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Search Results (3,109)

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Keywords = decision-making training

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33 pages, 4841 KiB  
Article
Research on Task Allocation in Four-Way Shuttle Storage and Retrieval Systems Based on Deep Reinforcement Learning
by Zhongwei Zhang, Jingrui Wang, Jie Jin, Zhaoyun Wu, Lihui Wu, Tao Peng and Peng Li
Sustainability 2025, 17(15), 6772; https://doi.org/10.3390/su17156772 - 25 Jul 2025
Abstract
The four-way shuttle storage and retrieval system (FWSS/RS) is an advanced automated warehousing solution for achieving green and intelligent logistics, and task allocation is crucial to its logistics efficiency. However, current research on task allocation in three-dimensional storage environments is mostly conducted in [...] Read more.
The four-way shuttle storage and retrieval system (FWSS/RS) is an advanced automated warehousing solution for achieving green and intelligent logistics, and task allocation is crucial to its logistics efficiency. However, current research on task allocation in three-dimensional storage environments is mostly conducted in the single-operation mode that handles inbound or outbound tasks individually, with limited attention paid to the more prevalent composite operation mode where inbound and outbound tasks coexist. To bridge this gap, this study investigates the task allocation problem in an FWSS/RS under the composite operation mode, and deep reinforcement learning (DRL) is introduced to solve it. Initially, the FWSS/RS operational workflows and equipment motion characteristics are analyzed, and a task allocation model with the total task completion time as the optimization objective is established. Furthermore, the task allocation problem is transformed into a partially observable Markov decision process corresponding to reinforcement learning. Each shuttle is regarded as an independent agent that receives localized observations, including shuttle position information and task completion status, as inputs, and a deep neural network is employed to fit value functions to output action selections. Correspondingly, all agents are trained within an independent deep Q-network (IDQN) framework that facilitates collaborative learning through experience sharing while maintaining decentralized decision-making based on individual observations. Moreover, to validate the efficiency and effectiveness of the proposed model and method, experiments were conducted across various problem scales and transport resource configurations. The experimental results demonstrate that the DRL-based approach outperforms conventional task allocation methods, including the auction algorithm and the genetic algorithm. Specifically, the proposed IDQN-based method reduces the task completion time by up to 12.88% compared to the auction algorithm, and up to 8.64% compared to the genetic algorithm across multiple scenarios. Moreover, task-related factors are found to have a more significant impact on the optimization objectives of task allocation than transport resource-related factors. Full article
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17 pages, 1565 KiB  
Article
Highway Autonomous Driving Decision Making Using Reweighting Ego-Attention and Driver Assistance Module
by Junyu Li and Liying Zheng
Drones 2025, 9(8), 525; https://doi.org/10.3390/drones9080525 - 25 Jul 2025
Abstract
Decision making is challenging in autonomous driving (AD) under highway scenarios because of the unpredictable behaviors of neighbor vehicles, leading to the necessity of accurately modelling interactions between vehicles. Though ego-attention, a variant of self-attention, provides a way for object interaction extraction, its [...] Read more.
