Search Results (16,581)

The rapid growth of rural e-commerce is reshaping rural development, yet its implications for territorial sustainability remain unclear. Using panel data from 1503 Chinese counties from 2014 to 2022, the effects of Taobao Village development and the mechanism of territorial sustainability are examined through causal, spatial, and mechanism analyses. The results show the following: (1) Taobao Village development significantly improves territorial sustainability, with stronger effects observed in central and northeastern China, while the impacts vary considerably across regions due to differences in economic foundations, digital infrastructure, and land-use conditions. (2) Spatial analysis reveals that the sustainability-enhancing effects of Taobao Villages are mainly localized, with no significant spillover effects to neighboring counties, indicating the constraints of existing administrative and spatial governance systems. (3) The ecological land-use change induced by Taobao Village development is characterized by quantity reduction with potential quality upgrading. The development of Taobao Village has reduced the ecological land area, but it does not mean a decline in ecological functions. The converted land may be composed of low-quality ecological plots, and the fiscal benefits driven by e-commerce and China’s land use compensation policy may maintain or enhance the overall ecological function. These findings highlight the importance of integrating digital rural development with ecological sustainability and territorial spatial governance. The study provides policy implications for promoting resilient and sustainable rural transformation through differentiated land-use strategies. Full article

Moderate wine consumption has historically been associated with a J-shaped relationship between dose and total mortality, driven primarily by cardiovascular protection, enhanced endothelial function, improved glucose metabolism, and anti-inflammatory and antioxidant effects attributable to both ethanol and grape-derived phenolic constituents. Yet in recent years, international alcohol guidelines have shifted markedly towards the assertion that ‘no safe level’ of alcohol exists. This transition has occurred despite the continued consistency of mechanistic, clinical, and epidemiological evidence demonstrating hormetic (biphasic) responses to wine and its constituents and despite methodological controversies over the mathematical models used in several recent national guideline revisions. This narrative review synthesises mechanistic evidence on hormesis and wine-specific biological effects—including polyphenols, resveratrol, nitric oxide signalling, platelet modulation, and endothelial benefits—with epidemiological data on J-shaped associations with cardiovascular disease, diabetes, and total mortality. It further examines key methodological changes in recent guidelines, particularly the adoption of the ‘Sheffield model’ and the exclusion of or reduced emphasis on cardioprotective outcomes. The review argues that these methodological decisions, rather than new scientific evidence, largely explain the departure from earlier guideline thresholds that allowed moderate wine consumption. Understanding wine’s biological specificity, the differential health implications of drinking patterns and contexts, and the limitations of model-driven policy changes is essential to develop coherent, evidence-informed public health recommendations. Full article

Geopolitical disruptions of natural gas supply have repeatedly forced importing countries to reorganize their electricity systems, yet it remains unclear whether such disruptions are followed by movement of structurally different economies toward comparable energy-security and sustainability outcomes. This study compares Germany, a high-income economy exposed to the 2022 curtailment of Russian pipeline gas, with Jordan, a developing import-dependent economy exposed to the repeated sabotage of the Arab Gas Pipeline after 2011, using harmonized generation-mix and carbon intensity data for Germany over 1985–2024 and Jordan over 2000–2022, supplemented by weekly German market data. The generation fuel mix concentration is measured with a Herfindahl-based Supply Concentration Index (SCI), structural change is estimated with segmented interrupted time series (ITS) regressions inferred through Newey–West heteroskedasticity- and autocorrelation-consistent standard errors, and the joint security–sustainability position of each country is summarized with a newly proposed Energy Vulnerability–Transition Index (EVTI) that combines diversification, renewable penetration, and carbon intensity performance. The results show that Jordan’s 2011 disruption was associated with a baseline estimated change in its carbon intensity trajectory from +3.32 to −12.63 gCO₂/kWh per year and with renewable growth of +2.39 percentage points per year from a near-zero base, while Germany’s 2022 disruption was associated with a temporary carbon intensity shock, visible in a coal reactivation index that peaked at 1.26 and a sixfold wholesale price increase, followed by a policy-supported return to the pre-existing decarbonization pathway. The Germany–Jordan EVTI ratio narrowed from 6.5× in 2014 to 1.8× in 2022, and this convergence is robust to alternative component weightings. The findings indicate that geopolitical gas disruptions, despite their high short-run costs, were followed in both contexts by measurable movement toward more diversified and lower-carbon electricity systems, with direct implications for Sustainable Development Goal (SDG) 7. Full article

