Determination of critical network interactions: an augmented Boolean pseudo-dynamics approach

Network theory has established that highly connected nodes in regulatory networks (hubs) show a strong correlation with criticality in network function. Although topological analysis is fully capable of identifying network hubs, it does not provide an objective method for ranking the importance of a particular node by relating its contribution to the overall network response. Towards this end, the authors have developed an augmented Boolean pseudo-dynamics approach to a priori determine the critical network interactions in biological interaction networks. The approach utilises network topology and dynamic state information to determine the set of active pathways. The active pathways are used in conjunction with the key cellular properties of efficiency and robustness, to rank the network interactions based on their importance in the sustenance of network function. To demonstrate the utility of the approach, the authors consider the well characterised guard cell signalling network in plant cells. An integrated analysis of the network revealed the critical mechanisms resulting in stomata closure in the presence and absence of abscisic acid, in excellent agreement with published results.     

Bayesian network modelling for supply chain risk propagation

Supply chain risk propagation is a cascading effect of risks on global supply chain networks. The paper attempts to measure the behaviour of risks following the assessment of supply chain risk propagation. Bayesian network theory is used to analyse the multi-echelon network faced with simultaneous disruptions. The ripple effect of node disruption is evaluated using metrics like fragility, service level, inventory cost and lost sales. Developed risk exposure and resilience indices support in assessing the vulnerability and adaptability of each node in the supply chain network. The research provides a holistic measurement approach for predicting the complex behaviour of risk propagation for improved supply chain risk management.     

Path entropy method for multiple-source water distribution networks

Over the past decade, one of the key research areas for water distribution networks has been the quantification of network reliability. An interesting approach in this research topic is the use of informational entropy as a surrogate measure for the reliability of water distribution network. Research on water distribution network entropy has progressed to the stage where practical applications are possible, but the actual meaning of the network entropy has never been fully elucidated. Recently, an alternative approach to calculate network entropy was proposed, which was termed the path entropy method (PEM). This alternative method was shown to be useful for the case of single-source water distribution networks. The purpose of this article is to explore the use of the PEM on multiple-source networks. A two-source and two-demand water distribution network is analysed to gain insight into the differences between single-source and multiple-source networks in terms of maximum-entropy flow distribution. This leads to the formal proof of the principle governing maximum-entropy flow ratios in multiple-source water distribution networks.     

Effective connectivity determines the critical dynamics of biochemical networks

Living systems comprise interacting biochemical components in very large networks. Given their high connectivity, biochemical dynamics are surprisingly not chaotic but quite robust to perturbations—a feature C.H. Waddington named canalization. Because organisms are also flexible enough to evolve, they arguably operate in a critical dynamical regime between order and chaos. The established theory of criticality is based on networks of interacting automata where Boolean truth values model presence/absence of biochemical molecules. The dynamical regime is predicted using network connectivity and node bias (to be on/off) as tuning parameters. Revising this to account for canalization leads to a significant improvement in dynamical regime prediction. The revision is based on effective connectivity, a measure of dynamical redundancy that buffers automata response to some inputs. In both random and experimentally validated systems biology networks, reducing effective connectivity makes living systems operate in stable or critical regimes even though the structure of their biochemical interaction networks predicts them to be chaotic. This suggests that dynamical redundancy may be naturally selected to maintain living systems near critical dynamics, providing both robustness and evolvability. By identifying how dynamics propagates preferably via effective pathways, our approach helps to identify precise ways to design and control network models of biochemical regulation and signalling.     

Some new insights on informational entropy for water distribution networks

Several researchers have studied the use of informational entropy as a surrogate measure for the reliability of water distribution networks. The hypothesis is that the numerical value of network entropy in some way reflects the reliability of water distribution networks, and this appears to be supported by the analysis of some example water distribution networks. However, the precise relationship between the entropy value and some measures of reliability has not been formally established. The primary objective in this paper is to present an alternative methodology to calculate the informational entropy of water distribution networks. This methodology is termed as the Path Entropy Method (PEM), which provides some insights into the entropy of branching-tree networks and maximum-entropy flows of single-source networks. In addition, a quick method of computing the maximum-entropy value of single-source networks is presented and termed as the Simplified Path Entropy Method (SPEM).     

