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  • 1
    In: Applied Sciences, MDPI AG, Vol. 12, No. 24 ( 2022-12-19), p. 13030-
    Abstract: This study looks at how incorporating nanofiller into sisal/flax-fibre-reinforced epoxy-based hybrid composites affects their mechanical and water absorption properties. The green Al2O3 NPs are generated from neem leaves in a proportion of leaf extract to an acceptable aluminium nitrate combination. Both natural fibres were treated with different proportions of NaOH to eliminate moisture absorption. The following parameters were chosen as essential to achieving the objectives mentioned above: (i) 0, 5, 10, and 15% natural fibre concentrations; (ii) 0, 2, 4, and 6% aluminium powder concentrations; and (iii) 0, 1, 3, and 5% NaOH concentrations. Compression moulding was used to create the hybrid nanocomposites and ASTM standards were used for mechanical testing such as tension, bending, and impact. The findings reveal that combining sisal/flax fibre composites with nanofiller improved the mechanical features of the nanocomposite. The sisal and flax fibre hybridised successfully, with 10% fibres and 4% aluminium filler. The water absorption of the hybrids rose as the fibre weight % increased, and during the next 60 h, all of the specimens achieved equilibrium. The failed samples were examined using scanning electron Microscopic (SEM) images better to understand the composite’s failure in the mechanical experimentations. Al2O3 NPs were confirmed through XRD, UV spectroscope and HPLC analysis. According to the HPLC results, the leaf’s overall concentrations of flavonoids (gallocatechin, carnosic acid, and camellia) are determined to be 0.250 mg/g, 0.264 mg/g, and 0.552 mg/g, respectively. The catechin concentration is higher than the phenolic and caffeic acid levels, which could have resulted in a faster rate of reduction among many of the varying configurations, 4 wt.% nano Al2O3 particle, 10 wt.% flax and sisal fibres, as well as 4 h of NaOH with a 5 wt.% concentration, producing the maximum mechanical properties (59.94 MPa tension, 149.52 Mpa bending, and 37.9 KJ/m2 impact resistance). According to the results, it can be concluded that botanical nutrients may be used effectively in the manufacturing of nanomaterials, which might be used in various therapeutic and nanoscale applications.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704225-X
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  • 2
    In: Fuel, Elsevier BV, Vol. 346 ( 2023-08), p. 128364-
    Type of Medium: Online Resource
    ISSN: 0016-2361
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2023
    detail.hit.zdb_id: 1483656-7
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  • 3
    Online Resource
    Online Resource
    MDPI AG ; 2023
    In:  Diagnostics Vol. 13, No. 17 ( 2023-08-24), p. 2746-
    In: Diagnostics, MDPI AG, Vol. 13, No. 17 ( 2023-08-24), p. 2746-
    Abstract: Convolutional neural networks (CNNs) have been extensively utilized in medical image processing to automatically extract meaningful features and classify various medical conditions, enabling faster and more accurate diagnoses. In this paper, LeNet, a classic CNN architecture, has been successfully applied to breast cancer data analysis. It demonstrates its ability to extract discriminative features and classify malignant and benign tumors with high accuracy, thereby supporting early detection and diagnosis of breast cancer. LeNet with corrected Rectified Linear Unit (ReLU), a modification of the traditional ReLU activation function, has been found to improve the performance of LeNet in breast cancer data analysis tasks via addressing the “dying ReLU” problem and enhancing the discriminative power of the extracted features. This has led to more accurate, reliable breast cancer detection and diagnosis and improved patient outcomes. Batch normalization improves the performance and training stability of small and shallow CNN architecture like LeNet. It helps to mitigate the effects of internal covariate shift, which refers to the change in the distribution of network activations during training. This classifier will lessen the overfitting problem and reduce the running time. The designed classifier is evaluated against the benchmarking deep learning models, proving that this has produced a higher recognition rate. The accuracy of the breast image recognition rate is 89.91%. This model will achieve better performance in segmentation, feature extraction, classification, and breast cancer tumor detection.
