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Search Results (255)

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Keywords = high-level task planning

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32 pages, 1456 KB  
Article
Optimization of the Human–Robot Collaborative Disassembly Process Using a Genetic Algorithm: Application to the Reconditioning of Electric Vehicle Batteries
by Salma Nabli, Gilde Vanel Tchane Djogdom and Martin J.-D. Otis
Designs 2025, 9(5), 122; https://doi.org/10.3390/designs9050122 - 17 Oct 2025
Abstract
To achieve a complete circular economy for used electric vehicle batteries, it is essential to implement a disassembly step. Given the significant diversity of battery geometries and designs, a high degree of flexibility is required for automated disassembly processes. The incorporation of human–robot [...] Read more.
To achieve a complete circular economy for used electric vehicle batteries, it is essential to implement a disassembly step. Given the significant diversity of battery geometries and designs, a high degree of flexibility is required for automated disassembly processes. The incorporation of human–robot interaction provides a valuable degree of flexibility in the process workflow. However, human behavior is characterized by unpredictable timing and variable task durations, which add considerable complexity to process planning. Therefore, it is crucial to develop a robust strategy for coordinating human and robotic tasks to manage the scheduling of production activities efficiently. This study proposes a global optimization approach to the scheduling of production activities, which employs a genetic algorithm with the objective of minimizing the total production time while simultaneously reducing the idle time of both the human operator and robot. The proposed approach is concerned with optimizing the sequencing of disassembly tasks, considering both temporal and exclusion constraints, to guarantee that tasks deemed hazardous are not executed in the presence of a human. This approach is based on a two-level adaptation framework developed in RoboDK (Robot Development Kit, v5.4.3.22231, 2022, RoboDK Inc., Montréal, (Québec,) Canada). At the first level, offline optimization is performed using a genetic algorithm to determine the optimal task sequencing strategy. This stage anticipates human behavior by proposing disassembly sequences aligned with expected human availability. At the second level, an online reactive adjustment refines the plan in real time, adapting it to actual human interventions and compensating for deviations from initial forecasts. The effectiveness of this global optimization strategy is evaluated against a non-global approach, in which the problem is partitioned into independent subproblems solved separately and then integrated. The results demonstrate the efficacy of the proposed approach in comparison with a non-global approach, particularly in scenarios where humans arrive earlier than anticipated. Full article
19 pages, 912 KB  
Article
Functional Independence Assessment in Children and Adolescents with Achondroplasia: A Multicenter Cross-Sectional Study Using the WeeFIM Scale
by Chung-Lin Lee, Hung-Hsiang Fang, Chih-Kuang Chuang, Dau-Ming Niu, Ju-Li Lin, Mei-Chyn Chao, Yen-Yin Chou, Pao Chin Chiu, Chia-Chi Hsu, Tzu-Hung Chu, Yin-Hsiu Chien, Huei-Ching Chiu, Ya-Hui Chang, Yuan-Rong Tu, Yun-Ting Lo, Hsiang-Yu Lin and Shuan-Pei Lin
Diagnostics 2025, 15(19), 2532; https://doi.org/10.3390/diagnostics15192532 - 7 Oct 2025
Viewed by 1092
Abstract
Background/Objectives: Achondroplasia is the most common skeletal dysplasia, affecting 1 in 25,000 births. Limited research exists on the assessment of functional independence using standardized tools in children and adolescents with achondroplasia. The WeeFIM scale provides a comprehensive evaluation of daily living skills across [...] Read more.
