Search Results (182)

Background: Developmental coordination Disorder (DCD) is a prevalent and persistent neurodevelopmental condition characterized by motor learning difficulties that significantly affect daily functioning and participation. Despite growing interest in artificial intelligence (AI) applications within healthcare, the extent and nature of AI use in the evaluation and intervention of DCD remain unclear. Objective: This scoping review aimed to systematically map the existing literature on the use of AI and AI-assisted approaches in the evaluation, screening, monitoring, and intervention of DCD, and to identify current trends, methodological characteristics, and gaps in the evidence base. Methods: A scoping review was conducted in accordance with the PRISMA extension for Scoping Reviews (PRISMA-ScR) guidelines and was registered on the Open Science Framework. Systematic searches were performed in Scopus, PubMed, Web of Science, and IEEE Xplore, supplemented by snowballing. Peer-reviewed studies applying AI methods to DCD-relevant populations were included. Data was extracted and charted to summarize study designs, populations, AI methods, data modalities, clinical purposes, outcomes, and reported limitations. Results: Seven studies published between 2021 and 2025 met the inclusion criteria following a literature search covering the period from January 2010 to 2025. One study listed as 2026 was included based on its early access online publication in 2025. Most studies focused on AI applications for assessment, screening, and classification, using supervised machine learning or deep learning models applied to movement-based data, wearable sensors, video recordings, neurophysiological signals, or electronic health records. Only one randomized controlled trial evaluated an AI-assisted intervention. The evidence base was dominated by early-phase development and validation studies, with limited external validation, heterogeneous diagnostic definitions, and scarce intervention-focused research. Conclusions: Current AI research in DCD is primarily centered on evaluation and early identification, with comparatively limited evidence supporting AI-assisted intervention or rehabilitation. While existing findings suggest that AI has the potential to enhance objectivity and sensitivity in DCD assessment, significant gaps remain in clinical translation, intervention development, and implementation. Future research should prioritize theory-informed, clinician-centered AI applications, including adaptive intervention systems and decision-support tools, to better support occupational therapy and physiotherapy practice in DCD care. Full article

Researchers are increasingly engaged in studies to determine and correct negative welfare consequences of animal husbandry and behaviour modification procedures, not least in response to industries’ growing need to maintain their social licence through demonstrable welfare standards that address public expectations. To ensure that welfare recommendations are scientifically credible, the studies must be rigorously designed and conducted, and the data produced must be interpreted with full regard to conceptual, methodological, and experimental design limitations. This commentary provides guidance on these matters. In addition to, and complementary with, the ARRIVE guidelines that deal with animal studies in general, there is a need for additional specific advice on the design of studies directed at procedures that alter behaviour, whether through training, handling, or restraint. The COMPASS Guidelines offer clear direction for conducting welfare-focused behaviour modification research. They stand for the following: Controls and Calibration, emphasising rigorous design, baseline measures, equipment calibration, and replicability; Objectivity and Open data, ensuring transparency, validated tools, and data accessibility; Motivation and Methods, with a focus on learning theory, behavioural science, and evidence-based application of positive reinforcers and aversive stimuli; Precautions and Protocols, embedding the precautionary principle, minimising welfare harms, listing stop criteria, and using real-time monitoring; Animal-centred Assessment, with multimodal welfare evaluation, using physiological, behavioural, functional, and objective indicators; Study ethics and Standards, noting the 3Rs (replacement, reduction, and refinement), welfare endpoints, long-term effects, industry independence, and risk–benefit analysis; and Species-relevance and Scientific rigour, facilitating cross-species applicability with real-world relevance and robust methodology. To describe these guidelines, the current article is organised into seven major sections that outline detailed, point-by-point considerations for ethical and scientifically rigorous design. It concludes with a call for continuous improvement and collaboration. A major purpose is to assist animal ethics committees when considering the design of experiments. It is also anticipated that these Guidelines will assist reviewers and editorial teams in triaging manuscripts that report studies in this context. Full article

