Search Results (5,988)

Medicine and vaccine supply chains in Nigeria are socio-technical systems exposed to persistent uncertainty and disruption. Existing studies rarely integrate systems thinking with uncertainty-aware decision tools to jointly prioritize challenges and policy responses. This study asks which policy mix most effectively strengthens these supply chains while balancing multiple, conflicting criteria and stakeholder judgments. We develop a two-stage Fermatean fuzzy framework that first weights 35 challenges using Fermatean Fuzzy Stepwise Weight Assessment Ratio Analysis (FF-SWARA) and then ranks four policy alternatives via Fermatean Fuzzy VIšeKriterijumska Optimizacija I Kompromisno Resenje (FF-VIKOR), based on expert elicitation and linguistic assessments. Results identify interruption of drug supplies, limited vaccine funding, cold-chain potency loss, human resource shortages, and product damage as the most critical challenges. FF-VIKOR prioritizes Effective Implementation of Existing Policies as the best alternative, followed by Improving Access to Medicines and Vaccines, indicating that governance quality and access-enabling infrastructure are complementary levers for resilience. To further enhance robustness, we embed the VIKOR outcomes into a policy-oriented game-theoretic analysis, where strategic weighting scenarios (e.g., cost-focused, infrastructure-driven, human-capital focused) interact with policy choices. The equilibrium results reveal that a mixed strategy combining Effective Implementation of Existing Policies and Strengthening Distribution and Storage Systems guarantees the best compromise performance across adversarial scenarios. The proposed framework operationalizes systems thinking for uncertainty-aware and strategically robust policy design and can be extended with real-time data integration, scenario planning, and regional replication to guide adaptive supply chain governance. Full article

Risk assessment is critical for securing and sustaining operational resilience in cloud computing. Traditional approaches often rely on single-objective or subjective weighting methods, limiting their accuracy and adaptability to dynamic cloud conditions. To address this gap, this study provides a framework for multi-layered decision-making using an Enhanced Hierarchical Holographic Modeling (EHHM) approach for cloud computing security risk assessment. Two methods were used, the Entropy Weight Method (EWM) and Criteria Importance Through Intercriteria Correlation (CRITIC), to provide a multi-factor decision-making risk assessment framework across the different security domains that exist with cloud computing. Additionally, fuzzy set theory provided the respective levels of complexity dispersion and ambiguities, thus facilitating an accurate and objective participation for a cloud risk assessment across asymmetric information. The trapezoidal membership function measures the correlation, rank, and scores, and was applied to each corresponding cloud risk security domain. The novelty of this re-search is represented by enhancing HHM with an expanded security-transfer domain that encompasses the client side, integrating dual-objective weighting (EWM + CRITIC), and the use of fuzzy logic to quantify asymmetric uncertainty in judgments unique to this study. Informed, data-related, multidimensional cloud risk assessment is not reported in previous studies using HHM. The different Integrated Weight measures allowed for accurate risk judgments. The risk assessment across the calculated cloud computing security domains resulted in a total score of 0.074233, thus supporting the proposed model in identifying and prioritizing risk assessment. Furthermore, the scores of the cloud computing dimensions highlight EHHM as a suitable framework to support and assist corporate decision-making in cloud computing security activity and informed risk awareness with innovative activity amongst a turbulent and dynamic cloud computing environment with corporate operational risk. Full article

This study develops an interpretable and calibrated XGBoost framework for probabilistic slope stability assessment using a 627-case database of circular-mode failures. Six predictors, namely, unit weight (γ), cohesion (c), friction angle (φ), slope angle (β), slope height (H), and pore-pressure ratio (r_u), were used to train a gradient-boosted tree model optimized through Bayesian hyperparameter search with five-fold stratified cross-validation. Physically based monotone constraints ensured that failure probability (P_f) decreases as c and φ increase and increases with β, H, and r_u. The final model achieved strong performance (AUC = 0.88, Accuracy = 0.80, MCC = 0.61) and reliable calibration, confirmed by a Brier score of 0.14 and ECE/MCE of 0.10/0.19. A 1000-iteration bootstrap quantified both epistemic and aleatoric uncertainties, providing 95% confidence bands for P_f-feature curves. SHAP analysis validated physically consistent influence rankings (φ > H ≈ c > β > γ > r_u). Predicted probabilities were classified into Low (P_f < 0.01), Medium (0.01 ≤ P_f ≤ 0.10), and High (P_f > 0.10) risk levels according to geotechnical reliability practices. The proposed framework integrates calibration, uncertainty quantification, and interpretability into a comprehensive, auditable workflow, supporting transparent and risk-informed slope management for infrastructure, mining, and renewable energy projects. Full article

