Higher education institutions (HEIs) have been permeated by the technological advancement that the Industrial Revolution 4.0 brings with it, and forces institutions to deal with a digital transformation in all dimensions. Applying the approaches of digital transformation to the HEI domain is an emerging field that has aroused interest during the recent past, as they allow us to describe the complex relationships between actors in a technologically supported education domain. The objective of this paper is to summarize the distinctive characteristics of the digital transformation (DT) implementation process that have taken place in HEIs. The Kitchenham protocol was conducted by authors to answer the research questions and selection criteria to retrieve the eligible papers. Nineteen papers (1980–2019) were identified in the literature as relevant and consequently analyzed in detail. The main findings show that it is indeed an emerging field, none of the found DT in HEI proposals have been developed in a holistic dimension. This situation calls for further research efforts on how HEIs can understand DT and face the current requirements that the fourth industrial revolution forced.

Background: Industry 4.0 technologies have been widely used in the railway industry, focusing mainly on maintenance and control tasks necessary in the railway infrastructure. Given the great potential that these technologies offer, the scientific community has come to use them in varied ways to solve a wide range of problems such as train failures, train station security, rail system control and communication in hard-to-reach areas, among others. For this reason, this paper aims to answer the following research questions: what are the main issues in the railway transport industry, what are the technologic strategies that are currently being used to solve these issues and what are the technologies from industry 4.0 that are used in the railway transport industry to solve the aforementioned issues? Methods: This study adopts a systematic literature review approach. We searched the Science Direct and Web of Science database inception from January 2017 to November 2021. Studies published in conferences or journals written in English or Spanish were included for initial process evaluation. The initial included papers were analyzed by authors and selected based on whether they helped answer the proposed research questions or not. Results: Of the recovered 515 articles, 109 were eligible, from which we could identify three main application domains in the railway industry: monitoring, decision and planification techniques, and communication and security. Regarding industry 4.0 technologies, we identified 9 different technologies applied in reviewed studies: Artificial Intelligence (AI), Internet of Things (IoT), Cloud Computing, Big Data, Cybersecurity, Modelling and Simulation, Smart Decision Support Systems (SDSS), Computer Vision and Virtual Reality (VR). This study is, to our knowledge, one of the first to show how industry 4.0 technologies are currently being used to tackle railway industry problems and current application trends in the scientific community, which is highly useful for the development of future studies and more advanced solutions. Funding: Colombian national organizations Minciencias and the Mining-Energy Planning Unit.

From the various perspectives of machine learning (ML) and the multiple models used in this discipline, there is an approach aimed at training models for the early detection (ED) of anomalies. The early detection of anomalies is crucial in multiple areas of knowledge since identifying and classifying them allows for early decision making and provides a better response to mitigate the negative effects caused by late detection in any system. This article presents a literature review to examine which machine learning models (MLMs) operate with a focus on ED in a multidisciplinary manner and, specifically, how these models work in the field of fraud detection. A variety of models were found, including Logistic Regression (LR), Support Vector Machines (SVMs), decision trees (DTs), Random Forests (RFs), naive Bayesian classifier (NB), K-Nearest Neighbors (KNNs), artificial neural networks (ANNs), and Extreme Gradient Boosting (XGB), among others. It was identified that MLMs operate as isolated models, categorized in this article as Single Base Models (SBMs) and Stacking Ensemble Models (SEMs). It was identified that MLMs for ED in multiple areas under SBMs’ and SEMs’ implementation achieved accuracies greater than 80% and 90%, respectively. In fraud detection, accuracies greater than 90% were reported by the authors. The article concludes that MLMs for ED in multiple applications, including fraud, offer a viable way to identify and classify anomalies robustly, with a high degree of accuracy and precision. MLMs for ED in fraud are useful as they can quickly process large amounts of data to detect and classify suspicious transactions or activities, helping to prevent financial losses.

La transformación digital en la Universidad se aborda como un punto de inflexión creado por nuevos paradigmas que surgen del mundo digital, y que la ha llevado a repensarse como institución para seguir siendo competitiva, visible en su dinámica de transformación social, sostenible a través del tiempo, y no perder su vigencia en el desarrollo tecnológico que surge, en constate cambio, atendiendo así las necesidades del entorno social del que forma parte. Al asumir este reto, la universidad reconoce que es de vital importancia, como primera medida, realizar una Transformación Cultural. Este artículo presenta el caso de la Universidad Nacional de Colombia (U.N.), que abordó este cambio, en su fase inicial, a través de la divulgación con los integrantes de la alta dirección. Para lograrlo, se diseñó una metodología, que prioriza su intervención en dos aspectos: el primero, la generación de la cultura organizacional alrededor de la Transformación Digital; el segundo, la creación del Proyecto Estratégico Institucional “Transformación Digital U.N. 2030”. Ambos aspectos buscan alinear la cultura corporativa existente con las nuevas realidades digitales de la Universidad Nacional de Colombia, a través de un estudio de prospectiva. La red Openergy y la Universidad Nacional de Colombia aúnan visiones sobre la definición de la Transformación Digital como un proceso de cambio cultural dentro de una entidad con impacto en todo su contexto. La Universidad Nacional de Colombia aporta a Openergy su experiencia de definición e implementación de la visión 2030, lo que supone aportaciones desde la gestión, la docencia, la tutorización y la interacción con los diversos actores de ese proceso. En este artículo se presentan los resultados más relevantes que se identificaron en la recopilación de información primaria, y en la caracterización de los imaginarios y expectativas de los diferentes participantes y actores del proceso de transformación digital. Lo que arroja una aproximación a un modelo que los autores han denominado el Gran Partenón, que se explicita en este artículo. Adicionalmente, se reflexiona sobre las características del proceso que pueden ser aplicadas a la red Openergy y cómo puede contribuir activamente. Finalmente, se presentan las conclusiones más relevantes que se han encontrado en este ejercicio de modelización, pretendiendo mostrar la alineación y coordinación de las políticas institucionales con el quehacer universitario.

This paper addresses supply risk in the oncological medicine supply chain (SC) in Colombia. A system dynamics model was developed for assessing supply risk impact on SC operation and performance. The costs of risk and logistics operation were evaluated in current conditions and in a risk mitigation scenario. The model was based on a systematic analysis of supply networks, mainly concerning the reduction of overall SC costs and improving service, quality and opportunity performance index. This article has resulted from research into SC risk management (SCRM) carried out by the Universidad Nacional de Colombia's SEPRO research group.