Search Results (495)

To be acceptable for precision agriculture applications, satellite imagery must be converted to surface reflectance. To be economical, the analytics must be delivered completely by automation and free of error to preserve farmer trust. CMAC (closed-form method for atmospheric correction) software was tested for this application along with established applications, Sen2Cor and FORCE—all three software packages seek to retrieve Sentinel-2 surface reflectance. Forty-three Sentinel-2 images were selected of farmland near Burley, Idaho, corrected by this software and evaluated as reflectance time series extracted from three irrigated corn fields. NDVI of irrigated corn presented an ideal test of precision and accuracy for surface reflectance retrieval. If accurate and precise, a plotted time series will smoothly display logistic growth during crop establishment followed by a plateau, then gradual senesce before harvest: divergences from this pattern indicate errors. CMAC followed the expected smooth pattern for this dataset while, in both FORCE and Sen2Cor, divergence occurred both above and below the CMAC time series for NDVI and from individual spectral band reflectance. These divergences were systematic and directly related to the degree of atmospheric effect—overcorrecting when clear, under-correcting when hazy. Only CMAC provided surface reflectance with the accuracy required for precision agriculture: applicable for Sentinel-2 as Tier 1 data and when haze or cloud- affected and unreliable, as Tier 2 infill from daily smallsat data. Additional analyses of the CMAC-corrected dataset were performed that were also applicable to Tier 2 daily-cadence smallsat data. Further analysis of this dataset indicated that, applied as NDVI, the application of broadband NIR, though sensitive to atmospheric water vapor, exhibited minimal errors compared to NDVI from narrowband NIR. These CMAC-corrected data provided an application to index crop start dates and were capable of distinguishing the uncorrectable data of cloud, cloud shadow, or extreme haze for removal under complete automation. Full article

Mountain Social–Ecological Systems (MtSES) are global assets, providing essential ecosystem services to nearly half of humanity, yet they are disproportionately vulnerable to global change, experiencing “polytraps” of depopulation, poverty, and environmental degradation. Despite the inherent human dimension in sustainability, the social pillar remains conceptually chaotic, forming a highly fragmented “publication labyrinth”, and is often neglected in favor of more easily quantifiable environmental and economic metrics. These oversights leave mountain communities in a precarious state, underscoring an urgent need for robust, context-specific assessment tools. This paper addresses this critical gap by employing a two-step methodology: first, a literature review identifies prevailing social sustainability issues in mountain contexts; second, a comparative analysis evaluates prominent frameworks and indicator-based tools against these themes, using Ostrom’s multi-tier Social–Ecological Systems (SES) framework as the theoretical lens. Our findings reveal a persistent environmental bias in MtSES research and highlight the necessity for frameworks that integrate local knowledge, address power imbalances, and support bottom-up governance. A tool is proposed with indicators specifically for mountainous contexts. This study contributes to theory by offering a structured approach to unpack the elusive “social” in SES and to practice by providing a model and tool for developing actionable, context-sensitive social sustainability assessments, thereby fostering resilience and equitable development in vulnerable mountain regions. Ultimately, by operationalizing these social dimensions, this research provides a direct roadmap for achieving key United Nations Sustainable Development Goals in marginalized high-altitude contexts, particularly focusing on No Poverty (SDG 1), Good Health and Well-being (SDG 3), Reduced Inequalities (SDG 10), Sustainable Communities (SDG 11), and Peace, Justice, and Strong Institutions (SDG 16). Full article

The rapid expansion of global commerce has escalated the complexity of money laundering schemes, making the detection of illicit transfers an urgent but highly challenging research problem. In operational anti-money laundering (AML) systems, the extreme rarity of illicit transactions often overwhelms compliance teams with false positives, leading to severe “alert fatigue.” To address this critical bottleneck, this paper introduces an enhanced, probability-driven risk-prioritization framework utilizing an XGBoost classifier integrated with Bayesian Optimization (BO-XGBoost). By optimizing directly for the Area Under the Precision–Recall Curve (PR-AUC), the model is specifically tailored to rank high-risk anomalies under severe class imbalance. We validate the proposed approach on a rigorously resampled transaction dataset simulating a realistic 5% laundering rate. The BO-XGBoost model demonstrates exceptional prioritization capability, achieving an ROC-AUC of 0.9686 and a PR-AUC of 0.7253. Most notably, it attains a near-perfect Precision@1%, meaning the top 1% of flagged transactions are 100% true illicit activities, entirely eliminating false positives at the highest priority tier. Comparative and SHAP-based interpretability analyses confirm that BO-XGBoost easily outperforms sequence-heavy deep learning baselines. Crucially, it matches computationally expensive stacking ensembles in peak predictive precision while significantly surpassing them in operational efficiency, indicating its immense promise for resource-optimized, real-world compliance screening. Full article

