Search Results (88,218)

Urban traffic congestion presents a complex challenge driven by intricate spatial dependencies and non-stationary temporal dynamics. While Multi-Agent Deep Reinforcement Learning has shown promise for Traffic Signal Control, existing approaches often struggle with partial observability and fail to coordinate effectively across large-scale, heterogeneous road networks. In this paper, we propose HydraLight (HYbrid Deep Reinforcement Learning Architecture for Traffic Lights), a novel spatio-temporal framework that integrates Graph Attention Networks and Temporal Transformers. To overcome the localized myopia of standard graph methods, HydraLight introduces a Global Pooling Context module that broadcasts macroscopic, citywide traffic summaries, enabling agents to proactively mitigate systemic gridlock. Furthermore, to facilitate robust multi-scenario training, we introduce a Unified Prioritized Experience Replay (Unified PER) module that normalizes Temporal-Difference errors, preventing task dominance across diverse topologies. Extensive experiments on the RESCO benchmark across five synthetic and real-world networks demonstrate that HydraLight consistently outperforms state-of-the-art baselines (including X-Light and CoSLight).Byreducing traffic congestion, travel delays, and idle waiting times, the proposed framework also contributes to more sustainable urban mobility through improved traffic flow efficiency, lower fuel consumption, and reduced vehicular carbon emissions. Notably, the proposed architecture excels in structurally irregular environments, achieving up to 13.07% reduction in average travel time on complex arterial networks and consistently improving queue stability and waiting-time minimization across both synthetic and real-world RESCO benchmarks compared to state-of-the-art baselines. Full article

Gold, as a critical material with both financial and industrial value, is widely used across numerous fields such as finance, aerospace and medical care. Under the global background of increasing geopolitical risks and the advancement of high-tech industries, the demand for gold continues to grow steadily. The main raw materials for extracting gold are mainly divided into ore and electronic waste. Currently, conventional cyanidation remains the dominant industrial method for gold recovery. However, issues such as pollution and high toxicity of cyanide tailings are driving global efforts to explore environmentally friendly alternatives. Therefore, the development of green and efficient gold extraction technology has become a global research hotspot. This article focuses on cyanide-free leaching technologies, providing a detailed review of their current developments, advantages, and limitations, and proposing future trends in gold extraction. The future development directions of gold extraction include the development of thiosulfate–glycine leaching systems, the combination of multi-technology collaborative processes such as ultrasonic assistance and biological treatment to enhance efficiency, the strengthening of microbial metallurgy technology, and the construction of a resource recycling system for electronic waste. This review provides new insights and development directions for extracting gold for sustainable development. Full article

With the rapid deployment of deep neural networks (DNNs) on edge devices, traditional hardware accelerators face significant challenges in terms of data security, computational redundancy caused by sparsity, and uneven utilization of on-chip resources. This paper proposes SaE-FPGA, a secure and efficient DNN accelerator designed specifically for edge FPGA platforms. The architecture introduces three core innovations: (1) Hash-Bypass Processing Unit (HBPU): Integrating a high-speed SHA-256 hardware engine with a hash-sparse bitmap mechanism, it enables real-time data integrity verification within a single clock cycle while skipping computations for redundant zero-value data. (2) Flexible Mixed-Precision Processing Element (FMP): By reconfiguring idle BRAM and LUT resources into an active lookup table multiplication engine, it overcomes the physical bit-width limitations of DSP blocks and supports INT8/INT6/INT4 mixed-precision multiplication. (3) Multi-mode Reconfigurable Streaming Frame (MRSF): A sparse-aware, elastic load balancing and data routing mechanism designed to mask long memory access latencies and ensure high hardware resource utilization. Experimental results on the Zynq 7045 platform demonstrate that SaE-FPGA reduces redundant computations by 23.2% while maintaining high precision and minimizing precision loss. The system effectively mitigates the risk of physical tampering. When tested on ResNet-50, it achieved a 27.2% improvement in energy efficiency and a 2.97× speedup compared to DSP-based FPGA solutions. Furthermore, by fully exploiting the hybrid BRAM-LUT and DSP configuration, the proposed accelerator achieves a remarkable peak throughput of 782.4 GOPS. Full article

