Search Results (25,920)

Global climate warming has led to the frequent occurrence of natural disasters, threatening the stability of agricultural production and the survival of agricultural enterprises. The existing literature presents mixed evidence regarding the impact of natural disasters on corporate ESG performance. Some studies argue that natural disasters promote ESG performance; however, such conclusions only hold for non-agricultural enterprises. Agricultural enterprises are highly dependent on natural conditions, and their core production factors are vulnerable to direct damage from natural disasters. Meanwhile, they are characterized by long production cycles and high asset specificity. After disaster shocks, agricultural enterprises have to prioritize production recovery, so natural disasters exert a dominant negative effect on their ESG performance. Based on the above context, here we take the performance of Chinese A-share listed agricultural companies between 2010 and 2023 as the research sample to explore the impact of natural disasters on the ESG performance of agricultural enterprises. The empirical results show that natural disasters significantly inhibit the ESG performance of agricultural enterprises. Mechanism tests indicate that natural disasters weaken ESG performance by damaging supply chain resilience, hindering green innovation, and disrupting internal control. A cross-sectional heterogeneity analysis reveals that the inhibitory effect is more pronounced for large-scale enterprises, enterprises with lower executive green cognition, and enterprises located in areas that are not major grain-selling areas. This study enriches the research on the economic consequences of natural disasters and the factors influencing corporate ESG performance. It also provides important practical implications for strengthening the ESG fulfillment of agricultural enterprises and accelerating the cultivation of new productive forces in agriculture. Full article

The commitment of Australian Universities in providing sustainability education and contributing to scholarly outputs in sustainability represents their critical efforts in supporting the United Nation’s Sustainable Development Goals (SDGs). Using data collected on sustainability-focused unit offerings and bibliometric analysis on 3119 scholarly outputs extracted from Scopus, this study investigates the extent of engagement and contribution of business schools across Australia in cultivating a culture of sustainability among graduates. The results indicate that 63.15 percent of business schools offer sustainability units in either undergraduate or postgraduate business programs, or in both. Empirical findings highlight that AACSB accreditation status, QS World University Rankings, and size of the business school significantly influenced sustainability-focused unit offerings. Additionally, a clear upward trajectory in scholarly outputs during the study period was observed. The bibliometric analysis reveals that academics coauthored with peers from 109 countries. Among the key themes identified are: “sustainability”, “sustainable development”, “ecotourism”, and “environmental sustainability”. These findings suggest that the social sustainability domain, and the application of the degrowth research paradigm in sustainability research are underexplored. This study is significant as it provides useful insights into the extent of commitment of Australian business schools in advancing the SDGs over the past three decades. The findings are useful in informing future course offerings and research directions. Full article

Background: Artificial intelligence (AI) is transitioning from experimental pilots to core public health functions such as disease surveillance, resource planning, and analysis of social and structural determinants of health. Yet, health data collection and stewardship remain fragmented across the globe; some jurisdictions still rely on paper-based systems, while others operate noninteroperable digital systems that can exacerbate inequities. Treating health data as a global good therefore requires governance that enables innovation while protecting rights, safety, and trust. This study aims to develop a conceptual meso-level capability framework that translates responsible AI principles into organizational practices for public health agencies. Methods: We developed the framework using a targeted narrative synthesis of contemporary governance guidance and documented early implementation experiences, purposively selected to represent major strands of current practice and debate. A structured expert panel consultation (n = 9) was subsequently conducted to assess the face validity and content validity of the proposed framework domains. Results: We propose the Public Health Responsible AI Capability (PH-RAIC) framework, which adapts principles of transparency, accountability, fairness, ethics, and safety to institutional realities faced by public health agencies. PH-RAIC identifies four interdependent capability domains: (1) strategic governance and alignment; (2) data and infrastructure stewardship; (3) participatory design, equity, and public engagement; and (4) lifecycle oversight, learning, and decommissioning. All four domains achieved Content Validity Index (CVI) values ≥ 0.85 in the expert panel consultation. The framework is presented as a conceptual, meso-level model that has undergone preliminary expert validation but requires further empirical testing in real-world agency settings. Conclusions: PH-RAIC links these domains to example practices, diagnostic questions, and illustrative measurement indicators to help agencies navigate efficiency–equity trade-offs and strengthen legitimacy and accountability in AI-enabled public health systems. It offers a validated conceptual basis for future empirical testing and operational readiness tools. Full article

