TMTSF2X Salts. Preparation,Structure and Effect of the Anions

The preparation of the TMTSF molecule and some of its properties are reviewed. The preparation of metallic and superconducting TMTSF X salts is described and some structural aspects are discussed, with emphasis of possible order-disorder transitions when X is a non-centrosymmetric anion. Preliminary results for TMTSF₂ TeF₅ which remain conducting to at least 5 K are presented.     

Uncovering the Hidden Landscape of Tris(4-pyridyl) Ligands: Topological Complexity Derived from the Bridgehead

Supramolecular main group chemistry is a developing field which parallels the conventional domain of metallo-organic chemistry. Little explored building blocks in this area are main group metal-based ligands which have the appropriate donor symmetry to build desired molecular or extended arrangements. Tris(pyridyl) main group ligands (E(py)₃, E=main group metal) are potentially highly versatile building blocks since shifting the N-donor arms from the 2- to the 3-positions and 4-positions provides a very simple way of changing the ligand character from mononuclear/chelating to multidentate/metal-bridging. Here, the coordination behaviour of the first main group metal tris(4-pyridyl) ligands, E(4-py)₃ (E=Sb, Bi, Ph−Sn) is explored, as well as their ability to build metal-organic frameworks (MOFs). The complicated topology of these MOFs shows a marked influence on the counter anion and on the ability of the E(4-py)₃ ligands to switch coordination mode, depending on the steric and donor character of the bridgehead. This structure-directing influence of the bridgehead provides a potential building strategy for future molecular and MOF design in this area.     

基于RBM的神经网络时间序列预测

为了克服神经网络依赖初始化结果,泛化能力不强的缺点,提出一种基于受限玻尔兹曼机(RBM)的神经网络模型.利用无监督学习方法优化神经网络的初始权值和阈值,将RBM与神经网络融合起来,模型与时间序列神经网络做实验对比,结果表明,基于受限的玻尔兹曼机的神经网络模型优于神经网络预测模型,模型可以提高预测的精准度,具有一定的应用意义.     

On exponential stability analysis for neural networks with time-varying delays and general activation functions

This paper is concerned with the exponential stability analysis for a class of cellular neural networks with both interval time-varying delays and general activation functions. The boundedness assumption of the activation function is not required. The limitation on the derivative of time delay being less than one is relaxed and the lower bound of time-varying delay is not restricted to be zero. A new Lyapunov-Krasovskii functional involving more information on the state variables is established to derive a novel exponential stability criterion. The obtained condition shows potential advantages over the existing ones since no useful item is ignored throughout the estimate of upper bound of the derivative of Lyapunov functional. Finally, three numerical examples are included to illustrate the proposed design procedures and applications.     

Localization of diffusion sources in complex networks with sparse observations

Locating sources in a large network is of paramount importance to reduce the spreading of disruptive behavior. Based on the backward diffusion-based method and integer programming, we propose an efficient approach to locate sources in complex networks with limited observers. The results on model networks and empirical networks demonstrate that, for a certain fraction of observers, the accuracy of our method for source localization will improve as the increase of network size. Besides, compared with the previous method (the maximum–minimum method), the performance of our method is much better with a small fraction of observers, especially in heterogeneous networks. Furthermore, our method is more robust against noise environments and strategies of choosing observers.