Search Results (3,180)

Ecosystem service value (ESV) is a critical indicator of regional ecological well-being. Assessing and forecasting ESV are essential for achieving the coordinated development of environmental and economic systems. This study employs the SD-PLUS model, integrating Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) to assess the spatiotemporal dynamics of land use and land cover change (LUCC), as well as ESV in Zhengzhou from 2030 to 2040. It analyses the impact of various driving factors on ESV and examines the spatial correlations among ecosystem services across different regions. The results indicate that the total ESV is expected to decrease by 73.53 × 10⁷ yuan, primarily due to significant reductions in cropland and water areas. By 2040, ESV is projected to increase by 14.51 × 10⁷ yuan under the SSP126 scenario, decrease by 73.18 × 10⁷ yuan under the SSP585 scenario, and show a moderate decline under the SSP245 scenario. Climate factors, transportation location, and topographical features have a significantly positive impact on ESV, while environmental and socioeconomic factors exert a negative influence. The analysis of interrelationships among ecosystem services shows that synergies dominate, especially between supporting and cultural services, with only localised trade-offs observed. These findings contribute valuable insights for the development of scientifically sound, well-reasoned, and efficient strategies for ecological conservation and sustainable development. Full article

Reconfigurable intelligent surfaces (RIS) have emerged as a promising enabler for beyond-5G and 6G networks, offering controllable propagation environments to enhance coverage and spectral efficiency. This study investigates and compares multiple RIS configuration strategies, including analytical baselines such as the phase gradient reflector (PGR) and focusing lens (FL), optimization-driven approaches via gradient-based optimization (GBO), and learning-assisted designs through hybrid Mixture-of-Experts (MoE) and CNN-based gating. A unified simulation framework was developed to evaluate amplitude and phase profiles, expert-selection heatmaps, and coverage improvement maps, alongside a detailed analysis of the average path gain evolution over iterations. Quantitative results show that PGR and FL achieve average path gains of −112 dB and −97 dB, respectively, while GBO attains the highest gain of approximately −92 dB. The Hybrid MoE achieves −93.5 dB with localized coverage enhancements exceeding 40 dB, whereas CNN-gating maintains smoother and more generalized coverage improvements up to 20 dB. Results demonstrate that while PGR and FL provide predictable yet limited performance, GBO yields the highest path gain at the cost of computational complexity. MoE balances interpretability and adaptability through smoother expert-weight distributions, whereas CNN-gating enforces sharper, binary-like spatial decisions, enhancing coverage in challenging blind spots. The comparative findings highlight a performance spectrum ranging from interpretable analytical models to highly adaptive learning-based schemes, revealing trade-offs between flexibility, computational cost, and generalization capability, while also underlining RIS’s potential for sustainable and energy-efficient networking. These insights position hybrid and learning-driven RIS designs as promising candidates for scalable, adaptive deployment in future wireless systems. Full article

This study focuses on highway service areas. Building upon prior research that identified key influencing factors through surveys and ISM–MICMAC analysis, it constructs a tripartite evolutionary game model involving the government, service area operators, and carbon reduction technology providers based on stakeholder theory. Combined with MATLAB simulations, the model reveals the dynamic patterns of the carbon reduction system. The results indicate that government strategies exert the strongest influence on the system and catalyze the other two parties, followed by service area operators. Carbon reduction technology providers adopt a more cautious stance in decision-making. Government actions shape system evolution through a “cost-benefit-incentive” triple mechanism, with its strategies exhibiting significant spillover effects on other actors. Enterprise behavior is markedly influenced by Xinjiang’s regional characteristics, where the core barriers to corporate carbon reduction lie in the costs of proactive equipment and technological investments. The willingness of technology providers to cooperate primarily depends on two drivers: incremental baseline benefits and enhanced economies of scale. The core trade-off in government decision-making lies between the cost of strong regulation (Cg1) and the cost of environmental governance under weak regulation (Cg2). An increase in Cg1 prolongs the government’s convergence time by 233.3% and indirectly suppresses the willingness of enterprises and technology providers due to weakened subsidy capacity. Enterprises are relatively sensitive to the investment costs of carbon reduction equipment and technology, with convergence time extending by 120%. Technology providers are highly sensitive to incremental baseline returns (Rt), with stabilization time extending by 500%. Compared to existing research, this model quantitatively reveals the “cost-benefit-incentive” triple transmission mechanism for carbon reduction coordination in “grid-end” regions, identifying key parameters for strategic shifts among stakeholders. Based on this, corresponding policy recommendations are provided for all three parties, offering precise and actionable directions for the sustainable advancement of carbon reduction efforts in service areas. The research conclusions can provide a replicable collaborative framework for decarbonizing transportation infra-structure in grid-end regions with high clean energy endowments. Full article

