Volume 5, Issue 5

In modern deregulated power industry, private sector has invested a lot to supply for extended power demand using the preexisting power system framework. This resulted into increased loading of transmission lines which has to work now to hit their thermal limits. The overloading of transmission line resulted in congestion and hence increase in loss of power in the system. One of the efficient ways to reduce congestion is by enhancing the available transfer capacity (ATC) of the power system. ATC enhancement can be achieved by application of FACTS devices. This paper presents an innovative method to mitigate congestion by locating TCSC in the IEEE 30 bus system. The allocation of TCSC is done by using ACPTDF sensitivity factors while the parameter setting is done by applying Grey Wolf Optimization (GWO) method. The effective application of GWO is demonstrated in this paper to reduce active power loss, enhancement of ATC value with reduction of reactive power loss and to optimize TCSC size through a multi objective function. The suitability of algorithm is established through concerned figures and tables.

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The aim of this paper is to identify a critical link in the man – machine – environment system in the case of an adverse event, such as a hay baling fire, based on a comprehensive risk assessment method. The rate of spread of fires in agriculture depends on meteorological conditions, with large areas affected and potentially endangering the surrounding buildings, facilities. Access to fires is difficult and can extend to forests. Water is often lacking at the scene of a fire, which should be extinguished, and water sources are usually located over long distances. The paper addressed a specific example using a comprehensive method. The process of risk assessment in the work process was determined by the following steps: assessment of the overall risk of the work equipment, assessment of environmental impact, assessment of the person’s ability to manage risk, calculation of the resulting risk value, comparison of calculated risk value and acceptability of risk value, proposal of measures. The result of the analysis was the finding that the primary cause of the fire is the environment, i.e. high ambient temperature. The critical element in hay baling work system is the work environment. The risk ratio was estimated at 5.78. The level of risk was low due to the rapid intervention of the human factor. Based on the results, the technical measures mentioned in the paper were proposed to the operator. The paper found that maintenance of the machine is important for protection against agricultural fires, where the human factor plays an important role in the man – machine – environment system.

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The difficulty of learning the underlying structure between processes is a common task found throughout the sciences, however not much work is dedicated towards this problem. In this paper, we attempt to use the language of structure learning to address learning the dynamic influence network between partially observable processes represented as dynamic Bayesian networks. The significance of learning an influence network is to promote knowledge discovery and improve on density estimation in the temporal space. We learn the influence network, defined by this paper, by learning the optimal structure for each process first, and thereafter apply redefined structure learning algorithms for temporal models. Our procedure builds on the language of probabilistic graphical model representation and learning. This paper provides the following contributions: we (a) provide a definition of influence between stochastic processes represented by dynamic Bayesian networks; (b) expand on the conventional structure learning literature by providing a structure score and learning procedure for temporal models; and (c) introduce the notion of a structural assemble which is used to associate two stochastic processes represented by dynamic Bayesian networks.

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The universally common mode of interaction is the human emotions. Thus, there are several advantages of automated recognition of human facial expressions. The primary objective of the proposed framework in this paper, is to classify a person’s facial expression into anger, contempt, disgust, fear, happiness, sadness and surprise. Firstly, CLAHE is performed on the image and the faces are identified using a histogram of oriented gradients. Then, using a model trained with the iBUG 300-W dataset the facial landmarks are predicted. Using the proposed method with the normalized landmarks, a feature vector is calculated. With this calculated feature vector, the emotions can be recognized using a Support Vector Classifier. The Support Vector Classifier was trained and tested for system accuracy using the famous Extended Cohn-Kanade database.

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A liquid petroleum storage facility (Terminal), is a platform used to store petroleum products. Terminals plays a strategic role on the oil and gas supply chain. There are three types of terminals: a fully automated terminal, a partially automated terminal and a fully manual terminal. To increase their efficiency and safety in a very competitive market, terminal companies might take the decision to invest in a supervisory control system DCS or SCADA. The advantages of DCS/SCADA systems are: improving the productivity and the proactivity, reducing cost, identifying quickly abnormal operating conditions and finally enhancing the efficiency and the safety. Due to the high cost of this project type, it is deemed vital to take the right decision when comparing the different options. The automation market offers multiple choices and opportunities, and taking the correct decision is a very challenging process. The key criteria for the technical evaluation are: 1) service offered, 2) interface capabilities,3) tender process efficiency and project execution, 4) after sales and product life cycle,5) references and finally 6) additional services. In this paper, the importance of each key criterion is identified and measured, leading to the development of a tool to be used for the assessment of different offers in order to successfully implement a supervision system. It consists of a framework based on a past experience of subject matter expert for a real case related to a successful implementation of a DCS system on a liquid petroleum storage facility. The paper provides as well a valuable feedback for vendors and can be used as a reference when preparing their commercial and technical strategy for future projects.

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The impact of Manufacture sand on UPV, compression strength and modulus of rupture has been experimentally investigated and discussed in this paper. Concrete grade 30 (30Mpa) were produced with different percentage of M sand substitution ranging from 25%, 50%, 75% and 100% by volume were selected as concrete mix design in this project. The strength for different percentage of M-sand incorporation was measured by the destructive test which are compression strength test and modulus of rupture test and non-destructive test which is UPV test for curing ages of 90, 28 and 7 days. Furthermore, the highest compression strength of concrete and modulus of rupture was found on the 90th day containing a proportion of 75% of M-sand as a substitution for natural fine aggregate. A novel empirical equations 1 & 4 are proposed for the relation of compression strength with the modulus of rupture and UPV.

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This Information technology (IT) has a crucial role to improve business processes in companies. Getting the best technologies rapidly becomes as significant as understanding and developing the business plan of organizations. Thus, different IT best practices and norms are used by companies to help their services and IT business.
These standards are set of best practices based on the experience and knowledge of numerous organizations; each of them focuses on specific governance issues such as ISO 27001 and ISO 27002 for IT security management, PMBOK and PRINCE2 for project management, ITIL for IT service management and COBIT for overall governance of an organization.
As part of a collective research project that focuses on the IT governance axis, we have developed a smart global IT-GRC platform that allows to IT manager to design his own repository, considering the powers of each best practice, the organization’s context and the IT strategic needs expressed by their stakeholders.
To ensure the durability and the continuity of this project, we must consider the evolution of the IT GRC market, the problem posed is how to integrate recent versions of IT GRC frameworks, and how to ensure a periodic update of the knowledge base of the global IT-GRC solution. It is the subject of this paper and the second part of an IT-GRC research project.

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This paper studies a trade-off between security (intercept probability (IP)) and reliability (outage probability (OP)) for a multi-hop decode-and-forward (DF) relaying protocol in an underlay cognitive radio network, in presence of a multi-antenna eavesdropper. In the considered protocol, all primary and secondary terminals are equipped with multiple antennas, and they employ transmit antenna selection (TAS) (at transmitter sides) and selection combining (SC) (at receiver sides) techniques to enhance the system performance. Relying on channel state information (CSI) of the primary-to-secondary interference links known or unknown, two efficient TAS/SC techniques are proposed for the secondary networks. Moreover, operating on the underlay spectrum sharing mode, the secondary transmitters including source and relays must adapt their transmit power so that OP of the primary network is not higher than a pre-determined threshold. Under impact of Rayleigh fading and co-channel interference, the end-to-end (e2e) OP and IP are expressed by exact closed-form expressions that are verified by Monte-Carlo simulations. Then, both simulation and theoretical results are presented to show the OP-IP trade-off.

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Vehicle systems are controlled by embedded electronic devices called electronic control units (ECUs). These ECUs are connected together with network protocols. The Controller Area Network (CAN) protocol is widely implemented due to its high fault tolerance. However, the CAN is a serial broadcast bus, and it has no protection against security threats. In this paper, we propose a fast stream cipher based on a chaos neural network (CNN) that is able to generate pseudo-random numbers at a high speed, faster than that of the Advanced Encryption Standard, to protect ECUs on the CAN bus by encrypting CAN messages. We discuss the chaotic orbit of the CNN and statistical properties of pseudo-random numbers from the CNN. For a stream cipher, it is very important to share the symmetric key. We designed a symmetric key that is shared with ID-based encryption without the need to use digital certificates. We evaluated our method’s performance with embedded boards and showed that the stream cipher is efficient for the embedded software of the ECU. Further, it does not need a hardware security module to accelerate the encryption.

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Low power OTAs are the most preferred circuits in the realization of continuous time filters of analog front end of wearable healthcare devices. A low transconductance OTA with series parallel current mirror to realize large time constant of the filter is designed. The differential pair of the OTA uses bulk driven PMOSFETs and the subthreshold operation of the circuit achieves 44 nW power with supply voltage of ±0.4 V. The designed OTA has DC gain of 29.59 dB and UGBW of 34.28 KHz. Using the proposed OTA, a multifunction filter which can operate as low pass and high pass filter, having cut-off frequency in the range 25 Hz – 225 Hz is designed in gpdk 180 nm CMOS technology. The simulation is performed using Cadence virtuoso design environment.

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Optimal integration of distributed generation (DG) into the distribution system results in reduced power losses and improved bus voltages. In this article, a combination of two techniques has been analyzed:
The integration of DG and reconfiguration of the distribution system by removing the Normally Open Point NOP in different places of the system.
These two techniques are applied to a real distribution network ” distribution network of Kenitra city in Morroco”, considering as key objectives the reduction of power loss and improvement of voltage profile.
To investigate the effectiveness and robustness of our system a model was performed using ETAP. The simulation results improve that we can minimize greatly the power losses in the distribution network by the implementation of DGs and reconfiguring our distribution network.

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Global changes caused by the IV Industrial revolution and globalization processes resulted in a redistribution of roles of participants in innovative infrastructures of countries. Universities are leading both in terms of generating R&D products and in terms of developing business activities. Now there is a problem of insufficient methodological support of technological universities for pricing R&D products developed and prepared for transfer to the business environment. Existing methods and models do not meet the needs of the market, which is growing rapidly. At the same time, the market is characterized by a high degree of volatility. The purpose of the article is to develop a method for modelling prices for R&D products from universities to the business environment, which takes into account: the specifics of the R&D product, modern market features for this R&D product; the nature of the transfer and commercialization of this R&D product. The article identifies the factors that determine the processes of transfer, commercialization and market launch of R&D products, which affect the pricing of R&D products. Groups of characteristics that characterize systematize these factors: 1) consumer value of R&D product; 2) market susceptibility of R&D product; 3) transfer and commercialization processes of R&D product. Justified a number of factor attributes within the formed groups and assigned them the values of linguistic terms for adjusting the price of R&D product using fuzzy set theory algorithms. The method takes into account elements of cost, revenue and comparative estimation approaches. The method makes it possible to adjust prices for R&D products, taking into account heterogeneous features in the composition of R&D products and compare them with market analogues of R&D products. This contributes to achieving a higher level of pricing accuracy for R&D products when they are transferred from the university to the business environment. The resulting prices are compared with market prices for competitive analogues, which makes it possible to determine the scenario of transfer and commercialization of R&D product; justify the strategy of market development of R&D product; increase the level of manoeuvrability of pricing management for R&D product. The model was tested on a number of R&D products developed at Lviv Polytechnic National University (Ukraine). Application of the proposed method is advisable in the short-and medium-term forecasting period.

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In this paper, the performance evaluation of a line-start three-phase Synchronous Reluctance Motor (SynRM) with symmetrical distributed brass rotor bars is presented. The machine, which has been designed from a conventional three-phase induction motor (IM) NEMA frame stator is proposed as an alternative to a squirrel cage induction motor (SCIM). The 2D Finite Element Analysis (FEA) under ac magnetic transient solution was used to study some performance parameters of interest during starting transients. The experimental measurements were carried out in order to validate the numerical computation, to analyze the starting transients, and to explore the dynamic responses due to load variations. The FEA and experimental results of the synchronous reluctance motor with brass rotor bars (SynRM-BRBs) are compared to the results of a conventional three-phase SCIM of the same NEMA frame stator. The results evidenced that the reluctance torque developed by the SynRM-BRBs has a compounding effect on the accelerating torque, reaching its steady-state operational condition faster than the SCIM. The dynamic response of the SynRM-BRBs is faster in contrast to the SCIM during load variations. Furthermore, it was noted through measured results that the proposed line-start three-phase SynRM had a reduced dynamic no-load, and load current as opposed to the SCIM, thus positioning itself as a good candidate to replace the SCIM in applications that require a line-start ac motor with good starting transients and fast dynamic responses.

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Dengue fever (DF) is a tropical disease spread by mosquitoes of the Aedes type. Therefore, a DF outbreak needs to be predicted to minimize the spread and death caused by it. The spread of dengue fever is a spatial problem. In this paper, we adopted the Multi Linear Perceptron (MLP) to solve the spatial problem, and we called it a spatial multi-layer perceptron model (Spatial MLP). In this proposed model, we consider two types of input neurons in the Spatial MLP, a region and the neighbourhood of that region. The spatial inputs dynamically change to the region. Additionally, the neighbourhood numbers of a region are also varied. So, the spatial inputs are changed in terms of the number of inputs and the neighbourhoods. As a result, the proposed model is outperformed the traditional MLP since it can adapt to the neighbourhoods. We can conclude the spatial MLP model can manage the information and predict the dengue fever outbreak in Surabaya

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The technology of Big Data got the capability to process large amounts of data, manage them effectively and make retrieval whenever it is required. Decision making in any organisation is a challenging task since decisions need to be made based on the accessibility of data and its status, this becomes more challenging especially in large organisations that generate massive amount of information and data every single day.
Implementation of latest advanced technologies like Big Data is imperative for any organization to make decisions which reduces time and pave the fourth industrial revolution. Even though this is vital in today’s business world, there are still few organizations that are hesitant to adopt it.
This paper illustrates the relation between Big Data and effective decision making by implementing a quantitative analysis through questionnaire. The conducted analysis is implemented using SPSS through correlational perspective, where percentage analysis, Chi-Square, correlation, and regression analyses are performed to obtain results.
It is clearly depicted that large organizations transition to adopt Big Data to aid their decision making, where medium and smaller organizations were slowly transitioning the adoption. In spite of this, most of employees, irrespective of the type of organization agree that Big Data is indeed a powerful advanced technology as they were satisfied by the organizational direction they have taken.

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Education has seen the rapid development of online learning. Many researchers have conducted studies on the use of recommendation systems in online learning. However, until now, several similar studies still focus on the accuracy of the prediction results. Various obstacles were encountered related to changes in the face to face learning process into online learning. This study uses the User-Item Collaborative Filtering method to predict student learning outcomes as a basis for providing recommendations to students. Data on student online learning outcomes were extracted using several methods as a basis for determining and assessing their learning outcomes. The dataset we use is dummy to match the original data. The findings of this study reveal that one of the reinforcing factors that affect student achievement in online learning is the quiz score. The students’ high achievement in the quizzes was also balanced by their active involvement during the learning process. Based on the evaluation of the recommendation system, it is known that the gradient boosted tree model is the best model for predicting the final score of student online learning with an accuracy calculated using the highest correlation of 0.7 and the smallest absolute error of 13.0 and root mean square error of 17.9. Based on the results of the evaluation, this study provides recommendations in the form of material links and learning archives that are useful for students to be able to carry out independent learning.

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Cancer is the leading cause of death in world. Currently, there are no approved vaccines to avoid the spreading of this disease. Drug discovery have played important role for discover of new potent drugs that could efficiently and cost-effectively. Pyrazoline analogue compounds known to have good potency as anti-cancer. The aim of this study is to observe the potency of these pyrazoline analogue as anticancer using molecular docking. In this study, ten pyrazoline analogue compounds were docked and tested on MCF7 cell line using MTT assay. Based on docking results, compound PH CN1-4F explored three interations with amino acid residues. These interactions are hydrogen bonding with Arg791, hydrophobic interaction with His790 and van der Waals interaction with Asp810. Biological assay was shown that this compound is also have big potency as breast cancer inhibitor with IC50 value of 61.22 µg/mL. Thus, this strategy can be used to identify new potent drugs as new inhibitor for breast cancer.

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The text’s production carried out in this research aimed to 5-year-old children from the IE 555 Inmaculada Concepción, under the modality dictated to adult, had a design and application of 22 sessions during three months with the experimental group. On their methodological approach was based on the expression ludocreative, articulating the written experience jointly with the art expression in the pedagogical fields of plastic, musical, scenography and corporal expression, propitiating the children´s prominence through a playful tone with deductive situations in the learning processes. For this, a quasi-experimental design was considered, with a control group of 15 children and an experimental group of 20 children. After selecting the groups in an intentional non-probabilistic manner, the questionnaire was applied to assess the production of texts to the respective groups. The results indicate the effect of the application of the proposal. This could be evidenced in the establishment of differences in the pre-test and post-test of both groups, and differences in the scores between both groups. In particular, it was found that the difference between pre and post-test was greater in the experimental group, obtaining a fundamental achievement to continue deepening in this playful and creative within the framework of pedagogical innovation.

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Suppose we want to classify a query item Q with a classification model that consists of a large set of predefined classes L and suppose we have a knowledge that indicates to us that the target class of Q belongs to a small subset from L. Naturally, this filtering will improve the accuracy of any classifier, even random guessing. Based on this principle, this paper proposes a new classification approach using convolutional neural networks (CNN) and computational geometry (CG) algorithms. The approach is applied and tested on the recognition of isolated handwritten Arabic characters (IHAC). The main idea of the proposed approach is to direct CNN using a filtering layer, which reduces the set of possible classes for a query item. The rules of the relative neighborhood graph (RNG) and Gabriel’s graph (GG) are combined for this purpose. The choice of RNG-GG was based on its great capacity to correctly reduce the list of possible classes. This capacity is measured by a new indicator that we call “the appearance rate”. In recent years and due to strong data growth, CNNs have performed classification tasks very well. On the contrary, CG algorithms yield limited results in huge datasets and suffer from high computational time, but they generally reach high appearance rates and do not require any training phase. Consequently, the proposed approach uses an optimal architecture to exploit the advantages of the two techniques and overcome the computational time issue. Experiments carried out on the IFHCDB database have shown that the suggested approach outperforms a normal CNN and yield satisfactory results.