Decision making is challenging in autonomous driving (AD) under highway scenarios because of the unpredictable behaviors of neighbor vehicles, leading to the necessity of accurately modelling interactions between vehicles. Though ego-attention, a variant of self-attention, provides a way for object interaction extraction, its feature expression still needs to improve. This paper improves the original ego-attention by reweighting the encoding vehicle features, forcing them to pay more attention to significant features. Moreover, we designed a rule-based driver assistance module (DAM) to alleviate mis-decisions by constraining action space. Finally, we constructed our final AD decision-making model by integrating the proposed reweighting ego-attention and the DAM into the dual-input decision-making framework trained by enhanced deep reinforcement learning (DRL). We evaluated our decision-making model on highway scenarios. The results show that our model achieves better performance in success step (39.95 steps/episode), speed (29.15 m/s), lane-changing times (5.64 times/episode), and task completion rate (98%) than existing models, including DRL-GAT-SA, AE-D3QN-DA, and ego-attention-based ones, implying the competitive driving accuracy, safety, and comfort of our model. Full article
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23 pages, 1667 KiB  
Review
Review of Advances in Multiple-Resolution Modeling for Distributed Simulation
by Luis Rabelo, Mario Marin, Jaeho Kim and Gene Lee
Information 2025, 16(8), 635; https://doi.org/10.3390/info16080635 - 25 Jul 2025
Abstract
Multiple-resolution modeling (MRM) has emerged as a foundational paradigm in modern simulation, enabling the integration of models with varying levels of granularity to address complex and evolving operational demands. By supporting seamless transitions between high-resolution and low-resolution representations, MRM facilitates scalability and interoperability, [...] Read more.
Multiple-resolution modeling (MRM) has emerged as a foundational paradigm in modern simulation, enabling the integration of models with varying levels of granularity to address complex and evolving operational demands. By supporting seamless transitions between high-resolution and low-resolution representations, MRM facilitates scalability and interoperability, particularly within distributed simulation environments such as military command and control systems. This paper provides a structured review and comparative analysis of prominent MRM methodologies, including multi-resolution entities (MRE), agent-based modeling (from a federation viewpoint), hybrid frameworks, and the novel MR mode, synchronizing resolution transitions with time advancement and interaction management. Each approach is evaluated across critical dimensions such as consistency, computational efficiency, flexibility, and integration with legacy systems. Emphasis is placed on the applicability of MRM in distributed military simulations, where it enables dynamic interplay between strategic-level planning and tactical-level execution, supporting real-time decision-making, mission rehearsal, and scenario-based training. The paper also explores emerging trends involving artificial intelligence (AI) and large language models (LLMs) as enablers for adaptive resolution management and automated model interoperability. Full article
(This article belongs to the Special Issue Editorial Board Members’ Collection Series: "Information Systems")
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23 pages, 13834 KiB  
Article
Using Shapley Values to Explain the Decisions of Convolutional Neural Networks in Glaucoma Diagnosis
by Jose Sigut, Francisco Fumero and Tinguaro Díaz-Alemán
Algorithms 2025, 18(8), 464; https://doi.org/10.3390/a18080464 - 25 Jul 2025
Abstract
This work aims to leverage Shapley values to explain the decisions of convolutional neural networks trained to predict glaucoma. Although Shapley values offer a mathematically sound approach rooted in game theory, they require evaluating all possible combinations of features, which can be computationally [...] Read more.
This work aims to leverage Shapley values to explain the decisions of convolutional neural networks trained to predict glaucoma. Although Shapley values offer a mathematically sound approach rooted in game theory, they require evaluating all possible combinations of features, which can be computationally intensive. To address this challenge, we introduce a novel strategy that discretizes the input by dividing the image into standard regions or sectors of interest, significantly reducing the number of features while maintaining clinical relevance. Moreover, applying Shapley values in a machine learning context necessitates the ability to selectively exclude features to evaluate their combinations. To achieve this, we propose a method involving the occlusion of specific sectors and re-training only the non-convolutional portion of the models. Despite achieving strong predictive performance, our findings reveal limited alignment with medical expectations, particularly the unexpected dominance of the background sector in the model’s decision-making process. This highlights potential concerns regarding the interpretability of convolutional neural network-based glaucoma diagnostics. Full article
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25 pages, 16941 KiB  
Article
KAN-Sense: Keypad Input Recognition via CSI Feature Clustering and KAN-Based Classifier
by Minseok Koo and Jaesung Park
Electronics 2025, 14(15), 2965; https://doi.org/10.3390/electronics14152965 - 24 Jul 2025
Abstract
Wi-Fi sensing leverages variations in CSI (channel state information) to infer human activities in a contactless and low-cost manner, with growing applications in smart homes, healthcare, and security. While deep learning has advanced macro-motion sensing tasks, micro-motion sensing such as keypad stroke recognition [...] Read more.
