Probabilistic Network Model Research Articles

Risk analysis for consumer-level utility gas and liquefied petroleum gas incidents using probabilistic network modeling: A case study of gas incidents in Japan

Utility gas and liquefied petroleum gas (LPG) are commonly used in Japan for their convenience as fuels for household and commercial needs. Although several efforts have been made to promote the safe use of gas, more gas accidents occur at the consumer level than in gas production facilities or in the supply chain. Incident investigations can acquire facts about the causes and effects of these accidents. In this paper, we propose a probabilistic network modeling approach in which the inherent characteristics of risk factors for consumer-level gas incidents are considered. In the approach, cause–effect chains are formulated for gas incidents, and network diagrams with probabilistic estimations are constructed to indicate the structure behind the occurrence of such incidents. The investigation shows that most gas incidents are caused by more than one risk factor, and one risk factor tends to cascade into others. These risk factors can be clustered according to their nature and can also be classified as originating causes or intermediate risk factors by analyzing their interdependencies in network diagrams. By identifying significant intermediate effects together with their causes, these risk factors can be reduced, which may reduce the occurrence of serious gas incidents at the consumer level.

Integrative approach to sporadic Alzheimer\u2019s disease:\xa0deficiency of TYROBP\xa0in cerebral A\u03b2 amyloidosis mouse normalizes clinical phenotype and complement subnetwork molecular pathology without reducing A\u03b2 burden

Integrative gene network approaches enable new avenues of exploration that implicate causal genes in sporadic late-onset Alzheimer’s disease (LOAD) pathogenesis, thereby offering novel insights for drug-discovery programs. We previously constructed a probabilistic causal network model of sporadic LOAD and identified TYROBP/DAP12, encoding a microglial transmembrane signaling polypeptide and direct adapter of TREM2, as the most robust key driver gene in the network. Here, we show that absence of TYROBP/DAP12 in a mouse model of AD-type cerebral Aβ amyloidosis (APPKM670/671NL/PSEN1Δexon9) recapitulates the expected network characteristics by normalizing the transcriptome of APP/PSEN1 mice and repressing the induction of genes involved in the switch from homeostatic microglia to disease-associated microglia (DAM), including Trem2, complement (C1qa, C1qb, C1qc, and Itgax), Clec7a and Cst7. Importantly, we show that constitutive absence of TYROBP/DAP12 in the amyloidosis mouse model prevented appearance of the electrophysiological and learning behavior alterations associated with the phenotype of APPKM670/671NL/PSEN1Δexon9 mice. Our results suggest that TYROBP/DAP12 could represent a novel therapeutic target to slow, arrest, or prevent the development of sporadic LOAD. These data establish that the network pathology observed in postmortem human LOAD brain can be faithfully recapitulated in the brain of a genetically manipulated mouse. These data also validate our multiscale gene networks by demonstrating how the networks intersect with the standard neuropathological features of LOAD.

Transcriptomic, Functional, and Network Analyses Reveal Novel Genes Involved in the Interaction Between Caenorhabditis elegans and Stenotrophomonas maltophilia.

The bacterivorous nematode Caenorhabditis elegans is an excellent model for the study of innate immune responses to a variety of bacterial pathogens, including the emerging nosocomial bacterial pathogen Stenotrophomonas maltophilia. The study of this interaction has ecological and medical relevance as S. maltophilia is found in association with C. elegans and other nematodes in the wild and is an emerging opportunistic bacterial pathogen. We identified 393 genes that were differentially expressed when exposed to virulent and avirulent strains of S. maltophilia and an avirulent strain of E. coli. We then used a probabilistic functional gene network model (WormNet) to determine that 118 of the 393 differentially expressed genes formed an interacting network and identified a set of highly connected genes with eight or more predicted interactions. We hypothesized that these highly connected genes might play an important role in the defense against S. maltophila and found that mutations of six of seven highly connected genes have a significant effect on nematode survival in response to these bacteria. Of these genes, C48B4.1, mpk-2, cpr-4, clec-67, and lys-6 are needed for combating the virulent S. maltophilia JCMS strain, while dod-22 was solely involved in response to the avirulent S. maltophilia K279a strain. We further found that dod-22 and clec-67 were up regulated in response to JCMS vs. K279a, while C48B4.1, mpk-2, cpr-4, and lys-6 were down regulated. Only dod-22 had a documented role in innate immunity, which demonstrates the merit of our approach in the identification of novel genes that are involved in combating S. maltophilia infection.