Assessing the effectiveness of protected areas is crucial for refining conservation policies, yet it remains challenging in remote, biodiversity-rich tropics due to logistical constraints. To address this challenge, we developed a remote sensing-based Pressure–State–Benefit (PSB) framework comprising four dimensions: human activity, ecosystem pattern, ecosystem quality, and ecosystem services. Using the Hainan Tropical Rainforest National Park (HTRNP) as a case study, we evaluated conservation effectiveness at the park scale by comparing ecological changes before (2015–2020) and after (2020–2025) its establishment. We further compared the Core Protection Zone (CPZ) and the General Control Zone (GCZ) to reveal differences in conservation outcomes under distinct management regimes. The establishment of HTRNP effectively curbed cropland and built-up land expansion. During 2020–2025, landscape patterns improved, characterized by enhanced connectivity and reduced fragmentation. Concurrently, fractional vegetation cover increased at an accelerated rate, indicating improved ecosystem quality, while key ecosystem services showed upward trends. Notably, the CPZ maintained superior ecological conditions with steady improvements under strict protection, whereas the GCZ, being more sensitive to human disturbances, exhibited fluctuating outcomes. This study demonstrates the effectiveness of HTRNP in enhancing regional ecosystem conditions and highlights the importance of zoned management. Furthermore, the proposed PSB framework offers a transferable approach for comprehensively assessing conservation effectiveness in other protected areas. Full article

Amid accelerating climate change, natural resource depletion, and rising consumer demand for sustainable, health-promoting products, circular agriculture has emerged as a strategic pathway for reconciling agricultural productivity with environmental and social sustainability; however, empirical evidence on how circular production, health-product innovation, and experiential tourism can be jointly evaluated within a single model remains limited. This study aims to propose a multidimensional value assessment framework for circular agriculture through a case study of the Tam Dao Mushroom Cooperative (TDMC) in Phu Tho Province, Vietnam, centered on the cooperative’s Cordyceps militaris-based production system, nanotechnology-enabled health-product processing, most notably the Nano Cordy Milk functional beverage and Science, Technology, Engineering, and Mathematics (STEM)-oriented experiential tourism activities, comprising guided production tours, hands-on cultivation and processing demonstrations, and on-site product tastings offered to school and university groups, domestic and international tourists, and visiting farmers and cooperatives. The research integrates a case study approach with the Fuzzy Analytic Hierarchy Process (Fuzzy-AHP) to determine the priority weights of value dimensions based on the evaluations of seven experts. The findings identify five key dimensions of value generated by the circular agricultural model: economic value, environmental value, social value, educational value, and tourism value. The Fuzzy-AHP results indicate that economic value has the highest priority weight (Best Non-fuzzy Performance, BNP = 0.4050), followed by environmental value (0.2715), social value (0.1307), educational value (0.1313), and tourism value (0.0621). The study demonstrates that the circular agriculture model not only generates economic benefits but also simultaneously creates significant environmental, social, educational, and tourism values. These findings contribute to the theoretical foundation for assessing the multidimensional value of circular agriculture while providing practical references and policy implications for the sustainable development of circular agricultural models. Full article

