Características psicológicas do paciente obeso grave e suas implicações pós-operatórias na cirurgia bariátrica

Obesity is a chronic disease that has multi-factorial aetiology, characterized by high degree of body fat; the degree of obesity will vary according to the Body Mass Index (BMI=m2 /kg). The severe degree of obesity is characterized by BMI>40 and it is regularly associated to endocrine-metabolic or mechanic clinical alterations, and to psychological disorders. Binge Eating (BE) results were overly high for this population. The Bariatric Surgery has been the treatment chosen by those diagnosed with severe obesity as this intervention provides prompt outcomes for loss of weight and clinical improvement conditions. However, recent research has acquiesced that after two years between 20% and 30% of people subject to this intervention gained weight. The main objective of this research is to assess the psychological and behavioral characteristics of those diagnosed with severe obesity that have been subject to Gastric Bypass Surgery in the past 24 months. Specific aspects were investigated: (1) characteristics of different personalities and diagnose of clinic and personality disorders; (2) BE and its relation with loss of weight; (2) the difference between the groups regarding post-surgery care, e.g. physical activity, psychological and dietician input. Method: 40 adults (women and men) aged 23 and 60 year-old who went through a bariatric surgery in the past 24 months, in the city of Natal-RN (Brazil); they were assembled in two groups n=20, Gain group displaying loss of < 50% of their initial surplus of weight, and the Loss group displaying loss of >50%. The research protocol is made of a socio-demographic questionnaire and 3 psychometric instruments: Rorschach – Comprehensive System; Millon Personality Inventory (MCMI-III); and the Binge Eating Scale (Escala de Compulsão Alimentar Periódica (ECAP). Through Rorschach significant differences between these groups were verified according to the kind of personality (EB) - more EB Extratensivo in Gain group and Intratensivo in Loss group – and the lack of control to express affect, increasing the answer for Color Pure at Group I. Concerning the people standardization, the sample as a whole tends to show psychic pain, denigrated selfperception, high levels of self-criticism, distorted perceptions, vulnerability to develop mood disorders and high scores regarding Suicide. MCMI-III results showed more clinic and personality disorders in Group I: Depressive Disorder and Schizotypal, Anxiety, Dysthymia, Major Depressive Disorder; Thought Disorder, Bipolar- Manic and Posttraumatic Stress Disorder. In relation to ECAP, the results indicated significant differences, showing increased BE results in Gain group. There were found significant differences between BE severity and the presence of clinic and personality disorders. Concerning the post-surgery care, the observed differences are statistically significant regarding physical activities with median-increased differences in Loss group. There is a difference between the initial weight and the time post-surgery, indicating that the higher the initial weight and the time after the surgery the higher the re-gain of weight post-surgery. Finally, the results show that the participants with more than 3 years of surgery will have Clinic and Major Depressive Disorders; Somatoform Disorder; Dysthymia. These results confirm prior studies related to BE post-surgery and re-gain of weight as well as the proneness of clinic disorders in severe obesity people. That means the results reinforce that the surgery process is a facet of the severe obesity treatment. The post-surgery process needs to be the main focus of attention and have a long-term input to sustain the care of the surgery results and the quality of life of the patients.

