Rhombellanic diamond

Rhombellanes are mathematical structures, proposed in 2017; they may appear both in crystal or quasicrystal networks, also in their homeomorphs, further possible becoming real molecules. The simplest rhombellane is the K_2.3 complete bipartite graph, a tile found in the linear polymeric staffanes. In this paper a new binodal crystal network, called here dia-rbl, is introduced; its repeating unit, ada-rbl, is a 4D structure. The crystal structure is characterized by connectivity and ring sequences and also by the Omega polynomial.     

Rhombellation: a builder for multi-shell clusters

Rhombellanes are mathematical structures, proposed by us in 2017; they may appear both in periodic crystals or in finite structures. The simplest rhombellane is rbl.5 or K_2.3, the complete bipartite graph. In this paper, rhombellation operation is iteratively applied on three classes of structures: cube, tori and cubic pcu-network, respectively. The structural topological parameters are detailed.     

每层3个节点的神经网络对连续函数的逼近

主要研究了多层前馈人工神经网络对Rd上连续函数的逼近,证得每层3个节点的n(n+d-1/d-1)层前馈人工神经网络可以按任意给定的精度逼近任一总次数为n的d元代数多项式,并给出d=1时的实例验证.此外,由Weierstrass定理,所构造的前馈人工神经网络可以按任意给定的精度逼近连续函数.最后,将该结论推广到多维输出的情形.     

一种适合异构P2P网络的树形结构覆盖层

为实现异构网络下的P2P应用,分析了具有异构融合特征的P2P网络的特点,提出了与之相适应的基于二叉树结构的覆盖层网络(TSOHEN)的设计方法.该方法根据节点的不同功能和属性将节点分为普通节点和混合节点两大类,并为每类节点设计了适应异构特征的路由表,对各类节点的加入和离开功能设计了相应的算法,并通过混合节点实现跨网的P2P查询操作.数值和仿真结果表明,该覆盖层设计能够有效地适应异构网络的环境,树形结构也没有使得根节点和叶节点的负荷产生明显的区别,各混合节点的负载也基本平衡.在大规模节点数量的情况下,TSOHEN的各种算法仍具有良好的收敛性.     

电影数字放映设备国产化之思考——在中国电子视像行业协会举办的大屏幕投影显示技术与产业高层论坛会上的讲话

本文对数字放映与胶片放映进行了比较,介绍了国内外数字电影放映的现状,指出了数字放映发展过程中存在的问题,尤其是重点论述了数字电影放映设备国产化的思路,提出了先从"光路"开始攻关的可行性发展途径.     

Crystal growth of rare-earth-doped ternary potassium lead chloride single crystals by the Bridgman method

High optical quality pure and rare-earth-doped ternary-potassium-lead-chloride (KPb₂Cl₅) single crystals have been grown using the Bridgman technique in a two-zone transparent vertical furnace. Combining the chlorination of the melt, to eliminate oxygen impurities, with a horizontal zone-refining, followed by the Bridgman growth itself using sealed silica ampoules, we successfully grew non-moisture-sensitive crystals of a high optical quality. The moisture content in the raw materials determines the quality of the resulting crystals.     

Topological Characterization of Book Graph and Stacked Book Graph

Degree based topological indices are being widely used in computer-aided modeling, structural activity relations, and drug designing to predict the underlying topological properties of networks and graphs. In this work, we compute the certain important degree based topological indices like Randic index, sum connectivity index, ABC index, ABC₄ index, GA index and GA₅ index of Book graph B_n and Stacked book graph B_m,n. The results are analyzed by using edge partition, and the general formulas are derived for the above-mentioned families of graphs.     

Artificial neural networks for pattern recognition

This tutorial article deals with the basics of artificial neural networks (ANN) and their applications in pattern recognition. ANN can be viewed as computing models inspired by the structure and function of the biological neural network. These models are expected to deal with problem solving in a manner different from conventional computing. A distinction is made between pattern and data to emphasize the need for developing pattern processing systems to address pattern recognition tasks. After introducing the basic principles of ANN, some fundamental networks are examined in detail for their ability to solve simple pattern recognition tasks. These fundamental networks together with the principles of ANN will lead to the development of architectures for complex pattern recognition tasks. A few popular architectures are described to illustrate the need to develop an architecture specific to a given pattern recognition problem. Finally several issues that still need to be addressed to solve practical problems using ANN approach are discussed. This paper is mostly a consolidation of work reported by several researchers in the literature, some of which is cited in the references. The author has borrowed several ideas and illustrations from the references quoted in this paper.     

