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This study focuses on the stochastic modelling and analysis of a serial production network consisting of two manufacturing stations operating under a make-to-stock inventory policy. The outcome is a single type of product and every manufacturing station includes a machine and an output buffer. Both machines are gradually deteriorating during their operation. Deterioration results in a reduced production rate. Continuous-time Markov chain was used to model all the possible states the network transits over time due to the occurrence of certain events, such as client arrival, deterioration failure, production or repair completion. The structure of the Markov chain was thoroughly studied providing useful information, supporting the effort of numerical solving to determine the steady-state probabilities enabling the calculation of useful performance metrics like equipment availability, down time, idle time, utilisation and average inventory. Through a series of numerical experiments, the behaviour of the serial production network was examined while alternating its parameters. Interesting conclusions emerged regarding the factors affecting the operation of such production systems subjected to gradual deterioration. [ABSTRACT FROM AUTHOR]

We here present CLUES2, a full-likelihood method to infer natural selection from sequence data that is an extension of the method CLUES. We make several substantial improvements to the CLUES method that greatly increases both its applicability and its speed. We add the ability to use ancestral recombination graphs on ancient data as emissions to the underlying hidden Markov model, which enables CLUES2 to use both temporal and linkage information to make estimates of selection coefficients. We also fully implement the ability to estimate distinct selection coefficients in different epochs, which allows for the analysis of changes in selective pressures through time, as well as selection with dominance. In addition, we greatly increase the computational efficiency of CLUES2 over CLUES using several approximations to the forward-backward algorithms and develop a new way to reconstruct historic allele frequencies by integrating over the uncertainty in the estimation of the selection coefficients. We illustrate the accuracy of CLUES2 through extensive simulations and validate the importance sampling framework for integrating over the uncertainty in the inference of gene trees. We also show that CLUES2 is well-calibrated by showing that under the null hypothesis, the distribution of log-likelihood ratios follows a χ2 distribution with the appropriate degrees of freedom. We run CLUES2 on a set of recently published ancient human data from Western Eurasia and test for evidence of changing selection coefficients through time. We find significant evidence of changing selective pressures in several genes correlated with the introduction of agriculture to Europe and the ensuing dietary and demographic shifts of that time. In particular, our analysis supports previous hypotheses of strong selection on lactase persistence during periods of ancient famines and attenuated selection in more modern periods.

Nuclear magnetic resonance (NMR) spectroscopy is an important analytical technique in synthetic organic chemistry, but its integration into high-throughput experimentation workflows has been limited by the necessity of manually analyzing the NMR spectra of new chemical entities. Current efforts to automate the analysis of NMR spectra rely on comparisons to databases of reported spectra for known compounds and, therefore, are incompatible with the exploration of new chemical space. By reframing the NMR spectrum of a reaction mixture as a joint probability distribution, we have used Hamiltonian Monte Carlo Markov Chain and density functional theory to fit the predicted NMR spectra to those of crude reaction mixtures. This approach enables the deconvolution and analysis of the spectra of mixtures of compounds without relying on reported spectra. The utility of our approach to analyze crude reaction mixtures is demonstrated with the experimental spectra of reactions that generate a mixture of isomers, such as Wittig olefination and C-H functionalization reactions. The correct identification of compounds in a reaction mixture and their relative concentrations is achieved with a mean absolute error as low as 1%.

BackgroundRecently-updated global guidelines for cervical cancer screening incorporated new technologies-most significantly, the inclusion of HPV DNA detection as a primary screening test-but leave many implementation decisions at countries' discretion. We sought to develop recommendations for Malawi as a test case since it has the second-highest cervical cancer burden globally and high HIV prevalence. We incorporated updated epidemiologic data, the full range of ablation methods recommended, and a more nuanced representation of how HIV status intersects with cervical cancer risk and exposure to screening to model outcomes of different approaches to screening.MethodsUsing a Markov model, we estimate the relative health outcomes and costs of different approaches to cervical cancer screening among Malawian women. The model was parameterized using published data, and focused on comparing "triage" approaches-i.e., lesion treatment (cryotherapy or thermocoagulation) at differing frequencies and varying by HIV status. Health outcomes were quality-adjusted life years (QALYs) and deaths averted. The model was built using TreeAge Pro software.ResultsThermocoagulation was more cost-effective than cryotherapy at all screening frequencies. Screening women once per decade would avert substantially more deaths than screening only once per lifetime, at relatively little additional cost. Moreover, at this frequency, it would be advisable to ensure that all women who screen positive receive treatment (rather than investing in further increases in screening frequency): for a similar gain in QALYs, it would cost more than four times as much to implement once-per-5 years screening with only 50% of women treated versus once-per-decade screening with 100% of women treated. Stratified screening schedules by HIV status was found to be an optimal approach.ConclusionsThese results add new evidence about cost-effective approaches to cervical cancer screening in low-income countries. At relatively infrequent screening intervals, if resources are limited, it would be more cost-effective to invest in scaling up thermocoagulation for treatment before increasing the recommended screening frequency. In Malawi or countries in a similar stage of the HIV epidemic, a stratified approach that prioritizes more frequent screening for women living with HIV may be more cost-effective than population-wide recommendations that are HIV status neutral.

