Search Results (9,857)

Accurate perception of dynamic carbon intensity is a prerequisite for low-carbon demand-side response. However, traditional grid-average carbon factors lack the spatio-temporal granularity required for real-time regulation. To address this, this paper proposes a “Prediction-Optimization” closed-loop framework for electric vehicle (EV) fleets. First, a hybrid forecasting model (VMD-BSLO-CTL) is constructed. By integrating Variational Mode Decomposition (VMD) with a CNN-Transformer-LSTM network optimized by the Blood-Sucking Leech Optimizer (BSLO), the model effectively captures multi-scale features. Validation on the UK National Grid dataset demonstrates its superior robustness against prediction horizon extension compared to state-of-the-art baselines. Second, a multi-objective Model Predictive Control (MPC) strategy is developed to guide EV charging. Applied to a real-world station-level scenario, the strategy navigates the trade-offs between user economy and grid stability. Simulation results show that the proposed framework simultaneously reduces economic costs by 4.17% and carbon emissions by 8.82%, while lowering the peak-valley difference by 6.46% and load variance by 11.34%. Finally, a cloud-edge collaborative deployment scheme indicates the engineering potential of the proposed approach for next-generation low-carbon energy management. Full article

Adipose tissue thermogenesis is a promising strategy to counter obesity and metabolic disease, but sustained activation of thermogenic adipocytes elevates oxidative and lipid-peroxidation stress, increasing susceptibility to ferroptotic cell death. Existing models often treat redox buffering, hypoxia signaling and ferroptosis as separate processes, which cannot explain why similar interventions—such as antioxidants, β-adrenergic agonists or iron modulators—alternately enhance thermogenesis or precipitate tissue failure. Here, we propose the Redox–Thermogenesis–Ferroptosis Compass (RTF-Compass) as a framework that maps adipose depots within a space defined by ferroptosis resistance capacity (FRC), ferroptosis signaling intensity (FSI) and HIF-1α-dependent hypoxic tone. Within this space, thermogenic output follows a hormetic, inverted-U trajectory, with a Thermogenic Ferroptosis Window (TFW) bounded by two failure states: a Reductive-Blunted state with excessive antioxidant buffering and weak signaling, and a Cytotoxic state with high ferroptotic pressure and inadequate defense. We use this model to reinterpret genetic, nutritional and pharmacological studies as state-dependent vectors that move depots through FRC–FSI–HIF space and to outline principles for precision redox medicine. Although the TFW is represented as coordinates in FRC–FSI–HIF space, we use ‘Compass’ to denote a coordinate framework in which perturbations act as vectors that orient depots toward thermogenic or cytotoxic outcomes. Finally, we highlight priorities for testing the model in vivo, including defining lipid species that encode ferroptotic tone, resolving spatial heterogeneity within depots and determining how metabolic memory constrains reversibility of pathological states. Full article

Ground-based full-physical experiments for space rendezvous and docking serve as a critical step in verifying the reliability of docking technology. The high-precision active attitude setting of spacecraft simulators represents a key technology for ground-based full-physical experiments. In order to satisfy the requirement for high-precision attitude control in these experiments, this paper proposes an enhanced method based on auto disturbance rejection control (ADRC). This paper addresses the limitations of traditional deadband–hysteresis relay controllers, which exhibit low steady-state accuracy and insufficient disturbance rejection capability. This approach employs a nonlinear extended state observer (NESO) to estimate and compensate for total system disturbances in real time. Concurrently, it incorporates an adaptive mechanism for deadband and hysteresis parameters, dynamically adjusting controller parameters based on disturbance estimates and attitude errors. This overcomes the trade-off between accuracy and power consumption that is inherent in fixed-parameter controllers. Furthermore, the method incorporates a nonlinear tracking differentiator (NTD) to schedule transitions, enabling rapid attitude settling without overshoot. The stability analysis demonstrates that the proposed controller achieves local asymptotic stability and global uniformly bounded convergence. The simulation results demonstrate that under three typical operating conditions (conventional attitude setting, pre-separation connector stabilisation, and docking initial condition establishment), the steady-state attitude error remains within ±0.01°, with convergence times under 3 s and no overshoot. These results closely match ground test data. This approach has been demonstrated to enhance the engineering applicability of the control system while ensuring high precision and robust performance. Full article