The design of active pneumatic upper limb exoskeletons is complicated by the challenge of reliably determining a kinematically safe workspace. Existing analytical kinematic methods are not sufficient to predict geometric collisions between elements of closed kinematic chains, which poses risks of mechanical damage and threats to user safety during exoskeleton operation. This paper proposes a hybrid algorithm for verifying the workspace of a pneumatic exoskeleton, combining analytical modelling in MATLAB R2020b based on the Product of Exponentials (PoE) method with high-performance static simulation in the Unity environment. At the initial stage, a discrete set comprising 758 million positions of the upper exoskeleton manipulator was generated. Subsequently, a multithreaded two-stage filtering process was implemented: analytical verification of rod stroke limits and angular constraints, followed by the detection of physical intersections of solid-state meshes using the PhysX engine. The results indicate that while the analytical model filters out 99.6% of invalid configurations. Yet, among the remaining positions—formally correct from a mathematical standpoint—up to 50% lead to critical geometric collisions or breaks in the kinematic chain. The computational efficiency of the proposed architecture enabled full static workspace verification in under 20 min. A reachable zone topology was established, revealing pronounced asymmetry and the presence of a “manoeuvrability core” in the user’s anterior hemisphere. The developed algorithm generates a verified set of kinematically safe exoskeleton states, providing a foundation for the kinematic safety layer of a hierarchical control system. These findings demonstrate the necessity of complementing analytical kinematics with physical collision detection when designing hybrid kinematic mechanisms, and the approach can be applied to verify collision-free movement trajectories in various robotic systems. The approach can be applied to verify collision-free movement trajectories in simulation, with physical validation deferred to future work. Full article

Two techniques of the Boundary Elements Method (BEM) are coupled for solving piecewise homogeneous elastic problems with body forces. The Domain Superposition Technique (DST), which is an alternative to the classical sub-regions approach, is used to approach the sectorial homogeneities, modeling the domain as a sum of a homogeneous surrounding sector and other complementary ones with different constitutive properties. The Galerkin tensor, with the adoption of a primitive function of the fundamental solution, transforms domain integrals. A classical boundary element matrix system is formed in which, unlike the sub-region idea, no interfaces exist between regions, and compatibility and equilibrium conditions are not imposed. The necessary correlation between the surrounding domain and all sub-domains is performed through the classic boundary element procedure of scanning, in which source points are used as the basis for integration along the boundaries. This research contributes to BEM literature by addressing a specific and non-trivial gap: the simultaneous treatment of body forces and material heterogeneity in a simplified and computationally efficient manner, without resorting to classical sub-region formulations. Full article

In Low Earth Orbit Beam-Hopping Satellite Systems (L-BHSS), co-channel interference among beams severely degrades communication quality. To address the inter-beam co-channel interference avoidance problem, this paper proposes a Parameter-Sharing Multi-Agent Deep Deterministic Policy Gradient-Based Beam Geometry Multi-Parameter Optimization Algorithm (PS-MADDPG-BGMPOA) for the joint optimization of satellite beam geometric parameters. The effects of free-space path loss, atmospheric impairments, and Rician fading are comprehensively considered, and a beam geometric multi-parameter optimization model is formulated with the objective of maximizing the long-term Signal-to-Interference-plus-Noise Ratio (SINR), incorporating beamwidth, beam center offset from the satellite nadir direction angle, inter-beam separation angle, and beam activation states. To tackle the resulting high-dimensional mixed action space, the proposed algorithm employs parameter sharing and grouped decision-making, which alleviates the dimensionality explosion problem and decouples the network scale from the number of beams, enabling efficient cooperative optimization with reduced training complexity. Simulation results show that, under various channel conditions and beam configurations, the proposed method effectively enhances communication quality and spectral efficiency while exhibiting superior real-time performance and stability. Full article

Off-grid electric vehicle (EV) charging infrastructure powered by hybrid solar–wind systems address critical adoption barriers in developing countries, where grid unreliability and sparse charging networks constrain transportation electrification. Despite growing research interest, no comprehensive review has systematically synthesized the interplay between hybrid renewable architectures, intelligent energy management strategies, and techno-economic viability specifically for off-grid EV charging in resource-constrained settings. This systematic review applies the PRISMA methodology to analyze 94 peer-reviewed publications (2013–2026), examining system architectures, intelligent control strategies, power electronics, battery storage, and deployment frameworks for standalone hybrid solar–wind EV charging stations. Key findings indicate that hybrid solar–wind configurations achieve 30–50% reductions in battery storage requirements and 15–25% lower levelized cost of energy (LCOE) (USD 0.08–0.15/kWh) compared with single-source systems, driven by diurnal and seasonal resource complementarity. Among intelligent control methods, the two-stage distributionally robust optimization (TSDRO) framework emerges as the most promising for data-scarce environments, outperforming conventional deterministic and stochastic approaches by 10–20% in managing renewable intermittency without requiring precise probability distributions. Wide-bandgap power semiconductors (SiC, GaN) enable 96–98% conversion efficiency, while lithium iron phosphate batteries provide 3000–5000 cycle lifetimes suited to tropical operating conditions. Critical gaps remain with field validation still predominantly simulation based, long-term operational data exceeding 24 months on equipment degradation and climate resilience are scarce, and scalable financing models for developing country contexts require further development. Nigeria is presented as an exemplar deployment context, with transferable insights for sub-Saharan Africa, South Asia, and Southeast Asia. Full article

