A temporal-causal network model for the effect of emotional charge on information sharing

Within Network-Oriented Modeling based on temporal-causal network models mathematical analysis of the dynamics of the behavior of the network models can be performed. This chapter addresses the analysis of some types of dynamic properties of a temporal-causal network model in an analytical mathematical manner. Properties addressed describe whether some values for the states exist for which no change occurs (equilibria), whether the values for these states converge to such a value as a limit value (attracting equilibria), whether states will show monotonically increasing or decreasing values over time (monotonicity), and whether situations occur in which no convergence takes place but in the end a specific sequence of values is repeated all the time (limit cycle). It is discussed how such analyses can be used for verification of the (implemented) model. Any discrepancies found, suggest there is something wrong in the implementation of the model. In this chapter some methods to analyse such properties of adaptive temporal-causal network models will be described and illustrated for a simple example model, for Hebbian learning, and for adaptive network models for evolving social interaction.

Analyses of the effectiveness of infectious disease control interventions often rely on dynamic transmission models to simulate intervention effects. We aim to understand how the choice of network or compartmental model can influence estimates of intervention effectiveness in the short and long term for an endemic disease with susceptible and infected states in which infection, once contracted, is lifelong. We consider 4 disease models with different permutations of socially connected network versus unstructured contact (mass-action mixing) model and heterogeneous versus homogeneous disease risk. The models have susceptible and infected populations calibrated to the same long-term equilibrium disease prevalence. We consider a simple intervention with varying levels of coverage and efficacy that reduces transmission probabilities. We measure the rate of prevalence decline over the first 365 d after the intervention, long-term equilibrium prevalence, and long-term effective reproduction ratio at equilibrium. Prevalence declined up to 10% faster in homogeneous risk models than heterogeneous risk models. When the disease was not eradicated, the long-term equilibrium disease prevalence was higher in mass-action mixing models than in network models by 40% or more. This difference in long-term equilibrium prevalence between network versus mass-action mixing models was greater than that of heterogeneous versus homogeneous risk models (less than 30%); network models tended to have higher effective reproduction ratios than mass-action mixing models for given combinations of intervention coverage and efficacy. For interventions with high efficacy and coverage, mass-action mixing models could provide a sufficient estimate of effectiveness, whereas for interventions with low efficacy and coverage, or interventions in which outcomes are measured over short time horizons, predictions from network and mass-action models diverge, highlighting the importance of sensitivity analyses on model structure. • We calibrate 4 models-socially connected network versus unstructured contact (mass-action mixing) model and heterogeneous versus homogeneous disease risk-to 10% preintervention disease prevalence.• We measure the short- and long-term intervention effectiveness of all models using the rate of prevalence decline, long-term equilibrium disease prevalence, and effective reproduction ratio.• Generally, in the short term, prevalence declined faster in the homogeneous risk models than in the heterogeneous risk models.• Generally, in the long term, equilibrium disease prevalence was higher in the mass-action mixing models than in the network models, and the effective reproduction ratio was higher in network models than in the mass-action mixing models.

In this chapter it is discussed how a personalised temporal-causal network model can be obtained that fits well to specific characteristics of a person, and his or her connections and further context. A model is an approximation, but always a form of abstraction of a real-world phenomenon. Its accuracy and correctness mainly depend on the chosen abstracting assumptions and the personal and contextual (network) characteristics defining the model. Depending on the complexity of the model, the number of its characteristics can vary from just a couple to thousands. These network characteristics usually represent specific features or properties of the modelled phenomenon, for example, for modelling human processes personality traits or social interaction properties. No values for such characteristics are given at forehand. From a more general and abstract view, they can be considered parameters of the model. Estimation of such parameters for a given model is a nontrivial task. In this chapter, it is discussed how this can be addressed for temporal-causal network models based on the parameter tuning method of Simulated Annealing and a specific component within the dedicated modeling environment, thereby making use of MATLAB’s built-in optimser Optimtool.KeywordsValidationSelf-modeling network modelsParameter tuningSimulated annealingRoot mean square error

In this paper, it is addressed by mathematical analysis how network-oriented modeling relates to the dynamical systems perspective on mental processes. It has been mathematically proven that any dynamical system can be modeled as a temporal-causal network model and that any adaptive dynamical system (of any order) can be modeled by a self-modeling network (of the same order).

