Search Results (6,741)

Cultivated land protection is fundamental to sustainable agricultural development, and reducing pesticide use is a key pathway for improving cultivated land quality. While policy-based agricultural insurance plays an important role in ensuring food security, whether it can promote pesticide-use reduction by influencing farmers’ production decisions remains unclear. Using survey data from 521 large-scale rice farmers in Jiangxi Province, China, this study takes the coverage depth of policy-based rice insurance as the core explanatory variable and employs ordinary least squares (OLS) estimation, the instrumental variable (IV) approach, and propensity score matching (PSM) to examine its effect on pesticide expenditure. Furthermore, the mediating roles of green technology adoption and farmland operational scale are explored. Specifically, for every one-unit increase in the coverage depth of policy-based rice insurance, pesticide expenditure decreases by 0.169 units. The results show that (1) greater insurance coverage depth significantly reduces pesticide expenditure among large-scale rice farmers, and this finding remains robust across a series of robustness tests and after addressing endogeneity concerns; (2) policy-based rice insurance promotes pesticide expenditure reduction by encouraging green technology adoption and expanding farmland operational scale; and (3) the pesticide-reduction effect is more pronounced among farmers with larger operational scales and more extensive farming experience. Based on these findings, efforts should be made to dynamically optimize the policy-based rice insurance system, strengthen its risk protection capacity, improve agricultural resource allocation efficiency, enhance its green incentive effects, and implement differentiated insurance support policies with more accurate compensation mechanisms. Full article

Sustainable and demand-adaptive railway operation requires frameworks capable of aligning service capacity with time-varying demand while ensuring safe, operationally feasible, and energy-efficient service. This paper proposes an integrated control-and-planning framework for modular pod-based railway operation based on virtual coupling. The framework combines a convoy control layer, which ensures safe and dynamically feasible virtually coupled operation, with a planning layer formulated as a mixed-integer linear programming (MILP) model for daily service allocation and convoy sizing. This hierarchical framework combines dynamically feasible convoy-control simulations with service-level planning to adapt capacity to passenger demand. The proposed methodology is evaluated through comparative simulations under peak-hour, shoulder-period, and off-peak demand scenarios, as well as over a daily schedule of 20 services. Its performance is compared with a conventional fixed-composition diesel–electric multiple unit (DEMU)-based operation. Results show that the pod-based configuration increases energy consumption under peak-hour conditions, remains comparable during shoulder periods, and substantially reduces energy consumption in off-peak operation, achieving a 57% saving in that regime. At the daily level, total energy consumption decreases from 3864 kWh to 3075 kWh, corresponding to a 20% reduction. These findings indicate that the main value of the proposed framework lies in transforming convoy composition into a demand-adaptive operational variable, thereby improving energy performance at the daily system level while preserving the safe and dynamically feasible operation of virtually coupled pod formations. Full article

Water scarcity threatens global stability, with demand set to surge 20–30% by 2050, pushing withdrawals from 4600 km³ yr⁻¹ today to 5500–6000 km³ yr⁻¹ under rising demographic and climatic pressures. This study presents a sequentially coupled econometric–hydrological–reduced-form economic framework that couples water supply dynamics with demand forecasting and macroeconomic impact assessment. Agriculture dominates current withdrawals at 70% (FAO AQUASTAT), followed by industry (20%) and domestic use (10%). Monte Carlo simulations (n = 1000) identify critical regional hotspots: Asia (stress ratio = 1.06), the Middle East (1.18), and Africa (0.99). The reduced-form economic module uses a target-calibrated scarcity elasticity (ε = 0.1865) applied against a fixed economic reference threshold (4600 km³ yr⁻¹). This internally calibrated parameter yields a first-order GDP loss estimate of approximately $16.0 trillion under the high-demand (+30%) 2050 scenario (6000 km³ yr⁻¹ demand), equivalent to 5.5% of projected 2050 global GDP ($290 trillion, PwC 2017 baseline). The resulting magnitude is broadly consistent with the order of GDP impacts discussed by OECD (2012) and GCEW (2024), although neither publication reports this specific elasticity value. This is not an independently predicted outcome; it is a calibrated scenario estimate produced by a reduced-form damage function designed to reproduce first-order magnitudes consistent with published structural model results. Mitigation strategies including efficiency improvements, pricing reforms, and AI-driven allocation can reduce demand by up to 40%, which within the model’s mathematical structure reduces the calibrated economic loss to zero. Sectoral water distribution is addressed through continuous linear programming with proportional rationing. This framework advances transparent, reproducible scenario-based understanding and informs policy decisions aimed at mitigating future water scarcity challenges globally, while explicitly acknowledging limitations relative to full structural CGE models and empirically estimated panel econometric models. Full article

