Transition Probability Model Research Articles

A unified in-time correction-based testability growth model and its application on test planning

Test management is a critical problem for in-time correction-based testability growth test. For the existing testability growth model, they are either too complex to be implemented in practice, or do not have the ability to draw a smooth test planning/projecting curve. This paper proposes a unified model for in-time correction-based testability growth test and presents its application on the test planning. Firstly, a simple model with only three parameters is developed, and its compatibility with the original Markov model is proved. The revised model only has three parameters, which can reduce the complexity of parameters estimation and increase the certainty of the test planning. Secondly, the paper incorporates the simplified transition probability model with the test cost model and studies the optimal test planning method with the minimum test cost criterion. To illustrate the efficacy of the proposed model, an application of the model to an attitude control system of a helicopter available in the open literature is given. The simulation demonstrates that the model and the method proposed in this paper are reasonable, and they are useful for effectively managing the testability growth test planning problem during system maturation.

Cost-Utility Analysis of Dabigatran and Warfarin for Stroke Prevention Among Patients With Nonvalvular Atrial Fibrillation in India

ObjectivesDabigatran has a better safety profile and requires less monitoring, but is costlier than warfarin. This study evaluated the cost-utility of dabigatran relative to warfarin for preventing stroke in nonvalvular atrial fibrillation (NVAF) in India. MethodsA Markov decision analysis model was developed to compare dabigatran (110 or 150 mg twice a day) to warfarin titrated to target prothrombin time in patients with NVAF at high risk of stroke. Model utilities and transition probabilities were based on literature and costs on market prices. Data on out-of-pocket expenses and income lost were taken from a nationally representative survey. We adopted a societal perspective and discounted both costs and outcomes at 3%. Ischemic stroke, intracranial bleed, other major bleeds, and death were outcomes of NVAF. The model projected the costs, life-years, and quality-adjusted life-years (QALYs) for each intervention over a lifetime. We used gross domestic product per capita of India (US dollars [US$]1889) as the cost-effectiveness threshold. Sensitivity analyses were conducted. ResultsTreatment with either dose of dabigatran was associated with gain in life-years and QALYs compared with warfarin. The discounted incremental cost-effectiveness ratios/QALYs for both doses of dabigatran (110 mg US$7519; 150 mg US$6634) were above the cost-effectiveness threshold, and the probability of being cost-effective at this threshold was low. Cost of dabigatran was an important factor in determining incremental cost-effectiveness ratio. Price reduction of 150 mg dose by 49% will make it cost-effective. ConclusionsDabigatran is not cost-effective in the Indian societal context. Reducing the price of dabigatran 150 mg by half will make it cost-effective.

Cost-effectiveness analysis of arthroscopic injection of a bioadhesive hydrogel implant in conjunction with microfracture for the treatment of focal chondral defects of the knee – an Australian perspective

Aim JointRep is a bioadhesive hydrogel arthroscopically injected to facilitate cartilage regeneration. The cost-effectiveness of JointRep with microfracture surgery compared to microfracture alone was evaluated from the Australian healthcare system perspective, in patients with symptomatic focal chondral defects (Outerbridge Grade 3 or 4) of the knee who had failed conservative treatment and were indicated for surgery. Materials and methods A de novo Markov model comprising two health states- ‘Alive’ and ‘Dead’ was developed. Model transition probability was based on the general population mortality rates. Clinical outcomes were assessed using the Western Ontario and McMaster Universities Osteoarthritis Index (WOMAC) scores, a validated patient-reported tool measuring pain, stiffness, and physical function. The utility was derived by mapping WOMAC scores to EQ-5D scores using a published algorithm. Cost inputs were based on published Australian costs from AR-DRGs, Medicare Benefits Schedule, and Prostheses List. Model outcomes included costs, Quality-adjusted life years (QALYs), and incremental cost-effectiveness ratio (ICER). Base-case analysis was conducted for a time horizon of 3 years and a cycle length of 1 year. Cost and health outcomes were discounted at 5% per annum. Sensitivity and scenario analyses were also conducted. Results Total QALYs were estimated to be higher for JointRep with microfracture surgery (2.61) compared to microfracture surgery alone (1.66), an incremental gain of 0.95 QALY. JointRep with microfracture surgery was associated with an incremental cost of $6,022 compared to microfracture surgery alone, thus leading to an ICER of $6,328. Results were substantially robust to varying parameters in the sensitivity analyses conducted, alternative model settings and assumptions in scenario analyses. Limitations The clinical inputs used in the model were based on data from short duration, non-randomized, post-market clinical trial. Conclusions JointRep with microfracture surgery is a cost-effective treatment option compared to microfracture alone from the Australian health care system perspective.