The brain produces diverse functions, from perceiving sounds to producing arm reaches, through the collective activity of populations of many neurons. Determining if and how the features of these exogenous variables (e.g., sound frequency, reach angle) are reflected in population neural activity is important for understanding how the brain operates. Often, high-dimensional neural population activity is confined to low-dimensional latent spaces. However, many current methods fail to extract latent spaces that are clearly structured by exogenous variables. This has contributed to a debate about whether or not brains should be thought of as dynamical systems or representational systems. Here, we developed a new latent process Bayesian regression framework, the orthogonal stochastic linear mixing model (OSLMM) which introduces an orthogonality constraint amongst time-varying mixture coefficients, and provide Markov chain Monte Carlo inference procedures. We demonstrate superior performance of OSLMM on latent trajectory recovery in synthetic experiments and show superior computational efficiency and prediction performance on several real-world benchmark data sets. We primarily focus on demonstrating the utility of OSLMM in two neural data sets: μECoG recordings from rat auditory cortex during presentation of pure tones and multi-single unit recordings form monkey motor cortex during complex arm reaching. We show that OSLMM achieves superior or comparable predictive accuracy of neural data and decoding of external variables (e.g., reach velocity). Most importantly, in both experimental contexts, we demonstrate that OSLMM latent trajectories directly reflect features of the sounds and reaches, demonstrating that neural dynamics are structured by neural representations. Together, these results demonstrate that OSLMM will be useful for the analysis of diverse, large-scale biological time-series datasets.

Dynamic functional connectivity investigates how the interactions among brain regions vary over the course of an fMRI experiment. Such transitions between different individual connectivity states can be modulated by changes in underlying physiological mechanisms that drive functional network dynamics, e.g., changes in attention or cognitive effort. In this paper, we develop a multi-subject Bayesian framework where the estimation of dynamic functional networks is informed by time-varying exogenous physiological covariates that are simultaneously recorded in each subject during the fMRI experiment. More specifically, we consider a dynamic Gaussian graphical model approach where a non-homogeneous hidden Markov model is employed to classify the fMRI time series into latent neurological states. We assume the state-transition probabilities to vary over time and across subjects as a function of the underlying covariates, allowing for the estimation of recurrent connectivity patterns and the sharing of networks among the subjects. We further assume sparsity in the network structures via shrinkage priors, and achieve edge selection in the estimated graph structures by introducing a multi-comparison procedure for shrinkage-based inferences with Bayesian false discovery rate control. We evaluate the performances of our method vs alternative approaches on synthetic data. We apply our modeling framework on a resting-state experiment where fMRI data have been collected concurrently with pupillometry measurements, as a proxy of cognitive processing, and assess the heterogeneity of the effects of changes in pupil dilation on the subjects propensity to change connectivity states. The heterogeneity of state occupancy across subjects provides an understanding of the relationship between increased pupil dilation and transitions toward different cognitive states.

Background: Traditional recombinant inbred lines (RILs) are generated from repeated self-fertilization or brother-sister mating from the F1 hybrid of two inbred parents. Compared with the F2 population, RILs cumulate more crossovers between loci and thus increase the number of recombinants, resulting in an increased resolution of genetic mapping. Since they are inbred to the isogenic stage, another consequence of the heterozygosity reduction is the increased genetic variance and thus the increased power of QTL detection. Self-fertilization is the primary form of developing RILs in plants. Brother-sister mating is another way to develop RILs but in small laboratory animals. To ensure that the RILs have at least 98% of homozygosity, we need about seven generations of self-fertilization or 20 generations of brother-sister mating. Prior to homozygosity, these lines are called pre-recombinant inbred lines (PRERIL). Phenotypic values of traits in PRERILs are often collected but not used in QTL mapping. To perform QTL mapping in PRERILs, we need the recombination fraction between two markers at generation t for t < 7 (selfing) or t < 20 (brother-sister mating) so that the genotypes of QTL flanked by the markers can be inferred. Results: In this study, we developed formulas to calculate the recombination fractions of PRERILs at generation t in self-fertilization, brother-sister mating, and random mating. In contrast to existing works in this topic, we used computer code to construct the transition matrix to form the Markov chain of genotype array between consecutive generations, the so-called recurrent equations. Conclusions: We provide R functions to calculate the recombination fraction using the newly developed recurrent equations of ordered genotype array. With the recurrent equations and the R code, users can perform QTL mapping in PRERILs. Substantial time and effort can be saved compared with QTL mapping in RILs. [ABSTRACT FROM AUTHOR]

We model the financial markets as a game and make predictions using Markov chain estimators. We extract the possible patterns displayed by the financial markets, define a game where one of the players is the speculator, whose strategies depend on his/her risk-to-reward preferences, and the market is the other player, whose strategies are the previously observed patterns. Then, we estimate the market's mixed probabilities by defining Markov chains and utilizing its transition matrices. Afterwards, we use these probabilities to determine which is the optimal strategy for the speculator. Finally, we apply these models to real-time market data to determine its feasibility. From this, we obtained a model for the financial markets that has a good performance in terms of accuracy and profitability. [ABSTRACT FROM AUTHOR]

