Abstract: Smart Grids have revolutionized the electricity industry by enabling more efficient control and monitoring of the electricity supply, with a key component being smart meters (SM). These collect information on demand, energy, and harmonic distortion, among others, which must be stored and managed efficiently in a metering data management system (MDMS). The MDMS must ensure that a complete set of data is obtained for use in algorithms to ensure the reliability and quality of the power supply. To address the challenge of management the big data generated by SM, short, medium, and long-term measurement forecasting techniques have been proposed, highlighting the use of artificial intelligence such as Artificial Neural Networks (ANN) and Deep Learning (DL) methods due to their ability to adapt to different input and output variables with various time horizons. In addition, the influence of the diversity of Information and Communication Technologies (ICT) on the update time and data storage in the MDMS is highlighted. In this sense, this work aims to identify which ANN or DL architecture(s) could be more suitable for enterprise, survey, or research applications, demonstrating favorable performance metrics in different scenarios of sampling frequency and typical data update times in Smart Grids. This is relevant due to the need for MDMS to perform multivariate and multi-pass predictions in the short term to complete the information until the information is available or updated.

Resumen: Las Redes Eléctricas Inteligentes de Distribución (REID) han revolucionado la industria eléctrica al permitir un control y monitoreo más eficiente del suministro eléctrico, con un componente clave siendo los medidores inteligentes (SM). Estos recopilan información sobre la demanda, energía, distorsión armónica, entre otros, que deben ser almacenados y gestionados eficientemente en un sistema de gestión de datos de medición (MDMS). El MDMS debe garantizar la obtención de un conjunto completo de datos para su uso en algoritmos que aseguren la confiabilidad y calidad del suministro eléctrico. Para abordar el desafío de gestionar la gran cantidad de datos generados por los SM, se han propuesto diferentes técnicas de predicción de mediciones a corto, mediano y largo plazo, destacando el uso de inteligencia artificial como las Redes Neuronales Artificiales (ANN) y métodos de Aprendizaje Profundo (DL) debido a su capacidad de adaptación a diferentes variables de entrada y salida con diversos horizontes temporales. Además, se destaca la influencia de la diversidad de Tecnologías de la Información y Comunicación (TIC) en el tiempo de actualización y almacenamiento de datos en un MDMS. En este sentido, este trabajo tiene como objetivo identificar qué arquitectura o arquitecturas de ANN o DL podrían ser más adecuadas para aplicaciones en empresas, estudios o investigaciones, demostrando métricas de rendimiento favorables en diferentes escenarios de frecuencia de muestreo y tiempos de actualización de datos típicos en un REID. Esto es relevante debido a la necesidad del MDMS de realizar predicciones multivariables y multipaso a corto plazo para completar la información, hasta que la misma esté disponible o se actualice.

Abstract Digital Welfare State in Ecuador: Citizen Datafication and Machine Learning in the Construction and Management of Poverty In the era of digital governance, the increasing use of Big Data and Artificial Intelligence (AI) in social protection systems has emerged as a prominent concern. The state's efforts to promote citizen datafication and the implementation of AI for managing and providing social services and benefits are guided by principles of neutrality, objectivity, and technological efficiency. Nevertheless, these technologies can not only improve the effectiveness of social benefits allocation, but also have the potential of controlling, monitoring, sanctioning, and excluding beneficiaries. In light of the prevailing techno-optimism evident in the design of contemporary social policy, particularly with regard to its allocation mechanisms, this study draws upon insights from Science, Technology, and Society Studies to shed light on how data systems and AI technologies transform the notion of poverty and impact the definition of beneficiaries and non-beneficiaries. This approach focuses on unraveling the political dimension of data systems and AI technologies that are emerging as mediators in the relationship between the state and citizens. Through the case study of Ecuador's Social Registry, the aim is to enrich the critical examination of digital welfare states in the Latin American region, analyzing their implications for society and their potential to produce or exacerbate inequality and exclusion; challenging deterministic views of technological change and public policy design.