Wi-Fi sensing leverages variations in CSI (channel state information) to infer human activities in a contactless and low-cost manner, with growing applications in smart homes, healthcare, and security. While deep learning has advanced macro-motion sensing tasks, micro-motion sensing such as keypad stroke recognition remains underexplored due to subtle inter-class CSI variations and significant intra-class variance. These challenges make it difficult for existing deep learning models typically relying on fully connected MLPs to accurately recognize keypad inputs. To address the issue, we propose a novel approach that combines a discriminative feature extractor with a Kolmogorov–Arnold Network (KAN)-based classifier. The combined model is trained to reduce intra-class variability by clustering features around class-specific centers. The KAN classifier learns nonlinear spline functions to efficiently delineate the complex decision boundaries between different keypad inputs with fewer parameters. To validate our method, we collect a CSI dataset with low-cost Wi-Fi devices (ESP8266 and Raspberry Pi 4) in a real-world keypad sensing environment. Experimental results verify the effectiveness and practicality of our method for keypad input sensing applications in that it outperforms existing approaches in sensing accuracy while requiring fewer parameters. Full article
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22 pages, 474 KiB  
Article
Neural Network-Informed Lotka–Volterra Dynamics for Cryptocurrency Market Analysis
by Dimitris Kastoris, Dimitris Papadopoulos and Konstantinos Giotopoulos
Future Internet 2025, 17(8), 327; https://doi.org/10.3390/fi17080327 - 24 Jul 2025
Abstract
Mathematical modeling plays a crucial role in supporting decision-making across a wide range of scientific disciplines. These models often involve multiple parameters, the estimation of which is critical to assessing their reliability and predictive power. Recent advancements in artificial intelligence have made it [...] Read more.
Mathematical modeling plays a crucial role in supporting decision-making across a wide range of scientific disciplines. These models often involve multiple parameters, the estimation of which is critical to assessing their reliability and predictive power. Recent advancements in artificial intelligence have made it possible to efficiently estimate such parameters with high accuracy. In this study, we focus on modeling the dynamics of cryptocurrency market shares by employing a Lotka–Volterra system. We introduce a methodology based on a deep neural network (DNN) to estimate the parameters of the Lotka–Volterra model, which are subsequently used to numerically solve the system using a fourth-order Runge–Kutta method. The proposed approach, when applied to real-world market share data for Bitcoin, Ethereum, and alternative cryptocurrencies, demonstrates excellent alignment with empirical observations. Our method achieves RMSEs of 0.0687 (BTC), 0.0268 (ETH), and 0.0558 (ALTs)—an over 50% reduction in error relative to ARIMA(2,1,2) and over 25% relative to a standard NN–ODE model—thereby underscoring its effectiveness for cryptocurrency-market forecasting. The entire framework, including neural network training and Runge–Kutta integration, was implemented in MATLAB R2024a (version 24.1). Full article
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18 pages, 1256 KiB  
Article
The Journey to Autonomy: Understanding Parental Concerns During the Transition of Children with Chronic Digestive Disorders
by Silvia Cristina Poamaneagra, Sorin Axinte, Carmen Anton, Elena Tătăranu, Catalina Mihai, Gheorghe G. Balan, Georgiana-Emmanuela Gîlca-Blanariu, Oana Timofte, Frenți Adina Mihaela, Oana Maria Roșu, Liliana Anchidin-Norocel and Smaranda Diaconescu
Medicina 2025, 61(8), 1338; https://doi.org/10.3390/medicina61081338 - 24 Jul 2025
Abstract
Background and Objectives: The transition from pediatric to adult-oriented healthcare is challenging and data on parental involvement and perception regarding the transition of children with chronic digestive diseases are scarce. Materials and Methods: Legal guardians of adolescents with chronic digestive diseases [...] Read more.
