Search Results (664)

Reliable climate data are essential for sustainable water management systems, especially under the challenges posed by climate change. In data-scarce regions, reanalysis products such as ERA5 can support flood and drought risk assessment and water security analysis. However, raw reanalysis precipitation is systematically biased relative to local observations and can distort hydrological indicators; bias correction is therefore needed. This study tests five bias correction techniques (Linear Scaling, Empirical Quantile Mapping, Quantile Mapping Spline Bias Correction, Mean Bias Subtraction, and Simple Linear Regression) on ERA5 precipitation data for Georgia, using classical and sliding window approaches at daily and monthly scales. Results show the importance of selecting the most appropriate method according to data availability and study objectives. The sliding window approach improved performance, especially at the daily scale, and distribution-based methods proved most effective in data-scarce regions. Full article

Background: Maritime operations increasingly rely on integrated, secure, and resilient architectures, yet the associated body of knowledge remains fragmented across conceptual, operational, logical, methodological, and governance-oriented perspectives. Objective: Our aim is to systematically review the literature on maritime integrated systems architecture in order to identify dominant themes, methodological tendencies, enabling technologies, and unresolved research gaps. Eligibility criteria: Peer-reviewed studies published in English were included when they addressed integrated systems architecture, or closely related architectural approaches, in maritime or naval contexts. Studies centred exclusively on isolated components, non-maritime settings without clear architectural transferability, or insufficient technical or methodological detail were excluded. Information sources: ACM Digital Library, IEEE Xplore, SpringerLink, ScienceDirect, MDPI, and IMarEST. Searches were carried out between January and March 2025, with the final search update for all sources completed in March 2025. Methods: The review was conducted and reported in accordance with PRISMA 2020. Three reviewers independently screened titles, abstracts, and full texts. Two reviewers independently extracted data, assessed methodological limitations and risk of bias using a review-specific qualitative appraisal framework, and evaluated the risk of bias due to missing results at the synthesis level. Disagreements were resolved through discussion and consensus, with third-reviewer arbitration when necessary. The synthesis combined qualitative thematic analysis across eleven predefined analytical categories with descriptive bibliometric and thematic mapping procedures. Results: Of 300 identified records, 60 studies met the inclusion criteria. Across non-mutually exclusive analytical categories, the literature was concentrated in Integrated Systems Architecture (52 studies), Development Processes (42), and Conceptual Models (37), whereas Zachman-based Methodology (4) and Maturity Models (3) were only marginally represented. Three recurrent patterns were observed across the corpus: the central role of cybersecurity and risk governance in architectural design; the growing importance of information technology and operational technology convergence for resilient monitoring, coordination, and decision support; and the increasing use of model-based and model-driven approaches to address architectural complexity. Overall confidence in the principal synthesized findings was judged to be moderate. Limitations: The review was limited to six databases and English-language publications, and the included studies varied in reporting depth, methodological transparency, and degree of empirical validation. Conclusions: The review organizes the field into a multilevel taxonomy spanning conceptual and operational models, logical and layered views, development processes, reference architectures, enabling technologies, and maturity-related perspectives. Taken together, the findings suggest that research in this area has progressed more clearly in architectural representation and integration than in long-term evaluation, particularly with regard to the practical operationalization of Zachman-based approaches and the development of maritime-specific maturity assessment frameworks. Funding: This review received no external funding. Registration: The review was not prospectively registered, and no publicly accessible protocol was prepared. Full article

