Search Results (1,169)

Due to a complex of factors, visually impaired people are facing difficulties and increased risks during fire emergencies and evacuations from different types of buildings. Even if a lot of studies have been conducted to improve the mobility and autonomy of people with visual impairment during emergency evacuation processes, these offer only partial solutions, especially in the presence of uncertainties characteristic of fire evolution. Aiming for a more comprehensive approach to the safe evacuation of people with visual impairments, this paper proposes a support system that integrates innovative aspects related to the architecture of the application, modeling and simulation methods, and experimental realization. The system is decentralized, capable of anticipating possible fire extensions and determining, in real-time, new corresponding evacuation routes. The overall design complies with the standard norms in emergency situations. Two models, one developed in Stateflow and the other based on Delay Time Petri Nets (DTPN), were constructed to describe the dynamic behavior of the system in the presence of unexpected events that can change the initial recommended evacuation path. To test the functionality and efficiency of the proposed system, the conditions created by potential fire sources were simulated as a part of realistic scenarios. Tests were conducted with visually impaired people. Simulation and prototype testing showed that the presented system can improve evacuation times, achieving a measurable gain compared to scenarios where there is no information regarding fire evolution. Full article

Introduction: Urinary and fecal incontinence, as well as the presence of an ostomy, are globally prevalent conditions with substantial implications for individuals’ daily lives. Among the psychological consequences, social isolation is a frequently reported experience but remains poorly explored in the existing literature. The aim of this scoping review is to explore how social isolation has been conceptualized and operationalized in research on individuals with incontinence and to synthesize evidence on its antecedents and outcomes. Methods: This review was conducted in accordance with the Joanna Briggs Institute guidelines and reported following the PRISMA-ScR checklist. Data were thematically synthesized and interpreted according to the Middle Range Theory of Social Isolation in Chronic Illness. Results: Twenty-three studies met the inclusion criteria. Findings indicate that social isolation among individuals with incontinence is a complex, multifactorial phenomenon. Predisposing factors included individual needs for social interaction and desire for approval, psychological resilience, toilet accessibility, education, income, gender, and age. Precipitating factors were related to illness trajectory and adaptation processes, including ostomy acceptance, time since ostomy creation or oncological treatment, sense of belonging, perceived social support, stigma, self-esteem, clinical severity, illness-related conditions, and loss of autonomy. Reported outcomes were consistently adverse, encompassing depression, anxiety, and reduced quality of life. Conclusions: Social isolation represents a core dimension of the lived experience of incontinence and should be recognized as a key clinical outcome. Systematic screening and targeted interventions should be integrated into continence care pathways. Future research should adopt longitudinal and interventional designs to clarify causal mechanisms and evaluate strategies to prevent and mitigate isolation. Full article

The integration of residential solar photovoltaic (PV) systems with electric vehicle (EV) charging infrastructure offers significant potential for reducing carbon emissions and enhancing energy autonomy. This study presents a real-world case of a solar-powered EV charging system installed at a residential property in Dublin, Ireland. Unlike prior studies that rely solely on simulation, this work covers the complete process from digital design using OpenSolar to on-site installation and performance evaluation. The system includes 16 high-efficiency solar panels (435 W each), a 4 kW hybrid inverter, a 5.3 kWh lithium-ion battery, and a smart EV charger. Real-time monitoring tools were used to collect energy performance data post-installation. The results indicate that 67% of the household’s solar energy was self-consumed, leading to a 50% reduction in electricity costs. In summer 2024, the client achieved full grid independence and received a €90 credit through feed-in tariffs. The system also enabled free EV charging and generated environmental benefits equivalent to planting 315 trees. This study provides empirical evidence supporting the practical feasibility and economic–environmental advantages of integrated PV–EV systems in temperate climates. Full article

