Article
Physics, Applied
Jan Kaiser, Supriyo Datta
Summary: The article discusses a probabilistic computer based on p-bits that can accelerate randomized algorithms used in various applications, such as Bayesian networks, optimization, Ising models, and quantum Monte Carlo. A generic architecture for p-computers and simulations with thousands of p-bits are proposed to demonstrate their potential for significant speedup.
APPLIED PHYSICS LETTERS
(2021)
Article
Environmental Sciences
Benjamin Beelen, Wayne Parker
Summary: This study developed a Monte Carlo algorithm to investigate methane emissions from a municipal sewer system in Canada. The results showed that force main reaches contributed 30% of the total methane emissions, despite only accounting for 4.4% of the total network length. The study also suggested that addressing methane generation in force main reaches could significantly reduce methane emissions.
JOURNAL OF ENVIRONMENTAL MANAGEMENT
(2022)
Article
Engineering, Geological
Kunal Gupta, Neelima Satyam, Vaasu Gupta
Summary: Probabilistic modelling is increasingly being used to assess landslide hazard by accounting for spatial and temporal uncertainties in geological, hydrological, geotechnical, seismological, and geomorphological parameters. In this study, a methodology was developed to model the uncertainties in a modified Newmark slope stability analysis model for assessing seismic landslide hazard in Uttarakhand, India. Statistical distributions were used to represent the uncertainties in input parameters, and probability density functions were simulated using the Monte Carlo method to create hazard maps.
Article
Biochemical Research Methods
Sanmitra Ghosh, Paul J. Birrell, Daniela De Angelis
Summary: In this paper, a computationally efficient method is proposed to quantify the uncertainty in transmission ability during an epidemic. This method does not make assumptions about the timing and magnitude of the impacts of external stimuli on transmission. The proposed approach is valuable for real-time monitoring of epidemics and has been demonstrated through examples of influenza, seasonality, and COVID-19 modeling.
PLOS COMPUTATIONAL BIOLOGY
(2023)
Article
Engineering, Geological
Jinwu Ouyang, Cuiying Zhou, Zhen Liu, Guijin Zhang
Summary: This study presents a method for building a 3D TIN geological model that incorporates probability analysis and random simulation to account for the uncertainty of stratigraphic attributes. By generating the Markov chain sequence of the stratum and combining it with the correlation of boreholes, a transition probability matrix of the virtual borehole is obtained to describe the probability of changes in the stratum. A probabilistic model of stratum thickness is constructed using Monte Carlo simulation, and a maximum probabilistic model of the study area is obtained through the adjustment of virtual boreholes and the addition of validation boreholes. The proposed modelling method effectively visualises the stratigraphic configuration and quantifies the stratigraphic uncertainty.
ENGINEERING GEOLOGY
(2023)
Article
Water Resources
Carles Beneyto, Jose Angel Aranda, Felix Frances
Summary: Stochastic weather generators are powerful tools that can extend precipitation records. However, they rely on available information, which is often scarce in arid and semi-arid regions. This study aims to investigate the uncertainty associated with the amount of information used in the weather generation calibration process. Monte Carlo simulation showed that incorporating a regional study of annual maximum daily precipitation reduced the uncertainty of all quantile estimates. It also highlighted the importance of integrating additional information in regions with extreme precipitation patterns.
HYDROLOGICAL SCIENCES JOURNAL
(2023)
Article
Geosciences, Multidisciplinary
Charles Doktycz, Mark Abkowitz, Hiba Baroud
Summary: This study introduces and applies a hybrid simulation model to improve understanding of the costs of extreme weather events based on empirical data. The model results show promising accuracy in estimating the expected costs for specific event types and locations, which can help make a business case for resilience investment.
INTERNATIONAL JOURNAL OF DISASTER RISK SCIENCE
(2022)
Article
Engineering, Multidisciplinary
Mahmoud G. Hemeida, Salem Alkhalaf, Tomonobu Senjyu, Abdalla Ibrahim, Mahrous Ahmed, Ayman M. Bahaa-Eldin
Summary: The paper presents a study on optimizing the location of distributed generators under load uncertainties using Monte Carlo simulation integrated with different bio-inspired algorithms, and compares the performance of four bio-inspired algorithms. Through experimental research, the superiority of different algorithms was evaluated.
