How well do CMIP5 climate simulations replicate historical trends and patterns of meteorological droughts?

Assessing the uncertainties and understanding the deficiencies of climate models are fundamental to developing adaptation strategies. The objective of this study is to understand how well Coupled Model Intercomparison-Phase 5 (CMIP5) climate model simulations replicate ground-based observations of continental drought areas and their trends. The CMIP5 multimodel ensemble encompasses the Climatic Research Unit (CRU) ground-based observations of area under drought at all time steps. However, most model members overestimate the areas under extreme drought, particularly in the Southern Hemisphere (SH). Furthermore, the results show that the time series of observations and CMIP5 simulations of areas under drought exhibit more variability in the SH than in the Northern Hemisphere (NH). The trend analysis of areas under drought reveals that the observational data exhibit a significant positive trend at the significance level of 0.05 over all land areas. The observed trend is reproduced by about three-fourths of the CMIP5 models when considering total land areas in drought. While models are generally consistent with observations at a global (or hemispheric) scale, most models do not agree with observed regional drying and wetting trends. Over many regions, at most 40% of the CMIP5 models are in agreement with the trends of CRU observations. The drying/wetting trends calculated using the 3 months Standardized Precipitation Index (SPI) values show better agreement with the corresponding CRU values than with the observed annual mean precipitation rates. Pixel-scale evaluation of CMIP5 models indicates that no single model demonstrates an overall superior performance relative to the other models

Translation and psychometrics of instrument of professional attitude for student nurses (IPASN) scale

Background: Achieving professional identity is one of the research priorities, and considering the importance of professional attitude among student nurses, it is necessary to identify a scale that is able to measure their achievement in professional attitude. Objectives: The present study was conducted with the aim of translation and psychometrics of instrument of professional attitude for student nurses (IPASN) scale. Methods: In this cross-sectional study, the translation and psychometrics of �instrument of professional attitude for student nurses scale� was performed based on the model of Wild et al. The third to eighth semester nursing students of Ilam University of Medical Sciences comprised the research population who were 300 students. After translation and retranslation, the editorial comments of the scale designer were applied. Then, content validity, face validity, confirmatory factor analysis, internal consistency, and test-retest reliability of the Persian version were calculated. Data were analyzed using SPSS software version 20 and EQS6.1. Results: The confirmatory factor analysis of the 28-item scale with its 8 sub-scales was confirmed by deleting the statement 7 and moving the items 10, 15, and 18. The reliability of the internal consistency was calculated to be α = 0.89 for the total scale, and (0.89), (0.45), (0.67), (0.69), (0.69), (0.73), (0.70), and (0.93) for the sub-scales, respectively. Pearson�s correlation coefficient was r = 0.79 for test-retest reliability (P < 0.005). Conclusions: This study shows that the modified Persian version of the instrument of professional attitude for student nurses scale with 27 statements is valid and reliable, and can be used to assess the nursing students towards their professional life. © 2020, Author(s)

Effect of acute caffeine administration on hyperalgesia and allodynia in a rat neuropathic pain model

Introduction: Damage to the central and peripheral nervous system causes neuropathic pain. Caffeine is a plant alkaloid and non-selective antagonist of A1, A2a and A2b adenosine receptors. It is reported that caffeine increases the threshold of pain. In this study, the effect of acute caffeine on behavioral responses of neuropathic pain was investigated. Materials and Methods: The present study was conducted on 56 adult male Wistar rats in the weight range of 220-250 g. Neuropathic pain was induced by chronic constriction injury (CCI(. Animals were randomly divided into 7 groups (n = 8): Control, Sham, CCI, CCI + Saline, and CCI + Caffeine (10, 50 and 100 mg/kg). Thermal hyperalgesia, mechanical and thermal allodynia has been done on days 4,7, 14, 21, 28 after CCI. Results: Neuropathic rats desmostrated increased pain thresholds. Notably, caffeine at a dose of 10 mg/kg significantly increased the thermal allodynia., but at doses of 50 and 100 mg/kg, it significantly decreased the thermal hyperalgesia and mechanical allodynia. Conclusion: Our findings indicated that the effects of caffeine on pain responses are dose-dependent. Probably the inhibition of adenosine A1 receptors by caffeine increases pain responses, while the inhibition of A2a and A2b adenosine receptors is associated with protective effect of caffeine against pain responses. © 2020, Semnan University of Medical Sciences. All rights reserved

