Markov Chain Model Research Articles

Markov Chains to Explore the Nanosystems for the Biophysical Studies of Cancers

The immune response is essential for the human body to function well and to survive against the sudden and chronic diseases such as viral & bacterial infections and cancers. In the immunosurveillance process, Natural Killer (NK) cells are one of the main elements in controlling the development of such infections and, for this reason, they have become the subject of “in-depth” studies especially for the application of new forms of immunotherapy. NK cells can rapidly destroy both autologous and tumor cells in vitro and for this reason the interest in their function is increasingly growing. Their presence in the tumor micro-environment (TME) also assumes prognostic value since the repertoire of NK cell receptors has been linked to anti-tumor function. In this work, a Markov chain modeling approach is proposed to analyze the network of interactions that NK cells carry out with other immune elements in the defense against cancer such as CD4+ cells and CD8+ cells and dendritic cells (DCs) that activate and enhance immune responses. The probabilistic approach used is promising since it helps to understand the balance and the communication in the micro-environment, in a realistic manner. The advantage of discrete time Markov chain approach is that, it can be further extended to complex networks using the state-of-the-art algorithms and can also be translated for the novel AI tools for the cytokines and protein databases.

A Markov chain model of Daphnia motion

Abstract Daphnia magna make turns through an antennae-whipping action. This action occurs every few seconds, so during the intervening time, the animal either remains in place or it continues movement roughly along its current course. We view their movement in two dimensions as being on a lattice, and construct a second-order Markov chain model to make predictions about which region of the lattice the animal moves to based on where it was at the last two time points. This model accounts for the animal’s morphology with respect to the antennae-whipping behavior.

Stochastic occupancy modeling for spaces with irregular occupancy patterns using adaptive B-Spline-based inhomogeneous Markov Chains

Stochastic occupancy modeling for spaces with irregular occupancy patterns using adaptive B-Spline-based inhomogeneous Markov Chains

A measure of the importance of moment for ball-strike counts in a baseball plate appearance

ABSTRACT This study constructs a discrete-time Markov Chain (DTMC) model for a baseball plate appearance (PA) employing Major League Baseball’s pitch-by-pitch dataset. Based on the DTMC model, we propose a novel measure for a baseball PA, termed the Importance of Moment (IOM). The IOM quantifies the criticality of each ball-strike count situation, by assessing the probabilistic difference between the pitcher’s and hitter’s favourable outcomes (out vs reaching base). If the favours significantly vary right after a particular ball-strike count, then the count is deemed critical and is assigned a high IOM value. We empirically verify that IOM explains pitchers’ behaviour of fastball speed. We then further investigate whether the behaviour of ace pitchers differs significantly from the majority. Several interesting properties are found from the analysis. Firstly, the path independence assumption generally holds, with the exception of the ball-strike count of 2B1S. Second, pitchers tend to throw the faster fastball at counts with higher IOM values. Lastly, ace pitchers are capable of pitching even faster fastball in two-strike situations in which IOM is high. The DTMC effectively models the probabilistic structure of a baseball PA, and the proposed IOM measure serves as a useful tool for explaining player behaviour.

Cost-effectiveness analysis of endovascular versus open repair in patients with abdominal aortic aneurysm in Iran: a cross-sectional study.

Cost-effectiveness analysis of endovascular versus open repair in patients with abdominal aortic aneurysm in Iran: a cross-sectional study.

Forecasting Land Use and Land Cover Changes in the Malaprabha Right Bank Canal Command Area through Cellular Automata and Markov Chain Modeling

Forecasting Land Use and Land Cover Changes in the Malaprabha Right Bank Canal Command Area through Cellular Automata and Markov Chain Modeling

Cost-effectiveness of adjuvant abemaciclib and ribociclib in high-risk, hormone receptor–positive, early breast cancer in India.

e12516 Background: Incorporating adjuvant cyclin-dependent kinase (CDK) 4/6 inhibitors abemaciclib and ribociclib along with endocrine therapy has been shown to improve invasive disease-free survival (iDFS) for hormone receptor-positive (HR+) human epidermal receptor 2 negative (HER2-) early breast cancer (EBC). This study assesses the cost-effectiveness of this strategy, along with adjuvant aromatase inhibitors (AI) from an Indian perspective. Methods: A Markov chain model evaluated the cost-effectiveness of abemaciclib and ribociclib with letrozole compared to letrozole alone for HR+/HER2- EBC from a payer perspective in India. Key measures included lifetime quality-adjusted life years (QALY), life-years (LY), and total costs. This study explores two scenarios for effectiveness: a best-case (BC) scenario, where the benefit of CDK4/6 inhibitors in improving iDFS lasts a lifetime, and a worst-case (WC) scenario, where benefits disappear after 5 years. Probabilistic sensitivity analyses (PSA) were used to account for simulation uncertainty. Results: In the BC scenario, abemaciclib added 2.17 QALY and 4.96 LY, costing $27,756.65 more. Ribociclib in the BC scenario added 0.98 QALY and 2.58 LY, with added costs of $20,494.6. The incremental cost-effectiveness ratio for abemaciclib and ribociclib surpassed the willingness to pay threshold for in India in both scenarios (Table). At current market prices there is 0% probability of cost-effectiveness of abemaciclib and ribociclib. The cost of abemaciclib and ribociclib needs to be reduce by 78.61% and 87.19%, respectively to achieve cost-effectiveness. Conclusions: The combination of adjuvant abemaciclib or ribociclib with letrozole is not cost-effective for HR+/HER2- EBC in India in either the best or worst-case scenario.[Table: see text]

