Hanjun Guo, Mingxiao Liu, Jin Xue, Izzy Yi Jian, Qian Xu, Qian-Cheng Wang
With the lifting of the COVID-19 lockdown, the construction industry is gradually moving towards a new normality. This study aims to evaluate the construction project performance in the post-COVID-19 pandemic context and proposes a roadmap framework to achieve project recovery in China. This paper follows a sequential mixed methodology with three core steps. First, the critical success factors (CSFs) and key performance indicators (KPIs) are derived from literature reviews and expert interviews. Second, the study conducts a questionnaire survey with 150 experts. Third, the research implements factor analysis and analytic hierarchy process (AHP) analysis for CSFs and characteristics and comparative analysis for KPIs. Based on the results, the study employs structural equational modelling (SEM) to connect the CSFs and KPIs and develop a roadmap towards the post-COVID-19 pandemic recovery of the construction projects. The study identifies 32 CSFs and 25 KPIs and categorises them into five clusters, respectively. The SEM analysis suggests that management and technological innovation significantly contribute to achieving enterprise strategic goals and advancing industrial development. The consistency of project goals and external expectations also positively affect the satisfaction level of stakeholders and social impact. In addition, the AHP clarifies that the stability of the external environment, the internal support, and the adequacy of resources are critical drivers to the post-COVID-19 recovery of construction projects. This research proffers a roadmap towards the project recovery of the construction industry in the post-COVID-19 era by connecting the performance indicators and their critical success drivers. The findings would guide comprehensive design and construction, project life cycle management, and assist in dealing with public health emergencies in construction project management to maximise the organisation’s profits and positive social impact.
{"title":"Post-COVID-19 Recovery: An Integrated Framework of Construction Project Performance Evaluation in China","authors":"Hanjun Guo, Mingxiao Liu, Jin Xue, Izzy Yi Jian, Qian Xu, Qian-Cheng Wang","doi":"10.3390/systems11070359","DOIUrl":"https://doi.org/10.3390/systems11070359","url":null,"abstract":"With the lifting of the COVID-19 lockdown, the construction industry is gradually moving towards a new normality. This study aims to evaluate the construction project performance in the post-COVID-19 pandemic context and proposes a roadmap framework to achieve project recovery in China. This paper follows a sequential mixed methodology with three core steps. First, the critical success factors (CSFs) and key performance indicators (KPIs) are derived from literature reviews and expert interviews. Second, the study conducts a questionnaire survey with 150 experts. Third, the research implements factor analysis and analytic hierarchy process (AHP) analysis for CSFs and characteristics and comparative analysis for KPIs. Based on the results, the study employs structural equational modelling (SEM) to connect the CSFs and KPIs and develop a roadmap towards the post-COVID-19 pandemic recovery of the construction projects. The study identifies 32 CSFs and 25 KPIs and categorises them into five clusters, respectively. The SEM analysis suggests that management and technological innovation significantly contribute to achieving enterprise strategic goals and advancing industrial development. The consistency of project goals and external expectations also positively affect the satisfaction level of stakeholders and social impact. In addition, the AHP clarifies that the stability of the external environment, the internal support, and the adequacy of resources are critical drivers to the post-COVID-19 recovery of construction projects. This research proffers a roadmap towards the project recovery of the construction industry in the post-COVID-19 era by connecting the performance indicators and their critical success drivers. The findings would guide comprehensive design and construction, project life cycle management, and assist in dealing with public health emergencies in construction project management to maximise the organisation’s profits and positive social impact.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75306289","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The prosperity of e-commerce has made more and more businesses willing to enter the e-commerce market, which has also brought a series of strategic collaboration between firms. This study considers game models with and without collaboration between the platform and the retailer. An e-commerce platform has relative logistics service sharing advantages while the retailer has relative procurement advantages. We formulated a multichannel supply chain consisting of a manufacturer and two retailers to explore the feasibility of the above strategic collaboration model. We utilized the Stackelberg game and Nash game approaches to obtain equilibrium solutions under both cooperative and noncooperative scenarios. Through a further analysis, we determined the impacts of the logistics sensitivity, the cost of the unit logistics service effort, the price of shared logistics service per unit, and the price competition intensity on optimal prices, the logistics service efforts, and the profits. Moreover, the collaborative exchange of advantages between the platform and the retailer needs to consider the interests of participating manufacturers in the game. Our extension suggests all three firms should actively promote deeper collaboration.