Simple Summary: Managing cow diseases effectively remains a major challenge in dairy farming. Our study introduces a simple model for predicting dairy cow diseases. To develop this model, we used categorized data and Markov chains to select the best prediction model based on minimal error distance. The results show that our model is not only highly accurate and reliable but also easy to use, even in low-tech farms. Our methodological approach can capture various data structures in different volumes and qualities, demonstrating its versatility and adaptability to a wide range of herd sizes. This universal applicability enables us to evaluate entire herds, regardless of size. Furthermore, while each farm records diseases differently, our model can accommodate these variations. As such, this model may help dairy farmers manage herd health, predict antibiotic costs, and plan farming strategies. Modelling and predicting dairy cow diseases empowers farmers with valuable information for herd health management, thereby decreasing costs and increasing profits. For this purpose, predictive models were developed based on machine learning algorithms. However, machine-learning based approaches require the development of a specific model for each disease, and their consistency is limited by low farm data availability. To overcome this lack of complete and accurate data, we developed a predictive model based on discrete Homogeneous and Non-homogeneous Markov chains. After aggregating data into categories, we developed a method for defining the adequate number of Markov chain states. Subsequently, we selected the best prediction model through Chebyshev distance minimization. For 14 of 19 diseases, less than 15% maximum differences were measured between the last month of actual and predicted disease data. This model can be easily implemented in low-tech dairy farms to project costs with antibiotics and other treatments. Furthermore, the model's adaptability allows it to be extended to other disease types or conditions with minimal adjustments. Therefore, including this predictive model for dairy cow diseases in decision support systems may enhance herd health management and streamline the design of evidence-based farming strategies. [ABSTRACT FROM AUTHOR]

We consider a stochastic matching model with a general compatibility graph, as introduced in Mairesse and Moyal (J Appl Probab 53(4):1064–1077, 2016). We prove that most common matching policies (including fcfm, priorities and random) satisfy a particular sub-additive property, which we exploit to show in many cases, the coupling-from-the-past to the steady state, using a backwards scheme à la Loynes. We then use these results to explicitly construct perfect bi-infinite matchings, and to build a perfect simulation algorithm in the case where the buffer of the system is finite. [ABSTRACT FROM AUTHOR]

Recently, driven by redundancy systems and matching systems, there has been renewed interest in models with product form stationary distributions. By a "product form," we mean that the stationary distribution can be expressed as a product of terms, each of which corresponds to a job in the system. Given the recent discovery of many such systems, it is natural to ask: how broad is this class of systems? In this paper, we consider extensions and generalizations of the recently-proposed pass-and-swap queue, which has a product-form stationary distribution. We make three main contributions. First, we identify sufficient conditions under which pass-and-swap queues can be connected in a closed network, while still preserving the product form. Second, we identify dimensions along which the pass-and-swap system can be extended while preserving the product-form stationary distribution. At the same time, we also identify cases in which generalizing the pass-and-swap queue causes the product-form nature of the stationary distribution to break. Finally, we identify questions that remain open and present a road map for future study. [ABSTRACT FROM AUTHOR]

We give rates of convergence in the almost sure invariance principle for sums of dependent random variables with semi-exponential tails, whose coupling coefficients decrease at a sub-exponential rate. We show that the rates in the strong invariance principle are in powers of log n . We apply our results to iid products of random matrices. [ABSTRACT FROM AUTHOR]

The menace of sedimentation to reservoirs has a significant implication for water quality, storage capacity and reservoir lifetime. Rainfall patterns and other anthropogenic and environmental impacts alter the erosion rate and, by extension, directly affect sedimentation rates if left unchecked. This research focused on using the integration of Markov Chains and Cellular Automata (MC – CA) models to estimate and forecast the future bathymetric surface of the Kainji reservoir in Nigeria for the year 2050. The bathymetric datasets used for this research comprise two different epochs (1990 and 2020). The datasets were acquired using a Single Beam Echosounder at Low and High frequencies of 20 kHz and 200 kHz. The preliminary investigation revealed that sedimentation is exacerbating a greater danger to the reservoir functionality. The results show that the maximum observed depth is 71.2 m, indicating a 7.53% loss in depth from the 1990 archived data and a 16.24% depth loss to sedimentation from 1968 to 2020 and 22.35% depth loss in the year 2050 as shown on the projected surface. Consequently, the integrated model (MC and CA) efficiently predicted the future bathymetric surface of the Kainji reservoir for the year 2050 based on the data characteristics. However, the proven techniques for analysing spatial data, such as the Markov Chain and Cellular Automata, best suited for analysing categorical transition data, show some artefacts (black spots) on the projected generated map which is subject to further investigation. [ABSTRACT FROM AUTHOR]

