Search Results (6,844)

Large-scale and widely distributed air-conditioning (AC) loads can be aggregated into load-type Virtual Power Plants (VPPs) to participate in peak-shaving ancillary services, thereby improving the allocation of demand-side electricity resources. However, current AC aggregation methods primarily focus on meeting peak-shaving instructions and generally employ fixed incentive pricing and proportional capacity allocation, making it difficult to balance user revenue and satisfaction and thereby constraining the flexibility of VPP demand-side regulation. This paper proposes a unified incentive-based demand response scheduling framework for both fixed- and variable-frequency AC loads across industrial, commercial, and residential scenarios. Based on the Equivalent Thermal Parameter model, AC loads are classified into curtailable and shiftable types, with their adjustable boundaries characterized by a Time-of-Use (TOU) elasticity-based interaction willingness model and a fuzzy load transfer rate model, respectively. A three-objective optimization model is established to maximize user revenue while minimizing user dissatisfaction and scheduling error, with incentive pricing and capacity allocation jointly optimized via Non-dominated Sorting Genetic Algorithm III (NSGA-III). Case studies are conducted on a load-type VPP covering three scenarios, namely a large industrial zone, a commercial zone, and a residential zone, under weekday and non-weekday TOU tariffs and three representative 1 h peak-shaving periods. Compared with a fixed-pricing benchmark, the proposed strategy increases total user revenue by 9.4% to 11.4% and reduces weighted average dissatisfaction by 0.27 to 1.92%. The case study results demonstrate that the proposed method can improve the trade-off between user revenue and satisfaction. Full article

Modern performance-based bridge design seeks to control damage in specific failure modes in order to balance safety and economy, particularly in high-seismic regions where inelastic and ductile deformation is expected to occur, both in the structure and soil, allowing potential reduction in seismic demand through fuse elements. In short-span bridges, abutments strongly influence longitudinal response, whereas transverse performance depends largely on seismic components such as shear keys and other energy-dissipation devices. Thus, performance assessment requires explicit representation of their hysteretic behavior. This study presents a numerical evaluation of the damping provided by common elements in typical bridge systems, using as reference damage observations from bridges affected by recent interface earthquakes in Mexico. Three-dimensional finite-difference models were developed, and nonlinear response-history analyses were performed to simulate ductile behavior and energy dissipation. The Sig3 hysteretic model available in FLAC^3D was used for abutments and foundation soils, while shear keys were represented as nonlinear springs. The results established a relationship between plastic deformation and energy dissipation, showing that incorporating the hysteretic behavior of both soil and sacrificial structural components enhanced the seismic bridge performance assessment, and led to more reliable and cost-efficient designs when inelastic deformation capacity was explicitly included in the numerical simulations. Full article

The squat is a fundamental multi-joint movement widely studied in strength training and biomechanics. While numerous experimental and computational studies have examined squat kinematics and joint loading, the mechanisms governing how squat technique adapts to increasing external load remain insufficiently understood. In particular, inverse-dynamics-based approaches often overlook explicit constraints imposed by limited joint moment capacity. This study presents a computational framework for predicting load-dependent adaptations of squat posture. The human body was represented as a multi-segment rigid-body system, with joints modeled as nonlinear rotational elements with bounded moment capacity. A reference squat trajectory was first generated kinematically, and a constrained optimization procedure was then applied at each motion frame to determine a mechanically admissible posture under increasing barbell load. The results show that higher loads lead to systematic posture adaptations, including increased torso inclination and redistribution of rotational demand from the knee toward the hip joint. For the highest load, peak torso pitch increased from 30° to over 40°, while joint utilization exceeded unity, indicating the onset of yielding. These findings identify joint moment capacity as a key constraint governing squat technique and demonstrate the potential of the proposed framework for predictive biomechanical analysis. Full article

Rosa canina L. is a medicinal and nutritionally valuable species with increasing industrial demand, yet its conventional propagation is limited by low rooting capacity and high genetic heterogeneity. In this study, a complete and reproducible in vitro micropropagation protocol was established, from explant introduction to plantlet acclimatization. Axillary buds were disinfected and introduced into Murashige and Skoog (MS) medium supplemented with 6-benzylaminopurine (BAP). Shoot multiplication was achieved using sequential cytokinin treatments, and shoot elongation was promoted by adding liquid MS medium containing activated charcoal (AC). The highest and fastest root induction percentage (up to 75%) was obtained on WPM with 2 mg·L⁻¹ IBA and under a 16 h light/8 h dark photoperiod. Light promoted adventitious root formation depending on the nutrient formulation. Thereafter, shoots developed well-structured root systems in vitro, and plantlets fully survived to ex vitro acclimatization. This protocol provides an efficient platform for the large-scale propagation of R. canina and demonstrates that its auxin-driven adventitious rooting is strongly conditioned by the interaction between basal medium composition and photoperiod. Full article