Background/Objectives: Achondroplasia is the most common skeletal dysplasia, affecting 1 in 25,000 births. Limited research exists on the assessment of functional independence using standardized tools in children and adolescents with achondroplasia. The WeeFIM scale provides a comprehensive evaluation of daily living skills across multiple functional domains. This study aimed to assess the functional independence levels in children and adolescents with achondroplasia using WeeFIM and analyze functional capabilities. Methods: This multicenter cross-sectional study included 46 participants aged 6–18 years with confirmed achondroplasia. Data were collected through standardized WeeFIM assessments from medical centers and online surveys (2021–2024). WeeFIM evaluates 18 functional items across 3 domains: self-care (8 items), mobility (5 items), and cognition (5 items), scored 1–7 (complete dependence to independence). Results: Participants included 26 males (56.5%) and 20 females (43.5%). Most (78.3%) were diagnosed during infancy. The mean functional scores were highest for cognition (34.0/35, 97.1%), followed by self-care (51.2/56, 91.4%) and mobility (31.5/35, 90.0%). Most participants achieved near-complete independence in cognitive functions. Mobility tasks, particularly stair climbing and bathtub transfers, showed the greatest challenges. Functional independence increased with age, with significant improvements during early childhood to adolescence transition. Conclusions: Children and adolescents with achondroplasia demonstrate high functional independence across daily activities, with cognitive abilities largely unaffected. Although specific mobility challenges exist, most participants achieve independence with appropriate accommodations. These findings provide valuable baseline data for clinical care planning and support optimistic functional outcomes for pediatric patients with achondroplasia. Full article
(This article belongs to the Section Clinical Diagnosis and Prognosis)
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23 pages, 4731 KB  
Article
Advancing Urban Roof Segmentation: Transformative Deep Learning Models from CNNs to Transformers for Scalable and Accurate Urban Imaging Solutions—A Case Study in Ben Guerir City, Morocco
by Hachem Saadaoui, Saad Farah, Hatim Lechgar, Abdellatif Ghennioui and Hassan Rhinane
Technologies 2025, 13(10), 452; https://doi.org/10.3390/technologies13100452 - 6 Oct 2025
Viewed by 497
Abstract
Urban roof segmentation plays a pivotal role in applications such as urban planning, infrastructure management, and renewable energy deployment. This study explores the evolution of deep learning techniques from traditional Convolutional Neural Networks (CNNs) to cutting-edge transformer-based models in the context of roof [...] Read more.
Urban roof segmentation plays a pivotal role in applications such as urban planning, infrastructure management, and renewable energy deployment. This study explores the evolution of deep learning techniques from traditional Convolutional Neural Networks (CNNs) to cutting-edge transformer-based models in the context of roof segmentation from satellite imagery. We highlight the limitations of conventional methods when applied to urban environments, including resolution constraints and the complexity of roof structures. To address these challenges, we evaluate two advanced deep learning models, Mask R-CNN and MaskFormer, which have shown significant promise in accurately segmenting roofs, even in dense urban settings with diverse roof geometries. These models, especially the one based on transformers, offer improved segmentation accuracy by capturing both global and local image features, enhancing their performance in tasks where fine detail and contextual awareness are critical. A case study on Ben Guerir City in Morocco, an urban area experiencing rapid development, serves as the foundation for testing these models. Using high-resolution satellite imagery, the segmentation results offer a deeper understanding of the accuracy and effectiveness of these models, particularly in optimizing urban planning and renewable energy assessments. Quantitative metrics such as Intersection over Union (IoU), precision, recall, and F1-score are used to benchmark model performance. Mask R-CNN achieved a mean IoU of 74.6%, precision of 81.3%, recall of 78.9%, and F1-score of 80.1%, while MaskFormer reached a mean IoU of 79.8%, precision of 85.6%, recall of 82.7%, and F1-score of 84.1% (pixel-level, micro-averaged at IoU = 0.50 on the held-out test set), highlighting the transformative potential of transformer-based architectures for scalable and precise urban imaging. The study also outlines future work in 3D modeling and height estimation, positioning these advancements as critical tools for sustainable urban development. Full article
(This article belongs to the Section Information and Communication Technologies)
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14 pages, 568 KB  
Brief Report
Wasting Despite Motivation: Exploring the Interplay of Perceived Ability and Perceived Difficulty on Food Waste Behavior Through Brehm’s Motivational Intensity Theory
by Paulina Szwed, Isabeau Coopmans, Rachel Lemaitre and Capwell Forbang Echo
Sustainability 2025, 17(19), 8836; https://doi.org/10.3390/su17198836 - 2 Oct 2025
Viewed by 413
Abstract
Household food waste remains a persistent challenge despite widespread pro-environmental intentions. Drawing on Brehm’s Motivational Intensity Theory, this study examined how perceived difficulty and perceived ability interact with motivation to predict self-reported food waste. We surveyed 939 participants in Flanders and Spain, measuring [...] Read more.