Background and Objectives: The transition from pediatric to adult-oriented healthcare is challenging and data on parental involvement and perception regarding the transition of children with chronic digestive diseases are scarce. Materials and Methods: Legal guardians of adolescents with chronic digestive diseases receiving care at a North-Eastern Romanian tertiary center and private offices were administered a 30-item survey. Results: There were 124 responders; 73.4% lived in rural areas; 81.5% were patients’ mothers. Positive correlations were found between parents’ perception of the child’s readiness for health-related decisions and appreciation of the children’s preparedness for transition (0.544; p = 0.000), between parents encouraging their children to maintain healthcare records and their perception of the children’s knowledge about their disease (0.67; p = 0.000), between parents’ fear of therapeutic breaks during transition and their perception of the need for transition training (0.704; p = 0.000), between fears for children’s impropriate health-related choices, fears of therapeutic breaks (0.573; p = 0.00) and parental perception that the adult physicians would be more patient-oriented and less family-centered (0.453; p < 0.000) and between parents’ trust in their children’s self-management skills and encouraging them to make decisions on their own (0.673; p < 0.000). Conclusions: The results of our study highlight the importance of addressing parental fears during special parent–children counseling sessions and promoting a child’s independence, chronic disease knowledge, records and alone consultations. Full article
(This article belongs to the Section Epidemiology & Public Health)
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15 pages, 1758 KiB  
Article
Eye-Guided Multimodal Fusion: Toward an Adaptive Learning Framework Using Explainable Artificial Intelligence
by Sahar Moradizeyveh, Ambreen Hanif, Sidong Liu, Yuankai Qi, Amin Beheshti and Antonio Di Ieva
Sensors 2025, 25(15), 4575; https://doi.org/10.3390/s25154575 - 24 Jul 2025
Abstract
Interpreting diagnostic imaging and identifying clinically relevant features remain challenging tasks, particularly for novice radiologists who often lack structured guidance and expert feedback. To bridge this gap, we propose an Eye-Gaze Guided Multimodal Fusion framework that leverages expert eye-tracking data to enhance learning [...] Read more.
Interpreting diagnostic imaging and identifying clinically relevant features remain challenging tasks, particularly for novice radiologists who often lack structured guidance and expert feedback. To bridge this gap, we propose an Eye-Gaze Guided Multimodal Fusion framework that leverages expert eye-tracking data to enhance learning and decision-making in medical image interpretation. By integrating chest X-ray (CXR) images with expert fixation maps, our approach captures radiologists’ visual attention patterns and highlights regions of interest (ROIs) critical for accurate diagnosis. The fusion model utilizes a shared backbone architecture to jointly process image and gaze modalities, thereby minimizing the impact of noise in fixation data. We validate the system’s interpretability using Gradient-weighted Class Activation Mapping (Grad-CAM) and assess both classification performance and explanation alignment with expert annotations. Comprehensive evaluations, including robustness under gaze noise and expert clinical review, demonstrate the framework’s effectiveness in improving model reliability and interpretability. This work offers a promising pathway toward intelligent, human-centered AI systems that support both diagnostic accuracy and medical training. Full article
(This article belongs to the Section Sensing and Imaging)
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11 pages, 830 KiB  
Article
Machine Learning-Based Prediction of Shoulder Dystocia in Pregnancies Without Suspected Macrosomia Using Fetal Biometric Ratios
by Can Ozan Ulusoy, Ahmet Kurt, Ayşe Gizem Yıldız, Özgür Volkan Akbulut, Gonca Karataş Baran and Yaprak Engin Üstün
J. Clin. Med. 2025, 14(15), 5240; https://doi.org/10.3390/jcm14155240 - 24 Jul 2025
Abstract
Objective: Shoulder dystocia (ShD) is a rare but serious obstetric emergency associated with significant neonatal morbidity. This study aimed to evaluate the predictive performance of machine learning (ML) models based on fetal biometric ratios and clinical characteristics for the identification of ShD [...] Read more.