MIDP: An MDP-based intelligent big data processing scheme for vehicular edge computing

The number of Vehicle Equipment (VE) connected to the Internet is increasing, and these VEs generate tasks that contain large amounts of data. Processing these tasks requires a lot of computing resources. Therefore, it is a promising issue that offloading compute-intensive tasks from resource-limited vehicles to Vehicular Edge Computing (VEC) servers, which involves big data transmission, processing and computation. In a network, multiple providers provide VEC servers. When a vehicle generates a task, our goal is to make an intelligent decision on whether and when to offload this task to VEC servers to minimize the task completion time and total big data processing time. When each vehicle passes VEC servers, the vehicle can decide to offload its task to the VEC server in the current communication range, or continue to drive until it reaches the next server's communication range. This issue can be considered as an asset selling problem. It is a challenging issue to make a smart decision for the vehicle with a location view because the vehicle is not sure when the next VEC server will be available and how much about the available computing capacity of the next VEC server. Firstly, this paper formulates the problem as a Markov Decision Process (MDP), defines and analyzes the state set, action set, reward model, and state transition probability distribution. Then it uses Asynchronous Advantage Actor-Critic (A3C) algorithm to solve this MDP problem, builds the various elements of the A3C algorithm, uses Actor (the strategy function) to generate two actions of the vehicle: offloading and moving without offloading. Thirdly, it uses Critic (the value function) to evaluate Actor's behavior, and guide Actor's actions in subsequent stages. The Actor starts from the initial state in the state space until it enters the termination state, forming a complete decision-making process. It minimizes the completion time of task offloading through learning thereby reducing the delay of big data processing. Compared to the Immediately Offload (IO) scheme and Expect Offload (EO) scheme, the MIDP scheme proposed in this paper reduces the average task offloading delay to 29.93% and 29.99%, close to the EO scheme in terms of task completion rate and up to 66.6% improvement compared to the IO scheme.

BJS.03Markov Decision Analysis of Treatment Pathways for Resectable Malignancy of the Oesophagus or Gastrooesophageal Junction

Abstract Aims To perform decision-analysis of treatment options for resectable malignancy of the oesophagus or gastroesophageal junction including: surgery alone, neoadjuvant chemoradiotherapy, neoadjuvant chemotherapy and surgery followed by adjuvant therapy based on current highest-level evidence. Methods A Markov decision analysis model in an advanced decision-tree format was constructedand populated with data from existing randomised controlled trials. Markov model transition probabilities were based on weighted pooled estimates of proportions from included studies, calculated using Freeman-Tukey arcsine square root transformation under random effects model to account for heterogeneity.Each Markov cycle equated with one month and Markov states within the model included: alive without disease, alive with disease and dead. Extensive deterministic and Monte Carlo probabilistic sensitivity analysis was performed to test all parameters contained within the model. Results 23 randomised controlled trials were included. Intention-to-treat analysis of the treatment pathways showed that neoadjuvant chemoradiotherapy was the superior pathway with an overall survival time of 50.52 months (42.26 QALMs). Monte Carlo sensitivity analysis run over 10000 iterations showed that neoadjuvant chemoradiotherapy was selected as the superior treatment pathway at a frequency of 93.21% followed by surgery followed by adjuvant therapy with a frequency of 6.79%. Subgroup analysis of only squamous cell carcinomas demonstrated that neoadjuvant chemoradiotherapy was the superior pathway with an overall survival time of 62.67 months (55.22 QALMs). Conclusions Based on current best available evidence this decision analysis supports neoadjuvant chemoradiotherapy as the treatment strategy of choice for resectable malignancy of the oesophagus or gastroesophageal junction.

