Significado psicológico de vida y muerte en jóvenes

Abstract. The objective was to investigate the psychological meaning of life and death for young people. The investigation was conducted with 60 subjects of both sexes, distributed equally. The technique of natural semantic networks was used and the applications were made collective. It was found that life was defined by women in terms of enjoyment, problems and emotional attachments, in comparison to men who related to life's biological and natural aspects. Regarding death, both groups experienced bereavement stages; however, women manifested these aspects more openly, in contrast to men who mentioned the consequences and favorable aspects of death. The study did not lose sight of the fact that one's conception of life and death depends to a great extent on one's social and cultural context.

Envisioning patient safety in Telehealth: a research perspective

This article explores the need for research into patient safety in large-scale Telehealth systems faced with the perspective of its development extended to healthcare systems. Telehealth systems give rise to significant advantages in improving the quality of healthcare services as well as bringing about the possibility of new types of risk. A theoretical framework is proposed for patient safety for its approach as an emerging property in complex socio-technical systems (CSTS) and their modelling in layers. As regards this framework, the differential characteristic Telehealth elements of the system have been identified, with a greater emphasis on the level of Telehealth system and its typical subsystems. The bases of the analysis are based on references in the literature and the experience accumulated by the researchers in the area. In particular, a case describing an example of Telehealth to control patients undergoing treatment with oral anticoagulants is used. As a result, a series of areas of research into and topics regarding Telehealth patient safety are proposed to cover the detectable gaps. Both the theoretical and practical implications of the study are discussed and future perspectives are reflected on.This research has been partially supported by grants FISPI09-90110 ‘Innovation Platform in new services based on telemedicine and e-health for chronic and dependent patients -PITES’ from the Ministry of Health & Consumer Affairs; and FISPI13-00508 ‘Innovation platform in new services based on Telemedicine and e- Health: definition, design and development of tools for interoperability, patient safety and support to decision (PITES-ISA)’ from the Ministry of Economy and Competitiveness (Secretary of State of Research, Development and Innovation). The funders had no role in the study, decision to publish, or drafting of the manuscript.S

Función de producción de la ganadería de carne en la zona sur del Estado de México

Se elaboró un función de producción Cobb_Douglas para la ganadería de carne en la zona sur del Estado de Méxic

Application of artificial neural networks for rapid flash calculations

Compositional reservoir simulation is widely used as an important tool for optimization of enhanced oil recovery processes. In compositional reservoir simulation, flash calculations are performed to solve for phase properties and amounts for each grid-block and each time step by use of a cubic equation of state (EOS). EOS flash calculation is one of the most time-consuming operations during compositional reservoir simulation. There has been a critical need for more efficient EOS flash for practical compositional reservoir simulation. The central idea tested in this thesis is to use artificial neural networks (ANNs) to replace the most fundamental, but time-consuming portion of EOS flash; that is, the evaluation of fugacity coefficients. ANNs are used for efficient feedforward approximation of the EOS fugacity coefficient function with a series of weights, bias, and activation functions. A set of weights and bias is found by using an algorithm that minimizes the mean squared error between the predicted and real values. This type of approximation is called supervised learning in machine learning applications. The thermodynamic model used is the Peng – Robinson equation of state with the van der Waals mixing rules and solved by the successive substitution algorithm for flash calculations. The implementation of the ANN-based fugacity coefficient function is straightforward because it only replaces the EOS-based fugacity coefficient in conventional flash calculation algorithms. Once an ANN-based fugacity coefficient function is built based on a cubic EOS, the EOS is required only when phase densities are calculated, usually at the final convergence. That is, ANN-based flash does not use an EOS during the iterative solution. We show comparisons between the conventional EOS flash calculations and the ANN flash calculations in terms of computational efficiency. Use of ANN flash can reduce on average 89.83% of the time needed by the conventional EOS flash for the cases studied in this thesis.Petroleum and Geosystems Engineerin