Abstract The removal of congo-red dye from waste water is crucial due to its toxicity and environmental impact. Adsorption of commercial alumina using Congo red dye was studied at various concentrations and pH. This study investigates the use of aluminum beads as a low-cost and eco-friendly adsorbent for Congo red adsorption. The result of the study shows that aluminum beads exhibit high adsorption capacity at 2.51 mg/g at 6.5 pH with 94% removal. The study revealed that the adsorbed dye was effectively removed using aluminum beads; therefore, this research demonstrates the potentiality of aluminum beads as a sustainable adsorbent for the removal of congo-red in waste water, as alumina gets protonated from acidic solutions, inhibiting the process. Adsorption decreased with increasing initial dye concentrations. Adsorbent dosage also affected adsorption, with increased dosage of aluminum beats which inhibited active sites and decreasing total adsorption due to the decreased surface area available for Congo red dye.Keywords: Congo-red dye, aluminum beads, waste water, adsorption capacity.

In this work, a general theoretical description of the electrochemical determination of Sudan dyes over copper sulfide nanoparticles has been made. The analysis of the correspondent mathematical models has shown that in the system is efficient from the electroanalytical point of view, although the electrooxidation mechanism is branched. The possibility for the oscillatory and monotonic instability has also been verified.

The area of computational chemistry and molecular modelling has undergone a revolution as a result of machine learning's emergence as a potent and adaptable tool for studying hydrogen bonding interactions. An overview of machine learning's uses and developments in hydrogen bonding characterisation is given in this abstract. Machine learning algorithms have been successfully used to predict hydrogen bond properties, identify donors and acceptors, and analyze complex molecular structures with high accuracy and efficiency. These algorithms include supervised and unsupervised learning methods, and deep learning architectures. Molecular interactions are better understood by combining machine learning with other computational techniques including quantum mechanical calculations, molecular dynamics simulations, and docking investigations. This has improved hydrogen bonding analysis. Additionally, explainable AI methods have improved the interpretability of models, enabling researchers to comprehend the variables driving model predictions. Our understanding of chemical and biological processes has substantially improved because to the widespread use of machine learning in hydrogen bonding analyses, which has also given us important new knowledge about molecular behaviour, drug design, materials research, and other fields of study. Future developments in algorithms and data representation are anticipated to further enhance the model's precision, understand ability, and scalability, fostering new ideas and scientific understanding of hydrogen bonding characterisation.

The study explored the effectiveness of an ethanolic extract derived from Urena lobata (UL) as a natural inhibitor of corrosion for mild steel when exposed to 0.3 M HCl acid. Various analytical methods including gravimetric analysis, Potentiodynamic Polarization (PDP), Fourier Transform Infrared Spectroscopy (FT-IR), and Scanning Electron Microscopy (SEM) were utilized. The experiments were conducted under different conditions including varying temperatures (303 K, 313 K, and 323 K), inhibitor concentrations (0.2 g/L, 0.4 g/L, and 0.6 g/L), at a fixed acid concentration of 0.3M, and with an immersion time of 4 hours. The findings revealed that the inhibition efficiency (%IE) increased with higher inhibitor concentrations but decreased with rising temperature and immersion time. The highest inhibition efficiency observed was 69.26%, while the lowest was 53.08%. SEM images confirmed the inhibitor's ability to shield the metal surface, while FT-IR spectra identified the functional groups responsible for corrosion inhibition. The enthalpy of activation (∆H) indicated an exothermic process, and the entropy of activation (∆S) pointed to a decrease in disorder, both reflected by negative values. The values of the Free Energy of Adsorption (∆Gads) indicated the spontaneity and viability of the process, with physical adsorption being proposed as the underlying mechanism. The Freundlich adsorption isotherm was deemed the most applicable model for UL. The activation energy (Ea) increased with higher plant extract concentrations, and first-order kinetics seemed to best describe the inhibition mechanism based on reported R2 values, rate constants, and half-life. Potentiodynamic polarization (PDP) result indicates that the inhibition efficiency was quite high showing 84.77 %IE and is a single type inhibitor (anodic). Phytochemical screening analysis indicates that U.L leaves contained alkaloids, tannins, sterols, glycosides, triterpenoid and saponins. Therefore, the leaves extract of UL moderately inhibits mild steel corrosion in hydrochloric acid.