Organisations increasingly face complex cybersecurity threats shaped not only by internal capabilities but also by external regulatory, institutional, and environmental conditions. While existing cybersecurity standards and maturity models provide valuable guidance, they often offer limited support for assessing organisational readiness in a manner that is both context-sensitive and diagnostically meaningful. This paper presents a context-aware cybersecurity readiness assessment framework designed to support organisational evaluation of cybersecurity readiness while explicitly accounting for external environmental influences. The framework adopts a two-tier architecture. Tier 1 assesses organisational awareness of and engagement with the external cybersecurity environment, including national regulatory obligations, institutional support mechanisms, and international collaboration. Tier 2 evaluates internal organisational cybersecurity readiness across governance, operational controls, awareness and culture, and external collaboration practices. The two tiers are designed to operate independently, enabling complementary interpretation without assuming deterministic relationships between external context and internal capability. The framework is developed and evaluated using a Design Science Research approach and is operationalised through a structured assessment instrument and an interpretable scoring model. Empirical validation is conducted across multiple organisational contexts operating in developing and emerging environments, with qualitative case study evidence where available. The results demonstrate that the framework differentiates meaningfully across readiness domains, avoids artificial score inflation or compression, and supports interpretable diagnosis of alignment gaps between external expectations and internal practices. The study contributes a validated assessment artefact that extends cybersecurity awareness research into a broader organisational readiness perspective. From a practical standpoint, the framework provides organisations, policymakers, and researchers with a structured tool to support incremental improvement, informed decision-making, and reflective engagement with both internal cybersecurity practices and external environmental conditions. Full article

Rainfall-induced shallow loess landslides pose a significant threat to human life and property. Early warning and risk assessment of these landslides are critical prerequisites for engineering control and disaster loss reduction. The Transient Rainfall Infiltration and Grid-Based Regional Slope-Stability Model (TRIGRS)-Three-dimensional Slope Stability Analysis Tool (Scoops 3D) joint model can overcome the shortcomings of using a single TRIGRS model for hydrological analysis and a single Scoops 3D model for slope stability analysis. Landslide risk assessment based on expected economic loss, on the other hand, can overcome the issue of maintaining the risk level edge and sorting at the same level. In this paper, the TRIGRS model’s head pressures were put into the Scoops 3D model, with the southeast of Fangta, a town in Shaanxi province, China, as the study area. The relationship between the slope gradient and the number of grids in each stable grade was certified. The rainfall thresholds for landslides, based on both rainfall intensity and rainfall duration, were obtained by rerunning the TRIGRS-Scoops 3D joint model. The landslide range and land uses of each dangerous slope were determined by maximum likelihood classification, and then the expected economic loss was calculated. To verify the reliability of the TRIGRS-Scoops 3D joint model, the identified dangerous slopes were compared with the results from landslide susceptibility mapping. The results show that the unstable grids are concentrated within a slope gradient of 30° to 35°, and the landslide early warning levels are divided into Tier 3, Tier 2, and Tier 1 Warnings. The occurrence of shallow loess landslides is affected by both rainfall intensity and rainfall duration, and the combined effect should be considered in early warning. The distribution of both extreme susceptible grids and high susceptible grids across all 23 dangerous slopes demonstrates the reasonableness of the TRIGRS-Scoops 3D joint model. The landslide susceptible probability within some dangerous slopes exhibits spatial variability. The mapping relationship between the slope gradient and loess landslides is extremely complex. This paper can provide a theoretical basis for the early warning and risk management for rainfall-induced shallow loess landslides; the proposed method is also applicable to other regions with similar geological and meteorological conditions. Full article

