Search Results (2,637)

Synergistic pollution–carbon mitigation is critical for China’s dual carbon targets. Taking coal-resource Shanxi Province as the case, this study developed an integrated Long-range Energy Alternatives Planning (LEAP)–Community Multiscale Air Quality (CMAQ) coupled framework combined with a three-dimensional vector model to simulate energy consumption, CO₂, and major air pollutant emissions (CO₂, CO, SO₂, NO₂, PM_2.5, and PM₁₀) under Baseline and Policy scenarios (2026–2050). The core novelty of this study lies in methodological innovation: the multi-model linkage realizes full-chain energy-emission-atmosphere simulation, remedying the isolation flaw of single models in prior research. The results indicated that low-carbon levels would rise steadily in both scenarios from 2026 to 2050. The Policy scenario achieved superior long-term low-carbon performance compared with the Baseline scenario and narrowed gaps in underdeveloped social subsystems, despite short-term transition costs. This scenario optimized the overall energy structure yet failed to fully reduce emission loads from residential and transport sectors. It drastically cut carbon and pollutant emissions, optimized spatial emission patterns, and decoupled most air pollutants from carbon emissions. However, this scenario still had prominent limitations: phased delays in emission abatement, strong coupling of CO, NO₂ and carbon emissions, and rising residential carbon emissions. Further pollution–carbon synergy assessment revealed worsening multi-dimensional imbalances under the Baseline scenario. While the Policy scenario experienced temporary systemic imbalance, its long-term coordination level improved steadily. This finding verified that systematic, long-term low-carbon governance constituted the core driver of Shanxi’s green transition. Targeted phased, classified collaborative governance strategies were proposed to resolve structural transformation risks for resource-based regions. Full article

Shale gas is a major unconventional energy resource in China. Its low porosity and permeability require large-scale volumetric fracturing to create conductive fracture networks. However, most induced fractures are propped discontinuously because shale reservoirs are geometrically complex. Fracture conductivity and proppant placement efficiency therefore directly control stimulation performance. Following SY/T 6302-2009, this study used linear flow-through experiments and a large-scale visual fracture simulation system to investigate the effects of proppant particle-size distribution, injection sequence, flow rate, and closure pressure on fracture conductivity and placement. The results show that the 20/40:40/70 mesh dual-particle-size combination at a 3:2 ratio provides the best overall performance. A fine-particle content of no more than 16.7% limits conductivity loss and improves the match between particle size and fracture aperture. Multilayer placement at fracture corners distributes high-stress loading and maintains conductivity. Injecting 70–140 mesh fine proppant before 40–70 mesh coarse proppant at 3.6 m³/h improves transport distance, coverage, and placement uniformity. The optimized scheme maintains stable conductivity at closure stresses of 10–80 MPa and achieves at least 95% propped-area coverage. These findings provide experimentally supported parameters for discontinuous propping and can inform shale gas fracturing design. Full article

The evolution of land-use types in mining cities is critical for balancing resource exploitation and ecological sustainability. Taking Jungar Banner as a case study, this study uses remote sensing imagery and auxiliary data (2010–2025) to examine spatiotemporal land-use changes, applies the OPGD model to detect driving factors, and integrates a Markov chain with an optimized NEGM-MOP-PLUS model to project 2030 land-use patterns under multiple scenarios. Results show that grassland shrank markedly, while cropland and built-up land expanded—the latter reaching 395.75 km² by 2025. After 2020, core mining areas became more contiguous, while peripheral zones showed increased fragmentation, with built-up land expansion becoming the dominant trend. Driving forces shifted from natural constraints to anthropogenic dominance: natural factors prevailed in 2010, mining impacts took the lead by 2015, and a mining–precipitation dual-core structure emerged by 2020. Future projections indicate continued grassland and bare land reduction, alongside water and built-up land expansion across all scenarios. Among them, the CDS, which balances economic and ecological objectives, is identified as the optimal spatial planning direction based on ecosystem service value (ESV) assessment. These findings offer practical guidance for managing land-use transitions in arid and semi-arid resource-based mining regions. Full article