PHC Production Management System Based on RFID

At present, in large precast concrete enterprises, the management over precast concrete component has been chaotic. Most enterprises take labor-intensive manual input method, which is time consuming and laborious, and error-prone. Some other slightly better enterprises choose to manage through bar-code or printing serial number manually. However, on one hand, this is also labor-intensive, on the other hand, this method is limited by external environment, making the serial number blur or even lost, and also causes a big problem on production traceability and quality accountability. Therefore, to realize the enterprise’s own rapid development and cater to the needs of the time, to achieve the automated production management has been a big problem for a modern enterprise. In order to solve the problem, inefficiency in production and traceability of the products, this thesis try to introduce RFID technology into the production of PHC tubular pile. By designing a production management system of precast concrete components, the enterprise will achieve the control of the entire production process, and realize the informatization of enterprise production management. RFID technology has been widely used in many fields like entrance control, charge management, logistics and so on. RFID technology will adopt passive RFID tag, which is waterproof, shockproof, anti-interference, so it’s suitable for the actual working environment. The tag will be bound to the precast component steel cage (the structure of the PHC tubular pile before the concrete placement), which means each PHC tubular pile will have a unique ID number. Then according to the production procedure, the precast component will be performed with a series of actions, put the steel cage into the mold, mold clamping, pouring concrete (feed), stretching, centrifugalizing, maintenance, mold removing, welding splice. In every session of the procedure, the information of the precast components can be read through a RFID reader. Using a portable smart device connected to the database, the user can check, inquire and management the production information conveniently. Also, the system can trace the production parameter and the person in charge, realize the traceability of the information. This system can overcome the disadvantages in precast components manufacturers, like inefficiency, error-prone, time consuming, labor intensity, low information relevance and so on. This system can help to improve the production management efficiency, and can produce a good economic and social benefits, so, this system has a certain practical value.

Estimation Technique of International Input-Output Model by Non-survey Method

The Asian International Input-Output (IO) Table that is compiled by Institute of Developing Economies-JETRO (IDE), was constructed in Isard type form. Thus, it required a lot of time to publish. In order to avoid this time-lag problem and establish a more simple compilation technique, this paper concentrates on verifying the possibility of using the Chenery-Moses type estimation technique. If possible, applying the Chenery-Moses instead of the Isard type would be effective for both impact and linkage analysis (except for some countries such as Malaysia and Singapore and some primary sectors. Using Chenery-Moses estimation method, production of the Asian International IO table can be reduced by two years. And more, this method might have the possibilities to be applied for updating exercise of Asian IO table.

Power Factor Calculation by the Finite Element Method

The use of finite element analysis (FEA) to design electrical motors has increased significantly in the past few years due the increasingly better performance of modern computers. Even though the analytical software remains the most used tool, the FEA is widely used to refine the analysis and gives the final design to be prototyped. The power factor, a standard data of motor manufactures data sheet is important because it shows how much reactive power is consumed by the motor. This data becomes important when the motor is connected to network. However, the calculation of power factor is not an easy task. Due to the saturation phenomena the input motor current has a high level of harmonics that cannot be neglected. In this work the FEA is used to evaluate a proposed (not limitative) methodology to estimate the power factor or displacement factor of a small single-phase induction motor. Results of simulations and test are compared.

An Alternative Optical Method for Acoustic Resonance Detection in HID Lamps

Acoustic resonances are observed in high-pressure discharge lamps operated with ac input modulated power frequencies in the kilohertz range. This paper describes an optical resonance detection method for high-intensity discharge lamps using computer-controlled cameras and image processing software. Experimental results showing acoustic resonances in high-pressure sodium lamps are presented.

Thermodynamic self-consistency issues related to the Cluster Variation Method: The case of the BCC Cr-Fe (Chromium-Iron) system

The Cluster Variation Method (CVM), introduced over 50 years ago by Prof. Dr. Ryoichi Kikuchi, is applied to the thermodynamic modeling of the BCC Cr-Fe system in the irregular tetrahedron approximation, using experimental thermochemical data as initial input for accessing the model parameters. The results are checked against independent data on the low-temperature miscibility gap, using increasingly accurate thermodynamic models, first by the inclusion of the magnetic degrees of freedom of iron and then also by the inclusion of the magnetic degrees of freedom of chromium. It is shown that a reasonably accurate description of the phase diagram at the iron-rich side (i.e. the miscibility gap borders and the Curie line) is obtained, but only at expense of the agreement with the above mentioned thermochemical data. Reasons for these inconsistencies are discussed, especially with regard to the need of introducing vibrational degrees of freedom in the CVM model. (C) 2008 Elsevier Ltd. All rights reserved.

A reproducible method for automated extraction of brain volumes from 3D human head MR images

An automated method for extracting brain volumes from three commonly acquired three-dimensional (3D) MR images (proton density, T1 weighted, and T2-weighted) of the human head is described. The procedure is divided into four levels: preprocessing, segmentation, scalp removal, and postprocessing. A user-provided reference point is the sole operator-dependent input required, The method's parameters were first optimized and then fixed and applied to 30 repeat data sets from 15 normal older adult subjects to investigate its reproducibility. Percent differences between total brain volumes (TBVs) for the subjects' repeated data sets ranged from .5% to 2.2%. We conclude that the method is both robust and reproducible and has the potential for wide application.