Modern machine learning approaches for vulnerability detection achieve strong performance within specific datasets, yet their reliability degrades under domain shift, limiting their effectiveness for real-world secure software development lifecycle (SDLC) decision-making. In particular, probabilistic vulnerability predictions, while well-calibrated, exhibit instability across heterogeneous codebases, reducing their suitability as standalone risk indicators. This paper introduces Intelligent Risk-Adaptive Secure SDLC (IRAS-SDLC), a lifecycle risk aggregation framework for Secure AI-Augmented Software Assurance under the Risk Management Framework (RMF) and Zero Trust. The proposed framework integrates model-derived vulnerability likelihood with structured security metrics, specifically exploitability and impact derived from standardized Common Vulnerability Scoring System (CVSS) data, to construct a unified and interpretable risk representation. This formulation enables consistent prioritization across SDLC phases while aligning with RMF control families and Zero Trust continuous verification principles. By combining learned semantic signals with domain-independent security factors, IRAS mitigates the instability of vulnerability likelihood under distributional shifts and provides a more robust basis for cross-domain risk assessment. The framework embeds risk evaluation early in the SDLC, enabling proactive identification of vulnerabilities during the requirements and design phases rather than post-implementation detection. Empirical evaluation demonstrates that IRAS-SDLC maintains meaningful risk estimation under domain shift and significantly improves lifecycle outcomes. In particular, early risk identification yields negative detection latency relative to conventional methods and reduces simulated remediation costs by up to an order of magnitude. IRAS-SDLC bridges the gap between machine learning-based vulnerability prediction and governance-aligned security assurance by providing a stable, interpretable, and lifecycle-aware risk assessment mechanism that is directly compatible with RMF-based compliance workflows and Zero Trust architectures. Full article

The rapid integration of Large Language Models (LLMs) into critical personal and professional environments has exacerbated security risks, particularly adversarial attacks such as prompt injection and jailbreaking, which aim to bypass safety alignment. This study evaluates the efficacy of NVIDIA’s Llama-3.1-nemoguard-8b-content-safety model acting as a semantic firewall to mitigate these threats. To ensure a robust assessment, we utilized the ‘Do Not Answer’ dataset, augmented with 939 synthetically generated benign prompts to create a balanced corpus of 1878 samples. The evaluation methodology encompasses a risk-category analysis, standard binary classification metrics, and a novel metric, the Compensation Rate, which measures the firewall’s ability to block responses when the underlying LLM fails. Results indicate a high Precision (94.57%) but a moderate Sensitivity (51.97%), uncovering a critical performance trade-off: the model exhibits a conservative bias, prioritizing high precision to minimize false positives at the expense of recall for nuanced adversarial prompts, particularly in categories involving sensitive data leakage and misinformation. Furthermore, the proposed Compensation Rate achieved 34.8%, suggesting that the semantic firewall successfully mitigated 34.8% of instances where the foundational LLM’s internal safety alignment failed. These findings indicate that while the system effectively blocks explicit threats, its efficacy as a secondary defense diminishes against context-dependent vulnerabilities, notably data exfiltration and misinformation. Full article

Background/Objectives: In critically ill patients, endotracheal intubation (EI) is often performed to secure the airway or mechanical ventilation. Accurately predicting the timing of intubation significantly affects patient outcomes. We developed an artificial intelligence (AI) model designed for real-time risk stratification of patients requiring EI. Methods: We utilized the Medical Information Mart for Intensive Care-IV (MIMIC-IV) 2.2 dataset and performed model development using 15 clinical variables, including vital signs, Glasgow Coma Scale (GCS) score, and arterial blood gas analysis results. Patients intubated before or within 1 h of intensive care unit (ICU) admission were excluded. Clinical data from the ICU inherently consists of continuous time-series measurements. Traditional machine learning models often treat this information as static tabular data, neglecting vital temporal dynamics and patient history. Conversely, deep learning time-series approaches can capture these complex patterns over time. Thus, we applied the Gated Recurrent Unit with Decay++ (GRU-D++) model to predict the need for EI. GRU-D++ is an extension of the GRU and GRU-D. It builds upon the GRU-D to provide improved performance when handling datasets with exceptionally high rates of missing values. GRU-D++ is a time series deep learning model with an automatic mechanism for imputing missing values. This built-in capability eliminates the need for additional data preprocessing and has previously demonstrated high predictive performance. Using the 15 variables, we evaluated the optimal timing for EI in ICU-admitted patients by applying various AI models. Results: Among these, the GRU-D++ model demonstrated AUROC of 0.888, AUPR of 0.481, sensitivity of 0.474, specificity of 0.995, precision of 0.511, and F1 score of 0.491 on MIMIC-IV dataset. For KNUH dataset, the model demonstrated AUROC of 0.913, AUPR of 0.063, sensitivity of 0.162, specificity of 0.997, precision of 0.137, and F1 score of 0.147 within the 2 h in advance scenario. Furthermore, when compared with conventional scoring systems such as the Heart rate, Acidosis, Consciousness, Oxygenation, Respiratory rate (HACOR) score and Respiratory rate-Oxygenation (ROX) index, the GRU-D++ model also showed better performance predictive accuracy. Conclusions: The AI-based intubation prediction model developed in this study holds potential as a real-time risk stratification tool, providing timely risk assessments regarding the need EI. While operational threshold recalibration is essential prior to clinical deployment, further prospective multicenter studies are required to validate the clinical utility of this model in real-time practice. Full article

