Search Results (8,837)

Peripheral artery disease (PAD) is a pervasive atherosclerotic condition affecting well over 100 million adults worldwide and associated with major functional limitations, reduced quality of life, and elevated risks of myocardial infarction, stroke, limb events, and mortality. Exercise therapy—preferably supervised or delivered through structured, monitored home-based programs—is a first-line, guideline-endorsed therapy that improves walking performance and patient-reported outcomes and contributes to comprehensive secondary prevention. This review synthesizes mechanistic underpinnings (endothelial, angiogenic, metabolic, and autonomic) and appraises the comparative effectiveness, safety, and implementation models of supervised exercise therapy (SET), structured home-based and hybrid programs, and alternative modalities in PAD. Finally, we summarize policy aspects and persistent gaps to guide clinical practice and future research. Full article

Coastal freak waves (CFWs) are sudden and hazardous wave events that occur near shorelines and can pose serious threats to coastal visitors and infrastructure. Due to the complex interactions among coastal bathymetry, wave dynamics, and environmental conditions, the mechanisms governing CFW formation remain poorly understood, making reliable prediction difficult. This study investigates the feasibility of applying machine learning techniques to predict CFW occurrences using observational environmental data. Three machine learning algorithms, the Random Forest (RF), Support Vector Machine (SVM), and Artificial Neural Network (ANN), were developed to generate probability-based predictions of CFW events. Environmental variables derived from buoy observations, including wave characteristics, wind conditions, swell parameters, wave grouping indicators, and nonlinear wave interaction indices, were used as model inputs. Hyperparameters were optimized using grid search combined with k-fold cross-validation. The results show that all three models achieved comparable predictive performance, with AUC values close to 0.80 and overall prediction accuracy around 74%. The ANN model achieved the highest recall, indicating strong capability in detecting CFW events, while the RF and SVM models showed more balanced precision and recall. Analysis of high-probability prediction events suggests that CFW occurrences are associated with swell-dominated conditions, strong wave grouping behavior, and enhanced nonlinear wave interactions. These results demonstrate that machine learning provides a promising framework for probabilistic prediction of coastal freak waves and has potential applications in coastal hazard assessment and early warning systems. Full article

Against the backdrop of accelerating climate change, extreme weather events have increasingly caused yield losses in agricultural crops. Meanwhile, they undermine the stability of production systems, posing an increasingly severe threat to agriculture. This study draws on the “diversity–stability” hypothesis to construct a country-level measure of agricultural production resilience in China (ARES). Using output time series for multiple agricultural products, we capture the co-movements of shocks and system resilience through output stability and volatility. By combining ARES with climate exposure measures, we assemble a panel dataset covering 1343 counties over the period 2000–2023 and employ a dynamic panel threshold model to jointly account for persistence in ARES and state-dependent nonlinearities in climate impacts. The results reveal significant path dependence in ARES and pronounced threshold effects across climate dimensions. In the full sample, extreme high-temperature days become significantly detrimental after crossing the threshold, whereas extreme low-temperature days become significantly beneficial in the high-exposure regime. Extreme rainfall days and extreme drought days generally exhibit positive effects that weaken markedly beyond their respective thresholds, indicating diminishing marginal gains in ARES under severe exposure. The comprehensive climate physical risk index significantly suppresses ARES when it is below the threshold value; however, after surpassing the threshold, its marginal effect becomes significantly weaker. Heterogeneity analyses across hilly, plain, and mountainous areas, as well as nationally designated key counties for poverty alleviation and development, further show that threshold locations and regime-specific effects differ substantially by terrain and development conditions. These findings highlight the need for “threshold-based” climate adaptation governance, emphasizing targeted investments and risk-financing instruments to prevent ARES collapse under tail-risk regimes. Full article