This study proposes TNAP-DDQN, a deep reinforcement learning method for urban low-altitude UAV path planning under residential noise threshold constraints. With time cost and safety risk as the optimization objectives, operational constraints such as collision risk and maximum AGL altitude are incorporated to achieve coordinated optimization of noise compliance, operational safety, and efficiency. To mitigate action space contraction and training instability induced by multiple constraints, a Noise-Degradation-Mask-based Action Bias Network (NDM-ABN) is introduced at the action selection layer. A three-tier degradation scheme prevents empty candidate sets, while bias-based decision making is applied to approximately tied actions to stabilize the policy. Moreover, multi-step prioritized experience replay (PER) improves sample efficiency and long-horizon return modeling, and potential-based reward shaping (PBRS) transforms sparse constraint signals into auxiliary rewards. Simulation results indicate that: (1) NDM-ABN is the key module for stabilizing the noise-exposure process by suppressing high-noise actions; (2) the required AGL is related to the UAV source noise level and local noise limits, implying the need for differentiated AGL altitude classes; and (3) the maximum admissible UAV source noise level increases as the threshold is relaxed. The proposed method provides quantitative guidance for noise-entry and AGL altitude regulation, while future work will incorporate additional metrics (e.g., A-weighted equivalent sound level) to better capture noise fluctuations and short-term peaks. Full article

Given less than five years remaining until the target year for the first phase of China’s dual carbon goals, this paper studies carbon allowance allocation with an in-depth study of Jiangsu Province due to its significant role in driving the Yangtze River Delta’s pioneering achievement of the dual carbon goals. This study considered 2017 (the intermediate target year) as the base year and incorporated socio-economic data such as population, GDP, and the urbanization rate. Then, methods including the entropy weight method, gravity model and social network analysis were applied to classify Jiangsu’s 95 counties. From a regional coordination perspective, carbon governance clusters were constructed with the Shapley value, based on which spatial heterogeneity patterns were analyzed, and a carbon quota allocation was proposed. The findings reveal that: (1) The dominant factors influencing cross-scale carbon reduction capacity at the county level are natural carbon sink capacity (indicator weight: 0.180) and urbanization rate (indicator weight: 0.145). (2) The correlation between carbon reduction factors among different districts and counties exhibits an uneven spatial pattern. And the spatial configuration exhibits a multi-tiered, network-like distribution. (3) Through conducting spatial analysis and spatial grouping, Jiangsu could be divided into 14 county-level carbon governance alliances, with the number of member counties ranging from 4 to 10 within each alliance. (4) The allocation of carbon quotas in Jiangsu exhibits a distinct descending gradient from the southern to the northern regions, which is coupled with the regional economic geography. This is exemplified by the highest quota in Jiangyin (496.46 Mt) in the south and the lowest in Lianyun (34.90 Mt) in the north. It is concluded that two carbon emission reduction pathways should be established as a priority: (a) Tongshan-Gulou (Xuzhou)-Yunlong-Quanshan-Jiawang and (b) Tianning-Jiangyin-Zhangjiagang-Changshu-Taicang-Kunshan. Full article

The combined impacts of urban pluvial flooding and traffic congestion can severely delay emergency response. However, existing studies often focus on isolated scenarios, failing to systematically quantify the reduction in overall service capability and specific route disruptions to critical functional nodes under compound hazards. To address this problem, this study proposes a three-tier analytical framework to systematically evaluate the resilience of emergency services under compound hazards. The framework first utilizes spatial network analysis to simulate the overall spatial evolution of service capabilities for Emergency Medical Service (EMS) and Fire and Rescue Service (FRS) across various return periods and traffic conditions. It then delves into the emergency response coverage for vulnerable population places. Finally, the fastest-route analysis is employed to identify variations in rescue routing. The study reveals several critical insights. (1) As rainfall intensity and traffic congestion intensify, the coverage areas of EMS and FRS exhibit significant contraction and boundary erosion. Notably, the service areas of FRS show a distinct fragmentation pattern. (2) The protection levels for vulnerable population places (e.g., kindergartens, primary and secondary schools, and nursing homes) show a pronounced stepwise decline. Under extreme rainfall and the heaviest congestion, the 5 min coverage for these sites drops from 89.9% to 23.6% for EMS, and from 72.4% to only 15.1% for FRS, revealing a severe risk exposure for vulnerable groups. (3) Road inundation leads to a substantial extension of rescue routes and even results in the complete isolation of 141 primary and secondary schools. Overall, the framework provides actionable decision support to enhance urban emergency response under compound hazards. Full article

