Search Results (5,450)

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

Estimating ground motion parameters at an unsampled site is challenging for seismologists and engineers alike. An attempt is made to apply Kriging interpolation to estimate peak ground accelerations at specific nuclear power plant sites. However, issues such as data quality and Kriging assumptions pose challenges to how practical and reasonable Kriging interpolation results may be in terms of estimating ground motion parameters. Peak ground acceleration data from the 2016 Gyeongju and 2017 Pohang earthquakes were taken from a local seismological agency. Peak ground acceleration, logarithms of the peak ground acceleration, and residuals between the recorded data and global and local ground motion models were used to select and derive empirical variogram models. The leave-one-out cross-validation process suggested estimating peak ground acceleration residuals from a locally developed ground motion model using an Exponential variogram model. Kriging estimates were compared to a site-specific ground motion model. These estimates appeared reasonable at one site but were significantly off at the other site. On the whole, Kriging estimates were lower than ground motion model predictions. When viewed relative to the nearest recordings, Kriging estimates appeared inconsistent across the two earthquake events. A nearest neighbor approach to computing Kriging estimates suggested a minimum of five data points but much more for modeling an empirical variogram. Results also suggest focusing on validation processes more than variogram selection. This suggests caution when applying Kriging for ground motion-related assessments in South Korea. Full article

Kuwait experiences persistently high levels of air pollution driven by industrial emissions, transportation, oil-related activities, and frequent desert dust storms. This study aims to synthesize and critically evaluate the available evidence on the relationship between air pollution, Air Quality Index (AQI), and health outcomes in Kuwait using a guided narrative review approach. A guided literature search identified 26 peer-reviewed studies published between 2014 and 2026 about Kuwait air pollution, which were assessed for methodological characteristics, pollutant types, health outcome categories, and vulnerable populations. The most frequently examined pollutants were particulate matter (PM_2.5: 69%; PM₁₀: 38%), followed by NO₂ (23%), multi-pollutant and AQI-based (19%), O₃ (12%), SO₂ (12%), VOCs and PAHs (8%). Health-related investigations most commonly addressed mortality and respiratory morbidity, while cardiovascular, metabolic, biomarker-based, and cancer-related outcomes were less frequently represented. Among studies reporting direct health outcomes, elevated PM_2.5 exposure was generally associated with increased risks of respiratory hospitalizations, cardiovascular events, and all-cause mortality. Susceptible populations identified across the literature include children, older adults, individuals with pre-existing chronic conditions, and outdoor workers, who may experience higher exposure levels and greater health vulnerability. However, a substantial proportion of the included studies focused primarily on exposure characterization or pollutant modeling without direct assessment of health outcomes. These studies nonetheless indicate consistently elevated pollutant levels and seasonal variability, which may plausibly contribute to population health risks. Overall, while the available Kuwait-specific evidence suggests potential adverse health effects linked to air pollution, the strength of direct epidemiological evidence remains limited. Important gaps persist, including the scarcity of long-term cohort studies, limited multi-pollutant analyses, and insufficient integration of AQI categories with health outcomes. These limitations highlight the need for more robust and longitudinal research to better quantify health risks and inform public health policy in Kuwait. Full article

Understanding how urban morphology regulates Land Surface Temperature (LST) is important in the context of rapid urbanization and increasingly frequent extreme climate events. Although both two-dimensional (2D) and three-dimensional (3D) morphological factors are known to affect urban thermal environments, their relative explanatory roles, factor-specific optimal scales, and nonlinear responses are still insufficiently quantified within a unified multi-scale framework. This study focuses on the area within Beijing’s Fifth Ring Road and applies an interpretable LightGBM-SHAP framework to examine the multi-scale relationships between integrated 2D/3D urban morphology and LST using a Landsat 8 image acquired during a typical summer daytime heatwave event. Five analytical scales (150, 300, 600, 900, and 1200 m) are evaluated to compare factor importance, identify optimal explanatory scales, and characterize threshold-like response patterns. The LightGBM models maintained relatively strong predictive performance across all scales under spatial cross-validation, with the highest mean R² observed at 600 m, followed closely by 300 m. The results indicate a clear scale-dependent contrast in explanatory dominance: 2D factors show stronger associations with LST at fine-to-medium scales, whereas 3D factors become more influential at coarser scales. From a process perspective, this contrast is consistent with differences in surface-cover-related and vertical-structure-related thermal regulation, although the underlying physical mechanisms are not directly tested in this study. SHAP analysis further identifies factor-specific nonlinear response intervals for several key indicators under the selected extreme-heat condition. For example, a cooling tendency is observed when Mean Building Height (MBH) exceeds 15 m at the 150 m scale. These findings provide scale-explicit and context-specific evidence for interpreting urban morphology–LST relationships and support heat-mitigation strategies that combine micro-scale surface-cover optimization with larger-scale regulation of building height variation and urban roughness. The identified response intervals should be interpreted as empirical references under a typical daytime heatwave condition rather than as universally transferable climatological thresholds. Full article

