Search Results (1,241)

LLM privacy risks arise across different lifecycle stages and architectural boundaries, and existing protection mechanisms provide only partial coverage. This paper analyzes the main families of privacy-preserving approaches for LLM systems through a two-axis structure that crosses lifecycle stages with system architecture layers. Some safeguards are operationally mature; others, such as confidential computing, have moved into production practice; stronger cryptographic methods, while most promising in principle, remain research-heavy in practice. No single mechanism provides complete end-to-end protection: different methods protect different assets, operate at different lifecycle stages, span distinct system layers, and carry distinct trust, performance, and deployment trade-offs. Practical LLM privacy is therefore a problem of layered system design rather than the search for a universal primitive, and hybrid architectures are emerging as the most realistic deployable pattern. Building on this analysis, we propose a six-dimensional evaluation framework for privacy in hybrid LLM deployments (a 0–5 ordinal scoring rubric designed for reproducible application, with explicit anchor language and per-score evidence requirements) and apply it to five representative confidential AI deployments, deriving the scores in full for two of them. The framework feeds a three-tier gap-closure roadmap and design principles for architecture-time use, connecting what privacy technologies promise, what they actually protect, and what is realistically deployable in modern LLM systems. Full article

Spatially explicit socioeconomic data are critical for regional analysis, yet they remain scarce at subnational scales in many African contexts. This study presents a transparent and reproducible open-data framework to generate high-resolution gridded Gross Domestic Product (GDP) and derived socioeconomic and energy indicators. The approach combines gridded population and Night-Time Light (NTL) through the LitPop method to downscale provincial GDP to 1 km resolution for the Inkomati-Usuthu Water Management Area (IUWMA) in South Africa. The resulting GDP dataset is subsequently used as a spatial proxy to disaggregate compensation of employees, gross capital formation, fixed capital stock, net exports, gross operational surplus and sectoral Total Final Energy Consumption (TFEC). Results show strong consistency with official provincial GDP totals, with deviations ±0.4% after 2017. In 2024, LitPop allocated 4.26 billion constant 2015 USD to the IUWMA, equivalent to 16% of Mpumalanga’s GDP, compared with 47.3% under area-based allocation and 51.3% under population-based allocation. These differences reveal the strong influence of spatially concentrated industrial and energy-intensive activity. The workflow provides a scalable and replicable solution to generate coherent gridded socioeconomic datasets for WEF Nexus modelling, although estimates remain proxy-based and sensitive to NTL-related biases, particularly the overrepresentation of highly illuminated industrial assets and the underrepresentation of less luminous activities. Full article

This study examines how the Internet of Things (IoT) acts as a key enabler of sustainability in industrial production systems within the Industry 4.0 paradigm, addressing the fragmented understanding of the mechanisms linking digital technologies to environmental, operational, and emerging human-centric outcomes. A systematic literature review was conducted following PRISMA 2020 guidelines using the Web of Science Core Collection. After applying explicit inclusion and exclusion criteria, 69 peer-reviewed studies published between 2016 and 2026 were analyzed through qualitative thematic synthesis and comparative analysis. The findings reveal that IoT functions as a foundational digital infrastructure enabling real-time monitoring, operational transparency, and data-driven decision-making in production environments. Four dominant application domains are identified: (i) energy and resource efficiency, (ii) production monitoring and control, (iii) predictive maintenance and asset management, and (iv) emerging human-centric production systems aligned with Industry 5.0. While IoT consistently improves operational reliability and resource efficiency, its contribution to the social dimension of sustainability remains comparatively underdeveloped. This study advances the existing literature by providing a mechanism-oriented synthesis that explains how IoT-enabled infrastructures generate sustainability outcomes across production systems. Furthermore, it establishes a conceptual bridge between Industry 4.0 digitalization and the transition toward human-centric and resilient manufacturing models associated with Industry 5.0. From a practical perspective, the results highlight that IoT adoption contributes to reducing energy consumption, optimizing resource utilization, and enhancing operational performance, while also supporting safer and more adaptive working environments. However, challenges related to data integration, workforce adaptation, and digital capability gaps persist, underscoring the need for inclusive and strategically aligned digital transformation processes. Full article

