INVESTIGATING HOW PARENTS, WHO GUIDE THEIR PRESCHOOL CHILDREN TOWARDS SPORTS, PERCEIVE SPORTS ACTIVITIES

Objectives: The aim of this study was to investigate parents’ expectations about general sport activities for their pre-school children. It is well documented that the awareness of sports knowledge of the parents are essential for the development of healthy life and acquiring a social dimension with a growth of healthy generations. Method: We administrated the ‘’Parents' Expectations of Their Children Questionnaire’’ developed by Keskin (2006). The questionnaire was a Likert type scale from ‘’totally agree’’ to ‘’totally disagree’’ and validity and reliability studies were reported by Keskin as Cronbach’s alpha was 0,86. A total of 125 participants (male; N = 39, Mage=35, 24 ± 5,48 , female; N = 86 and Mage=37,92 ± 6,65) were voluntarily participated from 10 different kinder gardens in Bursa province. The evaluation of the data was analysed with the Chi Square Test. Result: Our results revealed a statistical differences (p < .05) according to the sex groups of parents ‘’I believe my child will gain good eating habits by getting involved with sports activities’’ and age groups of parents; ‘’I believe by getting involved with sport activities, my child will stay away from psychological stress’’. Conclusion: According to parents’ belief and their expectations, attending sports activities for children provides physical, cognitive and social development for them. Developing countries (as well as developed ones) that are aware of the role of the parents on development of human being via sport and exercise activities should take into account their expectations especially in terms of sports policies.  Article visualizations

High carrier concentration induced effects on the bowing parameter and the temperature dependence of the band gap of Ga_xIn_1−xN

The influence of intrinsic carrier concentration on the compositional and temperature dependence of the bandgap of GaxIn1-xN is investigated in nominally undoped samples with Ga fractions of x = 0.019, 0.062, 0.324, 0.52, and 0.56. Hall Effect results show that the free carrier density has a very weak temperature dependence and increases about a factor of 4, when the Ga composition increases from x = 0.019 to 0.56. The photoluminescence (PL) peak energy has also weak temperature dependence shifting to higher energies and the PL line shape becomes increasingly asymmetrical and broadens with increasing Ga composition. The observed characteristics of the PL spectra are explained in terms of the transitions from free electron to localized tail states and the high electron density induced many-body effects. The bowing parameter of GaxIn1-xN is obtained from the raw PL data as 2.5 eV. However, when the high carrier density induced effects are taken into account, it increases by about 14% to 2.9 eV. Furthermore, the temperature dependence of the PL peak becomes more pronounced and follows the expected temperature dependence of the bandgap variation

Learning Context on a Humanoid Robot using Incremental Latent Dirichlet Allocation

In this article, we formalize and model context in terms of a set of concepts grounded in the sensorimotor interactions of a robot. The concepts are modeled as a web using Markov Random Field, inspired from the concept web hypothesis for representing concepts in humans. On this concept web, we treat context as a latent variable of Latent Dirichlet Allocation (LDA), which is a widely-used method in computational linguistics for modeling topics in texts. We extend the standard LDA method in order to make it incremental so that (i) it does not re-learn everything from scratch given new interactions (i.e., it is online) and (ii) it can discover and add a new context into its model when necessary. We demonstrate on the iCub platform that, partly owing to modeling context on top of the concept web, our approach is adaptive, online and robust: It is adaptive and online since it can learn and discover a new context from new interactions. It is robust since it is not affected by irrelevant stimuli and it can discover contexts after a few interactions only. Moreover, we show how to use the context learned in such a model for two important tasks: object recognition and planning.Scientific and Technological Research Council of TurkeyMarie Curie International Outgoing Fellowship titled “Towards Better Robot Manipulation: Improvement through Interaction

Kinematic landslide monitoring with Kalman filtering

International audienceLandslides are serious geologic disasters that threat human life and property in every country. In addition, landslides are one of the most important natural phenomena, which directly or indirectly affect countries' economy. Turkey is also the country that is under the threat of landslides. Landslides frequently occur in all of the Black Sea region as well as in many parts of Marmara, East Anatolia, and Mediterranean regions. Since these landslides resulted in destruction, they are ranked as the second important natural phenomenon that comes after earthquake in Turkey. In recent years several landslides happened after heavy rains and the resulting floods. This makes the landslide monitoring and mitigation techniques an important study subject for the related professional disciplines in Turkey. The investigations on surface deformations are conducted to define the boundaries of the landslide, size, level of activity and direction(s) of the movement, and to determine individual moving blocks of the main slide. This study focuses on the use of a kinematic deformation analysis based on Kalman Filtering at a landslide area near Istanbul. Kinematic deformation analysis has been applied in a landslide area, which is located to the north of Istanbul city. Positional data were collected using GPS technique. As part of the study, conventional static deformation analysis methodology has also been applied on the same data. The results and comparisons are discussed in this paper

Learning and Using Context on a Humanoid Robot Using Latent Dirichlet Allocation

2014 Joint IEEE International Conferences on Development and Learning and Epigenetic Robotics (ICDL-Epirob), Genoa, Italy, 13-16 October 2014In this work, we model context in terms of a set of concepts grounded in a robot's sensorimotor interactions with the environment. For this end, we treat context as a latent variable in Latent Dirichlet Allocation, which is widely used in computational linguistics for modeling topics in texts. The flexibility of our approach allows many-to-many relationships between objects and contexts, as well as between scenes and contexts. We use a concept web representation of the perceptions of the robot as a basis for context analysis. The detected contexts of the scene can be used for several cognitive problems. Our results demonstrate that the robot can use learned contexts to improve object recognition and planning.Scientific and Technological Research Council of Turkey (TUBiTAK

