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Systems, Volume 11, Issue 7 (July 2023) – 64 articles

Cover Story (view full-size image): The SeaLion CubeSat mission is a joint project between the Old Dominion University (ODU), the United States Coast Guard Academy (USCGA), and the Air Force Institute of Technology (AFIT). The mission is to launch a 3U CubeSat consisting of three payloads for on-orbit validation. Thus, a systems engineering approach was required to support this mission. The approach presented herein used a filesystem-based modelling language consisting of the model elements used in agile software engineering (i.e., elements for stakeholder needs, user stories, data structures, etc.) based on lightweight YAML-based syntax. View this paper
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25 pages, 4312 KiB  
Article
Evolution Mechanism of Public–Private Partnership Project Trust from the Perspective of the Supply Chain
Systems 2023, 11(7), 379; https://doi.org/10.3390/systems11070379 - 23 Jul 2023
Cited by 2 | Viewed by 999
Abstract
In the public–private partnership (PPP) supply chain, trust serves as the foundation for collaboration between investment companies and suppliers. However, due to many uncertain factors, the evolution of trust remains a “black box” phenomenon. In order to analyze the impact of the evolution [...] Read more.
In the public–private partnership (PPP) supply chain, trust serves as the foundation for collaboration between investment companies and suppliers. However, due to many uncertain factors, the evolution of trust remains a “black box” phenomenon. In order to analyze the impact of the evolution of trust in the PPP supply chain on investment companies and suppliers’ strategic choices, and promote the healthy and sustainable development of PPP supply chain projects, this paper establishes a trust evolutionary game model, which analyzes the evolutionary paths under different scenarios and explores the impact of parameters on the cooperative strategies of participants. The findings indicate that trust asymmetry or an increase in trust can facilitate investment companies and suppliers to opt for positive cooperation strategies. Furthermore, both parties’ strategies are less influenced by their initial willingness and more by trust degree. The moral risk coefficient and information asymmetry coefficient have a negative effect on the cooperative strategies, with the moral risk coefficient of investment companies exhibiting a more significant impact on the entire cooperation process. Moreover, both parties can only choose positive strategies when the information asymmetry coefficient is low. This study holds significant implications for promoting cooperation, enhancing contract performance, safeguarding the interests of all parties, and increasing cooperation satisfaction. Full article
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25 pages, 671 KiB  
Article
Optimal Government Subsidy Decision and Its Impact on Sustainable Development of a Closed-Loop Supply Chain
Systems 2023, 11(7), 378; https://doi.org/10.3390/systems11070378 - 23 Jul 2023
Cited by 2 | Viewed by 815
Abstract
Government subsidies generally play an important role in the sustainable operations management of a closed-loop supply chain (CLSC). This paper investigates the optimal government subsidy decision and its influence on the sustainable development of the CLSC, consisting of one manufacturer, one retailer, and [...] Read more.
Government subsidies generally play an important role in the sustainable operations management of a closed-loop supply chain (CLSC). This paper investigates the optimal government subsidy decision and its influence on the sustainable development of the CLSC, consisting of one manufacturer, one retailer, and one third-party collector, from the economic, environmental, and social perspectives. Based on game analysis technology, different Stackelberg game models among the government and the CLSC members are formulated to analyze the optimal decisions under different power structures. By conducting theoretic comparative and sensitivity analyses and a case study, the effects of the government subsidy and the power structure are explored from the total profit, environmental benefit, and social welfare. Results show that the subsidy is good for sustainable development of the CLSC, which improves the total profit of the CLSC members, environmental benefit, and social welfare and the improvement effect is more prominent when the CLSC members have unequal bargaining power. Moreover, according to the growth proportion of profit, the retailer and collector benefit more from the subsidy among the CLSC members when they have different bargaining power, otherwise, the CLSC members benefit equally from the subsidy, and the subsidy is more beneficial to the environment compared with the total supply chain profit and social welfare. Full article
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17 pages, 700 KiB  
Article
An Emotional Design Model for Future Smart Product Based on Grounded Theory
Systems 2023, 11(7), 377; https://doi.org/10.3390/systems11070377 - 23 Jul 2023
Cited by 1 | Viewed by 1951
Abstract
Recently, smart products have not only demonstrated more functionality and technical capabilities but have also shown a trend towards emotional expression. Emotional design plays a crucial role in smart products as it not only influences users’ perception and evaluation of the product but [...] Read more.