Rice cultivation accounts for roughly 10% of worldwide anthropogenic greenhouse gas emissions, making it a significant source of methane (CH₄) Despite modest observational constraints, estimates of worldwide CH₄ emissions from rice agriculture range from 18–115 Tg CH₄ yr⁻¹. CH₄ is a potent greenhouse gas, and its oxidation produces tropospheric ozone (O₃), which is harmful to public health and crop production when combined with nitrogen oxides (NOx) and sunlight. Elevated O₃ levels reduce air quality, crop productivity, and human respiratory health. This study presents an AI-driven framework that combines ensemble learning, hyperparameter optimisation (HPs), and SHAP-based explainability to enhance CH₄ emission predictions from rice paddies in India, Bangladesh, and Vietnam. The model consists of two stages: (1) a classification stage to distinguish between zero and non-zero CH₄ emissions, and (2) a regression stage to estimate emission magnitudes for non-zero situations. The framework also incorporates O₃ and asthma incidence data to assess the downstream impacts of CH₄-driven ozone formation on air quality and health outcomes. Understanding the factors that drive optimal model performance and the relative importance of features affecting model outputs is still an ongoing field of research. To address these issues, we present an integrated approach that utilises recent improvements in model optimisation and employs SHapley Additive ExPlanations (SHAP) to find the most relevant variables affecting methane (CH₄) emission forecasts. In addition, we developed a web-based artificial intelligence platform to help policymakers and stakeholders with climate strategy and sustainable agriculture by visualising methane fluxes from 2018 to 2020, ensuring practical applicability. Our findings show that ensemble learning considerably improves the accuracy of CH₄ emission prediction, minimises uncertainty, and shows the wider benefits of methane reduction for climate stability, air quality, and public health. Full article

This study assesses the quality of local public administration in European cities using an analytical algorithm based on the B-TOPSIS approach. It draws on the Quality of Life in European Cities survey, which includes five questions on citizens’ satisfaction with local administration, rated on a simplified four-point verbal scale with an option to skip. To process this type of group data, the study extends B-TOPSIS to handle ordinal scales, uncertainty, and missing responses. The method is applied to data from 2023 and compared with 2019 to detect temporal changes in satisfaction. The framework compensates for incomplete information, integrates a Monte Carlo-based protocol for robust results, enhances the ranking through almost first-order stochastic dominance, and supports cross-survey comparison. The results show that Zurich, Luxembourg, and Antalya rank highest in satisfaction, while Rome and Palermo rank lowest. Residents of medium-sized and very large cities report higher satisfaction, with EU and EFTA cities outperforming those in the Western Balkans. Overall, satisfaction levels have remained stable since 2019. These findings offer both methodological contributions and practical insights into governance quality and sustainability, constructing a unified performance index from dispersed survey responses. Full article

Background: The introduction of the “trace” category in the Xpert MTB/RIF Ultra assay has significantly improved the sensitivity of molecular tuberculosis diagnostics. While it enhances sensitivity, especially in paucibacillary and extrapulmonary cases, its specificity remains debatable, making its interpretation outside select populations a topic of clinical uncertainty. Objectives: This study evaluates the diagnostic and clinical significance of trace-positive results obtained with the Xpert MTB/RIF Ultra assay in the context of a high-incidence TB setting, examining their association with clinical, imaging, and microbiological findings. Methods: A retrospective analysis was conducted on 65 samples with trace-positive Xpert Ultra results, collected over a six-year period from 59 distinct patients in a general hospital in Romania. Correlations were assessed with microscopy, culture, clinical features, imaging, treatment initiation, and prior TB history. A composite reference standard was used for diagnostic accuracy evaluation. Results: Of the 65 trace-positive samples, 29 (44.6%) were culture-positive and 5 (7.7%) were smear-positive. A high proportion of patients, 56 (94.9%), presented with TB-compatible symptoms, and 47 (79.6% of those with imaging) had highly suggestive radiological findings. Based on the composite reference standard, 47 patients (79.7%) were ultimately diagnosed with active TB. Anti-TB treatment was initiated in 44 patients (74.5%). Trace positivity was observed across various specimen types, including sputum, pleural fluid, and cerebrospinal fluid. Conclusions: In high TB burden environments, trace-positive Xpert Ultra results frequently reflect true disease when interpreted within the appropriate clinical and imaging framework. Our findings indicate that, in regions with high tuberculosis incidence such as Romania, trace-positive Xpert Ultra results may contribute meaningfully to clinical decision-making when interpreted alongside clinical and radiological findings, in alignment with current WHO guidance. Full article