This study introduces a health-aware fuzzy logic (FL) energy management strategy (EMS) for fuel cell electric commercial vehicles (FCECVs) that aimed to improve energy efficiency and extending fuel cell system (FCS) lifespan. The FL-based EMS was developed using vehicle power demand and battery state of charge (SOC) as inputs, with the FCS power change rate as the output, aiming to mitigate degradation induced by abrupt load transitions. A multi-objective optimization framework was established to optimize the fuzzy logic controller (FLC) parameters, achieving a balanced trade-off between fuel economy and FCS longevity. The non-dominated sorting genetic algorithm-II (NSGA-II) was utilized for optimization across various driving cycles, with average Pareto-optimal solutions employed for real-time application. Performance evaluation under standard and stochastic driving cycles benchmarked the proposed strategy against dynamic programming (DP), charge-depletion charge-sustaining (CD-CS), conventional FL strategies, and a non-optimized baseline. Results demonstrated an approximately $38\%$ reduction in hydrogen consumption (HC) relative to CD-CS and over $75\%$ improvement in degradation mitigation, with performance superior to that of DP. Although the strategy exhibits an average $17.39\%$ increase in computation time compared to CD-CS, the average single-step computation time is only $2.1$ ms, confirming its practical feasibility for real-time applications. Full article

This research presents a harmonized optimal preview control strategy for a semi-active suspension system (SASS) with a controlled damper varied between the upper and lower bounds of the damping coefficient and an active aerodynamic surface (AAS) control. The preview control algorithm is based on a simplified bilinear 2-DOF quarter-car model to address the tradeoff between passenger ride comfort and road holding capabilities. While the active suspension with the actuator requires a significant amount of energy to provide control force, the semi-active suspension system with a variable damping coefficient mechanism consumes minimal energy to adapt quickly to the real-time operating conditions. Moreover, the dynamic performance of semi-active suspension with the preview controller in conjunction with the active aerodynamic surface is significantly improved. MATLAB^® (R2025b)-based numerical simulations for different road excitations were carried out for the evaluation of the proposed system. Both time-domain and frequency-domain results demonstrate enhanced vehicle dynamic performances in response to road bumps, asphalt road excitations, and harmonic input signals. The simulation performance results indicate that the proposed system extraordinarily reduced the variation in the mean-squared value of the car body vertical acceleration. At the same time, the system enhanced the wheel-road holding metric by decreasing the variation in the gripping force on the ground surface, while maintaining the necessary suspension rattle space constraints within the prescribed limit. Full article