Resumen En la era de la gobernanza digital, la utilización creciente de datos y tecnologías de Big Data e Inteligencia Artificial (IA) en los sistemas de protección social se ha convertido en un asunto relevante. Los esfuerzos del Estado por impulsar la datificación ciudadana y la implementación de la IA para la gestión y provisión de servicios y beneficios sociales están vehiculizados por las ideas de neutralidad, objetividad y eficiencia tecnológica. No obstante, estas tecnologías no solo pueden mejorar la efectividad en la asignación de beneficios sociales; sino que también tienen el potencial de controlar, monitorear, sancionar y excluir a los beneficiarios. Ante el tecno-optimismo que marca el diseño de la política social contemporánea, específicamente sus mecanismos de asignación, este trabajo apuesta por los Estudios de la Ciencia, Tecnología y Sociedad para dar luz sobre cómo los sistemas de datos y las técnicas de IA transforman la noción de pobreza e impactan en la definición de beneficiarios y no beneficiarios. Este enfoque se centra en desentrañar la dimensión política -de la politics- de los sistemas de datos y las tecnologías de la IA que comienzan a mediar la relación entre el Estado y los ciudadanos. A través del estudio del caso ecuatoriano del Registro Social, el objetivo recae en nutrir el examen crítico de los Estados de Bienestar Digital en la región latinoamericana, analizando sus implicaciones en la sociedad y su potencial para producir o amplificar desigualdad y exclusión; desafiando las visiones deterministas del cambio tecnológico y del diseño de políticas públicas.

The goal with the study reported on here was to determine pre-service music teachers' views regarding the effectiveness, utility, and sufficiency of technology use in music education as well as the associations between their personality traits and their attitudes towards this use. The participants were 224 pre-service teachers studying in music teacher training programmes in Türkiye. The use of technology in music education questionnaire, the big 5 inventory and the attitude towards using technology in music education scale were used as survey instruments to collect the data. The results reveal that attitudes towards using technology in music education showed a positive relation with openness, and a negative relation with neuroticism. A qualified majority of pre-service music teachers think that technology is beneficial and that they can use technology effectively in music education. Nonetheless, the majority of pre-service music teachers still thought that educational technologies were used inadequately during their music education.

ABSTRACT The evolution towards Industry 4.0 starts from previous industrial revolutions and culminates in the convergence of advanced technologies today. Industry 4.0 uses technologies such as the internet of things, big data analysis, artificial intelligence, augmented reality, among others, to improve the efficiency and adaptability of companies; organizational productivity is focused on the modernization of processes with emerging technologies. In this context, the accounting profession is undergoing significant changes such as the automation of routine tasks allowing accountants to focus on strategic analysis. Technology impacts accounting by improving efficiency, accuracy, and data security. Accountants must acquire technological competencies, such as proficiency in accounting software and skills in data analytics, cybersecurity, and digital communication. In terms of accounting ethics, the importance of confidentiality, integrity and transparency in the digital age is highlighted. Accountants must maintain high ethical standards and adapt to technological evolution. The research concludes that the accounting profession has transformed into a more strategic role, demanding technological skills and ethics adapted to the digital era. The connection between education, industry and technology skills is essential to prepare accountants for today's challenges.

RESUMEN La evolución hacia la Industria 4.0 parte de las revoluciones industriales anteriores y culmina en la convergencia de tecnologías avanzadas en la actualidad. La Industria 4.0 emplea tecnologías como el internet de las cosas, el análisis de grandes volúmenes de datos (big data), la inteligencia artificial, la realidad aumentada, entre otras, para mejorar la eficiencia y la adaptabilidad de las empresas, la productividad organizacional se centra en la modernización de procesos con tecnologías emergentes.En este contexto, la profesión contable experimenta cambios significativos como son en la automatización de tareas rutinarias, permite a los contadores enfocarse en análisis estratégicos. La tecnología impacta la contabilidad al mejorar la eficiencia, la precisión y la seguridad de los datos. Los contadores deben adquirir competencias tecnológicas, como el dominio de software contable y habilidades en análisis de datos, ciberseguridad y comunicación digital.En cuanto a la ética contable, se destaca la importancia de la confidencialidad, integridad y transparencia en la era digital. Los contadores deben mantener altos estándares éticos y adaptarse a la evolución tecnológica. La investigación concluye que la profesión contable se ha transformado en un papel más estratégico, exigiendo habilidades tecnológicas y una ética adaptada a la era digital. La conexión entre la educación, la industria y las competencias tecnológicas es esencial para preparar a los contadores para los desafíos actuales.