Neuroscience: What We Cannot Model, We Do Not Understand

We propose to use a conventional simulator, formulated on the topology of a coarse volumetric 3D grid, as a data-driven network model that seeks to reproduce observed and predict future well responses. The conceptual difference from standard history matching is that the tunable network parameters are calibrated freely without regard to the physical interpretation of their calibrated values. The simplest version uses a minimal rectilinear mesh covering the assumed map outline and base/top surface of the reservoir. The resulting CGNet models fit immediately in any standard simulator and are very fast to evaluate because of the low cell count. We show that surprisingly accurate network models can be developed using grids with a few tens or hundreds of cells. Compared with similar interwell network models (e.g., Ren et al., 2019, 10.2118/193855-MS), a typical CGNet model has fewer computational cells but a richer connection graph and more tunable parameters. In our experience, CGNet models therefore calibrate better and are simpler to set up to reflect known fluid contacts, etc. For cases with poor vertical connection or internal fluid contacts, it is advantageous if the model has several horizontal layers in the network topology. We also show that starting with a good ballpark estimate of the reservoir volume is a precursor to a good calibration.

Current constitutive theories face challenges when predicting the extremely large deformation and fracture of hydrogels, which calls for the demands to reveal the fundamental mechanism of the various mechanical behaviors of hydrogels from bottom up. Proper hydrogel network model provides a better approach to bridge the gap between the micro-structure and the macroscopic mechanical responses. This work summarizes the theoretical and numerical researches on the hydrogel network models, aiming to provide new insights into the effect of microstructure on the swelling-deswelling process, hyperelasticity, viscoelasticity and fracture of hydrogels. Hydrogel network models are divided into full-atom network models, realistic network models and abstract network models. Full-atom network models have detailed atomic structure but small size. Realistic network models with different coarse-graining degree have large model size to explain the swelling-deswelling process, hyperelasticity and viscoelasticity. Abstract network models abstract polymer chains into analytical interactions, leading to the great leap of model size. It shows advantages to reproduce the crack initiation and propagation in hydrogels by simulating chain scission. Further research directions on the network modeling are suggested. We hope this work can help integrate the merits of network modeling methods and continuum mechanics to capture the various mechanical behaviors of hydrogels. The random polymer network structure determines the macroscopic mechanical behaviors of hydrogels. This work summarizes the theoretical and numerical researches on the hydrogel network models. Full-atom network models depict the fundamental configurations of hydrogel network in atomic scale. Realistic network models based on different coarse-grain strategies have large model size. Abstract network models with much larger size are capable to not only bridge the underlying mechanism in microscale or mesoscale with the mechanical response in macroscale, but also integrate the merits of discrete methods and continuum mechanics.

In this paper, a software environment to support Network-Oriented Modeling is presented. The environment has been implemented in MATLAB. This code covers the principles of temporal-causal network models. The software environment has built-in options for network adaptation principles such as the Hebbian learning principle from neuroscience and the adaptation principle for bonding based on homophily from social science. The implementation is illustrated for an adaptive temporal-causal network model under acute stress for decision-making.

A substantial minority of the public express belief in conspiracy theories. A robust phenomenon in this area is that people who believe one conspiracy theory are more likely to believe in others. But the reason for this "positive manifold" of belief in conspiracy theories is unclear. One possibility is that a single underlying latent factor (e.g. "conspiracism") causes variation in belief in specific conspiracy theories. Another possibility is that beliefs in various conspiracy theories support one another in a mutually reinforcing network of beliefs (the "monological belief system" theory). While the monological theory has been influential in the literature, the fact that it can be operationalised as a statistical network model has not previously been recognised. In this study, we therefore tested both the unidimensional factor model and a network model. Participants were 1553 American adults recruited via Prolific. Belief in conspiracies was measured using an adapted version of the Belief in Conspiracy Theories Inventory. The fit of the two competing models was evaluated both by using van Bork et al.'s (Psychometrika, 83, 2018, 443, Multivariate Behavioral Research, 56, 2019, 175) method for testing network versus unidimensional factor models, as well as by evaluating goodness of fit to the sample covariance matrix. In both cases, evaluation of fit according to our pre-registered inferential criteria favoured the network model.

The collapse of buildings caused by earthquakes can lead to a large loss of life and property. Rapid assessment of building damage with remote sensing image data can support emergency rescues. However, current studies indicate that only a limited sample set can usually be obtained from remote sensing images immediately following an earthquake. Consequently, the difficulty in preparing sufficient training samples constrains the generalization of the model in the identification of earthquake-damaged buildings. To produce a deep learning network model with strong generalization, this study adjusted four Convolutional Neural Network (CNN) models for extracting damaged building information and compared their performance. A sample dataset of damaged buildings was constructed by using multiple disaster images retrieved from the xBD dataset. Using satellite and aerial remote sensing data obtained after the 2008 Wenchuan earthquake, we examined the geographic and data transferability of the deep network model pre-trained on the xBD dataset. The result shows that the network model pre-trained with samples generated from multiple disaster remote sensing images can extract accurately collapsed building information from satellite remote sensing data. Among the adjusted CNN models tested in the study, the adjusted DenseNet121 was the most robust. Transfer learning solved the problem of poor adaptability of the network model to remote sensing images acquired by different platforms and could identify disaster-damaged buildings properly. These results provide a solution to the rapid extraction of earthquake-damaged building information based on a deep learning network model.

