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  • 1
    Online Resource
    Online Resource
    Experimental and Statistical Analysis of Repeated Impact Records of Hybrid Fiber-Reinforced High-Performance Concrete
    MDPI AG ; 2023
    In:  Buildings Vol. 13, No. 3 ( 2023-03-03), p. 678-
    Details
    In: Buildings, MDPI AG, Vol. 13, No. 3 ( 2023-03-03), p. 678-
    Abstract: The effect of fiber type and fiber hybridization on the repeated impact strength was investigated experimentally using six high-performance concrete mixtures reinforced with a 2.5% fiber volume fraction. The fiber types considered in this study included short steel fibers (SF) with 6 mm length, long SF with 15 mm length, and polypropylene (PP) fibers. The repeated impact test was conducted using a specially made automatic testing machine following the test setup recommendations of the ACI 544-2R test, where cracking (Ncr) and failure (Nf) impact numbers were recorded and the failure mode and crack pattern were observed. The results were statistically analyzed using the normality test and variations were discussed. The test results showed that specimens with pure long SF (S15) obtained the highest Ncr and Nf values, which were 20% and 327% higher than those of the mixture with pure short SF (S6) owing to the better bond between fibers and the cementitious matrix in S15. Replacing 0.5% of the mixture’s SF with PP decreased the cracking resistance by 7% to 15%, while its effect on Nf was dependent on the length of SF. In most cases, the Ncr and Nf records did not exhibit a significant departure from normal distribution, according to the Anderson-darling test.
    Type of Medium: Online Resource
    ISSN: 2075-5309
    URL: Article
    DOI: 10.3390/buildings13030678
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2661539-3
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  • 2
    Online Resource
    Online Resource
    Drought Severity and Frequency Analysis Aided by Spectral and Meteorological Indices in the Kurdistan Region of Iraq
    MDPI AG ; 2022
    In:  Water Vol. 14, No. 19 ( 2022-09-26), p. 3024-
    Details
    In: Water, MDPI AG, Vol. 14, No. 19 ( 2022-09-26), p. 3024-
    Abstract: In the past two decades, severe drought has been a recurrent problem in Iraq due in part to climate change. Additionally, the catastrophic drop in the discharge of the Tigris and Euphrates rivers and their tributaries has aggravated the drought situation in Iraq, which was formerly one of the most water-rich nations in the Middle East. The Kurdistan Region of Iraq (KRI) also has catastrophic drought conditions. This study analyzed a Landsat time-series dataset from 1998 to 2021 to determine the drought severity status in the KRI. The Modified Soil-Adjusted Vegetation Index (MSAVI2) and Normalized Difference Water Index (NDWI) were used as spectral-based drought indices to evaluate the severity of the drought and study the changes in vegetative cover, water bodies, and precipitation. The Standardized Precipitation Index (SPI) and the Spatial Coefficient of Variation (CV) were used as meteorologically based drought indices. According to this study, the study area had precipitation deficits and severe droughts in 2000, 2008, 2012, and 2021. The MSAVI2 results indicated that the vegetative cover decreased by 36.4%, 39.8%, and 46.3% in 2000, 2008, and 2012, respectively. The SPI’s results indicated that the KRI experienced droughts in 1999, 2000, 2008, 2009, 2012, and 2021, while the southeastern part of the KRI was most affected by drought in 2008. In 2012, the KRI’s western and southern parts were also considerably affected by drought. Furthermore, Lake Dukan (LD), which lost 63.9% of its surface area in 1999, experienced the most remarkable shrinkage among water bodies. Analysis of the geographic distribution of the CV of annual precipitation indicated that the northeastern parts, which get much more precipitation, had less spatial rainfall variability and more uniform distribution throughout the year than other areas. Moreover, the southwest parts exhibited a higher fluctuation in annual spatial variation. There was a statistically significant positive correlation between MSAVI2, SPI, NDWI, and agricultural yield-based vegetation cover. The results also revealed that low precipitation rates are always associated with declining crop yields and LD shrinkage. These findings may be concluded to provide policymakers in the KRI with a scientific foundation for agricultural preservation and drought mitigation.