This article examines the influence of specific time series attributes on the efficacy of fuel demand forecasting. By utilising autoregressive models and Markov chains, the research aims to determine the impact of these attributes on the effectiveness of specific models. The study also proposes modifications to these models to enhance their performance in the context of the fuel industry's unique fuel distribution. The research involves a comprehensive analysis, including identifying the impact of volatility, seasonality, trends, and sudden shocks within time series data on the suitability and accuracy of forecasting methods. The paper utilises ARIMA, SARIMA, and Markov chain models to assess their ability to integrate diverse time series features, improve forecast precision, and facilitate strategic logistical planning. The findings suggest that recognising and leveraging these time series characteristics can significantly enhance the management of fuel supplies, leading to reduced operational costs and environmental impacts. [ABSTRACT FROM AUTHOR]

Producing prefabricated bathroom units (PBUs) involves a dry installation method that heavily relies on human labor. Therefore, strategically accomplishing efficient and agile workforce planning emphasizes the critical significance. However, previous studies that address workforce planning often overlook the long-term stochastic effects and assume a homogeneous workforce for the sake of computational simplicity. To overcome these limitations, this study adopts the Markovian approach to establish an explicit relationship between workforce cost and cycle time, considering a heterogeneous workforce and uncertain human-caused events. The proposed model has a hierarchical structure that addresses the behavioral tendencies that drive task allocation at the individual level and the aggregate effect of manpower allocation at the operation level. By integrating Little's law in queuing theory and metaheuristics optimization, the cycle time can be calculated while searching for the optimal workforce arrangement configuration. The study cross-validated the computational results with empirical data from a precast factory in Singapore and conducted a sensitivity analysis to verify the reliability. The results show that cross-training workers to multiple skills can lead to significant time savings, with a maximum of roughly 23 h saved in PBU cycle time. Ultimately, this research contributes to the body of knowledge by proposing a strategic workforce planning model that accounts for a heterogeneous workforce and uncertain human-caused events. It utilizes a partial cross-training configuration strategy to maximize productivity and flexibility for PBU production. Practical Applications: The fit-out process of prefabricated bathroom units (PBUs) relies heavily on manual labor, becoming a bottleneck in the supply chain. Efficient and agile workforce planning is critical to overcome this challenge. However, planning for a large, heterogeneous workforce for tasks in an uncertain environment is difficult. Therefore, this research aims to establish a clear relationship between workforce cost and cycle time to assist with quick decision making during the planning stage. The proposed model has a two-level hierarchical structure that addresses individual-level behavioral tendencies and the operational-level aggregate effect of manpower allocation. This approach ensures optimal workforce size, composition, cost, and flexibility for the project's needs. The model was tested using a real-world industrial case study, and the results showed that cross-training workers in multiple skills can significantly reduce PBU cycle time. The increased workforce flexibility is the main factor responsible for the time savings, with a maximum of roughly 23 h saved. [ABSTRACT FROM AUTHOR]

We determine diameters of Markov chains describing one-dimensional N-particle models with an exclusion interaction, namely the symmetric simple exclusion process (Ssep) and one of its non-reversible liftings, the lifted totally asymmetric simple exclusion process (Tasep). The diameters provide lower bounds for the mixing times, and we discuss the implications of our findings for the analysis of these models. [ABSTRACT FROM AUTHOR]

Docked Bike Sharing Systems often experience load imbalances among bike stations, leading to uneven distribution of bicycles and to challenges in meeting users' demand. To address the load imbalances, many docked Bike Sharing Systems employ rebalancing vehicles that actively redistribute bicycles across stations, ensuring a more equitable distribution and enhancing the availability of bikes for users. The determination of the number of rebalancing vehicles in docked Bike Sharing Systems is typically based on various criteria, such as the size of the system, the density of stations, the expected demand patterns, and the desired level of service quality. This is a determining factor, in order to increase the efficiency of customer service at a reasonable cost. To enable a cost-effective rebalancing, we have used a cluster-based approach, due to the large scale of the Bike Sharing Systems, and our model is based on Markov Chains, given their proven effectiveness in this domain. Degrees of subsystem load at station level were used for modeling purposes. Additionally, a quantization strategy around cluster load was developed, to avoid state space explosion. This allowed the computation of the probability of transitioning from one degree of system load to another. A new method was developed to determine the fleet size, based on the identified subsystem steady state, describing the rebalancing necessity. The model evaluation was performed on traffic data collected from the Citi Bike New York Bike Sharing System. Based on the evaluation results, the model transition rates were in accordance with the expected values, indicating that the rebalancing operations are efficient from the point of view of the fulfillment of on-time arrival constraints. [ABSTRACT FROM AUTHOR]

In this paper, we study the two-player zero-sum stochastic differential games with regime switching. The stochastic system is driven by Brownian motion and Markov chains. The cost functionals are defined by controlled backward stochastic differential equations (BSDEs). In this framework, we allow the control processes to depend on the information from the past. By making full use of the construction of Markov chains, we prove that the upper value function and lower value function are indeed deterministic, while the cost functionals are random. We establish the dynamic programming principle (DPP), and obtain that the lower and upper value functions are the unique viscosity solutions of the corresponding systems for partial differential equations (PDEs) of Hamilton-Jacobi-Bellman-Isaacs' (HJBI) type. [ABSTRACT FROM AUTHOR]

