Search Results (1,216)

In this paper, we present an optimized driving strategy for a dual RF input envelope tracking power amplifier (ET PA) exploiting load modulation. The dual-input architecture enables dynamic load modulation (LM), allowing real-time adjustment of the load impedance to enhance performance over the signal dynamics typical of digital modulation schemes. The proposed approach considers a GaN HEMT-based LM-ET PA characterized under pulsed excitation across multiple amplitude and phase conditions of the load modulation control. Optimizing the control parameters yields a suitable shaping function that extends conventional ET supply modulation to include amplitude and phase control of the auxiliary amplifier, thereby improving the efficiency, output power, and linearity of the main amplifier. Experimental data demonstrate that the proposed dual RF input GaN-based LM-ET PA at 3.6 GHz outperforms a conventional ET PA in both efficiency and linearity when tested with high peak-to-average ratio (PAPR) signals. Full article

Against the backdrop of the dual-carbon goals and the Digital China initiative, the urgent need for enterprises to pursue green innovation and transformation is evident. Supply chain digitalization serves as a critical enabler for enterprises to achieve a low-carbon industrial transformation and high-quality development through the effective coordination of data resources across the entire chain. This study examines A-share listed companies from 2012 to 2023, leveraging the 2018 Supply Chain Innovation and Application Pilot Policy to construct a quasi-natural experiment. Employing a difference-in-differences approach with multiple mediation effects, it investigates the impact of supply chain digitalization on corporate green innovation and its transmission mechanisms. Findings reveal that supply chain digitalization significantly enhances corporate green innovation levels, with this effect being more pronounced in substantive innovation, western regions, and firms with high customer concentration. Mechanism tests reveal that supply chain digitalization promotes green innovation not only through independent pathways of enhancing information transparency and improving ESG performance but also via a chained mediation effect: “supply chain digitalization → information transparency → ESG performance → green innovation”. This study enriches theoretical research on the relationship between supply chain digitalization and green innovation from the dual perspectives of information and governance, offering insights for government initiatives to advance data sharing, implement differentiated policies, and establish green governance systems. Full article

To promote the sustainable development of agriculture and reduce resource waste, this paper investigates sustainable financing strategies for a green fresh agri-food supply chain. We employ a purely theoretical Stackelberg game model and numerical simulations based on hypothetical parameters to develop three financing models for a supply chain consisting of one capital-constrained farmer and one retailer, considering consumers’ dual sensitivity to product freshness and greenness. Analytical and numerical results reveal that: (1) with low financing rates, internal financing effectively alleviates under investment in preservation, leading to higher wholesale/retail prices. In a green-sensitive market, the resulting price premium compensates for cost increases, avoiding the “low quality–low price” trap under external financing. (2) The retailer’s total profit decreases as the internal financing rate rises; higher interest income cannot offset demand loss caused by reduced preservation effort. Thus, a low- or zero-interest strategy maximizes the retailer’s operational profit. (3) As consumer sensitivity to freshness and greenness increases, profit growth under internal financing displays convexity. However, under extremely high freshness sensitivity, external financing yields stronger marginal incentives, suggesting that retailers should adjust profit allocation in the high-end market. The findings provide theoretical guidance for financing mode selection and practical insights for promoting green agricultural sustainable development. Full article