Systematic development of pH-independent controlled release tablets of carvedilol using central composite design and artificial neural networks

The purpose of this study was to apply the optimization method incorporating artificial neural network (ANN) using pH-independent release of weakly basic drug, carvedilol from HPMC-based matrix formulation. Because of weakly basic nature of carvedilol, drug shows pH-dependent solubility. The enteric polymer EUDRAGIT L100 was added formulations to overcome pH-dependent solubility of carvedilol. Effects of the Hydroxypropylmethyl cellulose (HPMC) K4M and EUDRAGIT L100 amount on drug release were investigated. For this purpose 13 kinds of formulations were prepared at three different levels of each variables. The optimization of the formulation was evaluated by using ANN method. Two formulation parameters, the amounts of HPMC K4M and Eudragit L100 at three levels (?1, 0, 1) were selected as independent/input variables. In-vitro dissolution sampling times at twelve different time points were selected as dependent/output variables. By using experimental dissolution results and amount of HPMC K4M and EUDRAGIT L100, percentage of dissolved carvedilol was predicted by ANN. Similarity factor (f₂) between predicted and experimentally observed profile was calculated and f₂ value was found 76.33. This value showed that there was no difference between predicted and experimentally observed drug release profile. As a result of these experiments, it was found that ANNs can be successfully used to optimize controlled release drug delivery systems.     

多元金属硫族化合物的合成和结构表征

介绍新的发展起来的反应性熔盐法及其在多元金属硫族化合物中温固相合成中的应用，并讨论了这类化合物的结构特征，低维性和热力学介稳性。     

An evaluation of the multi-state node networks reliability using the traditional binary-state networks reliability algorithm

A system where the components and system itself are allowed to have a number of performance levels is called the Multi-state system (MSS). A multi-state node network (MNN) is a generalization of the MSS without satisfying the flow conservation law. Evaluating the MNN reliability arises at the design and exploitation stage of many types of technical systems. Up to now, the known existing methods can only evaluate a special MNN reliability called the multi-state node acyclic network (MNAN) in which no cyclic is allowed. However, no method exists for evaluating the general MNN reliability. The main purpose of this article is to show first that each MNN reliability can be solved using any the traditional binary-state networks (TBSN) reliability algorithm with a special code for the state probability. A simple heuristic SDP algorithm based on minimal cuts (MC) for estimating the MNN reliability is presented as an example to show how the TBSN reliability algorithm is revised to solve the MNN reliability problem. To the author's knowledge, this study is the first to discuss the relationships between MNN and TBSN and also the first to present methods to solve the exact and approximated MNN reliability. One example is illustrated to show how the exact MNN reliability is obtained using the proposed algorithm.     

Identifying spreading influence nodes for social networks

The identification of spreading influence nodes in social networks, which studies how to detect important individuals in human society, has attracted increasing attention from physical and computer science, social science and economics communities. The identification algorithms of spreading influence nodes can be used to evaluate the spreading influence, describe the node’s position, and identify interaction centralities. This review summarizes the recent progress about the identification algorithms of spreading influence nodes from the viewpoint of social networks, emphasizing the contributions from physical perspectives and approaches, including the microstructure-based algorithms, community structure-based algorithms, macrostructure-based algorithms, and machine learning-based algorithms. We introduce diffusion models and performance evaluation metrics, and outline future challenges of the identification of spreading influence nodes.     

Deterministic global optimization approach to steady-state distribution gas pipeline networks

Natural gas is normally transported through a vast network of pipelines. A pipeline network is generally established either to transmit gas at high pressure from coastal supplies to regional demand points (transmission network) or to distribute gas to consumers at low pressure from the regional demand points (distribution network). In this study, the distribution network is considered. The distribution network differs from the transmission one in a number of ways. Pipes involved in a distribution network are often much smaller and the network is simpler, having no valves, compressors or nozzles. In this paper, we propose the problem of minimizing the cost of pipelines incurred by driving the gas in a distribute non-linear network under steady-state assumptions. In particular, the decision variables include the length of the pipes’ diameter, pressure drops at each node of the network, and mass flow rate at each pipeline leg. We establish a mathematical optimization model of this problem, and then present a global approach, which is based on the GOP primal-relaxed dual decomposition method presented by Visweswaran and Floudas (Global optimization in engineering design. Kluwer book series in nonconvex optimization and its applications. Kluwer, Netherlands, 1996), to the optimization model. Finally, results from application of the approach to data from gas company are presented.     

Topological rationality of supply chain networks

In this study, we apply a topologically distributed bounded rationality model to quantify the level of rationality in supply chain networks. We use the averaged Jensen-Shannon divergence values between Nash and Quantal Response equilibria for all inter-firm strategic interactions, which are represented as Prisoner’s Dilemma games, to characterise the average level of rationality in a given supply chain network. This is based on the game theoretic assumption that as the rationality of a particular interaction increases, it converges towards Nash equilibrium, in a certain strategic decision making scenario. Using this model, we demonstrate that hub-and-spoke topologies are collectively more rational compared to scale-free and random network topologies. Finally, we compare our theoretical results against the empirical findings reported for networked systems in various domains. In particular, it is shown that network topologies comprising higher average rationality levels emerge under increasingly competitive environments.     

