Article
Mathematics, Applied
Stefania Ottaviano
Summary: In this study, we investigate the threshold dynamics of a stochastic SAIRS-type model with vaccination, considering the explicit roles of asymptomatic and symptomatic infectious individuals in the epidemic dynamics. The disease transmission rate in the model can switch between different levels under a semi-Markov process. We provide sufficient conditions for almost sure epidemic extinction and persistence in time mean, and also explore the omega-limit set and conditions for the existence and uniqueness of an invariant probability measure in the case of disease persistence.
MATHEMATICAL METHODS IN THE APPLIED SCIENCES
(2023)
Article
Operations Research & Management Science
N. Azevedo, D. Pinheiro, S. Pinheiro
Summary: In this paper, we study a stochastic optimal control problem with state variable dynamics described by a stochastic differential equation modulated by a semi-Markov process. We provide a detailed proof of the dynamic programming principle and show that the value function can be characterized as a viscosity solution of the corresponding Hamilton-Jacobi-Bellman equation. We illustrate our results with an application to the generalization of Merton's optimal consumption-investment problem to financial markets with semi-Markov switching.
Article
Computer Science, Artificial Intelligence
Tao Li, Zhaojie Wang, Guoyu Yang, Yang Cui, Yuling Chen, Xiaomei Yu
Summary: Selfish mining attacks aim to obtain higher revenues compared with honest parties by exploiting vulnerabilities in the consensus mechanism, but are impractical due to high forking rates leading honest parties to exit the system. An improved selfish mining approach based on hidden Markov decision processes (SMHMDP) is proposed, which can balance revenues and forking rates by allowing semi-selfish miners to mine on the public chain with a small probability rho. Simulation results show that SMHMDP can benefit selfish miners within an acceptable forking rate without becoming an armchair strategist.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2021)
Article
Computer Science, Artificial Intelligence
Zepeng Ning, Bo Cai, Rui Weng, Lixian Zhang, Shun-Feng Su
Summary: This article investigates the stochastic stability analysis and stabilization problems for discrete-time Takagi-Sugeno fuzzy semi-Markov jump systems with upper-bounded sojourn time. The stability and stabilization conditions are established by part of the known semi-Markov kernel (SMK) information and then by all the known SMK information. The validity and the superiority of the proposed theoretical results are demonstrated through examples.
IEEE TRANSACTIONS ON FUZZY SYSTEMS
(2022)
Article
Automation & Control Systems
Jiamin Liu, Patrizio Colaneri, Paolo Bolzern, Zhao-Yan Li
Summary: This article investigates three different types of stochastic asymptotic stability for nonlinear stochastic semi-Markov jump systems (SSMJS). Novel sufficient conditions for p$$ p $$th (p>0)$$ \left(p>0\right) $$-moment asymptotic stability, almost sure asymptotic stability, and stochastic asymptotic stability in the large are obtained. It is remarkable that the obtained results in the latter case cover the results in existing literature. Finally, two examples are presented to confirm the validity of the obtained theoretical results.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2023)
Article
Mathematics
Vlad Stefan Barbu, Guglielmo D'Amico, Andreas Makrides
Summary: This paper introduces a class of stochastic processes in continuous time called step semi-Markov processes. It extends the classical semi-Markov process by incorporating multiple steps for transitions between two states. The models and main characteristics are defined, and the recursive evolution equations for two-step semi-Markov processes are derived.
Article
Mathematics
Thomas Spanninger, Beda Buchel, Francesco Corman
Summary: Train delays are a major inconvenience for passengers and railway operations. This study introduces an advanced Markov chain model to predict train delays using historical train operation data. By using process time deviations instead of absolute delays, and relaxing the stationarity assumptions for transition probabilities, our model achieves a prediction accuracy gain of 56% compared to state-of-the-art models based on absolute delays.
Article
Green & Sustainable Science & Technology
Channpisey Nop, Rasha M. Fadhil, Koichi Unami
Summary: This study establishes a Markov chain model for rainfall time series in temperate climates and employs stochastic dynamic programming to optimize the operation of rainwater harvesting systems. The transition probabilities of the model are based on gamma distribution assumptions with two parameters, contributing to stabilizing the optimal policy.