产品意象形态设计要素的均衡性评价

目的 探究产品意象形态各设计要素对产品整体设计的贡献程度,为设计师提供可靠的设计指导,分析产品意象形态设计要素对产品整体设计的贡献规律,基于信息熵和蛛网图评价法提出一种产品意象形态设计要素评价方法。方法 首先,基于信息熵理论计算各形态设计要素的熵值,并求得权重;其次,应用蛛网图评价法构建各产品意象形态设计要素评价向量,绘制产品意象形态设计要素蛛网图,并计算均衡性贡献系数和意象形态综合评价值;最后,应用产品形态美度评价方法计算各样本的美度评价,并进行对比验证,证明了该方法的可行性。结果 以奥迪A7系列汽车前脸为研究对象,经过计算得到均衡性贡献系数排序和意象形态综合评价排序,绘制样本意象形态设计要素蛛网图,直观展示各意象形态设计要素的贡献度与均衡性。结论 通过产品意象形态设计要素评价研究,量化产品意象形态各设计要素对产品整体设计的贡献度,有助于设计师在复合意象产品设计中的设计决策。     

Mitigating supply chain disruptions through interconnected logistics services in the Physical Internet

This paper investigates the resilience of inventory models using interconnected logistics services in the Physical Internet (PI). With traditional supply chain network design, companies define and optimise their own logistics networks, resulting in current logistics systems being a set of independent heterogeneous logistics networks. The concept of PI aims to integrate independent logistics networks into a global, open, interconnected system. Prior research has shown that new inventory models enabled by and applied to PI could help reduce inventory levels thanks to its high flexibility. Continuing along these lines, this paper examines how inventory models applying PI deal with disruptions at hubs and plants. To attain this, a single product inventory problem with uncertain demands and stochastic supply disruptions is studied. A simulation-based optimisation model is proposed to determine inventory control decisions. The results suggest that the PI inventory model, with greater agility and flexibility, outperforms the current classic inventory models in terms of resilience. Moreover, the difference in performance increases when the product value, penalty costs and disruption frequency increases. This paper indicates a novel approach to build a resilient supply network.     

The cost of attack in competing networks

Real-world attacks can be interpreted as the result of competitive interactions between networks, ranging from predator–prey networks to networks of countries under economic sanctions. Although the purpose of an attack is to damage a target network, it also curtails the ability of the attacker, which must choose the duration and magnitude of an attack to avoid negative impacts on its own functioning. Nevertheless, despite the large number of studies on interconnected networks, the consequences of initiating an attack have never been studied. Here, we address this issue by introducing a model of network competition where a resilient network is willing to partially weaken its own resilience in order to more severely damage a less resilient competitor. The attacking network can take over the competitor''s nodes after their long inactivity. However, owing to a feedback mechanism the takeovers weaken the resilience of the attacking network. We define a conservation law that relates the feedback mechanism to the resilience dynamics for two competing networks. Within this formalism, we determine the cost and optimal duration of an attack, allowing a network to evaluate the risk of initiating hostilities.     

Further Analysis of Global Synchronisation for Networks of Identical Cells with Delayed Coupling

Synchronisation is one of the most interesting collective motions observed in large-scale complex networks of interacting dynamical systems. We consider global synchronisation for networks of nonlinearly coupled identical cells with time delays, using an approach where the synchronisation problem is converted to solving an homogeneous linear system. This approach is extended to fit networks under more general coupling topologies, and we derive four delay-dependent and delay-independent criteria that ensure the coupled dynamical network is globally synchronised. Some examples show that the four criteria are not mutually inclusive, and numerical simulations also demonstrate our theoretical results.     

Living on the edge of chaos: minimally nonlinear models of genetic regulatory dynamics

Linearized catalytic reaction equations (modelling, for example, the dynamics of genetic regulatory networks), under the constraint that expression levels, i.e. molecular concentrations of nucleic material, are positive, exhibit non-trivial dynamical properties, which depend on the average connectivity of the reaction network. In these systems, an inflation of the edge of chaos and multi-stability have been demonstrated to exist. The positivity constraint introduces a nonlinearity, which makes chaotic dynamics possible. Despite the simplicity of such minimally nonlinear systems, their basic properties allow us to understand the fundamental dynamical properties of complex biological reaction networks. We analyse the Lyapunov spectrum, determine the probability of finding stationary oscillating solutions, demonstrate the effect of the nonlinearity on the effective in- and out-degree of the active interaction network, and study how the frequency distributions of oscillatory modes of such a system depend on the average connectivity.     