Household food waste remains a persistent challenge despite widespread pro-environmental intentions. Drawing on Brehm’s Motivational Intensity Theory, this study examined how perceived difficulty and perceived ability interact with motivation to predict self-reported food waste. We surveyed 939 participants in Flanders and Spain, measuring motivation to avoid waste, self-rated perceived ability to manage food, meal planning perceived difficulty, and food waste. Moderated moderation analyses revealed that motivation and perceived ability each independently predicted lower waste. Crucially, a significant three-way interaction showed that motivation most effectively reduced waste when perceived difficulty was low and perceived ability was high; when perceived difficulty exceeded perceived ability, motivation had no mitigating effect. These findings underscore that effort mobilization influenced by both individual capacity and situational demands is key to closing the intention–behavior gap in food waste. Practically, interventions should go beyond raising awareness to simplify tasks and bolster consumers’ skills, aligning action demands with realistic effort levels. Full article
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17 pages, 3960 KB  
Article
Development, Characteristics, and Implications of Landscape Performance Evaluation of Greenways in the United States
by Juanyu Wu, Zhiying Xian, Yi Luo and Yongmei Xiong
Land 2025, 14(10), 1968; https://doi.org/10.3390/land14101968 - 29 Sep 2025
Viewed by 387
Abstract
Greenways offer sustainable benefits at ecological, cultural and economic levels, enhancing human well-being. Landscape performance assessment is a crucial task for evaluating these benefits and guiding the sustainable development of greenways. To clarify the characteristics and roles of landscape performance in the development [...] Read more.
Greenways offer sustainable benefits at ecological, cultural and economic levels, enhancing human well-being. Landscape performance assessment is a crucial task for evaluating these benefits and guiding the sustainable development of greenways. To clarify the characteristics and roles of landscape performance in the development of the US greenway system, text analysis was conducted using KH Coder, and a meta-analysis was performed on three databases to select research cases on greenway performance evaluation in the US. The results show that the evaluation of social performance is higher than that of ecological and economic performances, and the data related to economic performance is more difficult to obtain. The efficacy of greenway projects varies with the construction stage and is influenced by social background and target benefits. The sustainability characteristics of high co-occurrence relationships are key to guiding greenway performance assessment, which helps in selecting indicators for evaluating greenways and providing references for improving planning directions. In the future, innovative technical means tailored to the goals of greenway landscapes should be used for performance evaluation. Full article
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18 pages, 4097 KB  
Article
Assessing and Optimizing Rural Settlement Suitability in Important Ecological Function Areas: A Case Study of Shiyan City, the Core Water Source Area of China’s South-to-North Water Diversion Project
by Yubing Wang, Chenyi Shi, Yingrui Wang, Wenyue Shi, Min Wang and Hai Liu
Sustainability 2025, 17(19), 8680; https://doi.org/10.3390/su17198680 - 26 Sep 2025
Viewed by 300
Abstract
China’s rural revitalization strategy has entered a new stage of development, in which optimizing the layout of rural settlements constitutes both a critical component and an urgent task for promoting integrated urban–rural development. Important ecological function areas play a vital role in maintaining [...] Read more.
