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Land fragmentation is one of the most important factors hindering the mechanization and scale of agriculture. To further alleviate the negative impact of arable land fragmentation, a more accurate model for measuring arable land fragmentation is needed. Using 0.1 resolution UAV images and farm survey data, we obtained spatial and tenure data of farming land in Baidu Village through ArcGis and other software, and analyzed the results and correlations of farming plot area, plot shape and plot dispersion indicators in the study area. A road accessibility index that integrates terrain slope and road network is proposed to characterize the dispersion of land parcels for the first time, and is compared with two road accessibility models that do not take into account terrain slope and road network. The results show that the dispersion index of farm plots is the most influential indicator on the fragmentation of farm plots, followed by the area index of farm plots, and finally the shape index of farm plots; the new model of measuring the fragmentation of farm plots based on natural surface elements and road networks is closer to the real situation and more accurate in portraying the degree of fragmentation of farm plots.

Health is a multidimensional concept that is challenging to measure, and in the rapidly evolving developmental changes that occur during the first 21 years of human life, requires a dynamic approach to accurately capture the transitions, and overall arc of a complex process of internal and external interactions. We propose an approach that integrates a lifecourse framework with a layered series of assessments, each layer using a many to many mapping, to converge on four fundamental dimensions of health measurement-Potential, Adaptability, Performance, and Experience. The four dimensions can conceptually be mapped onto a plane with each edge of the resulting quadrilateral corresponding to one dimension and each dimensions assessment calibrated against a theoretical ideal. As the plane evolves over time, the sequential measurements will form a volume. We term such a model the Prism Model, and describe conceptually how single domain assessments can be built up to generate the holistic description through the vehicle of a layer of Exemplar Cases. The model is theoretical but future work can use the framework and principles to generate scalable and adaptable applications that can unify and improve the precision of serial measurements that integrate environmental and physiologic influences to improve the science of child health measurement.

This paper aims to investigate the relationship between Italian teachers' well-being, socio-demographic characteristics and professional background. Using data from the 2015 wave of the Program for International Student Assessment (PISA) we considered information collected by the questionnaire completed by a total of 6,491 teachers in the sampled schools. Moving from existing literature on teachers' well-being, we investigate several aspects related to the teachers' working environment, career motivation and investment, and job satisfaction. We assess the variability in the observed outcomes attributable to school factors and heterogeneity between disciplines. Measurement models are combined in a multilevel setting in order to define teachers' well-being on a broad perspective while accounting for the multiple sources of heterogeneity due to several factors (e.g., discipline, teacher professional background, and individual differences) occurring at different levels of the data structure. In general, results show that the teachers' positive perception of the working environment in terms of availability of adequate human and physical resources, and professional development opportunities, provide a substantial state of well-being at work, and are related to teachers' job satisfaction. Moreover, results highlight the key role of transformational leadership in defining a teacher's well-being. Findings and implications are discussed.

This paper aims to investigate the relationship between Italian teachers' well-being, socio-demographic characteristics and professional background. Using data from the 2015 wave of the Program for International Student Assessment (PISA) we considered information collected by the questionnaire completed by a total of 6,491 teachers in the sampled schools. Moving from existing literature on teachers' well-being, we investigate several aspects related to the teachers' working environment, career motivation and investment, and job satisfaction. We assess the variability in the observed outcomes attributable to school factors and heterogeneity between disciplines. Measurement models are combined in a multilevel setting in order to define teachers' well-being on a broad perspective while accounting for the multiple sources of heterogeneity due to several factors (e.g., discipline, teacher professional background, and individual differences) occurring at different levels of the data structure. In general, results show that the teachers' positive perception of the working environment in terms of availability of adequate human and physical resources, and professional development opportunities, provide a substantial state of well-being at work, and are related to teachers' job satisfaction. Moreover, results highlight the key role of transformational leadership in defining a teacher's well-being. Findings and implications are discussed. [ABSTRACT FROM AUTHOR]

The author reflects on a study that utilized signal detection theory (SDT) approach to causal conditional reasoning, and mentions that SDT model is often unable to provide an informative characterization of data in, as it fails to separate argument strength and response bias. The authors further opine that reliance on receiver operating characteristics (ROC) shape in the study, to justify a separation between affirmation and denial problems, is unnecessary and misguided.