Question order effects are commonly observed in self-report measures of judgment and attitude. This article develops a quantum question order model (the QQ model) to account for four types of question order effects observed in literature. First, the postulates of the QQ model are presented. Second, an a priori, parameter-free, and precise prediction, called the QQ equality, is derived from these mathematical principles, and six empirical data sets are used to test the prediction. Third, a new index is derived from the model to measure similarity between questions. Fourth, we show that in contrast to the QQ model, Bayesian and Markov models do not generally satisfy the QQ equality and thus cannot account for the reported empirical data that support this equality. Finally, we describe the conditions under which order effects are predicted to occur, and we review a broader range of findings that are encompassed by these very same quantum principles. We conclude that quantum probability theory, initially invented to explain order effects on measurements in physics, appears to be a powerful natural explanation for order effects of selfreport measures in social and behavioral sciences, too.

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Protein contact map prediction is useful for protein folding rate prediction, model selection and 3D structure prediction. Here we describe NNcon, a fast and reliable contact map prediction server and software. NNcon was ranked among the most accurate residue contact predictors in the Eighth Critical Assessment of Techniques for Protein Structure Prediction (CASP8), 2008. Both NNcon server and software are available at http://casp.rnet.missouri.edu/nncon.html.

Highlights We proposed a novel framework, CHP-Net, to differentiate and localize COVID-19 from community acquired pneumonia. We used excessive data augmentation to extend the available dataset and optimize the CHP-Net generalization capability. Comparing to other ConvNet, CHP-Net works much more efficiently to extract feature information on chest X-Ray. All metrics, including categorical loss, accuracy, precision, recall and F1-score, proved CHP-Net fits good for the task. CHP-Net are better than the previous methods tested in detecting COVID-19 and exceeding to radiologist.