Uso de fármacos psicoestimulantes en drogodependencias

El uso de medicamentos estimulantes es una cuestión de plena actualidad en psiquiatría, aunque su utilización y prescripción es controvertida . Fármacos como el metilfenidato, las anfetaminas, o el modafinilo están siendo utilizados y estudiados en distintas enfermedades psiquiátricas como el trastorno por déficit de atención e hiperactividad (TDAH), la dependencia de cocaína, en trastornos del sueño y en la depresión resistente. Todos estos fármacos tienen en común, igual que las drogas de abuso, que son medicamentos que actúan sobre el sistema dopaminérgico, que constituye la base neurobiológica del refuerzo fisiológico. Los estimulantes como el metilfenidato o el modafinilo son fármacos eficaces en el TDAH y han sido estudiados en el tratamiento de la dependencia de cocaína. En niños con TDAH el metilfenidato es un factor protector para el desarrollo de fármaco en la dependencia de cocaína, aunque son estudios preliminares, por lo que no se debe considerar que este totalmente demostrado que los fármacos psicoestimulantes sean eficaces en el tratamiento de esta dependencia. Aunque no son conocidos todos los mecanismos fisiopatológicos, parece crítico que el refuerzo, y por lo tanto el riesgo de dependencia, aparece cuando se producen incrementos rápidos dopaminérgicos y que los efectos terapéuticos aparecen cuando son lentos y mantenidos. Las características de uso a dosis bajas administradas por vía oral disminuyen el riesgo de abuso. Para realizar una adecuada prescripción es necesario aclarar, definitivamente, los mecanismos neuroquímicos en los que intervienen, y sus indicaciones en drogodependenciasStimulant drugs prescription is a controversial and current topic in psychiatry. Drugs such as methylphenidate, amphetamine compounds and modafinil have been trialed and used in attention deficit hyperactivity disorder (ADHD), sleep conditions, cocaine dependence and as an adjunct to antidepressants for depression. All these drugs, like stimulant drugs abuse, increase extracellular dopamine in the brain.This effect is associated with reinforcing as well as therapeutic effects. Methylphenidate and modafinil treatment of ADHD are associated with a reduced risk for later substance abuse among ADHD patients. There is evidence of the beneficial effects of the use of modafinil in cocaine dependence, altough there isn't conclusive evidence for the stimulants' efficacy in treatment of the stimulants' dependence. At this time, the physiopathology of drug abuse and dependence is unknown, but it's known that the very critical point is that the reinforcing effects are associated with rapid changes in dopamine increases, whereas the therapeutic effects are associated with slowly and smoothly rising dopamine levels, such as are achieved with low doses and oral administration. Due to this, it's necessary to study the neurobiological bases on which stimulants drugs are related, and their clinical use in dependence treatment

Monitoring credit risk in the social economy sector by means of a binary goal programming model

The final publication is available at Springer via http://dx.doi.org/10.1007/s11628-012-0173-7Monitoring the credit risk of firms in the social economy sector presents a considerable challenge, since it is difficult to calculate ratings with traditional methods such as logit or discriminant analysis, due to the relatively small number of firms in the sector and the low default rate among cooperatives. This paper intro- duces a goal programming model to overcome such constraints and to successfully manage credit risk using economic and financial information, as well as expert advice. After introducing the model, its application to a set of Spanish cooperative societies is described.García García, F.; Guijarro Martínez, F.; Moya Clemente, I. (2013). Monitoring credit risk in the social economy sector by means of a binary goal programming model. 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Scheduling internal audit activities:A stochastic combinatorial optimization problem

The problem of finding the optimal timing of audit activities within an organisation has been addressed by many researchers. We propose a stochastic programming formulation with Mixed Integer Linear Programming (MILP) and Constraint Programming (CP) certainty-equivalent models. In experiments neither approach dominates the other. However, the CP approach is orders of magnitude faster for large audit times, and almost as fast as the MILP approach for small audit times. This work generalises a previous approach by relaxing the assumption of instantaneous audits, and by prohibiting concurrent auditin

Recurrent ANNs for Failure Predictions on Large Datasets of Italian SMEs

The prediction of failure of a firm is a challenging topic in business research. In this paper, we consider a machine learning approach to detect the state of asset shortfall in the Italian small and medium-sized enterprises’ context. More precisely, we use the recurrent neural networks to predict the insolvency of firms. The huge dataset we study allows us to overcome problems of distortions given by smaller sample sizes. The observed sample comes from AIDA database, and consider thirty variables replicated for five years. The main result is that recurrent neural networks outperform the multi-layer perceptron architecture used as benchmark. The obtained accuracy scores are in line with those found in the literature, and this suggests that the use of new techniques such as those tried out in this study could produce even better results

Do Declarative Process Models Help to Reduce Cognitive Biases Related to Business Rules?

Declarative process modeling languages, such as Declare, represent processes by means of temporal rules, namely constraints. Those languages typically come endowed with a graphical notation to draw such models diagrammatically. In this paper, we explore the effects of diagrammatic representation on humans' deductive reasoning involved in the analysis and compliance checking of declarative process models. In an experiment, we compared textual descriptions of business rules against textual descriptions that were supplemented with declarative models. Results based on a sample of 75 subjects indicate that the declarative process models did not improve but rather lowered reasoning performance. Thus, for novice users, using the graphical notation of Declare may not help readers properly understand business rules: they may confuse them in comparison to textual descriptions. A likely explanation of the negative effect of graphical declarative models on human reasoning is that readers interpret edges wrongly. This has implications for the practical use of business rules on the one hand and the design of declarative process modeling languages on the other

Risk and Information Disclosure in Google Drive Sharing of Tax Data

Risk abounds as individuals engage in activities involving the sharing of sensitive information through a third-party cloud storage tool. In this research, we investigate the storage and sharing of very sensitive information (an individual\u27s tax filing information) through a specific third-party technology provider, Google Drive. Within the specifics of such a potentially risky act we argue that information assurance mechanisms implemented by the cloud storage service provider reduce risk perceptions of individuals even when very sensitive information is being shared. Previous positive experience with a cloud service is likely to mitigate concerns on information sharing on the cloud. To elaborate this proposed relationship a research model of information assurance is proposed and tested in the context of tax filing sharing intention