Influencing dynamics on social networks without knowledge of network microstructure

Social network-based information campaigns can be used for promoting beneficial health behaviours and mitigating polarization (e.g. regarding climate change or vaccines). Network-based intervention strategies typically rely on full knowledge of network structure. It is largely not possible or desirable to obtain population-level social network data due to availability and privacy issues. It is easier to obtain information about individuals’ attributes (e.g. age, income), which are jointly informative of an individual’s opinions and their social network position. We investigate strategies for influencing the system state in a statistical mechanics based model of opinion formation. Using synthetic and data-based examples we illustrate the advantages of implementing coarse-grained influence strategies on Ising models with modular structure in the presence of external fields. Our work provides a scalable methodology for influencing Ising systems on large graphs and the first exploration of the Ising influence problem in the presence of ambient (social) fields. By exploiting the observation that strong ambient fields can simplify control of networked dynamics, our findings open the possibility of efficiently computing and implementing public information campaigns using insights from social network theory without costly or invasive levels of data collection.     

Incorporating individual health-protective decisions into disease transmission models: a mathematical framework

It is anticipated that the next generation of computational epidemic models will simulate both infectious disease transmission and dynamic human behaviour change. Individual agents within a simulation will not only infect one another, but will also have situational awareness and a decision algorithm that enables them to modify their behaviour. This paper develops such a model of behavioural response, presenting a mathematical interpretation of a well-known psychological model of individual decision making, the health belief model, suitable for incorporation within an agent-based disease-transmission model. We formalize the health belief model and demonstrate its application in modelling the prevalence of facemask use observed over the course of the 2003 Hong Kong SARS epidemic, a well-documented example of behaviour change in response to a disease outbreak.     

Consignment stock inventory policy: methodological framework and model

We consider the hierarchical coordinated control of a multi-procedure conveyor-serviced production station system with flexible stations deployed between adjacent procedures, which includes a dynamic intra-procedure switching control of the flexible stations for the goal of balancing different procedures and a dynamic inter-procedure production coordination of all of the stations within each procedure. It is complicated in terms of modelling and optimisation, and thus, it is difficult to find a solution using numerical methods; as a result, we refer to model-free learning optimisation methods. First, we establish a neuro-dynamic programming algorithm by utilising cerebellar model articulation controllers (CMACs) to approximate state-action values at an upper hierarchy. Second, according to the reaction-diffusion phenomenon, we combine a Wolf-PHC algorithm with a local information-interaction scheme to learn look-ahead control policies at the lower hierarchy. Simulation results show that, compared with traditional Q-learning and the backward Q-learning based Q-learning, our proposed CMAC-based learning optimisation methods have the advantages of yielding a higher processing rate and having a faster optimisation speed with a lower storage requirement.     

Xenon adsorbed in zeolite Na-Y: A systematic molecular dynamics study with a flexible framework approach

Summary Molecular dynamics simulations are performed in order to study systematically the behaviour of xenon in zeolite Na-Y over a wide range of temperatures and loadings. For these investigations a full flexible framework approach is used and the polarizability of xenon is taken into account. The good agreement between the results of the simulations and available experimental data indicates that the applied interaction model is a realistic approach. It is found that the energy of activation for the diffusion process strongly depends on the concentration of the sorbate atoms. Furthermore, the influence of the framework flexibility on the properties of the adsorbed species is studied by comparative simulations with a fixed framework approach. The results show that the main energetic properties, as well as the mobility of xenon, are hardly influenced by the vibrations of the host lattice under the conditions studied here. However, significant differences in the pair distribution functions of the xenon atoms are observed.     

Risk assessment for the supply chain of fast fashion apparel industry: a system dynamics framework

With the rapid progress of science and technology and continuously growing customer expectations, share of merchandise exhibiting characteristics of perishability is on the rise and a wide range of industries are affected by this phenomenon. This paper focuses on the fast fashion apparel industry due to its particular characteristics such as short life cycle products, volatile demand, low predictability, high level of impulse purchase, high level of price competition and global sourcing. A system dynamics model is proposed for analysing the behaviour and relationships of the fast fashion apparel industry with three supply chain levels. The Conditional Value at Risk measure is applied to quantify the risks associated with the supply chain of these products and also to determine the expected value of the losses and their corresponding probabilities. Multiple business situations for effective strategic planning and decision-making are generated. In particular, the impact of lead time and delivery delays on the supply chain performance (inventory, cost, backlog and risk) is analysed as the key to success for this industry is to satisfy customers’ needs in the shortest time.     