The growing deterioration of water resources and the energy requirements of conventional wastewater treatment technologies have increased interest in systems that combine organic matter removal with bioelectrochemical conversion. This study evaluated a laboratory-scale dual-chamber microbial fuel cell (MFC) operated with real wastewater using Scenedesmus acutus (S. acutus) and a native microbial consortium as anodic biocatalysts at 25 and 30 °C. The system was assessed through continuous monitoring of voltage, pH, temperature, and CH₄, H₂, and CO₂ signals in the anodic headspace, together with physicochemical characterization and chemical oxygen demand (COD) removal. COD removal efficiencies of 33.7 and 30.3% were obtained for S. acutus at 25 and 30 °C, respectively, whereas the native microbial consortium achieved 29.8 and 43.4% removal under the same conditions. The consortium at 30 °C showed the most favorable combination of COD removal and electrical response, whereas S. acutus at 30 °C reached the highest maximum voltage and stored energy, although with greater signal variability. The CH₄, H₂, and CO₂ signals differed among conditions and were consistent with the possible participation of fermentative and methanogenic processes alongside electrogenic activity, although gas production rates and the contribution of individual pathways were not quantified. Overall, the results demonstrate the operational feasibility of the proposed MFC for coupling wastewater treatment with a measurable electrical response and support further evaluation of native microbial consortia as anodic biocatalysts. Full article

Wind power is a cornerstone of China’s renewable energy development, supporting the green and low-carbon transformation and “dual-carbon” goals. With rapid capacity growth and an approaching wave of decommissioning for early onshore wind, assessing the carbon reduction potential from resource recovery is critical yet challenging. This study establishes a net carbon emission assessment framework covering operational and recycling stages, evaluating the carbon reduction potential of decommissioned materials including steel, copper, and resin. Results show that total carbon reduction from resource utilization grows steadily, under the assumed end-of-life resource-utilization pathways; the cumulative carbon reduction potential during 2025–2045 is estimated at approximately 90.85 million t CO₂-eq., with steel contributing roughly 60%. Between 2025 and 2045, carbon reduction from decommissioned wind power materials rises from 1.93 to 5.28 million tons of CO₂-eq., stabilizing the industry’s net emissions at 21 million tons from 2040. Four regional evolution patterns were identified: continuous growth in major wind power bases such as Inner Mongolia and Xinjiang; peak–fallback in early-developed provinces such as Henan and Ningxia; late-stage rise in eastern and central provinces such as Jiangsu and Guangdong; and platform fluctuation in northeastern provinces such as Heilongjiang, Jilin, and Liaoning. These estimates are based on static life-cycle emission factors, exclude transportation between wind farms and recycling facilities, and do not incorporate formal sensitivity or probabilistic uncertainty analysis; therefore, the absolute mitigation values should be interpreted as scenario-based estimates rather than precise forecasts. The findings suggest that authorities should implement differentiated regional decommissioning strategies and plan forward-looking wind power industrial chains to maximize resource recovery and support national dual-carbon objectives. Full article

Cross-view geo-localization (CVGL) between unmanned aerial vehicle (UAV) imagery and satellite imagery is a key technique for autonomous UAV navigation in Global Navigation Satellite System (GNSS)-denied environments. However, most existing methods rely on energy-intensive Artificial Neural Networks (ANNs), making them difficult to deploy on resource-constrained edge computing platforms. Spiking Neural Networks (SNNs) provide a promising alternative for energy-efficient inference, but their application to CVGL still faces two challenges that remain insufficiently addressed. First, the isotropic computation used by existing SNN backbones is mismatched with the directional characteristics of spike activations. Spike activations tend to form oriented aggregation patterns along elongated geographic structures, and isotropic computation can therefore dilute directional signals. Second, the limited representational capacity of SNNs further increases the sensitivity during training optimization. However, the standard triplet loss adopts a static weighting strategy and assigns the same weight to all triplets that violate the margin constraint, which is unfavorable for learning from hard negatives. To address these challenges, we propose a framework with two core contributions. At the feature extraction level, the Directional Adaptive Convolution Module (DACM) processes spike feature maps by sequentially performing horizontal strip convolution and vertical strip convolution, thereby capturing a more complete geometric structure of directional spike clusters. At the training supervision level, we propose a Dual-dimensional Progressive Reweighting (DPR) loss, which jointly characterizes sample difficulty from pairwise difficulty and positive-pair quality difficulty. A learnable fusion parameter is used to adaptively balance these two types of difficulty information. Experimental results on the University-1652 and SUES-200 benchmarks show that the proposed framework, when equipped with the same representation learning head as its ANN counterparts, achieves competitive and, in many settings, superior performance. In terms of energy efficiency, its estimated theoretical energy consumption is over $8.8\times$ lower than that of published ANN methods under their original configurations. Under a more rigorous matched ANN control that shares the identical architecture, the estimated energy is reduced from 29.84 mJ to 6.36 mJ, an approximately $4.7\times$ reduction obtained at a cost of only 2.29 percentage points in R@1. Full article

