Simplified,interpretable graph convolutional neural networks for small molecule activity prediction

Journal of Computer-Aided Molecular Design - We here present a streamlined, explainable graph convolutional neural network (gCNN) architecture for small molecule activity prediction. We first...     

Electroactivated Disposable Pencil Graphite Electrode – New,Cost-effective,and Sensitive Electrochemical Detection of Bioflavonoid Hesperidin

In this paper, for the first time, electroactivated disposable pencil graphite electrode (ePGE) was used for the detection of bioflavonoid hesperidin with cyclic and differential pulse voltammetry. The electroactivation efficiency of the pencil graphite electrode (PGE) was examined employing electrochemical impedance spectroscopy (EIS) and scanning electrochemical microscopy (SECM) and the enhancement of electron transfer kinetics of the PGE after the electroactivation was found. Hesperidin is irreversibly oxidized on the ePGE and its oxidation was the most pronounced at pH=5.0. Two electrode processes were detected, on one hand, a mixed diffusion and adsorption control was observed for the first electrode process. On the other hand, only diffusion control was observed in the second electrode process. Linear dependence between the peak current and the hesperidin concentration was obtained in the concentration range from 5×10⁻⁷ mol dm⁻³ to 1×10⁻⁵ mol dm⁻³ and the determined lower limit of detection (LOD) was 2×10⁻⁷ mol dm⁻³. Moreover, hesperidin in pharmaceutical formulation (containing active substance, hesperidin, and excipients) was quantified using ePGE. A good correlation was obtained between experimentally obtained hesperidin concentration by voltammetric analysis and concentration determined by standard HPLC technique (R²=0.9462).     

Levi-Civita connections for a class of spectral triples

Letters in Mathematical Physics - We give a new definition of Levi-Civita connection for a noncommutative pseudo-Riemannian metric on a noncommutative manifold given by a spectral triple. We prove...     

Data analytics and stochastic modeling in a semiconductor fab

The scale, scope and complexity of the manufacturing operations in a semiconductor fab lead to some unique challenges in ensuring product quality and production efficiency. We describe the use of various analytical techniques, based on data mining, process trace data analysis, stochastic simulation and production optimization, to address these manufacturing issues, motivated by the following two objectives. The first objective is to identify the sub‐optimal process conditions or tool settings that potentially affect the process performance and product quality. The second objective is to improve the overall production efficiency through better planning and resource scheduling, in an environment where the product mix and process flow requirements are complex and constantly changing. Copyright © 2010 John Wiley & Sons, Ltd.