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Drought influence on carbon assimilation and water use efficiency in Mediterranean ecosystems
Exercise-associated changes in leptin and irisin relate to cognitive function in older adults stratified by cognitive impairment
Lack of oncological significance of prophylactic splenectomy for type 4 gastric cancer invading the greater curvature: a multicenter retrospective study
Failure mechanisms and recycling potential of railway sleepers damaged during the 2023 Kahramanmaraş earthquakes
A computational framework for optimizing radioiodine therapy protocols in metastatic thyroid cancer
Clinician perspectives on preclinical radiology education: a qualitative study
Spleen and liver shear wave elastography for assessment of portal hypertension in children with biliary atresia
Wind turbine proximity and health-related quality of life in Germany 2002 to 2022
Abstract The expansion of wind energy has intensified debate about potential impacts on the health and wellbeing of nearby residents, yet empirical evidence remains mixed. Using data from the German Socio-Economic Panel (SOEP) linked to administrative records on onshore wind turbines, we analyse changes in health-related quality of life (HRQoL) associated with turbine construction in Germany between 2002 and 2022. We implement a quasi-experimental matched difference-in-differences design among residential non-movers, focusing on turbines with hub height ≥ 50 m and proximity bands from 1.5 km to 6 km. We find no evidence of adverse average HRQoL changes associated with turbine presence within 6 km in this setting for matched residential non-movers over the observed pre-post interval. Secondary analyses indicate heterogeneity by turbine characteristics and cumulative exposure, including patterns consistent with lower mental HRQoL where turbine density is higher. These results are interpreted cautiously, given multiple related specifications and limitations in assessing pre-trends with biennial HRQoL measurement. Overall, the findings highlight the importance of cumulative exposure and turbine characteristics for assessing the local health implications of wind energy deployment.
Explainable artificial intelligence models in predicting major cardiovascular events: insights from the PolyIran and PolyPars prospective studies
Comparison of hedonic hunger and intuitive eating status between pregnant and non-pregnant women
Principled XAI analysis of the deep learning-based landslide susceptibility prediction model
Abstract Research on applying machine learning (ML) and deep learning (DL) techniques to landslide susceptibility analysis is widespread, with increasingly accurate analyses through novel models. Predicting landslide susceptibility using ML models involves analyzing relationships between conditioning factors and landslide occurrences. Unlike traditional methods, ML models do not explicitly incorporate geotechnical or hydrological theories, raising concerns about result reliability despite high accuracy. This “black-box” limitation has prompted research applying eXplainable Artificial Intelligence (XAI) algorithms to interpret relationships between conditioning factors (digital elevation models (DEM), forest characteristics, soil properties, and geological features) and landslide susceptibility, thereby validating proposed ML models. In this paper, landslide susceptibility prediction models were developed using 20 conditioning factors and multiple architectures, including Support Vector Machine (SVM), Random Forest (RF), Multilayer Perceptron (MLP), and Convolutional Neural Networks (CNNs). Quantitative performances and XAI outcomes were compared. Specifically, the quantitative evaluation showed that the traditional point-based models (RF, SVM, and MLP) achieved Accuracies of 0.6931, 0.6621, and 0.7034, respectively, while the image-based CNNs achieved a higher Accuracy of 0.7586. Furthermore, regarding Recall—a critical metric for disaster management to minimize false negatives—the CNNs (0.8138) significantly outperformed the RF (0.6345), SVM (0.6276), and MLP (0.6828). These results underscore that capturing spatial context through image-wise inputs is far more effective for landslide susceptibility mapping than conventional pixel-level analysis. Because CNNs process input data differently, Gradient-weighted Class Activation Mapping (Grad-CAM) was applied alongside SHapley Additive exPlanations (SHAP) for CNNs, whereas only SHAP was applied to the other models. Results indicated specific patterns associated with certain conditioning factors in landslide susceptibility prediction. CNNs’ Grad-CAM heatmap effectively illustrated these patterns by treating data as images, improving interpretability and reliability of ML outputs.
Explainable ensemble learning using SHAP for ERP anomaly detection
Temperature regulation of a nonlinear CSTR using a global-guided optimization-based PID framework
Abstract Accurate temperature regulation in nonlinear continuous stirred tank reactors (CSTRs) remains a challenging task due to strong nonlinearities and operating-point sensitivity. Although numerous proportional-integral-derivative (PID) tuning approaches have been proposed, most existing studies primarily focus on nominal operating conditions, often resulting in degraded performance under varying process dynamics. To address this limitation, this study proposes an optimization-based PID with filter (PIDf) tuning framework that enhances consistency and reliability across different operating scenarios. A global-guided search mechanism is incorporated into the optimization process to improve convergence stability and solution quality without increasing computational complexity. The proposed framework is evaluated on a nonlinear jacketed CSTR system under setpoint variations and multiple operating conditions. Its performance is benchmarked against recent metaheuristic optimization methods and classical tuning strategies using time-domain specifications and error-based performance indices. The results indicate that the proposed approach achieves faster settling behavior, reduced overshoot, and improved consistency, while maintaining stable performance across repeated runs. Overall, the performance of the proposed approach is quantitatively assessed using standard time-domain and error-based metrics, providing a systematic evaluation of control quality. These findings highlight the applicability of the proposed framework for temperature regulation in nonlinear chemical processes.