{"title":"To Compete or to Collaborate? Logistics Service Sharing and Retailers' Resale in Competitive Online Channels","authors":"Xi Zhang, Shengping Zhang, Bisheng Du","doi":"10.3390/systems11070358","DOIUrl":"https://doi.org/10.3390/systems11070358","url":null,"abstract":"The prosperity of e-commerce has made more and more businesses willing to enter the e-commerce market, which has also brought a series of strategic collaboration between firms. This study considers game models with and without collaboration between the platform and the retailer. An e-commerce platform has relative logistics service sharing advantages while the retailer has relative procurement advantages. We formulated a multichannel supply chain consisting of a manufacturer and two retailers to explore the feasibility of the above strategic collaboration model. We utilized the Stackelberg game and Nash game approaches to obtain equilibrium solutions under both cooperative and noncooperative scenarios. Through a further analysis, we determined the impacts of the logistics sensitivity, the cost of the unit logistics service effort, the price of shared logistics service per unit, and the price competition intensity on optimal prices, the logistics service efforts, and the profits. Moreover, the collaborative exchange of advantages between the platform and the retailer needs to consider the interests of participating manufacturers in the game. Our extension suggests all three firms should actively promote deeper collaboration.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81216247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Despite the benefits of inventory transshipment, numerous behavioral experiments have revealed that retailers often deviate from the Nash-equilibrium ordering quantities, which in turn impacts the potential advantages. Motivated by this issue, we developed a behavioral model to analyze the deviation of ordering quantities among two independent retailers who engage in inventory transshipment from the perspective of analytical modeling. In our model, we incorporated bounded rationality with the quantal response equilibrium. Firstly, we established the existence of such a quantal response equilibrium and provided the conditions for its uniqueness. Secondly, we compared the quantal response equilibrium with the Nash equilibrium within a certain range of transshipment prices and observed that the limiting quantal response equilibrium is equivalent to the Nash equilibrium. Lastly, we design an iterative algorithm that incorporates the learning effects of the retailers to determine the quantal response equilibrium for the ordering quantity. The results indicate that the optimal ordering quantity and the nearby ordering quantities should be chosen with higher probabilities. Additionally, the retailer should gradually enhance their cognitive or computational abilities through repeated transshipment games to improve their decision-making process. Furthermore, to ensure a balanced inventory-sharing system, the evaluation of inventory strategies should consistently prioritize avoiding surplus instead of shortage.
{"title":"Decentralized Inventory Transshipments with Quantal Response Equilibrium","authors":"Qingren He, Taiwei Shi, Fei Xu, W. Qiu","doi":"10.3390/systems11070357","DOIUrl":"https://doi.org/10.3390/systems11070357","url":null,"abstract":"Despite the benefits of inventory transshipment, numerous behavioral experiments have revealed that retailers often deviate from the Nash-equilibrium ordering quantities, which in turn impacts the potential advantages. Motivated by this issue, we developed a behavioral model to analyze the deviation of ordering quantities among two independent retailers who engage in inventory transshipment from the perspective of analytical modeling. In our model, we incorporated bounded rationality with the quantal response equilibrium. Firstly, we established the existence of such a quantal response equilibrium and provided the conditions for its uniqueness. Secondly, we compared the quantal response equilibrium with the Nash equilibrium within a certain range of transshipment prices and observed that the limiting quantal response equilibrium is equivalent to the Nash equilibrium. Lastly, we design an iterative algorithm that incorporates the learning effects of the retailers to determine the quantal response equilibrium for the ordering quantity. The results indicate that the optimal ordering quantity and the nearby ordering quantities should be chosen with higher probabilities. Additionally, the retailer should gradually enhance their cognitive or computational abilities through repeated transshipment games to improve their decision-making process. Furthermore, to ensure a balanced inventory-sharing system, the evaluation of inventory strategies should consistently prioritize avoiding surplus instead of shortage.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90761951","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
With the emergence of the digital economy, digital technologies—such as artificial intelligence (AI)—have provided new possibilities for the green development of enterprises. Green total factor productivity is a key indicator of green sustainable development. While traditional total factor productivity does not consider the constraints of natural resources and the environment, green total factor productivity remedies this deficiency by incorporating environmental protection indicators, such as pollutant emissions, into the accounting system. To further clarify the relationship between AI technology and corporate green total factor productivity, this study uses a two-way fixed effects model to examine the impact of AI technology on the corporate green total factor productivity of A-share listed companies in China from 2013 to 2020 while examining how corporate slack resources affect the relationship between the two. The results show that the AI application positively contributes to the green total factor productivity of enterprises. Meanwhile, firms’ absorbed, unabsorbed, and potential slack resources all positively moderate the positive impact of AI technology on firms’ green total factor productivity. This study offers a theoretical basis for a comprehensive understanding of digital technology and enterprises’ green development. It also contributes practical insights for the government to formulate relevant policies and for enterprises to use digital technology to attain green and sustainable development.
