Highly active antiretroviral therapy can be administered safely and effectively to children and adolescents in resource-limited settings. Lopinavir/ritonavir-containing highly active antiretroviral therapy is a safe, effective, and durable treatment option for antiretroviral drug-experienced older children and adolescents with advanced HIV disease.

The subject of the present paper represents the result of a research that uses artificial intelligence, through the artificial neural networks, in order to simulate the Romanian Economic Sentiment Indicator (ESI-mentioned by The Economist and provided by the European CommissionEconomic and Financial Affairs website). The reason for the research is to determine a better method to forecast the Romania ESI considering its nonlinear behavior. For the simulation, a feed forward artificial neural network (ANN) was used. For this type of ANN, the best training algorithm is the back propagation algorithm. Training condition were set to a smaller than 5% error between the real data and the simulated data. The research is then extended to new input data (not available at the time of ANN training) used for comparison and forecasting of the real trends with the simulated ones. Even with new data, the use of the ANN determined forecasting results smaller than 5% (between -4.92; 5.16%). Also the ANN simulation offers an image about how indicators influence the ESI. In conclusion, the use of the ANN is considered a success and the authors determine the possibility that ANN research application be extended to other countries ESI or even to the European zone

No abstract

Lately, the global banking services industry has faced numerous challenges: the digitalization, increased competition, the instability of monetary and foreign exchange markets, and the volatility of exchange rates. However, at present, banks are facing the greatest challenge of all that is, placing the customers at the centre of business and the systematic follow-up of customer satisfaction. The present paper aims at assessing the influence of a series of determinants and socio-demographic factors on customer satisfaction with banking services in Romania, using the probit and logit models. The research focused on the Romanian banking market due to its distinctiveness within the European context – performance indicators above the European average during the past five years and a concentration level that discloses a significant growth potential. The results of the two models employed revealed similar results, with the most influential variables on customer satisfaction being convenience, e-banking, quality, and revenues.

The present paper shows the discussion and results of the research that simulated the fluctuation of the US Consumer Credit (CONS) using Artificial Neural Network (ANN). The research had several objectives, like: building, training and using an ANN as a possible tool for decision making, through the simulation of the US Consumer Credit. The condition for a successful training of the ANN was established as a smaller difference than 1.5% between the real data and the simulated data. A feed forward artificial neural network and a back propagation algorithm were used for the training and preparation of future use of the ANN. For the training result, two testing sessions were used. For the use of ANN in CONS forecasting, the research was extended with the simulation of CONS trend using trained ANN and a new set of consecutive values for each of the input data. Also, the new simulations determined a hierarchy of the inputs that were considered for the simulations of the CONS. In the conclusion, the researchers consider the ANN training and testing a success due to the values obtained: a difference of [-0.69; 0.32] % between the real and simulated CONS values. The trend simulation also shows the training success with accuracy smaller than 1.5%. The authors consider that the research can be extended to other countries or by adding others indicators.