The shortage of freshwater for irrigating vegetables in Akungba Akoko, Nigeria, is a critical concern during the dry season, demanding urgent attention. Local farmers rely heavily on polluted well and stream water for irrigation, which poses significant health risks due to contamination from refuse and pollutants. Addressing this challenge requires the development of a simple, cost-effective treatment facility to remove contaminants and make the water suitable for irrigation. This research aimed to assess the effectiveness of a straightforward filtration system using various physical materials to improve water quality. Conducted at Adekunle Ajasin University, Akungba-Akoko, the study focused on evaluating granite and river sand filtration on water collected from a local stream at Ibaka, Akungba-Akoko on April 7th, 2023. The filtered and unfiltered waters, categorized as follows: T0 = Borehole water (Control), T1 = Unfiltered water, T2 = water filtered with granite, T3 = water filtered with pure river sand, and T4 = water filtered with combined physical filters were subjected to physicochemical and microbiological analyses to determine its suitability for irrigation purposes. The study revealed that using single or combined physical filtration materials led to a notable decrease in microbial levels in the water samples. Additionally, significant reductions in total solids (TS), total suspended solids (TSS), and total dissolved solids (TDS) were observed in water filtered through these materials, either alone or in combination. Granite filtration (T2) resulted in notably higher pH (5.57 Ms/cm), EC (172.00 μ.S/cm) and nitrogen (27.00 mg/L) levels, while combined filtration (granite and pure river sand) (T4) showed higher levels of phosphorus (9.35 mg/L). These findings demonstrate the efficacy of both singular and combined physical filtration materials in improving wastewater quality. Thus, employing these filtration methods, either individually or in combination, is recommended for local farmers, especially in Akungba Akoko, South West Nigeria, to enhance water quality for agricultural purposes.

Contexto: Los sistemas de gestión de aprendizaje (LMS) han adquirido una relevancia creciente en los últimos años, desempeñando un papel crucial en las instituciones educativas al facilitar la administración de los procesos académicos y educativos de los estudiantes. Las instituciones deben ser capaces de seleccionar el sistema más adecuado según sus necesidades y prioridades académicas. Método: Este trabajo presenta una descripción detallada del concepto de los LMS, sus características, actividades y el funcionamiento que tienen en los centros educativos. Se utiliza el modelo AHP (Proceso Analítico Jerárquico), estableciendo criterios como procesos académicos, administrativos y tecnológicos. Resultados: Los resultados obtenidos del análisis de los LMS para Instituciones de Educación Superior en Ecuador son presentados a continuación. Conclusiones: El análisis AHP, basado en criterios académicos, administrativos y tecnológicos, determinó que Moodle es la mejor alternativa LMS para Instituciones de Educación Superior con un 49.2% de aceptación, seguida por Cambas LMS (23.4%), Blackboard (18.8%) y Sakai (8,6%). | Context: Learning management systems (LMS) have become increasingly relevant in recent years, playing a crucial role in educational institutions by facilitating the management of students' academic and educational processes. Institutions must be able to select the most appropriate system according to their academic needs and priorities. Method: This paper presents a detailed description of the concept of LMSs, their characteristics, activities and how they function in educational institutions. The AHP (Analytical Hierarchical Process) model is used, establishing criteria such as academic, administrative and technological processes. Results: The results obtained from the analysis of LMS for Higher Education Institutions in Ecuador are presented below. Conclusions: The AHP analysis, based on academic, administrative, and technological criteria, determined that Moodle is the best LMS alternative for Higher Education Institutions with a 49.2% acceptance rate, followed by Cambas LMS (23.4%), Blackboard (18.8%) and Sakai (8.6%).

