Markov Model Analysis Research Articles

Markov Modeling and Behavior Analysis of Steam Generation (SG) System in a Thermal Power Plant (TPP)

Steam generation (SG) system having five subsystems, namely Economizer, Reheater, Superheater, Furnace, Turbines, and Generator. The differential equations are acquired from the state transition diagram (STD) made pertaining to the real environment of the plant using Markov Method (MM). To get the performance of the system these equations are being worked out using normalizing conditions. The Performance values are attained by providing the apt values of failure and repair rates (FRRs) in the Markov model. Optimal Availability of the system is achieved with the Genetic algorithm (GA) technique.

Human Reliability Analysis for Cardiopulmonary Resuscitation Process in Emergency Medicine Using a Modified Hybrid Method Based on the Markov Model and Fault Tree Analysis

Introduction:In emergency departments (ED), human reliability assessment is essential for improving the quality of treatment and preventing medical accidents. A medical accident is expressed as an injury to a patient caused by the negligence of a doctor or nurse who is providing medical care. This study aimed to assess the human reliability in the cardiopulmonary resuscitation (CPR) process and recommend some comments to minimize human errors and improve patient safety.Materials and Methods: The main factors in the CPR process (such as rate and depth of chest compression and rate of ventilation) are identified based on the American heart association (AHA) roles. Data were recorded during three months in the evening shifts in the ED and CPR room of Imam Reza Hospital in Mashhad, Iran. In total, 42 samples were collected, and a modified hybrid approach according to the fault tree analysis and Markov method was proposed for the analysis of CPR team (including emergency medicine, medical interns, and nurses) reliability in the resuscitation process. Finally, the important basic events (errors) were selected using the Boruta algorithm by R software.Results: An FTA-Markov-based hybrid method is considered to compute the human reliability in the CPR process. The obtained results from human reliability analysis using the sensitivity analysis via Boruta algorithm and the proposed hybrid method show that an interrupt between chest compression process for rhythm control, the cycle of CPR, the depth of chest compression, and the discussion about reversible causes are the most effective factors in the human reliability of CPR process.Conclusion:The human reliability of the CPR process in the ED has been assessed using a hybrid method based on the FTA and Markov method for the first time. To improve the quality of treatment and prevent medical accidents during the CPR process, the main factors in the process are identified, and then, the proposed hybrid method is used to calculate human reliability.

A Comparative Analysis of Hidden Markov Model, Hybrid Support Vector Machines, and Hybrid Artificial Neural Fuzzy Inference System in Reservoir Inflow Forecasting (Case Study: The King Fahd Dam, Saudi Arabia)

The precise prediction of the streamflow of reservoirs is of considerable importance for many activities relating to water resource management, such as reservoir operation and flood and drought control and protection. This study aimed to develop and evaluate the applicability of a hidden Markov model (HMM) and two hybrid models, i.e., the support vector machine-genetic algorithm (SVM-GA) and artificial neural fuzzy inference system-genetic algorithm (ANFIS-GA), for reservoir inflow forecasting at the King Fahd dam, Saudi Arabia. The results obtained by the HMM model were compared with those for the two hybrid models ANFIS-GA and SVM-GA, and with those for individual SVM and ANFIS models based on performance evaluation indicators and visual inspection. The results of the comparison revealed that the ANFIS-GA model and ANFIS model provided superior results for forecasting monthly inflow with satisfactory accuracy in both training (R2 = 0.924, 0.857) and testing (R2 = 0.842, 0.810) models. The performance evaluation results for the developed models showed that the GA-induced improvement in the ANFIS and SVR forecasts was matched by an approximately 25% decrease in RMSE and around a 13% increase in Nash–Sutcliffe efficiency. The promising accuracy of the proposed models demonstrates their potential for applications in monthly inflow forecasting in the present semiarid region.

