Hudson模型适用性及储层微观建模应用*

为明确裂缝间相互作用对各向异性的影响，本文以Hudson模型为例分析了裂缝密度、裂缝倾角对地震波波场、弹性常数和Thomsen系数的影响规律，然后采用“基质-骨架-流体”组合化的方法进行了裂缝储层微观尺度的建模，并与实际测井资料进行了对比。结果表明该模型适用条件为低裂缝密度储层，二阶模型适用的裂缝密度范围比一阶模型大，但在裂缝密度过大时，二阶模型会出现不收敛的现象，模型便不再适用。裂缝储层纵横波速度随裂缝倾角增大而增大，纵波速度对裂缝倾角更为敏感。另外，在与实际测井曲线对比时，在高裂缝密度地层二阶模型的应用效果明显优于一阶模型，说明了在高裂缝密度储层考虑裂缝间的相互作用的必要性。     

Design and Synthesis of Imidazole and Triazole Pyrazoles as Mycobacterium Tuberculosis CYP121A1 Inhibitors

The emergence of untreatable drug-resistant strains of Mycobacterium tuberculosis is a major public health problem worldwide, and the identification of new efficient treatments is urgently needed. Mycobacterium tuberculosis cytochrome P450 CYP121A1 is a promising drug target for the treatment of tuberculosis owing to its essential role in mycobacterial growth. Using a rational approach, which includes molecular modelling studies, three series of azole pyrazole derivatives were designed through two synthetic pathways. The synthesized compounds were biologically evaluated for their inhibitory activity towards M. tuberculosis and their protein binding affinity (K_D). Series 3 biarylpyrazole imidazole derivatives were the most effective with the isobutyl ( 10 f ) and tert-butyl ( 10 g ) compounds displaying optimal activity (MIC 1.562 μg/mL, K_D 0.22 μM ( 10 f ) and 4.81 μM ( 10 g )). The spectroscopic data showed that all the synthesised compounds produced a type II red shift of the heme Soret band indicating either direct binding to heme iron or (where less extensive Soret shifts are observed) putative indirect binding via an interstitial water molecule. Evaluation of biological and physicochemical properties identified the following as requirements for activity: LogP >4, H-bond acceptors/H-bond donors 4/0, number of rotatable bonds 5–6, molecular volume >340 ų, topological polar surface area <40 Ų.     

On the inflation and deflation dynamics of liquid-filled,hyperelastic balloons

We derive a reduced-order model describing the inflation and deflation dynamics of a liquid-filled hyperelastic balloon, focusing on inviscid laminar flow and the extensional motion of the balloon. We initially study the flow and pressure fields for dictated motion of the solid, which throughout deflation are obtained by solving the potential problem. However, during inflation, flow separation creates a jet within the balloon, requiring a different approach. The analyses of both flow regimes lead to a simple piecewise model, describing the fluidic pressure during inflation and deflation, which is verified by finite element computations. We then use a variational approach to derive the equation describing the interaction between the extensional mode of the balloon and the entrapped fluid, yielding a nonlinear hybrid oscillator equation. Analytical and graphical investigations of the suggested model are presented, shedding light on its static and dynamic behaviour under different operating conditions. Our simplified model and its underlying assumptions are verified utilizing a fully coupled finite element scheme, showing excellent agreement.     

On the combination of kernel principal component analysis and neural networks for process indirect control

ABSTRACT

A new adaptive kernel principal component analysis (KPCA) for non-linear discrete system control is proposed. The proposed approach can be treated as a new proposition for data pre-processing techniques. Indeed, the input vector of neural network controller is pre-processed by the KPCA method. Then, the obtained reduced neural network controller is applied in the indirect adaptive control. The influence of the input data pre-processing on the accuracy of neural network controller results is discussed by using numerical examples of the cases of time-varying parameters of single-input single-output non-linear discrete system and multi-input multi-output system. It is concluded that, using the KPCA method, a significant reduction in the control error and the identification error is obtained. The lowest mean squared error and mean absolute error are shown that the KPCA neural network with the sigmoid kernel function is the best.     