China’s rural revitalization strategy has entered a new stage of development, in which optimizing the layout of rural settlements constitutes both a critical component and an urgent task for promoting integrated urban–rural development. Important ecological function areas play a vital role in maintaining ecological security; however, research focusing on the evaluation and optimization of rural settlement suitability within these regions remains limited, thereby constraining their sustainable development. Accordingly, this paper selects Shiyan City, situated within the core water source area of China’s South-to-North Water Diversion Project, as a case study. From an ecological perspective, a suitability evaluation system for rural settlements is developed, specifically tailored to important ecological function areas. This system integrates ecological factors including geological hazards, vegetation coverage, soil and water conservation, and soil erosion. Utilizing GIS spatial analysis and the minimum cumulative resistance model, the study assesses the suitability of rural settlements within these important ecological function areas. Furthermore, it proposes corresponding optimization types and strategies for rural settlements in such areas. The findings indicate the following: (1) The rural settlements in the study area demonstrate a “large dispersed settlements and small clustered settlements” distribution pattern, exhibiting an overall high-density agglomeration, though their internal layout remains fragmented and disordered due to geographical and ecological constraints. (2) The spatial comprehensive resistance values in the study area exhibit significant heterogeneity, with a general pattern of lower values in the north and higher values in the south. The region was categorized into five suitability levels: high yield, highly suitable, generally suitable, less suitable and unsuitable. The highly suitable areas, despite their limited spatial extent, support the highest density of rural settlements. In contrast, unsuitable areas occupy a substantially larger proportion of the territory, reaching 46.83%. These areas are strongly constrained by topographic and ecological factors, limiting their potential for development, and the spatial layout of villages requires further optimization, with emphasis placed on ecological conservation and adaptive sustainability. (3) Rural settlements are categorized into four optimized types: Urban–rural integration settlements, primarily located in high yield areas, are incorporated into urban development plans after optimization. Adjusted and improved settlements, mainly in highly suitable areas, enhance service quality and stimulate economic vitality post-optimization. Relocation and renovation settlements, including those in generally suitable and less suitable areas, achieve concentrated living and improved ecological livability after optimization. Restricted development settlements, predominantly in unsuitable areas, focus on ecological conservation and regional ecological security post-optimization. This study integrates ecological function protection factors with spatial optimization zoning for rural settlements in the study area, providing scientific reference for enhancing residential safety and ecological security for rural residents in important ecological function areas. Full article
(This article belongs to the Section Sustainable Urban and Rural Development)
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25 pages, 5138 KB  
Article
Off-Policy Deep Reinforcement Learning for Path Planning of Stratospheric Airship
by Jiawen Xie, Wanning Huang, Jinggang Miao, Jialong Li and Shenghong Cao
Drones 2025, 9(9), 650; https://doi.org/10.3390/drones9090650 - 16 Sep 2025
Viewed by 531
Abstract
The stratospheric airship is a vital platform in near-space applications, and achieving autonomous transfer has become a key research focus to meet the demands of diverse mission scenarios. The core challenge lies in planning feasible and efficient paths, which is difficult for traditional [...] Read more.
The stratospheric airship is a vital platform in near-space applications, and achieving autonomous transfer has become a key research focus to meet the demands of diverse mission scenarios. The core challenge lies in planning feasible and efficient paths, which is difficult for traditional algorithms due to the time-varying environment and the highly coupled multi-system dynamics of the airship. This study proposes a deep reinforcement learning algorithm, termed reward-prioritized Long Short-Term Memory Twin Delayed Deep Deterministic Policy Gradient (RPL-TD3). The method incorporates an LSTM network to effectively capture the influence of historical states on current decision-making, thereby improving performance in tasks with strong temporal dependencies. Furthermore, to address the slow convergence commonly seen in off-policy methods, a reward-prioritized experience replay mechanism is introduced. This mechanism stores and replays experiences in the form of sequential data chains, labels them with sequence-level rewards, and prioritizes high-value experiences during training to accelerate convergence. Comparative experiments with other algorithms indicate that, under the same computational resources, RPL-TD3 improves convergence speed by 62.5% compared to the baseline algorithm without the reward-prioritized experience replay mechanism. In both simulation and generalization experiments, the proposed method is capable of planning feasible paths under kinematic and energy constraints. Compared with peer algorithms, it achieves the shortest flight time while maintaining a relatively high level of average residual energy. Full article
(This article belongs to the Special Issue Design and Flight Control of Low-Speed Near-Space Unmanned Systems)
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32 pages, 4887 KB  
Article
Emerging Threat Vectors: How Malicious Actors Exploit LLMs to Undermine Border Security
by Dimitrios Doumanas, Alexandros Karakikes, Andreas Soularidis, Efstathios Mainas and Konstantinos Kotis
AI 2025, 6(9), 232; https://doi.org/10.3390/ai6090232 - 15 Sep 2025
Viewed by 1568
Abstract
The rapid proliferation of Large Language Models (LLMs) has democratized access to advanced generative capabilities while raising urgent concerns about misuse in sensitive security domains. Border security, in particular, represents a high-risk environment where malicious actors may exploit LLMs for document forgery, synthetic [...] Read more.