In this paper, we focus on online stochastic optimization problems in which random parameters follow time-varying distributions. In each round t, a decision is obtained from solving the current optimization problem. Then samples are drawn from distributions which are updated after obtaining the decision. The objective and constraint are updated in this process, and the updated problem is used to obtain the next decision. To solve the online stochastic optimization problem, we propose a model-based stochastic augmented Lagrangian method, which is referred to as the MSALM. In each round, we construct model functions for the sample objective and constraint functions based on their properties, which reduce computational complexity. The step size is designed in a dynamic way and decreases as t increases to accelerate convergence. Due to the setting of the online stochastic problem, we use stochastic dynamic regret and constraint violation to measure the performance of our algorithm. Under certain assumptions, we prove that our algorithm’s stochastic dynamic regret and constraint violation have a sublinear bound in terms of the total number of slots T. We design simulation experiments to verify the efficiency of our online algorithm. Its performance is evaluated on a range of information and system engineering problems, including adaptive filtering, online logistic regression, time-varying smart grid energy dispatch, online network resource allocation, and path planning. In addition, in the context of the path planning problem, we integrate our algorithm with supervised learning to demonstrate its enhanced capabilities. The experimental results validate the performance of our new algorithm in practical applications. Full article

Office openness comprises two physically distinct dimensions—spatial openness and visual openness—yet studies quantifying their independent contributions to cognitive efficiency at the individual level remain scarce. Prior research has predominantly reported group-mean effects, leaving bidirectional individual responses insufficiently examined. This study independently manipulated both dimensions and measured individual-level EEG responses in 24 adults using a 3 × 3 within-subject factorial design. The beta/alpha ratio change rate was computed as an index of cognitive efficiency. Substantial neurophysiological variation across conditions was confirmed in every participant. The absence of significant group-level effects was interpreted not as a lack of environmental influence but as the result of bidirectional individual responses canceling each other out in group averages. Spatial and visual openness induced response ranges of equivalent magnitude at the individual level, and individually optimal conditions were widely distributed across the nine experimental conditions. The correspondence rate between subjective preferences and EEG-identified optimal conditions did not exceed chance, and this bidirectional cancellation mechanism is proposed as an explanation for the contradictory findings that have long characterized open-office research. These results support design strategies that offer diverse combinations of spatial and visual openness within activity-based working environments, paired with feedback systems grounded in objective cognitive performance data. Full article

Climate change necessitates a global transition toward green and low-carbon development, underscoring the critical importance of energy efficiency. Buildings account for a substantial portion of urban energy consumption and carbon emissions, with central air-conditioning systems representing the largest energy-consuming component. This study focuses on optimizing equipment selection—including chillers, pumps, and cooling towers—for the refrigeration plant of a commercial complex in Xiamen. Following theoretical optimization, the operational performance of the implemented system was empirically analyzed using long-term monitoring data from 2024 to 2025. The results demonstrate an energy efficiency ratio (EER) of 5.44 in 2024 and 5.28 in 2025, surpassing the Grade I efficiency threshold (5.2) stipulated by the Chinese standard T/CRAAS 1039-2023. Monthly EER values consistently remained above 5.06 throughout the cooling season. Detailed performance analysis of individual equipment further confirmed that actual operational performance of chillers, pumps, and cooling towers closely matched or even exceeded rated performance metrics, with chiller efficiency deviations controlled within 5%. This study integrates optimized equipment selection at the design stage with empirical performance analysis based on actual operation, providing a validated approach for improving the energy efficiency of refrigeration plants in commercial buildings and offering valuable references for the revision of relevant energy efficiency standards. Full article

Carbon capture, utilization and storage (CCUS) is a key technology for carbon neutrality and efficient oilfield development. Oil well cement suffers serious carbonation degradation under high-temperature, high-pressure and high CO₂ partial pressure environments, leading to well cementing failure. In this study, TA@Gr-OTES (TGO) composite was prepared by surface grafting modification, and a hydrophobic oil well cement system suitable for CCUS was constructed. The anti-carbonation performance was tested under simulated formation conditions (130 °C, 25 MPa, CO₂ partial pressure 7 MPa). Results show that TGO-modified cement maintains stable hydrophobicity, with a 60-day compressive strength attenuation rate of only 6.7%, and its permeability and porosity are much lower than those of plain cement. TGO inhibits deep carbonation and corrosion product leaching, preserves hydration products, and reduces defect volume. The potential triple mechanisms of a hydrophobic barrier, graphene physical shielding and matrix densification effectively blocks CO₂ intrusion. This study provides theoretical and technical support for long-life cementing materials in CCUS wells. Full article

