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Coumarin derivatives have gathered major attention largely due to their versatile utility in a wide range of applications. In this framework, we report a comparative computational investigation on the optoelectronic properties of 3-phenylcoumarin and 3-heteroarylcoumarin derivatives established as enzyme inhibitors. Specifically, we concentrate on the variation in the optoelectronic characteristics for the hydroxyl group substitutions within the coumarin moiety. In order to realize our aims, all-electron density functional theory and time dependent density functional theory calculations were performed with a localized Gaussian basis-set matched with a hybrid exchange–correlation functionals. Molecular properties such as highest occupied molecular orbital (HOMO) and lowest unoccupied molecular orbital (LUMO) energies, vertical ionization (IEV) and electron affinity energies, absorption spectra, quasi-particle gap, and exciton binding energy values are examined. Furthermore, the influence of solvent on the optical properties of the molecules is considered. We found a good agreement between the experimental (8.72 eV) and calculated (8.71 eV) IEV energy values for coumarin. The computed exciton binding energy of the investigated molecules indicated their potential optoelectronics application.

Predicting clinical outcome following a specific treatment is a challenge that sees physicians and researchers alike sharing the dream of a crystal ball to read into the future. In Medicine, several tools have been developed for the prediction of outcomes following drug treatment and other medical interventions. The standard approach for a binary outcome is to use logistic regression (LR) [1,2] but over the past few years artificial neural networks (ANNs) have become an increasingly popular alternative to LR analysis for prognostic and diagnostic classification in clinical medicine [3]. The growing interest in ANNs has mainly been triggered by their ability to mimic the learning processes of the human brain. The network operates in a feed-forward mode from the input layer through the hidden layers to the output layer. Exactly what interactions are modeled in the hidden layers is still under study. Each layer within the network is made up of computing nodes with remarkable data processing abilities. Each node is connected to other nodes of a previous layer through adaptable inter-neuron connection strengths known as synaptic weights. ANNs are trained for specific applications through a learning process and knowledge is usually retained as a set of connection weights [4]. The backpropagation algorithm and its variants are learning algorithms that are widely used in neural networks. With backpropagation, the input data is repeatedly presented to the network. Each time, the output is compared to the desired output and an error is computed. The error is then fed back through the network and used to adjust the weights in such a way that with each iteration it gradually declines until the neural model produces the desired outpu

In this study a theoretical analysis of a collector augmented by a bottom booster reflector is presented. An analytical model has been developed and used to estimate the solar irradiation passing through the transparent cover of a flat collector, both with and without a bottom reflector. The analytical model is based on the anisotropic sky model and takes into account a finite length system with different angular configurations and reciprocal shading and reflections between reflector and collector. Computer simulations have been carried out in order to find the optimum angles of the reflector with respect to the plane of the collector. Optimal inclinations of the collector and reflector for each month at 39° N latitude have been identified

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