Background: Emergency department (ED) use for cancer-related pain is common, particularly among patients with metastatic disease who frequently require opioid analgesia. In October 2014, the U.S. Drug Enforcement Administration rescheduled hydrocodone combination products from Schedule III to Schedule II, introducing stricter prescribing and dispensing requirements. We evaluated whether hydrocodone rescheduling was associated with pain-related ED visits and hospitalizations among older adults with metastatic lung cancer. Methods: We conducted a retrospective SEER–Medicare cohort study of beneficiaries aged 66 years or older diagnosed with metastatic lung cancer. Diagnoses from January 2011 through September 2014 were classified as pre-policy, October 2014 was excluded as a transition month, and November 2014 through December 2018 constituted the post-policy period. Monthly 365-day cumulative incidences were estimated using the Aalen–Johansen estimator with death treated as a competing event. Segmented interrupted time-series models estimated immediate level and slope changes. Adjusted cause-specific Cox models evaluated time to first event. Results: The cohort included 52,371 beneficiaries. For narrowly defined neoplasm-related pain ED visits, the policy was associated with an immediate increase of 0.834 percentage points (95% CI, 0.330–1.338) and a post-policy slope increase of 0.030 percentage points per month (95% CI, 0.011–0.049). Pain-related hospitalizations increased immediately by 0.946 percentage points (95% CI, 0.336–1.556), with a slope increase of 0.024 percentage points per month (95% CI, 0.005–0.042). At 12 months, fitted cumulative incidences exceeded no-policy projections by 1.193 percentage points for ED visits and 1.228 percentage points for hospitalizations. Adjusted cause-specific hazard ratios were 1.14 for pain-related ED visits (95% CI, 1.00–1.30; p = 0.054) and 1.14 for hospitalizations (95% CI, 1.00–1.29; p = 0.049). Conclusions: Hydrocodone rescheduling was temporally associated with modest increases in acute-care encounters explicitly coded for neoplasm-related pain. The findings underscore the importance of preserving timely analgesic access for patients with advanced cancer. Full article

Dementia is a leading cause of disability and death worldwide, yet diagnosis in primary care remains substantially lower than expected, delaying access to treatment, support and future care planning. The Facing Dementia Together Practice Change Program was developed as a co-designed, multicomponent intervention to improve dementia diagnosis in Australian general practice. A mixed-methods process evaluation used the RE-AIM framework with the addition of Appropriateness. The intervention included Primary Health Network (PHN) engagement, GP education, clinical resources, audit and benchmarking reports, specialist support, and a dementia risk-alert tool. Data were collected through surveys, stakeholder interviews, website analytics and program documentation. Clinicians who participated reported increased confidence and changes in dementia-related clinical behaviours, while education, resources and benchmarking were perceived as valuable. However, overall reach was limited by competing clinical priorities, workforce pressures, lack of financial incentives, and PHN implementation challenges. The dementia risk-alert tool was feasible but achieved limited uptake because of software compatibility, usability concerns and incomplete electronic medical record data. Although the program was acceptable to participating clinicians, limited reach constrained its potential impact. These findings highlight organizational, workforce, digital infrastructure and policy factors that influenced implementation of multicomponent interventions in routine primary care. Full article

Land consolidation (LC) is increasingly expected to support food security, ecological restoration, rural development, and climate mitigation, yet evidence on its carbon effects remains fragmented across engineering, ecological, spatial, and governance research. This review combines bibliometric mapping with structured evidence synthesis of 355 records from WoS and CNKI to examine knowledge evolution, analytical paradigms, and their integration. The field has expanded from component-based assessments of construction emissions, soil carbon, and land-cover change toward life-cycle accounting, carbon fractions, ecosystem-service interactions, spatial optimization, and policy evaluation. WoS-indexed and Chinese-language literature show distinct but increasingly convergent orientations shaped by differences in intervention contexts, disciplinary traditions, analytical scales, and available evidence. Four complementary paradigms are identified: carbon accounting, biogeochemical processes, spatial land systems, and decision support and governance. Together, these paradigms reveal interconnected carbon pathways but remain constrained by inconsistent accounting boundaries, weak process–scale–time linkages, and limited integration with land-governance decisions. Future research should therefore advance standardized life-cycle and multi-scale accounting, mechanism-based assessment of long-term carbon and ecosystem-service dynamics, and digitally and institutionally enabled low-carbon governance. Full article

