ObjetivoAvaliar o estado nutricional e a ingestão alimentar de mulheres adultas sadias durante o ciclo menstrual. MétodosQuarenta e cinco voluntárias foram acompanhadas durante três meses. A avaliação do estado nutricional foi baseada no índice de massa corporal, porcentagem de gordura e água corporal. Foram aplicados seis registros alimentares para análise da ingestão dos grupos de alimentos, usando como base o guia alimentar da pirâmide. Para a observação do sintoma "desejos alimentares", foram utilizados três "mapas de sintomas diários". ResultadosOs valores médios de índice de massa corporal e de porcentagem de gordura corporal apresentaram-se normais em ambas as fases, entretanto foi observado maior percentual de mulheres com água corporal acima do padrão na fase lútea (77%). O consumo de alimentos do grupo complementar foi maior na fase lútea. Todos os outros grupos de alimentos, com exceção do grupo de carnes, apresentaram consumo inferior às recomendações, em ambas as fases. A intensidade do sintoma "desejos alimentares" foi leve durante o ciclo menstrual, não sendo observada diferença significativa entre as fases. O sintoma "desejos alimentares" associou--se positivamente com o aumento da ingestão do grupo complementar na fase lútea.

In the petroleum industry, sensor data and information are valuable. It can detect, predict and help to understand processes during oil production. Offshore wells require more attention. Once workovers, maintenance, and intervention are more costly than onshore wells. Coupling data-driven methods for well-monitoring applications, two unsupervised classification methods, one statistical and one machine learning-based, are proposed to detect anomalies in well data. The novelty is presented by applying a Control Chart using a 3 standard deviations window for the Permanent Downhole Gauge Pressure sensor (P-PDG), and a Fuzzy C-means algorithm to classify data from pressure and temperature sensors in an offshore field. The main goal in structuring a classified data set is using it to train machine learning models to monitor and manage petroleum production. Modeling applications for early fault detection systems in offshore production, based on real-time data from production sensors, require classified data sets. Then, labeling two target classes: "normal" and "fault" is a key step to be implemented in order to train the machine learning models. Therefore, this paper applies two methodologies to classify a real-time data set to create a training data set divided into "normal" and "fault" classes. Thus, it is possible to visualize the abnormal events pointed out by the methodologies and compare how sensible is each method. In addition, it is proposed a random forest application to test the performance of the classified data sets from both methods. The results have shown that the control chart method presents higher sensibility than fuzzy c-means, however, the differences between are insignificant. The random forest performance displayed sensitivity and specificity values of 99.91% and 100% for the data set classified by the control chart method and 94.01% and 99.98% for the data set classified by fuzzy c-means algorithm.

Detecting the early stages of failures is an old concern of petroleum industry. In order to tackle this problem, a novel sensor analysis methodology is proposed. The assessment of production sensors' behavior, individually or in a group, leads to a better understanding of failure modes during oil and gas production. Thus, Principal Components Analysis and Logistic Regression are incorporated as multivariate statistical modeling for studying the impact of different anomalies in production sensors. Therefore, a deep statistical analysis of these sensors can strengthen assumptions for supporting the modeling process of early fault detection systems. Based on a reliable public data set containing data from real wells, the application of the PCA approach combined with a Logistic Regression resulted in better visualization and understanding of some failures that occurred during petroleum production, such as the abrupt increase in BSW (Basic sediment and water), spurious closure of DHSV (Down hole Safety Valve), severe slugging, flow instability, productivity loss, quick restriction in PCK (production choke), scaling in PCK and hydrate formation in production lines. The two statistical approaches were used as a combined method to provide useful information regarding the failure modes in the dataset. Also, the dataset presented two classes that are important for anomaly detection in oil wells: "normal" and "abnormal", which allow detecting when production is outside its normal condition. Then, using the production sensors analysis with failure data can help to formulate better detection algorithms. By using PCA and Logistic Regression it was possible to identify which set of variables is better for detecting a specific type of problem. The application of these techniques boosts the modeling of early detection systems in oil and gas production. Besides, the assumptions led to conclusions about how to put groups of sensors and abnormalities together and how much time a well stands in a steady normal condition. Other conclusions showed the significance of transient information for fault detection modeling and the need for individual wells analyses. Hence, using PCA for treating and transforming the data brings important contributions for early fault detection modeling, once it allowed insight into how sensors and abnormal events can be related. Consequentially, the present paper has significant novelty contribution: it raises important assumptions that help to build solid knowledge about the anomalies behavior and help researchers to implement a better modeling strategy.

Hydraulic fracturing is widely used to increase oil well production and to reduce formation damage. Reservoir studies and engineering analyses are carried out to select the wells for this kind of operation. As the reservoir parameters have some diffuse characteristics, Fuzzy Inference Systems (FIS) have been tested for these selection processes in the last few years. This paper compares the performance of a neuro fuzzy system and a genetic fuzzy system used for selecting wells for hydraulic fracturing, with knowledge acquired from an operational data base to set the SIF membership functions. The training data and the validation data used were the same for both systems. We concluded that, despite the genetic fuzzy system being a newer process, it obtained better results than the neuro fuzzy system. Another conclusion was that, as the genetic fuzzy system can work with constraints, the membership functions setting kept the consistency of variable linguistic values.

In general, most capital budgeting problems deals with future and oftenuncertain data. To cope with this uncertainty, fuzzy sets theory has beenapplied to traditional deterministic approaches by replacing uncertain datawith fuzzy numbers. In this paper the bases of The Fuzzy Mathematics ofFinance, to model the uncertainties of data inputs is presented, providing anapproach to risk analysis. The goal is to capture and quantify the qualitativeinformation of the project coordinators using linguistics variables and modelthe uncertain cash flow and discount rates as triangular fuzzy numbers. Anexample of fuzzy cash flows and fuzzy resultant net present value (NPV) iscomputed from the model. Finally, a fuzzy project selection is performed byapplying different dominance rules and a comprehensive numerical example ispresent. Introduction Evaluating risk is often an important and complex job in the decisions ofcapital investments. Fluctuations of interest rates, technological changes andthe difficulty in forecasting the behavior of the processes parameters wherethe investments are being made, has turned the evaluation of risk involved, anobject of many studies in recent years (Bierman, H. & Bhimani, A.,1991).Bierman, H. (1986), suggests that the challenges of risk evaluations andconsiderations, were the main problems in the decision taking of capitalinvestments in American companies in the 90s. He says that little is yet knownabout why some companies are more effective managing risk than others. Projectmanagers work in a complex and dynamic environment and are constantly facingseveral risky and uncertain situations. It is thus essential, that in theprocess of decision making, the project managers take into consideration allpossibilities and risk factors, interpret how these factors interact andanalyze the beliefs as well as the information in this environment ofuncertainty. According to Tummala (1997 e 1995), Ho and Pike (1992) and Smister(1994), this is not widespread practice among project managers. The majorlimitations to the application of these practices are the time wasted on theanalysis, difficulty obtaining the data and the application of the necessarystatistical methods. The risk present in capital investments are there because of theuncertainties of the variables used for calculating the indicators necessaryfor the decision taking, the Present Net Value, Return Rate, etc. There areseveral statistical tools used for the processing of these variables, such assensitivity analysis, probability analysis, decision tree, Monte Carlosimulations, options theory, CAPM Models (Capital Asset Pricing Model) etc. Inall these techniques it is essential to determine the probabilities of theevent that influence the variables involved in the calculations of theinvestment indicators.