Abstract This article asks if the subject is an autonomous being in political deliberation or are the thoughts, positions and forms of political organization mediated by the different communicational tools to which the subject is exposed who determine it? Thus, it analyzes the relationship between Mass Media and politics with the deliberative capacity of the individual when making decisions in the political context based on Han and Chomsky. The methodology is qualitative with documentary design, the instrument are bibliographic sheets applied to primary and secondary sources. Databases are used with the algorithm: “Neoliberalism, Psycho-politic, Big Data, Mass Media and Democracy”. Thirteen (13) books and twenty (20) articles are analyzed. The argumentative text identifies the new forms and tools of political organization and the relationship between politics and the media. It is concluded that: 1) Neoliberalism directs the idea of a free market towards the individual himself; 2) Critical citizenship that has a more active role in democracy is necessary; 3) Psycho-politics is identified as a new form of political organization; 4) The new tool of politics is Big Data; 5) It is evident that the relationship between politics and Mass Media is linked to power groups; and 6) The emotionalization of political communication decreases individual autonomy.

Resumen El presente artículo indaga si ¿es el sujeto un ser autónomo en la deliberación política o son los pensamientos, las posturas y las formas de la organización política mediadas por las distintas herramientas comunicacionales a las que se ve expuesto quienes lo determinan? Así, analiza la relación que existe entre mass media y política, con la capacidad deliberativa del individuo al tomar decisiones en el contexto político, a partir de Chomsky y Han. La metodología es cualitativa con diseño documental, el instrumento son fichas bibliográficas aplicadas a fuentes primarias y secundarias. Se usan bases de datos con el algoritmo: “Neoliberalismo, psicopolítica, big data, medios de comunicación de masas y democracia”. Se analizan 13 libros y 20 artículos. Se identifican las nuevas formas y herramientas de organización política y la relación entre política y medios de comunicación. Se concluye que: 1) el neoliberalismo direcciona la idea de libre mercado hacia el individuo mismo, 2) es necesaria una ciudadanía crítica que tenga un rol más activo en la democracia, 3) se identifica a la psicopolítica como una nueva forma de organización política, 4) la nueva herramienta de la política es el big data, 5) se evidencia que la relación entre política y mass media está vinculada con los grupos de poder y 6) la emocionalización de la comunicación política disminuye la autonomía individual.

Abstract: While the Internet has brought convenience and speed to human life, it has also led to frequent privacy violations. In the context of epidemiological investigations and information disclosure regarding confirmed Covid-19 patients, many individuals have utilized the Internet as a means to disseminate information and engage in cyber manhunts, resulting in breaches of privacy for those involved. This phenomenon is particularly prevalent within the realm of the Internet, where the boundaries of privacy invasion become blurred. Various types of privacy infringements, both active and passive negligence, are evident on social networking platforms. The juxtaposition of the virtual world of the Internet with real-life scenarios presents novel challenges in the realm of privacy violations. The Internet era, coupled with the widespread use and integration of big data, has diminished the absolute right to privacy on the Internet. This paper examines the challenge of safeguarding the identity information of infectious patients through the lens of two theoretical frameworks -Kantianism and Utilitarianism- in an effort to address this ethical dilemma.

Resumo: Enquanto a Internet trouxe conveniência e velocidade à vida humana, ela também levou a frequentes violações da privacidade. No contexto de investigações epidemiológicas e divulgação de informações em relação a pacientes confirmados de Covid-19, muitos indivíduos utilizaram a Internet como um meio para disseminar informação e participar de uma caçada cibernética, resultando em violações da privacidade para aqueles envolvidos. Esse fenômeno é particularmente prevalente no âmbito da Internet, onde os limites de invasão da privacidade se tornaram borrados. Vários tipos de infrações da privacidade, tanto negligências ativa como passiva, são evidentes em plataformas de redes sociais. A justaposição do mundo virtual da Internet com cenários da vida real apresenta novos desafios no âmbito das violações da privacidade. A era da Internet, juntamente com o amplo uso e integração de megadados (big data), diminuiu o direito absoluto à privacidade na Internet. Esse artigo examina o desafio de proteger a informação de identidade de pacientes infectantes através das lentes de dois enquadres teóricos -Kantianismo e Utilitarismo- em um esforço para abordar esse dilema ético.

