Universidad César Vallejo

<p>Los nuevos retos de la sociedad de la información demandan de la universidad un severo cambio en sus rígidos cánones de formación. Los formatos basados en inteligencia artificial prometen una muy sustancial mejorar en la educación para todos los diversos niveles, con una mejora cualitativa sin precedentes: proporcionar al estudiante una certera personalización de su aprendizaje a la medida de sus requerimientos, logrando integrar las diversas formas de interacción humana y las tecnologías de la información y comunicación. El gran desafío de la universidad del nuevo milenio estriba en la urgente necesidad de planificar, diseñar, desarrollar e implementar competencias digitales a fin de formar mejores profesionales capaces de entender y desarrollar el entorno tecnológico en función a sus necesidades, así como implementar la universalización de un lenguaje digital sustentado en programas desarrollados bajo formatos de inteligencia artificial.</p>

Diseases that affect plant leaves stop the growth of their individual species. Early and accurate diagnosis of plant diseases may reduce the likelihood that the plant will suffer further harm. The intriguing approach needed more time, exclusivity, and skill. Images of leaves are used to identify plant leaf diseases. Research on deep learning (DL) appears to have a lot of potential for improved accuracy. The substantial advancements and expansions in deep learning have created the opportunity to improve the coordination and accuracy of the system for identifying and appreciating plant leaf diseases. This study presents an innovative deep learning technique for disease detection and classification named Ant Colony Optimization with Convolution Neural Network (ACO-CNN).The effectiveness of disease diagnosis in plant leaves was investigated using ant colony optimization (ACO). Geometries of colour, texture, and plant leaf arrangement are subtracted from the provided images using the CNN classifier. A few of the effectiveness metrics used for analysis and proposing a suggested method prove that the proposed approach performs better than existing techniques with an accuracy rate concert measures are utilized for the execution of these approaches. These steps are used in the phases of disease detection: picture acquisition, image separation, nose removal, and classification.

<p>El presente artículo brinda un análisis de las competencias digitales en el contexto actual. La creciente expectativa de las nuevas tecnologías y sus múltiples aplicaciones han producido trascendentes repercusiones en todas las esferas de la sociedad, y que, en concreto, en el aspecto de la educación superior donde las demandas son más que significativas buscando que cubrir sus implicancias. En primer lugar, se da a conocer el panorama teórico sobre las diferentes acepciones y conceptos en relación con las competencias digitales. Dicho enfoque brinda información sobre aspectos que atañen a lo relacionado a las competencias digitales. Seguidamente, se presentan algunos datos que permiten dar nuevas perspectivas, tales como la importancia y repercusiones, así como cuáles deberían ser las competencias digitales en los docentes universitarios en función de la demanda actual en la educación superior. Finalmente se dan a conocer una variedad de expectativas con relación a las vertientes que podrían desarrollarse en función de tan relevante tema.</p>

The digital transformation has profoundly affected every facet of human life, with technological advancements potentially reshaping the economy, society, and our daily living and working modalities. Artificial Intelligence (AI), particularly Generative AI (GAI), has emerged as a pivotal disruption in education, showcasing the capability to produce diverse and context-relevant content. Generative Artificial Intelligence (GAI) has revolutionized natural language processing, computer vision, and creative arts. Large language models (LLMs) like GPT-4 and Open Assistant and tools like DALL-E and Midjourney for the visual and creative domain are increasingly used for various tasks by students and others with critical information needs. AI presents novel avenues for crafting effective learning activities and developing enhanced technology-driven learning applications in the educational sector. However, integrating AI with a pedagogical focus pose challenge. Education 4.0, which integrates emerging technologies and innovative strategies, aims to prepare new generations for a technologically fluid world. This systematic literature review aims to analyze the use of LLMs in higher education within the context of Education 4.0’s pedagogical approaches, identifying trends and challenges from a selection of 83 relevant articles out of an initial set of 841 papers. The findings underscore the significant potential of LLMs to enrich higher education, aligning with Education 4.0 by fostering more autonomous, collaborative, and interactive learning. It highlights the necessity for human oversight to ensure the quality and accuracy of AI-generated content. It addresses ethical and legal challenges to ensure equitable implementation, suggesting an exploration of LLM integration that complements human interaction while maintaining academic integrity and pedagogical foundation.

