Search Results (19,812)

In the context of the coordinated operation of microgrids and community energy storage systems, achieving optimal resource allocation under complex and uncertain conditions has emerged as a prominent research focus. This study proposes a robust collaborative optimization model for microgrids and community energy storage systems under a game-theoretic environment where potential fraudulent behavior is considered. A multi-energy collaborative system model is first constructed, integrating multiple uncertainties in source-load pricing, and a max-min robust optimization strategy is employed to improve scheduling resilience. Secondly, a game-theoretic model is introduced to identify and suppress manipulative behaviors by dishonest microgrids in energy transactions, based on a Nash bargaining mechanism. Finally, a distributed collaborative solution framework is developed using the Alternating Direction Method of Multipliers and Column-and-Constraint Generation to enable efficient parallel computation. Simulation results indicate that the framework reduces the alliance’s total cost from CNY 66,319.37 to CNY 57,924.89, saving CNY 8394.48. Specifically, the operational costs of MG1, MG2, and MG3 were reduced by CNY 742.60, CNY 1069.92, and CNY 1451.40, respectively, while CES achieved an additional revenue of CNY 5130.56 through peak shaving and valley filling operations. Furthermore, this distributed algorithm converges within 6–15 iterations and demonstrates high computational efficiency and robustness across various uncertain scenarios. Full article

The sealing performance of ultra-high-pressure solenoid valves faces significant challenges, particularly under low-temperature conditions. Due to the difference in thermal expansion coefficients between the valve seat and the sealing tube, combined with material contraction at low temperatures, the bolt preload decreases, and consequently the contact force on the sealing surface and the average sealing specific pressure are reduced. This may result in an average sealing specific pressure falling below the required sealing specific pressure, causing leakage and failure of the ultra-high-pressure solenoid valve. To address this problem, this study utilizes theoretical and simulation analysis to examine the preload status in low-temperature environments and the causes of sealing failure in ultra-high-pressure solenoid valves. A corresponding optimization scheme is proposed, which involves increasing the torque from 120 N·m to 130 N·m and applying sealant to the threaded connection to enhance the sealing performance of the ultra-high-pressure solenoid valve. Following the increase in tightening torque and the application of thread sealant, the helium leakage rate at −40 °C is significantly reduced. Specifically, at a test pressure of 87.5 MPa, the helium leakage rate decreases from $1.6\times {10}^{-5}$ mbar·L/s to approximately $1.4\times {10}^{-6}$ mbar·L/s. At test pressures of 1.4 MPa and 10 MPa, the leakage rate is approximately $3.0\times {10}^{-7}$ mbar·L/s. Experimental verification shows that the proposed solution can significantly enhance the sealing reliability of ultra-high-pressure solenoid valves under extreme operating conditions. Full article

The increasing complexity and scale of service-oriented architectures in cloud computing have heightened the demand for intelligent, decentralized, and adaptive service composition techniques. This study proposes an advanced framework that integrates a Multi-Agent System (MAS) with a novel hybrid metaheuristic optimization method, the Integrated Particle-Ant Algorithm (IPAA), to achieve efficient, scalable, and Quality of Service (QoS)-aware service composition. The IPAA dynamically combines the global search capabilities of Particle Swarm Optimization (PSO) with the local exploitation strength of Ant Colony Optimization (ACO), thereby enhancing convergence speed and solution quality. The proposed system is structured into three logical layers—agent, optimization, and infrastructure—facilitating autonomous decision-making, distributed coordination, and runtime adaptability. Extensive simulations using a synthetic cloud service dataset demonstrate that the proposed approach significantly outperforms traditional optimization methods, including standalone PSO, ACO, and random composition strategies, across key metrics such as utility score, execution time, and scalability. Moreover, the framework enables real-time monitoring and automatic re-optimization in response to QoS degradation or Service-Level Agreement (SLA) violations. Through decentralized negotiation and minimal communication overhead, agents exhibit high resilience and flexibility under dynamic service availability. These results collectively suggest that the proposed IPAA-based framework provides a robust, intelligent, and scalable solution for service composition in complex cloud computing environments. Full article

