Asdrúbal López‐Chau

This article proposes a segmentation model of Latin American Startups based on unsupervised learning techniques (hierarchical agglomerative clustering with Ward's linkage) to identify the underlying structure of data that include financial and digital variables. The applied approach allowed for the identification of common patterns and significant differences, which facilitated establishing profiles that help understand business growth and consolidation processes. The sample includes 1,274 startups active at the time of data collection, which are first analyzed through descriptive statistics, tests of association, differentiation, and correlation. Subsequently, by applying hierarchical agglomerative clustering, three groups of Latin American Startups became evident: Cluster 1-Medium-aged companies without access to investment. Cluster 2-Young companies with early access to investment. Cluster 3-More advanced companies with greater capital and visibility. These findings allow for the modeling of the transitions that occur in startups: initiation, growth, and expansion. Spanish-language metadata / Metadatos en españolTítulo en español:Aplicación del agrupamiento jerárquico aglomerativo para la segmentación de startups en América Latina Resumen:Este artículo propone un modelo de segmentación de startups latinoamericanas basado en técnicas de aprendizaje no supervisado —agrupamiento jerárquico aglomerativo con el método de Ward— para identificar la estructura subyacente de datos que incluyen variables financieras y digitales. El enfoque aplicado permitió identificar patrones comunes y diferencias significativas, lo que facilitó el establecimiento de perfiles que contribuyen a comprender los procesos de crecimiento y consolidación empresarial. La muestra comprende 1.274 startups activas en el momento de la recopilación de los datos, las cuales se analizaron inicialmente mediante estadística descriptiva y pruebas de asociación, diferenciación y correlación. Posteriormente, mediante la aplicación del agrupamiento jerárquico aglomerativo, se identificaron tres grupos de startups latinoamericanas: Clúster 1: empresas de antigüedad intermedia sin acceso a inversión; Clúster 2: empresas jóvenes con acceso temprano a inversión; y Clúster 3: empresas más avanzadas, con mayor capital y visibilidad. Estos hallazgos permiten modelar las transiciones que experimentan las startups a través de tres etapas: inicio, crecimiento y expansión. Palabras Claves:Aprendizaje no supervisado, agrupamiento jerárquico, startups latinoamericanas, minería de datos. Smart citations:SciteAI. Dimensions.Open Alex.

This article presents a comparative analysis of machine learning (ML) models applied to machine fault classification, using the AI4I 2020 dataset. The performance of five algorithms (random forest, decision tree (J48), artificial neural networks (ANN), k-nearest neighbors (KNN), and Naive Bayes) is evaluated in six different data scenarios: synthetic minority oversampling (SMOTE) technique, undersampling, and original imbalanced distribution in the complete feature set and in the feature selection set. In addition, the impact of feature selection techniques on predictive power is evaluated. The goal is to identify the optimal predictive model and compare the performance of these approaches to determine the advantages of feature reduction and data balancing strategies. To ensure robust validation, the models are analyzed using precision, recall, specificity, accuracy, and F1 score. The main conclusions obtained demonstrate that it is possible to build reliable models capable of maximizing fault detection and minimizing false alarms, which is vital for predictive maintenance systems in industries where accuracy is critical.

This article describes the development of a conversational agent for managing and tracking academic procedures. Based on requirements analysis, the essential components were defined to provide real-time updates on processes and improve the user experience. The development phases included dialogue flow design, training with Generative Artificial Intelligence (GAI), implementation of a data warehouse, generators, database integration, a web interface, and a module. Follow-up. A crucial aspect was the importance of data order and structure. Standardization and organization of training content are fundamental to ensuring the accuracy and relevance of responses. The solution coherently articulates the components, ensuring robust interconnections. The follow-up module expands the agent's capabilities, offering comprehensive, efficient, and personalized interaction. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i2.1267Dimensions.Open Alex.

This research predicts the impedance function of mirror coatings using three curve fitting models implemented in MATLAB, such as: Polynomial fit (Polyfit), Cubic Spline fitting (CSAPS) and Local Regression Smoothing (LOESS) to carry out the analysis of the protection of mirror coatings in which the Electrochemical Impedance Spectroscopy (EIS) technique has been used. The electrochemical impedance parameters and their equivalent circuits have been measured from the data recorded at 5 and 24 hours of exposure of the sample under study to the aggressive agent sodium chloride at weight 3%. The methodology is structured into training (parameter calibration) and testing (performance evaluation) stages. The performance of the models in the training stage is carried out by cross-validation and in the testing stage it is quantified using the Root Mean Square Error (RMSE) and the determination coefficient (R2). The main results obtained show that the CSAPS method at 5 and 24 hours after exposing the system to the electrolyte is the most robust and consistent in cross-validation and the most accurate and reliable in performance when evaluating the complete data set with an 3.40 and 3.78, and in both cases .

Machine learning-based predictive maintenance systems have demonstrated their reliability, operational efficiency, and usefulness for informed decision-making. In this work, we evaluate the performance of various supervised machine learning algorithms to predict failures in industrial machinery, using sensor data. Two analysis approaches are applied: one without feature selection and another using the Neighborhood Component Analysis (NCA) method to reduce dimensionality and highlight the most relevant variables for classification. Discriminant Analysis (DA) model achieved the best overall performance, with superior precision, accuracy, and sensitivity metrics in both approaches. Feature selection using NCA improves the efficiency of several models, especially KNN and Linear Classifier, reducing complexity without compromising accuracy. This work demonstrates the potential of machine learning models for implementation in predictive maintenance industrial environments.

The accelerated growth of medical databases, particularly those storing images in DICOM (Digital Imaging and Communications in Medicine) format, has increased the need for efficient strategies to optimize the retrieval and querying of medical information. This work presents KÁIXAR, a horizontal fragmentation method for medical databases that integrates workload analysis, DICOM images, and content-based retrieval. The proposed method employs operation records (logs) to identify access patterns and, through the Pareto principle (80/20), selects the most representative queries for generating the fragmentation scheme. Subsequently, KÁIXAR proposes and allocates fragments by considering the hardware characteristics of servers located at the sites where the recorded queries originated. As a verification mechanism, the system enables the visualization of fragmented DICOM images and their associated metadata. Experimental evaluation showed scenario-dependent performance benefits. In an additional physical-site validation under a high-workload scenario, KÁIXAR reduced mean query latency from 108.735 ms to 15.062 ms relative to XAMANA, corresponding to an 86.15% reduction and a 7.22× speedup; the paired latency difference was statistically significant (p<0.001). These results support KÁIXAR as an efficient alternative for horizontal fragmentation in medical databases.

Since the deployment of the Internet of Things (IoT), it has transformed everyday life by enabling intelligent environments that improve efficiency and automate services in domains such as agriculture, healthcare, smart cities, and industry. However, the rapid proliferation of IoT devices has introduced significant security challenges, largely driven by the heterogeneity of devices, resource constraints, and the increasing exposure of network communications. This work proposes a lightweight security protection mechanism for IoT networks based on trust modelling. The proposed approach integrates machine learning techniques to evaluate IoT node behavior using network-layer (Layer 3) traffic features under different labeling granularities, including binary, categorical, and subcategorical classifications. By focusing on network-layer observations, the model remains applicable across heterogeneous IoT devices while preserving a low computational footprint. In addition, the Common Vulnerability Scoring System (CVSS) is incorporated as a standardized vulnerability severity metric, enabling the integration of probabilistic security evidence with contextual information about potential impact. This combination allows the estimation of trust to reflect not only the likelihood of anomalous behavior but also its associated severity. Experimental evaluation was conducted using a representative IoT traffic dataset, multiple preprocessing strategies, and several classical machine learning models. The results demonstrate that aggregating traffic-based intrusion detection outputs with vulnerability severity metrics enables a more robust, flexible, and interpretable trust estimation process. This approach supports the early identification of potentially compromised nodes while maintaining scalability and efficiency, making it suitable for deployment in heterogeneous IoT environments.

The deterioration of mirror coatings in aggressive environments is one of the main causes of staining, which is a manifestation of corrosion. Electrochemical Impedance Spectroscopy (EIS) allows the electrochemical behavior of these coatings to be evaluated. However, an issue with ANNs is that to perform predictions with high accuracy, it is necessary to adjust their parameters using a large amount of samples. Depending on the ANN architecture, the requirement can range from hundreds to thousands of data points. This is a problem in many real cases, since measurements are expensive in terms of time and resources. In this study, we use a Transfer Learning approach. First, we generate and use synthetic data to train a neural network; then, real data are used to fine-tune the model. The results show that the ANN can identify patterns of coating deterioration with high accuracy and provides an effective mechanism for early performance evaluation. A high accuracy of 0.98 is achieved in the advanced stage, which means that the ANN detects severely damaged protective coatings. For the initial stage, an accuracy of 0.71 and a recall of 0.56 are obtained, indicating that the model has significant difficulty detecting initial damage due to very subtle changes, in the low signal versus noise ratio and the behavior of the protective coatings’ properties when they are close to their intact state.

This research focuses on analysing the experiences of visitors to Alameda Central during the Easter period in order to examine the degree of personalisation of the tourist experience. To this end, the study adopts a quantitative research paradigm and a non-experimental, cross-sectional design, and applies machine learning (ML) clustering algorithms. The main conclusions suggest that ML can function as a clustering tool through which visitors are grouped according to behavioural patterns, thereby potentially informing decision-making processes. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2025.v16i4.803 Dimensions.Open Alex.

In studies of intelligent agents, the pursuit–evasion problem, commonly related to the predator–prey paradigm, has been used as a reference setting for examining decision-making, coordination, and adaptation in multi-agent systems. In this paper, pursuit and evasion strategies are reviewed from their theoretical foundations to the gradual incorporation of adaptive methods based on evolutionary algorithms and machine learning. Analytical formulations drawn from control theory, game theory, and graph-based models are considered together with learning-oriented methods, including multi-agent reinforcement learning, neuroevolution, evolutionary robotics, and competitive coevolution. The literature is arranged by methodological paradigm so that the scope, limitations, and applicability of each approach can be discussed in relation to dynamic and uncertain environments. Through this organization, classical models, algorithmic developments, and recent research trends are brought into the same discussion, while relevant gaps and possible future directions in pursuit–evasion research are identified. The contribution of this work is a structured synthesis in which analytical and adaptive perspectives are brought together within a unified reference for researchers and practitioners in robotics, artificial intelligence, and multi-agent systems.