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The dog is one of the first animals domesticated by human, and for thousands of years, it has been artificially bred into hundreds of types in order to provide certain traits that humans want. Nowadays, the selection of dogs by potential adopters has become a problem due to the availability of different type of breeds with their physical and mental characteristics. This study aims to design a decision support system through an analytical model that uses variety of data on the characteristics of purebred dogs obtained from different sources. Data from official sources is obtained from international purebred dog organizations that set the standards for each breed type, while data from unofficial sources is obtained from the dog lovers community, experts, and kennel owners. The result of the study provides appropriate recommendations to potential adopters in selecting a breed that suits their preferences and needs.

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This paper focuses on a string encoding algorithm, which produces sparse distributed representations of text data. A characteristic feature of the algorithm described here, is that it works without tokenizing the text and can avoid other data preparation steps, such as stemming and lemmatization. The text can be of arbitrary size, whether it is a single word or an entire book, it can be processed in the same way. Such approaches to text vectorization are not common in the machine learning literature. This sets the presented encoder apart from conventional text vectorizers. Two versions of the encoding algorithm are described and compared – the initial one and an improved version. The goal is to produce a robust data preparation procedure, capable of handling highly corrupted texts.

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Analysis and classification of lung diseases using X-ray images is a primary step in the procedure of pneumonia diagnosis, especially in a critical period as pandemic of COVID-19 that is type of pneumonia. Therefore, an automatic method with high accuracy of classification is needed to perform classification of lung diseases due to the increasing number of cases. Convolutional Neural Networks (CNN) based classification has gained a big popularity over the last few years because of its speed and level of accuracy on the image’s classification tasks. Through this article, we propose an implementation a CNN-based classification models using transfer learning technique to perform pneumonia detection and compare the results in order to detect the best model for the task according to certain parameters. As this has become a fast expanding field, there are several models but we will focus on the best outperforming algorithms according to their architecture, length and type of layers and evaluation parameters for the classification tasks. Firstly, we review the existing conventional methods and deep learning architectures used for segmentation in general. Next, we perform a deep performance and analysis based on accuracy and loss function of implemented models. A critical analysis of the results is made to highlight all important issues to improve.

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Ad-Hoc networks are self-organized wireless networks. Finding a secure and efficient route leading from a specific source node to an intended destination node is one of the serious concerns in mobile Ad-Hoc networks. ARANz is one of the significant protocols that has been proposed for such networks. ARANz implements the authentication methods used with the original Authenticated Routing for Ad-Hoc Networks (ARAN) and enhance security and attain robustness by dividing the network into zones and introducing several local certificate authority servers. Using restricted directional flooding, ARANz reveals improved scalability and performance.
The purpose of this paper is to discuss in details the misbehavior detection system used with ARANz protocol, along with presenting a detailed simulated security and performance evaluation of ARANz and other existing protocols. Through extensive simulation using GloMoSim simulator, a detailed security evaluation has been conducted to evaluate ARANz and compare it with the original ARAN and Ad-Hoc On-demand Distance Vector (AODV). Simulation results confirm the effectiveness of ARANz in discovering secure routes within quite large networks including large number of moving nodes, while retaining the minimum packet routing load. Results also prove that ARANz has superior performance regardless malicious nodes percentage conducting different types of attacks such as modification, black hole, grey hole and fabrication. Hence, ARANz can be a good choice for Ad-Hoc networks established among students on a campus or peers at a conference, where pre-deployment of some keys and certificates is possible.

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East Java province is a large province in Indonesia, in which Surabaya is the second largest metropolitan city after Jakarta. Various problems of development inequality in East Java have caused East Java to be defined as a disadvantaged area in 2015. The determination of disadvantaged regions is carried out every 5 years using 6 criteria and 33 indicators that have been set by the Ministry of Villages, Development of Disadvantaged Regions, and Transmigration. However, from several studies that have been conducted on the determination of disadvantaged regions, there is no research applies 33 indicators as a whole. So in this study, an evaluation of the determination of disadvantaged regions will be carried out using 33 indicators that have been determined by The Ministry of Villages, Development of Disadvantaged Regions, and Transmigration. Criteria data used are the results of the 2014 and 2018 surveys. These data are in the form of numerical data and categorical data. The method used is ensemble Robust Clustering Using Link (ROCK), which is a clustering method that can accommodate mixed data both categorical and numerical, using the concept of distance to measure the similarity or closeness between a pair of data points. The best cluster results for evaluating the determination of disadvantaged regions in 2020 consist of 4 clusters with the smallest Sw and Sb ratio of 0.3873984 and the optimum threshold value of 0.04. The results of the clustering, place Trenggalek, Bondowoso, Situbondo, Probolinggo, Tuban, Pamekasan, Sumenep, Bangkalan, and Sampang regions as disadvantaged regions in East Java.

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Since the release of of the IPv6 Routing protocol for Low-Power and Lossy Networks by the IETF ROLL working group, several enhancement schemes were proposed. In fact, They aim to extend the network lifetime, reduce congestion, mitigate end to end delay and moderate energy consumption. In fact, considering the vast area of Low-Power and Lossy Networks applications, the routing protocol was designed with a great deal of flexibility without dictating any specific routing metric/constraint to be used for building the routing topology. This paper presents a deep review of recent works on Routing Protocol for Low-Power and Lossy Networks and highlights Objective Function enhancements scheme. The objective is to provide an insight into relevant efforts such as novel metrics design and fuzzy logic techniques used for the Objective Function metric combination. The proposed enhancement schemes, highlight some limitations and give useful guidelines for future developments are also discussed.

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The evolution of IT keeps growing worldwide, especially mobile technology. Today, mobile technology not only held by adults but also kids. The proportion of kids who used smart- phones is increasing each year. The growing technology in mobile applications encourages developers to build educational apps to help kids learn to know alphabets, numbers, animals, and simple calculations in a fun way. This paper describes an educational “Homy Kid Lesson” as an Android-based application. The goal of this Homy Kid Lesson is helping children to know numbers, recognize letters, recognize animal names, and to perform simple calculations with an attractive interface. Research methods include data collection, design, and implementation. The development is done through the research literature, comparing it with similar apps, interviews, and questionnaires. Application design uses a storyboard. Its implementation refers to Extreme Programming (XP) model. The result is an educational application “Homy Kid Lesson” which can be a learning tool and as electronic media for learning with interest and fun concept. Contribution and novelty are increasing the ability of children to recognize letters and numbers early, increase the knowledge of animal names, introducing how to spell the letters and numbers to children also train the child’s ability to simple counting and introduced the method of learning with electronic media.

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The water heating with parabolic trough solar collectors (PTC) is a very widespread and at the same time quite promising solar technology. However, PTC presents several problems in terms of the profitability of water heating. For this reason, our study of water heating with PTC collectors consists of two main parts. In the first part, we investigate the effect of direct normal irradiation in the absorber tube using the TRNSYS software of the Errachidia city. In the second part, the study is entirely focused on the heat balance of the absorber tube in order to estimate the fluid outlet temperature. Besides, a mathematical model is developed to simulate and control the fluid outlet temperature circulating through the absorber tube of the collector. The water outlet temperature prediction was carried out by a thermal performance study of the PTC in weather conditions of Errachidia city (Morocco) using TRNSYS software and Matlab Code in the year’s typical days.

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Over the last decades, numerous distributed consensus-based algorithms have found a wide application as a complementary mechanism for data aggregation in wireless sensor networks. In this paper, we provide an analysis of the generalized Metropolis-Hastings algorithm for data aggregation with a fully-distributed stopping criterion. The goal of the implemented stopping criterion is to effectively bound the algorithm execution over wireless sensor networks. In this paper, we analyze and compare the performance of the mentioned algorithm with various mixing parameters for distributed averaging, for distributed summing, and for distributed graph order estimation. The algorithm is examined under different configurations of the implemented stopping criterion over random geometric graphs by applying two metrics, namely the mean square error and the number of the iterations for the consensus. The goal of this paper is to examine the applicability of the analyzed algorithm with the stopping criterion to estimating the investigated aggregate functions in wireless sensor networks. In addition, the performance of the algorithm is compared to the average consensus algorithm bounded by the same stopping criterion.

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Machine learning classifiers provide many promising solutions for data classification in different disciplines. However, data classification at run time is still a very challenging task for real-time applications. Acceleration of machine-learning hardware solutions is needed to meet the requirements of real-time applications. This paper proposes a new implementation of a machine learning classifier on Field Programmable Gate Arrays (FPGA). The proposed implementation utilizes the MicroBlaze soft-core processor on FPGA and uses the Advanced eXtensible Interface (AXI) bus to integrate the MicroBlaze with hardware peripherals. Experimental results shows that hardware-software co-design is a promising solution as it saves silicon area and provides a flexible configuration of decision tree algorithms at run time.

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Practically level of design element (i.e., explore, sharing, connect) is an essential of heutagogical approach. The self-determined learning process can be at ease with the implementation of these elements, and the critical step is reliability to measure teaching and learning feedback. Although various instruments were proposed in the literature to assess heutagogy elements, the specific potential Rasch Measurement Model to determine the practicality levels of heutagogy element is less emphasized. This paper aimed to validate the research instrument (six constructs with 65 items). The instrument was administered to N=175 students for a pilot study. The Rasch model was conducted to examine reliability (0.93, 0.94) with ? = 0.97, separation index (3.75, 4.01) for item and person, respectively. Besides, item fit (three-item dropped), polarity and standardized correlation residual (no overlapping items). The findings have shown that the instrument has high validity and reliability for use in measuring the practical level of heutagogy elements.

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With recent advancements in medical filed, the quantity of healthcare care data is increasing at a faster rate. Medical data classification is considered as a major research topic and numerous research works have been already existed in the literature. Presently, deep learning (DL) models offers an efficient method for developing a dedicated model to determine the class labels of the respective medical data. But the performance of the DL is mainly based on the hyperparameters such as, learning rate, batch size, momentum, and weight decay, which need expertise and wide-ranging trial and error. Therefore, the process of identifying the optimal configuration of the hyper parameters of a DL is still remains a major issue. To resolve this issue, this paper presents a new hyperparameters tuned DL models for intelligent medical diagnosis and classification. The proposed model is mainly based on four major processes namely pre-processing, feature extraction, classification and parameter tuning. The proposed method makes use of simulated annealing (SA) based feature selection. Then, a set of DL models namely recurrent neural network (RNN), gated recurrent units (GRU) and long short term memory (LSTM) are used for classification. To further increase the classification performance, differential evolution (DE) algorithm is applied to tune the hyperparameters of the DL models. A detailed simulation analysis takes place using three benchmark medical dataset namely Diabetes, EEG Eye State and Sleep stage dataset. The simulation outcome indicated that the DE-LSTM model have shown better performance with the maximum accuracy of 97.59%, 88.52% and 93.18% on the applied diabetes, EEG Eye State and Sleep Stage dataset.

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In the recent times power shortage has been a major setback to deal for the effective operation of power systems. Bridging the gap between generation and demand is known as Demand Side Management (DSM). For an effective DSM strategy to be implemented, it is crucial that both utility and customers be involved. By DSM, the energy generated is used more effectively. This reduces the burden of the utility to invest on additional generation. In this work, a DSM strategy has been performed on two systems: (i) on RTS 24 bus system with wind energy sources distributed at some nodes of the system (ii) on an institutional load with installed solar power plant. A generic DSM strategy to effectively utilize the generated energy and to minimize the utility bills for the customer has been proposed. An instantaneous billing scheme has been proposed. By implementing the instantaneous billing scheme, customers can be persuaded to change their consumption behavior, matching the demand with available generation. The results obtained are promising, with a resulting flat load profile and reduced utility bills for the customer.

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Application in the field of medical development has always been one of the most important research areas. One of these medical applications is the early prediction system for heart diseases especially; coronary artery disease (CAD) also called atherosclerosis. The need for a medical diagnosis support system is to detect atherosclerosis at the earlier stages to optimize the diagnosis, avoid the advanced cases, and reduce treatment costs. Earlier, the datasets are collected from specific medical sources and have evaluated against computer applications. In this paper, a supervised machine learning medical diagnosis support system (MDSS) for atherosclerosis prediction is presented that able to obtain and learn automatically knowledge from each patient’s clinical data. Therefore, we used three Machine Learning (ML) classifiers for the proposed MDSS for atherosclerosis. Thus, this work is accomplished using databases collected from the UCI repository (Cleveland, Hungarian) and Sani Z-Alizadeh dataset. The performance metrics were computed utilizing Accuracy, Recall and Precision. Furthermore, F1-score and Matthews’s correlation coefficient these measures were also calculated to greatly increase the proposed system performance. Additionally, 10-fold cross-validation methods have been used for proposed model performance evaluation that achieved 94% as the best accuracy average. Consequently, the proposed model can be used to support healthcare and facilitate large-scale clinical diagnostic of atherosclerosis diseases.

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Water is an important vital resource, and must be protected and saved. Unfortunately, millions of liters of water are wasted annually at the national level in Peru, one of the factors is the discomfort when people take a hot shower, because first the water is cold, which is contained in the pipes of domestic connections with the heating tank, the cold water is wasted by sending directly to the drain until the water to be in a pleasant temperature, that means about 5 liters of water are wasted. The technology within engineering has carried out a variety of innovations that respond and solve problems related to water saving, that’s why, this paper proposes to create an automated system that saves the wasted water in domestic showers, the proposal is characterized by being sustainable, for its environmental and economic impact; solving in this way a latent problem of society as well as raising public awareness of the benefits of saving water. As a result, a preliminary prototype was obtained, which demonstrates the basic operation of the proposed system.

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Sign language is one of the languages which are used to communicate with deaf people. By using it, they can communicate and understand each other. In Indonesia, there are two standards of sign language which are SIBI (Sistem Bahasa Isyarat) and BISINDO (Bahasa Isyarat Indonesia). Deep learning is a model that is used to apply to this topic. In this model, there are a lot of methods such as convolutional neural network, recurrent neural network, long-sort term memory, and each model has its characteristics. There are also some issues in deep learning by sign language recognition as the object such as data training, object position, pose, lighting, and the background of objects. This research will describe how to combine background subtraction and gaussian blur pre-processing, forwarding pre-processing background subtraction with CNN by using BISINDO, LSTM, and a combination between CNN and LSTM. In conclusion, this research shows that a combination between CNN and LSTM is the best model by explaining the accuracy and testing with sign language BISINDO as the object. The accuracy showed that for CNN 96%, LSTM 86%, and combination CNN and LSTM 96%, and the loss showed that for CNN 18%, LSTM 41%, and combination CNN and LSTM 17%.

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Solving equations does not require only to well master the techniques but also to well understand the different underlying concepts and processes. Many of the mistakes made by the students are often due to misinterpretation of the concepts taught, especially the use of letters which the main conceptual obstacle that students have to overcome. We present here an educational game, in the form didactic balance which may help the students to master resolving equations of the first degree. In our conception, we were very aware to avoid that our balance becomes a calculating machine that solves the equations instead of the student. The balance was designed to develop inside the student the rigor of mathematical reasoning, one of the didactic objectives behind the introduction of the notion of the equation of the one-degree equations at this stage of the learning process, that of the middle-school learners. The novelty is to offer to both teachers and students a didactic tool that may replace the traditional method of solving equations.

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To cultivate creative talent, ways to learn creative problem-solving skills is needed, and one of them is programming. Arduino is a well-known tool used for programming education and the usefulness has been demonstrated in various case studies. However, there are several problems in existing Arduino-compatible kits as education tools, including the need for understanding hardware and the difficulty of expanding the kits with third-party hardware. In this paper, the design of an Arduino-compatible modular kit, called as FRUTO, is proposed that can be easily connected and conveniently programmed to overcome the problems. The structure and features of the FRUTO kit that implements the proposed design are also shown. The FRUTO kit consists of the FRUTO module that uses a unified connector for easy and intuitive connection and the FRUTO library that abstracts hardware-dependent code for easy programming. The FRUTO kit is easier to use and more scalable than existing kits. Even more, it can be used in various ways depending on the students’ familiarity with hardware and programming. These strengths will make the kit to be an appropriate tool for various microcontroller-related education as well as programming education.

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The phenomenon of magnetic fields is affected depending on the polarity, the positive and negative poles will give a response of attraction and repulsion that can be easily observed. Being also important as an educational element where the theory materializes and is observed, which enriches all science. The objective of this work is to create a circuit that allows reaching the balance between the electronic components, making use of the fundamental electronics together with an LDR and a light sensor. Both generate a connection bridge in which the object that levitates to remain in the air fluctuates, achieving the objective of creating a levitator and being able to observe the nature of electronics and electromagnetism as a whole. As a result, it was obtained that there is a difference between the use of basic and advanced electronic circuits, in addition to identifying the variation in the voltage consumption between the coin and the screw, because the more voltage consumption, the greater the length of wave implying that the frequency decreases but the power intensifies. On the other hand, it should be noted that the use of a voltage generator to identify the voltage and power variations that were shown at the time of the different tests.

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A data analytic approach presented in this work covers both data descriptive and predictive modeling with two main objectives: (1) discovering factors related to longevity of populations in the two most populated nations, China and India, and (2) generating life expectancy predictive models for both countries. Descriptive modeling methods to explore major environmental and economic factors anticipating to affect longevity patterns of people are web graph analysis and chi-squared automatic interaction detection (CHAID) techniques. Web graph analysis has been applied for the ease of visualization and CHAID is for discovering factors leading to longevity. From the analysis results, particulate emission including ozone pollution and PM2.5 concentrations is the most important factor threatening life of populations in both China and India. To predict number of years an individual is expected to live based on the available environmental and economic factors, several statistical and machine learning techniques are applied and it turns out that a linear regression model yields the most accurate prediction result.

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This paper provides an overview of Intelligence, Surveillance, and Reconnaissance (ISR) data with respect on NATO standards and recommendations; further presents methods, tools, and experiences in ISR data processing in military operations. The steps of the Intelligence cycle and disciplines Business Intelligence (BI), Data Warehousing, Data Mining, and Big Data are presented in the introduction. The literature review is oriented to the analysis of intersections between ISR and BI methods. The next chapter describes the ISR data processing in detail; there are listed structures, formats, standards, and data from the operational point of view. The ISR operational picture is explained, and steps of the ISR data mart is completed. The last part is oriented to Big Data processing; NoSQL, in-memory and streaming databases. The last two chapters are focused on the description of research results in the processing of ISR data. The ISR data mart experiment processes the radio transmission data that consists of detected radio signals. Results are visualized in RapidMiner Studio. The Big Data experiment is realized in Apache Hadoop.