Holistic personas to increase the novice developer productivity

A deeper understanding and integration with system users' thoughts and emotional experiences are required for user-engaged development. User experience (UX) journey integrates user requirements and problem-solving approaches. The integration of data-driven techniques and user-centric approaches in software development is investigated in this study. It focuses on using the Markov chain model to predict developer productivity based on data gathered while creating personas across three projects. Organizations can gain valuable insights into user needs and requirements by conducting purposeful activities such as strength, weaknesses, opportunities, and threats (SWOT) analysis, competitor analysis, hypothesis formulation, identification of behavioral variables, mapping interviews, and defining characteristics and objectives. The model has predictive capabilities that allow for more informed decision-making, more efficient resource allocation, and better project planning. The goal of the activity and the model ensure the development of software products that effectively meet the needs of users, resulting in a higher success rate for software development initiatives. This study emphasizes the importance of integrating quantitative and qualitative analysis to drive successful software development projects and increase productivity while meeting user needs. According to the findings of the research conducted from the three projects completed, the proposed methods have similarities, and predictions using the Markov chain can determine the success of novice developers.

Physics-Informed Markov Chain Model to Identify Degradation Pathways of Lithium-Ion Cells

Physics-Informed Markov Chain Model to Identify Degradation Pathways of Lithium-Ion Cells

Green grant-free power allocation for ultra-dense Internet of Things: A mean-field perspective

Green grant-free power allocation for ultra-dense Internet of Things: A mean-field perspective

Tracing Student Activity Patterns in E-Learning Environments: Insights into Academic Performance

In distance learning educational environments like Moodle, students interact with their tutors, their peers, and the provided educational material through various means. Due to advancements in learning analytics, students’ transitions within Moodle generate digital trace data that outline learners’ self-directed learning paths and reveal information about their academic behavior within a course. These learning paths can be depicted as sequences of transitions between various states, such as completing quizzes, submitting assignments, downloading files, and participating in forum discussions, among others. Considering that a specific learning path summarizes the students’ trajectory in a course during an academic year, we analyzed data on students’ actions extracted from Moodle logs to investigate how the distribution of user actions within different Moodle resources can impact academic achievements. Our analysis was conducted using a Markov Chain Model, whereby transition matrices were constructed to identify steady states, and eigenvectors were calculated. Correlations were explored between specific states in users’ eigenvectors and their final grades, which were used as a proxy of academic performance. Our findings offer valuable insights into the relationship between student actions, link weight vectors, and academic performance, in an attempt to optimize students’ learning paths, tutors’ guidance, and course structures in the Moodle environment.

Quantifying land change dynamics, resilience and feedback: A comparative analysis of the lake Chad basin in Africa and Aral Sea basin in Central Asia

Quantifying land change dynamics, resilience and feedback: A comparative analysis of the lake Chad basin in Africa and Aral Sea basin in Central Asia

A markov chain based photovoltaic power simulation method

Photovoltaic power generation has volatility and randomness, which affects the safety and reliability of the power system. In order to achieve accurate simulation of photovoltaic output, this paper proposes a photovoltaic power timing simulation method based on rolling sampled Markov chain model. Firstly, establish a photovoltaic output model and analyze the actual output characteristics; Then, based on the first-order Markov chain model, the relationship between adjacent days is considered, and a multistate transition probability matrix is established to construct an annual time series output model; Finally, based on the annual output data of a photovoltaic power station and historical meteorological monitoring data, an example is simulated to verify the effectiveness of the proposed method.

Cost-effectiveness of ambroxol in the treatment of Gaucher disease type 2.

