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Probabilistic Boolean Networks can capture the dynamics of complex biological systems as well as other non-biological systems, such as manufacturing systems and smart grids. In this proof-of-concept manuscript, we propose a Probabilistic Boolean Network architecture with a learning process that significantly improves the prediction of the occurrence of faults and failures in smart-grid systems. This idea was tested in a Probabilistic Boolean Network model of the WSCC nine-bus system that incorporates Intelligent Power Routers on every bus. The model learned the equality and negation functions in the different experiments performed. We take advantage of the complex properties of Probabilistic Boolean Networks to use them as a positive feedback adaptive learning tool and to illustrate that these networks could have a more general use than previously thought. This multi-layered PBN architecture provides a significant improvement in terms of performance for fault detection, within a positive-feedback network structure that is more tolerant of noise than other techniques.

Pore network models of two‐phase flow in porous media are widely used to investigate constitutive relationships between saturation and relative permeability as well as capillary pressure. However, results of many studies show a discrepancy between calculated relative permeability and corresponding measured values. Often, calculated values overestimate the measured values. An important feature of almost all pore network models is that the resistance to flow is assumed to come from pore throats only; i.e., the resistance of pore bodies to the flow is considered to be negligible compare to the resistance of pore throats. We contend that this simplification may considerably affect the results for relative permeability curves. In this study, we present a new formulation for pore network modeling of two‐phase flow, which allows for the calculation of wetting phase fluxes in the edges of (partially) drained pores. In a quantitative investigation, we have shown the significance of this effect. The pore space is represented by cubic pore bodies and parallelepiped pore throats in a Multi‐Directional Pore Network model. This model allows for a distribution of coordination numbers ranging between 1 and 26. This topological property, together with geometrical distributions of pore sizes, is used to mimic the microstructure of real porous media. In the presence of the nonwetting phase, the wetting fluid is considered to fill only spaces along edges of cubic pore bodies. We show that the resistance to the flow of the wetting phase within these filaments of fluids are comparable to the resistance to the wetting phase flow within pore throats. Resulting saturation‐relative permeability relationships show very good agreement with measured curves. Explicit representation of wetting phase filaments and calculation of different fluxes within pore bodies may also lead to improved predictions of transport properties such as dispersivities and mass transfer coefficients. Key Points We present a new formulation for pore‐network modeling of two‐phase flow The resistance of pore bodies to the flow is taken into account Resulting saturation‐relative permeability relationships are shown and discussed

The study of the social world involves multiple, multidimensional, and endlessly dynamic competing systems evolving over time. This inherent complexity, however, does not mean that the social world is chaotic, random, or unstructured. Rather, structural forms do emerge and co-exist in social settings. It is the emergence, maintenance, and decay of these structures that allows researchers to detect temporary stability and provides them with the means to make predictions about continuity and change in social dynamics. Arguably then, the main challenge in the study of the social world consist of developing robust and consistent strategies or tools capable of tracing, mapping, or retrieving these structural forms in order to ultimately model this social complexity. Accordingly, the overarching purpose of this study consists of addressing this analytic and methodological challenge by proposing a groundbreaking analytic framework, and its corresponding software application, designed to extract temporal and dynamic structures in the social world relying on complex realism, complex systems, dynamic temporal network analyses, and data science and visualization techniques. Together, these frameworks constitute the foundations of Mapping, Organizing, and Visualizing Interdependent Events (MOVIE), an analytic framework [and software application] designed to ease the understanding of, individually-produced or interactively-generated, events and knowledge evolution, by tracing and recreating the processes that may have affected participants’ experiences, outcomes, and standpoints. To demonstrate MOVIE’s performance and rigor in capturing and recreating the dynamic complexity of micro-level interactions, the analyses relied on publicly available data sources on foreign policy and conflict resolution. All data elements and tools are provided with this study to make these analyses fully transparent and reproducible. MOVIE can trace/recreate the temporal elements embedded in existing qualitative databases (e.g. those generated with NVivo/MAXQDA/Atlas.ti), even if they were created without considering their dynamic time-evolving features, whose meaning-building relevance may help inform policy planning and action.

A pore network model (PNM) is proposed for the simulation of water transport inside the cathode side ‎gas diffusion layer (GDL) of polymer electrolyte fuel cells (PEFCs) during the transient start-up period as ‎well as the steady state. Numerous two-dimensional random networks representing GDL are generated ‎followed by statistical averaging of the results (Monte Carlo methods) to circumvent the uncertainties ‎imposed by random pore size distributions. The resulting liquid water saturation profiles within GDLs ‎exhibit concave patterns which is typically encountered in capillary fingering flow regimes in porous ‎media. The effect of GDL thickness and current collector rib width as two geometric parameters on water ‎transport dynamics are separately investigated. It turns out that thin and thick GDLs compared to the base ‎case can have contradicting outcomes on the account of total water saturation in the network. On the ‎other hand, wide current collector ribs give rise to liquid water saturation and build-up within GDL which ‎can lead to flooding. At the end, three-dimensional networks are generated demonstrating higher pore ‎connectivity which results in higher percolation times and different invasion patterns.‎

Competing endogenous RNA (ceRNA) regulations and crosstalk between various types of non-coding RNA in humans is an important and under-explored subject. Several studies have pointed out that an alteration in miRNA:target interaction can result in unexpected changes due to indirect and complex interactions. In this article, we defined a new network-based model that incorporates miRNA:ceRNA interactions with expression values. Our approach calculates network-wide effects of perturbations in the expression level of one or more nodes in the presence or absence of miRNA interaction factors such as seed type, binding energy. We carried out the analysis of large-scale miRNA:target networks from breast cancer patients. Highly perturbing genes identified by our approach coincide with breast cancer-associated genes and miRNAs. Our network-based approach takes the sponge effect into account and helps to unveil the crosstalk between nodes in miRNA:target network. The model has potential to reveal unforeseen regulations that are only evident in the network context. Our tool is scalable and can be plugged in with emerging miRNA effectors such as circRNAs, lncRNAs, and available as R package ceRNAnetsim: https://www.bioconductor.org/packages/release/bioc/html/ceRNAnetsim.html .