Recently, smart products have not only demonstrated more functionality and technical capabilities but have also shown a trend towards emotional expression. Emotional design plays a crucial role in smart products as it not only influences users’ perception and evaluation of the product but also promotes collaborative communication between users and the product. In the future, emotional design of smart products needs to be regarded as an important comprehensive design issue, rather than simply targeting a specific element. It should consider factors such as design systems, values, business strategies, technical capabilities, design ethics, and cultural responsibilities. However, currently, there is a lack of a design model that combines these elements. Currently, there are numerous practices in emotional design for smart products from different perspectives. They provide us an opportunity to build a comprehensive design model based on a large number of design case studies. Therefore, this study employed a standardized grounded theory approach to investigate 80 smart products and conducted interviews with 12 designers to progressively code and generate a design model. Through the coding process, this research extracted 547 nodes and gradually formed 10 categories, ultimately resulting in a design model comprising 5 sequential steps. This model includes user requirements, concept definition, design ideation, design implementation, and evaluation, making it applicable to most current and future emotional design issues in smart products. Full article
(This article belongs to the Special Issue Futures Thinking in Design Systems and Social Transformation)
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21 pages, 14463 KiB  
Article
An Industrial Case Study on the Monitoring and Maintenance Service System for a Robot-Driven Polishing Service System under Industry 4.0 Contexts
Systems 2023, 11(7), 376; https://doi.org/10.3390/systems11070376 - 22 Jul 2023
Viewed by 1447
Abstract
Remote monitoring and maintenance are important for improving the performance of production systems. However, existing studies on this topic usually focus on the monitoring and maintenance of the working conditions of the equipment and pay relatively less attention to the processing craft and [...] Read more.
Remote monitoring and maintenance are important for improving the performance of production systems. However, existing studies on this topic usually focus on the monitoring and maintenance of the working conditions of the equipment and pay relatively less attention to the processing craft and processing quality. In addition, as far as we know, there are relatively few industrial case studies on the real applications of remote monitoring and maintenance systems that include both conventional and advanced maintenance techniques under the context of Industry 4.0. Addressing these issues, an industrial case study on the monitoring and maintenance service system for a robot-driven carbon block polishing service system is presented, including its application background and engineering problems, software/hardware architecture and running logic, the monitoring and maintenance-related enabling techniques, and the configuration and operation workflows of the system in the form of screenshots of the functional WebAPPs of the software system. The case study can provide real examples and references for the industrial application of remote monitoring and maintenance service systems on industrial product service systems under the context of Industry 4.0. Advanced techniques such as the Industrial Internet of Things, digital twins, deep learning, and edge/cloud/fog computing have been applied to the system. Full article
(This article belongs to the Special Issue Manufacturing and Service Systems for Industry 4.0/5.0)
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23 pages, 4727 KiB  
Article
Derivation of Optimal Operation Factors of Anaerobic Digesters through Artificial Neural Network Technology
Systems 2023, 11(7), 375; https://doi.org/10.3390/systems11070375 - 22 Jul 2023
Cited by 1 | Viewed by 924
Abstract
The anaerobic digestion of sewage sludge in South Korean wastewater treatment plants is affected by seasonal factors and other influences, resulting in lower digestion efficiency and gas production, which cannot reach optimal yields. The aim of this study was to improve the digestion [...] Read more.
The anaerobic digestion of sewage sludge in South Korean wastewater treatment plants is affected by seasonal factors and other influences, resulting in lower digestion efficiency and gas production, which cannot reach optimal yields. The aim of this study was to improve the digestion efficiency and gas production of sludge anaerobic digestion in a wastewater treatment plant (WWTP) by using data mining techniques to adjust operational parameters. Through experimental data obtained from the WWTP in Daegu City, South Korea, an artificial neural network (ANN) technology was used to adjust the range of the organic loading rate (OLR) and hydraulic retention rate (HRT) to improve the efficiency and methane gas production from anaerobic sludge digestion. Data sources were normalized, and data analysis including Pearson correlation analysis, multiple regression analysis and an artificial neural network for optimal results. The results of the study showed a predicted 0.5% increase in digestion efficiency and a 1.3% increase in gas production at organic loads of 1.26–1.46 kg/m3 day and an HRT of 26–30 days. This shows that the ANN model that we established is feasible and can be used to improve the efficiency and gas production of sludge anaerobic digestion. Full article
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22 pages, 2239 KiB  
Article
Virtual Restoration System for 3D Digital Cultural Relics Based on a Fuzzy Logic Algorithm
Systems 2023, 11(7), 374; https://doi.org/10.3390/systems11070374 - 21 Jul 2023
Cited by 1 | Viewed by 859
Abstract
This research proposes a virtual restoration system and method for 3D digital cultural relics based on a fuzzy logic algorithm, aiming to solve the problems of the low classification accuracy and poor splicing effect of Terra Cotta Warrior fragments. This method adopts a [...] Read more.