Wood flooring manufacturers face complex challenges in dynamically allocating resources across multi-channel markets, characterized by channel conflicts, demand uncertainty, and long-term cumulative effects of decisions. Traditional static optimization or myopic approaches struggle to address these intertwined factors, particularly when critical market states like brand reputation and customer base cannot be precisely observed. This paper establishes a systematic and theoretically grounded online decision framework to tackle this problem. We first model the problem as a Partially Observable Stochastic Dynamic Game. The core innovation lies in introducing an unobservable market position vector as the central system state, whose evolution is jointly influenced by firm investments, inter-channel competition, and macroeconomic randomness. The model further captures production lead times, physical inventory dynamics, and saturation/cross-channel effects of marketing investments, constructing a high-fidelity dynamic system. To solve this complex model, we propose a hierarchical online learning and control algorithm named L-BAP (Lyapunov-based Bayesian Approximate Planning), which innovatively integrates three core modules. It employs particle filters for Bayesian inference to nonparametrically estimate latent market states online. Simultaneously, the algorithm constructs a Lyapunov optimization framework that transforms long-term discounted reward objectives into tractable single-period optimization problems through virtual debt queues, while ensuring stability of physical systems like inventory. Finally, the algorithm embeds a game-theoretic module to predict and respond to rational strategic reactions from each channel. We provide theoretical performance analysis, rigorously proving the mean-square boundedness of system queues and deriving the performance gap between long-term rewards and optimal policies under complete information. This bound clearly quantifies the trade-off between estimation accuracy (determined by particle count) and optimization parameters. Extensive simulations demonstrate that our L-BAP algorithm significantly outperforms several strong baselines—including myopic learning and decentralized reinforcement learning methods—across multiple dimensions: long-term profitability, inventory risk control, and customer service levels. Full article

Standard Deep Learning-based detectors generally face a trade-off between accuracy and latency, as well as a significant performance degradation when applied to unseen environments. To address these challenges, this study proposes a method that enhances both accuracy and latency by leveraging the static characteristics of fixed-camera pig pen monitoring. Specifically, we utilize background and infrastructure information obtained through a one-time preprocessing step upon camera installation. By integrating this information, we introduce three distinct modules, Background-suppressed Image Generator (BIG), Facility Image Generator (FIG), and Background Suppression Integration (BSI), that improve detection accuracy and operational efficiency without the need for model retraining. BIG creates background-suppressed images that integrate foreground and background information. FIG creates facility mask images that can be used to identify pigs that are occluded by facilities, enabling more efficient learning in unseen environments. BSI leverages both the input image and the background-suppressed image generated by BIG, feeding them into a 3D convolution layer for efficient feature fusion. This difference-aware fusion helps the model focus on foreground information and gradually reduce the domain gap. After training on the German pig dataset and testing on the unseen Korean Hadong pig dataset, the proposed method could improve AP₅₀ accuracy (from 75% to 86%) and Jetson Orin Nano latency (from 67 ms to 41 ms) compared to the baseline model YOLOV12m. Full article

