University of Cartagena

Abstract Human space flights in microgravity conditions lead to a spectrum of changes in the physiological and biomechanical properties of the central nervous system. These changes can pose challenges to the diagnosis, management, and recovery process for astronauts who sustain any neurotrauma. Understanding these interactions can be of particular importance to ensure the safety of astronauts and the development of space medicine, where valuable insight can be provided into neurotrauma care based on the experiences gained on Earth. The present narrative review discusses the neurophysiological effects of microgravity, its implications for neurotrauma, diagnostic challenges, and potential therapeutic interventions.

Actualmente, la educación infantil busca ofrecer experiencias didácticas integradoras que conecten el aprendizaje con el mundo del niño. En este contexto, el enfoque STEAM (Ciencia, Tecnología, Ingeniería, Arte y Matemáticas) se presenta como una propuesta innovadora que articula distintas disciplinas para fortalecer habilidades cognitivas, socioemocionales e imaginativas. Este artículo de investigación es resultado de un estudio que tuvo como objetivo fortalecer las prácticas pedagógicas en educación infantil en una institución pública del Distrito de Cartagena, Colombia, a través de la implementación del enfoque STEAM. Promoviendo el desarrollo integral de los niños niñas mediante el juego, la creatividad, la exploración del entorno y la resolución de problemas. Se utilizó una metodología cualitativa con diseño de investigación-acción pedagógica, donde el docente actúa como mediador activo. Las actividades se organizaron en proyectos con retos lúdicos, empleando materiales naturales, reciclados, tecnológicos y artísticos para estimular la exploración sensorial y la construcción del conocimiento de forma natural. Se fomentó el trabajo colaborativo y la participación familiar. Los resultados mostraron avances en el pensamiento lógico, la observación, la creatividad y la resolución de problemas. A nivel socioemocional, se evidenció mayor confianza, curiosidad y habilidades comunicativas. En conjunto, el enfoque STEAM potenció la motivación por aprender, transformando significativamente la formación integral en la educación inicial.

The rapid expansion of impervious surfaces in urban environments has significantly increased surface runoff and flood risk. Detention basins, implemented as part of Sustainable Urban Drainage Systems (SUDSs), are widely adopted worldwide to control peak discharges and mitigate recurrent flooding. In this study, an explicit flood routing model is applied to simulate the hydraulic behaviour of an urban detention reservoir, offering a computationally efficient alternative to traditional implicit numerical schemes by avoiding iterative solution procedures. In parallel, twenty-eight machine learning (ML) models are evaluated to estimate the percentage reduction in peak discharge required to comply with local regulatory constraints. The proposed framework integrates explicit hydrological routing with data-driven modelling to support decision-making during the design of detention systems. The methodology is applied to an urban catchment in Cartagena, Colombia, comparing an uncontrolled inflow hydrograph (without SUDSs) with an attenuated outflow hydrograph produced by the detention basin. The results demonstrate a substantial reduction in peak discharge and a delay in the time to peak, fully complying with Colombian regulations that require a minimum attenuation of 30%. Among the evaluated ML models, Squared Exponential Gaussian Process Regression achieved the best performance, yielding coefficient of determination (R2) values of 0.999 in both the validation and test sets. The findings confirm the potential of machine learning techniques to quantify peak-flow reduction requirements accurately and to support the planning and design of detention reservoirs in urban environments. The proposed approach constitutes a practical, efficient, and replicable tool for sustainable urban drainage design since the results of this research can be used to design detention pond systems employing ML tools.

The purpose of this study was to analyze the relationship between teachers’ emotional management competencies and students’ academic performance in rural educational institutions, considering the mediating role of classroom socioemotional variables and the moderating effect of the territorial context. The research was conducted using a quantitative approach with a cross-sectional correlational–mediational design. Participants included teachers and secondary school students from rural contexts in Colombia. Teachers’ emotional management competencies were assessed through a self-report scale measuring emotional awareness, emotional regulation, empathy, and socioemotional support, while academic performance was operationalized using institutional grade averages in core subjects. The results showed a positive and statistically significant relationship between teachers’ emotional management competencies and students’ academic performance. Additionally, findings indicated that this relationship was indirect and operated through socioemotional mediators, particularly classroom climate and academic engagement. Furthermore, the rural context exhibited a moderating effect, strengthening the impact of teachers’ emotional management competencies in settings characterized by higher socioeconomic vulnerability. It was concluded that teachers’ emotional management competencies represent a relevant pedagogical factor for optimizing learning processes in rural contexts, functioning as a compensatory resource in the face of structural and psychosocial limitations. This study provides contextualized empirical evidence that enhances the understanding of educational processes in rural territories and supports the development of teacher training initiatives and educational policies focused on strengthening socioemotional dimensions as a key component of educational quality and equity.

Heteroatom functionalization of graphene is an effective strategy for designing more sustainable lithium-ion battery electrodes, as it can tune both interfacial adhesion and the electronic features of the carbon lattice. In this work, we investigated the interfacial compatibility between three graphene sheets—pristine graphene, graphene doped with one nitrogen atom (Graphene–N), and graphene doped with one sulfur atom (Graphene–S)—and three lignocellulosic binders (carboxymethylcellulose (CMC); coniferyl alcohol (LcnA); and sinapyl alcohol (LsiA)) using density functional theory (DFT). Geometries were optimized using CAM-B3LYP and M06-2X in combination with the LANL2DZ basis set, while ωB97X-D/LANL2DZ was employed for dispersion-consistent single-point refinements. The computed adsorption energies indicate that all binder–surface combinations are thermodynamically favorable within the present finite-model framework (ΔEint ≈ −22.6 to −31.1 kcal·mol−1), with LSiA consistently showing the strongest stabilization across surfaces. Nitrogen doping produces a modest but systematic strengthening of adsorption relative to pristine graphene for all binders and is accompanied by electronic signatures consistent with localized donor/basic sites while preserving the delocalized π framework. In contrast, sulfur doping yields a more binder-dependent response: it maintains strong stabilization for LSiA but weakens LCnA relative to pristine/N-doped sheets, consistent with an S-induced local distortion/polarizability pattern that can alter optimal π–π registry depending on the adsorption geometry. A combined interpretation of adsorption energies, electronic descriptors (including ΔEgap as a model-dependent HOMO–LUMO separation), and topological analyses (AIM, ELF, LOL, and MEP) supports that Graphene–N provides the best overall balance between electronic continuity and chemically active interfacial sites, whereas Graphene–S can enhance localized anchoring but introduces more heterogeneous, lone-pair–dominated domains that may partially perturb electronic connectivity.

