Assessment of Corrosion in Naval Steels Submerged in Artificial Seawater Utilizing a Magnetic Non-Destructive Sensor.

This work presents a comprehensive evaluation of corrosion progression in DH36 naval steel through the integration of electrochemical impedance spectroscopy (EIS), weight loss, scanning electron microscopy (SEM), and advanced magnetic non-destructive techniques under artificial seawater (ASW, ASTM D1141) and natural marine conditions. Quantitative correlations are established between corrosion layer growth, electrochemical parameters, and magnetic permeability, demonstrating the magnetic sensor's capacity for the real-time, non-invasive assessment of marine steel degradation. Laboratory exposures reveal a rapid initial corrosion phase with the formation of lepidocrocite and goethite, followed by the densification of the corrosion product layer and a pronounced decline in corrosion rate, ultimately governed by diffusion-controlled kinetics.

eHealth literacy assessment as a promoter of user adherence in using digital health systems and services. A case study for balance physiotherapy in the TeleRehaB DSS project.

Improving patient adherence and compliance with digital health interventions requires the creation of eHealth literacy resources. This study examines the creation and application of a novel eHealth literacy tool for home-based balance physiotherapy as part of the TeleRehaB DSS project. This tool evaluates patients' digital literacy, in particular their ability to use the Internet of Things (IoT), Augmented Reality (AR) and smart device technologies.

Survey on the Application of Robotics in Archaeology.

This work explores the application of robotic systems in archaeology, highlighting their transformative role in excavation, documentation, and the preservation of cultural heritage. By combining technologies such as LiDAR, GIS, 3D modeling, sonar, and other sensors with autonomous and semi-autonomous platforms, archaeologists can now reach inaccessible sites, automate artifact analysis, and reconstruct fragmented remains with greater precision. The study provides a systematic overview of underwater, aerial, terrestrial, and other robotic systems, drawing on scientific literature that showcases their innovative use in both fieldwork and museum settings.

Corrosion and Antifouling Behavior of a New Biocide-Free Antifouling Paint for Ship Hulls Under Both Artificially Simulated and Natural Marine Environment.

This study involved covering naval steel samples with a biocide-free, innovative antifouling coating, which were subsequently immersed in either artificial seawater or a Greek maritime environment for durations ranging from 1 to 50 weeks. The objective was to assess the efficacy of the coating as an anticorrosion and antifouling barrier on the steel samples. Non-coated samples were analyzed alongside the coated samples for comparative purposes.

Multi-Area, Multi-Service and Multi-Tier Edge-Cloud Continuum Planning.

This paper presents the optimal planning of multi-area, multi-service, and multi-tier edge-cloud environments. The goal is to evaluate the regional deployment of the compute continuum, i.e.

Wood Waste Valorization and Classification Approaches: A systematic review.

This systematic literature review delves into various wood waste valorization and classification approaches, aiming to evaluate their efficacy in fostering sustainable wood resource management while enhancing the economic value of wood waste. By synthesizing findings from a diverse array of research studies, the review highlights the multifaceted nature of wood waste valorization, emphasizing the critical role of sorting and separation technologies in ensuring high-quality recovery of materials. It also identifies the wood classification practices in Europe, which are crucial for creating a harmonized valorization framework that aligns technological advancements with regulatory standards.

HEalthcare Robotics' ONtology (HERON): An Upper Ontology for Communication, Collaboration and Safety in Healthcare Robotics.

Healthcare robotics needs context-aware policy-compliant reasoning to achieve safe human-agent collaboration. The current ontologies fail to provide healthcare-relevant information and flexible semantic enforcement systems. HERON represents a modular upper ontology which enables healthcare robotic systems to communicate and collaborate while ensuring safety during operations.

Exploring the adaptation of the Nephroblastoma Oncosimulator to MRI scans, treatment data, and histological profiles of patients from different risk groups.

Introduction: Nephroblastoma or Wilms' tumor is the most prevalent type of renal tumor in pediatric oncology. Although the overall survival rate for this condition is excellent today (∼90%), there have been no significant improvements over the past two decades. In silico models aim to simulate tumor progression and treatment responses over time; they hold immense potential for enhancing the predictive accuracy and optimizing treatment protocols as they are inspired by the digital twin paradigm.

Bridging Data Gaps in Emergency Care: The NIGHTINGALE Project and the Future of AI in Mass Casualty Management.

In the context of mass casualty incident (MCI) management, artificial intelligence (AI) represents a promising future, offering potential improvements in processes such as triage, decision support, and resource optimization. However, the effectiveness of AI is heavily reliant on the availability of quality data. Currently, MCI data are scarce and difficult to obtain, as critical information regarding patient demographics, vital signs, and treatment responses is often missing or incomplete, particularly in the prehospital setting.

Evaluating the Usability of a Mobile Platform for Scoliosis Screening Assessment.

Scoliosis is curvature of the spine, often found in adolescents, which can impact on their quality of life. In recent years, smartphone applications (apps) and web-based applications may help the parents with the doctors' supervision in scoliosis screening and monitoring, thereby reducing the number of in-person visits. This paper suggests the usage of the SCOLIOSIS system to detect the onset of scoliosis.

The illness representations-physical well-being interplay over time in breast cancer patients.

Objective: Many studies have shown the prospective relation of illness representations to breast cancer patients' well-being. Still, very few have examined their bidirectional relationship over time. Here, the long-term mutual effects between physical well-being and illness representations were examined at the within-person level.

