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The primary goal of a power distribution system is to provide nominal voltages and power with minimal losses to meet consumer demands under various load conditions. In the distribution system, power loss and voltage uncertainty are the common challenges. However, these issues can be resolved by integrating distributed generation (DG) units into the distribution network, which improves the overall power quality of the network. If a DG unit with an appropriate size is not inserted at the appropriate location, it might have an adverse impact on the power system’s operation. Due to the arbitrary incorporation of DG units, some issues occur such as more fluctuations in voltage, power losses, and instability, which have been observed in power distribution networks (DNs). To address these problems, it is essential to optimize the placement and sizing of DG units to balance voltage variations, reduce power losses, and improve stability. An efficient and reliable strategy is always required for this purpose. Ensuring more stable, safer, and dependable power system operation requires careful examination of the optimal size and location of DG units when integrated into the network. As a result, DG should be integrated with power networks in the most efficient way possible to enhance power dependability, quality, and performance by reducing power losses and improving the voltage profile. In order to improve the performance of the distribution system by using optimal DG integration, there are several optimization techniques to take into consideration. Computational-intelligence-based optimization is one of the best options for finding the optimal solution. In this research work, a computational intelligence approach is proposed to find the appropriate sizes and optimal placements of newly introduced different types of DGs into a network with an optimized multi-objective framework. This framework prioritizes stability, minimizes power losses, and improves voltage profiles. This proposed method is simple, robust, and efficient, and converges faster than conventional techniques, making it a powerful tool of inspiration for efficient optimization. In order to check the validity of the proposed technique standard IEEE 14-bus and 30-bus benchmark test systems are considered, and the performance and feasibility of the proposed framework are analyzed and tested on them. Detailed simulations have been performed in “MATLAB”, and the results show that the proposed method enhances the performance of the power system more efficiently as compared to conventional methods. Full article

With the rapid development of new energy vehicle industry and battery technology, in addition to charging mode to supplement energy mode for electric vehicles, battery swapping mode is also about to become an important way for electric vehicles to recharge power. Therefore, in this context, this paper plans the demand response management framework of electric vehicles participating in the regional power grid under the battery swapping mode from the first time. On this basis, the time distribution of battery-swapping demand was proposed by the time series analysis model of different vehicle types of electric vehicles. Then, in order to reduce the peak-valley load difference in the regional power grid as the optimization management goal, the charging schedule optimization scheduling model of electric vehicles participating in the demand response of the regional power grid under the battery swapping mode was constructed. The case analysis shows that under the battery swapping mode, by participating in the demand response through the optimal management and scheduling of the charging load of the power battery, can help the grid balance the contradiction between supply and demand in the peak and valley and promote the full consumption of new energy. Full article

As a passive solar design technology, the Trombe wall can improve buildings’ energy efficiency and thermal comfort. However, the traditional Trombe wall heating efficiency is low and cannot meet the needs of continuous night heating of the building. To solve these problems, a new type of sheet-like composite adsorbent is proposed in this study, prepared from calcium chloride supported by a rock wool board, a high-porosity building material. The high adaptability of rock wool board to the building wall makes it possible for the composite adsorbent to be directly applied to the Trombe wall. The results show that the macroporous structure of the rock wool board provides a wealth of space for loading hydrated salts. The smaller the density and thickness, the more calcium chloride the rock wool board can carry, speeding up the absorption/deportation process. The rock wool slab-based calcium chloride composite adsorbent has a maximum adsorption capacity of 51% and a heat storage density of about 838 J/g. Achieving the desorbed balance within 8 h and applying it to the Trombe wall is expected to attain continuous heating of buildings and has significant potential in building energy conservation. Full article

The accurate, rapid, and stable prediction of electrical energy consumption is essential for decision-making, energy management, efficient planning, and reliable power system operation. Errors in forecasting can lead to electricity shortages, wasted resources, power supply interruptions, and even grid failures. Accurate forecasting enables timely decisions for secure energy management. However, predicting future consumption is challenging due to the variable behavior of customers, requiring flexible models that capture random and complex patterns. Forecasting methods, both traditional and modern, often face challenges in achieving the desired level of accuracy. To address these shortcomings, this research presents a novel hybrid approach that combines a robust forecaster with an advanced optimization technique. Specifically, the FS-FCRBM-GWDO model has been developed to enhance the performance of short-term load forecasting (STLF), aiming to improve prediction accuracy and reliability. While some models excel in accuracy and others in convergence rate, both aspects are crucial. The main objective was to create a forecasting model that provides reliable, consistent, and precise predictions for effective energy management. This led to the development of a novel two-stage hybrid model. The first stage predicts electrical energy usage through four modules using deep learning, support vector machines, and optimization algorithms. The second stage optimizes energy management based on predicted consumption, focusing on reducing costs, managing demand surges, and balancing electricity expenses with customer inconvenience. This approach benefits both consumers and utility companies by lowering bills and enhancing power system stability. The simulation results validate the proposed model’s efficacy and efficiency compared to existing benchmark models. Full article

