Transportation Research Part E-Logistics and Transportation Review最新文献

{"title":"An event-based model and hybrid genetic search algorithm for an inland multi-size container transportation problem","authors":"Meiyan Chi ,&nbsp;Xiaoning Zhu ,&nbsp;Baicheng Yan ,&nbsp;Kris Braekers","doi":"10.1016/j.tre.2025.104639","DOIUrl":"10.1016/j.tre.2025.104639","url":null,"abstract":"<div><div>This paper addresses a complex multi-size Container Drayage Problem (CDP) in the hinterland of a seaport, where a fleet of identical trucks is used to transport containers between customer locations, a container terminal, and a depot, and in which the repositioning of empty containers is also considered. Each truck can carry either one 40-ft container or two 20-ft containers simultaneously. The main target of the CDP is to determine the trucking schedule that satisfies all transport demands while minimizing the total cost. The problem is described using an event-based graph that considers capacity, pairing, precedence, and time-window constraints implicitly, based on which a compact Mixed-Integer Linear Programming (MILP) model is proposed. To reduce the model scale and enhance computational efficiency, we introduce tailored model enhancement methods to eliminate infeasible event nodes and arcs based on time window feasibility checks. The results of numerical experiments prove that the event-based model can solve small-scale instances effectively. For large-scale instances, we develop a Hybrid Genetic Search (HGS) algorithm that incorporates a Dynamic Programming (DP)-optimized enumeration method to handle multi-size container loading schemes and time-window constraints effeciently. Extensive computational experiments show that our proposed algorithm significantly outperforms the commercial solver <span>CPLEX</span> on large-scale instances, demonstrating its scalability for real-world applications.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104639"},"PeriodicalIF":8.8,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978646","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}

{"title":"A multi-channel retail store location model considering customer retry purchasing patterns","authors":"Lifen Yun ,&nbsp;Runfeng Yu ,&nbsp;Hongqiang Fan ,&nbsp;Yuanjie Tang ,&nbsp;Xun Weng","doi":"10.1016/j.tre.2026.104674","DOIUrl":"10.1016/j.tre.2026.104674","url":null,"abstract":"<div><div>The rapid growth of e-commerce has driven retailers to establish multiple retail channels to enhance service quality for their customers. Despite retailers’ efforts to serve customers, services may not always be available due to various reasons. In such cases, customers often retry their purchases through their preferred channel. This behavior, along with the complexities of multi-channel retailing, complicates both the structure and costs of last-mile network design. To optimize store locations and costs, this paper proposes a mixed-integer programming (MIP) model for tactical store location planning, considering customer retry purchasing patterns and three channels: ship from store (SFS), buy online and pick up in store (BOPS), and offline shopping (OS). Given that the problem is <em>NP-hard</em> in the strong sense, we develop an iterative two-phase Lagrangian relaxation and granular tabu search heuristic (LR-GTS) to tackle large-scale instances. In each iteration, the LR operator decomposes the model and produces high-quality location schemes, while the GTS operator improves the vehicle routing in the SFS channel. Numerical results demonstrate that our heuristic exhibits strong performance in solving large-scale problems involving 600 customers. Additionally, we apply our model to real-world cases, offering valuable managerial insights derived from the sensitivity analysis results.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104674"},"PeriodicalIF":8.8,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978645","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}

{"title":"How does supply chain finance impact supply chain resilience − based on three levels of supply and demand decision making","authors":"Limeng Chai ,&nbsp;Longyu Zong ,&nbsp;Kee-hung Lai","doi":"10.1016/j.tre.2026.104686","DOIUrl":"10.1016/j.tre.2026.104686","url":null,"abstract":"<div><div>Enhancing supply chain resilience has emerged as a key focus for industries worldwide. Based on the theory of resource orchestration, this paper uses annual reports of Chinese companies to analyze how supply chain finance improves supply chain resilience in three dimensions, including matching supply and demand, fostering supply–demand relationships, and ensuring supply quality. The results indicate that supply chain finance contributes positively to improving resilience across these dimensions. Furthermore, the moderating analysis examines the positive moderating effects of artificial intelligence, merchant guild culture and the level of vertical integration on supply chain finance and supply chain resilience. Finally, this paper also explores the threshold effect of company risk-taking and the heterogeneity of industry characteristics. The results show that company risk-taking level has a certain threshold impact, and the supply chain finance in the manufacturing industry has a more significant effect on enhancing supply chain resilience, which emphasizes that companies should attach importance to aspects such as technology, risk management and cultural maintenance, when they carry out supply chain finance business. This research provides a new perspective for optimizing supply chain resilience.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104686"},"PeriodicalIF":8.8,"publicationDate":"2026-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978647","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}

