Abstract

Abstract
This study reflects the assumption that all links in a supply chain (SC) must share responsibility for socio-environmental issues. One of the main barriers to ensuring the sustainability of an SC is the difficulty in accessing partners' information, especially beyond the first tier. Due to the great geographical dispersion, large number of small companies, and, mainly, the growth of the fast fashion industry, the textile sector is recognised as a priority when it comes to social sustainability issues. Moreover, consumers are increasingly demanding information about the social footprint of products. Thus, this paper aims to contribute to a better understanding of how SC visibility can contribute to increasing the social sustainability of textile SCs. Using a longitudinal perspective and adopting mixed methods integrated into a design science strategy, we evaluate SC visibility in the context of two Portuguese textile supply chains, before and after the development of a technology-based solution.

Abstract
This research aims to leverage digital drawing as a non-verbal language to transcend the communication barriers faced by individuals with partial to complete locked-in syndrome (LIS). It will explore the possibility of using the human body as an interface, through assistive technology, in accordance with its limitation in functionality, to facilitate social reconnection and emotional expression through drawing. This approach is grounded in the understanding that creative expression and communication are fundamental human needs and can significantly impact the well-being and quality of life of individuals with severe motor impairments. This paper will focus on the development of the drawing activities. These activities will run under a mixed reality set that can be tailored by caregivers or therapists to the end-user's needs and preferences, ensuring functionality and user satisfaction through an accessible, enriching, and emotionally rewarding experience. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

Abstract
Background/Objectives: Type 2 diabetes mellitus (T2DM) is a chronic condition marked by hyperglycemia, which can affect metabolic, vascular, and hematological parameters. A low-carbohydrate, high-fat (LCHF) diet has been shown to improve glycemic control and blood pressure regulation. Exercise in hypoxia (EH) enhances insulin sensitivity, erythropoiesis, and angiogenesis. The combination of LCHF and EH may offer a promising strategy for managing T2DM and hypertension (HTN), although evidence remains limited. This study aimed to assess the effects of an eight-week normobaric EH intervention at 3000 m simulated altitude combined with an LCHF diet on hematological and lipid profiles, inflammation, and blood pressure in older patients with T2DM and HTN. Methods: Forty-two diabetic patients with HTN were randomly assigned to three groups: (1) control group (control diet + exercise in normoxia), (2) EH group (control diet + EH), and (3) intervention group (EH+LCHF) Baseline and eight-week measurements included systolic, diastolic, and mean blood pressure (SBP, DBP, MAP), hematological and lipid profiles, and inflammation biomarkers. Results: Blood pressure decreased after the intervention (p < 0.001), with no significant differences between groups (SBP: p = 0.151; DBP: p = 0.124; MAP: p = 0.18). No differences were observed in lipid profile or C-reactive protein levels (p > 0.05). Mean corpuscular hemoglobin (MCH) increased in the EH group (p = 0.027), while it decreased in the EH+LCHF group (p = 0.046). Conclusions: Adding hypoxia or restricting carbohydrates did not provide additional benefits on blood pressure in T2DM patients with HTN. Further elucidation of the mechanisms underlying hematological adaptations is imperative.

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Abstract
The large gap between the generalization level of state-of-the-art machine learning and human learning systems calls for the development of artificial intelligence (AI) models that are truly inspired by human cognition. In tasks related to image analysis, searching for pixel-level regularities has reached a power of information extraction still far from what humans capture with image-based observations. This leads to poor generalization when even small shifts occur at the level of the observations. We explore a perspective on this problem that is directed to learning the generative process with causality-related foundations, using models capable of combining symbolic manipulation, probabilistic reasoning, and pattern recognition abilities. We briefly review and explore connections of research from machine learning, cognitive science, and related fields of human behavior to support our perspective for the direction to more robust and human-like artificial learning systems.