Modern smart cities increasingly depend on resilient and intelligent energy infrastructures to maintain critical urban services during large-scale disturbances and multi-fault conditions. Conventional restoration approaches are often limited by centralized operation, delayed response, and inadequate coordination of distributed energy resources (DERs) under emergency conditions. To address these challenges, this paper proposes a Federated AI-Driven Urban Energy Resilience Framework for Smart City Critical Infrastructure Restoration using Virtual Power Plant (VPP) coordination, blockchain-enabled peer-to-peer (P2P) energy trading, and intelligent distributed energy management. The proposed framework is validated on the IEEE 118-bus radial distribution system under severe dual-fault outage conditions, representing urban disaster-induced infrastructure interruptions. Critical urban service zones, including healthcare support systems, emergency loads, smart residential sectors, and EV charging corridors, are considered during the restoration process. The Seagull Optimization Algorithm (SOA) is employed to optimize DER dispatch and improve restoration performance under operational constraints. A progressive restoration strategy comprising conventional outage conditions, VPP-assisted restoration, blockchain-enabled decentralized energy trading, and AI-driven coordinated restoration is analyzed. Simulation results demonstrate that the proposed framework significantly enhances urban energy resilience by increasing load restoration from 55.05% to 94.20%, reducing Energy Not Supplied (ENS), improving voltage stability, and lowering interruption-related economic losses. The minimum bus voltage improves to 0.965 p.u. under the proposed coordinated restoration strategy. The results show that coordinated VPP operation and blockchain-based energy sharing can support reliable restoration of critical urban infrastructure during major outage conditions. The results indicate that integrating AI-assisted VPP coordination with secure decentralized energy trading can effectively support smart city critical infrastructure continuity during extreme outage conditions. The proposed framework provides a scalable and resilient solution for future intelligent urban energy systems and disaster-resilient smart city applications. Full article

In summary, near-surface icing risk in complex alpine terrain is jointly controlled by freezing conditions, moisture supply, freeze–thaw transitions, and topographic energy processes. Traditional approaches relying on sparse station data or single temperature thresholds fail to capture spatial heterogeneity, and frequent cloud cover together with topographic errors severely limit the application of thermal infrared remote sensing. Taking the area along the Duku Highway in the Tianshan Mountains as the study region, a daily icing risk assessment framework at 250 m resolution was constructed using multi-source remote sensing, ERA5-Land reanalysis data, topographic correction, and an energy–moisture dual-constrained model. A diurnal temperature cycle model, the CAP index, and physics-constrained machine learning were integrated to reconstruct the daily minimum land surface temperature ($T_{s,\mathrm{m}\mathrm{i}\mathrm{n}}$) at 250 m resolution under all weather conditions. A probabilistic two-tier risk assessment model was then established by incorporating moisture, topography, and freeze–thaw transitions. The results show that high-risk zones occur primarily in valleys and topographically constrained corridors rather than the coldest elevations. Validation against Landsat LST (r = 0.886) and the Bayanbulak station (bias −0.76 °C, RMSE 5.62 °C, r = 0.91) confirms spatial and seasonal accuracy. Sensitivity and Monte Carlo analyses indicate the RiskScore is mainly controlled by the low-temperature weight, while upstream parameters are less influential. The framework is best applied as a screening and early-warning product to identify sub-kilometer potential icing corridors, complementing point measurements and short-range forecasts. Full article

Ecological zoning is important for understanding spatial heterogeneity and supporting landscape-level management. However, existing approaches rarely integrate ecosystem service supply with ecological risk, and their underlying nonlinear relationships remain insufficiently explored. This study aims to develop an integrated framework linking ecosystem service value (ESV) and landscape ecological risk (LER) based on a two-dimensional quadrant model. This framework integrates ESV and LER from complementary benefit–risk perspectives, advancing ecological zoning beyond single-indicator approaches. Using the Miyun Reservoir Basin as a case study, multi-source data from 2000 to 2020 were used to quantify ESV and LER and to examine their spatiotemporal dynamics. The ESV-LER framework was applied to identify ecological functional zones. In addition, the XGBoost-SHAP model combined with the Geographical Detector was used to explore the nonlinear effects and interactions of natural and anthropogenic drivers. ESV showed a “decline-recovery” trend, whereas LER exhibited an opposite “decrease-increase” pattern. Areas with both high ESV and high LER were mainly distributed around the reservoir and river networks, suggesting a spatial mismatch between ecological value and risk. Ecological improvement and conservation zones accounted for approximately 79% of the basin, while ecological risk prevention zones expanded over time, indicating increasing human disturbance. NDVI was identified as a dominant factor with dual effects, enhancing ESV while reducing LER, whereas population density and NPP exhibited nonlinear threshold effects that increased ecological risk. Overall, this study advances ecological zoning by integrating functional value and risk perspectives while explicitly revealing their nonlinear drivers. The proposed framework provides a transferable and interpretable approach for watershed-scale ecological management and supports more targeted and differentiated governance strategies. Full article