Image noise degrades image quality in applications such as medical imaging, surveillance, and remote sensing, where real-time processing and high accuracy are critical. Software-based denoising can be flexible, but often suffers from latency and low throughput when deployed on embedded or edge systems. A Field Programmable Gate Array (FPGA)-based system offers parallelism and lower latency, but the existing work typically focusses on fixed architectures rather than scalable framework supporting multiple filter models. This paper presents a high-performance, resource-efficient FPGA-based framework for real-time noise reduction. The modular, pipelined architecture integrates median and adaptive filters, managed by a state machine-based control unit to enhance processing efficiency. Implemented on a Cyclone V FPGA using Quartus Prime 22.1std, the system provides scalability through adjustable Random Access Memory (RAM) and supports multiple denoising algorithms. Tested on Leena images with salt-and-pepper noise, it processes 10% noise in 1.724 ms in a simulated environment running at 800 MHz; it was compared with Python version 3.11.2 with the OpenCV-library version 4.8.076 on a general-purpose Central Processing Unit (CPU) (0.0201 ms). The proposed solution demonstrates low latency and high throughput, making it well-suited for embedded and edge computing applications. Full article

Biodiversity knowledge is fundamental to conservation planning and sustainable environmental decision-making; however, general-purpose Large Language Models (LLMs) frequently produce hallucinations when responding to biodiversity-related queries. To address this challenge, we propose BioChat, a domain-specific question-answering system that integrates a Retrieval-Augmented Generation (RAG) framework with a Re-Ranker–based retrieval and routing mechanism. The system is built upon a verified biodiversity dataset curated by the National Institute of Biological Resources (NIBR), comprising 25,593 species and approximately 970,000 structured data points. We systematically evaluate the effects of embedding selection, routing strategy, and generative model choice on factual accuracy and hallucination mitigation. Experimental results show that the proposed Re-Ranker-based routing strategy significantly improves system reliability, increasing factual accuracy from 47.9% to 71.3% and reducing hallucination rate from 34.0% to 24.4% compared with Naive RAG baseline. Among the evaluated LLMs, Qwen2-7B-Instruct achieves the highest factual accuracy, while Gemma-2-9B-Instruct demonstrates superior hallucination control. By delivering transparent, verifiable, and context-grounded biodiversity information, BioChat supports environmental education, citizen science, and evidence-based conservation policy development. This work demonstrates how trustworthy AI systems can serve as sustainability-enabling infrastructure, facilitating reliable access to biodiversity knowledge for long-term ecological conservation and informed public decision-making. Full article

Background and Objectives: Paediatric palliative care in Romania is underdeveloped and unevenly distributed, which creates major difficulties in accessing services for children with life-limiting illnesses and their families. The lack of a dedicated national strategy, the shortage of specialised staff, and socio-economic barriers exacerbate the vulnerability of these groups. This study aimed to explore parents’ and caregivers’ experiences, to analyse the perspectives of public institutions and NGOs involved in supporting these children, and to identify the main barriers and facilitators in accessing paediatric palliative care. Materials and Methods: Given that all data were collected exclusively through focus group discussions, this study employed a qualitative design based on three focus groups guided by a semi-structured interview guide. The analysis was conducted using MAXQDA software, which enabled the coding and synthesis of emerging themes. Participants were parents/caregivers of children with life-limiting illnesses, representatives of public institutions, and members of relevant NGOs in Bacău County, Romania. Purposive sampling was used to capture diverse and experience-based perspectives, resulting in a total of 24 participants across three focus groups—parents and caregivers (n = 11), public institution representatives (n = 7), and NGO representatives (n = 6). No individual semi-structured interviews were conducted. Results: The analysis highlighted a complex typology of medical, emotional, social, educational, and spiritual needs of children and their families. Parents reported experiences of abandonment in the curative system, emphasising the importance of pain control, safety, and community support. Public institutions acknowledged the lack of skills and inter-sectoral coordination, while NGOs pointed to structural barriers and the low visibility of these children. Major needs include access to specialised care, psychological support, respite services for carers, financial and administrative assistance, education, and spiritual counselling. A significant obstacle is the lack of up-to-date statistical data needed to inform public policy. Conclusions: Paediatric palliative care should be considered a national priority through the development of a dedicated strategy, the expansion of specialised services, and the strengthening of partnerships between the public and non-governmental sectors. An integrated, child- and family-centred approach addressing the medical, social, emotional, and spiritual dimensions of care is essential. The results highlight the need for continuous staff training, information campaigns, and community support mechanisms to reduce inequalities and improve the quality of life of children with life-limiting illnesses. Full article