Background and Objectives: Parkinson’s disease is the second most common neurodegenerative disorder after Alzheimer’s disease, and its incidence increases with age, being particularly high in people over 70 years of age. For patients with this condition, medical rehabilitation can have a profound impact, helping to improve mobility, preserve functional autonomy, and enhance quality of life. Focal vibration stimulation is a promising, well-tolerated, and easy-to-apply method with potential to facilitate motor activity and support the motor learning process, making it also useful in gait reeducation for patients with various neurological conditions. This systematic review aims to analyze the existing scientific evidence on the effectiveness of focal muscle vibration therapy in managing symptoms of Parkinson’s disease. Materials and Methods: This systematic review of the literature was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and the protocol was registered in PROSPERO under the protocol registration number CRD420251120737. Searches were conducted in five databases (PubMed, PEDro, ScienceDirect, Cochrane Library, Web of Science). The selection criteria targeted original clinical studies, published in English between 2010 and the present, that investigated focal muscle vibration therapy in patients with Parkinson’s disease and were fully available, excluding review papers, meta-analyses, books, and articles inaccessible in full text. Version 2 of the Cochrane risk-of-bias tool for randomized trials (RoB 2) was used to assess the quality of the included studies. Results: The results of the studies were interpreted individually for each study, and the main information was synthesized in a comparative table to facilitate analysis. The final analysis included five studies that investigated the effects of focal muscle vibration in patients with Parkinson’s disease. The results suggest that this form of stimulation may offer benefits for patients with gait disorders, improving balance and stability. Among the study’s limitations are the small number of included articles (n = 5) and the restriction to English-language publications, which may limit the applicability of the results. Conclusions: Given the promising results, focal muscle vibration therapy could represent a useful option in the management of Parkinson’s disease. Integrating this method into rehabilitation plans could bring significant functional benefits, but further studies are needed to confirm its long-term effectiveness and to establish standardized application protocols. No external funding was received for the conduct of this review. Full article

Evidence-based medicine (EBM) supplies the best available data, yet clinicians still face low-value care, surrogate-driven reversals, and pseudoscientific claims. We propose Semi-Imperative Evidentialism (SIE), a normative framework that links evidential warrant to proportionate professional duties while preserving patient autonomy. Using a targeted narrative review in philosophy of science, bioethics, and clinical epidemiology, we distilled six binary attributes to classify activities as Science, Pseudoscience, or Non-science. Scientific items enter a two-tier ladder—Tier 1 (established clinical evidence) or Tier 2 (emerging or preclinical evidence)—with status re-scored as randomized trials, living meta-analyses, and post-marketing safety signals accrue. SIE maps tiers to action: Tier 1 should be offered or strongly recommended, with reasons documented if declined; Tier 2 should be discussed with explicit consent, preferably within trials or registries; Pseudoscience should be refused or discontinued with corrective education; Non-science may be acknowledged as contextual support when safe and non-substitutive. Worked examples—antiarrhythmic suppression post–myocardial infarction (CAST) and “complementary cancer cures”—illustrate earlier and more transparent course-correction. SIE provides a fallibilist bridge from evidence to duty, constraining discretion without eroding autonomy; prospective audits and cluster trials should test its impact on prescribing and consent. Full article

The research explores how artificial intelligence-based chatbots transform psychological and legal assistance in situations of gender-based violence, evaluating their effect on women’s digital empowerment. A cross-sectional design with a mixed approach was used, combining a 25-item survey of 1000 women and a quantitative analysis using multiple correspondences and clustering techniques, supplemented by semi-structured interviews. The findings show that 64% considered the use of chatbots useful for accessing information, although only 27% used them to report incidents due to structural and digital barriers. Participants from rural areas faced severe connectivity limitations and expressed distrust of artificial intelligence, while those who interacted frequently demonstrated greater autonomy, decision-making capacity, and confidence in seeking support. Qualitative analysis showed that users valued confidentiality and anonymity as essential elements for sharing experiences of violence that they did not reveal in face-to-face settings. They also highlighted that immediate interaction with chatbots created a perception of constant support, reducing isolation and motivating users to seek formal help. The conclusion is that designing gender-focused chatbots and integrating them into care systems is an innovative and effective way to expand access to justice and psychological care. Full article