Digital infrastructure (DI) plays a dual role in the low-carbon transition. It supports economic operation but also consumes substantial energy. This study explores DI’s impact on carbon emission efficiency (CEE) using data on 41 cities in China’s Yangtze River Delta from 2011 to 2024. The methods used in this study include a two-way fixed effects model, mediation analysis, a panel threshold model, and a spatial Durbin model. The results show that the impact of DI on CEE is U-shaped. Industrial upgrading and technological innovation are the potential channels through which DI affects CEE. Energy efficiency has a single threshold value of 8.533. DI enhances CEE when energy efficiency exceeds this threshold. Spatial analysis indicates that both the direct and indirect effects of DI follow a U-shaped pattern. Heterogeneity analysis indicates that the environmental impact of DI varies depending on resource endowments, policy environments, and economic development levels. This study provides insights for global urban agglomerations to balance digital transformation and sustainable development. Full article

This study examines how government policy shapes the relationship between specialization strategy and business resilience in Indonesia’s cosmetic raw-materials industry. Drawing on the Resource-Based View and resilience theory, cross-sectional survey data from 189 key informants, each representing one supplier firm, were analyzed using partial least squares structural equation modeling. Specialization Strategy was positively and significantly associated with Business Resilience (β = 0.271, t = 5.286, p < 0.001), and Government Policy also showed a positive direct association with Business Resilience (β = 0.240, t = 4.421, p < 0.001). The interaction between Government Policy and Specialization Strategy was negative and significant (β = −0.089, t = 4.605, p < 0.001), indicating that stronger perceived policy support attenuated, rather than reversed, the positive relationship between Specialization Strategy and Business Resilience. The findings therefore indicate a dual policy role: Government Policy was positively associated with Business Resilience overall, while higher perceived policy support reduced the marginal contribution of Specialization Strategy to resilience. The study contributes to strategic management and administrative science by integrating firm-specific capabilities with external policy conditions in an import-dependent emerging economy. The findings also suggest that industrial policy may be more effective when support instruments are differentiated, adaptive, and aligned with the strategic and operational characteristics of affected firms. Full article

This review examines the clinical and practical implications of embedding molecular profiles directly into the 2023 FIGO staging system for endometrial carcinoma, in the context of the 2025 ESGO/ESTRO/ESP guidelines. The primary purpose is to navigate a central conflict in modern oncology: how to deliver increasingly personalized care while maintaining a globally accessible, equitable, and standardized cancer classification system. The 2023 FIGO update represents a paradigm shift from the traditional dualistic model (Type I versus Type II) by allowing molecular findings to redefine stage itself. While this integration offers clear benefits, it introduces significant challenges. First, the system depends on advanced molecular testing, creating a “rich-poor” divide where patients in resource-limited settings are systematically overtreated because testing is unavailable. Second, stage becomes unstable, changing with sequential histologic and molecular re-review, which causes confusion for patients and clinicians. Third, the system lumps prognostically distinct histotypes (Serous, Clear Cell, Carcinosarcoma, and Grade 3 Endometrioid) into a single aggressive stage, obscuring meaningful differences in survival. Fourth, it relies on subjective parameters such as “substantial” lymphovascular space invasion, for which no standardized definition exists, leading to high inter-observer variability. After analyzing these controversies, the review proposes a pragmatic solution: decouple anatomical staging from molecular risk stratification. Staging should remain a purely anatomical, universally applicable descriptor of tumor extent, while molecular and histologic data are used separately within a dynamic risk assessment model, as suggested by the European guidelines. This dual-track approach preserves global comparability, reduces inequity, and maintains diagnostic stability, while still enabling personalized treatment where advanced diagnostics are available. Full article

