Search Results (1,285)

Global stocktakes reveal that studies on natural hazards triggering technological accidents (Natech) in the context of climate change are limited in Southeast Asia, which is characterized by rapid industrial growth and high exposure to extreme weather. This study assesses the status of Natech hazards, including their consideration in national climate change adaptation policies, and translation to local stakeholders for disaster risk reduction using a case study in Malaysia. A qualitative approach was deployed, combining document analysis and semi-structured interviews in the case study area. The findings reveal that all 11 countries in Southeast Asia are susceptible to Natech hazards; however, only Malaysia, Timor-Leste (East Timor), and Vietnam mention them explicitly in their respective National Communications to the United Nations Framework Convention on Climate Change (UNFCCC), while Indonesia, Singapore, and Thailand reference them indirectly. The results of the interviews (n = 35) in IKS Kuala Selangor, Malaysia, indicate that, despite 30% of stakeholders having been directly affected by floods, fewer than 10% are prepared for Natech hazards. Potential policy entry points for mainstreaming Natech hazards include linking them to regulations on Environmental Impact Assessments and building codes for new projects, plans for climate proofing existing structures, and means for capacity enhancement. Full article

Facing China’s complex operating environment, in which the spot electricity market is still at an early stage of development and the mechanism price policy for new energy is implemented in parallel, this paper addresses the mechanism adaptability challenges that arise when high-proportion new energy participates in the spot electricity market on a full-volume basis. A trading and settlement mechanism with trading flexibility, multi-timescale coordination, and policy alignment is then constructed. Full-volume electricity on both the generation and consumption sides is incorporated into spot market clearing and settlement, thereby strengthening the role of the spot market in resource allocation. Differentiated bidding, settlement, and responsibility-bearing mechanisms are further designed according to electricity type and participant category, thereby clarifying responsibility boundaries and supporting imbalance-fund allocation. A multi-timescale trading mechanism is constructed to link short-cycle flexible trading, day-ahead bidding, intraday rolling adjustment, and real-time balancing. In addition, a differentiated medium- and long-term contract mechanism and a secondary settlement system are designed to meet the needs of system peak-regulation and market risk management. These designs also support coordination with the mechanism price policy for new energy, priority generation of hydropower and nuclear power, guaranteed electricity consumption, and grid-company agency purchasing. Case studies show that the proposed mechanism can help mitigate market efficiency distortions, improve the adaptability of the spot market to high-proportion new energy, and support the reasonable allocation of policy-related compensation costs and system regulation costs. Full article

As cryptocurrency adoption accelerates globally, cities face a critical question: can digital currencies be adapted by different agencies without compromising financial stability? This paper develops an agent-based modeling framework that enables transit authorities to systematically explore cryptocurrency integration strategies. We simulate 1000 heterogeneous agents with varying risk tolerance, technological proficiency, and social influence susceptibility over 365 days, testing five policy regimes across a comprehensive scenario matrix comprising four risk-attitude compositions, four technology-adoption levels, four social-influence intensities, and three market conditions (bullish, neutral, bearish)—creating 192 distinct population-market configurations evaluated across all five policies with 15 independent replications per configuration (14,400 total simulation runs). The framework produces adoption outcomes ranging from near-zero to over 49% depending on scenario assumptions, with technology familiarity emerging as the dominant driver. The framework provides transit authorities with a practical tool for scenario-based planning: testing policy interventions, stress-testing financial stability under various market conditions, identifying potential vulnerabilities before deployment, and comparing alternative strategies across diverse demographic contexts. This simulation-based approach enables data-driven decision-making in the absence of real-world precedent, offering a structured methodology for evaluating cryptocurrency integration while managing financial stability risks. Full article