Sparse probabilistic Boolean network problems: A partial proximal-type operator splitting method

The sparse probabilistic Boolean network (SPBN) model has been applied in various fields of industrial engineering and management. The goal of this model is to find a sparse probability distribution based on a given transition-probability matrix and a set of Boolean networks (BNs). In this paper, a partial proximal-type operator splitting method is proposed to solve a separable minimization problem arising from the study of the SPBN model. All the subproblem-solvers of the proposed method do not involve matrix multiplication, and consequently the proposed method can be used to deal with large-scale problems. The global convergence to a critical point of the proposed method is proved under some mild conditions. Numerical experiments on some real probabilistic Boolean network problems show that the proposed method is effective and efficient compared with some existing methods.

A probabilistic network model for structural transitions in biomolecules.

Biological macromolecules often undergo large conformational rearrangements during a functional cycle. To simulate these structural transitions with full atomic detail typically demands extensive computational resources. Moreover, it is unclear how to incorporate, in a principled way, additional experimental information that could guide the structural transition. This article develops a probabilistic model for conformational transitions in biomolecules. The model can be viewed as a network of anharmonic springs that break, if the experimental data support the rupture of bonds. Hamiltonian Monte Carlo in internal coordinates is used to infer structural transitions from experimental data, thereby sampling large conformational transitions without distorting the structure. The model is benchmarked on a large set of conformational transitions. Moreover, we demonstrate the use of the probabilistic network model for integrative modeling of macromolecular complexes based on data from crosslinking followed by mass spectrometry.

Implementation of Knowledge-Based Expert System Using Probabilistic Network Models

The latest development in machine learning techniques has enabled the development of intelligent tools which can identify anomalies in the system in real time. These intelligent tools become expert systems when they combine the algorithmic result of root cause analysis with the domain knowledge. Truth maintenance, fuzzy logic, ontology classification are just a few out of many techniques used in building these systems. Logic is embedded in the code in most of the traditional computer program, which makes it difficult for domain experts to retrieve the underlying rule set and make any changes. These system bridge the gap by making information explicit rather than implicit. In this paper, we present a new approach for developing an expert system using decision tree analysis with probabilistic network models such as Bayes-network. The proposed model facilitate the process of correlation between belief probability with the unseen data by use of logical flowcharting, loopy belief propagation algorithm, and decision trees analysis. The performance of the model will be measured by evaluation and cross validation techniques.

Constructing Reliable Virtual Backbones in Probabilistic Wireless Sensor Networks

Most existing algorithms used for constructing virtual backbones are based on the ideal deterministic network model (DNM) in which any pair of nodes is either fully connected or completely disconnected. Different from DNM, the probabilistic network model (PNM), which presumes that there is a probability to connect and communicate between any pair of nodes, is more suitable to the practice in many real applications. In this paper, we propose a new algorithm to construct reliable virtual backbone in probabilistic wireless sensor networks. In the algorithm, we firstly introduce Effective Degree of Delivery Probability (EDDP) to indicate the reliable degree of nodes to transfer data successfully, and then exclude those nodes with zero EDDP from the candidate dominator set to construct a reliable connected dominating set (CDS). Moreover, each dominatee selects the neighbor dominator with the maximum delivery probability to transfer data. Through simulations, we demonstrate that our proposed algorithm can remarkably prolong the network lifetime compared with existing typical algorithms.