Web application firewalls (WAFs) are critical defenses against persistent threats to web applications, yet their security evaluation remains challenging. Traditional manual testing methods are often inefficient and resource-intensive, while existing reinforcement learning (RL)-based automated approaches face two key limitations: (1) attackers cannot perceive opaque WAF rule logic; (2) boolean feedback from WAFs results in sparse/delayed rewards—sparse rewards trap agents in blind exploration, and delayed rewards hinder the association between early actions and final outcomes, adversely affecting learning efficiency. To address those challenges, we propose Ouroboros—a framework integrating genetic algorithm-based symbolic rule reconstruction (translating WAF rules into interpretable RNNs for fine-grained confidence scoring), timing side-channel analysis (evaluating rule-matching depth), and a multi-tiered reward mechanism to enable self-evolving RL testing. Experiments show that the framework reaches 89.2% bypass success rate on signature-based WAFs. This paper presents an efficient solution for automated WAF testing and delivers insights for optimizing rule logic and anomaly detection mechanisms. Full article

The current research undertook a comprehensive examination of global research related to the use of measurement, reporting, and verification (MRV) techniques for quantifying and tracking greenhouse gas (GHG) emissions from agriculture and livestock farming. Data were collected using a bibliometric analysis of 5340 studies published in the period (1990–2025) and a systematic literature review of 100 studies published in the period (2020–2025). The insights from the findings showed that four MRV techniques were broadly adopted across different regions: (1) inventory techniques (IPCC Tiers, national systems), (2) accounting at the project/product level (LCA, carbon footprint protocols), (3) MRV based on measurement and models (chambers, remote sensing, farm models, AI/ML), and (4) frameworks for governance and standardization (UNFCCC, Paris ETF, PAS 2050, etc.). The findings further revealed the impact of the MRV techniques on agriculture and livestock farming, showing that they facilitated the uptake of low-carbon practices. In agriculture, the MRV techniques showed that lower emissions emerged from mixed cropping, while in livestock farming, the emissions varied based on the feeding stage and type of diet used. However, various challenges arose in the adoption of MRV techniques where there was limited data related to GHG emissions, thereby reducing generalizability. In future work, there is a need for scholars to consider integrating the different MRV techniques to develop an understanding of the problem area. Full article

Quantitative evaluation of public transit networks (PTNs) with complex-network models informs route optimization and operational adjustments. Prior studies emphasize large cities and pay limited attention to small-sized urban systems. This study examines the bus network of Cangzhou City, Hebei Province, China, to broaden the empirical scope and characterize PTNs in smaller cities. The dataset for this study comprises route and stop records, passenger boarding logs, and bus GPS traces. We develop a general workflow for bus data cleaning and completion. To characterize the dynamic bus network and compare it with the static network, we construct a static network and Directed Weighted Dynamic Network I (DWDN I) using the L-space method, and we construct Directed Weighted Dynamic Network II (DWDN II) using the P-space method. We calculated network metrics including degree, weighted degree, clustering coefficient, path length, network diameter, network efficiency, and small-world coefficient. The principal results show that: (1) at the macroscopic level, the dynamic PTN tracks passenger demand, as the average degree, weighted average degree, and clustering coefficient fluctuate in concert with passenger flows; (2) key stations concentrate in the urban core, and stations with high weighted degree display pronounced spatial autocorrelation; (3) the exponential form of the weighted-degree distribution indicates that the examined bus network is not scale-free, while the dynamic network’s small-world coefficient exceeds that of the static network across time periods, reflecting stronger small-world characteristics. This study integrates network and spatial attributes of the PTN to offer an exploratory case for investigating public transit networks in third-tier cities. The findings can inform comparable studies and offer practical guidance for bus operators. Full article

Local governments are key actors in driving urban renewal. To implement urban renewal initiatives, in-depth research into their policy backgrounds, institutional characteristics, and governance logic is essential. Traditional policy analysis often neglects the value dimension, which undermines the effectiveness of embedding informal institutional values. To complement existing research, this study examines 50 urban renewal policy documents issued in Guangzhou between 1978 and 2025. Using content analysis and grounded theory methods, this study incorporates the value dimension into the traditional “supply–demand–environment” policy analysis framework to examine local governments’ policy preferences in urban renewal, and to interpret its governance logic from the perspective of Williams’ four-level framework. The findings are as follows: (1) Guangzhou’s urban renewal has formed a policy system centered on supply-side policies, supported by environmental policy improvements, with value embedding, demand-driven measures, and multi-dimensional guidance as supplementary components. Local governments show a distinct preference for supply-oriented policy tools. (2) Guangzhou’s urban renewal policies present a pyramid structure with resource allocation at the core and governance structure as the foundation. The policies focus on the optimal allocation of land resources, collaborative actions among government, market, and society, the deep integration of public values, the clarification of property rights rules, and the application of digital technologies. (3) The governance logic of urban renewal forms a four-tier progressive closed-loop: from value anchoring to rule linkage, then to multi-stakeholder collaboration, and finally to factor empowerment, establishing a systematic governance mechanism that balances people-centricity and efficiency. Accordingly, urban renewal should prioritize value embedding and cultural preservation, balance investment in physical assets and human capital, optimize governance structures and policy mixes, coordinate the roles of an effective market and a capable government, improve supply–demand matching and the efficiency of resource allocation, and adjust the complementarity and applicability of policy tools. Full article

