Abstract
The application of digital twin technology to the ocean is often referred to as Digital Twins of the Ocean (DTO). One notable initiative funded under Horizon Europe programs - Green Deal is the ILIAD - Digital Twin of the Ocean project. One of the objectives of ILIAD is to establish interoperable, data-intensive, and cost-effective DTO pilots. This paper focuses on one such pilot dedicated to environmental monitoring and water quality assessment associated with the OceanLab infrastructure in the Trondheim Fjord, Norway. This paper outlines the architecture and concept of the pilot while providing detailed insights into its application for various what-if scenarios. The scenario presented in this paper is a case study that analyzes the impact of a hypothetical oil spill at the Trondheim terminal. It focuses on the spread of surface oil over a 30-hour period using various pilot modules. The paper also discusses the potential replication of this study in another geographical location.

Abstract
Introduction: Together with sociodemographic and clinical features, locus of control and self-efficacy may impact the processes underlying changes in eating habits. Objectives: To study the relationships of sociodemographic and clinical characteristics, locus of control, general self-efficacy and eating self-efficacy with the perception of adherence to healthy eating among patients undergoing treatment for overweight. Methodology: A convenience sample of 74 overweight (BMI = 25.0 kg/m2) individuals (77.0% females, mean age = 41 years, SD = 11) attending nutrition consultations was studied regarding sociodemographic and clinical data, stages of change towards healthy eating, health locus of control (Health Locus of Control Scale), eating self-efficacy (General Eating Self-Efficacy Scale) and general self-efficacy (Self-Concept Clinical Inventory’s self-efficacy factor). Results: Approximately two-thirds (67.6%) of participants were in the “Action/Maintenance” stage towards healthy eating. In the total locus of control scale, general self-efficacy and eating self-efficacy, participants showed average scores slightly higher than the midpoint of the respective scales. In a binary logistic regression model, sociodemographic, clinical, locus of control and self-efficacy variables significantly predicted being in the action/maintenance stage towards healthy eating (p < 0.001; Nagelkerkle’s R2 = 48.4%). A higher proportion of weight loss (adjusted Exp(ß) = 1.074, p = 0.017) and higher eating self-efficacy (adjusted Exp(ß) = 1.317, p = 0.005) were significantly associated with higher odds of being in the “Action/Maintenance” stage. Conclusions: Most participants attending nutrition consultations to treat overweight considered following a healthy diet. Higher eating self-efficacy and greater weight loss associated to being in the “Action/Maintenance” stage towards healthy eating.

Abstract
Background: The study aims to present and explain the development stages of a mobile app designed to improve health literacy for self-management of oncological diseases. Through the integration of gamification, the app aims to enhance patient engagement and education in an interactive manner. Methods: The methodology of Design Science in Information Systems and Software Engineering was employed, which included stages of needs identification, requirements definition, prototyping, and iterative validation of the developed artifact. A total of 132 participants, consisting of patients and healthcare professionals, were involved in the development of the PocketOnco application. The subsequent implementation of the App, PocketOnco, involved usability testing, System Usability Scale assessment, and the collection of qualitative feedback. Results: The usability testing analysis revealed excellent acceptance of PocketOnco, with the gamified elements such as quizzes and reward systems being particularly appreciated for their ability to consistently engage and motivate users. Conclusion: The various stages in the development of this resource ensure the quality of its purpose. The application proved to be a viable and attractive solution for both patients and healthcare professionals, suggesting a promising path for future digital interventions in the field of oncology.

Abstract
Quality inspection inspection systems are critical for maintaining product integrity. Being a repetitive task, when performed by operators only, it can be slow and error-prone. This paper introduces an automated inspection system for quality assessment in casting aluminum parts resorting to a robotic system. The method comprises two processes: filing detection and hole inspection. For filing detection, five deep learning modes were trained. These models include an object detector and four instance segmentation models: YOLOv8, YOLOv8n-seg, YOLOv8s-seg, YOLOv8m-seg, and Mask R-CNN, respectively. Among these, YOLOv8s-seg exhibited the best overall performance, achieving a recall rate of 98.10%, critical for minimizing false negatives and yielding the best overall results. Alongside, the system inspects holes, utilizing image processing techniques like template-matching and blob detection, achieving a 97.30% accuracy and a 2.67% Percentage of Wrong Classifications. The system improves inspection precision and efficiency while supporting sustainability and ergonomic standards, reducing material waste and reducing operator fatigue.

Abstract
ILIAD focuses on developing an ecosystem of interoperable Digital Twins for the Ocean by connecting to existing ocean data infrastructures, enhancing ocean data infrastructures with additional observation technologies and citizen science, employing numerical models and executing AI models, and aiding operational decision-making of marine and maritime activities. This work focuses on the diverse ILIAD Pilots and emphasizes sensors, data collection, and data management. Emphasis is given on new, low-cost sensors, their objectives, the novel technical aspects, the generated data, and how they can be used in the ILIAD project framework and the operation of ILIAD DTs.

Abstract
Background/Objectives: Obesity is a major contributor to cardiovascular disease, yet traditional risk assessment methods may overlook behavioral and circadian influences that modulate metabolic health. Chronotype, physical activity, sleep quality, eating speed, and breakfast habits have been increasingly associated with cardiometabolic outcomes. This study aims to evaluate the associations between these behavioral factors and both anthropometric and biochemical markers of cardiovascular risk among obese candidates for bariatric surgery. Methods: A cross-sectional study was conducted in a sample of 286 obese adults (78.3% females, mean 44.3 years, SD = 10.8, mean BMI = 42.5 kg/m2, SD = 6.2) followed at a central Portuguese hospital. Chronotype (reduced Morningness-Eveningness Questionnaire), sleep quality (Pittsburgh Sleep Quality Index), physical activity (Godin-Shephard Questionnaire), eating speed, and breakfast skipping were assessed. Cardiovascular risk markers included waist-to-hip ratio (WHR), waist-to-height ratio, A Body Shape Index (ABSI), Body Roundness Index, atherogenic index of plasma (AIP), triglyceride-glucose index (TyG), and homeostatic model assessment for insulin resistance (HOMA-IR). Results: Men exhibited significantly higher WHR, ABSI, HOMA-IR, TyG, and AIP. Eveningness was associated with higher insulin (r = -0.168, p = 0.006) and HOMA-IR (r = -0.156, p = 0.011). Poor sleep quality was associated with higher body fat mass (r = 0.151, p = 0.013), total cholesterol (r = 0.169, p = 0.005) and LDL cholesterol (r = 0.132, p = 0.030). Faster eating speed was associated with a higher waist circumference (r = 0.123, p = 0.038) and skeletal muscle mass (r = 0.160, p = 0.009). Conclusions: Male sex, evening chronotype, and poor sleep quality were associated with more adverse cardiometabolic profiles in individuals with severe obesity. These findings support the integration of behavioral and circadian factors into cardiovascular risk assessment strategies.