Markovian Analysis Research Articles

A Kernelized Classification Approach for Cancer Recognition Using Markovian Analysis of DNA Structure Patterns as Feature Mining.

Nucleotide-based molecules called DNA and RNA are essential for several biological processes that affect both normal and cancerous cells. They contain the critical genetic material needed for normal cell growth and functioning. The DNA structure patterns that make up the genetic code affect cells' growth, behavior, and control. Different DNA structure patterns indicate different physiological effects in the cell. Knowledge of these patterns is necessary to identify the molecular origins of cancer and other disorders. Analyzing these patterns can help in the early detection of diseases, which is essential for the effectiveness of cancer research and therapy. The novelty of this study is to examine the patterns of dinucleotide structure in many genomic regions, including the non-coding region sequence (N-CDS), coding region sequence (CDS), and whole raw DNA sequence (W.R. sequence). It provides an in-depth discussion of dinucleotide patterns related to these diverse genetic environments and contains malignant and non-malignant DNA sequences. The Markovian modeling that predicts dinucleotide probabilities also reduces feature complexity and minimizes computational costs compared to the approaches of Kernelized Logistic Regression (KLR) and Support Vector Machine (SVM). This technique is effectively evaluated in essential case studies, as indicated by accuracy metrics and 10-fold cross-validation. The classifier and feature reduction, which are generated by Markovian probability, operate well together and can help predict cancer. Our findings successfully distinguish DNA sequences related to cancer from those diagnostics of non-cancerous diseases by analyzing the W.R. DNA sequence as well as its CDS and N-CDS regions.

Markovian analysis of U.S. Treasury volatility: Asymmetric responses to macroeconomic announcements

Extensive is the literature supporting the announcement effect of the release of pre-scheduled macroeconomic news on U.S. Treasuries conditional moments; nevertheless, none of them considered possible asymmetric responses to these news arrival depending on the latent regimes characterizing the U.S. economy. Owing to the peculiar U.S. interest rate environment over the last 23 years, not allowing for stochastic-switching states even in the GARCH-class models may miss important volatility pattern. Hence, the therein model proposes the first partitioning of Treasuries excess returns in different regimes (normal, crash and low-rate) through hidden Markov chains to, only in a subsequent moment, apply a conditional volatility GARCH-class model augmented by dummy variables accounting for macroeconomic release days. The same model is then re-run to assess the explanatory power of U.S. Treasury auctions within volatility modeling. Results point to a statistically significant and asymmetric effects imparted by both prescheduled macroeconomic releases and new issuances on U.S. Treasury excess returns depending on both the regime and the securities’ maturities, while evidencing the very different characteristics of GARCH and ARCH coefficients depending on the regime.

A Markovian Analysis of an IEEE-802.11 Station with Buffering

The purpose of this paper is to analyze the so-called backoff tech- nique of the IEEE 802.11 protocol with buffers. This protocol rules the trans- missions on a radio channel between nodes (or stations) of a network exchanging packets of information. In contrast to existing models, packets arriving at a sta- tion which in the backoff state are not discarded, but are stored in a buffer of in nite capacity. The backoff state corresponds to the number of time-intervals (mini-slot) that a node must wait before its packet is actually transmitted. As in previous studies, the key point of our analysis hinges on the assumption that the time on the radio channel is viewed as a random succession of trans- mission slots (whose duration corresponds to a packet transmission time) and mini-slots, which stand for the time intervals during which the backoff of the station is decremented. During these mini-slots the channel is idle, which im- plies that there is no packet transmission. These events occur independently with given probabilities, and the external arrivals of messages follow a Pois- son process. The state of a node is represented by a three-dimensional Markov chain in discrete-time, formed by the triple (backoff counter, number of packets at the station, number of transmission attempts). Stability (ergodicity) condi- tions are obtained for an arbitrary station and interpreted in terms of maximum throughput. Several approximations related to these models are also discussed.