Wildfires are a growing threat in Mediterranean regions where climate variability and land-use practices increase vulnerability to fire risk. Developing effective prediction models is essential for robust wildfire management, particularly in such data-scarce environments. Focusing on data-scarce Mediterranean environments, with reference to environmental conditions observed in Morocco, this review presents prediction models across three methodological categories: spatial risk mapping, temporal forecasting, and fire spread simulation, alongside the satellite data products that support their deployment. Each category is assessed in terms of predictive performance, data requirements, and adaptability to low-resource environments. XGBoost showed strong applicability in data-scarce Mediterranean contexts, while ARIMA was validated for forecasting fire-relevant time series under limited data resources. Freely accessible MODIS-derived products represent a significant asset to the region. Based on this synthesis, a hybrid XGBoost-ARIMA framework incorporating MODIS-derived inputs and SHAP-based interpretability is proposed as a promising candidate architecture to be validated after further investigation. The findings aim to support researchers, land managers, and policymakers in strengthening local wildfire prevention and mitigation efforts by aligning model capabilities with regional data and environmental constraints. Full article

The ongoing evolution of Industry 4.0 technologies necessitates novel and effective modes of human–machine interaction within production environments. This work presents a modular approach to the design and implementation of graphical user interfaces (GUI) in mixed reality, leveraging the Asset Administration Shell (AAS) standard. The proposed method enables the dynamic rendering of GUI elements in a Mixed Reality setting based on structured data retrieved from an AAS server. Developed for the Microsoft HoloLens 2 using the Unity engine and the Microsoft Reality Toolkit 3 (MRTK3), the system allows for the spatial placement of interface components either at predefined coordinates or in relation to specific elements of a production line model. Additionally, it incorporates a real-time distributed architecture utilizing OPC UA PubSub and MQTT protocols for processing and visualising live data. The prototype demonstrates the viability of using AAS as a flexible framework for defining and generating GUI components in immersive environments and lays the groundwork for further research into standardised, easily deployable user interface solutions for industrial applications. Full article

Knowledge of river flood risk in semiarid rural areas is often based on outdated, low-resolution geoinformation. Consequently, identification of exposed settlements, assets and risk-reduction measures remains challenging. This dataset provides up-to-date, fine-grained information for a rural area spanning 931 km² that is exposed to flooding from the Niger River and the Karma Wadi. The dataset includes information on (i) areas exposed to the two flood types that characterise the river’s hydrological regime and flash floods from the wadi, (ii) flood-prone crops, buildings and (iii) measures for risk treatment. Discharge data, a 4 m horizontal-resolution digital elevation model, and two-dimensional hydraulic modelling with BASEMENT were used to identify flood-prone areas. Visual interpretation of high-resolution satellite imagery in Google Earth, together with field inspections, enabled the identification of exposed assets. The Information System on Rural Markets of Niger and house compensation values recognised during resettlement-related works enabled asset valuation. Risk was expressed in monetary terms as the product of flood probability and expected damage. Risk-reduction measures were identified with stakeholders through a SWOT analysis and prioritised using eight criteria. The dataset can support emergency plans, flood early warning systems, rescue and recovery operations and flood risk management. Full article