Green low-carbon data centers operate as coupled cyber-energy systems whose dispatch must coordinate renewable generation, grid exchange, battery storage, cooling load, flexible computing workload, carbon-intensity signals, and reliability constraints. This study develops and evaluates a safety-constrained deep reinforcement learning framework for source–load–storage coordinated operation of a grid-connected green data center. The operating problem is formulated as a constrained Markov decision process with state variables describing the IT load, deferrable workload backlog, renewable availability, electricity price, marginal carbon intensity, battery state of charge, server-room temperature, reserve margin, and calendar context. The action space covers grid import and export, renewable utilization, storage charge and discharge, workload shifting, and cooling control. The learning architecture combines a constrained actor–critic policy, adaptive Lagrangian safety critics, and a control barrier function (CBF)-based action shield that projects unsafe actions onto an explicitly defined operating set before plant execution. The shield is specified as a low-dimensional quadratic projection over state-dependent SOC, thermal, reserve, SLA, and grid-interface constraints, while cumulative risks are priced through Lagrangian safety budgets during policy training. The evaluation uses a controlled and auditable benchmark simulation with normalized public-data-compatible profiles, declared scenarios, random seeds, neural-network settings, and mechanism-matched baselines; it is not a telemetry-based verification or hardware certification of a deployed data center. Within this declared benchmark, the proposed safe DRL controller produces a simulated 13.1% emission reduction relative to the Rule-based controller, 95.8% renewable utilization, a normalized annual cost of 0.91, and fewer boundary contacts than the tested unconstrained, Lagrangian-only, and shield-only PPO variants. These percentages are simulator outputs relative to the stated benchmark and must not be interpreted as measured field savings. The results show how separating reward learning, cumulative safety pricing, and one-step engineering projection changes low-carbon dispatch within the specified model. Full article

Flooding is a destructive natural hazard intensified by climate change, posing challenges to sustainable disaster risk management. This study developed and evaluated machine learning models for flood severity prediction within a hexagonal grid system (H3, resolution 8) under rainy season conditions in Maha Sarakham, Thailand. Four models Random Forest (RF), XGBoost, Gradient Boosting (GB), and Support Vector Machine (SVM) were trained using 11 environmental variables across historical years (2018, 2021, 2022) and tested on a projected year (2025) under SSP scenarios. XGBoost demonstrated the most stable performance (accuracy > 0.95 across all years), while SVM achieved high historical accuracy (0.970 average) but failed to detect positive flood cases in 2025 (recall = 0), highlighting the importance of temporal validation. Topographic variables were the most consistent predictors, but NSMI (soil moisture) emerged as the top SHAP predictor in 2025 (r = 0.52), suggesting a shift in flood-generating mechanisms under climate change. A polarization pattern was observed: flood-affected area declined to 6.8% in 2025 (79% reduction from 2022), yet maximum flood point counts remained high at 14.0, indicating more concentrated but intense flooding. Under SSP projections, using the historical baseline (27.5%), SSP1-2.6 (45.2%) and SSP2-4.5 (45.0%) indicate increased flood risk relative to the historical baseline through 2040. The SSP5-8.5 projection (3.4%) is identified as a model extrapolation artifact through formal out-of-distribution assessment (Mahalanobis distance = 8.72, p < 0.001) and is therefore excluded from policy recommendations. Although GRU and LSTM achieved marginally higher AUC values in retrospective validation, we recommend XGBoost for operational forecasting due to its temporal stability, computational efficiency, and interpretability. We further recommend integrating real-time soil moisture monitoring into early warning systems and shifting to hotspot-targeted adaptation strategies. Full article

Public responses during health crises are shaped by interacting risk perceptions, affect, trust, information needs, overload, misinformation, and protective behavior. Existing applications of the Risk Information Seeking and Processing (RISP) model are largely static and therefore cannot represent stochastic multichannel exposure, delayed correction, or policy feedback. We develop the Stochastic Probabilistic Dynamic RISP (SP-D-RISP) model, which recasts RISP as a bounded stochastic state-space system. Its symmetry structure is explicit: the channel-allocation mechanism is equivariant under simultaneous relabeling of channels and their parameter blocks, while the multi-agent dynamics are invariant to agent relabeling under exchangeable sampling and a label-independent policy. Channel-specific effects, heterogeneous traits, rumor shocks, and interventions generate symmetry breaking. The model combines softmax–multinomial channel competition, discounted Bayesian trust updating, and policy-coupled state transitions. Projection guarantees feasible states by construction, whereas stronger stochastic stability is conditional on a coefficient-level small-gain criterion. For the stationary bounded-memory specification, this criterion is sufficient for Wasserstein contraction, uniqueness of the invariant distribution, and geometric forgetting of initial conditions. The criterion is formulated at the coefficient level and is kept distinct from finite-horizon simulation diagnostics. For the fully disclosed semi-synthetic coefficient vector, the scenario-specific gain matrices have spectral radii between $0.852765$ and $0.857123$; the worst-case column-sum norm is $0.983948$. Thus, the fixed-policy kernels satisfy the stated contraction certificate. For deterministic time-varying paths, the calculation is used only as a common-path one-step certificate, and for the threshold-adaptive rule, it is used only mode by mode rather than as a stationary invariant-law claim. While concentration bounds and Monte Carlo inference quantify population and replication uncertainty, a semi-synthetic experiment with 2500 heterogeneous agents over 90 days examines trust and literacy heterogeneity, clarification delays, communication volume, and intervention portfolios. Within the calibrated SP-D-RISP scenarios, the simulations suggest that higher communication volume may reduce modeled protective behavior when overload effects dominate knowledge gains, delayed clarification may increase transient misinformation, and an integrated portfolio can yield a more favorable simulated outcome profile than the evaluated single-lever strategies. Full article