JOURNAL OF CLEANER PRODUCTION
(2021)
Article
Mathematics
Ali Labriji, Abdelkrim Bennar, Mostafa Rachik
Summary: The use of conditional probabilities in various fields has become increasingly popular, especially with the availability of large datasets to maximize estimation algorithms. However, handling such a large volume of data often requires significant computational capacity and compilation time.
JOURNAL OF MATHEMATICS
(2021)
Article
Engineering, Multidisciplinary
Wenhuang Wu, Ling He, Zhilian Yan, Jianping Zhou
Summary: This paper focuses on event-triggered extended dissipativity stabilization of uncertain semi-Markov switching systems with external disturbances. The goal is to ensure the stochastic stability and extended dissipativity of the systems using event-triggered control. A mode-and disturbance-dependent switched event-triggered mechanism is proposed to reduce the number of triggered events. A time-and mode-dependent Lyapunov functional is constructed to establish criteria for stochastic stability and extended dissipativity. A co-design scheme for the feedback-gain and event-triggered matrices is presented, and the effectiveness of the designed controller is illustrated using DC motor and robotic arm models.
APPLIED MATHEMATICAL MODELLING
(2023)
Article
Multidisciplinary Sciences
Michael B. Bonsall, Emily A. Holmes
Summary: Traumatic events can lead to distressing memories that intrude to mind unbidden and recur, especially in mental disorders such as post-traumatic stress disorder. Addressing these intrusive memories is important for treatment, but existing cognitive and descriptive models lack quantitative structure and empirical validation. In this study, a quantitative framework using stochastic process theory is developed to better understand the temporal dynamic processes of trauma memory and provide a probabilistic description of memory mechanisms. The framework highlights how interventions targeting intrusive memories can be enhanced based on intervention and reminder strength, as well as the probability of memory consolidation. Parametrizing the framework with empirical data suggests that weakening multiple reactivation cues may be more effective in reducing intrusive memories than using stronger cues. This approach also provides a way to link neural mechanisms of memory with broader cognitive processes.
JOURNAL OF THE ROYAL SOCIETY INTERFACE
(2023)
Article
Automation & Control Systems
Min Zhang, Jun Huang, Yueyuan Zhang
Summary: This article investigates the stochastic stability analysis and state feedback stabilization for nonlinear stochastic differential semi-Markov jump systems with incremental quadratic constraints. Conditions ensuring the systems' stability are formulated using linear matrix inequalities and a state feedback controller is designed to achieve stochastic stability. The results are illustrated through an example of a helicopter, showing their superiority and effectiveness.
INTERNATIONAL JOURNAL OF ROBUST AND NONLINEAR CONTROL
(2021)
Article
Economics
Valentina Chiariello, Francesca Rotondo, Domenico Scalera
Summary: This paper empirically investigates the efficiency of Italian regions in providing public primary and secondary education for the period 2011-18 using stochastic frontier analysis. It identifies strong interregional differences, particularly a clear North-South geographical pattern. Context variables such as per capita GDP, poverty, institutional quality, and adult education are shown to be highly relevant in shaping regional efficiency. When interregional disparities in socio-economic factors are considered, no residual geographical pattern in regional efficiency emerges.
Article
Computer Science, Artificial Intelligence
Ying Liu, Zhiyao Zhao, Haibiao Ma, Quan Quan
Summary: A stochastic approximation method is proposed in this study to predict the docking success probability during the docking phase of aerial refueling. If the success probability is lower than the specified value, the receiver aircraft needs to increase its relative distance from the tanker aircraft to reduce the risk.
APPLIED SOFT COMPUTING
(2021)
Article
Automation & Control Systems
Xiaotai Wu, Peng Shi, Yang Tang, Shuai Mao, Feng Qian
Summary: This article studies the problem of exponential stability for semi-Markov jump stochastic nonlinear systems. A new stochastic analysis method is developed to investigate the almost surely exponential stability problem without additional constraints. Mode-dependent linear comparable relationships are assumed to reduce conservatism.