    Type of Medium: Online Resource
    ISSN: 2075-4418
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662336-5
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  • 4
    In: Machines, MDPI AG, Vol. 11, No. 9 ( 2023-08-31), p. 874-
    Abstract: The reliable operation of monoblock centrifugal pumps (MCP) is crucial in various industrial applications. Achieving optimal performance and minimizing costly downtime requires effectively detecting and diagnosing faults in critical pump components. This study proposes an innovative approach that leverages deep transfer learning techniques. An accelerometer was adopted to capture vibration signals emitted by the pump. These signals are then converted into spectrogram images which serve as the input for a sophisticated classification system based on deep learning. This enables the accurate identification and diagnosis of pump faults. To evaluate the effectiveness of the proposed methodology, 15 pre-trained networks including ResNet-50, InceptionV3, GoogLeNet, DenseNet-201, ShuffleNet, VGG-19, MobileNet-v2, InceptionResNetV2, VGG-16, NasNetmobile, EfficientNetb0, AlexNet, ResNet-18, Xception, ResNet101 and ResNet-18 were employed. The experimental results demonstrate the efficacy of the proposed approach with AlexNet exhibiting the highest level of accuracy among the pre-trained networks. Additionally, a meticulous evaluation of the execution time of the classification process was performed. AlexNet achieved 100.00% accuracy with an impressive execution (training) time of 17 s. This research provides invaluable insights into applying deep transfer learning for fault detection and diagnosis in MCP. Using pre-trained networks offers an efficient and precise solution for this task. The findings of this study have the potential to significantly enhance the reliability and maintenance practices of MCP in various industrial settings.
    Type of Medium: Online Resource
    ISSN: 2075-1702
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2704328-9
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  • 5
    In: Frontiers in Materials, Frontiers Media SA, Vol. 10 ( 2023-8-4)
    Abstract: Composite materials are increasingly replacing synthetic fiber combinations in various applications. However, certain extreme environments on Earth and in space require structures to operate under low temperatures, specifically cryogenic conditions, which can significantly affect material reactions. Therefore, the main focus of this study is to develop and evaluate hybridized biocomposites, specifically assessing their tensile, bending, and impact strengths in a controlled liquid nitrogen environment (77 K). Utilizing the Taguchi optimization method, the statistical analysis of wearing characteristics was carried out utilizing cryogenic treatment hours, load, sliding distance, and weight percentage of abaca and sisal fibers. When 20 percent abaca and sisal were mixed, tensile performance increased from 28.96 to 36.58 MPa. Likewise, the same mixture increased bending strength from 59.63 to 75.68 MPa, and impact strength improved from 59.36 to 71.25 J/m. The cryogenic treatment of composite materials for 15–30 min improved the mechanical characteristics of the materials by enhancing the binding between reinforcements and substrate. The Taguchi 27 test outcomes showed a decreased friction coefficient of 7.79 × 10 5 mm 3 /Nm in the 10th trial with 30 min of cold working, 10% hybrid fibers, 600 m slide distance, and a 4 N load combination. Frictional coefficient data indicated the lowest rate during the third experiment with 15 min of cryogenic treatment, 10% hybrid fibers, 1,500 m slide length, and a 12 N load combination. The microstructural analysis of the fractured specimen was evaluated by scanning electron microscopy. Finally, such composite materials are employed in liquid propellant tanks, satellites, spaceships, rocket constructions, aeroplane components at cruising altitudes, and other applications.
    Type of Medium: Online Resource
    ISSN: 2296-8016
    Language: Unknown
    Publisher: Frontiers Media SA
    Publication Date: 2023
    detail.hit.zdb_id: 2759394-0
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  • 6
    In: ACS Omega, American Chemical Society (ACS)
    Type of Medium: Online Resource
    ISSN: 2470-1343 , 2470-1343
    Language: English
    Publisher: American Chemical Society (ACS)
    Publication Date: 2023
    detail.hit.zdb_id: 2861993-6
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  • 7
    In: Energies, MDPI AG, Vol. 15, No. 19 ( 2022-10-08), p. 7385-
    Abstract: A unified active power control scheme is devised for the grid-integrated permanent magnet synchronous generator-based wind power system (WPS) to follow the Indian electricity grid code requirements. The objective of this paper is to propose control schemes to ensure the continuous integration of WPS into the grid even during a higher percentage of voltage dip. In this context, primarily a constructive reactive power reference is formulated to raise and equalize the point of common coupling (PCC) potential during symmetrical and asymmetrical faults, respectively. A simple active power reference is also proposed to inject a consistent percentage of generated power even during faults without violating system ratings. Eventually, the efficacy of the proposed scheme is demonstrated in terms of PCC voltage enhancement, DC-link potential, grid real, and reactive power oscillation minimization using the PSCAD/ EMTDC software.