Under varying demand, pump states, load allocation, speed settings, and supply pressure are strongly coupled in parallel pumping systems. This study develops a steady-state energy-efficiency optimization framework for homogeneous and heterogeneous three-branch systems by integrating continuous pump-performance modeling, hard hydraulic-feasibility handling, mixed-variable optimization, and demand-dependent pressure regulation. An improved snake optimizer (ISO) is constructed through targeted strategies for population initialization, global exploration, and local exploitation. Flow–head and flow–power models established from LVR5-5 and CDL3-50 experimental data achieved a total-power MAPE of 2.36% and R² = 0.9972 over 40 implemented steady-state schemes. Deterministic constrained solutions for all effective pump-state combinations were used as references for the pumping-system optimization. Under the same budget of 21,000 function evaluations, ISO, SO, PSO, DE, and GA completed 1500 independent runs; ISO achieved the best overall search performance, with all 300 runs reaching the 0.1% neighborhood of the reference solution and the lowest mean NAUC and final gaps. Using ISO for both pressure-control modes, variable-pressure operation reduced mean power consumption by 10.92% and 7.51% for the homogeneous and heterogeneous systems, respectively, relative to constant-pressure operation under the same minimum service-pressure requirement. The results demonstrate that combining high-quality mixed-variable optimization with demand-dependent pressure regulation can effectively improve the steady-state energy efficiency of parallel pumping systems. Full article

Ground source heat pump (GSHP) systems contribute to sustainable building decarbonization while confronting two intertwined challenges: long-term ground thermal imbalance in shallow borefields and the requirement for coordinated operation between thermal systems and electrical grid dynamics. Hybrid shallow–deep borefield configurations have been proposed to mitigate thermal imbalance for sustainable geothermal resource exploitation, yet their grid-interactive demand–response potential remains unexplored. Here, we develop a coupled thermal–electrical model for a shallow–deep mixed GSHP (SDBHE) system equipped with water-tank thermal storage, validated against scaled sand-tank experiments (3.5–8.3% error), and assess its year-round performance under time-of-use electricity tariffs for a 200,000 m² residential district in cold-climate conditions. The SDBHE system reduces the required shallow borehole count by 28% and total drilling length by 22% compared with a shallow-only baseline, saving 11% on operational electricity costs over 10 years. Integrating water-tank thermal storage with a 50% load-shifting strategy yields an additional 10.9–11% cost reduction without degrading the system’s coefficient of performance. Under higher load-shifting ratios, the combined capital and operational savings reach 19–29%, with the optimal allocation assigning the incremental high-price-period load preferentially to deep boreholes (COP 6.29 versus 5.25 for shallow). These results demonstrate that integrating shallow and deep geothermal tiers with thermal storage enables both capital-efficient borefield design and economically viable demand-side grid participation. The findings are bound by the cold-climate residential context and the rule-based control scheme; field-scale validation and lifecycle cost analysis are needed to generalize the conclusions. Full article

The growth of cloud services and immersive applications based on extended reality (XR), including Augmented Reality (AR), Virtual Reality (VR), and Mixed Reality (MR), imposes increasingly demanding requirements on access networks. The large-scale integration of IoT devices in smart buildings further increases the need for high throughput, low latency and jitter, and reliable connectivity. Traditional Fiber-to-the-Home (FTTH) networks with a single access point (AP) become quite limiting when there are high performance requirements, with many users with indoor mobility and high device density. Fiber-to-the-Room (FTTR) is an extension of FTTH, which brings fiber optics to each room of the house through a Main FTTR Unit (MFU) and several Sub FTTR Units (SFU) along with the APs, with centralized device management. Green FTTR networks are characterized by their energy efficiency through centralized control of signal power and Dynamic Bandwidth Allocation (DBA) management. The fgONT architecture allows for deterministic network slicing, enabling the allocation of specific resources isolated from the rest of the network traffic, allowing for predictable bandwidth and QoS. This work presents a framework that allows for a comparative analysis of FTTR and FTTH networks in different scenarios in order to ensure a compromise between transmission quality, network energy efficiency, and the user’s perceived experience. The results obtained show that, in high device density scenarios, FTTR reduces the average packet loss from 52.69% to less than 0.08%, decreases the average latency from 151 ms to less than 2 ms, and maintains the overall QoE above 0.974, compared to 0.27 in FTTH with a single AP. Full article