Search-and-rescue (SAR) missions following earthquakes and other disasters require aerial video perception systems that do more than detect objects in isolated frames. Operators must maintain the identities of access points, vehicles, responders, hazards, and other mission-relevant targets despite UAV ego-motion, dust, debris, occlusion, scale variation, and abrupt scene transitions. This paper presents E’CHIT (Edge-Oriented Colour Histogram Instance-Guided Tracking), a deployment-oriented, operator-centric UAV tracking framework for real-world disaster-response applications. Its primary scientific contribution is an identity-stabilised, detector-assisted tracking methodology. YOLOv8-seg proposals trained on D’RespNeT initialise and refresh tracks; a Custom-RE3 recurrent module propagates target states through short detector dropouts; and a lightweight EOMC verifier, based on edge orientation, mean colour, and shape consistency, determines whether tracks should be accepted, refreshed, or reacquired. A scene-cut watchdog that combines luminance mean absolute difference (MAD) with HSV histogram divergence prevents stale identities from carrying over after hard edits or sudden feed changes. Custom-RE3 is the continuation module implemented and evaluated in this study. The surrounding E’CHIT wrapper follows an initialise–reseed–verify–reset cycle and is tracker-adaptable at the software-interface level: another compatible SOT or MOT continuation module can be integrated through adapter modifications, state and bounding-box conversion, and method-specific retuning, followed by independent validation. All reported quantitative results therefore apply to the Custom-RE3 implementation. D’RespNeT, the optional reinforcement learning (RL) warm start, the HUD, and the deployment stack support this central tracking contribution. D’RespNeT provides 28 polygon-annotated SAR classes. An author-developed PPO/SAC script is used only during offline detector training. In the reported runs, it produces different early optimisation trajectories for selected difficult or under-represented classes, while the default supervised schedule remains the strongest final global mAP reference. No RL policy runs during deployment; the detector architecture, parameter count, and inference graph remain unchanged. Evaluation on D’RespNeT and authentic disaster-response UAV footage shows that E’CHIT increases Success@IoU ≥ 0.5 from 0.62 to 0.79, reduces identity switches by approximately 71%, and maintains real-time 1080p performance, achieving 164–330 FPS for single-target tracking and 24–100+ FPS for end-to-end multi-target operation on an RTX-class GPU using FP16. The VOT2014, NT-VOT211, and VOTS2024 figures reproduce historical result spaces reported in the literature and include a clearly labelled, non-official E’CHIT operating-point marker solely for context. This marker was not produced using the corresponding official datasets, toolkits, reset rules, or submission routes; it is excluded from the primary quantitative claims and must not be interpreted as a leaderboard rank or a protocol-identical comparison. Overall, the system demonstrates how identity-stable UAV tracks can provide actionable operator cues for target monitoring, entry-point assessment, and UAV–UGV/ground-team coordination in cluttered disaster scenes. Full article

China’s pursuit of its dual-carbon targets amid rapid artificial intelligence (AI) development raises an important question: do the environmental implications of AI innovation change as regional innovation advances? Using a balanced panel of 30 Chinese provinces from 2011 to 2024, this study measured patent-based AI innovation intensity using applications identified through a strict AI patent classification. Linear and quadratic models with province and year fixed effects were estimated, and the Lind–Mehlum test was used to assess the shape of the relationship within the observed range. The preferred specification indicates an inverted-U-shaped association: carbon emission intensity initially increases with patent-based AI innovation but declines beyond an interior turning point. The negative quadratic coefficient remains stable when the emissions data source, patent classification, sample period, treatment of outliers, and timing of the AI terms are varied. Supplementary Bartik and copula-control analyses preserve the negative curvature, although their identification limitations preclude a definitive causal interpretation. A Kaya-based exact decomposition shows that the estimated curvature is concentrated in energy intensity rather than the carbonization factor. Human capital strengthens the estimated concavity, while the clearest regional contrast is observed between central and eastern China, with the strongest curvature in the central provinces. These findings suggest that greater AI patenting does not automatically reduce emissions. Its environmental implications depend on the stage of regional innovation and its interaction with energy efficiency and absorptive capacity. Policies promoting AI innovation should therefore be coordinated with cleaner energy supply, efficiency improvements, and human capital investment. More broadly, the study provides a stage-sensitive basis for evaluating the sustainability implications of patent-based AI innovation through measurable changes in carbon emission intensity. Full article