Sparse matrix–matrix multiplication (SpMM) is essential for deep learning models and scientific computing. Recently, Tensor Cores (TCs) on GPUs, originally designed for dense matrix multiplication with mixed precision, have gained prominence. However, utilizing TCs for SpMM is challenging due to irregular memory access patterns and a varying number of non-zero elements in a sparse matrix. To improve data locality, previous studies have proposed reordering sparse matrices before multiplication, but this adds computational overhead. In this paper, we propose Tensor Core-Adapted SpMM (TCA-SpMM), which leverages TCs without requiring matrix reordering and uses the compressed sparse row (CSR) format. To optimize TC usage, the SpMM algorithm’s dot product operation is transformed into a blocked matrix–matrix multiplication. Addressing load imbalance and minimizing data movement are critical to optimizing the SpMM kernel. Our TCA-SpMM dynamically allocates thread blocks to process multiple rows simultaneously and efficiently uses shared memory to reduce data movement. Performance results on sparse matrices from the Deep Learning Matrix Collection public dataset demonstrate that TCA-SpMM achieves up to 29.58× speedup over state-of-the-art SpMM implementations optimized with TCs. Full article

The dietary recommendations for individuals with diabetes focus on maintaining a balanced nutritional intake to manage blood sugar levels. This study suggests a nutritional strategy to improve glycemic control based on an analysis of a dietary optimization problem. The goal is to minimize the overall glycemic loads (GLs) of specific foods. Two variations of the particle swarm optimization (PSO) method, as well as random quantum process optimization (GQPSO), are introduced. The findings demonstrate that the quantum and random methods are more effective than the traditional techniques in reducing the glycemic loads of diets and addressing nutritional deficiencies while also aligning nutrient intake with the recommended levels. The resolution of this diet optimization model, executed multiple times with adjustments to the parameters of both methods, enables dynamic exploration and provides a wide range of diverse and effective food choices. Full article

Real-time status monitoring is crucial in ensuring the safety of railway tunnel traffic. The primary monitoring method currently involves deploying sensors to form a Wireless Sensor Network (WSN). Due to the linear characteristics of railway tunnels, the resulting sensor networks usually have a linear topology known as a thick Linear Wireless Sensor Network (LWSN). In practice, sensors are deployed randomly within the area, and to balance the energy consumption among nodes and extend the network’s lifespan, this paper proposes a self-organizing network and routing method based on thick LWSNs. This method can discover the topology, form the network from randomly deployed sensor nodes, establish adjacency relationships, and automatically form clusters using a timing mechanism. In the routing, considering the cluster heads’ load, residual energy, and the distance to the sink node, the optimal next-hop cluster head is selected to minimize energy disparity among nodes. Simulation experiments demonstrate that this method has significant advantages in balancing network energy and extending network lifespan for LWSNs. Full article

Background: Dietary composition is one of the factors influencing the acid–base balance of the body by providing acid or base precursors. One of the methods for assessing the acid-forming potential of a diet is to calculate its potential renal acid load (PRAL). The aim of this study was to identify the sociodemographic, lifestyle, and health factors related to the PRAL. Methods: Dietary intake was assessed among 133 individuals aged 70+ years using the three-day record method. Results: The average PRAL value was 15.7 mEq/day (range from −42.4 to +101.7). The diets of a majority of the participants (71.4%) had acid-forming potential (PRAL > 0). From a univariate analysis, the acid-forming potential of the diets was linked mainly to women (65.3% in PRAL > 0 group vs. 10.5% in PRAL < 0 group), people using dietary supplements, those who consumed alcohol, those who assessed their health as being at least good, people with osteoporosis, those hospitalized during the previous year, and those with rather lower physical activity. Conclusions: From a multivariate analysis, gender was the strongest predictor of an acid-forming diet, but the following also contributed: an average self-rated health status (compared to good), a good health status (compared to poor), alcohol drinking, hospitalization, lack of nutritional knowledge, and, to a lesser extent, non-frail status (compared to pre-frail). Therefore, more extensive nutritional education in the identified groups is required. Full article