    Type of Medium: Online Resource
    ISSN: 1996-1073
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2437446-5
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  • 8
    In: Energies, MDPI AG, Vol. 15, No. 23 ( 2022-11-29), p. 9024-
    Abstract: Optimal energy management has become a challenging task to accomplish in today’s advanced energy systems. If energy is managed in the most optimal manner, tremendous societal benefits can be achieved such as improved economy and less environmental pollution. It is possible to operate the microgrids under grid-connected, as well as isolated modes. The authors presented a new optimization algorithm, i.e., Oppositional Gradient-based Grey Wolf Optimizer (OGGWO) in the current study to elucidate the optimal operation in microgrids that is loaded with sustainable, as well as unsustainable energy sources. With the integration of non-Renewable Energy Sources (RES) with microgrids, environmental pollution is reduced. The current study proposes this hybrid algorithm to avoid stagnation and achieve premature convergence. Having been strategized as a bi-objective optimization problem, the ultimate aim of this model’s optimal operation is to cut the costs incurred upon operations and reduce the emission of pollutants in a 24-h scheduling period. In the current study, the authors considered a Micro Turbine (MT) followed by a Wind Turbine (WT), a battery unit and a Fuel Cell (FC) as storage devices. The microgrid was assumed under the grid-connected mode. The authors validated the proposed algorithm upon three different scenarios to establish the former’s efficiency and efficacy. In addition to these, the optimization results attained from the proposed technique were also compared with that of the results from techniques implemented earlier. According to the outcomes, it can be inferred that the presented OGGWO approach outperformed other methods in terms of cost mitigation and pollution reduction.
    Type of Medium: Online Resource
    ISSN: 1996-1073
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2437446-5
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  • 9
    In: Energies, MDPI AG, Vol. 16, No. 1 ( 2022-12-20), p. 6-
    Abstract: Leading to the enhancement of smart grid implementation, the peer-to-peer (P2P) energy transaction concept has grown dramatically in recent years allowing the end-users to successfully exchange their excess generation and demand in a more profitable way. This paper presents local energy market (LEM) architecture with various market strategies for P2P energy trading among a set of end-users (consumers and prosumers) in a smart residential locality. In a P2P fashion, prosumers/consumers can export/import the available generation/demand in the LEM at a profit relative to utility prices. A common portal known as the transactive energy market operator (TEMO) is introduced to manage the trading in the LEM. The goal of the TEMO is to develop a transaction agreement among P2P players by establishing a price for each transaction based on the price and trading demand provided by the participants. A few case studies on a location with ten residential P2P participants validate the performance of the proposed TEMO.
    Type of Medium: Online Resource
    ISSN: 1996-1073
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2437446-5
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  • 10
    In: Applied Sciences, MDPI AG, Vol. 12, No. 22 ( 2022-11-17), p. 11686-
    Abstract: The most popular method collaborative filter approach is primarily used to handle the information overloading problem in E-Commerce. Traditionally, collaborative filtering uses ratings of similar users for predicting the target item. Similarity calculation in the sparse dataset greatly influences the predicted rating, as less count of co-rated items may degrade the performance of the collaborative filtering. However, consideration of item features to find the nearest neighbor can be a more judicious approach to increase the proportion of similar users. In this study, we offer a new paradigm for raising the rating prediction accuracy in collaborative filtering. The proposed framework uses rated items of the similar feature of the ’most’ similar individuals, instead of using the wisdom of the crowd. The reliability of the proposed framework is evaluated on the static MovieLens datasets and the experimental results corroborate our anticipations.
    Type of Medium: Online Resource
    ISSN: 2076-3417
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2704225-X
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