Markov State Transition Matrix Research Articles

Finite Difference Model of Substrate Channeling in TCA Cycle Enzymes

The Tricarboxylic Acid (TCA) cycle is a highly optimized metabolic pathway. Of particular interest in the TCA cycle is the malate dehydrogenase—citrate synthase (MDH-CS) complex, which accomplishes citrate production from malate with an oxaloacetate intermediate. This complex was studied experimentally by Bulutoglu et al, who showed that mutation of positively-charged residues on the complex surface effected the dynamic response of citrate production as measured by lag time experiments.1 More recently, we have created a Markov State model that was used to predict the transfer efficiency of each complex and determine the reaction pathways on the surface.2 Utilizing the kinetic parameters of both the recombinant and mutant complexes determined experimentally1and Markov state transition matrix produced by previous modeling2, we report a finite difference model to calculate time trajectories of the surface and bulk occupancies by OAA. Solution of a system of differential mass balance equations, based on the transient matrix, provide the occupancy probability at each node in the network. The lag time of MDH-CS was determined computationally to be comparable to experiment for both the recombinant and mutant complex with lag times of 0.44 and 2.1 seconds, respectively.1 Using the model, the dynamics of each of the four possible reaction pathways between the the two source (MDH) active sites and the two sink (CS) sites could be studied independently. This analysis provides a dynamic model for intermediate transport in an electrostatically channeled system, and can be used as a predictive tool to provide mechanistic insight into pathway dominance. We gratefully acknowledge support from the Army Research Office MURI (#W911NF1410263) via The University of Utah. References B. Bulutoglu, K. E. Garcia, F. Wu, S. D. Minteer, and S. Banta, ACS Chemical Biology, 11, 2847–53 (2016). doi:10.1021/acschembio.6b00523Y. Xie, S. D. Minteer, S. Banta, and S. Calabrese Barton, ACS Nanoscience Au, 2, 414-421 (2022). doi:10.1021/acsnanoscienceau.2c00011 Figure 1

E2DTF: An End-to-End Detection and Tracking Framework for Multiple Micro-UAVs With FMCW-MIMO Radar

Due to the weak radar echoes and strong background clutters in low-altitude airspace, the detection and tracking for multiple micro-unmanned aerial vehicles (UAVs) have posed formidable challenges in radar surveillance field. Consequently, this paper proposes an end-to-end detection and tracking framework (E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTF) for multiple micro-UAVs by utilizing the frequency modulated continuous wave-multiple input multiple output (FMCW-MIMO) radar. To address the low signal-to-noise ratio (SNR) problem, E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTF presents a frame-range-Doppler-azimuth information fusion filter to integrate the target energy by exploiting the spatio-temporal dependence of positions within a sequence of unthresholded frames. Additionally, considering that a target may enter/leave the radar field-of-view (FOV), E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTF introduces a target model state, updated by an extended Markov state transition matrix sequentially, to realize an unknown, time-varying number of micro-UAVs tracking. Another nice feature of E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTF is that it avoids the complex data association procedure thanks to removing the threshold-decision operation. Finally, both numerical simulations and experiments with five different scenarios, i.e., horizontal line, cross-trajectory, circular loop, rainy condition and 3D trajectory tracking are presented to verify the effectiveness of the proposed method. The results show that E <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> DTF can obtain superior detection and tracking performance for multiple micro-UAVs in contrast to the state-of-the-art methods considering detection and tracking processes independently, especially under low SNR conditions.

Influence of different playing styles among the top three teams on action zones in the World Cup in 2018 using a Markov state transition matrix.

In football, attacking has seen evolving for decades and attacking pattern detection is an important topic in this sport. The purpose of this study was to identify the general and threatening attacking patterns of different playing styles in world top football matches, which represented the latest evolvement of soccer attacking. Attacking sequence data of the top three teams from 21 matches in the 2018 World Cup were collected. The three teams were classified into two playing styles according to a previous study, France was a direct-play team, and Croatia and Belgium were possession-play teams. The football field was divided into 12 zones and Markov transition matrix-based zone models were applied to assess the attacking pattern in the 21 matches. Both descriptive analysis and simulative analysis were conducted using this model. The results revealed that (1) flanker attacks were frequently taken among all three teams, and possession playing teams (Croatia and Belgium) played more often than direct playing teams (France) in their center of the midfield zone and (2) forward passes across/through zones toward the middle of attacking quarter (A1/4) have a positive impact of creating a chance of a goal. Using Markov transition matrix, general and threatening attacking patterns were found. The combination of possession play and counterattack was a new trend that emerged in the 2018 World Cup. These findings can help coaches to develop corresponding strategies when facing opponents of different playing styles.

