Predicting transient particle transport in periodic ventilation using Markov chain model with pre-stored transition probabilities

2021 ◽  
pp. 108730
Author(s):  
Wenjie Huang ◽  
Yuting An ◽  
Yue Pan ◽  
Jinghua Li ◽  
Chun Chen
Keyword(s):  
Markov Chain ◽  
Particle Transport ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

scholarly journals Calibration of Transition Intensities for a Multistate Model: Application to Long-Term Care

Risks ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 37
Author(s):  
Manuel L. Esquível ◽  
Gracinda R. Guerreiro ◽  
Matilde C. Oliveira ◽  
Pedro Corte Real
Keyword(s):  
Markov Chain ◽  
Continuous Time ◽  
Long Term Care ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model ◽  
Chain Process ◽  
Term Care ◽  
National Network

We consider a non-homogeneous continuous time Markov chain model for Long-Term Care with five states: the autonomous state, three dependent states of light, moderate and severe dependence levels and the death state. For a general approach, we allow for non null intensities for all the returns from higher dependence levels to all lesser dependencies in the multi-state model. Using data from the 2015 Portuguese National Network of Continuous Care database, as the main research contribution of this paper, we propose a method to calibrate transition intensities with the one step transition probabilities estimated from data. This allows us to use non-homogeneous continuous time Markov chains for modeling Long-Term Care. We solve numerically the Kolmogorov forward differential equations in order to obtain continuous time transition probabilities. We assess the quality of the calibration using the Portuguese life expectancies. Based on reasonable monthly costs for each dependence state we compute, by Monte Carlo simulation, trajectories of the Markov chain process and derive relevant information for model validation and premium calculation.

scholarly journals Will this search end up with booking? Modeling airline booking conversion of anonymous visitors

2020 ◽  
Vol 27 (2) ◽  
pp. 237-250
Author(s):  
Misuk Lee
Keyword(s):  
Markov Chain ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Distribution Channel ◽  
Stochastic Approach ◽  
Tourism Industry ◽  
Chain Model ◽  
Online Search ◽  
Clickstream Data ◽  
Content Type

Purpose Over the past two decades, online booking has become a predominant distribution channel of tourism products. As online sales have become more important, understanding booking conversion behavior remains a critical topic in the tourism industry. The purpose of this study is to model airline search and booking activities of anonymous visitors. Design/methodology/approach This study proposes a stochastic approach to explicitly model dynamics of airline customers’ search, revisit and booking activities. A Markov chain model simultaneously captures transition probabilities and the timing of search, revisit and booking decisions. The suggested model is demonstrated on clickstream data from an airline booking website. Findings Empirical results show that low prices (captured as discount rates) lead to not only booking propensities but also overall stickiness to a website, increasing search and revisit probabilities. From the decision timing of search and revisit activities, the author observes customers’ learning effect on browsing time and heterogeneous intentions of website visits. Originality/value This study presents both theoretical and managerial implications of online search and booking behavior for airline and tourism marketing. The dynamic Markov chain model provides a systematic framework to predict online search, revisit and booking conversion and the time of the online activities.

scholarly journals Insight into earthquake sequencing: analysis and interpretation of time-series constructed from the directed graph of the Markov chain model

2015 ◽  
Vol 2 (1) ◽  
pp. 399-424
Author(s):  
M. S. Cavers ◽  
K. Vasudevan
Keyword(s):  
Time Series ◽  
Markov Chain ◽  
Directed Graph ◽  
State Transition ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Fano Factor ◽  
Chain Model ◽  
Intrinsic Mode Functions ◽  
Allan Factor

Abstract. Directed graph representation of a Markov chain model to study global earthquake sequencing leads to a time-series of state-to-state transition probabilities that includes the spatio-temporally linked recurrent events in the record-breaking sense. A state refers to a configuration comprised of zones with either the occurrence or non-occurrence of an earthquake in each zone in a pre-determined time interval. Since the time-series is derived from non-linear and non-stationary earthquake sequencing, we use known analysis methods to glean new information. We apply decomposition procedures such as ensemble empirical mode decomposition (EEMD) to study the state-to-state fluctuations in each of the intrinsic mode functions. We subject the intrinsic mode functions, the orthogonal basis set derived from the time-series using the EEMD, to a detailed analysis to draw information-content of the time-series. Also, we investigate the influence of random-noise on the data-driven state-to-state transition probabilities. We consider a second aspect of earthquake sequencing that is closely tied to its time-correlative behavior. Here, we extend the Fano factor and Allan factor analysis to the time-series of state-to state transition frequencies of a Markov chain. Our results support not only the usefulness the intrinsic mode functions in understanding the time-series but also the presence of power-law behaviour exemplified by the Fano factor and the Allan factor.

scholarly journals Markov Chain Modeling of HIV, Tuberculosis, and Hepatitis B Transmission in Ghana

