Reliability Engineering & System Safety最新文献_第10页

Integrated multi-agent-based outpatient building fire response modeling for risk-driven resource use and retrofitting strategies: A case study

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-25 DOI: 10.1016/j.ress.2025.110970

Aokun Yu , Haichao Bu , Tianyi Luan , Wenmei Gai

{"title":"Integrated multi-agent-based outpatient building fire response modeling for risk-driven resource use and retrofitting strategies: A case study","authors":"Aokun Yu , Haichao Bu , Tianyi Luan , Wenmei Gai","doi":"10.1016/j.ress.2025.110970","DOIUrl":"10.1016/j.ress.2025.110970","url":null,"abstract":"<div><div>To evaluate and optimize the evacuation capability of an outpatient building from a risk-driven perspective, a multi-agent-based modeling framework for emergency response that integrates the processes of fire evolution, emergency evacuation and rescue was developed. Then explored evacuation capacity optimization strategies from the perspectives of resource use and building retrofitting. To verify the effectiveness and applicability of the method, an evacuation model that simulates a fire in the outpatient building was developed. It was found that few occupants with restricted mobility took up the most evacuation time, need to improve the vertical mobility of these occupants; To prevent unplanned evacuation network interruptions due to fire, stairwell availability needs to be protected; The use of elevators for evacuation needs to be centralized to speed up the evacuation. The results of the current study will help public emergency authorities develop an effective plan for optimizing the emergency response system in outpatient buildings.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110970"},"PeriodicalIF":9.4,"publicationDate":"2025-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An adaptive mixture prior in Bayesian convolutional autoencoder for early detecting anomalous degradation behaviors in lithium-ion batteries

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-24 DOI: 10.1016/j.ress.2025.110926

Sun Geu Chae, Suk Joo Bae

{"title":"An adaptive mixture prior in Bayesian convolutional autoencoder for early detecting anomalous degradation behaviors in lithium-ion batteries","authors":"Sun Geu Chae, Suk Joo Bae","doi":"10.1016/j.ress.2025.110926","DOIUrl":"10.1016/j.ress.2025.110926","url":null,"abstract":"<div><div>Accurate and timely detection of anomalies in lithium-ion batteries is crucial for ensuring their reliability and safety. Complex degradation patterns and limited availability of labeled data pose significant challenges in identifying abnormal behaviors in battery usage. This paper proposes an unsupervised adaptive mixture distribution-based Bayesian convolutional autoencoder (AMDBCAE) method for detecting anomalous degradation behaviors in lithium-ion batteries at earlier cycles of reliability test. As the prior for the model parameters, we propose a mixture of the Laplace and Student’s <span><math><mi>t</mi></math></span> distributions by taking uncertainties in the weights of the convolutional network and their heavy-tailed characteristics into account. Using a modified form of the Bayes by backprop algorithm, the parameter of mixture proportion is adaptively updated to capture diverse and complex degradation patterns in battery degradation data more efficiently. Extracted latent features are then processed through unsupervised clustering algorithms to identify abnormal degradation behaviors of lithium-ion batteries. The analyses of two real-world lithium-ion battery datasets demonstrate the efficiency and accuracy of the proposed unsupervised framework with limited number of testing data. The proposed method addresses the limitations of manual feature extraction and the need for extensive experimental knowledge by leveraging the adaptive BCAE model to automatically extract latent features as a virtual health indicator in sparse data environments.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"259 ","pages":"Article 110926"},"PeriodicalIF":9.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143488960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Knowledge embedded spatial–temporal graph convolutional networks for remaining useful life prediction

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-24 DOI: 10.1016/j.ress.2025.110928

