Search Results (8,298)

The rapid growth of Industrial Internet of Things (IIoT) terminals has resulted in tasks exhibiting increased concurrency, heterogeneous resource demands, and dynamic priorities, significantly increasing the complexity of task scheduling in edge computing. Cloud–edge–end collaborative computing leverages cross-layer task offloading to alleviate edge node resource contention and improve task scheduling efficiency. However, existing methods generally neglect the joint optimization of task offloading, resource allocation, and priority adaptation, making it difficult to balance task execution and resource utilization under resource-constrained and competitive conditions. To address this, this paper proposes a two-stage dynamic-priority-aware joint task offloading and resource allocation method (DPTORA). In the first stage, an improved Multi-Agent Proximal Policy Optimization (MAPPO) algorithm integrated with a Priority-Gated Attention Module (PGAM) enhances the robustness and accuracy of offloading strategies under dynamic priorities; in the second stage, the resource allocation problem is formulated as a single-objective convex optimization task and solved globally using the Lagrangian dual method. Simulation results show that DPTORA significantly outperforms existing multi-agent reinforcement learning baselines in terms of task latency, energy consumption, and the task completion rate. Full article

The rapid rise in atmospheric CO₂ concentrations has intensified the need for scalable, sustainable, and economically viable carbon sequestration technologies. This study introduces a cost-effective CaO/Ca(OH)₂-based mineralization process that not only enables efficient CO₂ removal but also allows the simultaneous recovery of high-purity calcite nanoparticles as value-added products. The process involves hydrating CaO, followed by controlled carbonation under optimized CO₂ flow rates, temperature conditions, and and additive use, yielding nanocrystalline calcite with an average particle size of approximately 100 nm. Comprehensive characterization using X-ray diffraction, transmission electron microscopy, and energy-dispersive X-ray spectroscopy confirmed a polycrystalline structure with exceptional chemical purity (99.9%) and rhombohedral morphology. Techno-economic analysis further demonstrated that coupling CO₂ sequestration with nanoparticle production can markedly improve profitability, particularly when utilizing CaO/Ca(OH)₂-rich industrial residues such as steel slags or lime sludge as feedstock. This hybrid, multi-revenue strategy—integrating carbon credits, nanoparticle sales, and waste valorization—offers a scalable pathway aligned with circular economy principles, enhancing both environmental and economic performance. Moreover, the proposed system can be applied to CO₂-emitting plants and facilities, enabling not only effective carbon dioxide removal and the generation of carbon credits, but also the production of calcite nanoparticles for diverse applications in agriculture, manufacturing, and environmental remediation. These findings highlight the potential of CaO/Ca(OH)₂-based mineralization to evolve from a carbon management technology into a platform for advanced materials manufacturing, thereby contributing to global decarbonization efforts. Full article

The rapid growth of 5G base stations (BSs) and electric vehicles (EVs) introduces significant challenges for distribution network operation due to high energy consumption and variable loads. This paper proposes a coordinated day-ahead scheduling framework that integrates 5G BS task migration, storage utilization, and EV charging or discharging with mobility constraints. A mixed-integer second-order cone programming (MISOCP) model is formulated to optimize network efficiency while ensuring reliable power supply and maintaining service quality. The proposed approach enables dynamic load adjustment via 5G computing task migration and coordinated operation between 5G BSs and EVs. Case studies demonstrate that the proposed method can effectively generate an optimal day-ahead scheduling strategy for the distribution network. By employing the task migration strategy, the computational workloads of heavily loaded 5G BSs are dynamically redistributed to neighboring stations, thereby alleviating computational stress and reducing their associated power consumption. These results highlight the potential of leveraging the joint flexibility of 5G infrastructures and EVs to support more efficient and reliable distribution network operation. Full article

The advent of sixth-generation (6G) wireless networks and cell-free massive multiple-input multiple-output (MIMO) architectures underscores the need for efficient resource allocation to support federated learning (FL) at the network edge. Existing approaches often treat communication, computation, and learning in isolation, overlooking dynamic heterogeneity and fairness, which leads to degraded performance in large-scale deployments. To address this gap, we propose a joint optimization framework that integrates communication–computation co-design, fairness-aware aggregation, and a hybrid strategy combining convex relaxation with deep reinforcement learning. Extensive experiments on benchmark vision datasets and real-world wireless traces demonstrate that the framework achieves up to 23% higher accuracy, 18% lower latency, and 21% energy savings compared with state-of-the-art baselines. These findings advance joint optimization in federated learning (FL) and demonstrate scalability for 6G applications. Full article