Runoff forecast and analysis of the probability of dry and wet transition in the Hanjiang River Basin

Runoff prediction plays an important guiding role in water resources planning and management, flood and drought prevention. As the Hanjiang River Basin (HRB) is the water source of the middle line of the South-to-North Water Diversion Project, it has higher requirements for water resources accurate prediction. In order to analyze the prediction capabilities of different prediction methods for the HRB runoff, this study constructed 12 prediction models to simulate and predict the runoff of four hydrological stations in the HRB. Furthermore, the Markov Chain Monte Carlo (MCMC) method was used to analyze the transition probability of runoff from low-to-high (high-to-low). The results showed that the runoff of four hydrological stations in the HRB all showed a downward trend, and most of the sudden changes occurred in the 1980 s. The smoother the runoff changes, the easier it is to make accurate prediction. Among the 12 models, the quadric spline Markov forecasting model (QS-MFM), moving average Markov forecasting model (MA-MFM), Markov forecasting model (MFM), deep neural networks (DNN), and cubic exponential smoothing (CES) methods have stronger generalization ability and can more accurately predict the runoff of the HRB. The average relative error during the validation period is 0.27, 0.28, 0.33, 0.34 and 0.39, respectively. The logistic model can accurately simulate the change of runoff status in the HRB. The wet threshold of Baihe (BH), Huanglongtan (HLT), Huangjiagang (HJG), and Huangzhuang (HZ) is 819.9 m3/s, 207.4 m3/s, 1313.9 m3/s and 1681.7 m3/s, and the dry threshold is 480.4 m3/s, 130.6. m3/s, 817.8 m3/s and 1083.4 m3/s, respectively. The MCMC method can accurately estimate the parameters of the logistic model, and the low-high (high-low) runoff transition probability model constructed in the HRB can accurately calculate the low to high (high to low) runoff transition probability.

Cell Cycle Commitment and the Origins of Cell Cycle Variability.

Exit of cells from quiescence following mitogenic stimulation is highly asynchronous, and there is a great deal of heterogeneity in the response. Even in a single, clonal population, some cells re-enter the cell cycle after a sub-optimal mitogenic signal while other, seemingly identical cells, do not, though they remain capable of responding to a higher level of stimulus. This review will consider the origins of this variability and heterogeneity, both in cells re-entering the cycle from quiescence and in the context of commitment decisions in continuously cycling populations. Particular attention will be paid to the role of two interacting molecular networks, namely the RB-E2F and APC/CCDH1 “switches.” These networks have the property of bistability and it seems likely that they are responsible for dynamic behavior previously described kinetically by Transition Probability models of the cell cycle. The relationship between these switches and the so-called Restriction Point of the cell cycle will also be considered.

Time series simulation for multiple wind farms based on HMMs and regular vine copulas

The randomness and fluctuation of wind power cause difficulties in electric utilities operation and dispatch. Although the methods for simulating wind power time series have been studied by researchers for decades, methods designed for the single wind farm output or the aggregated output of multiple wind farms are inefficient in capturing the spatial dependence among regional multiple wind farms. Also, abnormal or missing data are quite common in the wind power field, which is often ignored by traditional multi-dimensional correlation modeling methods, resulting in being imprecise and unstable encountering the situation of missing data. In this paper, the discrete hidden Markov model (HMM) and regular vine copulas are applied to accurately reproduce the joint distribution of regional wind farms. The regular vine copulas can take various bivariate copulas as blocks to precisely and flexibly describe the dependence structures of multiple wind farm outputs, and using HMM, we were able to model transition probability for different dependence structures. Enough synthetic time series of multiple wind farms can be generated by the proposed method, and it is of great significance for the long-term scheduling of power systems. The effectiveness of the proposed method is tested using the datasets of five wind farms in Northwest China as a case. The simulation results prove that the proposed method can accurately model the dependence structures among multiple wind farms and statistical characteristics of power outputs, and are more robust when there are missing data in wind power records especially.