Kneeosteoarthritis (KOA) is a common degenerative joint disease that causes pain, stiffness, and impaired mobility. Automated Kellgren–Lawrence (KL) grading from knee X-ray images facilitates efficient screening and follow-up assessment. However, models trained on a single-source dataset frequently suffer performance degradation when applied to external cohorts, due to heterogeneities in image quality, acquisition protocols, class distributions, and annotation patterns. Furthermore, conventional domain adaptation approaches typically treat all source samples uniformly, making them vulnerable to negative transfer induced by ambiguous or distributionally divergent instances. To overcome these limitations, the present study develops a supervised contrastive domain adaptation framework designed for robust KOA severity grading under domain shift. The framework incorporates two task-specific modules: (1) a source-domain sample screening module that dynamically allocates class-wise quotas based on transferability and identifies high-value source samples by evaluating target intra-class affinity, inter-class separability, and source-class compactness; and (2) a target-balanced ordinal contrastive learning module that aligns the screened source samples with target features and imposes stronger constraints on negative pairs with larger KL-grade distances. The framework was evaluated bidirectionally on KneeKL (8260 images) and MedicalExpert-I (1650 images), two public knee radiograph datasets for KOA grading. With ResNet-18, it achieved a Quadratic Weighted Kappa (QWK) of 0.8557 for KneeKL-to-MedicalExpert-I transfer, exceeding source-only training and direct source–target merging by 0.2652 and 0.0468, respectively. Comparisons with representative existing methods and multiple experimental analyses further validate the competitiveness of the proposed framework. Full article

Cloud task scheduling is a critical component of cloud computing systems because it directly affects resource allocation, workload distribution, execution efficiency, and service cost. However, many metaheuristic algorithms suffer from population diversity loss, premature convergence, and an inadequate balance between global exploration and local exploitation when solving complex and large-scale optimization problems. To address these limitations, this study develops an Enhanced Human Urbanization Algorithm (EHUA) for numerical optimization and cloud task scheduling. Inspired by the collaborative resource-allocation behavior of modern logistics networks, three coordinated mechanisms are reformulated within the adventurer–city–citizen structure of the original Human Urbanization Algorithm: a logistics-hub-guided adaptive exploration mechanism, a supply–demand-based dynamic redistribution mechanism, and a cooperative logistics delivery exploitation mechanism. These mechanisms reduce excessive dependence on a single capital, adaptively regulate city search ranges, and strengthen citizen-level solution refinement. The performance of EHUA is evaluated on the CEC2014 and CEC2020 benchmark suites using convergence analysis, box plots, numerical statistics, Wilcoxon signed-rank tests, Friedman rankings, and ablation experiments. EHUA obtains the best mean fitness values on 20 of the 30 CEC2014 functions under both 30- and 50-dimensional settings, on 8 of the 10 CEC2020 functions at 10 dimensions, and on all 10 functions at 20 dimensions, demonstrating strong overall competitiveness and repeatability without implying universal superiority on every problem. EHUA is further applied to cloud task scheduling under workload scales ranging from 100 to 10,000 tasks. Considering comprehensive cost, monetary cost, execution time, and load cost, the proposed method consistently achieves low comprehensive scheduling costs and maintains favorable trade-offs among individual objectives as the workload increases. These results indicate that EHUA provides an effective and scalable optimization framework for complex benchmark problems and cloud task scheduling applications. Full article