BACKGROUND: Nosocomial Infections (NI) are a frequent and relevant problem. The purpose of this study was to determine the epidemiology of the three most common NI in a Pediatric Intensive Care Unit from a developing country. METHODS: We performed a prospective study in a single Pediatric Intensive Care Unit during 12 months. Children were assessed for 3 NI: bloodstream infections (BSI), ventilator-associated pneumonia (VAP) and urinary tract infections (UTI), according to Center for Disease Control criteria. Use of devices (endotracheal tube [ETT], central venous catheter [CVC] and urinary catheter [UC]) was recorded. RESULTS: Four hundred fourteen patients were admitted; 81 patients (19.5%) developed 85 NIs. Density of incidence of BSI, VAP and UTI was 18.1, 7.9 and 5.1/1000 days of use of CVC, ETT and UC respectively. BSI was more common in children with CVCs than in those without CVCs (20% vs. 4.7%, p < 0.05). Candida spp. was the commonest microorganism in BSI (41%), followed by Coagulase-negative Staphylococcus (17%). Pseudomonas (52%) was the most common germ for VAP and Candida (71%) for UTI. The presence of NI was associated with increased mortality (38.2% vs. 20.4% in children without NI; p < 0.001) and the median length of ICU stay (23 vs. 6 days in children without NI; p < 0.001). Children with NI had longer average hospital stay previous to diagnosis of this condition (12.3 vs. 6 days; p < 0.001). CONCLUSIONS: One of every 5 children acquires an NI in the PICU. Its presence was associated with increased mortality and length of stay. At the same time a longer stay was associated with an increased risk of developing NI.

Digital skills in the work of higher education is a very fruitful field that is constantly developing due to the adequacy of teachers in the face of the demand for information and communication technologies and their impact on the educational field. This article deals with the current panorama and its dynamics against the requirements that the teacher must develop and the perspectives of the university and students. On the other hand, the perspective of new trends in relation to the panorama of virtual learning environments and their link in university education, is addressed. In addition, a final discussion is presented on the possibilities of the development of competencies in the higher academic task.

Abstract Artificial intelligence (AI) has been integrated into higher education (HE), offering numerous benefits and transforming teaching and learning. Since its launch, ChatGPT has become the most popular learning model among Generation Z college students in HE. This study aimed to assess the knowledge, concerns, attitudes, and ethics of using ChatGPT among Generation Z college students in HE in Peru. An online survey was administered to 201 HE students with prior experience using the ChatGPT for academic activities. Two of the six proposed hypotheses were confirmed: Perceived Ethics (B = 0.856) and Student Concerns (B = 0.802). The findings suggest that HE students’ knowledge and positive attitudes toward ChatGPT do not guarantee its effective adoption and use. It is important to investigate how attitudes of optimism, skepticism, or apathy toward AI develop and how these attitudes influence the intention to use technologies such as the ChatGPT in HE settings. The dependence on ChatGPT raises ethical concerns that must be addressed with responsible use programs in HE. No sex or age differences were found in the relationship between the use of ChatGPTs and perceived ethics among HE students. However, further studies with diverse HE samples are needed to determine this relationship. To promote the ethical use of the ChatGPT in HE, institutions must develop comprehensive training programs, guidelines, and policies that address issues such as academic integrity, privacy, and misinformation. These initiatives should aim to educate students and university teachers on the responsible use of ChatGPT and other AI-based tools, fostering a culture of ethical adoption of AI to leverage its benefits and mitigate its potential risks, such as a lack of academic integrity.

Early detection of diabetes is essential to prevent serious complications in patients. The purpose of this work is to detect and classify type 2 diabetes in patients using machine learning (ML) models, and to select the most optimal model to predict the risk of diabetes. In this paper, five ML models, including K-nearest neighbor (K-NN), Bernoulli Naïve Bayes (BNB), decision tree (DT), logistic regression (LR), and support vector machine (SVM), are investigated to predict diabetic patients. A Kaggle-hosted Pima Indian dataset containing 768 patients with and without diabetes was used, including variables such as number of pregnancies the patient has had, blood glucose concentration, diastolic blood pressure, skinfold thickness, body insulin levels, body mass index (BMI), genetic background, diabetes in the family tree, age, and outcome (with/without diabetes). The results show that the K-NN and BNB models outperform the other models. The K-NN model obtained the best accuracy in detecting diabetes, with 79.6% accuracy, while the BNB model obtained 77.2% accuracy in detecting diabetes. Finally, it can be stated that the use of ML models for the early detection of diabetes is very promising.