    Type of Medium: Online Resource
    ISSN: 2073-4441
    URL: Article
    DOI: 10.3390/w14193024
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2521238-2
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  • 3
    Online Resource
    Online Resource
    Integrating Remote Sensing Techniques and Meteorological Data to Assess the Ideal Irrigation System Performance Scenarios for Improving Crop Productivity
    MDPI AG ; 2023
    In:  Water Vol. 15, No. 8 ( 2023-04-20), p. 1605-
    Details
    In: Water, MDPI AG, Vol. 15, No. 8 ( 2023-04-20), p. 1605-
    Abstract: To increase agricultural productivity and ensure food security, it is important to understand the reasons for variations in irrigation over time. However, researchers often avoid investigating water productivity due to data availability challenges. This study aimed to assess the performance of the irrigation system for winter wheat crops using a high-resolution satellite, Sentinel 2 A/B, combined with meteorological data and Google Earth Engine (GEE)-based remote sensing techniques. The study area is located north of Erbil city in the Kurdistan region of Iraq (KRI) and consists of 143 farmer-owned center pivots. This study also aimed to analyze the spatiotemporal variation of key variables (Normalized Difference Moisture Index (NDMI), Normalized Difference Vegetation Index (NDVI), Precipitation (mm), Evapotranspiration (ETo), Crop evapotranspiration (ETc), and Irrigation (Hours), during the wheat-growing winter season in the drought year 2021 to understand the reasons for the variance in field performance. The finding revealed that water usage fluctuated significantly across the seasons, while yield gradually increased from the 2021 winter season. In addition, the study revealed a notable correlation between soil moisture based on the (NDMI) and vegetation cover based on the (NDVI), and the increase in yield productivity and reduction in the yield gap, specifically during the middle of the growing season (March and April). Integrating remote sensing with meteorological data in supplementary irrigation systems can improve agriculture and water resource management by boosting yields, improving crop quality, decreasing water consumption, and minimizing environmental impacts. This innovative technique can potentially enhance food security and promote environmental sustainability.
    Type of Medium: Online Resource
    ISSN: 2073-4441
    URL: Article
    DOI: 10.3390/w15081605
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2521238-2
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  • 4
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    Online Resource
    Voice Pathology Detection and Classification Using Convolutional Neural Network Model
    MDPI AG ; 2020
    In:  Applied Sciences Vol. 10, No. 11 ( 2020-05-27), p. 3723-
    Details
    In: Applied Sciences, MDPI AG, Vol. 10, No. 11 ( 2020-05-27), p. 3723-
    Abstract: Voice pathology disorders can be effectively detected using computer-aided voice pathology classification tools. These tools can diagnose voice pathologies at an early stage and offering appropriate treatment. This study aims to develop a powerful feature extraction voice pathology detection tool based on Deep Learning. In this paper, a pre-trained Convolutional Neural Network (CNN) was applied to a dataset of voice pathology to maximize the classification accuracy. This study also proposes a distinguished training method combined with various training strategies in order to generalize the application of the proposed system on a wide range of problems related to voice disorders. The proposed system has tested using a voice database, namely the Saarbrücken voice database (SVD). The experimental results show the proposed CNN method for speech pathology detection achieves accuracy up to 95.41%. It also obtains 94.22% and 96.13% for F1-Score and Recall. The proposed system shows a high capability of the real-clinical application that offering a fast-automatic diagnosis and treatment solutions within 3 s to achieve the classification accuracy.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    URL: Article
    DOI: 10.3390/app10113723
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2704225-X
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  • 5
    Online Resource
    Online Resource
    A Framework for Susceptibility Analysis of Brain Tumours Based on Uncertain Analytical Cum Algorithmic Modeling
    MDPI AG ; 2023
    In:  Bioengineering Vol. 10, No. 2 ( 2023-01-22), p. 147-
    Details
    In: Bioengineering, MDPI AG, Vol. 10, No. 2 ( 2023-01-22), p. 147-
    Abstract: Susceptibility analysis is an intelligent technique that not only assists decision makers in assessing the suspected severity of any sort of brain tumour in a patient but also helps them diagnose and cure these tumours. This technique has been proven more useful in those developing countries where the available health-based and funding-based resources are limited. By employing set-based operations of an arithmetical model, namely fuzzy parameterised complex intuitionistic fuzzy hypersoft set (FPCIFHSS), this study seeks to develop a robust multi-attribute decision support mechanism for appraising patients’ susceptibility to brain tumours. The FPCIFHSS is regarded as more reliable and generalised for handling information-based uncertainties because its complex components and fuzzy parameterisation are designed to deal with the periodic nature of the data and dubious parameters (sub-parameters), respectively. In the proposed FPCIFHSS-susceptibility model, some suitable types of brain tumours are approximated with respect to the most relevant symptoms (parameters) based on the expert opinions of decision makers in terms of complex intuitionistic fuzzy numbers (CIFNs). After determining the fuzzy parameterised values of multi-argument-based tuples and converting the CIFNs into fuzzy values, the scores for such types of tumours are computed based on a core matrix which relates them with fuzzy parameterised multi-argument-based tuples. The sub-intervals within [0, 1] denote the susceptibility degrees of patients corresponding to these types of brain tumours. The susceptibility of patients is examined by observing the membership of score values in the sub-intervals.