{"title":"Artificial Intelligence and Green Total Factor Productivity: The Moderating Effect of Slack Resources","authors":"Ying Ying, Xiaoyan Cui, Shanyue Jin","doi":"10.3390/systems11070356","DOIUrl":"https://doi.org/10.3390/systems11070356","url":null,"abstract":"With the emergence of the digital economy, digital technologies—such as artificial intelligence (AI)—have provided new possibilities for the green development of enterprises. Green total factor productivity is a key indicator of green sustainable development. While traditional total factor productivity does not consider the constraints of natural resources and the environment, green total factor productivity remedies this deficiency by incorporating environmental protection indicators, such as pollutant emissions, into the accounting system. To further clarify the relationship between AI technology and corporate green total factor productivity, this study uses a two-way fixed effects model to examine the impact of AI technology on the corporate green total factor productivity of A-share listed companies in China from 2013 to 2020 while examining how corporate slack resources affect the relationship between the two. The results show that the AI application positively contributes to the green total factor productivity of enterprises. Meanwhile, firms’ absorbed, unabsorbed, and potential slack resources all positively moderate the positive impact of AI technology on firms’ green total factor productivity. This study offers a theoretical basis for a comprehensive understanding of digital technology and enterprises’ green development. It also contributes practical insights for the government to formulate relevant policies and for enterprises to use digital technology to attain green and sustainable development.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73266887","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
One of the most vital issues in electrical systems involves optimally operating microgrids (MGs) using demand-side management (DSM). A DSM program lowers utility operational costs in one sense but also needs policies that encourage financial incentives in the other. The present study formulates the optimum functioning of MGs using DSM in the form of a problem of optimization. DSM considers load shifting to be a viable option. There are operational limitations and executive limitations that affect the problem, and its objective function aims at minimizing the overall operational prices of the grid and the load-shifting prices. The major problem has been solved using an improved butterfly optimization scheme. Furthermore, the suggested technique was tested in various case studies that consider types of generation unit, load types, unit uncertainties, grid sharing, and energy costs. A comparison was made between the suggested scheme and various algorithms on the IEEE 33-bus network to demonstrate the proficiency of the suggested scheme, showing that it lowered prices by 57%.
{"title":"The Innovative Research on Sustainable Microgrid Artwork Design Based on Regression Analysis and Multi-Objective Optimization","authors":"Shuang Chang, Dian Liu, Bahram Dehghan","doi":"10.3390/systems11070354","DOIUrl":"https://doi.org/10.3390/systems11070354","url":null,"abstract":"One of the most vital issues in electrical systems involves optimally operating microgrids (MGs) using demand-side management (DSM). A DSM program lowers utility operational costs in one sense but also needs policies that encourage financial incentives in the other. The present study formulates the optimum functioning of MGs using DSM in the form of a problem of optimization. DSM considers load shifting to be a viable option. There are operational limitations and executive limitations that affect the problem, and its objective function aims at minimizing the overall operational prices of the grid and the load-shifting prices. The major problem has been solved using an improved butterfly optimization scheme. Furthermore, the suggested technique was tested in various case studies that consider types of generation unit, load types, unit uncertainties, grid sharing, and energy costs. A comparison was made between the suggested scheme and various algorithms on the IEEE 33-bus network to demonstrate the proficiency of the suggested scheme, showing that it lowered prices by 57%.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90921315","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Digitization is a megatrend that shapes the economy and society, driving major transformations. Enterprises, as the most important microeconomic entities, are critical carriers for society in conducting digital transformation and practicing sustainable development to achieve socioeconomic and environmental sustainability. Exploring the relationship and mechanisms between digital transformation and sustainable corporate development is crucial. This study investigates the influence of digital transformation on sustainable corporate development as well as its moderating mechanisms. A two-way fixed effects model is used on a research sample of Chinese A-share listed companies in Shanghai and Shenzhen from 2010 to 2020. Three methods are used for robustness testing to alleviate endogeneity issues. The empirical results show that digital transformation can significantly enhance sustainable corporate development, whereas empowered management and highly educated employees are essential complementary human resources that effectively strengthen the contribution of digitalization to sustainability. Additionally, internal controls are internal drivers that have a positive moderating effect on the digital transformation to improve corporate sustainability. This study reveals that digital transformation is an important tool for promoting corporate sustainability, broadening the literature in related fields, and providing insights for corporate management and government policymakers to advance corporate sustainability.