Resumen: Aunque Internet ha aportado comodidad y rapidez a la vida humana, también ha dado lugar a frecuentes violaciones de la intimidad. En el contexto de las investigaciones epidemiológicas y la divulgación de información relativa a pacientes confirmados de covid-19, muchas personas han utilizado Internet como medio para difundir información y participar en cibercacerías, lo que ha dado lugar a violaciones en la intimidad de los implicados. Este fenómeno prevalece en el ámbito de Internet, donde los límites de la invasión de la intimidad se vuelven vagos. En las redes sociales, se manifiestan diversos tipos de violaciones de la intimidad, tanto por negligencia activa como pasiva. La yuxtaposición entre el mundo virtual de Internet con escenarios de la vida real plantea nuevos retos en el ámbito de las violaciones de la intimidad. La era de Internet, junto con el uso generalizado y la integración del bigdata, han mermado el derecho absoluto a la privacidad. Este artículo examina el reto de salvaguardar la información sobre la identidad de los pacientes infecciosos a través de la lente de dos marcos teóricos -el kantianismo y el utilitarismo- en un esfuerzo por abordar este dilema ético.

Abstract: The relevance of the Big Mac as a proxy indicator for Purchasing Power Parity becomes highly significant during periods of high currency depreciation. Are currencies undervalued or overvalued concerning the dollar? Is absolute and relative purchasing power parity (PPP) observed in Colombia? We address these issues by using a longitudinal data structure and estimating a panel data model. For the Colombian case, there were instances where official reports indicated an appreciation of the peso against the dollar, but the Big Mac index showed a real depreciation. The estimates suggest empirical evidence in favor of absolute PPP, i.e., based on the log-transformed equation estimated using the GLS method, while there is no empirical evidence in favor of meeting PPP in relative terms.

Resumen: El Big Mac como indicador proxy de la paridad de poder de compra cobra mucha relevancia durante periodos de alta depreciación de la moneda. ¿Se encuentran subvaluadas o sobrevaloradas las monedas con respecto al dólar?, ¿se cumple la paridad del poder de compra (PPC) absoluta y relativa en Colombia? Mediante una estructura de datos de corte longitudinal y la estimación de un modelo de datos de panel, damos respuesta a estas preguntas. Para Colombia se han observado momentos en los que oficialmente se informaba una apreciación del peso frente al dólar, el índice Big Mac detectó una depreciación real. Las estimaciones indican que existe evidencia empírica a favor de la PPC absoluta, es decir, a partir de la ecuación transformada por logaritmos y estimada mediante el método GLS, mientras que no existe evidencia empírica que apoye el cumplimiento de la PPC en términos relativos. Clasificación JEL: F31; F41; G15; C23; C32.

Abstract: This paper presents a text mining approach for extracting valuable patterns from social media documents in the context of U.S. immigration. The paper points out the uncovering of statistical features alongside linguistic elements based on graph techniques. The use of graphs provide rich data structures for representing lexical and syntactic aspects of texts, allowing the discovery of complex patterns that used by experts could provide valuable insight. The proposed method is applied over a Twitter-X/-Reddit dataset that comprise English and Spanish language samples from 2016 up to 2019. Experimental results showed that our interpretation of classic statistic techniques provide a baseline understanding of the topic while a more robust analysis (graphs) permits to uncover/predict hidden patterns over large amount of samples. In particular, the use of a co-occurrence graph helped to obtain relevant words, phrases and sentences while a user-interaction graph allow to detect important users, communities and interactions among themselves.

