Using neural networks in the identification of Preisach-typehysteresis models

The identification process of the classical Preisach-type hysteresis model reduces to the determination of the weight function of elementary hysteresis operators upon which the model is built. It is well known that the classical Preisach model can exactly represent hysteretic nonlinearities which exhibit wiping-out and congruency properties. In that case, the model identification can be analytically and systematically accomplished by using first-order reversal curves. If the congruency property is not exactly valid, the Preisach model can only be used as an approximation. It is possible to improve the model accuracy in this situation by incorporating more appropriate experimental data during the identification stage. However, performing this process using the traditional systematic techniques becomes almost impossible. In this paper, the machinery of neural networks is proposed as a tool to accomplish this identification task. The suggested identification approach has been numerically implemented and carried out for a magnetic tape sample that does not possess the congruency property. A comparison between measured data and model predictions suggests that the proposed identification approach yields more accurate results     

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.     

Optimal control policy for probabilistic Boolean networks with hard constraints

It is well known that the control/intervention of some genes in a genetic regulatory network is useful for avoiding undesirable states associated with some diseases like cancer. For this purpose, both optimal finitehorizon control and infinite-horizon control policies have been proposed. Boolean networks (BNs) and its extension probabilistic Boolean networks (PBNs) as useful and effective tools for modelling gene regulatory systems have received much attention in the biophysics community. The control problem for these models has been studied widely. The optimal control problem in a PBN can be formulated as a probabilistic dynamic programming problem. In the previous studies, the optimal control problems did not take into account the hard constraints, i.e. to include an upper bound for the number of controls that can be applied to the captured PBN. This is important as more treatments may bring more side effects and the patients may not bear too many treatments. A formulation for the optimal finite-horizon control problem with hard constraints introduced by the authors. This model is state independent and the objective function is only dependent on the distance between the desirable states and the terminal states. An approximation method is also given to reduce the computational cost in solving the problem. Experimental results are given to demonstrate the efficiency of our proposed formulations and methods.     

Robust control of uncertain context-sensitive probabilistic Boolean networks

Uncertainty is an intrinsic phenomenon in control of gene regulatory networks (GRNs). The presence of uncertainty is related to impreciseness of GRN models due to: (1) Errors caused by imperfection of measurement devices and (2) Models' inability to fully capture a complex structure of the GRN. Consequently, there is a discrepancy between actual behaviour of the GRN and what is predicted by its mathematical model. This can result in false control signals, which can drive a cell to an undesirable state. To address the problem of control under uncertainties, a risk-sensitive control paradigm is proposed. Robustness is accomplished by minimisation of the mean exponential cost as opposed to, for instance, minimisation of the mean square cost by risk-neutral controllers. The authors derive an optimal risk-sensitive controller when a GRN is modelled by a context-sensitive probabilistic Boolean network (CSPBN). By using a relation between the relative entropy and free-energy, a relative stability of the cost achieved by the risk-sensitive controller is demonstrated when the distribution of the CSPBN attractors is perturbed, as opposed to the cost of the risk-neutral controller that exhibits increase. The use of the relation between the relative entropy and free-energy to analyse the influence of a particular attractor on the robustness of the controller is studied. The efficiency of the risk-sensitive controller is tested for the CSPBN obtained from the study of malignant melanoma.     

Autonomous Boolean modelling of developmental gene regulatory networks

During early embryonic development, a network of regulatory interactions among genes dynamically determines a pattern of differentiated tissues. We show that important timing information associated with the interactions can be faithfully represented in autonomous Boolean models in which binary variables representing expression levels are updated in continuous time, and that such models can provide a direct insight into features that are difficult to extract from ordinary differential equation (ODE) models. As an application, we model the experimentally well-studied network controlling fly body segmentation. The Boolean model successfully generates the patterns formed in normal and genetically perturbed fly embryos, permits the derivation of constraints on the time delay parameters, clarifies the logic associated with different ODE parameter sets and provides a platform for studying connectivity and robustness in parameter space. By elucidating the role of regulatory time delays in pattern formation, the results suggest new types of experimental measurements in early embryonic development.     

Elementary models for biological interaction

This paper provides a brief review of two theories of biological interaction of hazardous exposures, the Hewlett-Plackett theory and the sufficient-component cause theory. Although the former has its origin in bioassay and the latter in epidemiology, it is possible to show that the two theories are isomorphic in that they imply identical relationships between biological interaction and disease rates. The relationship of biological interaction to statistical and public health interaction is also reviewed. In particular, the presence or absence of biological interaction under the two theories does not correspond in a one-to-one fashion with the presence or absence of any proposed form of statistical or public health interaction. This observation confirms the importance of clearly distinguishing different concepts of interaction.     

A recursive method for breaking complex logic loops in Boolean system models

A finite iterative method is developed for solving system fault trees containing logic loops. The procedure yields correct expressions for the top events as well as for all intermediate gates.     