Background: Stress is a critical determinant of mental health, yet conventional monitoring approaches often rely on subjective self-reports or physiological signals that lack real-time responsiveness. Recent advances in large language models (LLMs) offer opportunities for speech-driven, adaptive stress detection, but existing systems are limited to retrospective text analysis, monolingual settings, or detection-only outputs. Methods: We developed a real-time, speech-driven stress detection framework that integrates audio recording, speech-to-text conversion, and linguistic analysis using transformer-based LLMs. The system provides multimodal outputs, delivering recommendations in both text and synthesized speech. Nine LLM variants were evaluated on five benchmark datasets under zero-shot and few-shot learning conditions. Performance was assessed using accuracy, precision, recall, F1-score, and misclassification trends (false-negatives and false-positives). Real-time feasibility was analyzed through latency modeling, and user-centered validation was conducted across cross-domains. Results: Few-shot fine-tuning improved model performance across all datasets, with Large Language Model Meta AI (LLaMA) and Robustly Optimized BERT Pretraining Approach (RoBERTa) achieving the highest F1-scores and reduced false-negatives, particularly for suicide risk detection. Latency analysis revealed a trade-off between responsiveness and accuracy, with delays ranging from ~2 s for smaller models to ~7.6 s for LLaMA-7B on 30 s audio inputs. Multilingual input support and multimodal output enhanced inclusivity. User feedback confirmed strong usability, accessibility, and adoption potential in real-world settings. Conclusions: This study demonstrates that real-time, LLM-powered stress detection is both technically robust and practically feasible. By combining speech-based input, multimodal feedback, and user-centered validation, the framework advances beyond traditional detection only models toward scalable, inclusive, and deployment-ready digital mental health solutions. Full article

Accelerating urbanization and the intensifying pace of climate change have heightened the occurrence of urban pluvial flooding, threatening urban sustainability. As the preferred approach to urban stormwater management, coupled gray and green infrastructure (GI–GREI) integrates GREI’s rapid runoff conveyance with GI’s infiltration and storage capacities, and their siting and scale can affect life-cycle cost (LCC) and urban drainage system (UDS) resilience. Focusing on Fengxi New City, China, this study develops a multi-objective optimization framework for the GI–GREI system that integrates GI suitability and pipe-network importance assessments and evaluates the Pareto set through entropy-weighted TOPSIS. Across multiple rainfall return periods, the study explores optimal trade-offs between UDS resilience and LCC. Compared with the scenario where all suitable areas are implemented with GI (maximum), the TOPSIS-optimal schemes reduce total life-cycle cost (LCC) by CNY 3.762–4.298 billion (53.36% on average), rebalance cost shares between GI (42.8–47.2%) and GREI (52.8–57.2%), and enhance UDS resilience during periods of higher rainfall return (P = 20 and 50). This study provides an integrated optimization framework and practical guidance for designing cost-effective and resilient GI–GREI systems, supporting infrastructure investment decisions and climate-adaptive urban development. Full article

This study explores the fabrication and characterization of hybrid aluminum matrix composites reinforced with boron carbide (B₄C) and microsilica, produced via ultrasonically assisted stir casting followed by T6 heat treatment. Pure aluminum was selected as the base matrix to evaluate the combined effects of B₄C and microsilica reinforcements. Microstructural analyses showed that ultrasonic treatment effectively dispersed nanoparticles, reduced agglomeration, and enhanced particle–matrix interfacial bonding. T6 heat treatment further refined the grain structure through Zener pinning and promoted the formation of reaction layers at particle interfaces. Mechanical testing revealed that Al/B₄C composites provided the highest strength and hardness, while Al/microsilica systems retained superior ductility. The hybrid Al/B₄C/microsilica composites demonstrated a balanced combination of yield strength (38.6 MPa), ultimate tensile strength (82.6 MPa), and elongation (35.2%), confirming a synergistic strengthening–toughening effect. These results highlight the potential of Al/B₄C/microsilica hybrid reinforcements to optimize the trade-off between strength and ductility in aluminum-based composites. Full article

