Survival of Virus Particles in Water Droplets: Hydrophobic Forces and Landauer’s Principle

Many small biological objects, such as viruses, survive in a water environment and cannot remain active in dry air without condensation of water vapor. From a physical point of view, these objects belong to the mesoscale, where small thermal fluctuations with the characteristic kinetic energy of k_BT (where k_B is the Boltzmann’s constant and T is the absolute temperature) play a significant role. The self-assembly of viruses, including protein folding and the formation of a protein capsid and lipid bilayer membrane, is controlled by hydrophobic forces (i.e., the repulsing forces between hydrophobic particles and regions of molecules) in a water environment. Hydrophobic forces are entropic, and they are driven by a system’s tendency to attain the maximum disordered state. On the other hand, in information systems, entropic forces are responsible for erasing information, if the energy barrier between two states of a switch is on the order of k_BT, which is referred to as Landauer’s principle. We treated hydrophobic interactions responsible for the self-assembly of viruses as an information-processing mechanism. We further showed a similarity of these submicron-scale processes with the self-assembly in colloidal crystals, droplet clusters, and liquid marbles.     

Asymptotic Information-Theoretic Detection of Dynamical Organization in Complex Systems

The identification of emergent structures in complex dynamical systems is a formidable challenge. We propose a computationally efficient methodology to address such a challenge, based on modeling the state of the system as a set of random variables. Specifically, we present a sieving algorithm to navigate the huge space of all subsets of variables and compare them in terms of a simple index that can be computed without resorting to simulations. We obtain such a simple index by studying the asymptotic distribution of an information-theoretic measure of coordination among variables, when there is no coordination at all, which allows us to fairly compare subsets of variables having different cardinalities. We show that increasing the number of observations allows the identification of larger and larger subsets. As an example of relevant application, we make use of a paradigmatic case regarding the identification of groups in autocatalytic sets of reactions, a chemical situation related to the origin of life problem.     

Application of Euler Neural Networks with Soft Computing Paradigm to Solve Nonlinear Problems Arising in Heat Transfer

In this study, a novel application of neurocomputing technique is presented for solving nonlinear heat transfer and natural convection porous fin problems arising in almost all areas of engineering and technology, especially in mechanical engineering. The mathematical models of the problems are exploited by the intelligent strength of Euler polynomials based Euler neural networks (ENN’s), optimized with a generalized normal distribution optimization (GNDO) algorithm and Interior point algorithm (IPA). In this scheme, ENN’s based differential equation models are constructed in an unsupervised manner, in which the neurons are trained by GNDO as an effective global search technique and IPA, which enhances the local search convergence. Moreover, a temperature distribution of heat transfer and natural convection porous fin are investigated by using an ENN-GNDO-IPA algorithm under the influence of variations in specific heat, thermal conductivity, internal heat generation, and heat transfer rate, respectively. A large number of executions are performed on the proposed technique for different cases to determine the reliability and effectiveness through various performance indicators including Nash–Sutcliffe efficiency (NSE), error in Nash–Sutcliffe efficiency (ENSE), mean absolute error (MAE), and Thiel’s inequality coefficient (TIC). Extensive graphical and statistical analysis shows the dominance of the proposed algorithm with state-of-the-art algorithms and numerical solver RK-4.     

Study of Nonlinear Models of Oscillatory Systems by Applying an Intelligent Computational Technique

In this paper, we have analyzed the mathematical model of various nonlinear oscillators arising in different fields of engineering. Further, approximate solutions for different variations in oscillators are studied by using feedforward neural networks (NNs) based on the backpropagated Levenberg–Marquardt algorithm (BLMA). A data set for different problem scenarios for the supervised learning of BLMA has been generated by the Runge–Kutta method of order 4 (RK-4) with the “NDSolve” package in Mathematica. The worth of the approximate solution by NN-BLMA is attained by employing the processing of testing, training, and validation of the reference data set. For each model, convergence analysis, error histograms, regression analysis, and curve fitting are considered to study the robustness and accuracy of the design scheme.     