This work presents the design and prototyping of two reconfigurable BLE-based devices developed to overcome the limitations of commercial platforms in terms of configurability, data transparency, and energy efficiency. The first is a wearable smart wristband integrating inertial and biometric sensors, while the second is a configurable beacon (ASIA Beacon) able to dynamically adjust key transmission parameters such as channel selection and power level. Both devices were engineered with energy-aware components, OTA update support, and flexible 3D-printed enclosures optimized for residential environments. The firmware, developed under Zephyr RTOS, exposes data through standardized interfaces (GATT, MQTT), facilitating their integration into IoT architectures and research-oriented testbeds. Initial experiments carried out in an anechoic chamber demonstrated improved RSSI stability, extended autonomy (up to 4 months for beacons and 3 weeks for the wristband), and reliable real-time data exchange. These results highlight the feasibility and potential of the proposed devices for future deployment in ambient assisted living environments, while the focus of this work remains on the hardware and software development process and its validation. Full article

Modern parenting styles cover a wide spectrum, from strict tiger parenting to supportive lighthouse parenting. Among these, helicopter parenting remains particularly controversial: while some consider it a protective approach, others believe that excessive control can hinder the development of a child’s autonomy and independence. Our research is significant because it approaches the topic from a rarely examined pedagogical perspective, thereby contributing to the psychology-dominated literature. We conducted our analysis using the EBSCO Discovery Service search engine and followed the steps of the PRISMA protocol. The aim of the study is to explore the relationship between helicopter parenting and academic achievement, as well as to review the factors—such as career-related decisions and adaptation—that may play a mediating role in this relationship. It reviews 33 studies published between 2012 and 2024, most of which use quantitative methods. Most of these studies were conducted in the United States and focus primarily on the college-age demographic. Our findings show that recognising and addressing helicopter parenting behaviours is essential to supporting student success. Furthermore, educational institutions should more actively make use of the potential resources and opportunities offered by such parenting behaviours. Overall, our findings confirm that helicopter parenting remains an under-researched topic at both the national and European levels, particularly in the context of education and public education systems, where further research is clearly needed. Full article

Traditional lectures in veterinary anatomy often limit student engagement and higher-order thinking. The flipped classroom (FC) model shifts foundational content to independent study using interactive tools such as H5P^® and Wooclap^®, reserving classroom time for collaborative problem-solving. Objective: To evaluate the impact of the FC model on student engagement, preparation habits, and cognitive performance in veterinary anatomy, focusing on the respiratory and cardiovascular systems. Methodology: The intervention was implemented over two academic years (2023/24 and 2024/25) and included continuous assessment, cognitive-level evaluations based on Marzano’s taxonomy, platform analytics, and anonymous student surveys. Results: Platform data showed high engagement, with completion rates exceeding 90%. Students who prepared 2–3 days in advance performed better on application and integration tasks. Survey responses indicated a shift from passive video viewing to active learning strategies, such as structured note-taking and strategic time management. By 2024/25, 85% of students dedicated 30+ min to preparation, compared to 48% the previous year. Conclusion: The FC model fostered autonomy, spatial reasoning, and clinical contextualization. Aligned with constructivist principles, it supported Intended Learning Outcomes through adaptive scaffolding. Despite institutional challenges, the model proved scalable and pedagogically coherent, warranting further longitudinal research and broader curricular integration. Full article