This study presents a comprehensive performance evaluation of hybrid global navigation satellite system (GNSS) configurations in urban canyon environments across South Korea, focusing on the integration of Global Positioning System (GPS) with the BeiDou, GLONASS, Galileo, Quasi Zenith Satellite System (QZSS), and Navigation with Indian Constellation (NavIC) constellations. Simulation scenarios representing pedestrian, vehicular, and unmanned aerial vehicle (UAV) movements are used to analyze the positioning accuracy and reliability of each hybrid system. The results indicate that GPS–BeiDou and GPS–QZSS combinations consistently provide superior accuracy and continuous satellite visibility, with GPS–BeiDou achieving centimeter-level precision in the UAV scenario. In contrast, GPS–GLONASS and GPS–NavIC systems exhibit higher error rates and less stable performance. These findings emphasize the critical role of satellite availability, receiver altitude, and signal compatibility in achieving robust positioning. Although the results are specific to South Korea, the proposed evaluation framework is broadly applicable and can help other countries assess hybrid GNSS performance to guide the design and optimization of their regional navigation satellite systems. Full article

Unmanned aerial vehicles (UAVs) are increasingly favored for emergency search and rescue operations due to their high adaptability to harsh environments and low operational costs. However, the dynamic nature of search path endpoints, influenced by target movement, limits the applicability of shortest path models between fixed points in moving target search problems. Consequently, the moving target search problem using UAVs in complex environments presents considerable challenges, constituting an NP-hard problem. The Grey Wolf Optimizer (GWO) is known for addressing such problems. However, it suffers from limitations, including premature convergence and instability. To resolve these constraints, a Grey Wolf Optimizer with variable population and strategy (GWO-VPS) is developed in this work. GWO-VPS implements a variable encoding scheme for UAV movement patterns, combining motion-based encoding with path-based encoding. The algorithm iteratively alternates between global optimization and local smoothing phases. The global optimization phase incorporates: (1) a Q-learning-based strategy selection; (2) position updates with obstacle avoidance and energy consumption reduction; and (3) adaptive exploration factor. The local smoothing phase employs four path smoothing operators and Q-learning-based strategy selection. Experimental results demonstrate that GWO-VPS outperforms both enhanced GWO variants and standard algorithms, confirming the algorithm’s effectiveness in UAV-based moving target search simulations. Full article

As wind energy development continues to expand toward nearshore and deep-sea regions, enhancing the aerodynamic efficiency of vertical axis wind turbines (VAWTs) in complex marine environments has become a critical challenge. To address this, a composite flow control strategy combining leading-edge suction and trailing-edge gurney flap is proposed. A two-dimensional unsteady numerical simulation framework is established based on CFD and the four-equation Transition SST (TSST) transition model. The key control parameters, including the suction slot position and width as well as the gurney flap height and width, are systematically optimized through orthogonal experimental design. The aerodynamic performance under single (suction or gurney flap) and composite control schemes is comprehensively evaluated. Results show that leading-edge suction effectively delays flow separation, while the gurney flap improves aerodynamic characteristics in the downwind region. Their synergistic effect significantly suppresses blade load fluctuations and enhances the wake structure, thereby improving wind energy capture. Compared to all other configurations, including suction-only and gurney flap-only blades, the composite control blade achieves the most significant increase in power coefficient across the entire tip speed ratio range, with an average improvement of 67.24%, demonstrating superior aerodynamic stability and strong potential for offshore applications. Full article

