A repeated measures experiment of school playing environment to increase physical activity and enhance self esteem in UK school children

School playtime provides daily opportunities for children to be active outdoors, but only makes small contributions to physical activity (PA) requirements. Natural environments facilitate unstructured PA and children report a preference for play in nature. Thus, play on the school field might encourage children to be more active during playtime. The primary aim of this study was to examine the impact of the school playing environment on children's PA. Descriptive data and fitness were assessed in 25 children aged 8–9 years from a single primary school. Over two consecutive weeks participants were allocated to either play on the school field or playground during playtime. The order of play in the two areas was randomised and counterbalanced. Moderate to vigorous PA (MVPA) was assessed during playtime on the last two days of each week using accelerometers. There was a significant interaction of environment and sex on MVPA during morning play (F(1,22) = 6.27; P<0.05; np2 = 0.222), but not during lunch (P>0.05; np2 = 0.060) or all of playtime combined (P>0.05; np2 = 0.140). During morning play boys were significantly more active than girls on the playground (t(23) = 1.32; P<0.01; n2 = 0.291), but not on the field (P>0.05; n2 = 0.071). For lunch (F(1,22) = 24,11; P<0.001; np2 = 0.523) and all of playtime combined (F(1,22) = 33.67; P<0.001; np2 = 0.616) there was a significant effect of environment. There was also a significant main effect of sex during lunch (F(1,22) = 11.56; P<0.01; np2 = 0.344) and all of playtime combined (F(1,22) = 12.37; P<0.01; np2 = 0.371). MVPA was higher on the field and boys were more active than girls. Play on the field leads to increases in MVPA, particularly in girls. The promising trend for the effect of the natural environment on MVPA indicates that interventions aimed at increasing MVPA should use the natural environment and that schools should encourage greater use of their natural areas to increase PA.

A repeated measures experiment of green exercise to improve self-esteem in UK school children

Exercising in natural, green environments creates greater improvements in adult's self-esteem than exercise undertaken in urban or indoor settings. No comparable data are available for children. The aim of this study was to determine whether so called ‘green exercise’ affected changes in self-esteem; enjoyment and perceived exertion in children differently to urban exercise. We assessed cardiorespiratory fitness (20 m shuttle-run) and self-reported physical activity (PAQ-A) in 11 and 12 year olds (n = 75). Each pupil completed two 1.5 mile timed runs, one in an urban and another in a rural environment. Trials were completed one week apart during scheduled physical education lessons allocated using a repeated measures design. Self-esteem was measured before and after each trial, ratings of perceived exertion (RPE) and enjoyment were assessed after completing each trial. We found a significant main effect (F (1,74), = 12.2, p<0.001), for the increase in self-esteem following exercise but there was no condition by exercise interaction (F (1,74), = 0.13, p = 0.72). There were no significant differences in perceived exertion or enjoyment between conditions. There was a negative correlation (r = −0.26, p = 0.04) between habitual physical activity and RPE during the control condition, which was not evident in the green exercise condition (r = −0.07, p = 0.55). Contrary to previous studies in adults, green exercise did not produce significantly greater increases in self-esteem than the urban exercise condition. Green exercise was enjoyed more equally by children with differing levels of habitual physical activity and has the potential to engage less active children in exercise.

Repeat-measures longitudinal study evaluating behavioural and gastrointestinal symptoms in children with autism before, during and after visceral osteopathic technique (VOT)