Finite difference time domain (FDTD) method for modeling the effect of switched gradients on the human body in MRI

Numerical modeling of the eddy currents induced in the human body by the pulsed field gradients in MRI presents a difficult computational problem. It requires an efficient and accurate computational method for high spatial resolution analyses with a relatively low input frequency. In this article, a new technique is described which allows the finite difference time domain (FDTD) method to be efficiently applied over a very large frequency range, including low frequencies. This is not the case in conventional FDTD-based methods. A method of implementing streamline gradients in FDTD is presented, as well as comparative analyses which show that the correct source injection in the FDTD simulation plays a crucial rule in obtaining accurate solutions. In particular, making use of the derivative of the input source waveform is shown to provide distinct benefits in accuracy over direct source injection. In the method, no alterations to the properties of either the source or the transmission media are required. The method is essentially frequency independent and the source injection method has been verified against examples with analytical solutions. Results are presented showing the spatial distribution of gradient-induced electric fields and eddy currents in a complete body model.

Matrix Converter-Based Unified Power-Flow Controllers: Advanced Direct Power Control Method

This paper presents a direct power control (DPC) for three-phase matrix converters operating as unified power flow controllers (UPFCs). Matrix converters (MCs) allow the direct ac/ac power conversion without dc energy storage links; therefore, the MC-based UPFC (MC-UPFC) has reduced volume and cost, reduced capacitor power losses, together with higher reliability. Theoretical principles of direct power control (DPC) based on sliding mode control techniques are established for an MC-UPFC dynamic model including the input filter. As a result, line active and reactive power, together with ac supply reactive power, can be directly controlled by selecting an appropriate matrix converter switching state guaranteeing good steady-state and dynamic responses. Experimental results of DPC controllers for MC-UPFC show decoupled active and reactive power control, zero steady-state tracking error, and fast response times. Compared to an MC-UPFC using active and reactive power linear controllers based on a modified Venturini high-frequency PWM modulator, the experimental results of the advanced DPC-MC guarantee faster responses without overshoot and no steady-state error, presenting no cross-coupling in dynamic and steady-state responses.

The daily and hourly energy consumption and load forecasting using artifitial neural network method: a case study using a set of 93 households in Portugal

It is important to understand and forecast a typical or a particularly household daily consumption in order to design and size suitable renewable energy systems and energy storage. In this research for Short Term Load Forecasting (STLF) it has been used Artificial Neural Networks (ANN) and, despite the consumption unpredictability, it has been shown the possibility to forecast the electricity consumption of a household with certainty. The ANNs are recognized to be a potential methodology for modeling hourly and daily energy consumption and load forecasting. Input variables such as apartment area, numbers of occupants, electrical appliance consumption and Boolean inputs as hourly meter system were considered. Furthermore, the investigation carried out aims to define an ANN architecture and a training algorithm in order to achieve a robust model to be used in forecasting energy consumption in a typical household. It was observed that a feed-forward ANN and the Levenberg-Marquardt algorithm provided a good performance. For this research it was used a database with consumption records, logged in 93 real households, in Lisbon, Portugal, between February 2000 and July 2001, including both weekdays and weekend. The results show that the ANN approach provides a reliable model for forecasting household electric energy consumption and load profile. © 2014 The Author.