The sustainability of buildings and the built environment extends beyond energy and environmental performance to circular resource use, health and safety, digital infrastructure, and stakeholder collaboration. This exploratory integrative literature review examines how these established but insufficiently connected domains reshape business models in the built environment. Since the built environment is a major source of global carbon emissions and waste, a primary research stream concerns the transition toward circular economy principles beyond traditional profit-maximization logics. The literature also highlights the potential of health- and safety-oriented innovations to reduce risks and improve indoor environments. Other studies highlight the potential of digital innovations to improve life-cycle management, resource efficiency, and risk mitigation. However, their widespread adoption faces systemic barriers, including data interoperability and cybersecurity issues, implementation costs, skills shortages, organizational resistance, and regulatory and governance challenges. The literature often emphasizes technical potential while paying less attention to value-capture mechanisms and the allocation of costs, risks, benefits, and responsibilities among the actors involved. Integrating sustainable practices, digital infrastructures, circular-economy principles, and collaborative governance is therefore essential to develop economically viable, organizationally feasible, and ethically responsible business models for the built environment, across the building life cycle and among public and private stakeholders. Full article

Artificial intelligence is rapidly reshaping sustainability reporting, influencing how environmental, social, and governance (ESG) information is collected, analysed, and disclosed. While AI-assisted reporting improves efficiency and analytical capability, it also raises important concerns regarding transparency, accountability, verification, and AI-enabled greenwashing, creating new challenges for the credibility of sustainability disclosures. This study adopts a doctrinal legal research design supported by qualitative analysis, comparative regulatory assessment, and a structured review of legal, regulatory, and academic sources. It examines how emerging approaches to AI governance and sustainability reporting address these challenges and identifies the governance principles required to support trustworthy AI-assisted ESG reporting. Existing regulatory initiatives strengthen important aspects of sustainability reporting, yet AI governance, ESG disclosure, and greenwashing continue to be addressed through separate regulatory instruments. To bridge this gap, the study develops an integrated governance framework that combines transparency, meaningful human oversight, AI auditing, sustainability verification, and clearly allocated accountability within a coherent governance structure. The proposed framework contributes to the literature by offering a structured governance model specifically designed for AI-assisted sustainability reporting. The framework also provides practical guidance for regulators, standard setters, organisations, and assurance providers seeking to strengthen reporting integrity and stakeholder confidence in AI-assisted ESG reporting. Full article

Global warming poses a serious environmental challenge worldwide. Agriculture, as a significant source of greenhouse gas (GHG) emissions, exerts considerable influence on the atmospheric environment. Chengdu, renowned for its thriving agricultural sector, serves as a key grain production center in China. Reducing agricultural greenhouse gas (AGHG) emissions is essential for mitigating the impact of climate change on Chengdu. Firstly, this paper employed the IPCC (Intergovernmental Panel on Climate Change) coefficient method and the Super-SBM-Undesired model to calculate the AGHG emissions and emission efficiency in Chengdu, respectively. Then, center of gravity shift analysis, kernel density estimation and spatial autocorrelation theory were used to analyze the spatial–temporal evolution characteristics of AGHG emissions. Finally, this paper conducted an in-depth analysis based on the STIRPAT model to identify key factors affecting AGHG emissions. The results show that: (1) From 2007 to 2021, Chengdu experienced an overall decline in both AGHG emissions and emission intensity, with reductions of 22.32% and 66.20%, respectively. And the AGHG emission efficiency was largely low. (2) AGHG emissions display regional variations and spatial clustering phenomena, characterized by a pattern of “high in the east, low in the west, high outside and low inside”. (3) AGHG emissions are highly increased by the sown area (S) and pesticide and fertilizer utilization (F) and may be reduced by the agricultural industrial structure (V) and the urbanization rate (U). These findings provide valuable scientific insights into the spatial–temporal dynamics of regional agricultural emissions. Furthermore, this study offers practical references for local governments to optimize agricultural resource allocation, formulate tailored low-carbon agricultural policies, and promote sustainable rural development. Full article

