ve measures of COVID-19 infection to combat against disease transmission and environmental pollution.

The online version of this article (10.1007/s10668-020-01151-9) contains supplementary material, which is available to authorized users.

The online version of this article (10.1007/s10668-020-01151-9) contains supplementary material, which is available to authorized users.The accurate determination of the nonpolar surface area of glycans is vital when utilizing liquid chromatograph/mass spectrometry (LC-MS) for structural characterization. A new approach for defining and computing nonpolar surface areas based on continuum solvation models (CS-NPSA) is presented. It is based on the classification of individual surface elements representing the solvent accessible surface used for the description of the polarized charge density elements in the CS models. Each element can be classified as polar or nonpolar according to a threshold value. The summation of the nonpolar elements then results in the NPSA resulting in a very fine resolution of this surface. The further advantage of the CS-NPSA approach is the straightforward connection to standard quantum chemical methods and program packages. The method has been analyzed in terms of the contributions of different atoms to the NPSA. The analysis showed that not only atoms normally classified as nonpolar contributed to the NPSA, but at least partially also atoms next to polar atoms or N atoms. By virtue of the construction of the solvent accessible surface, atoms in the inner regions of a molecule can be automatically identified as not contributing to the NPSA. The method has been applied to a variety of examples such as the phenylbutanehydrazide series, model dextrans consisting of glucose units and biantennary glycans. Linear correlation of the CS-NPSA values with retention times obtained from liquid chromatographic separations measurements in the mentioned cases give excellent results and promise for more extended applications on a larger variety of compounds.Extant literature has increased our understanding of the multifaceted nature of the digital divide, showing that it entails more than access to information and communication resources. Research indicates that digital inequality mirrors to a significant extent offline inequality related to socioeconomic resources. Bridging digital divides is critical for sustainable digitalized societies. Ιn this paper, we present a literature review of Information Systems research on the digital divide within settings with advanced technological infrastructures and economies over the last decade (2010-2020). The review results are organized in a concept matrix mapping contributing factors and measures for crossing the divides. Building on the results, we elaborate a research agenda that proposes [1] extending established models of digital inequalities with new variables and use of theory, [2] critically examining the effects of digital divide interventions, and [3] better linking digital divide research with research on sustainability.

The online version contains supplementary material available at 10.1007/s10796-020-10096-3.

The online version contains supplementary material available at 10.1007/s10796-020-10096-3.Metal-free electrocatalysts have been widely used as cathodes for the reduction of hexavalent chromium [Cr(VI)] in microbial fuel cells (MFCs). Guggulsterone E&Z manufacturer The electrocatalytic activity of such system needs to be increased due to the low anodic potential provided by bacteria. In this study, graphite paper (GP) was treated by liquid nitrogen to form three-dimensional graphite foam (3DGF), improving the Cr(VI) reduction by 17% and the total Cr removal by 81% at 30 h in MFCs. X-ray absorption spectroscopy confirmed the Cr(VI) reduction product as Cr(OH)3. Through the spectroscopy characterizations, electrochemical measurements, and density functional theory calculations, the porous structures, edges, and O-doped defects on the 3DGF surface resulted in a higher electroconducting rate and a lower mass transfer rate, which provide more active sites for the Cr(VI) reduction. Additionally, the scrolled graphene-like carbon nanosheets and porous structures on the 3DGF surface might limit the OH- diffusion and result in a high local pH, which accelerated the Cr(OH)3 formation. The results of this study are expected to provide a simple method to manipulate the carbon materials and insights into mechanisms of Cr(VI) reduction in MFCs by the 3DGF with in situ exfoliated edges and O-functionalized graphene.Twin support vector regression (TSVR) is generally employed with ε -insensitive loss function which is not well capable to handle the noises and outliers. According to the definition, Huber loss function performs as quadratic for small errors and linear for others and shows better performance in comparison to Gaussian loss hence it restrains easily for a different type of noises and outliers. Recently, TSVR with Huber loss (HN-TSVR) has been suggested to handle the noise and outliers. Like TSVR, it is also having the singularity problem which degrades the performance of the model. In this paper, regularized version of HN-TSVR is proposed as regularization based twin support vector regression (RHN-TSVR) to avoid the singularity problem of HN-TSVR by applying the structured risk minimization principle that leads to our model convex and well-posed. This proposed RHN-TSVR model is well capable to handle the noise as well as outliers and avoids the singularity issue. To show the validity and applicability of proposed RHN-TSVR, various experiments perform on several artificial generated datasets having uniform, Gaussian and Laplacian noise as well as on benchmark different real-world datasets and compare with support vector regression, TSVR, ε -asymmetric Huber SVR, ε -support vector quantile regression and HN-TSVR. Here, all benchmark real-world datasets are embedded with a different significant level of noise 0%, 5% and 10% on different reported algorithms with the proposed approach. The proposed algorithm RHN-TSVR is showing better prediction ability on artificial datasets as well as real-world datasets with a different significant level of noise compared to other reported models.