Starting from the notion of algebraic hidden processes introduced in Ref.9, we propose a definition of quantum hidden Markov process which extends the notion of quantum Markov chain (QMC) in the same sense in which classical hidden Markov processes (HMPs) extend classical Markov chains. We give several structure theorems for this new family of quantum states which shares with the QMCs the unique property that the finite dimensional joint expectations are explicitly given without these states being linearly equivalent to product states (as the Gaussian ones). The special feature of this new class of quantum states is best exemplified by looking at their restrictions to diagonal sub-algebras: we prove that they lead to a new family of classical stochastic processes that generalize in a non-trivial way the classical HMPs. [ABSTRACT FROM AUTHOR]

Motivation Metastasis formation is a hallmark of cancer lethality. Yet, metastases are generally unobservable during their early stages of dissemination and spread to distant organs. Genomic datasets of matched primary tumors and metastases may offer insights into the underpinnings and the dynamics of metastasis formation. Results We present metMHN, a cancer progression model designed to deduce the joint progression of primary tumors and metastases using cross-sectional cancer genomics data. The model elucidates the statistical dependencies among genomic events, the formation of metastasis, and the clinical emergence of both primary tumors and their metastatic counterparts. metMHN enables the chronological reconstruction of mutational sequences and facilitates estimation of the timing of metastatic seeding. In a study of nearly 5000 lung adenocarcinomas, metMHN pinpointed TP53 and EGFR as mediators of metastasis formation. Furthermore, the study revealed that post-seeding adaptation is predominantly influenced by frequent copy number alterations. Availability and implementation All datasets and code are available on GitHub at https://github.com/cbg-ethz/metMHN. [ABSTRACT FROM AUTHOR]

Abstract Background Traditional recombinant inbred lines (RILs) are generated from repeated self-fertilization or brother-sister mating from the F1 hybrid of two inbred parents. Compared with the F2 population, RILs cumulate more crossovers between loci and thus increase the number of recombinants, resulting in an increased resolution of genetic mapping. Since they are inbred to the isogenic stage, another consequence of the heterozygosity reduction is the increased genetic variance and thus the increased power of QTL detection. Self-fertilization is the primary form of developing RILs in plants. Brother-sister mating is another way to develop RILs but in small laboratory animals. To ensure that the RILs have at least 98% of homozygosity, we need about seven generations of self-fertilization or 20 generations of brother-sister mating. Prior to homozygosity, these lines are called pre-recombinant inbred lines (PRERIL). Phenotypic values of traits in PRERILs are often collected but not used in QTL mapping. To perform QTL mapping in PRERILs, we need the recombination fraction between two markers at generation t for t

At present, an actual trend in the development of fuel and energy complexes in a number of countries is the diversification of generation via the involvement of local types of solid fuel. In this case, thermochemical processing of fuel is often carried out in a fluidized state. A significant proportion of dispersed fuels cannot be transferred to a state of stable fluidization. The solution in such cases is to create a fluidized bed of inert carrier, into which particles of the target fuel component are then introduced. In this work, a computational and experimental study of the fluidization of inert bulk material (haydite granules) was carried out. The key purpose of the work was to develop a mathematical model for the formation of a fluidized bed, which makes it possible to calculate the process based on its local characteristics, as well as to identify the model parameters and test it using experiment data. During the study the problems of developing a numerical method for calculating the distribution of velocities and concentrations along the height of the apparatus were solved, parametric identification of the proposed mathematical model was carried out, and empirical verification of the modeling results was carried out. The mathematical apparatus of the theory of Markov chains was used as the mathematical basis for constructing the model. The data from our own laboratory full-scale experiment were used to identify the parameters of the model and verify it. A comparison of calculated and experimental data showed the high predictive efficiency of the model for the given granulometric composition of the fluidizing product. The results of the laboratory full-scale experiments also showed a significant evolution of the granulometric composition of haydite granules during their long-term stay in a fluidized bed, which requires a separate study, as well as the introduction of appropriate amendments to the mathematical model for its further improvement.

The main goal of the paper is to identify the causes of non-realization of project prediction and to propose a new framework for project prediction. A secondary goal is to explain why the approaches to project prediction used currently do not provide satisfactory results. The research was realised in the form of qualitative research using semi-structured interviews. The findings reveal that the main causes of non-realization of project prediction are follows: there is no methodology that could be practically used; simplified approaches to project prediction usually have low reliability for which reason they are generally unusable; suitable input data and information for project prediction are not available. The main contribution made by the paper is the identification of causes of non-realization of project prediction and the proposal of a new framework for project prediction that respects changing conditions during the lifecycle of the project and changes in the way of thinking in project prediction. A prerequisite for its application is a functioning system of knowledge management in projects, including the realization of post-project analysis.