Introduction: Drug side effects are a relevant problem for patient safety and public health, and traditional methods have limitations in capturing complex patterns between clinical and pharmacological variables. Objective: To evaluate machine learning models to probabilistically predict multiple side effects associated with drug use. Materials and methods: A cross-sectional computational study was carried out with data from 1000 medications that included clinical condition, dosage and duration of treatment. Random Forest, Decision Tree, Support Vector Classifier and KNN were trained and optimized using Grid Search and an 80:20 split for training and testing. Chi-square tests and Principal Component Analysis were applied to explore associations and overlap between categories. Results: Significant associations were found between side effects and clinical condition (p < 0.05) and the drug administered (p < 0.05). The PCA showed a high overlap between categories, which justified a probabilistic approach. Tree-based models showed better performance (accuracy ≈ 0.35). Conclusions: Prediction of side effects is a multifactorial and non-deterministic problem; probabilistic machine learning models allow for estimating several plausible adverse events and can support clinical decision-making and pharmacovigilance. Full article

Severe wintertime particulate pollution (PM₁₀ and PM_2.5) affects the Santiago Metropolitan Region in Chile and is intensified by basin topography and frequent thermal inversions. Local authorities rely on the Critical Episodes Management (CEM) forecasting system, yet its predictive performance is variable. This study assesses CEM to identify operational vulnerabilities and propose data-driven improvements for urban air-quality governance. About ~1.2 million hourly meteorological and air-quality records (2017–2022) were analyzed using Generalized Additive Models (GAMs) to characterize key nonlinear relationships, and we evaluated the operational skill of the Cassmassi-1 PM₁₀ model and the WRF-Chem-based PM_2.5 forecasting component used by the system. Cassmassi-1 missed more than 50% of critical episodes and showed a false-alarm rate above 60%, consistent with limitations associated with static or incomplete emission representations. By contrast, the WRF-Chem-based component achieved episode prediction accuracy above 70%. GAM results indicate that wind speeds below 2 m s⁻¹, high diurnal temperature range, and relative humidity below 65% are strongly associated with extreme events. Considering the results, we recommend transitioning to nonlinear forecasting approaches that explicitly incorporate these meteorological thresholds and vertical stability indicators to improve alert reliability, strengthen urban resilience, and reduce population exposure. Full article

Data on wildfires (burned area ≥ 100 ha) in South Korea were compiled for 2000–2025 and analyzed together with the national geospatial inventories of hazardous fuel facilities to characterize wildfire-triggered Natech exposure and potential consequence distances. In total, 47 large wildfire events were identified, burning approximately 139,800 ha, with all events occurring during the late winter–spring window (February–May). The spatial overlays of wildfire footprints with facility locations identified 805 gasoline/diesel stations and 227 LPG filling stations located within wildfire-affected districts, corresponding to 14.1% of gas stations and 11.5% of LPG stations in the nationwide facility dataset. Facility exposure was geographically clustered, with the highest concentrations occurring in the eastern and southeastern wildfire hotspots. To quantify potential technological impact extents under wildfire escalation, ALOHA simulations were conducted for a wildfire-induced BLEVE/fireball scenario involving a 10,000 L mobile tank with representative fuels (propane for LPG, n-octane for gasoline, and n-dodecane for diesel). The modeled thermal radiation threat zone radii (10, 5, and 2 kW·m⁻²) were 228/322/502 m for propane, 250/353/550 m for n-octane, and 254/358/559 m for n-dodecane. Together, the event-based wildfire dataset, facility overlay results, and scenario-based impact distances provide an integrated, quantitative basis for assessing wildfire-triggered Natech conditions at the wildland–urban/industrial interface in South Korea. Full article

