Separation of Lactic Acid from Diluted Solution by Hybrid Short Path Evaporation and Reactive Distillation

This work describes the separation and purification of lactic acid from diluted solution by HSPE （hybrid short path evaporation） and RD （reactive distillation） as coupled process. The results showed that it is possible to increase lactic acid concentration up to 4.7 times higher than the raw material concentration.

Comparative Skill of Numerical Weather Forecasts in Eastern Amazonia

The present study evaluates the performance of three numerical weather forecasting models: Global Forecast System (GFS), Brazilian Regional Atmospheric Modelling System (BRAMS) and ETA Regional Model (ETA), by means of the Mean Error (ME) and the Root Mean Square Error (RMSE), during the most rainy four months period (January to April 2012) on Eastern Amazonia. The models displayed errors of superestimation and underestimation with respect to the observed precipitation, mainly over center-north of Pará and all of Amapá, where the precipitation is higher. Among the analyzed models, GFS shows the best performance, except during January and March, when the model to underestimated precipitation, possibly due to the anomalously high values recorded.

Simulation of Molecular Distillation Process for Lactic Acid

In this work, a procedure to simulate the MD （molecular distillation） process for lactic acid purification was developed. The simulation was carried out with the aid of the Aspen Plus Process Simulator. Flash vessel was used to represent the MD process since the software does not present this unit operation. The simulation results with efficiency factors were in agreement with previously reported experimental data.

Facts and Fiction. Events and Stories in Relation to Texts and the World

This paper, as a part of a larger research project entitled Amazon. Realities in the Novels and the Imaginary Reports of Travelers, focuses on the following objectives： （1） the ＂referentiality＂ of the text to the external world （goal, empirical stuff）; and （2） understanding fictionality in terms of＂make-believe＂ and the principle of＂internal coherence＂. The main section sets out the theoretical foundation of the relation between facts/events and fiction, both as pragmatic and fictional texts. Then, the paper investigates in which forms and ways existing facts, data, occurrences, events and narratives in various kinds of texts constitute a representation and interpretation of the world. The central focus of the study is on the Amazon region. The study is an experiment in joining together different types of texts around the notion of＂referentiality＂; emphasizing reference to the external world （factuality）, the reading of the component elements of a language and a discourse （narratology）, historical reading against the horizon of expectation （reception theory） and the interpretation of the retrospective.

The Cartography and the Spatial Representations:Search by Perfect Map

The activities of the professionals working on cartographic products have been highlighted especially in recent years. The history, the concepts and categories of cartography cannot be neglected in using this type of tool since the use of elements that make a cartographic product, as an example: map, chart, plan, globe, and a scale model should be used since the unawareness of techniques of cartography and the absence of the components of a map may hinder the understanding of what is intended to represent cartographically. This text is a contribution to the discussion of advances in cartography including errors and absence of maps, including a brief discussion of the characteristics of cartographic language nowadays and the search for perfection in cartography.

Prognosis of forest production using machine learning techniques

Forest production and growth are obtained from statistical models that allow the generation of information at the tree or forest stand level.Although the use of regression models is common in forest measurement,there is a constant search for estimation procedures that provide greater accuracy.Recently,machine learning techniques have been used with satisfactory performance in measuring forests.However,methods such as Adaptive NeuroFuzzy Inference System(ANFIS)and Random Forest are relatively poorly studied for predicting the volume of wood in eucalyptus plantations in Brazil.Therefore,it is essential to check whether these techniques can provide gains in terms of accuracy.Thus,this study aimed to evaluate the use of Random Forest and ANFIS techniques in the prognosis of forest production.The data used come from continuous forest inventories carried out in stands of eucalyptus clones.The data were divided into 70%for training and 30%for validation.The algorithms used to generate rules in ANFIS were Subtractive Clustering and Fuzzy-C-Means.Besides,training was done with the hybrid algorithm(descending gradient and least squares)with the number of seasons ranging from 1 to 20.Several RFs were trained,varying the number of trees from 50 to 850 and the number of observations by five leaves to 35.Artificial neural networks and decision trees were also trained to compare the feasibility of the techniques.The evaluation of the estimates generated by the techniques for training and validation was calculated based on the following statistics:correlation coefficient(r),relative Bias(RB),and the relative root mean square error(RRMSE)in percentage.In general,the techniques studied in this work showed excellent performance for the training and validation data set with RRMSE values<6%,RB<0.5%,and r>0.98.The RF presented inferior statistics about the ANFIS for the prognosis of forest production.The Subtractive Clustering(SC)and Fuzzy-C-Means(FCM)algorithms provide accurate baseline and volume projection estimates;both techniques are good alternatives for selecting variables used in modeling forest production.