Historic districts are important built environments in which architectural form and cultural meaning shape residents’ place-based identity and engagement with conservation under urban renewal. However, empirical evidence on which features of the historic environment most strongly support local identity and conservation participation in migrant-intensive, fast-growing cities remains limited. This study investigates the relationships among architectural heritage, religious elements, cultural activities, local identity, sense of belonging, and conservation participation in five historic districts in Shenzhen, China. Using a residents’ questionnaire survey, we applied hierarchical multiple regression and mediation analysis to examine these relationships. The results indicate that architectural heritage is the strongest predictor of local identity, whereas religious atmosphere and cultural activities show comparatively weak effects. Local identity is positively associated with conservation participation, with only limited mediation through sense of belonging. The findings indicate that in migrant-intensive urban settings, architectural distinctiveness plays a stronger role in shaping local identity than religious or other cultural practices. Overall, the study argues that architectural heritage should be seen not only as a physical fabric to be preserved, but also as a resource that can strengthen local identity, participation, and socially sustainable urban renewal. Full article

Vision–Language–Action (VLA) policies promise flexible long-horizon manipulation, but deployment under domain shift requires both reliable uncertainty estimates and a workable runtime-assurance policy. We study a model-agnostic uncertainty-calibrated safety-gating wrapper that estimates online failure risk and routes control among policy execution, pause-and-reobserve, and a fallback planner. Using a cleaned and consistently aggregated benchmark pipeline, we evaluate two long-horizon manipulation tasks in NVIDIA Isaac Sim 5.0 under lighting, texture, occlusion, sensor, and combined shifts. Relative to an ungated VLA baseline, calibrated gating improves mean shifted success from 57.5% to 77.2% and reduces aggregate expected calibration error from 0.303 to 0.100. The largest success gains occur under occlusion and combined shift, including improvements from 48.3% to 85.2% on the drawer task and from 59.4% to 87.8% on clutter sort. The results also expose a systems trade-off: an aggressive uncalibrated threshold baseline attains stronger raw success and collision metrics, but requires nearly twice as many interventions per shifted episode (21.6 vs. 11.5). The main contribution is, therefore, an empirical characterization of the reliability–intervention trade-off created by calibrated supervision, not a claim that the calibrated supervisor is universally the best terminal controller. We frame calibrated gating as a better-calibrated, lower-intervention supervisor that materially improves robustness relative to an ungated VLA while revealing the open problem of mapping calibrated risk into efficient intervention policies. Additional threshold-sensitivity, signal-diagnostic, overhead, and residual-failure analyses show that the selected operating point is meaningful but not universal: the calibrated risk threshold captures most shifted failures in retrospective logs, yet residual contacts still arise during pause and fallback states. These findings provide controlled simulation evidence for trustworthy VLA supervision under distribution shift and clarify the reliability–intervention frontier that future embodied-control systems must navigate. Full article

Housing inequalities remain a major challenge for contemporary urban governance, as they combine economic, social, spatial, and demographic dynamics that are difficult to capture through single indicators. This paper develops a data-driven assessment of housing inequalities in Portugal between 2015 and 2025, drawing on official national and European statistics and applying a Business Intelligence (BI) and urban analytics framework oriented towards policy monitoring. Official data from Statistics Portugal and Eurostat are integrated through an analytical pipeline including automated extraction via public APIs, data enrichment, and visual analytics. The workflow follows a CRISP-DM-inspired structure, creating a set of normalized indicators to capture different dimensions of housing conditions. The results point to a structurally polarized housing market. Housing valuations increased across all regions, but at uneven rates, reinforcing territorial disparities rather than convergence. Metropolitan and tourism-oriented regions experienced faster appreciation and indirect effects, while year-over-year growth in completed dwellings slowed after 2021–2022, indicating an uneven supply response. Beyond its empirical findings, the primary contribution of this study lies in demonstrating how BI and data science methodologies can be operationalized to monitor housing inequalities using official statistics. The proposed framework is replicable and can be adapted to other territorial and policy contexts. Full article