Background: While physical activity has been shown to enhance cognitive function, little is known about whether stretching, a low-intensity, accessible form of exercise, can elicit similar benefits, particularly over time. The study adopted a self-control (within-subject) design. Purpose: This study aimed to investigate the time-dependent effects of self-stretching on cognitive performance in healthy adults. Methods: Thirty healthy participants performed a self-stretching protocol targeting the neck and shoulder muscles. Cognitive performance was assessed in terms of attention (Stroop test), working memory (N-back test), and short-term memory (digit span-based memory test) at baseline, and at 10, 20, and 30 min post-intervention. Paired t-tests and Wilcoxon signed-rank tests were used to analyze changes in cognitive performance. One-way ANOVA was conducted on baseline data to confirm the homogeneity across the three measurement groups. Results: One-way ANOVAs confirmed no significant baseline differences among the three groups in any cognitive measure (p > 0.05). Stroop test results showed a significant reduction in reaction time at 10 (p = 0.01) and 20 min (p = 0.02) post-stretching, indicating an improvement in information processing speed. The effect size (Hedges’ g) for this improvement was very large (−1.01) at 10 min and large (−0.87) at 20 min. However, no significant improvements were observed in Stroop accuracy scores, memory performance, or N-back task results at any time point. Conclusions: Self-stretching produced short-term improvements only in reaction time during attention tasks, while no changes were observed in memory or working memory accuracy. These findings suggest that stretching may offer brief, task-specific benefits related to processing speed rather than broad cognitive enhancement. Full article

Background: Diastasis rectus abdominis (DRA) is a frequent concern following childbirth, particularly following a cesarean section (CS). Mobile exercise applications offer promising opportunities for enhancing physical therapy services, with potential positive outcomes. Purpose: This study compared the effect of a specific DRA-targeted mobile app-based exercise program on inter-recti distance (IRD) and multiple clinical measures to a traditional abdominal exercise program in post-CS mothers with DRA. Methods: This two-armed, parallel-group, randomized controlled trial involved 40 primiparous women undergoing CS; they were classified into two equal groups. Group A participated in Just Fit, a mobile app-based abdominal exercise program, while Group B received a traditional abdominal exercise program. Both exercise programs lasted 30 min, 3 times/week, for 8 weeks as a home program with follow-up sessions once weekly at an outpatient physical therapy clinic. Ultrasonography was used to measure IRD, a manual muscle test assessed abdominal muscle strength, a tape measure gauged circumferences, and a questionnaire evaluated satisfaction. Results: Both programs showed significant improvements in pre- and post-treatment measures of IRD, muscle strength, and girth (p ≤ 0.001 for all comparisons). Women in Group A exhibited significant post-treatment improvements in IRD above the umbilicus, abdominal muscle strength, girth measurements, and satisfaction compared with those in Group B. However, there were non-significant differences in IRD below the umbilicus and hip circumference between the two groups. Conclusions: The mobile app-based exercise program was associated with greater improvements in IRD above the umbilicus, abdominal muscle strength, waist and umbilical circumferences, and patient satisfaction compared with the traditional exercise program for post-CS DRA. These findings suggest that mobile app-guided rehabilitation may serve as an effective and accessible adjunct to traditional post-CS exercise programs, although larger trials are recommended to confirm these results. Full article

This study presents a novel low-cost workflow integrating smartphone-based photogrammetry, Building Information Modeling (BIM), infrared thermography, and real-time interactive visualization to create digital twins for comprehensive energy assessment of existing buildings. Unlike conventional approaches requiring expensive laser scanning equipment and specialized software, this methodology democratizes advanced building diagnostics through accessible technologies and academic licenses. The research aims to develop and validate a replicable workflow that enables architects, engineers, and educators to conduct detailed energy assessments without high-end equipment, while establishing technical criteria for accurate geometric reconstruction, thermal data integration, and interactive visualization. The workflow combines terrestrial photogrammetry using smartphone cameras for 3D reconstruction, BIM modeling in Autodesk Revit for semantic building representation, infrared thermography for thermal performance documentation, and Unreal Engine for immersive real-time visualization. The approach is validated through application to the historic control tower of the former Rabassa aerodrome at the University of Alicante, documenting data capture protocols, processing workflows, and integration criteria to ensure methodological replicability. Results demonstrate that functional digital twins can be generated using consumer-grade devices (high-end smartphones) and academically licensed software, achieving geometric accuracy sufficient for energy assessment purposes. The integrated platform enables systematic identification of thermal anomalies, heat loss patterns, and envelope deficiencies through intuitive three-dimensional interfaces, providing a robust foundation for evidence-based energy assessment and renovation planning. The validated workflow offers a viable, economical, and scalable solution for building energy analysis, particularly valuable in resource-constrained academic and professional contexts, advancing both scientific understanding of accessible digital twin methodologies and practical applications in building energy assessment. Full article

