Notwithstanding universities’ many laudable aims, incidents of serious bullying, academic harassment and sexual harassment in academic settings are reported with increasing regularity globally. However, the human rights violations involved in bullying and academic harassment have not received attention by the literature. In this article, we pierce the veil of silence surrounding university environments and provide a systematic account of the breaches of international and European human rights law involved in academic bullying and harassment. By adopting a socio-legal lens, we shed (...) light onto such practices and tactics, the breaches of specific human rights and norms and the reluctance of states and regulators to intervene in what is perceived to be universities’ ‘sovereign’ sphere of jurisdiction in order to prevent human rights’ violations and to protect the victims of human rights abuse. We call for a multi-faceted and multi-agency approach to tackle academic bullying and harassment, and make a number of institutional and policy recommendations to ensure universities’ compliance with human rights standards and the effective protection of academics under attack. (shrink)

Wheat is the most imperative crop for man feeding and is planted in numerous countries under rainfed conditions in semiarid zones. It is necessary for decision-makers and governments to predict the yield of rainfed wheat before harvest and to determine the effect of the major factors on it. Different methods have been suggested for forecasting yield with various levels of accuracy. One of these approaches is the statistical regression model, which is simple and applicable for regions with scarce data available. (...) Since the weather is the most important factor affecting the production of wheat, particularly in rainfed cultivation, regression models using weather parameters are very common. However, the coefficients of these models are location based and should be determined locally. Therefore, in this research, backward multiple linear regression technique based on relative importance metrics was used to determine the most important effective weather parameters on rainfed wheat productions in Fars Province, south of Iran, during 2006–2013. The influence of each parameter in the final model was analyzed using the values of LMG relative importance metric. The result indicated that sunshine hours had the biggest LMG and, therefore, was the most effective parameter. Also, among the other considered parameters, rainy days, minimum relative humidity, and average relative humidity with LMG values of 21.97%, 21.69%, and 21.62%, respectively, had the most effects on rainfed wheat yield in the studied area. All parameters except for the sunshine hours positively affected rainfed wheat yield. The most important reason for the significance of these parameters can be the prevailing dry and semidry climate in the southern areas of Iran. The proposed model for determination of weather parameters effects on rainfed wheat could be a great guidance and aid for different stakeholders such as farmers, decision-makers, and governments. (shrink)

In this paper, the covariance control algorithm for nonlinear stochastic systems using covariance feedback is studied. Covariance control of nonlinear systems scenario involves the theory of covariance control based on the idea of the covariance feedback. Therefore, the proposed covariance control algorithm is derived for our case, firstly by applying the covariance control method and linear approximation of nonlinear systems, and then it is achieved by adopting this method for a class of nonlinear stochastic systems by using feedback linearization idea (...) and a covariance feedback controller. The effectiveness of the proposed covariance feedback algorithm is studied using numerous simulations concerning different nonlinear case studies. (shrink)

The productivity of researchers and the impact of the work they do are a preoccupation of universities, research funding agencies, and sometimes even researchers themselves. The h-index is the most popular of different metrics to measure these activities. This research deals with presenting a practical approach to model the h-index based on the total number of citations and the duration from the publishing of the first article. To determine the effect of every factor on h, we applied a set of (...) simple nonlinear regression. The results indicated that both NC and D1 had a significant effect on h. The determination of coefficient for these equations to estimate the h-index was 93.4% and 39.8%, respectively, which verified that the model based on NC had a better fit. Then, to record the simultaneous effects of NC and D1 on h, multiple nonlinear regression was applied. The results indicated that NC and D1 had a significant effect on h. Also, the determination of coefficient for this equation to estimate h was 93.6%. Finally, to model and estimate the h-index, as a function of NC and D1, multiple nonlinear quartile regression was used. The goodness of the fitted model was also assessed. (shrink)