The elaboration of school timetables is a complex and laborious task when performed manually, due to the large number of requirements and constraints that must be considered for its correct construction. In this work, the problem is addressed by using a simple Genetic Algorithms (GA) and compared with an improved approach that incorporates both an initialization function and a repair function. The study uses real data from the Computer Engineering course at the Centro Universitario UAEM Valle de México. The results obtained show that the initialization function significantly reduces errors from the first generations, while the repair function further accelerates the reduction of class or teacher splices. Thus, the effectiveness of the proposed approach to solve the scheduling problem in the university center is demonstrated. Smart citations: https://scite.ai/reports/10.61467/2007.1558.2026.v17i1.1144Dimensions.Open Alex.

Los chatbots evolucionaron de ser simples programas de laboratorio a convertirse en herramientas ampliamente utilizadas en diversos sectores, incluyendo el empresarial, educativo y gubernamental. Hoy en día, existen múltiples arquitecturas de chatbots, que van desde modelos básicos hasta sistemas avanzados basados en técnicas de aprendizaje profundo. En este artículo se revisaron 46 publicaciones recientes con el objetivo de identificar las tendencias actuales en arquitecturas y aplicaciones de chatbots. A partir de est e análisis, se ofrece un panorama general que permite comprender la dirección en la que se está desarrollando esta tecnología.

La luz es fundamental para la visión: al entrar al ojo, atraviesa sus diferentes estructuras hasta llegar a los fotorreceptores, que convierten esa luz en señales que el cerebro interpreta, permitiéndonos reconocer y comprender nuestro entorno. La pupila, que es el orificio en el centro del iris, regula cuánta luz ingresa al ojo. Estudiar el comportamiento de la pupila ha abierto nuevas oportunidades en campos tan diversos como el marketing, la medicina y la educación. Esta investigación propone una alternativa basada en video-oculografía, una técnica de registro no invasivo que utiliza cámaras para estudiar el movimiento de los ojos, para analizar los reflejos de la pupila cuando se exponen a estímulos de luz de colores primarios (rojo, verde y azul, conocidos como RGB). El objetivo es conocer, comprender y obtener datos significativos sobre el comportamiento de la pupila. A partir de estos datos, es posible realizar análisis estadísticos que, en el futuro, permitirán desarrollar sistemas basados en inteligencia artificial para prevenir problemas visuales y apoyar el diagnóstico temprano de enfermedades oculares.

El presente trabajo describe la implementación de un sistema de video-oculografía (VOG) y pupilometría para medir reflejos pupilares en 20 voluntarios (21 ± 3 años) bajo estímulos de luz azul y blanca en un ambiente controlado, aunque es una muestra muy pequeña los resultados demuestran que el sistema puede ser útil para obtener datos pupilares teniendo una evaluación positiva tanto el sistema como la infraestructura empleada. Se utilizaron tres estímulos de 10 ms con intervalos de 10 s, registrando áreas pupilares entre 3000 y 9000 píxeles, con contracciones máximas de 3000-5000 píxeles. Los resultados muestran que la luz azul induce contracciones ligeramente más pronunciadas (PCA promedio: 444-449 píxeles²) que la luz blanca (PCA promedio: 410-417 píxeles²), con una disminución en la respuesta pupilar post-iluminación (PIPR) en el tercer ciclo debido a fatiga de las células ganglionares intrínsecamente fotosensibles (ipRGCs). Este sistema proporciona una base de datos preliminar para futuros estudios con inteligencia artificial, para identificar biomarcadores potenciales para desordenes oculares del sistema parasimpático.

The ever-evolving landscape of healthcare demands innovative solutions, particularly in light of the global health crisis of 2020 and the aging global population. Technological advancements and new approaches in remote health monitoring systems have helped to bridge the gap for vulnerable individuals such as older adults. This review explores methods for the analysis of physiological signals using remote and intelligent systems and mobile and web-based applications, mostly linked to wearable devices, focusing primarily on the elderly population. The main objective is to identify crucial advancements in the development or integration of technology applied to addressing challenges of this magnitude. The research is structured following the PRISMA-ScR guidelines. The search strategy was implemented in databases such as the ACM Digital Library, IEEE Xplore, PubMed, Science Direct, Scopus, and Springer Link. A total of 411 articles were collected, and inclusion and exclusion criteria were applied to focus on studies published between 2020 and 2024. Ultimately, 100 articles from 35 countries were selected for data extraction. The findings reveal significant progress in remote monitoring technologies but emphasize the need for rigorous validation to ensure accuracy and reliability across diverse populations. To develop robust systems that provide equitable and high-quality healthcare, it is essential to address critical challenges such as data privacy, security, accessibility, and ethical considerations.

El Parkinson es una enfermedad neurodegenerativa que afecta a aproximadamente a cuatro o cinco millones de personas en el mundo. En México, el Instituto Nacional de Neurología y Neurocirugía reporta una incidencia anual de 50 nuevos casos por cada 100,000 habitantes. Se realizó una revisión sistemática en revistas científicas publicadas en los últimos cinco años, utilizando el método PRISMA para desarrollar un meta-análisis. El objetivo fue identificar avances en el uso de herramientas tecnológicas con sensores de registro de movimiento, empleadas para apoyar el diagnóstico clínico y el seguimiento de la enfermedad. Se analizaron 1,064 documentos obtenidos de bases de datos científicas: PubMed (645 artículos), Scopus (285 artículos) y Redalyc (134 artículos), todas enfocadas en áreas médicas, salud e ingeniería. Los resultados subrayan la importancia de combinar evaluaciones clínicas tradicionales con tecnologías de monitoreo basadas en sensores de movimiento, las cuales han demostrado ser útiles en el diagnóstico y manejo de la enfermedad de Parkinson. Aunque estos avances tecnológicos ofrecen soluciones para el control y seguimiento de síntomas, la enfermedad permanece incurable. Sin embargo, las revisiones sistemáticas y los meta-análisis aportan evidencias valiosas que respaldan la toma de decisiones clínicas, optimizando los tratamientos y mejorando la calidad de vida de los pacientes. Este estudio enfatiza la necesidad de continuar investigando herramientas innovadoras que permitan un abordaje más eficaz de esta compleja enfermedad.

This article presents the comprehensive design and evaluation of a digital storytelling system tailored forchildren aged 4 to 6 in Latin America. This system is based on generative artificial intelligence.Tests were conducted encompassing the system’s functionality, content diversity, generation times,voice evaluation, intonation, speed, and pronunciation quality. The results confirm the system’s correctoperation and intuitive nature. The generated stories exhibit a high degree of diversity, as evidenced bythe calculation of Jaccard indices, with a maximum value of 0.2 observed in the evaluations of 30 analyzedstories. As anticipated, generation times increase with the length of the stories. It was determined thatthe voice best suited for storytelling is Onyx from OpenAI’s TTS. However, pronunciation errors werenoted in all TTS model voices. According to the conducted analysis, the system produces diverse stories that promote values among Spanish-speaking children, thus emphasizing the importance of inclusivity for individuals with different abilities. It is noteworthy that no inappropriate content for children was found in any of the stories

El presente trabajo presenta una comparación de algoritmos de clasificación para reconocimiento de los caracteres manuscritos correspondientes a los dígitos del cero al nueve, utilizando como fuente de información la base de datos MNIST. Se utilizarán técnicas de búsqueda exhaustiva de hiperparámetros de los algoritmos de machine learning KNN, regresión logística, Nayve Bayes, máquina de soporte de vectores, árbol de decisión y bosque aleatorio. El resultado de los algoritmos anteriores será comparado con una red neuronal multicapa y otra convolucional también diseñadas en el trabajo, se emplearán las métricas de clasificación exactitud, precisión, exhaustividad y F1-score con tal fin.

In the present research, the prediction of the academic performance of university students of an undergraduate educational program is carried out by applying Machine Learning (ML) with the purpose of determining the students with academic difficulties and excellence in school performance. It is an applied research in a population of 327 students, to which a representative sample of 74 students is determined, using a proportional stratified probability sampling, in which the stratum is the semester the student is studying out of the nine that make up the study plan. The work is an applied study with a pre-experimental design of a single group, because after applying ML the results are observed and the measurements is carried out. The main conclusions obtained allow establishing a methodology for the application of ML methods in the prediction of academic performance. The best performing algorithms used are Support Vector Machine (SVM) and Neural Network (NN).

El presente documento tiene como propósito revisar los adjetivos, verbos y sustantivos recurrentes en las definiciones de la Administración Pública, a través de la revisión de los planteamientos de 68 autores y 150 definiciones. Se utilizó una “nube dinámica de palabras” para representar de forma resumida, la frecuencia del uso de palabras en los textos revisados en los diferentes periodos. Se concluye que: primera, en el campo disciplinario se ha recurrido a los términos “administración”, “pública”, “gobierno”, “ciencia” y “sociedad” como las voces más representativas. Segunda, las definiciones son construcciones teóricas que corresponden a su contexto histórico relacionado con el papel del Estado; tercera, explican el desarrollo de la expansión del Estado y las actividades administrativas encomendadas a la administración pública; y cuarta, son aproximaciones para un mayor entendimiento tanto como materia administrativa y como disciplina científica.

The year 2024 stands out as a pivotal year marked by significant political transformations across the globe. Some countries, such as Mexico and the United States, could be deeply affected by political polarization and echo chambers. This study employed sentiment analysis and machine learning techniques to investigate political polarization on Twitter during the 2018 Mexican presidential election. The findings reveal that the winning candidate exhibited the highest level of polarization. This underscores the pivotal role of social media in elections. For some time now, social media platforms like Twitter have contributed to intensified political polarization and the creation of echo chambers. Further research is essential to understand the influence of polarization on voter decision-making and democratic procedures. Establishing ethical guidelines for using machine learning in policy analysis is critical to preserving the integrity of democratic processes while reaping the potential benefits of new technologies.

Lexicons are a fundamental resource for sentiment analysis, offensive language identification, trend detection, and document classification. Lexicons have the advantage of being easy to use, but most of the existing lexicons have been created manually. Recently, researchers have been interested in extending the use of lexicons to different fields. In this paper, an easy-to-compute statistics-based method for extracting lexicons in specific domains or ad-hoc lexicons is shown. The proposed method was evaluated on two datasets and achieved 80% accuracy in document classification. This novel approach is expected to be a valuable tool for researchers and practitioners who need to quickly and efficiently create domain-specific and ad-hoc lexicons.