Abstract Artificial intelligence (AI) and its many aliases, including machine learning, deep learning and big data, have invaded modern medicine impacting most aspects of modern practice. One of the most controversial and potentially impactful, is artificial intelligence use in medical imaging. While most commercial and academic attention has focused on higher cost imaging modalities such as magnetic imaging resonance (MRI) and computed tomography (CT), ultrasound has also become the target of AI application developers. Ultrasound presents additional barriers to AI application development and execution, not seen in axial imaging such as MRI and CT. Point-of-care ultrasound (POCUS), with its lack of standardization and plethora of inexperienced users, poses the greatest imaging challenge to AI. However, POCUS is also the key to widespread access to diagnostic and interventional ultrasound at the patient’s bedside throughout the world. This article discusses AI, it utilization in POCUS, current challenges, risks, limitations, needs and future possibilities.

Resumo A inteligência artificial (IA) e os seus muitos pseudónimos, incluindo a aprendizagem automática, a aprendizagem profunda e os grandes volumes de dados, invadiram a medicina moderna, afetando a maioria dos aspetos da prática moderna. Um dos mais controversos e potencialmente impactantes é a utilização da inteligência artificial na imagiologia médica. Embora a maior parte da atenção comercial e académica se tenha centrado em modalidades de imagiologia de custo mais elevado, como a ressonância magnética (RM) e a tomografia computorizada (TC), os ultrassons também se tornaram o alvo dos criadores de aplicações de IA. O ultrassom apresenta barreiras adicionais ao desenvolvimento e execução de aplicações de IA, não observadas na imagiologia axial, como a RM e a TC. A ecografia point-of-care (POCUS), com a sua falta de normalização e a multiplicidade de utilizadores inexperientes, representa o maior desafio de imagiologia para a IA. No entanto, a POCUS também é a chave para o acesso generalizado ao diagnóstico e à ultrassonografia intervencionista à beira do leito do paciente em todo o mundo. Este artigo discute a IA, sua utilização em POCUS, desafios atuais, riscos, limitações, necessidades e possibilidades futuras.

Abstract The key benefits of qualitative research are rich insights and thick data. However, this may come at the cost of small sample sizes and low generalisability of findings. With traditional focus group sessions (FGDs), this could be addressed by conducting multiple groups. However, this requires significant investment of time and manpower. It was prudent to explore methods to gather thick data quickly, to effectively replace FGDs. This would increase the number of respondents in a single session, without increasing manpower or lengthening fieldwork, and maintaining data quality. This paper details the experience of running a pilot study of a 1.5-hour online discussion with N=103 respondents, to capture in-depth responses at scale. Using pre-programmed questions and artificial intelligence (AI) to provide instant visual analyses of responses and additional probes to respondents live, a full qualitative study was run, with a larger sample and in the same duration required for a typical FGD. The discussion was text-based - respondents could view and give their agreement or disagreement to what others may have said without directly interacting. The data was compared to data collected from a previous study where a total of 35 respondents across 5 FGDs discussed a similar topic, and analysed from an operational aspect of conducting research and gathering insights from both methodologies. While this methodology does not replace traditional FGD, it has proven effective in scaling up qualitative research by gathering large amounts of qualitative data within a short duration, in real-time. It has its limitations, primarily the inability to further nuance responses. Despite this, the pilot appears to be a successful attempt conceptually, as the AI generated valuable instant insights while the study was ongoing, particularly from open-ended (OE) responses. It may add value to specific use cases such as large-scale engagement studies which require both breadth and scalability.

Resumen Los Smart Ports (Puertos Inteligentes) se enfocan en desarrollar procesos más competitivos bajo criterios de costos, tiempos e información alrededor de toda la cadena de suministro con ayuda de las tecnologías pertenecientes a la industria 4.0. La presente investigación, tiene como objetivo analizar el uso de tecnologías emergentes empleadas en Smart Ports mediante consultas de análisis bibliométrico y revisión de literatura de la base de datos Scopus, donde se categorizaron por temáticas comunes. Se concluye el auge y desarrollo de la adopción de tecnologías emergentes en los puertos, principalmente el Internet de las Cosas (IoT), Big Data, Blockchain e Inteligencia Artificial; estas tecnologías han ayudado a que los procesos logísticos portuarios sean seguros y eficientes. También se identifica un énfasis por aportar a procesos de sostenibilidad ambiental, garantizando mediante tecnologías acciones de mejora en las terminales portuarias. Los principales ejemplos que están utilizando tecnologías de industria 4.0 en puertos se ubican en Shanghai, Huanghua, Rotterdam, Hamburgo, Barcelona, Salerno, Rávena, Vancouver y los Ángeles.