The rapid proliferation of Large Language Models (LLMs) has democratized access to advanced generative capabilities while raising urgent concerns about misuse in sensitive security domains. Border security, in particular, represents a high-risk environment where malicious actors may exploit LLMs for document forgery, synthetic identity creation, logistics planning, or disinformation campaigns. Existing studies often highlight such risks in theory, yet few provide systematic empirical evidence of how state-of-the-art LLMs can be exploited. This paper introduces the Silent Adversary Framework (SAF), a structured pipeline that models the sequential stages by which obfuscated prompts can covertly bypass safeguards. We evaluate ten high-risk scenarios using five leading models—GPT-4o, Claude 3.7 Sonnet, Gemini 2.5 Flash, Grok 3, and Runway Gen-2—and assess outputs through three standardized metrics: Bypass Success Rate (BSR), Output Realism Score (ORS), and Operational Risk Level (ORL). Results reveal that, while all models exhibited some susceptibility, vulnerabilities were heterogeneous. Claude showed greater resistance in chemistry-related prompts, whereas GPT-4o and Gemini generated highly realistic outputs in identity fraud and logistics optimization tasks. Document forgery attempts produced only partially successful templates that lacked critical security features. These findings highlight the uneven distribution of risks across models and domains. By combining a reproducible adversarial framework with empirical testing, this study advances the evidence base on LLM misuse and provides actionable insights for policymakers and border security agencies, underscoring the need for stronger safeguards and oversight in the deployment of generative AI. Full article
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29 pages, 20970 KB  
Article
A Semantic Energy-Aware Ontological Framework for Adaptive Task Planning and Allocation in Intelligent Mobile Systems
by Jun-Hyeon Choi, Dong-Su Seo, Sang-Hyeon Bae, Ye-Chan An, Eun-Jin Kim, Jeong-Won Pyo and Tae-Yong Kuc
Electronics 2025, 14(18), 3647; https://doi.org/10.3390/electronics14183647 - 15 Sep 2025
Viewed by 431
Abstract
Intelligent robotic systems frequently operate under stringent energy limitations, especially in complex and dynamic environments. To enhance both adaptability and reliability, this study introduces a semantic planning framework that integrates ontology-driven reasoning with energy awareness. The framework estimates energy consumption based on the [...] Read more.
Intelligent robotic systems frequently operate under stringent energy limitations, especially in complex and dynamic environments. To enhance both adaptability and reliability, this study introduces a semantic planning framework that integrates ontology-driven reasoning with energy awareness. The framework estimates energy consumption based on the platform-specific behavior of sensing, actuation, and computational modules while continuously updating place-level semantic representations using real-time execution data. These representations encode not only spatial and contextual semantics but also energy characteristics acquired from prior operational history. By embedding historical energy usage profiles into hierarchical semantic maps, this framework enables more efficient route planning and context-aware task assignment. A shared semantic layer facilitates coordinated planning for both single-robot and multi-robot systems, with the decisions informed by energy-centric knowledge. This approach remains hardware-independent and can be applied across diverse platforms, such as indoor service robots and ground-based autonomous vehicles. Experimental validation using a differential-drive mobile platform in a structured indoor setting demonstrates improvements in energy efficiency, the robustness of planning, and the quality of the task distribution. This framework effectively connects high-level symbolic reasoning with low-level energy behavior, providing a unified mechanism for energy-informed semantic decision-making. Full article
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23 pages, 37380 KB  
Article
SAM2MS: An Efficient Framework for HRSI Road Extraction Powered by SAM2
by Pengnian Zhang, Junxiang Li, Chenggang Wang and Yifeng Niu
Remote Sens. 2025, 17(18), 3181; https://doi.org/10.3390/rs17183181 - 14 Sep 2025
Viewed by 621
Abstract
Road extraction from high-resolution remote sensing images (HRSIs) provides critical support for downstream tasks such as autonomous driving path planning and urban planning. Although deep learning-based pixel-level segmentation methods have achieved significant progress, they still face challenges in handling occlusions caused by vegetation [...] Read more.