Traditional particle swarm optimization (PSO) easily falls into premature convergence, while differential evolution (DE) is highly sensitive to fixed control parameters. Existing PSO-DE hybrid frameworks suffer from static population sizes and insufficient cross-population information exchange. This paper proposes PSO-DE-ADP, a hybrid optimizer with sinusoidal adaptive parameters, elite-guided mutation, ring neighborhood-weighted PSO and fitness-driven dynamic dual-population allocation. Four complementary mechanisms are integrated: (i) sine-wave perturbation superimposed on linear decay adaptively adjusts PSO inertia weight, acceleration factors and DE scaling/crossover coefficients to balance search stages; (ii) global elite individuals are embedded into DE mutation to reduce blind random search; (iii) ring topology with weighted learning realizes bidirectional information interaction between PSO and DE subpopulations; (iv) the proportion of PSO/DE individuals is dynamically adjusted according to elite ratio to allocate computing resources. Experiments adopt the CEC2017 30-dimensional benchmark with 30 test functions covering unimodal, multimodal, hybrid and composite landscapes. Compared with 8 state-of-the-art metaheuristics, PSO-DE-ADP achieves the lowest Friedman rank (1.08 vs. 2.23–4.90 for PSO variants; 1.53 vs. 2.07–5.00 for non-PSO algorithms). Ablation tests prove each component significantly boosts accuracy; The algorithm only costs 0.172 s average runtime, superior to all competitors. Statistical Wilcoxon and Friedman tests verify its significant superiority. Future work extends this method to multi-objective, constrained and real engineering optimization tasks. Full article

Production data transparency in smart manufacturing simultaneously enhances and impairs employee performance across organizational contexts. Existing research has not resolved this theoretical contradiction. Drawing on socio-technical systems theory, cognitive appraisal theory, and conservation of resources theory, this study develops a dual-pathway model. Data transparency influences adaptive performance through a bright empowerment pathway and a dark surveillance pathway mediated by EPM-induced strain. Procedural justice of data governance and digital self-efficacy operate as a perceived-institutional boundary condition and an individual-capability boundary condition, respectively. Latent moderated structural equations were applied to survey data from 412 employees in Chinese smart manufacturing enterprises. Results support both pathways and reveal a theoretically consequential asymmetry between these boundary conditions. Johnson–Neyman analysis indicates that institutional justice attenuates the strain pathway to non-significance within the observed distribution of responses. Conversely, digital self-efficacy requires near-ceiling levels to achieve the same pattern. This asymmetry indicates that continuous moderation produces sharply different practical outcomes across the observed data range. Institutional and individual remedies therefore address the visibility paradox on different practical scales. Governance adequacy represents a more attainable managerial lever than individual capability development. These findings advance socio-technical systems theory by detailing the asymmetric buffering capacities of different organizational resources. Full article

To address the issue where information asymmetry in third-party logistics (3PL)-led low-carbon supply chain coordination undermines coordination efficiency, thereby threatening the sustainability and resilience of supply chain cooperation, this paper develops a Stackelberg dynamic game model with the 3PL as the leader. This model is constructed within the context where consumers’ low-carbon preferences influence product demand, and a government carbon cap policy is implemented. By comparing decentralized and centralized equilibria, we verify that centralized collaboration achieves dual gains: higher carbon reduction levels and greater overall supply chain profits, which strengthens sustainability and resilience. To address efficiency losses from three types of information asymmetry, we propose a two-stage dynamic coordination mechanism adapted to evolving cooperation transparency. At the initial stage with opaque information, a bargaining-power-weighted profit-sharing contract is adopted, where negotiation weights are quantified by enterprise scale, resource control and industry influence. After data transparency improves, the system switches to a Nash bargaining framework supported by blockchain carbon data sharing to realize stable long-term collaboration. Numerical cases and sensitivity analysis demonstrate that manufacturer cost information asymmetry is the primary constraint on coordination efficiency. The proposed dynamic coordination scheme effectively mitigates systemic complexity, balancing economic benefits and carbon reduction targets. This study provides practical pathways for supply chain participants to navigate complex low-carbon environments and advance sustainable, resilient supply chain operation. Full article