Traditional teaching methods are often far from aligning with professional practice demands. Team-Based Learning (TBL), a variant of Problem-Based Learning, may foster motivation, autonomy, and deeper knowledge acquisition, especially in those educative contexts linked to practical knowledge. The objective of the present research was to explore the impact of a TBL program with digital support on Sport Sciences students’ psychological and learning outcomes. A quasi-experimental design with pre- and post-tests was applied to 68 fourth-year students (mean age = 21.45 ± 1.57 years). The intervention spanned 12 weeks, where the students had to solve specific case studies linked to the theoretical content of the subject and its applicability. Variables measured included motivational climate, satisfaction of basic psychological needs, intrinsic motivation, transversal competences, and academic performance. Significant improvements were observed in task- and ego-oriented climate, autonomy, competence, relatedness, knowledge scores, and competence in scientific searches and academic dissemination (p < 0.05). No significant changes were found in intrinsic motivation or audiovisual material competence. Sex influenced several outcomes, while project marks and prior transversal skills did not. TBL combined with digital tools enhanced learning outcomes and key psychological needs, though intrinsic motivation remained unchanged. Findings highlight the value of active methodologies in higher education, while underscoring the need for long-term, broader studies. Full article

High-definition maps (HDMaps) are critical for safe autonomy on structured roads. Yet traditional production—relying on dedicated mapping fleets and manual quality control—is costly and slow, impeding large-scale, frequent updates. Recently, standard-definition maps (SDMaps) derived from remote sensing have been adopted as priors to support HDMap perception, lowering cost but struggling with subtle urban changes and localization drift. We propose Loop-MapNet, a self-evolving, multimodal, closed-loop mapping framework. Loop-MapNet effectively leverages surround-view images, LiDAR point clouds, and SDMaps; it fuses multi-scale vision via a weighted BiFPN, and couples PointPillars BEV and SDMap topology encoders for cross-modal sensing. A Transformer-based bidirectional adaptive cross-attention aligns SDMap with online perception, enabling robust fusion under heterogeneity. We further introduce a confidence-guided masked autoencoder (CG-MAE) that leverages confidence and probabilistic distillation to both capture implicit SDMap priors and enhance the detailed representation of low-confidence HDMap regions. With spatiotemporal consistency checks, Loop-MapNet incrementally updates SDMaps to form a perception–mapping–update loop, compensating remote-sensing latency and enabling online map optimization. On nuScenes, within 120 m, Loop-MapNet attains 61.05% mIoU, surpassing the best baseline by 0.77%. Under extreme localization errors, it maintains 60.46% mIoU, improving robustness by 2.77%; CG-MAE pre-training raises accuracy in low-confidence regions by 1.72%. These results demonstrate advantages in fusion and robustness, moving beyond one-way prior injection and enabling HDMap–SDMap co-evolution for closed-loop autonomy and rapid SDMap refresh from remote sensing. Full article

This study explores a conceptual framework for integrating Artificial Intelligence (AI) into Enterprise Resource Planning (ERP) systems, emphasising its transformative potential in highly automated industrial environments, often referred to as ‘dark factories’, where operations are carried out with minimal human intervention using robotics, AI, and IoT. These lights-out manufacturing environments demand intelligent, autonomous systems that go beyond traditional ERP functionalities to deliver sustainable enterprise operations and supply chain management. Drawing from secondary data and a comprehensive review of existing literature, the study identifies significant gaps in current AI-ERP research and practice, namely, the absence of a unified adoption framework, limited focus on AI-specific implementation challenges, and a lack of structured post-adoption evaluation metrics. In response, this paper proposes a novel integrated conceptual framework that combines the Technology–Organisation–Environment (TOE) framework, the Technology Acceptance Model (TAM), and the Information Systems (IS) Success Model. The model incorporates industry-specific dark factors, such as AI autonomy, human–machine collaboration, operational agility, and sustainability, by optimising resource efficiency, enabling predictive maintenance, enhancing supply chain resilience, and supporting circular economy practices. The primary research aim of the current study is to provide a theoretical foundation for further empirical research on the input of AI-ERP systems into autonomous industry settings. The framework provides a robust theoretical foundation and actionable guidance for researchers, technology leaders, and policy-makers navigating the integration of AI and ERP in sustainable enterprise operations and supply chain management. Full article