Xiao Cai , Dingcheng Zhang , Yang Yu , Min Xie

{"title":"Knowledge embedded spatial–temporal graph convolutional networks for remaining useful life prediction","authors":"Xiao Cai , Dingcheng Zhang , Yang Yu , Min Xie","doi":"10.1016/j.ress.2025.110928","DOIUrl":"10.1016/j.ress.2025.110928","url":null,"abstract":"<div><div>Accurate prediction of remaining useful life (RUL) is crucial for prognostics and health management of equipment. Deep learning methods have gained significant attention in this field, leveraging the abundance of monitoring data captured from sensor networks. However, these methods often overlook the spatial interactions among sensor signals. Moreover, they primarily focus on pattern extraction from sensor data and neglect the utilization of available prior knowledge that could enhance prediction accuracy and stability. To address these limitations, a knowledge-embedded spatial–temporal graph convolutional networks (KEST-GCN) method is proposed. In KEST-GCN, the relationship triplets are established based on the system structure knowledge and sensor position information. Then, these triplets are transformed into low-dimensional vector embeddings using an energy-based knowledge embedded algorithm. After that, the graph dataset is generated, where the embeddings of sensors are utilized to construct the graph edges and weighted adjacency matrix. The weights are dynamically updated by an attention mechanism. Finally, a GCN layer with a multi-head attention mechanism, an LSTM layer and a fully connected layer are employed to extract spatial–temporal degradation patterns and obtain the RUL prediction results. The effectiveness and stability of our proposed method is demonstrated using an aero-engine dataset and a cutting tool dataset, respectively.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"259 ","pages":"Article 110928"},"PeriodicalIF":9.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143511316","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Reliability improvement of rolling stock planning with maintenance requirements for high-speed railway

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-24 DOI: 10.1016/j.ress.2025.110972

Jiaxin Niu, Ke Qiao, Peng Zhao

{"title":"Reliability improvement of rolling stock planning with maintenance requirements for high-speed railway","authors":"Jiaxin Niu, Ke Qiao, Peng Zhao","doi":"10.1016/j.ress.2025.110972","DOIUrl":"10.1016/j.ress.2025.110972","url":null,"abstract":"<div><div>The rolling stock planning problem is a key step in the high-speed railway transit planning process. When trip delays, railway departments may need to incur considerable additional costs for plan adjustments to quickly resume operations. Therefore, we consider the impact of trip delay probabilities during rolling stock planning, aiming to minimise the total travel cost and maximise plan robustness to enhance the reliability of plan execution. A space–time–state network is established to describe the operation of rolling stocks considering accumulated mileage and running time constraints for maintenance, representing the rolling stock planning problem as a mixed-integer nonlinear programming model. Then, an alternating direction method of multipliers (ADMM)-based decomposition mechanism that decomposes the model into a set of rolling stock route selection subproblems is introduced, where each subproblem is efficiently solved by a maintenance-constrained shortest path algorithm related to reliability. A set of different scale real-life cases based on trips managed by a depot in China are used to verify the effectiveness of the proposed model and algorithm. The results show that the ADMM considerably outperforms the traditional Lagrangian relaxation (LR) method. On a set of larger-scale cases, the proposed ADMM with enhancements obtains an optimality gap of 2.42 % on average. This result is substantially better than LR, which provides optimality gaps of 32.55 % on average. Finally, the model in this paper effectively enhances the probability of successful rolling stock route execution in trip delay scenarios, resulting in a rolling stock plan with improved reliability.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"259 ","pages":"Article 110972"},"PeriodicalIF":9.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143510493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Creating an incident investigation framework for a complex socio-technical system: Application of multi-label text classification and Bayesian network structure learning

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-24 DOI: 10.1016/j.ress.2025.110971

Mohammadreza Karimi Dehkordi, Fereshteh Sattari, Lianne Lefsrud

{"title":"Creating an incident investigation framework for a complex socio-technical system: Application of multi-label text classification and Bayesian network structure learning","authors":"Mohammadreza Karimi Dehkordi, Fereshteh Sattari, Lianne Lefsrud","doi":"10.1016/j.ress.2025.110971","DOIUrl":"10.1016/j.ress.2025.110971","url":null,"abstract":"<div><div>The power distribution sector presents a complex socio-technical system where accidents frequently occur from various technical, human, environmental, and organizational factors, resulting in fatalities and substantial economic losses. The dynamic operational environment and complex interactions among the causal factors further complicate effective risk management and accident prevention. This research proposes a methodology to identify various risk factors and develop causal networks representing the complex relationships among these factors in power distribution incident reports. First, machine learning multi-label text classification identifies the risk factors from the incident reports. Then, the relationship among these factors is determined by integrating experts’ domain knowledge and data-driven Bayesian network structure learning approaches. Finally, the most influential causal factors and their direct/indirect effects on the incidents are identified, and proper risk control measures are recommended. The proposed methodology is applied to an incident database from a Canadian power distribution company, covering power outages, injuries, environmental issues, and near misses collected from 2013 to 2020. The results highlight that human and technical factors are the most influential and affected by organizational and environmental factors. Considering their complex interaction, implementing targeted risk management for high-risk direct/indirect causal factors could prevent further incidents and improve the companies’ overall safety.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110971"},"PeriodicalIF":9.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effects of inhomogeneity and statistical and material anisotropy on THM simulations

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-24 DOI: 10.1016/j.ress.2025.110921