This research proposes a virtual restoration system and method for 3D digital cultural relics based on a fuzzy logic algorithm, aiming to solve the problems of the low classification accuracy and poor splicing effect of Terra Cotta Warrior fragments. This method adopts a series of steps to improve the efficiency and accuracy of fragment splicing. Firstly, features such as curvature, torsion, and left and right chord lengths were extracted from the fracture surface contour lines of the cultural relic fragments to form feature vectors. Then, the feature vector was fused and compressed by using the multilayer perceptron. The multilayer perceptron is a neural network model that can process and learn input data via multiple levels of computation, resulting in more expressive feature representations. Next, we used the calculation results of the multilayer perceptron to perform the splicing operation on the fragments. This means that, based on the calculation results of the feature vectors, the system can automatically select appropriate splicing methods to accurately match and splice fragments. Finally, by adjusting the weight of the multilayer perceptron, the error rate of fragment splicing can be reduced, further improving the accuracy of repair. The experimental results show that the method proposed in this article is significantly better than traditional methods in terms of time consumption and can effectively improve the efficiency of fragment matching and stitching. Conclusion: The fragment-stitching algorithm based on multi-feature adaptive fusion improved the speed and effectiveness of stitching in fragment-stitching tasks. In summary, the fragment-stitching algorithm based on multi-feature adaptive fusion can improve the speed and effectiveness of stitching in fragment-stitching tasks. The application of this method is expected to play an important role in the field of cultural relic protection, such as the restoration of Terra Cotta Warrior fragments. Full article
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17 pages, 831 KiB  
Article
Risk Control for Synchronizing a New Economic Model
Systems 2023, 11(7), 373; https://doi.org/10.3390/systems11070373 - 20 Jul 2023
Viewed by 766
Abstract
Risk analysis in control problems is a critical but often overlooked issue in this research area. The main goal of this analysis is to assess the reliability of designed controllers and their impact on applied systems. The chaotic behavior of fractional-order economical systems [...] Read more.
Risk analysis in control problems is a critical but often overlooked issue in this research area. The main goal of this analysis is to assess the reliability of designed controllers and their impact on applied systems. The chaotic behavior of fractional-order economical systems has been extensively investigated in previous studies, leading to advancements in such systems. However, this chaotic behavior poses unpredictable risks to the economic system. This paper specifically investigates the reliability and risk analysis of chaotic fractional-order systems synchronization. Furthermore, we present a technique as a new mechanism to evaluate controller performance in the presence of obvious effects. Through a series of simulation studies, the reliability and risk associated with the proposed controllers are illustrated. Ultimately, we show that the suggested technique effectively reduces the risks associated with designed controllers. Full article
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26 pages, 4121 KiB  
Article
Students’ Classroom Behavior Detection System Incorporating Deformable DETR with Swin Transformer and Light-Weight Feature Pyramid Network
Systems 2023, 11(7), 372; https://doi.org/10.3390/systems11070372 - 20 Jul 2023
Cited by 3 | Viewed by 1153
Abstract
Artificial intelligence (AI) and computer vision technologies have gained significant prominence in the field of education. These technologies enable the detection and analysis of students’ classroom behaviors, providing valuable insights for assessing individual concentration levels. However, the accuracy of target detection methods based [...] Read more.
Artificial intelligence (AI) and computer vision technologies have gained significant prominence in the field of education. These technologies enable the detection and analysis of students’ classroom behaviors, providing valuable insights for assessing individual concentration levels. However, the accuracy of target detection methods based on Convolutional Neural Networks (CNNs) can be compromised in classrooms with multiple targets and varying scales, as convolutional operations may result in the loss of location information. In contrast, transformers, which leverage attention mechanisms, have the capability to learn global features and mitigate the information loss caused by convolutional operations. In this paper, we propose a students’ classroom behavior detection system that combines deformable DETR with a Swin Transformer and light-weight Feature Pyramid Network (FPN). By employing a feature pyramid structure, the system can effectively process multi-scale feature maps extracted by the Swin Transformer, thereby improving the detection accuracy for targets of different sizes and scales. Moreover, the integration of the CARAFE lightweight operator into the FPN structure enhances the network’s detection accuracy. To validate the effectiveness of our approach, extensive experiments are conducted on a real dataset of students’ classroom behavior. The experimental results demonstrate a significant 6.1% improvement in detection accuracy compared to state-of-the-art methods. These findings highlight the superiority of our proposed network in accurately detecting and analyzing students’ classroom behaviors. Overall, this research contributes to the field of education by addressing the limitations of CNN-based target detection methods and leveraging the capabilities of transformers to improve accuracy. The proposed system showcases the benefits of integrating deformable DETR, Swin Transformer, and the lightweight FPN in the context of students’ classroom behavior detection. The experimental results provide compelling evidence of the system’s effectiveness and its potential to enhance classroom monitoring and assessment practices. Full article
(This article belongs to the Special Issue Human–AI Teaming: Synergy, Decision-Making and Interdependency)
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30 pages, 7015 KiB  
Article
Production Logistics in Industry 3.X: Bibliometric Analysis, Frontier Case Study, and Future Directions
Systems 2023, 11(7), 371; https://doi.org/10.3390/systems11070371 - 19 Jul 2023
Cited by 1 | Viewed by 1519
Abstract
At present, the development of the global manufacturing industry is still in the transition stage from Industry 3.0 to Industry 4.0 (i.e., Industry 3.X), and the production logistics system is becoming more and more complex due to the individualization of customer demands and [...] Read more.