Development of a mesoscopic framework spanning nanoscale protofibril dynamics to macro-scale fibrin clot formation

Thrombi form a micro-scale fibrin network consisting of an interlinked structure of nanoscale protofibrils, resulting in haemostasis. It is theorized that the mechanical effect of the fibrin clot is caused by the polymeric protofibrils between crosslinks, or to their dynamics on a nanoscale order. Despite a number of studies, however, it is still unknown, how the nanoscale protofibril dynamics affect the formation of the macro-scale fibrin clot and thus its mechanical properties. A mesoscopic framework would be useful to tackle this multi-scale problem, but it has not yet been established. We thus propose a minimal mesoscopic model for protofibrils based on Brownian dynamics, and performed numerical simulations of protofibril aggregation. We also performed stretch tests of polymeric protofibrils to quantify the elasticity of fibrin clots. Our model results successfully captured the conformational properties of aggregated protofibrils, e.g., strain-hardening response. Furthermore, the results suggest that the bending stiffness of individual protofibrils increases to resist extension.     

Data-driven uncertainty quantification and propagation in structural dynamics through a hierarchical Bayesian framework

In the presence of modeling errors, the mainstream Bayesian methods seldom give a realistic account of uncertainties as they commonly underestimate the inherent variability of parameters. This problem is not due to any misconceptions in the Bayesian framework since it is robust with respect to the modeling assumptions and the observed data. Rather, this issue has deep roots in users’ inability to develop an appropriate class of probabilistic models. This paper bridges this significant gap, introducing a novel Bayesian hierarchical setting, which breaks time-history vibration responses into several segments so as to capture and identify the variability of inferred parameters over the segments. Since the computation of the posterior distributions in hierarchical models is expensive and cumbersome, novel marginalization strategies, asymptotic approximations, and maximum a posteriori estimations are proposed and outlined in a computational algorithm aiming to handle both uncertainty quantification and propagation. For the first time, the connection between the ensemble covariance matrix and hyper distribution parameters is characterized through approximate estimations. Experimental and numerical examples are employed to illustrate the efficacy and efficiency of the proposed method. It is observed that, when the segments correspond to various system operating conditions and input characteristics, the proposed method delivers robust parametric uncertainties with respect to unknown phenomena such as ambient conditions, input characteristics, and environmental factors.     

Some aspects of chaotic and stochastic dynamics for structural systems

In this paper, the bifurcation behaviour of an externally excited four-dimensional nonlinear system is examined. Throughout this paper, a two-degree-of-freedom shallow arch structure under either a periodic or a stochastic excitation will be considered. For the case when the excitation is periodic, the local and global behaviour is examined in the presence of principalsubharmonic resonance and1:2 internal resonance. The method of averaging is used to obtain the first order approximation of the response of the system under resonant conditions. A standard Melnikov type perturbation method is used to show analytically that the system may exhibit chaotic dynamics in the sense of Smale horseshoe for the 1:2 internal resonance case in the absence of dissipation. In the case of stochastic excitation, the stability of the stationary solution is examined by determining themaximal Lyapunov exponent andmoment Lyapunov exponent in terms of system parameters. An asymptotic method is used to obtain explicit expressions for various exponents in the presence of weak dissipation and noise intensity. These quantities provide almost-sure stability boundaries in parameter space. When the system parameters lie outside these boundaries, it is essential to understand the nonlinear behaviour. The method of stochastic averaging is applied to obtain a set of approximate Itô equations which are then examined to describe the local bifurcation behaviour.     