Abstract Smart Ports focus on developing more competitive processes based on criteria such as costs, time, and information across the entire supply chain with the assistance of Industry 4.0 technologies. This research aims to analyze the use of emerging technologies in Smart Ports through a literature review from the Scopus database until 2022. Bibliometrix and Vosviewer are employed for quantitative analysis, followed by a qualitative approach in an exploratory study to categorize common themes in the addressed publications. The study concludes the rise and development of emerging technology adoption in ports, primarily IoT, Big Data, Blockchain, and Artificial Intelligence. These technologies have contributed to securing and streamlining port logistics processes. There is also a notable emphasis on contributing to environmental sustainability processes through technologies ensuring improvements in port terminals. Key examples of ports incorporating Industry 4.0 technologies include Shanghai, Huanghua, Rotterdam, Hamburg, Barcelona, Salerno, Ravenna, Vancouver, and Los Angeles.

Resumen El ámbito de la atención médica, impulsado por el crecimiento constante de datos relacionados con la salud humana y el curso en desarrollo de la transformación digital, está experimentando una notable evolución. En este estudio de carácter experimental, se llevó a cabo una comparativa de técnicas de Inteligencia Artificial, específicamente redes neuronales, Random Forest y árbol de decisión, con el propósito de evaluar su eficacia en el diagnóstico de enfermedades cardiovasculares. Esto se logró aprovechando datos clínicos disponibles en bases de datos de acceso abierto. La metodología se enfocó en la identificación de las variables más influyentes en el diagnóstico de enfermedades cardiovasculares mediante una revisión exhaustiva de la literatura. Luego, se determinaron las técnicas de Aprendizaje automático a emplear y se adquirió el conjunto de datos más apropiado para estas variables. Los resultados revelaron que las tres técnicas de Inteligencia Artificial demostraron un buen desempeño en el diagnóstico de enfermedades cardiovasculares. Es importante resaltar que el modelo basado en redes neuronales destacó con una precisión del 89%, consolidándose como una herramienta de gran relevancia para respaldar el diagnóstico oportuno de estas enfermedades. Estos hallazgos sugieren un posible impacto positivo en la práctica clínica y la atención médica futura al proporcionar a los profesionales de la salud un recurso valioso para tomar decisiones informadas en el diagnóstico y tratamiento de enfermedades cardiovasculares. En última instancia, esto podría mejorar la calidad de la atención y la vida de los pacientes. Este estudio refuerza la noción de que las técnicas de Aprendizaje automático desempeñan un rol fundamental en la transformación de la atención médica y la toma de decisiones clínicas en el ámbito de la salud, ofreciendo nuevas perspectivas para la prevención y el tratamiento de enfermedades cardiovasculares y otros trastornos médicos.

Abstract The field of healthcare, driven by the continuous growth of data related to human health and the ongoing course of digital transformation, is undergoing a significant evolution. In this experimental study, a comparison of Artificial Intelligence techniques, specifically neural networks, Random Forest, and decision tree, was conducted to evaluate their effectiveness in diagnosing cardiovascular diseases. This was achieved by leveraging clinical data available in open-access databases. The methodology focused on identifying the most influential variables in cardiovascular disease diagnosis through a comprehensive literature review. Subsequently, the Machine Learning techniques to be employed were determined, and the most suitable dataset for these variables was acquired. The results revealed that all three Artificial Intelligence techniques demonstrated good performance in diagnosing cardiovascular diseases. It is worth highlighting that the neural network-based model excelled with an accuracy of 89%, establishing itself as a highly relevant tool for supporting timely disease diagnosis. These findings suggest a potential positive impact on clinical practice and future healthcare by providing healthcare professionals with a valuable resource for making informed decisions in the diagnosis and treatment of cardiovascular diseases. Ultimately, this could enhance the quality of patient care and their overall well-being. This study reinforces the notion that Machine Learning techniques play a crucial role in transforming healthcare and clinical decision-making in the field of health, offering new perspectives for the prevention and treatment of cardiovascular diseases and other medical disorders.