{"title":"Two-stage virtual coupling high-speed railway train operation control method considering train operation curve optimization","authors":"Wencheng Huang ,&nbsp;Baiquan Tai","doi":"10.1016/j.tre.2026.104687","DOIUrl":"10.1016/j.tre.2026.104687","url":null,"abstract":"<div><div>In order to maintain the train tracking accuracy of the virtual coupled train formation (VCTF) while reducing energy consumption during operation, in this paper, we propose a two-stage virtual coupling high-speed train operation control method that considers optimizing the train operation curve. Based on train dynamics, we take minimizing energy consumption and operation time deviation as the main objectives for the train operation curve optimization in Stage I, establish a dynamic programming model with time discretization, and design an algorithm to satisfy the requirements of precise parking and on-time operation of the train, in which a variable time step solving algorithm is considered to improve the solving efficiency. The optimal energy-saving train operation curve obtained in Stage I is used as the expected operation curve of the leader train in the VCTF. In Stage II, we adopt the Distributed Model Predictive Control (DMPC) to establish the virtual coupling high-speed railway train operation control model. With the goal of minimizing the position error and speed error of the VCTF, and the train safety interval, train speed, train control performance and passenger comfort constraints, the designed DMPC ensures the stability and safety of the operation of VCTF. Finally, simulation analysis is conducted on three different scenarios, the research results show that the methodology proposed in this paper can effectively reduce the energy consumption of trains during operation while improving the tracking accuracy of trains in VCTF. In terms of energy saving, the proposed method is superior to previous heuristic algorithms and reinforcement learning methods, with improvements of 16.12 % and 2.52 %, respectively. In terms of tracking accuracy, the peak distance and speed errors of the VC trains do not exceed 2 m and 0.24 m/s. In terms of solving efficiency, the variable time step dynamic programming method can improve the solving speed by nearly 30 % while ensuring the solving accuracy. Moreover, the energy consumption of Train 0 and Train 1 increased by 1.7 % and 0.66 % respectively compared to the expected operation curve. Simulation results with different expected intervals show that setting an appropriate expected tracking interval in actual operation requires a balance between train operation safety and energy efficiency.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104687"},"PeriodicalIF":8.8,"publicationDate":"2026-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978614","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}

{"title":"ISAC-empowered deep reinforcement learning scheme for eVTOL approach trajectory optimization with radar point cloud","authors":"Leyan Chen ,&nbsp;Yuhao Wang ,&nbsp;Shulu Chen ,&nbsp;Yong Sun ,&nbsp;Kai Wang","doi":"10.1016/j.tre.2026.104681","DOIUrl":"10.1016/j.tre.2026.104681","url":null,"abstract":"<div><div>Urban air mobility (UAM) has emerged as a promising solution to alleviate ground traffic congestion by enabling the use of low-altitude airspace for passenger and cargo transportation. Among UAM, electric vertical take-off and landing (eVTOL) aircraft are expected to play a central role in future urban transport systems due to their flexibility, zero-emission operation, and compatibility with existing infrastructure. However, ensuring the safe and efficient operation of eVTOLs during the approach and landing phase remains a major challenge, especially in urban environments where intelligent unmanned aerial vehicles (UAVs) simultaneously perform logistics and monitoring tasks at low altitudes. Reliable sensing and communication are therefore essential to guarantee operational safety, connectivity, and energy efficiency. To address these challenges, this paper proposes an integrated sensing and communication (ISAC) framework for eVTOL approach trajectory optimization. The framework integrates radar point cloud (RPC) sensing with the three-dimensional radio knowledge map (RKM) to enhance environmental awareness and communication reliability in dense urban airspace. Based on this framework, a fusion deep point reinforcement learning (FDPRL) algorithm is developed to optimize eVTOL trajectories under age-of-information and energy constraints jointly. It includes the RPC feature extraction module for UAV sensing, the RKM feature extraction module for communication enhancement, and a decision-making module for trajectory control. Simulation results demonstrate that the proposed FDPRL achieves challenge performance, outperforming all the benchmarks, enabling the eVTOL to adaptively adjust its approach trajectory to avoid UAVs while maintaining efficient communication, thus achieving superior total communication capacity and maximum residual energy.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104681"},"PeriodicalIF":8.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978650","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}