Erratum to: Probabilistic Boolean network modeling and model checking as an approach for DFMEA for manufacturing systems

Erratum to: Probabilistic Boolean network modeling and model checking as an approach for DFMEA for manufacturing systems

CURRENT STATE OF DEVELOPMENT IN THE FIELD OF ECONOMIC AND MATHEMATICAL MODELING OF THERMAL POWER

Urgency of the research. To overcome the image of one of the most energy-intensive countries in Europe, to reduce gas consumption in Ukraine, the need for compensation from the state budget the difference in tariffs for communal heatgenerating and heat supply companies may decrease if the cost of services, to take into account and eliminate heat loss. The priority of the Government of Ukraine has been the introduction of new mechanisms to promote energy efficiency on the consumer side (in particular, the payment of the loan, which the inhabitants of the house take for its thermo-or to go to alternative heating, etc.), while according to experts 80% of heat loss it occurs in areas generating and transporting. Tasks relating to the modernization and reorientation of the country's heating systems in order to reduce consumption of natural gas are those that actually increase national, primarily the energy security of Ukraine and are highly relevant and important state. Target setting. Creating application of economic and mathematical tools for decision support in the process of modernization and renewal of generating capacity of thermal generation, construction of replacement facilities using modern technologies to achieve European standards for emissions of pollutants and the use of other fuels, renovation of trunk and distribution heat networks allow perform analysis of alternative management actions and will boost the development of energy-efficient, technically reliable and safe operation of heating systems of society with minimal economic burden on end users. Actual scientific researches and issues analysis. Scientific, methodological and applied aspects of economic and mathematical modelling of power system development processes are devoted to the study Y. Bakhshan, S. Motadayen Aval, F. Kamel and A. Hajhossini, J. Okrajni, K. Mutwil and M. Cieśla, B. Yakovlev, A. Sapuhina, V. Kurochkina and S. Novikov, S. Orlovsky, V. Volkov and O. Timofeenko, B. Polovnikova and V. Huzeeva, V. Klimenko and Y. Orlov, D. Chernikov, A. Aleksakhina and A. Boblovskogo, A. Bujak, O. Havrysya, K. Mahkotilo and other domestic and foreign scientists. Uninvestigated parts of general matters defining. However, until now no one has proposed to apply the models and methods of the theory of planning and network management, as well as still have not developed a science-based system of economic and mathematical models of decision support with respect to the modernization of communal heat power. The research objective. The main objective of this work is to systematize and review of scientific works of foreign and domestic scientists, containing analogues and prototypes, which are the basis for further improvement of the concept of rational functioning of the market of energy materials and products based on the creation of a complex of economic and mathematical models of resource management and costs in the process of modernization of communal heat energy in Ukraine. The statement of basic materials. The paper systematically analyses and scientific works of foreign and domestic scientists, containing analogues and prototypes, serve as the basis for further improvement of the functioning of the concept of sustainable market for energy materials and products based on the creation of a complex of economic-mathematical models of management of resources and costs in the process of modernization municipal power system in Ukraine. It is proved that the idea of adapting the methods of graph theory, as well as deterministic and probabilistic network models taking into account the time, cost and resources for planning and management practice, modernization, reconstruction and replacement of process equipment heating stations and thermal networks enterprises of power in Ukraine, characterized by scientific novelty and is a tool inform decisions heat generating and heat supply companies, investment companies, the legislative bodies regarding the technically reliable and safe functioning of the energy supply company systems. Conclusions. The use of modern information technology and network of economic and mathematical models and methods to simultaneously consider all the economic and physical conditions and to determine the best option in the process of modernization of communal thermal power will become one of the components forming a system of support for the introduction of new management systems to the innovative development of the energy sector of Ukraine , will provide decision-making in favour of the power supply systems, which are the most beneficial to the safety position, the reliability (continuity), the quality of energy supply and the provision of affordable and attractive for the environmental consequences of energy services.