Resumen En la era de la Industria 4.0, caracterizada por avances tecnológicos transformadores que dan forma a los procesos de fabricación, el big data se ha convertido en una práctica común en la inteligencia empresarial. Este abarca el uso de datos con técnicas avanzadas de análisis y desempeña un papel importante en los aspectos comerciales y la elección del cliente. En este contexto, el objetivo principal de esta investigación es comprender la relación entre el big data y la competitividad empresarial. La investigación se basa en una revisión de 83 artículos publicados en la Web of Science en el período de 2016 a 2023. A través del análisis de clusters, se identifican cuatro grupos de categorías de investigación en esta área (big data y IA en la Industria 4.0, análisis de datos para la toma de decisiones, innovación empresarial y big data, y el Internet de las cosas como fuente de datos). Las implicaciones prácticas de esta investigación son pertinentes para las actividades de gestión organizacional relacionadas con procesos de innovación y toma de decisiones, con implicaciones directas para la competitividad de las pequeñas y medianas empresas. A nivel teórico, las categorías identificadas proporcionan un marco para futuras investigaciones en la comprensión de la conexión entre el big data y la competitividad en el contexto de la Industria 4.0.

Abstract In the era of Industry 4.0, characterized by transformative technological advancements reshaping manufacturing processes, big data has become a common practice in business intelligence. It encompasses the use of data with advanced analytics techniques and plays an important role in business aspects and customer choice. In this context, the primary goal of this research is to comprehend the relationship between big data and the competitiveness of businesses. The research is based on a review of 83 articles published on the Web of Science in the period 2016 and 2023. Through cluster analysis, four groups of research categories are identified in this area (big data and AI in Industry 4.0, analysis of data for decision-making, big data and business innovation, and Internet of Things as a data source). The practical implications of this research are pertinent to organizational management activities involving innovation processes and decision-making, with direct implications for small and midsize enterprises competitiveness. On a theoretical level, the identified categories provide a framework for future research in understanding the connection between big data and competitiveness in the context of industry 4.0.

Resumen: El presente artículo aborda la comunicación digital, emitida por 10 presidentes sudamericanos, en el contexto de salida de la pandemia del Covid-19 (segundo semestre 2021). En ello, se analiza el liderazgo comunicacional presidencial en el ámbito de las relaciones internacionales y, particularmente, respecto de la atención que en sus comunicaciones brindan a temáticas del plano internacional, así como la dirección que dicha atención asume.La estrategia metodológica puesta en práctica adapta mecanismos utilizados en estudios similares, combinando elementos del big data, con aquellos propios del análisis de contenido. En total se analizaron 11.616 datos/mensajes.Los hallazgos obtenidos explicitan que la comunicación digital presidencial en Sudamérica presta poca atención al ámbito internacional y, en los escasos espacios que se le reserva a esta temática, dicha comunicación se direcciona en virtud de temáticas de orden global, por sobre las bilaterales o multilaterales. Ello, en parcial contraste con nuestra hipótesis, que postula que la comunicación digital presidencial en Sudamérica presta sí poca atención al ámbito internacional, pero que en los escasos espacios que se le reserva a esta temática, dicha comunicación se direcciona en virtud de rivalidades bilaterales.

Abstract: Presidential leadership in South America plays a crucial role in the understanding of political behavior in both international relations and the formulation and implementation of foreign policies in each country. Beyond the inherent logics that underpin the presidential systems in Latin America, this leadership has been assuming significant communicational characteristics. These are primarily characterized by deployment in the digital environment that -with even greater autonomy- the region's presidents put into practice; during the COVID-19 pandemic, this leadership became even more vertiginous. In this article we address the communication that South American presidents put into practice in the digital political territory during the second half of 2021. This, in order to understand three analytical dimensions: a) presence and technical strategies put into practice; b) levels of thematic attention placed at the international level; and c) direction that such attention assumes, either bilaterally, multilaterally or globally. It is these three levels that order the questions that guide this research and the analysis of the 11,616 data that compose it. The adopted methodological approach combines content analysis and big data components, using methods from previous investigations. The resulting hypothesis follows the following logic: South American presidential digital communication pays little attention to the international sphere, and in the few spaces reserved to this topic, said communication is addressed either because of global aspects along the same lines or because of "rivalries" from which domestic benefits can be derived. There won't be many regional or multinational guidelines at that point. The political and economic environment in which this study is conducted is also crucial to take into account, as the COVID-19 epidemic and its repercussions accounted for a significant portion of the presidential initiatives examined.