As a rapidly developing extreme drought event, flash drought poses an increasingly serious threat to agricultural production, ecosystem carbon sequestration, and regional ecological security. However, systematic understanding remains limited regarding the occurrence characteristics of flash drought across different cropland types and the mechanisms by which it affects gross primary productivity (GPP). Using root-zone soil moisture, meteorological variables, and GPP data for China from 2000 to 2020, this study characterized flash drought events across different cropland ecosystems, quantified the response frequency and intensity of GPP, and further explored the dominant driving factors using eXtreme Gradient Boosting and SHapley Additive exPlanations. The results showed that flash drought occurred more frequently in cropland than in non-cropland areas, and that rainfed cropland experienced flash drought more frequently and developed more rapidly than irrigated cropland. The mean GPP response frequency in cropland was 0.43, indicating that nearly half of flash drought events suppressed GPP. Regions with high sensitivity were mainly concentrated in northwestern and northeastern China, with northwestern China showing the lowest resistance to flash drought. Climatic background and hydro-meteorological anomalies were the dominant factors controlling GPP responses in cropland, and the dominant driving factors differed significantly among cropland types, exhibiting pronounced nonlinear and threshold effects. This study reveals the spatial heterogeneity and driving mechanisms of flash drought impacts across different cropland ecosystems in China and provides a scientific basis for agricultural drought-risk assessment and differentiated adaptive management. Full article

Agricultural ecosystem health is critical for regional sustainable development and ecological security, yet existing assessment frameworks often lack explicit quantification of ecosystem integrity. Focusing on the Ili River Valley—a major arid Northwest China grain base—this study developed a novel multi-dimensional VOR-ES-I framework (Vitality–Organization–Resilience–Ecosystem Services–Integrity), elevating ecosystem integrity as a core independent dimension alongside traditional components. Using multi-source spatiotemporal data, the framework assessed farmland ecosystem health dynamics, while an interpretable XGBoost-SHAP model identified key drivers and their non-linear mechanisms. Future 2030 patterns were projected by coupling the assessment with the PLUS model under different scenarios. The results showed the composite Ecosystem Health Index (EHI) generally increased during 2000–2024, with a spatial gradient of higher values in the mountains (ranging from 0.53 to 0.80) and lower in the central plains (ranging from 0.11 to 0.52). Potential evapotranspiration (PET) and slope were the top drivers, with notable synergistic interactions and threshold effects (e.g., PET ~557 mm). Climatic factors (especially temperature) grew more influential over time, while socio-economic drivers had weaker direct effects. Scenario projections indicated the Farmland Protection scenario would best enhance ecosystem health (mean EHI = 0.290), whereas the Urban Development scenario might reduce EHI and intensify ecological degradation risks in expansion zones. This study contributes a refined VOR-ES-I framework and methodology, strengthening integrity diagnosis and complex driver interpretation. It provides a scientific basis for integrated assessment, sustainable management, and spatial planning of arid oasis farmland ecosystems. Full article