Background: Performance status is often cited as an independent predictor of survival after metastatic spine tumor surgery (MSTS), but its standalone predictive value for short-term outcomes remains unclear. We aimed to evaluate how well Karnofsky (KPS) and Eastern Cooperative Oncology Group performance status (ECOG-PS) predict 90-day survival, a common surgical candidacy threshold, in patients managed with MSTS. Methods: We conducted a retrospective study of 175 adult patients who underwent MSTS at a single institution (2012–2025). All patients had documented preoperative KPS and ECOG-PS scores. Univariable logistic regression was used to assess associations with 90-day survival. Predictive performance was assessed by discrimination (AUC), diagnostic accuracy, calibration (Brier score), and clinical utility (decision curve analysis). Results: The crude 90-day survival rate was 73%. Both KPS (OR 1.02 [95% CI 1.01 to 1.05]; p = 0.001) and ECOG-PS (OR 0.51 [95% CI 0.36 to 0.73]; p < 0.001) were statistically associated with survival. However, discrimination was modest (AUC 0.65 for KPS, 0.68 for ECOG-PS), with the most balanced diagnostic accuracy achieved at KPS ≥ 70 (sensitivity 0.66, specificity 0.62) and ECOG-PS ≤ 2 (sensitivity 0.76, specificity 0.5). Calibration was fair (Brier scores 0.185 and 0.182, respectively). Decision curve analysis showed minimal net benefit across most threshold probabilities, with ECOG-PS performing slightly better at intermediate thresholds (30–60%), the zone of greatest clinical uncertainty. Conclusions: Despite being widely cited as an independent predictor of postoperative survival in patients with metastatic spine disease, performance status assessed via the KPS and ECOG-PS demonstrated only modest overall discriminatory ability, diagnostic accuracy, calibration, and clinical utility when used alone to predict 90-day survival after MSTS. While both scores retained meaningful value at the extremes (i.e., patients with very poor or very good performance status had more predictable outcomes), caution is warranted in intermediate cases, where performance status alone may be insufficient to guide treatment decisions. These findings highlight the critical difference between statistical association and the real-world clinical utility of a single metric to predict outcome in this patient population. Full article

One of the sources of risk inherent to construction projects is the quality of spatial data. Damage to buried pipes and cables often causes accidents, delays, or stoppages of construction works. Fuzzy logic is a method for studying the risk. It is employed to describe complex or poorly defined phenomena that can hardly be characterised with probabilistic methods. The article proposes a method for assessing the risk of damaging underground utilities based on a fuzzy inference engine. The author first defined linguistic variables and assigned them values based on risk factors. The membership functions for the linguistic variables were modelled using expert judgement. Then, the author determined qualitative fuzzy sets with the rule base. Finally, the values were converted into crisp values. The defuzzification technique employed was the centre of gravity. The proposed method can assess the risk of damage to underground utilities for spatial data exhibiting diverse quality classes. It will be employed to generate large-scale risk maps. The proposed fuzzy logic solution is an effective and appropriate tool for assessing the risk of damage to underground utilities arising from the quality of subsurface data. It should not be regarded as a universal substitute for PRA (Probabilistic Risk Assessment) but as a complementary methodology that is particularly well-suited to risk assessment in data-poor environments characterised by epistemic uncertainty and reliance on qualitative expert judgement. Full article

