Objective: A systematic review of cost-utility and cost-effectiveness research works of telemedicine, electronic health (e-health), and mobile health (m-health) systems in the literature is presented. Materials and Methods: Academic databases and systems such as PubMed, Scopus, ISI Web of Science, and IEEE Xplore were searched, using different combinations of terms such as “cost-utility” OR “cost utility” AND “telemedicine,” “cost-effectiveness” OR “cost effectiveness” AND “mobile health,” etc. In the articles searched, there were no limitations in the publication date. Results: The search identified 35 relevant works. Many of the articles were reviews of different studies. Seventy-nine percent concerned the cost-effectiveness of telemedicine systems in different specialties such as teleophthalmology, telecardiology, teledermatology, etc. More articles were found between 2000 and 2013. Cost-utility studies were done only for telemedicine systems. Conclusions: There are few cost-utility and cost-effectiveness studies for e-health and m-health systems in the literature. Some cost-effectiveness studies demonstrate that telemedicine can reduce the costs, but not all. Among the main limitations of the economic evaluations of telemedicine systems are the lack of randomized control trials, small sample sizes, and the absence of quality data and appropriate measures.

Resistive switching conduction in Ni/HfO ₂ /Si capacitors is studied at temperatures ranging from 77 to 473 K. A model for the low-resistance state (LRS) consistent with the experimental data is proposed. The LRS current-voltage (I-V) curves show a maximum resistance, R ₀ , at zero bias and a minimum value, R _∞ , at voltages close to reset, which indicates a departure from linearity. A three-parameter model for the I-V curves is reported and its temperature dependence analyzed.

Artificial intelligence methods based on deep learning (DL) have recently made significant progress in many different areas including free text classification and sentiment analysis. We believe that corporate governance is one of these areas, where DL can generate very valuable and differential knowledge, for example, by analyzing the biographies of independent directors, which allows for qualitative modeling of their profile in an automatic way. For this technology to be accepted it is important to be able to explain how it generates its results. In this work we have developed a six-dimensional labeled dataset of independent director biographies, implemented three recurrent DL models based on LSTM and transformers along with four ensembles, one of which is an innovative proposal based on a multi-layer perceptron (MLP), trained them using Spanish language and economics and finance terminology and performed a comprehensive test study that demonstrates the accuracy of the results. We have also performed a complete study of explainability using the SHAP methodology by comparatively analyzing the developed models. We have achieved a mean error (MAE) of 8% in the modeling of the open text biographies, which has allowed us to perform a case study of time analysis that has detected significant variations in the composition of the Standard Expertise Profile (SEP) of the boards of directors, related to the crisis of the period 2008–2013. This work shows that DL technology can be accurately applied to free text analysis in the finance and economic domain, by automatically analyzing large volumes of data to generate knowledge that would have been unattainable by other means.

This paper presents a Deep Learning (DL) model for natural language processing of unstructured CVs to generate a six-dimensional profile of the professional experience of the Spanish companies' board of directors. We show the complete process starting with open data extraction and cleaning, the generation of a labeled dataset for supervised learning, the development, training and validation of a DL model capable of accurately analyzing the dataset, and, finally, a data analysis work based on the automated generation of the professional profiles of more than 6,000 directors of Spanish listed companies between 2003 and 2020. An RNN-LSTM neural network has been trained in three phases starting from a random initial state, (1) learning of basic structures of the Spanish language, (2) fine tuning for scientific texts in the field of economics and finance, and (3) regression modeling to generate a six-dimensional profile based on a generalization of sentiment classification systems. The complete training has been carried out with very low computational requirements, having a total duration of 120 hours of processing in a low-end GPU. The results obtained in the validation of the DL model show great accuracy, obtaining a value for the standard deviation of the mean error between 0.015 and 0.033. As a result, we have been able to outline with a high degree of reliability the profile of the listed Spanish companies' board of directors. We found that the predominant profile is that of directors with experience in executive or consultancy positions, followed by the financial profile. The results achieved show the potential of DL in social science research, particularly in Finance.

Nowadays, financial institutions increasingly leverage artificial intelligence to enhance decision-making and optimize investment strategies. A specific application is the automatic analysis of large volumes of unstructured textual data to extract relevant information through deep learning (DL) methods. However, the effectiveness of these methods is often limited by the scarcity of high-quality labeled data. To address this, we propose a new data augmentation technique, Concatenation Augmentation (CA). This is designed to overcome the challenges of processing unstructured text, particularly in analyzing professional profiles from corporate governance reports. Based on Mixup and Label Smoothing Regularization principles, CA generates new text samples by concatenating inputs and applying a convex additive operator, preserving its spatial and semantic coherence. Our proposal achieved hit rates between 92.4% and 99.7%, significantly outperforming other data augmentation techniques. CA improved the precision and robustness of the DL models used for extracting critical information from corporate reports. This technique offers easy integration into existing models and incurs low computational costs. Its efficiency facilitates rapid model adaptation to new data and enhances overall precision. Hence, CA would be a potential and valuable data augmentation tool for boosting DL model performance and efficiency in analyzing financial and governance textual data.