Management of smart greenhouses represents a transformative advancement in precision agriculture, enabling sustainable intensification of food production through the integration of multi-sensor networks, intelligent control, and sophisticated data filtering techniques. Unlike conventional greenhouses that rely on manual monitoring, smart greenhouses combine environmental sensors, Internet of Things (IoT) platforms, and artificial intelligence (AI)-driven decision making to optimize microclimates, improve yields, and enhance resource efficiency. This review systematically investigates three key technological pillars, multi-sensor monitoring, intelligent control, and data filtering techniques, for smart greenhouse environment management. A structured literature screening of 114 peer-reviewed studies was conducted across major databases to ensure methodological rigor. The analysis compared sensor technologies such as temperature, humidity, carbon dioxide (CO₂), light, and energy to evaluate the control strategies such as IoT-based automation, fuzzy logic, model predictive control, and reinforcement learning, along with filtering methods like time- and frequency-domain, Kalman, AI-based, and hybrid models. Major findings revealed that multi-sensor integration enhanced precision and resilience but faced changes in calibration and interoperability. Intelligent control improved energy and water efficiency yet required robust datasets and computational resources. Advanced filtering strengthens data integrity but raises concerns of scalability and computational cost. The distinct contribution of this review was an integrated synthesis by linking technical performance to implementation feasibility, highlighting pathways towards affordable, scalable, and resilient smart greenhouse systems. Full article

This research investigates the integration of green cores as central biophilic elements in residential architecture, proposing a climate-responsive design methodology grounded in architectural optimization. The study begins with the full-scale refurbishment of a compact urban apartment, wherein interior partitions, fenestration and material systems were reconfigured to embed vegetated zones within the architectural core. Light exposure, ventilation potential and spatial coherence were maximized through data-driven design strategies and structural modifications. Integrated planting modules equipped with PAR-specific LED systems ensure sustained vegetation growth, while embedded environmental infrastructure supports automated irrigation and continuous microclimate monitoring. This plant-centered spatial model is evaluated using quantifiable performance metrics, establishing a replicable framework for optimized indoor ecosystems. Photosynthetically active radiation (PAR)-specific LED systems and embedded environmental infrastructure were incorporated to maintain vegetation viability and enable microclimate regulation. A programmable irrigation system linked to environmental sensors allows automated resource management, ensuring efficient plant sustenance. The configuration is assessed using measurable indicators such as daylight factor, solar exposure, passive thermal behavior and similar elements. Additionally, a post-occupancy expert assessment was conducted with several architects evaluating different aspects confirming the architectural and spatial improvements achieved through the refurbishment. This study not only demonstrates a viable architectural prototype but also opens future avenues for the development of metabolically active buildings, integration with decentralized energy and water systems, and the computational optimization of living infrastructure across varying climatic zones. Full article

Deploying Large Language Models (LLMs) in edge environments faces two major challenges: (i) the conflict between limited device resources and high computational demands, and (ii) the dynamic impact of user mobility on model partitioning and task offloading decisions. To address these challenges, this paper proposes the Dynamic Adaptive Partitioning and Offloading (DAPO) framework, an intelligent solution for multi-user, multi-edge Mobile Edge Intelligence (MEI) systems. DAPO employs a Deep Deterministic Policy Gradient (DDPG) algorithm to jointly optimize the model partition point and the task offloading destination. By mapping continuous policy outputs onto valid discrete actions, DAPO efficiently addresses the high-dimensional hybrid action space and dynamically adapts to user mobility. Through extensive simulations, we demonstrate that DAPO outperforms baseline strategies and mainstream RL methods, achieving up to 27% lower latency and 18% lower energy consumption compared to PPO and A2C, while maintaining fast convergence and scalability in dynamic mobile environments. Full article

Indoor positioning remains a recurrent and significant challenge in research. Unlike outdoor environments, where the Global Positioning System (GPS) provides reliable location information, indoor scenarios lack direct line-of-sight to satellites or cellular towers, rendering GPS inoperative and requiring alternative positioning techniques. Despite numerous approaches, indoor contexts with resource limitations, energy constraints, or physical restrictions continue to suffer from unreliable localization. Many existing methods employ a fixed number of reference anchors, which sets a hard balance between localization accuracy and energy consumption, forcing designers to choose between precise location data and battery life. As a response to this challenge, this paper proposes an energy-aware indoor positioning strategy based on Mobile Ad Hoc Networks (MANETs). The core principle is a self-adaptive control loop that continuously monitors the network’s positioning accuracy. Based on this real-time feedback, the system dynamically adjusts the number of active anchors, increasing them only when accuracy degrades and reducing them to save energy once stability is achieved. The method dynamically estimates relative coordinates by analyzing node encounters and contact durations, from which relative distances are inferred. Generalized Multidimensional Scaling (GMDS) is applied to construct a relative spatial map of the network, which is then transformed into absolute coordinates using reference nodes, known as anchors. The proposal is evaluated in a realistic simulated indoor MANET, assessing positioning accuracy, adaptation dynamics, anchor sensitivity, and energy usage. Results show that the adaptive mechanism achieves higher accuracy than fixed-anchor configurations in most cases, while significantly reducing the average number of required anchors and their associated energy footprint. This makes it suitable for infrastructure-poor, resource-constrained indoor environments where both accuracy and energy efficiency are critical. Full article