基于贝叶斯网络的计算机网络端到端服务故障诊断

研究了利用贝叶斯网络不确定推理技术实现端到端服务故障诊断的方法，详细描述了贝叶斯网络故障诊断模型的建立方法，设计了基于Pearl信念传播机制的故障诊断算法，并对其进行了改进，以提高诊断效果．最后，通过仿真验证了该方法的有效性，并提出了下一步的研究方向．     

Proton transfer reactions and hydrogen-bond networks in protein environments

In protein environments, proton transfer reactions occur along polar or charged residues and isolated water molecules. These species consist of H-bond networks that serve as proton transfer pathways; therefore, thorough understanding of H-bond energetics is essential when investigating proton transfer reactions in protein environments. When the pK_a values (or proton affinity) of the H-bond donor and acceptor moieties are equal, significantly short, symmetric H-bonds can be formed between the two, and proton transfer reactions can occur in an efficient manner. However, such short, symmetric H-bonds are not necessarily stable when they are situated near the protein bulk surface, because the condition of matching pK_a values is opposite to that required for the formation of strong salt bridges, which play a key role in protein–protein interactions. To satisfy the pK_a matching condition and allow for proton transfer reactions, proteins often adjust the pK_a via electron transfer reactions or H-bond pattern changes. In particular, when a symmetric H-bond is formed near the protein bulk surface as a result of one of these phenomena, its instability often results in breakage, leading to large changes in protein conformation.     

Deep convolutional neural networks for eigenvalue problems in mechanics

We show that deep convolutional neural networks (CNNs) can massively outperform traditional densely connected neural networks (NNs) (both deep or shallow) in predicting eigenvalue problems in mechanics. In this sense, we strike out in a new direction in mechanics computations with strongly predictive NNs whose success depends not only on architectures being deep but also being fundamentally different from the widely used to date. We consider a model problem: predicting the eigenvalues of one-dimensional (1D) and two-dimensional (2D) phononic crystals. For the 1D case, the optimal CNN architecture reaches 98% accuracy level on unseen data when trained with just 20 000 samples, compared to 85% accuracy even with 100 000 samples for the typical network of choice in mechanics research. We show that, with relatively high data efficiency, CNNs have the capability to generalize well and automatically learn deep symmetry operations, easily extending to higher dimensions and our 2D case. Most importantly, we show how CNNs can naturally represent mechanical material tensors, with its convolution kernels serving as local receptive fields, which is a natural representation of mechanical response. Strategies proposed are applicable to other mechanics' problems and may, in the future, be used to sidestep cumbersome algorithms with purely data-driven approaches based upon modern deep architectures.     

High-pressure phase transition in Ho2O3

High-pressure X-ray diffraction and Raman studies on holmium sesquioxide (Ho₂O₃) have been carried out up to a pressure of ∼17 GPa in a diamond-anvil cell at room temperature. Holmium oxide, which has a cubic or bixbyite structure under ambient conditions, undergoes an irreversible structural phase transition at around 9.5 GPa. The high-pressure phase has been identified to be low symmetry monoclinic type. The two phases coexist to up to about 16 GPa, above which the parent phase disappears. The high-pressure laser-Raman studies have revealed that the prominent Raman band ∼370 cm⁻¹ disappears around the similar transition pressure. The bulk modulus of the parent phase is reported.     

Nonlinear mechanics of soft fibrous networks

Mechanical networks of fibres arise on a range of scales in nature and technology, from the cytoskeleton of a cell to blood clots, from textiles and felts to skin and collageneous tissues. Their collective response is dependent on the individual response of the constituent filaments as well as density, topology and order in the network. Here, we use the example of a low-density synthetic felt of athermal filaments to study the generic features of the mechanical response of such networks including strain stiffening and large effective Poisson ratios. A simple microscopic model allows us to explain these features of our observations, and provides us with a baseline framework to understand active biomechanical networks.     

Detecting outbreaks in temporally dependent networks

Dynamic networks require effective methods of monitoring and surveillance in order to respond promptly to unusual disturbances. In many applications, it is of interest to identify anomalous behavior within a dynamic interacting system. Such anomalous interactions are reflected by structural changes in the network representation of the system. In this paper, a dynamic random graph model is proposed that takes into account the past activities of the individuals in the social network and also represents temporal dependency of the network. The model parameters are appearance and disappearance probabilities of an edge which are estimated using a maximum likelihood approach. A generalization of a single path‐dependent likelihood ratio test is employed to detect changes in the parameters of the proposed model. Through monitoring the estimated parameters, one can effectively detect structural changes in a temporal‐dependent network. The proposed model is employed to describe the behavior of a real network, and its parameters are monitored via dependent likelihood ratio test and multivariate exponentially weighted moving average control chart. Results indicate that the proposed dynamic random graph model is a reliable mean to modeling and detecting changes in temporally dependent networks.