Emprego de redes neurais artificiais supervisionadas e não supervisionadas no estudo de parâmetros reológicos de excipientes farmacêuticos sólidos

In this paper artificial neural network (ANN) based on supervised and unsupervised algorithms were investigated for use in the study of rheological parameters of solid pharmaceutical excipients, in order to develop computational tools for manufacturing solid dosage forms. Among four supervised neural networks investigated, the best learning performance was achieved by a feedfoward multilayer perceptron whose architectures was composed by eight neurons in the input layer, sixteen neurons in the hidden layer and one neuron in the output layer. Learning and predictive performance relative to repose angle was poor while to Carr index and Hausner ratio (CI and HR, respectively) showed very good fitting capacity and learning, therefore HR and CI were considered suitable descriptors for the next stage of development of supervised ANNs. Clustering capacity was evaluated for five unsupervised strategies. Network based on purely unsupervised competitive strategies, classic "Winner-Take-All", "Frequency-Sensitive Competitive Learning" and "Rival-Penalize Competitive Learning" (WTA, FSCL and RPCL, respectively) were able to perform clustering from database, however this classification was very poor, showing severe classification errors by grouping data with conflicting properties into the same cluster or even the same neuron. On the other hand it could not be established what was the criteria adopted by the neural network for those clustering. Self-Organizing Maps (SOM) and Neural Gas (NG) networks showed better clustering capacity. Both have recognized the two major groupings of data corresponding to lactose (LAC) and cellulose (CEL). However, SOM showed some errors in classify data from minority excipients, magnesium stearate (EMG) , talc (TLC) and attapulgite (ATP). NG network in turn performed a very consistent classification of data and solve the misclassification of SOM, being the most appropriate network for classifying data of the study. The use of NG network in pharmaceutical technology was still unpublished. NG therefore has great potential for use in the development of software for use in automated classification systems of pharmaceutical powders and as a new tool for mining and clustering data in drug development

Avaliação da qualidade final de efluentes de lagoas de estabilização com vistas ao reúso urbano

Stabilization pond is the most used sewage treatment system in the country, corresponding to approximately 90% of all systems. The systems evaluated were stabilization ponds system of sewage treatment in the city of Natal / RN. This research aimed to analyze the possible uses through physical-chemical and bacteriological of these final effluent ponds for urban uses depending on the characteristics after passage around the treatment system. The parameters used were chosen according to those established by Chernicharo et al. (2006), in order to characterize the effluent. The parameters evaluated were: DO, temperature, pH, conductivity, organic nitrogen, ammonia, NTK, total phosphorus, and series of solid fecal coliforms. Generally, the characteristics of the effluent followed variability found in the literature. The results showed an efficiency that is technically feasible to use the effluent end of some of STPs analyzed when checked parameters alone, if fitting in unrestricted urban use, restricted use and urban land use