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In this paper we design a large scale reconfigurable digital bio-inspired computing model. We consider the reconfigurable and event driven parameters in the developed field-programmable neuromorphic computing system. The various Intellectual Property (IP) cores are developed for the modules such as Block RAM, Differential Clock, Floating Point, and First In First Out (FIFO) for the design of the neuron model in Xilinx ISE, with exploration of register transfer logic (RTL) and hardware synthesis using Verilog code. The architecture for design at device level offers the best possible design tradeoff for specific processor architectures and development choices. In this paper we perform algorithmic design of a large scale reconfigurable logical bio-inspired computing model. The proposed algorithm is implemented on Field Programmable Gate Array (FPGA) to develop a neuron model to be utilized in neuromorphic computing system.

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This study aims to analyze a person’s intention in using self-service technology (SST) services during the pandemic COVID-19. Where this time, raising problems one of the social distancing that affects a service provided, especially on services that use technology that applies SST. This study develops from previous research where the customer has satisfaction in using SST services. Still, this use does not know a person intention to use SST services, so it is necessary to develop with several factors the adoption of technology, namely influence that can make a person intention to use. The research framework of this research consists of several factors and indicators, namely efficiency (4 indicators), Reliability (4 indicators), security (3 indicators), Convenience (3 indicators), Ease of use (4 indicators). This study uses an online survey with google form for users of self-service technology services in 5 major cities in Indonesia. Dissemination of the survey for three months, and the obtained were 100 respondents valid. This research conducts testing using Smarpls v3.0. The results of this study found two factors that influence perceived usefulness, namely: efficiency and reliability, and found three factors that did not have an impact, namely Security factors, Convenience Factors, Perceived Ease of use, and Service Trust do not have an impact on perceived usefulness. This result is a very interesting finding because during the period pandemic COVID-19. These factors are no longer the top priority in the use of SST services and do not affect who intention to use a technology service. Thus, it can be concluded that whose intention at a certain level of the problem will prioritize the reliability and efficiency of the system for service. This factor can be the focus of the company in developing systems that adopt Self-service technology.

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The research will explore the challenges of using local water sources inside the city for a self-sufficient urban water supply by developed a system dynamics model. This study aims to evaluate and understand the Pesanggrahan River appropriateness as a raw drinking water source through a conceptual model that can accurately represent the interactions between the water supply and demand system. A set of time series data for the monthly precipitation and river flow rates at two stations from 2002 to 2016 were used to calculate the 90% dependable river flow fluctuations over one year. The results showed that water availability becomes limited in July, August, and September. Simulation results demonstrated that the Pesanggrahan River could supply 450 liters/s. The water demand exceeded the supply if the average water consumption 150 liters/capita/day for 100% service coverage. However, they will balance when service coverage 66 %, but reducing water consumption to 99 liters/capita/day will increase service coverage to 100%. The average water consumption and service coverage forming a linear equation relationship Y = 99.20x-0.99 with a correlation factor R2 = 0.99. This research contributes to enhancing the resilience of the water supply system. It provides a well-founded, flexible, and realistic approach to recognize and deal with challenges to local raw water resources limitation that inherent with uncertainties in water resources management.

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Crowdwork is a system that brings job providers and crowdworkers in a portal. Nowadays, many companies are turning to freelancing rather than hiring full-time employees. In addition, the workers have also switched to working as freelancers. New problems also arise in the crowdwork system. Trust is one of the main issues that arise in the crowdwork system. It happens because the job providers and workers do not meet each other on the crowdwork portal. This research aims to examine the components that affect trust in a crowdwork system. The benefit is that stakeholders can be aware of how to increase trust in the crowdwork system. The method used in this research is systematic literature review, by analyzing Scopus based journals related to trust in the crowdwork system. This research produces 11 components and 38 indicators. There are three components that needs concern to stakeholders who want to take advantage of crowdwork systems and focus on trust. These components are reciprocal voting, monetary reward, and cognitive effort. The platform should enable stakeholders to have reciprocal voting, less cognitive effort, as well as clean and clear monetary reward procedure.

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Harmonic distortion is a problem that can be caused by the use of power electronic devices. The effect of harmonics has an impact on changes in the input voltage source waveform which is referred to as wave defects. This incident has an impact on electronic faults and overheating of the power transformer coil as a supplier. One cause of the emergence of harmonics is the use of non-linear loads on the electric power system. Utilization of non-linear loads such as arc fires (metal casting), welding, magnetic core in transformers and rotating machines, synchronous machines, adjustable speed drives, solid-state switches High voltage DC transmission and Photovoltaic invertors can produce input wave defects. The filter is modelled through a reference current which is used as a PWM pulse generator reference signal to trigger the inverter, further generating a filter current that is injected into the system. Inverter control uses the Propositional Integrator (PI) control approach. From the simulation test results using the Psim software, it is shown that the recorded input waves due to harmonic distortion can be corrected by placing an active filter into the power system.

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Bearings are the key components of many rotating machines, in which serious failure or even major breakdown may occur due to their abnormal operation and defects. Thus, accurate fault diagnoses of bearing elements are essential for proactive predictive maintenance. However, the using of multiple sensors with high sampling rate reveal considerable shortages in the analysis of big data acquisition. Therefore, compressive sensing (CS) proposes in this study to overcome the aforementioned problems and support the fault diagnostic approach of ball bearing defects. The amount of data processed by CS technique can be significantly reduced to be more reliable for backup data. It can be a collaborative reconstruction method to compress the sampling data size and reliably exploiting similar sparsity structure of the acquired signal. Little attention has been paid for practically used sparseness of the CS converted signal in early fault detection of defects in ball bearing. Envelope analysis and CS technique are employed on experimental vibration data for fault detection in inner race and outer race of ball bearing. The results show that the reconstructed CS signal can characterize reliable features for bearing fault detection with some limitations in the range of compression ratio (up to 40%) and the selection of reconstructed sparse bandwidth. Hence, envelope analysis can provide optimal bandwidth to reconstruct the sparse modulated signal of ball bearing to overcome the limitation of the CS method.

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The accurate identification and characterization of microbiological species is a must that allows us to design engineered pharmacology to tackle down the diversity of diseases derived of them. This paper presents a study about the usage of both classical dynamical and electrodynamics in conjunction to the Feynman’s path integral to describe as well as identify the displacement of bacteria in closed spaces. Our methodology consists in the in the usage of the usage of probability amplitude to investigate the theoretical motility of bacteria can be disturbed through electrical interactions from the fact that them contain ions in their biochemical composition.Our study yields that bacteria might exhibit a pattern of probabilities as function of space and time. It would be advantageous for an engineered nanodevice that would sense them uniquely through electrical interactions.

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There have been many activities to train the brain to remain active, one of its activities is by playing games. Games with the right presentation can train the brain as a whole. This research is focused on High-Order Thinking Skills (HOTS) in order to be able to train the brain’s ability in players. Can be concluded that the Web scoreboard with Hypertext Prepocessor (PHP) and Structured Query Language (SQL) has been successfully developed. Web consists of three main menus namely the home menu, score menu, contact menu. unity code accessed to a web page to get data from a web server. This is a simple module for retrieving URL data.

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Online transportation in Indonesia is a new trend of transportation that is currently used among the lower to upper society. The change in behavior began in 2011 and is growing to this day, The comments that are growing on social media are very important for the online transportation company the negative comments lower the level of users while the good comments increase the users’ level. Thus, the comments influence the overall trust of their customers. Among social media, Twitter is a place where many people convey feelings of pleasure and displeasure timely, especially at a time when the COVID-19 pandemic is becoming a serious outbreak. Through these “tweets,” many customers express their experience with the service. In this paper, we aim to analyze the experience of online transportation consumers using Support Vector Machine. The data were taken in two periods, i.e. April 2019 (“the normal era”) and June 2020 (“the COVID-19 pandemic outbreak. Class logging is done based on 3 categories namely positive, negative and normal, while in mining the data we labeled with the keywords @grabID and @gojekindonesia, 1618 data were obtained with a ratio of 1183 is normal era data and 435 data in the era of COVID-19 pandemic, The highest accuracy results occurred in the normal era with a ratio of 10% as test data and 90% as its training data on linear and sigmoid kernels of 0.8060 while the COVID-19 era only got the highest accuracy of 0.59 in linear kernels with a ratio of 60:40. This is a sign that the COVID-19 pandemic does not contribute to decreasing trust in the service.

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In this paper, the multi-objective optimization process was applied for the surface grinding process of 3X13 steel using an aluminum oxide grinding wheel (WA46J7V1A). For each experiment, three cutting parameters, including the workpiece velocity, feed rate, and the depth of cut, were controlled to change according to the experimental matrix. At each experiment, surface roughness and vibrations of the technology system in three directions (X, Y, and Z) were measured and analyzed. The surface roughness and vibration amplitudes were modeled as a quadratic function of workpiece velocity, feed rate, and the depth of cut. The General Reduced Gradient (GRG) algorithm and Harmonic Mean (HM) method were combined to solve the multi-objective optimization problem about the surface grinding process of 3X13. The optimized values of surface roughness and vibration amplitudes (in X, Y, Z directions) were 0.901 µm, 0.815 µm, 1.594 µm, and 0.599 µm, respectively. These results that were determined at the cutting conditions were workpiece velocity of 14.428 m/min, the feed rate of 5.789 mm/stroke, and cutting depth of 0.013 mm. By conducting the grinding process at the optimal values of grinding conditions, the experimental results of surface roughness and system vibration amplitudes were quite close to that one of the predicted results. This proposed approach can be applied to improve the machining quality of the surface grinding process of 3X13 steel.

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This paper studied the bending analysis of an isotropic rectangular plate for the effects of aspect ratio, shear and deflection on the critical lateral load of the plates using the polynomial shear deformation theory (PSDT). One of the plate is clamped at opposite edge clamped and the other opposite edge simply supported (CSCS). The other of the plate is simply supported at the first and fourth edge, clamped at second edge and free of support at the third edge (SCFS). Direct variational method of analysis was adopted using strain-displacement expressions to obtain the direct governing equations for the determination of the coefficient of deflection and shear deformation along the direction of x and y coordinate. From the established equation, a new model for determination of the critical lateral imposed load of the plate is developed. The study revealed that: (i) as the specified thickness of the plate increases, the value of critical lateral imposed load increase (ii) the critical lateral imposed load decrease as the plates span increases. Numerical comparison was conducted to verify and demonstrate the efficiency of the present theory. The result obtained are in good agreement with those in the literature.

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The ability to verify the critical risk factors related to an effective diagnosis is very crucial for improving accuracy on coronary heart disease prediction. The objective of this research is to find the best predictive model for coronary heart disease diagnosis. Three approaches are set up to achieve the goals (1) investigating the classifier algorithms that are most suitable for the Thai heart disease dataset in this study (2) exploring features analyzed to be the significant risk factors in the predictive model, both major risk factors, and socioeconomic status and (3) rediscretizing the predefined clinical values on certain major risk factors. In order to achieving the optimal model before incorporating with feature selection process, several classifier approaches are conducted in this experiment. The study shows that the most effective classifiers ranked from the highest accuracy are Support Vector Machine, Naïve Bayes, Decision Tree, and Multi Layer Perceptron. Support Vector Machine produces the highest accuracy of 88.18%, with respect to both major risk factors and socioeconomic factors. Moreover, when adjusted thirteen major risk factors and five socioeconomic factors altogether, the accuracy is proved to be better than conducting each one alone. To investigate the better predictive performance of our study, feature selection methods of both filter and wrapper groups are employed with exploring the hybrid models to identify the most relevant features for Thai coronary heart disease. Relief Attribute Evaluation with Bayes Theorem is proved to be the best one with the accuracy of 92.59%, classified by SVM. To prove the accuracy enhancement, we perform rediscretization model on predefined medical values to examine different physical and personalized information of each person which can be incurred the coronary heart disease in different situation. The findings found that equal-depth rediscretization values on 7 major risk factors as Obesity, Hypertension, age, LDL, HDL, Fasting Blood Sugar, and Triglyceride, influences and improves with the better accuracy than predefined values of 95.50% classified by SVM. Thus, this finding shows that the proposed technique definitely outperforms predefined values from medical field.

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In this article, we concentrate on the use of a metaheuristic technique based on an Evolutionary Algorithm (EA) for determining the optimal geometrical parameters of spiral inductors for RF circuits. For this purpose, we have opted for an optimization procedure through an enhanced Differential Evolution (DE) algorithm. The proposed tool allows the design of optimized integrated inductors not only with a maximum quality factor(Q), but also with a maximum self-resonant frequency (SRF), and a minimum surface area, in addition to being adapted to any model of any technology. This paper presents also a comparison between performances of the optimized inductors (inductor square shape and inductor circular shape), in terms of the quality factor, SRF, and circuit size. For the purpose of mitigating the impact of parasitic effects, design basics have been taken into consideration. Then, in order to investigate the efficacy of evaluated results, an (EM) simulator has been employed.

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The Music Composition Library of the great composers constitutes an intellectual heritage. This article introduces an algorithm of artificial Oriental composing music based on the descriptors determined on a large learning base to automatically write Oriental music as the logic identical to any composer. Musical words are called a grammatical alphabet. Each word derived is created with the descriptors through its very own alphabet by crossing a number of filters removing all improper combinations and maintaining the features correctly responding with each filtering process while honoring the grammar of oriental music. A musical word is a combination of a rhythmic word and a symbolic word.

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Nigerian power utility companies particularly the distribution and generation aspects were recently in the process of national power reform converted from public to private service by privatization. Prior to these development, power utility companies’ performance is low due to poor operational style that leads to inadequate revenue generation. Thus, the task before the privatized companies includes autonomy, high reliability operation and brake-even management. To achieve these goal, frequent outages and system collapses must be minimized. One of the methods of achieving this is using power flow to improve the reliability of power system which will subsequently improve other lacking factors. A developed software for Newton-Raphson power flow was tested with a known solution network and the results obtained are accurate and reliable. Therefore, this paper presents an application of this software on real-time transmission network. Nigerian 330kV transmission grid is considered as case study. The power flow analysis of this grid was carried out and the network operational parameters were obtained. These results are stated and carefully analyzed. In practice, power utility distribution network of medium voltage of 11kV feeder was also tested with this NR-Software in ascertaining network reliability and in the course of adding public transformers to utility feeder network.

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The widespread presence of Coronavirus (COVID-19) is causing organizations and individuals major economics downsizing. The way this virus is transmitted from one individual to another is the real cause of the problem. For that, researchers in different fields started seriously looking for touch-less and contact-less exchange. Particularly in the finance world, cash transactions and keypad based transactions are becoming obsolete because they are some of the major causes of the spread of this virus (and other viruses and bacteria). Cryptocurrency could be one of the solutions to the above mentioned situation. This novel money is based on Blockchain technology, which is based on cryptography algorithms for the safety and the security of the transactions. This paper exhibits a comparative study of the asymmetric cryptography algorithms. This helps the user to best choose the most secure, safe and reliable method to encrypt the Blockchain.

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Big data analysis has important applications in many areas such as sensor networks and connected healthcare. High volume and velocity of big data bring many challenges to data analysis. One possible solution is to summarize the data and provides a manageable data structure to hold a scalable summarization of data for efficient and effective analysis. This research extends our previous work on developing an effective technique to create, organize, access, and maintain summarization of big data and develops algorithms for Bayes classification and entropy discretization of large data sets using the multi-resolution data summarization structure. Bayes classification and data discretization play essential roles in many learning algorithms such as decision tree and nearest neighbor search. The proposed method can handle streaming data efficiently and, for entropy discretization, provide su the optimal split value.

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Machine learning has been dramatically advanced over several decades, from theory context to a general business and technology implementation. Especially in healthcare research, it is obvious to perceive the scrutinizing implementation of machine learning to warranty the rewarded benefits in early disease detection and service recommendation. Many practitioners and researchers have eventually recognized no absolute winner approach to all kinds of data. Even when implicit, the learning algorithms rely on learning parameters, hyperparameters tuning to find the best values for these coefficients that optimize a particular evaluation metric. Consequently, machine learning is complicated and should not rely on one single model since the correct diagnosis can be controversial in a particular circumstance. Hence, an effective workflow should effortlessly incorporate a diversity of learning models and select the best candidate for a particular input data. In addressing the mentioned problem, the authors present processes that interpret the most appropriate learning models for each of the different clinical datasets as the foundation of developing and recommending diagnostic procedures. The whole process works as (i) automatic hyperparameters tuning for picking the most appropriate learning approach, and (ii) mobile application is developed to support clinical practices. A high F1-measurement has been achieved up to 1.0. Numerous experiments have been investigated on eight real-world datasets, applying several machine learning models, including a non-parameter approach, parameter model, bagging, and boosting techniques.

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Contextual continuous word representation showed promising performances in different natural language processing tasks. It stems from the fact that these word representations consider the context in which a word appears. But until recently, very little attention was paid to the contextual representations in Arabic question classification task. In the present study, we employed a contextual representation called Embeddings from Language Models (ELMo) to extract semantic and syntactic relations between words. Then, we build different deep neural models according to three types: Simple models, CNN and RNN mergers models, and Ensemble models. These models are trained on Arabic questions corpus to optimize the cross entropy loss given questions representations and their expected labels. The dataset consists of 3173 questions labeled according the Arabic taxonomy and an updated version of the Li & Roth taxonomy. We performed various comparisons with models based on the widely known context- free word2vec word representation. These evaluations confirm that ELMo representation achieves top performances. The best model scores up to 94.17%, 94.07%, 94.17% in accuracy, macro F1 score, and weighted F1 score, respectively.

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There is some agricultural equipment for post harvesting of Orange fruit Juice, all the machine is geared towards extraction of the fruit juice. Fruit juice extraction is the act of wringing out the juice content of fruits by way of an effective processing and storage which enhance reduction in wastage. Fruit juices which literally have high antioxidants help in increasing serum capacity of the body and at the same time balances the oxidative stress and discomfort normally caused by high-fatty and sugar meals. The history of juice extraction dates back to the nineteenth century. The extraction of juice from its fruit has progressed tremendously from the old tedious method of squeezing to an automated juice extracting machine across the world, making it an essential tool for citrus farmers. Juice extractor machine are classified broadly into four types centrifugal, masticating, Triturating and Press juicers which may be operated manually or electrically. Many attempts have been made to extract oranges using both manual and mechanized means. Traditionally the task of extraction of orange is easy but time consuming. The manual still requires the operator to remove the pulps and the seeds while the mechanized is a whole automated process made easy.
Both mechanized and manual fruit juice machines discussed in this work is developed to provide an affordable and user-friendly machine. These machine models exist all over the world with very few becoming popular while the rest got fizzle out due to their limitations. Then simple portable machines that squeeze the juices from the orange are to be reviewed in this work for better and healthy juice extraction.