A proposed new generation of evidence-based microsimulation models to inform global control of cervical cancer

Health decision models are the only available tools designed to consider the lifetime natural history of human papillomavirus (HPV) infection and pathogenesis of cervical cancer, and the estimated long-term impact of preventive interventions. Yet health decision modeling results are often considered a lesser form of scientific evidence due to the inherent needs to rely on imperfect data and make numerous assumptions and extrapolations regarding complex processes. We propose a new health decision modeling framework that de-emphasizes cytologic-colposcopic-histologic diagnoses due to their subjectivity and lack of reproducibility, relying instead on HPV type and duration of infection as the major determinants of subsequent transition probabilities. We posit that the new model health states (normal, carcinogenic HPV infection, precancer, cancer) and corollary transitions are universal, but that the probabilities of transitioning between states may vary by population. Evidence for this variability in host response to HPV infections can be inferred from HPV prevalence patterns in different regions across the lifespan, and might be linked to different average population levels of immunologic control of HPV infections. By prioritizing direct estimation of model transition probabilities from longitudinal data (and limiting reliance on model-fitting techniques that may propagate error when applied to multiple transitions), we aim to reduce the number of assumptions for greater transparency and reliability. We propose this new microsimulation model for critique and discussion, hoping to contribute to models that maximally inform efficient strategies towards global cervical cancer elimination.

Cost effectiveness of DPYD genotyping to screen for dihydropyrimidine dehydrogenase (DPD) deficiency prior to adjuvant chemotherapy for colon cancer.

55 Background: Fluoropyrimidine chemotherapy agents, including 5-fluorouracil and capecitabine, are the backbone of adjuvant treatment for colon cancer, and adjuvant chemotherapy substantially reduces recurrence and mortality after surgical resection of stage 3 colon cancer. While fluoropyrimidine chemotherapy is generally safe, the risk of severe, potentially fatal chemotherapy toxicity is substantially increased for the 2-3% of U.S. patients with DPD deficiency caused by pathogenic variants in the DPYD gene. DPYD genotype testing is readily available in the U.S. but has not been widely adopted. We evaluated the cost effectiveness of DPYD genotyping prior to adjuvant chemotherapy for colon cancer in the U.S. Methods: We constructed a Markov model to simulate screening for DPD deficiency with DPYD genotyping (versus no screening) among patients receiving fluoropyrimidine-based adjuvant chemotherapy for stage 3 colon cancer. Screen-positive patients were modeled to receive dose-reduced fluoropyrimidine chemotherapy. Model transition probabilities for treatment-related toxicities were derived from published clinical trial data with annotation of DPYD genotype and chemotherapy dosing strategy. Our analysis is from the healthcare perspective, with a time horizon of five years and an annual discount rate of 3% for future costs and benefits. Direct healthcare costs and health utilities were estimated from published sources and converted to 2020 US dollars, and post-treatment survival was modeled from SEER data. The primary outcome was the incremental cost-effectiveness ratio (ICER), defined as dollars per quality-adjusted life year (QALY). We used a value of $100,000/QALY as the cost-effectiveness threshold. One-way sensitivity analyses were used to examine model uncertainty. Results: Compared with no screening, screening for DPD deficiency with DPYD genotyping increased per-patient costs by $106 and improved quality-adjusted survival by 0.0028 QALYs, leading to an ICER of $37,300/QALY. In one-way sensitivity analyses, the ICER exceeded $100,000/QALY when the carrier frequency of pathogenic DPYD gene variants was less than 1.17%, and when the specificity of DPYD genotyping was less than 98.9%. Cost-effectiveness estimates were not sensitive to the cost of DPYD genotyping, the cost of toxicity-related hospitalizations, or the health utility associated with grade 3-4 toxicity. Conclusions: Among patients receiving adjuvant chemotherapy for stage 3 colon cancer, screening for DPD deficiency with DPYD genotyping is a cost-effective strategy for preventing infrequent but severe, sometimes fatal toxicities of fluoropyrimidine chemotherapy.

A theory of memory for binary sequences: Evidence for a mental compression algorithm in humans.

