Parameter identification for viscoelastic materials

Starting from the general stress-strain relation for a linear Boltzmann-Volterra material, which is in agreement with the principle of inertia, a new identification procedure is proposed. Instead of running one long-range relaxation experiment, following asingle suitably specified deformation history, material characterization is done using the data ofn short relaxation experiments followingn different deformation histories. To interpret these data a direct non-iterative algorithm has been developed. Compared with other methods, for example curve fitting by using Gauss' method, this direct method is numerically stable and allows a simple direct evaluation of the error due to the scattering of experimental data. The method has been applied to the determination of the relaxation times of an unsaturated polyester material.     

石墨炔管能带和力学性质的第一性原理研究

基于第一性原理计算,这篇文章研究了单壁锯齿型和扶手型石墨炔管的几何结构、电子结构以及杨氏模量.计算表明:石墨炔管是一类具有一定能隙的直接带隙半导体管,其带隙在0.4-1.3eV的能量范围,且随管径的增大而变小.而石墨炔管的杨氏模量在0.44-0.50Tpa区间变化.对于锯齿型石墨炔管,其杨氏模量随着半径的增大而变小而锯齿型石墨炔管的杨氏模量随其半径的增大而增大.     

柱形分布的电荷产生的电势

将无限长线电荷看成二维平面上的一个“源”,利用电势叠加原理计算了无限长柱形均匀分布的电荷产生的电势.这些柱体的截面电荷分布包括可解析表示和不可求解表示两种,后者可以通过数值计算给出结果.因此这种方法实际上可以计算任意截面柱形电荷分布的电势.通过计算,展示了各种电荷分布所产生电场的特性和共同特征.     

单轴晶体内表面入射e光线的双反射

基于光线遵从的Fermat原理,从几何学的角度讨论和分析了在单轴晶体中入射的e光线在界面上的双反射,得到了确定光线反射方向的一般公式,并对几种特殊情况进行了讨论.指出了上述结果与由电磁波的边界条件计算所得结果是一致的.     

Hamaker微观连续介质理论的数字密度和Hamaker常数分析

针对Hamaker微观连续介质理论在微观接触力计算中存在的问题,根据Hamaker假设,用连续介质法计算2个原子之间的相互作用力,发现作用力同经典的Lennard-Jones势所反映的作用力不一致.通过分析数字密度,发现数字密度并非是如Hamaker所认为的常数,而是随间距变化的;并得到Hamaker微观连续介质理论仅在间距大于7倍的原子半径时才成立的结论.通过分析Hamaker常数,发现Hamaker常数也随间距变化.     

Coding of Gibbs function flows from nucleotide pools

The biochemical machinery of living systems obeys kinetic laws, but is driven by Gibbs function flows. Both the kinetic and thermodynamic aspects of Gibbs gain, transmission, and utilization are considered. An information-theoretic approach is used to find conditions under which the kinetics encodes the associated Gibbs function flow with the lowest possible error.     

On the Nature of Functional Differentiation: The Role of Self-Organization with Constraints

The focus of this article is the self-organization of neural systems under constraints. In 2016, we proposed a theory for self-organization with constraints to clarify the neural mechanism of functional differentiation. As a typical application of the theory, we developed evolutionary reservoir computers that exhibit functional differentiation of neurons. Regarding the self-organized structure of neural systems, Warren McCulloch described the neural networks of the brain as being “heterarchical”, rather than hierarchical, in structure. Unlike the fixed boundary conditions in conventional self-organization theory, where stationary phenomena are the target for study, the neural networks of the brain change their functional structure via synaptic learning and neural differentiation to exhibit specific functions, thereby adapting to nonstationary environmental changes. Thus, the neural network structure is altered dynamically among possible network structures. We refer to such changes as a dynamic heterarchy. Through the dynamic changes of the network structure under constraints, such as physical, chemical, and informational factors, which act on the whole system, neural systems realize functional differentiation or functional parcellation. Based on the computation results of our model for functional differentiation, we propose hypotheses on the neuronal mechanism of functional differentiation. Finally, using the Kolmogorov–Arnold–Sprecher superposition theorem, which can be realized by a layered deep neural network, we propose a possible scenario of functional (including cell) differentiation.     

Calibration Invariance of the MaxEnt Distribution in the Maximum Entropy Principle

The maximum entropy principle consists of two steps: The first step is to find the distribution which maximizes entropy under given constraints. The second step is to calculate the corresponding thermodynamic quantities. The second part is determined by Lagrange multipliers’ relation to the measurable physical quantities as temperature or Helmholtz free energy/free entropy. We show that for a given MaxEnt distribution, the whole class of entropies and constraints leads to the same distribution but generally different thermodynamics. Two simple classes of transformations that preserve the MaxEnt distributions are studied: The first case is a transform of the entropy to an arbitrary increasing function of that entropy. The second case is the transform of the energetic constraint to a combination of the normalization and energetic constraints. We derive group transformations of the Lagrange multipliers corresponding to these transformations and determine their connections to thermodynamic quantities. For each case, we provide a simple example of this transformation.     

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.