Accurate early warning of rainfall-induced landslides poses a critical challenge in geological disaster risk management. Conventional deterministic rainfall threshold models often overlook the heterogeneity of regional geological conditions, while landslide susceptibility assessment is plagued by uncertainties in selecting non-landslide samples. To address these issues, this paper took Zhushan County in Hubei Province as the study area, and the semi-supervised random forest (SRF) model was adopted to conduct landslide susceptibility assessment. The critical rainfall (Effective Rainfall-Duration, EE-D) threshold curves were constructed based on the antecedent effective rainfall (EE) and rainfall duration (D). Furthermore, EE-D threshold curves with different geological condition characteristics were established and analyzed according to the thickness, slope, and area of the landslides, respectively. By coupling the landslide susceptibility results with a classified multi-level rainfall threshold model, a spatiotemporally refined regional framework for tiered landslide early warning was developed. The results show that the SRF model solves the problem of non-landslide sample selection error in traditional supervised learning. The Area Under Curve (AUC) value reaches 0.91, which is better than the analytic hierarchy process, logistic regression, etc. Moreover, the models of landslide susceptibility and EE-D threshold can effectively achieve the hierarchical early warning of rainfall-induced landslide hazards. Full article

Purpose: Telehealth (TH) offers promising solutions for enhancing the management of chronic obstructive pulmonary disease (COPD), particularly in resource-limited or remote settings. However, regulatory uncertainty remains a significant barrier to adopting and integrating TH technologies into routine care. This systematic review aims to evaluate the role of regulatory guidelines in implementing and adopting TH solutions for COPD care and to identify key barriers and facilitators shaping these regulatory efforts. Methods: Following PRISMA guidelines, a comprehensive search of five databases up to 18 October 2025 (PubMed, Web of Science, Scopus, CINAHL, and JSTOR) and grey literature was conducted. Studies and governmental reports were included if they examined regulatory frameworks, stakeholder perspectives, or implementation challenges related to TH in COPD care. Study quality was assessed using the Critical Appraisal Skills Programme (CASP) tool. Narrative and data synthesis were employed. Results: From 343 identified records, 33 sources (18 peer-reviewed studies and 15 governmental/organizational reports) met the inclusion criteria. Findings revealed wide disparities in the existence, specificity, and enforcement of TH regulatory guidelines across countries. Developed nations often had more structured yet nonspecific frameworks, while emerging health systems, such as Saudi Arabia, exhibited fragmented but evolving regulatory landscapes. Common barriers included unclear stakeholder roles, inadequate funding, technological limitations, and resistance to organizational change. Conclusions: Clear, inclusive, and context-sensitive regulatory guidelines are essential to support the successful integration of TH in COPD care. Enhanced regulatory clarity can improve patient trust, engagement, and adherence by addressing safety, accountability, and accessibility concerns. Future research should focus on stakeholder-informed policies that reflect the practical realities of healthcare delivery in both developed and emerging systems. Full article

Monitoring pilot cognitive state in real time is becoming increasingly important as automation plays a larger role in aviation. Traditional workload assessments, such as questionnaires or task-based performance metrics, provide useful insights but can be limited in rapidly changing flight environments. Physiological measures, including heart rate, respiration, and electroencephalogram (EEG), offer continuous data streams, yet their variability and complexity present challenges for analysis. This study explores the use of a hierarchical Bayesian framework to quantify patterns from physiological signals recorded during high-fidelity flight simulations. Five certified pilots flew scenarios that varied in automation level and working memory demand while heart rate, respiration rate, and EEG-derived workload estimates were monitored. The model generated individualized and condition-specific estimates, quantified uncertainty, and remained stable with a small participant pool. Heart rate appeared to be the most consistent indicator, followed by EEG-derived workload, while respiration rate was less reliable across conditions. These results suggest that Bayesian inference may provide a promising way to interpret physiological data in aviation settings and could support the development of adaptive automation that responds to pilot workload. The approach emphasizes transparency and efficiency, offering complementary value to existing modeling techniques for aerospace human factors and flight deck applications. Full article

