Usporedba QSPR modela zasnovanih na vodikom-popunjenim molekularnim grafovima i na grafovima atomskih orbitala

QSPR models are studied for normal boiling points of alkanes, alkylbenzenes, and polyaromatic hydrocarbons, in terms of optimized correlation weights of local invariants of the hydrogen- -filled graphs (HFGs) and of the graphs of atomic orbitals (GAOs). Morgan extended connectivities of the zeroth, first, and second order of the HFGs and GAOs were employed. The best QSPR model obtained is based on optimized correlation weights of the extended connectivity of the first order of the GAO. The statistical characteristics of this model are: n = 70, r superscript(2) = 0.9988, s = 5.8 °C, F = 57437 (training set); n = 70, r superscript(2) = 0.9985, s = 6.7 °C, F = 45154 (test set).Istraživani su QSPR modeli za normalnu točku vrelišta alkana, alkilbenzena i poliaromatskih ugljikovodika, zasnovani na optimiziranim korelacijskim te`inama lokalnih invarijanti vodikom-popunjenih molekularnih grafova (HFG) i grafova atomskih orbitala (GAO). Primjenjeni su Morganovi indeksi proširene povezanosti nultoga, prvoga i drugoga reda, kako za HFG tako i za GAO. Najbolji QSPR model je dobiven na osnovi optimiziranih korelacijskih težina za proširenu povezanost prvoga reda za GAO. Statističke karakteristike ovoga modela su: n = 70, r superscript(2) = 0.9988, s = 5.8 °C, F = 57437 (training set); n = 70, r superscript(2) = 0.9985, s = 6.7 °C, F = 45154 (test set)

Sub-space approximations for MDO problems with disparate disciplinary variable dependence

The research leading to these results have been funded by the European Union Seventh Framework Programme FP7-PEOPLE-2012-ITN under grant agreement 316394, Aerospace Multidisciplinarity Enabling DEsign Optimization (AMEDEO) Marie Curie Initial Training Network

Aerodynamic CFD Based Optimization of Police Car Using Bezier Curves

This paper investigates the optimization of the aerodynamic design of a police car, BMW 5-series which is popular police force across the UK. A Bezier curve fitting approach is proposed as a tool to improve the existing design of the warning light cluster in order to reduce drag. A formal optimization technique based on Computational Fluid Dynamics (CFD) and moving least squares (MLS) is used to determine the control points for the approximated curve to cover the light-bar and streamline the shape of the roof. The results clearly show that improving the aerodynamic design of the roofs will offer an important opportunity for reducing the fuel consumption and emissions for police vehicles. The optimized police car has 30% less drag than the non-optimized counter-part

Statistical analysis of high-speed jet flows

The spatiotemporal dynamics of pressure fluctuations of a turbulent jet flow is examined from the viewpoints of symbolic permutations theory and Kolmogorov-Smirnov statistics. The methods are applied to unveil hidden structures in the near-field of the two jets corresponding to the NASA SHJAR SP3 and SP7 experiments. Large Eddy Simulations (LES) are performed using the high-resolution Compact Accurately Boundary-Adjusting high-REsolution Technique (CABARET) accelerated on Graphics Processing Units (GPUs). It is demonstrated that the decomposition of the LES pressure solutions into symbolic patterns of simpler temporal structure reveals the existence of some orderly structures in the jet flows. To separate the non-linear dynamics of the revealed structures from the linear part, the results based on the pressure signals obtained from LES are compared with the surrogate dataset constructed from the original data

Advancement of predictive modeling of zeta potentials (ζ) in metal oxide nanoparticles with correlation intensity index (CII)

It was expected that index of the ideality of correlation (IIC) and correlation intensity index (CII) could be used as possible tools to improve the predictive power of the quantitative model for zeta potential of nanoparticles. In this paper, we test how the statistical quality of quantitative structure-activity models for zeta potentials (ζ, a common measurement that reflects surface charge and stability of nanomaterial) could be improved with the use of these two indexes. Our hypothesis was tested using the benchmark data set that consists of 87 measurements of zeta potentials in water. We used quasi-SMILES molecular representation to take into consideration the size of nanoparticles in water and calculated optimal descriptors and predictive models based on the Monte Carlo method. We observed that the models developed with utilization of CII are statistically more reliable than models obtained with the IIC. However, the described approach gives an improvement of the statistical quality of these models for the external validation sets to the detriment for the training sets. Nevertheless, this circumstance is rather an advantage than a disadvantage

QSAR Model for Cytotoxicity of Silica Nanoparticles on Human Embryonic Kidney Cells1

Abstract A predictive model for cytotoxicity of 20 and 50 nm silica nanoparticles has been built using so-called optimal descriptors as mathematical functions of size, concentration and exposure time. These parameters have been encoded into 31 combinations 'concentration-exposure-size'. The calculation has been carried out by means of the CORAL software ( http://www.insilico.eu/coral/ ) using three random splits of the obtained systems into training and test sets. The statistical quality of the best model for cell viability (%) of cultured human embryonic kidney cells (HEK293) exposed to different concentrations of silica nanoparticles measured by MTT assay is satisfactory

QSPR modeling for enthalpies of formation of organometallic compounds by means of SMILES-based optimal descriptors

Organometallic compounds are an important class of chemicals, because many of them have vital biochemical features. There are studies on the quantitative structure-property/activity relationships (QSPR/QSAR) for organic substances [1-5], but only a few articles have deal with QSPR for organometallic compounds Simplified molecular input line entry system (SMILES) [9-13] has been used as an alternative for molecular graphs in the QSPR/QSAR analyses SMILES-based optimal descriptors were calculated a

Delay and distortion of slow light pulses by excitons in ZnO

Light pulses propagating through ZnO undergo distortions caused by both bound and free excitons. Numerous lines of bound excitons dissect the pulse and induce slowing of light around them, to the extend dependent on their nature. Exciton-polariton resonances determine the overall pulse delay and attenuation. The delay time of the higher-energy edge of a strongly curved light stripe approaches 1.6 ns at 3.374 eV with a 0.3 mm propagation length. Modelling the data of cw and time-of-flight spectroscopies has enabled us to determine the excitonic parameters, inherent for bulk ZnO. We reveal the restrictions on these parameters induced by the light attenuation, as well as a discrepancy between the parameters characterizing the surface and internal regions of the crystal.Comment: 4 pages, 4 figure

Application of Genetic Programming and Artificial Neural Network Approaches for Reconstruction of Turbulent Jet Flow Fields

Two Machine Learning (ML) methods are considered for reconstruction of turbulet signals corresponding to the Large Eddy Simulation database obtained by application of the high-resolution CABARET method accelerated on GPU cards for flow solutions of NASA Small Hot Jet Acoustic Rig (SHJAR) jets. The first method is the Feedforward Neural Networks technique, which was successfully implemented for a turbulent flow over a plunging aerofoil in (Lui and Wolf, 2019). The second method is based on the application of Genetic Programming, which is well-known in optimisation research, but has not been applied for turbulent flow reconstruction before. The reconstruction of local flow velocity and pressure signals as well as timedependent principle coefficients of the Spectral Proper Orthogonal Decomposition of turbulent pressure fluctuations are considered. Stability and dependency of the ML algorithms on the smoothness property and the sampling rate of the underlying turbulent flow signals are discussed