Summary: This study investigated the influence of visceral osteopathic technique (VOT) on the behaviour and gastrointestinal (GI) symptoms of children with autism using a validated questionnaire to measure outcome. Methods: The 49 recruited autistic children suffered GI symptoms and impaired social interaction and communication, but were otherwise healthy. Thirty minute VOT sessions were applied to the abdomens of the children over a 6 week period whilst their GI and behavioural parameters were recorded. Outcomes were measured using a modified Autism Research Institute Secretin Outcomes Survey Form, the ‘S.O.S Form’. Four questionnaires were completed by parents before treatment (control period), four completed during treatment (treatment period) and one completed six weeks after the last treatment (post treatment period). Subjects acted as their own controls. Results: Results from repeat ANOVA demonstrated a positive, overall significant, symptomatic improvement (p < 0.05) in ‘social behaviour and communication’ and ‘digestive signs’ subscales of the questionnaire comparing before and after VOT. Significant improvement in vomiting (p = 0.00029), poor appetite (p = 0.039) and eye contact (p = 0.035) was also demonstrated after VOT application. Discussion and conclusion: The experimental hypothesis has been supported indicating a positive effect of VOT on some of the measured GI symptoms and behavioural patterns in this group of children with autism. This data indicates that the application of VOT may be of benefit to children with autism and GI disturbance.

A Fuzzy-Wavelet Neural Network Model for the Detection of Meat Spoilage using an Electronic Nose

Food product safety is one of the most promising areas for the application of electronic noses. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). This paper proposes a fuzzy-wavelet neural network model which incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modeling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results indicated that the proposed modeling scheme could be considered as a valuable detection methodology in food microbiology

A Rapid Detection of Meat Spoilage using an Electronic Nose and Fuzzy-Wavelet systems

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. To address the rapid detection of meat spoilage microorganisms during aerobic or modified atmosphere storage, an electronic nose with the aid of fuzzy wavelet network has been considered in this research. The proposed model incorporates a clustering pre-processing stage for the definition of fuzzy rules. The dual purpose of the proposed modelling approach is not only to classify beef samples in the respective quality class (i.e. fresh, semi-fresh and spoiled), but also to predict their associated microbiological population directly from volatile compounds fingerprints. Comparison results against neural networks and neurofuzzy systems indicated that the proposed modelling scheme could be considered as a valuable detection methodology in food microbiology

Neuro-Fuzzy based Identification of Meat Spoilage using an Electronic Nose

Freshness and safety of muscle foods are generally considered as the most important parameters for the food industry. The performance of a portable electronic nose has been evaluated in monitoring the spoilage of beef fillet stored aerobically at different storage temperatures (0, 4, 8, 12, 16 and 20°C). An adaptive fuzzy logic system model that utilizes a prototype defuzzification scheme has been developed to classify beef samples in their respective quality class and to predict their associated microbiological population directly from volatile compounds fingerprints. Results confirmed the superiority of the adopted methodology and indicated that volatile information in combination with an efficient choice of a modeling scheme could be considered as an alternative methodology for the accurate evaluation of meat spoilage

Application of adaptive neuro-fuzzy inference for wind power short-term forecasting

The increased integration of wind power into the electric grid, as nowadays occurs in Portugal, poses new challenges due to its intermittency and volatility. Hence, good forecasting tools play a key role in tackling these challenges. In this paper, an adaptive neuro-fuzzy inference approach is proposed for short-term wind power forecasting. Results from a real-world case study are presented. A thorough comparison is carried out, taking into account the results obtained with other approaches. Numerical results are presented and conclusions are duly drawn. (C) 2011 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

Increase of the delivered energy probability in DES using a fuzzy probabilistic modeling

The paper proposes a methodology to increase the probability of delivering power to any load point by identifying new investments in distribution energy systems. The proposed methodology is based on statistical failure and repair data of distribution components and it uses a fuzzy-probabilistic modeling for the components outage parameters. The fuzzy membership functions of the outage parameters of each component are based on statistical records. A mixed integer nonlinear programming optimization model is developed in order to identify the adequate investments in distribution energy system components which allow increasing the probability of delivering power to any customer in the distribution system at the minimum possible cost for the system operator. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 180 bus distribution network.