Integer programming‐based method for observability of singleton attractors in Boolean networks

Boolean network (BN) is a popular mathematical model for revealing the behaviour of a genetic regulatory network. Furthermore, observability, an important network feature, plays a significant role in understanding the underlying network. Several studies have been done on analysis of observability of BNs and complex networks. However, the observability of attractor cycles, which can serve as biomarker detection, has not yet been addressed in the literature. This is an important, interesting and challenging problem that deserves a detailed study. In this study, a novel problem was first proposed on attractor observability in BNs. Identification of the minimum set of consecutive nodes can be used to discriminate different attractors. Furthermore, it can serve as a biomarker for different disease types (represented as different attractor cycles). Then a novel integer programming method was developed to identify the desired set of nodes. The proposed approach is demonstrated and verified by numerical examples. The computational results further illustrates that the proposed model is effective and efficient.Inspec keywords: integer programming, Boolean algebra, complex networks, diseasesOther keywords: disease, consecutive nodes, biomarker detection, attractor cycles, complex networks, genetic regulatory network, mathematical model, Boolean networks, singleton attractors, integer programming‐based method     

Search for all MCs in networks with unreliable nodes and arcs

A simple method is proposed to search for all minimal cutsets (MCs ) for imperfect networks reliability subject to both arc and node failures under the condition that all of the MCs in the network with perfect nodes are given in advance. The proposed method does not require re-enumeration for all of the MCs for additional node failure consideration. All of the MC candidates found in the proposed algorithm are actual MCs without any need for further verification. This algorithm is more effective than the existing algorithm in which every MC candidate is not verified as a MC. No identical MCs are found using the proposed algorithm, which does not duplicate MCs and is more efficient than the existing methods. Only simple concepts are used to implement the proposed algorithm, which makes it easier to understand and implement. With considering unreliable nodes, the proposed method is also more realistic and valuable for reliability analysis in an existing network. The correctness of the proposed algorithm will be analyzed and proven. One example is used to illustrate how all MCs are generated in a network with arc and node failures solved using the proposed algorithm.     

Operational planning models for distribution networks

The design and optimisation of a logistic network deals with a wide set of decisions, e.g. the determination of the best location and capacity of the different logistic facilities (production plants, distribution centres, transit points, wholesalers, etc.), the allocation of the product demand coming from customers in presence (or absence) of fractionable flows of material, the determination of the best transportation mode (truck, rail, etc.) as well as loading and routing of vehicles. These decisions involve multiple stages of a distribution network: customers-regional distribution centres (RDC), RDCs-central distribution centres (CDC) and CDCs-production plants and sources, in presence of multiple products and the variable time (i.e. time-dependent product demand and flows of material). This paper presents a top-down methodology that joins the strategic planning, the tactical planning and the operational planning of distribution networks with a special focus on the development of effective heuristic methods to face the vehicle routing problem. Original models and heuristic algorithms for the operational planning are illustrated. The impact of the strategic and tactical decisions on the performance of the operational planning is evaluated by the application of the proposed hierarchical approach to two realistic case studies. Obtained results are illustrated in a what-if experimental analysis conducted on multiple problem settings and realistic scenarios.     

Locating ineffective sensor nodes in wireless sensor networks

Ineffective sensor node (InESN) in a wireless sensor network (WSN) is defined as one whose position cannot be estimated by traditional localisation methods. Incremental localisation method is investigated and the existence of the InESNs is confirmed. By analysing the existing characteristics, the InESNs are classified into three categories: InESNs connecting with one known node, InESNs connecting with two known nodes and InESNs standing alone. It is impossible to locate the InESNs of the third category because they cannot receive any information from the known nodes. With a moving target in the WSN, a constrained least-squares formulation is developed to estimate the InESNs of the first two categories. Numerical evaluations are carried out to examine the performance of the proposed method and show that it is indeed effective for locating the InESNs. By incorporating the InESNs in the tracking applications, the performance of the target tracking can be greatly enhanced.     

Inference of Boolean networks under constraint on bidirectional gene relationships

The coefficient of determination (CoD) has been used to infer Boolean networks (BNs) from steady-state data, in particular, to estimate the constituent BNs for a probabilistic BN. The advantage of the CoD method over design methods that emphasise graph topology or attractor structure is that the CoD produces a network based on strong predictive relationships between target genes and their predictor (parent) genes. The disadvantage is that spurious attractor cycles appear in the inferred network, so that there is poor inference relative to the attractor structure, that is, relative to the steady-state behaviour of the network. Given steady-state data, there should not be a significant amount of steady-state probability mass in the inferred network lying outside the mass of the data distribution; however, the existence of spurious attractor cycles creates a significant amount of steady-state probability mass not accounted for by the data. Using steady-state data hampers design because the lack of temporal data causes CoD design to suffer from a lack of directionality with regard to prediction. This results in spurious bidirectional relationships among genes in which two genes are among the predictors for each other, when actually only one of them should be a predictor of the other, thereby creating a spurious attractor cycle. This paper characterises the manner in which bidirectional relationships affect the attractor structure of a BN. Given this characterisation, the authors propose a constrained CoD inference algorithm that outperforms unconstrained CoD inference in avoiding the creation of spurious non-singleton attractor. Algorithm performances are compared using a melanoma-based network.     

