Supply Chain Analytics最新文献_第4页

Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques 评估机器学习方法对工程变更时间表的早期预测性能--利用预测性流程监控技术的案例研究

Supply Chain Analytics Pub Date : 2024-10-24 DOI: 10.1016/j.sca.2024.100087

Ognjen Radišić-Aberger, Peter Burggräf, Fabian Steinberg, Alexander Becher, Tim Weißer

{"title":"Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques","authors":"Ognjen Radišić-Aberger, Peter Burggräf, Fabian Steinberg, Alexander Becher, Tim Weißer","doi":"10.1016/j.sca.2024.100087","DOIUrl":"10.1016/j.sca.2024.100087","url":null,"abstract":"<div><div>By applying machine learning algorithms, predictive business process monitoring (PBPM) techniques provide an opportunity to counteract undesired outcomes of processes. An especially complex variation of business processes is the engineering change (EC) process. Here, failing to adhere to planned implementation dates can have severe impacts on assembly lines, and it is paramount that potential negative cases are identified as early as possible. Current PBPM research, however, has seldomly investigated the predictive performance of machine learning approaches and their applicability at early process steps, let alone for the EC process. In our research, we show that given adequate feature encoding, shallow learners can accurately predict schedule adherence after process initialisation. Based on EC data from an automotive manufacturer, we provide a case sensitive performance overview on algorithm-encoding combinations. For that, three algorithms (XGBoost, Random Forest, LSTM) were combined with four encoding techniques. The encoding techniques used were the two common aggregation-based and index-based last state encoding, and two new combinations of these, which we term advanced aggregation-based and complex aggregation-based encoding. The study indicates that XGBoost-index-encoded approaches outclass regarding average predictive performance, whereas Random-Forest-aggregation-encoded approaches perform better regarding temporal stability due to reduced influence by dynamic features. Our research provides a case-based reasoning approach for deciding on which algorithm-encoding combination and evaluation metrics to apply. In doing so, we provide a blueprint for an early warning and monitoring method within the EC process and other similarly complex processes.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100087"},"PeriodicalIF":0.0,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142554016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A bibliometric exploration of environmental sustainability in supply chain research 供应链研究中的环境可持续性文献计量学探索

Supply Chain Analytics Pub Date : 2024-10-22 DOI: 10.1016/j.sca.2024.100086

Brintha Rajendran , Manivannan Babu , Naliniprava Tripathy , Veeramani Anandhabalaji

{"title":"A bibliometric exploration of environmental sustainability in supply chain research","authors":"Brintha Rajendran , Manivannan Babu , Naliniprava Tripathy , Veeramani Anandhabalaji","doi":"10.1016/j.sca.2024.100086","DOIUrl":"10.1016/j.sca.2024.100086","url":null,"abstract":"<div><div>This study undertakes a bibliometric examination of the literature on supply chain sustainability (SCS). The analysis includes exploring co-authorship, examining keyword co-occurrences, conducting citation analysis, bibliographic coupling, co-citation analysis for performance evaluation, and employing science mapping techniques. The paper thus explores the significant facets of the literature on SCS. We studied the literature on SCS management from 1996 to 2024 and extracted 6898 articles retrieved from the Scopus database. In the preliminary phase, the investigation employs the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow methodology alongside a designated search strategy. Secondly, it employs Biblioshiny, an RStudio package, and VOSviewer. The study finds that the SCS field has evolved with a major focus on collaboration, innovation, and sustainability. Furthermore, the findings indicate that China, the USA, the UK, and India lead in research contributions, emphasizing the importance of international collaboration. Additionally, the findings signpost that technology such as blockchain enhances sustainability efforts. Social sustainability also gains recognition alongside environmental concerns. These findings can inform researchers, highlighting the need for international cooperation, technology integration, and emphasis on social sustainability in advancing the management of supply chains. This study makes novel contributions by providing global coverage of publications, adopting an inclusive approach encompassing case studies and empirical research articles, addressing social desirability bias by reporting positive as well as negative aspects of sustainability in supply chain practices, and identifying alternative areas for future research within the discipline.</div></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100086"},"PeriodicalIF":0.0,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142537375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A systematic review of supply chain analytics for targeted ads in E-commerce 电子商务定向广告供应链分析系统综述

Supply Chain Analytics Pub Date : 2024-09-28 DOI: 10.1016/j.sca.2024.100085

Shrestha Pundir, Hardik Garg, Devnaad Singh, Prashant Singh Rana

引用次数: 0

An integrated supply chain network design for advanced air mobility aircraft manufacturing using stochastic optimization 利用随机优化为先进的空中机动飞机制造设计综合供应链网络