Reliable and energy efficient topology control in probabilistic Wireless Sensor Networks via multi-objective optimization

In Wireless Sensor Networks (WSNs) instead of using the possible network connectivity to its maximum extent, a deliberate choice must be made to restrict the topology of the network. Constructing a virtual backbone network using Connected Dominating Sets (CDS) is a promising choice for topology control. Currently, almost all existing studies employ heuristic and/or meta-heuristic optimizations for formulating minimum-sized CDS under the deterministic network model. In this paper, we address the problem of constructing energy efficient CDS in WSNs while improving network reliability. The problem is modelled as a multi-objective optimization that simultaneously maximizes two contradictory parameters: reliability and energy efficiency. Unlike most of the existing studies, the reliability parameter is expressed as a probabilistic inference using probabilistic network model due to uncertainty in connections among sensor nodes. Extensive simulation results indicate that the proposed approach in this paper achieves more reliability, longer stability period and more energy efficient CDS compared to other approaches in the literature.

On the analytical approximation of joint aggregate queue-length distributions for traffic networks: A stationary finite capacity Markovian network approach

This paper is motivated by recent results in the design of signal plans for Manhattan that highlight the importance of providing signal control algorithms with an analytical description of between-link dependencies. This is particularly important for congested networks prone to the occurrence of spillbacks. This paper formulates a probabilistic network model that proposes an aggregate description of the queue-length, and then approximates the joint aggregate queue-length distribution of subnetworks. The goal is to model between-queue dependencies beyond first-order moments, yet to do so in a tractable manner such that these techniques can be used for optimization purposes.This paper models an urban road network as a finite space capacity Markovian queueing network. Exact evaluation of the stationary joint queue-length distribution of such a network with arbitrary size and topology can be obtained numerically. Nonetheless, the main challenge to such an approach remains the dimensionality of the network state space, which is exponential in the number of queues. This paper proposes to address the dimensionality issue by: 1) describing the state of the network aggregately, and 2) decomposing the network into overlapping subnetworks. We propose an analytical approximation of the stationary aggregate joint queue-length distribution of a subnetwork. The model consists of a system of nonlinear equations with a dimension that is linear, instead of exponential, in the number of queues and that is independent of the space capacity of the individual queues. The method is derived for tandem Markovian finite capacity queueing networks. The proposed model is computationally tractable and scalable, it can be efficiently used for the higher-order distributional analysis of large-scale networks. The model is validated versus simulation estimates and versus other decomposition methods. We then use it to address an urban traffic control problem. We show the added value of accounting for higher-order spatial between-queue dependency information in the control of congested urban networks.

Mapping resource conflicts with probabilistic network models

Resource conflict management is an increasingly interesting topic for both scientific advancement and policy analysis. This study provides decision makers with a data-driven tool as a valid support to institutional action in planning and implementing policies. The probabilistic network model here proposed is built using (1) historical data and (2) a general taxonomy on world resource conflicts. Predicting the most likely conflict outcome is a prerequisite for carrying out some scenario building, which is also illustrated in the paper. However, the strength of our study lies not only in the prediction model, which takes into account specific political, socio-economic and geographical features of the areas involved in resource conflicts, but also in the statistical approach proposed herein for assessing the impact of various policy decisions on conflict dynamics and outcome. The empirical analysis shows that economic and social factors play a central role not only as triggers of claims on the use of natural resources, but also on the salience and persistence of the conflict. Moreover, from scenario simulations it appears that bilateral negotiations most likely drive conflicts in coastal areas (biodiversity protection etc.) to end with an agreement between the parties. Not only the importance of economic interests on coastlines is much higher than in other areas, but also issues on the preservation of natural site and biodiversity are more likely to insist on littorals than in a general framework.

Latent space inference of internet-scale networks

The rise of Internet-scale networks, such as web graphs and social media with hundreds of millions to billions of nodes, presents new scientific opportunities, such as overlapping community detection to discover the structure of the Internet, or to analyze trends in online social behavior. However, many existing probabilistic network models are difficult or impossible to deploy at these massive scales. We propose a scalable approach for modeling and inferring latent spaces in Internet-scale networks, with an eye towards overlapping community detection as a key application. By applying a succinct representation of networks as a bag of triangular motifs, developing a parsimonious statistical model, deriving an efficient stochastic variational inference algorithm, and implementing it as a distributed cluster program via the Petuum parameter server system, we demonstrate overlapping community detection on real networks with up to 100 million nodes and 1000 communities on 5 machines in under 40 hours. Compared to other state-of-the-art probabilistic network approaches, our method is several orders of magnitude faster, with competitive or improved accuracy at overlapping community detection.