Abstract This paper presents the construction of intelligent systems for selecting the optimum concentration of geopolymer matrix components based on ranking optimality criteria. A peculiarity of the methodology is replacing discrete time intervals with a sequence of states. Markov chains represent a synthetic property accumulating heterogeneous factors. The computational basis for the calculations was the digitization of experimental data on the strength properties of fly ashes collected from thermal power plants in the Czech Republic and used as additives in geopolymers. A database and a conceptual model of priority ranking have been developed, that are suitable for determining the structure of relations of the main factors. Computational results are presented by studying geopolymer matrix structure formation kinetics under changing component concentrations in real- time. Multicriteria optimization results for fly-ash as an additive on metakaolin-based geopolymer composites show that the optimal composition of the geopolymer matrix within the selected variation range includes 100 g metakaolin, 90 g potassium activator, 8 g silica fume, 2 g basalt fibers and 50 g fly ash by ratio weight. This ratio gives the best mechanical, thermal, and technological properties.

In this paper, we propose an algebraic approach to the definition of Hidden Markov Processes (HMP) which has two advantages: on the one hand, it prepares the ground for the quantum extension of these processes (discussed in the second part of this paper), on the other hand it suggests two natural extensions of these processes: one of them highlights a different notion of conditional independence between the observable and the hidden process; the other one drops the assumption that the hidden process is Markov. The latter extension is motivated by the examples of hidden, but not hidden Markov processes produced in the second part of the paper. Furthermore, we prove that any hidden Markov process can be interpreted as a restriction, to a suitable sub-algebra, of a Markov process. [ABSTRACT FROM AUTHOR]

Limited research has been directed toward coastal protection infrastructure compared to other types of infrastructure, despite the increasing global population in low-elevated coastal regions and the threats posed by climate change. This paper presents a risk-based asset management framework for coastal protection structures that improves accuracy with each inspection. The framework consists of five components: the Coastal Asset Inventory (CAI), Inspection and Condition Assessment (ICA) module, Backward Markovian Deterioration Model (BMDM), Forward Markovian Deterioration Model (FMDM), and Intervention Policy Engine (IPE). The framework addresses challenges in accurately predicting coastal structure deterioration due to uncertainties in wave loading conditions and the need for frequent inspections. It is applied to rubble-mound breakwaters in Alexandria, Egypt. The BMDM and FMDM models are developed based on inspection data, and the IPE optimizes interventions considering structural condition, risk thresholds, and budget constraints. Results showed that long-term deterioration estimates occur at an accelerated rate with an increase in inspection points, triggering earlier interventions. However, the framework proves reliable even with only two inspection points, allowing asset managing agencies to implement the model based on the structural condition at the year of construction and a minimum of two inspections. The proposed risk-based asset management framework provided a comprehensive approach to managing coastal protection infrastructure, reducing risks to life and property. By accurately predicting deterioration and optimizing intervention decisions, the framework can greatly assist in the effective management and maintenance of coastal assets. This is vital in ensuring the safety of coastal populations facing global climate change and demographic growth. Coastal protection structures are crucial for safeguarding coastal populations in the face of climate change and demographic growth. This research paper introduces a risk-based asset management framework for coastal protection structures, with practical applications for coastal management agencies and practitioners. The framework addresses the challenges of predicting coastal structure deterioration and optimizing intervention decisions. The framework consists of five components: the Coastal Asset Inventory (CAI), Inspection and Condition Assessment (ICA), Backward Markovian Deterioration Model (BMDM), Forward Markovian Deterioration Model (FMDM), and Intervention Policy Engine (IPE). It has been successfully applied to rubble-mound breakwaters in Alexandria, Egypt. The practical implications are significant: The framework enables asset managers to make informed decisions on coastal infrastructure maintenance and interventions, reducing risks to life and property. It provides a comprehensive approach to managing coastal protection infrastructure and ensuring the safety of coastal populations. By accurately predicting deterioration and optimizing interventions, the framework supports effective management and maintenance of coastal assets. Its reliability and flexibility make it a valuable tool for coastal management practitioners, promoting sustainable development and safeguarding coastal areas. With its potential for broader application, this risk-based asset management framework offers practical solutions for coastal protection worldwide. [ABSTRACT FROM AUTHOR]

A Hidden Markov Model (HMM) is a couple of stochastic processes: A hidden Markov process and an observed emission process. Generally, the HMMs are used to study the hidden behavior of random systems through some observed emission sequences generated by the phenomenon under study. In this frame-work, we propose to solve the likelihood and the decoding problems of HMMs whose state space is composed of a continuous part and a discrete part. We adapt forward, backward and Viterbi algorithms to the case of our proposal. Numerical examples and Monte Carlo simulations are considered to show the efficiency and the adaptation of the algorithms for the proposed model. [ABSTRACT FROM AUTHOR]

Powerful actors have engaged in information control for centuries, restricting, promoting, or influencing the information environment as it suits their evolving agendas. In the Digital Age, information control has moved online, and information operations now target the online platforms that play a critical role in news engagement and civic debate. In this paper, we use a discrete-time stochastic model to analyze coordinated activity in an online social network, representing the behaviors of accounts as interacting Markov chains. From a dataset of 31,521 tweets posted by 206 accounts, half of which were identified by Twitter as participating in a state-linked information operation, we evaluate the coordination, measured by the apparent influence, between pairs of state-linked accounts compared to unaffiliated accounts. Our analysis reveals that state-linked actors demonstrate significantly higher levels of coordination among themselves compared to their coordination with unaffiliated accounts. Furthermore, the degree of coordination observed between state-linked accounts is more than seven times greater than the coordination observed between unaffiliated accounts. Moreover, we find that the account that represented the most coordinated activity in the network had no followers, demonstrating the power of our modeling approach to unearth hidden connections even in the absence of explicit network structure. [ABSTRACT FROM AUTHOR]