Although rural livelihoods act as a critical mediator between poverty alleviation policies and sustainable outcomes, the spatial heterogeneity of this interaction remains underexplored within those agrarian systems that are crucial for food production. This study examines how China’s Targeted Poverty Alleviation policies shape livelihood strategies and the livelihood diversity of rural households across different spatial contexts in Jilin Province, a main grain production region of Northeast China. Using survey data from 2306 households, this study employs multiple logistic and linear regression models. The results indicate that (1) industrial and employment policies are associated with development-oriented strategies, whereas enterprise-driven and cash transfer policies tend to reinforce asset-based or welfare-dependent livelihoods; (2) these policy effects exhibit significant spatial heterogeneity, mediated by local agricultural productivity conditions, labor endowments, and off-farm livelihood availability; and (3) industrial policies show stronger associations with agricultural livelihoods in the east, while financial policies are more effective in sustaining agricultural engagement in the capital-constrained west. Integrating the Sustainable Livelihoods Framework with a spatial lens, this study shifts the focus of policy assessment from static outcome metrics to process-oriented analysis and reveals the mechanisms underlying the spatial divergence of livelihood strategies, providing a nuanced analytical framework for assessing the impacts of PAPs across diverse agricultural contexts. Based on these findings, this study highlights that spatially differentiated, livelihood context-sensitive policies are essential for securing sustainable and long-term poverty reduction in grain production regions, offering a replicable template for policy evaluation and practical implications for achieving SDGs 1 and 2 in agrarian regions. Full article

Addressing the issues of forest resource fragmentation and difficulties in value realization caused by traditional development models, China has explored the Natural Resource Asset Portfolio Supply Model (PSM), offering a new pathway for realizing Forest Ecosystem Service Value (FESV). However, existing studies are mostly descriptive case summaries and have yet to reveal the process mechanisms through which PSM drives forest value enhancement. Accordingly, this study selects five typical cases released by the Ministry of Natural Resources and employs multi-case research and grounded theory to deeply analyze their evolutionary pathways. The findings show that PSM promotes forest value enhancement through a gradient evolutionary pathway of “asset aggregation, functional coupling, and property rights conversion”. Asset aggregation addresses fragmentation through resource integration; functional coupling generates synergies through element combination; and property rights conversion transforms ecosystem services into transferable value carriers through institutional innovation, completing the transition from physical assets to capital. The study further identifies two roles of forest resources in composite asset packages, namely dominant resources and background resources, along with their distinct value enhancement pathways, and reveals how institutional innovation in property rights releases ecosystem services from physical constraints into transferable value carriers. The gradient evolutionary pathway constructed in this paper provides a novel process explanation for theoretical research on ecosystem service value realization, and its cross-context applicability offers a theoretical reference for natural resource management in similar global contexts. Practically, it provides managers with actionable value enhancement pathway choices and institutional design references, while also offering a viable analytical tool for policy optimization of PSM. Full article

As a non-rivalrous, replicable, and non-consumable production factor, data offers conditions for resource-efficient value creation, and the conversion from scattered data resources into measurable data assets sits at the center of firm competitiveness and sustainable allocation of digital factors. How artificial intelligence supports this conversion, and how executive cognition shapes its strength, are taken up within a framework drawing on the resource-based view, dynamic capability, and upper-echelons theory. Using 24,251 firm-year observations from Chinese A-share listed firms over 2012–2022, panel fixed-effects estimation yields a positive association between AI and data asset formation, stable across instrumental-variable estimation, propensity score matching, Heckman correction, and alternative measures of both variables. AI deepens data mining capability through stronger research and development investment and widens data-carrying capacity through expanded digital infrastructure, with the two channels opening up the relationship. Cognitive flexibility improves the fit between AI and shifting business scenarios, while cognitive complexity supports balanced allocation of technological resources across competing constraints; both characteristics strengthen the main association. The pattern is more pronounced among state-owned enterprises and firms in eastern and central regions, with industry differences less clear-cut. The findings inform differentiated policy design for sustainable digital development in emerging-market settings. Full article