The Music-Related Quality of Life Measure (MuRQoL): A Scoping Review of Its Validation and Application.

Background: The Music-Related Quality of Life (MuRQoL) was launched in 2017 as a valid psychometric measure of Cochlear Implant (CI) users' music experience and its impact on Quality of Life (QoL). This scoping review aimed to explore the implementation and effectiveness of the instrument since its introduction.

Methods: PubMed and Google Scholar databases were searched for publications written in English reporting a translation, validation or application of the MuRQoL.

Augmented intelligence with voice assistance and automated machine learning in Industry 5.0.

Augmented intelligence puts together human and artificial agents to create a socio-technological system, so that they co-evolve by learning and optimizing decisions through intuitive interfaces, such as conversational, voice-enabled interfaces. However, existing research works on voice assistants relies on knowledge management and simulation methods instead of data-driven algorithms. In addition, practical application and evaluation in real-life scenarios are scarce and limited in scope.

oncology: a mechanistic multiscale model of clinical prostate cancer response to external radiation therapy as the core of a digital (virtual) twin. Sensitivity analysis and a clinical adaptation approach.

Introduction: Prostate cancer (PCa) is the most frequent diagnosed malignancy in male patients in Europe and radiation therapy (RT) is a main treatment option. However, current RT concepts for PCa have an imminent need to be rectified in order to modify the radiotherapeutic strategy by considering (i) the personal PCa biology in terms of radio resistance and (ii) the individual preferences of each patient.

Methods: To this end, a mechanistic multiscale model of prostate tumor response to external radiotherapeutic schemes, based on a discrete entity and discrete event simulation approach has been developed.

Digital Self-Management Intervention Paths for Early Breast Cancer Patients: Results of a Pilot Study.

Background: Despite excellent prognosis of early breast cancer, the patients face problems related to decreased quality of life and mental health. There is a need for easily available interventions targeting modifiable factors related to these problems. The aim of this study was to test the use of a new digital supportive intervention platform for early breast cancer patients.

Assault rifle injuries in civilians: ballistics of wound patterns, assessment and initial management.

Purpose: Wounds from assault rifles and their commercial offspring have been encountered with increasing frequency in civilian practice. Our aim is to summarize wound ballistics related to the main injury patterns that can also affect management strategies.

Methods: An online search of the PubMed was conducted for research and review articles published after 2000 in English, using the MeSH terms "gunshot wounds", "mass casualty incidents", "war-related injuries", "soft tissue injuries", "vascular system injuries", "colon injuries", "wound infection", "antibiotic prophylaxis", "debridement", "hemorrhage", "penetrating head injuries", "pneumothorax" and additional free-text terms.

Towards verifiable cancer digital twins: tissue level modeling protocol for precision medicine.

Cancer exhibits substantial heterogeneity, manifesting as distinct morphological and molecular variations across tumors, which frequently undermines the efficacy of conventional oncological treatments. Developments in multiomics and sequencing technologies have paved the way for unraveling this heterogeneity. Nevertheless, the complexity of the data gathered from these methods cannot be fully interpreted through multimodal data analysis alone.

Data Analytics to Support Policy Making for Noncommunicable Diseases: Scoping Review.

Background: There is an emerging need for evidence-based approaches harnessing large amounts of health care data and novel technologies (such as artificial intelligence) to optimize public health policy making.

Objective: The aim of this review was to explore the data analytics tools designed specifically for policy making in noncommunicable diseases (NCDs) and their implementation.

Methods: A scoping review was conducted after searching the PubMed and IEEE databases for articles published in the last 10 years.

Optimized efficient attention-based network for facial expressions analysis in neurological health care.

Facial Expression Analysis (FEA) plays a vital role in diagnosing and treating early-stage neurological disorders (NDs) like Alzheimer's and Parkinson's. Manual FEA is hindered by expertise, time, and training requirements, while automatic methods confront difficulties with real patient data unavailability, high computations, and irrelevant feature extraction. To address these challenges, this paper proposes a novel approach: an efficient, lightweight convolutional block attention module (CBAM) based deep learning network (DLN) to aid doctors in diagnosing ND patients.

A Robust End-to-End IoT System for Supporting Workers in Mining Industries.

The adoption of the Internet of Things (IoT) in the mining industry can dramatically enhance the safety of workers while simultaneously decreasing monitoring costs. By implementing an IoT solution consisting of a number of interconnected smart devices and sensors, mining industries can improve response times during emergencies and also reduce the number of accidents, resulting in an overall improvement of the social image of mines. Thus, in this paper, a robust end-to-end IoT system for supporting workers in harsh environments such as in mining industries is presented.

A Multidisciplinary Hyper-Modeling Scheme in Personalized In Silico Oncology: Coupling Cell Kinetics with Metabolism, Signaling Networks, and Biomechanics as Plug-In Component Models of a Cancer Digital Twin.

The massive amount of human biological, imaging, and clinical data produced by multiple and diverse sources necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell-signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data.

Data-driven modelling of brain activity using neural networks, diffusion maps, and the Koopman operator.

We propose a machine-learning approach to construct reduced-order models (ROMs) to predict the long-term out-of-sample dynamics of brain activity (and in general, high-dimensional time series), focusing mainly on task-dependent high-dimensional fMRI time series. Our approach is a three stage one. First, we exploit manifold learning and, in particular, diffusion maps (DMs) to discover a set of variables that parametrize the latent space on which the emergent high-dimensional fMRI time series evolve.