Aqeel Afzal Chaudhry , Chao Zhang , Oliver G. Ernst , Thomas Nagel

{"title":"Effects of inhomogeneity and statistical and material anisotropy on THM simulations","authors":"Aqeel Afzal Chaudhry , Chao Zhang , Oliver G. Ernst , Thomas Nagel","doi":"10.1016/j.ress.2025.110921","DOIUrl":"10.1016/j.ress.2025.110921","url":null,"abstract":"<div><div>When modeling the material properties of host rocks for thermo-hydro-mechanical simulations in barrier integrity investigations for deep geological disposal of radioactive waste, numerous modeling aspects must be considered. If complete information were available, the material properties would be functions of space, with inhomogeneity and anisotropy expressed by spatially varying and tensor-valued coefficients. In practice, uncertainty is present in particular related to spatial variability of physical properties. This variability can be modeled by random fields, whose realizations are functions of space. A common choice is a Gaussian random field, determined by its mean and two-point covariance function. Anisotropy can occur both in the statistical covariance structure, resulting in different correlation lengths along principal axes, and in the physical properties themselves, leading to tensor-valued random fields. In this study, we focus on both cases, considering dominant material properties such as thermal conductivity, intrinsic permeability, and Young’s modulus, and present numerical simulations illustrating the effects of inhomogeneity, randomness, and anisotropy. Since spatial variability is a key feature in the analysis of in-situ data, this study quantifies the individual contribution of each of the listed effects in a well-controlled synthetic case and discusses them in the context of scale.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110921"},"PeriodicalIF":9.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Optimal operation and maintenance scheduling in generalized repairable m-out-of-n standby systems with common shocks

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-23 DOI: 10.1016/j.ress.2025.110967

Gregory Levitin , Liudong Xing , Yuanshun Dai

{"title":"Optimal operation and maintenance scheduling in generalized repairable m-out-of-n standby systems with common shocks","authors":"Gregory Levitin , Liudong Xing , Yuanshun Dai","doi":"10.1016/j.ress.2025.110967","DOIUrl":"10.1016/j.ress.2025.110967","url":null,"abstract":"<div><div>This paper contributes by modeling a new class of repairable, dynamic <span><math><mi>m</mi></math></span>-out-of-<span><math><mi>n</mi></math></span> standby systems operating in random shock environments. Operating components are exposed to a common shock process and can fail due to external shocks and/or internal deterioration, causing the failure of the entire mission. Therefore, it is pivotal to implement an operation and maintenance schedule (OMS), according to which any operating component may be preventively replaced by a standby component to undergo perfect maintenance during the mission. Due to heterogeneity of system components, different OMSs incur different expected mission cost (EMC) and mission success probability (MSP). We formulate a new optimization problem to determine the optimal OMS that minimizes the EMC while satisfying a certain level of MSP. The solution methodology encompasses a new recursive procedure to evaluate MSP and the realization of genetic algorithm. A case study of a chemical reactor cooling system is conducted to showcase the proposed model and study the effects of component heterogeneity as well as several key model parameters on the system performance. The mission cost sensitivity analysis is also demonstrated, providing insights on the most cost-effective component performance or shock resistance improvement. The proposed model extends the OMS study of standby systems in literature from non-shock to shock operating environments.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110967"},"PeriodicalIF":9.4,"publicationDate":"2025-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143529770","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new fault detection method based on an updatable hybrid model for hard-to-detect faults in nonstationary processes

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-22 DOI: 10.1016/j.ress.2025.110920

Jie Dong , Daye Li , Zhiyu Cong , Kaixiang Peng

{"title":"A new fault detection method based on an updatable hybrid model for hard-to-detect faults in nonstationary processes","authors":"Jie Dong , Daye Li , Zhiyu Cong , Kaixiang Peng","doi":"10.1016/j.ress.2025.110920","DOIUrl":"10.1016/j.ress.2025.110920","url":null,"abstract":"<div><div>Fault detection is an effective means to guarantee the stable operation of industrial production. Fault signals are easily masked by nonstationary trends in the variables, which leads to hard-to-detect faults in nonstationary processes. In this paper, an updatable hybrid model for fault detection is proposed for the nonstationary characteristics and hard-to-detect faults of industrial processes. First, the stationary residuals of the nonstationary variables are combined with the stationary variables to form a combined matrix. Second, a monitoring model based on slow-feature-analysis-local-outlier-factor (SFA-LOF) is constructed, which extracts the slow features in the combined matrix and introduces a local outlier factor as the monitoring index. Third, the sensitive variables of faults that are hard to detect using SFA-LOF are screened, and refined models based on Kullback–Leibler divergence are constructed for hard-to-detect faults. Then, an updatable hybrid model based on the SFA-LOF model and the refined model is proposed. The hybrid model matches the detection models to the faults and is able to update the hybrid model by developing refined models. Finally, the Tennessee Eastman process is used to validate the effectiveness of the proposed fault detection framework.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"259 ","pages":"Article 110920"},"PeriodicalIF":9.4,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143474337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A new multi-layer performance analysis of unmanned system-of-systems within IoT