Regulatory sandboxes have evolved from specialised FinTech tools into broader mechanisms of regulatory experimentation. As artificial intelligence (AI) applications become embedded in credit decisioning, payment-fraud detection, identity verification, crypto-asset compliance, customer-facing advice and supervisory analytics, sandbox design increasingly affects how legal and institutional responsibility is allocated among regulators, participating firms, technology vendors and users. This article provides a comparative doctrinal and institutional analysis of accountability and liability in AI-related financial regulatory sandboxes. It clarifies the relevant AI modalities, distinguishes accountability (answerability and enforceability during sandbox participation) from liability (contractual, tort/product and regulatory/public law responsibility after harm), and maps framework-level safeguards across the European Union, the United Kingdom, Singapore, Norway and Hungary. The analysis does not seek to measure sandbox effectiveness empirically. Instead, it examines how publicly available legal and regulatory materials structure the allocation of duties before, during and after sandbox testing. The article shows that sandboxes generally do not operate as liability shields. Their legal significance lies in reallocating ex ante accountability duties—documentation, disclosure, monitoring, human oversight and exit planning—while preserving baseline liability rules. An Accountability and Liability Protocol is proposed to clarify roles, protect baseline consumer rights, support evidentiary traceability and connect sandbox learning to enforceable post-sandbox obligations. Full article

This paper integrates the Elastic Net-TVP-VAR-BK framework and constructs a high-dimensional multilayer frequency-domain network, including short-, medium-, and long-term layers, to investigate extreme risk spillovers among 56 industries in the Chinese stock market. We examine the topology of the multilayer network at the system, cross-sector, and industry levels, as well as from both static and dynamic perspectives. Using daily data on 56 industry indices from 1 March 2007 to 30 September 2024, our empirical results show that: (1) All multilayer network topologies, including edge structures, node characteristics, and spillover strengths, exhibit significant frequency heterogeneity, and the dynamic topology of the three-layer network shows fluctuations and directional differences during critical periods. (2) In most periods, the short-term layer exhibits stronger average spillover intensity and denser inter-industry linkages, suggesting that short-horizon risk transmission plays a more prominent role in rapid contagion. However, the medium- and long-term layers remain important for identifying persistent and structural risk transmission. (3) At the industry level, capital markets and textiles, apparel, and luxury goods within the short-term layer, food products, household products, and road and rail in the medium-term layer as well as construction and engineering, industrial conglomerates, trading companies and distributors, metals and mining, and distributors in the long-term layer, all demonstrate high cross-industry systemic importance and total systemic importance, thereby establishing themselves as key nodes within their respective frequency domains. The findings provide theoretical support for policymakers in formulating strategies to address market risks and offer important references for investors in asset allocation and risk management decisions. Full article

As global industrialization and energy demands rise, excessive reliance on fossil fuels escalates carbon emissions, making clean energy alternatives an urgent priority for sustainable development. As a key transition pathway, wind and solar power can be converted into hydrogen via electrolyzers for electricity generation, thermal supply, or natural gas synthesis. This enables flexible multi-energy coordination and improves overall renewable energy utilization efficiency. However, conventional electrolyzer scheduling approaches typically assume fixed hydrogen production efficiency, failing to account for dynamic variations in operating conditions, efficiency attenuation, and lifetime degradation under fluctuating renewable inputs. This inadequacy compromises the long-term sustainability of green hydrogen systems. To address these challenges, this paper proposes a hybrid AEL-PEM electrolyzer power allocation and operating condition array rotation strategy. Piecewise linear models are established to characterize the efficiency and full life cycle degradation of both electrolyzer types across normal operation, overload, and start–stop transitions. A mixed-integer linear programming (MILP) model is formulated with an objective function incorporating energy purchase costs, start–stop penalty costs, and electrolyzer lifetime degradation costs, and is solved using the Gurobi solver. Simulation validation is conducted using a 24 h typical summer day dataset with a 15 min resolution. Three comparative schemes are evaluated to verify the strategy’s effectiveness in minimizing total system operation costs and enhancing renewable energy utilization efficiency through optimized operating condition management. Results demonstrate that the proposed strategy reduces total system costs by 23%, entirely eliminates renewable energy curtailment, and balances electrolyzer lifespan degradation across all units, collectively advancing the economic efficiency, asset sustainability, and long-term operational reliability of green hydrogen systems. Full article