No-gi Brazilian jiu-jitsu: A Markovian analysis of elite-level combat dynamics

No-gi Brazilian jiu-jitsu is a grappling-based combat sport performed without the jiu-jitsu uniform. It’s fast-growing popularity has been influenced by the contributions to competitors’ performance in mixed martial arts tournaments. This study aimed to analyze the frequency of actions, the transitions between them, and their reward–risk balance—the probability of submitting minus the probability of being submitted—among top-level competitors. Data encompassed all 93 matches (90 competitors) of the World Submission Fighting Championship-2019. Reliability was assessed by two experts (sports scientists, more than 10 years as no-gi Brazilian jiu-jitsu competitors). Data analyses used Bayesian methods for inference. Posterior distributions of the mean frequencies of actions indicate that the main actions (e.g. takedown, sweep, and back-take) tended to occur less than once per competitor in each match, except by the submission attempt (point estimate of 1.03 per match). The highest estimates for the transition probabilities between match actions were within-competitor—a guard pass action to another guard pass action (0.30); between-competitors—a takedown attempt to a submission attempt (0.15). Most of the actions presented positive reward-risk balance, particularly for the back-take, which showed the highest probability of direct transition to submission (0.45). The dynamic analysis of no-gi Brazilian jiu-jitsu matches evidenced some preferential actions’ transitions associated with winning. Findings should enhance the accuracy of performance prediction and provide evidence-based recommendations for coaches.

Defining adaption measures for organisational multi-agent systems

Designing adaptive multi-agent Systems (MAS) is a challenging development effort. A key point of adaptive systems is that they provide alternative options for acting and designers have to weight the number and elaboration of these alternatives. Here, we concentrate on organisation-oriented MAS and show that organisational models provide suitable means for identifying key measures of this adaptivity. We derive measures both from the static description of the organisation as well as from the induced adaptation dynamics grounded on a Markovian analysis of the behaviour. These measures allow for a goal-directed planning of adaption.

RNA velocity unraveled.

We perform a thorough analysis of RNA velocity methods, with a view towards understanding the suitability of the various assumptions underlying popular implementations. In addition to providing a self-contained exposition of the underlying mathematics, we undertake simulations and perform controlled experiments on biological datasets to assess workflow sensitivity to parameter choices and underlying biology. Finally, we argue for a more rigorous approach to RNA velocity, and present a framework for Markovian analysis that points to directions for improvement and mitigation of current problems.

Dynamic Performance Prediction in Batch-Based Assembly System with Bernoulli Machines and Changeovers

Worldwide competition and diverse demand of customers pose great challenges to manufacturing enterprises. How to organize production to achieve high productivity and low cost becomes their primary task. In the mean time, the rapid pace of technology innovation has contributed to the development of new types of flexible automation. Hence, increasing manufacturing enterprises convert to multi-product and small-batch production, a manufacturing strategy that brings increased output, reduced costs, and quick response to the market. A distinctive feature of small-batch production is that the system operates mainly in the transient states. Transient states may have a significant impact on manufacturing systems. It is therefore necessary to estimate the dynamic performance of systems. As the assembly system is a typical class of production systems, in this paper, we focus on the problem of dynamic performance prediction of the assembly systems that produce small batches of different types of products. And the system is assumed to be characterized with Bernoulli reliability machines, finite buffers, and changeovers. A mathematical model based on Markovian analysis is first derived and then, the analytical formulas for performance evaluation of three-machine assembly systems are given. Moreover, a novel approach based on decomposition and aggregation is proposed to predict dynamic performance of large-scale assembly systems that consist of multiple component lines and additional processing machines located downstream of the assemble machine. The proposed approach is validated to be highly accurate and computationally efficient when compared to Monte Carlo simulation.

Efficient computational stochastic framework for performance optimization of E-waste management plant

PurposeReliability and maintainability are the key system effectiveness measures in process and manufacturing industries, and treatment plants, especially in E-waste management plants. The present work is proposed with a motto to develop a stochastic framework for the e-waste management plant to optimize its availability integrated with reliability, availability, maintainability, and dependability (RAMD) measures and Markovian analysis to estimate the steady-state availability of the E-waste management plant. In the analysis an effort is also made to identify the best performing algorithm for availability optimization of the e-waste plant. MethodologyA stochastic model for a particular plant is developed and its availability is optimized using various metaheuristic approaches like a genetic algorithm (GA), particle swarm optimization (PSO), and differential evolutions (DE). The most sensitive component is identified using RAMD methodology while the effect of deviation in various failure and repair rates are observed by the proposed model. The failure and repair rates follow an exponential distribution. All time-dependent random variables are statistically independent. Originality/NoveltiesA novel stochastic model is presented for an e-waste management plant and optimum availability is obtained using metaheuristic approaches. The proposed methodology is not so far discussed in the reliability analysis of process industries. FindingsThe numerical results of the proposed model compared to identify the most efficient algorithm. It is observed that genetic algorithm provides the maximum value (0.92330969) of availability at a population size 2500 after 500 iterations. PSO algorithm attained the maximum value (0.99996744) of availability just after 50 iterations and 100 population size. So, its rate of convergence is faster than GA. The optimum value of availability is 0.99997 using differential evolution after 500 iterations and population size of more than 1000. These findings are very beneficial for system designers. Practical ImplicationsThe proposed methodology can be utilized to find the reliability measures of other process industries.