In estuarine environments, machine learning (ML) methods have been widely applied to predict water-level variations prone to flooding. However, most studies have focused on low-frequency components driven by tides and surges, neglecting high-frequency oscillations such as seiches. This study addresses this gap by assessing the ability of ML methods to predict seiche-influenced water levels. The application was conducted in the upper Elorn estuary (France), where seiches exceeded 0.6 m in height, with first-mode periods of 45–70 min. The ML procedure relied on a series of recurrent neural networks (RNNs, LSTM, and GRUs) and was implemented in a two-step framework to separately predict (i) low-frequency water-level variations and (ii) high-frequency seiche oscillations. The model accurately reproduced low-frequency dynamics (with a coefficient of determination of 0.98) and captured a substantial portion of seiches-related variability during major events. The integration of seiches improved peak total water-level predictions, reducing the mean absolute error by 30% during tidal cycles characterized by strong seiches (amplitude exceeding 0.1 m). Furthermore, the inclusion of seiches enhanced the estimation of the highest 10% peak water levels while reducing the tendency to underestimate measurements. These findings emphasize the importance of integrating seiche-generating physical processes into ML-based forecasting frameworks. Full article

Geohazards such as landslides, earthquakes, debris flows, and floods are governed by complex interactions among geological, hydrological, and human processes. Traditional data-driven models have improved hazard prediction but often lack interpretability and adaptability. This review examines the evolution of knowledge-guided approaches in geohazard research, highlighting how knowledge representation and artificial intelligence have progressively converged to enhance understanding, reasoning, and model transparency. A bibliometric analysis of 1410 publications indexed in the Web of Science reveals an evolution from early ontology-based knowledge engineering for expert reasoning to knowledge graphs (KG), frameworks enabling multi-source data integration and relational inference, and more recently, to large language model (LLM), augmented systems for automated knowledge extraction and cognitive geoscience. This review synthesizes advances in knowledge representation, knowledge graphs, and LLM-based reasoning, demonstrating how hybrid models that embed physical laws and expert knowledge can improve the interpretability and generalization of machine learning. These developments enable new forms of knowledge-driven geohazard intelligence and support applications in hazard monitoring, early warning, and risk communication. There are challenges we still face, including semantic fragmentation, limited causal reasoning, and sparse data for extreme events. Future directions require unified knowledge–data–mechanism architectures, causality-aware modeling, and interoperable standards to advance trustworthy and explainable geohazard intelligence. Full article

Addressing the prevalent issue of “physical preservation but spiritual silence” in the revitalisation of China’s national industrial heritage, this study proposes and empirically validates a “dual-track narrative” design framework that systematically translates cultural values into spatial experiences. The framework integrates a “figure–history” narrative, which crystallises historical lineage and symbolic spirit through spatial sequences, commemorative landmarks, and authentic remains, with a “scene–activity” narrative, which transforms former production spaces into dynamic, culturally vibrant stages through ecological restoration displays, industrial landscape transformation, and flexible activity implantation. Using Nantong Guangsheng Oil Mill as a single-case study, the research employs qualitative methods including archival analysis, field observation, and semi-structured interviews to examine how the dual-track framework operates in practice. The findings reveal that the “figure–history” narrative manifests in a walkable “time corridor” along the north–south axis, where architectural remnants from different eras are organised to materialise Zhang Jian’s industrial salvation ethos and the collective memory of generations of workers. Meanwhile, the “scene–activity” narrative activates underutilised spaces—such as the repurposing of acid treatment ponds into constructed wetlands and paved grounds into public stages—enabling ongoing cultural production, community interaction, and ecological healing. The study demonstrates that the dual-track framework bridges the historical and contemporary dimensions often treated separately in heritage practice, establishing a systematic “translation mechanism” from cultural decoding to design intervention. Theoretically, it contributes to industrial heritage research by integrating narratology, memory studies, heritage interpretation, and situationism into a coherent design methodology. Practically, it offers decision-makers evaluation criteria beyond the preservation-versus-development binary, provides designers with a mode of creative transformation grounded in material authenticity, and suggests to operators a content-driven, event-based model for sustaining heritage spaces. By spatialising and eventising narratives, the dual-track approach enables industrial heritage to function as a catalyst for cultural identity, social vitality, and economic sustainability, offering a transferable paradigm for the adaptive reuse of industrial heritage in contemporary urban contexts. Full article