{"title":"What if rebalancing fleets could adapt? A two-stage stochastic model for dynamic bike redistribution","authors":"Mohammadreza Eslamipirharati ,&nbsp;Maryam Motamedi ,&nbsp;John Doucette ,&nbsp;Nooshin Salari","doi":"10.1016/j.tre.2025.104640","DOIUrl":"10.1016/j.tre.2025.104640","url":null,"abstract":"<div><div>Bike-sharing systems are an important mode of transportation, enabling individuals to rent bikes for short trips and return them to any station throughout the city. However, the dynamic nature of user arrivals at each station leads to imbalances between bike supply and demand, resulting in unsatisfied users. An essential challenge lies in efficiently deploying and scheduling rebalancing vehicles for bike redistribution, as these decisions have a considerable effect on the efficiency of the system. To tackle this challenge, we propose a dynamic rebalancing model that integrates tactical and operational decisions within a single optimization framework. Unlike approaches that treat these decisions separately, our model captures the interaction between the two: in the first stage, it determines how many vehicles should be deployed over the planning horizon (tactical decision), and in the second stage, it assigns stations to dynamic rebalancing groups and allocates vehicles to these groups in response to demand realizations (operational decisions). To address the computational challenge, we propose two approaches: an Improved Integer L-shaped decomposition algorithm and a heuristic that combines machine learning with an early stopping criterion to estimate the second-stage cost function. Moreover, we generate forecasts of rental and return demand and incorporate them into the optimization model to enhance decision-making under demand uncertainty. Our numerical results show that the proposed heuristic is highly effective in minimizing the unsatisfied demand while reducing the computational costs efficiently.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104640"},"PeriodicalIF":8.8,"publicationDate":"2026-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145978648","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}

{"title":"Impact Effects of Transport Structure Changes on Urban Traffic Congestion: A Case Study of Core Cities in China","authors":"Heng Chen,&nbsp;Naqi Yuan Liu,&nbsp;Yumei Yang,&nbsp;Yanying Wang,&nbsp;Qian Li,&nbsp;Yingji Shan","doi":"10.1016/j.tre.2026.104676","DOIUrl":"10.1016/j.tre.2026.104676","url":null,"abstract":"<div><div>Accelerated urbanization and population expansion in developing countries have led to excessive reliance on road transport within logistics systems, exacerbating urban traffic congestion and constraining high-quality socioeconomic development. Consequently, optimizing urban logistics-transport structures becomes crucial for alleviating congestion and achieving sustainable economic growth. This study employs Chinese core cities as samples, adopting a comparative perspective on passenger/freight transport structure changes. Kernel density estimation and Markov chain models analyze spatiotemporal evolution patterns between logistics-transport restructuring and congestion, exploring their mechanistic relationships. Benchmark panel regressions and spatial econometric models provide empirical verification. Key findings include: (1) China’s urban logistics-transport structures demonstrate overall temporal optimization, specifically upward adjustments in passenger transport structures (rising ratios of railway-to-highway passenger volumes) and upward adjustments in freight transport structures (rising ratios of railway-to-highway freight volumes). Traffic congestion shows marginal mitigation. Changes in logistics-transport structures and spatial transitions of congestion remain relatively stable. China’s urban transportation exhibits a stepped imbalance pattern: “eastern superiority and central-western weakness in rising transport structures” alongside “eastern-central superiority and western inferiority in congestion alleviation”. (2) Nationally, absent spatial considerations, increased railway-to-highway passenger and freight volume ratios suppress congestion. When incorporating spatial factors, these ratio improvements alleviate local congestion while mitigating neighboring areas’ congestion. (3) Regionally without spatial effects, enhanced railway-passenger structures reduce congestion in eastern-western cities. Central cities’ inadequate transport network upgrades prevent full utilization of railway advantages, intensifying congestion. Improved railway-freight structures alleviate congestion in eastern-central cities, whereas western mountainous terrain constraints limit rail freight capacity, failing to meet growing demand and worsening congestion. With spatial effects, railway-freight improvements effectively reduce eastern-central congestion, while railway-passenger enhancements may exacerbate central cities’ congestion.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104676"},"PeriodicalIF":8.8,"publicationDate":"2026-01-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961767","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}

{"title":"Enhancing ship collision risk assessment by integrating virtual ship-based shared nearest neighbor clustering and game-theoretic modeling","authors":"Congcong Zhao ,&nbsp;Zhuoyi Li ,&nbsp;Tsz Leung Yip ,&nbsp;Bing Wu","doi":"10.1016/j.tre.2026.104675","DOIUrl":"10.1016/j.tre.2026.104675","url":null,"abstract":"<div><div>With the rapid growth in global shipping activities, the assessment of vessel collision risk has become a critical concern for maritime safety management. This study develops a comprehensive framework for identifying high-risk areas in congested waterways by integrating the Shared Nearest Neighbor Density-Based Spatial Clustering of Applications with Noise (SNN-DBSCAN) algorithm and Stackelberg game-theoretic model. The proposed framework first applies SNN-DBSCAN to enable robust waterway regionalization and vessel clustering under highly heterogeneous traffic densities, enhancing the accuracy and efficiency of collision risk assessment. To prevent critical crossing interactions from being fragmented by purely spatial clustering, we introduce a virtual-ship representation based on a risk-invariance principle, ensuring that high-risk encounters are preserved in the interaction set. Furthermore, a leader-follower game is employed to characterize strategic vessel responses by jointly considering safety, efficiency, and decision uncertainty to predict the next actions of the target vessel. The proposed framework is validated using empirical data from the busy waters of Hong Kong under daytime, nighttime, heavy precipitation, and strong winds. The results reveal pronounced scenario-dependent changes in vessel collision risk. Reduced nighttime visibility shifts hotspots and elevates risk, daytime port activities create new high-risk zones, and severe weather drives vessels to typhoon shelters where higher density and poorer maneuverability increase danger. The proposed approach captures these shifts and yields an interpretable, actionable tool for collision risk assessment and maritime traffic management, supporting future maritime safety management.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104675"},"PeriodicalIF":8.8,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145961769","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}