{"title":"How Does Digital Transformation Increase Corporate Sustainability? The Moderating Role of Top Management Teams","authors":"Yaxin Zhang, Shanyue Jin","doi":"10.3390/systems11070355","DOIUrl":"https://doi.org/10.3390/systems11070355","url":null,"abstract":"Digitization is a megatrend that shapes the economy and society, driving major transformations. Enterprises, as the most important microeconomic entities, are critical carriers for society in conducting digital transformation and practicing sustainable development to achieve socioeconomic and environmental sustainability. Exploring the relationship and mechanisms between digital transformation and sustainable corporate development is crucial. This study investigates the influence of digital transformation on sustainable corporate development as well as its moderating mechanisms. A two-way fixed effects model is used on a research sample of Chinese A-share listed companies in Shanghai and Shenzhen from 2010 to 2020. Three methods are used for robustness testing to alleviate endogeneity issues. The empirical results show that digital transformation can significantly enhance sustainable corporate development, whereas empowered management and highly educated employees are essential complementary human resources that effectively strengthen the contribution of digitalization to sustainability. Additionally, internal controls are internal drivers that have a positive moderating effect on the digital transformation to improve corporate sustainability. This study reveals that digital transformation is an important tool for promoting corporate sustainability, broadening the literature in related fields, and providing insights for corporate management and government policymakers to advance corporate sustainability.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75375691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Archana Tikayat Ray, B. Cole, Olivia Fischer, Anirudh Prabhakara Bhat, Ryan T. White, D. Mavris
The increased complexity of modern systems is calling for an integrated and comprehensive approach to system design and development and, in particular, a shift toward Model-Based Systems Engineering (MBSE) approaches for system design. The requirements that serve as the foundation for these intricate systems are still primarily expressed in Natural Language (NL), which can contain ambiguities and inconsistencies and suffer from a lack of structure that hinders their direct translation into models. The colossal developments in the field of Natural Language Processing (NLP), in general, and Large Language Models (LLMs), in particular, can serve as an enabler for the conversion of NL requirements into machine-readable requirements. Doing so is expected to facilitate their standardization and use in a model-based environment. This paper discusses a two-fold strategy for converting NL requirements into machine-readable requirements using language models. The first approach involves creating a requirements table by extracting information from free-form NL requirements. The second approach consists of an agile methodology that facilitates the identification of boilerplate templates for different types of requirements based on observed linguistic patterns. For this study, three different LLMs are utilized. Two of these models are fine-tuned versions of Bidirectional Encoder Representations from Transformers (BERTs), specifically, aeroBERT-NER and aeroBERT-Classifier, which are trained on annotated aerospace corpora. Another LLM, called flair/chunk-english, is utilized to identify sentence chunks present in NL requirements. All three language models are utilized together to achieve the standardization of requirements. The effectiveness of the methodologies is demonstrated through the semi-automated creation of boilerplates for requirements from Parts 23 and 25 of Title 14 Code of Federal Regulations (CFRs).
