18 research outputs found
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. Service Business. 7(3):483-495. doi:10.1007/s11628-012-0173-7S48349573Alfares H, Duffuaa S (2009) Assigning cardinal weights in multi-criteria decision making based on ordinal rankings. J Multicriteria Decis Anal 15:125–133Altman EI (1968) Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. J Financ 23:589–609Altman EI, Hadelman RG, Narayanan P (1977) Zeta analysis: a new model to identify bankruptcy risk of corporations. J Bank Financ 1:29–54Andenmatten A (1995) Evaluation du risque de défaillance des emetteurs d’obligations: Une approche par l’aide multicritère á la décision. Presses Polytechniques et Univertitaires Romandes, LausanneBeaver WH (1966) Financial ratios as predictors of failure. J Account Res 4:71–111Boritz JE, Kennedey DB (1995) Effectiveness of neural network types for prediction of business failure. Expert Syst Appl 9:503–512Bottomley P, Doyle J, Green R (2000) Testing the reliability of weight elicitation methods: direct rating versus point allocation. J Mark Res 37:508–513Casey M, McGee V, Stinkey C (1986) Discriminating between reorganized and liquidated firms in bankruptcy. Account Rev 61:249–262Cruz S, Gonzalez T, Perez C (2010) Marketing capabilities, stakeholders’ satisfaction, and performance. Serv Bus 4:209–223Díaz M, Marcuello C (2010) Impacto económico de las cooperativas. La generación de empleo en las sociedades cooperativas y su relación con el PIB. CIRIEC 67:23–44Dimitras AI, Zopounidis C, Hurson C (1995) A multicriteria decision aid method for the assessment of business failure risk. Found Comput Decis Sci 20:99–112Dimitras AI, Slowinski R, Susmaga R, Zopounidis C (1999) Business failure prediction using rough sets. Eur J Oper Res 114:263–280Elmer PJ, Borowski DM (1988) An expert system approach to financial analysis: the case of S&L bankruptcy. Financ Manage 17:66–76Frydman H, Altman EI, Kao DL (1985) Introducing recursive partitioning for financial classification: the case of financial distress. J Financ 40:269–291García F, Guijarro F, Moya I (2008) La valoración de empresas agroalimentarias: una extensión de los modelos factoriales. Rev Estud Agro-Soc 217:155–181Gupta MC, Huefner RJ (1972) A cluster analysis study of financial ratios and industry characteristics. J Account Res 10:77–95Jensen RE (1971) A cluster analysis study of financial performance of selected firms. Account Rev 16:35–56Juliá J (2011) Social economy: a responsible people-oriented economy. Serv Bus 5:173–175Keasey K, Mcguinnes P, Short H (1990) Multilogit approach to predicting corporate failure: further analysis and the issue of signal consistency. Omega-Int J Manage S 18:85–94Li H, Adeli H, Sun J, Han JG (2011) Hybridizing principles of TOPSIS with case-based reasoning for business failure prediction. Comput Oper Res 38:409–419Luoma M, Laitinen EK (1991) Survival analysis as a tool for firm failure prediction. Omega-Int J Manage S 19:673–678March I, Yagüe RM (2009) Desempeño en empresas de economía social. Un modelo para su medición. CIRIEC 64:105–131Martin D (1977) Early warning of bank failure: a logit regression approach. J Bank Financ 1:249–276Mateos A, Marín M, Marí S, Seguí E (2011) Los modelos de predicción del fracaso empresarial y su aplicabilidad en cooperativas agrarias. CIRIEC 70:179–208McKee T (2000) Developing a bankruptcy prediction model via rough sets theory. Int J Intell Syst Account Finan Manage 9:159–173Messier WF, Hansen JV (1988) Inducing rules for expert system development: an example using default and bankruptcy data. Manage Sci 34:1403–1415Ohlson JA (1980) Financial ratios and the probabilistic prediction of bankruptcy. J Account Res 18:109–131Peel MJ (1987) Timeliness of private firm reports predicting corporate failure. Invest Anal J 83:23–27Saaty TL (1980) The analytic hierarchy process. McGraw-Hill, New YorkScapens RW, Ryan RJ, Flecher L (1981) Explaining corporate failure: a catastrophe theory approach. J Bus Finan Account 8:1–26Skogsvik R (1990) Current cost accounting ratios as predictors of business failures: the Swedish case. J Bus Finan Account 17:137–160Slowinski R, Zopounidis C (1995) Application of the rough set approach to evaluation of bankruptcy risk. Int J Intell Syst Account Finan Manage 4:24–41Vranas AS (1992) The significance of financial characteristics in predicting business failure: an analysis in the Greek context. Found Comput Decis Sci 17:257–275Westgaard S, Wijst N (2001) Default probabilities in a corporate bank portfolio: a logistic model approach. Eur J Oper Res 135:338–349Wilson RL, Sharda R (1994) Bankruptcy prediction using neuronal networks. Decis Support Syst 11:545–557Zavgren CV (1985) Assessing the vulnerability to failure of American industrial firms. A logistic analysis. J Bus Financ Account 12:19–45Zmijewski M (1984) Methodological issues related to the estimation of financial distress prediction models. Studies on Current Econometric Issues in Accounting Research. J Account Res 22:59–86Zopounidis C, Doumpos M (2002) Multicriteria classification and sorting methods: a literature review. Eur J Oper Res 138:229–24
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 managerial ethics and legal education influence online privacy policies in Greater China?
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