This research focuses on the need to clearly define the lateral design force distribution shape for the seismic design of reinforced concrete (RC) buildings. It specifically looks at the effects of soil-structure interaction (SSI) and inelastic structural behavior. Traditional seismic design methods usually assume fixed-base conditions and overlook SSI. This can result in inaccurate predictions of how much the structure will deform. To create a more reliable, data-driven seismic design framework, the study conducted extensive nonlinear time-history analyses on various regular RC buildings using OpenSeesPy both for generating a building database based on current code provisions and for assessing their dynamic response. These models vary across important factors, including fundamental periods, slenderness ratios, lambda(str), and soil conditions. The spectral acceleration at the first mode period (S) over tilde (a(T1)) served as the measure of ground motion intensity (IM). The collected data were used to derive classical regression equations and to train machine learning models, including Neural Network Regression and Gradient-Boosting Regression Trees, to predict the optimal lateral force profile shape. These methods are essential for understanding the complex, nonlinear relationships between seismic input factors and the dynamic char acteristics of a building, as well as engineering demand parameters (EDPs) such as storey drift. The study found that the key factors affecting nonlinear lateral displacement and force-shape profile include the structure's slenderness ratio lambda(str) , the fixed-base and flexible-base fundamental periods, T-n and (T) over bar (n), and the IM, (S)over tilde>(a(T1)). By providing a probabilistic assessment, this methodology seeks to improve the outcomes of seismic design codes and enhance performance-based design for RC buildings.
This work presents an interdisciplinary university outreach experience focused on the development of therapeutic creams based on local essential oils, mainly eucalyptus, to support physiotherapy treatments for low-income older adults in the neighbourhood of La Vicentina (Quito, Ecuador). The project was part of PUCE Solidaria, with the participation of Chemistry and Physiotherapy students who prepared and applied the products in a socially vulnerable community context. The distillation method was used to obtain essential oils, and creams with a warming effect were subsequently formulated. More than fifty people benefited, reporting improvements in mobility as well as relief of muscular and joint pain. Furthermore, the experience enabled students to apply scientific knowledge in a socially relevant environment, strengthening their commitment to chemistry in the social sphere and to community hope. This study combines scientific rigor and social engagement, in line with the university’s mission to contribute to justice and collective well-being.
This work presents the statistical analysis of Tseltal language, and the results are based on the Bible books. It constitutes one of the first systematic efforts to characterize the linguistic structure of Tseltal and to quantitatively assess its similarity with other dialects. The corpus is analyzed by ordering words according to their frequency of occurrence, fitting the resulting distributions to Zipf’s law and Zipf-Mandelbrot law, by linear regression and maximum likelihood method. The results indicate that the Zipf exponent for Tseltal is close to one, a behavior consistent with reported exponent values for other languages, including some within the Mayan family, finally, the calculated parameters are discussed.
Loneliness among older adults represents a complex and growing challenge, with significant emotional and social repercussions. In this context, the Co-bien project adopts a co-design approach to address this issue through empathy, active listening, and socially meaningful technology. The aim of this study is to develop an adapted engagement protocol that enables the exploration of experiences, needs, and expectations of older adults at risk of experiencing loneliness. Qualitative methods such as Cultural Probes and in-depth interviews were employed within participatory workshops involving older adults from diverse contexts. Preliminary findings suggest that these tools foster self-reflection, build trust, and facilitate access to subjective meanings that are difficult to capture through traditional methods. Preliminary results suggest that combining Cultural Probes and in-depth interviews not only enables access to intimate experiences usually inaccessible to traditional methods, but also increases motivation, trust and participants’ sense of agency—laying the groundwork for co-design processes that are ethically stronger and more operationally robust.
This paper presents a teaching experience in Biomedical Engineering based on Ignatian Pedagogy, seeking deep learning committed to the Sustainable Development Goals (SDGs). The project integrates the development of Embedded Systems (using Arduino) with training in key transversal learning outcomes (teamwork, communication, and critical learning). The use of an active methodology and the focus on real challenges in the field of health have proven to be effective, motivating students to apply technical knowledge and principles of responsibility. The results confirm that by aligning technology with social commitment, a comprehensive education is achieved that prepares future professionals to address complex problems in a sustainable manner.