    Type of Medium: Online Resource
    ISSN: 2306-5354
    URL: Article
    DOI: 10.3390/bioengineering10020147
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2746191-9
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  • 6
    Online Resource
    Online Resource
    MAFC: Multi-Agent Fog Computing Model for Healthcare Critical Tasks Management
    MDPI AG ; 2020
    In:  Sensors Vol. 20, No. 7 ( 2020-03-27), p. 1853-
    Details
    In: Sensors, MDPI AG, Vol. 20, No. 7 ( 2020-03-27), p. 1853-
    Abstract: In healthcare applications, numerous sensors and devices produce massive amounts of data which are the focus of critical tasks. Their management at the edge of the network can be done by Fog computing implementation. However, Fog Nodes suffer from lake of resources That could limit the time needed for final outcome/analytics. Fog Nodes could perform just a small number of tasks. A difficult decision concerns which tasks will perform locally by Fog Nodes. Each node should select such tasks carefully based on the current contextual information, for example, tasks’ priority, resource load, and resource availability. We suggest in this paper a Multi-Agent Fog Computing model for healthcare critical tasks management. The main role of the multi-agent system is mapping between three decision tables to optimize scheduling the critical tasks by assigning tasks with their priority, load in the network, and network resource availability. The first step is to decide whether a critical task can be processed locally; otherwise, the second step involves the sophisticated selection of the most suitable neighbor Fog Node to allocate it. If no Fog Node is capable of processing the task throughout the network, it is then sent to the Cloud facing the highest latency. We test the proposed scheme thoroughly, demonstrating its applicability and optimality at the edge of the network using iFogSim simulator and UTeM clinic data.
    Type of Medium: Online Resource
    ISSN: 1424-8220
    URL: Article
    DOI: 10.3390/s20071853
    Language: English
    Publisher: MDPI AG
    Publication Date: 2020
    detail.hit.zdb_id: 2052857-7
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  • 7
    Online Resource
    Online Resource
    Innovative Artificial Intelligence Approach for Hearing-Loss Symptoms Identification Model Using Machine Learning Techniques
    MDPI AG ; 2021
    In:  Sustainability Vol. 13, No. 10 ( 2021-05-12), p. 5406-
    Details
    In: Sustainability, MDPI AG, Vol. 13, No. 10 ( 2021-05-12), p. 5406-
    Abstract: Physicians depend on their insight and experience and on a fundamentally indicative or symptomatic approach to decide on the possible ailment of a patient. However, numerous phases of problem identification and longer strategies can prompt a longer time for consulting and can subsequently cause other patients that require attention to wait for longer. This can bring about pressure and tension concerning those patients. In this study, we focus on developing a decision-support system for diagnosing the symptoms as a result of hearing loss. The model is implemented by utilizing machine learning techniques. The Frequent Pattern Growth (FP-Growth) algorithm is used as a feature transformation method and the multivariate Bernoulli naïve Bayes classification model as the classifier. To find the correlation that exists between the hearing thresholds and symptoms of hearing loss, the FP-Growth and association rule algorithms were first used to experiment with small sample and large sample datasets. The result of these two experiments showed the existence of this relationship, and that the performance of the hybrid of the FP-Growth and naïve Bayes algorithms in identifying hearing-loss symptoms was found to be efficient, with a very small error rate. The average accuracy rate and average error rate for the multivariate Bernoulli model with FP-Growth feature transformation, using five training sets, are 98.25% and 1.73%, respectively.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su13105406
    Language: English
    Publisher: MDPI AG
    Publication Date: 2021
    detail.hit.zdb_id: 2518383-7
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