Abstract
Objective: This scoping review synthesized and mapped the breadth of the existing literature on technological resources used to support individuals undergoing hemodialysis treatment. Methods: Following the methodological guidelines of the Joanna Briggs Institute (JBI) for scoping reviews and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) checklist, comprehensive searches were conducted across the following databases: MEDLINE, Cumulative Index to Nursing and Allied Health Literature (CINAHL), PsycINFO, Scopus, Scientific Electronic Library Online (SciELO), MedicLatina, and the Cochrane Central Register of Controlled Trials, with no time restrictions. Results: Thirty-nine studies conducted between 2003 and 2023 met the inclusion criteria. These studies covered a range of technological innovations developed specifically for hemodialysis treatment, including virtual reality, exergames, websites, and mobile applications. These technologies were designed with diverse objectives: to facilitate physical exercise, optimize dietary and medication management, improve disease adherence and management, and promote self-efficacy and self-care in patients. Conclusions: The review revealed a wide range of technological resources available to hemodialysis patients. These digital solutions show great potential to transform care by promoting more engaged and personalized health practices. Although this study did not directly assess the impact of these technologies, it provides a solid foundation for future investigations that can explore in-depth how such innovations contribute to effective disease management and improvement in clinical outcomes.

Abstract
In this paper is presented a mixed-integer linear programming (MILP) model that maximizes the Photovoltaic-based (PV-based) hosting capacity (HC) in unbalanced and active distribution networks. The model takes into account the controlled charge of electric vehicles (EVs) and incorporates a demand-response program (DRP), for demand-side load shifting. The model's solution determines the optimal operation of distributed generators (DGs), switched capacitor banks (SCBs), energy storage devices (ESDs), coordination of the EVs charging, and DRP. Linear formulation is obtained from a mixed-integer non-linear programming (MINLP) model, ensuring tractability and guarantee convergence, since it can be efficiently solved using commercial optimization solvers of convex optimization. The model's effectiveness is demonstrated through tests on a 123-bus, three-phase unbalanced distribution system. Four case studies are conducted to assess the effect of different distributed energy resources (DERs). Results show that the simultaneous optimization of DERs, EVs charging and DR scheduling can significantly increase the PV-based HC -reaching up more than the substation capacity- while reducing total power losses. These findings demonstrate the technical potential of integrated DER coordination in enhancing PV penetration and improving the operational efficiency of active distribution systems.

Abstract
In recent years, the task of person re-identification (Re-ID) has improved considerably with the advances in deep learning methodologies. However, occluded person Re-ID remains a challenging task, as parts of the body of the individual are frequently hidden by various objects, obstacles, or other people, making the identification process more difficult. To address these issues, we introduce a novel data augmentation strategy using artificial occlusions, consisting of random shapes and objects from a small image dataset that was created. We also propose an end-to-end methodology for occluded person Re-ID, which consists of three branches: a global branch, a feature dropping branch, and an occlusion detection branch. Experimental results show that the use of random shape occlusions is superior to random erasing using our architecture. Results on six datasets consisting of three tasks (holistic, partial and occluded person Re-ID) demonstrate that our method performs favourably against state-of-the-art methodologies.

Abstract
BackgroundIndividuals with spinal cord injury (SCI) face complex and ongoing rehabilitation needs. In this context, mobile health applications have emerged as promising tools to support self-management and rehabilitation.ObjectiveTo map and characterize mobile applications specifically developed to support rehabilitation of individuals with SCI.MethodsA scoping review was conducted in accordance with PRISMA-ScR guidelines. A systematic search was performed across five electronic databases (PubMed, Scopus, Web of Science, and CINAHL). Studies published between 2015 and 2024 describing the use of mobile applications in the rehabilitation of adults with SCI were included.ResultsA total of 24 studies were included. We synthesized the identified applications descriptively into four domains: self-management and health education; gamification and motivation for physical rehabilitation; monitoring and prevention of secondary complications; and assistive technology and advanced rehabilitation. A consistent adoption of user-centered design principles was observed. Despite high levels of reported usability, challenges remain regarding long-term engagement, technological complexity, and sustained adherence.ConclusionMobile applications represent a promising complementary resource to support rehabilitation and health management in individuals with SCI. However, more robust longitudinal studies with larger sample sizes are required to assess the clinical impact and long-term feasibility of these interventions.

Abstract
[No abstract available]

Abstract
Diabetic retinopathy (DR) is a leading cause of vision loss in working-age adults. While screening reduces the risk of blindness, traditional imaging is often costly and inaccessible. Artificial intelligence (AI) algorithms present a scalable diagnostic solution, but concerns regarding fairness and generalization persist. This work evaluates the fairness and performance of image-trained models in DR prediction, as well as the impact of disentanglement as a bias mitigation technique, using the diverse mBRSET fundus dataset. Three models, ConvNeXt V2, DINOv2, and Swin V2, were trained on macula images to predict DR and sensitive attributes (SAs) (e.g., age and gender/sex). Fairness was assessed between subgroups of SAs, and disentanglement was applied to reduce bias. All models achieved high DR prediction performance in diagnosing (up to 94% AUROC) and could reasonably predict age and gender/sex (91% and 77% AUROC, respectively). Fairness assessment suggests disparities, such as a 10% AUROC gap between age groups in DINOv2. Disentangling SAs from DR prediction had varying results, depending on the model selected. Disentanglement improved DINOv2 performance (2% AUROC gain), but led to performance drops in ConvNeXt V2 and Swin V2 (7% and 3%, respectively). These findings highlight the complexity of disentangling fine-grained features in fundus imaging and emphasize the importance of fairness in medical imaging AI to ensure equitable and reliable healthcare solutions. © 2025 Elsevier B.V., All rights reserved.