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-22 DOI: 10.1016/j.ress.2025.110953

Kaixuan Wang , Tingdi Zhao , Yuan Yuan , Zhenkai Hao , Zhiwei Chen , Hongyan Dui

{"title":"A new multi-layer performance analysis of unmanned system-of-systems within IoT","authors":"Kaixuan Wang , Tingdi Zhao , Yuan Yuan , Zhenkai Hao , Zhiwei Chen , Hongyan Dui","doi":"10.1016/j.ress.2025.110953","DOIUrl":"10.1016/j.ress.2025.110953","url":null,"abstract":"<div><div>Internet of Things (IoT)-enabled unmanned system-of-systems (USoS) is vital in disaster management, rescue operations, and military missions. However, research on performance loss and improvement strategies of USoS under multiple shocks has been limited. Thus, evaluating performance loss and developing improvement strategies for USoS is critical to boosting mission capability and efficiency. This paper presents a multi-layer performance analysis method for USoS within the IoT framework. Firstly, we established a multi-layer USoS structure, dividing it into physical, communication, and command layers to address variable performance and mission baselines. Secondly, an USoS performance loss model is established based on the degradation-threshold-shock models and the signal-to-noise-and-interference ratio to enhance USoS performance evaluation accuracy. Thirdly, performance improvement strategies of USoS are proposed by combining the observe, orient, decide, and act (OODA) loop with the minimum cost maximum flow theory to optimize resource allocation and reconfigure emergency links. Finally, a sea-air collaborative USoS serves as a case study to validate the efficacy of the proposed method, showing significant post-implementation performance gains, and offering a reference for mitigating performance loss and enhancing reliability during multiple shocks.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"259 ","pages":"Article 110953"},"PeriodicalIF":9.4,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143479973","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Enhanced prediction of pipe failure through transient simulation-aided logistic regression

IF 9.4 1区工程技术

Reliability Engineering & System Safety Pub Date : 2025-02-22 DOI: 10.1016/j.ress.2025.110913

Dan Zhong, Chaoyuan Huang, Wencheng Ma, Liming Deng, Jinbo Zhou, Ying Xia

{"title":"Enhanced prediction of pipe failure through transient simulation-aided logistic regression","authors":"Dan Zhong, Chaoyuan Huang, Wencheng Ma, Liming Deng, Jinbo Zhou, Ying Xia","doi":"10.1016/j.ress.2025.110913","DOIUrl":"10.1016/j.ress.2025.110913","url":null,"abstract":"<div><div>To reduce leakage and improve the stability of the water supply system, water companies are increasingly adopting pipe failure prediction models based on hydraulic and non-hydraulic factors. However, these companies often face the challenge of limited data and conventional hydraulic factors have limited predictive capability in capturing the complex dynamics of pipe failures. This study proposed a logistic regression model based on hydraulic transient simulation, illustrated with the real case of a Chinese city. The data recorded included 246 pipe failures in one year. The model considered the influence of pressure, flow rate variations, and the network topology of the water supply system through hydraulic transient simulation and quantitatively analyzed the simulation results. The logistic regression model combined non-hydraulic factors with the quantitative analysis results of hydraulic factors to predict pipe failures. This study risk-categorized six areas that were prone to pipe failures. The developed model demonstrated significant accuracy and reliability in predicting pipe failures at high-risk levels. 75.61 % of true failure events were correctly predicted and the area under the curve values (AUC) value increased from 0.706 to 0.809 when incorporating transient simulation. This demonstrates that the model is effective in capturing the dynamic characteristics of the hydraulic factors and exhibits a high degree of accuracy even with a limited amount of data. This provides a feasible solution for water companies to accurately predict pipe failures.</div></div>","PeriodicalId":54500,"journal":{"name":"Reliability Engineering & System Safety","volume":"260 ","pages":"Article 110913"},"PeriodicalIF":9.4,"publicationDate":"2025-02-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143547939","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0