AIN SHAMS ENGINEERING JOURNAL
(2021)
Article
Environmental Sciences
Maeva Caillat, Valentin Pibernus, Sylvain Girard, Mathieu Ribatet, Patrick Armand, Christophe Duchenne
Summary: In the event of accidental or malevolent atmospheric releases, decision-makers need to take swift mitigating measures. However, the determination of danger zones and safe zones based on atmospheric dispersion models is uncertain due to unreliable input data. This paper proposes a methodology to accurately estimate the probability of exceeding a concentration threshold, taking into account spatial correlation and providing more accurate risk assessments in low probability areas. This methodology shows promise in creating useful maps for decision-makers and can be implemented in a decision-support tool.
ATMOSPHERIC ENVIRONMENT
(2023)
Article
Energy & Fuels
Lewis Waswa, Munyaradzi Justice Chihota, Bernard Bekker
Summary: This paper proposes a novel input-process-output stochastic-probabilistic CSS framework for distribution feeders with DERs to address technical challenges in high DER penetration scenarios. The method is effective in determining conductor sizes for future loading conditions under DER allocation uncertainty, providing practicality for planners working on new electrification projects.
Article
Engineering, Geological
Chao Zhao, Wenping Gong, Tianzheng Li, C. Hsein Juang, Huiming Tang, Hui Wang
Summary: Accurate characterization of subsurface stratigraphic configuration is crucial to geotechnical engineering work, but uncertainty can be significant due to complexity and limited data availability. This paper presents a method for characterizing subsurface stratigraphy with limited borehole data, demonstrating its effectiveness and advantages through comparative analyses and a case study in Western Australia.
ENGINEERING GEOLOGY
(2021)
Article
Chemistry, Physical
Aditya Kumar, Abhijit Chatterjee
Summary: We introduce a probabilistic microkinetic modeling (MKM) framework that integrates the short-ranged order (SRO) evolution of adsorbed species on a catalyst surface. The model incorporates adsorbate-adsorbate interactions, surface diffusion, adsorption, desorption, and catalytic reaction processes using a system of ordinary differential equations. By accurately describing the adspecies ordering/arrangement with SRO parameters and utilizing the reverse Monte Carlo (RMC) method, the relevant local environment probability distributions are extracted and applied to the MKM. The resulting reaction kinetics is comparable to the kinetic Monte Carlo (KMC) method but with significantly faster computational time.
JOURNAL OF CHEMICAL PHYSICS
(2023)
Article
Biochemical Research Methods
Simon A. Martina Perez, Heba Sailem, Ruth E. A. Baker
Summary: Researchers identified three distinct types of gene knockdowns during wound healing, each displaying unique cell behaviors through a combination of mathematical modeling and experimental imaging. By applying detailed models to a large dataset of gene knockdowns, they revealed the importance of density-dependent interactions in the process of wound healing.
PLOS COMPUTATIONAL BIOLOGY
(2022)
Article
Engineering, Aerospace
Dooyoul Lee, Hwanjeong Cho, Min-Saeng Kim, Kybeom Kwon
Summary: This study proposes a framework for the probabilistic risk analysis of aircraft self-collisions, focusing on military aircraft with external stores. A case study involving an ejected gun shell is analyzed, taking into account uncertain factors such as random shell rotation. The probability of collision and corresponding risks are evaluated using a Monte Carlo simulation and probabilistic ballistic model.
Article
Environmental Sciences
Mohammad Mehdi Riyahi, Hossien Riahi-Madvar
Summary: Detention rockfill dams are important in flood control projects due to their minimal technical requirement, low cost, minimal environmental side effects, and self-automotive operation process. However, reliable design of these dams is challenging due to the complexity of Non-Darcian flow interactions with stability and uncertainties of dam. This study examines the effects of uncertainties in probabilistic design of these dams and proposes a reliable design framework with a focus on stability analysis.
ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH
(2023)
Article
Engineering, Industrial
Zhaojun Hao, Francesco Di Maio, Enrico Zio
Summary: This paper discusses the O&M strategies for reliable and safe production and supply of CPESs, considering the uncertainty in energy demand and supply due to renewable energy sources and the need to avoid severe accidents for safety reasons. A Deep Reinforcement Learning approach is developed to search for the best strategy, taking into account the health conditions and remaining useful life of system components, and possible accident scenarios. The approach integrates Proximal Policy Optimization and Imitation Learning, and incorporates a CPES model with component RUL estimator and failure process model. An application to the ALFRED reactor demonstrates that the optimal solution found by DRL outperforms state-of-the-art O&M policies.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Zhiyao Zhang, Xiaohui Chen, Enrico Zio, Longxiao Li
Summary: This article focuses on the remaining useful life (RUL) prediction of aero-engines under working-condition shift scenarios. A multi-task learning-boosted method (MTLTrans) is proposed, which utilizes Transformer and two auxiliary tasks to improve prediction accuracy.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Fei Zuo, Enrico Zio, Yue Xu
Summary: Risk response is an essential part of project risk management, which helps reduce the negative impacts of risks. The paper proposes a flow-based continuous-time bi-objective optimization model for risk-related resource planning in response to the scarcity of resources. The model is successfully applied to a case study, obtaining the global optimal solution through parameter tuning. Additionally, a rule-based metaheuristic algorithm is developed to handle large-scale projects by incorporating improved population initialization and genetic operators. The computational results of the case study and numerical experiments validate the effectiveness of the algorithm and highlight the significance of the precautionary principle and redundant resources in project risk management.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Engineering, Industrial
Yucheng Hao, Limin Jia, Enrico Zio, Yanhui Wang, Zhichao He
Summary: This paper proposes a multi-objective optimization-based approach to identify critical elements in a high-speed train system. The system is modeled as an interdependent machine-electricity-communication network, and cascading failure models and metrics for robustness are developed. By formulating a multi-objective optimization model, the impact of critical element failure on network robustness is maximized while minimizing their number. The results show that critical elements are mainly located within the machine and communication networks, and their importance is not related to their topological importance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2023)
Article
Green & Sustainable Science & Technology
Ozcel Cangul, Roberto Rocchetta, Murat Fahrioglu, Edoardo Patelli
Summary: A novel financial metric called Unit Financial Impact Indicator (UFII) is proposed to optimize the payback period and quantify the financial efficiency of solar photovoltaic (PV) system investments. The study also introduces a new probabilistic framework for robust and optimal positioning and sizing of utility-scale PV systems in a transmission network, taking into account environmental and operational uncertainties. The results from testing the proposed approach on a 14-bus IEEE power grid case study confirm its applicability and effectiveness in quantifying the financial effectiveness of solar PV investments.
Article
Computer Science, Artificial Intelligence
Roberto Rocchetta, Alexander Mey, Frans A. Oliehoek
Summary: This work investigates formal generalization error bounds for support vector machines (SVMs) in realizable and agnostic learning problems. It focuses on the parallels between probably approximately correct (PAC)-learning bounds and novel error guarantees derived within scenario theory. The numerical comparison of different error bounds for SVMs trained on real-life problems suggests that scenario theory provides tighter and more informative results compared to other approaches.
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
(2023)
Article
Computer Science, Hardware & Architecture
Wenqiang Zheng, Hanxiao Zhang, Yan-Fu Li
Summary: High-level maintenance is crucial for safe operation of high-speed trains, but manual scheduling lacks optimization based on dynamic time intervals. This article presents state-of-the-art scheduling models for high-level maintenance of high-speed trains. The one-time maintenance scheduling problem is a key issue, and a more efficient mathematical model and solution are proposed. Additionally, a periodic scheduling model is developed to accommodate multiple maintenance actions and achieve global optimal plans. Both models are mixed 0-1 linear integer models solvable by commercial solvers. Real-case study using 100 high-speed trains in China validates the effectiveness and comparisons of the proposed models, serving as a basis for high-level maintenance scheduling in either periodic or one-time cases.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Automation & Control Systems
Hui Wu, Yan-Fu Li
Summary: This article proposes a novel method for weakly-supervised anomaly detection by integrating label propagation and manifold graph learning into a support vector data description model. The method is shown to be effective through experiments on benchmark datasets and a real-world example of fault detection for high-speed train wheels.
IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING
(2023)
Article
Energy & Fuels
Lin Fan, Huai Su, Enrico Zio, Yuejun Li, Li Zhang, Shiliang Peng, Yuxuan He, Yucheng Hao, Jinjun Zhang
Summary: This work proposes a method for joint optimization of maintenance and spare parts inventory to maximize the reliability of a pipeline system's gas supply. The method considers maintenance costs and the risk of gas shortages. Various steps are performed, including system failure probability calculation, system maximum supply capacity analysis, joint optimization modeling, and system maintenance planning. A genetic algorithm is used to optimize the inspection interval and spare ordering time, taking into account the stochastic behavior of lead time.
GAS SCIENCE AND ENGINEERING
(2023)
Article
Computer Science, Artificial Intelligence
Zedu Chen, Bineng Zhong, Guorong Li, Shengping Zhang, Rongrong Ji, Zhenjun Tang, Xianxian Li
Summary: SiamBAN is a simple and effective tracker that predicts target boxes in a per-pixel fashion through a fully convolutional network, addressing the challenge of variation in scales or aspect ratios. It divides the tracking problem into classification and regression tasks to handle inconsistency, and achieves promising performance in benchmark tests.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
(2023)
Article
Computer Science, Hardware & Architecture
Yue Xu, Dechang Pi, Shengxiang Yang, Enrico Zio
Summary: This article focuses on the problem of multitask selective maintenance for multistate complex systems and proposes a novel approach using reliability evaluation and a multifactorial evolutionary algorithm. Numerical experiments show that the proposed method outperforms the original method.
IEEE TRANSACTIONS ON RELIABILITY
(2023)
Article
Mathematics, Interdisciplinary Applications
Wanqing Song, Jianxue Chen, Zhen Wang, Aleksey Kudreyko, Deyu Qi, Enrico Zio
Summary: This study proposes a method based on adaptive fractional Levy stable motion (AfLSM), Mellin-Stieltjes transform, and Monte Carlo simulation for accurate estimation of the remaining useful life (RUL) of lithium-ion batteries. The proposed model exhibits flexibility in capturing long-range dependence, has a non-Gaussian distribution and heavy-tailed properties, and allows for adaptive updates of the drift coefficients based on the degradation trajectory.
FRACTAL AND FRACTIONAL
(2023)
Article
Engineering, Industrial
Lida Naseh Moghanlou, Francesco Di Maio, Enrico Zio
Summary: This paper proposes a probabilistic scenario analysis framework to quantify service losses and assess the economic losses and transport reliability of road-power infrastructure under different car accident scenarios. The framework allows comparing alternative designs and evaluating their performance using graphical representations.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Green & Sustainable Science & Technology
Huan Wang, Yan-Fu Li, Ying Zhang
Summary: This study proposes a method for battery health state monitoring based on spiking neural networks and electrochemical impedance spectroscopy, achieving accurate SOH estimation through simulating the feature processing mechanism of brain neurons and low power consumption.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS
(2023)
Article
Engineering, Industrial
Dario Valcamonico, Piero Baraldi, Enrico Zio, Luca Decarli, Anna Crivellari, Laura La Rosa
Summary: This study investigates the possibility of using information from reports on process safety events (PSEs) in hydrocarbon production assets to support quantitative risk assessment (QRA). A novel methodology combining natural language processing (NLP) and Bayesian networks (BNs) is proposed to estimate the probability of different severity levels of PSEs and identify the factors that have the most influence on their occurrence. The results show that the proposed methodology can identify critical factors for the severity of PSE consequences and inform decisions on system safety improvements.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Mateusz Oszczypala, Jakub Konwerski, Jaroslaw Ziolkowski, Jerzy Malachowski
Summary: This article discusses the issues related to the redundancy of k-out-of-n structures and proposes a probabilistic and simulation-based optimization method. The method was applied to real transport systems, demonstrating its effectiveness in reducing costs and improving system availability and performance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Wencheng Huang, Haoran Li, Yanhui Yin, Zhi Zhang, Anhao Xie, Yin Zhang, Guo Cheng
Summary: Inspired by the theory of degree entropy, this study proposes a new node identification approach called Adjacency Information Entropy (AIE) to identify the importance of nodes in urban rail transit networks (URTN). Through numerical and real-world case studies, it is found that AIE can effectively identify important nodes and facilitate connections among non-adjacent nodes.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Liwei Chen
Summary: This paper discusses the four phases of the system life cycle and the different costs associated with each phase. It proposes an improvement importance method to optimize system reliability and analyzes the process of failure risk under limited resources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Xian Zhao, Chen Wang, Siqi Wang