Both the latent variable model and the network model have been widely used to conceptualize mental disorders. However, it has been pointed out that there is no clear dichotomy between the two models, and a combination of these two model could enable a better understanding of psychopathology. The recently proposed latent network model (LNM) has provided a statistical framework to enable this combination. Evidence has shown that posttraumatic stress disorder (PTSD) could be a suitable candidate disorder to study the combined model. In the current study, we initiated the first investigation of the latent network of PTSD symptoms. The latent network of DSM-5 PTSD symptoms was estimated in 1196 adult survivors of China's 2008 Wenchuan earthquake. Validation testing of the latent network was conducted in a replication sample of children and adolescent who experienced various trauma types. PTSD symptoms were measured by the PTSD Checklist for DSM-5 (PCL-5). The latent network was estimated using the seven-factor hybrid model of DSM-5 PTSD symptoms, analysed using the R package lvnet. The latent network model demonstrated good fit in both samples. A strong weighted edge between the intrusion and avoidance dimensions was identified (regularized partial correlation = 0.75). The externalizing behaviour dimension demonstrated the highest centrality in the latent network. This study is the first to investigate the latent network of DSM-5 PTSD symptoms. Results suggest that both latent symptom dimension and associations between the dimensions should be considered in future PTSD studies and clinical practices.

We present in this paper PETFEN, a Performance Evaluation Tool for Flow-level network modeling of Ethernet Networks. Flow-level network models are a useful tool to dimension and predict various performances of networks with TCP and UDP flows, providing information such as mean flow bandwidths, link utilizations or queue sizes. While the literature on flow-level network models is extensive, there is still a lack of tools for numerical evaluations on user provided topologies. In this paper, we describe the three components of PETFEN: (i) an effective domain specific language used for algorithmically describing topologies, (ii) a mathematical toolbox for the numerical evaluation of flow-level network models on the provided topologies, (iii) modules for the evaluation of the topologies with external tools. Via various numerical evaluations, we compare the results of PETFEN with results of SimGrid, another tool based on flow-level network models, as well as results of the discrete event simulator OMNeT++.

Network approaches to psychopathology have become increasingly common in mental health research, with many theoretical and methodological developments quickly gaining traction. This article illustrates contemporary practices in applying network analytical tools, bridging the gap between network concepts and their empirical applications. We explain how we can use graphs to construct networks representing complex associations among observable psychological variables. We then discuss key network models, including dynamic networks, time‐varying networks, network models derived from panel data, network intervention analysis, latent networks, and moderated models. In addition, we discuss Bayesian networks and their role in causal inference with a focus on cross‐sectional data. After presenting the different methods, we discuss how network models and psychopathology theories can meaningfully inform each other. We conclude with a discussion that summarizes the insights each technique can provide in mental health research.

Podophyllum hexandrum Royle is an important high-altitude plant of Himalayas with immense medicinal value. Earlier, it was reported that the cell wall hydrolases were up accumulated during radicle protrusion step of Podophyllum seed germination. In the present study, Podophyllum seed Germination protein interaction Network (PGN) was constructed by using the differentially accumulated protein (DAP) data set of Podophyllum during the radicle protrusion step of seed germination, with reference to Arabidopsis protein-protein interaction network (AtPIN). The developed PGN is comprised of a giant cluster with 1028 proteins having 10,519 interactions and a few small clusters with relevant gene ontological signatures. In this analysis, a germination pathway related cluster which is also central to the topology and information dynamics of PGN was obtained with a set of 60 key proteins. Among these, eight proteins which are known to be involved in signaling, metabolism, protein modification, cell wall modification, and cell cycle regulation processes were found commonly highlighted in both the proteomic and interactome analysis. The systems-level analysis of PGN identified the key proteins involved in radicle protrusion step of seed germination in Podophyllum.

Among primates, humans are special in their ability to create and manipulate highly elaborate structures of language, mathematics, and music. Here we show that this sensitivity to abstract structure is already present in a much simpler domain: the visual perception of regular geometric shapes such as squares, rectangles, and parallelograms. We asked human subjects to detect an intruder shape among six quadrilaterals. Although the intruder was always defined by an identical amount of displacement of a single vertex, the results revealed a geometric regularity effect: detection was considerably easier when either the base shape or the intruder was a regular figure comprising right angles, parallelism, or symmetry rather than amore irregular shape. This effectwas replicated in several tasks and in all human populations tested, including uneducated Himba adults and French kindergartners. Baboons, however, showed no such geometric regularity effect, even after extensive training. Baboon behavior was captured by convolutional neural networks (CNNs), but neither CNNs nor a variational autoencoder captured the human geometric regularity effect. However, a symbolic model, based on exact properties of Euclidean geometry, closely fitted human behavior. Our results indicate that the human propensity for symbolic abstraction permeates even elementary shape perception. They suggest a putative signature of human singularity and provide a challenge for nonsymbolic models of human shape perception.