Search

Wearable devices face a significant challenge in balancing battery life with performance, often leading to frequent recharging and reduced user satisfaction. In this paper, we introduce the SmartAPM (Smart Adaptive Power Management) framework, a novel approach that leverages deep reinforcement learning (DRL) to optimize power management in wearable devices. The key objective of SmartAPM is to prolong battery life while enhancing user experience through dynamic adjustments to specific usage patterns. We compiled a comprehensive dataset by integrating user activity data, sensor readings, and power consumption metrics from various sources, including WISDM, UCI HAR, and ExtraSensory. Synthetic power profiles and device specifications were incorporated into the dataset to enhance training. SmartAPM employs a multi-agent deep reinforcement learning framework that combines on-device and cloud-based learning techniques, as well as transfer learning, to enhance personalization. Simulations on wearable devices demonstrate that SmartAPM can extend battery life by 36% compared to traditional methods, while also increasing user satisfaction by 25%. The system adapts to new usage patterns within 24 h and utilizes less than 5% of the device's resources. SmartAPM has the potential to revolutionize energy management in wearable devices, inspiring a new era of battery efficiency and user satisfaction. [ABSTRACT FROM AUTHOR]

A hybrid microgrid powered by hydrogen is an energy infrastructure that depends on hydrogen as its primary energy carrier within a localized network. This study proposed a novel bi-level optimization approach to enhance power quality and cost efficiency of the system. In the quest to improve energy management systems (EMS) and enhance power quality, a bi-level optimization approach named Particle swarm optimization-Modified water wave optimization (PSO-MWWO) has been proposed. This method integrates adaptive population size and an adaptive wavelength coefficient to boost its overall effectiveness. It is attractive with specialists in energy management systems (EMS), control systems, and hydrogen technologies can significantly augment the efficiency of coordination endeavours. PSO-MWWO can incorporate environmental considerations, such as minimizing emissions or exploiting the use of renewable energy resources (RESs) in hydrogen production and consumption. This paper thoroughly examines its implementation, operation, and unique features, with a particular emphasis on the power quality of a hydrogen based microgrid. The achieved results and numerical analysis affirm the superiority of the proposed technique compared to other traditional methods like mixed integer linear programming (MILP), HOMER, Variable mesh optimization (VMO), and Cataclysmic genetic algorithm in optimizing component sizing, renewable production, hydrogen production, reliability, cost effective, and overall efficacy. This substantiates its practical utility in real-time applications. The efficacy of this method is empirically demonstrated through the implementation in MATLAB software. [ABSTRACT FROM AUTHOR]

Model optimization is a problem of great concern and challenge for developing an image classification model. In image classification, selecting the appropriate hyperparameters can substantially boost the model's ability to learn intricate patterns and features from complex image data. Hyperparameter optimization helps to prevent overfitting by finding the right balance between complexity and generalization of a model. The ensemble genetic algorithm and convolutional neural network (EGACNN) are proposed to enhance image classification by fine-tuning hyperparameters. The convolutional neural network (CNN) model is combined with a genetic algorithm GA) using stacking based on the Modified National Institute of Standards and Technology (MNIST) dataset to enhance efficiency and prediction rate on image classification. The GA optimizes the number of layers, kernel size, learning rates, dropout rates, and batch sizes of the CNN model to improve the accuracy and performance of the model. The objective of this research is to improve the CNN-based image classification system by utilizing the advantages of ensemble learning and GA. The highest accuracy is obtained using the proposed EGACNN model which is 99.91% and the ensemble CNN and spiking neural network (CSNN) model shows an accuracy of 99.68%. The ensemble approaches like EGACNN and CSNN tends to be more effective as compared to CNN, RNN, AlexNet, ResNet, and VGG models. The hyperparameter optimization of deep learning classification models reduces human efforts and produces better prediction results. Performance comparison with existing approaches also shows the superior performance of the proposed model. [ABSTRACT FROM AUTHOR]