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This study aims to provide a systematic and complete knowledge map for researchers in the field of virtual academic communities (VACs) and to help them quickly understand the key knowledge, evolution trends and research frontiers. This paper adopts the bibliometric method, with the help of bibliometric analysis software Citespace and VOSviewer quantitative analyze the retrieved literature data, and the analysis results are presented in the form of tables and visualization maps. Analysis of 372 literature data related to VACs from the Web of Science database shows that: the development of research in this field has gone through three stages and produced a number of representative key scholars and highly cited literature; the document co-citation knowledge map and keyword clustering map show the research hotspots in VACs; the results of burst detection and disciplinary overlay analysis reveal the research frontier, development trend and disciplinary coverage in VACs.

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A distribution company in Mexico covers the travel expenses for 21 sales representatives. Currently, the routes they follow are not established clearly, which can lead to high costs in this subject. A reduction of such cost is sought after, by optimizing the routes for each one of them. The following research finds an improvement on the routes for the sales representative of a distribution company in Mexico. It was done by using the Traveling Salesman Problem with Hotel Selection or Base selections via a Simulated Annealing algorithm. The results show an improvement in a reasonable timeframe by using the Simulated Annealing. It also shows that the maximum process time was of 156.63 minutes, and the least amount of improvement was 24.44% over the current route selection. Applying this model will be beneficial for the company as the company is trying to reduce costs related to the sales representatives such as; fuel cost, hotel cost, and travel expenses.

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Age and growth are two essential rice biophysics parameters used to determine the health parameters and production rate. The spatial data of both parameters can utilize remote sensing technology, which in turn makes use of several vegetation indices to achieve accurate estimation. However, due to the rapid changes in rice plants’ characteristics, it is essential to study vegetation index utilization using a multitemporal method to improve its accuracy. Therefore, this research uses a multitemporal Enhanced Vegetation Index (EVI) to estimate rice’s age and growth model. The multitemporal EVI patterns were observed to estimate the Time Early Planting (TEP) and the maximum EVI value of rice in an area. The results showed that the maximum EVI value in the rice fields of Demak Regency has a class range of 0.4 to more than 0.9. The highest value is in the class of 0.80 – 0.85 covering 12023.28 ha, followed by 0.75 – 0.80 at 11834.19 ha. Furthermore, the multitemporal EVI method on Landsat 8 images was used to estimate the rice age with accuracy or RMSE of 7.7 days. The result also showed that this value is good enough because the RMSE is still in the same range of paddy growth phases.

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The paper is devoted to the presentation of a new methodological approach (new philosophy) to the formulation and solution of directed search and synthesis of electromechanical objects for a given function problems. The object under the study is a class of electrical machines and electromechanical devices which operation is carried out with a variable structure or geometry of the active zone. The research is relevant due to the characteristic trend in the evolution of modern technology which is associated with the creation of complex technical systems with the ability to change the structure and spatial geometry of the executive body in accordance with changing of external factors. The novelty of the synthesis methodology is determined by genetic nature of the technical evolution of electromechanical objects and by genetic programs of structure formation using. According to the results of research the genetic principles and macrogenetic programs of electromechanical objects with variable spatial geometry of active parts have been determined for the very first time. The area of existence and results of genetic synthesis of “elastic” electromechanics objects are presented. The reliability of genetic models, genetic programs and the results of the electromechanical objects with adaptive spatial geometry of the active zone synthesis is confirmed by the results of evolutionary experiments.

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This paper deals with a multi closed-loop adaptive neuro-fuzzy inference system (ANFIS) design for the under-actuated quadrotor systems. First, the training data set for the fuzzy inference system is obtained using a proportional integral derivative controller. Then, an initial ANFIS controller is designed, where the integral control action is preserved in the multi-closed-cloop ANFIS for each quadrotor system state. Thereafter, scaling gains are added to the controller inputs/outputs, and a multidimensional PSO algorithm is used to tune all the control parameters. Besides, using a simulation example, the aerial vehicle performances are investigated in the presence of an unknown payload mass parameter. Specifically, the position tracking performances of the proposed multi closed-loop PSO-based ANFIS plus integral control strategy is compared with the classical PID, conventional ANFIS, and non-optimized ANFIS plus integral controllers. Thus, using the conducted simulation results, it results that the multi closed-loop PSO-based ANFIS plus integral can achieve perfect translational trajectory-tracking and ensure better attitude stabilization despite unknown quadrotor payload mass parameter. Therefore, the proposed new multi closed-loop PSO-based control strategy may be considered as an efficient controller when considering an arbitrary trajectory-tracking problem for the quadrotor system.

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In this industry 4.0 and digital era, we are more dependent on the use of communication and various transaction such as financial, exchange of information by various means. These transaction needs to be secure. Differentiation between the use of benign and malware is one way to make these transactions secure. We propose in this work a malware classification scheme that constructs a model using low-end computing resources and a very large balanced dataset for malware. To our knowledge, and search the complete dataset is used the first time with the XGBoost GBDT machine learning technique to build a classifier using low-end computing resources. The model is optimized for efficiency with the removal of noisy features by a reduction in features sets of the dataset by domain expertise in malware detection and feature importance functionality of XGboost and hyperparameter tuning. The model can be trained in low computation resources at less time in 1315 seconds with a reduction in feature set without affecting the performance for classification. The model gives improved performance for accuracy with the tuning of the hyperparameter and achieve higher accuracy of 98.5 and on par AUC of .9989.

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The pre-reason of the Smolensk catastrophe was a dense fog. The pilots took three trials to find the proper way to airfield. Each case the tower communicated: you are on the curse and path. Pilots, however, resigned. In third critical trial the co-pilot prolonged the response second ring to 8 seconds and the engines could not already take the plane up. It collided with a thick tree and made an upside down. Next it crashed on the ground and left a bloody trace of merely ~100 m long. This means that a mean acceleration at initial speed of 100 m/s and linear braking reached -50 m/s2. The real values could be even higher as the peak slowdown is always higher the mean. The clue of the Smolensk crash was then a fog and high azimuth error of radar. If it was correct but the ceiling too low, the plane could lose the under-carriage but avoided the upside down and the bloody crash.

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Previous study of butterfly life-cycle algorithms (BLCA) has been done to construct a novel algorithm by impersonating the life-cycle of a real butterfly. The algorithm was tested in measuring the company’s growth performance using a balanced scorecard (BSC) strategic planning model. This paper is proposed to continue the previous study by expanding the concept in measuring company growth, not only using BSC but also SWOT analysis as the strategic planning model and then to develop its application through coding procedure and more focused to code the assessment of BLCA. Then, we build the application using Java programming language. As a result, the model could be applied in companies to measure company’s growth performance by comparing those two strategic planning models.

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Millimeter-wave (mmW) antenna is one of the most important parts of the fifth-generation (5G) systems because of its advanced characteristics, for example, wideband and high transmission rate. In this paper, an mmW 4×1 array antenna with high gain and wideband based on an aperture coupled feeding patch (ACFP) antenna is presented. The proposed array antenna operates at 28-GHz frequency. The antenna has a wide operating bandwidth of around 12.6 % at -10 dB bandwidth that covers 26.65 GHz to 30.35 GHz. The peak gain of the array antenna is approximately 13 dBi at 28 GHz and kept maintained in all interested frequency band. The proposed antenna is designed using a 0.127-mm thick Duroid 5880 substrate with a compact substrate of dimensions of 25 mm x 48 mm x 0.754 mm.

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In the last decade, deep learning(DL) has witnessed excellent performances on a variety of problems, including speech recognition, object recognition, detection, and natural language processing (NLP) among many others. Of these applications, one common challenge is to obtain ideal parameters during the training of the deep neural networks (DNN). These typical parameters are obtained by some optimisation techniques which have been studied extensively. These research have produced state-of-art(SOTA) results on speed and memory improvements for deep neural networks(NN) architectures. However, the SOTA optimisers have continued to be an active research area with no compilations of the existing optimisers reported in the literature. This paper provides an overview of the recent advances in optimisation algorithms and techniques used in DNN, highlighting the current SOTA optimisers, improvements made on these optimisation algorithms and techniques, alongside the trends in the development of optimisers used in training DL based models. The results of the search of the Scopus database for the optimisers in DL provides the articles reported as the summary of the DL optimisers. From what we can tell, there is no comprehensive compilation of the optimisation algorithms and techniques so far developed and used in DL research and applications, and this paper summarises these facts.

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A rarefied gas flow is modeled inside two cases of triangular lid-driven microcavity using single (SRT) and multi-relaxation time (MRT) lattice Boltzmann approaches. In the first one, the right angle is in the top-left corner and the upper wall moves with positive horizontal velocity. However, in the second case, the right angle is in the bottom-left corner and the bottom wall moves with negative horizontal velocity. Unlike the classical form of square cavities, widely treated in the literature, the triangular form has a diagonal wall that affects the flow motion. At the moving wall, diffuse scattering boundary condition (DSBC) is employed while at the stationary sides, a combination of bounce-back and specular reflection boundary conditions (BSBC) is used. The computations are primarily performed in the slip and early transition regimes. The rarefaction effect, given by the Knudsen number (Kn) value, on the profiles of velocity components, is examined for both approaches. This study proves that for the higher values of Kn, the SRT-LBM approach cannot provide accurate results, particularly, near the inclined wall. However, the MRT-LBM approach confirms its validity even in the transition regime. A comparison with Direct Simulation Monte Carlo (DSMC) results for horizontal velocity contours shows the efficiency of the MRT-LBM approach than the SRT-LBM one which breaks down for rarefied flows.

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Modern digitalized cyber domains are extremely complex ensemble. Cyber attacks or incidents against system may affect capricious effects for another system or even for physical devices. For understanding and training to encounter those effects requires an effective and complex simulation capability. Cyber Security Exercises are an effective expedient for training and learning measures and operations with their outcomes in that complex cyber domain. Learning in cyber security exercises is relevant for different level actors in organisation hierarchy. Technical experts are able to train the technical capabilities whereas decision makers are able to train the decision-making capabilities under hectic cyber incident. In this paper, the pedagogical aspects of cyber security exercises are discussed in accordance with the law of the lifecycle of the cyber security exercise: planning phase, implementation phase, and feedback phase.

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ECG analysis is commonly used by medical practitioners and cardiologists for monitoring cardiac health. A high-performance automatic ECG classification system is a challenging area because there is difficulty in detecting and clustering various waveforms in the signal, especially in the manual analysis of electrocardiogram (ECG) signals. In this paper, an accurate (ECG) classification and monitoring system are proposed using the implementation of 1D Convolutional Neural Networks (CNNs) and Long Short Term Memory (LSTM). The learned features are captured from the CNN model, and then fed to the LSTM model. No handcraft features are required for the model for the ECG classification. The result of the CNN-LSTM model has demonstrated superior performance than several state-of-the-arts that cited in the result section. The proposed models are evaluated on MIT-BIH arrhythmia and PTB Diagnostics datasets. Based on the obtained results, the CNN-LSTM method can improve the accuracy rate, such that 98.1 % and 98.66 % on Myocardial Infarction (MI) and arrhythmia classification, respectively.

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Increased recycling of PVC has become a requirement in industrial and scientific research level. Several studies will be realized. To confirm and check the recycled materials performance, it will be important to go through numerical modeling, which consists not only in validating the results of the experiments, but also in predicting what happens when the material is loaded. PVC material is more used in different fields, that ultimately means, after service life, an increase in waste requiring a high recycling rate. This article presents an approach validating the aging model as well as a numerical analysis predicting the mechanical properties of PVC after aging. The analysis samples (rigid and flexible) PVC which are subject to two types of accelerated aging, allows to obtain an aging model. Numerical modeling of PVC after aging is carried out using the finite element method and has been able to confirm the results obtained experimentally. Predicting the mechanical properties of rigid PVC after aging loaded with coconut and cow horn fibers after a first recycling is made by the finite element method, Mori-Tanaka and Double inclusion models. The obtained results have showed an improvement in the mechanical characteristics of the PVC studied using this natural bio-loading with these two fibers which respect the environment and have a lower cost and more lightness.

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The most common non-communicable diseases, such as cardiovascular diseases and cancer, are a problem in global and national growth. The World Health Organization considers it a priority to study the specific causes of these diseases for trend monitoring. The aim of this paper is to identify a hierarchy of clusters of Portuguese mortality by non-communicable diseases using the agglomerative hierarchical cluster analysis. The Euclidean distance with complete linkage and average linkage criteria are used. These methods identify six clusters with both criteria, indicating some order of disease severity in the way clusters joint together. Special attention should be given to diseases in the last two clusters, where the last one is formed by ischemic heart disease, cerebrovascular diseases and larynx / trachea / bronchi and lung malignant tumor, all for males. In fact, these clustering results show that male gender seems to be a risk factor for at least two groups of the non-communicable diseases. Other suggested risk factors and / or pathophysiological mechanisms that in a direct or indirect way may enhance the common development of the pathologies found in the clusters arising from this study should also be an object of priority study.

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The study aims to demonstrate how the use of the design process can align a brand’s strategy with the interests of its target audience through a digital product based on a case study. Currently, Lexus internal studies show that there is a possibility to meet the needs of new audiences, beyond the traditional ones (men, 50 In order to achieve this goal, the following objectives have been defined: communicate the brand and its cars’ main values and philosophy, namely ecology, economy, safety and comfort; promote its services and products; and design an interface that guarantees users easy, pleasant and attractive navigation. The work process consisted of identifying the brand’s strategy and values, identifying users, analyzing the main competing brands in the market, and designing a prototype within the framework of User-Centered Design, for which it had to address crucial issues such as the application of character models, UX and UI design, the creation of wireframes and user flows, interface design and the development of usability tests. The results demonstrate that it is possible to align the strategic interests of brands with the needs, objectives and expectations of users in a context of increasing global concerns of citizens related to reuse and sustainability. In this sense, it is vitally important that brands adopt Design processes in order to converge their own brand interests with people’s demands.

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The objective of this article is to highlight the relationship between the integration of sustainable development practices in Moroccan Small and Medium Enterprises (Smes) and the challenge sought by managers in order to ensure competitiveness and sustainability. Indeed, present-day Smes are operating in a world where economic, social, societal or environmental constraints are constantly evolving. The Sme has to face, on a daily basis, new challenges such as economic stability and sustainability alongside the major structures because the challenge of any company is to be able to differentiate itself by adding its own trace promoting a more stable positioning in a fairly hostile environment. The investigation is carried out on a sample 30 Smes in the city of Kénitra due to the importance that this city holds in terms of both geographical dimension and economic inputs on the regional scale, 13.6% of the jobs generated by the SME according to the report of the high Planning Commission (HCP 2017). The results obtained reveal that the major concern of the managers of the Moroccan Sme is a purely commercial concern and that the integration of the various practices related to sustainable development in their managerial vision is only a means and does not constitute an objective to ensure the expected survival and sustainability.

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This study primarily aims to identify the factors that helped emo kids in 2010 move through the emo-identity discrimination and be able to obtain a certain level of achievement. Facebook is the social network that allows users to track friends’ posts back over 10 years. Content analysis was conducted by using two coders to rate 1,432 Facebook posts published during 2010 until 2019, based on the variables: (1) emo-related content, (2) emotion expression, (3) being a part of fandom, (4) the group of fandom, (5) friend(s) and (6) family appearance. The results from logistic regression analysis reveal that past emo kids’ Facebook posts are increasing in the contents about friends, family, and fan objects over time. However, the number of posts with emo music or emo fan object was reduced. Having social support and belonging to social group, like fandom, might help past emo kids overcome the hard time that they had got prejudiced. Future studies should develop a model or existing theory to explain the complexity of individuals’ overlapping identities blended in social networking profiles.

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This paper proposes a closed association rule generation technique to investigate the association patterns of diseases that are frequent co-occurrence. Diseases records of 5,000 patients are studied to find the association patterns of disease co-occurrence. The CHARM algorithm is adapted to find frequent diseases that can cover all-important patterns with a small number. Then the association patterns of disease co-occurrence are created in a form of association rules from the frequent diseases. The rules represent diseases associated with other diseases. Accuracy and prediction ratio are defined to evaluate the generated association patterns. From the experimental results, the generated association patterns give 79.76% of accuracy and 84.03% of prediction ratio although the number of generated association patterns is small. Moreover, the top-10 association patterns of disease co-occurrence are investigated. Besides, the 5 most frequent diseases are found to deeply study the other related diseases of them. From the investigation, we found that diabetes mellitus, metabolic disorders, and renal failure are highly related to hypertensive diseases with 88.81% of confidence. In addition, we found that influenza and pneumonia, plastic and other anemias are highly related to metabolic disorders.

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Feature extraction is an important process in image classification for achieving an efficient accuracy for the classification learning models. One of these methods is using the convolution neural networks. The use of the trained classic deep convolution neural networks as features extraction gives a considerable results in the remote sensing images classification models. So, this paper proposes three classification approaches using the support vector machine where based on the use of the ImageNet pre-trained weights classic deep convolution neural networks as features extraction from the remote sensing images. There are three convolution models that used in this paper; the Densenet 169, the VGG 16, and the ResNet 50 models. A comparative study is done by extract features using the outputs of the mentioned ImageNet pre-trained weights convolution models after transfer learning, and then use these extracted features as input features for the support vector machine classifier. The used datasets in this paper are the UC Merced land use dataset and the SIRI-WHU dataset. The comparison is based on calculating the overall accuracy to assess the classification model performance.

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Storage-Class Memory (SCM) like OptaneTM has advanced as a fast storage medium, and conventional memory management systems designed for the hard disk storage need to be reconsidered. In this article, we revisit the memory management system that adopts SCM as the underlying storage medium and discuss the issues in two layers: file caching and virtual memory paging. Our first observation shows that file caching in the SCM storage is profitable only if the cached data is referenced more than once, which is different from the file caching in hard disks, where a single hit is also beneficial. Our second observation in virtual memory paging shows that the page size in the SCM storage is sensitive to the memory system performance due to the influence of memory address translation and storage access cost. Our simulation studies show that the performance of paging systems can be improved by adjusting the page size appropriately considering application characteristics, storage types, and available memory capacities. However, the page size will not be a significant issue in mobile platforms like Android, where applications are killed before the memory space is exhausted, making situations simpler. We expect that the analysis shown in this article will be useful in configuring file caches and paging systems with the emerging SCM storage.