The theft of auto parts in Mexico has become a serious problem in recent years. The implementation of artificial intelligence methods can help identify criminal behaviors that are commonly associated with this crime. However, there is no publicly available dataset focused on solving this security problem; furthermore, there are no specific references in the literature regarding the identification of auto parts theft. This paper proposes the creation of a dataset related to this type of theft, and the application of predictive models for the automatic identification of behaviors exhibited by individuals before committing the mentioned crime

El objetivo es probar y medir el impacto del software de Sistema para la Enseñanza y Traducción de la Lengua de Señas Mexicana UAEMex creado inteligencia artificial para facilitar la comunicación en personas con discapacidades auditivas o del habla. Se utilizó método cuantitativo de tipo exploratorio y descriptivo para probar y medir el impacto de los niveles de enseñanza-aprendizaje y traducción en tres grupos experimentales de personal pertenecientes a tres instituciones públicas dedicadas a la atención de personas con discapacidades auditivas o del habla y con corte longitudinal por las mediciones en los tres módulos de enseñanza-aprendizaje y por el seguimiento tres meses con el cuestionario del método FODA. Obteniendo resultados en aprendizaje y traducción de los tres módulos fue del 80% en velocidad normal y 73% velocidad rápida. En la aplicabilidad del sistema realizado a los tres meses y ejecutado pos personal mostro fortaleza internas 100%, en oportunidades 90%, en debilidades 68.33% y en amenazas 72.5%. Por lo que se puede decir que en beneficios y logros que el usuario pudo constatar en la vida laboral es muy alto y las oportunidades de mejora es medio bajo en las cuales se debe de trabajar para perfeccionar el software.

This paper presents a proposal for parameter extraction of a resistive load inverter circuit, with a Thin Film Transistor (TFT), using Artificial Neural Networks, Random Forest, Decision Trees and Support Vector Regression. Although analytical and optimization methods are usually used for this purpose, they have disadvantages such as the need for expertise or complex implementation. This work shows that these supervised learning methods are useful for this task because they can learn the parameters of the device transfer curves, obtaining a good fit between the measurements and the extracted parameters. The different methods were trained using a data set constructed from simulations performed with AIM-Spice software, where the parameters affecting different regions of the inverter characteristic curve were extracted. In the experimental stage, the Neural Networks obtained better results, with an average error rate of 6.04%. The method was also applied to real NMOS measurements and yielded minimum errors of up to 0.43%.

El objetivo es medir los tipos de personalidad, los perfiles de estrés y los tipos de afrontamiento resiliente con el Test Perfil de Estrés de Nowack y la Escala Resilience Scale RS-25 en pacientes con cáncer para conocer su salud mental, elemento indispensable para proponer estrategias que ayuden a reducir la morbilidad. Investigación cuasiexperimental de tipo explicativa clínica y corte transversal, realizada en dos muestras independientes seleccionadas por diagnóstico médico de los servicios cuello y cabeza y atención a columna. Se utilizó estadística descriptiva e inferencial para conocer las características de la muestra y la correlación entre las variables. Resultados: los pacientes que tienen una personalidad de tipo C poseen conductas de riesgo por padecer periodos prolongados de estrés por evitar la confrontación o huida, provocando físicamente bajas defensas biológicas y desarrollo de tumores malignos y metástasis. El tratamiento de quimioterapia presenta efectos secundarios provocando reacciones negativas físicas y emocionales.

Introducción: El rendimiento académico, nos permite evaluar la eficacia y calidad de los procesos educativos de los estudiantes y para las autoridades educativas es un indicador relevante que permita orientar hacia la toma de decisiones. Objetivo: Describir los factores familiares, escolares, de personalidad y perfil escolar en el rendimiento académico en los estudiantes de la licenciatura en enfermería. Metodología: Se llevó a cabo una investigación no experimental, enfoque cuantitativo, con estudio descriptivo, corte transversal, unidad de análisis estudiantes de la Licenciatura de enfermería, la muestra quedo conformada de 122 estudiantes, muestreo estratificado, se utilizó el instrumento de (Medmar), ya que, integra los factores familiares, escolares, de personalidad y perfil escolar. Resultados: Edad mínima 18 máxima 43 años, predomino la mujer, solteros, promedio semestral: mínima 8, máxima 9.7, el promedio general: mínima 7.9, máxima 9.3. factores familiares tienen asociación con el perfil escolar son la variable se obtuvo significancia independientemente que la muestra estudia tenían promedio general que oscilaba 7.9 a 9.3. el factor familiar: nivel medio-bajo 93%, es poco el apoyo familiar Conclusión: factores familiares tienen asociación con el perfil escolar son la variable se obtuvo significancia independientemente que la muestra estudiada tenía promedio general que oscilaba 7.9 a 9.3.

Binary neural networks have emerged as an efficient solution for resource-constrained devices due to their reduced computational, memory, and storage requirements. However, binary neural networks often suffer from decreased accuracy compared to floating-point models. In this study, we propose a binary residual multilayer perceptron architecture that mitigates the degradation caused by binarization through the incorporation of normalization layers and residual connections. By leveraging design recommendations from state-of-the-art binary architectures, we aim to create a user-friendly model that can be easily implemented without requiring extensive neural network design expertise. This paper presents the empirical results of our proposed architecture, demonstrating its effectiveness in reducing degradation and improving performance for hardware-constrained devices.

The proper storage and management of multimedia data is a topic of great interest to industry and academia. Database fragmentation plays a fundamental role as a mechanism to guarantee cost reduction and improve response time performance in distributed data management environments. Multimedia database access patterns are constantly changing; due to this, it is important that the partitioning schemes also adapt to these changes. Dynamic fragmentation techniques offer this advantage and represent a reduction of the tasks that an administrator must perform and the complete autonomy to determine when to carry out a new fragmentation based on a cost model. This work proposes a new method of dynamic horizontal fragmentation for multimedia databases, including a way to contemplate content-based queries in the creation of new fragments. The use of content-based queries is on the rise, as multimedia elements are often presented within databases, and for this reason new fragmentation strategies must include this aspect to provide better-performing schemas. The method included in this research is placed within a current web application called XAMANA. We performed some experiments to demonstrate the effectiveness of our approach.

Affective polarization is a phenomenon that has invaded the political arena empowered by social networks. In this chapter, the authors analyze the Capitol riot posts on Twitter. To achieve this, the authors use affective computing introducing the multi-emotional charge combined with statistical analysis based on the t-student test and Welch's t-test. The research questions guiding this study are: How do social media platforms' messages impact on inciting? Do social media platforms' messages with negative emotional charge affect legitimizing of the Capitol protest? Findings identify the significant influence of Donald Trump on Twitter during the Capitol riot. Moreover, data analysis identifies positive and negative emotions towards Donald Trump as well as similarities in the showed emotions of Trump and the audience.

Data warehousing gives frameworks and means for enterprise administrators to methodically prepare, comprehend, and utilize the data to improve strategic decision-making skills. One of the principal challenges to data warehouse designers is fragmentation. Currently, several fragmentation approaches for data warehouses have been developed since this technique can decrease the OLAP (online analytical processing) query response time and it provides considerable benefits in table loading and maintenance tasks. In this paper, a horizontal fragmentation method, called FTree, that uses decision trees to fragment data warehouses is presented to take advantage of the effectiveness that this technique provides in classification. FTree determines the OLAP queries with major relevance, evaluates the predicates found in the workload, and according to this, builds the decision tree to select the horizontal fragmentation scheme. To verify that the design is correct, the SSB (star schema benchmark) was used in the first instance; later, a tourist data warehouse was built, and the fragmentation method was tested on it. The results of the experiments proved the efficacy of the method.

Stress is a problem that affects the quality of life of many people around the world. Recent studies [1] show that Mexico ranks first in the world with 75% of people that they suffer from stress, surpassing for the first-time countries such as China and the United States, which occupied the first places. Of this figure, close to 40% of employees in Mexico suffer the consequences of work-related stress, approximately 18.4 million people live with this health problem on a daily basis. Derived from this situation, it is proposed to design a mobile application that evaluates work stress in Mexican workers, implementing the questionnaires for the analysis of work stress MBI (Maslach Burnout Inventory) and OIT-OMS supported by Ivancevich and Matteson in 1989, methods of psychological analysis that will help to measure work stress and suggest timely attention to each worker

A test of independence is commonly used to determine differences (or associations) between samples in a nominal level measurement. Fisher’s exact test and Chi-square test are two of the most widely applied tests of independence used in the data analyses in different areas such as information technologies, biostatistics, psychology and health sciences. In some cases, contingency tables with null entries (also called random zeros) arise, particularly if the number of samples is small, and the variables analyzed are multilevel. This situation becomes a problem because if one or more entries in a contingency table are zero or have small values, then the tests of independence produce unreliable results. In this paper, we propose a method to address that issue. The method merges one or more levels of the variables analyzed to create contingency tables with only one degree of freedom, avoiding applying a test of independence on contingency tables with random zeros. The source code (Python) of the method is publicly available for use. The results obtained using our method give a complete panorama of the associations between the variables of a data set. To show the effectiveness of our approach to find dependencies between variables, we use four data sets publicly available on the Internet.

This paper is twofold. First, the authors analysed the state of the art on the explainable artificial intelligence (XAI) applied to affective computing, emotion analysis, and sentiment analysis. Second, based on Kaptein’s approach and using the personality, emotion, and mood model proposed by Egges, the authors tested an Emotion-aware XAI framework to determine the affective state of predefined-behaviour autonomous entities in uncertainty contexts. The affective state analysis of eight selected formal use cases was divided into four stages: personality settings, event evaluation, emotions and moods generation, and results classification. Findings suggest that the affective state depends on the type of predominant personality and the influence of the events in the environment.

En este documento, utilizando la teoría del intercambio social como base teórica, proponemos identificar la presencia de acoso laboral en profesores universitarios, a través de máquinas de vectores soporte, y la aplicación de un instrumento que mida la satisfacción en el trabajo, en lugar de evaluar el nivel de acoso laboral explícitamente. La muestra fue de 248 docentes de cuatro universidades públicas en México. Obtuvimos los siguientes resultados: la desvalorización del trabajo es el tipo de acoso más frecuente, mientras que el mobbing personal es el menos frecuente. El kernel RBF es la mejor opción para predecir acoso laboral en las dimensiones: sobrecarga de trabajo, mobbing personal y devalorización del trabajo; el núcleo polinomial es el mejor para el mobbing organizacional. La precisión de clasificación de los modelos es superior al 91%, y la puntuación F = 0.93, ambos en el peor de los casos. Según el rendimiento de los modelos, se puede predecir el acoso laboral con precisión.