2019 ◽  
Vol 2019 ◽  
pp. 1-8 ◽  
Author(s):  
Clement Twumasi ◽  
Louis Asiedu ◽  
Ezekiel N. N. Nortey
Keyword(s):  
Markov Chain ◽  
Hepatitis B ◽  
Discrete Time ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Chain Model ◽  
Disease Dynamics ◽  
Epidemiological Models ◽  
Discrete Time Markov Chain ◽  
Expected Time

Several mathematical and standard epidemiological models have been proposed in studying infectious disease dynamics. These models help to understand the spread of disease infections. However, most of these models are not able to estimate other relevant disease metrics such as probability of first infection and recovery as well as the expected time to infection and recovery for both susceptible and infected individuals. That is, most of the standard epidemiological models used in estimating transition probabilities (TPs) are not able to generalize the transition estimates of disease outcomes at discrete time steps for future predictions. This paper seeks to address the aforementioned problems through a discrete-time Markov chain model. Secondary datasets from cohort studies were collected on HIV, tuberculosis (TB), and hepatitis B (HB) cases from a regional hospital in Ghana. The Markov chain model revealed that hepatitis B was more infectious over time than tuberculosis and HIV even though the probability of first infection of these diseases was relatively low within the study population. However, individuals infected with HIV had comparatively lower life expectancies than those infected with tuberculosis and hepatitis B. Discrete-time Markov chain technique is recommended as viable for modeling disease dynamics in Ghana.

scholarly journals Integration of fast fluid dynamics and Markov chain model for predicting transient particle transport in buildings

2019 ◽  
Vol 111 ◽  
pp. 04030
Author(s):  
Wei Liu ◽  
Chun Chen
Keyword(s):  
Fluid Dynamics ◽  
Markov Chain ◽  
Particle Transport ◽  
Markov Chain Model ◽  
Lagrangian Model ◽  
Air Distribution ◽  
Chain Model ◽  
Indoor Environments ◽  
Eulerian Model ◽  
Simulation Tools

Fast simulation tools for the prediction of transient particle transport are critical in designing the air distribution indoors to reduce the exposure to indoor particles and associated health risks. This investigation proposed a combined fast fluid dynamics (FFD) and Markov chain model for fast predicting transient particle transport indoors. The solver for FFD-Markov-chain model was programmed in OpenFOAM, an open-source CFD toolbox. This study used a case from the literature to validate the developed model and found well agreement between the transient particle concentrations predicted by the FFD-Markov-chain model and the experimental data. This investigation further compared the FFD-Markovchain model with the CFD-Eulerian model and CFD-Lagrangian model in terms of accuracy and efficiency. The accuracy of the FFD-Markov-chain model was similar to that of the other two models. For the studied case, the FFD-Markov-chain model was 4.7 times faster than the CFD-Eulerian model, and it was 137.4 times faster than the CFD-Lagrangian model in predicting the steady-state airflow and transient particle transport. Therefore, the FFD-Markov-chain model is able to greatly reduce the computing cost for predicting transient particle transport in indoor environments.

scholarly journals A Markov chain model for predicting transient particle transport in enclosed environments

2015 ◽  
Vol 90 ◽  
pp. 30-36 ◽  
Author(s):  
Chun Chen ◽  
Wei Liu ◽  
Chao-Hsin Lin ◽  
Qingyan Chen
Keyword(s):  
Markov Chain ◽  
Particle Transport ◽  
Markov Chain Model ◽  
Chain Model

Characterization of endothelial cell locomotion using a Markov chain model

1995 ◽  
Vol 73 (7-8) ◽  
pp. 461-472 ◽  
Author(s):  
Yih Lee ◽  
Larry V. Mclntire ◽  
Kyriacos Zygourakis ◽  
Pauline A. Markenscoff
Keyword(s):  
Markov Chain ◽  
Stationary State ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Cell Movement ◽  
Chain Model ◽  
Trajectory Data ◽  
Cell Locomotion ◽  
Cell Motion ◽  
Cells Cultured

A Markov chain model was developed to characterize the two-dimensional locomotion of bovine pulmonary artery endothelial (BPAE) cells cultured with or without basic fibroblast growth factor (bFGF). This model provides a detailed description of the migration process by computing the following locomotory parameters: (i) the speed of cell locomotion; (ii) the expected duration of cell movement in any given direction; (iii) the probability distribution of turn angles that will decide the next direction of cell movement; (iv) the frequency of cell stops; and (v) the duration of cell stops. Eight directional states and a stationary state were used in our Markov analysis. From cell trajectory data, the transition probabilities among the various states and the waiting times for the directional and the stationary states were computed. The steady-state probabilities were also calculated to obtain the ultimate direction of cell motion and, thus, determine whether cell motion was random. Our results showed how the addition of bFGF enhanced the locomotory capability of BPAE cells. Cells cultured with 30 ng/mL bFGF had lower probability of moving to the stationary state than those cultured without bFGF In addition, cells cultured with 30 ng/mL bFGF remained in the stationary state for shorter periods of time than cells cultured without bFGF. In both these cases, however, the transition probabilities from the stationary state to any directional state were uniformly distributed and were not affected by the presence of bFGF.Key words: Markov chain model, stochastic process, cell locomotion, endothelial cells.

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