In this paper, we study one kind of stochastic recursive optimal control problem with Markovian Switching. In this problem, the cost functional is described by the solution of backward stochastic differential equations with Markov chains. We prove the dynamic programming principle for this kind of optimal control problem and show that the value function is the unique viscosity solution of the corresponding Hamilton-Jacobi-Bellman equation. [ABSTRACT FROM AUTHOR]

For any fixed real a > 0 and x ∈ R d , d ≥ 1 , we consider the real-valued random process (S n) n ≥ 0 defined by S 0 = a , S n = a + ln | g n ⋯ g 1 x | , n ≥ 1 , where the g k , k ≥ 1 , are i.i.d. nonnegative random matrices. By using the strategy initiated by Denisov and Wachtel to control fluctuations in cones of d-dimensional random walks, we obtain an asymptotic estimate and bounds on the probability that the process (S n) n ≥ 0 remains nonnegative up to time n and simultaneously belongs to some compact set [ b , b + ℓ ] ⊂ R ∗ + at time n. [ABSTRACT FROM AUTHOR]

Inaccuracy and information measures based on cumulative residual entropy are quite useful and have received considerable attention in many fields, such as statistics, probability, and reliability theory. In particular, many authors have studied cumulative residual inaccuracy between coherent systems based on system lifetimes. In a previous paper (Bueno and Balakrishnan, Prob. Eng. Inf. Sci. 36 , 2022), we discussed a cumulative residual inaccuracy measure for coherent systems at component level, that is, based on the common, stochastically dependent component lifetimes observed under a non-homogeneous Poisson process. In this paper, using a point process martingale approach, we extend this concept to a cumulative residual inaccuracy measure between non-explosive point processes and then specialize the results to Markov occurrence times. If the processes satisfy the proportional risk hazard process property, then the measure determines the Markov chain uniquely. Several examples are presented, including birth-and-death processes and pure birth process, and then the results are applied to coherent systems at component level subject to Markov failure and repair processes. [ABSTRACT FROM AUTHOR]

Runtime Enforcement (RE) is a technique aimed at monitoring the executions of a system at runtime and ensure its compliance against a set of formal requirements (properties). RE employs an enforcer (a safety wrapper for the system) which modifies the (untrustworthy) output by performing actions such as delaying (by storing/buffering) and suppressing events, when needed. In this paper, to handle practical applications with memory constraints, we propose a new RE paradigm where the memory of the enforcer is bounded/finite. Besides the property to be enforced, the user specifies a bound on the enforcer memory. Bounding the memory poses various challenges such as how to handle the situation when the memory is full, how to optimally discard events from the buffer to accommodate new events and let the enforcer continue operating. We define the bounded-memory RE problem and develop a framework for any regular property. All of our results are formalized and proved. We also analyze probabilistically how much memory is required on an average case for a given regular property, such that the output of the bounded enforcer is equal to that of the unbounded enforcer up to a fixed probability. The proposed framework is implemented and a case study is worked out to show the practicability and usefulness of the bounded enforcer in the real-world and to show the usage of the aforementioned probabilistic analysis on them. The performance is evaluated via some examples from application scenarios and it indicates linear changes in the execution time of the enforcers in response to increases in trace length, property complexity, and buffer sizes. [ABSTRACT FROM AUTHOR]

Markov chain analysis is a key technique in formal verification. A practical obstacle is that all probabilities in Markov models need to be known. However, system quantities such as failure rates or packet loss ratios, etc. are often not—or only partially—known. This motivates considering parametric models with transitions labeled with functions over parameters. Whereas traditional Markov chain analysis relies on a single, fixed set of probabilities, analysing parametric Markov models focuses on synthesising parameter values that establish a given safety or performance specification φ . Examples are: what component failure rates ensure the probability of a system breakdown to be below 0.00000001?, or which failure rates maximise the performance, for instance the throughput, of the system? This paper presents various analysis algorithms for parametric discrete-time Markov chains and Markov decision processes. We focus on three problems: (a) do all parameter values within a given region satisfy φ ?, (b) which regions satisfy φ and which ones do not?, and (c) an approximate version of (b) focusing on covering a large fraction of all possible parameter values. We give a detailed account of the various algorithms, present a software tool realising these techniques, and report on an extensive experimental evaluation on benchmarks that span a wide range of applications. [ABSTRACT FROM AUTHOR]