The intersection of financial innovation and environmental sustainability offers important opportunities for low- and middle-income (LMI) countries. This study examines the association between FinTech investment, geopolitical-economic uncertainty, urbanization, economic development, and carbon dioxide (CO₂) emissions in LMI countries. CO₂ emissions per capita are used as an environmental outcome indicator rather than as a direct measure of green finance. Using a panel dataset covering 2010–2021, the study applies fixed-effects panel regressions as the main empirical approach and reports one-step difference the Generalized Method of Moments (GMM) estimates as exploratory dynamic evidence. The fixed-effects results indicate that GDP per capita is positively and significantly associated with CO₂ emissions, while FinTech investment and urbanization do not show consistent significant associations. Geopolitical risk is positively associated with CO₂ emissions in some static specifications, but this association becomes insignificant once gross domestic product (GDP) per capita is included. The exploratory GMM results, estimated with collapsed instruments and restricted lag depth, do not provide statistically significant evidence that FinTech investment is associated with lower CO₂ emissions. Overall, the findings suggest that FinTech investment may be relevant for environmental outcomes in LMI countries, but its role is neither automatic nor uniform and remains sensitive to model specification. Policy implications emphasize the need to strengthen digital financial infrastructure, regulatory transparency, institutional stability, urban planning, and climate-oriented investment channels to support FinTech-driven environmental performance. Full article

Optical and Synthetic Aperture Radar (SAR) sensors capture complementary and consistent information, and their fusion enhances remote sensing image quality. Existing pyramid decomposition-based methods suffer from insufficient texture–edge discrimination. Additionally, the manual setting of parameters during pyramid decomposition introduces uncertainty in the fusion results. To address this problem, we propose an optical and SAR image fusion framework based on local extrema adaptive pyramid decomposition (LEAPFusion), which enhances edge preservation and improves parameter adaptability. Specifically, by leveraging the edge-preserving properties of local extrema, we introduce them into the image pyramid decomposition framework to construct complementary local extrema and Laplacian pyramids. Then, we introduce an explicit parameter adaptation strategy in which the decomposition levels and local extrema kernel sizes are automatically determined from image size and pyramid scale, enabling consistent multi-scale representation and reducing parameter sensitivity compared to empirically tuned settings. Finally, by exploiting the complementary properties of the two pyramids, we implement a multi-type fusion strategy: weighted averaging for low-frequency components and parameter-adaptive pulse-coupled neural network (PAPCNN) for high-frequency details. Our decomposition framework seamlessly integrates three representative edge-preserving filters—a median filter, a guided filter, and a rolling guidance filter—demonstrating strong generalization capability across different filtering paradigms. Extensive experiments on two benchmark datasets demonstrate that our method outperforms seven state-of-the-art algorithms, achieving the best results across diverse scenes with improvements of up to 13.38% in SF and 18.90% in SCD compared to the second-best methods. Full article

Grain production resilience forms a critical foundation for national food security, and the ongoing development of land transfer provides essential momentum for establishing a more resilient grain production system. Using panel data from 30 provincial-level regions from 2013 to 2024, this study constructs a multi-dimensional evaluation system for grain production resilience and calculates the comprehensive grain production resilience index using the entropy value method. This study applies two-way fixed effects and mediation models to empirically analyze the impact of land transfer on grain production resilience and its underlying mechanisms. The results show the following: (1) Land transfer significantly enhances grain production resilience: a 1 percentage point increase in the land transfer rate leads to a 0.0014-point increase in the resilience index, equivalent to 0.64% of the sample mean, and this finding remains robust after model replacement, extreme value trimming, and variable substitution. (2) Land transfer exerts its positive effect through three mediating pathways: agricultural insurance (scale dimension), specialized farmer cooperation, and agricultural mechanization. (3) Heterogeneity analysis reveals significant regional differences: the enhancing effect is more pronounced in non-major grain-producing regions and areas with underdeveloped agricultural service systems; while in major grain-producing regions and high-service-level regions, the relationship presents an inverted U-shape, with turning points at 66.794% and 71.921% of the land transfer rate respectively. Accordingly, this study proposes that China should further improve the institutional design of land transfer to systematically support the development of grain production resilience, optimize relevant policy pathways, and implement region-specific measures for targeted and effective intervention. Full article

The agricultural industry is at a critical juncture, experiencing global pressures in the form of climate volatility, a shortage of labor, and an increase in production costs. Although artificial intelligence (AI) has the potential for revolution due to its predictive analytics and self-controlled machinery, it has not achieved widespread and even distribution for use, especially among small-to-medium-sized farms in the Midwestern United States. This study formulates and empirically examines a comprehensive socio-technical model to determine the drivers and barriers to the adoption of AI in this agricultural region. Based on a synthesized framework of the “Unified Theory of Acceptance and Use of Technology” (UTAUT) and “Task–Technology Fit” (TTF), the study incorporates agriculture-specific contextual factors such as “environmental risk, access to broadband, economic constraints, and policy support”. The analyses of the 489 farmers in the U.S. Midwest were conducted through the “partial least squares structural equation modeling” (PLS-SEM) “SmartPLS v.3.9”. The findings provide full empirical evidence of the proposed model, which supports 11 hypothesized relationships. The key results show that the strongest positive predictors of adoption intention are “performance expectancy, effort expectancy, and trust”. On the other hand, data security concerns and financial restrictions are strong deterrents. The paper also outlines the significant facilitating functions of the broadband infrastructure and policy support in building farmer perceptions of technology’s ease-of-use and facilitating conditions. These lessons can provide policymakers, ag-tech developers, and extension agencies with a roadmap on how to create more equitable and contextual interventions that overcome the rural digital divide and create resilient data-driven farming systems. Full article