This paper describes Rescue in Collapsed Building (RICB) a telemetry system for recovering victims inside a collapsed building due to an earthquake. The use of Artificial Neural Networks (ANN) with Raspberry Pi and sensors are being used in several fields, for example: to recognize pedestrians on streets, to identify human activities such as running, walking and sitting, and to collect garbage collection. RICB’s field is human support and rescue. RICB is proposed as an Artificial Intelligence tool to improve the identification of human patterns according to the readings of sensors such as movement, sound and temperature. These measurements will be collected from a Raspberry Pi Zero W device inside a transparent acrylic sphere with a Human Radiating Sensor (HRS) that is our own elaboration in this paper. Thereafter, it will be analyzed by the ANN to determine if there is a body of data in the set of transmitted data to a server. That application lets an expert monitor and manipulate RICB to read the measurements of sensors. When RICB processes this data, the user can see the probability of the victim being alive to enable communication with him/her and local rescue teams.

Este estudio se centra en identificar los factores que influyen en la percepción de la calidad del consumidor en restaurantes de servicio a mesa en el pueblo mágico Real del Monte, Hidalgo, México. La metodología se basa en dos perspectivas, en primer lugar, en el análisis de los factores más importantes de los resultados de una encuesta de 22 ítems por medio de redes neuronales artificiales aplicada a 320 comensales y, en segundo lugar, en la aplicación de entrevistas semiestructuradas a ocho comensales. Los hallazgos muestran que los aspectos fundamentales que influyen en la percepción de los consumidores son la capacidad del personal para responder preguntas, la música de fondo, así como la calidad y el sabor de los alimentos.

Every computer task generates response times depending on the computer hardware and software. The response times of tasks executed in real-time operating systems such as RT-Linux can vary as their instances evolve even though they always execute the same algorithm. This variation decreases as the priority of the tasks increases; however, the minimum and maximum response times are still present in the same task, and this complicates its monitoring, decreasing its level of predictability in case of contingency or overload, as well as making resource sizing difficult. Therefore, the need arises to propose a model capable of reconstructing the dynamics of response times for the instances of a task with high priority in order to analyze their offline behavior under specific working conditions. For this purpose, we develop the necessary theory to build the response time reconstruction model. Then, to test the proposed model, we set up a workbench consisting of a single board computer, PREEMPT_RT, and a high priority task generated by the execution of a matrix inversion algorithm. This work demonstrates the application of the theory in an experimental process, presenting a way to model and reconstruct the dynamics of response times by a high-priority task on RT-Linux.

This chapter presented an analysis of the application of lexicon-based political sentiment analysis in social media. The aim is to identify the most frequently used lexicons in political sentiment analysis, their results, similarities, and differences. For this, the authors conducted a systematic literature review based on PRISMA methodology. Afinn, NRC, and SenticNet lexicons are tested and combined for data analysis from the 2020 U.S. presidential campaign. Findings show that political sentiment analysis is a new field studied for only 10 years. Political sentiment analysis could generate benefits in understanding problems such as political polarization, discourse analysis, politician influence, candidate profiling, and improving government-citizen interaction, among other problems in the public sphere, enhanced by the combination of lexicons and multimodal analysis. The authors conclude that polarity was one of the critical dimensions identified for finding variations in the behavior and polarity of sentiments. Limitations and future work also are presented.

Today, new applications demand an internet of things (IoT) infrastructure with greater intelligence in our daily use devices. Among the salient features that characterize intelligent IoT systems are interoperability and dynamism. While service-oriented architectures (SOA) offer a well-developed and standardized architecture and protocols for interoperability, answering whether SOA offers enough dynamism to merge IoT with artificial intelligence (AI) is still in its beginnings. This paper proposes an SOA model, called SCM-IoT (service composition model for IoT), for incorporating AI into IoT systems, addressing their coordination by a mediator offering services for storage, production, discovery, and notification of relevant data for client applications. The model allows IoT systems to be incrementally developed from three perspectives: a conceptual model, platform-independent computational model, and platform-dependent computational model. Finally, as a case of study, a domotic IoT system application is developed in SCM-IoT to analyze the characteristics and benefits of the proposed approach.

One of the pillars of connected government is citizen centricity: an approach in which citizen participation is essential. In Mexico, social networks are currently one of the most important means by which citizens express their needs and provide opinions to the government. The goal of this chapter is to contribute to citizen centricity by adapting the methodology of sentiment analysis of social media posts to an expanded version for crisis situations. The main difference in this approach from the normally accepted one is that instead of using pre-defined classes (positive and negative) for sentiments, the authors first determined the different data categories and then applied them to the classic process of sentiment analysis. This approach was tested using posts on Mexico's earthquake in 2017. They found that needs, demands, and claims made in the posts reflect sentiments in a better way, and this can help to improve the government-citizen connection.

The financial profile of natural and legal persons allows financial institutions and financial services companies to identify the level of default of credit applicants. In this way, maintaining a healthy financial profile is derived from a positive history and status, the result of good financial decisions. In Latin America, the vast majority of people pay little attention to strengthening this type of profile, and it is a subject that is rarely addressed on the most popular social media platforms. In this chapter, a search for opinions of Latin Americans regarding the financial profile on the Twitter social network was made, finding that there is a very small number of responses to publications on this topic. Therefore, a web platform is proposed to provide advice on this important issue. The general design of a module for text analysis using sentiment analysis and machine learning techniques is also proposed.

The COVID-19 pandemic has become a critical and disruptive event that has substantially changed the way people live and work. Although several studies have examined the effects of remote work on organizational outcomes and behaviors, only a few have inquired into how its opportune implementation impacts aggregate emotions over time. This chapter aims to conduct a sentiment analysis with public reactions on Twitter about telework during the pandemic period. The results showed fluctuations in emotional polarity, starting with a higher positive charge in the early pandemic scenarios that became weaker, and the negative polarity of emotions increased. Fear, sadness, and anger were the emotions that increased the most during the pandemic. Knowledge about people's sentiments about telework is important to complement organizational research and to complement the framework for the development of efficient telework implementation strategies.

El objetivo de la presente investigación es probar la utilidad del prototipo de Bastón Blanco Inteligente UAEM con sensores ultrasónicos (disparadores de alarmas y vibraciones) y sistema GPS (Sistema de Posicionamiento de Posición Global) para usuarios con discapacidad visual. La tecnología incluida en el Bastón Blanco Inteligente UAEM le proporciona al usuario con discapacidad visual diversas ventajas para ampliar su movilidad de forma segura, lo que en definitiva sirve para mejorar su calidad de vida. Sin embargo, su uso adecuado requiere de un entrenamiento especializado que ayude al usuario a obtener la utilidad que el prototipo promete. Para probar esos beneficios se efectuó un estudio exploratorio donde se analizaron las experiencias de entrenamiento y de consumo de 20 participantes adultos con discapacidad visual severa y ceguera. El análisis estadístico fue descriptivo, y permitió registrar la satisfacción de los usuarios ante las características físicas y los beneficios ofrecidos por el prototipo. Como resultado se observó la asociación entre las vibraciones, los sonidos y los diferentes mensajes emitidos (obstáculos diversos a pequeñas o grandes distancias). El Bastón Blanco Inteligente UAEM con sensores ultrasónicos y sistema GPS es un prototipo que ayuda a la movilidad segura y autónoma, lo que eleva la calidad de vida del usuario porque el dispositivo es ligero, plegable y su material es resistente; además, contiene aditamentos sonoros y vibratorios que proporcionan la simulación de un mapa físico a bajo precio.

This study analyzes the workflow experiences in the teaching-learning process of teachers and students enrolled in mixed (face to face /distance education) and distance education systems. The main objective is to evaluate Moodle, Learning Management System with the systematized Brief Inventory of Experiences. In this study we measure and compare the flow and comfort experiences of a population of 8140 participants, divided into two groups: 320 teachers and 7820 students. We can conclude that there is a significant difference in the p <0.05 level, in the following two variables: 1) When performing the activity, I forget about the problems and concerns and 2) I do things spontaneously and automatically. Tasks they must face in order to provide the students the appropriate tools in order to learn. In addition to this challenge, the teacher does not achieve an optimal level of comfort throughout the teaching process due the lack of positive feedback. Encouraging the teachers to guide the students to takeoff the learning and the teacher to get positive feedback. While the students receive and assimilate, the teacher gives and experiments. Because the transmission of knowledge is a function of who assimilates the legacy.

Abstract Backgroud:Investors are always playing with the fears and desires of buyers and sellers. Stock exchange markets are not the exception. Financial sentiment analysis allows us to understand the effect of reactions and emotions on social media in the stock market. In this research, we analyze Twitter data and financial indices to answer the question: How do polarity generated by the posts on Twitter influence financial indices behavior in pandemic seasons? Methods:The study is based on the sentiment analysis of influential Twitter accounts in this field and its relationship with the behavior of important financial indices. To achieve this, we tested four lexicons to detect polarity on Twitter. Results:Our findings shows that the period in which the markets reacted was 6 to 13 days after the information was shared and disseminated on Twitter in the COVID-19 season, and 1 to 2 day for H1N1 season. Furthermore, in our analysis, we found that the lexicons that got the best results for sentiment analysis on Twitter were S140 and Affin.Conclusions:Financial sentiment analysis is an important technique to forecasting stock market and polarity is the most widely used technique in the financial area. There is a relationship between the polarity in Twitter and the financial indexes behavior. The most influential Twitter accounts during the pandemic season were The New York Times, Bloomberg, CNN News, and Investing, presenting a very high relation between sentiments on Twitter and the stock market behavior.

Abstract In this study, we present a nucleus segmentation proposal of white blood cells (WBCs) using chromatic features. It is human inspired on perception of color: a person locates the nucleus of the WBCs by the chromatic contrast between the nucleus and the other elements of the blood smear. To implement that, we segment the nucleus by selecting the pixels with high chromatic variance. First, an unsupervised neural network, which was trained offline to recognize different colors is applied to the images. Thereby, the hue of the pixels is normalized, and the chromatic variance is accurately computed. Processing the hue and using the unsupervised neural network the brightness and staining variations are robustly estimated. In previous related works, the color components are processed separately as uncorrelated intensity channels, and the mathematical operations are selected intuitively. Unlike that, we use color as a feature without separating the hue components, keeping their correlation, so the formal treat becomes systematic. Experiments use the RGB and HSV spaces with three public image databases: ALL‐IDB2, CellaVision, and JTSC. A pixel‐level segmentation evaluation is performed by comparing the segmented images with the ground truth. Our proposal competes with current methods since the values in accuracy, specificity, precision, sensitivity, dice coefficient, kappa index, and true positive rate all are similar to or improved upon the state of the art. The performance of our approach is classified as excellent regarding the kappa index value, and it detects at least 80% of the cells with an average dice coefficient larger than 0.9.