Frequent extreme heatwaves (HWs) have significantly exacerbated urban thermal risks, yet the regulatory mechanisms of urban morphology remain poorly understood. This study focuses on the core urban areas of Beijing and develops a Local Climate Zone (LCZ)-constrained spatiotemporal data fusion model (LCZ-FSDAF) to generate high-resolution Land Surface Temperature (LST) datasets from 2015 to 2024. By integrating urban–rural gradient analysis with the XGBoost-SHAP model, this study quantitatively resolves the spatiotemporal evolution of land surface temperature during heatwaves and the nonlinear threshold effects of urban morphological parameters, using a representative extreme heatwave event in July 2023 as a case study. The results indicate that the LCZ-FSDAF model achieves high precision across complex urban underlying surfaces (up to 0.946, RMSE as low as 0.762 K), effectively capturing the spatial heterogeneity of the urban thermal environment. Over the past decade, heatwave events in Beijing have exhibited a significant trend of increasing frequency, duration, and intensity. During these events, LST displays a concentric core-high, periphery-low structure; however, the peak temperature shifts toward high-density built-up areas in the sub-core, manifesting a distinct heat island core shift phenomenon. Furthermore, the impact of urban morphology on LST is characterized by significant nonlinearity, with the Normalized Difference Vegetation Index (NDVI) and Mean Building Height (MBH) identified as dominant factors. Notably, Building Coverage (BC) and Sky View Factor (SVF) exhibit pronounced threshold effects across different thermal indicators. Findings of this study are useful for guiding urban planning, optimizing spatial configurations, formulating urban heat island mitigation policies under heatwaves, and promoting the Sustainable Development Goals (SDGs) of cities and communities. Full article

Introduction: Chimeric antigen receptor (CAR) T-cell therapies have revolutionized treatment for relapsed/refractory hematologic malignancies, targeting CD19 in B-cell neoplasms and BCMA in multiple myeloma, with response rates exceeding 80%. However, long-term risks, including therapy-related myeloid neoplasms, such as myelodysplastic syndrome (MDS) and acute myeloid leukemia (AML), are emerging 6–24 months post infusion, potentially linked to lymphodepleting chemotherapy, clonal hematopoiesis expansion, and inflammatory milieus. This FAERS pharmacovigilance analysis quantified MDS/AML reporting across seven FDA-approved CAR-T products to detect antigen-specific signals unattainable in pivotal trials with limited follow-up. Methods: Adverse event reports from FAERS (1 January 2013–10 February 2025) were queried for tisagenlecleucel, axicabtagene ciloleucel, brexucabtagene autoleucel, lisocabtagene maraleucel, obecabtagene autoleucel, idecabtagene vicleucel, and ciltacabtagene autoleucel, focusing on MedDRA terms for MDS/AML. Duplicates and ambiguous cases were excluded. Disproportionality was assessed using reporting odds ratios (RORs; lower 95% CI >1 signaling significance), comparing CAR-T-event pairs to database background, with subgroup analyses by antigen target. Results: Among 14,093,557 reports, CAR-T products linked to 303 MDS (brexucabtagene autoleucel ROR 97.93 [72.18–132.87], n = 44; axicabtagene ciloleucel ROR 58.70 [50.34–68.44], n = 172) and 129 AML cases (axicabtagene ciloleucel ROR 22.89 [18.23–28.73], n = 76). Signals were consistent across CD19- and BCMA-directed agents, absent only for recently approved obecabtagene autoleucel. Conclusions: CAR-T therapies exhibit disproportionate MDS/AML reporting in FAERS, supporting class-wide late hematologic toxicity in pretreated patients with clonal hematopoiesis. Enhanced surveillance, baseline profiling, and marrow evaluation for cytopenias are warranted, balancing curative benefits. Full article