Working memory capacity can be improved by recoding the memorized information in a condensed form. Here, we tested the theory that human adults encode binary sequences of stimuli in memory using an abstract internal language and a recursive compression algorithm. The theory predicts that the psychological complexity of a given sequence should be proportional to the length of its shortest description in the proposed language, which can capture any nested pattern of repetitions and alternations using a limited number of instructions. Five experiments examine the capacity of the theory to predict human adults’ memory for a variety of auditory and visual sequences. We probed memory using a sequence violation paradigm in which participants attempted to detect occasional violations in an otherwise fixed sequence. Both subjective complexity ratings and objective violation detection performance were well predicted by our theoretical measure of complexity, which simply reflects a weighted sum of the number of elementary instructions and digits in the shortest formula that captures the sequence in our language. While a simpler transition probability model, when tested as a single predictor in the statistical analyses, accounted for significant variance in the data, the goodness-of-fit with the data significantly improved when the language-based complexity measure was included in the statistical model, while the variance explained by the transition probability model largely decreased. Model comparison also showed that shortest description length in a recursive language provides a better fit than six alternative previously proposed models of sequence encoding. The data support the hypothesis that, beyond the extraction of statistical knowledge, human sequence coding relies on an internal compression using language-like nested structures.

Using Penalized EM Algorithm to Infer Learning Trajectories in Latent Transition CDM.

Cognitive diagnostic models (CDMs) have arisen as advanced psychometric models in the past few decades for assessments that intend to measure students' mastery of a set of attributes. Recently, quite a few studies attempted to extend CDMs to longitudinal versions, and they all tended to model transition probabilities from non-mastery to mastery or vice versa for each attribute separately, with an exception of a few studies (e.g., Chen et al. 2018; Madison & Bradshaw 2018). However, these pioneering works have not taken into consideration the attribute relationships and the ever-changing attributes in a learning period. In this paper, we consider a profile-level latent transition CDM (TCDM hereafter), which can not only identify transition probabilities across the same attributes over time, but also the transition pathways across different attributes. Two versions of the penalized expectation-maximization (PEM) algorithms are proposed to shrink the probabilities associated with impermissible transition pathways to 0 and, thereby, help explore attribute relationships in a longitudinal setting. Simulation results reveal that PEM with group penalty holds great promise for identifying learning trajectories.

Interacting T-S fuzzy particle filter algorithm for transfer probability matrix of adaptive online estimation model

For the problem of inaccurate or difficult to obtain statistical characteristics of non-Gaussian noise, an interacting T-S fuzzy modeling algorithm is proposed to incorporate spatial-temporal information into particle filtering. In the proposed method, feature information is characterized by multiple semantic fuzzy sets, and the model transition probabilities are estimated by using the fuzzy set transition probabilities, which can be derived by the closeness degrees between the fuzzy sets. Furthermore, the correntropy can capture the statistical information to solve the non-Gaussian noise, thus a kernel fuzzy C-regression means (FCRM) based on correntropy and spatial-temporal information is proposed to adaptively identify the premise parameters of T-S fuzzy model, and a modified strong tracking method is used to estimate the consequence parameters. By using the proposed interacting T-S fuzzy model, an efficient importance density function is constructed for the particle filtering algorithm. Finally, the simulation results show that the tracking performance of the proposed algorithm is effective in processing non-Gaussian noise.

Evaluation of a Heart Failure Telemonitoring Program Through a Microsimulation Model: Cost-Utility Analysis.

BackgroundHeart failure (HF) is a major public health issue in Canada that is associated with high prevalence, morbidity, and mortality rates and high financial and social burdens. Telemonitoring (TM) has been shown to improve all-cause mortality and hospitalization rates in patients with HF. The Medly program is a TM intervention integrated as standard of care at a large Canadian academic hospital for ambulatory patients with HF that has been found to improve patient outcomes. However, the cost-effectiveness of the Medly program is yet to be determined.ObjectiveThis study aims to conduct a cost-utility analysis of the Medly program compared with the standard of care for HF in Ontario, Canada, from the perspective of the public health care payer.MethodsUsing a microsimulation model, individual patient data were simulated over a 25-year time horizon to compare the costs and quality-adjusted life years (QALYs) between the Medly program and standard care for patients with HF treated in the ambulatory care setting. Data were sourced from a Medly Program Evaluation study and literature to inform model parameters, such as Medly’s effectiveness in reducing mortality and hospitalizations, health care and intervention costs, and model transition probabilities. Scenario analyses were conducted in relation to HF severity and TM deployment models. One-way deterministic effectiveness analysis and probabilistic sensitivity analysis were performed to explore the impact on the results of uncertainty in model parameters.ResultsThe Medly program was associated with an average total cost of Can $102,508 (US $77,626) per patient and total QALYs of 5.51 per patient compared with the average cost of Can $97,497 (US $73,831) and QALYs of 4.95 per patient in the Standard Care Group. This led to an incremental cost of Can $5011 (US $3794) and incremental QALY of 0.566, resulting in an incremental cost-effectiveness ratio of Can $8850 (US $6701)/QALY. Cost-effectiveness improved in relation to patients with advanced HF and with deployment models in which patients used their own equipment. Baseline and alternative scenarios consistently showed probabilities of cost-effectiveness greater than 85% at a willingness-to-pay threshold of Can $50,000 (US $37,718). Although the results showed some sensitivity to assumptions about effectiveness parameters, the intervention was found to remain cost-effective.ConclusionsThe Medly program for patients with HF is cost-effective compared with standard care using commonly reported willingness-to-pay thresholds. This study provides evidence for decision makers on the use of TM for HF, supports the use of a nurse-led model of TM that embeds clinically validated algorithms, and informs the use of economic modeling for future evaluations of early-stage health informatics technology.