Aviation is under increasing pressure to reduce carbon emissions in conventional transports and support the growth of low-altitude operations such as long-endurance eVTOLs. Hybrid-electric propulsion addresses these challenges by integrating the high specific energy of fuels or hydrogen with the controllability and efficiency of electrified powertrains. At present, the field of hybrid-electric aircraft is developing rapidly. To systematically study hybrid-electric propulsion control in aviation, this review focuses on practical aspects of system development, including propulsion architectures, system- and component-level modeling approaches, and energy management strategies. Key technologies in the future are examined, with emphasis on aircraft power-demand prediction, multi-timescale control, and thermal integrated energy management. This review aims to serve as a reference for configuration design, modeling and control simulation, as well as energy management strategy design of hybrid-electric propulsion systems. Building on this reference role, the review presents a coherent guidance scheme from architectures through modeling to energy-management control, with a practical roadmap toward flight-ready deployment. Full article

Electrical grids are currently facing new demands due to increased power consumption, growing interconnections, and limitations regarding transmission capacity. These factors introduce considerable challenges for the dispatch and operation of large-scale power systems, often resulting in congestion, energy losses, and high operating costs. To address these issues, this study presents a transmission line switching strategy, which is formulated as an optimal power flow problem with binary variables and solved via mixed-integer nonlinear programming. The proposed methodology was tested using MATLAB’s MATPOWER toolbox version 8.1, focusing on power systems with five and 3374 nodes. The results demonstrate that operating costs can be reduced by redistributing power generation while observing the system’s reliability constraints. In particular, disconnecting line 6 in the 5-bus system yielded a 13.61% cost reduction, and removing line 1116 in the 3374-bus system yielded cost savings of 0.0729%. These findings underscore the potential of transmission line switching in enhancing the operational efficiency and sustainability of large-scale power systems. Full article

The development of high-performance supercapacitor electrodes is crucial to meet the increasing demand for efficient and sustainable energy storage systems. Cobalt oxide (Co₃O₄), with its high theoretical capacitance, is a promising electrode material, but its practical application is hindered by poor conductivity limitations and structural instability during cycling. In this work, lanthanum La³⁺-doped Co₃O₄ nanocubes were synthesized via a hydrothermal approach to tailor their structural and electrochemical properties. Different doping concentrations (1, 3, and 5%) were introduced to investigate their influence systematically. X-ray diffraction confirmed the retention of the spinel phase with clear evidence of La³⁺ incorporation into the Co₃O₄ lattice. Also, Raman spectroscopy validated the structural integrity through characteristic Co-O vibrational modes. Scanning electron microscopy analysis revealed uniform cubic morphologies across all samples. The formation of the cubic spinel structure of 1% La³⁺-doped Co₃O₄ are confirmed from XPS and TEM studies. Electrochemical evaluation in a 3 M KOH electrolyte demonstrated that 1% La³⁺-doped Co₃O₄ nanocubes delivered the highest performance, achieving a specific capacitance of 1312 F g⁻¹ at 1 A g⁻¹ and maintaining a 79.8% capacitance retention and a 97.12% Coulombic efficiency over 10,000 cycles at 5 Ag⁻¹. It can be demonstrated that La³⁺ doping is an effective strategy to enhance the charge storage capability and cycling stability of Co₃O₄, offering valuable insights for the rational design of next-generation supercapacitor electrodes. Full article