Helminth infections caused by soil-transmitted species, like Ascaris lumbricoides, Trichuris trichiura, and hookworms, affect over one billion people worldwide, yet accurate diagnosis remains challenging due to low sensitivity of microscopy in detecting eggs in stool samples, especially in low-intensity infections. Molecular diagnostics, particularly PCR-based detection of helminth DNA in stool samples, have emerged as more sensitive and specific alternatives. Here we review advances in DNA extraction methods that overcome inhibitors in stool, multiplex PCR assays, and next-generation sequencing technologies enabling species differentiation and detection of drug resistance markers. These molecular tools enhance epidemiological surveillance and inform control strategies. Despite challenges such as sample complexity and cost, ongoing improvements in molecular diagnostics hold promise for more effective helminth detection and management in clinical and field settings.

ABSTRACT This paper introduces the Transformer Linear Regression (TLR) algorithm, employed to predict extreme air pocket pressures in water pipelines during these processes. The proposed TLR model adopts a hybrid approach, combining the contextual learning capabilities of Transformer encoders with the interpretability of a linear regression output layer. The methodology was validated using an experimental facility focused on filling processes, and was replicated in a large-scale water pipeline case study involving emptying operations. The TLR model demonstrated excellent predictive accuracy across both scenarios, achieving coefficients of determination greater than 0.95 for all operations, alongside very low RMSE values. Moreover, SHAP analysis confirmed the predominant influence of specific input variables. While the practical deployment of TLR-based prediction requires reliable estimation of air exchange through valves and adequate piezometric monitoring, the approach represents a significant advancement and holds promise as monitoring technologies for transient pressures and air flows continue to evolve.

Los linfangiomas son malformaciones hamartomatosas benignas compuestas por linfa, y son lesiones muy infrecuentes. Estas se pueden presentar en diferentes áreas de la anatomía; sin embargo, solo se presentan en el 5% en el abdomen y representan solo el 0,2% de las neoplasias del páncreas. Los linfangiomas quísticos pancreáticos tienen la particularidad de presentar un difícil diagnóstico por técnicas de imagenología convencional, por lo que la ultrasonografía endoscópica permite su diagnóstico y drenaje. Se presenta el caso de un hombre en la quinta década de la vida que se encontraba en estudio de dolor abdominal secundario a litiasis renal y que durante su protocolo de diagnóstico se identificó de forma incidental una masa en la cabeza del páncreas y que no se logró clasificar por medio de técnicas de radiología convencional, por lo que fue necesario realizar una ultrasonografía endoscópica que no solo permitió el acercamiento diagnóstico, si no que por medio de esta técnica se logró su drenaje y el envío del contenido a estudio, lo que permitió realizar el diagnóstico de un linfangioma quístico pancreático.

Poly(vinyl chloride) (PVC) is one of the most widely used polymers due to its strength, low cost, and light weight. Industrial production is mainly conducted by suspension polymerization, which facilitates the control of the emissions of vinyl chloride monomer (VCM), a known carcinogen. However, the process consumes large amounts of water and energy and generates residual compounds such as polyvinyl alcohol (PVA) and polymerization initiators, which must be properly managed to mitigate environmental impacts. To improve sustainability, this study applied mass- and energy-integration strategies together with a zero-liquid-discharge (ZLD) water-regeneration system that uses sequential aerobic and anaerobic reactors to recirculate process water with reduced PVA. Although these measures reduce resource consumption, they can displace or intensify other impacts; therefore, a comprehensive evaluation of the system is necessary. Accordingly, the objective of this study is to quantify and compare the potential environmental impacts (PEIs) of the improved PVC production process through a scenario-based assessment using a waste reduction algorithm (WAR). This is applied to four operating scenarios in order to identify the stages and flows that contribute most to the environmental burden. According to our literature review, there is limited published evidence that simultaneously combines mass/energy integration and a ZLD system in PVC processes; thus, this work provides an integrated assessment useful for industrial design. The environmental performance of the improved process was evaluated using WAR GUI software (v 1.0.17, which quantifies PEIs in categories such as toxicity, climate change, and acidification. Four scenarios were compared: Case 1 (excluding both product and energy), Case 2 (product only), Case 3 (energy only), and Case 4 (product and energy). The total PEI increased from 2.46 PEI/day in Case 1 to 6230 PEI/day in Case 4, with the largest contributions from acidification (5140 PEI/day) and global warming (496 PEI/day), mainly due to natural gas consumption (5184 GJ/day). In contrast, Cases 1 and 2 showed negative PEI values (−3160 and −2660 PEI/day), indicating that converting the toxic VCM (LD50: 500 mg/kg; ATP: 26 mg/L) into PVC (LD50: 2000 mg/kg; ATP: 100 mg/L) can reduce the environmental burden in certain respects. In addition, the ZLD system contributed to maintaining low aquatic toxicity in Case 4 (90.70 PEI/day).