The rapid development of space exploration demands real-time backhaul of massive sensing payload data in space-ground integrated telemetry, tracking, and command (TT&C) networks. However, traditional narrow-band TT&C links suffer from severe congestion during massive data backhaul. Since most TT&C applications are inherently task-oriented and do not require pixel-perfect data reconstruction, we propose a task-oriented joint resource allocation framework based on semantic communications. Specifically, we introduce an adaptive semantic split computing mechanism that extracts and transmits only compact, decision-critical features instead of raw bitstreams, fundamentally mitigating the bandwidth bottleneck. The joint optimization of computation offloading, semantic splitting, and continuous on-board computing allocation is formulated as a stochastic mixed-integer nonlinear programming (MINLP) problem. We propose a decoupled algorithm based on Hierarchical Multi-Agent Proximal Policy Optimization (HMAPPO) to solve it. An outer layer employs multi-agent reinforcement learning (MARL) for distributed discrete decision-making, while an inner layer utilizes a Karush–Kuhn–Tucker (KKT)-based solver for continuous space-based computing allocation. This bi-level architecture overcomes the curse of dimensionality and mathematically guarantees zero-violation of physical capacity constraints. Simulations demonstrate that HMAPPO rapidly converges and sustains a high weighted success rate under heavy traffic congestion, significantly improving system utility compared to state-of-the-art baselines. Full article

Fieldwork carries a high risk of irregular, non-compressible traumatic wounds, which often initiate a vicious cycle of “traumatic bleeding-insect bite-secondary infection”. Conventional dressings cannot combine rapid hemostasis with physical protection against venomous insects, creating an urgent demand for multifunctional field trauma dressings. To solve this problem, this study developed a shape-adaptive bilayer hydrogel that concurrently provides rapid hemostasis, promotes wound repair, and acts as a robust physical barrier. The hydrogel adopts a layered design: the bottom layer (PPTY) achieves autogelation within 3 s upon blood contact, while the top armor protective layer (AP) withstands pressures up to 942 kPa. By incorporating chitosan and sodium citrate into the AP precursor solution, the hydrogel achieved in situ formation within 50 s and developed a stable self-renewing armor layer. The tightly bonded bilayer showed complementary functions. In rat models of femoral artery puncture and tail vein bleeding, PPTY-AP hydrogel significantly reduced blood loss and shortened hemostasis time. Moreover, the hydrogel demonstrated excellent tissue adhesion and moisture retention capacity, promoting full-thickness skin wound healing. This multifunctional, rapidly deployable hydrogel presents a promising solution for field trauma management and offers a new design paradigm for advanced wound dressings. Full article