Safety and efficacy of BCG re-vaccination in relation to COVID-19 morbidity in healthcare workers: A double-blind, randomised, controlled, phase 3 trial

SummaryBackgroundBCG vaccination prevents severe childhood tuberculosis (TB) and was introduced in South Africa in the 1950s. It is hypothesised that BCG trains the innate immune system by inducing epigenetic and functional reprogramming, thus providing non-specific protection from respiratory tract infections. We evaluated BCG for reduction of morbidity and mortality due to COVID-19 in healthcare workers in South Africa.MethodsThis randomised, double-blind, placebo-controlled trial recruited healthcare workers at three facilities in the Western Cape, South Africa, unless unwell, pregnant, breastfeeding, immunocompromised, hypersensitivity to BCG, or undergoing experimental COVID-19 treatment. Participants received BCG or saline intradermally (1:1) and were contacted once every 4 weeks for 1 year. COVID-19 testing was guided by symptoms. Hospitalisation, COVID-19, and respiratory tract infections were assessed with Cox proportional hazard modelling and time-to-event analyses, and event severity with post hoc Markovian analysis. This study is registered with ClinicalTrials.gov, NCT04379336.FindingsBetween May 4 and Oct 23, 2020, we enrolled 1000 healthcare workers with a median age of 39 years (IQR 30–49), 70·4% were female, 16·5% nurses, 14·4% medical doctors, 48·5% had latent TB, and 15·3% had evidence of prior SARS-CoV-2 exposure. Hospitalisation due to COVID-19 occurred in 15 participants (1·5%); ten (66·7%) in the BCG group and five (33·3%) in the placebo group, hazard ratio (HR) 2·0 (95% CI 0·69–5·9, p = 0·20), indicating no statistically significant protection. Similarly, BCG had no statistically significant effect on COVID-19 (p = 0·63, HR = 1·08, 95% CI 0·82–1·42). Two participants (0·2%) died from COVID-19 and two (0·2%) from other reasons, all in the placebo group.InterpretationBCG did not protect healthcare workers from SARS-CoV-2 infection or related severe COVID-19 disease and hospitalisation.FundingFunding provided by EDCTP, grant number RIA2020EF-2968. Additional funding provided by private donors including: Mediclinic, Calavera Capital (Pty) Ltd, Thys Du Toit, Louis Stassen, The Ryan Foundation, and Dream World Investments 401 (Pty) Ltd. The computations were enabled by resources in project SNIC 2020–5–524 provided by the Swedish National Infrastructure for Computing (SNIC) at UPPMAX, partially funded by the Swedish Research Council through grant agreement No. 2018–05,973.

Markovian analysis of unreliable multi-machine flexible manufacturing cell.

In this paper, a Markovian model is constructed to test a flexible manufacturing cell’s (FMC) performance. The considered FMC includes a conveyer belt, robot, and n machines. The conveyer belt delivers the working part to the robot, and the robot picks it up and loads it onto the machines. The movement of a working part from one step to the next depends on the availability of the tool in the next step (i.e., conveyer belt, robot, and machine). Any machine is assumed to potentially fail during the processing time as a result of high loading stresses. First, a Markovian model is constructed for single-machine and double-machine FMCs. Then, a generalized FMC with an n-machine is constructed. The introduced model is illustrated with two numerical examples for both the single- and triple-machine. The Markov chain model can be used to estimate the FMC performance measures (i.e., overall utilization of machines and production rate). It is used to analyze the response of these measures under varying parameters (i.e., conveyor belt delivery rate, robot loading rate, processing rate of a machine, failure rate of a machine, and down machines’ repairing rate). Moreover, an economic model based on the Markov chain model is introduced to analyze the FMC’s net profit under these varying parameters.