At present, the development of the global manufacturing industry is still in the transition stage from Industry 3.0 to Industry 4.0 (i.e., Industry 3.X), and the production logistics system is becoming more and more complex due to the individualization of customer demands and the high frequency of order changes. In order to systematically analyze the research status and dynamic evolution trend of production logistics in the Industry 3.X stage, this paper designed a Log-Likelihood ratio-based latent Dirichlet allocation (LLR-LDA) algorithm based on bibliometrics and knowledge graph technology, taking the literature of China National Knowledge Infrastructure and Web of Science database as the data source. In-depth bibliometric analysis of literature was carried out from research progress, hotspot evolution, and frontier trends. At the same time, taking the case of scientific research projects overcome by our research group as an example, it briefly introduced the synchronized decision-making framework of digital twin-enabled production logistics system. It is expected to broaden the research boundary of production logistics in the Industry 3.X stage, promote the development and progress of the industry, and provide valuable reference for steadily moving towards the Industry 4.0 stage. Full article
(This article belongs to the Special Issue Manufacturing and Service Systems for Industry 4.0/5.0)
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15 pages, 1539 KiB  
Article
A Method to Identify Main Paths of Knowledge Diffusion for Collaborative Innovation Projects
Systems 2023, 11(7), 370; https://doi.org/10.3390/systems11070370 - 19 Jul 2023
Viewed by 756
Abstract
The main paths of the knowledge diffusion network can reveal the important actors and diffusion process, which has an important significance in improving the efficiency of knowledge diffusion. Due to the independent path choice of project actors, knowledge diffusion networks show a dynamic [...] Read more.
The main paths of the knowledge diffusion network can reveal the important actors and diffusion process, which has an important significance in improving the efficiency of knowledge diffusion. Due to the independent path choice of project actors, knowledge diffusion networks show a dynamic characteristic in collaborative innovation projects. Taking into account this dynamic characteristic, dynamic main path analysis method for project context is proposed. The method, constructed by MATLAB simulation modeling, proposes calculation index and analysis strategies. Contrastive application of two main types of path analysis (the main path analysis method and dynamic main path analysis method) is carried out through a collaborative innovation project case, in order to verify the effectiveness and applicability of this method. The comparison results show that the main paths identified by the new method are more consistent with the actual main paths in knowledge diffusion practice, and the level of knowledge flow through main paths is higher. Therefore, our conclusion is that the dynamic main path analysis method proposed in this research has high applicability and accuracy for identifying the main paths in the collaborative innovation projects. Full article
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21 pages, 714 KiB  
Article
Does Green Finance Expand China’s Green Development Space? Evidence from the Ecological Environment Improvement Perspective
Systems 2023, 11(7), 369; https://doi.org/10.3390/systems11070369 - 19 Jul 2023
Cited by 8 | Viewed by 1093
Abstract
It is important to explore the intrinsic mechanism of green finance’s role in widening the green development space for China, in order to optimize the structure of green financial development and accelerate the construction of a modernized economic system. Taking ecological environment improvement [...] Read more.