This systematic review examined, through the UTAUT2 model, the factors influencing the acceptance of artificial intelligence (AI) applications in university contexts. A total of 50 scientific texts published between 2018 and 2023 were analyzed and selected after a rigorous search of specialized databases. These findings confirm the versatility of UTAUT2 in elucidating technological adoption processes in higher education. Performance expectancy and hedonic motivation emerged as significant predictors of intentions and effective use among students, faculty, and administrative staff. Among students, perceived ease of use and social influence were also relevant. The analysis revealed differences in adoption patterns between STEM and non-STEM disciplines and between public and private institutions. Despite widespread positive perceptions of AI's potential, barriers such as distrust and lack of knowledge persist. The research also identified moderating and mediating factors, such as prior technology experience and technological self-efficacy. These results have important implications for the implementation of AI in higher education, suggesting the need for differentiated approaches according to the characteristics of each group and institutional context. It is recommended to develop strategies that address the identified barriers and leverage facilitators, with an emphasis on training, ethical design, and contextual adaptation of AI applications. Future research should explore the longitudinal evolution of these factors and examine AI adoption in non-STEM disciplines in greater depth.

El objetivo de la presente investigación fue determinar la relación entre ansiedad por Covid - 19 y salud mental en 356 estudiantes universitarios (227 mujeres y 129 hombres, Medad = 22.36 años, DE = 2.46). Asimismo, se comparó la ansiedad por Covid - 19 y salud mental entre algunas variables sociodemográficas. Se aplicó la versión en español de la Coronavirus Anxiety Scale (CAS) y el Mental Health Inventory-5 (MHI). Los resultados muestran que una mayor ansiedad por COVID – 19 se relaciona con una disminución de la salud mental (ρ = −.67, p &lt;.01). Asimismo, respecto a las comparaciones realizadas se evidencian diferencias estadísticamente significativas en función a las variables sociodemográficas previamente mencionadas. El estudio confirma que a mayor ansiedad por COVID – 19 menor salud mental en una muestra de estudiantes universitarios peruanos.

&lt;p&gt;Las tecnologías de la información y comunicación (TIC), se han convertido en un recurso determinante en el campo educativo, y a la vez una variable indispensable en la práctica académica donde se busca aprovechar cada uno de los recursos que nos ofrece la Web 2.0. El objetivo de este artículo es realizar un análisis y revisión crítica de los aspectos conceptuales frente a la formación del docente, en el uso de las TIC y su implicancia en su labor diaria, así como en el proceso de enseñanza-aprendizaje. Se concluye en resaltar la importancia de la creación de dimensiones pedagógicas que señalen las competencias de formación, que debe presentar un docente, frente a la nueva tendencia tecnológica.&lt;/p&gt;

In these times of global tragedy due to the pandemic that caused COVID-19, distance learning relies on the resources of the digital field, as well as on the management of ICT and the development of digital skills. Therefore, this research has been aimed at corroborating the existing links between the integration of ICT and digital skills pandemic times. A study with a quantitative, non-experimental, cross-sectional, correlational approach was developed. The sample consisted of 168 students from a public university in Lima, Peru. Two tools were adapted: 1) integration of ICT, 18 items and 2) digital skills, 30 items, with reliability coefficients by Cronbach's Alpha of 0.976 and 0.889, respectively. The questionnaires were developed and taken through Google forms. The results showed that the level of integration of ICT was high (89.9%) as well as digital skills (86.9%). Spearman's Rho correlation analysis concluded that there was a positive and high relationship between integration of ICT and digital skills (0.761, p &lt; 0.05). Finally, discussions were raised about the development of aspects related to ICT during the current pandemic.

OBJECTIVES: The Patient Health Questionnaire (PHQ) and Generalised Anxiety Disorder Scale (GAD) are widely used screening tools, but their sensitivity and specificity in low-income and middle-income countries are lower than in high-income countries. We conducted a study to determine the sensitivity and specificity of different versions of these scales in a Peruvian hospital population. DESIGN: Our study has a cross-sectional design. SETTING: Our participants are hospitalised patients in a Peruvian hospital. The gold standard was a clinical psychiatric interview following ICD-10 criteria for depression (F32.0, F32.1, F32.2 and F32.3) and anxiety (F41.0 and F41.1). PARTICIPANTS: The sample included 1347 participants. A total of 334 participants (24.8%) were diagnosed with depression, and 28 participants (2.1%) were diagnosed with anxiety. RESULTS: The PHQ-9's≥7 cut-off point showed the highest simultaneous sensitivity and specificity when contrasted against a psychiatric diagnosis of depression. For a similar contrast against the gold standard, the other optimal cut-off points were: ≥7 for the PHQ-8 and ≥2 for the PHQ-2. In particular, the cut-off point ≥8 had good performance for GAD-7 with sensitivity and specificity, and cut-off point ≥10 had lower levels of sensitivity, but higher levels of specificity, compared with the cut-off point of ≥8. Also, we present the sensitivity and specificity values of each cut-off point in PHQ-9, PHQ-8, PHQ-2, GAD-7 and GAD-2. We confirmed the adequacy of a one-dimensional model for the PHQ-9, PHQ-8 and GAD-7, while all PHQ and GAD scales showed good reliability. CONCLUSIONS: The PHQ and GAD have adequate measurement properties in their different versions. We present specific cut-offs for each version.