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The insulation condition of a transformer decides the longevity of the equipment. The unpredicted failure of power transformer will lead to major disaster in the distribution network and it affects both environment and public safety. Nowadays synthetic oil and natural esters are alternatives to transformer oil because of the biodegradable nature. In this paper, investigations were carried out to study the performance of the blended ester. The different properties investigated were viscosity, breakdown voltage, flash point, dielectric dissipation factor and moisture content. Comparisons of the properties were made between mineral oil, vegetable oil without additives and with additives. Further Investigation was carried out to study the impact of antioxidants and degasification. The results indicated that the addition of antioxidants and degasification of the vegetable oil improve significantly its voltage withstanding capacity. The Indigenous oil is code named as DM; Indigenous oil with DBPC is codenamed as DM1, Indigenous oil with BHA is codenamed as DM2. The results have been tabulated and found to be satisfactory.

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Cloud computing is the development of information technology to provides resources that can be accessed through network. Security and privacy in cloud computing are major concern for companies. Therefore, cloud computing architecture strategy and design are needed to reduce costs and ensure the security of company assets in cloud computing. In this study, we are using multi-layered security strategy that contain of Next-Generation Firewall and Web Application Firewall technologies. We conducted an evaluation on the design by using the SQL injection and malicious file injection method. The results of the evaluation show that the cloud computing architecture design that we proposed manage to prevent SQL injection and malicious file injection threats.

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The functional priority of Health Information Exchanges (HIEs) is to ensure a timely transference of health data. Because of the frequency at which health records are transferred and the variety of health data HIEs receive, there are major challenges in efficiently accomplishing this. Cloud technology has become a generally accepted solution to remediate data transference issues but for HIEs, there are usually no clear paths to adoption.
Enterprise Architecture (EA) is a tool that can ensure the smooth adoption and implementation of technologies. This paper is an extension of a proposal originally presented in the 14th International Conference on Ubiquitous Information Management and Communication (IMCOM) [1]. In that article, an enterprise architecture that supports an HIE migration to a cloud architecture was proposed. This paper presents an EA migration plan and proposes an institutionalization framework, highlighting how the proposed EA can be applied to an HIE. A validation framework is provided as a means of testing the implementation of the HIE migration EA.

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The smartphone is a phenomenal device or tool that recently transform the habit of people. In this part, the smartphone serves not only as a communication device but also more complex functionality. The early research reported that smartphones are used by university students to support their social and academic activities. However, the research report that explores how much influence factor of smartphone for educational university student activities is scarce, especially for academic achievement index. Therefore, this explanatory research examines the exploration of the smartphone influence factor with university student background (such as gender, achievement index), academic, and social activities. This study applied SPSS tools with the Correlation Bivariate technique to investigate the influence factor and successfully obtained 17 essentials correlation or relationship between factors.

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Information on alternative technologies or new prototypes was analyzed to help improve the quality of business management services. The problem is the lack of application of new technologies that improve the quality of service and reduce disagreements in the management of organizations. The objective is to present a prototype for the management of engineering and ICT companies to improve the quality of services. The deductive method and exploratory research were used to analyze the information. The result was a mixed conceptual model of quality of service approach, a dual-security architecture for business management, control of algorithms with processes for quality of service and control of algorithms with information filter. It was concluded that an information filter with Blockchain is considered a fundamental alternative to provide control, reliability, immutability and improved quality of service.

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Dynamic modeling has been a crucial study in many areas of the engineering field. In this paper, we apply the Newton-Euler equation of motion to a two-DOF parallel mechanism solar tracker which is a close loop mechanism. The aim of this study is to show a simulation of the dynamical model with feedback control using a PD controller to orientate the solar panel perpendicular to the sun rays. The mechanism is modeled in the form of a system of algebraic differential equations. First, kinematic constraint equations were constructed in the form of algebraic equations to specify the dynamic interactions at joints. We use the Baumgarte stabilization method, a constraint violation method to eliminate computational error incurred by numerical approximation. Then, the dynamic equations of the system were formulated using the Newton- Euler equation of motion. To describe the translation and rotation motions, we apply Cartesian coordinates and Euler parameters. Simulation of driving the solar panel to reach the desired configuration is made, and the result shows that the PD controller provides good performance of the mechanism regardless of the complexity of the dynamic behavior of the mechanism.

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The crisis linked to the COVID-19 and the uncertainty it generates in the unprecedented health, societal, economic and financial fields have had a strong impact on the stock markets. Indeed, in such a climate of very high uncertainty, it is to be expected that the excessive stock market price movements will continue, with both declines and technical rebounds, and that the resulting volatility will remain particularly high. In order to cope with this crisis, investors and portfolio managers must mobilize all portfolio selection strategies. In particular, portfolio management and construction are based on the concepts of return and risk. This couple has been at the center of all the concerns of managers and investors in portfolio optimization issues since the introduction of the mean-variance model by Markowitz [1], [2]. However, many studies have proposed different measures of risk to overcome the drawbacks of variance. The objective of this paper is to present and compare the portfolio compositions and performance of four different portfolio optimization models using different risk measures, including variance, Mean Absolute Deviation, Gini coefficient and Lower Partial Moments (LPM). The results of this study show that the Mean-Lower Partial Moments (MLPM) model outperforms other models. The Mean-Lower Partial Moments (MLPM) model is suitable for investors during the crisis period (COVID-19) in the Moroccan financial market.

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To properly simulate and implement a quadcopter flight control for intended load and flight conditions, the quadcopter model must have parameters on various relationships including propeller thrust-torque, thrust-PWM, and thrust–angular speed to a certain level of accuracy. Thrust-torque modeling requires an expensive reaction torque measurement sensor. In the absence of sophisticated equipment, the study comes up with alternative methods to complete the quadcopter model. The study also presents a method of modeling the rotational aerodynamic drag on the quadcopter. Although the resulting model of the reaction torque generated by the quadcopter’s propellers and the model of the drag torque acting on the quadcopter body that are derived using the methods in this study may not yield the true values of these quantities, the experimental modeling techniques presented in this work ensure that the derived dynamic model for the quadcopter will nevertheless behave identically with the true model for the quadcopter. The derived dynamic model is validated by basic flight controller simulation and actual flight implementation. The model is used as basis for a quadcopter design, which eventually is used for test purposes of basic flight control. This study serves as a baseline for fail-safe control of a quadcopter experiencing an unexpected motor failure.

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This paper has explored the technology beliefs of university students considering four parameters. We have proposed an automatic belief identification system for academic institutions. For this, we used two different clustering algorithms to segment the student group with different beliefs about the technology. In the Hierarchical Clustering (HC), the Agglomerative approach was followed. The beliefs were segmented with Ward’s method and Squared Euclidean Distance (SED). The HC method recommended a maximum of three and a minimum of two optimal clusters. Later, we applied K-Means clustering on 37 features to validate the initial cluster solution. Based on ANOVA’s results, we select 20 significant features that contributed most to detect dissimilarity in students’ beliefs. The findings of the paper proved that suggested features stabilized clustering as compared to all features. The novel features provided three clusters: cluster 1 with 27.61%; cluster 2 with 34.36%; cluster 3 with 38.04% students with similar beliefs about the technology. Based on the results provided, we found the high (mean>3.5), undecided (mean:1.73-3.63), and hybrid (mean:1.34-4.68) beliefs towards the technology available at university. We also recommended the selected features to be used as predictors for the online belief detection system. The university administration needs to cure students belonged to undecided groups.

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Electrical and optical properties of polymer nanocomposite thin films have been analyzed to study their reliability and competency as a component for optoelectronic devices such as LED and solar cells. Polymer nanocomposite encounters various challenges, such as the dispersion of nanoparticles in the matrix that hinders their efficiency for potential devices. In this paper, two types of polymer nanocomposites have been fabricated, and their Johnson noise, current density-voltage, and optical have been measured. The first type of nanocomposite produced through an in-situ method, that is by impregnating CdS or CdSe nanoparticles in conjugated polymer, P3HT (NP-CdX:P3HT). The nucleation of the nanoparticles was done using gas exposure. The second type is by directly adding CdS or CdSe quantum dots into P3HT (QD-CdX:P3HT). Both kinds of polymer nanocomposite thin films were fabricated using modified Langmuir-Blodgett technique. Results showed that for frequency above 10 Hz, the Johnson noise was less than 1× 10-27 A2/Hz, regardless of the quantity of quantum dots or nanoparticles. The J-V results show (NP-CdX:P3HT) electrical performance compared with QD-CdX:P3HT. High polymer crystallization of NP-CdX:P3HT thin films is revealed by UV-Vis absorbance spectra. The quantum confinement effect is evidence through peak shifting and depreciation of absorption. The photoluminescence intensity of thin films decreased when they were exposed to the gas. It can be concluded that the NP-CdX:P3HT nanocomposites can be further studied as they have greater potential to be exploited in optoelectronic devices.

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This paper is an extension of Novel Flexible Chip-less Bow-Tie RFID tag in which we presented the design, testing and fabrication of the tag and compared the results with Octagonal Chipless RFID tag. The chipless RFID tag was designed by using simulation software CST microwave studio and fabricated by using laser etching technique on a flexible polymer substrate Polyethylene Terephthalate (PET). The tag operates at frequency ranging from 8 to 18 GHz uses the Frequency Selective Surface (FSS) approach. A series of experiments are performed to measure the Radar Cross Section (RCS) in an anechoic chamber. The tag design is composed of six concentric Bow-Tie shaped loop resonators with one unitary element. In this paper, we demonstrated the Singularity Expansion Method (SEM) based circuit modelling and the transient behaviour of the RFID tag is performed. The coupling coefficients and the induced currents over the surface of Bow tie shaped rings are evaluated. The maximum read range is evaluated and the Bow-Tie RFID tag is proved to be more accurate and efficient with the variation of distance up to 1.8m at 0dBm which is extendable to 2.14m for higher input power. This range is maximum to our knowledge for such a high-frequency range of 8-18GHz. The 4-bits Bow-Tie Chipless RFID tag design is compact and can be deployed commercially for general IoT applications.

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Brain tumors are a type of tumor with a high mortality rate. Since multifocal-looking tumors in the brain can resemble multicentric gliomas or gliomatosis, accurate detection of the tumor is required during the treatment process. The similarity of neurological and radiological findings also complicates the classification of these tumors. Fast and accurate classification is important for brain tumors. Computer aided diagnostic systems and deep neural network architectures can be used in the diagnosis of multicentric gliomas and multiple lesions. In this study, the Deep Neural Network classification model with Synthetic Minority Over-sampling Technique pre-processing was used on the Visually Accessible Rembrandt Images dataset. The proposed model for the classification of brain tumors consists of 1319 trainable parameters and the proposed method has achieved 95.0% accuracy rate. Precision, Recall, F1-measure values are 95.4%, 95.0% and 94.9% respectively. The proposed decision support system can be used to give an idea to doctors in the detection of glioma type tumors.

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This paper presents a comparative study for using the classic networks in remote sensing images classification. There are four deep convolution models that used in this comparative study; the DenseNet 196, the NASNet Mobile, the VGG 16, and the ResNet 50 models. These learning convolution models are based on the use of the ImageNet pre-trained weights, transfer learning, and then adding a full connected layer that compatible with the used dataset classes. There are two datasets are used in this comparison; the UC Merced land use dataset and the SIRI-WHU dataset. This comparison is based on the inspection of the learning curves to determine how well the training model is and calculating the overall accuracy that determines the model performance. This comparison illustrates that the use of the ResNet 50 model has the highest overall accuracy and the use of the NASNet Mobile model has the lowest overall accuracy in this study. The DenseNet 169 model has little higher overall accuracy than the VGG 16 model.

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Estimation of displacement is an information required for daily operation monitoring systems to monitor human health or to locate users in buildings, basements, tunnels and similar places which under the same conditions that the global positioning signal (GPS) level is from very weak to completely absent; and is the measurement technique by using multimetric data fusion. Most current displacement estimation methods require a lot of infrastructures and devices such as UWB, wifi access points, cameras. Hence, estimation methods that ultilize inertial measurement unit (IMU) and integrate acceleration to get diplacement are effective alternatives since the three-axis accelerometer embedded in IMU usually low cost, easy to adjust and low noise. The advantage of this approach is that the IMU-based device is compact, easy to install and put on user’s body. However, these methods expose some weaknesses when used in large-scale indoor structures such as multi-storey buildings due to the need to compensate azimuth estimation which is drifted overtime and is employed for calculating displacement with refer to earth frame as a base station. This article proposes a low-cost wireless displacement estimation system developed with IMU. The system employs a Kalman-filter type in indirect form for orientation estimates and Median-filter algorithm for classification of motion modes. In order to verify the proposed system in terms of accuracy and feasibility, a device was designed in a wearable form and tetsted on a multi-storey building in university. The wearable device ultilized IMU model MPU9250 and results recored wirelessly via Xbee devices in order to test the system performance in such senarios as climbing/descending staircases only, climbing/descending staircases through one floor combined with walking. Experiments are repeated for root mean square error (RMSE) computation based on the ground-truth. The proposed system performance is evaluated accordingly to RMSE. The experimental results demonstrate RMSE of 3.56%, 1.43%, for climbing/descending staircases only, climbing/descending staircases one floor combined with walking, respectively.

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As manufacturing efficiency has become a main focus of today’s business, it is very critical to surge the throughput by developing different test strategies. With throughput, testing cost also has been recognized as the major challenge in the future of leading semiconductors. Reducing test time is a significant effort to maximize throughput as the complexity increases in future generation outcomes and devices. So, low-cost Automatic Test Equipment (ATE) with parallel test can be promoted as the obvious solution for challenges said above. In parallel testing, multiple devices-under-test (DUT) can be tested at a time that enhances way of testing by increasing product flow, limiting gross test times, and efficient usage of tester. The proposed Integrated Circuit (IC) tester is used to implement multi-site testing (Quad-Site testing) and concurrent testing. It exhibits multi-site efficiency which substantially enhances the throughput by reducing test time. Modular, re-configurable test system provides cost-saving solution. To confirm these effects, authors have presented experimental results for Quad site testing of different ICs namely Decoder, Buffer, Multiplexer and Logic gates. This portable IC Tester handles variety of IC packages like Dual Inline Package (DIP), Small Outline Integrated Circuit (SOIC), Thin Shrink Small Outline Package (TSSOP). With functional test, the proposed tester also verified the AC Parametric tests (i) Propagation Delay is 20ns (ii) Operating frequency with 50MHz for Decoder IC (74HC138). The proposed IC tester consumes 70% less power and throughput enhanced by 11% compared to existing IC testers.

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In recent years, the transport of large packages with super weight from 100 tons to several thousand tons is no longer a difficult problem due to the continuous development of technology. Experienced transport companies, specializing in transporting heavy goods in Vietnam, have invested in very modern equipment and machinery such as self-propelled trailers of Cometto (Italy) in order to transport safely mentioned parcels of great economic value arrive at the requested location. This trailer can be self-propelled, does not need to use a tractor, and only needs to use a remote-control handheld device. Moreover, the trailer gear shaft can rotate 360 degrees. In particular, the hydraulic system supports trailers operating with high accuracy and absolute safety including functions such as 360-degree rotation, lifting, transmission, braking, etc. In order to improve the performance of trailers when actually used in large projects, an important detail in the trailer’s hydraulic system has been inserted a throttle valve with to increase the safety of the hydraulic pump and the entire system as well as the safety of the goods that trailers are transporting. The trailer system has transported the rig with a capacity of up to 3,200 tons in Vietnam, the shipment of 15,000 tons in the world and beyond in the future.

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This work presents an innovative solution for parking-sharing of private institutions based on daily occupancy patterns and using different real time tracking methods of vacant parking slots. The research objective consists in finding the most accurate cars detection method, for determining of vacant parking slots and updating them on application web page. Beside the technical innovation represented by image processing algorithms used, this paper promotes the concept of sharing economy with many social benefits like car flow optimization, reducing fuel, pollution, loss of time and creating financial advantages for parking owners. The main software component is a web application which is connected with Raspberry Pi microcontroller, 2 Pi cameras and one fix camera for parking management. It facilitates reserving a place, opening the barrier and allows entering, exiting and revising the number of vacant slots and synchronization with the web application and the supporting database. The web application provides the following facilities: real time parking status view, reservation on a specific time by license plate number, administration module that includes payment system and updates about users and prices, implementation of the gamification concept in the management of parking spaces. The solution was piloted at Lucian Blaga University of Sibiu (LBUS) Romania. The developed solution is flexible, extensible and applicable to crowded university cities, but also to other private organizations that have inefficiently operated parking slots.

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Visualization plays an important role in teaching and learning. The ability of the learner to grasp the visual contents are better than that of textual contents. Traditional teaching methods often revolved around instructions and recitation techniques. However, most of these approaches were dormant and did not call for active learning. The proposed AGDO- Analyze, Group, Design & Optimize methodology was inspired by these obstacles and creates an engaging and wholesome experience for the students. A sample of 47 undergraduate students was used to analyses the feasibility and effectiveness of this teaching mechanism. Students first tackled and analyzed the problem statement with their preconceived knowledge. They were then segregated into groups wherein they designed solutions collectively. Finally, they tried to find an optimized solution considering all the suggested designs. Observations were validated based on feedback and how well the students were able to perform. The results revealed that approaches such as AGDO facilitate an immersive learning environment and ensure the quality of teaching. The implications and methodology were further discussed.

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Hearing impairments have a negative impact in the lives of individuals living with them and those around such individuals. Different applications and technological tools have been developed to help reduce this negative impact. Most mobile applications that have been developed that use Speech-to-Text technology have been inconsistent such that they are not inclusive of all types of hearing impaired individuals, only work under specifically predefined environments and do not support conversations with multiple participants. This makes the present tools less effective and makes hearing impaired participants feel like they are not completely part of the conversation. This paper presents a model that aims to address this by introducing the use of Multiple-Speaker Classification technology in the design of mobile applications for hearing impaired people. Furthermore we present a prototype of a mobile application called Deaf Chat that uses the newly designed model. A survey was conducted in order to evaluate the potential that this application has to address the needs of hearing-impaired people. The results of the evaluation presented a good user acceptance and proved that a platform like Deaf Chat could be useful for the greater good of those who have hearing impairment.