Interferometric signals in space-borne Gravitational Wave Detectors are measured by digital phasemeters. The phasemeter processes signals generated by multiple interferometers, with its primary function being micro-radian level phase measurements. The Science Interferometer is responsible for inter-spacecraft measurements, including relative ranging, absolute ranging, laser communication, and clock noise transfer. Since the scientific interferometer incorporates multiple functions and various signals are simultaneously coupled into the heterodyne signal, establishing a suitable evaluation environment is a crucial foundation for achieving micro-radian level phase measurement during ground testing and verification. This paper evaluates the phase measurement noise of the science interferometer by simulating the heterodyne signal and establishing a test environment. The experimental results show that when the simulated heterodyne signal contains the main beat-note, upper and lower sideband beat-notes, and PRN modulation simultaneously, the phase measurement noise of the main beat-note, upper and lower sideband beat-notes all reach 2π μrad/Hz^1/2@(0.1 mHz–1 Hz), meeting the requirements of the space gravitational wave detection mission. An experimental verification platform and performance reference benchmark have been established for subsequent research on the impact of specific noise on phase measurement performance and noise suppression methods. Full article

The diversity of energy resources in distribution networks requires new strategies for planning and operation. In this context, microgrids are solutions that can integrate renewable energy sources, energy storage systems (ESSs), and demand response (DR), thereby decentralizing operations and utilizing digital technologies to create more proactive energy markets. Given the above, this work proposes a distributed optimal dispatch strategy for microgrids with multiple energy resources, with a focus on scalability. Simulations are performed using agent modeling on the Python Agent Development (PADE) platform, leveraging distributed computing resources and agent communication. A co-simulation environment, coordinated by Mosaik, synchronizes data exchange, while a plug-and-play system allows dynamic agent modification. The main contribution of the present study relies on a system integration approach, combining a multi-agent system (MAS) and Mosaik co-simulation framework with plug-and-play agent support for the very short-term (five-minute) dispatch of energy resources. Optimization algorithms, namely particle swarm optimization (PSO) and multi-agent particle swarm optimization (MAPSO), are framed as an incremental improvement tailored to this distributed architecture. Case studies show that distributed MAPSO performs better, with lower objective function values and a smaller relative standard deviation (15.6%), while distributed PSO had a higher deviation (33.9%). Although distributed MAPSO takes up to three times longer to provide a solution, with an average of 9.0 s, this timeframe is compatible with five-minute dispatch intervals. Full article

Modern energy grids, with their regional diversity and complex consumption patterns, require accurate short-term forecasting for operational efficiency and reliability. This study introduces a Stacking Ensemble Forecasting (SEF) framework for multi-region household energy demand, utilizing an optimized stacking ensemble model tuned via Bayesian Optimization to achieve superior predictive accuracy. The framework significantly improved accuracy across Diyarbakır, Istanbul, and Odemis, with a final model demonstrating up to 16.47% RMSE reduction compared to the best baseline models. The final model’s real-world performance was validated through a Simulated Digital Twin (SDT) environment, where scenario-based testing demonstrated its robustness against behavioral changes, data quality issues, and device failures. The proposed SEF-SDT framework offers a generalizable solution for managing diverse regions and consumption profiles, contributing to efficient and sustainable energy management. Full article