Speaker identification is a classification task which aims to identify a subject from a given time-series sequential data. Since the speech signal is a continuous one-dimensional time series, most of the current research methods are based on convolutional neural network (CNN) or recurrent neural network (RNN). Indeed, these methods perform well in many tasks, but there is no attempt to combine these two network models to study the speaker identification task. Due to the spectrogram that a speech signal contains, the spatial features of voiceprint (which corresponds to the voice spectrum) and CNN are effective for spatial feature extraction (which corresponds to modeling spectral correlations in acoustic features). At the same time, the speech signal is in a time series, and deep RNN can better represent long utterances than shallow networks. Considering the advantage of gated recurrent unit (GRU) (compared with traditional RNN) in the segmentation of sequence data, we decide to use stacked GRU layers in our model for frame-level feature extraction. In this paper, we propose a deep neural network (DNN) model based on a two-dimensional convolutional neural network (2-D CNN) and gated recurrent unit (GRU) for speaker identification. In the network model design, the convolutional layer is used for voiceprint feature extraction and reduces dimensionality in both the time and frequency domains, allowing for faster GRU layer computation. In addition, the stacked GRU recurrent network layers can learn a speaker’s acoustic features. During this research, we tried to use various neural network structures, including 2-D CNN, deep RNN, and deep LSTM. The above network models were evaluated on the Aishell-1 speech dataset. The experimental results showed that our proposed DNN model, which we call deep GRU, achieved a high recognition accuracy of 98.96%. At the same time, the results also demonstrate the effectiveness of the proposed deep GRU network model versus other models for speaker identification. Through further optimization, this method could be applied to other research similar to the study of speaker identification.

The effect of limitations in the structural detail available in a network model on contamination warning system (CWS) design was examined in case studies using the original and skeletonized network models for two water distribution systems (WDSs). The skeletonized models were used as proxies for incomplete network models. CWS designs were developed by optimizing sensor placements for worst-case and mean-case contamination events. Designs developed using the skeletonized network models were transplanted into the original network model for evaluation. CWS performance was defined as the number of people who ingest more than some quantity of a contaminant in tap water before the CWS detects the presence of contamination. Lack of structural detail in a network model can result in CWS designs that (1) provide considerably less protection against worst-case contamination events than that obtained when a more complete network model is available and (2) yield substantial underestimates of the consequences associated with a contamination event. Nevertheless, CWSs developed using skeletonized network models can provide useful reductions in consequences for contaminants whose effects are not localized near the injection location. Mean-case designs can yield worst-case performances similar to those for worst-case designs when there is uncertainty in the network model. Improvements in network models for WDSs have the potential to yield significant improvements in CWS designs as well as more realistic evaluations of those designs. Although such improvements would be expected to yield improved CWS performance, the expected improvements in CWS performance have not been quantified previously. The results presented here should be useful to those responsible for the design or implementation of CWSs, particularly managers and engineers in water utilities, and encourage the development of improved network models.

This paper introduces the broadband over power lines-enhanced network model (BPLeNM) that is suitable for efficiently delivering the generated data of wireless sensor networks (WSNs) of overhead high-voltage (HV) power grids to the substations. BPLeNM exploits the high data rates of the already installed BPL networks across overhead HV grids. BPLeNM is compared against other two well-verified network models from the relevant literature: the linear network model (LNM) and the optimal arrangement network model (OANM). The contribution of this paper is threefold. First, the general mathematical framework that is necessary for describing WSNs of overhead HV grids is first presented. In detail, the general mathematical formulation of BPLeNM is proposed while the existing formulations of LNM and OANM are extended so as to deal with the general case of overhead HV grids. Based on these general mathematical formulations, the general expression of maximum delay time of the WSN data is determined for the three network models. Second, the three network models are studied and assessed for a plethora of case scenarios. Through these case scenarios, the impact of different lengths of overhead HV grids, different network arrangements, new communications technologies, variation of WSN density across overhead HV grids, and changes of generated WSN data rate on the maximum delay time is thoroughly examined. Third, to assess the performance and the feasibility of the previous network models, the feasibility probability (FP) is proposed. FP is a macroscopic metric that estimates how much practical and economically feasible is the selection of one of the previous three network models. The main conclusion of this paper is that BPLeNM defines a powerful, convenient, and schedulable network model for today’s and future’s overhead HV grids in the smart grid (SG) landscape.