Artificial intelligence has been applied to different sectors in the government from data analysis to predictive analytics, as well as, policing, combating the COVID-19 pandemic, and political election campaigns. In the academic field, the exploitation and understanding of data generated in social media have mostly focused on unimodal sentiment analysis, based on one-dimensional sentiment analysis. This research proposes an analysis of the emotional charge for the U.S. presidential elections in 2020, based on a hybrid approach that combines affective computing and classic statistical analysis. We analyze the multi-emotional charge of candidates and voters, as well as the potential relationship of the candidates' emotions on the voters. Through this analysis is possible to determine the degree of agreement between candidates and voters. Future research is proposed for the area of affective computing in political campaigns.

El objetivo de la investigación es medir el impacto de los tutoriales para la prevención y combate en contra del Hostigamiento y Acoso Sexual Cibernético, delito que ha aumentado de forma exponencial debido al mal uso de los medios digitales. Los tutoriales tienen la función de emitir mensajes de: conductas preventivas, seguridad, identificación del victimario, manejo personal del suceso y denuncia del delito ante autoridades. Se utilizó método cuantitativo, corte transversal y análisis descriptivo del cuestionario de satisfacción en muestra de 4,104 participantes. Los resultaron fueron: los hombres prefieren visualizar el mensaje de identificación del victimario para evitar ser confundido con éste (61%) y denunciar el delito en medios electrónicos (51%). Las mujeres solicitan atención personalizada, profesional e integral con justicia pronta y expedita (68%) para terminar con la impunidad y erradicar la cultura misógina. Concluyendo con un buen nivel de satisfacción ante la información emitida por los tutoriales (88%).

Las Plataformas de Financiamiento Fintech (PFFs) han revolucionado la forma en que se otorgan créditos a nivel mundial, generando un gran interés por su estudio. En este sentido, se realizó un artículo de revisión, cuyo objetivo es describir el estado actual de la investigación científica sobre las PFFs, a través de la recuperación de documentos científicos de impacto y de autores prolíficos en el tema. Entre los principales hallazgos se identifica que ha iniciado la ola de investigación con evidencia empírica, aunque escasa del tipo cuantitativo, limitándose al análisis de un reducido número de PFFs de Estados Unidos y China. Con ello, salen a la luz diferencias entre estos dos países, centradas en el tipo de datos que exponen a los inversionistas, los inversionistas, la homogeneidad entre PFFs y el enfoque de investigación. Asimismo, se observan más artículos enfocados hacia las PFFs que satisfacen el consumo, sin embargo se visualizan algunos estudios empíricos, sobre otras, cuyo objetivo es financiar empresas o proyectos, esto en la región Europea. Finalmente, los científicos enfatizan que las variables que tienen mayor incidencia en el crecimiento de las PFFs son la incorporación tecnológica y la posición privilegiada en el tema de regulación..

Fragmentation is a design technique widely used in multimedia databases, because it produces substantial benefits in reducing response times, causing lower execution costs in each operation performed. Multimedia databases include data whose main characteristic is their large size, therefore, database administrators face a challenge of great importance, since they must contemplate the different qualities of non-trivial data. These databases over time undergo changes in their access patterns. Different fragmentation techniques presented in related studies show adequate workflows, however, some do not contemplate changes in access patterns. This paper aims to provide an in-depth review of the literature related to dynamic fragmentation of multimedia databases, to identify the main challenges, technologies employed, types of fragmentation used, and characteristics of the cost model. This review provides valuable information for database administrators by showing essential characteristics to perform proper fragmentation and to improve the performance of fragmentation schemes. The reduction of costs in fragmentation methods is one of the most desired main properties. To fulfill this objective, the works include cost models, covering different qualities. In this analysis, a set of characteristics used in the cost models of each work is presented to facilitate the creation of a new cost model including the most used qualities. In addition, different data sets or reference points used in the testing stage of each work analyzed are presented.

La presente investigación tiene el objetivo conocer la relación existente entre la certificación de calidad educativa evaluada por organismos acreditadores y la satisfacción labores de docentes e investigadores de una Institución de Educación Superior (IES).La investigación fue realizada con 24 organismos acreditadores y con 744 docentes e investigadores. Los resultados de los análisis documentales y estadísticos mostraron que los organismos acreditadores otorgan certificados de calidad educativa al 87% de los programas de la IES estudiada. Cuando la IES solo cumplió con los indicadores de Prerrequisitos e Imprescindibles. El 13% restante de la certificación a programas, corresponden a los indicadores no cumplidos, afectando a 5,854 estudiantes (13%) y la situación laboral de 403 docentes e investigadores (8%).

One of the main problems faced by Data Warehouse designers is fragmentation.Several studies have proposed data mining-based horizontal fragmentation methods.However, not exists a horizontal fragmentation technique that uses a decision tree. This paper presents the analysis of different decision tree algorithms to select the best one to implement the fragmentation method. Such analysis was performed under version 3.9.4 of Weka, considering four evaluation metrics (Precision, ROC Area, Recall and F-measure) for different selected data sets using the Star Schema Benchmark. The results showed that the two best algorithms were J48 and Random Forest in most cases; nevertheless, J48 was selected because it is more efficient in building the model.

Fragmentation, allocation and replication are techniques widely used in relational databases to improve the performance of operations and reduce their cost in distributed environments. This article shows an analysis of different methods for database fragmentation, allocation and replication and a Web application called FRAGMENT that adopts the work technique that was selected in the analysis stage, because it presents a fragmentation and replication method, it is applied to a cloud environment, it is easy to implement, it focuses on improving the performance of the operations executed on the database, it shows everything necessary for its implementation and is based on a cost model. FRAGMENT analyzes the operations performed in any table of a database, proposes fragmentation schemes based on the most expensive attributes and allocates and replicates a scheme chosen by the user in a distributed environment in the cloud. This work shows a common problem in fragmentation methods, overlapping fragments, and provides an algorithm with an approach to address it. This algorithm results in the predicates that will define each fragment in a distributed environment. To validate the implemented technique, a second web application is presented, dedicated to simulate operations on sites and focused on producing a log file for the main application. Experiments with the TPC-E benchmark demonstrated lower response time of the queries executed against the distributed database generated by FRAGMENT compared with a centralized database.

The use of a simple categorization of emotions or even the use of universal expressions of emotions is unsuitable to properly identify sentiments in posts in some situations. The main goal of this paper is to analyze impressions of Twitter messages in the 19S Mexican earthquake of 2017 through machine learning techniques, specifically with classification algorithms. To identify impressions, we applied sentiment analysis based on supervised methods, and we identified a customized list of terms that we called impressions, which reflects the nature of tweets related to the event of study. Our proposed impressions analysis is useful to understand Twitter messages during different events since impressions adapt to each situation and context, based on emotional frameworks. We found that Twitter is useful to prove or disprove the information disseminated by the mass media and mainly for asking for help. Analyzing this kind of data in real-time will be useful for decision-making. The contribution of this paper is to fill the gap in the sentiment analysis area and the automatic identification of eleven impressions for disaster events in Twitter using machine learning techniques. This method has been called impression analysis.

La aplicacion de disciplinas como la ergonomia y el estudio del elemento finito mediante tecnicas computarizadas de simulacion pueden ayudar al ingeniero en la construccion de un nuevo producto o equipo. El diseno y construccion de productos que estan sometidos a esfuerzos mecanicosrequieren de un analisis y pruebas que permitan garantizar la resistencia, la ergonomia y la seguridad estructural, el analisis del elemento finito serarealizado mediante Simulacion computarizada con los programas SolidWorks y Matlab, el uso de estos es descrito mediante una prueba estatica en una estructura simple definida por Diseno Asistido con Computadora (cad, por sus siglas en ingles) y analizada con Ingenieria Asistida con Computadora (cae , por sus siglas en ingles), el cual sirve como introduccion a calculos de estructuras mas complejas. Se trata de un paso inicial para estudiantes y/o ingenieros que deseen aplicar estudios estructurales estaticos en geometrias tridimensionales y aplicar los resultados para la construccion de productos o equipos con los materiales adecuados.

One of the pillars of connected government is citizen centricity: an approach in which citizen participation is essential. In Mexico, social networks are currently one of the most important means by which citizens express their needs and provide opinions to the government. The goal of this chapter is to contribute to citizen centricity by adapting the methodology of sentiment analysis of social media posts to an expanded version for crisis situations. The main difference in this approach from the normally accepted one is that instead of using pre-defined classes (positive and negative) for sentiments, the authors first determined the different data categories and then applied them to the classic process of sentiment analysis. This approach was tested using posts on Mexico's earthquake in 2017. They found that needs, demands, and claims made in the posts reflect sentiments in a better way, and this can help to improve the government-citizen connection.

A model and real-time simulation of a gas turbine engine (GTE) by real-time tasks (RTT) is presented. A Kalman filter is applied to perform the state vector identification of the GTE model. The obtained algorithms are recursive and multivariable; for this reason, ANSI C libraries have been developed for (a) use of matrices and vectors, (b) dynamic memory management, (c) simulation of state-space systems, (d) approximation of systems using equations in matrix finite difference, (e) computing the mean square errors vector, and (f) state vector identification of dynamic systems through digital Kalman filter. Simulations were performed in a Single Board Computer (SBC) Raspberry Pi 2® with a real-time operating system. Execution times have been measured to justify the real-time simulation. To validate the results, multiple time plots are analyzed to verify the quality and convergence time of the mean square error obtained.

The research question that leads this effort is: How do Twitter messages impact the polarization of emotions in the presidential election in Mexico in 2018? We collected 6,515 tweets posted by the five candidates that stood for the presidency of Mexico in 2018. We developed a methodology based on artificial intelligence techniques using machine learning and classifiers to predict the emotions of each candidate and the effects of those emotions on the other candidates. The results show that the candidate with the greatest polarization in the generated emotions won the electoral contest and that the emotions generated in Twitter affected other candidates.

At present, the demand for information in distributed database systems is large and growing day by day. While this is happening, new challenges arise to improve the performance of the databases. Data fragmentation and replication methods have a leading role in distributed systems over the cloud, which is why this paper presents the design of a horizontal fragmentation, allocation and replication method in the cloud. This research proposes an algorithm for solving the problem of overlapping horizontal fragments in a data fragmentation and replication method in the cloud. The design of a Web application using the aforementioned method is also presented, this allows the fragmentation, assignment and replication scheme proposed by the method to be directly applied on a distributed database.