Solubility of tridecanedioic acid in pure solvent systems: An experimental and computational study

Solubility has been extensively investigated by the phase equilibria approach at the mesoscale level, but its origin on the molecular and electronic levels is poorly understood. This study explored the solubility behaviour of crystalline solid in selected pure solvents with various functional groups by using both phase equilibria and molecular modelling methods. The model compound tridecanedioic acid (TDDA) solubility in methanol, ethanol, acetic acid, acetone, and ethyl acetate was determined from T = (283.15 to 323.15) K by a static method. It was found that almost all solutions studied exhibit non-ideal behaviour and deviate positively from Raoult’s law indicating the important role of homo-molecules interactions. Thermodynamic analyses of solution suggest that both enthalpy and entropy of solution govern the dissolution process. Computational studies on solubility behaviour were performed by using both density functional theory (DFT) calculations and molecular dynamic (MD) simulations. The results conclude that the (solute + solvent) interaction is not the only factor determining solubility, and (solvent + solvent) interaction also plays an important role. The simulated results are found to be qualitatively consistent with experimental values. Finally, solubility values were correlated by the empirically modified Apelblat equation and two local composition models of Wilson and NRTL.     

Tri-peptide reference structures for the calculation of relative solvent accessible surface area in protein amino acid residues

Relative amino acid residue solvent accessibility values allow the quantitative comparison of atomic solvent-accessible surface areas in different residue types and physical environments in proteins and in protein structural alignments. Geometry-optimised tri-peptide structures in extended solvent-exposed reference conformations have been obtained for 43 amino acid residue types at a high level of quantum chemical theory. Significant increases in side-chain solvent accessibility, offset by reductions in main-chain atom solvent exposure, were observed for standard residue types in partially geometry-optimised structures when compared to non-minimised models built from identical sets of proper dihedral angles abstracted from the literature. Optimisation of proper dihedral angles led most notably to marked increases of up to 54% in proline main-chain atom solvent accessibility compared to literature values. Similar effects were observed for fully-optimised tri-peptides in implicit solvent. The relief of internal strain energy was associated with systematic variation in N, C^α and C^β atom solvent accessibility across all standard residue types. The results underline the importance of optimisation of ‘hard’ degrees of freedom (bond lengths and valence bond angles) and improper dihedral angle values from force field or other context-independent reference values, and impact on the use of standardised fixed internal co-ordinate geometry in sampling approaches to the determination of absolute values of protein amino acid residue solvent accessibility. Quantum chemical methods provide a useful and accurate alternative to molecular mechanics methods to perform energy minimisation of peptides containing non-standard (chemically modified) amino acid residues frequently present in experimental protein structure data sets, for which force field parameters may not be available. Reference tri-peptide atomic co-ordinate sets including hydrogen atoms are made freely available.     

Mathematical Model and Numerical Simulation of Conductometric Biosensor of Urea

In this article, a mathematical model was developed to describe and optimize the configuration of the urea biosensor. The biosensor is based on interdigitated gold microelectrodes modified with a urease enzyme membrane. The model presented here focuses on the enzymatic reaction and/or diffusion phenomena that occur in the enzyme membrane and in the diffusion layer. Numerical resolution of differential equations was performed using the finite difference technique. The mathematical model was validated using experimental biosensor data. The responses of the biosensor to various conditions were simulated to guide experiments, improve analytical performance, and reduce development costs.     

Exploring AuRh Nanoalloys: A Computational Perspective on the Formation and Physical Properties

We studied the formation of AuRh nanoalloys (between 20–150 atoms) in the gas phase by means of Molecular Dynamics (MD) calculations, exploring three possible formation processes: one-by-one growth, coalescence, and nanodroplets annealing. As a general trend, we recover a predominance of Rh@Au core-shell ordering over other chemical configurations. We identify new structural motifs with enhanced thermal stabilities. The physical features of those selected systems were studied at the Density Functional Theory (DFT) level, revealing profound correlations between the nanoalloys morphology and properties. Surprisingly, the arrangement of the inner Rh core seems to play a dominant role on nanoclusters’ physical features like the HOMO-LUMO gap and magnetic moment. Strong charge separations are recovered within the nanoalloys suggesting the existence of charge-transfer transitions.