To address the application requirements of energy storage devices for new energy electric vehicles—including high energy density, high-power density, fast charging and discharging, and long-term cycling stability—traditional symmetric supercapacitors are often limited by low energy density and poor compatibility between the anode and cathode, making it difficult to meet the high-efficiency energy storage demands under the dynamic operating conditions of electric vehicles. This study focuses on the regulation of hierarchical thin-film structures and the innovative heterogeneous coating interface engineering with precise slurry coating and film-forming optimization and designs and fabricates NiCo₂O₄/NiFeO composite thin-film electrodes and Mn-doped porous carbon (Mn-PC) thin-film electrodes. The uniform, compact and stable coating formation on nickel foam substrates via controllable slurry coating facilitates the efficient integration of active materials and conductive supports. The electrode slurries were coated onto conductive nickel foam substrates, and high-performance aqueous supercapacitors were assembled using an asymmetric configuration. A systematic study was conducted covering material preparation, structural characterization, electrochemical testing, and full-device performance evaluation. Using techniques such as XRD, XPS, SEM, TEM, BET, and an electrochemical workstation, the study revealed the structure–activity relationships among material morphology, crystalline phases, pore structure, and electrochemical performance, elucidating the charge storage mechanisms of the composite electrode films and the principles of synergistic adaptation between the anode and cathode. The results indicate that NiCo₂O₄ nanowires decorated with in situ-grown NiFeO nanosheets to form a composite structure; when coated onto nickel foam, this forms a uniform, porous electrode film with a specific surface area of 171.3 m²/g, a specific capacitance as high as 1746 F/g at 1 A/g, and a capacity retention rate of 94.0% after 10,000 cycles. After coating and film formation, the Mn-PC anode introduced pseudocapacitive active sites through uniform Mn doping, resulting in a film electrode specific capacitance of 348 F/g and significantly improved rate and cycling performance. The assembled NiCo₂O₄/NiFeO//Mn-PC asymmetric supercapacitor exhibits a thin-film electrode specific capacitance of 153 F/g at 1 A/g, with a maximum energy density of 52 Wh/kg. Even at a power density of 9000 W/kg, it maintains 45 Wh/kg, and retains 89.5% of its capacity after 10,000 cycles, with overall performance outperforming most previously reported transition metal-based devices. This coating-engineered electrode fabrication strategy breaks through the interface mismatch and structural instability bottlenecks of traditional thin-film electrodes, providing a novel material system and an efficient coating assembly strategy for high-performance supercapacitor thin-film electrodes in new energy electric vehicles, and offers experimental evidence and technical references for the development and application of high-power energy storage coating devices for automotive use, as well as the innovative design of electrode coating engineering in energy storage fields. Full article

Kazakhstan, the largest landlocked country and the ninth-largest country by land area in the world, played a central role in the Soviet Virgin Lands campaign. However, decades of cultivation left the soil degraded and vulnerable to erosion. This legacy, along with worldwide water scarcity, drives the search for alternative farming methods such as hydroponics. This study investigates the feasibility of powering an indoor hydroponic system with photovoltaic (PV) technology in different regions of Kazakhstan. Three PV configurations, 16, 20, and 24 panels, were simulated in PVsyst (8.0.12) to meet the monthly energy demand of the system. The goal was to determine the minimum PV size and storage capacity for continuous year-round operation. Results showed that 16 panels were sufficient only from April to July, whereas 20- and 24-panel systems provided better reliability throughout the year. Optimal designs varied by region. For instance, those in the south, such as Turkistan, required smaller setups (6.8 kWp, 26 panels, 7 batteries), whereas those in the north, such as Akmola, needed larger ones (10.9 kWp, 42 panels, 10 batteries). Performance ratios ranged from 41% to 66% depending on the region. These results indicate that PV-powered hydroponic systems are feasible in Kazakhstan, although system configurations must be adapted to specific regional solar conditions. Full article

Human squat motion is commonly analyzed using inverse dynamics, where joint moments are computed from experimentally measured kinematics. Such analyses typically assume that the observed motion is mechanically feasible and do not explicitly account for limitations of joint moment capacity. In this study, a computational framework is proposed for the load-constrained reconstruction of squat motion that integrates kinematic motion generation with a mechanical model of moment-limited joints. The human body is represented as a multi-segment system consisting of feet, shanks, thighs, pelvis, and torso. Joint behavior is modeled using nonlinear rotational springs with bounded moment capacity, allowing elastic response followed by allowing bounded moment response and redistribution of mechanical demand as critical moment levels are approached. A reference squat trajectory is first generated kinematically, after which a constrained optimization problem is solved at each motion frame to obtain a mechanically admissible posture under external loading. The objective function combines trajectory tracking with joint energy contributions, while gravitational loading from a barbell applied at the shoulders introduces external work. The formulation enables automatic correction of the reference motion when joint moment limits are exceeded, resulting in mechanically admissible squat postures. Numerical examples illustrate the evolution of pelvis trajectory, torso inclination, lower-limb segment angles, and reconstructed body configurations throughout the squat cycle. The results confirm that joint moment capacity directly influences the reconstructed motion and leads to load-dependent adaptation of squat posture. Full article