{"title":"Agile Methodology for the Standardization of Engineering Requirements Using Large Language Models","authors":"Archana Tikayat Ray, B. Cole, Olivia Fischer, Anirudh Prabhakara Bhat, Ryan T. White, D. Mavris","doi":"10.3390/systems11070352","DOIUrl":"https://doi.org/10.3390/systems11070352","url":null,"abstract":"The increased complexity of modern systems is calling for an integrated and comprehensive approach to system design and development and, in particular, a shift toward Model-Based Systems Engineering (MBSE) approaches for system design. The requirements that serve as the foundation for these intricate systems are still primarily expressed in Natural Language (NL), which can contain ambiguities and inconsistencies and suffer from a lack of structure that hinders their direct translation into models. The colossal developments in the field of Natural Language Processing (NLP), in general, and Large Language Models (LLMs), in particular, can serve as an enabler for the conversion of NL requirements into machine-readable requirements. Doing so is expected to facilitate their standardization and use in a model-based environment. This paper discusses a two-fold strategy for converting NL requirements into machine-readable requirements using language models. The first approach involves creating a requirements table by extracting information from free-form NL requirements. The second approach consists of an agile methodology that facilitates the identification of boilerplate templates for different types of requirements based on observed linguistic patterns. For this study, three different LLMs are utilized. Two of these models are fine-tuned versions of Bidirectional Encoder Representations from Transformers (BERTs), specifically, aeroBERT-NER and aeroBERT-Classifier, which are trained on annotated aerospace corpora. Another LLM, called flair/chunk-english, is utilized to identify sentence chunks present in NL requirements. All three language models are utilized together to achieve the standardization of requirements. The effectiveness of the methodologies is demonstrated through the semi-automated creation of boilerplates for requirements from Parts 23 and 25 of Title 14 Code of Federal Regulations (CFRs).","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77872929","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Arguing and communicating are basic skills in the mathematics curriculum. Making arguments in written form facilitates rigorous reasoning. It allows peers to review arguments, and to receive feedback about them. Even though it requires additional cognitive effort in the calculation process, it enhances long-term retention and facilitates deeper understanding. However, developing these competencies in elementary school classrooms is a great challenge. It requires at least two conditions: all students write and all receive immediate feedback. One solution is to use online platforms. However, this is very demanding for the teacher. The teacher must review 30 answers in real time. To facilitate the revision, it is necessary to automatize the detection of incoherent responses. Thus, the teacher can immediately seek to correct them. In this work, we analyzed 14,457 responses to open-ended questions written by 974 fourth graders on the ConectaIdeas online platform. A total of 13% of the answers were incoherent. Using natural language processing and machine learning algorithms, we built an automatic classifier. Then, we tested the classifier on an independent set of written responses to different open-ended questions. We found that the classifier achieved an F1-score = 79.15% for incoherent detection, which is better than baselines using different heuristics.
{"title":"Automatically Detecting Incoherent Written Math Answers of Fourth-Graders","authors":"Felipe Urrutia, R. Araya","doi":"10.3390/systems11070353","DOIUrl":"https://doi.org/10.3390/systems11070353","url":null,"abstract":"Arguing and communicating are basic skills in the mathematics curriculum. Making arguments in written form facilitates rigorous reasoning. It allows peers to review arguments, and to receive feedback about them. Even though it requires additional cognitive effort in the calculation process, it enhances long-term retention and facilitates deeper understanding. However, developing these competencies in elementary school classrooms is a great challenge. It requires at least two conditions: all students write and all receive immediate feedback. One solution is to use online platforms. However, this is very demanding for the teacher. The teacher must review 30 answers in real time. To facilitate the revision, it is necessary to automatize the detection of incoherent responses. Thus, the teacher can immediately seek to correct them. In this work, we analyzed 14,457 responses to open-ended questions written by 974 fourth graders on the ConectaIdeas online platform. A total of 13% of the answers were incoherent. Using natural language processing and machine learning algorithms, we built an automatic classifier. Then, we tested the classifier on an independent set of written responses to different open-ended questions. We found that the classifier achieved an F1-score = 79.15% for incoherent detection, which is better than baselines using different heuristics.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77976305","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ahmad Alshami, Moustafa Elsayed, Eslam Ali, A. E. Eltoukhy, T. Zayed
Systematic reviews (SR) are crucial in synthesizing and analyzing existing scientific literature to inform evidence-based decision-making. However, traditional SR methods often have limitations, including a lack of automation and decision support, resulting in time-consuming and error-prone reviews. To address these limitations and drive the field forward, we harness the power of the revolutionary language model, ChatGPT, which has demonstrated remarkable capabilities in various scientific writing tasks. By utilizing ChatGPT’s natural language processing abilities, our objective is to automate and streamline the steps involved in traditional SR, explicitly focusing on literature search, screening, data extraction, and content analysis. Therefore, our methodology comprises four modules: (1) Preparation of Boolean research terms and article collection, (2) Abstract screening and articles categorization, (3) Full-text filtering and information extraction, and (4) Content analysis to identify trends, challenges, gaps, and proposed solutions. Throughout each step, our focus has been on providing quantitative analyses to strengthen the robustness of the review process. To illustrate the practical application of our method, we have chosen the topic of IoT applications in water and wastewater management and quality monitoring due to its critical importance and the dearth of comprehensive reviews in this field. The findings demonstrate the potential of ChatGPT in bridging the gap between traditional SR methods and AI language models, resulting in enhanced efficiency and reliability of SR processes. Notably, ChatGPT exhibits exceptional performance in filtering and categorizing relevant articles, leading to significant time and effort savings. Our quantitative assessment reveals the following: (1) the overall accuracy of ChatGPT for article discarding and classification is 88%, and (2) the F-1 scores of ChatGPT for article discarding and classification are 91% and 88%, respectively, compared to expert assessments. However, we identify limitations in its suitability for article extraction. Overall, this research contributes valuable insights to the field of SR, empowering researchers to conduct more comprehensive and reliable reviews while advancing knowledge and decision-making across various domains.