Forecasting of Turkey inflation with hybrid of feed forward and recurrent artifical neural networks

Enflasyon öngörülerinin elde edilmesi önemli bir ekonomik problemdir. Öngörülerin doğru bir şekilde elde edilmesi daha doğru kararlara neden olacaktır. Enflasyon öngörüsü için literatürde çeşitli zaman serileri teknikleri kullanılmıştır. Son yıllarda zaman serisi öngörü probleminde esnek modelleme yeteneği nedeniyle, Yapay Sinir Ağları (YSA) tercih edilmektedir. Yapay sinir ağları doğrusal veya eğrisel belirli bir model kalıbı, durağanlık ve normal dağılım gibi ön koşullara ihtiyaç duymadığından herhangi bir zaman serisine kolaylıkla uygulanabilmektedir. Bu çalışmada Tüketici Fiyat Endeksi (TUFE) için ileri ve geri beslemeli yapay sinir ağları yaklaşımı kullanılarak öngörüler elde edilmiştir. Çözümlemede kullanılan YSA modellerinin öngörülerinin girdi olarak kullanıldığı, YSA’ya dayalı yeni bir melez yaklaşım önerilmiştir.Obtaining the inflation prediction is an important problem. Having this prediction accurately will lead to more accurate decisions. Various time series techniques have been used in the literature for inflation prediction. Recently, Artificial Neural Network (ANN) is being preferred in the time series prediction problem due to its flexible modeling capacity. Artificial neural network can be applied easily to any time series since it does not require prior conditions such as a linear or curved specific model pattern, stationary and normal distribution. In this study, the predictions have been obtained using the feed forward and recurrent artificial neural network for the Consumer Price Index (CPI). A new combined forecast has been proposed based on ANN in which the ANN model predictions employed in analysis were used as data

Use of a tandem affinity purification assay to detect interactions between West Nile and dengue viral proteins and proteins of the mosquito vector

AbstractWest Nile and dengue viruses are (re)emerging mosquito-borne flaviviruses that cause significant morbidity and mortality in man. The identification of mosquito proteins that associate with flaviviruses may provide novel targets to inhibit infection of the vector or block transmission to humans. Here, a tandem affinity purification (TAP) assay was used to identify 18 mosquito proteins that interact with dengue and West Nile capsid, envelope, NS2A or NS2B proteins. We further analyzed the interaction of mosquito cadherin with dengue and West Nile virus envelope protein using co-immunoprecipitation and immunofluorescence. Blocking the function of select mosquito factors, including actin, myosin, PI3-kinase and myosin light chain kinase, reduced both dengue and West Nile virus infection in mosquito cells. We show that the TAP method may be used in insect cells to accurately identify flaviviral–host protein interactions. Our data also provides several targets for interrupting flavivirus infection in mosquito vectors

An Epidemiologic Study of Antimicrobial Resistance of \u3cem\u3eStaphylococcus\u3c/em\u3e Species Isolated from Equine Samples Submitted to a Diagnostic Laboratory

Background Antimicrobial resistance limits traditional treatment options and increases costs. It is therefore important to estimate the magnitude of the problem so as to provide empirical data to guide control efforts. The aim of this study was to investigate the burden and patterns of antimicrobial resistance (AMR) among equine Staphylococcus samples submitted to the University of Kentucky Veterinary Diagnostic Laboratory (UKVDL) from 1993 to 2009. Retrospective data of 1711 equine Staphylococcus samples submitted to the UKVDL during the time period 1993 to 2009 were included in the study. Antimicrobial susceptibility testing, that included 16 drugs, were performed using cultures followed by the Kirby-Bauer disk diffusion susceptibility test. The proportion of resistant isolates by animal breed, species of organism, sample source, and time period were computed. Chi-square and Cochran-Armitage trend tests were used to identify significant associations and temporal trends, respectively. Logistic regression models were used to investigate predictors of AMR and multidrug resistance (MDR). Results A total of 66.3% of the isolates were resistant to at least one antimicrobial, most of which were Staphylococcus aureus (77.1%), while 25.0% were MDR. The highest level of resistance was to penicillins (52.9%). Among drug classes, isolates had the highest rate of AMR to at least one type of β-lactams (49.2%), followed by aminoglycosides (30.2%). Significant (p \u3c 0.05) associations were observed between odds of AMR and horse breed, species of organism and year. Similarly, significant (p \u3c 0.05) associations were identified between odds of MDR and breed and age. While some isolates had resistance to up to 12 antimicrobials, AMR profiles featuring single antimicrobials such as penicillin were more common than those with multiple antimicrobials. Conclusion Demographic factors were significant predictors of AMR and MDR. The fact that some isolates had resistance to up to 12 of the 16 antimicrobials assessed is quite concerning. To address the high levels of AMR and MDR observed in this study, future studies will need to focus on antimicrobial prescription practices and education of both practitioners and animal owners on judicious use of antimicrobials to slow down the development of resistance