Objective: Shoulder dystocia (ShD) is a rare but serious obstetric emergency associated with significant neonatal morbidity. This study aimed to evaluate the predictive performance of machine learning (ML) models based on fetal biometric ratios and clinical characteristics for the identification of ShD in pregnancies without clinical suspicion of macrosomia. Methods: We conducted a retrospective case-control study including 284 women (84 ShD cases and 200 controls) who underwent spontaneous vaginal delivery between 37 and 42 weeks of gestation. All participants had an estimated fetal weight (EFW) below the 90th percentile according to Hadlock reference curves. Univariate and multivariate logistic regression analyses were performed on maternal and neonatal parameters, and statistically significant variables (p < 0.05) were used to construct adjusted odds ratio (aOR) models. Supervised ML models—Logistic Regression (LR), Random Forest (RF), and Extreme Gradient Boosting (XGB)—were trained and tested to assess predictive accuracy. Performance metrics included AUC-ROC, sensitivity, specificity, accuracy, and F1-score. Results: The BPD/AC ratio and AC/FL ratio markedly enhanced the prediction of ShD. When added to other features in RF models, the BPD/AC ratio got an AUC of 0.884 (95% CI: 0.802–0.957), a sensitivity of 68%, and a specificity of 83%. On the other hand, the AC/FL ratio, along with other factors, led to an AUC of 0.896 (95% CI: 0.805–0.972), 68% sensitivity, and 90% specificity. Conclusions: In pregnancies without clinical suspicion of macrosomia, ML models integrating fetal biometric ratios with maternal and labor-related factors significantly improved the prediction of ShD. These models may support clinical decision-making in low-risk deliveries where ShD is often unexpected. Full article
(This article belongs to the Section Obstetrics & Gynecology)
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25 pages, 5142 KiB  
Article
Wheat Powdery Mildew Severity Classification Based on an Improved ResNet34 Model
by Meilin Li, Yufeng Guo, Wei Guo, Hongbo Qiao, Lei Shi, Yang Liu, Guang Zheng, Hui Zhang and Qiang Wang
Agriculture 2025, 15(15), 1580; https://doi.org/10.3390/agriculture15151580 - 23 Jul 2025
Viewed by 48
Abstract
Crop disease identification is a pivotal research area in smart agriculture, forming the foundation for disease mapping and targeted prevention strategies. Among the most prevalent global wheat diseases, powdery mildew—caused by fungal infection—poses a significant threat to crop yield and quality, making early [...] Read more.
Crop disease identification is a pivotal research area in smart agriculture, forming the foundation for disease mapping and targeted prevention strategies. Among the most prevalent global wheat diseases, powdery mildew—caused by fungal infection—poses a significant threat to crop yield and quality, making early and accurate detection crucial for effective management. In this study, we present QY-SE-MResNet34, a deep learning-based classification model that builds upon ResNet34 to perform multi-class classification of wheat leaf images and assess powdery mildew severity at the single-leaf level. The proposed methodology begins with dataset construction following the GBT 17980.22-2000 national standard for powdery mildew severity grading, resulting in a curated collection of 4248 wheat leaf images at the grain-filling stage across six severity levels. To enhance model performance, we integrated transfer learning with ResNet34, leveraging pretrained weights to improve feature extraction and accelerate convergence. Further refinements included embedding a Squeeze-and-Excitation (SE) block to strengthen feature representation while maintaining computational efficiency. The model architecture was also optimized by modifying the first convolutional layer (conv1)—replacing the original 7 × 7 kernel with a 3 × 3 kernel, adjusting the stride to 1, and setting padding to 1—to better capture fine-grained leaf textures and edge features. Subsequently, the optimal training strategy was determined through hyperparameter tuning experiments, and GrabCut-based background processing along with data augmentation were introduced to enhance model robustness. In addition, interpretability techniques such as channel masking and Grad-CAM were employed to visualize the model’s decision-making process. Experimental validation demonstrated that QY-SE-MResNet34 achieved an 89% classification accuracy, outperforming established models such as ResNet50, VGG16, and MobileNetV2 and surpassing the original ResNet34 by 11%. This study delivers a high-performance solution for single-leaf wheat powdery mildew severity assessment, offering practical value for intelligent disease monitoring and early warning systems in precision agriculture. Full article