A Sticky Sampling and Markov State Transition Matrix Based Driving Cycle Construction Method for EV

Driving cycle (DC) plays an important role in designing and evaluating EVs, and many Markov chain-based DC construction methods describe driving profiles of unfixed-line vehicles with Markov state transition probability. However, for fixed-line electric vehicles, the time-sequence of microtrips brings huge influences on their brake, drive, and battery management systems. Simply describing topography, traffic, location, driving features, and environment in a stochastic manner cannot reflect the continuity characteristics hidden in a fixed route. Thus, in this paper, we propose a sticky sampling and Markov state transition matrix based DC construction algorithm to describe both randomness and continuity hidden in a fixed route, in which a data structure named “driving pulse chain” was constructed to describe the sequence of the driving scenarios and several Markov state transition matrices were constructed to describe the random distribution of velocity and acceleration in same driving scenarios. Simulation and experimental analysis show that with sliding window and driving pulse chain, the proposed algorithm can describe and reflect the continuity characteristics of topography, traffic, and location. At the same time, the stochastic nature of the driving cycle can be preserved.

Prediction and analysis of domestic water consumption based on optimized grey and Markov model

Abstract With the rapid development of urbanization and the continuous improvement of living standards, China's domestic water consumption shows a growing trend. However, in some arid and water deficient areas, the shortage of water resources is a crucial factor affecting regional economic development and population growth. Therefore, it is essential to reliably predict the future water consumption data of a region. Aiming at the problems of poor prediction accuracy and overfitting of non-growth series in traditional grey prediction, this paper uses residual grey model combined with Markov chain correction to predict domestic water consumption. Based on the traditional grey theory prediction, the residual grey prediction model is established. Combined with the Markov state transition matrix, the grey prediction value is modified, and the model is applied to the prediction of domestic water consumption in Shaanxi Province from 2003 to 2019. The fitting results show that the accuracy grade of the improved residual grey prediction model is “good”. This shows that the dynamic unbiased grey Markov model can eliminate the inherent error of the traditional grey GM (1,1) model, improve the prediction accuracy, have better reliability, and can provide a new method for water consumption prediction.

Product technical life prediction based on multi-modes and fractional Lévy stable motion

Some equipment degradation processes have long-range dependence (LRD) and multi-modes characteristics. The multi-modes are caused by changes of the external environment, the operating conditions and the loads throughout the lifetime of the equipment. In the present paper, a multi-modal Fractional Lévy Stable Motion (FLSM) degradation model is developed to predict the product technical life or remaining useful life (RUL) of equipment. The advantage of FLSM lies in its LRD characteristics and its ability to describe multiple stochastic distributions as the tail parameter α changes. Multi-modes, switching points and modal categories are identified by change point detection and clustering algorithms, and a Markov state transition matrix describes the modes switching law. The probability density function (PDF) of RUL is established by Monte Carlo Simulation. The effectiveness of the prediction model is verified by a practical example of a blast furnace.

PSO-Markov residual correction method based on Verhulst-Fourier prediction model

Macroeconomic predicting is a research hotspot in the field of predicting. The accuracy of predicting often directly affects the rationality of decision-making, especially for defense expenditure predicting. This paper studies the residual correction method of prediction model based on time series. Firstly, based on the grey nonlinear Verhulst prediction model, Fourier series is introduced in this paper to correct the residual sequence once and establish a residual correction model. On this basis, this paper also introduces Markov related concepts, creatively introduces the two-dimensional residual data into Markov state transition matrix, classifies it by K-means clustering analysis, and calculates its parameters by PSO algorithm to realize the secondary accurate correction of residual. Finally, a PSO-Markov residual correction method based on Verhulst-Fourier model is proposed. Tested by examples, this method effectively improves the prediction accuracy of the model, and the prediction is more reliable and accurate.

Noise and Parameter Heterogeneity in Aggregate Models of Thermostatically Controlled Loads

Aggregate models are used in the analysis and control of large populations of thermostatically controlled loads (TCLs), such as air-conditioners and water heaters. The fidelity of such models is studied by analyzing the influences of noise and parameter heterogeneity on TCL aggregate dynamics. While TCLs can provide valuable services to the power systems, control may cause their temperatures to synchronize, which may then lead to undesirable power oscillations. Recent works has shown that the aggregate dynamics of TCLs can be modeled by tracking the evolution of probability densities over discrete temperature ranges or bins. To accurately capture oscillations in aggregate power, such bin-based models require a large number of bins. The process of obtaining the Markov state transition matrix that governs the dynamics can be computationally intensive when using Monte Carlo based system identification techniques. Existing analytical techniques are further limited as noise and heterogeneity in several thermal parameters are difficult to incorporate. These challenges are addressed by developing a fast analytical technique that incorporates noise and heterogeneity into bin-based aggregate models. Results show the identified and the analytical models match very closely. Studies consider the influence of model error, noise and parameter heterogeneity on the damping of oscillations.