Cost-Effectiveness of Multitarget Stool DNA Testing vs Colonoscopy or Fecal Immunochemical Testing for Colorectal Cancer Screening in Alaska Native People

ObjectiveTo estimate the cost-effectiveness of multitarget stool DNA testing (MT-sDNA) compared with colonoscopy and fecal immunochemical testing (FIT) for Alaska Native adults. Patients and MethodsA Markov model was used to evaluate the 3 screening test effects over 40 years. Outcomes included colorectal cancer (CRC) incidence and mortality, costs, quality-adjusted life-years (QALYs), and incremental cost-effectiveness ratios (ICERs). The study incorporated updated evidence on screening test performance and adherence and was conducted from December 15, 2016, through November 6, 2019. ResultsWith perfect adherence, CRC incidence was reduced by 52% (95% CI, 46% to 56%) using colonoscopy, 61% (95% CI, 57% to 64%) using annual FIT, and 66% (95% CI, 63% to 68%) using MT-sDNA. Compared with no screening, perfect adherence screening extends life by 0.15, 0.17, and 0.19 QALYs per person with colonoscopy, FIT, and MT-sDNA, respectively. Colonoscopy is the most expensive strategy: approximately $110 million more than MT-sDNA and $127 million more than FIT. With imperfect adherence (best case), MT-sDNA resulted in 0.12 QALYs per person vs 0.05 and 0.06 QALYs per person by FIT and colonoscopy, respectively. Probabilistic sensitivity analyses supported the base-case analysis. Under varied adherence scenarios, MT-sDNA either dominates or is cost-effective (ICERs, $1740-$75,868 per QALY saved) compared with FIT and colonoscopy. ConclusionEach strategy reduced costs and increased QALYs compared with no screening. Screening by MT-sDNA results in the largest QALY savings. In Markov model analysis, screening by MT-sDNA in the Alaska Native population was cost-effective compared with screening by colonoscopy and FIT for a wide range of adherence scenarios.

Searching for Dark Matter with a Superconducting Qubit.

Detection mechanisms for low mass bosonic dark matter candidates, such as the axion or hidden photon, leverage potential interactions with electromagnetic fields, whereby the dark matter (of unknown mass) on rare occasion converts into a single photon. Current dark matter searches operating at microwave frequencies use a resonant cavity to coherently accumulate the field sourced by the dark matter and a near standard quantum limited (SQL) linear amplifier to read out the cavity signal. To further increase sensitivity to the dark matter signal, sub-SQL detection techniques are required. Here we report the development of a novel microwave photon counting technique and a new exclusion limit on hidden photon dark matter. We operate a superconducting qubit to make repeated quantum nondemolition measurements of cavity photons and apply a hidden Markov model analysis to reduce the noise to 15.7dB below the quantum limit, with overall detector performance limited by a residual background of real photons. With the present device, we perform a hidden photon search and constrain the kinetic mixing angle to ε≤1.68×10^{-15} in a band around 6.011GHz (24.86 μeV) with an integration time of 8.33s. This demonstrated noise reduction technique enables future dark matter searches to be sped up by a factor of 1,300. By coupling a qubit to an arbitrary quantum sensor, more general sub-SQL metrology is possible with the techniques presented in this Letter.

Evaluating the cost-effectiveness of universal hepatitis B virus vaccination in Iran: a Markov model analysis

ABSTRACT Vaccination is an essential way to prevent the transmission of hepatitis B virus (HBV). Various studies have been published on the cost-effectiveness of HBV vaccination, but since the results vary according to the target population and related health outcomes, this study examined the cost-effectiveness of the universal HBV vaccination in Iran. In this economic evaluation study, a decision tree with the Markov model was used to compare the universal HBV vaccination with a strategy of non-vaccination. Health states used in the model included healthy, chronic hepatitis B, compensated cirrhosis, decompensated cirrhosis, hepatocellular carcinoma, and death. Analyses were performed from a payer’s perspective. Incremental cost-effectiveness ratio (ICER) per life-year gained, and quality-adjusted life-years (QALYs) gained were calculated at a 5% annual discount rate. The sensitivity analysis was conducted using Monte Carlo simulation. Analyses were performed using Microsoft Excel and TreeAge Pro 2011 software. In 2017, the estimated cost per dose for any HBV vaccine was $3.20 USD. The universal HBV vaccination was economically advantageous compared to non-vaccination, and the estimated cost of this program per life-year and QALY gained were $6,319 and negative (-) $1,183.85 USD, respectively. Given the uncertainty of all parameters, the model remained robust and reliable. In Iran, the universal HBV vaccination strategy for both health outcomes of QALY and life-years gained was cost-effective and advantageous. The vaccination strategy saved money, increased life years and improved quality of life. Therefore, it is recommended that this program continues to be provided.

