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-N^G-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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In this paper, 81 Turkish provinces with different development levels were ranked using the TOPSIS method. To evaluate the ranking with TOPSIS, we presented an improvement to Mahalanobis distances, by considering a robust MM estimator of the covariance matrix to deal with the presence of outliers in the dataset. Additionally, the homogenous subsets, which were obtained from the robust Mahalanobis distance-based TOPSIS were compared with robust cluster analysis. According to our findings, robust TOPSIS-M scores reflect the inter-class differences in economic developments of provinces spanning from the extremely low to the extremely high level of economic developments. Considering indicators of economic development, which are often used in the literature, İstanbul ranked first, Ankara second, and İzmir third according to the Robust TOPSIS-M method. Moreover, with the Robust Cluster analysis, these provinces were diagnosed as outliers and it was seen that obtained clusters were compatible with the ranking of Robust TOPSIS-M.

Although the determinants of crime rates have been widely studied in the literature, previous studies have not taken into account the existence of outliers that may affect the estimation accuracy. In this study, socioeconomic determinants of crime rates against property and people were investigated using Path analysis based on both classical and robust (MM-estimator) approaches. Findings present that unemployment and migration rates are effective on crime rates against people according to both estimation approaches. However, crime rates against property are only affected from migration rates with regards to the classical approach, whereas income and unemployment rates are unique significant determinants according to the robust approach.

The article addresses a topical issue which is extremely relevant in crisis periods – evaluation of the level of the shadow economy in all Lithuanian regions. By applying the MIMIC modelling, three equations were developed for three different periods: economic upturn, economic downturn (crisis) and economic recovery. The number of immigrants, employment rate and population’s density were identified as the major shadow economy determinants in Lithuanian regions. The determinants identified are unique in the case of Lithuania because they reveal that the labour market (employment rate, the number of immigrants) and population’s density are the key factors that show how municipalities address the issues of the shadow economy. 10 municipalities with respectively high or low levels of the shadow economy were ranked for each period under consideration. The maps developed for different periods illustrate the general trends of the evolution of the shadow economy. This is the first study that estimates the size of the shadow economy in 60 municipalities (a small regional division) with different economic periods taken into account. Scientific novelty manifests through consideration of the regional shadow economy and proving significance of the labour market and immigration in reducing regional disparities.