The outbreak of the COVID-19 pandemic has dramatically shaped higher education and seen the distinct rise of e-learning as a compulsory element of the modern educational landscape. Accordingly, this study highlights the factors which have influenced how students perceive their academic performance during this emergency changeover to e-learning. The empirical analysis is performed on a sample of 10,092 higher education students from 10 countries across 4 continents during the pandemic’s first wave through an online survey. A structural equation model revealed the quality of e-learning was mainly derived from service quality, the teacher’s active role in the process of online education, and the overall system quality, while the students’ digital competencies and online interactions with their colleagues and teachers were considered to be slightly less important factors. The impact of e-learning quality on the students’ performance was strongly mediated by their satisfaction with e-learning. In general, the model gave quite consistent results across countries, gender, study fields, and levels of study. The findings provide a basis for policy recommendations to support decision-makers incorporate e-learning issues in the current and any new similar circumstances.
The purpose of this study was to investigate the effect of gentamicin (100 mg/kg/day, i.p.) treatment on endothelium-dependent and -independent vasodilation in isolated perfused rat kidney, and the effect of amino acid L-arginine (in the drinking water, 2.25 g/l) on renal dysfunction induced by gentamicin. When gentamicin-treated groups were compared with the control group, it was observed that BUN and creatinine levels increased significantly. Also, the relaxant responses induced by acetylcholine, sodium nitroprusside and pinacidil decreased. Histopathological examination indicated acute tubular necrosis in this group. In animals treated with gentamicin together with L-arginine, there was a significant amelioration in the BUN and creatinine levels. The vasodilator responses were similar to those of the control group. Histopathological examination indicated only hydropic degeneration in tubular epithelium of kidney. Co-administration of L-NG-nitroarginine methyl ester (L-NAME) (112.5 mg/l), an inhibitor of nitric oxide synthase, and L-arginine to rats treated with gentamicin did not change the protective effect of L-arginine. In rats receiving L-NAME alone, the level of BUN and creatinine and vasodilation to acetylcholine were not significantly different when compared to those of the control group, while relaxant responses to sodium nitroprusside and pinacidil were increased. These results suggest that gentamicin leads to an impairment in vascular smooth muscle relaxation in addition to acute tubular necrosis in the rat kidney. Supplementation of L-arginine has an important protective effect on gentamicin-induced nephropathy.
The seemingly unrelated regression (SUR) model is a generalization of a linear regression model consisting of more than one equation, where the error terms of these equations are contemporaneously correlated. The standard feasible generalized linear squares (FGLS) estimator is efficient as it takes into account the covariance structure of the errors, but it is also very sensitive to outliers. The robust SUR estimator of Bilodeau and Duchesne (Canadian Journal of Statistics, 28:277-288, 2000) can accommodate outliers, but it is hard to compute. First, we propose a fast algorithm, FastSUR, for its computation and show its good performance in a simulation study. We then provide diagnostics for outlier detection and illustrate them on a real dataset from economics. Next, we apply our FastSUR algorithm in the framework of stochastic loss reserving for general insurance. We focus on the general multivariate chain ladder (GMCL) model that employs SUR to estimate its parameters. Consequently, this multivariate stochastic reserving method takes into account the contemporaneous correlations among run-off triangles and allows structural connections between these triangles. We plug in our FastSUR algorithm into the GMCL model to obtain a robust version.
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