Monitoring dam stability is critical to ensure structural safety and operational reliability. This study integrates Persistent Scatterer Interferometry (PSI) based on Sentinel-1 SAR imagery (2020–2023) with Finite Element Method (FEM) simulations to assess the behavior of the Koyna Dam in India. PSI detected crest displacements between −1.0 and −1.8 mm yr⁻¹, while FEM simulations predicted a maximum vertical displacement of approximately −3.2 mm at the crest. Although these results represent different quantities (time-averaged displacement rates versus peak static displacement), both approaches indicate millimeter-scale deformation and a consistent pattern of settlement at the dam crest, supporting the interpretation of hydrologically driven structural response. The observed differences are primarily attributed to differences in spatial resolution and methodology between point-based FEM outputs and pixel-averaged satellite observations. The study demonstrates that combining satellite-based monitoring with numerical simulations provides a robust and cost-effective framework for dam safety assessment. This integrated approach supports improved interpretation of deformation behavior and offers practical value in extreme conditions, such as during flood events or climate-driven hydrological changes. Furthermore, continued advances in remote sensing and numerical modeling are expected to enhance the reliability of such approaches, making this methodology a transferable and sustainable solution for dam management worldwide. Full article

This study carries out the research on event-triggered output feedback control tailored for discrete-time switched linear systems. A dynamic event-triggered mechanism (DETM) is utilized to mitigate the triggering frequency. To ensure stability and control performance, it is assumed that an event is triggered whenever the system undergoes a switch. First, the closed-loop stability of the underlying switched system with DETM is analyzed via the switched Lyapunov function method, followed by the establishment of a stability criterion for the system under arbitrary switching. Based on this criterion, a dynamic event-triggered output feedback control strategy is devised. The viability and application potential of our proposed control strategy is validated through simulation trials using a morphing aircraft model. Furthermore, when we pit dynamic event-triggered control (DETC) against its static (SETC) version, the proposed DETM reduces the trigger events and prolongs the inter-event intervals versus the SETM, while retaining nearly identical control accuracy and energy consumption, thus providing an efficient solution for resource-constrained networked control systems. Full article

Single-event effects (SEEs) present a significant challenge to the radiation reliability of integrated circuits. Conventional SEE analysis methods for single-photon avalanche diode (SPAD) devices primarily rely on Sentaurus Technology Computer-Aided Design (TCAD) numerical simulation, which is computationally intensive and time-consuming. In this study, we propose a generalized deep learning (DL) model, using a silicon-based SPAD device with a double-junction double-buried-layer (DJDB) structure fabricated in 180 nm CMOS process as the research subject. By incorporating key parameters that influence SEEs as model inputs, the proposed approach enables rapid prediction of critical parameter metrics, including transient current peaks and dark count rates. Experimental results show that the DL model achieves a prediction accuracy of 97.32% for transient current peaks and 99.87% for dark count rates, demonstrating extremely high prediction precision. To further validate the generalization capability of the proposed network, the model is applied to predict the detection performance of the DJDB-SPAD device. The prediction accuracies for four key performance parameters all exceed 97.5%, further confirming the accuracy and robustness of the developed model. Meanwhile, compared with the conventional Sentaurus TCAD simulation method, the proposed method achieves a 336-fold improvement in computational efficiency. Overall, this method realizes the dual advantages of high precision and high efficiency, which provides an efficient and accurate technical solution for the rapid characteristic analysis and reliability evaluation of SPAD devices under single-event effects (SEEs). Full article