Supply Chain Analytics Pub Date : 2024-09-12 DOI: 10.1016/j.sca.2024.100083

Esrat Farhana Dulia , Syed A.M. Shihab

{"title":"An integrated supply chain network design for advanced air mobility aircraft manufacturing using stochastic optimization","authors":"Esrat Farhana Dulia , Syed A.M. Shihab","doi":"10.1016/j.sca.2024.100083","DOIUrl":"10.1016/j.sca.2024.100083","url":null,"abstract":"<div><p>Electric vertical takeoff and landing (eVTOL) aircraft manufacturers await numerous pre-orders for eVTOLs and expect demand for such advanced air mobility (AAM) aircraft to rise dramatically soon. However, eVTOL manufacturers (EMs) cannot commence mass production of commercial eVTOLs due to a lack of supply chain planning for eVTOL manufacturing. The eVTOL supply chain differs from traditional ones due to stringent quality standards and limited suppliers for eVTOL parts, shortages in skilled labor and machinery, and contract renegotiations with major aerospace suppliers. The emerging AAM aircraft market introduces uncertainties in supplier pricing and capacities, eVTOL manufacturing costs, and eVTOL demand, further compounding the supply chain planning challenges for EMs. Despite this critical need, no study has been conducted to develop a comprehensive supply chain planning model for EMs. To address this research gap, we propose a stochastic optimization model for integrated supply chain planning of EMs while maximizing their operating profits under the abovementioned uncertainties. We conduct various numerical cases to analyze the impact of 1) endogenous eVTOL demand influenced by the quality of eVTOLs, 2) supply chain disruptions caused by geopolitical conflicts and resource scarcity, and 3) high-volume eVTOL demand similar to that experienced by automotive manufacturers, on EM supply chain planning. The results indicate that our proposed model is adaptable in all cases and outperforms established benchmark stochastic models. The findings suggest that EMs can commence mass eVTOL production with our model, enabling them to make optimal decisions and profits even under potential disruptions.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100083"},"PeriodicalIF":0.0,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000268/pdfft?md5=b604a803c52861a058956e6fd8a64ecf&pid=1-s2.0-S2949863524000268-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comparative assessment of holt winter exponential smoothing and autoregressive integrated moving average for inventory optimization in supply chains 霍尔特冬季指数平滑法与自回归综合移动平均法在供应链库存优化中的比较评估

Supply Chain Analytics Pub Date : 2024-09-05 DOI: 10.1016/j.sca.2024.100084

Lalji Kumar , Sudhakar Khedlekar , U.K. Khedlekar

{"title":"A comparative assessment of holt winter exponential smoothing and autoregressive integrated moving average for inventory optimization in supply chains","authors":"Lalji Kumar , Sudhakar Khedlekar , U.K. Khedlekar","doi":"10.1016/j.sca.2024.100084","DOIUrl":"10.1016/j.sca.2024.100084","url":null,"abstract":"<div><p>Precise demand forecasting and agile pricing strategies are crucial in modern business. This study aims to enhance these strategies by evaluating the efficacy of Holt-Winters Exponential Smoothing (HWES) and Autoregressive Integrated Moving Average (ARIMA) models. The study assesses their performance in predicting demand amid unpredictable factors and develops robust forecasting algorithms using real-world data. It evaluates HWES and ARIMA in capturing demand fluctuations, considering seasonality, market trends, and cyclic patterns. A comprehensive comparative analysis is conducted under stable and unstable economic conditions. The study also focuses on a dynamic pricing model for limited sale seasons, examining lost sales patterns over time. In the context of supply chain and inventory management, efficient demand forecasting and dynamic pricing are essential for optimizing inventory levels and minimizing costs. Supply chains must adapt quickly to demand fluctuations to avoid overstocking or stockouts, which lead to revenue losses and inefficiencies. The findings reveal that ARIMA consistently outperforms HWES in minimizing lost sales, demonstrating its efficacy in demand forecasting, mitigating stockouts, and reducing revenue losses, particularly in varying economic conditions. This research significantly contributes to current knowledge by developing tailored forecasting algorithms and a dynamic pricing model, enhancing supply chain resilience and performance in uncertain business environments.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100084"},"PeriodicalIF":0.0,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S294986352400027X/pdfft?md5=98f10ccd1d31fdd03db055c77fb3faa2&pid=1-s2.0-S294986352400027X-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An explainable artificial intelligence model for predictive maintenance and spare parts optimization 用于预测性维护和备件优化的可解释人工智能模型

