Modeling and Analysis of Postdisaster Aviation Medical Rescue Process Using SPN-MC by Qingsong Zhang, Xuezheng Wang, Yijia Jin, Xingna Luo

“…The dynamic discrete modeling of the postdisaster aviation medical rescue process was carried out in combination with the stochastic Petri net (SPN) theory, and the effectiveness of the process of postdisaster aviation medical rescue was analyzed by using the isomorphism relationship between SPN and Markov chain (MC). Finally, the Sichuan Jiuzhaigou Earthquake event was taken as a case study to carry out the performance analysis and sensitivity analysis of the postdisaster aviation medical rescue process model. …”
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Global Multi-Phase Path Planning Through High-Level Reinforcement Learning by Babak Salamat, Sebastian-Sven Olzem, Gerhard Elsbacher, Andrea M. Tonello

“…Our approach utilizes a Markov Decision Process (MDP) framework and high-level reinforcement learning techniques to ensure trajectory smoothness, continuity, and compliance with constraints. …”
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Low-Redshift Observational Constraints on Dark Energy Cosmologies by Mohammad Malekjani

“…Applying the Markov chain Monte Carlo algorithm and using low-redshift observational data, we put cosmological constraints on dark energy cosmologies. …”
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Power Data Access Viewer-Based Meteorological Drought Analysis and Rainfall Variability in the Nile River Basin by Birara Gebeyhu

“…Bias correction procedures were implemented to refine the monthly rainfall data for Bahirdar, Markos, Nekemt, and Muger stations, resulting in notable improvements in the coefficient of determination (R2) that were increased from 0.74 to 0.93, 0.72 to 0.89, 0.71 to 0.96, and 0.69 to 0.84, respectively. …”
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Uncertainty Quantification of GEKO Model Coefficients on Compressible Flows by Yeong-Ki Jung, Kyoungsik Chang, Jae Hyun Bae

“…The affine invariant ensemble algorithm (AIES) is selected to characterize the posterior distribution via Markov chain Monte Carlo sampling. Calibrated model coefficients are extracted from posterior distributions obtained through Bayesian inference, which is based on the point-collocation nonintrusive polynomial chaos (NIPC) method. …”
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Application and Analysis of Multicast Blocking Modelling in Fat-Tree Data Center Networks by Guozhi Li, Songtao Guo, Guiyan Liu, Yuanyuan Yang

“…Then the blocking probability of downlink in the next time-slot is calculated in multicast subnetwork by using Markov chains theory. With the obtained probability, we select the optimal downlink based on the available core switch. …”
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Does Regime-Dependent Volatility Drive Dynamism in Investor Herding? by Komal Jindal, Meera Bamba, Mamta Aggarwal

“…Hence, the objective of this paper is to investigate the dynamic herd behaviour for S&P BSE 500 from 2009-2023 using the Markov Regime Switching model. Results exhibit the occurrence of three regimes, namely, high, low, and extremely volatile regimes. …”
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Analysis of anomalous behaviour in network systems using deep reinforcement learning with convolutional neural network architecture by Mohammad Hossein Modirrousta, Parisa Forghani Arani, Reza Kazemi, Mahdi Aliyari‐Shoorehdeli

“…The objective is to develop a system for intrusion detection based on deep reinforcement learning. On the basis of the Markov decision procedure, the developed system can construct patterns appropriate for classification purposes based on extensive amounts of informative records. …”
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A Novel Energy Consumption Prediction Model Integrating Real-Time Traffic State Recognition and Velocity Prediction of BEVs by Yue Li, Yu Jiang, Jianhua Guo, Dong Xie

“…In addition, a Fuzzy-Markov-based velocity prediction model is developed to generate future velocity profiles under diverse traffic scenarios. …”
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Application of CNN-LSTM Model for Vehicle Acceleration Prediction Using Car-following Behavior Data by Shuning Tang, Yajie Zou, Hao Zhang, Yue Zhang, Xiaoqiang Kong