基于自适应核学习相关向量机的乳腺X线图像微钙化点簇处理方法研究

采用自适应核学习相关向量机方法, 结合形态学滤波和Kallergi分簇标准, 研究了乳腺X线图像中微钙化点簇的处理. 首先将微钙化点检测看作一个监督学习问题, 然后应用自适应核学习相关向量机作为分类器判断图像中每一个位置是否为微钙化点并采用形态学处理滤除干扰噪声, 最后对获得的微钙化点采用Kallergi标准进行分簇. 为提高运算速度, 在微钙化点检测时将整个图像分解为多个子图像并行运算, 实现了一种基于自适应核学习相关向量机的微钙化点簇快速处理方法. 实验结果和分析表明, 自适应核学习相关向量机方法算法性能优于相关向量机方法, 特别是实现的快速方法能进一步降低微钙化点簇的处理时间. 关键词： 乳腺X线图像 微钙化点簇 相关向量机 自适应核学习     

电场诱导氧化石墨烯的极化动力学特性研究

本文通过外加电场改变氧化石墨烯团簇分子的共振能量,利用激光激发氧化石墨烯产生的共振荧光特性测量氧化石墨烯在电场作用下的极化动力学特性. 发现存在外加电场使得荧光共振峰的半高全宽趋于饱和的时间特性,而不同的氧化石墨烯团簇分子的荧光共振峰的暂态特性同时反映了电场对氧化石墨烯产生定向极化和变形极化的动力学特性. 关键词： 氧化石墨烯 团簇分子 电场 极化动力学     

Evolution of geometrical structures,stabilities and electronic properties of neutral and anionic Li n Cu λ (n = 1–9, λ = 0, −1) clusters: compare with pure lithium clusters

The structural evolution, stabilities, and electronic properties of copper-doped lithium Li _n Cu^λ (n?=?1–9, λ?=?0, ?1) clusters have been systematically investigated using a density functional method at PW91PW91 level. Extensive searches for ground-state structures were carried out, and the results showed the copper tends to occupy the most highly coordinated position and form the largest probable number of bonds with lithium atoms. By calculating the binding energies per atom, fragmentation energies and the HOMO-LOMO gaps, we found LiCu, Li₇Cu, LiCu^?, Li₂Cu^? and Li₈Cu^? clusters have the stronger relative stability and enhanced chemical stability. The content and pattern of frontier MOs for the most stable doped isomers were analysed to investigate the bond nature of interaction among Li and Cu atoms. The results show some σ-type and π-type bonds are formed among them, and with small admixture of the Cu d characters. To achieve a deep insight into the electron localization and reliable electronic structure information, the natural population analysis and electron localization function were performed and discussed.     

Spin-polarised DFT study of cobalt-doped gallium clusters

The structural, electronic and magnetic properties of small gallium clusters doped with Cobalt have been studied using spin-polarised density functional theory. The binding energy per atom, second-order differences of total energies and fragmentation energies of equilibrium geometries of the host Ga_n+1 and doped Ga_nCo (n = 1–12) clusters are computed. Doped clusters are found to be more stable than pure Ga clusters; Ga₃Co, Ga₅Co and Ga₈Co clusters are exceptionally stable. Doping with Co changes the highest occupied molecular orbital-lowest unoccupied molecular orbital (HOMO–LUMO) gap, and also affects the magnetic moments of clusters.     

DRDF analysis of wide-angle x-ray scattering of natural rubber

A differential radial distribution function (DRDF) of molten natural rubber (NR) was derived from its wide-angle x-ray scattering (WAXS) data. Three peaks with Bragg spacings 4.85, 2.31, and 1.23 Å corresponding, respectively, to interchain, C₁-C₃ and C₁ -C₂ distances are present in the corrected WAXS curve. The derived DRDF, which is shown to be greatly improved than the ones published so far, contains six peaks located at 1.51, 2.48, 3.98, 5.68, 10.65, and 15.25 Å. Maximum contributions of intramolecular atomic distances to the radial distribution peaks were estimated and compared with the experimental results. The comparison shows that the first three peaks are intramolecular in origin and the remaining peaks are predominantly due to intermolecular regularities. A periodicity of an approximately constant interval of 5.26 Å is found between the intermolecular peaks and is attributable to the organizations of more or less parallel chain segments in the material. This finding provided additional evidence for the presence of local lateral ordering to the extent of about 30 Å in molten-state natural rubber.     

Defect clustering in double doped fluorite crystals with Lu and Gd

Abstract

Solid solutions Ca¹ x-^yLu^xGd^y F_2+x+y for 10^?4 ≤ x ≤ 2 × 10^?2 and y=0.0001 have been studied by electron paramagnetic resonance (EPR) and ionic thermal currents (ITC). It has been found that the ITC spectrum from 77 to 420 K is very weak and the main peak is attributed to the relaxation of both Lu³⁺-F^? _x and Gd³⁺F^? _i nn dipoles. No polarizable clusters are present in the temperature range explored here. The EPR spectra show the presence of Gd³⁺ tetragonal and cubic centers due to the local and non local compensation, respectively. The continuous decrease in the molar fraction of Gd³⁺ tetragonal centers together with the low concentration of Lu nn dipoles is an evidence of the existence at these low and intermediate concentrations of large clusters such as the cubo-octahedral hexamer which has been proposed for CaF₂ crystals very highly doped with small trivalent cations.