Supply Chain Analytics Pub Date : 2024-08-26 DOI: 10.1016/j.sca.2024.100078

Ufuk Dereci , Gülfem Tuzkaya

{"title":"An explainable artificial intelligence model for predictive maintenance and spare parts optimization","authors":"Ufuk Dereci , Gülfem Tuzkaya","doi":"10.1016/j.sca.2024.100078","DOIUrl":"10.1016/j.sca.2024.100078","url":null,"abstract":"<div><p>Maintenance strategies are vital for industrial and manufacturing systems. This study considers a proactive maintenance strategy and emphasizes using analytics and data science. We propose an Explainable Artificial Intelligence (XAI) methodology for predictive maintenance. The proposed method utilizes a machine learning project cycle and Python libraries to interpret the results using the Local Interpretable Model-agnostic Explanations (LIME) method. We also introduce an early concept of spare parts management, presenting insights from predictive maintenance outcomes and providing explanations for decision-makers to enhance their understanding of the influential factors behind predictions. This study demonstrates that utilizing machine learning models in predictive maintenance is highly beneficial; however, the binary outcomes of these models can be misunderstood by decision-makers. Detailed explanations provided to decision-makers will directly impact maintenance decisions and improve spare part management.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100078"},"PeriodicalIF":0.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000219/pdfft?md5=de370f4dd5787db3d883f746b49da463&pid=1-s2.0-S2949863524000219-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142130182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A two-stage optimization model for relief distribution to disaster survivors under two-fold uncertainty 双重不确定性下向灾难幸存者发放救济的两阶段优化模型

Supply Chain Analytics Pub Date : 2024-08-23 DOI: 10.1016/j.sca.2024.100079

Palash Sahoo

{"title":"A two-stage optimization model for relief distribution to disaster survivors under two-fold uncertainty","authors":"Palash Sahoo","doi":"10.1016/j.sca.2024.100079","DOIUrl":"10.1016/j.sca.2024.100079","url":null,"abstract":"<div><p>Disasters are unforeseen occurrences requiring extensive transport deployment to support and relieve victims. Sometimes, this transportation is not feasible directly from some supply points to some destination points. Due to this tragedy, it is unclear precisely what is available at supply points, what is needed at destinations, how much transportation capacity there is, and what the routes are like. In this study, we investigate a two-stage multi-item fixed charge four-dimensional transportation problem using the concept of big data theory under the two-fold uncertainties. Here, the model’s parameters such as unit transportation costs, availabilities of items at the suppliers, fixed charges, capacities of conveyances, and demands of the items at the retailers are considered type-2 zigzag uncertain variables. Using big data theory and based on uncertain programming theory, two novel uncertain models are developed such as chance-constrained programming and expected value programming model. These two uncertain models transformed into the deterministic form via uncertainty inverse distribution theory. A critical value based reduction method with three categories (i.e., expected value, pessimistic value, and optimistic value) is applied to reduce the type-2 zigzag uncertain variable to the type-1 zigzag uncertain variable. The genetic algorithm and particle swarm optimization techniques have been proposed to find the optimal solution for the two deterministic models. The efficiency of our proposed approach is demonstrated with a real-life numerical example.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"8 ","pages":"Article 100079"},"PeriodicalIF":0.0,"publicationDate":"2024-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000220/pdfft?md5=1fce1edb90ad4c3e82f399b2092acdb8&pid=1-s2.0-S2949863524000220-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142095580","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A home healthcare routing-scheduling optimization model considering time-balancing and outsourcing 考虑时间平衡和外包的家庭医疗路由-调度优化模型

Supply Chain Analytics Pub Date : 2024-08-13 DOI: 10.1016/j.sca.2024.100077

Shabnam Rekabi , Babak Moradi , Farima Salamian , Niloofar Fadavi , Mahsa Zokaee , Amir Aghsami

{"title":"A home healthcare routing-scheduling optimization model considering time-balancing and outsourcing","authors":"Shabnam Rekabi , Babak Moradi , Farima Salamian , Niloofar Fadavi , Mahsa Zokaee , Amir Aghsami","doi":"10.1016/j.sca.2024.100077","DOIUrl":"10.1016/j.sca.2024.100077","url":null,"abstract":"<div><p>Home care services have a significant role in lowering healthcare expenditure. Supply chain management in home healthcare (HHC) ensures efficient delivery of medical supplies and equipment to patients' homes, improving overall quality of care and patient outcomes. This study proposes a routing and scheduling optimization model for HHC by prioritizing patients, developing an effective delivery strategy, and considering home care logistics and services. The model primarily concerns reducing logistics activities’ overall expenses while considering patients’ priorities. A bi-objective optimization model for a multi-period HHC problem is developed by prioritizing patients with urgent critical needs. The best-worst method (BWM) and technique for order of preference by similarity to ideal solution (TOPSIS) are used to prioritize patients using a linear programming metric (Lp-metric). The BWM and TOPSIS have been uniquely used in this study for routing and scheduling in HHC. Eventually, the applicability of the proposed method is demonstrated through a real-life case study with a series of numerical examples and sensitivity analysis. For instance, by analyzing privilege, we see patients are carefully matched with caregivers possessing advanced skills, leading to increased patient satisfaction. Based on assigned routes, caregivers prioritize patients with higher weight and emergency conditions at the start of each path, followed by patients with less urgent conditions. This ensures that patients with more severe conditions are serviced first.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"7 ","pages":"Article 100077"},"PeriodicalIF":0.0,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000207/pdfft?md5=2d8a84385cc284572728b28c1022b38c&pid=1-s2.0-S2949863524000207-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141993532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unlocking the potential of digital twins in supply chains: A systematic review 释放数字孪生在供应链中的潜力：系统回顾