In the present paper, we investigate the structural and electrical properties of gadolinium (Gd)-modified barium zirconate titanate (BZT) ceramics, i.e., Ba1-xGd2x/3Zr0.05Ti0.95O3 (BGZT; x = 0.01–0.05). The high-temperature solid-state reaction method has been used to synthesize the ceramics. The X-ray diffraction and Rietveld refinement analysis show the existence of dual-phase tetragonal (P4mm) and orthogonal (Amm2) in all the compositions. In the XRD data, the peak shifts to a lower angle up to x = 0.03 and then toward the higher angle side indicating the doping at the A site for (x ≤ 0.03 and at the B site for higher concentration. The grain size is found to be reduced from around ~ 2 μm to ~ 0.5 μm with Gd doping. The real and imaginary impedance values increase to 0.8 MΩ for x = 0.03 and then reduce for higher concentrations. The complex impedance spectra indicate that the grain and the grain boundary resistance are decreasing with increasing concentrations of Gd until x = 0.03 with a minimum value of ~ 40kΩ (at 300 ℃) and then rising for higher concentrations. The excellent fitting of the Kohlrausch–Williams–Watts function to the dielectric modulus indicates the transition from long-range to short-range mobility of charge carriers. The temperature-dependent ac conductivity shows that the conductivity rises until x = 0.03 and then reduces due to the incorporation of Gd in the Ti site producing oxygen vacancies. The activation energy for both the conduction and relaxation processes was obtained using the Arrhenius plots. The activation energy for the relaxation process is less than ~ 0.5 eV and for the conduction process found to be more than ~ 0.5 eV indicating different hoping mechanisms are involved in the composition. [ABSTRACT FROM AUTHOR]

In this method, we presented an attractive and efficient methodology for the preparation of 3,4-dihydropyrimidin-2-(1H)one derivatives through Biginelli reaction of aromatic aldehydes, 1,3-dicarbonyl compounds and urea or thiourea in the presence of Fe3O4@SiO2-AP/Diamine-Cu(II) nanocomposite as a novel magnetically recoverable catalyst in ChCl-Urea as green solvent. The Fe3O4@SiO2-AP/Diamine-Cu(II) nanocatalyst was constructed by simple method via available materials and its structure was well identified by several analysis. Under this method, a wide range of aromatic aldehydes with electron-donating and electron-withdrawing substituents on the benzene ring were evaluated in these reactions, and in all cases the products were synthesized with very high yields. The presented method has a series of noteworthy features, which can be mentioned as follows: synthesis of products with very high yields, reaction in ChCl-Urea as a green solvent, applicability of the catalytic system for a wide range of aldehydes, simple preparation of catalyst, simple separation of catalyst, high reusability of catalyst, good identification of catalyst structure and synthesized products.Graphical Abstract: In this method, we presented an attractive and efficient methodology for the preparation of 3,4-dihydropyrimidin-2-(1H)one derivatives through Biginelli reaction of aromatic aldehydes, 1,3-dicarbonyl compounds and urea or thiourea in the presence of Fe3O4@SiO2-AP/Diamine-Cu(II) nanocomposite as a novel magnetically recoverable catalyst in ChCl-Urea as green solvent. The Fe3O4@SiO2-AP/Diamine-Cu(II) nanocatalyst was constructed by simple method via available materials and its structure was well identified by several analysis. Under this method, a wide range of aromatic aldehydes with electron-donating and electron-withdrawing substituents on the benzene ring were evaluated in these reactions, and in all cases the products were synthesized with very high yields. The presented method has a series of noteworthy features, which can be mentioned as follows: synthesis of products with very high yields, reaction in ChCl-Urea as a green solvent, applicability of the catalytic system for a wide range of aldehydes, simple preparation of catalyst, simple separation of catalyst, high reusability of catalyst, good identification of catalyst structure and synthesized products. [ABSTRACT FROM AUTHOR]

Tolerance allocation and transfer are two important engineering tasks. In fact, the assembly quality improvement and tolerances cost reduction are relied on optimal transfer and tolerance allocation while respecting the functions depend and manufacturing requirements. The traditional approaches reduce the tolerance values during the transfer of the design dimension on the manufacturing dimensions and expand the tolerance manufacturing cost as well. However, these approaches are unsuitable for a concurrent engineering context. This paper proposes a unique transfer approach of mechanism–dimension from the functional requirements to the manufacturing dimensions of parts. The traditional approaches for the transfer of mechanisms–dimensions in parts-dimensions are firstly presented. Then, a case study of the manufacturing dimensions specification based on Bourdet and Wade is detailed. The obtained results lead to avoid the tolerance reduction generated by the double dimensions transfer of traditional industrial approaches. A comparative study of allocated tolerances of manufacturing dimensions is presented for each approach. Therefore, the compared approaches classification is performed according to various criteria as the number of manufacturing dimensions, the values of the allocated tolerance and the calculated total cost based on the tolerances' manufacturing cost and quality loss. In addition, the proposed method based on the unique transfer promotes the field of interactive engineering. In fact, the unique transfer requires the collaborative work and interactive exchange of expertise between different actors of the product development cycle. [ABSTRACT FROM AUTHOR]

