Factors affecting nutrition behavior among middle-class adolescents in urban area of Northern region of Brazil

INTRODUCTION: Brazil has been called a nation in nutrition transitional because of recent increases in the prevalence of obesity and related chronic diseases. With overweight conditions already prevalent among middle-income populations, there exists a need to identify factors that influence nutrition behavior within this group. OBJECTIVE: To address this subject, a research study was implemented among middle-class adolescents attending a large private secondary school in Manaus, Amazonas, Brazil. The study determined the availability and accessibility of snack foods as well as subjects’ attitudes and preferences towards, and the influence of family and friends on healthy (high-nutrient density) snack choices. METHODS: The 4-stage process included: (a) a nutrition expert focus group discussion that reported local nutrition problems in general and factors related to adolescent nutrition, (b) an adolescent pilot survey (n=63) that solicited information about snacking preferences and habits as well as resources for nutrition information and snack money; (c) a survey of various area food market sources to determine the availability and accessibility of high nutrient density snacks; and (d) a follow-up adolescent survey (n=55) that measured snack food preferences and perceptions about their cost and availability. RESULTS: Results included the finding that, although affordable high nutrient density snacks were available, preferences for low nutrient density snacks prevailed. The adolescents were reportedly more likely to be influenced by and obtain nutrition information from family members than friends. CONCLUSION: From study results it is apparent that a focus on food availability will not automatically result in proper nutritional practices among adolescents. This fact and the parental influence detected are evidence of a need to involve adolescents and their parents in nutrition education campaigns to improve adolescent snack food choices.

A comparative study of alternative approaches for common factors identification

Preliminary version

A Step-up mu-Power Converter for Solar Energy Harvesting Applications, using Hill Climbing Maximum Power Point Tracking

This paper presents a step-up micro-power converter for solar energy harvesting applications. The circuit uses a SC voltage tripler architecture, controlled by an MPPT circuit based on the Hill Climbing algorithm. This circuit was designed in a 0.13 mu m CMOS technology in order to work with an a-Si PV cell. The circuit has a local power supply voltage, created using a scaled down SC voltage tripler, controlled by the same MPPT circuit, to make the circuit robust to load and illumination variations. The SC circuits use a combination of PMOS and NMOS transistors to reduce the occupied area. A charge re-use scheme is used to compensate the large parasitic capacitors associated to the MOS transistors. The simulation results show that the circuit can deliver a power of 1266 mu W to the load using 1712 mu W of power from the PV cell, corresponding to an efficiency as high as 73.91%. The simulations also show that the circuit is capable of starting up with only 19% of the maximum illumination level.

Occupational accidents among middle and high school students of the state of São Paulo, Brazil

OBJECTIVES: To estimate the prevalence of occupational injuries and identify their risk factors among students in two municipalities. METHODS: A cross-sectional survey was conducted in public schools of the municipalities of Santo Antonio do Pinhal and Monteiro Lobato, Brazil. A stratified probabilistic sample was drawn from public middle and high schools of the study municipalities. A total of 781 students aged 11 to 19 years participated in the study. Students attending middle and high school answered a comprehensive questionnaire on living and working conditions, as well as aspects of work injuries, and health conditions. Multiple logistic regression models were fitted to estimate risk factors of previous and present occupational injuries. RESULTS: Of 781 students, 604 previously had or currently have jobs and 47% reported previous injuries. Among current workers (n=555), 38% reported injuries on their current job. Risk factors for work injuries with statistically significant odds ratio >2.0 included attending evening school, working as a housekeeper, waiter or brickmaker, and with potentially dangerous machines. CONCLUSIONS: The study results reinforce the need of restricting adolescent work and support communities to implement social promotion programs.

Energy resource scheduling considering reactive power management

The reactive power management is an important task in future power systems. The control of reactive power allows the increase of distributed energy resources penetration as well as the optimal operation of distribution networks. Currently, the control of reactive power is only controlled in large power units and in high and very high voltage substations. In this paper a reactive power control in smart grids paradigm is proposed, considering the management of distributed energy resources and of the distribution network by an aggregator namely Virtual Power Player (VPP).