Sub-meter-resolution Polarimetric Synthetic Aperture Radar (PolSAR) imagery enables precise building footprint extraction but introduces complex scattering correlated with fine spatial structures. This change renders both traditional methods, which rely on simplified scattering models, and existing deep learning approaches, which sacrifice spatial detail through multi-looking, inadequate for high-precision extraction tasks. To address this, we propose an Orthogonal Dual-Resolution Network (ODRNet) for end-to-end, precise segmentation directly from single-look complex (SLC) data. Unlike complex-valued neural networks that suffer from high computational cost and optimization difficulties, our approach decomposes complex-valued data into its orthogonal real and imaginary components, which are then concurrently fed into a Dual-Resolution Branch (DRB) with Bilateral Information Fusion (BIF) to effectively balance the trade-off between semantic and spatial details. Crucially, we introduce an auxiliary Polarization Orientation Angle (POA) regression task to enforce physical consistency between the orthogonal branches. To tackle the challenge of diverse building scales, we designed a Multi-scale Aggregation Pyramid Pooling Module (MAPPM) to enhance contextual awareness and a Pixel-attention Fusion (PAF) module to adaptively fuse dual-branch features. Furthermore, we have constructed a VHR PolSAR building footprint segmentation dataset to support related research. Experimental results demonstrate that ODRNet achieves 64.3% IoU and 78.27% F1-score on our dataset, and 73.61% IoU with 84.8% F1-score on a large-scale SLC scene, confirming the method’s significant potential and effectiveness in high-precision building extraction directly from SLC. Full article

With the rapid development of the Internet of Things (IOT), 5G, and modern power systems, demand-side loads are becoming increasingly observable and remotely controllable, which enables demand-side flexibility to participate more actively in grid dispatch and emergency support. Under extreme rainstorm conditions, however, component failure risk rises and the availability and dispatchability of demand-side flexibility can change rapidly. This paper proposes a risk-aware emergency regulation framework that translates rainstorm information into actionable multi-load aggregation decisions for urban power systems. First, demand-side resources are quantified using four response attributes, including response speed, response capacity, maximum response duration, and response reliability, to enable a consistent characterization of heterogeneous flexibility. Second, a backpropagation (BP) neural network is trained on long-term real-world meteorological observations and corresponding reliability outcomes to estimate regional- or line-level fault probabilities from four rainstorm drivers: wind speed, rainfall intensity, lightning warning level, and ambient temperature. The inferred probabilities are mapped onto the IEEE 30-bus benchmark to identify high-risk areas or lines and define spatial priorities for emergency response. Third, guided by these risk signals, a two-level coordination model is formulated for a load aggregator (LA) to schedule building air conditioning loads, distributed photovoltaics, and electric vehicles through incentive-based participation, and the resulting optimization problem is solved using an adaptive genetic algorithm. Case studies verify that the proposed strategy can coordinate heterogeneous resources to meet emergency regulation requirements and improve the aggregator–user economic trade-off compared with single-resource participation. The proposed method provides a practical pathway for risk-informed emergency regulation under rainstorm conditions. Full article

Hierarchical Federated Learning (HFL) alleviates the trade-off between communication overhead and privacy protection in mobile scenarios via multi-level aggregation and mobility consideration. However, its idealized convex loss assumption and full-dimension parameter upload deviate from real-world non-convex tasks and edge channel constraints, causing excessive energy consumption, high communication cost, and compromised convergence that hinder practical deployment. To address these issues in mobile/UAV networks, this paper proposes an energy-efficient optimization scheme for HFL under non-convex loss, integrating a dynamically adjustable partial-dimension model upload mechanism. By screening key update dimensions, the scheme reduces uploaded data volume. We construct a total energy minimization model that incorporates communication/computation energy formulas related to upload dimensions and introduces an attendance rate constraint to guarantee learning performance. Using Lyapunov optimization, the long-term optimization problem is transformed into single-round solvable subproblems, with a step-by-step strategy balancing minimal energy consumption and model accuracy. Simulation results show that compared with the original HFL algorithm, our proposed scheme achieves significant energy reduction while maintaining high test accuracy, verifying the positive impact of mobility on system performance. Full article

Heat exchangers are central to energy and process industries, yet performance is bounded by the trade-off between higher heat transfer and greater pressure drop. This review targets indirect-type heat exchangers and organizes bioinspired strategies through a multi-scale lens of surface, texture, and network scales. It provides a structured comparison of their thermo-hydraulic behaviors and evaluation methods. At the surface scale, control of wettability and liquid-infused interfaces suppresses icing and fouling and stabilizes condensation. At the texture scale, microstructures inspired by shark skin and fish scales regulate near-wall vortices to balance drag reduction with heat-transfer enhancement. At the network scale, branched and bicontinuous pathways inspired by leaf veins, lung architectures, and triply periodic minimal surfaces promote uniform distribution and mixing, improving overall performance. The survey highlights practical needs for manufacturing readiness, durability, scale-up, and validation across operating ranges. By emphasizing analysis across scales rather than reliance on a single metric, the review distills design principles and selection guidelines for next-generation bioinspired heat exchangers. Full article