Filipino immigrants play a central role in Canada’s essential workforce, particularly in health care, caregiving, and service sectors. This paper examines how their labour market outcomes have changed since the onset of the COVID-19 pandemic, focusing on employment, exposure to precarious work, and earnings. Using Labour Force Survey data from 2016 to 2025, we compare Filipino immigrants with other major immigrant groups, using Canadian-born workers as a benchmark. The results point to a consistent pattern: since the start of the pandemic, Filipino immigrants became more likely to experience unemployment and to be concentrated in precarious forms of work. At the same time, their earnings did not keep pace with those of Canadian-born workers, resulting in a widening gap. Compared to other immigrant groups, Filipino immigrants stand out as facing a combination of worsening job conditions and slower earnings growth. These findings suggest that the pandemic reshaped the labour market in ways that left Filipino immigrants increasingly vulnerable, despite their central role in essential work. The paper concludes by discussing policy measures that could better protect immigrant workers in future periods of economic disruption. Full article

Multipath routing in wireless sensor networks (WSNs) improves reliability by providing alternative forwarding paths when a route fails. However, mobile sinks make path maintenance difficult because sink movement can invalidate previously constructed source-to-sink routes. Existing protocols typically depend on either global path reconstruction, which increases control overhead, or footprint-chaining, which accumulates detours through previous sink positions and may weaken path independence. To address this problem, this paper proposes QL-LGMPRP, a reliability-aware local-grid-based multipath routing protocol that combines a sink-centered local grid, two-path delivery, link-quality-aware forwarding, and lightweight tabular Q-learning for waypoint adaptation. Mobility-related route changes are confined to the sink-centered grid, whereas a compact tabular Q-learning policy adjusts the primary-path direction using grid, link-quality, and energy-related state variables. The sink constructs a local grid around its current position, with cells sized to keep in-grid forwarding locally bounded. When an event occurs, the source computes an entry point on the grid perimeter and constructs two greedy paths: a primary path through a Q-learning-selected waypoint near the grid boundary and a backup path toward the current sink position. The Q-learning agent uses a compact tabular state representation that includes the boundary-cell index, residual-energy level, sink-grid position, and local link-quality information, and learns waypoint offsets using a reward that combines delivery success, transmission energy, and delay. This design confines routing adaptation to the sink-centered grid while allowing the waypoint policy to respond to heterogeneous link conditions. Simulation results under different sink speeds and interference conditions show that QL-LGMPRP maintains high delivery reliability while reducing detour-related forwarding costs relative to footprint-chaining and showing lower weak-link exposure than the geometric-forwarding comparison schemes. Full article

Inland container depot (ICD) truck schedules must absorb new orders, service delays, appointment changes, congestion, and port cut-offs without destabilizing an already executed plan. This study asks whether event-triggered local repair can be separated into an explicit business-rule audit and a learned ranking of feasible task–vehicle actions. The proposed decision-support framework connects a static baseline, candidate task chains, six modeled hard-feasibility predicates, a Transformer encoder trained with proximal policy optimization (Transformer-PPO), and discrete-event execution logs. A five-seed, 120-episode confirmation gave Transformer-PPO a held-out online completion proxy (α_online) of 0.3226 and reward of 110.58, compared with 0.2581 and 61.87 for the matched multilayer perceptron (MLP); deterministic rules and search remained competitive. An independent audit of 4,968,000 action cells across 552 decision states found no disagreement with an independently coded oracle for the implemented hard predicates, while a reward-weight screen exposed the expected efficiency-stability trade-off. Together with a rolling-horizon comparator and a three-scale by three-disturbance stress test, the evidence supports an auditable system-integration contribution, not a new generic reinforcement learning (RL) algorithm or universal performance superiority. Claims are limited to synthetic simulation-based decision support. Full article