Supply Chain Analytics Pub Date : 2024-07-29 DOI: 10.1016/j.sca.2024.100075

Syed Adeel Haneef Zaidi , Sharfuddin Ahmed Khan , Amin Chaabane

{"title":"Unlocking the potential of digital twins in supply chains: A systematic review","authors":"Syed Adeel Haneef Zaidi , Sharfuddin Ahmed Khan , Amin Chaabane","doi":"10.1016/j.sca.2024.100075","DOIUrl":"10.1016/j.sca.2024.100075","url":null,"abstract":"<div><p>Digital Twins (DTs) developments are still in the pilot stages of deployment in supply chain management (SCM), and their full integration with real-time synchronization and autonomous decision-making poses many challenges. This paper aims to identify these common challenges and provide a conceptual framework for establishing a Digital Twin (DT) system to improve supply chain management performance. The paper presents a systematic literature review of 129 research papers on DT applications for SCM improvement. The selected papers were reviewed and classified into three categories: manufacturing and production, supply chain, and logistics. The development of digital technologies such as the Internet of Things (IoT), Radio Frequency Identification (RFID) devices, cloud computing, cyber-physical systems (CPSs), cybersecurity (CS), and simulation modeling has increased the opportunities to explore the creation of supply chain DTs. However, there are limitations and various challenges due to the complexity of most systems. The results indicate that DT for SCM should include external links (i.e. suppliers, distributors) and internal links (i.e. procurement, production, logistics) to deal with any disruption through data-driven modeling with real-time synchronization. Based on the review findings, this study proposes a three-layered conceptual framework to improve supply chain management performance. The proposed framework provides future directions for DT research in SCM. It provides a holistic and integrated approach to DT implementation, the common DT technologies, and data analytics techniques for improved supply chain performance.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"7 ","pages":"Article 100075"},"PeriodicalIF":0.0,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000189/pdfft?md5=4487bd56f93ddc361cd12675e1dc8f76&pid=1-s2.0-S2949863524000189-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Application of artificial intelligence in reverse logistics: A bibliometric and network analysis 人工智能在逆向物流中的应用：文献计量和网络分析

Supply Chain Analytics Pub Date : 2024-07-27 DOI: 10.1016/j.sca.2024.100076

Oyshik Bhowmik , Sudipta Chowdhury , Jahid Hasan Ashik , GM Iqbal Mahmud , Md Muzahid Khan , Niamat Ullah Ibne Hossain

{"title":"Application of artificial intelligence in reverse logistics: A bibliometric and network analysis","authors":"Oyshik Bhowmik , Sudipta Chowdhury , Jahid Hasan Ashik , GM Iqbal Mahmud , Md Muzahid Khan , Niamat Ullah Ibne Hossain","doi":"10.1016/j.sca.2024.100076","DOIUrl":"10.1016/j.sca.2024.100076","url":null,"abstract":"<div><p>Despite abundant research on the application of artificial intelligence (AI) in reverse logistics, no comprehensive study with bibliometric and network analysis has been conducted. This study uses bibliometric analysis to derive the prominent research statistics in AI-centric reverse logistics, considering 2929 articles from the last three decades. The most impactful contributors and countries that employ AI in reverse logistics are identified using various bibliometric tools. Also, network analysis is performed to reveal the most influential articles and emerging trends and map the relationships via clustering. The results of keyword co-occurrence and co-citation analyses reveal that machine learning and deep learning techniques have been commonly used for addressing reverse logistics challenges with higher frequency in recent years. Furthermore, a systematic review is carried out, considering the influential articles from recent years. The review is conducted following the systematic literature review framework, and 79 articles are chosen to be studied thoroughly. Subsequently, the articles are divided based on various reverse logistics processes, and the most frequently used AI techniques are identified and categorized into five distinct groups. The comprehensive investigation of AI techniques reveals the use-case scenario of AI algorithms in the reverse logistics domain. This study concludes with implications and recommendations for prospects by addressing the shortcomings of the current studies and providing future researchers and practitioners with a robust roadmap to investigate reverse logistics in their research further.</p></div>","PeriodicalId":101186,"journal":{"name":"Supply Chain Analytics","volume":"7 ","pages":"Article 100076"},"PeriodicalIF":0.0,"publicationDate":"2024-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2949863524000190/pdfft?md5=675a03af106c4975660c495cacb17d46&pid=1-s2.0-S2949863524000190-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141962009","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0