Esta investigación tiene por objetivo presentar una solución emergente basada en Tecnologías BCI para pacientes en terapia de rehabilitación de drogas. El estudio presenta un marco introductorio sobre la expansión de los mercados ilícitos y el aumento del consumo global de drogas; de tal manera, destacando la necesidad de enfoques innovadores, como las interfaces cerebro-computadora (BCI), para mejorar la rehabilitación y personalización de tratamientos en adicciones. Posteriormente, se emplea un dispositivo EEG para captar señales cerebrales durante la rehabilitación de adicciones, procesadas con el software EEGLAB. Finalmente, los investigadores elaboraron un prototipo BCI para la monitorización en tiempo real de patrones cerebrales en pacientes en rehabilitación de adicciones. Utilizando el dispositivo EEG Emotiv Insight y el software EEGLAB, se capturan y procesan señales cerebrales para personalizar terapias, mejorando la precisión y efectividad del tratamiento. Los resultados iniciales muestran la capacidad del prototipo para detectar variaciones en la actividad cerebral, optimizando la intervención terapéutica y abriendo nuevas posibilidades en salud mental. | This research aims to present an emerging solution based on BCI Technologies for patients in drug rehabilitation therapy. This work presents an introductory framework on the expansion of illicit markets and the increase in global drug consumption; thus, highlighting the need for innovative approaches, such as brain-computer interfaces (BCI), to improve rehabilitation and personalize addiction treatments. Subsequently, an EEG device is used to capture brain signals during addiction rehabilitation, processed with EEGLAB software. Finally, the researchers developed a BCI prototype for real-time monitoring of brain patterns in patients in addiction rehabilitation. Using the Emotiv Insight EEG device and EEGLAB software, brain signals are captured and processed to personalize therapies, improving the precision and effectiveness of the treatment. Initial results show the prototype's ability to detect variations in brain activity, optimizing therapeutic intervention and opening new possibilities in mental health.

Context: This work presents the possible optimization of passenger transportation at one intersection in San Jose, Costa Rica through the application of preferential treatments for public bus transportation, through the use of a simulation program compatible with said treatments. The main indicator used corresponds to the average delays per passenger; these delays were obtained at intersections with and without the use of preferential treatments. Methodology: The input data correspond to vehicle counts that were carried out with the help of video cameras at intersections; Data were obtained, by vehicle type, for the morning and afternoon peak hours. The intersections were recreated according to the site geometry and modeled, the volumes were entered into the simulation model. In order to simulate preferential treatment, bus detectors were added to the simulation, whose function is to detect the moment in which the bus passes and with this modify the phases of the traffic light to instantly benefit the bus. The strategies used to give bus phase priority (TSP) are green time extension and red suppression.  Results: In the simulation models created, preferential treatments were determined to reduce average delays per passenger by up to 38 % at the intersection studied. Annual savings in time and fuel of up to 60 thousand dollars were determined. Conclusions: Calculating delays per person, instead of the traditional calculation per vehicle, can provide a different perspective to the analysis of capacity at intersections. The implementation preferential treatments for transit could be feasible in economic terms. | Contexto: Este trabajo estudia la optimización del transporte de pasajeros en una intersección de San José, Costa Rica, mediante tratamientos preferenciales para buses. Se evalúan las demoras promedio por pasajero en escenarios con y sin estos tratamientos. Metodología: Se realizaron conteos vehiculares con cámaras de video durante horas pico, clasificando por tipo de vehículo. La simulación incorporó la geometría del sitio y detectores de autobuses que modifican las fases del semáforo mediante extensión del tiempo verde y supresión del rojo. Se aprovecharon bahías existentes para simular el salto de cola. Resultados: Los tratamientos preferenciales redujeron las demoras promedio por pasajero hasta en un 38%. Se calcularon ahorros anuales de 60 mil dólares en tiempo y combustible. El tiempo de recuperación de la inversión se estimó en 8 meses. Conclusiones: El análisis de demoras por persona, versus el tradicional cálculo por vehículo, brinda una nueva perspectiva para evaluar la capacidad en intersecciones. Los tratamientos preferenciales para transporte público demuestran ser económicamente viables.