Summary: This paper proposes a new rebalancing strategy for balanced systems by switching standby components. Different switching rules are provided based on different balance conditions. The system reliability is derived using the finite Markov chain imbedding approach, and numerical examples and sensitivity analysis are presented for validation.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Fengyuan Jiang, Sheng Dong
Summary: Corrosion defects are the primary causes of pipeline burst failures. The traditional methodologies ignore the effects of random morphologies on failure behaviors, leading to deviations in remaining strength estimation and reliability analysis. To address this issue, an integrated methodology combining random field, non-linear finite element analysis, and Monte-Carlo Simulation was developed to describe the failure behaviors of pipelines with random defects.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Guoqing Cheng, Jiayi Shen, Fang Wang, Ling Li, Nan Yang
Summary: This paper investigates the optimal joint inspection and mission abort policies for a multi-component system with failure interaction. The proportional hazards model is used to characterize the effect of one component's deterioration on other components' hazard rates. The optimal policy is studied to minimize the expected total cost, and some structural properties of the optimal policy are obtained.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Hongyan Dui, Yaohui Lu, Shaomin Wu
Summary: A new resilience model is proposed in this paper for systems under competing risks, and related indices are introduced for evaluating the system's resilience. The model takes into account the degradation process, external shocks, and maintenance interactions of the system, and its effectiveness is demonstrated through a case study.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yang Li, Jun Xu
Summary: This paper proposes a translation model based on neural network for simulating non-Gaussian stochastic processes. By converting the target non-Gaussian power spectrum to the underlying Gaussian power spectrum, non-Gaussian samples can be generated.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yanyan Liu, Keping Li, Dongyang Yan
Summary: This paper proposes a new random walk method, CBDRWR, to analyze the potential risk of railway accidents. By combining accident causation network, we assign different restart probabilities to each node and improve the transition probabilities. In the case study, the proposed method effectively quantifies the potential risk and identifies key risk sources.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Nan Hai, Daqing Gong, Zixuan Dai
Summary: The current risk management of utility tunnel operation and maintenance is of low quality and efficiency. This study proposes a theoretical model and platform that offer effective decision support and improve the safety of utility tunnel operation and maintenance.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Tomoaki Nishino, Takuya Miyashita, Nobuhito Mori
Summary: A novel modeling methodology is proposed to simulate cascading disasters triggered by tsunamis considering uncertainties. The methodology focuses on tsunami-triggered oil spills and subsequent fires and quantitatively measures the fire hazard. It can help assess and improve risk reduction plans.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Mingjiang Xie, Yifei Wang, Jianli Zhao, Xianjun Pei, Tairui Zhang
Summary: This study investigates the effect of rockfall impact on the health management of pipelines with fatigue cracks and proposes a crack propagation prediction algorithm based on rockfall impact. Dynamic SIF values are obtained through finite element modeling and a method combining multilayer perceptron with Paris' law is used for accurate crack growth prediction. The method is valuable for decision making in pipeline reliability assessment and integrity management.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Saeed Jamalzadeh, Lily Mettenbrink, Kash Barker, Andres D. Gonzalez, Sridhar Radhakrishnan, Jonas Johansson, Elena Bessarabova
Summary: This study proposes an integrated epidemiological-optimization model to quantify the impacts of weaponized disinformation on transportation infrastructure and supply chains. Results show that disinformation targeted at transportation infrastructure can have wide-ranging impacts across different commodities.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Jiaxi Wang
Summary: This paper investigates the depot maintenance packet assignment and crew scheduling problem for high-speed trains. A mixed integer linear programming model is proposed, and computational experiments show the effectiveness and efficiency of the improved model compared to the baseline one.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
Article
Engineering, Industrial
Yuxuan Tian, Xiaoshu Guan, Huabin Sun, Yuequan Bao
Summary: This paper proposes a DFMs searching algorithm based on the graph neural network (GNN) to improve computational efficiency and adaptively identify DFMs. The algorithm terminates prematurely when unable to identify new DFMs.
RELIABILITY ENGINEERING & SYSTEM SAFETY
(2024)