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This examination plans to investigate the task issue in the educational program designer group utilizing the Ones Assignment Method. The calculation of this technique as follows. At first characterized the task grid, at that point by utilizing determinant portrayal acquired a diminished framework which has in any event one in each line and segments. At long last, it acquired an ideal answer for task issues by allocating ones to each line and every segment. This strategy depends on making somewhere in the range of ones in the task network and afterward attempt to locate a total task. The huge favorable position of this technique is a methodical system, simple to apply, and can be used for a wide range of task issues with amplify or limit target capacities. The contextual analysis of this exploration is a counseling organization creating science educational program with 5 specialists and 5 phases of educational program arranging and improvement process. The exploration bring about case minimization time of 48 days.

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A meaningful User Experience can indicate successful of the application, so it is important to provide positive experience which aims to maximize the usability of an application. The purpose of this current study is to evaluate the user experience of an application called Teksologi: Gamification interactive typing for visually impaired children in Indonesia. User Experience Questionnaire (UEQ) were applied to measure several aspects include attractiveness, dependability, efficiency, perspicuity, novelty, and stimulation. A total of 22 visually impaired participants had been involved for this study. Besides, Alpha Cronbach test and directly check the means of each items inside the scales to draw the conclusions were conducted to examine the consistency of each aspect, then comparison to UEQ benchmark also applied. The result show that all the quality aspect is positive. At the same time, stimulation had the highest value, but perspicuity had the smallest value that enhancement will be needed to make user feel easy to get familiar with the application, easy to learn to use the application.

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Obstructive Sleep Apnea (OSA) is a potentially common sleep disorder manifested in upper airways’ collapse, either partially or completely. If diagnosed late and untreated, it may result in serious complications. The standard golden method, useful for treating OSA, remains the full night Continuous Positive Airway Pressure (CPAP). Yet, due to the ensuing discomfort it incurs on patients, researchers have been motivated to investigate other efficient alternatives, whereby, OSA can be effectively treated. More recently, an increasingly popular OSA treatment solution has been developed that consists in activating the protrusion muscles of the tongue by stimulating the Hypoglossal Nerve (HGN). After discussing the classification topologies of some neural stimulator output design stages, we consider putting forward the design of output stage of Hypoglossal Nerve Stimulator (HGNS), in compliance with the HGN requirements. The proposed HGNS is simulated in cadence software using the 0.35µm CMOS technology and Matlab software. It proved to enable the delivery of a maximum stimulation current of 1mA through 1k? resistive load under 3.3V voltage supply. The output stage managed to achieve large voltage compliance of 94% of the voltage supply and high output impedance of 275M? at full scale stimulus current. The total power consumption is about 705.87µW.

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Objectives: Describe the clinical characteristics and home care of patients diagnosed with COVID-19 in a Primary Healthcare Facility of the “Mi Peru” district, in the Callao Region, Peru. Materials and methods: Observational and descriptive cross-sectional study. A total of 84 subjects with positive results for Rapid Test IgM / IgG or molecular test (RT-PCR) participated. Results: Of the sample, 59.5% (n=50) were males, 60.7% (n=51) aged 30 to 59 years and 21.4% (n=18) aged 60 to over of age, 67.9% (n=57) were positive on the rapid test and the rest on the molecular test. Diabetes and high blood pressure were the main pathological antecedents. Among the most frequent clinical manifestations were general malaise, fever and cough, with 40.5% (n=34), 36.9% (n=31) and 31% (n=26), respectively. The most frequently prescribed medications were acetaminophen and azithromycin, 25% (n=21), and 21.4% (n=18), respectively. Regarding home monitoring of patients diagnosed with COVID-19, the first week was performed with a higher proportion of those affected, progressively decreasing in the second week. Conclusions: Evidence of an important response to COVID-19 is shown in the scenario of the first level of care. Mild and moderate cases can be treated outpatient and complemented with home monitoring, contributing to the containment of the pandemic.

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This investigation deals with the identification of the suitable empirical models for predicting radio wave propagation path losses in Erbil city of Kurdistan region in Iraq. For this purpose, two sites of Korek Telecom operating at 1800 MHz and 2100 MHz have been selected at urban and sub-urban environments in the city and seven different empirical path loss model named free space model, ECC-33, Stanford Interim University (SUI), Optimized Cost-231, Okumura-Hata, Egli, and Ericson models are tested against experimentally measured path loss values. The path loss was measured experimentally using Sony Ericson handset with complete required equipment and the results are compared to that predicted by each of the mentioned empirical models. The results which have been analyzed through the identification of Root Mean Square Error value achieved by each model revealed that the Optimized Cost-231 and Ericson model provide lowest Root Mean Square Error values. They provide RMSE values of 9.21, 9.38 and 1.58 and 4.12 at
operating frequency of 1800 MHz, while at frequency 2100 MHz they gave 7.28, 6.07, 11.86 and 7.54, for sub-urban and urban areas, respectively. Therefore, it was concluded that the Optimized Cost-231 followed by Ericson model would be more reliable and best candidate for use in planning and designing network communication in the urban and suburban areas in the investigated area.

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This paper extends the gamification interactive typing for Primary School Visually Impaired in Indonesia with some development according to previous user’s feedback. This study focuses in to renew the application and evaluate the updated application for visually impaired children developed by utility Unity software. Besides, standards of good gamification are worthy of study and can increase the motivation to learn, and it only can happen if it meets the needs of the user. To achieve those goals, it has completed some development on several sections includes the homepage, input text, text size, and scoring. In this paper, System Usability Scale (SUS) is utilized, and some statistical model is conducted such as average, mean, Pearson-Product Moment correlation, T-test, and ANOVA. T-test results show that no difference between the partial and fully visually impaired participant, gender, and participants who used a similar application and not. The grades do not affect the SUS score, so it proved that the collected SUS score as average 75 is an objective result from the users although the average grade is 46. Moreover, both variable usability (0.884) and variable learnability (0.771) are positively correlated toward the System Usability Scale, notwithstanding variable usability and variable learnability is not correlated (0.383). The impact of this research can improve the industries especially the education field in Indonesia and some expectations from that result are included experience, knowledge, and skills of the users that need to be evaluated in further research. Hence, in the future, by using this application, we can increase the standard of living visually impaired people and enhance industry 4.0 in Indonesia.

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Nowadays, heart diseases cause the maximum death in the world. Also, due to the noticeable increase of heart diseases, studying this field is one of the important matters in medical community. Therefore, this study tries to benefit using information in data base of cardiac arrhythmia and employ arterial intelligent and neural network, in order to improve the speed in getting cardiac signals with minimum errors and maximum certainty.
The dataset for the project is taken from the UCI machine learning repository https://archive.ics.uci.edu/m1/datasets/Arrhythmia. Used data base has 279 characteristics taken from 364 patients that includes general characteristics and ECG signals received from patients. In this study, firstly the primary classification has done with all characteristics, so some parts of information in data base of cardiac arrhythmia with values near zero has omitted. Considering the improvement of accuracy of the classification after omitting the characteristics near to zero, in next level the second series of data in neural networks that has negative effect on classification has omitted. In order to increase the accuracy of neural network and minimize the number of characteristics, the characteristics has classified in multiple classes and the obtained ratio has improved using genetic algorithm. In this level, the best accuracy of the neural classification has obtained but in order to get a network with minimum characteristics possible and preserve the 100% accuracy of the classification, ineffective characteristics has omitted using PCA algorithm

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This study aims to review English as a foreign language learning students’ perception of blended learning on various aspects of learning process. The study seeks to answer the question as to whether there is any correlation among students derived independent variable and their perception with respect to the merits and demerits of online blended learning. The Q-methodology was used whereby the questionnaire method was applied and data collection was supported by the Q-Sort data collection model which involves 251 learners who voluntarily presented themselves for Q-Method procedural participation and learning at third and fourth grade of English Language Teaching at the university identified within United Arab Emirates. The results showed no statistical difference in means for students’ perceptions towards blended learning at significant level 0.05. This thus confirms a higher degree of acceptance for blended learning versus old class mode of learning. The study, therefore, concludes that individual student success was highly increased in blended learning encounters as compared to either complete online or wholly face to face learning encounters. Online blended learning has been seen to optimize maximum benefits of old teaching methods and access to online learning materials.

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This paper highlights the importance of on-campus online job recruitment system and its role in helping students grab the available job opportunities. It highlights the problems associated with the traditional way of hiring, especially for college students. It also presents some findings and results obtained through various surveys conducted within the campus before and after the deployment of this system. The work presented in this paper is based on an e-recruitment system built for one of the leading engineering institute in Nepal, Pulchowk campus. With some features like job recommendation based on various levels of skill, smart multi-criteria search, graduate tracking, this system proves to be useful for all i.e. companies, students and the campus as well.

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An experimental study on the effect of a cover number of solar distillers with a continuous seawater feeding system was conducted. The seawater feeding in this study was a continuous feeding, which was not utilized yet in the previous studies. Three identical distillers i.e. single caver distiller, double cover distiller, and triple cover distiller were designed and examined. The material tested in this study was seawater taken from Tanjungkarang beach, Mataram, NTB, Indonesia, and converted into distilled water through distillation processes. The overall size of the distillers used was 1136 mm x 936 mm x 574 mm (outer dimension), while the absorber plat size was 0.8 m x 1 m. The experiment was performed in July 2019 from 09.00 to 16.00 local time. The results showed that increasing the number of glass cover decreased the amount of distilled water. The single cover distiller resulted in 949 ml a day, while the double and triple cover distillers resulted in 260 ml and 88 ml a day. The distiller with 3 glass covers was hotter than others so that the seawater vapour could not condense on the glass cover. Therefore, the distiller with a single glass cover was recommended.

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Due to high failure rates many students end up spending unnecessary years struggling to qualify and subsequently accumulate unnecessary debt. In this paper, our principal contribution is to provide an expert system that statistically predicts the success of a first year student in an undergraduate Science programme given only academic merit in their subject matter. Over the past decades, much work has been done in the field of predicting student success in first year computer science and in other first year courses. Historically, other authors focused on using linear statistical models to predict student success. These models had limitations as the prediction was designed for inference as compared to machine learning techniques. This paper presents an approach of using the naïve Bayes classifier, support vector machines and decision trees as models that can be used to predict the completion of an undergraduate science degree. This was done by firstly training the classifiers and then testing them. The support vector machine achieved the best accuracy (87%) in predicting the completion of a science degree based only on first year marks, this was followed by the naïve Bayes model (86.36%) and the decision tree (65.62%) came last.

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Stop and Wait (SW) ARQ, Go Back N (GBN) ARQ, and Selective Repeat (SR) ARQ are the main ARQ (Automatic-Repeat-reQuest) protocols used to ensure reliable delivery of digital data at correct sequence. These protocols are implemented at the DLC (Data Link Control) sub layer of Data Link Layer (DLL) of OSI (Open System Interconnection) network model. The main task of such protocols is controlling errors and providing smooth and reliable transmission between communicating nodes. This is mostly done by using acknowledgements and timeouts to satisfy reliable data transmission over unreliable channels. This paper completes measuring the performance of SR ARQ protocol. We study and analyze the service time distribution of SR ARQ protocol used in digital data transmission over unreliable channels. Stochastic Process and Markov Chains have been used to study the proposed network model. Closed and analytic expressions of the Probability Generating Function (PGF) of service time are calculated considering two different situations (short and long messages). Moreover, expressions for first and second moments of the service time are derived. ARQ protocols are basically applied on shortwave radio to provide reliable delivery of signals and in peer-to-peer protocols that provide reliable data transmission. The obtained results can be applied in simulations of similar communication systems and may be adopted in approximating some relevant systems.

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This article discusses the problem of handwriting recognition in Kazakh and Russian languages. This area is poorly studied since in the literature there are almost no works in this direction. We have tried to describe various approaches and achievements of recent years in the development of handwritten recognition models in relation to Cyrillic graphics. The first model uses deep convolutional neural networks (CNNs) for feature extraction and a fully connected multilayer perceptron neural network (MLP) for word classification. The second model, called SimpleHTR, uses CNN and recurrent neural network (RNN) layers to extract information from images. We also proposed the Bluechet and Puchserver models to compare the results. Due to the lack of available open datasets in Russian and Kazakh languages, we carried out work to collect data that included handwritten names of countries and cities from 42 different Cyrillic words, written more than 500 times in different handwriting. We also used a handwritten database of Kazakh and Russian languages (HKR). This is a new database of Cyrillic words (not only countries and cities) for the Russian and Kazakh languages, created by the authors of this work.

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Several challenges like changes in brightness, dynamic background, occlusion and inconsistency of camera position make the recognition of hand gestures dif?cult in any vision-based method. Diversity in finger shape, size, distribution and motion dynamics is also a big constraint. This leads to the motivation in developing a dense Scale Invariant Feature Transform (SIFT) flow based architecture for recognizing dynamic hand gestures. Initially, a combination of three frames differencing and skin filtering technique is used for hand detection to reduce the computational complexity followed by a SIFT flow technique to extract the features from the detected hand region. SIFT flow vectors obtained from every pixel can lead to over?lling, data redundancy and dimension disaster. A dual layer belief propagation algorithm is utilized to optimize the feature vectors to resolve the dimensionality problem. Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) classifiers are used to evaluate the performance of the developed framework. Experiments were conducted on hand gesture database for HCI, Sebastien Marcel Dynamic Hand Posture Database and RWTH German finger spelling database. The simulation results demonstrate that the developed architecture has excellent performance on the uneven background and varying camera position and it is robust against image noise. A comparative analysis with the state of the art methods illustrates the effectiveness of the architecture.

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The effect of the moving dispersive conducting complex scatterer on the scattering of incident H-wave is investigated herein. In this research, a simulation shows how the scattered phase and magnitude of a moving (rotating and translating) circular cylinder with higher conductivity, made of dispersive material, is affected in the case of incident H-wave (TE-mode) polarization. The Franklin transformation is applied to study the scattering of incident wave by a rotating dispersive higher conductivity cylinder, and then the effect of translating dispersive higher conductivity cylinder is investigated by applying the Lorentz transformation. This effect is studied at different speed of rotation. Also, this work shows that the pattern of scattered phase and magnitude are changed in terms of incident frequency. Moreover, a created model is used to demonstrate the impact of moving dispersive higher conductivity cylinder using backscattered static data which is generated using a comprehensive computational electromagnetics software(FEKO). The comparison between patterns of scattering of incident H-wave by a moving dispersive and non-dispersive higher conductivity cylinder is considered to show clear behavior of scattering patterns, in terms of the material of scatterer.

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The family can be referred to as a basic nucleus of society, where it must be fully formed as a group and guarantee the safety and development of its members. Increasing social inequality affects society and also the families that comprise it. It is a study with a quantitative approach, with a non-experimental, descriptive and cross-sectional design. The population consisted of 240 heads of families who go to 12 Popular Dining Rooms of a Vulnerable Area of Collique. The data collection technique was the survey-interview and the instrument used was the RFT 5-33 questionnaire of 5 dimensions and 33 items. The total family risk in families who go to Popular Dining Rooms of a Vulnerable Area of Collique in Comas, it is presented as follows, 172 participants representing 72% are threatened families; 41 participants representing 17% are families with low risk and 27 participants representing 11% are families with high risk. Regarding dimensions, threatened families predominated in all, in psycho-affective conditions with 94%, in health services and practices with 91%, in housing and neighborhood conditions with 62%, in socioeconomic situation with 85% and in child management with 85%. The total family risk that predominated is threatened families, followed by families with high risk and families with low risk. Regarding the dimensions of the main variable, threatened families predominate in all of them. The dimension with the highest high-risk value is housing and neighborhood conditions.

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The Health-related quality of life corresponds to the perception that people have of their level of well-being, considering aspects of their lives and their impact on their state of health. The studies were initially carried out in a population that presented both acute and chronic pathologies and covered practically all areas of medical specialties; subsequently, the interest focused on studying health-related quality of life in the general population and then in the population of children and adolescents. That is why, in this research work, the evaluation of the adolescent to prevent future problems was raised; to carry out the evaluation, the questionnaire called KIDSCREEN 52 was used, which consists of 52 closed questions divided into 10 dimensions, providing an idea of how the adolescent feels. The following results were obtained: The Health-related quality of life in adolescents of an Educational Institution of Ventanilla, where 430 students representing 59.1% have high quality of life and 298 students representing 40.9% have medium quality of life; according to its dimensions, adolescents from the Educational Institution of Ventanilla, its most affected dimension was economic resources, with 64 students representing 8.8% having a low level. In the relationship with parents’ dimension, it appreciates that 3.4% of students do not have a good family atmosphere since they do not live with their parents, but with uncles and/or grandparents.

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The Covid-19 is a pandemic issue that is causing widespread global disruption forcing lockdowns in many countries including Malaysia as an effort in disconnecting the virus from spreading. As an initiative, the Malaysia government has call for the Movement Control Order (MCO) as one of the lockdown alternatives starting on 18th March 2020. Due to that, the MCO has given a huge impact on Malaysia’s industries including the construction industry. Hence, to successfully deliver projects, time and cost become the essential component which in this current scenario, most of the on-going projects declined to deliver the project according to the time and cost as planned. Thus, this paper intends to explore the consequences of the Movement Control Order (MCO) towards project success. In getting an authentic data and fast feedback from the respondents during this current scenario, uses alternate methods as face to face interview is not encouraged. The interview sessions were held thru telephone conversation with 8 contractors in Klang Valley and Penang which randomly selected based on contractor listing in Malaysian Construction Industry Development Board (CIDB) website and content analysis are used to figure out the main consequences using Nvivo software. Results from the interviews found that the MCO has resulted in 6 main impacts consists of (1) regulation compliance; (2) safety; (3) additional time for project delivery; (4) increase in development cost; (5) limited human resources supplies; and (6) limited resource availability on-site. These shows the MCO has given the negative impact to the project success. This result is important for the government to ensure their awareness on the consequences of the MCO towards construction industry and facilitate the construction players to overcome the six factors mentioned in this paper.

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This paper examines the impact of innovation on the performance of manufacturing enterprises in Vietnam. Innovation is measured by product innovation (3 observed variables), technology innovation (8 observed variables), and organization innovation (6 observed variables) while firm performance is measured by revenue and profit. The OLS regression model was used with data collected from 806 enterprises in four industrial sectors. The results show that innovation has a positive effect on firm performance. From the results, some implications are proposed to improve the performance of manufacturing enterprises in Vietnam.