The transition toward carbon-neutral cities and sustainable infrastructure requires massive capital mobilization, yet traditional static valuation models like discounted cash flow (DCF) systematically undervalue green projects due to high initial capital expenditures and long-term uncertainty. To address this critical gap in sustainable finance, this study proposes a novel Artificial Intelligence–Blockchain–Multiple Real Options (AI-MRO) integrated framework. This model aligns infrastructure profitability with Environmental, Social, and Governance (ESG) criteria and United Nations Sustainable Development Goals (SDGs), specifically SDG 11 (Sustainable Cities), SDG 13 (Climate Action), and SDG 9 (Industry, Innovation, and Infrastructure). The core approach integrates AI-based probabilistic forecasting for carbon footprint optimization and cash flow prediction, MRO-based operational flexibility assessment, and blockchain-based smart contracts (Security Token Offerings, STOs) to ensure transparent green finance governance and social inclusion. Through empirical validation at Singapore’s Punggol Digital District (PDD)—a flagship smart city project featuring a district-level smart grid reducing 1700 tonnes of CO₂ and generating 3000 MWh of solar energy annually—this model successfully captured investment resilience (Extended Net Present Value, ENPV > 0) even in crisis scenarios where conventional DCF models failed. The results demonstrate that integrating digital twins and AI-driven ESG metrics structurally reduces the risk premium and amplifies the strategic value of sustainable investments. This study represents a substantial methodological contribution toward data-driven, automated, and transparent governance, offering a scalable financial framework for global net-zero infrastructure development. Full article

Smart manufacturing depends on operational data that remain continuous, interpretable, and reusable in practice. In constrained small- and medium-sized enterprise (SME) factories, however, the main bottleneck often lies not in later-stage analytics or AI applications but in securing an operationally viable data foundation under real deployment conditions. A lifecycle-based analysis of smart manufacturing data pipelines, together with recurrent SME deployment constraints identified in prior studies, led this study to derive six recurring operational risks. On this basis, this study proposes an Operational Data Foundation Framework structured around core requirements of continuity, governance, diagnosability, operability, reprocessability, and evolvability. These requirements are further articulated through design principles and assessable operational invariants. The framework was instantiated in a real SME factory, where heterogeneous field sources were integrated into a coherent operational data foundation for smart manufacturing through constrained communication paths, durable edge-side capture, cloud-side stream processing, controlled data normalization, and monitoring and alerting functions. Requirement-based evidence from the field implementation showed that the system preserved stable semantics across the pipeline, made failures traceable to specific lifecycle segments, preserved historical records for later reprocessing, and remained manageable under constrained deployment conditions. A representative field case further demonstrated the framework’s practical value: severe communication instability was diagnosed through lifecycle-segment discrepancy analysis and improved from approximately 33% to 95% packet reception after targeted intervention. This study contributes a field-grounded and assessable design logic for operational data foundations, with field evidence from a single food manufacturing SME supporting its feasibility and operational relevance. Full article

Conservation agriculture (CA) is widely promoted as a climate-smart approach to improve productivity and resilience, especially among small-scale farmers who face socioeconomic and climate-related risks that threaten their livelihoods. However, evidence linking CA adoption to household food-security outcomes in South Africa remains limited. This study examines patterns and determinants of CA adoption and assesses its implications for household food security among small-scale farmers in three municipalities of Mpumalanga Province. A quantitative cross-sectional survey was conducted among 391 farmers selected through stratified random sampling. Data were collected using a structured questionnaire and analyzed using descriptive statistics, chi-square tests, Kruskal–Wallis tests, and binary logistic regression. Results show that CA adoption was widespread but largely partial, with most farmers adopting one or two principles rather than the full CA package. Access to CA-related resources and information, household size, livelihood strategy, farm income, and farm size significantly influenced adoption. Higher adoption intensity was consistently associated with improved food-security outcomes, including increased production, lower food-insecurity severity, greater crop diversification, higher likelihood of year-round production, and increased market participation. The study concludes that conservation agriculture can contribute positively to multiple dimensions of household food security when adopted as an integrated system, but partial adoption yields limited benefits. Targeted extension support, improved access to resources, and context-specific interventions are required to enhance sustained and holistic CA adoption among small-scale farmers. Full article