Ensuring the long-term sustainable utilisation of supplemented cropland has become a critical challenge for implementing China’s requisition–compensation balance of farmland (RCBF) policy, particularly in the fragmented hilly and mountainous regions of Southwest China. Existing studies generally evaluate land suitability and utilisation performance separately, making it difficult to identify mismatches between theoretical suitability and actual utilisation and thereby limiting targeted regulation. To address this limitation, this study proposes a suitability–utilisation deviation identification (SUDI) framework, which integrates four sequential analytical components: three-dimensional suitability assessment, suitability–utilisation deviation identification, driving mechanism analysis, and sustainable regulation. Taking Beibei District of Chongqing as a case study, supplemented cropland parcels were identified using the 2020–2024 land change survey data. A three-dimensional suitability evaluation system incorporating production, ecological, and utilisation attributes was established to quantify theoretical land suitability. Actual utilisation performance was characterised using the land economic utilisation coefficient, and suitability–utilisation deviation was identified through residual analysis between theoretical suitability and utilisation intensity. A Bayesian-optimised Extreme Gradient Boosting-SHAP (XGBoost-SHAP) model was subsequently employed to reveal the nonlinear effects and interaction mechanisms of the driving factors. The results indicate the following: (1) supplemented cropland in Beibei District is predominantly characterised by medium-to-high suitability, with high-suitability patches exhibiting a mosaic spatial pattern of local aggregation and overall dispersion; (2) suitability–utilisation deviation is dominated by under-utilised plots, whereas well-matched and over-intensified plots account for substantially smaller proportions, indicating that insufficient realisation of land suitability is the prevailing utilisation pattern; and (3) the land economic utilisation coefficient is the dominant factor driving suitability–utilisation deviation, while high-standard farmland construction and plot area exhibit significant mitigating effects. Moreover, significant interaction effects between utilisation intensity and location-related variables reveal that unfavourable spatial conditions amplify deviation risk under intensive land use. The proposed SUDI framework extends conventional suitability assessment by explicitly linking suitability evaluation with utilisation performance, driving mechanism analysis, and differentiated regulation. It provides a transferable analytical framework for diagnosing suitability–utilisation mismatch and supports dynamic management and sustainable utilisation of supplemented cropland in fragmented hilly and mountainous regions. Full article

The article explores the evolution of energy transition research through a bibliometric co-occurrence analysis of author keywords extracted from 146 scientific publications. The analysis reveals a shift from the technical aspects of energy systems and traditional decision-support approaches (2014–2018) towards climate change, decarbonization, renewable energy, investments, and energy policy (2018–2021). Recent studies (2022–2024) increasingly emphasize renewable energy, sustainable development, and intelligent decision-support systems, reflecting the growing digitalization of energy systems and the transition towards intelligent energy management. Based on these findings, the study develops a Digital-Twin-Oriented Techno-Economic Decision-Support Framework (DTOTEDSF) for optimizing and managing carbon-reduction strategies under dynamic energy transition conditions. Rather than representing a fully implemented digital twin (DT), the proposed framework constitutes the analytical foundation for its future development. It integrates techno-economic modeling, optimization, scenario analysis, and sensitivity assessment into a unified decision-support methodology. To demonstrate its practical applicability, the framework was applied to four industrial CCS case studies in the cement sector using publicly available technical and economic data. Its analytical core combines technical, economic, and optimization models to evaluate CCS performance under alternative operating conditions. Consequently, the proposed framework provides a methodological basis for the future implementation of fully operational DTs and contributes to the development of intelligent decision-support tools for industrial decarbonization and the sustainable energy transition. Full article