Understanding how climate change will reshape drought dynamics is essential for planning sustainable water and agricultural systems in tropical regions. However, large uncertainties in existing projections limit effective adaptation. To address this, we applied machine learning-enhanced climate projections and satellite-based drought indices to assess drought dynamics in Ethiopia’s Ganale Dawa Basin as a case study. Agricultural and hydrological droughts were analyzed for a historical baseline (1982–2014) and three future periods (2015–2040, 2041–2070, 2071–2100) under SSP2-4.5 (a moderate-emission pathway) and SSP5-8.5 (a high-emission pathway) scenarios. Results show that agricultural droughts occurred 34 times during the historical baseline. Under SSP2-4.5, their frequency declined to 10 in the mid-future, before rising to 16 events in the far future. In contrast, SSP5-8.5 projected increased variability with 33 events in the near future, dropping to 2 in the mid-future, and increasing again to 19 in the far future. Hydrological droughts were more persistent, with a baseline frequency of 31 events, and 26–36 events over future periods under both scenarios. These findings reveal increasing variability in agricultural drought and continued recurrence of hydrological drought. The findings emphasize a dual adaptation approach combining immediate agricultural responses with sustained water management and climate mitigation. Full article

Coastal vulnerability describes the susceptibility of a system to adverse effects from natural hazards. It is typically evaluated using spatial data on geographical attributes and is often synthesized using tools such as a Coastal Vulnerability Index (CVI). However, the literature highlights that there is no universal method for assessing vulnerability, emphasizing the importance of site-specific adaptations. A key challenge in coastal risk management is dealing with the inherent uncertainty of environmental variables and their future dynamics. Incorporating this uncertainty is essential for producing reliable assessments and informed decision-making. In this paper, we present an R package that facilitates the implementation of probabilistic graphical models explicitly incorporating epistemic uncertainty. This approach allows for vulnerability assessments even in situations where data availability is limited. The proposed methodology aims to deliver a more flexible and transparent framework for vulnerability analysis under uncertainty, providing valuable support to local policymakers, in particular during the early phases of intervention planning and technology selection for coastal mitigation strategies. Full article

The maritime industry faces increasing pressure to improve energy efficiency, a challenge that extends to the luxury yacht sector. This study presents a comprehensive hydrodynamic assessment for retrofitting a bespoke Energy Saving Device (ESD) onto a 45 m motor yacht. A full-scale self-propulsion Computational Fluid Dynamics (CFD) model was developed and validated directly against dedicated sea trial data, ensuring high fidelity and bypassing traditional scaling uncertainties. The validated model was then utilized to design and optimize a custom pre-propeller duct system. A parametric study varying the duct’s angle of attack identified an optimal configuration of ${20}^{\circ }$, which achieves a definitive power saving of 4.7% at the vessel’s cruise speed of 12.3 knots. Analysis of the propulsive factors reveals that the gain is primarily driven by a substantial increase in the hull efficiency, ${\eta }_H$, achieved by conditioning the propeller inflow. This improvement successfully compensates for the corresponding decrease in the propeller’s open-water efficiency, ${\eta }_o$. This work demonstrates a successful end-to-end numerical workflow for designing and verifying an effective, retrofittable ESD, highlighting a practical solution for reducing fuel consumption in existing motor yachts. Full article

Scalp image detection faces challenges such as limited evaluation dimensions, difficulties in quantifying user perception, and insufficient discriminative power of traditional assessment methods. To address these issues, this paper proposes a multi-attribute decision-making model for deep learning algorithm selection. The model integrates subjective and objective weighting through a hybrid approach, where natural language processing (NLP) techniques extract perceptual preferences from user reviews, and the Interval-valued neutrosophic set analytic hierarchy process (IVNS-AHP) and entropy weight method (EWM) are employed to determine subjective and objective weights, respectively. The combined weights are used within the IVNS-VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje) framework, enhanced by a possibility distribution (PD) to improve discriminative capability. Experiments were conducted using multiple performance metrics, including Precision, Recall, mean Average Precision at IoU = 0.5 (mAP@50), F1 Score, frames per second (FPS), and Parameters, to evaluate mainstream scalp detection algorithms. The results demonstrate that YOLOv8n achieves the highest comprehensive ranking with strong stability across different decision preferences. Comparative analyses with TODIM (an acronym in Portuguese of interactive and multiple attribute decision-making), TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution), and fuzzy VIKOR variants confirm that the proposed PD-VIKOR method provides superior ranking stability and discriminative precision, offering a more reliable and robust evaluation under uncertainty. Full article