Urea as a Protein LID Opener: Overcoming Substrate Inhibition in Adenylate Kinase by Tuning Conformational Dynamics

Enzymes are designed to accelerate vital chemical reactions by multiple orders of magnitude. Many of them harness large-scale motions of domains and subunits to promote their activity. Studying structural dynamics is hence essential in order to decipher how protein machines function. Combined with H2MM, a photon-by-photon hidden Markov model analysis, single-molecule FRET studies on the enzyme adenylate kinase (AK) recently revealed very fast domain motions, two orders of magnitude faster than the turnover of the enzyme. AK may use numerous cycles of conformational rearrangement in order to find a relative orientation of its substrates that is optimal for the chemical reaction, the interconversion of adenine nucleotides. In the present work we studied the impact of urea on the domain motions of AK, particularly those of its ATP-binding LID-domain. Although urea has been commonly used as a protein denaturant, it is reported that in minor amounts it actually enhances the catalytic activity of AK. We show that this activation is caused by an effect on substrate inhibition. High concentrations of the substrate AMP decrease the rate of LID-domain opening, disturb the balance between open and closed conformations and reduce turnover. Minor amounts of urea partially revert this effect by opening the domains and allow the enzyme to regain activity. Importantly, in absence of inhibitory concentrations of AMP, the activity of AK is not enhanced by urea and can even be reduced, although urea here again favors the open conformation This suggests a delicate balance between domain closure dynamics and substrate binding, which might not be a unique feature of AK, and is likely employed by a multitude of enzymes to regulate their activity.

The Role of Calcium in Regulating the Conformational Dynamics of d-Galactose/d-Glucose-Binding Protein Revealed by Markov State Model Analysis.

The d-glucose/d-galactose-binding protein (GGBP) from Escherichia coli is a substrate-binding protein (SBP) associated with sugar transport and chemotaxis. It is also a calcium-binding protein, which makes it unique in the SBP family. However, the functional importance of Ca2+ binding is not fully understood. Here, the calcium-dependent properties of GGBP were explored by all-atom molecular dynamics simulations and Markov state model (MSM) analysis as well as single-molecule Förster resonance energy transfer (smFRET) measurements. In agreement with previous experimental studies, we observed the structure stabilization effect of Ca2+ binding on the C-terminal domain of GGBP, especially the Ca2+-binding site. Interestingly, the MSMs of calcium-depleted GGBP and calcium-bound GGBP (GGBP/Ca2+) demonstrate that Ca2+ greatly stabilizes the open conformation, and smFRET measurements confirmed this result. Further analysis reveals that Ca2+ binding disturbs the local hydrogen bonding interactions and the conformational dynamics of the hinge region, thereby weakening the long-range interdomain correlations to favor the open conformation. These results suggest an active regulatory role of Ca2+ binding in GGBP, which finely tunes the conformational distribution. The work sheds new light on the study of calcium-binding proteins in prokaryotes.

The Cloud Resource Forecasting Model Based HMM

One of the most important goals for companies that provide cloud computing services is to maintain high availability on large computer systems. In order to accomplish such objective, it is necessary to discovery the reason of poor availability. Software aging is a main factor in cloud computing services, leading to software failures, poor performances and may result in system downtime. This paper investigates the software aging effects on the OpenStack cloud computing platform and describes a forecasting model based on Hidden Markov Models. The prediction analysis of the Hidden Markov Models on the key performance data of the system shows that the Hidden Markov Models has excellent predictive performance and is suitable for the prediction of the reliability of the cloud server system.