This review goal is to reflect on the challenges and prospects for water quality in the face of the pandemic caused by the new SARS-CoV-2 coronavirus (COVID-19). Based on the information available so far, the detection of SARS-CoV-2 RNA in wastewater has raised interest in using it as an early warning method, to detect the resurgence of infections and to report the risk associated with contracting SARS-CoV-2 in contact with untreated water or inadequately treated wastewater is discharged. The wastewater-based epidemiological approach can be used as an early indicator of infection within a specific population. On the other hand, it is necessary to collect information from the managers of drinking water supply companies and professionals who are related to water quality, to know SARS-CoV-2 data and information, and its influence on drinking water quality. The basic purpose of this review article is to try to provide a valuable and quick reference guide to COVID-19. Important topics were discussed, such as detection of SARS-CoV-2 in wastewater in various parts of the world; wastewater screening to monitor COVID-19; persistence of SARS-CoV-2 in aquatic systems; the presence of SARS-CoV-2 in drinking water; clean water as a mechanism to deal with the COVID-19 pandemic; chlorine as a disinfectant to eliminate SARS-CoV-2 and damage to ecosystems by the use of chlorine. Currently does not exist extensive literature on the effectiveness of water and wastewater treatment processes that ensure the correct elimination of SARS-CoV-2. Excessive use of disinfectants such as chlorine is causing effects on the environment. This document highlights the need for further research to establish the behavior of the SARS-CoV-2 virus in aquatic systems. This study presents an early overview of the observed and potential impacts of COVID-19 on the environment.

El propósito del artículo, fue identificar las pruebas no paramétricas no sujetos a una distribución de probabilidad normalizada para el análisis inferencial adecuado de datos provenientes de muestras pequeñas. Mediante la teoría fundamentada se describió su fundamento y uso: 1 muestra (Binomial, Chi-cuadrado, Kolmogorov-Smirnov, de rachas), 2 muestras independientes (Moses, Kolmogorov-Smirnov, rachas de Wald-Holfowitz, U-Mann Whitney), 2 muestras pareadas (De signo, McNemar, Wilcoxon), m muestras no pareadas (Mediana, Kruskal-Wallis, Jonckeere-Terpstra) y m muestras pareadas (Fridman, Q-Cochran, W-Kendall). Se concluye que estas pruebas son valiosas y robustas, la elección está sujeto al diseño, número y escala de medición de las variables.

Esta investigación consiste en una revisión literaria de publicaciones científicas en el área de la inteligencia artificial (IA), pertenecientes a revistas científicas encontradas en el portal SCImago Journal &amp; Country Rank. La búsqueda de información se realizó utilizando palabras clave y títulos de investigaciones publicadas entre los años 1970 y 2020 en la base de datos de Scopus. El objetivo de este artículo es identificar los aportes de la IA en la educación en las últimas cinco décadas, dara conocer las revistas científicas con los índices de impacto más altos en el área de la IA en los últimos 10 años, y analizar el papel que desempeñará la IA en la educación post Covid-19. Los resultados evidencian aportes significativos de la IA en la educación, empleando técnicas de redes neuronales, big data, visión por computador, asistentes digitales virtuales, aprendizaje automático y análisis predictivo, siendo Estados Unidos el país que posee el mayor número de revistas científicas (siete) dedicadas al área de la IA. Finalmente, destacamos la necesidad de involucrar la IA en el proceso de enseñanza y aprendizaje en una educación post Covid-19.

The importance of leadership in teaching, especially at UTELVT Santo Domingo, will be reviewed. A quantitative approach was used to analyze the effectiveness and efficiency of teachers in decision-making and teamwork, as well as to identify factors that affect their ability to teach subjects. The sample included 19 teachers and data was collected through surveys. The results indicate that teachers at UTELVT practice leadership in their activities, contributing to the positive and constructive formation of students. The study concludes by highlighting the importance of leadership in teaching to maintain participant involvement and achieve the best possible educational performance. UTELVT teachers in Santo Domingo appear to be implementing leadership in their teaching and work with students. The research suggests that this type of approach in teaching can lead to positive effects and better educational outcomes.