The research presented provides an overview of the latest progress in data-driven control methods used for industrial heating furnaces. Although the data-driven methodologies reviewed provide good performance metrics compared to conventional control strategies, they lack the integration of energy efficiency considerations into the controller design process. This research presents a comprehensive control design framework for a novel energy-efficient data-driven controller applied to an industrial heating furnace. It proposes a novel Hybrid Mamdani–ANFIS controller developed using real-time data from an industrial heating furnace. A novel ANFIS-based energy model is also presented in this work to evaluate the energy efficiency of the presented controller models. The results demonstrated that the proposed novel Hybrid Mamdani–ANFIS controller outperforms both the Fuzzy PID and conventional Fuzzy controller in terms of energy efficiency, achieving approximately 30% energy savings and exhibiting a faster disturbance response time. This study makes a considerable contribution to the field of control theory by synthesizing existing knowledge, addressing identified research gaps, and introducing a novel control design framework that enhances energy efficiency, robustness, and adaptability across a wide spectrum of control applications in industrial heating furnace systems. Full article

Retrofit strategies to improve the energy performance of buildings have gained significant importance worldwide; however, asbestos in older residential buildings is considered a serious threat to both human health and the environment. Existing studies have generally focused on the health effects of asbestos, the properties of insulation materials, or individual aspects of energy performance, while a coherent and comparative conceptual framework for sustainable retrofit systems is limited. This review aims to systematically integrate the current scientific evidence on asbestos management, alternative insulation materials, life cycle assessment (LCA), and circular economy principles to present a literature-informed conceptual decision-support framework for sustainable retrofit. The study used the PRISMA-based literature selection approach, while the evidence from different peer-reviewed studies was comparatively organized in the context of process workflows, risk considerations, lifecycle impacts, and building-physics-related findings. The literature-based results indicate that incorporating safe asbestos management, low-carbon insulation materials, and circular retrofit strategies into an integrated approach can improve energy efficiency and environmental sustainability. However, this study is not based on a validated numerical simulation, an executed optimization model, or calibrated engineering analysis, but rather on a comparative synthesis and conceptual interpretation of the existing literature and presents a decision-support framework that can guide future low-carbon and safe construction strategies. Full article

The thermodynamic prediction of the fast refueling process for vehicular hydrogen storage cylinders faces the complex problem of modeling multi-layer composite walls. Drawing on the series thermal resistance principle, this paper introduces an equivalent thermal conductivity approach, simplifying the multi-layer structure into homogeneous material. Combined with the real-gas-state equation, a coupled thermodynamic framework combining zero-dimensional gas dynamics and one-dimensional cylinder wall heat transfer is developed. The comparison and verification with the 70 MPa fast charging experimental data have demonstrated that the proposed model exhibits sufficient accuracy and robustness for the problem. By comparing the temperature rise changes of different volume type-III gas cylinders, it was found that the surface area-to-volume ratio (A/V) was the primary geometric factor—the key geometric parameter that governs the temperature rise behavior. Larger volume gas cylinders exhibit more significant temperature rise due to their lower heat dissipation efficiency. A further comparison of the thermal response characteristics between Type-III and Type-IV cylinders demonstrates that the equivalent thermal conductivity is the dominant parameter determining the temperature rise behavior: The lower this coefficient, the stronger the limitation on the cylinder’s heat dissipation capacity, and the more pronounced the temperature rise. The proposed method not only ensures accuracy but also reduces the complexity of the modeling process, providing an efficient theoretical tool for optimizing the refueling strategy and conducting thermal safety assessment of vehicular hydrogen storage systems. Full article

In intensive animal production, the overuse of antibiotics has exacerbated bacterial antimicrobial resistance and environmental pollution. Together with gut microbiota dysbiosis and recurrent disease outbreaks, these challenges severely constrain the sector’s high-quality development. Quorum sensing (QS), a cell-density-dependent bacterial communication mechanism, can be modulated through agents that specifically inhibit or activate QS circuitry to regulate microbial community functions. Such QS modulators possess notable advantages, such as environmental benignity and high target specificity, and thus offer innovative strategies to decrease antibiotic reliance, enhance production efficiency, and reduce environmental emissions. This review examines QS modulators sourced from plants, microorganisms, animals, and synthetic processes, while highlighting key challenges such as environmental interference, resistance development, high costs, and the lack of standardized biosafety evaluations. Future research should focus on enhancing specificity, stability, affordability, and safety, with an emphasis on rational design, synergistic systems, improved manufacturing processes, and multi-target modulators. This review may provide a theoretical basis for translating QS-regulation technologies into farm-level applications, thereby advancing sustainable animal production and antibiotic-free husbandry. Full article