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In this paper, we present a new method to deduce minimal cut sets depending on the minimal path sets of the complex systems (networks) to generate the Incidence Matrix, and then compared it with the truth table of the system. This comparison, based on some algebraic properties, gives minimal-cut sets of the complex network with an algorithm in Mathematica software. In addition, the minimal cut sets completely characterize the operating state of the system and equal to the complex system structural function information. So, the distinguish of the operational states of the system give us information about the binary operational states for some components. The system failure time is also given immediately if the failure times of the component parts are known.

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Horse power – the prime mover that has been there with humans for ages; chiefly used for transportation in the early days, and later, also used as an energy source leading to the conception of the term – horse power (hp). The current paper presents an interdisciplinary effort that brings forward an approach for evaluation of the potential of renewable power extractable from this prime mover once again. Specifically, the focus is towards extracting power from the equine treadmills (dry-type) whereby the incline of the machine is replaced by an equivalent (or pseudo-equivalent) energy generation mechanism leading to the coupling of renewable power with an equine workout. This approach comprises aerobic power (evaluated from oxygen uptake data) as well as anaerobic power (evaluated from plasma lactate data) – each of which is estimated from the difference values between equine running on a flat treadmill and when the mill is inclined. Furthermore, a literature review mentioning different inventions as well as some research efforts directed towards tapping somatic energy of animals is included in the manuscript. Also, a section dedicated towards assumptions/weak points helps judge the applicability of the presented work.

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We describe the control design strategy used to control a soft robotic artificial muscle composed of silicone rubber using hand gesture signals. This artificial muscle is actuated with pneumatics, and therefore, the control strategy employed is through the regulation of air pressure within the inner chambers. Using the hand gestures of bringing the hands apart and together, the artificial muscle can be made to expand and contract with the gesture interface from the leap motion sensor. The advantage of the employed hand gesture control compared to switch control is that it provides a more natural interface for the regulation of air pressure within the artificial muscle through the use of electronic and automatic control. Possible areas of application include the use of the soft muscles for rehabilitation purposes and the combined system for developing a physiotherapy gaming device to exercise the hands and fingers of individuals that need to strengthen the muscles of the hands and fingers.

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Novel ionic polymers were synthesized by crosslinking of poly (vinylalcohol) (PVA) with sulfosuccinic acid (SSA) and silicotungstic acid (SiWA) with or without silica. The polymer electrolyte membrane fuel cell (PEMFC) was developed using solution casting method. Infrared (IR) spectra revealed that the Keggin structure was insered in the PVA films. The thermal decomposition of the PVA/SSA/SiWA/SiO2 membranes showed good thermal stability up to 200°C. Water uptake ranged between 31% and 88%. The maximum conductivity has been found to be 6,72.10-3 S.cm-1 at room temperature for PVA/SSA/SiWA containing 10% of silica weight. The ion exchange capacity of this membrane was 1,75 mmol.g-1. The results showed that these membranes presented very promising performances for use in Proton Exchange Membrane Fuel Cells.

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Durum wheat (Triticum turgidum L. var durum) is among the most important crops in the world. High and stable grain yield in diverse environments is the major objective in durum breeding programs. This trait is linked to the quantitative trait loci (QTL). For the detection of QTL linked to the grain yield, it is necessary to construct a high-density genetic linkage map. The aims of this study were to detect the candidate genes comprised in the QTLs on 2A chromosome linked to grain yield and annotate the single nucleotide polymorphisms (SNPs). The linkage map of Lahn/Cham1 population was used to identify QTLs. In multi-environmental analysis and employing bioinformatic approaches, 583 sequences corresponding to the SNPs markers selected from the detected QTL regions were analyzed, 122 SNP sequences were annotated of which 53% of the candidate genes were involved in stresses tolerance, 29.5% in plant development and growth, and 3.3% in cell transport. Moreover, 1.6% of candidate genes were retrotransposon and transposon 2.4% with unknown function. Further 9.8% were related in other cellular processes. The results also showed that 66.7% of the candidate genes harbored on 4B chromosome, were involved in stresses tolerance and 33.3% in plant development and growth. Additionally, in the specific and stressed environments analysis, the DNA sequences of the four QTL detected on 2A chromosome were used for homology search, 546 candidate genes were identified of which some were present in several QTL (F-box gene family, hydroxyproline-rich glycoprotein-like), retrotransposons and transposons and others. This study provided information on employing SNPs markers to detect candidate genes linked with grain yield trait in durum wheat in contrasting environments (dry, cold, hot).

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Ethereum smart contract system has seen a steady adoption as it continues to support tens of thousands of contracts. This feature has evolved to give a practical shape to the ideas leading up to fractional ownership transfer, using advanced smart contracts such as ERC-981. However, alongside its numerous benefits, various risks arise with the actual implementation of the ERC-981. This paper documents high-level processes and risk factors involved in the transfer-system, building a theoretical risk model based on Electre Tri-framework belonging to MCDA classification/sorting models. This model deals with detecting problems that are pre-defined on a central reference. The approach is illustrated through several stages: following comparison between the methods of risk analysis towards a risk assessment model, proposing recommendations and solutions. The framework was able to detect 18 major risks and bugs assigned to 6 categories.

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Social skills and resilience are very important aspects for mental health; therefore, it is necessary to take into account the positive contribution in the development of adolescents, they are in a vulnerable stage, adapting to physical, mental, emotional, etc. changes. Adolescents, not knowing how to handle the difficulties that may arise, if they do not have the ability to face it and show their positive qualities, can easily fall into depression, student desertion, early pregnancy, addictions to toxic substances (alcohol, tobacco and drugs). That is why in this study to measure social skills, the Elena Gismero Scale of Social Skills questionnaire data collection instrument was used, it has 33 items, 28 refer to the lack of assertion or social skills deficit, 5 of them refers to a positive sense. For resilience, the Connor-Davidson Resilience Scale will be used, it has 25 items. The results obtained with respect to social skills show that in the dimension positive interactions with the opposite sex shows the low level 14.3%, which is equivalent to 45 students, they are presenting difficulties in relating to the opposite sex, regarding resilience in the dimension of spirituality, the low level of 21.7%, indicates that 65 students do not have positive attitudes to fulfill their purposes. The minimum age was 11 years, the maximum age was 17 years, the mean being 13.39 in terms of sex, males predominate with a number of 154 students and 146 females.

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Piezoelectric energy harvesters (PEH) can be used in many areas of application, including human walking, railways, pavements and bridges. Piezoelectric energy harvesters are currently based on two types of external forces, namely pressure load and mechanical oscillation or vibration. A vibration energy harvesting (VEH) is a mechanical oscillation in a piezoelectric energy harvester that harvested electric energy. In the market, there is available energy harvesting device in good electric energy harvesting and very sensitivity. However, the price is too high and the fabrication process is too complex. Furthermore, one of the aimed of the research is to install the energy harvesting device at rotary compressor machine which has noise vibration frequency at 1 kHz to 10 kHz. This paper presented a cavity structure-based flexible piezoelectric vibration energy harvester (FPVEH) based on an IDE circuit for low-frequency vibration applications. A cavity structure (IDE circuity) combine with the flexible circuit (polyimide) and flexible membrane (polyvinylidene fluoride, PVDF) will increase the electric energy harvesting and sensitivity of the device. Therefore, the four designs (Design A to D) are used to investigate the effect of the electrode finger width and the gap between the electrode fingers (to investigate the cavity structure applying in the design). All designs have been characterized by FEA simulation using COMSOL Multiphysics 5.0 and experimental work using a sieve shaker vibration machine. A sieve shaker machine is worked as vibration frequency calibrator. However, the sieve machine can operate at 5 kHz and 7 kHz. Since these two vibration frequencies are in targeted vibration frequency. It is used as vibration frequency calibrator in this experimental work. The results from the FEA simulation and experimental work show the Design D has the highest electric energy harvesting compare to other designs. It has electric energy harvesting at 27.3 V for 1 minutes period. Design D has a wide electrode finger width and the wide gap between electrodes compare to other designs. The vibration frequency was also given the impact to energy harvesting whereby the vibration frequency at 5 kHz has the highest electric energy harvesting compare to vibration frequency at 7 kHz.

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A real-time embedded system requires plenty of measurements to fallow the Nyquist criteria. The hardware built for such a large number of measurements, is facing the challenges like storage and transmission rate. Practically it is very much complex to build such costly hardware. Compressive Sensing (CS) will be a future alternate technique for the Nyquist rate, specific to some applications where sparsity property plays a major role. Software implementation of Compressive Sensing takes more time to reconstruct a signal from CS measurements using the Matching Pursuit (MP) algorithm because of fetching, decoding, and execution policy. It is necessary to build hardware in CS. The author proposes one such VLSI Architecture (Hardware) for 256 X 256 and 512 X 512image. Various random matrices like Bernoulli, Partial Hadmard, Uniform Spherical, and Random Matrix are used to build hardware. FHT (Foreward Transform) with ±2 to 6 threshold is applied to get CS measurements. The reconstruction time, Signal to Noise ratio (SNR), and Mean Square Error (MSE) are measured. Multiple time experiments are carried out and results show that for an image of size 256 x 256, SNR is 25 dB and MSE is 166. For the image of size 512 x 512, the values are 27dB and 182. However, both the input images are resized to 256 X 256 so the reconstruction time is 2.62µsec? which is less is compared to software implementation.

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This paper addresses essential features/issues and proposes solutions that would improve the overall performance of a multi-phase buck converter. Low efficiency at light load is addressed with phase shedding, load fast transient and regulated output voltage spiking/dipping is addressed with novel helper technique at the point of load (output node). An Integrated current sensing is utilized to implement over-current-protection (OCP), in addition to its inherent function in current mode control. The phase shedding developed technique was used to enhance the efficiency of the converter. The number of phases rather increases or decreases, depending on the desired load. The proposed fast transient helper circuit is tested by inserting a 500mA transient current step in 100µs. Worst case spike of a 79.1mV was achieved at the output node; which is a reduction of 49.4% of the original response (160mV without the helper circuit). Worst case of a 35mV of output voltage dip was achieved; which is a reduced by 45.4% compared to the original response (77.1mV without the helper circuit). An integrated current sensing technique using current mirroring to equalize the drain voltages of main and replica (sense) PMOS devices was utilized. With this technique, the current in the replica (sense) PMOS device is a scaled down version of the current in the main PMOS device. The sensed currents from all three phases are added up and converted to a voltage. This voltage is compared to a reference voltage that represents the limit for over-current. This reference voltage is set to be 20% higher than the average total currents in the three phases combined.

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This article is part of the field of Multi-Criteria Decision Aid (MCDA), where several criteria must be considered in decision making. All criteria are generally as varied as possible and express different dimensions, and aspects of the decision problem posed. For more than four decades, several MCDA methods have emerged and have been applied perfectly to solve a large number of multi-criteria decision problems. Several studies have tried to compare these methods directly with one another. Since each method has its disadvantages and advantages, a direct comparison between the two methods is normally far from common sense and becomes subjective. In this article, we propose a rational and objective approach that will be used to compare the methods between them. This approach consists of using the famous correlation measure to evaluate the quality of the results obtained by different MCDA approaches. To prove the effectiveness of the proposed approach, experimental examples, as well as a study of real cases, will be studied. Indeed, a set of indicators, known as The Europe 2020 indicators, are defined by the European Commission (EC) to control the smart, sustainable and inclusive growth performance of the European Union countries (EU). In this proposed real study, a subset of indicators is used to compare the performance of environmental preservation and protection of the EU states. For this, the two-renowned methods MCDA ELECTRE II and TOPSIS are used to classify from the best to the worst CE countries with regard to environmental preservation.
The results of the experiment that the proposed ranking quality measure is significant. For the case study shows that the ELECTRE II method results in a better ranking than that obtained by the TOPSIS method.

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In this paper, performances of two variations of chaos based algorithms are compared. First algorithm is a self-adaptive color image encryption algorithm is proposed based on Radial Hilbert Transform and chaos. This technique uses chaotic random phase masks operated on the transformed pixels to increase the randomness in confusion and diffusion operations. Also, a random jumbling process is used at the final stage to increase the randomness in the cipher image. Part of the plain image is used to generate the keys for encrypting another part. Second algorithm is inspired by the bio operations resembling confusion and diffusion. Use of a scrambler improves the performance of this algorithm. Proposed work elaborates the results of the suitability analysis conducted on various kinds of input images, namely, satellite images, face images and handwritten signature images. Performance parameters considered for the analysis include horizontal correlation, vertical correlation, diagonal correlation, and net changing pixel rate, unified average change in intensity, entropy and encryption time taken by the encryption techniques.

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Caregivers experience lower back pain due to their awkward postures while handling patients. Therefore, a monitoring system to supervise caregivers’ postures using wearable sensors is being developed. This study proposed a postural recognition method for caregivers during postural change while handling a patient on a bed. The proposed method recognizes foot positions and arm movements by a machine learning algorithm using inertial data on the trunk and foot pressure data obtained from wearable sensors. An experiment was conducted to evaluate whether the proposed method could recognize three foot positions and three arm movements. Participants provided postural change for a simulated patient on a bed (patient: supine to lateral recumbent) under nine conditions, including different combinations of the three foot positions and three arm movements; the experiment was repeated ten times for each condition. Experimental results showed that the proposed method using an artificial neural network with all features obtained from an inertial measurement unit and insole pressure sensors could recognize arm movements and foot positions with an accuracy of approximately 0.75 and 0.97, respectively. These results suggest that the proposed method can be used in a monitoring system tracking a caregiver’s posture.

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The undulated power supply has dropped to its worst reliability index in most parts of the city despite the installations of distribution transformers to improve the power. In this work, examination of Port Harcourt Town Zone 4 (Z4), Rivers State power distribution system forcing on its operation, planning for future expansion of the system, and sharing of power between utilities was done. The objective was to unravel the problematic recurrent blackouts as a result high power loss, that is (I2R) in the line; low voltage profile, poor cos? at the load end, excessive loading of feeder transformers, and conductors rating inadequacy at the receiving end of the 33KV Distribution part of the substation. A comprehensive study was carried out on the system with the formation of node admittance matrix. Programmable codes were written using MATLAB script to resolve the static power flow equations defined applying Fast decoupled-Newton Raphson calculation procedure centered on the advantages of time and PC memory space (PC-MS). Thus, the node voltage and the other variables like branch flows and phase angles were gotten, and network losses were reduced. However, the results obtained were compared with that gotten from Electrical Transient Analyzer Program (ETAP) application software. It was seen that the two results got were related. The general net power gotten was (129.741 MW, 83.818 MVAr) applying the Fast Decoupled-Newton-Raphson load flow technique in MATLAB programming environment after the addition of receptive power through the means of the capacitor bank to the affected nodes. The total net power that is real and reactive got employing ETAP programming were (125.765 MW, 92.782 MVAr).
The overall line losses were enhanced by 0.246 reductions. That is from (4.75MW, 10.05MVAr) to (3.58MW, 7.57MVAr) of the entire real power losses.

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After studying the aviation problems in Iraq, on one hand, and because Iraq has various weather characteristics due to its different terrains (i.e. mountains, plain fields, and deserts). We discussed these problems with pilots and aviation specialists, and came to the conclusion that the main offset faced is vision deficiency is foggy and dusty weather. Thus, the Iraqi aviation reconnaissance system was developed to overcome this hurdle by implementing instant image rendering by developing this technology to remove fog and such hazy particles utilizing dark channel prior (DCP) image processing algorithm since this method is highly capable of resolving the fog/haze weather condition problems. The DCP algorithm suggested showed a reasonable enhancement of the images with foggy, hazy, and dusty conditions when the Unmanned Aerial Vehicle (UAV) was used to capture images at various altitudes correspondent to the same height most helicopters and reconnaissance UAVs fly at. Our main contribution is based on the evaluation of optimal values of DCP parameters/metrics to the hazy images and performs UAV image dehazing and enhancement.

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After studying the aviation problems in Iraq, on one hand, and because Iraq has various weather characteristics due to its different terrains (i.e. mountains, plain fields, and deserts). We discussed these problems with pilots and aviation specialists, and came to the conclusion that the main offset faced is vision deficiency is foggy and dusty weather. Thus, the Iraqi aviation reconnaissance system was developed to overcome this hurdle by implementing instant image rendering by developing this technology to remove fog and such hazy particles utilizing dark channel prior (DCP) image processing algorithm since this method is highly capable of resolving the fog/haze weather condition problems. The DCP algorithm suggested showed a reasonable enhancement of the images with foggy, hazy, and dusty conditions when the Unmanned Aerial Vehicle (UAV) was used to capture images at various altitudes correspondent to the same height most helicopters and reconnaissance UAVs fly at. Our main contribution is based on the evaluation of optimal values of DCP parameters/metrics to the hazy images and performs UAV image dehazing and enhancement.

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The purpose of this research is to study the relevance of factors for the analysis of the effectiveness of suitable educational institutions that illustrate the significance of the characteristics and attributes of the student’s academic achievements and to identify the acceptance and tolerance of each attribute, which supports lifelong learning. The data used in this research is 1109 students who used and tested the institution recommender system based on student context and educational institution application. The research methodology focuses on the study of user involvement and application analysis. There are six significant phases of the research: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. The machine learning tools and data mining techniques are k-means, k-medoids, decision trees, cross-validation methods, and confusion matrix. From the data analysis, it can be concluded that the overall level of satisfaction with the application is accepted (average = 3.70, S.D. = 0.84). In addition, the prototype model has been developed for predicting and recommending appropriate institutions for the learner has moderate accuracy levels (92.25%), and the results of the self-test data model are very accurate at the highest level, which is equal to 93.78%. Finally, this research demonstrates the relevance and success of education engineering projects. It demonstrates a worthy accomplishment. For future research, the researchers aim to construct and develop applications that promote and support the findings of this research.

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Visual Simultaneous Localization and Mapping known as V-SLAM, is an essential task for autonomous vehicles. It can be carried out using several sensors, in particular with on board cameras. To locate a vehicle, SLAM algorithms are based on two main tasks. The first task (front-end kernel) is intended to process images in order to provide features (called also landmarks or primitives) of the perceived environment. The second task (back-end kernel) is intended for localization and environment reconstruction.
This work focuses on the front-end task which uses extractors (detectors and descriptors) in a stereo-vision system. Several feature detectors and descriptors exist in the state of the art. The aim of this paper is to evaluate the possible combinations of detectors and descriptors to achieve a precise localization while considering the processing times. The study is extended to bio-inspired extractors. The evaluation is achieved with SLAM algorithms over well-known indoor and outdoor datasets. Experimental results highlight the performance of bio-inspired extractors and their potential integration in designing vision systems for real-time SLAM applications.