Internal representations,external representations and ergonomics: towards a theoretical integration

Ergonomics is concerned with how humans interact with systems to perform tasks that achieve goals. This interaction requires thought processes that construct and manipulate mental representations of situations to enable the selection of task-oriented actions having predicted outcomes. Mental representations are, then, central to task performance and therefore ergonomics. The present review assesses the current state of knowledge regarding the nature of mental representations, focusing particularly on: (1) the vast range of existing terminology that underpins discussions about representational constructs; (2) the important role of memory in understanding mental representation; (3) the tight coupling between external and internal representations during task performance. The review concludes by proposing an integrated framework that shows how key proposals concerning mental and external representations from leading theorists can be brought together to complement one another.     

The transition mechanism of DNA overstretching: a microscopic view using molecular dynamics

The overstretching transition in torsionally unconstrained DNA is studied by means of atomistic molecular dynamics simulations. The free-energy profile as a function of the length of the molecule is determined through the umbrella sampling technique providing both a thermodynamic and a structural characterization of the transition pathway. The zero-force free-energy profile is monotonic but, in accordance with recent experimental evidence, becomes two-state at high forces. A number of experimental results are satisfactorily predicted: (i) the entropic and enthalpic contributions to the free-energy difference between the basic (B) state and the extended (S) state; (ii) the longitudinal extension of the transition state and (iii) the enthalpic contribution to the transition barrier. A structural explanation of the experimental finding that overstretching is a cooperative reaction characterized by elementary units of approximately 22 base pairs is found in the average distance between adenine/thymine-rich regions along the molecule. The overstretched DNA adopts a highly dynamical and structurally disordered double-stranded conformation which is characterized by residual base pairing, formation of non-native intra-strand hydrogen bonds and effective hydrophobic screening of apolar regions.     

A‐scalability and an integrated computational technology and framework for non‐linear structural dynamics. Part 2: Implementation aspects and parallel performance results

An integrated framework and computational technology is described that addresses the issues to foster absolute scalability (A‐scalability) of the entire transient duration of the simulations of implicit non‐linear structural dynamics of large scale practical applications on a large number of parallel processors. Whereas the theoretical developments and parallel formulations were presented in Part 1, the implementation, validation and parallel performance assessments and results are presented here in Part 2 of the paper. Relatively simple numerical examples involving large deformation and elastic and elastoplastic non‐linear dynamic behaviour are first presented via the proposed framework for demonstrating the comparative accuracy of methods in comparison to available experimental results and/or results available in the literature. For practical geometrically complex meshes, the A‐scalability of non‐linear implicit dynamic computations is then illustrated by employing scalable optimal dissipative zero‐order displacement and velocity overshoot behaviour time operators which are a subset of the generalized framework in conjunction with numerically scalable spatial domain decomposition methods and scalable graph partitioning techniques. The constant run times of the entire simulation of ‘fixed‐memory‐use‐per‐processor’ scaling of complex finite element mesh geometries is demonstrated for large scale problems and large processor counts on at least 1024 processors. Copyright © 2003 John Wiley & Sons, Ltd.     

Setup Error Adjustment: Sensitivity Analysis and a New MCMC Control Rule

In this paper, we focus on the performance of adjustment rules for a machine that produces items in batches and that can experience errors at each setup operation performed before machining a batch. The adjustment rule is applied to compensate for the setup offset in order to bring back the process to target. In particular, we deal with the case in which no prior information about the distribution of the offset or about the within‐batch variability is available. Under such conditions, adjustment rules that can be applied are Grubbs' rules, the exponentially‐weighted moving average (EWMA) controller and the Markov chain Monte Carlo (MCMC) adjustment rule, based on a Bayesian sequential estimation of unknown parameters that uses MCMC simulation. The performance metric of the different adjustment rules is the sum of the quadratic off‐target costs over the set of batches machined. Given the number of batches and the batch size, different production scenarios (characterized by different values of the lot‐to‐lot and the within‐lot variability and of the mean offset over the set of batches) are considered. The MCMC adjustment rule is shown to have better performance in almost all of the cases examined. Furthermore, a closer study of the cases in which the MCMC policy is not the best adjustment rule motivates a modified version of this rule which outperforms alternative adjustment policies in all the scenarios considered. Copyright © 2005 John Wiley & Sons, Ltd.     