The increasing deployment of interconnected devices in Smart Cities and Industrial Internet of Things (IIoT) environments has significantly enhanced operational efficiency, automation, and real-time data analytics. However, this rapid digitization also introduces complex security and privacy challenges, particularly in the handling of sensitive data across heterogeneous and resource-constrained networks. This review explores the current landscape of privacy-preserving protocols designed for Smart City and IIoT infrastructures. We examine state-of-the-art approaches including lightweight cryptographic schemes, secure data aggregation, anonymous communication protocols, and blockchain-based frameworks. The paper also analyzes practical trade-offs between security, latency, and computational overhead in real-world deployments. Open research challenges such as secure interoperability, privacy in federated learning, and resilience against AI-driven cyberattacks are discussed. Finally, the paper outlines promising research directions and technologies that can enable scalable, secure, and privacy-aware network infrastructures for future urban and industrial ecosystems. Full article

Agrivoltaics—the co-location of photovoltaic energy production with agriculture—offers a promising pathway to address growing pressures on land, food, and clean energy resources. This study evaluates the first agrivoltaic pilot installation in Jordan, located in Amman (935 m above sea level; hot-summer Mediterranean climate), during its first operational year. Two 11.1 kWp bifacial photovoltaic (PV) systems were compared: (i) a south-facing array tilted at 10°, and (ii) a vertical east–west “fence” configuration. The tilted system achieved an annual specific yield of 1962 kWh/kWp, approximately 35% higher than the 1288 kWh/kWp obtained from the vertical array. Seasonal variation was observed, with the performance gap widening to ~45% during winter and narrowing to ~22% in June. As expected, the vertical system exhibited more uniform diurnal output, enhanced early-morning and late-afternoon generation, and lower soiling losses. The light profiles measured for the year indicate that vertical systems barely impede the light requirements of crops, while the tilted system splits into distinct profiles for the intra-row area (akin to the vertical system) and sub-panel area, which is likely to support only low-light requirement crops. This configuration increases the levelized cost of electricity (LCOE) by roughly 88% compared to a conventional ground-mounted system due to elevated structural costs. In contrast, the vertical east–west system provides an energy yield equivalent to about 33% of the land area at the tested configuration but achieves this without increasing the LCOE. These results highlight a fundamental trade-off: elevated tilted systems offer greater land-use efficiency but at higher cost, whereas vertical systems preserve cost parity at the expense of lower energy density. Full article

Wireless sensor networks (WSNs) contend with the critical challenge of balancing energy conservation against data transmission delay, a trade-off that protocols such as PEGASIS—while being strong in energy efficiency—fail to manage optimally due to resulting high latency, unbalanced load distribution, and suboptimal cluster formation. To address these limitations, this paper introduces the Enhanced Multi-Objective PEGASIS (EMO-PEGASIS) protocol, which is designed and implemented using a dual-phase machine learning strategy. This multi-objective approach works in two stages. First, it utilises K-means clustering to achieve robust spatial partitioning of the network. Second, it employs K-Nearest Neighbours (K-NN) classification to enable adaptive and intelligent routing. The simulation was performed using MATLAB R2025a, and the results show that EMO-PEGASIS addresses this multi-objective optimisation problem. The proposed EMO-PEGASIS protocol achieves a 45% reduction in average energy consumption, a 38% decrease in end-to-end delay, and a 67% increase in network lifetime compared to the original PEGASIS protocol. Additionally, EMO-PEGASIS demonstrates enhanced stability and effective load balancing under heterogeneous network configurations, while maintaining an excellent packet delivery ratio of 96.8%. These findings underscore the effectiveness of integrating machine learning techniques, which ultimately yield enhanced performance and enable reliable multi-objective optimisation within energy- and delay-constrained WSN environments. Full article