The advancement of communication technologies has facilitated the deployment of numerous sensors, terminal human–machine interfaces, and smart devices in various complex environments for data collection and analysis, providing automated and intelligent services. The increasing urgency of monitoring demands in complex environments necessitates low-cost and efficient network deployment solutions to support various monitoring tasks. Distributed networks offer high stability, reliability, and economic feasibility. Among various Low-Power Wide-Area Network (LPWAN) technologies, Long Range (LoRa) has emerged as the preferred choice due to its openness and flexibility. However, traditional LoRa networks face challenges such as limited coverage range and poor scalability, emphasizing the need for research into distributed routing strategies tailored for LoRa networks. This paper proposes the Optimizing Link-State Routing Based on Load Balancing (LB-OLSR) protocol as an ideal approach for constructing LoRa distributed multi-hop networks. The protocol considers the selection of Multipoint Relay (MPR) nodes to reduce unnecessary network overhead. In addition, route planning integrates factors such as business communication latency, link reliability, node occupancy rate, and node load rate to construct an optimization model and optimize the route establishment decision criteria through a load-balancing approach. The simulation results demonstrate that the improved routing protocol exhibits superior performance in node load balancing, average node load duration, and average business latency. Full article

The environmental degradation in the Middle East and North Africa (MENA) region leads to significant challenges regarding economic sustainability and the attainment of sustainable development goals (SDGs). The extensive use of fossil fuels in the region, as well as rapid urbanization and economic growth, has led to significant carbon emissions, together with unprecedented ecological footprints compromising environmental sustainability. The study aims to elucidate the influence exerted by technological innovation, trade openness, and natural resources on environmental sustainability in Turkey and Egypt for the period 1990–2022. In assessing the empirical relations, the study employed the Fourier function incorporate estimation techniques, that is, Fourier ADF for unit root test, Fourier ARDL, and Fourier NARDL for long-run and short-run elasticities of technological innovation (TI), trade openness (TO,) and natural resources rent (NRR) on load capacity factor (LCF) and inverted LCF (ILCF); finally, the directional causality evaluate through Fourier TY causality test. The results revealed that both Turkey and Egypt have severe environmental problems due to their high carbon emissions and ecological footprints. Technological change and international trade separately negatively affect environmental sustainability; however, these negative impacts have mixed character. On the one hand, technology can improve efficiency and reduce ecological footprints by obviating the use of high-impact processes or allowing cleaner production systems. In the same vein, trade openness helps transfer green technologies more quickly, but it can also lead to unsustainable resource extraction and pollution. The findings of the paper propose that in order to move forward, Turkey and Egypt need strategic policy shifts to ensure environmental sustainability, including transitioning towards renewable energy from fossil fuels while bolstering their capacity for energy efficiency. Policymakers must balance economic development with environmental conservation to reduce the harmful effects of climate degradation and help safeguard continued economic survival in the face of increasing climatic instability. This research helps to inform policy and investment decisions about how the SDGs can be achieved and how they are relevant for sustainable development in the MENA region. Full article

This study presents a molecular dynamics (MD) investigation of the evolution of local atomic environments (LAEs) in a Cu₆₆Zr₃₄ bulk metallic glass (BMG), both at rest and under constant shear deformation. LAEs were characterized using Voronoi polyhedra analysis. Even in the absence of external load, LAEs frequently transformed into one another due to short-ranged atomic position fluctuations. However, as expected, each transition from one polyhedra to another was balanced by the reverse transition, thereby preserving the proportions of the different polyhedra. Cu-centered icosahedral LAEs were observed to preferentially transform into and from <1,0,9,3,0>, <0,1,10,2,0>, and <0,2,8,2,0> LAEs. Upon applying pure shear, the simulation box was first deformed in one direction up to a strain of 25% and then in the opposite direction to the same strain level. Shear deformation induced large nonaffine atomic displacements in the directions parallel to the shear, which were concentrated in specific regions of the BMG, forming band-like regions. From the onset, shear deformation led to the destabilization of Cu-centered icosahedral LAEs, as indicated by more frequent transitions to and from other polyhedra. Unlike other Cu-centered LAEs, icosahedra were also found to be more sensitive to yielding. The destruction of Cu-centered icosahedra was primarily a result of net transformations into <1,0,9,3,0> and <0,2,8,2,0> LAEs in the BMG subjected to pure shear, with a minor contribution of transformations involving the <0,1,10,2,0> polyhedra. Full article