Multiple Model Poisson Multi-Bernoulli Mixture Filter for Maneuvering Targets

The Poisson multi-Bernoulli mixture (PMBM) filter is conjugate prior composed of the union of a Poisson point process (PPP) and a multi-Bernoulli mixture (MBM). Considering that the single model is not enough to guarantee stable tracking performance for maneuvering targets, in this article, a multiple model PMBM (MM-PMBM) filter is proposed to cope with this problem. The proposed MM-PMBM filter extends the single-model PMBM filter recursion to multiple motion models by exploiting the jump Markov system (JMS). The performance of the proposed algorithm is examined from two scenarios with different detection probabilities. Moreover, the robustness of Markovian model transition probability matrices (TPMs) for the proposed MM-PMBM filter is also explored. The simulation results demonstrate that the proposed MM-PMBM filter performs well in terms of the tracking accuracy, including the target states and cardinality estimates, and also has good tolerance with respect to different TPMs.

Multi-temperature vibrational energy relaxation rates in CO2

Rates of vibrational energy relaxation in carbon dioxide are studied in the framework of the three-temperature kinetic-theory approach. Vibrational–translational transitions in the bending mode and inter-mode exchange of vibrational quanta are considered. In the zero-order approximation of the generalized Chapman–Enskog method, the energy relaxation rates in the coupled symmetric–bending and asymmetric modes are expressed in terms of thermodynamic forces similar to chemical reaction affinities, and a compact representation for the vibrational energy production rates is proposed. Linearized theory is developed, and analytical ratios of linearized relaxation rates to those defined by the original Landau–Teller (LT) theory are obtained. The relaxation rates are calculated using the Schwartz–Slawsky–Herzfeld (SSH) and forced harmonic oscillator models for the vibrational energy transition probabilities in the temperature range 200 K–10 000 K. For inter-mode exchanges, using the SSH theory yields significantly underpredicted relaxation rates. The ranges of applicability for the LT formula and linearized theory are estimated; the original LT formula for inter-mode vibrational energy exchanges is not capable of accounting for the excitation of both vibrational modes; linearized models yield better results. Possible steps for improving the numerically efficient LT model are proposed.

Estimating Transition Probabilities from Published Evidence: A Tutorial for Decision Modelers.

This tutorial presents practical guidance on transforming various types of information published in journals, or available online from government and other sources, into transition probabilities for use in state-transition models, including cost-effectiveness models. Much, but not all, of the guidance has been previously published in peer-reviewed journals. Our purpose is to collect it in one location to serve as a stand-alone resource for decision modelers who draw most or all of their information from the published literature. Our focus is on the technical aspects of manipulating data to derive transition probabilities. We explain how to derive model transition probabilities from the following types of statistics: relative risks, odds, odds ratios, and rates. We then review the well-known approach for converting probabilities to match the model’s cycle length when there are two health-state transitions and how to handle the case of three or more health-state transitions, for which the two-state approach is not appropriate. Other topics discussed include transition probabilities for population subgroups, issues to keep in mind when using data from different sources in the derivation process, and sensitivity analyses, including the use of sensitivity analysis to allocate analyst effort in refining transition probabilities and ways to handle sources of uncertainty that are not routinely formalized in models. The paper concludes with recommendations to help modelers make the best use of the published literature.