Oncology teams operate in highly demanding clinical environments marked by recurrent acute and chronic stressors that can impair optimal quality of care. Although the practice environment is known to influence team processes, the specific contribution of team resilience at work to team effectiveness remains insufficiently delineated in oncology. This cross-sectional study investigated whether team resilience at work mediates the associations between the teamwork practice environment and two core indicators of team effectiveness: team functioning and team cohesion. A total of 189 oncology team members in Québec (Canada) completed an e-questionnaire between February 2022 and June 2023. Structural equation modeling was conducted to assess an evidence-informed mediation model. The tested model revealed acceptable fit. Findings showed significant indirect effects consistent with a mediating role of team resilience at work in the relationships between the teamwork practice environment and team functioning and team cohesion. Some dimensions—resourcefulness, alignment, efforts to understand problems, wellness awareness and being proud to work in the team—loaded strongly on the resilience concept. These results highlight the relevance of reinforcing team resilience capacities to sustain high-quality care in oncology settings. Interventions aiming to enhance team effectiveness may benefit from explicitly integrating strategies designed to strengthen resilience-related dimensions within oncology teams. Full article

Against the backdrop of accelerating low-carbon transformation in the global energy system and decarbonization in the transportation sector, the widespread adoption of electric vehicles has intensified grid load imbalances and highlighted challenges in integrating intermittent renewable energy generation. Vehicle-to-Grid (V2G) technology has emerged as a key solution to these challenges. This paper systematically traces the global evolution of V2G technology from conceptualization to large-scale deployment, focusing on localized practices in China’s scaled V2G applications. It dissects the logic behind policy evolution, identifies three distinct Chinese V2G models—centralized, distributed, and battery-swapping—and validates the practical outcomes of representative pilot projects. Research reveals three core constraints hindering China’s large-scale V2G adoption: the absence of battery capacity degradation management mechanisms, fragmented standardization systems, and rigid market mechanisms. Based on this, the paper proposes recommendations for scaling V2G in China across three dimensions: power battery second-life utilization, standardization system construction, and market mechanism optimization. Furthermore, aligning with the global demand for large-scale V2G implementation, this paper proactively proposes innovative market models. These include establishing a coordinated trading mechanism between green power and V2G, developing a digitally driven distributed trust and transaction system, and exploring financialization and risk hedging models for battery assets. These concepts provide theoretical foundations and decision-making references for achieving high-quality, large-scale V2G applications worldwide. Full article

AI data-center (DC) growth is increasingly constrained by limited deliverable electricity, interconnection capacity, and cooling demand. This study develops a boundary-consistent screening framework for waste-to-energy (WtE)-coupled AI DC cooling, treating cooling as an energy service that can be supplied through grade matching rather than solely through electricity-driven mechanical chilling. The framework translates plant-side exportable heat into corridor-level planning objects by explicitly accounting for thermal attenuation, absorption-based conversion, and parasitic electricity associated with delivery and auxiliaries. Three results structure the analysis. First, a reference-case energy-service ledger shows how a representative regulated WtE plant with municipal solid-waste throughput of 1500 t/day and lower heating value of 10 MJ/kg yields ~78.1 MWth of exportable driving heat and, at a 20 km corridor, ~53.0 MWcool of delivered cooling and ~8.0 MWe of net avoided cooling electricity after parasitic debiting. Second, the coupled system is governed by operating regimes, not a single efficiency score. Under the baseline package, full thermal coverage is maintained up to ~20.9 km, the stricter quality-adjusted criterion remains positive to ~22.9 km, and the electricity–relief criterion remains positive to ~44.7 km. Third, deployment-scale translation for a 1 GW IT campus (u = 0.70, L = 5 km) implies a net grid relief of ~116.9–264.4 MW across scenario packages, while the required WtE footprint ranges from roughly three to 148 equivalent representative plants, or about 0.6–40 full-load-equivalent plants at a 25% displacement target. The contribution is a siting-ready planning framework that identifies when WtE-coupled cooling remains corridor-feasible, when it becomes hybrid and marginal, and when infrastructure scale rather than thermodynamic benefit becomes the binding constraint. It is intended as a screening tool for planning and comparison, not as a project-specific hydraulic or plant-cycle design. Full article