Hybrid fuzzy Monte Carlo technique for reliability assessment in transmission power systems

This paper proposes a new methodology to reduce the probability of occurring states that cause load curtailment, while minimizing the involved costs to achieve that reduction. The methodology is supported by a hybrid method based on Fuzzy Set and Monte Carlo Simulation to catch both randomness and fuzziness of component outage parameters of transmission power system. The novelty of this research work consists in proposing two fundamentals approaches: 1) a global steady approach which deals with building the model of a faulted transmission power system aiming at minimizing the unavailability corresponding to each faulted component in transmission power system. This, results in the minimal global cost investment for the faulted components in a system states sample of the transmission network; 2) a dynamic iterative approach that checks individually the investment’s effect on the transmission network. A case study using the Reliability Test System (RTS) 1996 IEEE 24 Buses is presented to illustrate in detail the application of the proposed methodology.

Fuzzy Monte Carlo mathematical model for load curtailment minimization in transmission power systems

This paper presents a methodology which is based on statistical failure and repair data of the transmission power system components and uses fuzzyprobabilistic modeling for system component outage parameters. Using statistical records allows developing the fuzzy membership functions of system component outage parameters. The proposed hybrid method of fuzzy set and Monte Carlo simulation based on the fuzzy-probabilistic models allows catching both randomness and fuzziness of component outage parameters. A network contingency analysis to identify any overloading or voltage violation in the network is performed once obtained the system states by Monte Carlo simulation. This is followed by a remedial action algorithm, based on optimal power flow, to reschedule generations and alleviate constraint violations and, at the same time, to avoid any load curtailment, if possible, or, otherwise, to minimize the total load curtailment, for the states identified by the contingency analysis. In order to illustrate the application of the proposed methodology to a practical case, the paper will include a case study for the Reliability Test System (RTS) 1996 IEEE 24 BUS.

Logic programming and fuzzy Monte Carlo for distribution network reconfiguration

This paper present a methodology to choose the distribution networks reconfiguration that presents the lower power losses. The proposed methodology is based on statistical failure and repair data of the distribution power system components and uses fuzzy-probabilistic modeling for system component outage parameters. The proposed hybrid method using fuzzy sets and Monte Carlo simulation based on the fuzzyprobabilistic models allows catching both randomness and fuzziness of component outage parameters. A logic programming algorithm is applied, once obtained the system states by Monte Carlo Simulation, to get all possible reconfigurations for each system state. To evaluate the line flows and bus voltages and to identify if there is any overloading, and/or voltage violation an AC load flow has been applied to select the feasible reconfiguration with lower power losses. To illustrate the application of the proposed methodology, the paper includes a case study that considers a 115 buses distribution network.

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.

Controladores linguísticos Fuzzy

Trabalho Final de Mestrado para obtenção do grau de Mestre em Engenharia Mecânica

An integrated recycling approach for GFRP pultrusion wastes: recycling and reuse assessment into new composite materials using Fuzzy Boolean Nets

In this study, efforts were made in order to put forward an integrated recycling approach for the thermoset based glass fibre reinforced polymer (GPRP) rejects derived from the pultrusion manufacturing industry. Both the recycling process and the development of a new cost-effective end-use application for the recyclates were considered. For this purpose, i) among the several available recycling techniques for thermoset based composite materials, the most suitable one for the envisaged application was selected (mechanical recycling); and ii) an experimental work was carried out in order to assess the added-value of the obtained recyclates as aggregates and reinforcement replacements into concrete-polymer composite materials. Potential recycling solution was assessed by mechanical behaviour of resultant GFRP waste modified concrete-polymer composites with regard to unmodified materials. In the mix design process of the new GFRP waste based composite material, the recyclate content and size grade, and the effect of the incorporation of an adhesion promoter were considered as material factors and systematically tested between reasonable ranges. The optimization process of the modified formulations was supported by the Fuzzy Boolean Nets methodology, which allowed finding the best balance between material parameters that maximizes both flexural and compressive strengths of final composite. Comparing to related end-use applications of GFRP wastes in cementitious based concrete materials, the proposed solution overcome some of the problems found, namely the possible incompatibilities arisen from alkalis-silica reaction and the decrease in the mechanical properties due to high water-cement ratio required to achieve the desirable workability. Obtained results were very promising towards a global cost-effective waste management solution for GFRP industrial wastes and end-of-life products that will lead to a more sustainable composite materials industry.