With the growing global population, intensifying resource constraints, and deepening climate change impacts, agriculture faces dual challenges of ensuring food security and advancing sustainable development. Artificial intelligence (AI) has emerged as a transformative technology, penetrating the entire crop production chain and offering innovative solutions to traditional agricultural bottlenecks. This paper systematically reviews AI applications in five core domains: biotic stress monitoring, soil health management, precision operation, supply chain optimization, and climate-resilient agriculture. It further categorizes and analyzes four key technical pathways—deep learning, sensor fusion, data-driven methods, and hybrid modeling—while critically examining major challenges across data, technology, implementation, and ethics/policy dimensions. Future directions are discussed from technological innovation, scenario expansion, implementation guarantees, and sustainability orientation. Research findings show that AI has achieved technical validation in pest/disease detection, soil parameter modeling, and intelligent spraying, with accuracy exceeding 85% in some cases. However, regional data bias, insufficient model generalization, and the digital divide still hinder large-scale deployment. Moving forward, coordinated efforts in technological innovation and policy support are required to promote inclusive, standardized, and sustainable AI applications in crop production. Full article

Against the background of China’s dual carbon goals, high-renewable-power systems suffer severe resilience threats from destructive ice disasters, and existing recovery approaches fail to fully exploit multi-type flexible resources with unsatisfying computational efficiency. Targeting this gap, this work establishes a resilience enhancement framework for ice-affected power grids. This model quantifies line failure probability considering time-varying ice thickness and wind load, generates representative fault scenarios via sequential Monte Carlo and K-means clustering, and innovatively incorporates mobile energy storage systems (MESSs) and low-temperature-corrected PEM electrolyzers into coordinated post-fault dispatch; an improved parrot optimization (PO) algorithm with Chebyshev chaos, random mutation and adaptive t-distribution is designed to boost solving efficiency. Tested on the IEEE 39-bus system, the proposed method reduces average load shedding to 3.7% and raises renewable accommodation to 95.6%, outperforming fixed energy storage and literature-based strategies by cutting load curtailment by 45.6% and 30.2% respectively, while multi-condition sensitivity analyses validate its stable applicability under varying disaster intensity and renewable penetration. This coordinated scheduling strategy supplies feasible technical support for practical anti-icing resilience promotion of new-type power grids. Full article

China regards shale gas as a key energy source for ensuring energy security, promoting the transformation of the energy structure, and addressing climate change. However, at this stage, the scarcity of land resources, coupled with various institutional restrictions, has brought numerous practical obstacles to the large-scale commercial development of shale gas. By analyzing the restrictive provisions concerning shale gas development in China’s current laws, this paper points out three major institutional constraints faced by the use of agricultural land for shale gas development: first, stringent land use control policies; second, the legal acquisition system for surface land remains unstable; third, institutional gaps in the supervision of subsurface space on collectively owned land. To overcome these institutional barriers, this study proposes fundamental reform measures for the current land legal framework. If comprehensive reform cannot be achieved immediately, partial breakthroughs may be sought within the existing institutional framework. The sequence has three phases. Near-term one to three years: negative-list quotas, refined land classification, land linkage, benefit balance, and community guidance. No law changes needed; provinces can act. Medium-term three to seven years: regulations and the mining land chapter in the revised Mineral Resources Law. Long-term beyond seven years: constitutional amendment for collective land transfer and dual-track supply reform. This study provides a theoretical reference for solving the land use issues in China’s shale gas development, and its conclusions also provide a reference for resolving the conflicts between shale gas development and agricultural land use in other jurisdictions. Full article