Road extraction from high-resolution remote sensing images (HRSIs) provides critical support for downstream tasks such as autonomous driving path planning and urban planning. Although deep learning-based pixel-level segmentation methods have achieved significant progress, they still face challenges in handling occlusions caused by vegetation and shadows, and often exhibit limited model robustness and generalization capability. To address these limitations, this paper proposes the SAM2MS model, which leverages the powerful generalization capabilities of the foundational vision model, segment anything model 2 (SAM2). Firstly, an adapter-based fine-tuning strategy is employed to effectively transfer the capabilities of SAM2 to the HRSI road extraction task. Secondly, we subsequently designed a subtraction block (Sub) to process adjacent feature maps, effectively eliminating redundancy during the decoding phase. Multiple Subs are cascaded to form the multi-scale subtraction module (MSSM), which progressively refines local feature representations, thereby enhancing segmentation accuracy. During training, a training-free lossnet module is introduced, establishing a multi-level supervision strategy that encompasses background suppression, contour refinement, and region-of-interest consistency. Extensive experiments on three large-scale and challenging HRSI road datasets, including DeepGlobe, SpaceNet, and Massachusetts, demonstrate that SAM2MS significantly outperforms baseline methods across nearly all evaluation metrics. Furthermore, cross-dataset transfer experiments (from DeepGlobe to SpaceNet and Massachusetts) conducted without any additional training validate the model’s exceptional generalization capability and robustness. Full article
(This article belongs to the Section Remote Sensing Image Processing)
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16 pages, 896 KB  
Article
Parental Self-Efficacy in Managing Pediatrics’ Medications and Treatments in Jordan: A Cross-Sectional Study
by Abdallah Y. Naser and Hassan Al-Shehri
Healthcare 2025, 13(18), 2280; https://doi.org/10.3390/healthcare13182280 - 12 Sep 2025
Viewed by 568
Abstract
Background: Parents make vital decisions regarding their children’s health and safety. Poor parental self-efficacy is associated with unfavorable health outcomes among their children. This study aims to investigate parental self-efficacy in managing pediatric medications and treatments in Jordan. Methods: This is an online [...] Read more.
Background: Parents make vital decisions regarding their children’s health and safety. Poor parental self-efficacy is associated with unfavorable health outcomes among their children. This study aims to investigate parental self-efficacy in managing pediatric medications and treatments in Jordan. Methods: This is an online cross-sectional survey study that was conducted in Jordan between 20 April and 4 July 2025. Self-efficacy in managing medications and treatments for children was assessed utilizing a previously validated questionnaire, including healthcare information or decision-making, symptom identification or management, general treatment management, general healthcare navigation, and feeding management. Logistic regression analysis was performed to identify predictors of a higher level of self-efficacy. Results: A total of 597 parents were included in this study. The majority of parents reported high levels of confidence (self-efficacy) in managing various aspects of their child’s care. The highest proportion of parents indicated they were very confident in knowing when their child needs to visit a healthcare provider (35.2%) and in following their child’s diet or nutrition plan (36.9%). Very confident was the most selected response for knowing how to contact healthcare providers (38.4%) and scheduling an appointment (37.0%). Higher income was strongly linked to greater self-efficacy, with parents earning 1001–1500 Jordanian dinars (JOD) showing significantly higher odds (odds ratio (OR) = 4.44, 95% confidence interval (CI): 2.42–8.15, p < 0.001) compared to those earning less than 500 JOD. Parents working in medical fields also had higher odds (OR = 3.30, 95% CI: 1.69–6.45, p < 0.001) compared to those not working. Parents with 2–3 children (OR = 1.73, 95% CI: 1.00–3.00, p = 0.049) or 4–5 children (OR = 1.59, 95% CI: 1.05–3.63, p = 0.03) had greater odds of self-efficacy compared to those with one child. Conclusions: The majority of the parents in this study expressed strong self-efficacy in managing their child’s care, specifically in healthcare-related tasks. Higher self-efficacy was significantly associated with parents’ socioeconomic characteristics such as marital status, medical employment, income, insurance coverage, and number of children. At the same time, lower confidence levels and self-efficacy were observed among divorced parents. More support should be directed towards low-income families and parents who work outside the medical field to enhance their self-efficacy and ultimately the health outcomes of their children. Full article
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15 pages, 593 KB  
Article
Nurses’ Perceptions of Electronic Medical Record Effectiveness at Ministry of Health Hospitals in Jeddah City: A Cross-Sectional Study
by Ebtihal Abdullah Rajab, Sabah Mahmoud Mahran and Nabeela Al Abdullah
Nurs. Rep. 2025, 15(9), 329; https://doi.org/10.3390/nursrep15090329 - 9 Sep 2025
Viewed by 689
Abstract
Background: Globally, there is a growing demand for the adoption of electronic health systems and the transition toward digital processes within healthcare organizations. Electronic Medical Records (EMRs) play a vital role in enhancing documentation accuracy, improving healthcare delivery, and minimizing medical errors. However, [...] Read more.
