Abstract

Abstract
An automatic system for pathology classification in chest X-ray scans needs more than predictive performance, since providing explanations is deemed essential for fostering end-user trust, improving decision-making, and regulatory compliance. CLARE-XR is a novel methodology that, when presented with an X-ray image, identifies the associated pathologies and provides explanations based on the presentation of similar cases. The diagnosis is achieved using a regression model that maps an image into a 2D latent space containing the reference coordinates of all findings. The references are generated once through label embedding, before the regression step, by converting the original binary ground-truth annotations to 2D coordinates. The classification is inferred minding the distance from the coordinates of an inference image to the reference coordinates. Furthermore, as the regressor is trained on a known set of images, the distance from the coordinates of an inference image to the coordinates of the training set images also allows retrieving similar instances, mimicking the common clinical practice of comparing scans to confirm diagnoses. This inherently interpretable framework discloses specific classification rules and visual explanations through automatic image retrieval methods, outperforming the multi-label ResNet50 classification baseline across multiple evaluation settings on the NIH ChestX-ray14 dataset.

Abstract
Background: The role of mindful eating (ME) and intuitive eating (IE) in improving eating behavior, diet quality, and health is an area of increasing interest. Objective: The objective of this review was to identify the instruments used to assess ME and IE among higher education students and outcomes related to these dimensions. Methods: This review was carried out according to the PRISMA statement, through systematic searches in PubMed, Web of Science, PsycInfo, and Scopus. The inclusion criteria selected for higher education students, levels of ME and/or IE reported, and observational and clinical studies. The exclusion criteria selected against reviews, qualitative studies, and case studies. Quality was assessed using the Academy of Nutrition and Dietetics Quality Criteria Checklist. Results: A total of 516 initial records were identified, from which 75 were included. Cross-sectional studies were the most common research design (86.7%). Most studies were conducted with samples that were predominantly female (90.7%), White (76.0%), aged 18 to 22 years (88.4%), with BMI < 25 kg/m(2) (83.0%), and in the United States (61.3%). The Intuitive Eating Scale (IES), the Mindful Eating Questionnaire (MEQ), and their different versions were the most used instruments. The outcomes most studies included were eating behavior and disorders (77.3%), anthropometric assessments (47.8%), mental health (42.0%), and body image (40.6%). Regarding the quality of studies, 34.7% of studies were assigned a positive, 1.3% a negative, and 64.0% a neutral rate. Conclusions: IES and MEQ were the most used instruments. RCT and cohort studies are scarce, and future research with a higher level of quality is needed, especially on the topics of food consumption, diet quality, and biochemical markers.

Abstract

Abstract
The Puzzle -based Learning approach has been applied to several fields of knowledge. In education research papers, the instructional usage of puzzles is considered to improve learners' motivation and engagement and help them to develop critical skills but difficulties concerning learners' interaction with puzzles have also been pointed out. Our paper investigates the dynamics of the concept of a puzzle and its interface to provide a better understanding of its form and functions, and help learners interact with puzzles. We consider Puzzle -based Learning tenets as well as their educational impacts on both critical thinking and learner engagement and provide an original proposal concerning the understanding of puzzles. Our proposal centered on the dynamics of puzzles bears conceptual and educational facets. Conceptually, puzzle dynamics is viewed as composed of two elements: a mechanism, the Puzzle Trigger, and a process, the Puzzle -Solving. From an educational point of view, the rationale for integrating Puzzle Triggers in Puzzle -based Learning is meant to help learners interact with puzzles and consequently become motivated and engaged in the Puzzle -Solving process. This way, learners' critical thinking skills are reinforced and focused on finding solutions to challenges. We illustrate the implementation of Puzzle Triggers and Puzzle -Solving by considering two instructional activities in a Software Development undergraduate course of an online learning Informatics Engineering Program.

Abstract
Proactive participation of uncertain renewable generation in the day-ahead (DA) wholesale market effectively reduces the system marginal price and carbon emissions, whilst significantly increasing the volumes of real-time balancing mechanism prices to ensure system security and stability. To solve the conflicting interests over the two timescales, this article: 1) proposes a novel hierarchical optimization model to align with the actual operation paradigms of the hierarchical market, whereby the capacity allocation matrix is adopted to coordinate the DA and balancing markets; 2) mathematically formulates and quantitatively analyses the long-term driving factors of balancing actions, enabling system operators (SOs) to design efficient and well-functioning market structures to meet economic and environmental targets; 3) empowers renewable generating units and flexible loads to participate in the balancing market (BM) as 'active' actors and enforces the non-discriminatory provision of balancing services. The performance of the proposed model is validated on a modified IEEE 39-bus power system and a reduced GB network. Results reveal that with effective resource allocation in different timescales of the hierarchical market, the drop speed of balancing costs soars while the intermittent generation climbs. The proposed methodology enables SOs to make the most of all resources available in the market and balance the system flexibly and economically. It thus safeguards the climate mitigation pathways against the risks of substantially higher balancing costs.