Deconstructing the core dynamics from a complex time-lagged regulatory biological circuit

Complex regulatory dynamics is ubiquitous in molecular networks composed of genes and proteins. Recent progress in computational biology and its application to molecular data generate a growing number of complex networks. Yet, it has been difficult to understand the governing principles of these networks beyond graphical analysis or extensive numerical simulations. Here the authors exploit several simplifying biological circumstances which thereby enable to directly detect the underlying dynamical regularities driving periodic oscillations in a dynamical nonlinear computational model of a protein?protein network. System analysis is performed using the cell cycle, a mathematically well-described complex regulatory circuit driven by external signals. By introducing an explicit time delay and using a `tearing-and-zooming? approach the authors reduce the system to a piecewise linear system with two variables that capture the dynamics of this complex network. A key step in the analysis is the identification of functional subsystems by identifying the relations between statevariables within the model. These functional subsystems are referred to as dynamical modules operating as sensitive switches in the original complex model. By using reduced mathematical representations of the subsystems the authors derive explicit conditions on how the cell cycle dynamics depends on system parameters, and can, for the first time, analyse and prove global conditions for system stability. The approach which includes utilising biological simplifying conditions, identification of dynamical modules and mathematical reduction of the model complexity may be applicable to other well-characterised biological regulatory circuits.     

Editorial - Selected papers from the 2nd international symposium on optimization and systems biology

One of the major challenges for post-genomic biology is to understand how genes, proteins and small molecules interact to form cellular systems. It has been recognised that a complicated living organism cannot be fully understood by merely analysing individual components, and that interactions of those components or networks are ultimately responsible for an organism?s form and functions. Instead of analysing individual components or aspects of the organism, systems biology is the study of an organism, viewed as a dynamical and interacting network of biomolecules which give rise to a complicated life. With increasingly accumulated data from high-throughput technologies, molecular networks and their dynamics have been studied extensively from various aspects of living organisms. Many mathematical methods have been adopted in computational systems biology; in particular, optimisation and statistics play a key role in analysing and understanding biological mechanisms from system-wide viewpoints.     

Some observations on energy loss and network entropy in water distribution networks

The design of water distribution networks has been extensively studied with the aid of computers, and the focus has shifted from merely satisfying the hydraulic requirements to fulfilling the need for reliability. One of the interesting research areas recently is the use of informational entropy as a surrogate measure for reliability. The actual meaning of the informational entropy for water distribution network has not been fully elucidated, and it was hypothesed that reliability is in some way proportional to the numerical value of network entropy. The purpose of this paper is to explore the relationship between energy loss and network entropy. The investigation approach used is to take a simple looped network and individually vary the pipe diameters from zero to infinity, and calculate the corresponding network entropy and energy loss for the water distribution network. The results revealed many interesting observations in the variation of network entropy with pipe resistance coefficient K _i , and the revelation of local maximum and minimum points.     

Optimized Evaluation of Mobile Base Station by Modern Topological Invariants

Due to a tremendous increase in mobile traffic, mobile operators have started to restructure their networks to offload their traffic. New research directions will lead to fundamental changes in the design of future Fifth-generation (5G) cellular networks. For the formal reason, the study solves the physical network of the mobile base station for the prediction of the best characteristics to develop an enhanced network with the help of graph theory. Any number that can be uniquely calculated by a graph is known as a graph invariant. During the last two decades, innumerable numerical graph invariants have been portrayed and used for correlation analysis. In any case, no efficient assessment has been embraced to choose, how much these invariants are connected with a network graph. This paper will talk about two unique variations of the hexagonal graph with great capability of forecasting in the field of optimized mobile base station topology in setting with physical networks. Since K-banhatti sombor invariants (KBSO) and Contrharmonic-quadratic invariants (CQIs) are newly introduced and have various expectation characteristics for various variations of hexagonal graphs or networks. As the hexagonal networks are used in mobile base stations in layered, forms called honeycomb. The review settled the topology of a hexagon of two distinct sorts with two invariants KBSO and CQIs and their reduced forms. The deduced outcomes can be utilized for the modeling of mobile cellular networks, multiprocessors interconnections, microchips, chemical compound synthesis and memory interconnection networks. The results find sharp upper bounds and lower bounds of the honeycomb network to utilize the Mobile base station network (MBSN) for the high load of traffic and minimal traffic also.     