Mechanized harvesting in the industrial tomato sector is currently bottlenecked by excessive mechanical injuries and elevated levels of foreign materials generated during electro-mechanical combine harvesting operations. To combat these limitations, this comprehensive review explores recent breakthroughs in harvester-mounted smart grading systems engineered specifically for complex, open-field conditions. Rather than relying solely on conventional optical inspection, the study examines the transition toward advanced, heterogeneous edge-computing frameworks—incorporating FPGAs and embedded GPUs—deployed within electro-mechanical harvesting platforms. This architectural evolution plays a crucial role in mitigating unpredictable processing delays caused by intense operational vibrations, although achieving absolute real-time stability under extreme field conditions remains an ongoing challenge. To minimize bruising and physical deterioration, our analysis synthesizes findings from multi-scale explicit dynamic finite element simulations, unpacking the underlying microstructural failure modes of the crop. We illustrate how regulating applied forces via soft robotic effectors can help approach a ‘damage-free’ handling threshold, though empirical results vary depending on fruit maturity and dynamic operational speeds. Furthermore, coupling multi-modal sensor fusion with Convolutional Neural Networks (CNNs) shows promising potential for non-destructive internal property evaluation under the vibration, dust, and throughput constraints of electro-mechanical harvesters, pending broader validation across diverse field datasets. Ultimately, by projecting future trends in onboard electro-mechanical harvester separation and advocating for a closer synergy between agronomic practices and machine engineering, this paper delivers a comprehensive blueprint for building next-generation, highly resilient, and gentle sorting machinery. Full article

This study proposes an explainable framework for ESG portfolio rebalancing by integrating carbon credit signals, technical indicators, and Transformer-inspired forecasting into a unified decision process. The investable universe consists of six ESG-themed ETFs, namely ESGU, SUSA, ICLN, TAN, KRBN, and KGRN. Carbon-related sustainability information is represented by four S&P carbon indices, including GCC, CCA, EUA, and UCITS. Within the proposed framework, Transformer, Informer, and Temporal Fusion Transformer are used to predict next-day returns, and the forecast outputs are translated into portfolio decisions through threshold filtering, Softmax-based allocation, and inertia smoothing under fixed transaction costs. The empirical results show that the proposed framework remains competitive against Equal Weight, Risk Parity, and Momentum benchmarks, although its advantage is conditional rather than uniformly dominant across all metrics. Informer delivers the strongest Sharpe ratio among the model-based strategies, while Transformer exhibits a more stable risk profile. The ablation results indicate that the smoothing mechanism has the clearest effect on turnover and allocation stability, whereas the incremental value of carbon-related inputs is most visible in Informer. The uncertainty assessment further shows that many benchmark differences are not consistently significant under repeated resampling, but the performance weakening caused by removing carbon inputs in Informer remains identifiable. The subperiod analysis shows that benchmark rules are more competitive in 2024H1, whereas model-based strategies gain relative strength in 2024H2. The explainability analysis indicates that carbon-feature contributions are concentrated more strongly in Intermediate and Carbon-Sensitive asset groups and remain weaker in Broad ESG assets; feature-level and SHAP beeswarm evidence further shows that the three architectures rely on GCC, CCA, EUA, and UCITS in different ways. These findings suggest that carbon-related sustainability signals can provide economically meaningful allocation information in selected settings when they are combined with suitable model architecture and disciplined rebalancing control, thereby supporting a competitive and explainable ESG portfolio rebalancing framework. Full article