This paper addresses the need for accessible and interoperable supervision solutions within the Industry 4.0 paradigm, particularly for small-scale or resource-constrained environments. The proposed system integrates a web-based architecture using opensource technologies to enable real-time industrial monitoring and data acquisition. A hybrid setup was developed, combining a virtual glass manufacturing process in Factory IO with a physical three-phase induction motor controlled by a Modicon M580 PLC. The system architecture includes a local HMI developed in Control Expert and a remote interface built with React and Node.js, both synchronized through a MySQL 8.0 database populated via Python 3.13 using the Modbus TCP/IP protocol. Experimental results demonstrate consistent data synchronization, reliable multi-platform integration, and an average end-to-end latency of 156 ms, validating the feasibility of the approach for IIoTbased applications. The solution demonstrates how general-purpose web technologies can be effectively repurposed for industrial use, offering a cost-effective and scalable alternative to traditional SCADA systems. The proposed architecture is easily replicable, adaptable to various process configurations, and suitable for academic, prototyping, and SME environments. Full article

With the increasing prevalence of Internet of Things (IoT) devices in areas like authentication, data protection, and access control, general purpose microcontrollers (MCUs) have become the primary platform for security-critical apps. However, the expense of these attacks has decreased significantly in recent years, making them a viable threat to MCU-based devices. We present a framework-driven perspective with a comparative survey of MCU fault injection resilience for IoT. The survey supports—and is organized around—the procedural evaluation framework we introduce. We discuss the basic requirements for security first, and then categorize the common types of hardware intrusion, including side-channel attacks, fault injection attacks, and invasive methods. We synthesize reported security technologies employed by MCU vendors, such as TrustZone/TEE, Physical Unclonable Functions (PUF), secure boot, flash encryption, secure debugging, and tamper detection, in the context of FIA scenarios. A comparison of representative MCUs—STM32U585, NXP LPC55S69, Nordic nRF54L15, Espressif ESP32-C6, and Renesas RA8M1—highlights cost–security trade-offs relevant to token-class deployments. We position this work as a framework/perspective: an evidence-first FI evaluation protocol for token-class MCUs, a portable checklist unifying PSA/SESIP/CC expectations, and a set of concrete case studies (e.g., ESP32-C6 secure boot hardening). We do not claim a formal systematic review. Full article

This research is framed within a larger project, whose general objective is the implementation of a SMG (Smart Micro-Grid) for distributed generation from renewable energy sources, with hydrogen as a backup. This project in-corporates an energy management system to optimize the operation of each of the systems involved while ensuring energy demand. Additionally, a hydrogen management strategy is included to maximize performance in its production, storage, and consumption within the SMG. Specifically, this work focuses on the design and implementation of a monitoring and optimization system for a SMG composed of a photovoltaic generator, a short-term energy storage system using a lithium battery, and a system for the generation, storage, and use of hydrogen produced in a fuel cell. The objective is the development of an HMI (Human Machine Interface) based on a touch operator panel KTP700 by the manufacturer Siemens, which runs in parallel with the existing SCADA (Supervisory Control and Data Acquisition) application implemented using the graphical programming software LabVIEW. The purpose of this HMI is to complement the SCADA system in such a way that it allows for direct, simple, and immediate interaction with all the equipment that comprises the SMG. This will provide quick and secure access to the monitoring of relevant variables and the parameterization of the hydrogen generator. Furthermore, due to the robustness and reliability of the industrial operator panels, the aim is to establish a supervision system with continuous and permanent operation, similar to the industrial plants automation and management systems. Full article