Background/Objectives: Nivolumab plus ipilimumab (IO–IO) provides durable clinical benefit in metastatic renal cell carcinoma (mRCC), yet long-term real-world data focusing on progression-free and treatment-free (PF–TF) survival remain limited. This study aimed to evaluate the long-term outcomes of IO–IO with a particular focus on the frequency and clinical characteristics of PF–TF. Methods: We retrospectively analyzed 63 patients with mRCC treated with first-line IO–IO across eight institutions with a minimum potential follow-up of five years. Progression-free survival (PFS), PFS2, and overall survival (OS) were assessed. PF–TF was defined as absence of disease progression and any cancer-directed therapy at the five-year landmark. Clinical and treatment-related factors were compared between patients with and without PF–TF. Results: The median PFS, PFS2, and OS were 7.5 (95% confidence interval [CI], 5.1–13.3), 26.2 (95% CI, 13.6–46.6), and 47.4 months (95% CI, 29.3–not reached), respectively. At 5 years, 11 patients (17%) achieved PF–TF. Baseline characteristics, IMDC risk classification, and peripheral blood biomarkers were not predictive of PF–TF. PF–TF was associated with the absence of bone metastases, presence of lymph node metastases, and occurrence of immune-related adverse events (irAEs), as well as the delayed onset of irAEs. No PF–TF patients required corticosteroid pulse therapy, and durable PF–TF was observed even after early treatment discontinuation due to adverse events. Conclusions: IO–IO demonstrated sustained long-term efficacy in real-world practice, with a subset achieving durable PF–TF. These findings highlight IO–IO as a strategy capable of providing long-term disease control with reduced treatment burden in selected patients with mRCC. Full article

Transcatheter aortic valve implantation (TAVI) has become the standard of care for patients with severe aortic stenosis. Despite significant procedural refinement, vascular complications (VCs) remain among the most frequent and clinically relevant adverse events associated with TAVI. These complications are closely associated with adverse clinical outcomes and continue to represent one of the most significant limiting factors for the broader expansion of TAVI indications to larger patient populations. Over the past decade, their incidence has declined substantially, largely due to device evolution, improved closure techniques, and the widespread adoption of meticulous pre-procedural imaging and planning. This narrative review provides a comprehensive overview of VCs in TAVI, focusing on contemporary incidence rates, underlying mechanisms, and patient as well as procedural-related risk factors. Additionally, the role of alternative access routes is discussed, alongside emerging technologies and future perspectives aimed at further reducing complication rates. Full article

Ramadan fasting substantially alters meal timing, sleep patterns, and daily activity, thereby increasing the risk of hypoglycaemia in adults with type 1 diabetes (T1D). Although continuous glucose monitoring (CGM) systems provide real-time alerts, these are largely reactive or limited to short prediction horizons, offering insufficient warning under fasting-related behavioural and circadian disruption. This study aims to evaluate whether behaviour-aware, temporally enriched recurrent deep learning models, leveraging multimodal CGM and wearable-derived signals, can forecast hypoglycaemia one hour ahead during Ramadan and the post-fasting period. In an observational, free-living cohort study conducted in Qatar, 33 adults with T1D were monitored using CGM and a wrist-worn wearable during Ramadan 2023 and the subsequent month. Multimodal data were aggregated into hourly features and organised into rolling 36 h sequences. In addition to physiological signals, explicit temporal and circadian proxy features were engineered, including cyclic time encodings, day–night indicators, and Ramadan-specific behavioural windows (e.g., pre-iftar, iftar, post-iftar, and fasting phases). Recurrent models, including LSTM and BiLSTM architectures, were trained using patient-wise, leak-free splits, with focal loss applied to address class imbalance. Model performance was evaluated on a held-out, naturally imbalanced test set using ROC AUC, precision–recall AUC, recall, and probability calibration, alongside cross-phase evaluation between Ramadan and post-fasting periods. Following quality control, 1164 participant-days were retained, with hypoglycaemia accounting for approximately 4% of hourly observations. Temporal feature enrichment and the use of a 36 h lookback window improved both discrimination and calibration, with performance stabilizing beyond this horizon. On the imbalanced test set, the best-performing multimodal model achieved an ROC AUC of 0.867 and a precision–recall AUC of 0.341, identifying 77% of next-hour hypoglycaemic events at a sensitivity-focused operating point (precision = 0.14). The selected BiLSTM model demonstrated good probability calibration (Brier score ≈ 0.03). Models trained using wearable-derived inputs alone achieved comparable discrimination and, in some configurations, higher precision–recall AUC than CGM-only baselines. Notably, models trained on the original imbalanced data outperformed resampled variants, suggesting that temporal and behavioural features provided sufficient discriminatory signal without requiring aggressive class balancing. Cross-phase evaluation indicated robust generalisation, particularly for the BiLSTM model. Overall, behaviour-aware, temporally enriched multimodal models can provide calibrated, hour-ahead hypoglycaemia risk estimates during Ramadan fasting in adults with T1D, enabling proactive intervention beyond reactive CGM alerts. Explicit modelling of circadian and behavioural dynamics enhances predictive performance under real-world class imbalance. Furthermore, integrating wearable-derived behavioural and physiological signals adds predictive value beyond CGM alone, supporting robustness across varying levels of contextual data availability. External validation and prospective clinical evaluation are required prior to deployment. Full article