Rock-filled concrete (RFC) is an advanced construction material that integrates high-performance self-compacting concrete (HSCC) with large rocks exceeding 300 mm, providing advantages such as reduced hydration heat and increased construction processes. The elastic modulus of RFC is a critical parameter that directly influences its structural performance, making it vital for modern construction applications that require strength and stiffness. However, there is a scientific gap in understanding the effects of rock size, shape, arrangement, and volumetric ratio on this parameter. This study investigates these factors using mesoscale finite element models (FEMs) with spherical and polyhedral rocks. The results reveal that polyhedral rocks increase the elastic modulus compared to spherical rocks, enhancing RFC’s load-bearing capacity. Additionally, a 5% increase in the elastic modulus was observed when the rockfill ratio was increased from 50% to 60%, demonstrating a direct correlation between rock volume and mechanical performance. Furthermore, the elastic modulus rises significantly in the early stages of placement, followed by a gradual increase over time. Optimal rock sizes and a balanced mix of rock shapes allow for improved concrete flow and mechanical properties, making RFC a highly efficient material for construction. These findings offer valuable insights for designers and engineers looking to optimize RFC for structural applications. Full article

This study proposes an improved few-shot learning model of the Siamese network residual Visual Geometry Group (VGG). This model combined with time–frequency domain transformation techniques effectively enhances the performance of across-load fault diagnosis for induction motors with limited data conditions. The proposed residual VGG-based Siamese network consists of two primary components: the feature extraction network, which is the residual VGG, and the merged similarity layer. First, the residual VGG architecture utilizes residual learning to boost learning efficiency and mitigate the degradation problem typically associated with deep neural networks. The employment of smaller convolutional kernels substantially reduces the number of model parameters, expedites model convergence, and curtails overfitting. Second, the merged similarity layer incorporates multiple distance metrics for similarity measurement to enhance classification performance. For cross-domain fault diagnosis in induction motors, we developed experimental models representing four common types of faults. We measured the vibration signals from both healthy and faulty models under varying loads. We then applied the proposed model to evaluate and compare its effectiveness in cross-domain fault diagnosis against conventional AI models. Experimental results indicate that when the imbalance ratio reached 20:1, the average accuracy of the proposed residual VGG-based Siamese network for fault diagnosis across different loads was 98%, closely matching the accuracy of balanced and sufficient datasets, and significantly surpassing the diagnostic performance of other models. Full article

This study presents the design and a thorough analysis of a six-bar crank-driven leg mechanism integrated with a skew pantograph, developed for walking robots. The mechanism’s dimensions were optimized using a rigorous dimensional synthesis process in GIM software (version 2024). Subsequently, a detailed kinematic analysis was performed in GIM to simulate the leg’s motion trajectory, velocity, and acceleration. In parallel, kinematic equations were formulated using the vector loop method, implemented in MATLAB (version R2013-b), and compared with the GIM results for validation, demonstrating the strong agreement between both tools. These results confirm the mechanism’s ability to generate a compact, high-lift foot trajectory while maintaining system stability and energy efficiency. An inverse dynamic analysis was carried out to determine the actuator’s driving torque, ensuring efficient operation under expected load conditions. Furthermore, topology optimization conducted in SOLIDWORKS (version 2021) significantly reduced the weight of the ground-contacting link while preserving its structural integrity. A subsequent stress analysis validated the mechanical viability of the optimized design, supporting its feasibility for real-world implementation. This research provides a robust foundation for the development of a functional prototype. Its potential applications include mobile robots for sectors such as agriculture and all-terrain vehicles, where efficient, reliable, and adaptive locomotion is crucial. The proposed mechanism strikes an optimal balance between mechanical simplicity, cost-effectiveness, and high performance, making it well-suited for challenging operational environments. Full article

The purpose of this paper is to present a detailed design procedure for a highly efficient sequential load-modulated balanced amplifier (SLMBA) to provide an in-depth analysis of this complex power amplifier (PA) architecture. SLMBA’s basic theory is presented and discussed. An SLMBA with a frequency range from 2.45 GHz to 2.65 GHz was implemented and then measured in order to validate the proposed design methodology. In both saturation and back-off states, the fabricated SLMBA exhibits extremely high efficiency and output power. It delivers a maximum output power of 43~44.4 dBm and a drain efficiency (DE) of 71.6~75% at saturation, a DE of 63.5~66% at 6 dB output back-off (OBO) state, a DE of 61.8~66% at 10 dB OBO state, and a DE of more than 51% at 12 dB OBO state in the targeted frequency band. The achieved results demonstrate the effectiveness of the proposed design procedure. Full article