Deep Burst Denoising

Noise is an inherent issue of low-light image capture, one which is exacerbated on mobile devices due to their narrow apertures and small sensors. One strategy for mitigating noise in a low-light situation is to increase the shutter time of the camera, thus allowing each photosite to integrate more light and decrease noise variance. However, there are two downsides of long exposures: (a) bright regions can exceed the sensor range, and (b) camera and scene motion will result in blurred images. Another way of gathering more light is to capture multiple short (thus noisy) frames in a "burst" and intelligently integrate the content, thus avoiding the above downsides. In this paper, we use the burst-capture strategy and implement the intelligent integration via a recurrent fully convolutional deep neural net (CNN). We build our novel, multiframe architecture to be a simple addition to any single frame denoising model, and design to handle an arbitrary number of noisy input frames. We show that it achieves state of the art denoising results on our burst dataset, improving on the best published multi-frame techniques, such as VBM4D and FlexISP. Finally, we explore other applications of image enhancement by integrating content from multiple frames and demonstrate that our DNN architecture generalizes well to image super-resolution

Simple, Accurate, and Robust Nonparametric Blind Super-Resolution

This paper proposes a simple, accurate, and robust approach to single image nonparametric blind Super-Resolution (SR). This task is formulated as a functional to be minimized with respect to both an intermediate super-resolved image and a nonparametric blur-kernel. The proposed approach includes a convolution consistency constraint which uses a non-blind learning-based SR result to better guide the estimation process. Another key component is the unnatural bi-l0-l2-norm regularization imposed on the super-resolved, sharp image and the blur-kernel, which is shown to be quite beneficial for estimating the blur-kernel accurately. The numerical optimization is implemented by coupling the splitting augmented Lagrangian and the conjugate gradient (CG). Using the pre-estimated blur-kernel, we finally reconstruct the SR image by a very simple non-blind SR method that uses a natural image prior. The proposed approach is demonstrated to achieve better performance than the recent method by Michaeli and Irani [2] in both terms of the kernel estimation accuracy and image SR quality

Projected changes of rainfall seasonality and dry spells in a high greenhouse gas emissions scenario

In this diagnostic study we analyze changes of rainfall seasonality and dry spells by the end of the twenty-first century under the most extreme IPCC5 emission scenario (RCP8.5) as projected by twenty-four coupled climate models contributing to Coupled Model Intercomparison Project 5 (CMIP5). We use estimates of the centroid of the monthly rainfall distribution as an index of the rainfall timing and a threshold-independent, information theory-based quantity such as relative entropy (RE) to quantify the concentration of annual rainfall and the number of dry months and to build a monsoon dimensionless seasonality index (DSI). The RE is projected to increase, with high inter-model agreement over Mediterranean-type regions---southern Europe, northern Africa and southern Australia---and areas of South and Central America, implying an increase in the number of dry days up to 1Â month by the end of the twenty-first century. Positive RE changes are also projected over the monsoon regions of southern Africa and North America, South America. These trends are consistent with a shortening of the wet season associated with a more prolonged pre-monsoonal dry period. The extent of the global monsoon region, characterized by large DSI, is projected to remain substantially unaltered. Centroid analysis shows that most of CMIP5 projections suggest that the monsoonal annual rainfall distribution is expected to change from early to late in the course of the hydrological year by the end of the twenty-first century and particularly after year 2050. This trend is particularly evident over northern Africa, southern Africa and western Mexico, where more than 90% of the models project a delay of the rainfall centroid from a few days up to 2Â weeks. Over the remaining monsoonal regions, there is little inter-model agreement in terms of centroid changes