This study evaluates the environmental impact of earthquake-resistant structural design choices in high-risk seismic regions through life cycle assessment. As climate change concerns intensify, understanding the environmental implications of structural design decisions becomes crucial for sustainable construction. Examining a building in Quito, Ecuador, the research compares three structural systems: Optimized Framed System (OFS), Optimized Dual System (ODS), and Equivalent Framed System (EFS). The assessment quantifies emissions through a ‘cradle to gate’ approach, encompassing materials fabrication, transportation, and construction processes. The results demonstrate that the ODS achieves optimal seismic performance equal to the EFS while reducing emissions by 38%, with only 5% higher emissions than the OFS. The findings establish that effective earthquake-resistant design can simultaneously achieve structural resilience and environmental sustainability, providing valuable insights for sustainable structural engineering practices in seismic regions.

This study presents the effect of spray‐coated graphene nanoplatelets on the mechanical response of various polymers to cyclic loadings. The substrates material (three polymers) and the coating (various numbers of coating layers) are assessed. The experimental results suggest that the compressive stiffness, compressive modulus, damping, and energy dissipation of the samples coated with graphene nanoplatelets improve with respect to the uncoated samples. The outcomes of this experimental research highlight the feasibility of utilizing films of graphene nanoplatelets to improve the mechanical properties of polymers for vibration isolation, foreseeing application in various environments, for instance, in buildings and infrastructures (bridges, railways) for seismic and acoustic isolation.

Oil palm empty fruit bunch fibers (OPEFBF), in three size ranges (small: 250–450 µm, medium: 450–600 µm, large: 600–800 µm), were investigated as a filter-bed material in biofilters for the removal of organic matter and nutrients. After saturation, these fibers (post) were used in the removal of methylene blue through batch processes. The batch adsorption tests included optimizing the adsorbent dosage (0.5–32.0 g/L) and contact time (2.5–60.0 min). Experimental data were fitted to various kinetic/isotherm models. Instrumental characterization of both raw and post fibers was conducted. Post fibers underwent morphological/compositional changes due to the presence of microorganisms and their byproducts. Efficiencies reached up to 94% for chemical oxygen demand (COD), 88.4% for total nitrogen and 77.2% for total phosphorus. In batch adsorption, methylene blue removal exceeded 90%, underscoring the effectiveness of small raw OPEFBF and large post OPEFBF. Kinetic models indicated that raw OPEFBF better fit the pseudo-first-order model, while post OPEFBF better fit the pseudo-second-order model. Both types of OPEFBF showed a good fit with the Freundlich model (higher R2, lower χ2 and SSE). Particularly, large post OPEFBF stood out as the most efficient adsorbent, achieving a maximum adsorption capacity of 12.02 mg/g for methylene blue. Therefore, raw/post OPEFBF could be an alternative to remove contaminants from wastewater.

This study addresses the critical issue of indoor air quality (IAQ) and pathogen transmission within enclosed spaces at high altitudes, focusing on university classrooms in Quito, an Andean city in South America. The aim is to establish safety thresholds for room occupancy and permissible durations of exposure, tailored to this unique environmental context. Through an experimental approach conducted at an elevation of 2900 m above sea level, various natural ventilation strategies were evaluated for their efficacy in mitigating pathogen transmission risks. The study employs the Concentration Decay Test Method to characterize air changes per hour (ACH) and utilizes the Bazant mathematical model to predict occupancy levels based on ventilation, dimensions of the room, respiratory activity, infectiousness rates, and other parameters. Findings highlight the significant impact of ventilation strategies on room occupancy. Notably, higher infectiousness rates and large exposure times drastically reduce permissible occupancy levels, underscoring the importance of effective ventilation in maintaining safety. This research contributes valuable insights for informed decision‐making regarding classroom capacity and safety protocols in Andean higher education settings.