Background: The optimization of treatment for pediatric cancer has increased 5-year survivor rates to over 80%. Currently, there are almost half a million survivors of a pediatric cancer alive in the United States, with numbers increasing worldwide. Despite increased survivorship, pediatric cancer survivors (PCSs) are at high risk for long-term chronic disease, including cardiometabolic dysregulation at an early age due to cancer-related treatments. PCSs often have increased adiposity, perturbation in the gut microbiome, and chronic systemic inflammation compared to age-matched controls. Time-restricted eating (TRE) has emerged as an effective dietary intervention to promote weight loss in individuals with increased adiposity and cardiometabolic disease. Prebiotic supplements may enhance the efficacy of TRE by promoting satiety via the gut microbiome. Given the accessibility of both TRE and prebiotic supplements, this type of dietary intervention may be ideal for young adult PCSs. The purpose of this study was to determine the feasibility and acceptability of 12 weeks of TRE with and without a prebiotic supplement among young adult PCSs. Changes in body weight, body composition, and cardiometabolic disease risk markers were explored. Methods: Feasibility was measured based on recruitment (n = 20), retention (>80%), and adherence to the TRE eating window and prebiotic (>80%), and acceptability was measured based on a validated survey. Body weight, body composition, blood pressure, and additional blood-based cardiometabolic disease risk markers were also measured before and following the intervention. Results: Feasibility was not met based on recruitment (n = 13), but retention and adherence exceeded the a priori hypothesis. Acceptability also met the a priori hypothesis. Improvements were observed in some cardiometabolic disease risk markers, including a significant decrease in fat mass and visceral fat mass in both groups following the intervention. Conclusions: Given the positive outcomes related to retention, adherence, and acceptability, as well as some cardiometabolic disease risk markers, a larger and longer study of TRE and prebiotic supplementation in PCSs is warranted. However, innovative recruitment strategies should be implemented, such as leveraging social media and targeting larger geographical areas, given recruitment challenges. Full article

Background and Purpose: There remains a need for effective and accessible education methods to support recovery after total hip replacement. To evaluate the effects of video-based education on daily living activities and wound healing of patients undergoing total hip replacement surgery. Methods: A randomised controlled trial was used. Eligible participants were those aged 18 years and over who had undergone total hip replacement surgery in a training and research hospital. The intervention group received video-based training, while the control group received only routine care. Results: Patients in the video-based training group showed significantly greater improvement in daily living activities, hip function, and wound healing on postoperative days 5 and 30 compared to the control group (p < 0.01). Conclusions: Video-based education significantly improved daily living activities, hip function, and wound healing in patients undergoing total hip replacement. Clinicaltrials ID: NCT06523829 Full article

Surrogate models provide virtual representations that mirror physical objects or processes, serving distinct purposes in simulations and digital transformation. This review article examines how integrating surrogate modelling with artificial intelligence (AI) techniques can facilitate the iterative development of surrogate models and identify instances where additional data acquisition is necessary to enhance the performance of a surrogate model. This demonstrates the potential of combining AI with surrogate modelling in addressing some of the key challenges in the food and drink manufacturing industry. The paper also provides an accessible examination of AI and surrogate modelling in the food and drink manufacturing industry, offering a summary of current applications and advancements within the field. The key areas addressed by this article include the application of AI and ML in process control, prediction, and modelling for food manufacturing, as well as the advantages and limitations of AI-based surrogate modelling (SM), among other issues addressed. Based on the literature reviewed herein, AI-based surrogate models can be employed to optimise production processes and reduce the need for extensive physical prototyping in the food and drink manufacturing industry. This review emphasises AI-based surrogate modelling techniques tailored for complex food processing systems and distinguishes itself by bridging method-specific insights with practical industrial relevance. Additionally, this article reviews challenges and limitations in the food and drink manufacturing industry and the application of surrogate modelling, along with future directions for research in this rapidly evolving field. Full article

Virtual reality (VR) has emerged as a transformative tool in the treatment of phobias and the cultivation of emotional resilience. This study aims to explore the potential of VR to create controlled, immersive environments that facilitate exposure therapy, enabling individuals to confront and desensitize themselves to their fears in a safe and personalized manner. The flexibility of VR systems allows therapists to tailor scenarios to the unique needs of patients, addressing specific phobias such as acrophobia, arachnophobia, and social anxiety disorders. Beyond phobia treatment, VR’s capacity to simulate challenging or stress-inducing scenarios presents opportunities for fostering emotional resilience by building adaptive coping mechanisms and reducing stress responses over time. The integration of biofeedback and machine learning further enhances VR applications, enabling real-time adjustments based on physiological and psychological responses. In this article, the current advancements, underlying mechanisms, and challenges in leveraging VR technology for therapeutic purposes are discussed with a focus on its implications for mental health care. By combining immersive technology with evidence-based practices, VR offers a promising pathway for improving mental health outcomes and expanding the accessibility of therapeutic interventions. Full article