VYUŽITIE VYBRANÝCH METÓD OPERAČNÉHO VÝSKUMU PRI PLÁNOVANÍ OCHRANY SÍL VO VZDUŠNÝCH OPERÁCIÁCH

At the beginning of the article, we characterize the purpose, conditions, utilization and objectives of force protection for air operations. We focus on selected modern methods applicable in the planning process of force protection for air operations. The article presents the research based on the Markov model, scenario method (scenario planning) and SWOT analysis in aviation with direct implementation into the planning process in this field. We briefly define the selected methods and identify their specific use in force protection planning for air operations. Specifically, we examine the method application as efficiently as possible in real-life situations (in operations) or complex exercises before the actual deployment of forces.

Distinct <i>in vivo</i> Dynamics of Excitatory Synapses Onto Cortical Pyramidal Neurons and Inhibitory Interneurons

Cortical function relies on the balanced activation of excitatory and inhibitory neurons. However, little is known about the organization and dynamics of shaft excitatory synapses onto cortical inhibitory interneurons, which cannot be easily identified morphologically. Here, we fluorescently visualize the excitatory postsynaptic marker PSD-95 at endogenous levels as a proxy for excitatory synapses onto layer 2/3 pyramidal neurons and parvalbumin-positive (PV+) inhibitory interneurons in the mouse barrel cortex. Longitudinal in vivo imaging reveals that, while synaptic weights in both neuronal types are log-normally distributed, synapses onto PV+ neurons are less heterogeneous and more stable. Markov-model analyses suggest that the synaptic weight distribution is set intrinsically by ongoing cell type-specific dynamics, and substantial changes are due to accumulated gradual changes. Synaptic weight dynamics are multiplicative, i.e., changes scale with weights, though PV+ synapses also exhibit an additive component. These results reveal that cell type-specific processes govern cortical synaptic strengths and dynamics.

Motor-related oscillatory activity in schizophrenia according to phase of illness and clinical symptom severity

Magnetoencephalography (MEG) measures magnetic fields generated by synchronised neural current flow and provides direct inference on brain electrophysiology and connectivity, with high spatial and temporal resolution. The movement-related beta decrease (MRBD) and the post-movement beta rebound (PMBR) are well-characterised effects in magnetoencephalography (MEG), with the latter having been shown to relate to long-range network integrity. Our previous work has shown that the PMBR is diminished (relative to controls) in a group of schizophrenia patients. However, little is known about how this effect might differ in patients at different stages of illness and degrees of clinical severity. Here, we extend our previous findings showing that the MEG derived PMBR abnormality in schizophrenia exists in 29 recent-onset and 35 established cases (i.e., chronic patients), compared to 42 control cases. In established cases, PMBR is negatively correlated with severity of disorganization symptoms. Further, using a hidden Markov model analysis, we show that transient pan-spectral oscillatory ”bursts”, which underlie the PMBR, differ between healthy controls and patients. Results corroborate that PMBR is associated with disorganization of mental activity in schizophrenia.

Adaptive Multi-Channel Event Segmentation and Feature Extraction for Monitoring Health Outcomes.

To develop a multi-channel device event segmentation and feature extraction algorithm that is robust to changes in data distribution. We introduce an adaptive transfer learning algorithm to classify and segment events from non-stationary multi-channel temporal data. Using a multivariate hidden Markov model (HMM) and Fisher's linear discriminant analysis (FLDA) the algorithm adaptively adjusts to shifts in distribution over time. The proposed algorithm is unsupervised and learns to label events without requiring a priori information about true event states. The procedure is illustrated on experimental data collected from a cohort in a human viral challenge (HVC) study, where certain subjects have disrupted wake and sleep patterns after exposure to an H1N1 influenza pathogen. Simulations establish that the proposed adaptive algorithm significantly outperforms other event classification methods. When applied to early time points in the HVC data, the algorithm extracts sleep/wake features that are predictive of both infection and infection onset time. The proposed transfer learning event segmentation method is robust to temporal shifts in data distribution and can be used to produce highly discriminative event-labeled features for health monitoring. Our integrated multisensor signal processing and transfer learning method is applicable to many ambulatory monitoring applications.