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This study conducted experiments to investigate the effects of resting actions of e-learners during break times on keeping and improving the concentrations of learners in e-learning. Two smart toys (a dog-type robot “aibo” and a toy drone) were used for the resting actions. Two types of experiments were conducted to examine the effects: one is a work type experiment (simple mathematical calculation) and the other is a memorizing type experiment (learning of English words). In those experiments, the learners played with one of the smart toys during the resting times between the learning sessions and performed certain actions accordingly. The experimental results were combined with the previous study using a humanoid robot “RoBoHoN.” A questionnaire was also employed to investigate the feeling of the learners. The experiments showed that the “playing with aibo” action refreshed the learners effectively in the mathematical experiment, while the “playing with RoBoHoN” action was effective in the learning experiment.

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The proactive caching has been an emerging approach to cost-effectively boost the network capacity and reduce access latency. While the performance of which extremely relies on the content prediction. Therefore, in this paper, a proactive cache policy is proposed in a distributed manner considering the prediction of the content popularity and user location to minimise the latency and maximise the cache hit rate. Here, a backpropagation neural network is applied to predict the content popularity, and prediction by partial matching is chosen to predict the user location. The simulation results reveal our proposed cache policy is around 27%-60% improved in the cache hit ratio and 14%-60% reduced in the average latency, compared with the two conventional reactive policies, i.e., LFU and LRU policies.

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The revolution in the electrification of conventional vehicles due to increased petroleum prices and environmental concerns has had its impact on three-wheeler vehicles as well. Motorized three-wheeled vehicles, known as Auto-rickshaws, are a standard mode of trans- portation in India. Existing battery operated electric rickshaws (known as E-rickshaws) available in major cities of India faces challenges like insufficient charging facility, low driving range, high battery cost, battery replacement/disposal, etc. A battery-ultracapacitor (UC) hybrid energy source is proposed in this paper to overcome these issues. Two energy sources of complementary characteristics, when operated in tandem, can enhance the over- all performance of a vehicle in terms of weight, volume, and efficiency. An Erickshaw with battery-UC hybrid energy sources is modeled in Matlab-Simulink in this paper. The vehicle dynamics are calculated real-time based using a GPS based PerformanceBox tool of VBOX motorsport for Surat city. A fuzzy logic-based approach was employed for efficient energy management between these two sources with efficient utilization of regenerative power generated while braking. The system was later tested on Real-Time Hardware-in-Loop (HIL) environment to validate the simulation results. It was observed that the addition of an additional source with complementary characteristics had enhanced the performance of vehicle operation.

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The adoption of Engine-in-the-loop technology shows real behaviour. This study presents a test runs simulation platform with real engine data. In addition, a test bench model is a demand approach that offers a significant potential to provide an excellent reproducibility of test runs. The platform includes the data integration to upgrade tests run and a comparison with previous results using the advancing control techniques designed. The dynamometer system presents significantly non-linearity. The adaptive control approach, integrated into the Model Predictive Control on the vehicle, allows increasing the tests run performance. The results show how the real data can improve performance and the validation of the system integrating the updated driving cycle and maintaining EiL approach. The conclusion showed the significant benefits regarding the control methods used.

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This paper presents a research about the modelling of surface roughness in the grinding process. Based on the analyzed results about the surface roughness models from the previous studies, this study was performed to develop a surface roughness model in the grinding process. The surface roughness model is proposed with two hypotheses about the shape and ratio of the number of scratches of the grinding grains leaving on the workpiece surface. The first hypothesis is that the shapes of scratches of the grinding grains leave on the workpiece surface with five types including the triangle, the curved arc, the parabolic, the semi-circular, and the curved arc of a quarter circle. The second hypothesis is that the ratios of the number of the scratches with different shapes of the grinding grains leaving on the workpiece surface are the same (all are equal to 20%). This research paper discusses about the modelling of surface roughness when considering many shapes of scratches of the grinding grains left on the machining surface. This proposed model can be used to calculate the surface roughness in grinding processes of the different pairs of grinding wheels and workpieces. The developed surface roughness model has been applied to calculate the surface roughness during the grinding of SUJ2 steel using an aluminum oxide grinding wheel. Calculated surface roughness using proposed model were quite close to the experimental results. The average difference between calculated and the experimental results was about 14.84%. This study offers a promising ability to calculate the machining surface roughness in the grinding processes.

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English remains the language of choice for database courses and widely used for instruc- tion in nearly all South African universities, and also in many other countries. Novice programmers of native origins are mostly taught Structured Query Language (SQL) through English as the medium of instruction. Consequently, this creates a myriad of problems in understanding the syntax of SQL as most native learners are not too proficient in English. This could affect a learner’s ability in comprehending SQL syntaxes. To resolve this problem, this work proposes a tool called local language narrations to SQL (Local-Nar-SQL) that uses a type of Finite Machine, such as a Jumping Finite Automaton to translate local lan- guage narratives into SQL queries. Further, the generated query extracts information from a sample database and presents an output to the learner. This paper is an extension of work originally presented in a previous study in this field. A survey involving 145 participants concluded that the majority found Local-Nar-SQL to be helpful in understanding SQL queries from local languages. If the proposed tool is used as a learning aid, native learners will find it easier to work with SQL, which will eliminate many of the barriers faced with English proficiencies in programming pedagogies.

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Due to pandemic spreads of COVID-19 and increasing populations of se- niors, exercises or performance practices at home have become important to maintain healthy lives around the world. World Health Organization (WHO) has announced the physical health determines the Quality of Life (QoL) of a human. Unfortunately, a lot of people have no exercise and may be in unhealthy conditions. In this paper, we propose an Exercise and Per- formance Learning Assistant System (EPLAS) to assist people practicing exercises or learning performances by themselves at home. EPLAS adopts inexpensive devices and free software for low-cost implementation. It offers a video content of model actions by an instructor to be followed by the user, where the reaction is rated by comparing the feature points of the human bodies extracted by an open-source software OpenPose. For evaluations, we conduct experiments of applying EPLAS with five Yoga poses to 41 persons in Indonesia, Japan, and Taiwan, and confirm the effectiveness of the proposal.

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From amateur players who enjoy sports throughout their lives to top athletes who participate in international competitions, interest in improving sports skills is growing. Coaches and their coaching are indispensable for improving sports skills, but it is difficult for many athletes, especially amateur athletes, to secure coaching. However, we thought that anyone could easily receive coaching through the use of an artificial intelligence (AI) coach. In order to bring about AI coaching, learning is important. The set of learning data must include data such as players’ skills as they correspond to their gaze and performance.
In particular, it is thought that analyzing gaze movement during sports may provide insight into exceptional athletic skills. In this study, we propose a skill sampling method of collecting learning data for the specific purpose of creating an AI coach, using a wireless eye movement measurement device and 4K imaging.

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There have been disruptions in local and global food supply chains around the world due to the COVID-19 pandemic. It has led to rethink various aspects and to consider various trends in the food sector, encourage a more rapid transition and evolution to the Sustainable Supply Chain Management (SSCM), to counteract current problems and to help towards a more sustainable and competitive vision. However, there is a strategic disconnection in SSCM between sustainability and strategies related to competitive advantage. Therefore, the objective of this research is to propose a decision-making model to assess the sustainability and competitiveness strategically of a Mexican agri-food focal SME (Small and Medium-sized Enterprise) that leads its supply chain. It is validated with comparative analysis in the productive taro activity through the Analytic Hierarchy Process (AHP) that contributes to prevent or to face problems such as the COVID-19 pandemic and climate change. This research is based on a case study methodology divided into four stages. Three presented alternatives are assessed and compared by thirty-five criteria: a) A current situation without sustainability, b) Cost leadership strategy considering sustainability, and c) Differentiation strategy considering sustainability. The conclusions indicate more feasibility and compatibility to achieve sustainability with a differentiation strategy than with a cost leadership strategy. The current crisis should lead us to think beyond COVID-19, as we have more challenges ahead, such as climate change, environmental impacts, poverty, among other aspects that could cause instability around the world. Therefore, the current situation should be an impetus to further progress towards the 17 Sustainable Development Goals, in this case, through the theory of SSCM. That is why this model contributes strategically to the SSCM, to develop greater long-term resilience in the Mexican agri-food focal SMEs and their supply chains.

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Objective: Individuals with aphasia often experience significant problems in their daily lives and social participation. Technologies that address speech and language disorders deficit in merging between therapist’s major role and reinforcing the training between sessions at home. It also lacks the Arabic language attention; however, current systems are typically expensive and lack amusement. Moreover, cumulative feedback for both patient and therapist incapacitates the whole home rehabilitation process. This project sought to address these issues by developing an interactive rehabilitation-based system for people with aphasia. Methods: A virtual reality (VR) environment is created providing real-life situations with task specific training of comprehension in addition to a virtual speech-language pathologist (SLP) representing lip motions for correct pronunciation of target words. A speech recognition convolutional neural networks (CNN) algorithm based on signal processing is created and trained on ten isolated Arabic words. We tempted log-spectrograms and Mel-frequency cepstral coefficients (MFCC) as feature extractors for the CNN model which is then integrated for accurate evaluation of input speech from the aphasic patient providing a real-time feedback resulting in measuring speech improvement and sends it to the SLP through the network via a website platform. Results: Our speech recognition assessment algorithm results in a recognition accuracy of 95.2 % using Log-Spectrograms feature extraction method and 92.6 % using MFCC. Significance & Conclusion: We hypothesize that this interactive VR therapy combined with speech function training will result in faster word retrieval and improve language ability of patients with aphasia and that our outcomes contribute to the development of a home-based language and speech therapy.

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This paper showcases our previous and continuously improving development at Ateneo Innovation Center (AIC) and partners in designing and further enhancing the existing Low-cost Phototherapy Light System (LPLS) and Improved Low-cost Phototherapy Light System (ILPLS) to the new Smart Low-cost Phototherapy Light System (Smart LPLS) with non-invasive jaundice monitoring for newborns with Neonatal Jaundice (NNJ). Developing this tool will help determine the intensity of yellowish color in infants and can monitor NNJ in a non-invasive way. The system is envisioned to be integrated with Mobile or Near Cloud as part of Smart Nursing Station together with other hospital equipment for monitoring, collection, and management of medical records and services. Its solar-power features for off-grid and remote deployments were also explored. This contribution is an extension of the Intelligent Sensors and Monitoring System for Low-cost Phototherapy Light for Jaundice Treatment that was presented in the International Symposium on Multimedia and Communication Technology (ISMAC) in 2019.

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he present work is undertaken as part of research studies aiming to provide sociotechnical systems with a decision-making tool that supports them in assessing the resilience of their processes. The ultimate objective is to fix the identified imperfections in order to steadily gain strength and effectiveness to cope with new and existing threats and challenges. In that respect, this paper presents a framework called 3R process whose goal is to calculate the resilience score for a process based on several parameters and grade it on the resilience scale. In order to achieve this, a fuzzy model has been put in place and graphic user interfaces have been designed so that the 3R process becomes an integral part of daily working practices. Finally, a real case company example in the context of COVID-19 is exposed towards putting into practice the proposed model.

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Lately, the use of mobile learning has increased in higher education at a global level. Especially after the new Coronavirus (Covid-19). Therefore, trust issues towards the benefits of mobile learning are still important matters just as important as quality dimensions (e.g. service quality, information quality and system quality) for both technology and its content. These three factors are existing in the Information System Success Model (ISSM), but the trust is not clearly present. Hence, this study is attempting to explain the impact of trust in addition to the three quality dimensions of the ISSM on the usage intention of mobile learning among students in two Jordanian universities. The data were gathered by a self-administered questionnaire and SPSS version 23 is used by performing linear regression analysis to analyze it. The findings showed that trust, service quality, system quality, and information quality positively and significantly affect the intention to use mobile learning. This study will help researchers, decision-makers and system designers by gaining valuable implications in developing proper strategies and creating effective and practical approaches through learning via mobile devices.

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Smart agriculture concept uses innovative solutions including IoT and Cloud storage features, dedicated sensors for monitoring basic agricultural parameters, new communications protocols, etc. SmartAgro architecture comprises a telemetry system for Key Performance Indicators (KPIs) such as air & soil temperature, air & soil relative humidity, leaf wetness, etc. The current paper outlines the reliability of the implemented system by comparing and analyzing data collected in spring 2019 and spring 2020. The relevance of this season consists in great air variations due to the transition from winter to summer. Being monitored in a vine area near Bucharest, these data may be useful for different statistics related to grapes culture in this season and can be used by interested parties for future predictions related to vine crops. Moreover, in this paper, data fusion will allow advanced data management and coherence achievement among collected data.

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This paper is an extension of work originally presented in IPFA 2019. In the original work, a new memory bit-counting method in physical failure analysis (PFA) using laser deprocessing technique (LDT) is introduced. In the present paper, LDT will be further exploited and the methodology applied to PFA will be fully discussed. Compared to the conventional methods that involve high-cost equipment such as focused ion beam (FIB) and reactive ion etcher (RIE), the novel LDT method using a laser system instead lowers the cost by more than 5 times and shortens the failure analysis (FA) cycle time by up to 45%. The new improved methodology can significantly increase PFA throughput especially in semiconductor foundries, and facilitate more applications in other types of FA labs.

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Big data is a vast data set that was used in many areas. Online applications are subject to theft of confidential information because opponents can exploit software errors to access private data, and because curious or malicious officials can capture and lose data. CryptDB is a functional system that provides security and confidentiality through a set of operations. The obvious confidentiality of these attacks is for applications supported by SQL databases. It works by executing SQL queries on encrypted data using robust coding systems that support SQL. Word2Vec outputs word vectors that can be displayed as a large piece of text, or even we first train data. Word2Vec forms and word similarity assessment. Without a doubt, this article calls for proper research that sheds light on the security features using CryptDB to prevent data theft and privacy breaches in the server. The motivation of this research is to have an overview of CryptDB and Word2Vec implementation on the existing research approaches.

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The large and ever-increasing variety of remote sensing sensors used in satellite imagery today explains why detecting changes between identical locations in images that are captured, at two separate times, from heterogeneous capturing systems is a major and challenging recent research problem in the field of satellite imaging for fast and accurate determination of temporal changes. This work presents an original concentric circular invariant convolution model that aims at projecting the first satellite image into the imaging modality of the second image. This allows the two images to have identical statistics so that one can then effectively use a classical monomodal change detection method. The invariant circular convolution kernel parameters are estimated in the least squares sense using a conjugate gradient routine whose optimal direction is determined by a quadratic line search algorithm. After the projection of the before image into the imaging modality domain associated with the after image is achieved, a basic pixel- by-pixel difference permits the estimation of a relevant soft difference map which is segmented into two classes by a multilevel Markovian technique. A series of experiments conducted on several pair of satellite images acquired under different imaging modalities, resolution scales, noise characteristics, change types and events, validates the effectiveness of this strategy. The experimentation shows that the proposed model can process different image pairs with less re-striction about the source images and natural event, coming from different sensors or from the same sensor, for detecting natural changes.

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In this paper, a portable Frequency Modulated Continuous Wave (FMCW) radar system was designed and implemented for human movements and breathing detection. The radar operates with a frequency band ranges from 4.7 to 4.9GHz. The radar sub-systems were designed and simulated using up to date computer-aided-design tools before implementation. The Voltage Controlled Oscillators (VCO), high gain antenna, low loss power divider/combiner, and a high selectivity bandpass filter were implemented, and their parameters were measured using a microwave analyzer. The simulated results and the measured results show a significant correlation. The new RF front end module components enhanced the radar signal-noise-ratio (SNR), and the breathing rate detection. The Digital Signal Processing (DSP) is implemented on the Field Programmable Gate Array (FPGA) board for movement and breathing signals detection processing in real-time. Highly sensitive detection, configurability, low cost, low power consumption, and portability were considered in the designed system. We believe that the enhanced reconfigurable radar system will be helpful in several biomedical monitoring applications.

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Logistics is a science and art that has been widely used by large commercial chains, whose main objectives focus on the distribution of products or services. This understanding has generated various logistics studies directed towards those businesses, without considering that science can also be applied to small or medium enterprises.
The present study initially executes a bibliographic review of multiple works done by several authors that confirm that studies related to the field of logistics can be aimed at any organization. The theoretical investigation of this study is therefore widely supported.
This work is focused on helping a small enterprise continue offering its services during the pandemic caused by the worldwide presence of COVID-19. Hence, the purpose of this research is to present a logistics model that will allow a small supermarket to continue its commercial activities in Quito, Ecuador, in compliance with diverse regulations issued by the Ecuadorian Government. The logistical model uses the methodology called savings method developed by Clarke and Wright.
The designed model produces as a main outcome the establishment of short-distance home delivery routes of provision to customers by the supermarket, thereby achieving significant savings in time and money. Additionally, the design and implementation of the model can serve as an example to be replicated in similar businesses.

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Knowledge-based approach is wield used in various NLP applications. For example, to evaluate the semantic similarity between words, the semantic evidence in lexical ontologies (wordnets) is commonly used. The success of the English WordNet (EnWN) in this domain has inspired the creation of several wordnets in different languages, including the Arabic WordNet (ArWN). The English synsets have been extended to Arabic synsets through translation, which have introduced semantic gaps in ArWN structure. Therefore, compared to EnWN, ArWN has limited coverage in terms of lexical and semantic knowledge. This paper explores to what degree the richness of the wordnets’ semantic structure influences the semantic evidence that can be used in wordnet-based applications, in particular the effect of filling the semantic gaps in ArWN. The paper studies the performance of applying English-based and Arabic-based similarity measures over ArWN. A set of experiments was performed by applying six path-based semantic similarity measures over Arabic benchmark dataset to investigate the usability and efficacy of the enriched structure of ArWN. The Performance measures, Person Correlation and Mean Square Error, are computed against and compared to human judgment benchmark. The obtained results demonstrate that the semantic similarity between words can be significantly improved when filling the semantic gaps. In addition, the experiment findings show that Arabic-based measures competitively perform well compared to the English-based measures. Further, ArWN enhanced structure is also available for public.

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