Background: Globally, there is a growing demand for the adoption of electronic health systems and the transition toward digital processes within healthcare organizations. Electronic Medical Records (EMRs) play a vital role in enhancing documentation accuracy, improving healthcare delivery, and minimizing medical errors. However, limited research has explored nurses’ perceptions of EMR effectiveness within Ministry of Health hospitals in Jeddah City. Methods: A quantitative descriptive cross-sectional design was employed in four governmental hospitals affiliated with the Ministry of Health in Jeddah. A convenience sampling technique was used to recruit 911 full-time registered nurses from inpatient and outpatient departments. Data was collected through an electronic self-administered questionnaire evaluating EMR use, system quality, and user satisfaction. Descriptive and inferential statistical analyses were conducted using SPSS version 26. Results: The global EMR score (82%) reflected a high level of acceptance and integration of EMR systems among the nurses surveyed. The use of order entry received the highest mean score (84.8%), indicating that nurses find EMRs particularly effective in streamlining administrative and clinical tasks, such as medication orders and care plans. The strong correlation between system quality and user satisfaction (rs = 0.911) underscores the importance of well-designed EMRs in fostering trust and confidence among clinical users. Conclusions: The findings indicate that nurses perceive EMRs as effective tools for improving documentation, care coordination, and workflow efficiency. This study recommends the establishment of structured feedback mechanisms that enable nurses to report issues, suggest improvements, and share success stories—thereby fostering a culture of continuous system enhancement. Full article
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27 pages, 8196 KB  
Article
Enhancing Electric Vehicle Charging Infrastructure Planning with Pre-Trained Language Models and Spatial Analysis: Insights from Beijing User Reviews
by Yanxin Hou, Peipei Wang, Zhuozhuang Yao, Xinqi Zheng and Ziying Chen
ISPRS Int. J. Geo-Inf. 2025, 14(9), 325; https://doi.org/10.3390/ijgi14090325 - 24 Aug 2025
Viewed by 777
Abstract
With the growing adoption of electric vehicles, optimizing the user experience of charging infrastructure has become critical. However, extracting actionable insights from the vast number of user reviews remains a significant challenge, impeding demand-driven operational planning for charging stations and degrading the user [...] Read more.
With the growing adoption of electric vehicles, optimizing the user experience of charging infrastructure has become critical. However, extracting actionable insights from the vast number of user reviews remains a significant challenge, impeding demand-driven operational planning for charging stations and degrading the user experience. This study leverages three pre-trained language models to perform sentiment classification and multi-level topic identification on 168,129 user reviews from Beijing, facilitating a comprehensive understanding of user feedback. The experimental results reveal significant task-model specialization: RoBERTa-WWM excels in sentiment analysis (accuracy = 0.917) and fine-grained topic identification (Micro-F1 = 0.844), making it ideal for deep semantic extraction. Conversely, ELECTRA, after sufficient training, demonstrates a strong aptitude for coarse-grained topic summarization, highlighting its strength in high-level semantic generalization. Notably, the models offer capabilities beyond simple classification, including autonomous label normalization and the extraction of valuable information from comments with low information density. Furthermore, integrating textual and spatial analyses revealed striking patterns. We identified an urban–rural emotional gap—suburban users are more satisfied despite fewer facilities—and used geographically weighted regression (GWR) to quantify the spatial differences in the factors affecting user satisfaction in Beijing’s districts. We identified three types of areas requiring differentiated strategies, as follows: the northwestern region is highly sensitive to equipment quality, the central urban area has a complex relationship between supporting facilities and satisfaction, and the emerging adoption area is more sensitive to accessibility and price factors. These findings offer a data-driven framework for charging infrastructure planning, enabling operators to base decisions on real-world user feedback and tailor solutions to specific local contexts. Full article
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25 pages, 3070 KB  
Article
Feeding Urban Rail Transit: Hybrid Microtransit Network Design Based on Parsimonious Continuum Approach
by Qian Ye, Yunyu Zhang, Kunzheng Wang, Xinghua Liu and Chunfu Shao
Information 2025, 16(8), 702; https://doi.org/10.3390/info16080702 - 18 Aug 2025
Viewed by 531
Abstract
In recent years, the passenger flow volume of conventional transit in major cities has declined steadily. Ground public transit often suffers from congestion during rush hours caused by frequent stops (e.g., conventional fixed-route buses) or excessively high operating costs (e.g., demand-responsive transit). While [...] Read more.