The article presents the results of a study on modeling cloud systems. In this research, the authors developed both analytical and simulation models. System analysis was conducted at the level of virtual machine support, corresponding to Infrastructure as a Service (IaaS). The models assumed that virtual machines of different sizes are offered as part of IaaS, reflecting the heterogeneous nature of modern systems. Additionally, it was assumed that due to limitations in access to physical server resources, only a portion of these resources could be used to create virtual machines. The model is based on Markov chain analysis for state-dependent systems. The system was divided into an external structure, represented by a collection of physical machines, and an internal structure, represented by a single physical machine. The authors developed a novel approach to determine the equivalent traffic, approximating the real traffic appearing at the input of a single physical machine under the assumptions of request distribution. As a result, it was possible to determine the actual request loss probability in the entire system. The results obtained from both models (simulation and analytical) were summarized in common graphs. The studies were related to the actual parameters of commercially offered physical and virtual machines. The conducted research confirmed the high accuracy of the analytical model and its independence from the number of different instances of virtual machines and the number of physical machines. Thus, the model can be used to dimension cloud systems. [ABSTRACT FROM AUTHOR]

A very important class of models widely used nowadays to describe and predict, at least in stochastic terms, the behavior of many-particle systems (where the word "particle" is not meant in the purely mechanical sense: particles can be cells of a living tissue, or cars in a traffic flow, or even members of an animal or human population) is the Kinetic Theory for Active Particles, i.e., a scheme of possible generalizations and re-interpretations of the Boltzmann equation. Now, though in the literature on the subject this point is systematically disregarded, this scheme is based on Markov Chains, which are special stochastic processes with important properties they share with many natural processes. This circumstance is here carefully discussed not only to suggest the different ways in which Markov Chains can intervene in equations describing the stochastic behavior of any many-particle system, but also, as a preliminary methodological step, to point out the way in which the notion of a Markov Chain can be suitably generalized to this aim. As a final result of the discussion, we find how to develop new very plausible and likely ways to take into account possible effects of the external world on a non-isolated many-particle system, with particular attention paid to socio-economic problems. [ABSTRACT FROM AUTHOR]

A Bernoulli scheme with unequal harmonic success probabilities is investigated, together with some of its natural extensions. The study includes the number of successes over some time window, the times to (between) successive successes and the time to the first success. Large sample asymptotics, statistical parameter estimation, and relations to Sibuya distributions and Yule–Simon distributions are discussed. This toy model is relevant in several applications including reliability, species sampling problems, record values breaking and random walks with disasters. [ABSTRACT FROM AUTHOR]

The multiple global crisis has made the economies of the world's countries, including EU's economy, vulnerable through the downgrading of the pandemic and the subsequent outbreak of geo-political conflict. These two events had the effect of decelerating the European economy and increasing the poverty level of the population, even that these developments are weaker than in rest of the world. The main objective of the present scientific approach is to identify a risk function based on Markov probability chains and to assess the possibilities of economic recovery through a package of policies structured over different time horizons. The used methods consist of meta-analysis, statistical analysis and geo-spatial and temporal modelling. The results of the study capture the integrated developments of risk-generating macroeconomic elements such as inflation, unemployment, public debt growth in a regionally segregated manner. These elements are useful for supranational decision-makers to increase the economic survival rate after multiple shocks through our proposed policy package. [ABSTRACT FROM AUTHOR]

The article proposes the concept of assessing the level of optimal load modes in terms of tasks (flow of tasks) for two groups of students in the process of integrated education in the specialty 126 "Information systems and technologies" and specialty 022 "Web design", by using the toolkit of Markov chains and equations Kolmogorov. The proposed approach allows you to reproduce the dynamics of states and the results of the mutual influence of two social groups, which are represented by a contingent of students in the context of processing the flow of tasks. Possible options for research may include comparing the level of productivity of each group, the influence of factors affecting their work, as well as the calculation of probabilities of states of social groups over time and marginal probabilities. [ABSTRACT FROM AUTHOR]

Objective To introduce 2 R-packages that facilitate conducting health economics research on OMOP-based data networks, aiming to standardize and improve the reproducibility, transparency, and transferability of health economic models. Materials and Methods We developed the software tools and demonstrated their utility by replicating a UK-based heart failure data analysis across 5 different international databases from Estonia, Spain, Serbia, and the United States. Results We examined treatment trajectories of 47 163 patients. The overall incremental cost-effectiveness ratio (ICER) for telemonitoring relative to standard of care was 57 472 €/QALY. Country-specific ICERs were 60 312 €/QALY in Estonia, 58 096 €/QALY in Spain, 40 372 €/QALY in Serbia, and 90 893 €/QALY in the US, which surpassed the established willingness-to-pay thresholds. Discussion Currently, the cost-effectiveness analysis lacks standard tools, is performed in ad-hoc manner, and relies heavily on published information that might not be specific for local circumstances. Published results often exhibit a narrow focus, central to a single site, and provide only partial decision criteria, limiting their generalizability and comprehensive utility. Conclusion We created 2 R-packages to pioneer cost-effectiveness analysis in OMOP CDM data networks. The first manages state definitions and database interaction, while the second focuses on Markov model learning and profile synthesis. We demonstrated their utility in a multisite heart failure study, comparing telemonitoring and standard care, finding telemonitoring not cost-effective. [ABSTRACT FROM AUTHOR]