Este estudio tuvo como propósito determinar cómo el uso de la metodología educativa Aprendizaje Basado en Problemas mejora el pensamiento crítico en estudiantes de secundaria. Para lograr lo propuesto, se desarrolló un análisis sistemático de la información, a través de la búsqueda de artículos científicos de acceso libre, en las bases de datos Dialnet, Scielo, Redalyc y Google académico. Como criterio de selección se consideró estudios empíricos que presenten resultados de intervenciones implementadas con Aprendizaje Basado en Problemas para mejorar el Pensamiento crítico, independiente de su género o idioma, publicados en los últimos seis años; artículos con intervención en muestras de estudiantes de educación secundaria. Los artículos seleccionados se organizaron en tablas para identificar sus objetivos, tipos de investigación y metodologías. Se destaca como resultado un mayor reporte de investigaciones realizadas en Asia. Se concluye que el Aprendizaje Basado en Problemas mejora significativamente el Pensamiento crítico en estudiantes de secundaria.

The Marburg virus (MV), identified in 1967, has caused deadly outbreaks worldwide, the mortality rate of Marburg virus disease (MVD) varies depending on the outbreak and virus strain, but the average case fatality rate is around 50%. However, case fatality rates have varied from 24 to 88% in past outbreaks depending on virus strain and case management. Designated a priority pathogen by the National Institute of Allergy and Infectious Diseases (NIAID), MV induces hemorrhagic fever, organ failure, and coagulation issues in both humans and non-human primates. This review presents an extensive exploration of MVD outbreak evolution, virus structure, and genome, as well as the sources and transmission routes of MV, including human-to-human spread and involvement of natural hosts such as the Egyptian fruit bat ( Rousettus aegyptiacus ) and other Chiroptera species . The disease progression involves early viral replication impacting immune cells like monocytes, macrophages, and dendritic cells, followed by damage to the spleen, liver, and secondary lymphoid organs. Subsequent spread occurs to hepatocytes, endothelial cells, fibroblasts, and epithelial cells. MV can evade host immune response by inhibiting interferon type I (IFN-1) synthesis. This comprehensive investigation aims to enhance understanding of pathophysiology, cellular tropism, and injury sites in the host, aiding insights into MVD causes. Clinical data and treatments are discussed, albeit current methods to halt MVD outbreaks remain elusive. By elucidating MV infection’s history and mechanisms, this review seeks to advance MV disease treatment, drug development, and vaccine creation. The World Health Organization (WHO) considers MV a high-concern filovirus causing severe and fatal hemorrhagic fever, with a death rate ranging from 24 to 88%. The virus often spreads through contact with infected individuals, originating from animals. Visitors to bat habitats like caves or mines face higher risk. We tailored this search strategy for four databases: Scopus, Web of Science, Google Scholar, and PubMed. we primarily utilized search terms such as “Marburg virus,” “Epidemiology,” “Vaccine,” “Outbreak,” and “Transmission.” To enhance comprehension of the virus and associated disease, this summary offers a comprehensive overview of MV outbreaks, pathophysiology, and management strategies. Continued research and learning hold promise for preventing and controlling future MVD outbreaks. GRAPHICAL ABSTRACT

This article aimed to carry out a systematic review of rainwater harvesting and storage systems (RWHSS) between 2012 and 2022. This study used the guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement (PRISMA) as reviewing method. The systematic review process involved four stages: identification, screening, eligibility and inclusion. To carry out this research, the Scopus, ScienceDirect and Springer Link digital databases were consulted using the keywords “rainwater”, “storage”, “harvesting”, “rural”, “treatment”; initially obtaining 581 results, after filtering the information through PRISMA, 15 articles were obtained to carry out the analysis of the results linked to the questions raised in this work. The results showed that all the RWHSS have four main components: 1) Catchment area, 2) gutters, 3) pipes and 4) storage system. Regarding the most used material in the system's catchment area, it is galvanized metal with 25.71% of the studies and for the construction of the cistern, it is concrete with 41.66%. The quality of the rainwater collected in the RWHSS varies according to some factors such as the material, maintenance, weather conditions, etc. The main rainwater quality parameters considered by the authors at the time of implementing and using an RWHSS were identified, and compliance of the parameters with the WHO standard values was also evaluated. The main parameters considered by the authors were: pH (66.66%), turbidity (53.33%), E. Coli (53.33%), lead (40%) and nitrates (40%).