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19 pages, 313 KiB  
Article
Survey on the Role of Mechanistic Interpretability in Generative AI
by Leonardo Ranaldi
Big Data Cogn. Comput. 2025, 9(8), 193; https://doi.org/10.3390/bdcc9080193 - 23 Jul 2025
Viewed by 155
Abstract
The rapid advancement of artificial intelligence (AI) and machine learning has revolutionised how systems process information, make decisions, and adapt to dynamic environments. AI-driven approaches have significantly enhanced efficiency and problem-solving capabilities across various domains, from automated decision-making to knowledge representation and predictive [...] Read more.
The rapid advancement of artificial intelligence (AI) and machine learning has revolutionised how systems process information, make decisions, and adapt to dynamic environments. AI-driven approaches have significantly enhanced efficiency and problem-solving capabilities across various domains, from automated decision-making to knowledge representation and predictive modelling. These developments have led to the emergence of increasingly sophisticated models capable of learning patterns, reasoning over complex data structures, and generalising across tasks. As AI systems become more deeply integrated into networked infrastructures and the Internet of Things (IoT), their ability to process and interpret data in real-time is essential for optimising intelligent communication networks, distributed decision making, and autonomous IoT systems. However, despite these achievements, the internal mechanisms that drive LLMs’ reasoning and generalisation capabilities remain largely unexplored. This lack of transparency, compounded by challenges such as hallucinations, adversarial perturbations, and misaligned human expectations, raises concerns about their safe and beneficial deployment. Understanding the underlying principles governing AI models is crucial for their integration into intelligent network systems, automated decision-making processes, and secure digital infrastructures. This paper provides a comprehensive analysis of explainability approaches aimed at uncovering the fundamental mechanisms of LLMs. We investigate the strategic components contributing to their generalisation abilities, focusing on methods to quantify acquired knowledge and assess its representation within model parameters. Specifically, we examine mechanistic interpretability, probing techniques, and representation engineering as tools to decipher how knowledge is structured, encoded, and retrieved in AI systems. Furthermore, by adopting a mechanistic perspective, we analyse emergent phenomena within training dynamics, particularly memorisation and generalisation, which also play a crucial role in broader AI-driven systems, including adaptive network intelligence, edge computing, and real-time decision-making architectures. Understanding these principles is crucial for bridging the gap between black-box AI models and practical, explainable AI applications, thereby ensuring trust, robustness, and efficiency in language-based and general AI systems. Full article
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18 pages, 1154 KiB  
Article
Predicting Major Adverse Cardiovascular Events After Cardiac Surgery Using Combined Clinical, Laboratory, and Echocardiographic Parameters: A Machine Learning Approach
by Mladjan Golubovic, Velimir Peric, Marija Stosic, Vladimir Stojiljkovic, Sasa Zivic, Aleksandar Kamenov, Dragan Milic, Vesna Dinic, Dalibor Stojanovic and Milan Lazarevic
Medicina 2025, 61(8), 1323; https://doi.org/10.3390/medicina61081323 - 23 Jul 2025
Viewed by 53
Abstract
Background and Objectives: Despite significant advances in surgical techniques and perioperative care, major adverse cardiovascular events (MACE) remain a leading cause of postoperative morbidity and mortality in patients undergoing coronary artery bypass grafting and/or aortic valve replacement. Accurate preoperative risk stratification is essential [...] Read more.