Tolerance allocation is a very important assignment. In fact, knowing how to allocate efficiently tolerances for assembly parts in a computer-aided design (CAD) and computer-aided manufacturing (CAM) system is a very key issue because the cost and quality depend directly on dimension tolerances. The traditional optimization approaches complain about a lack regarding the evaluation of manufacturing difficulty to solve tolerance allocation problem. In this paper, a new approach based on difficulty coefficient evaluation (DCE) and Lagrange multiplier (LM) method is presented to obtain an economical tolerance allocation. In fact, DCE technique, based on the failure mode, effects and criticality analysis (FMECA) tool, is used to quantify the manufacturing difficulty of mechanical parts, as well as the LM method is developed to optimize the proposed approach while respecting the functions and manufacturing requirements. A case study is established to verify the highlights of this work. Indeed, the computed result shows that the method can produce tolerance allocation economically and accurately according to difficulty requirements. Consequently, the proposed method is performed to exploit fully design for manufacturing and assembly (DFMA). For this, an integrated CAD/tolerancing model based on manufacturing difficulty (ICADTMD) is developed using graphical user interface (GUI) in MATLAB. [ABSTRACT FROM AUTHOR]

In a product life cycle, the tolerances present major impacts on the quality and cost of mechanisms. This paper presents an innovative methodology for tolerance allocation. The proposed approach allows tolerance integration in a computer-aided design (CAD) model, while considering functional and manufacturing requirements in an early phase of digital mock-up (DMU). The purpose is to consider the manufacturing process in the tolerance allocation using tools for the study and analysis of reliability of the design or the process, as the Failure Mode, Effects and Criticality Analysis (FMECA) and Ishikawa diagram. The results lead to the broadening tolerance values of difficult machined dimensions while respecting the functional requirements. Thus, the total cost of assembly decreases. The model is validated through a case study of tolerance allocation using various approaches. [ABSTRACT FROM AUTHOR]

Providing new and efficient methods for synthesizing amides remains a paramount challenge in organic chemistry due to their prevalence in natural products, pharmaceuticals, and industrial applications. Given the importance of easy separation and recyclability, magnetic nanocatalysts offer significant advantages over traditional heterogeneous catalysts. In this research, we developed a palladium(0) complex immobilized on dopamine-bipyridine functionalized Fe3O4 nanoparticles (MNPs-Dopamine-BiPy-Pd(0)) and investigated its catalytic activity in the mild synthesis of amides via azidation/carbonylation of aryl halides. A wide array of aryl iodides, incorporating both electron-donating and electron-withdrawing groups, were successfully converted to amides using sodium azide as the nitrogen source and Mo(CO)6 as the carbonyl source, achieving 75–97% yields within 5 h. Notably, the MNPs-Dopamine-BiPy-Pd(0) catalyst demonstrated excellent recyclability, maintaining high catalytic activity after eight consecutive cycles.Graphical Abstract: Providing new and efficient methods for synthesizing amides remains a paramount challenge in organic chemistry due to their prevalence in natural products, pharmaceuticals, and industrial applications. Given the importance of easy separation and recyclability, magnetic nanocatalysts offer significant advantages over traditional heterogeneous catalysts. In this research, we developed a palladium(0) complex immobilized on dopamine-bipyridine functionalized Fe3O4 nanoparticles (MNPs-Dopamine-BiPy-Pd(0)) and investigated its catalytic activity in the mild synthesis of amides via azidation/carbonylation of aryl halides. A wide array of aryl iodides, incorporating both electron-donating and electron-withdrawing groups, were successfully converted to amides using sodium azide as the nitrogen source and Mo(CO)6 as the carbonyl source, achieving 75–97% yields within 5 h. Notably, the MNPs-Dopamine-BiPy-Pd(0) catalyst demonstrated excellent recyclability, maintaining high catalytic activity after eight consecutive cycles. [ABSTRACT FROM AUTHOR]