The integrity of halal supply chains is increasingly threatened by fragmented paper-based records, certificate fraud, and the absence of real-time traceability. This paper presents HalalChain, a blockchain-based halal product traceability system that enforces role-based access control (RBAC) through three Solidity smart contracts deployed on an Ethereum-compatible blockchain. HalalChain is designed for production deployment on an EVM-compatible Layer-2 or sidechain such as Polygon or BNB Chain, on which the contracts run without code changes. A dual-storage architecture synchronises every supply chain event to both a PostgreSQL relational database and the blockchain, balancing on-chain immutability with off-chain query performance. The system supports five stakeholder roles, namely administrator, supplier, manufacturer, logistics, and retailer, each restricted to specific supply chain event types enforced at the smart contract level. Consumers can verify product halal status and full supply chain history by scanning a QR code linked to a public verification endpoint that cross-checks database records against on-chain event counts, producing a chain-integrity indicator. As the current chain-integrity check is count-base, it can detect missing or extra database rows, but it cannot detect content-level modification if the row count remains unchanged. A total of 107 automated test cases were executed covering functional correctness, edge cases, end-to-end integration, and gas performance benchmarks. Core smart contract operations consume between 25,365 and 213,684 gas units, indicating feasible deployability on Ethereum-compatible networks. An exploratory analysis was carried out with a preliminary survey of 40 respondents (mean = 4.10 on a 5-point Likert scale), suggesting that consumer demand for blockchain-verified halal certification is encouraging. The results demonstrate that HalalChain provides a tamper-evident, role-enforced traceability foundation for the halal food industry. The system secures the digital chain of custody cryptographically and the physical–digital binding between the QR code, and the product remains a separate trust assumption requiring complementary anti-tamper mechanisms. Full article

Solar-driven interfacial water evaporation has been recognized as an effective measure to address freshwater scarcity. Photothermal materials lie at the core of this process and have been extensively studied. However, conventional carbon-based materials typically suffer from high thermal emissivity, leading to significant heat loss. Here, we report a tungsten carbide/carbon composite polyvinyl alcohol hydrogel evaporator (PWC) for solar-driven interfacial seawater evaporation. Specifically, a tungsten carbide/carbon (WC/C) composite was synthesized via a straightforward one-step molten salt coating method and exhibited a remarkable photothermal conversion efficiency of 67.1%, attributed to the plasmon resonance absorption effect of WC nanoparticles. When incorporated into a polyvinyl alcohol (PVA) hydrogel via a physical-chemical dual-crosslinking strategy, the resulting PWC evaporator achieved a high evaporation rate of 2.99 kg m⁻² h⁻¹ and a conversion efficiency of 90.9% in a 5 wt% NaCl solution under 1 kW m⁻² illumination. In addition, the evaporator can purify seawater and effectively remove a variety of organic dyes. This study provides a viable strategy for a sustainable freshwater supply. Full article

To address the challenges of quantifying expert experience, handling missing data, and managing class imbalance in evaluating the autonomous controllability of power equipment, this paper proposes a quantitative evaluation method that integrates expert prior rules with machine learning. First, building upon a five-dimensional evaluation indicator system, expert decision logic—including dimension-average threshold judgments, multi-dimensional weakness-based cumulative downgrading mechanisms, and key sub-item interaction rules—is formalized into a 15-dimensional rule prior feature vector, which is concatenated with the original 21-dimensional raw indicators to construct a RAW + RULE augmented feature space. Second, a KNN algorithm is employed for missing value imputation, while cost-sensitive learning combined with the SMOTE is adopted in a dual-path parallel scheme to address class imbalance. Six machine learning models are evaluated and compared via 30 repeated stratified cross-validations on a real-world dataset of 97 high-voltage bushing suppliers. Experimental results show that, on complete datasets, the RAW + RULE configuration with the Random Forest model achieves a mean test accuracy of 0.936 and a Kappa of 0.938, substantially outperforming the pure raw-feature model (accuracy 0.769, Kappa 0.766). Under weighted random missingness ranging from 10% to 50%, the RAW + RULE configuration demonstrates superior robustness, with ensemble tree models maintaining mean accuracies of 0.614–0.636 even at a 50% missing rate. This study provides a practically deployable technical solution and methodological reference for the quantitative assessment of autonomous controllability levels and early security warning in the power equipment supply chain. Full article