Sustainable hydrogen policy and national strategy documents are rich in qualitative information whose systematic evaluation still relies largely on subjective SWOT frameworks. This study proposes a reproducible hybrid methodology that couples expert-supervised Natural Language Processing (NLP) with a stochastic techno-economic model of the Levelized Cost of Hydrogen (LCOH) to convert policy discourse into quantitative, evidence-based recommendations. TF-IDF vectorization, K-Means clustering, Shannon entropy and Correspondence Analysis (CA) are applied to a manually annotated corpus of 107 sentences drawn from Türkiye’s national hydrogen strategy documents (Cohen’s κ = 0.81, substantial agreement). CA positions Regulation/Legislation and Financing near the Weakness quadrant, Renewable Resource Potential in the Strength quadrant, and Export/Demand Risk near Opportunity—revealing structural bottlenecks that challenge the sustainable energy transition. These qualitative findings are subjected to a quantitative consistency check via a Monte Carlo simulation (N = 10,000 iterations) propagating joint uncertainty in CAPEX, electricity price, electrolyzer efficiency, annual operating hours, discount rate and plant lifetime. The deterministic 2025 LCOH baseline of 4.89 €/kg H₂ carries a P10–P90 interval of [3.95; 5.92] €/kg. Global Sobol sensitivity analysis identifies electricity price as the dominant driver (S₁ ≈ 0.52), suggesting that financing is discursively surfaced by the textual layer. Under business-as-usual technological learning, the probability of reaching a globally competitive LCOH (≤2 €/kg H₂) by 2050 is only 17.8%; a stylized proactive policy intervention (carbon pricing + subsidies) raises this probability to 78.4%. The framework is adaptable to other countries and languages (though the current implementation is Turkish-specific), providing a scalable, open methodology for evidence-based sustainable clean energy planning. Full article

Against accelerating urban expansion, climate risks, and green transition, fortifying urban ecological resilience (UER) has become central to global urban sustainability. However, there is as yet no systematic evidence to suggest how the National Innovative City Pilot Policy (NICPP) promotes green technology innovation (GTI) and strengthens UER. To address this gap, this paper builds upon the traditional resistance–adaptability–resilience (RAR) paradigm and its extension, the driving force–resistance–adaptability–resilience (DRAR) framework to formulate a comprehensive driving force–pressure–resistance–adaptability–resilience (DPRAR) assessment system. Moreover, this paper investigates a dataset of 271 cities in China spanning the period from 2006 to 2023. By treating the NICPP as a quasi-natural experiment, this paper integrates an entropy balancing technique into a staggered difference–in–differences (DID) model to identify its effect on UER. The results are outlined below. (1) From 2006 to 2023, UER across Chinese municipalities generally increased, though spatial imbalances persisted. (2) The NICPP enhanced urban UER, with effects characterized by time-lagged and cumulative patterns. (3) Both the quantity and quality structure of urban GTI served as channels through which the NICPP affected UER. (4) The NICPP effects are strongest in the central region, followed by those in the western and eastern regions, as well as in resource-dependent cities and cities with stricter environmental regulation. This research provides novel empirical perspectives linking innovation-driven development, GTI, and UER, with implications for NICPP optimization and UER enhancement in China. Full article

The growing complexity of cryptocurrency ecosystems has created substantial difficulties for anti-money laundering (AML) enforcement across jurisdictions. Existing AML information-sharing mechanisms are fragmented, slow, and constrained by privacy regulations. This paper presents CAHE-AML, a risk-adaptive architecture built on a context-aware hybrid encryption framework designed to enable secure and privacy-preserving cross-border AML data exchange between regulatory authorities and virtual asset service providers (VASPs). CAHE-AML integrates symmetric encryption, attribute-based encryption (ABE), a decentralized key governance model, and heuristic risk-scoring mechanisms for dynamic policy adaptation. Using an Ethereum Fraud Dataset, we compute risk scores to dynamically assign context-aware access policies across three hierarchical tiers: local, regional, and global. Experimental evaluation on an enterprise-grade multi-core system demonstrates high computational efficiency, achieving substantial throughput and low processing latency. The system identifies integrity breaches with high accuracy while maintaining a manageable data expansion ratio. These results highlight the computational feasibility of CAHE-AML’s cryptographic layer for real-time, high-frequency AML data processing in decentralized financial systems. Full article