This study aims to address gaps in understanding healthcare accessibility inequality in rural China, where traditional distance-based assessments and urban-centric biases are insufficient. By integrating real-time travel data from Amap and the two-step floating catchment area (2SFCA) method, we conducted a high-resolution (1 km grid) analysis across transportation modes, administrative scales, and time-sensitive populations. Results reveal that driving enables more stable, equitable access (characterized by higher supply–demand ratios and lower variability) than public transport, which distorts ratios due to limited coverage. Accessibility disparities are most pronounced at the county scale, with eastern rural counties (e.g., Yangtze River Delta) showing far higher accessibility (log₁₀(A-value) > 5.0) than remote western counties (log₁₀(A-value) < 1.5). High time-sensitive populations (urgent care) face extreme accessibility gaps, with only 15% of counties providing optimal access. In contrast, low time-sensitive groups benefit from extended travel time thresholds, achieving 62% coverage of optimal access. Targeted interventions—investing in rural high-tier hospitals, enhancing transit frequency, and county-specific policies—are needed to advance health equity. The findings of this study provide the first nationwide high-resolution healthcare accessibility map for rural China, improve assessment accuracy via real-time data, and identify county-level gaps—offering data-driven insights for targeted policies to advance health equity and support rural revitalization. Full article

As the rapid evolution and expansion of Internet of Things (IoT) devices continues to accelerate, modern infrastructures face increasing cyber risks, largely driven by device inter-connectivity, limited security maturity, and interdependent attack propagation across networks. Traditional cyber insurance models often overlook these IoT-specific characteristics, relying on uniform or simplified risk assumptions that fail to capture real-world vulnerabilities. To address this gap, this paper presents a maturity-aware cyber insurance optimization framework tailored for interconnected IoT environments. The framework integrates organizational security maturity, interdependent risk propagation modeled through a modified Susceptible–Infected–Susceptible (SIS) process, and a Stackelberg game formulation that captures strategic interactions between the insurer and the defender. Through numerical studies on representative IoT topologies, we demonstrate that maturity-aware, risk-sensitive premium structures quantitatively outperform uniform pricing baselines in cost-efficiency and insurer sustainability. Specifically, our experimental results reveal that operating at an optimal intermediate maturity level ($M=3$) reduces the defender’s total expected cost by approximately $40\%$ (from $255.38$ k to $152.36$ k) compared to the baseline state ($M=1$). Furthermore, this structural hardening triggers an $88.3\%$ reduction in full-coverage insurance premiums (from $225.38$ k to $26.36$ k). In contrast, our uniform-pricing baseline exhibits reduced profitability in our experiments due to cross-subsidization effects, reinforcing the value of tiered, risk-proportional pricing for mitigating adverse-selection incentives. In summary, this work establishes a tractable, economically viable framework for cyber insurance in IoT ecosystems and provides a foundation for future extensions to richer network settings. Full article

Telemedicine at the national scale must balance clinical quality, privacy, latency, and sustainability. This study aims to develop a system architecture and methodology for low-energy edge AI combined with green data center routing to reduce energy per consultation while maintaining clinical-grade performance. The results present (1) an energy-aware edge inference stack for physiological sensing and video triage; (2) a carbon-aware, service level agreement (SAL)-constrained routing strategy across regional data centers using software-defined networking and dynamic workload placement; (3) a techno-environmental methodology linking patient-level service key performance indexes to energy neutrality factor, grams CO₂e per encounter, and latency–reliability envelopes; and (4) national rollout playbooks covering network tiers (household/clinic/edge/cloud), facilities upgrades, and governance. Scenarios in urban, peri-urban, and rural/remote environments show 37–62% energy savings and 28–49% carbon reductions relative to cloud-only baselines, with median end-to-end latency ≤120 ms for triage and ≤40 ms for vitals alarms, meeting the World Health Organization and the International Telecommunication Union latency expectations for eHealth. Trade-offs, risks (drift, network volatility), and policy levers (green SLAs, data residency, open standards) are evaluated to scale sustainable telemedicine without compromising safety or equity. Full article