Hyperhidrosis (HH) is a condition characterized by excessive sweating beyond physiological needs, affecting patients’ quality of life. This study introduces an innovative technological solution focused on the development of a graphical application to support the clinical study of hyperhidrosis, a condition characterized by excessive sweating. The application, developed using Python, Raspberry Pi, and temperature sensors, enables real-time monitoring of thermal variations. This system provides a novel approach to clinical data collection, specifically focusing on how biometric data is measured and how visual and auditory alerts are generated. The application’s design and implementation, highlighting the integration of PyQt4 and Matplotlib for a Python-based GUI, are the primary focus of this paper.
This study, conducted within the scope of the FAIST project, explores the design and development of a digital interface for personalized footwear. The primary objective is to create an intuitive and user-centered interface that enables individuals to scan their feet, personalize footwear models, and complete purchases. This paper presents the outcomes of the first research phase, which comprises a comprehensive literature review on gamification, e-commerce, UX/UI design, and usability heuristics, along with a comparative analysis of leading digital interfaces for personalized footwear acquisition. The main outcome of the investigation was the identification of essential design requirements that will serve as guidelines for the future stages of prototype development.
The present study examines the impact of artificial intelligence (AI) on human-centered service in the boutique hospitality sector, focusing on the Portuguese Boutique Hotel: Hotel da Oliveira, known for its personalized guest experiences. The chapter investigates how the implementation of AI can alter the essential human contact that defines hotel identity and the guest experience. This research employs a qualitative methodology, utilizing semi-structured interviews to elicit in-depth professional insights and experiences related to maintaining the human touch in daily operations. The empirical findings suggest that although AI can be used to improve operational efficiency, employees emphasized the need to maintain the human touch to preserve the boutique hotel experience. This research discusses the challenges small hotels face when implementing AI, especially the balance between using technology and preserving the core values of human-centered service.
The emergence of new Artificial Intelligence technologies has brought significant changes to collaborative processes in Graphic Design. In addition to the widely promoted tools, their integration into the daily practices of professionals requires the adoption of new working methodologies and the development of novel technical skills, essential for their appropriate use. This raises pertinent questions: Do these tools diminish decision-making power or creative agency? Can they replace a substantial portion of professional tasks? Are the outcomes they generate ethically and legally acceptable? This literature review aims to examine this emerging model of co-creation, with particular attention to the typology of tools, the contexts in which they are employed, and the quality of the results they enable. Professionals now face new challenges, as co-creation with Artificial Intelligence tools presents a dual reality: on the one hand, it offers a new creative paradigm, enhancing process optimization and relieving users of repetitive tasks; on the other hand, it raises concerns regarding the origin, legality, and ethical implications of the outputs generated. The productive potential that Artificial Intelligence contributes to the creative process is undeniable, facilitating greater workflow, efficiency, and problem-solving capabilities. Nevertheless, its indiscriminate use risks devaluing Graphic Design as a discipline and may lead to the erosion of essential skills such as critical thinking and professional autonomy. Rather than focusing solely on the availability of these technologies, it is crucial to reflect on how they are applied and perceived, acknowledging the pressing need for clearer regulation and, above all, comprehensive training and ongoing professional support.
The abstract nature of mathematical concepts often impedes learners’ comprehension, particularly within geometry, a difficulty accentuated at the transition from secondary to higher education, where students shift from procedural fluency to formal, axiomatic reasoning. The goal of this study was to understand how a hybrid flipped learning design, mediated by Moodle and supported by artificial intelligence (AI), articulating tangram, GeoGebra, and Polypad, improves performance in geometry and qualifies students’ geometric reasoning, by mapping the appropriation of AI-generated explanations in asynchronous interactions. Methodologically, we adopted a mixed-methods, single-group pre-/post-design (n = 22) within a hybrid flipped learning cycle streamlined to asynchronous preparation via Moodle and AI tools and studio-style, in-class sessions; qualitative data comprised forum posts analyzed through directed content analysis. Pre-/post-comparisons showed statistically significant gains on all four domains, robust to parametric and non-parametric tests; effect sizes ranged from large to very large, with distributional shifts across outcomes. Individually, improvement was most widespread for spatial reasoning and mathematical problem-solving; geometric properties improved for 15 students; geometric deduction was heterogeneous. Qualitatively, students increasingly named and justified properties, described transformations with greater precision, and used AI-generated explanations as scaffolds to verify reasoning, explore alternative representations, and correct misconceptions while maintaining authorship of arguments. These findings indicate the promise of multimodal, AI-supported hybrid designs for early undergraduate geometry learning, while acknowledging limits of causal inference, small sample size, and absent follow-up.