Traditional financial distress early-warning models mostly rely on lagged structured financial indicators, which fail to capture the potential credit risks associated with the climate transition of green enterprises. Taking A-share listed green companies from 2015 to 2024 as research samples, this paper centers on the core research question of whether mandatory climate narratives in annual reports can deliver incremental risk warning information beyond accounting indicators. Based on textual data from annual reports, this study constructs a corporate climate resilience indicator by integrating word frequency statistics and sentiment analysis. Two data-partitioning schemes (random sampling and time-series extrapolation) are adopted to compare the predictive performance of four ensemble learning models. Extended tests are further conducted via SHAP values, partial dependence plots, polynomial Logit regression, interaction effect regression and grouped regression. The results indicate that the climate resilience indicator carries incremental information supplementary to financial indicators and possesses predictive power for financial distress. XGBoost demonstrates optimal adaptability to the hybrid feature framework, combining financial data and climate textual features. The climate resilience indicator exerts synergistic effects with financial variables and presents a non-linear statistical correlation with default probability. This study verifies that climate narratives disclosed in annual reports can serve as valid early-warning signals for credit risks. The conclusions provide empirical evidence for financial risk control, corporate disclosure management and the formulation of climate regulatory policies. Full article

Connected and Automated Electric Vehicles (CAEVs) are rapidly evolving into complex Cyber-Physical-Social Systems (CPSS), generating structural tensions between technological innovation and public safety. Current research in public governance exhibits significant fragmentation. Scholars frequently isolate data privacy compliance from algorithmic safety auditing, treating them as distinct silos. To bridge this gap, this study applies the PRISMA framework to systematically synthesize 135 core peer-reviewed articles, exposing the endogenous limitations of unidimensional regulatory paradigms. Our analysis yields three central insights. First, traditional “notice-and-consent” models fail under the ubiquitous data collection demands of modern V2X environments. Macro-level policies must translate into foundational Privacy-Enhancing Technologies (PETs) through “Law-as-Code” mechanisms. Second, the opacity of end-to-end algorithmic decision-making deconstructs traditional tort liability systems. This necessitates ex-ante quantitative auditing mechanisms—such as Explainable Artificial Intelligence (XAI) and enhanced Threat Analysis and Risk Assessment (TARA 2.0)—to mitigate adversarial attacks and physical-level safety hazards. Third, overcoming cross-national regulatory fragmentation requires constructing a “dual-track synergistic” governance architecture. This framework institutionalizes the coupling of data lifecycle quality workflows with the algorithmic Safety of the Intended Functionality (SOTIF). Ultimately, this review advocates for adaptive regulatory sandboxes and advances the harmonization and mutual recognition of global standards (e.g., ISO/SAE 21434, UN R155/156). Addressing current methodological and empirical data constraints, future academic inquiry must pivot. Researchers should target the value alignment challenges of Large Language Models (LLMs) in autonomous driving and implement multi-stakeholder participatory policy pilots designed to reconcile diverse social values. Full article

UAV-based weed detection in precision agriculture is constrained by asymmetric error costs: a missed weed patch causes herbicide under-treatment and yield loss, whereas a false alarm only prompts an unnecessary spot treatment. Cost-sensitive active learning (cAL) addresses this through an asymmetric misclassification penalty $r^{+}$, but the optimal value is scene-dependent and cannot be determined without domain expertise or advance knowledge of scene difficulty—a fundamental barrier to autonomous UAV monitoring workflows. We propose AdaRisk-Agent, the first LLM-orchestrated framework for adaptive $r^{+}$ calibration in cAL-based UAV weed detection. We validate the framework on four UAV multispectral scenes from two public datasets—WeedsGalore (Germany, five-band maize) and WeedyRice (Vietnam, four-band paddy)—spanning two crop types, two sensor configurations, and weed prevalence from 3.1% to 30.5%. Adaptive calibration reduces the false-negative rate (FNR) by up to $80\%$ relative to symmetric-cost baselines across all scenes. The deterministic surrogate (AdaRisk-Rule) surpasses the fixed-policy oracle (cAL $r^{+}=7$) on two of four scenes without advance scene knowledge, achieving a $50\%$ FNR reduction on the most spectrally challenging scene. A context-feature ablation confirms that budget urgency is the primary calibration signal and that test-set-independent deployment is feasible. Each calibration decision is accompanied by a natural-language justification, enabling auditable deployment in operational precision agriculture workflows. Future work will extend AdaRisk-Agent to multi-class weed species detection and multi-scene meta-learning for compact offline surrogate policies. Full article