To promote efficient land utilization while maintaining ecosystem stability, ecological management zoning in the Songnen Plain must balance ecological benefits and landscape ecological risk. Using land use data spanning 1990 to 2020, this study combined multi-scenario land use simulation with assessments of ecosystem service value (ESV) and landscape ecological risk (LER), and further applied these results to ecological management zoning. For 2030, land use configurations were projected across three alternative scenarios, namely cropland preservation (CP), ecological preservation (EP), and sustainable development (SD). ESV and LER were evaluated at a 5.1 km × 5.1 km grid scale based on 11,012 grid cells, and Z-score standardization was used to compare zoning results across scenarios. The results showed that land use patterns differed markedly among scenarios and led to asynchronous responses of ESV and LER. Compared with 2020, ESV under the CP, EP, and SD scenarios showed changes of −2.01%, 10.32%, and 2.56%. The EP scenario produced the highest ESV, but it was also associated with relatively high ecological risk, whereas the SD scenario showed lower integrated risk and a more balanced zoning pattern. Overall, the SD scenario was more suitable as the basis for optimizing ecological management zoning in the Songnen Plain. Future land governance should therefore promote coordinated optimization of ecological protection, risk control, and land use while ensuring food security. Full article

The electrical system is a crucial infrastructure of modern society. It provides the energy needed for society to continue its development. However, this critical infrastructure is increasingly threatened by the extreme weather events driven by the escalating climate crisis, posing significant challenges to sustainable development and energy security. Therefore, it is important to conduct comprehensive risk analyses of the electrical system to prepare for future challenges. This paper presents an electrical risk assessment conducted within the European project ICARIA, aiming to evaluate the effects of global climate change on critical infrastructure resilience. The study improves on the first risk assessment conducted, evaluating the electrical system’s vulnerability to flooding events, such as heavy rains or rising sea levels, in the Metropolitan Area of Barcelona. A key contribution to this research is the integration of direct impact assessments and cascading effect analyses, which identify how localised failures in electrical assets can spread throughout the system, potentially leading to a blackout. The research focuses on modelling various flood projections, using extreme weather scenarios and return periods ranging from 1 to 100 years. These projections are employed to evaluate the risk assessment methodology and quantify potential impacts on the electrical grid, including Expected Annual Damage (EAD) and Energy Not Supplied Cost (ENSC). The results aim to provide policymakers and grid operators with valuable insights, enabling the development of data-driven adaptation strategies and climate-resilient infrastructure planning to mitigate the risks posed by extreme weather events. Full article

Thailand is facing a rapidly aging society, raising concerns about how retiring workers will maintain their quality of life. Insured persons in the social security system—especially voluntary members under Section 40 of the Social Security Act B.E. 2533 (1990), who are often informal workers—frequently lack formal retirement plans, underscoring the need for interventions that address financial security and subjective well-being (SWB) in later life. This study aimed to develop and evaluate a retirement planning program designed to enhance subjective well-being and improve the quality of life for pre-retirees in Songkhla Province. A Research and Development (R&D) design was employed in four phases. Phase 1 (R1) involved a needs assessment: survey data from 500 insured individuals (ages 40–60) were collected to identify gaps between current and desired retirement preparedness. Phase 2 (D1) utilized the needs assessment results and theoretical frameworks to design a Subjective Well-being Retirement Planning Program, encompassing financial, health, and psychosocial components. Content-relevance experts validated the draft program. Phase 3 (R2) involved implementing the program with 15 volunteer participants over four weekly workshops (each 3 h long) and evaluating its short-term pilot outcomes using pretest-posttest measures of subjective well-being. Phase 4 (D2) refined the program based on evaluation findings and expert feedback. Results indicated that following participation in the program, participants’ overall subjective well-being and all sub-dimensions (life satisfaction, positive and negative affect balance, sense of meaning, social connectedness, security, and health) were significantly higher than before (p < 0.001). Additionally, the proportion of participants classified as inadequately prepared for retirement (high-risk due to low planning) decreased markedly, suggesting increased readiness within the pilot group. Expert evaluations of the program design reflected a high content validity index and strong agreement on the program’s accuracy, appropriateness, and usefulness for the target group. In conclusion, the developed retirement planning program was associated with short-term improvements in subjective well-being and quality-of-life indicators among insured pre-retirees. This theory-informed program, developed through an R&D process, offers a model for supporting aging workers in the social security system, with implications for policymakers and practitioners seeking to promote healthy, happy, and secure retirements in an aging society. Full article