Assessment of Rainfall–Runoff Due to the Impacts of Land-Use Changes by Integrated Geospatial Empirical Approach: Study on Koraiyar Basin, Tiruchirappalli City, India

The changes that occur in various forms of land classes are known as land-use and land-cover changes (LULC). Such changes have a substantial impact on the contours of an urban basin and consequently have an effect on the surface runoff of the rainfall that occurs in the area. The runoff features obviously deteriorate because of the decrease in initial abstraction, and an increase in imperviousness causes enhanced runoff. The particular challenges in the urban basin are specificity calculation and quantification of surface runoff. Geospatial techniques are adopted along with the Soil Conservation Service-curve number (SCS-CN) technique to reliably predict and accurately measure runoff. The GIS is used to prepare the transformed layers of land classes from remotely sensed data. In this explorative study, LULC change and its impact on the urban Koraiyar basin, Tiruchirappalli city, South India, is studied by using SCS-CN with the aid of GIS techniques. As the basin that passes is located in a developing city, rapid changes in LULC are observed in and around the periphery of the basin. The LULC changes and their impact on surface runoff are analyzed using GIS with multi-dated Landsat satellite images for the years 1986–2016 at intervals of every 10 years. The supervised classification algorithm is used to develop LULC maps. From the study, it is observed that there is a continuous increase in settlement area of 1.04% from 1986 to 2016, especially in the northern part of the basin. A Markov model analysis is done from the historically developed LULC maps to predict the anticipated future LULC changes for the years 2026 and 2036 along with an estimation of the surface runoff. The predicted maps show that there is likely to be an increase in settlement area of 1.04% and decrease in 12.29% of agricultural land. The various thematic layers like slope, soil and curve number (CN) maps are also prepared using GIS. The composite CNs are generated for various land classes in the basin from 1986 to 2036. The increase in CN from 72 to 74.76 and their influence on runoff are studied. Finally, the study attempts to reliably estimate the present and future LULC status and its effect on surface runoff in the Koraiyar river basin.

Mining Design Heuristics for Additive Manufacturing Via Eye-Tracking Methods and Hidden Markov Modeling

Abstract In this research, we collected eye-tracking data from nine engineering graduate students as they redesigned a traditionally manufactured part for additive manufacturing (AM). Final artifacts were assessed for manufacturability and quality of final design, and design behaviors were captured via the eye-tracking data. Statistical analysis of design behavior duration shows that participants with more than 3 years of industry experience spend significantly less time removing material and revising than those with less experience. Hidden Markov modeling (HMM) analysis of the design behaviors gives insight to the transitions between behaviors through which designers proceed. Findings show that high-performing designers proceeded through four behavioral states, smoothly transitioning between states. In contrast, low-performing designers roughly transitioned between states, with moderate transition probabilities back and forth between multiple states.

Comparison of water availability in 2015 and 2022 based on land cover in the Maros River Basin

Forest as one of permanent vegetation can have an impact either to increase or decrease the quality of a watershed due to the changes in land cover. Land use activities that are changing landscapes within a watershed can often affect water yield. This study aims to determine the availability of water in Maros watershed in 2015 and 2022 and to identify the sub-watershed experiencing changes in water availability. Data analysis in this study uses the SWAT (Soil and Water Assessment Tool) model. Input data used in the analysis are the 2015 land use map, 2022 land cover projected map (Markov model analysis), soil type map, and Digital Elevation Model (DEM) data. The results showed that land cover in 2015 and 2022 was projected to change. The number of settlement and rice fields area was increased, while the forest vegetation was decreased. The change in land cover will affect water availability. The result of the SWAT modeling analysis showed that the availability of water in Maros Watershed in 2015 was 1,303,169,370 m3, and in 2022 was 1,322,495,380 m3. The volume of water availability from 2015 to 2022 has increased by 19,326,010 m3. The increase that occurred was influenced by the surface flow, which increased from 2015 to 2022. There are 38 sub-watershed, which have decreased water availability, 26 sub-watersheds in which the water availability was increased while one sub-watershed did not change at all.