This study investigates time-varying market efficiency and cross-market correlations in cryptocurrency markets across South Korea, the United States, and Japan. Using rolling-window multifractal detrended fluctuation analysis (MF-DFA) and dynamic conditional correlation–generalized autoregressive conditional heteroskedasticity (DCC-GARCH), we analyze 11 cryptocurrency–fiat pairs—Bitcoin (BTC), Ethereum (ETH), Ripple (XRP), and Bitcoin Cash (BCH) denominated in Korean Won (KRW), US Dollar (USD), and Japanese Yen (JPY)—from January 2018 to September 2025. MF-DFA results confirm persistent multifractality and significant time-variation in market efficiency across all markets, consistent with the Adaptive Market Hypothesis (AMH). DCC-GARCH estimates reveal a structural divergence between return integration and efficiency correlations: return-based correlations for same-asset cross-fiat pairs are exceptionally high (mean dynamic conditional correlation of approximately 0.96–0.98), whereas efficiency-based correlations are far more heterogeneous, with cross-asset pairs approaching near-zero synchronization. We interpret the Kimchi Premium as a product of institutional frictions that impede price-level arbitrage while leaving volatility transmission largely unaffected. These findings suggest that cryptocurrency market integration is multidimensional—globally synchronized in risk dynamics, yet locally segmented in the structural quality of information processing. Full article

This study develops a production-based asset pricing model that incorporates time-varying disaster risk together with model uncertainty. Within an extended relative-entropy framework, agents’ distorted beliefs and ambiguity aversion are characterized, and the corresponding Hamilton–Jacobi–Bellman–Isaacs (HJBI) equation is derived under a stochastic robust-control setting. The framework implies that the equity premium can be decomposed into three components: diffusion and jump risk premiums associated with conventional risk aversion and an additional rare-event premium generated by ambiguity aversion. Numerical experiments show that ambiguity aversion reduces the equilibrium risk-free rate, whereas aversion to rare disasters significantly raises compensation for bearing risk, helping reconcile both the equity premium puzzle and the risk-free rate puzzle. In addition, equity return volatility increases with the probability of disaster events, but at a diminishing rate. Overall, the results underscore the importance of model uncertainty and time-varying disaster risk in the determination of asset prices and risk premia. Full article

This review analyzes the economic, social, and environmental dimensions of water buffalo (Bubalus bubalis) production and its contribution to the Sustainable Development Goals (SDGs). A scoping review following PRISMA-ScR guidelines was conducted using the Web of Science (2020–2026), resulting in 225 included studies. Buffalo production is a multipurpose system that generates value through milk, meat, hides, manure, draft power, and animal-assisted services, with greater longevity than most livestock species. Economically, it supports income diversification, resource efficiency, and functions as a financial asset that can be sold to cover unexpected expenses. Socially, it enhances food security by providing nutrient-dense products, particularly milk with bioactive compounds associated with potential health benefits, and promotes women’s participation in livestock management and household economies. Environmentally, buffalo systems efficiently utilize low-quality forages, are adapted to marginal conditions, contribute to wetland conservation, and provide ecosystem services. These contributions align with several SDGs (1, 2, 5, 8, 12, 13, and 15). However, sector expansion is constrained by limitations in nutrition, management, veterinary services, and reproductive efficiency, as well as environmental challenges related to methane emissions and life cycle impacts. While global methane emissions from buffalo are lower due to their smaller population, emission intensity remains system-dependent and represents a critical challenge. In conclusion, water buffalo production represents a multifunctional and context-dependent system with significant potential to support sustainable development, although targeted innovations are required to improve productivity and address environmental challenges. Future research should integrate One Health and One Welfare approaches, develop long-term studies, and expand research under diverse experimental and field conditions to better characterize the potential health implications of buffalo-derived products. In addition, strengthening circular economy strategies, including region-specific diets to reduce emissions, remains a priority. Full article