Functional relationships for unsaturated flow in soils, including those between capillary pressure, saturation, and relative permeabilities, are often described using analytical models based on the bundle‐of‐tubes concept. These models are often limited by, for example, inherent difficulties in prediction of absolute permeabilities, and in incorporation of a discontinuous nonwetting phase. To overcome these difficulties, an alternative approach may be formulated using pore‐scale network models. In this approach, the pore space of the network model is adjusted to match retention data, and absolute and relative permeabilities are then calculated. A new approach that allows more general assignments of pore sizes within the network model provides for greater flexibility to match measured data. This additional flexibility is especially important for simultaneous modeling of main imbibition and drainage branches. Through comparisons between the network model results, analytical model results, and measured data for a variety of both undisturbed and repacked soils, the network model is seen to match capillary pressure–saturation data nearly as well as the analytical model, to predict water phase relative permeabilities equally well, and to predict gas phase relative permeabilities significantly better than the analytical model. The network model also provides very good estimates for intrinsic permeability and thus for absolute permeabilities. Both the network model and the analytical model lost accuracy in predicting relative water permeabilities for soils characterized by a van Genuchten exponent n≲3. Overall, the computational results indicate that reliable predictions of both relative and absolute permeabilities are obtained with the network model when the model matches the capillary pressure–saturation data well. The results also indicate that measured imbibition data are crucial to good predictions of the complete hysteresis loop.

We reconstruct 3-D sandstone models which give a realistic description of the complex micro-structure observed in actual sandstones. The essence of our approach is to build sandstone models which are analogs of actual sandstones by stochastically model the results of the main sandstone forming processes - grain sedimentation, compaction, and diagenesis. Topological and geometrical analyses are used to construct pore networks which replicate the microstructure of the reconstructed sandstones. The generated networks are used as input to a two-phase network model. The network model simulates primary drainage and water injection for both water wet and mixed wet systems. Predicted transport properties for different reconstructed sandstones are found to be in good agreement with available experimental data. Introduction The micro structure of a porous medium and the physical characteristics of the solid and the fluids which occupy the pore space determine several macroscopic properties of the medium. These properties include transport properties of interest such as permeability, electrical conductivity, relative permeability, and capillary pressure. In principle, it should be possible to determine these properties by appropriately averaging the equations describing the physical processes occurring on the pore-scale. The prediction of average or macroscopic transport properties from the associated pore-scale parameters is a long-standing issue which has been the subject of much investigation. One commonly applied tool in this investigation is the network model. The premise of the network model is that the pore space can be represented by an equivalent network of interconnected pores in which larger pores (pore bodies) are connected by smaller pores (pore throats). Since the pioneering work of Fatt, network models have been used extensively to study different displacement processes in simple or idealised porous media. Seldom, however, do such models claim to be representative of reservoir rocks. The extension of network modelling techniques to real porous media is hampered by the difficulty of adequately describing the complex nature of the pore space. Advanced techniques such as microtomographic imaging and serial sectioning provide a detailed description of the pore space at micrometer resolution. In practice, however, information about the microstructure of reservoir rocks is limited to 2-D thin section images and to pore throat entry sizes determined from mercury injection data. These data are insufficient to directly construct a 3-D pore network which replicates the microstructure of the porous medium. As a result, simplifying assumptions about the pore structure must be invoked. Despite these simplifications. network models have proved to be powerful tools for extrapolating limited measured data and for developing valuable insight into complex multiphase flow phenomena such as capillary pressure and relative permeability hysteresis, the effect of wettability, and three-phase flow. The difficulty in adequately describing the pore network of reservoir rocks has, however, prevented network models from being used as a predictive tool, thus greatly limiting their application in the oil industry. In the present work, geostatistical information obtained from image analysis of 2-D thin sections are used to generate a reliable reconstruction of the complex rock-pore system in 3-D. The network representation of the pore space is constructed from topological and geometrical analyses of the fully characterised reconstructed sample. The pore network is subsequently used as input to network simulators of single- and two-phase flow. Predicted values for permeability. electrical conductivity, and relative permeabilities for different reconstructed sandstones are compared with experimental data. Sandstone Reconstruction A sandstone sample and its petrographical parameters are the end result of all the geological and hydrodynamical processes which have affected the sedimentary basin. We do not attempt to model the detailed dynamics of these processes. P. 369^