IEEE TRANSACTIONS ON AUTOMATIC CONTROL
(2022)
Article
Green & Sustainable Science & Technology
Zulqarnain H. Khattak, Asad J. Khattak
Summary: This study found that higher gasoline prices contribute to the adoption of battery electric vehicles, while the perceived disadvantages of AFVs for long commutes hinder their wider adoption. Additionally, consumers who frequently use the internet are more likely to purchase hybrid vehicles. West Coast residents are a significant portion of early adopters and are more inclined to purchase hybrids rather than battery electric vehicles.
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION
(2023)
Article
Green & Sustainable Science & Technology
Zulqarnain H. Khattak, Asad J. Khattak
Summary: Travel increases with urban sprawl, leading to congestion and emissions. The development of new technologies like Mobility as a Service (MaaS) provides alternative transport options including ride-hailing, carsharing and bike sharing. The study investigates the travel choices and shared use of electric and hybrid vehicles in MaaS, finding that ride-hailing involving these vehicles can reduce greenhouse gas emissions. Factors like personal interest in technologies influence the use of alternative fuel vehicles (AFVs) for travel. This research has implications for policy decisions and promoting the purchase and shared mobility use of AFVs for MaaS.
INTERNATIONAL JOURNAL OF SUSTAINABLE TRANSPORTATION
(2023)
Article
Transportation
Zulqarnain H. Khattak, Jackeline Rios-Torres, Michael D. Fontaine, Asad J. Khattak
Summary: Advancements in sensing technology have allowed for the collection of extensive driving behavior data, which can be used for real-time monitoring and identification of safety critical events. This study developed a deep learning approach using convolutional neural networks to infer such events, finding that shallow CNN architectures performed better in detection accuracy.
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Transportation
Licheng Zhang, Kun Peng, Xiangmo Zhao, Asad J. Khattak
Summary: A novel computational model was developed to improve eco-driving in intelligent transportation systems. The model accurately predicted fuel consumption by dividing the volatile state into eight types and considering instantaneous driving decisions. It outperformed existing models in new routes with lower errors.
JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS
(2023)
Article
Transportation Science & Technology
Cheng Wei, Fei Hui, Zijiang Yang, Shuo Jia, Asad J. Khattak
Summary: This study proposes a new lane-changing trajectory prediction model, including a method for segmenting the LC process and a predictive model based on deep neural networks. Experimental results show that the proposed prediction model has high accuracy and long-term prediction capability, allowing for fine-grained LC description.
TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES
(2022)
Article
Engineering, Civil
Antora Mohsena Haque, Iman Mahdinia, A. Latif Patwary, Asad J. Khattak
Summary: This study aims to explore the spatial heterogeneity in damage to the remainder of affected parcels and analyze the factors contributing to spatial variations in percentage damage. The results reveal that a geographically weighted Gaussian regression model outperforms the global model, and key factors affecting spatial variations in damage include acquisition ratio, adverse change in utility, major acquisition of landscape, change in highest and best use, and damage to access.
TRANSPORTATION RESEARCH RECORD
(2022)
Article
Engineering, Civil
A. Latif Patwary, Asad J. Khattak
Summary: The COVID-19 pandemic has emphasized the significance of information and communication technologies (ICTs) in facilitating virtual engagement. This study explores the interdependencies between online shopping, working from home (WFH), and travel behavior, and suggests that WFH can be explicitly treated as an alternative to commuting in travel demand models for future planning.
TRANSPORTATION RESEARCH RECORD
(2022)
Article
Ergonomics
Iman Mahdinia, Amin Mohammadnazar, Asad J. Khattak
Summary: This study examines the survival time of pedestrians in fatal injury crashes and extracts valuable information to understand the factors that affect their survival. The results show that EMS response time, speeding, and some pedestrian behaviors are the most important factors. However, the effects vary spatially and temporally.