New scaling relation for information transfer in biological networks

We quantify characteristics of the informational architecture of two representative biological networks: the Boolean network model for the cell-cycle regulatory network of the fission yeast Schizosaccharomyces pombe (Davidich et al. 2008 PLoS ONE 3, e1672 (doi:10.1371/journal.pone.0001672)) and that of the budding yeast Saccharomyces cerevisiae (Li et al. 2004 Proc. Natl Acad. Sci. USA 101, 4781–4786 (doi:10.1073/pnas.0305937101)). We compare our results for these biological networks with the same analysis performed on ensembles of two different types of random networks: Erdös–Rényi and scale-free. We show that both biological networks share features in common that are not shared by either random network ensemble. In particular, the biological networks in our study process more information than the random networks on average. Both biological networks also exhibit a scaling relation in information transferred between nodes that distinguishes them from random, where the biological networks stand out as distinct even when compared with random networks that share important topological properties, such as degree distribution, with the biological network. We show that the most biologically distinct regime of this scaling relation is associated with a subset of control nodes that regulate the dynamics and function of each respective biological network. Information processing in biological networks is therefore interpreted as an emergent property of topology (causal structure) and dynamics (function). Our results demonstrate quantitatively how the informational architecture of biologically evolved networks can distinguish them from other classes of network architecture that do not share the same informational properties.     

用分段法与链表法的二维布尔运算

二维布尔运算是计算机图形学中的基本算法。通过把两个二维几何形体分别按入点与出点位置顺序分段，组合成一个新的图形，形成了—种新的二维布尔运算算法：分段法。笔者介绍了分段法的几何原理和基本步骤，并利用面向对象技术实现复杂数据结构操作的基本方法——链表法实现了常用的二维布尔运算算法，并将两种方法作了比较。     

Nonparametric statistics of dynamic networks with distinguishable nodes

The study of random graphs and networks had an explosive development in the last couple of decades. Meanwhile, techniques for the statistical analysis of sequences of networks were less developed. In this paper, we focus on networks sequences with a fixed number of labeled nodes and study some statistical problems in a nonparametric framework. We introduce natural notions of center and a depth function for networks that evolve in time. We develop several statistical techniques including testing, supervised and unsupervised classification, and some notions of principal component sets in the space of networks. Some examples and asymptotic results are given, as well as two real data examples.     

无线传感器网络中不良节点的判断与定位

为降低整个无线传感器网络的定位误差,基于图论相关原理和方法,对无线传感器网络中存在的边缘节点和亚孤立节点进行了判断,通过对此类不良节点周围的节点密度、接收锚节点的范围和方向进行分析,给出了不良节点定位误差偏大的理论解释和对其进行修正定位的解决方案,确立了无线传感器网络中边缘节点和亚孤立节点的判断与定位方法.不同场景下定位性能的仿真实验显示,运用这种方法,对规则的网络拓扑,经修正后不良节点的定位精度比修正前一般可以提高17%以上;对不规则的网络拓扑,经修正后的亚孤立节点定位精度比修正前一般可以提高10%以上.     

Optimization models for operative planning in drinking water networks

The topic of this paper is minimum cost operative planning of pressurized water supply networks over a finite horizon and under reliable demand forecast. Since this is a very hard problem, it is desirable to employ sophisticated mathematical algorithms, which in turn calls for carefully designed models with suitable properties. The paper develops a nonlinear mixed integer model and a nonlinear programming model with favorable properties for gradient-based optimization methods, based on smooth component models for the network elements. In combination with further nonlinear programming techniques (Burgschweiger et al. in ZIB Report ZR-05-31, Zuse Institute Berlin, 2005), practically satisfactory near-optimum solutions even for large networks can be generated in acceptable time using standard optimization software on a PC workstation. Such an optimization system is in operation at Berliner Wasserbetriebe.     

Parameter identification for human body segment models

For the practical implementation of computer simulation or analysis of human motion in ergonomics, orthopaedics, sports, and other areas, the respective models must be individualized by assigning them subject-specific parameter values such as those for segment parameters. Several methods and their efficiency are discussed for determining this parameter set for a given subject. It is shown that the anthropometrico-computational method is presently the most accurate and reliable technique with potential for further improvement.     

Bottleneck identification in supply chain networks

In supply chain risk management, it is essential to identify firms that induce high losses due to supply chain disruptions in a focal firm or the supply chain network as a whole (bottlenecks). In this article, we describe supply chain networks as complex systems of firms and their suppliers. We revisit some established network measures and compare their predictions with a new methodology for detecting bottlenecks. In this bottom-up approach, production disruptions on the firm level are modelled with stochastic point processes, and a mechanism for the propagation of losses through the network is defined. The individual firms' emerging loss contributions to the total losses of the focal firm provide, then, an alternative risk-adjusted measure. Our methodology and findings enable more informed and transparent decisions to be made for optimal supply chain network design.