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Physical education professionals aim to develop quality programmes for physical education. This study aimed to develop and validate a scale using professionals’ perceptions of Quality Physical Education QPE in Asia using twenty-four items regarding QPE quality issues. The items covered status and roles, development of educational elements and supportive features in physical education. A sample of N = 799 sport and physical education professionals from eleven Asian cities participated in this questionnaire survey. Twenty-four items relating to QPE were examined via exploratory factor analysis (EFA) using maximum likelihood extraction and direct oblimin rotation methods. Nevertheless, only 20 items were extracted following the EFA examination. Items 1, 9, 14 and 18 were excluded because of low factor loadings. The remaining items were clustered into four subscales: Development and Supportive Elements for Quality Physical Education in Schools (DSFQPE; α = .918), Core Values of Quality Physical Education (CVPE; α = .908), Curriculum Arrangement of Physical Activities (CAPA; α = .884) and Provision and Norms in Physical Education (PNPE; α = .865). The Cronbach’s alpha coefficient (α = .875) indicated excellent internal consistency for the overall measure. Furthermore, the 4 retained factors from the EFA were assessed via robust confirmatory factor analysis (CFA). The 4-factor model demonstrated a good fit with the data (CMIN/DF = 3.450, CFI = .928, TLI = .916, PCFI = .801, RMSEA = .078). The study identified a 4-factor structure with internal consistency and acceptable interfactor correlations. The structure seemed to be applicable, including the twenty items identified as useful and necessary tools for the framework of analysis in the investigation of diverse settings for the study of quality physical education. [ABSTRACT FROM AUTHOR]

Targeted environmental and ecosystem management remain crucial in control of dengue. However, providing detailed environmental information on a large scale to effectively target dengue control efforts remains a challenge. An important piece of such information is the extent of the presence of potential dengue vector breeding sites, which consist primarily of open containers such as ceramic jars, buckets, old tires, and flowerpots. In this paper we present the design and implementation of a pipeline to detect outdoor open containers which constitute potential dengue vector breeding sites from geotagged images and to create highly detailed container density maps at unprecedented scale. We implement the approach using Google Street View images which have the advantage of broad coverage and of often being two to three years old which allows correlation analyses of container counts against historical data from manual surveys. Containers comprising eight of the most common breeding sites are detected in the images using convolutional neural network transfer learning. Over a test set of images the object recognition algorithm has an accuracy of 0.91 in terms of F-score. Container density counts are generated and displayed on a decision support dashboard. Analyses of the approach are carried out over three provinces in Thailand. The container counts obtained agree well with container counts from available manual surveys. Multi-variate linear regression relating densities of the eight container types to larval survey data shows good prediction of larval index values with an R-squared of 0.674. To delineate conditions under which the container density counts are indicative of larval counts, a number of factors affecting correlation with larval survey data are analyzed. We conclude that creation of container density maps from geotagged images is a promising approach to providing detailed risk maps at large scale. [ABSTRACT FROM AUTHOR]

Accurately predicting reference evapotranspiration (ET0) with limited climatic data is crucial for irrigation scheduling design and agricultural water management. This study evaluated eight machine learning models in four categories, i.e. neuron-based (MLP, GRNN and ANFIS), kernel-based (SVM, KNEA), tree-based (M5Tree, XGBoost) and curve-based (MARS) models, for predicting daily ET0 with maximum/maximum temperature and precipitation data during 2001–2015 from 14 stations in various climatic regions of China, i.e., arid desert of northwest China (NWC), semi-arid steppe of Inner Mongolia (IM), Qinghai-Tibetan Plateau (QTP), (semi-)humid cold-temperate northeast China (NEC), semi-humid warm-temperate north China (NC), humid subtropical central China (CC) and humid tropical south China (SC). The results showed machine learning models using only temperature data obtained satisfactory daily ET0 estimates (on average R2 = 0.829, RMSE = 0.718 mm day−1, NRMSE = 0.250 and MAE = 0.508 mm day−1). The prediction accuracy was improved by 7.6% across China when information of precipitation was further considered, particularly in (sub)tropical humid regions (by 9.7% in CC and 12.4% in SC). The kernel-based SVM, KNEA and curve-based MARS models generally outperformed the others in terms of prediction accuracy, with the best performance by KNEA in NWC and IM, by SVM in QTP, CC and SC, and very similar performance by them in NEC and NC. SVM (1.9%), MLP (2.0%), MARS (2.6%) and KNEA (6.4%) showed relatively small average increases in RMSE during testing compared with training RMSE. SVM is highly recommended for predicting daily ET0 across China in light of best accuracy and stability, while KNEA and MARS are also promising powerful models. [ABSTRACT FROM AUTHOR]