Randomised and distributed methods for reliable peer-to-peer data communication in wireless ad hoc networks

Peer-to-peer (P2P) communications have attracted a great deal of attention from the network research community in recent years. However, due to the fundamental limitations of wireless environments, providing reliable data availability for P2P applications over wireless ad hoc networks is still a major challenge. To address the problem, a distributed and randomised scheme based on self-avoiding walks is proposed. The scheme concatenates disparate network layers, with the goal of recovering from routing failures that disrupt P2P data accessibility. In addition, a probabilistic approach is presented that explores the tradeoffs between several system parameters. Some new analysis tools, such as path coupling, are utilised which provide a better understanding of the system's operations. That the proposed concepts and techniques make a significant contribution to the design of effective and efficient P2P applications in wireless ad hoc networks is believed.     

No warning for slow transitions

A rise in fragility as a system approaches a tipping point may be sometimes estimated using dynamical indicators of resilience (DIORs) that measure the characteristic slowing down of recovery rates before a tipping point. A change in DIORs could be interpreted as an early warning signal for an upcoming critical transition. However, in order to be able to estimate the DIORs, observational records need to be long enough to capture the response rate of the system. As we show here, the required length of the time series depends on the response rates of the system. For instance, the current rate of anthropogenic climate forcing is fast relative to the response rate of some parts of the climate system. Therefore, we may expect difficulties estimating the resilience from modern time series. So far, there have been no systematic studies of the effects of the response rates of the dynamical systems and the rates of forcing on the detectability trends in the DIORs prior to critical transitions. Here, we quantify the performance of the resilience indicators variance and temporal autocorrelation, in systems with different response rates and for different rates of forcing. Our results show that the rapid rise of anthropogenic forcing to the Earth may make it difficult to detect changes in the resilience of ecosystems and climate elements from time series. These findings suggest that in order to determine with models whether the use of the DIORs is appropriate, we need to use realistic models that incorporate the key processes with the appropriate time constants.     

Generalized synchronization in complex networks

The phenomenon of generalized synchronization (GS) in networks with a complex topology of links between elements (nodes) representing chaotic dynamical systems has been studied. It is shown that GS onset in these networks can be detected as the moment of transition of the second-order Lyapunov exponent from a positive to a negative value. The results of the analysis are confirmed by the nearest-neighbor method. It is established that the network topology significantly influences the GS development.     

Mean Opinion Score Estimation for Mobile Broadband Networks Using Bayesian Networks

Mobile broadband (MBB) networks are expanding rapidly to deliver higher data speeds. The fifth-generation cellular network promises enhanced-MBB with high-speed data rates, low power connectivity, and ultra-low latency video streaming. However, existing cellular networks are unable to perform well due to high latency and low bandwidth, which degrades the performance of various applications. As a result, monitoring and evaluation of the performance of these network-supported services is critical. Mobile network providers optimize and monitor their network performance to ensure the highest quality of service to their end-users. This paper proposes a Bayesian model to estimate the minimum opinion score (MOS) of video streaming services for any particular cellular network. The MOS is the most commonly used metric to assess the quality of experience. The proposed Bayesian model consists of several input data, namely, round-trip time, stalling load, and bite rates. It was examined and evaluated using several test data sizes with various performance metrics. Simulation results show the proposed Bayesian network achieved higher accuracy overall test data sizes than a neural network. The proposed Bayesian network obtained a remarkable overall accuracy of 90.36% and outperformed the neural network.     

Fostering emergent resilience: the complex adaptive supply network of disaster relief

The frequency and intensity of disasters continue to increase. Following large-scale and catastrophic disasters, local organisations integrate with other responding organisations to form hastily disaster relief supply chain networks. Such supply networks are infrequently activated in a single location, generate unparalleled uncertainty, change quickly, and are driven by the urgency of saving lives and restoring livelihoods. Unfortunately, even where sound supply chain management practices are used, supply networks have encountered diverse levels of resilience and adequate disaster relief performance has remained elusive. In this paper, several unique characteristics that disaster relief efforts exhibit are examined as compared with demand-driven, steady-state supply chains. Important differences in the flows of resource, money, and information are identified. A complex adaptive supply network (CASN) lens is used to frame what existing literature has uncovered regarding disaster relief efforts, showing how relief organisations, their interactions, and their environmental context help determine the level of resilience that supply networks experience following disasters. This CASN characterisation is leveraged to help explain why traditional supply chain management practices lead to varied results in disaster relief. Finally, complexity science theory is drawn on to set forth eight testable propositions that may help to enhance supply network resilience.