Against the backdrop of multi-source water transfers and increasingly frequent extreme rainfall, short-term deterioration of reservoir inflow water quality has become a key risk to intake safety, treatment operations, and urban water-supply security. Traditional assessments based on static thresholds and annual or seasonal averages often fail to identify high-risk periods at the event scale. Using continuous online monitoring data from 2021 to 2024 for the inflow of Yuqiao Reservoir, Tianjin, China, this study developed a month-specific dynamic-threshold framework and green/yellow/red risk windows and integrated a reach-wise river–reservoir routing scheme; a two-box decay model; and a three-class risk trigger into a unified analytical framework for long-term background characterization, event propagation analysis, source-contribution interpretation, and early-warning evaluation. Results show that the permanganate index (COD_Mn) exhibits an overall stable-to-declining background with pronounced wet-season pulses, whereas total nitrogen (TN) and total phosphorus (TP) remain at moderate-to-high levels, with yellow/red risk windows clustering markedly in the wet season. In typical red and yellow events, nitrogen contributions from upstream control sections progressively accumulate toward the reservoir inlet along the river–reservoir cascade system, whereas in some events the residual contribution from unmonitored near-inlet inflows becomes dominant. The COD_Mn-based three-class trigger achieves an overall accuracy of approximately 71.5% and shows comparatively strong identification of yellow-level risk, while remaining conservative for red-level alarms. These findings indicate that coupling month-specific dynamic thresholds with event-scale routing-decay analysis and trigger-based classification can support inflow monitoring, intake-risk early warning, and coordinated operation of key upstream reaches and near-reservoir control zones in water-transfer–reservoir integrated systems. Full article

In contemporary debates on the United Nations Sustainable Development Goals, there is growing recognition that artificial intelligence (AI) may contribute meaningfully to SDG 2 (Zero Hunger), particularly by enhancing the efficiency of food aid distribution and resource allocation. However, such optimism must be critically situated within the broader institutional and ethical contexts in which AI operates. This study argues that the effectiveness of AI in conflict-affected settings is contingent not only on technical capacity but also on governance structures, ethical safeguards, and institutional trust, dimensions closely aligned with SDG 16 (Peace, Justice, and Strong Institutions). Using the Gaza Strip as a case study, this article demonstrates that AI-driven food assistance mechanisms may inadvertently reinforce structural vulnerabilities. Specifically, algorithmic targeting of aid risks deepening dependency, exacerbating digital exclusion, and weakening already fragile governance systems. The absence of robust data accountability frameworks further complicates these dynamics, raising concerns regarding transparency, fairness, and long-term sustainability. The findings caution against privileging technical efficiency at the expense of socio-political stability. Rather, they highlight that the sustainability of AI interventions in humanitarian contexts fundamentally depends on the credibility and legitimacy of institutions. Accordingly, this study proposes a conceptual model for AI in hunger relief and digital humanitarianism that integrates technical innovation with institutional accountability and social trust. This study presents a narrative review informed by structural searching that examines the influence of AI on food security interventions in fragile contexts. This analysis applies a combined ethical governance and sustainability lens to assess current applications and risks. This research advances a broader analytical framework that moves beyond purely technical interpretations of AI, emphasizing its role as a socio-political tool, through identifying five key pillars for sustainable AI governance: data sovereignty, algorithmic accountability, inclusive system design, community-led governance, and market integrity. Full article