Intensive yak fattening in cold alpine regions requires essential long-term high-concentration feeding, which disrupts rumen microbial homeostasis and causes inefficient digestion of nutrients. Lactobacillus may have probiotic potential for ruminants, yet its regulation of rumen function is poorly understood under high-energy diets. During a 120-day feeding experiment, 120 male Pamir yaks were allocated to four dietary treatments, with LEG and LLG serving as the primary comparison for evaluating 0.02% Lactobacillus supplementation to explore the regulatory effects of Lactobacillus supplementation on the rumen microbiota and host metabolism of yaks during a fattening process based on a concentrate feed diet via phenotypic data (body wight and nutrient digestibility) and multi-omics analyses (rumen microbial sequencing and rumen epithelial transcriptome) in a concentrate-based rearing yak model. The results showed that Lactobacillus intervention reduced OTU (Operational Taxonomic Unit) richness during the early fattening period and subsequently promoted microbial recovery through colonization resistance, which significantly enhanced microbial diversity (p < 0.05), and restructured the microbial community structure toward efficient energy utilization under high-concentrate feeding by reducing Prevotellaceae and Ruminococcaceae abundance, increasing the Bacillota/Bacteroidota ratio (p < 0.05). Concurrently, Lactobacillus enhanced the apparent digestibility of dry matter, crude protein, and fibrous components (p < 0.05). According to the transcriptomic analysis, there was an activation of signaling pathways related to IL-18 and TNF, and up-regulation of immune-and metabolism-related genes, in addition to strengthening the rumen mucosal barrier function. Multi-omics integration supported that dietary supplementation of Lactobacillus can optimize rumen fermentation, enhance nutrient digestion, and strengthen immune defense in Pamir yaks fed high-concentrate in cold seasons. These modifications demonstrate the positive effects of the Lactobacillus supplementation strategy on yak rumen health without interfering with the high-energy intensive rearing pattern. The present research presents a scientific basis for the use of targeted probiotic strategies to improve the rumen health and efficiency of alpine yak production systems. Full article

With the rapid penetration of electric vehicles (EVs) and renewable energy generation in distribution networks, the coordinated scheduling of flexible EV loads and uncertain renewable resources has become a critical research focus in modern power systems. This study investigates the collaborative optimal dispatch of thermal units, EVs, and renewable power generation. Different from conventional closed-loop game-based bi-level optimization, this paper constructs a transmission–distribution integrated scheduling framework and proposes a sequential hierarchical progressive optimization strategy for EV charging and discharging dispatch to fully tap the cross-level coordination potential of power grids. The upper transmission layer optimizes the joint operation of thermal units, wind power, and photovoltaic units to minimize the overall power supply cost, where the inequality power balance constraint is reasonably adopted to reserve power regulation margin for renewable fluctuation and meet practical engineering operation requirements. To effectively address the severe uncertainty of renewable power output without relying on accurate probability distribution information, information gap decision theory (IGDT) is employed to realize robust scheduling with risk-averse and opportunity-seeking decision adaptability. In the lower distribution layer, a theoretically grounded nodal electricity price (NEP) model integrating node loss sensitivity (NLS) and node load rate (NLR) is applied to substitute iterative power flow calculation, which realizes the spatial optimal allocation of EV charging and discharging nodes while significantly improving computational efficiency. The proposed framework comprehensively minimizes network power loss and user charging cost. Finally, extensive simulations based on the IEEE 33-node distribution system verify the effectiveness, computational superiority, and robustness of the proposed sequential hierarchical coordinated scheduling strategy. Full article

Timely and accurate defect classification is critical for ensuring product quality and safety in industrial inspection scenarios. The widespread deployment of Internet of Things (IoT) devices equipped with sensing, computing, and communication capabilities has promoted the development of AI-enabled industrial applications. However, conventional AI approaches typically rely on centralized data collection and processing, which become impractical in real-world IoT environments due to growing privacy concerns and constrained device resources. To address these challenges, this paper proposes a communication-efficient adaptive federated learning algorithm for heterogeneous defect classification tasks. The proposed approach jointly accelerates the training process through three mechanisms: (i) adaptive local updates that balance communication and computation overheads; (ii) parameter compression that trades off communication cost against model accuracy; (iii) joint bandwidth and computation-power allocation that optimizes per-round communication and computation time across participating devices. We further analyze the joint effects of these three mechanisms and provide a convergence analysis. Extensive simulations show that the proposed method achieves competitive classification accuracy while reducing single-round training time by up to 70%. 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