In this study, we designed a delayed payment mechanism in laboratory second price auctions (SPAs), under which subjects received a cash endowment two weeks after the experiment day and had to use their own money to pay the experimental losses (if any) on the experiment day. We compared the effect of delayed payment on overbidding in the induced value SPAs with the conventional “on-the-spot” payment mechanism where the subjects received an endowment on the experiment day, and the prepaid mechanism where the subjects received the endowment two weeks before the experiment day. Each auction was repeated for 20 rounds to provide sufficient learning opportunities to the bidders. Our results showed that bids converged to the corresponding values over auction rounds and overbidding was reduced by previous losses, consistently with the adaptive learning theory. Moreover, overbidding seems to depend significantly on bidders’ cash holding, and the magnitude of the payment treatment effects depends crucially on liquidity constraints. In the presence of liquidity constraints, both delayed and prepaid payment mechanisms reduced overbidding, while in the absence of liquidity constraints, only the delayed endowment mechanism reduced overbidding. Furthermore, when controlling the degree of liquidity constraints, subjects with higher GPAs were less likely to overbid and the delayed endowment mechanism significantly reduced overbidding compared to other payment mechanisms. These results suggest that overbidding in SPAs might be caused by bounded rationality and could be reduced by adaptive learning especially when overbidding becomes “truly” costly to subjects. [ABSTRACT FROM AUTHOR]

Introduction: Factors associated with persistently high Human Immunodeficiency Virus (HIV) prevalence levels in several districts of India are not well understood. This study was undertaken to determine the association of socio-demographic characteristics, economic factors, awareness about HIV and Sexually Transmitted Infections (STIs), and condom use with consistently high HIV prevalence in the Indian districts and to ascertain whether these associations differed across various regions of India. Methods: This study was carried out including all 640 districts of India. Secondary analysis of data obtained from the Census of India-2011, HIV Sentinel Surveillance in India and District Level Household Survey-III was done. Population profile, socio-economic characteristics, levels of HIV/STI/condom awareness and condom use, were compared between the districts with and without consistently high HIV prevalence. Due to the presence of collinearity among predictor variables, we used principal component analysis and the principal component scores were included as covariates for further analysis. Considering the districts at level 1 and the regions at level 2, multi-level analysis was done by generalised linear mixed models. Variance partition coefficient and median odds ratio were also calculated. Results: Sixty-three districts with consistently high HIV prevalence were found clustered in the South and the North-east regions of India. Population size, density and urbanisation were found to be positively associated with consistently high HIV prevalence in these districts. Higher levels of literacy, better socio-economic status, higher proportion of population in reproductive age group and late marriages were positively associated with consistently high HIV prevalence in all regions of India except in the Southern region. Higher levels of knowledge about the role of condoms in HIV prevention and condom use were associated with low HIV prevalence at the district level. Conclusions: Considerable heterogeneity among factors associated with consistently high HIV prevalence at the district level in different regions of India necessitates special region-specific strategies for HIV control. Increasing awareness about HIV alone is not sufficient for controlling the HIV epidemic and there is a need to raise knowledge levels about preventive measures against HIV and promote the use of condoms amongst population. [ABSTRACT FROM AUTHOR]

Introduction: Safety climate, which provides a snapshot of safety culture, is rarely measured in Indonesian healthcare organisations because there are no validated surveys that can be administered in its native language, Bahasa Indonesia. The objectives of this study were to translate and linguistically adapt the Safety Attitudes Questionnaire into Bahasa Indonesia, and investigate the internal construct validity and reliability of the translated survey. Methods: The Safety Attitudes Questionnaire was translated into Indonesian language through forward and backward translation. The internal construct validity and reliability of the translated survey was assessed using Rasch analysis which examines overall model fit, unidimensionality, response format, targeting, internal consistency reliability and item bias. Results: A total of 279 nurses (response rate 82%) completed the Indonesian version of the Safety Attitudes Questionnaire. Most respondents were Division 2 registered nurses (n = 209; 75%), female (n = 174; 62%), and aged less than 30 years (n = 187; 67%). All six domains of the Indonesian version of the Safety Attitudes Questionnaire demonstrated unidimensionality (t-test less than 0.05 threshold value). However, suboptimal targeting (ceiling effect) was observed in all domains, and had at least one misfitting item (item fit residual beyond ±2.5) Item bias was also evident in most domains. Conclusion: This study has translated and validated an Indonesian version of the Safety Attitudes Questionnaire for the first time. Whilst there was general support to sum items to obtain domain scores, further work is required to refine the response options as well as the wording and number of items in this survey to improve its overall measurement properties. [ABSTRACT FROM AUTHOR]

We test and compare different incentives in motivating water conservation using a randomized controlled trial. In a field experiment carried out with Singaporean households, regular feedback was given, with informative, normative and monetary incentives provided to different groups. Evidence shows that all households saved an average of 4 Litres of water per person per day, with no difference in treatment effect found across various groups. Perhaps unsurprisingly, the water saving effect is also found to be more significant with high baseline users, who saved up to 5.9 Litres per person per day. High baseline households also respond more positively to the non-monetary incentives. [ABSTRACT FROM AUTHOR]