Incorporating human factors into the AAMT selection: a framework and process

Human factors such as employee morale and workers skills greatly influence the successful adoption of automated and advanced manufacturing technologies. For newly industrialised countries, the evaluation of these factors before technology selection is particularly paramount. Countries such as Brazil are in the critical early stages of technology adoption and low rates of secondary education and scarcity of technicians reinforce the importance of assessing human factors before the actual technology implementation. Although methods have been proposed to evaluate intangible aspects, the lack of a structured approach to identify and quantify human factors still constitutes a major hurdle. The paper describes a framework and process to assist managers in identifying and evaluating human factors in the selection. The approach was tested in eight companies in Brazil. The results indicated that the main advantages of the proposed approach are: (a) provide a comprehensive justification of technology adoption by identifying and quantifying intangible aspects; and (b) supply a practical process to be incorporated into the selection decision-making process.     

The habitability framework: a conceptual approach towards linking human behaviour and physical environment

This paper presents the background, rationale and elements of the habitability framework which links human behaviour to aspects of the physical environment. The conceptual framework is accompanied by examples of applications, including behaviour-based facility programming and post-occupancy evaluation.     

Effects of heterogeneity in infection-exposure history and immunity on the dynamics of a protozoan parasite

Infection systems where traits of the host, such as acquired immunity, interact with the infection process can show complex dynamic behaviour with counter-intuitive results. In this study, we consider the traits ‘immune status’ and ‘exposure history’, and our aim is to assess the influence of acquired individual heterogeneity in these traits. We have built an individual-based model of Eimeria acervulina infections, a protozoan parasite with an environmental stage that causes coccidiosis in chickens. With the model, we simulate outbreaks of the disease under varying initial contaminations. Heterogeneity in the traits arises stochastically through differences in the dose and frequency of parasites that individuals pick up from the environment. We find that the relationship between the initial contamination and the severity of an outbreak has a non-monotonous ‘wave-like’ pattern. This pattern can be explained by an increased heterogeneity in the host population caused by the infection process at the most severe outbreaks. We conclude that when dealing with these types of infection systems, models that are used to develop or evaluate control measures cannot neglect acquired heterogeneity in the host population traits that interact with the infection process.     

行波热声发电系统自激振荡的非线性动力学研究

利用非线性热声网络模型建立了一种行波热声发电系统的简化动力学演化方程,考察了系统自激振荡过程。重点探究非线性热声效应的影响,并与仅考虑线性热声效应的情况进行对比。结果表明,当仅考虑线性热声效应时,振荡曲线不断增大,无法达到稳定值;而考虑非线性热声效应时,自激振荡最后达到稳定幅值。进一步研究得出,系统电机的非线性阻尼系数越大,系统稳定后的振荡幅值越小。对不同电机非线性阻尼系数时系统产生的振荡曲线作频谱分析,计算结果和实际情况相近。最后考察了回热器未达临界温度梯度时系统的自激振荡过程。     

Dissecting the determinants of malaria chronicity: why within-host models struggle to reproduce infection dynamics

The duration of infection is fundamental to the epidemiological behaviour of any infectious disease, but remains one of the most poorly understood aspects of malaria. In endemic areas, the malaria parasite Plasmodium falciparum can cause both acute, severe infections and asymptomatic, chronic infections through its interaction with the host immune system. Frequent superinfection and massive parasite genetic diversity make it extremely difficult to accurately measure the distribution of infection lengths, complicating the estimation of basic epidemiological parameters and the prediction of the impact of interventions. Mathematical models have qualitatively reproduced parasite dynamics early during infection, but reproducing long-lived chronic infections remains much more challenging. Here, we construct a model of infection dynamics to examine the consequences of common biological assumptions for the generation of chronicity and the impact of co-infection. We find that although a combination of host and parasite heterogeneities are capable of generating chronic infections, they do so only under restricted parameter choices. Furthermore, under biologically plausible assumptions, co-infection of parasite genotypes can alter the course of infection of both the resident and co-infecting strain in complex non-intuitive ways. We outline the most important puzzles for within-host models of malaria arising from our analysis, and their implications for malaria epidemiology and control.     