This study focuses on how mesoscale eddies at the Kuroshio boundary in the East China Sea modulate underwater acoustic propagation. Using high-resolution reanalysis data from the Hybrid Coordinate Ocean Model (HYCOM) and validated acoustic ray-tracing simulations, the OW + SLA method is employed for eddy identification and classification. Statistical analysis of 120 eddy events from 2015 to 2020 clarifies their seasonal variation characteristics. Warm eddies shift the convergence zone 15–30 km away from the sound source and broaden it by 20–40%, while cold eddies shift it 10–25 km toward the source and narrow it by 15–35%. A linear relationship exists between eddy amplitude and acoustic transmission loss (TL = 72.4 + 0.42 h, R² = 0.61), where TL is the transmission loss in decibels (dB) and h is the eddy amplitude in meters (m), and there are depth-dependent transmission loss modulation effects. These results provide practical guidance not only for sonar system design and acoustic communication optimization but also for error correction in underwater acoustic navigation systems operating in eddy-prone environments. Full article

Groundwater drainage-induced karst collapse is a major geohazard in coal-mining regions of central Hunan, threatening residential safety and infrastructure. This study focuses on the Tuoshan minefield in the Jinzhushan mining area by integrating multi-source field data, including surveys of 170 collapse points, long-term groundwater monitoring at six boreholes, and high-density electrical geophysics. A topographically corrected MODFLOW seepage-field model is developed and calibrated for 2014 (RMSE = 0.32 m; NSE = 0.85) and validated for 2015–2016 (RMSE = 0.41 m; NSE = 0.81). To address the large groundwater-level simulation errors commonly encountered in subtropical hilly karst mining settings, the model incorporates a topographic correction, improving simulation accuracy by 12% relative to an uncorrected model. The simulations capture rapid “steep rise–slow fall” groundwater dynamics: Heavy rainfall (>100 mm/day) raises groundwater levels by 2.8–3.1 m within 2–3 days, whereas pumping (200 m³/h) causes a 1.9–2.2 m decline within one week. A 1.2 km drawdown funnel forms and overlaps with 89% of collapse points, indicating that seepage-field evolution and groundwater-level decline control collapse clustering, with soil suffusion and soil–water–rock interaction acting as key amplifying processes. Based on Terzaghi’s effective stress principle and the Theis solution, a collapse prediction formula is derived and validated using measured events (accuracy = 87.5%), and a region-specific critical hydraulic gradient (i_n = 0.85) is determined, lower than values reported for North China. The proposed workflow provides quantitative thresholds and model-based guidance for karst collapse prevention in subtropical mining areas. Full article

Background/Objectives: Bloodstain pattern analysis (BPA) is widely used in crime scene investigation (CSI), yet its practical application, evidential limits, and interpretive role are often discussed in fragmented or technique-focused terms. This systematic literature review examines how BPA is used in CSI, with emphasis on its operational functions, interpretive scope, and scientific robustness. Methods: The review followed PRISMA 2020 guidelines. A comprehensive search was conducted in Scopus using predefined Boolean strings. After screening, eligibility assessment, and manual review, 18 peer-reviewed research articles published between 1996 and 2026 were included. Data were extracted systematically and analysed using thematic synthesis. Results: The findings show that BPA is applied in CSI as an integrated evidential pathway rather than as a single analytical procedure. Its uses include bloodstain detection and documentation, geometric reconstruction through trajectory and area-of-origin analysis, differentiation of mechanisms and sources to prevent misclassification, activity-level inference based on transfer and contact phenomena, and temporal reasoning related to trace formation. The review also highlights the role of validation infrastructures, including blood substitutes, animal analogues, and computational methods, which support training, experimentation, and reproducibility under ethical and practical constraints. Across the literature, reconstruction accuracy is shown to be sensitive to documentation quality, measurement assumptions, environmental conditions, and contextual limitations. Conclusions: Overall, BPA contributes to CSI by enabling structured, context-aware interpretation of blood evidence while remaining subject to measurement assumptions, contextual influences, and cognitive factors that may affect reconstruction outcomes. Its evidential value lies not only in reconstructing events, but also in supporting transparent, testable, and defensible forensic reasoning. Full article