Healthy building concepts are increasingly recognized as important for improving occupant health and well-being, yet empirical evidence on their understanding and implementation in sub-Saharan African contexts remains limited. This study provides an exploratory assessment of construction professionals’ awareness and self-reported application of healthy building concepts in Gabon. Using a structured questionnaire survey of 45 construction professionals, including architects, engineers, and contractors, the study examines sources of awareness, patterns of application across project stages, and health-related dimensions prioritized in practice. The results indicate high levels of conceptual awareness within the surveyed group, but uneven and context-dependent application. Implementation is strongly concentrated at the design stage, while continuity during construction and operation remains limited. Professionals tend to prioritize tangible and measurable dimensions such as lighting, materials, air quality, and thermal comfort, whereas psychosocial and community-related aspects receive less attention. Based on these empirical patterns, the study proposes an empirically informed and context-sensitive framework structured around six strategic pillars to support the gradual integration of healthy construction practices in Gabon. Rather than offering a prescriptive model, the framework serves as an analytical reference to inform future research, professional capacity building, and policy dialog. Given the exploratory nature of the study and its reliance on self-reported data, the findings should be interpreted as indicative rather than generalizable. Full article

The widespread adoption of Generative Artificial Intelligence (GenAI) in regulated enterprises is currently hindered by the “Static Alignment Trap”: the inability of traditional safety methods, such as Reinforcement Learning from Human Feedback (RLHF), to adapt to rapidly shifting compliance landscapes without costly retraining. This paper introduces and evaluates the Agreement Validation Interface (AVI), a modular governance architecture that functions as a deterministic middleware layer. By decoupling governance from the core inference engine, AVI implements Dynamic Bilateral Alignment (DBA), enforcing policy constraints at both the input and output stages through vector-based semantic retrieval. Adopting a Design Science Research (DSR) methodology, we validated the system against the FinanceBench financial benchmark ($N=150$ queries, three repeated runs, 450 total observations) and a proprietary Russian-language provocative content dataset developed internally at MWS AI ($N=201$ queries; not publicly available). The empirical results demonstrate that the architecture achieves an 83.2% Large Language Model (LLM)-judge compliance rate (95% confidence interval, CI: 79.4–87.1%), statistically significantly exceeding the unfiltered baseline of 63.7% ($\Delta =+19.5$ percentage points (pp), $t=4.02$, $p=0.002$). The vector-based input filter achieves perfect detection performance (Precision $=1.000$, Recall $=1.000$, F1 $=1.000$). Cross-domain validation on 201 Russian-language provocative queries confirms generalizability (Recall $=0.985$, LLM compliance among triggered queries $=0.977$). The operational Time-to-Compliance for enforcing new rules was reduced from hours (model fine-tuning) to under five seconds (vector indexing). These findings suggest that enterprise AI safety requires an architectural shift from model-centric training to system-centric control, complemented by system-prompt-level anti-inference governance. We conclude that AVI offers a scalable, cost-effective, and statistically validated framework for auditable AI compliance, independent of the underlying model provider. Full article

Background/Objectives: The evaluation of cognition is central to many neurological conditions, including traumatic brain injury, Alzheimer’s disease, Lewy body disease, frontotemporal degeneration, and vascular disorders. In clinical practice, particularly in aging populations, cognitive complaints often arise in the context of mixed neurological processes, requiring careful integration of cognitive and non-cognitive findings. Despite this, there remains limited clarity regarding the respective roles of neurologists and clinical neuropsychologists and the distinction between cognitive and neuropsychological assessments, terms that are often used interchangeably despite important differences in methodology and scope. This lack of a shared framework has practical consequences. Cognitive test results, when interpreted in isolation for diagnosis, may be misconstrued as comprehensive measures of brain function, particularly when non-cognitive neurological features such as motor, cerebellar, or vestibular abnormalities should have been considered (but were not). Methods: In this narrative review, we synthesize clinical guidelines, consensus statements, regulatory sources, and representative empirical literature to articulate a competence-based framework in which cognitive assessment is a medically integrated process incorporating history, functional evaluation, neurological examination, and the targeted use of standardized neuropsychological instruments. Results: Neurologists are trained to establish medical diagnoses and integrate cognitive findings into the context of neurological disease, while neuropsychologists contribute detailed psychometric characterization, culturally and demographically informed interpretation, cognitive phenotyping, functional characterization, and validity assessment in complex clinical and medicolegal contexts. Although neuropsychologists are qualified to diagnose neurocognitive disorders using standardized diagnostic criteria, attribution to specific neurological etiologies requires a comprehensive medical evaluation that extends beyond cognitive testing alone. Conclusions: We outline a tiered approach to evaluation that aligns assessment methods with clinical questions and supports accurate diagnosis, interdisciplinary collaboration, and patient-centered care. Full article