Modelling of flexible manufacturing system: a review

A flexible manufacturing system (FMS) due to its ability of being flexible in nature is concerned with automatic production of different parts in medium range. In short, it can be regarded as an automated manufacturing system. In this paper, an insight on previous work in the area of FMS modelling has been provided with an overview of research and development for better understanding of FMS. It serves as a medium for investigation of work that are accomplished by using different modelling techniques in FMS like mathematical, artificial intelligence, hierarchical, multi criteria decision-making method, Petri Nets and simulation. This paper will help researchers who are keen to do research in the area of FMS modelling, by highlighting contribution of available techniques in the field of FMS. It will also help them in deciding which modelling techniques can be used for a given problem. At last, comparison of different parameters considered in the recent papers of modelling FMS has been provided in the form of Table, along with a clear vision related to those works that still need to be investigated.     

DNA double-strand break repair: a theoretical framework and its application

DNA double-strand breaks (DSBs) are formed as a result of genotoxic insults, such as exogenous ionizing radiation, and are among the most serious types of DNA damage. One of the earliest molecular responses following DSB formation is the phosphorylation of the histone H2AX, giving rise to γH2AX. Many copies of γH2AX are generated at DSBs and can be detected in vitro as foci using well-established immuno-histochemical methods. It has previously been shown that anti-γH2AX antibodies, modified by the addition of the cell-penetrating peptide TAT and a fluorescent or radionuclide label, can be used to visualize and quantify DSBs in vivo. Moreover, when labelled with a high amount of the short-range, Auger electron-emitting radioisotope, ¹¹¹In, the amount of DNA damage within a cell can be increased, leading to cell death. In this report, we develop a mathematical model that describes how molecular processes at individual sites of DNA damage give rise to quantifiable foci. Equations that describe stochastic mean behaviours at individual DSB sites are derived and parametrized using population-scale, time-series measurements from two different cancer cell lines. The model is used to examine two case studies in which the introduction of an antibody (anti-γH2AX-TAT) that targets a key component in the DSB repair pathway influences system behaviour. We investigate: (i) how the interaction between anti-γH2AX-TAT and γH2AX effects the kinetics of H2AX phosphorylation and DSB repair and (ii) model behaviour when the anti-γH2AX antibody is labelled with Auger electron-emitting ¹¹¹In and can thus instigate additional DNA damage. This work supports the conclusion that DSB kinetics are largely unaffected by the introduction of the anti-γH2AX antibody, a result that has been validated experimentally, and hence the hypothesis that the use of anti-γH2AX antibody to quantify DSBs does not violate the image tracer principle. Moreover, it provides a novel model of DNA damage accumulation in the presence of Auger electron-emitting ¹¹¹In that is supported qualitatively by the available experimental data.     

Exponential random graph modeling for diagnosis of out-of-control signals in social network surveillance: Signal interpretation in social networks with ERG modeling

Monitoring of the social networks for detecting anomalous behavior could be vital for the system's survival. This anomalous behavior could raise from any changes in behavior or attributes of a particular individual or groups of individuals in the network and causes structural changes. Multivariate statistical process control charts are effective tools for this purpose while Exponential Random Graph Models are used to model highly interdependent data of the network. So after selecting a model for specific network, T² control charts are used for monitoring the network data to detect any anomalous behavior. Then the Mason, Tracy, and Young method is utilized for interpreting an out-of-control condition. Finally, some real-world examples are used to evaluate the performance of the proposed diagnosis approach. Since complicated dependency in a social network makes different choices in model selection for Exponential Random Graph Models and this causes various results in the evaluation study, if the impact of diagnosis result is not seen in model selection, the appropriate model will not be necessarily selected and this will affect the effectiveness of the whole system. So, in this paper for improving the performance of diagnosis, two indices are introduced and added to model selection criteria and then the appropriate model could be selected based on the decision-maker's preferences.