With the large-scale integration of renewable energy into distribution networks, the intermittency and uncertainty of renewable generation pose significant challenges to the voltage security of the power grid under extreme scenarios. To address this issue, this paper proposes an optimal scheduling method for power grids under extreme scenarios, based on an improved Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm. By simulating potential extreme scenarios in the power system and formulating targeted secure scheduling strategies, the proposed method effectively reduces trial-and-error costs. First, the time series clustering method is used to construct the extreme scene dataset based on the principle of maximizing scene differences. Then, a mathematical model of power grid optimal dispatching is constructed with the objective of ensuring voltage security, with explicit constraints and environmental settings. Then, an interactive scheduling model of distribution network resources is designed based on a multi-agent algorithm, including the construction of an agent state space, an action space, and a reward function. Then, an improved MADDPG multi-agent algorithm based on specific information fusion is proposed, and a hybrid optimization experience sampling strategy is developed to enhance the training efficiency and stability of the model. Finally, the effectiveness of the proposed method is verified by the case studies of the distribution network system. Full article

Fuel cell trucks are a promising solution to reduce the disproportionately high greenhouse gas emissions of heavy-duty long-haul transportation. However, unlike conventional diesel vehicles, they lack combustion engine waste heat for cabin heating. As a result, electric heaters are often employed, which increase auxiliary energy consumption and reduce driving range. To address this challenge, advanced control strategies are needed to improve heating efficiency while maintaining passenger comfort. This study proposes and validates a methodology for implementing Model Predictive Control (MPC) in the cabin heating system of a fuel cell truck. Vehicle experiments were conducted to characterize dynamic heating behavior, passenger comfort indices, and to provide validation data for the mathematical models. Based on these models, an MPC strategy was developed in a Model-in-the-Loop simulation environment. The proposed approach achieves energy savings of up to 8.1% compared with conventional control using purely electric heating, and up to 21.7% when cabin heating is coupled with the medium-temperature cooling circuit. At the same time, passenger comfort is maintained within the desired range (PMV within ±0.5 under typical winter conditions). The results demonstrate the potential of MPC to enhance the energy efficiency of fuel cell trucks. The methodology presented provides a validated foundation for the further development of predictive thermal management strategies in heavy-duty zero-emission vehicles. Full article

The increasing penetration of renewable energy sources and the consequent rise in forecast uncertainty have underscored the need for robust operational strategies in transmission power systems. This paper introduces a risk-averse, data-driven distributionally robust optimization framework that integrates unit commitment and power flow constraints to enhance both reliability and operational security. Leveraging advanced forecasting techniques implemented via gradient boosting and enriched with cyclical and lag-based time features, the proposed methodology forecasts renewable generation and demand profiles. Uncertainty is quantified through a quantile-based analysis of forecasting residuals, which forms the basis for constructing data-driven ambiguity sets using Wasserstein balls. The framework incorporates comprehensive network constraints, power flow equations, unit commitment dynamics, and battery storage operational constraints, thereby capturing the intricacies of modern transmission systems. A worst-case net demand and renewable generation scenario is computed to further bolster the system’s risk-averse characteristics. The proposed method demonstrates the integration of data preprocessing, forecasting model training, uncertainty quantification, and robust optimization in a unified environment. Simulation results on a representative IEEE 24-bus network reveal that the proposed method effectively balances economic efficiency with risk mitigation, ensuring reliable operation under adverse conditions. This work contributes a novel, integrated approach to enhance the reliability of transmission power systems in the face of increasing uncertainty. Full article

Environmental vibrations may affect the functional use of engineering structures and even lead to disastrous consequences. Vibration suppression and energy harvesting based on Nonlinear Energy Sink (NES) and the piezoelectric effect have gained significant attention in recent years. The harvested electrical energy can supply power to the structural health monitoring sensor device. In this work, the electromechanical-coupled governing equations of the primary structure coupled with the series-connected 2-degree-of-freedom NES (2-DOF NES) integrated by a piezoelectric energy harvester are derived. The absorption and dissipation performances of the system under varying transient excitation intensities are investigated. Additionally, the targeted energy transfer mechanism between the primary structure and the two NESs oscillators is investigated using the wavelet analysis. The reduced slow flow of the dynamical system is explored through the complex-variable averaging method, and the primary factors for triggering the target energy transfer phenomenon are revealed. Furthermore, a comparison is made between the vibration suppression performance of the single-degree-of-freedom NES (S-DOF NES) system and the 2-DOF NES system as a function of external excitation velocity. The results indicate that the vibration suppression performance of the first-level NES (NES1) oscillator is first stimulated. As the external excitation intensity gradually increases, the vibration suppression performance of the second-level NES (NES2) oscillator is also triggered. The 1:1:1, high-frequency, and low-frequency transient resonance captures are observed between the primary structure and NES1 and NES2 oscillators over a wide frequency range. The 2-DOF NES demonstrates superior efficiency in suppressing vibrations of the primary structure and exhibits enhanced robustness to varying external excitation intensities. This provides a new strategy for structural vibration suppression and online power supply for health monitoring devices. Full article