Catholic University of Uruguay

Plant-based cheese alternatives (PBCAs) are considered sustainable substitutes for dairy-based cheese (DB); however, their consumer acceptance remains limited, particularly in dairy-dominant cultural contexts such as Uruguay, where specific consumer insights are scarce. This study investigated the influence of sensory experience, product information, and conceptual expectations on consumer perceptions of commercially available mozzarella-style PBCAs in Uruguay. Three sessions were conducted: blind and informed tastings (n = 110) and an expectation survey with a separate participant group (n = 236). Liking, purchase intention, and conceptual associations were evaluated using hedonic scales, CATA questions, and cluster analyses. Dairy-based cheese consistently achieved the highest liking and purchase intention scores. Positive sensory drivers for acceptance included a smooth mouthfeel, creaminess, and shiny appearance, whereas artificial flavors and strong aftertastes reduced acceptance. One PBCA showed an acceptable texture but lacked flavor. Crucially, providing product information did not enhance PBCA acceptance, underscoring the importance of sensory experiences. Cluster analysis revealed two consumer segments: “high purchase intention group” (n = 176), who are younger, and associate PBCAs with sustainability and health; and “low purchase intention group” (n = 60), who were older and perceived PBCAs as artificial and unappealing. PBCAs hold potential among sustainability-oriented consumers, but improving flavor authenticity and sensory quality is crucial. Addressing these sensory barriers and strategically positioning products around health and environmental benefits are crucial for broadening overall acceptance and facilitating sustainable dietary shifts in the population.

Test-Time Adaptation (TTA) enables pre-trained models to adjust to distribution shift by learning from unlabeled test-time streams. However, existing methods typically treat these streams as independent samples, overlooking the supervisory signal inherent in temporal dynamics. To address this, we introduce Order-Aware Test-Time Adaptation (OATTA). We formulate test-time adaptation as a gradient-free recursive Bayesian estimation task, using a learned dynamic transition matrix as a temporal prior to refine the base model's predictions. To ensure safety in weakly structured streams, we introduce a likelihood-ratio gate (LLR) that reverts to the base predictor when temporal evidence is absent. OATTA is a lightweight, model-agnostic module that incurs negligible computational overhead. Extensive experiments across image classification, wearable and physiological signal analysis, and language sentiment analysis demonstrate its universality; OATTA consistently boosts established baselines, improving accuracy by up to 6.35

Athlete burnout has traditionally been examined as a phenomenon mainly associated with high-performance sport; however, growing evidence suggests that it may also affect amateur athletes. This study aimed to analyze burnout in high-performance and amateur athletes, integrating gender differences and a person-centered approach through cluster analysis. A cross-sectional design was applied to 511 athletes (38.0% high-performance and 62.0% amateur) using the Revised Athlete Burnout Inventory (IBD-R). Descriptive and inferential analyses examined differences by competitive level and gender, while hierarchical and k-means cluster analyses identified burnout profiles. Results showed similar levels of emotional exhaustion and depersonalization across competitive levels, with specific differences in reduced personal accomplishment. Four burnout profiles were identified: Healthy, Reduced Accomplishment, Emotional Exhaustion, and Depersonalization-dominant burnout, differentially distributed by gender and competitive level. These findings indicate that burnout is a transversal phenomenon in sport and highlight the value of person-centered approaches for prevention and intervention across competitive contexts.

This study illustrates the application of Acceptance and Commitment Therapy (ACT) for a 12-year-old boy with separation anxiety and his mother. Over 23 sessions, ACT strategies promoted psychological flexibility, values-based parenting, and adaptive behaviors. The intervention reduced the child's experiential avoidance, anxiety, and depressive symptoms, while increasing value-oriented actions, while the mother showed improved psychological flexibility and life satisfaction. The results were sustained at a three-month follow-up. This case study highlights the potential of ACT in treating childhood separation anxiety by simultaneously involving parents, demonstrating its feasibility and efficacy. The findings provide guidance for adapting ACT for families and child populations.

Charge-based MOSFET compact models provide a physically consistent framework to describe transistor charges and capacitances across operating regimes. Unlike current-based approaches, they enforce charge conservation and yield reliable predictions of dynamic and RF behavior. This paper reviews the main charge-based formulations, ranging from industrial standards (BSIM, PSP, HiSIM) to academic compact models such as EKV and the recent ACM-2 five-parameter approach. We contrast their philosophies, complexity, and accuracy, highlighting the trade-offs between highly parameterized industrial models and compact analytical formulations oriented to design and education. Representative applications in analog/RF design, digital timing and power estimation are discussed. Particular attention is given to the lightweight ACM-2 model as a paradigmatic example of simplicity and analytical clarity. We conclude by outlining current challenges-advanced device architectures, quantum effects, and automated parameter extraction-and perspectives for future compact modeling in deeply scaled technologies.