The Application of Improved Genetic Algorithm in the Markov State Transition Matrix to SME Technical Efficiency and Scale Revenue Calculation Methods

To solve a state transfer matrix using a Markov model, an improved genetic algorithm approach is proposed. A Markov prediction model is employed to study structures in the SME technical efficiency and scale gains calculation method. The obtained results are compared to the data shown in the China Statistical Yearbooks. It is found that our proposed genetic algorithm approach provides references for optimizing an agriculture SME industrial structure and hence improves the prediction precision.

Modified Page Rank Model in Investigation of Criminal Gang

In this paper, we consider a criminal investigation on the collective guilt of part members in a working group. Assuming that the statistics we used are reliable, we present the Page Rank Model based on mutual information. First, we use the average mutual info rmation between non-suspicious topics and the suspicious topics to score the topics by degree of suspicion. Second, we build the correl ation matrix based on the degree of suspicion and acquire the corresponding Markov state transition matrix. Then, we set the original v alue for all members of the working group based on the degree of suspicion. At the last, we calculate the suspected degree of each me mber in the working group. In the small 10-people case, we build the improved Page Rank model. By calculating the statistics of this c ase, we acquire a table which indicates the ranking of the suspected degree. In contrast with the results given in this issue, we find thes e two results basically match each other, indicating the model we have built is feasible. In the current case, firstly, we obtain a ranking list on 15 topics in order of suspicion via Page Rank Model based on mutual information. Secondly, we acquire the stable point of Mar kov state transition matrix using the Markov chain. Then, we build the connection matrix based on the degree of suspicion and acquire the corresponding Markov state transition matrix. Last, we calculate the degree of 83 candidates. From the result, we can see that those suspicious are on the top of the ranking list while those innocent people are at the bottom of the list, representing that the model we ha ve built is feasible. When suspicious topics and conspirators changed, a relatively good result can also be obtained by this model. In th e current case, we have the evidence to believe that Dolores and Jerome, who are the senior managers, have significant suspicion. It is recommended that future attention should be paid to them. The Page Rank Model, based on mutual information, takes full account of t he information flow in message distribution network. This model can not only deal with the statistics used in conspiracy, but also be ap plied to detect the infected cells in a biological network. Finally, we present the advantages and disadvantages of this model and the dir ection of improvements.

The Reliability Research of the Good Receipting and being Put in Storage Process of X City's Tobacco Logistics Distribution Center

In this paper,X city's tobacco logistics distribution center is taken as a research subject.Firstly,the author used the availability analysis method to do the reliability analysis of the good receipting and being put in the storage process of this logistics system,a discrete model of the system's reliability based on the Markov process was being put forward .Then, the Markov state transition matrix was being worked out in order to get the computational formulas used to work out the steady state effectiveness and the failure rate of this system.Meanwhile, it also evaluated the reliability of this system and gave the improvement suggestions.

Motivation for Occupational Fraud: An Analysis of the “Fraud Triangle” Using Economic Logic

The “fraud triangle” constitutes a very popular criminological model of occupational that explains the dynamics underlying occupational fraud. However, given that occupational is essentially an economic crime, there is little evidence in the extant literature that provides an explanation of the “fraud triangle” in purely economic terms. In this paper, we separately consider each of the three vertexes of the fraud triangle and establish an underlying economic linkage that coherently connects each of the vertexes. We first consider the vertex and hypothesize and prove that in equilibrium, the valence (i.e. net incentive to an employee from an act of occupational fraud) is zero. It is when the equilibrium is disturbed that a positive valence for arises, identified in our framework by an increase in the probability of an employee not being caught after committing fraud. We then move to the second vertex i.e. opportunity and derive, using a finite-state Markov chain, a separate, closed-form expression for the equilibrium probability measure identified in our analysis of the first vertex. Finally, we show that the last vertex - rationalization; represented in our framework via an equilibrium penalty-to-reward ratio for an act of occupational from an employee's viewpoint is very sensitively dependent on the likelihood of the organizational culture towards continuing to remain in a slack state in the future given that it is currently in a slack state. We also provide a hypothetical numerical illustration of our framework for a specific setting of the Markov state transition matrix. We believe that our analysis is a first step in providing a rigorous mathematical ratification of the criminological theory of fraud triangle and therefore helps to open up the field for more meaningful empirical enquiry in future.