Background and Objectives: Despite significant advances in surgical techniques and perioperative care, major adverse cardiovascular events (MACE) remain a leading cause of postoperative morbidity and mortality in patients undergoing coronary artery bypass grafting and/or aortic valve replacement. Accurate preoperative risk stratification is essential yet often limited by models that overlook atrial mechanics and underutilized biomarkers. Materials and Methods: This study aimed to develop an interpretable machine learning model for predicting perioperative MACE by integrating clinical, biochemical, and echocardiographic features, with a particular focus on novel physiological markers. A retrospective cohort of 131 patients was analyzed. An Extreme Gradient Boosting (XGBoost) classifier was trained on a comprehensive feature set, and SHapley Additive exPlanations (SHAPs) were used to quantify each variable’s contribution to model predictions. Results: In a stratified 80:20 train–test split, the model initially achieved an AUC of 1.00. Acknowledging the potential for overfitting in small datasets, additional validation was performed using 10 independent random splits and 5-fold cross-validation. These analyses yielded an average AUC of 0.846 ± 0.092 and an F1-score of 0.807 ± 0.096, supporting the model’s stability and generalizability. The most influential predictors included total atrial conduction time, mitral and tricuspid annular orifice areas, and high-density lipoprotein (HDL) cholesterol. These variables, spanning electrophysiological, structural, and metabolic domains, significantly enhanced discriminative performance, even in patients with preserved left ventricular function. The model’s transparency provides clinically intuitive insights into individual risk profiles, emphasizing the significance of non-traditional parameters in perioperative assessments. Conclusions: This study demonstrates the feasibility and potential clinical value of combining advanced echocardiographic, biochemical, and machine learning tools for individualized cardiovascular risk prediction. While promising, these findings require prospective validation in larger, multicenter cohorts before being integrated into routine clinical decision-making. Full article
(This article belongs to the Section Intensive Care/ Anesthesiology)
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19 pages, 2215 KiB  
Article
Evaluation of the Effectiveness of Driver Training in the Use of Advanced Driver Assistance Systems
by Małgorzata Pełka and Adam Rosiński
Appl. Sci. 2025, 15(15), 8169; https://doi.org/10.3390/app15158169 - 23 Jul 2025
Viewed by 79
Abstract
This paper evaluates the effectiveness of driver training programmes aimed at the proper use of Advanced Driver Assistance Systems (ADASs). Participants (N = 49) were divided into the following three groups based on the type of training received: practical training, e-learning, and brief [...] Read more.
This paper evaluates the effectiveness of driver training programmes aimed at the proper use of Advanced Driver Assistance Systems (ADASs). Participants (N = 49) were divided into the following three groups based on the type of training received: practical training, e-learning, and brief manual instruction. The effectiveness of the training methods was assessed using selected parameters obtained from driving simulator studies, including reaction times and system activation attempts. Given the large volume and nonlinear nature of the input data, a heuristic, expert-based approach was used to identify key evaluation criteria, structure the decision-making process, and define fuzzy rule sets and membership functions. This phase served as the foundation for the development of a fuzzy logic model in the MATLAB environment. The model processes inputs to generate a quantitative performance score. The results indicate that practical training (mean score = 4.0) demonstrates superior effectiveness compared to e-learning (3.09) and manual instruction (mean score = 3.01). The primary contribution of this work is a transparent, data-driven evaluation tool that overcomes the inherent subjectivity and bias of traditional trainer-based assessments. This model provides a standardised and reproducible approach for assessing driver competence, offering a significant advancement over purely qualitative, trainer-based assessments and supporting the development of more reliable certification processes. Full article
(This article belongs to the Section Transportation and Future Mobility)
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22 pages, 2337 KiB  
Article
From Misunderstanding to Safety: Insights into COLREGs Rule 10 (TSS) Crossing Problem
by Ivan Vilić, Đani Mohović and Srđan Žuškin
J. Mar. Sci. Eng. 2025, 13(8), 1383; https://doi.org/10.3390/jmse13081383 - 22 Jul 2025
Viewed by 196
Abstract
Despite navigation advancements in enhanced sensor utilization and increased focus on maritime training and education, most marine accidents still involve collisions with high human involvement. Furthermore, navigators’ knowledge and application of the most often misunderstood Rule 10 Traffic Separation Schemes (TSS) according to [...] Read more.