ACCIDENT ANALYSIS AND PREVENTION
(2022)
Article
Ergonomics
Numan Ahmad, Ramin Arvin, Asad J. Khattak
Summary: This study investigates the impact of different driving errors, violations, and roadway environments on the instability of driving speed, which contributes to safety-critical events. The findings show that driving errors and violations not only directly increase the risk of events but also indirectly through the instability in driving speed.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Ergonomics
Yangsong Gu, Diyi Liu, Ramin Arvin, Asad J. Khattak, Lee D. Han
Summary: This study investigates a new Artificial Intelligence technique called Geographical Random Forest (GRF) to accurately predict rear-end crash frequency at intersections. The results show that the proposed GRF outperforms Global Random Forest in terms of test error and fit, and identifies key indicators of rear-end crashes.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Ergonomics
Numan Ahmad, Behram Wali, Asad J. Khattak
Summary: This study aims to improve the prediction accuracy of crash frequency on roadway segments by using statistical and machine learning methods, with stacking being the most accurate and robust technique. The study applies stacking to model crash frequency on urban and suburban arterials, comparing its performance with other statistical models and machine learning techniques. Results show that stacking outperforms the alternative methods in terms of prediction accuracy.
JOURNAL OF SAFETY RESEARCH
(2023)
Article
Engineering, Civil
Laura Harris, Numan Ahmad, Asad Khattak, Subhadeep Chakraborty
Summary: The objective of this work was to determine the effect of visibility-related factors and some environmental and human factors on the severity of pedestrian-vehicle crashes. It was found that higher speed limits, less light conditions, and no traffic controls were significantly correlated with increased pedestrian injury severity. Dusk and dark with or without lighting were also factors correlated with increased pedestrian injury severity, while inclement weather was correlated with lower pedestrian injury severity.
TRANSPORTATION RESEARCH RECORD
(2023)
Article
Ergonomics
A. Latif Patwary, Asad J. Khattak
Summary: Major concerns have been raised about the increase in crash fatalities during the COVID-19 pandemic in the US, despite the decrease in traffic. This study analyzes the correlation between fatalities, crashes, and crash harm using a comprehensive time-series database in Tennessee. The results indicate that fatal crashes during the pandemic are associated with more speeding and reckless behaviors, varied across jurisdictions, and involve commercial trucks. Policymakers can use these findings to strengthen traffic law enforcement through appropriate countermeasures.
ACCIDENT ANALYSIS AND PREVENTION
(2023)
Article
Computer Science, Artificial Intelligence
Cheng Wei, Fei Hui, Asad J. Khattak, Yutan Zhang, Wenbo Wang
Summary: Virtual simulation testing has become the main method for testing autonomous driving systems and algorithms. This study proposes a method for batch generating human-like behavior and trajectory data for background vehicles, improving the coverage and reliability of virtual simulation testing.
EXPERT SYSTEMS WITH APPLICATIONS
(2023)
Article
Ergonomics
A. Latif Patwary, Antora Mohsena Haque, Iman Mahdinia, Asad J. Khattak
Summary: Recent research has explored the relationship between disadvantaged communities and traffic safety by analyzing census data. The findings suggest that factors such as health, resilience, and transportation barriers are associated with more fatal crashes, while a higher percentage of the population with bachelor's degrees and increased use of public transportation are correlated with fewer fatal crashes. Additionally, disadvantaged census tracts with a higher proportion of Hawaiian or other Pacific Islander, and American Indian or Alaska Native populations have a higher rate of fatal crashes. These insights are important for developing more equitable traffic safety interventions.
ACCIDENT ANALYSIS AND PREVENTION
(2024)
Article
Economics
Songyot Kitthamkesorn, Anthony Chen, Seungkyu Ryu, Sathaporn Opasanon
Summary: The study introduces a new mathematical model to determine the optimal location of park-and-ride facilities, addressing the limitations of traditional models and considering factors such as route similarity and user heterogeneity.
TRANSPORTATION RESEARCH PART B-METHODOLOGICAL
(2024)