IntroductionThe hippocampal volume is a well-known biomarker to detect and diagnose neurological, psychiatric, and psychological diseases. However, other morphological descriptors are not analyzed. Furthermore, not available databases, or studies, were found with information related to the hippocampal morphology from Latin-American patients living in the Andean highlands.MethodsThe hippocampus is manually segmented by two medical imaging specialists on normal brain magnetic resonance images. Then, its morphological qualitative and quantitative descriptors (volume, sphericity, roundness, diameter, volume-surface ratio, and aspect ratio) are computed via 3D digital level-set-based mathematical representation. Furthermore, other morphological descriptors and their possible correlation with the hippocampal volume is analyzed.ResultsWe introduce a first database with the hippocampus’ morphological characterization of 63 patients from Quito, Ecuador, male and female, aged between 18 and 95 years old.DiscussionThis study provides new research opportunities to neurologists, psychologists, and psychiatrists, to further understand the hippocampal morphology of Andean and Latin American patients.

El estudio del material granular almacenado en silos se lo ha realizado habitualmente con las formulaciones de la mecánica del medio continuo y los elementos finitos. Sin embargo, existen diversas limitaciones al cuantificar la interacción entre partículas y su comportamiento individual. Por lo tanto, se plantea la utilización del método del elemento discreto (DEM) para evitar las limitaciones intrínsecas de modelos continuos en el análisis del flujo de maíz (materia granular) durante los procesos de descarga en silos. El elemento discreto es una eficaz herramienta mecánico-computacional que permite modelar ensambles granulares al considerar sus propiedades físicas y mecánicas tanto al nivel individual como de conglomerado. En esta investigación, los ensambles diseñados son representaciones numéricas de granos de maíz almacenado en silos. Los resultados de las simulaciones se cuantifican en términos de perfiles de velocidad, cadenas de fuerza, esfuerzos en las paredes del silo, y deformaciones del conglomerado granular. Uno de los principales hallazgos de esta investigación es la importancia del ángulo de reposo del maíz en la descarga de silos ya que los esfuerzos, deformaciones y cadenas de fuerza varían dependiendo de este valor (27°).

This study shows the influence of the inclusion of abaca fiber (Musa Textilis) on the coefficients of consolidation, expansion, and compression for normally consolidated clayey silt organic soil specimens using reconstituted samples. For this purpose, abaca fiber was added according to the dry mass of the soil, in lengths (5, 10, and 15 mm) and concentrations (0.5, 1.0, and 1.5%) subjected to a curing process with sodium hydroxide (NaOH). The virgin and fiber-added soil samples were reconstituted as slurry, and one-dimensional consolidation tests were performed in accordance with ASTM D2435. The results showed a reduction in void ratio (compared to the soil without fiber) and an increase in the coefficient of consolidation (Cv) as a function of fiber concentration and length, with values corresponding to 1.5% and 15 mm increasing from 75.16 to 144.51 cm2/s. Although no significant values were obtained for the compression and expansion coefficients, it was assumed that the soil maintained its compressibility. The statistical analysis employed hierarchical linear models to assess the significance of the effects of incorporating fibers of varying lengths and percentages on the coefficients, comparing them with the control samples. Concurrently, mixed linear models were utilized to evaluate the influence of the methods for obtaining the Cv, revealing that Taylor’s method yielded more conservative values, whereas the Casagrande method produced higher values.

This study addresses the challenge of optimizing flat-plate solar collector design, traditionally reliant on trial-and-error and simplified engineering design methods. We propose using physics-informed neural networks (PINNs) to predict optimal design conditions in a range of data that not only characterized the highlands of Ecuador but also similar geographical locations. The model integrates three interconnected neural networks to predict global collector efficiency by considering atmospheric, geometric, and physical variables, including overall loss coefficient, efficiency factors, outlet fluid temperature, and useful heat gain. The PINNs model surpasses traditional simplified thermodynamic equations employed in engineering design by effectively integrating thermodynamic principles with data-driven insights, offering more accurate modeling of nonlinear phenomena. This approach enhances the precision of solar collector performance predictions, making it particularly valuable for optimizing designs in Ecuador’s highlands and similar regions with unique climatic conditions. The ANN predicted a collector overall loss coefficient of 5.199 W/(m2·K), closely matching the thermodynamic model’s 5.189 W/(m2·K), with similar accuracy in collector useful energy gain (722.85 W) and global collector efficiency (33.68%). Although the PINNs model showed minor discrepancies in certain parameters, it outperformed traditional methods in capturing the complex, nonlinear behavior of the data set, especially in predicting outlet fluid temperature (55.05 °C vs. 67.22 °C).