ABSTRACT Learning analytics is the subject of multiple interpretations and weightings, depending on the analysis and use given to it by each scientific area. The objective of the work is to evaluate different concepts of learning analytics, formulated by various authors to provide an inclusive definition with those components that are required to be present in said concept. The research is based on a qualitative approach. It is classified as exploratory. It uses heuristic, historical-logical, analytical-synthetic, inductive-deductive methods, internal criticism, document analysis, triangulation and modeling from praxis. The results allow us to affirm that the different meanings of the concept given by the various authors, for the most part, unload their weight on the techno-pedagogical element, understood as Big Data, without sufficiently taking into account the other components that intervene in the teaching processes. educational and teaching-learning, in which the true act of learning of the student as a psycho-biological-social being occurs. It is concluded that learning analytics is essential for better management of educational institutions and, in particular, for achieving greater progress in the teaching-learning process, given that it must be complemented with qualitative data.

RESUMEN La analítica del aprendizaje es objeto de múltiples interpretaciones y ponderaciones, en dependencia del análisis y empleo dado a la misma por cada área científica. El objetivo del trabajo es valorar diferentes conceptos de analítica del aprendizaje, formulados por diversos autores para proporcionar una definición incluyente con aquellos componentes de obligada presencia en dicho concepto. La investigación se sostiene en un enfoque cualitativo. Se clasifica como exploratoria. Emplea los métodos heurísticos, histórico- lógico, analítico-sintético, inductivo-deductivo, la crítica interna, el análisis de documentos, la triangulación y la modelación desde la praxis. Los resultados permiten afirmar que las diferentes acepciones del concepto entregadas por los diversos autores, en su mayoría, descargan su peso en el elemento tecno pedagógico, entiéndase Big Data, sin tomar en cuenta de manera suficiente los demás componentes que intervienen en los procesos docente-educativo y de enseñanza-aprendizaje, en los cuales ocurre el verdadero acto de aprendizaje del alumno como ser psico-biológico-social. Se concluye que la analítica del aprendizaje resulta imprescindible para un mejor manejo de las instituciones educativas y, en particular, para el logro de superiores avances en el proceso de enseñanza-aprendizaje, habida cuenta de que esta debe complementarse con datos de índole cualitativa.

RESUMO A análise da aprendizagem é objeto de múltiplas interpretações e ponderações, dependendo da análise e utilização que lhe é dada por cada área científica. O objetivo do trabalho é avaliar diferentes conceitos de análise de aprendizagem, formulados por diversos autores para fornecer uma definição inclusiva dos componentes que devem estar presentes nesse conceito. A pesquisa baseia-se em uma abordagem qualitativa. É classificado como exploratório. Utiliza métodos heurísticos, histórico-lógicos, analítico-sintéticos, indutivo-dedutivos, crítica interna, análise documental, triangulação e modelagem a partir da práxis. Os resultados permitem-nos afirmar que os diferentes sentidos do conceito dados pelos vários autores, na sua maioria, descarregam o seu peso no elemento técnico-pedagógico, entendido como Big Data, sem ter suficientemente em conta as outras componentes que intervêm no os processos de ensino, educativo e ensino-aprendizagem, nos quais ocorre o verdadeiro ato de aprendizagem do aluno como ser psicobiológico-social. Conclui-se que a análise da aprendizagem é essencial para uma melhor gestão das instituições de ensino e, em particular, para alcançar maiores avanços no processo de ensino-aprendizagem, visto que deve ser complementada com dados qualitativos.