Long-term time series forecasting (LTSF) is critical for modern power systems, energy management, and grid planning. Yet virtually all existing forecasting models employ stationary activation functions that apply identical nonlinear mappings regardless of temporal context—a fundamental mismatch with real-world load data, which exhibits strongly regime-dependent dynamics such as summer demand peaks, winter heating patterns, and overnight low-load periods. We address this gap by proposing TC-KAN (Time-Conditioned Kolmogorov–Arnold Network), the first forecasting architecture to augment KAN activation functions with position-aware coefficient parameterisation. The core innovation replaces the static polynomial coefficients in standard KAN activations with position-conditioned coefficients produced by a lightweight positional-embedding MLP, providing additional learnable capacity beyond standard KAN while adding negligible parameter overhead. TC-KAN further integrates a dual-pathway processing block—combining depthwise convolution for local temporal pattern extraction with the time-conditioned KAN layer for enhanced nonlinear transformation—within a channel-independent framework with Reversible Instance Normalisation. Experiments were conducted on four standard ETT benchmark datasets and the high-dimensional Weather dataset. TC-KAN achieves superior or competitive accuracy in most configurations while requiring merely 51K parameters—approximately 40% of DLinear and ∼100× fewer than iTransformer. On ETTh2, TC-KAN reduces the mean squared error by up to 61.4% over DLinear, and matches the current state-of-the-art iTransformer on ETTm2 at a fraction of the computational cost. This extreme parameter reduction circumvents the steep memory bottlenecks endemic to massive Transformer models, positioning TC-KAN as a highly practical architecture tailored precisely for resource-constrained edge deployments—such as on-device load forecasting inside smart grid sensors and industrial IoT controllers. Full article

Magnesium alloys exhibit promising application prospects in medical orthopedic implants. However, their practical applications are limited by rapid corrosion, suboptimal osseointegration, and implant-related infections. Although conventional drug-eluting polymer coatings can provide various biological functions, the uncontrolled drug release often compromises long-term therapeutic efficacy. In this study, a self-healing Mg-poly(ε-caprolactone) (PCL)@OHF coating is designed and prepared on WE43 Mg by spin coating to achieve ultrasound-triggered release of osthole. OHF consists of osthole-loaded hollow mesoporous silica nanoparticles (HMSs) modified with Pluronic F127. Drug release studies show that the nanocapsules respond to ultrasound stimulation, with the cumulative release increasing from 39.94% to 75.93% after 7 days. Furthermore, the coating demonstrates intrinsic self-healing capacity upon thermal treatment at 50 °C. Electrochemical and immersion tests reveal that the composite coating provides good barrier protection for the WE43 Mg alloy, evidenced by a decrease in corrosion current density from 2.04 × 10⁻⁶ to 5.94 × 10⁻⁷ A/cm². In vitro biological assays confirm the antibacterial efficacy against Staphylococcus aureus and Escherichia coli, as well as the ability to promote osteogenic differentiation. The results reveal a surface modification strategy that combines self-healing, anticorrosion, and on-demand drug release, offering a promising approach for advanced orthopedic implants. Full article

The large-scale integration of wind energy introduces increased uncertainty and variability in modern power systems, with direct implications for both system design and operation. In addressing similar aspects, energy storage plays a pivotal role as a key source of system flexibility. However, the design and sizing of storage systems remain challenging, especially under conditions of increased uncertainty. In this context, the present study proposes an alternative methodological framework, based on an inverse sizing pathway, i.e., from operations to design. More specifically, the uncertainty embedded in day-ahead forecasting of residual errors, associated with wind power generation and load demand, is currently exploited as a design-relevant signal, while energy storage is treated explicitly as a risk-hedging mechanism. Forecasting residuals spanning a year of operation are incorporated in the problem through probabilistic modeling, leading to the generation of trajectories that correspond to different risk levels and are managed as design scenarios. Regarding the modeling of uncertainties, the study examines two different strategies, namely a global modeling approach and a k-means clustering strategy. Accordingly, by mapping the interplay between storage capacity, uncertainty levels (or risk tolerance), achieved RES shares and system-level costs, we highlight the role of energy storage as a risk-hedging entity rather than merely a means of energy balancing. Our results to that end demonstrate that the achieved shares of RES exhibit increased sensitivity, even within constrained regions of wind power variation, while storage capacity features distinct zones of hedging value and hedging saturation effects emerging beyond certain storage levels. Moreover, evaluation of the two modeling strategies reflects on their complementary character, with the global modeling approach ensuring continuity and the clustering strategy capturing local asymmetries within different operational regimes. In conclusion, the methodology presented in this study bridges the gap between operational forecasting and long-term system design, offering a risk-aware framework for storage sizing, grounded in actual operational signals rather than relying on stationary historical data and relevant scenarios. Full article