“…The analysis results from the coupled hidden Markov model (CHMM) are used to evaluate the driving behavior differences between different drivers by Wasserstein distance. …”
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Predicting Wet-Road Crashes Using the Finite-Mixture Zero-Truncated Negative Binomial Model by Ying Chen, Zhongxiang Huang

“…The model is applied to three-year wet-road crash data on 395 divided roadway segments (total 586 km), and the parameters are estimated using the Markov chain Monte Carlo (MCMC) method. Comparison indicates that the proposed FMZTNB model has better fitting performance and is more accurate in predicting the number of wet-road crashes. …”
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Calibration verification for stochastic agent-based disease spread models. by Maya Horii, Aidan Gould, Zachary Yun, Jaideep Ray, Cosmin Safta, Tarek Zohdi

“…The first calibration method is a Bayesian inference approach using an empirically-constructed likelihood and Markov chain Monte Carlo (MCMC) sampling, while the second method is a likelihood-free approach using approximate Bayesian computation (ABC). …”
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Is non-contrast-enhanced magnetic resonance imaging cost-effective for screening of hepatocellular carcinoma? by Genevieve Jingwen Tan, Chau Hung Lee, Yan Sun, Cher Heng Tan

“…Methods: Cost-effectiveness analysis of no surveillance, US surveillance and NCEMRI surveillance was performed using Markov modelling and microsimulation. At-risk patient cohort was simulated and followed up for 40 years to estimate the patients’ disease status, direct medical costs and effectiveness. …”
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Crisis Monitoring in Financial Sectors Using CAMEL Partial Triadic Analysis Model by Rody Guzman-Garzon, Purificacion Galindo-Villardon, Purificacion Vicente-Galindo

“…Future research could extend the model by integrating all balance sheet variables against the base CPTA model through the application of Hidden Markov models.…”
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Multiple Unmanned Aerial Vehicle Collaborative Target Search by DRL: A DQN-Based Multi-Agent Partially Observable Method by Heng Xu, Dayong Zhu

“…The simulation environment for UAV target search is constructed through the custom Markov Decision Process. By comparing the PODQN algorithm with random strategy, DQN, Double DQN, Dueling DQN, VDN, QMIX, it is demonstrated that the proposed PODQN algorithm has the best performance under different agent configurations.…”
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Advanced Monte Carlo for Acquisition Sampling in Bayesian Optimization by Javier Garcia-Barcos, Ruben Martinez-Cantin

“…We introduce a simplified version of Boltzmann sampling, and we analyze multiple Markov chain Monte Carlo (MCMC) methods with a numerically improved log EI implementation for acquisition sampling. …”
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Multifeature Contrast Enhancement Algorithm for Digital Media Images Based on the Diffusion Equation by Jijun Wang, Yi Yuan, Guoxiang Li

“…In this paper, the proposed algorithm experiments with several sets of Kor Kor resolution remote sensing images, respectively, and the Markov random field model and fully convolutional network (FCN) algorithm are used as the comparison algorithm. …”
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Clinical and Economic Evaluation of Transdermal Oxybutynin in the Treatment of Overactive Bladder in Spain by Carlos Crespo, Pedro Blasco, Marcelo Guigini, Jordi Galván

“…**Materials and Methods:** A Markov model was built with monthly cycles for a 5-year time frame. …”
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Cost-effectiveness analysis of eribulin versus dacarbazine in patients with advanced liposarcoma by Miaomiao Zhang, Jinlong Huang, Xiaochun Zheng, Ping Huang, Xiuli Yang

“…We established a 10-year Markov model to compare the cost-effectiveness of eribulin and dacarbazine regimens. …”
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A Midcourse Guidance Method Combined with Trajectory Prediction for Antinear-Space-Gliding Vehicles by JiaQing Wan, Qian Zhang, Hao Zhang, JiaNing Liu

“…An interactive multiple-model method of variable structure is proposed to identify the characteristics of the maneuver mode by introducing a fading factor and the modified Markov probability transfer matrix and then predict the trajectory through numerical integration. …”
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