Composition of Greenhouse Gas Emissions in Spain: an Input-Output Analysis

Extending the traditional input-output model to account for the environmental impacts of production processes reveals the channels by which environmental burdens are transmitted throughout the economy. In particular, the environmental input-output approach is a useful technique for quantifying the changes in the levels of greenhouse emissions caused by changes in the final demand for production activities. The inputoutput model can also be used to determine the changes in the relative composition of greenhouse gas emissions due to exogenous inflows. In this paper we describe a method for evaluating how the exogenous changes in sectorial demand, such as changes in private consumption, public consumption, investment and exports, affect the relative contribution of the six major greenhouse gases regulated by the Kyoto Protocol to total greenhouse emissions. The empirical application is for Spain, and the economic and environmental data are for the year 2000. Our results show that there are significant differences in the effects of different sectors on the composition of greenhouse emissions. Therefore, the final impact on the relative contribution of pollutants will basically depend on the activity that receives the exogenous shock in final demand, because there are considerable differences in the way, and the extent to which, individual activities affect the relative composition of greenhouse gas emissions. Keywords: Greenhouse emissions, composition of emissions, sectorial demand, exogenous shock.

Efficient knowledge retrieval to calibrate input variables in forest fire prediction

Forest fires are a serious threat to humans and nature from an ecological, social and economic point of view. Predicting their behaviour by simulation still delivers unreliable results and remains a challenging task. Latest approaches try to calibrate input variables, often tainted with imprecision, using optimisation techniques like Genetic Algorithms. To converge faster towards fitter solutions, the GA is guided with knowledge obtained from historical or synthetical fires. We developed a robust and efficient knowledge storage and retrieval method. Nearest neighbour search is applied to find the fire configuration from knowledge base most similar to the current configuration. Therefore, a distance measure was elaborated and implemented in several ways. Experiments show the performance of the different implementations regarding occupied storage and retrieval time with overly satisfactory results.

An interregional input-output analysis of the pollution content of trade flows and environmental trade balances between five states in the US Mid-West

In this paper we attempt an empirical application of the multi-region input-output (MRIO) method in order to enumerate the pollution content of interregional trade flows between five Mid-West regions/states in the US –Illinois, Indiana, Iowa, Michigan and Wisconsin – and the rest of the US. This allows us to analyse some very important issues in terms of the nature and significance of interregional environmental spillovers within the US Mid-West and the existence of pollution ‘trade balances’ between states. Our results raise questions in terms of the extent to which authorities at State level can control local emissions where they are limited in the way some emissions can be controlled, particularly with respect to changes in demand elsewhere in the Mid-West and US. This implies a need for policy co-ordination between national and state level authorities in the US to meet emissions reductions targets. The existence of an environmental trade balances between states also raises issues in terms of net losses/gains in terms of pollutants as a result of interregional trade within the US and whether, if certain activities can be carried out using less polluting technology in one region relative to others, it is better for the US as a whole if this type of relationship exists.

Water Reallocation in the Input-Output Model

Water reallocation between economic agents has been –and continues to be- the subject of a considerable amount of research. This paper proposes a method for evaluating how water is reallocated within the economy in response to changes in final demand and changes in the technical water needs of activities and consumers. The empirical application, which is for the Catalan economy, shows important asymmetries in the effects that exogenous inflows and changes in water technical requirements cause on water reallocation. In the process of water distribution, exogenous inflows mostly benefit agriculture and damage private consumers. On the other hand, increases in technical water requirements have negative effects on agriculture and positive effects on the other production activities. The results of the study suggest that agriculture is an important activity not only in terms of water distribution but also in terms of water reallocation due to changes in final demand and technical water needs. Keywords: Water reallocation, water distribution, exogenous shock, technical water needs.

Merging the hypothetical extraction method and the classical multiplier approach: a hybrid possibility for identifying key distributive sectors

The two main alternative methods used to identify key sectors within the input-output approach, the Classical Multiplier method (CMM) and the Hypothetical Extraction method (HEM), are formally and empirically compared in this paper. Our findings indicate that the main distinction between the two approaches stems from the role of the internal effects. These internal effects are quantified under the CMM while under the HEM only external impacts are considered. In our comparison, we find, however that CMM backward measures are more influenced by within-block effects than the proposed forward indices under this approach. The conclusions of this comparison allow us to develop a hybrid proposal that combines these two existing approaches. This hybrid model has the advantage of making it possible to distinguish and disaggregate external effects from those that a purely internal. This proposal has also an additional interest in terms of policy implications. Indeed, the hybrid approach may provide useful information for the design of ''second best'' stimulus policies that aim at a more balanced perspective between overall economy-wide impacts and their sectoral distribution.