Probabilistic Boolean network modeling and model checking as an approach for DFMEA for manufacturing systems

Modeling manufacturing processes assists the design of new systems, allowing predictions of future behaviors, identifying improvement areas and evaluating changes to existing systems. Probabilistic Boolean networks (PBN) have been used to study biological systems, since they combine uncertainty and rule-based representation. A novel approach is proposed to model the design of an automated manufacturing assembly processes using PBNs to generate quantitative data for occurrence assessment in design failure mode and effects analysis. FMEA is a widely used tool in risk assessment (RA) to ensure design outputs consistently deliver the intended level of performance. Effectiveness of RA depends upon the robustness of the data used. Temporal logic is applied to analyze state successions in a transition system, while interactions and dynamics are captured over a set of Boolean variables using PBNs. Designs are therefore enhanced through assessment of risks, using proposed tools in the early phases of design of manufacturing systems. A two-sample T test demonstrates the proposed model provides values closer to expected values; consequently modeling observable phenomena (\(p\,\hbox {value} > 0.05\)). Simulations are used to generate data required to conduct inferential statistical tests to determine the level of correspondence between model prediction and real machine data.

A Bayesian network model to predict accidents on Swiss highways

Although in 2014, Switzerland had an average of less than two fatalities per billion vehicle-kilometres, making its roads among the safest in Europe, still more than 17 000 traffic accidents occurred on Swiss communal roads, cantonal roads and national highways. On the highway network of approximately 1800 km alone, there were almost 1700 accidents involving personal injuries. In order to further reduce this number of accidents, it is important that accident risks are assessed as accurately as possible. A state-of-the-art methodology is used to develop a Bayesian probabilistic network model to estimate the number of accidents involving personal injury on the Swiss highway network. The developed model predicts the number of accidents on a given highway segment and can be used to identify segments with a high expected number of accidents. During validation, the number of accidents was correctly predicted on 86% of the segments with a tolerance of 25%. The model can also be used to conduct parametric studies, which help to ensure that the risk reduction interventions are effective and efficient. Road traffic and road infrastructure engineers and managers can use the model during the decision-making processes in the planning, construction and maintenance of road networks.

Reorganization of the interchromosomal network during keratinocyte differentiation.

The well-established human epidermal keratinocyte (HEK) differentiation model was investigated to determine possible alterations in chromosome territory (CT) association during differentiation. The seven human chromosomes (1, 4, 11, 12, 16, 17, and 18) selected for this analysis are representative of the chromosome size and gene density range of the overall human genome as well as including a majority of genes involved in epidermal development and differentiation (CT1, 12, and 17). Induction with calcium chloride (Ca(2+)) resulted in morphological changes characteristic of keratinocyte differentiation. Combined multi-fluorescence in situ hybridization (FISH) and computational image analysis on the undifferentiated (0h) and differentiated (24h after Ca(2+) treatment) HEK revealed that (a) increases in CT volumes correspond to overall nuclear volume increases, (b) radial positioning is gene density-dependent at 0h but neither gene density- nor size-dependent at 24h, (c) the average number of interchromosomal associations for each CT is gene density-dependent and similar at both time points, and (d) there are striking differences in the single and multiple pairwise interchromosomal association profiles. Probabilistic network models of the overall interchromosomal associations demonstrate major reorganization of the network during differentiation. Only ~40% of the CT pairwise connections in the networks are common to both 0 and 24h HEK. We propose that there is a probabilistic chromosome positional code which can be significantly altered during cell differentiation in coordination with reprogramming of gene expression.

