Abstract
Objective.The detection of arterial pulsating signals at the skin periphery with Photoplethysmography (PPG) are easily distorted by motion artifacts. This work explores the alternatives to the aid of PPG reconstruction with movement sensors (accelerometer and/or gyroscope) which to date have demonstrated the best pulsating signal reconstruction. Approach. A generative adversarial network with fully connected layers is proposed for the reconstruction of distorted PPG signals. Artificial corruption was performed to the clean selected signals from the BIDMC Heart Rate dataset, processed from the larger MIMIC II waveform database to create the training, validation and testing sets. Main results. The heart rate (HR) of this dataset was further extracted to evaluate the performance of the model obtaining a mean absolute error of 1.31 bpm comparing the HR of the target and reconstructed PPG signals with HR between 70 and 115 bpm. Significance. The model architecture is effective at reconstructing noisy PPG signals regardless the length and amplitude of the corruption introduced. The performance over a range of HR (70-115 bpm), indicates a promising approach for real-time PPG signal reconstruction without the aid of acceleration or angular velocity inputs.

Abstract
The rapid growth of data traffic and evolving service demands are driving a shift from traditional network architectures to advanced solutions. While 5G networks provide reduced latency and higher availability, they still face limitations due to reliance on integrated hardware, leading to configuration and interoperability challenges. The emerging Open Radio Access Network (O-RAN) paradigm addresses these issues by enabling remote configuration and management of virtualized components through open interfaces, promoting cost-effective, multi-vendor interoperability. Network slicing, a key 5G enabler, allows for tailored network configurations to meet heterogeneous performance requirements. The main contribution of this paper is a private Standalone 5G network based on O-RAN, featuring a dynamic Data Radio Bearer Management xApp (xDRBM) for real-time metric collection and traffic prioritization. xDRBM optimizes resource usage and ensures performance guarantees for specific applications. Validation was conducted in an emulated environment representative of real-world scenarios. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2025.

Abstract
Purpose:To synthesizethe availableevidenceaboutthe use of HealthInformationTechnology(HIT)to supportpatientsduringhemodialysis.Methods:TheJoannaBriggsInstitute's methodologicalguidelinesfor scopingreviewsandthe PRISMA-ScRchecklistwereemployed.BibliographicsearchesacrossMEDLINE (R), CINAHL (R), PsychologyandBehavioralSciencesCollection,Scopus,MedicLatina,and Cochraneyielded932 records.Results:Eighteenstudiespublishedbetween2003and2023wereincluded.Theyexploreda rangeof HITs,includingvirtualreality,exergames,websites,and mobileapplications,all specificallydevelopedfor use duringthe intradialyticperiod.Conclusion:Thisstudyhighlightsthe HITsdevelopedfor use duringhemodialysistreatment,supportingphysicalexercise,diseasemanagement,and enhancementof self-efficacyand self-care.

Abstract
This paper addresses the problem of maximizing the expected size of a matching in the case of unreliable vertices and/or edges. The assumption is that the solution is built in several steps. Ina given step, edges with successfully matched vertices are made permanent; but upon edge or vertex failures, the remaining vertices become eligible for reassignment. This process maybe repeated a given number of times, and the objective is to end with the overall maximum number of matched vertices. An application of this problem is found in kidney exchange programs, going on in several countries, where a vertex is an incompatible patient-donor pair and an edge indicates cross-compatibility between two pairs; the objective is to match these pairs so as to maximize the number of served patients. A new scheme is proposed for matching rearrangement in case of failure, along with a prototype algorithm for computing the optimal expectation for the number of matched edges (or vertices), considering a possibly limited number of rearrangements. Computational experiments reveal the relevance and limitations of the algorithm, in general terms and for the kidney exchange application.

Abstract
Management and reuse of wood waste can be a challenging process due to the frequent presence of hazardous contaminants. Conventional detection methods are often limited by the need for excessive sample preparation and lengthy and expensive analysis. Laser-induced Breakdown Spectroscopy (LIBS) is a rapid and micro- destructive technique that can be a promising alternative, providing in-situ and real-time analysis, with minimal to no sample preparation required. In this study, LIBS imaging was used to analyze wood waste samples to determine the presence of contaminants such as As, Ba, Cd, Cr, Cu, Hg, Pb, Sb, and Ti. For this analysis, a methodology based on detecting three lines per element was developed, offering a screening method that can be easily adapted to perform qualitative analysis in industrial contexts with high throughput operations. For the LIBS experimental lines selection, control and reference samples, and a pilot set of 10 wood wastes were analysed. Results were validated by two different X-ray Fluorescence (XRF) systems, an imaging XRF and a handheld XRF, that provided spatial elemental information and spectral information, respectively. The results obtained highlighted LIBS ability to detect highly contaminated samples and the importance of using a 3-line criteria to mitigate spectral interferences and discard outliers. To increase the dataset, a LIBS large-scale study was performed using 100 samples. These results were only corroborated by the XRF-handheld system, as it provides a faster alternative. In particular cases, ICP-MS analysis was also performed. The success rates achieved, mostly above 88 %, confirm the capability of LIBS to perform this analysis, contributing to more sustainable waste management practices and facilitating the quick identifi- cation and remediation of contaminated materials.

Abstract
Deep learning techniques have demonstrated significant potential in computer-assisted diagnosis based on medical imaging. However, their integration into clinical workflows remains limited, largely due to concerns about interpretability. To address this challenge, we propose Efficient-Proto-Caps, a lightweight and inherently interpretable model that combines capsule networks with prototype learning for lung nodule characterization. Additionally, an innovative Davies-Bouldin Index with multiple centroids per cluster is employed as a loss function to promote clustering of lung nodule visual attribute representations. When evaluated on the LIDC-IDRI dataset, the most widely recognized benchmark for lung cancer prediction, our model achieved an overall accuracy of 89.7 % in predicting lung nodule malignancy and associated visual attributes. This performance is statistically comparable to that of the baseline model, while utilizing a backbone with only approximately 2 % of the parameters of the baseline model’s backbone. State-of-the-art models achieved better performance in lung nodule malignancy prediction; however, our approach relies on multiclass malignancy predictions and provides a decision rationale aligned with globally accepted clinical guidelines. These results underscore the potential of our approach, as the integration of lightweight and less complex designs into accurate and inherently interpretable models represents a significant advancement toward more transparent and clinically viable computer-assisted diagnostic systems. Furthermore, these findings highlight the model’s potential for broader applicability, extending beyond medicine to other domains where final classifications are grounded in concept-based or example-based attributes. © 2013 IEEE.