A Markovian Analysis of Cooperative ARQ with Random Access

A Markovian Analysis of Cooperative ARQ with Random Access

Markovian Analysis of Covid-19 Dynamics

Covid-19 is an emergency and viral infection with its outbreak being termed as one of the great epidemics in the 21st century causing so many deaths, which made WHO declare it as a pandemic emergency. This virus is new and comes with its characteristics of which randomness and uncertainty are among its common features. In this paper, we developed a model for carrying out an analysis of COVID-19 dynamics using Markov-chain theory methodology. Here, we employed the use of conditional probability distribution as embedded in the Markov property of our chain to construct the transition probabilities that were used in determining the probability distributions of COVID-19 patients as well as predicting its future spread dynamics. We provide a step-by-step approach to obtaining probability distributions of infected and recovered individuals, of infected and recovering and of a recovered patient being getting infected again. This study reveals that irrespective of the initial state of health of an individual, we will always have probabilities P_RI/〖(P〗_IR+P_RI) of an individual being infected and P_RI/〖(P〗_IR+P_RI) of an individual recovering from this disease. Also, with increasing ‘n’, we have an equilibrium that does not depend on the initial conditions, the implication of which is that at some point in time, the situation stabilizes and the distribution X_(n+1) is the same as that of X_n. We envision that the output of this model will assist those in the health system and related fields to plan for the potential impact of the pandemic and its peak.

On the Throughput Optimization in Large-Scale Batch-Processing Systems

We analyze a data-processing system with n clients producing jobs which are processed in batches by m parallel servers; the system throughput critically depends on the batch size and a corresponding sub-additive speedup function that arises due to overhead amortization. In practice, throughput optimization relies on numerical searches for the optimal batch size which is computationally cumbersome. In this paper, we model this system in terms of a closed queueing network assuming certain forms of service speedup; a standard Markovian analysis yields the optimal throughput in w n4 time. Our main contribution is a mean-field model that has a unique, globally attractive stationary point, derivable in closed form. This point characterizes the asymptotic throughput as a function of the batch size that can be calculated in O(1) time. Numerical settings from a large commercial system reveal that this asymptotic optimum is accurate in practical finite regimes.

Multi-type products and dedicated buffers-based flexible production process analysis of serial Bernoulli lines

In the contemporary manufacturing environment, flexible production is growing in popularity due to its quick adaptation and response to the market changes and customized requirements. Since multi-type products under small-batch-based operations are commonly seen in flexible production, dedicated buffers are sometimes used to separate different types of parts that are being processed on one production line. Besides, the steady-state analysis may be ineffective in analyzing such systems because of the small-batch-based operation. As a result, transient-based production process analysis becomes indispensable in studying the dynamic behavior of flexible production systems. In this work, we investigate the production process in flexible production systems with multi-type products, machines obeying the Bernoulli reliability model, and dedicated buffers. In the first place, based on Markovian analysis, a set of auxiliary production lines are presented to decouple the dynamics of the original system. Next, the process performance measures of interest are approximately evaluated from the auxiliary production lines to avoid the issue of system state explosion, which is frequently encountered when applying the Markovian approach. Besides, the proposed analytical algorithm is validated to be with high precision through numerical experiments in contrast with the Monte Carlo simulation. Finally, by using the proposed method and applying the genetic algorithm, we investigate the problem of buffer allocation to minimize the completion time of the system.

Modern Random Access: An Age of Information Perspective on Irregular Repetition Slotted ALOHA

Age of information (AoI) is gaining attention as a valuable performance metric for many IoT systems, in which a large number of devices report time-stamped updates to a central gateway. This is the case, for instance, of remote sensing, monitoring, or tracking, with broad applications in the industrial, vehicular, and environmental domain. In these settings, AoI provides insights that are complementary to those offered by throughput or latency, capturing the ability of the system to maintain an up-to-date view of the status of each transmitting device. From this standpoint, while a good understanding of the metric has been reached for point-to-point links, relatively little attention has been devoted to the impact that link layer solutions employed in IoT systems may have on AoI. In particular, no result is available for modern random access protocols, which have recently emerged as promising solutions to support massive machine-type communications. To start addressing this gap we provide in this paper the first study of the AoI of a scheme in this family, namely irregular repetition slotted ALOHA (IRSA). By means of a Markovian analysis, we track the AoI evolution at the gateway, prove that the process is ergodic, and derive a compact closed form expression for its stationary distribution. Leaning on this, we compute exact formulations for the average AoI and the age violation probability. The study reveals non-trivial design trade-offs for IRSA and highlights the key role played by the protocol operating frame size. Moreover, a comparison with the performance of a simpler slotted ALOHA strategy highlights a remarkable potential for modern random access schemes in terms of information freshness.