Postdisaster Housing Stages: A Markov Chain Approach to Model Sequences and Duration Based on Social Vulnerability.

Housing recovery is an unequal and complex process presumed to occur in four stages: emergency shelter, temporary shelter, temporary housing, and permanent housing. This work questions the four-stage typology and examines how different types of shelter align with multiple housing recovery stages given different levels of social vulnerability. This article also presents a Markov chain model of the postdisaster housing recovery process that focuses on the experience of the household. The model predicts the sequence and timing of a household going through housing recovery, capturing households that end in either permanent housing or a fifth possible stage of failure. The probability of a household transitioning through the stages is computed using a transition probability matrix (TPM). The TPM is assembled using proposed transition probability models that vary with the social vulnerability of the household. Monte Carlo techniques are applied to demonstrate the range of sequences and timing that households experience going through the housing recovery process. A set of computational rules are established for sending a household to the fifth stage, representing a household languishing in unstable housing. This predictive model is exemplified on a virtual community, Centerville, where following a severe earthquake scenario, differences in housing recovery times exceed four years. The Centerville analysis results in nearly 5% of households languishing in unstable housing, thereby failing to reach housing recovery. These findings highlight the disparate trajectories experienced by households with different levels of social vulnerability. Recommendations are provided at the end for more equitable postdisaster recovery policies.

Analyzing land use structure efficiency with carbon emissions: A case study in the Middle Reaches of the Yangtze River, China

China has a large population and limited land resources. The dearth of available land poses considerable challenges for economic development and environmental protection. In order to realize maximum benefits through the optimization of land use structure, land use structure efficiency (LUSE) becomes an important planning guideline for the sustainable development of a region. This paper conducts a case study in the Middle Reaches of the Yangtze River (MRYR) and investigates the LUSE of the region considering the effect of carbon emissions from 1995 to 2018 by using the slack-based measure with undesirable output (SBM-Undesirable) model and Markov transition probability matrix with spatial effect. The results show that: (1) the average LUSE in the study area is 0.652 when the carbon emissions are not considered, compared with that of 0.569 when the carbon emissions are factored in. The comparative result indicates that carbon emissions have a great impact on the LUSE; (2) the LUSE has a rising trend during the study period (except for 2010) starting with a value of 0.463 in 1995 and ending with a value of 0.671 in 2018. Furthermore, the spatial pattern of the LUSE in the study area has been through a drastic change, with the high-value areas clustered at a single center in 1995 evolving to a multiple-center pattern in 2018; (3) High LUSE areas have a positive spillover effect on its neighboring areas, while low LUSE areas have negative spillover effects. The results can provide insights into the optimization of land use structure and the efficient utilization of land resources. This study also suggests that policy makers should optimize the industrial structure and propose policy initiatives oriented towards goals of energy-saving and emission reduction to facilitate regional planning and sustainable development.

Cross-Layer Defense Methods for Jamming-Resistant CBTC Systems

Communication-based Train Control (CBTC) systems are the burgeoning directions for developing future train control systems. With the adoption of wireless communication and network techniques, train control systems are more vulnerable to cyber-attacks. Notably, the jamming attacks, aiming at the handoff process that is the weakest part of train ground communication systems, will cause long disruption of communication. It will have a severe impact on train control operation efficiency. Current research regarding industry control system security is hard to model the impact of the jamming attacks on the train control system quantitatively, and current countermeasure schemes against jamming attacks are not designed for the operating mechanism of train control systems. This paper first builds the train control security state transition probability model under jamming attacks. A cross-layer defense scheme is then proposed from the aspect of the physical layer, the cyber layer and the management layer. In the physical layer, this paper designs a model prediction control algorithm to track dynamic target signals, in the hopes of eventually tracking the dynamic target quickly and smoothly. In the cyber layer, a multi-stage and zero-sum stochastic game model is built for the channel selection for the attack and the defense, whereby the channel selection randomized policy will be obtained. In the management layer, a dynamic train travel speed profile generation algorithm is proposed to mitigate the jamming attacks’ impact on train control systems. Extensive simulation results are shown that jamming attack impact on CBTC can be mitigated effectively with our proposed cross-layer defense scheme.