Henry Laniado

Los beneficios de las TIC en las pymes se resumen en mayor rendimiento, crecimiento, expansión y nuevos productos. La investigación en gestión y sistemas de información suele abordar las TIC sin distinguir el efecto de diferentes tipos de tecnologías y su nivel de uso en las organizaciones. El objetivo de esta investigación fue analizar el efecto de la disposición y el nivel de uso de distintas herramientas y aplicaciones de TIC, y de las competencias digitales en el desempeño operativo de pymes del sector turístico del Valle del Cauca, Colombia, mediante una encuesta aplicada a 100 representantes legales o gerentes de empresas turísticas, a partir de una población de 2434 pymes. Para el análisis de las variables y sus indicadores se usó PLS-SEM. Los resultados evidenciaron que el nivel de TIC influye positiva y significativamente en el desempeño operativo, aunque las competencias digitales no presentaron un efecto moderador destacable en la relación entre el nivel de TIC y el desempeño operativo. Se concluye que, en la muestra analizada, el nivel de TIC constituye un predictor relevante del desempeño operativo, aunque su efecto no depende necesariamente del nivel general de competencias digitales. Estos hallazgos aportan evidencia empírica sobre el valor estratégico de las TIC en pymes turísticas de economías emergentes y sugieren la necesidad de profundizar en otros factores organizacionales que podrían potenciar dicha relación.

Functional data analysis has gained significant attention due to its wide applicability. This research explores the extension of statistical analysis methods for functional data, with a primary focus on supervised classification techniques. It provides a review on the existing depth-based methods used in functional data samples. Building on this foundation, it introduces an extremality-based approach, which takes the modified epigraph and hypograph indexes properties as classification techniques. To demonstrate the effectiveness of the classifier, it is applied to both real-world and synthetic data sets. The results show its efficacy in accurately classifying functional data. Additionally, the classifier is used to analyze the fluctuations in the S\&P 500 stock value. This research contributes to the field of functional data analysis by introducing a new extremality-based classifier. The successful application to various data sets shows its potential for supervised classification tasks and provides valuable insights into financial data analysis.

This paper presents an associative neural network based on Long Short-Term Memory (LSTM) networks to predict the opening, minimum, maximum and closing prices ​​of the Shanghai composite index, PetroChina Company Limited (PetroChina), and Zhongxing Telecommunications Equipment Corporation (ZTE). The data is transformed with time series techniques to render them stationary. Once good results are obtained in terms of the mean absolute percentage error (MAPE), the model is tested with the American Nasdaq Composite Index (IXIC). Similar works have been carried out, such as that of Ding & Qin (2020) where they predict the opening, minimum and maximum prices ​​of an asset. This study goes a step further to predict the closing value following the proposed associative network methodology. Having the opening price and the closing price, it is possible to make investments to generate profitability based on the daily net change in value of the asset. Resumen Este trabajo presenta una red neuronal asociativa basada en redes LSTM (Long Short-Term Memory) para predecir los precios de apertura, mínimo, máximo y cierre del índice compuesto de Shanghai, PetroChina y Zhongxing Telecommunications Equipment Corporation (ZTE). Los datos son transformados con técnicas de series temporales para hacerlos estacionarios. Una vez obtenidos buenos resultados en términos del error porcentual absoluto medio (MAPE), el modelo es probado con el American Nasdaq Composite Index (IXIC). Trabajos similares han sido realizados, como el de Ding & Qin (2020), donde predicen los precios de apertura, mínimos y máximos de un activo. Este studio va un paso más adelante en la predicción del valor de cierre siguiendo la metodología de redes asociativas propuesta. Teniendo el precio de apertura y el precio de cierre, es posible realizar inversiones para generar rentabilidad en base al cambio neto diario en valor del activo.

This article presents a novel nonparametric approach to generate synthetic data using copulas, which are functions that explain the dependency structure of the real data. The proposed method addresses several challenges faced by existing synthetic data generation techniques, such as the preservation of complex multivariate structures presented in real data. By using all the information from real data and verifying that the generated synthetic data follows the same behavior as the real data under homogeneity tests, our method is a significant improvement over existing techniques. Our method is easy to implement and interpret, making it a valuable tool for solving class imbalance problems in machine learning models, improving the generalization capabilities of deep learning models, and anonymizing information in finance and healthcare domains, among other applications.

Dengue is a disease of high interest for public health in the affected localities. Dengue virus is transmitted by Aedes species and presents hyperendemic behaviors in tropical and subtropical regions. Colombia is one of the countries most affected by the dengue virus in the Americas. Its central-west region is a hot spot in dengue transmission, especially the Department of Antioquia, which has suffered from multiple dengue outbreaks in recent years (2015–2016 and 2019–2020). In this article, we perform a retrospective analysis of the confirmed dengue cases in Antioquia, discriminating by both subregions and dengue severity from 2015 to 2020. First, we conduct an exploratory analysis of the epidemic data, and then a statistical survival analysis is carried out using a Cox regression model. Our findings allow the identification of the hazard and socio-demographic patterns of dengue infections in the Colombian subtropical region of Antioquia from 2015 to 2020.