Recognition of Blinks Activity Patterns during Stress Conditions Using CNN and Markovian Analysis

This paper investigates eye behaviour through blinks activity during stress conditions. Although eye blinking is a semi-voluntary action, it is considered to be affected by one’s emotional states such as arousal or stress. The blinking rate provides information towards this direction, however, the analysis on the entire eye aperture timeseries and the corresponding blinking patterns provide enhanced information on eye behaviour during stress conditions. Thus, two experimental protocols were established to induce affective states (neutral, relaxed and stress) systematically through a variety of external and internal stressors. The study populations included 24 and 58 participants respectively performing 12 experimental affective trials. After the preprocessing phase, the eye aperture timeseries and the corresponding features were extracted. The behaviour of inter-blink intervals (IBI) was investigated using the Markovian Analysis to quantify incidence dynamics in sequences of blinks. Moreover, Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) network models were employed to discriminate stressed versus neutral tasks per cognitive process using the sequence of IBI. The classification accuracy reached a percentage of 81.3% which is very promising considering the unimodal analysis and the noninvasiveness modality used.

Monitoring and forecasting spatio-temporal LULC for Akure rainforest habitat in Nigeria

Abstract For several decades, Nigerian cities have been experiencing a decline in their biodiversity resulting from rapid land use land cover (LULC) changes. Anticipating short/long-term consequences, this study hypothesised the effects of LULC variables in Akure, a developing tropical rainforest city in south-west Nigeria. A differentiated trend of urban LULC was determined over a period covering 1999–2019. The study showed the net change for bare land, built-up area, cultivated land, forest cover and grassland over the two decades to be −292.68 km2, +325.79 km2, +88.65 km2, +8.62 km2 and −131.38 km2, respectively. With a projected population increase of about 46.85%, the study identified that the built-up land cover increased from 1.98% to 48.61%. The change detection analysis revealed an upsurge in built area class. The expansion indicated a significant inverse correlation with the bare land class (50.97% to 8.66%) and grassland class (36.33% to 17.94%) over the study period. The study observed that the land consumption rate (in hectares) steadily increased by 0.00505, 0.00362 and 0.0687, in the year 1999, 2009 and 2019, respectively. This rate of increase is higher than studies conducted in more populated cities. The Cellular Automata (CA) Markovian analysis predicted a 37.92% growth of the study area will be the built-up area in the next two decades (2039). The 20-year prediction for Akure built-up area is within range when compared to CA Markov prediction for other cities across the globe. The findings of this study will guide future planning for rational LULC evaluation.

On the Throughput Optimization in Large-scale Batch-processing Systems

We analyse a data-processing system with n clients producing jobs which are processed in batches by m parallel servers; the system throughput critically depends on the batch size and a corresponding sub-additive speedup function. In practice, throughput optimization relies on numerical searches for the optimal batch size, a process that can take up to multiple days in existing commercial systems. In this paper, we model the system in terms of a closed queueing network; a standard Markovian analysis yields the optimal throughput in ωn4 time. Our main contribution is a mean-field model of the system for the regime where the system size is large. We show that the mean-field model has a unique, globally attractive stationary point which can be found in closed form and which characterizes the asymptotic throughput of the system as a function of the batch size. Using this expression we find the asymptotically optimal throughput in O(1) time. Numerical settings from a large commercial system reveal that this asymptotic optimum is accurate in practical finite regimes.

Modal Interval Probability: Application to Bonus-Malus Systems

Classical intervals have been a very useful tool to analyze uncertain and imprecise models, in spite of operative and interpretative shortcomings. The recent introduction of modal intervals helps to overcome those limitations. In this paper, we apply modal intervals to the field of probability, including properties and axioms that form a theoretical framework applied to the Markovian analysis of Bonus-Malus systems in car insurance. We assume that the number of claims is a Poisson distribution and in order to include uncertainty in the model, the claim frequency is defined as a modal interval; therefore, the transition probabilities are modal interval probabilities. Finally, the model is exemplified through application to two different types of Bonus-Malus systems, and the attainment of uncertain long-run premiums expressed as modal intervals.

BAYESIAN ANALYSIS OF DOUBLY STOCHASTIC MARKOV PROCESSES IN RELIABILITY

Markov processes play an important role in reliability analysis and particularly in modeling the stochastic evolution of survival/failure behavior of systems. The probability law of Markov processes is described by its generator or the transition rate matrix. In this paper, we suppose that the process is doubly stochastic in the sense that the generator is also stochastic. In our model, we suppose that the entries in the generator change with respect to the changing states of yet another Markov process. This process represents the random environment that the stochastic model operates in. In fact, we have a Markov modulated Markov process which can be modeled as a bivariate Markov process that can be analyzed probabilistically using Markovian analysis. In this setting, however, we are interested in Bayesian inference on model parameters. We present a computationally tractable approach using Gibbs sampling and demonstrate it by numerical illustrations. We also discuss cases that involve complete and partial data sets on both processes.