Under a dual-carbon background, promoting substantive green innovation in manufacturing enterprises has become a central topic in green transition research. This paper constructs an evolutionary game model involving manufacturing enterprises and consumers under market mechanisms and government intervention to analyze the evolutionary patterns and stability conditions of their strategic choices. Using case data and numerical simulations, it explores the role of government guidance in addressing market failures and fostering green innovation in manufacturing. The findings reveal the following: (1) Under market mechanisms, system evolution is influenced by multiple factors. If enterprises prioritize short-term gains by accelerating symbolic green innovation, consumer trust erodes, leading to a shift toward traditional consumption and ultimately driving the system toward market failure. (2) Under government intervention, incentive subsidies must reach a specific threshold to effectively guide manufacturers toward substantive green innovation. Such subsidies also lower the marginal cost of low-carbon consumption, enhancing consumer willingness to purchase green products. Furthermore, government regulation demonstrates positive promoting effects on the green behaviors of both manufacturers and consumers, with a more pronounced impact on the former. (3) The policy combination of incentive subsidies and government supervision significantly shapes evolutionary trajectories through a synergistic mechanism of “reward incentives and regulatory rigidity.” Policy mismatches may trap the system in market failure. Only when subsidy intensity sufficiently compensates for innovation costs and regulatory capacity exceeds enforcement efficiency thresholds can the system stably evolve toward a substantive green innovation, low-carbon consumption state, fostering a virtuous cycle of supply–demand synergy. Full article

Conventional full-space heating systems waste massive fossil-derived energy on unoccupied indoor areas and cause uncomfortable “warm head, cold feet” issues against sustainable building targets. To fill this gap and advance low-carbon indoor heating solutions for sustainable office development, this study proposes an innovative localized heating terminal combining radiant panels and downward laminar air supply. An experimental platform was established, with twelve testing cases covering varied supply air velocity, supply air temperature and radiant panel temperature to explore its thermal comfort and energy-saving sustainability performance. Experimental results demonstrate that, under the optimal operating condition (0.55 m/s airflow, 23.5 °C supply air, 36 °C radiant panel), the vertical head–foot temperature difference reduces to merely 1.2 °C, far below the 3–5 °C threshold of conventional heating equipment; the draught rate approaches zero to eliminate cold draft discomfort. Critically, 65–75% of total supplied heat concentrates within human-occupied zones, drastically cutting redundant heat loss and advancing building heating sustainability. The terminal features dual working modes: convection contributes 78.7–94.4% of total heat for rapid warm-up while radiant heat maintains stable long-term comfortable surroundings. Such flexible dual-mode design supports sustainable part-load operation matching intermittent office occupancy, making this terminal a feasible low-carbon option for modern sustainable office buildings prioritizing energy efficiency and a healthy indoor environment. Full article

To address the issues of low voltage levels and insufficient reliability in dynamic regulation and voltage stabilization in existing high-voltage power supplies for electron-curtain accelerators, this paper presents a 150 kV/30 kW DC high-voltage power supply specifically designed for electron-curtain accelerators. The main circuit employs an LC high-frequency resonant topology and a step-up transformer with eight secondary windings, utilizing a parallel step-up and series output architecture to increase the output voltage level. During the charging phase, a dual-closed-loop frequency conversion scheme combined with duty cycle feedforward is employed to accelerate charging speed, while the voltage stabilization phase utilizes hysteresis burst control to improve accuracy. Simulation results indicate that the system can charge to 155 kV in 102 ms, with a voltage ripple less than 0.1%, a linear regulation of 0.01%, and a load regulation of 0.5%. Tests on a low-voltage prototype confirmed that the power devices can achieve zero-current soft switching, with a resonant current peak of 40 A and overall efficiency reaching 96%. The accompanying filament power supply can stably output 24 V/20 A, and the closed-loop voltage regulation is stable and reliable, providing technical support for the engineering application of high-voltage power supplies in high-power electron beam accelerators. Full article