Despite navigation advancements in enhanced sensor utilization and increased focus on maritime training and education, most marine accidents still involve collisions with high human involvement. Furthermore, navigators’ knowledge and application of the most often misunderstood Rule 10 Traffic Separation Schemes (TSS) according to the Convention on the International Regulations for Preventing Collisions at Sea (COLREG) represents the first focus in this study. To provide insight into the level of understanding and knowledge regarding COLREG Rule 10, a customized, worldwide survey has been created and disseminated among marine industry professionals. The survey results reveal a notable knowledge gap in Rule 10, where we initially assumed that more than half of the respondents know COLREG regulations well. According to the probability calculation and chi-square test results, all three categories (OOW, Master, and others) have significant rule misunderstanding. In response to the COLREG misunderstanding, together with the increasing density of maritime traffic, the implementation of Decision Support Systems (DSS) in navigation has become crucial for ensuring compliance with regulatory frameworks and enhancing navigational safety in general. This study presents a structural approach to vessel prioritization and decision-making within a DSS framework, focusing on the classification and response of the own vessel (OV) to bow-crossing scenarios within the TSS. Through the real-time integration of AIS navigational status data, the proposed DSS Architecture offers a structured, rule-compliant architecture to enhance navigational safety and the decision-making process within the TSS. Furthermore, implementing a Fall-Back Strategy (FBS) represents the key innovation factor, which ensures system resilience by directing operator response if opposing vessels disobey COLREG rules. Based on the vessel’s dynamic context and COLREG hierarchy, the proposed DSS Architecture identifies and informs the navigator regarding stand-on or give-way obligations among vessels. Full article
(This article belongs to the Special Issue Advances in Navigability and Mooring (2nd Edition))
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12 pages, 262 KiB  
Article
Engaging Fathers in Home-Based Parenting Education: Home Visitor Attitudes and Strategies
by Heidi E. Stolz and Melissa Rector LaGraff
Fam. Sci. 2025, 1(1), 3; https://doi.org/10.3390/famsci1010003 - 22 Jul 2025
Viewed by 90
Abstract
Much U.S. research supports the effectiveness of parenting education delivered via the home visiting method. Home visitors are essential to reaching fathers in this context, but not all have favorable attitudes toward father engagement or feel confident working with fathers. Given that father [...] Read more.
Much U.S. research supports the effectiveness of parenting education delivered via the home visiting method. Home visitors are essential to reaching fathers in this context, but not all have favorable attitudes toward father engagement or feel confident working with fathers. Given that father involvement is important for a wide range of child and adolescent outcomes and that fathers benefit from parenting education, it is important to better understand the forces that shape home visitors’ attitudes toward fathers, and thus their subsequent efforts to include them in publicly funded programming. Using survey data from 95 home visitors in Tennessee, this study explores whether home visitors’ beliefs about fathers and attitudes toward father engagement vary as a function of home visitor or agency characteristics. Results suggest training in social work, reporting father-friendly organizational attitudes and behaviors at one’s agency, and reporting supervisor support specifically for father engagement relate to various favorable fathering attitudes. Home visitors’ strategies to engage fathers in home visiting are presented, including strategies for before, during, and after the home visit. Overall, family service agency administrators are in key positions to make decisions that can improve agency father-friendliness, home visitor attitudes toward fathers, and subsequent outcomes for fathers, mothers, and children. Full article
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