Person Re-Identification (Re-ID), a critical component of intelligent surveillance and security systems, seeks to match individuals across disjoint camera networks under complex real-world conditions. While deep learning has revolutionized Re-ID through enhanced feature representation and domain adaptation, a holistic synthesis of its advancements, unresolved challenges, and ethical implications remains imperative. This survey offers a structured and critical examination of Re-ID in the deep learning era, organized into three pillars: technological innovations, persistent barriers, and future frontiers. We systematically analyze breakthroughs in deep architectures (e.g., transformer-based models, hybrid global-local networks), optimization paradigms (contrastive, adversarial, and self-supervised learning), and robustness strategies for occlusion, pose variation, and cross-domain generalization. Critically, we identify underexplored limitations such as annotation bias, scalability-accuracy trade-offs, and privacy-utility conflicts in real-world deployment. Beyond technical analysis, we propose emerging directions, including causal reasoning for interpretable Re-ID, federated learning for decentralized data governance, open-world lifelong adaptation frameworks, and human-AI collaboration to reduce annotation costs. By integrating technical rigor with societal responsibility, this review aims to bridge the gap between algorithmic advancements and ethical deployment, fostering transparent, sustainable, and human-centric Re-ID systems. Full article

Zero-heat-flux (ZHF) thermometry is a clinically validated method for non-invasive core body temperature monitoring, yet its broad adoption in wearable applications is constrained by the high power consumption of the heater element. In this study, we numerically investigate the role of microporous insulation in minimizing energy demand while preserving measurement accuracy. A three-dimensional finite element model of a ZHF probe was implemented in COMSOL Multiphysics 5.4, consisting of a resistive heater, a microporous insulation shell, and a skin-equivalent substrate regulated by proportional–integral–derivative (PID) control. A Taguchi L9 orthogonal array was utilized to systematically investigate the effects of porosity (0–90%), insulation thickness (2–4 mm), and the convective heat transfer coefficient (5–15 W/m²·K) on the thermal performance of the ZHF thermometry system. Two performance metrics—heater energy consumption and settling time—were analyzed using analysis of variance (ANOVA). The results indicated that porosity accounted for more than 95% of the variance in heater power and over 80% of the variance in settling time. The configuration with φ = 90% and t = 3 mm demonstrated a balanced trade-off between energy efficiency and transient response for low-power ZHF thermometry. These findings provide design insights for energy-efficient wearable temperature sensors. Full article

Soil degradation, declining fertility, and rising greenhouse gas emissions highlight the urgent need for sustainable soil management strategies. Among them, biochar has gained recognition as a multifunctional material capable of enhancing soil fertility, sequestering carbon, and valorizing biomass residues within circular economy frameworks. This review synthesizes evidence from 186 peer-reviewed studies to evaluate how feedstock diversity, pyrolysis temperature, and elemental composition shape the agronomic and environmental performance of biochar. Crop residues dominated the literature (17.6%), while wood, manures, sewage sludge, and industrial by-products provided more targeted functionalities. Pyrolysis temperature emerged as the primary performance driver: 300–400 °C biochars improved pH, cation exchange capacity (CEC), water retention, and crop yield, whereas 450–550 °C biochars favored stability, nutrient concentration, and long-term carbon sequestration. Elemental composition averaged 60.7 wt.% C, 2.1 wt.% N, and 27.5 wt.% O, underscoring trade-offs between nutrient supply and structural persistence. Greenhouse gas (GHG) outcomes were context-dependent, with consistent Nitrous Oxide (N₂O) reductions in loam and clay soils but variable CH₄ responses in paddy systems. An emerging trend, present in 10.6% of studies, is the integration of artificial intelligence (AI) to improve predictive accuracy, adsorption modeling, and life-cycle assessment. Collectively, the evidence confirms that biochar cannot be universally optimized but must be tailored to specific objectives, ranging from soil fertility enhancement to climate mitigation. Full article

We present a systematic machine learning study of the solubility of diverse pharmaceutical acids in deep eutectic solvents (DESs). Using an automated Dual-Objective Optimization with Iterative feature pruning (DOO-IT) framework, we analyze a solubility dataset compiled from the literature for ten pharmaceutically important carboxylic acids and augment it with new measurements for mefenamic and niflumic acids in choline chloride- and menthol-based DESs, yielding N = 1020 data points. The data-driven multi-criterion measure is applied for final model selection among all collected accurate and parsimonious models. This three-step procedure enables extensive exploration of the model’s hyperspace and effective selection of models fulfilling notable accuracy, simplicity, and also persistency of the descriptors selected during model development. The dual-solution landscape clarifies the trade-off between complexity and cost in QSPR for DES systems and shows that physically meaningful energetic descriptors can replace or enhance explicit COSMO-RS predictions depending on the application. Full article