Este artículo presenta los resultados de la investigación cuyo objetivo consistió en la elaboración de un instrumento innovador, ágil, veraz y confiable para evaluar y certificar al personal académico universitario. Un instrumento capaz de detectar cuatro niveles de conocimientos y uso en tecnologías de la información y la comunicación (TIC) y tecnologías del aprendizaje y el conocimiento (TAC). Se trató de un estudio cuasiexperimental dividido en tres fases: a) elaboración de la prueba con diseño mixto transformativo secuencial; b) evaluación de la prueba con diseño mixto secuencial, y c) sistematización con App Ionic 3. Una vez realizados los análisis cualitativos y cuantitativos, el instrumento resultó versátil y transferible en 1) selección y contratación de personal, 2) identificar el nivel conocimientos que tiene el personal, 3) crear programas y/o cursos que refuerce o cree nuevas habilidades en TIC y TAC, 4) categorizar al personal de acuerdo su nivel de conocimientos, 5) promocionar al personal para adquirir beneficios o prestaciones laborales, 6) evidenciar el nivel que tiene los académicos en la evaluación que realizan los organismos acreditadores y 7) constancia curricular de posesión de nivel de conocimiento y uso en TIC y TAC en área académica. Dicha prueba puede ser aplicable de forma individual o colectiva y obtener calificaciones a través de un método manual, por lo que se anexa una guía de usuario, o electrónico, para lo cual se requiere solicitar la página web a los autores.

Las computadoras han logrado una mayor independencia a la hora de resolver situaciones dificiles o cambiantes; asimilan la informacion y toman decisiones. El aprendizaje automatico para el que estan siendo programados diversos aparatos digitales ya esta presente en varias plataformas de internet y, de cierta forma, los datos que adquieren son de uso constante en nuestra vida. Sin embargo, no hay suficientes especialistas que saquen un mayor provecho de todo el conocimiento que estos dispositivos acumulan; ahi esta el reto.

Content-Based Image Retrieval (CBIR) is a technique to obtain images based on visual characteristics like color, texture and shape.In this article, an analysis of different CBIR systems is presented; also an inventory system that allows content-based queries is proposed to help the employees of a company that sells agricultural machinery and spare parts, called AGROMAQ.This system identifies different products, all with the aim of reducing customer service time, since an expert is currently needed to identify them, which increases attention time not only in hours but also in days.

This research identifies the factors influencing the reduction of autopsies in a hospital of Veracruz. The study is based on the application of data mining techniques such as association rules and Bayesian networks in data sets obtained from opinions of physicians. We analyzed, for the exploration and extraction of the knowledge, algorithms like Apriori, FPGrowth, PredictiveApriori, Tertius, J48, NaiveBayes, MultilayerPerceptron, and BayesNet, all of them provided by the API of WEKA. To generate mining models and present the new knowledge in natural language, we also developed a web application. The results presented in this study are those obtained from the best-evaluated algorithms, which have been validated by specialists in the field of pathology.

Introducción. Las estrategias didácticas utilizadas para la enseñanza y el aprendizaje de la programación de software son complejas porque se debe adquirir, codificar y recuperar información con base en el pensamiento lógico-matemático para diseñar instrucciones que serán ejecutadas por un ordenador con el fin de resolver problemas de diversa naturaleza. Esto justifica por qué estos contenidos tienen alta demanda en la educación superior de la actualidad, a pesar de que los índices de titulación sean, por el contrario, muy bajos. Objetivo. Proponer y evaluar estrategias didácticas generales y específicas para la enseñanza y aprendizaje de programación en software con el fin de crear una alternativa para disminuir los registros de bajo rendimiento y la deserción en las carreras que incluyan temas de la mencionada área. Método. Diseño cuasiexperiemtal, análisis cuantitativo no paramétrico, longitudinal (cuatro evaluaciones), con una muestra de 78 estudiantes de la carrera de ingeniería en Computación, con distribución al azar en dos grupos experimentales y dos grupos de control en escenarios de aulas inteligentes. Material. Programas de software con lenguajes C y C++ en plataforma educativa, así como uso de Autogradr y del Inventario de estrategias metacognitivas. Resultados. Se empleó una prueba de Kruskal-Wallis debido a que se tuvo una muestra sin distribución normal y con escala por jerarquías. Se observó la aceptación de la hipótesis nula en ambas pruebas: 1) H = 0.04, en rendimiento académico superior en los grupos experimentales, la interdependencia en conocimientos es regular alta 0.657, con diferencia de 1.7 en rendimiento promedio, y 2) H = 0.24, en estrategias metacognitivas superior en los grupos experimentales, la interdependencia en estrategias metacognitivas es baja 0.342. Se resalta el área de planificación como fase elemental para la solución de problemas. Conclusiones. Se demostró que la estrategia didáctica expuesta en tres bloques específicos de enseñanza y aprendizaje elevó el rendimiento académico y la metacognición de los estudiantes.

Currently, the technological and administrative modernity has revolutionized the dynamics of work in organizations. In recent years, the existence of mobbing has been pointed out, which represents an obstacle to the integral development of workers; therefore, job satisfaction plays a fundamental role in the quality of life of people. In the present work a quantitative research was carried out, a transversal-correlational design was used, to determine if there is a statistically significant relationship between the mobbing variables and job satisfaction of the teachers of the Higher Education Institutions (HEI). The sample consisted of 248 teachers from four HEI in central Mexico. The hypotheses were evaluated through the chi-square statistical test. The results reveal the dependence between the dimensions of the variables analyzed.

A partir de la modernidad administrativa y tecnológica se ha generado un cambio en la dinámica en el interior de las organizaciones, por ello, buscar el desarrollo integral de sus miembros es un elemento clave para generar venta­ja competitiva. En este artículo se analiza la dependencia entre las variables mobbing y satisfacción laboral, con los datos de una muestra obtenida de cuatro Instituciones de Educación Superior (IES) del centro de México. Los hallazgos revelan que dichas variables tienen una fuerte dependencia estadística, de acuerdo a la prueba estadística de chi-cuadrada aplicada.

Resumen.Las generaciones actuales de jóvenes conviven en entornos donde es común la presencia de dispositivos electrónicos conectados a Internet.Entre las diferentes aplicaciones disponibles se encuentran las de e-learning.Sin embargo, un aspecto poco abordado, es la utilización de técnicas estadísticas, aprendizaje automático y minería de datos para retroalimentar tanto a discentes y profesores para mejorar su desempeño académico o su función docente.En este artículo, se propone una arquitectura escalable para aplicaciones e-learning para dispositivos móviles, con capacidad de ofrecer retroalimentación automática a los usuarios basadas en la interacción con el sistema y las calificaciones obtenidas.La arquitectura propuesta permite incorporar diversas técnicas, incluyendo las de inteligencia artificial, para mejorar la calidad de la retroalimentación.Para demostrar la viabilidad del enfoque, se desarrolló un prototipo de aplicación que permite identificar posibles dificultades académicas y emitir recomendaciones a discentes

Commonly, classic plant identification methods use dichotomous or multi-access keys that compare characteristics of the leaves, asking if they are lobed, unlobed, simple or compound, among others leaf features.However, in the literature very little attention has been paid to make an automatic distinction of leaves using such features.In this paper, we contribute to fill this gap.We propose a novel method to differentiate between types of leaves.The proposal is invariant to rotation and also to scaling.In order to show the effectiveness of the proposal, we tested it with more than 1,900 images of leaves which are publicly available on the Internet, achieving correct identification rates greater than 86%.

Automatic plant identification has been an important issue in the last years. Most of the state-of-the-art methods for this purpose use leaf features to predict the species. Despite there are many methods to extract different leaf features, just few of them are focused on discriminating between simple and compound leaves. In this work, we introduce a method to detect compound leaves. Our method uses concentric circles to explore the surface of the leaf to count the changes of color in binary images, then, the changes are analyzed to detect compound leaves. The method predicts correctly more than 96% of the leaves in the Flavia data set. We also tested the method with some images of leaves available on the Internet, with 100% of correctness. The information on whether a leaf is or not compound, was used on experiments to observe whether this improves the performance of classifiers.

In the last years, a significant reduction in the number of autopsies realized in the hospitals of the world has been observed.Since medics are the closest people to this problematic, they can offer information that helps clarify why the decreasing of this practice has occurred.In this paper, data mining techniques are applied to perform an analysis of medical opinions regarding the realization of autopsies in a hospital of Veracruz, in Mexico.The opinions were collected through surveys applied to 85 medics of the hospital.The result is a model represented by a set of rules that suggests some of the factors that are related to the decrease in the number of autopsies in the hospital, according to the survey respondents.

La presente investigación propone la implementación del Sistema Operativo LimeSurvey en la Encuesta Electrónica ALFA TUNING para medir competencias académicas de egresados y su empleabilidad en región de Zumpango.Material: Encuesta Electrónica ALFA TUNING.Objetivo: ayudar a obtener información sobre la empleabilidad de los egresados, indicadores requeridos por los organismos certificadores para medir la calidad educativa.Metodología: diseño no experimental, uso de muestras aleatorias, con cortes trasversales y estadística descriptiva. Dividido en cuatro fases: Fase 0, validez de contenidos del Cuestionario ALFA TUNING con coordinadores académicos; Fase 1, prueba piloto de la Encuesta Electrónica ALFA TUNING; fase 2, aplicación de la encuesta a egresados; fase 3, aplicación de encuentra a empleadores.Resultados: La comparación de seis investigaciones podemos decir que la en encuesta utilizada para el Programa de Movilidad Universitaria Internacional (PMUI) y el sistema LimeSurvey; ambas presenta tiempos estimados menor a dos horas, pero el tiempo utilizado en el sistema LimeSurvey es seis veces menor al sistema utilizado en la plataforma PMUI. Referente al costo en uso, la Encuesta Electrónica ALFA TUNING a través del sistema operativo LimeSurvey presenta un costo menor que todos los sistemas operativos.

In this paper we present an approach for fruit recognition using artificial vision, towards to employ it as an application for supermarkets. The fruit's visual features extracted are shape, texture and -we focus on- the extraction of the color chromaticity. From the few related works on fruit recognition, the color extraction is performed in the RGB (Red, Green, Blue) space. We claim as necessary to employ the chromaticity of colors to characterize the color of the fruits. Thus, we propose to use the HSV (Hue, Saturation, Value) space because it is possible to extract and process the chromaticity data, but without the undesirable intensity effects of the RGB space. On the other hand, before the color is characterized, we complete a selection of the chromaticities that contribute with important data about the fruits. Our approach's strength is validated by performing test on 20 common fruit classes: this proposal outperforms the color characterization of related work.

Plant identification belongs to a specific application domain of data mining. Images of plant leaves are usually used as the main element to distinguish a plant from another. For proper identification, feature extraction is necessary. In the literature, most plant recognition systems use the features along with a classification method, which has been adapted or modified to face this type of application. In this paper, we propose three new geometric features that describe the vertical and horizontal symmetry of leaves. These features are simple to extract from images. According to the results of experiments, when these features are used in conjunction with other well-known geometric characteristics, the performance of classical classification methods is remarkably improved. To show the effectiveness of the proposal, we test seven classifiers with images of leaves publicly available on the Internet.