Electrical networks face operational challenges from power quality-affecting disturbances. Since disturbance signatures directly affect classifier performance, optimized feature selection becomes critical for accurate power quality assessment. The pursuit of robust feature extraction inevitably constrains the dimensionality of the discriminative feature set, but the complexity of the recognition model will be increased and the recognition speed will be reduced if the feature vector dimension is too high. Building upon the aforementioned requirements, in this paper, we propose a feature extraction framework that combines improved symplectic geometric mode decomposition, refined generalized multiscale quantum entropy, and refined generalized multiscale reverse dispersion entropy. Firstly, based on the intrinsic properties of power quality disturbance (PQD) signals, the embedding dimension of symplectic geometric mode decomposition and the adaptive mode component screening method are improved, and the PQD signal undergoes tri-band decomposition via improved symplectic geometric mode decomposition (ISGMD), yielding distinct high-frequency, medium-frequency, and low-frequency components. Secondly, utilizing the enhanced symplectic geometric mode decomposition as a foundation, the perturbation features are extracted by the combination of refined generalized multiscale quantum entropy and refined generalized multiscale reverse dispersion entropy to construct high-precision and low-dimensional feature vectors. Finally, a double-layer composite power quality disturbance model is constructed by a deep extreme learning machine algorithm to identify power quality disturbance signals. After analysis and comparison, the proposed method is found to be effective even in a strong noise environment with a single interference, and the average recognition accuracy across different noise environments is 97.3%. Under the complex conditions involving multiple types of mixed perturbations, the average recognition accuracy is maintained above 96%. Compared with the existing CNN + LSTM method, the recognition accuracy of the proposed method is improved by 3.7%. In addition, its recognition accuracy in scenarios with small data samples is significantly better than that of traditional methods, such as single CNN models and LSTM models. The experimental results show that the proposed strategy can accurately classify and identify various power quality interferences and that it is better than traditional methods in terms of classification accuracy and robustness. The experimental results of the simulation and measured data show that the combined feature extraction methodology reliably extracts discriminative feature vectors from PQD. The double-layer combined classification model can further enhance the model’s recognition capabilities. This method has high accuracy and certain noise resistance. In the 30 dB white noise environment, the average classification accuracy of the model is 99.10% for the simulation database containing 63 PQD types. Meanwhile, for the test data based on a hardware platform, the average accuracy is 99.03%, and the approach’s dependability is further evidenced by rigorous validation experiments. Full article

Population aging intensifies the demand for rehabilitation services, which are already suffering from staff shortages. In response to this challenge, the implementation of new technologies in physiotherapy is needed. For such a task, rehabilitation exoskeletons can be used. While designing such tools, their functionality and safety must be ensured. Therefore, simulations of their strength and kinematics must meet set criteria. This paper aims to present a methodology for simulating the dynamics of rehabilitation exoskeletons during activities of daily living and determining the reactions in the construction’s joints, as well as the required driving torques. The methodology is applied to the SmartEx-Home exoskeleton. Two versions of a multibody model were developed in the Matlab/Simulink environment—a rigid-only version and one with deformable components. The kinematic chain of construction was reflected with the driven rotational joints and modeled passive sliding open bearings. The simulation outputs include the driving torques and joint reaction forces and the torques for various input trajectories registered using IMU sensors on human participants. The results obtained in the investigation show that in general, to mobilize shoulder flexion/extension or abduction/adduction, around 30 Nm of torque is required in such a lightweight exoskeleton. For elbow flexion/extension, around 10 Nm of torque is needed. All of the reactions are presented in tables for all of the characteristic points on the passive and active joints, as well as the attachments of the extremities. This methodology provides realistic load estimations and can be universally used for similar structures. The presented numerical results can be used as the basis for a strength analysis and motor or force sensor selection. They will be directly implemented for the process of mass minimization of the SmartEx-Home exoskeleton based on computational optimization. Full article

Climate change significantly affects the well-being of urban populations. Thus, there is an increasing need for public green spaces in cities, as biologically active surfaces play a critical role in modifying the urban climate—cooling temperatures and providing shelter. Some institutional gardens, like cemeteries and hospital gardens, are hidden treasures: they are open but excluded from citizens’ mental maps, while usually having a rich green mass. This article aims to explore these hidden green surface elements, presenting their advantages and disadvantages by measuring their local climate effects. Three institutional gardens located in different urban environments were selected for analysis in the sample area of Budapest to explore how the surrounding built-up areas of the city modify the urban climate. The climate analyses were prepared with the ENVI-met climate simulation program. In the case of both hospital gardens and cemeteries, our studies show that their green spaces have great potential to increase the sense of comfort for both users of the green spaces and inhabitants of the neighborhood. In densely built-up urban areas, it is particularly important to involve institutional green spaces in public use, because with appropriate development they can contribute to cities’ adaptation to climate change. Full article