{"title":"With whom to ally? Alliance strategy for EV battery supplier considering echelon utilization and disassembly recycling","authors":"Zhangzhen Fang ,&nbsp;Yuhan Guo ,&nbsp;Gaoxiang Lou ,&nbsp;Zhixuan Lai ,&nbsp;Haicheng Ma ,&nbsp;Li Zhou","doi":"10.1016/j.tre.2025.104656","DOIUrl":"10.1016/j.tre.2025.104656","url":null,"abstract":"<div><div>The rapid expansion of the electric vehicle (EV) industry has heightened the need for sustainable and efficient closed-loop supply chains (CLSC) that can simultaneously improve economic returns and mitigate environmental impacts. To address this challenge, this study develops a game-theoretic model from the perspective of the power battery supplier and examines four inter-firm alliance modes: Non-alliance (N), supplier-manufacturer alliance (SM), supplier-recycler alliance (SR), and comprehensive alliance (SMR). The results reveal that (1) in the forward supply chain, suppliers under the SM and SMR modes consistently achieve higher battery capacity and EV sales. In the reverse supply chain, suppliers in alliance modes (SM, SR, SMR) are able to pay lower recycling prices while securing higher recycling quantities. (2) When recycling competition is weak, alliance with the manufacturer improves economic performance, whereas that with the recycler enhances environmental outcomes; however, the two benefits cannot be achieved simultaneously. By contrast, under intense recycling competition, forming a comprehensive alliance allows suppliers to improve both environmental and economic performance. (3) When extending the analysis to include suppliers’ investment in echelon utilization technology innovation, increased recycling competition intensity leads to a decline in the supplier’s echelon utilization performance, thereby amplifying the advantage of the comprehensive alliance.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104656"},"PeriodicalIF":8.8,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956534","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}

{"title":"Algorithmic pricing in supply chains: implications for product quality, pricing, and profits","authors":"Kui Song ,&nbsp;Jing Chen ,&nbsp;Hui Yang ,&nbsp;Bintong Chen ,&nbsp;Honghu Huang","doi":"10.1016/j.tre.2026.104679","DOIUrl":"10.1016/j.tre.2026.104679","url":null,"abstract":"<div><div>The rapid adoption of algorithmic pricing by retailers, enabled by big data analytics, is reshaping decisions in supply chains and affecting consumer surplus. We develop a game-theoretic model to examine how a retailer’s operation under an algorithmic-pricing regime, compared with a uniform-pricing regime, influences the manufacturer’s product quality and wholesale pricing decisions, as well as profits and consumer surplus. We uncover three key findings. First, algorithmic pricing affects product quality through two opposing effects: the demand segmentation effect, which encourages quality improvement by better matching products to heterogeneous consumers, and the profit compression effect, which discourages quality investment when the consumer distribution is highly skewed. Second, algorithmic pricing generates asymmetric profit impacts for supply chain members. While the retailer benefits more directly from pricing precision, both firms can benefit, particularly under a balanced mix of consumer types, through increased market coverage and reduced channel conflict. Third, when algorithmic reliability is high and consumer heterogeneity is moderate, algorithmic pricing can improve consumer surplus by aligning prices with willingness-to-pay and incentivizing higher quality. As reliability improves and the consumer distribution becomes more balanced, the system can achieve a tripartite win–win that benefits the manufacturer, the retailer, and consumers. These findings highlight the dual, condition-dependent role of algorithmic pricing as both a coordination tool and a quality-enhancement mechanism in supply chains. They also offer managerial implications for the strategic deployment of algorithmic pricing tools and inform policy debates on regulating algorithm-driven markets.</div></div>","PeriodicalId":49418,"journal":{"name":"Transportation Research Part E-Logistics and Transportation Review","volume":"208 ","pages":"Article 104679"},"PeriodicalIF":8.8,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145956533","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}