Evaluating electric vehicle (EV) motor performance over dynamic drive cycles is essential for accurate energy efficiency prediction and system-level optimization. While conventional steady-state models enable rapid generation of efficiency maps, they can introduce significant errors due to grid interpolation and the omission of transient dynamics. Limited understanding exists regarding how grid coarseness and modeling approach affect the discrepancy between steady-state and time-stepping solutions. This study quantifies these differences for a laboratory-scale induction motor (IM) operating under down-scaled drive cycles, using experimental time-stepping measurements as a reference. Efficiency maps are developed using three methods—analytic modeling, finite element analysis (FEA), and experimental testing—while time-stepping simulations are conducted using an analytic model. The study evaluates both total drive cycle energy efficiency errors and pointwise deviations across the torque–speed envelope for various grid resolutions. Results are compared against laboratory-based time-stepping measurements to identify trade-offs between computational efficiency and accuracy. Additionally, the analysis evaluates the impact of operating point (OP) placement within the grid and temperature variation on the accuracy of efficiency maps. Full article

UAV object detection is still difficult to achieve due to large-scale variation, dense small objects, a complicated background, and resource constraints from onboard computing. To solve these problems, we develop a diffusion-enhanced detection network, DL-DEIM, tailored for aerial images. The proposed scheme generalizes the DEIM baseline across three orthogonal axes. First, we propose a lightweight backbone network called DCFNet, which utilizes a DRFD module and a FasterC3k2 module to maintain spatial information and reduce computational complexity. Second, we propose a LFDPN module, which can conduct bidirectional multi-scale fusion via frequency-spatial self-attention and deep feature refinement and largely enhance cross-scale contextual propagation for small objects. Third, we propose LAWDown, an adaptive-content-aware downsampling to preserve the discriminative representation with higher accuracy at lower resolutions, which can effectively capture the spatially-variant weights and group channel interactions. On the VisDrone2019 dataset, DL-DEIM achieves a mAP@0.5 of 34.9% and a mAP@0.5:0.95 of 20.0%, outperforming the DEIM baseline by +4.6% and +2.9%, respectively. The model maintains real-time inference speed (356 FPS) with only 4.64 M parameters and 11.73 GFLOPs. Ablation studies validate the fact that DCFNet, LFDPN, and LAWDown collaboratively contribute to the accuracy and efficiency. Visualizations also display clustered and better localized activation in crowded scenes. These results show that DL-DEIM achieves a good tradeoff between detection probability and computation burden and it can be used in practice on resource-limited UAV systems. Full article

Stochastic differential equations (SDEs) with multiple conserved quantities are ubiquitous in scientific fields, modeling systems from molecular dynamics to celestial mechanics. While geometric numerical integrators that preserve single invariants are well-established, constructing efficient and high-order numerical schemes for SDEs with multiple conserved quantities remains a challenge. Existing approaches often suffer from high computational costs or lack desirable numerical properties like symmetry. This paper introduces two novel classes of projection-based numerical methods tailored for SDEs with multiple conserved quantities. The first method projects the increments of an underlying numerical scheme onto a discrete tangent space, ensuring all invariants are preserved by construction. The second method leverages a local coordinates approach, transforming the SDE onto the manifold defined by the invariants, solving it numerically, and then projecting back, guaranteeing the solution evolves on the correct manifold. We prove that both methods inherit the mean-square convergence order of their underlying schemes. Furthermore, we propose a simplified strategy that reduces computational expense by redefining the multiple invariants into a single one, offering a practical trade-off between exact preservation and efficiency. Numerical experiments confirm the theoretical findings and demonstrate the superior efficiency and structure-preserving capabilities of our methods. Full article