Spaceflight and microgravity (μg) environments induce numerous cardiovascular changes that affect cardiac structure and function, and understanding these effects is essential for astronaut health and tissue engineering in space. This review compiles and analyzes over 30 years of research on the impact of real and simulated μg on cardiomyocytes. A comprehensive literature search was conducted across five databases, and 62 eligible studies involving cardiac cells under μg or spaceflight conditions were compiled and analyzed. Despite the great heterogeneity in terms of cardiac model, microgravity platform, and exposure duration, multiple studies consistently reported alterations in Ca²⁺ handling, metabolism, contractility, and gene expression. Three-dimensional human-induced pluripotent stem cell-derived cardiomyocyte (HiPSC-CM) models generally showed enhanced tissue maturation and proliferation parameters, suggesting potential therapeutic benefits, while 2D models mostly exhibited stress-related dysfunction. In vivo simulated microgravity studies, such as the hindlimb unloading (HU) model, show structural and functional cardiac remodeling, and real μg studies confirmed various effects seen under the HU model in multiple rodent species. Thus, μg exposure consistently induces cardiac changes at the cellular and molecular level, while model choice, microgravity platform, and exposure duration critically influence the outcomes. Full article

This paper presents a hybrid computational architecture for efficient and robust digital transmission inspired by helical genetic structures. The proposed system integrates advanced modulation schemes, such as multi-pulse-position modulation (MPPM), high-order quadrature amplitude modulation (QAM), and chirp spread spectrum (CSS), along with Reed–Solomon error correction and quantum-assisted search, to optimize performance in noisy and non-line-of-sight (NLOS) optical environments, including VLC channels modeled with log-normal fading. Through mathematical modeling and simulation, we demonstrate that the number of helical transmissions required for genome-scale data can be drastically reduced—up to 95% when using parallel strands and high-order modulation. The trade-off between redundancy, spectral efficiency, and error resilience is quantified across several configurations. Furthermore, we compare classical genetic algorithms and Grover’s quantum search algorithm, highlighting the potential of quantum computing in accelerating decision-making and data encoding. These results contribute to the field of operations research and supply chain communication by offering a scalable, energy-efficient framework for data transmission in distributed systems, such as logistics networks, smart sensing platforms, and industrial monitoring systems. The proposed architecture aligns with the goals of advanced computational modeling and optimization in engineering and operations management. Full article

With the development of modern agricultural technology, plant factories have become an important way to achieve efficient and sustainable crop production. Accurate understanding of the light received by plants is the key to improving the light energy utilization efficiency of lamps and ensuring the benefits of plant factories. Ray tracing technology, as one of the key technologies in plant factories, is of great significance to analyze the growing light environment of vegetables. Spinach has high nutritional value and is loved by the public and is one of the main crops grown in plant factories. In this paper, LightTools, TracePro, and Ansys Lumerical FDTD Solution, which are currently mature light environment tracking software in the field of lighting, are selected as the research objects to investigate their performance in simulating the light environment of spinach leaf surfaces under different planting arrangements and different lamp source distances. The results show as follows: Under the rectangular planting arrangement, the leaves received more light, and the plants grew faster. Different planting arrangements of plants had little effect on the simulation effect of the same kind of software, but the simulation effect of the three kinds of software under the same planting arrangement was significantly different, and the difference between the simulated value and the measured value of TracePro was the least. Further, TracePro was used to trace and simulate the leaf surface light conditions of spinach under a rectangular planting arrangement at different lighting distances, and the simulation results showed that there was no significant difference between the software simulation value and the measured value, and the simulation accuracy was the highest when the distance from the light source was 30 cm. Therefore, TracePro software can accurately simulate the light intensity of spinach leaves during the growth process and is most suitable for monitoring the change of light environment of spinach growth in plant factories. Full article