CeO₂ doping is a well-established strategy for enhancing the properties of zirconia (ZrO₂) ceramics, with the prior literature indicating an optimal doping range of around 10–15 wt.% for specific attributes. Building upon this foundation, this study provides a systematic investigation into the concurrent evolution of mechanical, tribological, and thermophysical properties across a broad compositional spectrum (0–20 wt.% CeO₂). The primary novelty lies in the holistic correlation of these often separately examined properties, revealing their interdependent trade-offs governed by microstructural development. The 15Ce-ZrO₂ composition, consistent with the established optimal range, achieved a synergistic balance: hardness increased by 27.6% to 310 HV₁, the friction coefficient was minimized to 0.205, and the wear rate was reduced to 1.81 × 10⁻³ mm³/(N m). Thermally, it exhibited a 72.2% reduction in the thermal expansion coefficient magnitude at 1200 °C and a low thermal conductivity of 0.612 W/(m·K). The enhancement mechanisms are consistent with solid solution strengthening, grain refinement, and likely enhanced phonon scattering, potentially from point defects such as oxygen vacancies commonly associated with aliovalent doping in oxide ceramics, while performance degradation beyond 15 wt.% is linked to CeO₂ agglomeration and duplex microstructure formation. This work provides a relatively comprehensive insight into the dataset and mechanism, which is conducive to the fine design of multifunctional ZrO₂ bulk ceramics. It is not limited to determining the optimal doping level, but also aims to clarify the comprehensive performance map, providing reference significance for the development of advanced ceramic materials with synergistically optimized hardness, wear resistance, and thermal properties. Full article

The delisting of Binance USD (BUSD) constitutes a major regulatory intervention in the stablecoin market and provides a unique opportunity to examine how targeted regulation affects liquidity allocation, market concentration, and short-run systemic risk in crypto-asset markets. Using daily data for 2023 and a linear and nonlinear Local Projections event-study framework, this paper analyzes the dynamic market responses to the BUSD delisting across major stablecoins and cryptocurrencies. The results show that liquidity displaced from BUSD is reallocated primarily toward USDT and USDC, leading to a measurable increase in stablecoin market concentration, while decentralized and algorithmic stablecoins absorb only a limited share of the shock. At the same time, Bitcoin and Ethereum experience temporary liquidity contractions followed by a relatively rapid recovery, suggesting conditional resilience of core crypto-assets. Overall, the findings document how a regulatory-induced exit of a major stablecoin reshapes short-run market dynamics and concentration patterns, highlighting potential trade-offs between regulatory enforcement and market structure. The paper contributes to the literature by providing the first empirical analysis of the BUSD delisting and by illustrating the usefulness of Local Projections for studying regulatory shocks in cryptocurrency markets. Full article

Plant roots form highly specialized apoplastic barriers that regulate the exchange of water, ions, and solutes between the soil and vascular tissues, thereby protecting plant survival under environmental stress. Among these barriers, the endodermis and exodermis play essential roles, enhanced by suberin lamellae and lignin-rich Casparian strips (CS). Recent advances have shown that these barriers are not static structures but are dynamic systems, rapidly adapting in response to drought, salinity and nutrient limitation. The R2R3-MYB transcription factor (TF) family is essential to this adaptive plasticity. These TFs serve as key regulators of hormonal and developmental signals to regulate suberin and lignin biosynthesis. Studies across different species demonstrate both conserved regulatory structure and species-specific adaptations in barrier formation. Suberization provides a hydrophobic structure that limits water loss and ion toxicity, while lignification supports structural resilience and pathogen defense, with the two pathways exhibiting adaptive and interactive regulation. However, significant knowledge gaps remain regarding MYB regulation under combined abiotic stresses, its precise cell-type-specific activity, and the associated ecological and physiological trade-offs. This review summarizes the central role of root barrier dynamics in plant adaptation, demonstrating how MYB TFs regulate suberin and lignin deposition to enhance crop resilience to environmental stresses. Full article