To develop secure, fast, and interoperable smart substations, it is vital to understand the current situation and potential future directions of the technologies involved. This study presents the evolution and state of the art of the Generic Object Oriented Substation Event (GOOSE) communication protocol, defined by the International Electrotechnical Commission (IEC) 61850 standard. A Systematic Literature Review (SLR) was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. This included journal articles published from 2004 to 2025 and conference papers from 2020 to 2025, written in English within Engineering. Only studies primarily focusing on GOOSE, citing it at least ten times, and indexed in the Scopus, IEEE Xplore, and Web of Science databases were included. The quantitative analysis used SciMAT software, complemented by a qualitative analysis. Due to the bibliometric and thematic nature of this review, potential biases were considered at the review level rather than by applying a formal study-level risk-of-bias tool. The final analysis comprised 82 journal articles and 84 conference papers. The results offer a comprehensive mapping of GOOSE research evolution, identify nine main challenges and limitations from the last 22 years, and highlight current research directions. The literature reveals methodological heterogeneity, a predominance of simulation-based approaches, and limited large-scale empirical validation. Full article

Background/Objectives: Influenza remains a primary cause of severe illness and death in adults over 60. In this group, immunosenescence and existing health conditions make infections more dangerous and traditional vaccines less effective. The MF59-adjuvanted vaccine was specifically designed to overcome these limitations by enhancing the body’s immune activation and antigen presentation. While the vaccine shows clear benefits, some recent concerns regarding vaccine safety have been raised without supporting scientific evidence. Therefore, this systematic review focuses on providing a comprehensive evaluation of its safety outcomes compared to standard vaccines. Methods: Following the PRISMA guidelines, a systematic review and meta-analysis were conducted; two researchers independently assessed the eligibility of the studies, and the risk of bias was assessed using RoB2 and ROBINS tools for randomized clinical trials and observational studies, respectively. Pooled risk estimates were calculated using a random-effects model. Results: Ten RCTs and three non-RCTs meeting the inclusion criteria were included. No significant differences were found for severe systemic outcomes: Guillain–Barré syndrome (RR 1.01, 95% CI 0.64–1.80) and encephalitis (RR 1.23, 95% CI 0.85–1.78). For other systemic adverse effects, there were no significant differences between adjuvanted and non-adjuvanted vaccines; only myalgia showed a small but significant increase with adjuvanted vaccines (RR 1.35, 95% CI 1.02–1.78) compared with non-adjuvanted vaccines. Conclusions: MF59-adjuvanted influenza vaccines have a favorable and well-characterized safety profile in adults aged 60 years and older. Adverse events are predominantly mild and transient, with no evidence of increased risk of serious or immune-mediated outcomes compared with non-adjuvanted vaccines. Full article

Parallel, data-intensive applications are now commonly executed on infrastructures that combine Cloud, Fog, and Edge resources. In these environments, execution takes place on devices with markedly different computational power and over networks whose latency and bandwidth can fluctuate over time. Under these conditions, overall performance is influenced not only by processing speed but also by communication delays arising from data dependencies between tasks. This leads to a basic issue: whether scheduling strategies developed under computation-focused assumptions continue to perform well once communication costs are made explicit. This work examines the behavior of simple and widely adopted scheduling heuristics when network effects are modeled directly within the system. No new scheduling algorithms are introduced. Instead, the analysis focuses on how execution time and monetary cost change for deterministic parallel workloads deployed on hierarchical Cloud–Edge infrastructures exposed to stochastic latency and bandwidth variations. For this purpose, we introduce CLOWNSim, a lightweight discrete-event simulation framework that supports large-scale Monte Carlo experiments on fixed task graphs, allowing infrastructural and scheduling effects to be examined independently of workload variability. The experimental analysis covers fully centralized Cloud deployments, intermediate Fog configurations, and resource-constrained IoT scenarios. Scheduling policies based on computational speed, execution cost, or random device selection are evaluated across these settings. In Cloud and Fog environments, communication latency and data transfers represent a substantial portion of the overall makespan, weakening the impact of scheduling decisions driven primarily by computation. In IoT scenarios, limited processing capacity becomes the main limiting factor, while communication overhead remains present but less influential in comparison. The results indicate that performance trends across the Cloud–Edge continuum cannot be attributed to scheduler choice alone. Execution behavior arises from the combined effects of workload structure, placement decisions, and network properties, with different elements becoming dominant depending on the deployment context. The proposed simulation framework offers a practical way to study these interactions and to assess cost–performance trade-offs under communication conditions that reflect realistic operating environments. Full article