Contexto: Moodle es un SGA que por medio de sus actividades almacena datos de cumplimiento de cada uno de los usuarios que participe de las mismas. Este estudio no solo presenta el diseño e implementación de un módulo dashboard en Moodle para el seguimiento de actividades, sino que también evalúa su efectividad mediante métricas de rendimiento y un análisis estadístico que incluye la comparación con grupos de control. Método: La presente investigación propone el diseño e implementación de un módulo dashboard con el objetivo de realizar el seguimiento de actividades en el SGA Moodle. Resultados: Para validar el presente trabajo investigativo se realizó el seguimiento de actividades para los estudiantes de una materia dentro de un aula virtual en un ambiente espejo controlado del sistema de gestión de aprendizaje de la Universidad de Guayaquil que se encuentra en producción. Se evaluó la efectividad del dashboard mediante métricas de rendimiento como el tiempo de acceso a la información, la frecuencia de uso y la tasa de finalización de actividades. Se realizó un análisis estadístico que incluyó la comparación con un grupo de control. Conclusiones: Los resultados que se obtuvieron a partir de este proyecto son positivos debido a que optimiza el tiempo de análisis de las actividades de los estudiantes hacia los docentes. El análisis de datos generado por el dashboard permitió a los docentes identificar áreas de mejora y necesidades específicas de los estudiantes, lo que promovió un aprendizaje más personalizado y optimizó el proceso de enseñanza-aprendizaje. | Context: Moodle is a LMS that through its activities stores compliance data of each of the users who participate in them. This study not only presents the design and implementation of a dashboard module in Moodle for activity tracking, but also evaluates its effectiveness through performance metrics and a statistical analysis that includes comparison with control groups. Method: This research proposes the design and implementation of a dashboard module with the objective of tracking activities in the Moodle LMS. Results: To validate the present research work, the monitoring of activities for students of a subject within a virtual classroom in a controlled mirror environment of the learning management system of the University of Guayaquil that is in production was carried out. The effectiveness of the dashboard was evaluated using performance metrics such as information access time, frequency of use, and task completion rate. A statistical analysis was performed that included comparison with a control group. Conclusions: The results obtained from this project are positive because it optimizes the time of analysis of the activities of the students to the teachers. The data analysis generated by the dashboard allowed teachers to identify areas for improvement and specific needs of the students, which promoted more personalized learning and optimized the teaching-learning process.

Este artículo se centra en el desarrollo de técnicas interpretativas para un sistema de recomendación basado en Inteligencia Artificial (IA) aplicado a procesos de contratación pública. El proyecto busca no solo implementar soluciones técnicas, sino también abordar desafíos estructurales y organizacionales en la contratación, mejorando la eficiencia y la justicia. Se destaca el crecimiento exponencial de la dependencia tecnológica en diversos sectores, impulsada por avances en IA y Machine Learning, y la adopción de la Inteligencia Artificial Explicable (XAI). A diferencia de la IA tradicional, la XAI equilibra la precisión con la interpretabilidad humana, crucial para su aplicación en sistemas de recomendación. Este enfoque holístico tiene como objetivo mejorar la transparencia, confianza y eficiencia en la selección de proveedores, abordando la opacidad y los riesgos de sesgo en la toma de decisiones automatizada, y resaltando la importancia de la XAI en la creación de sistemas más éticos y confiables. | This article focuses on the development of interpretive techniques for an Artificial Intelligence (AI)-based recommendation system applied to public procurement processes. The project seeks not only to implement technical solutions, but also to address structural and organizational challenges in procurement, improving efficiency and fairness. It highlights the exponential growth of technological dependence in various sectors, driven by advances in AI and Machine Learning, and the adoption of Explainable Artificial Intelligence (XAI). Unlike traditional AI, XAI balances accuracy with human interpretability, crucial for its application in recommendation systems. This holistic approach aims to improve transparency, trust, and efficiency in supplier selection, addressing opacity and