In recent years, the passenger flow volume of conventional transit in major cities has declined steadily. Ground public transit often suffers from congestion during rush hours caused by frequent stops (e.g., conventional fixed-route buses) or excessively high operating costs (e.g., demand-responsive transit). While rail transit offers reliable service with dedicated right-of-way, its high capital and operational costs pose challenges for integrated planning with other transit modes. The joint design of rail, conventional buses, and DRT remains underexplored. To bridge this gap, this paper proposes and analyses a new hybrid transit system that integrates conventional transit service with demand-adaptive transit (DAT) to feed urban rail transit (the system hence called hybrid microtransit system). The main task is to optimally design the hybrid microtransit system to allocate resources efficiently across different modes. Both the conventional transit and DAT connect passengers from their origin/destination to the rail transit stations. Travelers can choose one of the services to access urban rail transit, or directly walk. Accordingly, we divide the service area into three parts and compute the user costs to access rail transit by conventional transit and DAT. The optimal design problem is hence formulated as a mixed integer program by minimizing the total system cost, which includes both the user and agency (operating) costs. Numerical experiment results demonstrate that the hybrid microtransit system performs better than the system that only has conventional transit to feed under all demand levels, achieving up to a 7% reduction in total system cost. These may provide some evidence to resolve the “first-mile” challenges of rail transit in megacities by designing better conventional transit and DAT. Full article
(This article belongs to the Special Issue Big Data Analytics in Smart Cities)
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19 pages, 619 KB  
Review
Condition-Based Maintenance in Complex Degradation Systems: A Review of Modeling Evolution, Multi-Component Systems, and Maintenance Strategies
by Hui Cao, Jie Yu and Fuhai Duan
Machines 2025, 13(8), 714; https://doi.org/10.3390/machines13080714 - 12 Aug 2025
Viewed by 1331
Abstract
This review systematically examines the evolution of maintenance strategies for complex systems, with a focus on the advancements in condition-based maintenance (CBM) decision-making methodologies. Traditional approaches, such as post-failure maintenance and time-based maintenance, are increasingly supplanted by CBM due to challenges like high [...] Read more.
This review systematically examines the evolution of maintenance strategies for complex systems, with a focus on the advancements in condition-based maintenance (CBM) decision-making methodologies. Traditional approaches, such as post-failure maintenance and time-based maintenance, are increasingly supplanted by CBM due to challenges like high costs or inefficiency in resource allocation. CBM leverages system reliability models in conjunction with component degradation data to dynamically establish maintenance thresholds, optimizing resource utilization while minimizing operational risks and repair costs. Research has expanded from single-component degradation systems to multi-component systems, leveraging degradation models and optimization algorithms to propose strategies addressing multi-level control limits, economic dependencies, and task constraints. Recent studies emphasize multi-component interactions, incorporating structural influences, imperfect repairs, and economic correlations into maintenance planning. Despite progress, challenges persist in modeling coupled degradation mechanisms and coordinating maintenance decisions for interdependent components. Future research directions should encompass adaptive learning strategies for dynamic degradation processes, such as those employed in intelligent agents for real-time environmental adaptation, and the incorporation of intelligent predictive technologies to enhance system performance and resource utilization. Full article
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