Resumen.La cantidad y diversidad de software malicioso (Malware) en la actualidad es enorme.Se espera que en un corto tiempo se incrementen todavía más las amenazas cibernéticas.Uno de los enfoques más utilizados para enfrentar este problema, es analizar la dinámica de propagación de Malware utilizando modelos matemáticos basados en sistemas de ecuaciones diferenciales propuestos en la década de los años 20.Una desventaja de ese enfoque es la dificultad que se presenta para relacionar el valor de los parámetros de las ecuaciones con aspectos específicos del mundo real.En este artículo, se presenta una propuesta para simular y analizar la propagación de Malware considerando elementos que se presentan cotidianamente en la realidad.Se propone un modelo en el que se introducen dos conceptos nuevos, el primero es remarcar la diferencia entre individuos y dispositivos; el segundo es realizar la distinción entre propietarios y usuarios de dispositivos.Estos elementos son introducidos como parámetros del modelo para analizar la evolución de la propagación.El modelo presentado se implementó como una plataforma funcional, que se utilizó para realizar simulaciones con 1,000 dispositivos.De acuerdo a los resultados de los experimentos realizados, se encuentra evidencia del efecto de los conceptos introducidos sobre la dinámica de propagación de Malware.

La fragmentación híbrida es una técnica reconocida para lograr la optimización de consultas tanto en bases de datos relacionales como en bases de datos orientadas a objetos. Debido a la creciente disponibilidad de aplicaciones multimedia, surgió el interés de utilizar técnicas de fragmentación en bases de datos multimedia para tomar ventaja de la reducción en el número de páginas requeridas para responder una consulta, así como de la minimización del intercambio de datos entre sitios. Sin embargo, hasta ahora sólo se ha utilizado fragmentación vertical y horizontal en estas bases de datos. Este artículo presenta un método de fragmentación híbrida para bases de datos multimedia. Este método toma en cuenta el tamaño de los atributos y la selectividad de los predicados para generar esquemas de fragmentación híbridos que reducen el costo de ejecución de las consultas. También, se desarrolla un modelo de costo para evaluar esquemas de fragmentación híbridos en bases de datos multimedia. Finalmente, se presentan algunos experimentos en una base de datos de prueba con el fin de demostrar la eficiencia del método de fragmentación propuesto.

Resumen. En el area de aprendizaje automatico, el problema de desbalance en los datos es uno de los mas desafiantes. Desde hace mas de una decada, se han desarrollado nuevos metodos para mejorar el desempeno de los metodos de clasificacion para este tipo de problema. En este articulo se presenta una modificacion al algoritmo C4.5 usando el concepto de entroṕia minoritaria. La propuesta esta basada en la correccion de un error que observamos en una publicacion previa. La implementacion del nuevo metodo presentado es probada con conjuntos publicamente disponibles. Los resultados obtenidos muestran la utilidad del metodo desarrollado.

Resumen.El análisis de la dinámica del tecleo es una técnica usada para verificar la identidad de usuarios de sistemas informáticos.Actualmente, existen pocos conjuntos de datos públicamente disponibles de usuarios reales, y los que se encuentran corresponden a teclados tipo QUERTY.El teclado tipo numérico, que es usado en diversas aplicaciones importantes, ha recibido poca atención por parte de la comunidad científica.En este trabajo se presenta el desarrollo de un sistema para sistema operativo Android, cuya finalidad es la captura de los tiempos de tecleo usados comúnmente en biometría informática.Los datos generados por usuarios reales usando este sistema, se han publicado en la Internet para su libre descarga.En este artículo se presenta un resumen de los datos y se presentan trabajos futuros.Palabras clave: dinámica de tecleo, biometría, Android. IntroducciónLa biometría informática se refiere al conjunto de técnicas, principalmente estadísticas, que son aplicadas para verificar la autenticidad de individuos, tomando como base sus rasgos físicos o de conducta.La biometría informática se divide en estática y dinámica.La primera usa las características físicas de los sujetos, mientras que la segunda emplea sus rasgos conductuales.Entre los rasgos físicos más utilizados se encuentran las huellas dactilares y algunas partes del ojo como la retina o iris.Algunos ejemplos de características conductuales incluyen la voz, la forma de firmar, y el modo de caminar.Recientemente, el grupo de hackers "Chaos Computer Club" 1 demostró que utilizar el tipo de biometría informática estática para proteger sistemas ya no es confiable.Ya que mediante simples fotografías tomadas con teléfonos inteligentes es posible copiar las huellas dactilares.Debido a lo anterior, la biometría informática dinámica cobra mayor importancia en diversas aplicaciones, y es actualmente usada cada vez más en sitios Web 2 y sistemas informáticos 3 .

Electoral processes using electronic devices allow voters to cast electronically. Devices such as personal computers, Direct Record Machines (DRE) voting machines, and smartcards, among others, in conjunction with private nets or the Internet have been the main tools to implement electronic voting systems (e-voting). Some years ago, mobile devices had not been considered due to their storage restrictions and low computational power; however, nowadays the technology in these devices has advanced and it is possible to implement complicated applications that offer security services such as confidentiality and authentication. In this paper, a reliable and efficient e-voting system for Android Smartphone is implemented. The e-voting proposed herein is composed of three entities: the authentication server (AS), the voting server (VS) and the voter (V) which runs on an Android Smartphone. Two short messages, between V and AS, are necessary to generate an electronic ballot. In order to cast a vote, one more message containing the ballot is sent from V to VS. Bilinear pairing functions are used to verify the signatures contained in the electronic ballot. Each ballot has short lengths, implying improvements in transmission times. Additionally, considering that the most costly operations such as bilinear pairing are not performed in V, the results obtained in tests show that the e-voting system is efficient.

problema de desbalance en clasificación se presenta en conjuntos

Support vector machine (SVM) is not suitable for classification on large data sets due to its training complexity. Convex hull can simplify SVM training, however the classification accuracy becomes lower when there are inseparable points. This paper introduces a novel method for SVM classification, called convex-concave hull. After a grid processing, the convex hull is used to find extreme points. Then we detect a concave (non-convex) hull, the vertices of it are used to train SVM. We applied the proposed method on several problems. Experimental results demonstrate that our approach has good classification accuracy while the training is significantly faster than other state of the art methods.

Support Vector Machine (SVM) is an important classification method used in a many areas. The training of SVM is almost O(n^{2}) in time and space. Some methods to reduce the training complexity have been proposed in last years. Data selection methods for SVM select most important examples from training data sets to improve its training time. This paper introduces a novel data reduction method that works detecting clusters and then selects some examples from them. Different from other state of the art algorithms, the novel method uses a decision tree to form partitions that are treated as clusters, and then executes a guided random selection of examples. The clusters discovered by a decision tree can be linearly separable, taking advantage of the Eidelheit separation theorem, it is possible to reduce the size of training sets by carefully selecting examples from training sets. The novel method was compared with LibSVM using public available data sets, experiments demonstrate an important reduction of the size of training sets whereas showing only a slight decreasing in the accuracy of classifier.

An important objective of health monitoring systems for tall buildings is to diagnose the state of the building and to evaluate its possible damage. In this paper, we use our prototype to evaluate our data‐mining approach for the fault monitoring. The offset cancellation and high‐pass filtering techniques are combined effectively to solve common problems in numerical integration of acceleration signals in real‐time applications. The integration accuracy is improved compared with other numerical integrators. Then we introduce a novel method for support vector machine (SVM) classification, called convex‐concave hull. We use the Jarvis march method to decide the concave (nonconvex) hull for the inseparable points. Finally the vertices of the convex‐concave hull are applied for SVM training.

In this paper, we present an algorithm to speed up the training of SVM. The proposed algorithm is based on SV candidates selection strategy, exploiting the observation that typically from a set of elements with the same label, if there exist SV, then most of them are on the boundary of the set. We compute the non convex hull sets that envelop the elements with the same label, this sets have in general a few elements compared with the entire data set. To train the SVM we use only the non convex hulls sets, which improves the training time. According to the results, our algorithm gives good accuracy and the training time is reduced considerably (under certain run conditions), the proposed algorithm is suitable for datasets with small number of features yet.

The Concurrent Real-Time Tasks (TTRC), carry out all the activities of a System in Real-Time (STR); the Scheduler of Concurrent Real-Time Tasks (PTTRC) is the responsible of selecting the Real Time Task and the moment that it will take the resources, (PTTRC) is an algorithm that assigns the resources from the processor to different tasks in different moments. However, is necessary to verify effectiveness of the algorithm using an adequate approach that provides a metric between 0 and 1; the measuring should be carried out in local form and in a global way. In this work we intend the measuring of the efficiency of the scheduler through of the evolution of the instances of the Concurrent Tasks and their temporary restrictions.

RESUMEN El medir la velocidad angular real de una maquina rotatoria es un problema que tiene que valorase, ya que si dicha medicion retroalimenta a un sistema de control, la respuesta de este ultimo pudiera no ser la adecuada. Para medir la velocidad angular, es comun utilizar un codificador incremental un dispositivo que produce pulsos electricos conforme gira la fecha de la maquina rotatoria,junto con circuitos electronicos digitales. La implementacion de este tipo de soluciones digitales, en general introduce un error en las mediciones, ya que solamente pueden contarse numero de enteros de pulsos en cada periodo de tiempo, o si se mide el intervalo de tiempo entre un pulso y otro, el error se introduce de acuerdo a la resolucion del temporizador utilizado. En este trabajo se detallan dos metodos para minimizar el error en la medicion de velocidad angular cuando se usan medios digitales.

Aplicación de inteligencia artificial y tecnologías para la transformación digital: Pymes, desarrollo sostenible e inclusión social es un libro que presenta resultados de investigación de diferentes áreas del conocimiento. Profesores-investigadores de varias universidades de reconocimiento y prestigio en el país muestran en la era de la inteligencia artificial (IA) como a través del uso de herramientas digitales se pueden buscar alternativas de solución a problemáticas sociales, económicas, académicas y ambientales en sectores productivos, escolares, médicos, turísticos y comerciales con el propósito de favorecer a personas vulnerables en regiones marginadas de México. La presente obra refleja el esfuerzo por obtener resultados de investigación desde un enfoque multidisciplinario, considerando varias perspectivas y marcos de referencia que permitan tener un mayor alcance que si se realizaran desde una única disciplina, y cuyo objetivo principal es que la tecnología constituya un pilar en la inclusión social a pesar de las brechas que pudiesen existir.