{"title":"Harnessing the Power of ChatGPT for Automating Systematic Review Process: Methodology, Case Study, Limitations, and Future Directions","authors":"Ahmad Alshami, Moustafa Elsayed, Eslam Ali, A. E. Eltoukhy, T. Zayed","doi":"10.3390/systems11070351","DOIUrl":"https://doi.org/10.3390/systems11070351","url":null,"abstract":"Systematic reviews (SR) are crucial in synthesizing and analyzing existing scientific literature to inform evidence-based decision-making. However, traditional SR methods often have limitations, including a lack of automation and decision support, resulting in time-consuming and error-prone reviews. To address these limitations and drive the field forward, we harness the power of the revolutionary language model, ChatGPT, which has demonstrated remarkable capabilities in various scientific writing tasks. By utilizing ChatGPT’s natural language processing abilities, our objective is to automate and streamline the steps involved in traditional SR, explicitly focusing on literature search, screening, data extraction, and content analysis. Therefore, our methodology comprises four modules: (1) Preparation of Boolean research terms and article collection, (2) Abstract screening and articles categorization, (3) Full-text filtering and information extraction, and (4) Content analysis to identify trends, challenges, gaps, and proposed solutions. Throughout each step, our focus has been on providing quantitative analyses to strengthen the robustness of the review process. To illustrate the practical application of our method, we have chosen the topic of IoT applications in water and wastewater management and quality monitoring due to its critical importance and the dearth of comprehensive reviews in this field. The findings demonstrate the potential of ChatGPT in bridging the gap between traditional SR methods and AI language models, resulting in enhanced efficiency and reliability of SR processes. Notably, ChatGPT exhibits exceptional performance in filtering and categorizing relevant articles, leading to significant time and effort savings. Our quantitative assessment reveals the following: (1) the overall accuracy of ChatGPT for article discarding and classification is 88%, and (2) the F-1 scores of ChatGPT for article discarding and classification are 91% and 88%, respectively, compared to expert assessments. However, we identify limitations in its suitability for article extraction. Overall, this research contributes valuable insights to the field of SR, empowering researchers to conduct more comprehensive and reliable reviews while advancing knowledge and decision-making across various domains.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80019072","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Bokui Chen, Yaohui Chen, Yao Wu, Yuxuan Xiu, Xiao-Rong Fu, Kai Zhang
This paper studies how autonomous vehicles (AVs) influence future traffic and energy consumption when adopting various route guidance strategies, which can be divided into global information strategies and local information strategies according to the scope of information collection. A mixed traffic flow model is established. Then, an autonomous vehicle model is improved to investigate the impact of six route guidance strategies. The selected strategies are specifically evaluated in a double-route scenario, considering both single-exit and dual-exit configurations. Three indicators are chosen to evaluate traffic efficiency, including traffic flow, average speed and quantity of vehicles. Consumption per unit flux is the indicator of energy consumption level. Simulation results show that autonomous vehicles can improve traffic efficiency and reduce energy consumption.
{"title":"The Effects of Autonomous Vehicles on Traffic Efficiency and Energy Consumption","authors":"Bokui Chen, Yaohui Chen, Yao Wu, Yuxuan Xiu, Xiao-Rong Fu, Kai Zhang","doi":"10.3390/systems11070347","DOIUrl":"https://doi.org/10.3390/systems11070347","url":null,"abstract":"This paper studies how autonomous vehicles (AVs) influence future traffic and energy consumption when adopting various route guidance strategies, which can be divided into global information strategies and local information strategies according to the scope of information collection. A mixed traffic flow model is established. Then, an autonomous vehicle model is improved to investigate the impact of six route guidance strategies. The selected strategies are specifically evaluated in a double-route scenario, considering both single-exit and dual-exit configurations. Three indicators are chosen to evaluate traffic efficiency, including traffic flow, average speed and quantity of vehicles. Consumption per unit flux is the indicator of energy consumption level. Simulation results show that autonomous vehicles can improve traffic efficiency and reduce energy consumption.","PeriodicalId":52858,"journal":{"name":"syst mt`lyh","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82049860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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