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.

Study on the glycerolysis reaction of high free fatty acid oils for use as biodiesel feedstock

Biodiesel is the main alternative to fossil diesel and it may be produced from different feedstocks such as semi-refined vegetable oils, waste frying oils or animal fats. However, these feedstocks usually contain significant amounts of free fatty acids (FFA) that make them inadequate for the direct base catalyzed transesterification reaction (where the FFA content should be lower than 4%). The present work describes a possible method for the pre-treatment of oils with a high content of FFA (20 to 50%) by esterification with glycerol. In order to reduce the FFA content, the reaction between these FFA and an esterification agent is carried out before the transesterification reaction. The reaction kinetics was studied in terms of its main factors such astemperature, % of glycerin excess, % of catalyst used, stirring velocity and type of catalyst used. The results showed that glycerolysis is a promising pretreatment to acidic oils or fats (> 20%) as they led to the production of an intermediary material with a low content of FFA that can be used directly in thetransesterification reaction for the production of biodiesel. (C) 2011 Elsevier B.V. All rights reserved.

An integrated approach for distributed energy resource short term scheduling in smart grids considering realistic power system simulation

The large increase of distributed energy resources, including distributed generation, storage systems and demand response, especially in distribution networks, makes the management of the available resources a more complex and crucial process. With wind based generation gaining relevance, in terms of the generation mix, the fact that wind forecasting accuracy rapidly drops with the increase of the forecast anticipation time requires to undertake short-term and very short-term re-scheduling so the final implemented solution enables the lowest possible operation costs. This paper proposes a methodology for energy resource scheduling in smart grids, considering day ahead, hour ahead and five minutes ahead scheduling. The short-term scheduling, undertaken five minutes ahead, takes advantage of the high accuracy of the very-short term wind forecasting providing the user with more efficient scheduling solutions. The proposed method uses a Genetic Algorithm based approach for optimization that is able to cope with the hard execution time constraint of short-term scheduling. Realistic power system simulation, based on PSCAD , is used to validate the obtained solutions. The paper includes a case study with a 33 bus distribution network with high penetration of distributed energy resources implemented in PSCAD .

Transmission costs allocation based on optimal re-dispatch

Congestion management of transmission power systems has achieve high relevance in competitive environments, which require an adequate approach both in technical and economic terms. This paper proposes a new methodology for congestion management and transmission tariff determination in deregulated electricity markets. The congestion management methodology is based on a reformulated optimal power flow, whose main goal is to obtain a feasible solution for the re-dispatch minimizing the changes in the transactions resulting from market operation. The proposed transmission tariffs consider the physical impact caused by each market agents in the transmission network. The final tariff considers existing system costs and also costs due to the initial congestion situation and losses. This paper includes a case study for the 118 bus IEEE test case.

Power systems reliability calculation based on fuzzy data mining

This paper presents a methodology supported on the data base knowledge discovery process (KDD), in order to find out the failure probability of electrical equipments’, which belong to a real electrical high voltage network. Data Mining (DM) techniques are used to discover a set of outcome failure probability and, therefore, to extract knowledge concerning to the unavailability of the electrical equipments such us power transformers and high-voltages power lines. The framework includes several steps, following the analysis of the real data base, the pre-processing data, the application of DM algorithms, and finally, the interpretation of the discovered knowledge. To validate the proposed methodology, a case study which includes real databases is used. This data have a heavy uncertainty due to climate conditions for this reason it was used fuzzy logic to determine the set of the electrical components failure probabilities in order to reestablish the service. The results reflect an interesting potential of this approach and encourage further research on the topic.

Intelligent power system

Artificial intelligence techniques are being widely used to face the new reality and to provide solutions that can make power systems undergo all the changes while assuring high quality power. In this way, the agents that act in the power industry are gaining access to a generation of more intelligent applications, making use of a wide set of AI techniques. Knowledge-based systems and decision-support systems have been applied in the power and energy industry. This article is intended to offer an updated overview of the application of artificial intelligence in power systems. This article paper is organized in a way so that readers can easily understand the problems and the adequacy of the proposed solutions. Because of space constraints, this approach can be neither complete nor sufficiently deep to satisfy all readers’ needs. As this is amultidisciplinary area, able to attract both software and computer engineering and power system people, this article tries to give an insight into themost important concepts involved in these applications. Complementary material can be found in the reference list, providing deeper and more specific approaches.