Service Level Agreement (SLA) compliance in hospital transport processes is essential in terms of patient safety, service continuity, and resource efficiency. However, transport requests occur as irregular events, limiting the applicability of equally spaced time-series assumptions. The presented study jointly addresses two complementary objectives in an event-based framework: predicting the interarrival time between consecutive transport requests (next-event forecasting) and forecasting the total request count within forward SLA horizons (forward-count forecasting). Machine learning methods such as Ridge Regression, Extra Trees, and Histogram-based Gradient Boosting, as well as deep learning architectures such as Long Short-Term Memory and Gated Recurrent Unit, were compared under different time horizons and adaptive history windows on time-stamped transport request records from the operational system supporting a private hospital in Turkey, including patient, specimen, and material transport requests. Results indicate that deep learning methods yield lower errors in demand count prediction at short time horizons; as the horizon lengthens, machine learning performs similarly and even outperforms in some cases; and as the history window increases, the prediction error for the next request occurrence systematically decreases. The lowest mean absolute error values in request counts were obtained for demand forecasting within a 30 min time window; 2.10 for material transport, 3.88 for patient transport, and 2.84 for specimen transport. Additionally, R² value reached 0.98 for next-event forecasting with a rolling-memory window of 20 events. Overall, the findings suggest that hospital transport demand is substantially predictable and that event-based forecasting can support SLA-oriented staffing, task dispatching, and delay mitigation. Full article

Background and Objectives: Cutaneous squamous cell carcinoma (cSCC) displays heterogeneous metastatic potential, and the role of sentinel lymph node biopsy (SLNB) in clinically node-negative patients remains debated. To evaluate tumor thickness and histological grade as predictors of sentinel lymph node (SLN) metastasis in high-risk cSCC and to assess the performance of a simplified pathology-based predictive model. Materials and Methods: This retrospective single-center study included consecutive patients with high-risk cSCC and clinically N0 status who underwent SLNB. Associations were examined using univariate and multivariable logistic regression, ROC analysis with bootstrap internal validation (2000 iterations), and decision curve analysis. Results: Thirty-four patients were analyzed; 12 (35.3%) had SLN metastases. SLN-positive patients had greater tumor thickness (median 5.5 mm vs. 3.0 mm, p = 0.006) and higher frequency of G2–G3 histological grade (91.7% vs. 45.5%, p = 0.011). Histological grade was the strongest independent predictor in multivariable analysis (OR 14.61, 95% CI 1.63–131.12). The combined model demonstrated apparently high discrimination in this small cohort (AUC 0.91; bootstrap 95% CI 0.79–0.99), though this estimate should be interpreted with caution given the limited number of events. A 4.0-mm threshold yielded sensitivity 83.3% and NPV 86.7%. Conclusions: In this exploratory single-center study, tumor thickness and histological grade were complementary predictors of SLN metastasis in cSCC. These findings are preliminary and require validation in larger prospective cohorts. Full article

Multimodal freight transportation networks are increasingly exposed to natural and human-made disruptions, yet prior research remains fragmented in how disruptions are represented, which modeling techniques are applied, and how results are validated, limiting comparability and actionable guidance for resilient planning. This study presents a PRISMA-guided systematic review of disruption modeling in multimodal freight networks. A total of 21 studies were identified and coded to address three research questions concerning (RQ1) which analytical and computational modeling techniques are applied; (RQ2) to what extent models represent cross-modal interdependencies, cascading failures, and recovery processes; and (RQ3) what validation, calibration, and empirical testing strategies are employed. The review shows that optimization-based approaches and hybrid frameworks dominate the literature, complemented by fewer network science and data-driven methods. Most studies model disruptions as node/link failures and/or capacity degradation using static single-event scenarios, and explicit representations of cascading effects, operational delay propagation, and time-evolving recovery trajectories remain relatively rare. While many studies rely on real network data, formal calibration and historical backtesting against observed disruption events are uncommon, and validation is primarily case study-based. These findings highlight the need for more dynamic resilience modeling, stronger uncertainty quantification, standardized reporting of performance and resilience metrics, and greater use of empirically grounded validation to improve the generalizability and decision relevance of multimodal freight resilience models. Full article