ANÁLISIS CIENTÍFICO DESDE DISTINTAS PERSPECTIVAS COLECCIÓN CIENTÍFICA EDUCACIÓN, EMPRESA Y SOCIEDAD Primera Edición 2022 Vol. 17 Editorial EIDEC Sello Editorial EIDEC (978-958-53018) NIT 900583173-1 ISBN: 978-628-95419-0-8 Formato: Digital PDF (Portable Document Format) DOI: https://doi.org/10.34893/h0578-1489-6417-x Publicación: Colombia Fecha Publicación: 30/11/2022 Coordinación Editorial Escuela Internacional de Negocios y Desarrollo Empresarial de Colombia – EIDEC Centro de Investigación Científica, Empresarial y Tecnológica de Colombia – CEINCET Red de Investigación en Educación, Empresa y Sociedad – REDIEES Revisión y pares evaluadores Centro de Investigación Científica, Empresarial y Tecnológica de Colombia – CEINCET Red de Investigación en Educación, Empresa y Sociedad – REDIEES

La investigación tiene como objetivo identificar los perfiles psicológicos y conductual de las víctimas y de los victimarios que intervienen en el Hostigamiento y Acoso Sexual (HAS) cibernético en la población estudiantil universitaria para conocer los móviles que intervienen en esta agresión. Se utilizó el método cuantitativo para obtener los perfiles, a través de una electrónica con: Cuestionario del Módulo sobre Ciberacoso (MOCIBA) 2019, Test de Empatía Cognitiva y Afectiva TECA y el Inventario de Comportamientos Sexuales No Convencionales. Aplicada una nuestra aleatoria de 4916 estudiantes universitarios. Resultados a) perfil de la víctima fue en estudiantes que tenía entre 18 a 21 años, género femenino en el área de ciencias sociales, quienes mostraron altos niveles de estrés empático revelando su fragilidad ante emociones negativas, así como realización de pocas acciones contra el HAS cibernético, porque eventualmente cambia contraseñas y solo elimina publicación, mensaje o vídeo que considera inapropiados, b) perfil del victimario es poco definido por la inconsistencia de datos, provocado por el anonimato, solo obteniendo frecuencias altas en participantes entre 26 a 29 años, del género masculino, quienes demuestran fantasías sexuales asociadas a la presentación u ostentación del propio cuerpo o del prójimo, asociadas a la dominación o sometimiento.

Investigación sobre la calidad educativa como se ve afectada por las condiciones laborales de docentes e investigadores

Las aplicaciones moviles tienen un gran impacto en la sociedad debido a los avances tecnologicos; existen usuarios de diferentes edades que cuentan con algun dispositivo movil con distintas aplicaciones, como herramientas de ayuda o de entretenimiento. Ante el inconveniente de que los usuarios no se encuentran fisicamente en un mismo lugar, las aplicaciones E-Learning tienen un area de oportunidad en la creacion de grupos de trabajo. En el presente articulo se expone una arquitectura en la que se visualiza un modulo de recomendacion para la creacion de grupos de trabajo con caracteristicas similares las cuales seran extraidas de la red social Facebook para la Aplicacion Movil tipo E-Learning “Mochuelo Learning” utilizando algoritmos de agrupamiento para formar grupos de trabajo

El sistema LimeSurvey ayuda a medir competencia académicas requeridas por los organismos certificadores. Material: Guía para la Autoevaluación de Programas de Educación Superior (GRAPE) y sistema LimeSurvery. Objetivo: Aplicar el sistema LimeSurvey para crear una Quía electrónica de GAPES, lo que va agilizar la obtención de índices requeridos para la autoevaluación y organismos acreditadores. Metodología: Diseño cuasiexperimental con dos grupos (control y experimental), muestra no probabilística, seleccionada con características específicas, corte trasversal y estadística descriptiva. Dividido en cuatro fases: Fase 0 validez de contenidos del Quía electrónica de GAPES con coordinadores académicos. Fase 1 Prueba piloto de la Quía electrónica de GAPES. Fase 2 aplicación de la quía electrónica y guía en papel de GAPES. Fase 3 medir los beneficios de las aplicación de la quía. Resultados: La comparación de dos métodos (electrónica y en papel) para la autoevaluación encomendada por CIEES para obtener las certificaciones por los organismos certificadores. Observando sistematización de la Quía GAPES a través del sistema LimeSurvey ayuda a disminuir en las horas de trabajo en 103.77 hrs. representando un costo $ 6,918.34 y aumento de trabajo en equipo de forma simultanea para realizar la actividad con multi-áreas académicas.

En esta investigacion se realizo un estudio sobre los factores de riesgo laboral que condicionan el desarrollo de SM en el profesional de Enfermeria. Es importante destacar que el estudio fue cuantitativo, transversal, correlacional y prospectivo; en el cual se aplico un instrumento validado al profesional de Enfermeria de Unidades dedicadas al cuidado critico, en un Hospital de Segundo Nivel del Estado de Mexico, encontrandose que los principales factores de riesgo laboral para esta poblacion son la edad, los malos habitos alimenticios laborales y el tipo de alimentacion durante la jornada aboral. Existe la presencia del SM en casi el 50% de la poblacion estudiada, considerando los criterios del PANEL III del Colesterol. Resulta contradictorio encontrar la presencia de malos habitos alimenticios y el sedentarismo en el profesional de Enfermeria, ya que deberia ser lo contrario, por ser profesionales dedicados al cuidado de la salud, y poseer el conocimiento sobre lo saludable y lo no saludable, lo que da como respuesta un alto indice de obesidad y sobrepeso. El profesional estudiado que aun no presenta SM, tiene un riesgo latente de padecerlo en un futuro, si continua con su estilo de vida actual, ya que aproximadamente el 95% de la poblacion total en general tienen malos alimenticios (realizan entre dos y tres comidas por dia, su actividad fisica es limitada y en algunos casos nula, realizan comidas fuera de casa mas de tres veces a la semana). Por lo que el Hospital donde se realizo este estudio, aunque probablemente la situacion sea similar en otros Hospitales, deberian de preocuparse por la alimentacion de sus trabajadores y estar al pendiente de su salud, ya que si ellos enferman o peor aun si fallecen, por esta condicion la Institucion podria verse afectada. Como recomendaciones para eliminar, disminuir o retrasar la complicaciones derivadas del SM, se proponen las siguientes: (a) Formar un comite de salud dentro del Hospital para verificar que se lleven a cabo los programas dirigidos al profesional de la salud, y que se les de seguimiento; tales como el de “Activate, muevete por tu salud”. (b) Realizar somatometria (peso y talla) a cada individuo, por lo menos cada tres meses, para mantener un control del IMC. (c) Realizar pruebas bioquimicas, por lo menos una vez al ano, con seguimiento de casos en pruebas alteradas o con cifras por encima de lo normal. Esto para mantener un control de los criterios del SM. (d) Brindar alimentos lo mas saludables posibles a los trabajadores, y si es posible la supervision de un nutriologo para verificar las dietas. (e) Supervisar a los puestos aledanos al hospital, para promover la venta de alimentos balanceados y equilibrados. Como trabajo futuro, se tiene planeado extender esta investigacion a mas hospitales, y considerar realizar un estudio mas completo o de laboratorio para determinar parametros no contenidos en este estudio tal es el caso de cHDL. Por otra parte, se sugiere que los programas de prevencion que se llevan en otras Instituciones como las educativas, se implementen de igual manera en los Hospitales, para poder acreditarlos en un futuro como “Hospital Saludable”. Donde al trabajador se le puede crear una conciencia de educacion sobre estilos de vida saludable, y ser el portador de este conocimiento hacia los pacientes

In a population of 40 patients of a Hospital of Second Level of Care, the grade of compliance of the F1-PIVUPSVI / 02 format obtained a low overall compliance. It was found a finding a significant dependence between the academic degree and the compliance degree. It can be observed a slight increase in compliance according to the academic degree. Among the nine criteria, bag level, id and connected drainage was the most complied, and those of low compliance were hygienic and guidance measures. In terms of category, specialists in nursing report better compliance, the general nurses and graduates in nursing achieve the least compliance. Intensive therapy ranked first in compliance. For the inferential analysis, chi squared was applied, finding a significant dependence between the criteria for reporting data on urinary tract infection and the performance of hygienic measures on the patient. Carrying out hygienic measurements to the patient with the recording of the probe. As well as the counseling measures provided to the patient and relative to the record of functioning of the probe.

Automatic face recognition has evolved significantly with the use of deep learning techniques, enabling more robust systems that can withstand variations in lighting, pose, and expression. This paper presents the implementation of a face recognition system for video recordings using the MTCNN face detection algorithm, which is used for its accuracy in identifying and locating faces in uncontrolled environments with varying lighting conditions. After face detection, the VGG-16 model was used as a deep feature extractor, leveraging its architecture to employ transfer learning techniques to learn hierarchical and discriminative representations of the face. The system processes the video frames using the OpenCV library, then uses the MTCNN network for facial identification, and finally assigns identities through a classifier trained, as previously mentioned, with the VGG-16 model. In recent studies, authors report that convolutional neural networks consistently outperform traditional methods due to their ability to automate the extraction of relevant features and handle complex environmental conditions. Preliminary results indicate that the MTCNN–VGG-16 integration forms an effective strategy for facial recognition in real-world scenarios, demonstrating high accuracy and stability under diverse visual conditions. This work provides an accessible and reproducible architecture for potential applications in video analytics, surveillance, and intelligent automation

La clasificación en conjuntos de datos no balanceados es un problema actual e \nimportante (Khan2012). Ejemplos de aplicaciones del mundo real en las que se \ngeneran este tipo de datos son, por ejemplo, diagnósticos médicos de \nenfermedades tales como cáncer o diabetes mellitus, detección de errores en \ncódigo fuente, ataques informáticos en servidores e identificación de transacciones \nbancarias fraudulentas. En este tipo de escenarios, es sumamente importante \ndetectar casos que generalmente ocurren con poca frecuencia con respecto a la \ncantidad de datos obtenidos, ya que representan un riesgo que hay que observar \noportunamente. Para los ejemplos de aplicaciones mencionadas anteriormente, \nestos casos poco frecuentes serían los pacientes enfermos de cáncer o diabetes, \nlos fragmentos de código fuente con errores, los accesos inválidos a servidores y \nlas transacciones ilícitas. Sin embargo, como se mencionó anteriormente, los \nalgoritmos de clasificación, presentan problemas con conjuntos de datos no \nbalanceados (He2009).