The Internet of Vehicles (IoV) relies on frequent vehicle-to-roadside-unit (RSU) access over open wireless channels, making efficient authentication and key establishment essential. In many existing authentication and key agreement (AKA) schemes, a target RSU receives and processes a request before an invalid sender is rejected, which can waste roadside computation under dense invalid-request traffic. This paper presents EDAKA-IoV, an elliptic-curve-cryptography-based AKA scheme that separates admission filtering from end-to-end session-key establishment. A trusted authority (TA) performs Lightweight Polynomial-based Pre-Verification (LPPV) to discard invalid authentication requests before they reach the target RSU, while the vehicle and RSU establish the final session key. A current–pending dual-state mechanism prevents permanent de-synchronization during dynamic pseudo-identity renewal without adding another communication round. Formal analysis under an eCK-style model, ProVerif verification, and heuristic analysis evaluate session-key secrecy, injective mutual authentication, privacy, and resistance to the considered attacks. Optional offline precomputation moves two fixed-base scalar multiplications outside the online phase and reduces the non-polynomial online computation component by approximately 48.8%. With fixed-length compressed point encoding, the authentication exchange requires 2336 bits. The workload analysis shows that the RSU-side processing reduction is proportional to the invalid-request ratio, while the TA still performs record lookup, hashing, and degree-dependent polynomial evaluation for every received request. EDAKA-IoV therefore provides a balanced authentication solution for resource-sensitive IoV deployments. Full article

While continuously promoting agricultural water-saving and efficiency improvement, Ningxia is confronted with problems such as deepening groundwater tables and growing ecological vulnerability. Exploring the trade-off relationships and their evolutionary characteristics between socioeconomic development and water resource carrying capacity, as well as between water use efficiency improvement and groundwater-ecosystem maintenance, is of great significance for coordinated water resource governance in arid irrigation districts. Based on time-series data covering 2000–2024, this paper establishes a DPSIR evaluation model and constructs a progressive quantitative analytical framework coupling the entropy-weight-Tapio decoupling, rate-scissors difference and PLS-SEM models. During the study period, the growth rate of the response (R) dimension (13.76%) was far higher than that of the state (S) dimension (3.23%) from 2011 to 2020, confirming the objective existence of dual trade-offs. The two categories of trade-offs underwent a three-stage evolution of “latent-intensified-remediation”, showing the counter-intuitive feature of “effective total-volume control alongside continuous groundwater table deepening”. Hidden transmission barriers were identified for 2008–2016 (θ₁, θ₂ dropped to 0.46–1.32°): the transfer of water-saving dividends to industry caused groundwater extraction to rise rather than fall to a certain extent. PLS-SEM analysis reveals that structural lock-in acts as the core inhibiting factor for ecological protection. The total effect of socioeconomic development on ecology reaches 0.921, whereas structural lock-in produces a chained negative mediating effect of −0.192 by suppressing water use efficiency. Improvement in water use efficiency presents dual characteristics of overall ecological gain and localized groundwater-recharge loss. It can be concluded that engineering-only water-saving measures cannot balance water-intake reduction and recharge deficits. It is necessary to simultaneously advance low-water-consumption cropping-pattern restructuring, rigid enforcement of the 2.5 m ecological groundwater table threshold, and the substitution mechanism for saved-water volume between industry and agriculture, so as to build a coordinated “water-saving-recharge-ecology” regulation system. Full article

In the context of the global green development wave and the rapid iteration of digital technology, digital empowerment has become the core driving force for high-end manufacturing enterprises to achieve green transformation. At present, China’s manufacturing industry is facing the dual pressures of tightening resource and environmental constraints and industrial upgrading. How to break the bottleneck of green development through digital technology innovation has become a key issue to be solved urgently. Based on the techno-economic paradigm, green development theory and value creation theory, this study constructs a theoretical analysis framework for the green development of a digital-enabling manufacturing industry and deeply analyzes the mechanisms of digital technology (such as big data, Internet of Things, artificial intelligence, etc.) in optimizing energy allocation, improving production efficiency and reducing environmental emissions. By selecting 303 manufacturing enterprises of different scales in China as samples, the structural equation model is used for empirical tests. The results show that (1) digital empowerment has a significant positive impact on the green value performance of manufacturing enterprises, and (2) green development plays an intermediary role between digital empowerment and the green value performance of enterprises; that is, digital technology indirectly promotes green development by improving energy conservation and emission reduction, green innovation and green upgrading of enterprises. The research reveals the internal logic of digitally enabling the green development of Chinese manufacturing enterprises and provides a theoretical basis and implementation path for enterprises to formulate the innovation mechanism of digital–green development. Full article