Probabilistic Assessment of the Occurrence and Duration of Ice Accretion on Cables

This paper presents an operational framework for assessing the probability of occurrence of in-cloud and precipitation icing and its duration. The framework utilizes the features of the Bayesian Probabilistic Networks and its performance is illustrated through a case study of the cable-stayed Öresund Bridge. The Bayesian Probabilistic Network model used for the estimation of the occurrence and duration probabilities is studied and it is found to be robust with respect to changes in the choice of distribution types used to model the meteorological variables that influence the two icing mechanisms and their duration. The model is found to be more sensitive to changes in the discretization levels of the input variables. Finally, it is shown how forecasting of the meteorological variables, that is the probabilities of the occurrence of ice accretion and its duration, can be used to update the model. The updated probabilities can be used as a decision support tool for the management of risk and safety with respect to falling ice.

On the Value of Forecasting in Cable Ice Risk Management

This paper addresses the issue of risk management of cable structures with respect to icing events, which may lead to safety issues related to human life, functional disruptions and the associated economic consequences. Emphasis is placed on the value of early warning of icing events, which is based on the monitoring of environmental conditions and short-term forecasting. Decision problems in risk management can be supported by quantifying the value of structural health monitoring (SHM). The approach for the assessment of the value of SHM is based on structural risk assessments together with the Bayesian pre-posterior decision analysis and builds upon the quantification of Value of Information (Vol). Using known probabilistic models, which relate environmental conditions to the events of icing in terms of occurrence, a framework developed earlier is presented for the assessment of the expected value of consequences associated with cable icing events. The consequences are evaluated for different outputs of the probabilistic model to provide a basis for prioritizing risk management decision alternatives. It is shown how forecasting of the environmental characteristics, that is the input to a Bayesian Probabilistic Network model, can be used to update the probability tables of the BPN model and thereby facilitate updating of the ice occurrence probability, which can serve as a decision support tool for the risk management of a cable-supported bridge. The approach is illustrated by an example considering the safety management of a cable-supported bridge subject to the risk of falling ice from the cables. The example shows how the expected value of SHM and forecasting can be assessed, and a sensitivity study concerning the assumptions underlying the consequence modelling is reported.

Ergm.graphlets: A Package for ERG Modeling Based on Graphlet Statistics

Exponential-family random graph models are probabilistic network models that are parametrized by sufficient statistics based on structural (i.e., graph-theoretic) properties. The ergm package for the R statistical computing environment is a collection of tools for the analysis of network data within an exponential-family random graph model framework. Many different network properties can be employed as sufficient statistics for exponential- family random graph models by using the model terms defined in the ergm package; this functionality can be expanded by the creation of packages that code for additional network statistics. Here, our focus is on the addition of statistics based on graphlets. Graphlets are classes of small, connected, induced subgraphs that can be used to describe the topological structure of a network. We introduce an R package called ergm.graphlets that enables the use of graphlet properties of a network within the ergm package of R. The ergm.graphlets package provides a complete list of model terms that allows to incorporate statistics of any 2-, 3-, 4- and 5-node graphlets into exponential-family random graph models. The new model terms of the ergm.graphlets package enable both exponential-family random graph modeling of global structural properties and investigation of relationships between node attributes (i.e., covariates) and local topologies around nodes.

Identification of probabilistic graphical network model for root-cause diagnosis in industrial processes

Identification of faults in process systems can be based purely on measurement (e.g. PCA), or can exploit knowledge of process model structure to construct a causal network. This work introduces a method to identify most likely causal network in cases when process model is not known. An incidence matrix, showing location of measurements in the plant network structure, and historical process data are used to identify the optimal causal network structure by means of maximizing Bayesian scores for alternative causal networks. Causal subnetworks, corresponding to subgraphs of the process network, are identified by finding the most probable graph based on highest posterior probability of graph features computed via Markov Chain Monte Carlo simulation. Novel Bayesian contribution indices within the probabilistic graphical network are proposed to identify the potential root-cause variables. Application to Tennessee Eastman Chemical plant demonstrates that the presented method is significantly more accurate than the current methods.