Climate change poses significant environmental and socio-economic challenges for Small Island Developing States (SIDS), including Timor-Leste. This systematic review aimed to synthesise evidence on climate change impacts, vulnerabilities, adaptation pathways, and research gaps in Timor-Leste. Following PRISMA 2020 guidelines, literature searches were conducted in Web of Science, Scopus, and Google Scholar through May 2025. Eligible sources included peer-reviewed studies, technical reports, policy documents, and institutional publications. Source quality was assessed based on relevance, methodological consistency, credibility, and thematic contribution, and evidence was synthesised using a qualitative meta-synthesis approach. A total of 79 peer-reviewed studies and 8 international reports met the eligibility criteria, resulting in a final corpus of 87 documents. The evidence indicates a warming trend of 0.16 °C/decade and a sea-level rise of 5.5 mm/year with significant implications for agriculture, food security, water resources, and coastal systems. Approximately 70% of the population depends on climate-sensitive livelihoods, increasing exposure to climate-related risks. Evidence remains limited by data scarcity and methodological heterogeneity. Overall, Timor-Leste faces substantial climate vulnerability, highlighting the need for strengthened adaptation planning, improved climate information systems, and targeted policy interventions. Full article

Advanced Persistent Threats (APTs) pose a serious threat to Internet of Things (IoT) systems because of their stealthiness, persistence, and ability to adapt to defensive responses. Communication-Based Train Control (CBTC) systems, as IoT-enabled railway signaling infrastructures, have evolved from relatively closed operational environments into interconnected cyber-physical networks, exposing train control systems to coupled cyber intrusion and operational-safety risks. To address this challenge, this paper proposes an alert-driven active defense framework for CBTC systems that integrates Bayesian belief updating, hypergame-based cognitive-bias modeling, and Hierarchical Reinforcement Learning (HRL). The framework converts intrusion detection system (IDS) alerts, network traffic observations, and cyber-physical observations into belief-state, transition, and reward inputs. The Bayesian model estimates attacker type and attack stage, the hypergame model represents deception-induced asymmetric cognition between attackers and defenders, and the HRL decouples strategic defense posture selection from tactical defense execution. The scenario-driven simulations in a CBTC APT defense setting show that the proposed model strategy achieves an 87.1% defense success rate against APT attacks while consuming 62.7% of the normalized defense resources, outperforming DQN, PG, and PPO under the same test conditions. These results suggest that explicitly coupling cyber observations, CBTC operational constraints, and hierarchical deception-aware policies can improve cost-aware active defense for railway signaling infrastructures. Full article

Background/Objectives: Traditional community oral health programs (COHPs) often remain reactive, clinic-based, and insufficiently integrated with medical, social, and aged-care systems. As a result, they may be poorly positioned to address the needs of older adults experiencing multimorbidity, mobility limitations, cognitive impairment, care dependency, and financial or geographic barriers. This paper proposes SPACES, an implementation-oriented conceptual framework for designing, delivering, and evaluating community oral health programs for older adults. Methods: SPACES was developed through a targeted interpretive synthesis of peer-reviewed literature, policy guidance, and implementation-oriented sources related to oral health, healthy aging, integrated care, access barriers, workforce development, and sustainable service delivery. Results: The framework comprises six interconnected domains: Scalable Prevention and Education, Partnerships Across Sectors, Adaptive Care Coordination, Community Access Solutions, Empowered Workforce Development, and Sustainable Service Models. It is intended primarily for adults aged 60 years and older, while allowing adaptation in jurisdictions that use 65 years and older as the eligibility threshold. The framework applies across community, home care, assisted living, and long-term care settings. SPACES organizes evidence-informed components for older-adult oral health, including routine screening, caregiver-supported daily mouth care, risk-stratified prevention, referral pathways, proximity-based service delivery, geriatric workforce competencies, task sharing, sustainable financing, governance, and quality improvement. Conclusions: SPACES is presented not as a tested intervention but as a conceptual and program-planning tool to help planners clarify responsibilities, identify equity gaps, align cross-sector resources, and select measurable implementation indicators. Future stakeholder refinement and empirical evaluation are needed through co-design, pragmatic implementation studies, and real-world assessment of feasibility, acceptability, cost, equity impact, and oral health outcomes. Full article