Research on evaluation system of emergency disposal plan for power plant incidents

This paper starts with the analysis of the unusual inherence mechanism, from two aspects: accumulating and human error. We put forward twelve factors affected the decision of the emergency treatment plan in practice and summarized the evaluation index system combining with literature data. Then we screened out eighteen representative indicators by used the FDM expert questionnaire in the first phase. Hereafter, we calculated the weight of evaluation index and sorted them by the FAHP expert questionnaire, and came up with the frame of the evaluation rule by combined with the experience. In the end, the evaluation principles are concluded.

Optimal dispatch with reactive power compensation by Genetic Algorithm

This paper presents a Unit Commitment model with reactive power compensation that has been solved by Genetic Algorithm (GA) optimization techniques. The GA has been developed a computational tools programmed/coded in MATLAB. The main objective is to find the best generations scheduling whose active power losses are minimal and the reactive power to be compensated, subjected to the power system technical constraints. Those are: full AC power flow equations, active and reactive power generation constraints. All constraints that have been represented in the objective function are weighted with a penalty factors. The IEEE 14-bus system has been used as test case to demonstrate the effectiveness of the proposed algorithm. Results and conclusions are dully drawn.

Pediculus capitis infestation according to sex and social factors in Argentina

OBJECTIVE: To determine the intensity of Pediculus capitis infestation (abundance) among Argentinean schoolchildren. Children's sex and social stratum were analyzed as modifiers of the general prevalence and degree of parasitism. METHODS: The study included 1,370 schoolchildren (692 girls, 678 boys) from 26 schools of the province of La Rioja (21 public schools, five private schools). Classic prevalence was obtained as the percentage of children with nits and/or lice. Moreover, five degrees of parasitism were classified: 0) children with no signs of pediculosis; 0+) children with evidence of past infestation; 1) children with a recent infestation and low probability of active parasitism; 2) children with a recent infestation and high probability of active parasitism; 3) children with mobile lice (active pediculosis). RESULTS: The general prevalence was 61.4% (girls: 79%; boys: 44%, p<0.001). Private schools showed lower prevalence than public schools (p=0.02), especially due to the low prevalence in boys. Fifty percent of children were classified in classes 0 and 0+, 22% in class 1; and 28% in grades 2 and 3. The proportion of children in grade 3 was higher in public schools than in private schools. There were significant sexual differences in the intensity of parasitism for grades 2 and 3, where girls' rates exceeded twice those of boys'. CONCLUSIONS: Sex and social stratum are important modifiers of P. capitis general prevalence and degree of infestation. The classification of children by intensity of infestation allowed a more precise delimitation of this condition, which is especially important for disease surveillance and application of control measures.

AI in power systems and energy markets

In a world increasingly conscientious about environmental effects, power and energy systems are undergoing huge transformations. Electric energy produced from power plants is transmitted and distributed to end users through a power grid. The power industry performs the engineering design, installation, operation, and maintenance tasks to provide a high-quality, secure energy supply while accounting for its systems’ abilities to withstand uncertain events, such as weather-related outages. Competitive, deregulated electricity markets and new renewable energy sources, however, have further complicated this already complex infrastructure.Sustainable development has also been a challenge for power systems. Recently, there has been a signifi cant increase in the installation of distributed generations, mainly based on renewable resources such as wind and solar. Integrating these new generation systems leads to more complexity. Indeed, the number of generation sources greatly increases as the grid embraces numerous smaller and distributed resources. In addition, the inherent uncertainties of wind and solar energy lead to technical challenges such as forecasting, scheduling, operation, control, and risk management. In this special issue introductory article, we analyze the key areas in this field that can benefi t most from AI and intelligent systems now and in the future.We also identify new opportunities for cross-fertilization between power systems and energy markets and intelligent systems researchers.