Hidden Markov model technique for dynamic spectrum access

Dynamic spectrum access is a paradigm used to access the spectrum dynamically. A hidden Markov model (HMM) is one in which you observe a sequence of emissions, but do not know the sequence of states the model went through to generate the emissions. Analysis of hidden Markov models seeks to recover the sequence of states from the observed data. In this paper, we estimate the occupancy state of channels using hidden Markov process. Using Viterbi algorithm, we generate the most likely states and compare it with the channel states. We generated two HMMs, one slowly changing and another more dynamic and compare their performance. Using the Baum-Welch algorithm and maximum likelihood algorithm we calculated the estimated transition and emission matrix, and then we compare the estimated states prediction performance of both the methods using stationary distribution of average estimated transition matrix calculated by both the methods.

Public health and cost consequences of time delays to thrombectomy for acute ischemic stroke.

To determine public health and cost consequences of time delays to endovascular thrombectomy (EVT) for patients, health care systems, and society, we estimated quality-adjusted life-years (QALYs) of EVT-treated patients and associated costs based on times to treatment. The Markov model analysis was performed from US health care and societal perspectives over a lifetime horizon. Contemporary data from 7 trials within the Highly Effective Reperfusion Evaluated in Multiple Endovascular Stroke Trials (HERMES) collaboration served as data source. Aside from cumulative lifetime costs, we calculated the net monetary benefit (NMB) to determine the economic value of care. We used a contemporary willingness-to-pay threshold of $100,000 per QALY for NMB calculations. Every 10 minutes of earlier treatment resulted in an average gain of 39 days (95% prediction interval 23-53 days) of disability-free life. Overall, the cumulative lifetime costs for patients with earlier or later treatment were similar. Patients with later treatment had higher morbidity-related costs but over a shorter time span due to their shorter life expectancy, resulting in similar lifetime costs as in patients with early treatment. Regarding the economic value of care, every 10 minutes of earlier treatment increased the NMB by $10,593 (95% prediction interval $5,549-$14,847) and by $10,915 (95% prediction interval $5,928-$15,356) taking health care and societal perspectives, respectively. Any time delay to EVT reduces QALYs and decreases the economic value of care provided by this intervention. Health care policies to implement efficient prehospital triage and to accelerate in-hospital workflow are urgently needed.

Beam tracking algorithm for marine applications using visible light communication.

Visible light communication (VLC) offers a unique advantage of electromagnetic interference immunity and wide bandwidth. It has gradually become the main candidate in marine communication with great potential. As a green communication link, VLC requires reliable beam tracking as a prerequisite. Therefore, based on the received signal strength of a single-input-multiple-output system in the VLC scenario, this paper first proposes a geometrical algorithm for transmitter localization. On this basis, a linear iterative algorithm using Taylor expansion, implicit function theorem, and time-domain expansion is presented to realize online beam tracking. Under the marine VLC system, an iterative denoising algorithm based on the hidden Markov model and principal component analysis is proposed for denoising. Simulation results show that the proposed algorithms have predominance in high tracking accuracy (within 10 cm) and desirable real-time performance.

Spatial Dependence of Stably Stratified Nocturnal Boundary-Layer Regimes in Complex Terrain

The stably stratified atmospheric boundary layer (SBL) has been found in previous studies to display distinct regimes of behaviour. In particular, a contrast is often drawn between the weakly (wSBL) and very (vSBL) stable boundary layers. Time series of SBL regime affiliation obtained from hidden Markov model analyses of data from three different towers at the Los Alamos National Laboratory are used to investigate the spatial dependence of SBL regime occupation and SBL transitions. The local topography influences the flow such that south-west to north-east flow prevails, for which wSBL and vSBL conditions respectively are more likely to occur. Joint probabilities of shared regime occupation at the three towers (with and without conditioning on wind direction) are much larger than would be expected from statistically independent regime sequences at the different locations. Very persistent wSBL nights (without any transitions to the vSBL) have a higher probability of occurring across the entire tower network domain than very persistent vSBL nights. Many regime transitions occur within a narrow time window between the different towers; occurrence probabilities of such events are much higher than would be expected from statistically independent regime transitions. Of such events, transitions occurring at exactly the same time across the tower network occur most often. Many co-occurring turbulence recovery events can be associated with high-intensity intermittent turbulence events. Our results imply that the scale on which the SBL regime occupation and transitions are dependent exceeds 10 km in this region of complex terrain.