Our aim was to compare the costs and efficacy of ambroxol in combination with imiglucerase with the costs and efficacy of imiglucerase only in the treatment of Gaucher disease type 2 (GD2) in the socio-economic settings of the Republic of Serbia, an upper-middle-income European economy. The perspective of the Serbian Republic Health Insurance Fund was chosen for this study, and the time horizon was 6 years. The main outcomes of the study were quality-adjusted life years gained with ambroxol + imiglucerase and comparator, and direct costs of treatment. The study was conducted through the generation and simulation of the Markov chain model. The model results were obtained after Monte Carlo microsimulation of a sample with 1,000 virtual patients. Treatment with ambroxol in combination with imiglucerase was cost-effective when compared with imiglucerase only and was associated with positive values of net monetary benefit regardless of the onset of the disease. Such beneficial result for ambroxol and imiglucerase combination is primarily driven by the low cost of ambroxol and its considerable clinical effectiveness in slowing the progression of neural complications of GD2. If ambroxol and imiglucerase are used in combination for the treatment of GD2, it is more cost-effective than using imiglucerase alone.

The Use of Markov Chain Model for Rating Location Based Turbine Performance

It is shown how a 12-state Markov chain model can be used in rating turbine performance in a given location. A 1.8 MW wind turbine exposed to wind speed in San Angelo, USA is used for illustration. The model fits the data. As such, features of the model are used in providing indices for rating the performance of the turbine in this location. Probability distribution of the wind speed in this location is introduced into each of the traditional methods of computing average power output from the turbine power curve and expected extract-able power. The estimates obtained are 937 kW and 826 kW, respectively.

DPGazeSynth: Enhancing eye-tracking virtual reality privacy with differentially private data synthesis

DPGazeSynth: Enhancing eye-tracking virtual reality privacy with differentially private data synthesis

Incorporating endogenous human behavior in models of COVID-19 transmission: A systematic scoping review

Incorporating endogenous human behavior in models of COVID-19 transmission: A systematic scoping review

Random-Effects Substitution Models for Phylogenetics via Scalable Gradient Approximations.

Phylogenetic and discrete-trait evolutionary inference depend heavily on an appropriate characterization of the underlying character substitution process. In this paper, we present random-effects substitution models that extend common continuous-time Markov chain models into a richer class of processes capable of capturing a wider variety of substitution dynamics. As these random-effects substitution models often require many more parameters than their usual counterparts, inference can be both statistically and computationally challenging. Thus, we also propose an efficient approach to compute an approximation to the gradient of the data likelihood with respect to all unknown substitution model parameters. We demonstrate that this approximate gradient enables scaling of sampling-based inference, namely Bayesian inference via Hamiltonian Monte Carlo, under random-effects substitution models across large trees and state-spaces. Applied to a dataset of 583 SARS-CoV-2 sequences, an HKY model with random-effects shows strong signals of nonreversibility in the substitution process, and posterior predictive model checks clearly show that it is a more adequate model than a reversible model. When analyzing the pattern of phylogeographic spread of 1441 influenza A virus (H3N2) sequences between 14 regions, a random-effects phylogeographic substitution model infers that air travel volume adequately predicts almost all dispersal rates. A random-effects state-dependent substitution model reveals no evidence for an effect of arboreality on the swimming mode in the tree frog subfamily Hylinae. Simulations reveal that random-effects substitution models can accommodate both negligible and radical departures from the underlying base substitution model. We show that our gradient-based inference approach is over an order of magnitude more time efficient than conventional approaches.

Reliability and Detectability of Emergency Management Systems in Smart Cities under Common Cause Failures.

Urban areas are undergoing significant changes with the rise of smart cities, with technology transforming how cities develop through enhanced connectivity and data-driven services. However, these advancements also bring new challenges, especially in dealing with urban emergencies that can disrupt city life and infrastructure. The emergency management systems have become crucial elements for enabling cities to better handle urban emergencies, although ensuring the reliability and detectability of such system remains critical. This article introduces a new method to perform reliability and detectability assessments. By using Fault Tree Markov chain models, this article evaluates their performance under extreme conditions, providing valuable insights for designing and operating urban emergency systems. These analyses fill a gap in the existing research, offering a comprehensive understanding of emergency management systems functionality in complex urban settings.

Markov Chain Model for NIFTY50 Index

For the last two decades, financial market forecasting has been one of the sensational topics in research, but the market prediction is not easy because of its uncertainty. Though the trend is uncertain by its nature, some researcher is attempted to trace the movement with more accuracy. This study focuses on creating and analyzing a stochastic model that employs the Markov Chains method to anticipate stock prices on the stock exchange. By supposing that prices fluctuate have the feature of Markov dependence with their transition probabilities, study, four states are determined to construct the Markov model in this study. The daily fluctuations in the NIFTY 50 Index, which includes the top 50 firms of Indian stocks are tracked and analyzed. The TPM and IPV, the anticipated number of transitions, and the expected number of return times are established.