The global transition to a low-carbon energy system requires innovative solutions that integrate renewable energy production with storage and utilization technologies. The growth in energy demand, combined with the intermittency of these sources, highlights the need for advanced management models capable of ensuring system stability and efficiency. This paper presents the development of an optimized energy management system integrating renewable sources, with a focus on green hydrogen production via electrolysis, storage, and use through a fuel cell. The system aims to promote energy autonomy and support the transition to a low-carbon economy by reducing dependence on the conventional electricity grid. The proposed model enables flexible hourly energy flow optimization, considering solar availability, local consumption, hydrogen storage capacity, and grid interactions. Formulated as a Mixed-Integer Linear Programming (MILP) model, it supports strategic decision-making regarding hydrogen production, storage, and utilization, as well as energy trading with the grid. Simulations using production and consumption profiles assessed the effects of hydrogen storage capacity and electricity price variations. Results confirm the effectiveness of the model in optimizing system performance under different operational scenarios. Full article

The global food crises and growing population necessitate efficient agricultural land use. Weeds cause up to 40% yield loss in major crops, resulting in over USD 100 billion in annual economic losses. Camera-equipped UAVs offer a solution for automatic weed detection, but the high computational and energy demands of deep learning models limit their use to expensive, high-end UAVs. In this paper, we present a low-cost UAV system built from off-the-shelf components, featuring a custom-designed on-board computing system based on the NVIDIA Jetson Nano. This system efficiently manages real-time image acquisition and inference using the energy-efficient Squeeze U-Net neural network for weed detection. Our approach ensures the pipeline operates in real time without affecting the drone’s flight autonomy. We also introduce the AgriAdapt dataset, a novel collection of 643 high-resolution aerial images of salad crops with weeds, which fills a key gap by providing realistic UAV data for benchmarking segmentation models under field conditions. Several deep learning models are trained and validated on the newly introduced AgriAdapt dataset, demonstrating its suitability for effective weed segmentation in UAV imagery. Quantitative results show that the dataset supports a range of architectures, from larger models such as DeepLabV3 to smaller, lightweight networks like Squeeze U-Net (with only 2.5 M parameters), achieving high accuracy (around 90%) across the board. These contributions distinguish our work from earlier UAV-based weed detection systems by combining a novel dataset with a comprehensive evaluation of accuracy, latency, and energy efficiency, thus directly targeting deep learning applications for real-time UAV deployment. Our results demonstrate the feasibility of deploying a low-cost, energy-efficient UAV system for real-time weed detection, making advanced agricultural technology more accessible and practical for widespread use. Full article

Background/objectives: Despite global efforts to improve childhood immunization rates, gender-related barriers continue to hinder equitable access to vaccines worldwide. This study synthesizes gender barrier analyses conducted in various countries to better understand these challenges. This evidence synthesis aims to (1) identify the main gender-related barriers affecting immunization focusing on zero-dose targets, HPV, and COVID-19 vaccination campaigns; and (2) summarize key recommendations and lessons that have emerged from countries to overcome those gender barriers. Methods: A documentary analysis was used by reviewing data from gender barrier analyses that were conducted by multiple governments with UNICEF support. The study classified barriers using the socio-ecological model (SEM), encompassing systemic, health service, community, household, and individual-level gender barriers. Descriptive statistics and inductive thematic coding were used to analyze data. Results: This synthesis includes 24 documents representing gender barrier analyses across 29 countries. Findings highlight multiple barriers, including systemic discrimination against women in public and healthcare spaces, limited political will to address gender disparities, and limited (sex)-disaggregated and gender data. At the community and household levels, social norms restrict women’s autonomy in seeking immunization services, while household duties (culturally assigned to women) also restrict their access to immunization services. Adolescents face additional challenges, particularly regarding HPV vaccination, due to misconceptions and stigma from families and peers. Conclusions: Addressing gender-related barriers requires a multi-level approach, integrating gender-responsive policies, and comprehensively addressing gender barriers that are hindering the progress of vaccination efforts. UNICEF’s commitment to gender-responsive immunization strategies is critical for achieving the Immunization Agenda 2030 and ensuring equitable vaccine access for all. Full article