It is important to explore the intrinsic mechanism of green finance’s role in widening the green development space for China, in order to optimize the structure of green financial development and accelerate the construction of a modernized economic system. Taking ecological environment improvement as a new research perspective, this paper presents the impacts and mechanisms of green finance on the green development space of the economy and society through the fixed-effect model and moderating-effect model, based on panel data from 30 provinces and municipalities in China from 2011 to 2020. The findings show that green finance development in China significantly expands the green development space of the economy and society, and this conclusion did not change after robustness tests such as replacing the main variables, adjusting the study interval, and considering endogeneity. In terms of its mechanism of action, ecological environment improvement plays an important mediating and regulating role in the process of green finance, essentially magnifying the green development space of the economy and society. In terms of a heterogeneity analysis, the effect of green finance on the expansion of the green development space is the largest in the eastern region, followed by the northeastern region, and the smallest in the central and western regions. In addition, the positive effect of green finance is relatively larger in regions with a higher urbanization level, government fiscal expenditure level, foreign investment level, and advanced industrial structure. The main contribution of this paper is to the field of green development, revealing the important role of the ecological benefits of green finance, which can help to achieve high-quality sustainable development in the economy and society. Full article
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20 pages, 971 KiB  
Article
Assessment of Dynamic Object Information Utilization Service in a Control Center for Each Urban Scale via Fuzzy AHP
Systems 2023, 11(7), 368; https://doi.org/10.3390/systems11070368 - 18 Jul 2023
Cited by 1 | Viewed by 728
Abstract
Recently, the demand for citizen-sensible service solutions such as traffic, crime prevention, and disasters in smart cities is increasing. In order to provide technology-based smart city services, local government control centers could be utilized. Accordingly, this paper presented a method for selecting a [...] Read more.
Recently, the demand for citizen-sensible service solutions such as traffic, crime prevention, and disasters in smart cities is increasing. In order to provide technology-based smart city services, local government control centers could be utilized. Accordingly, this paper presented a method for selecting a control center-based dynamic object information utilization service model through in-depth interviews with 26 related local government control center operation personnel. A comparative analysis according to the size of the local government to which the evaluator belongs was also performed. As a methodology, Fuzzy AHP was used, which can support rational decision-making by mathematically expressing ambiguous phenomena such as subjective and uncertain judgments. The summary of the research results is as follows. Services related to recent incidents in South Korea (e.g., school zone traffic accidents and lowland inundation) were identified as very important. These social issues are significant factors in policy decisions. In comparing the results for each urban scale, the importance of pedestrian safety services on backside roads and main road traffic services was found to be important in the metropolitan area and regional, medium, and small cities, respectively. This was attributed to metropolitan cities with high population density, and medium and small cities experiencing alienated traffic information. In metropolitan areas, new services are highly important owing to the demand for a more scientific control service and future mobility based on a sound control infrastructure. In medium and small cities, facility management services were assessed relatively highly owing to the poor conditions of regional cities with a lack of supervising personnel in the field and a lack of surveillance system infrastructure. This paper was able to confirm the difference in service preference by city size, and it is necessary to select the optimal service model considering these results. Full article
(This article belongs to the Section Systems Practice in Social Science)
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13 pages, 336 KiB  
Article
Systems Precision Medicine: Putting the Pieces Back Together
Systems 2023, 11(7), 367; https://doi.org/10.3390/systems11070367 - 18 Jul 2023
Cited by 1 | Viewed by 1263
Abstract
Systems precision medicine is an interdisciplinary approach that recognises the complexity of diseases and emphasises the integration of clinical knowledge, multi-omics data, analytical models, and the expertise of physicians and data analysts to personalise the care pathway in complex diseases, such as cancer [...] Read more.
Systems precision medicine is an interdisciplinary approach that recognises the complexity of diseases and emphasises the integration of clinical knowledge, multi-omics data, analytical models, and the expertise of physicians and data analysts to personalise the care pathway in complex diseases, such as cancer or diabetes. The aim is to gain a comprehensive understanding of diseases by analysing individual components and identifying relevant aspects for therapy and diagnosis. Key components, their interactions and emerging patterns can be studied using statistical, mathematical and computational tools. The combination of data analysis and clinical evaluation is crucial to effective decision-making, emphasising the need for an integrative approach rather than relying on data alone. Therefore, the crucial point discussed in this paper is that the “computational” part and the “artistic” part (i.e., the physician’s intuition) cannot be separated, and therefore, systems precision medicine can be configured as a collective work of art, involving not only different medical professionals but also, and above all, professional data analysts. The work is “artistic” because data and mathematics alone, without medical knowledge of the context, are not enough. But the work is also “collective” in the sense that it must be the place of cultural integration between the professional intuition of the physician, which cannot be translated into mathematical formulas, and the ability to extract information from multi-omics data of the data analysts, who instead use formal and computational mathematical methods. However, to drive the medical revolution and reassemble a patient’s parts, data analysts need to be involved in the hospital context, and precision medicine physicians should embrace data analytical perspectives. This will require ongoing dialogue, new languages of communication, and education that promotes continuous learning and collaboration between professions, fostering a new level of interdisciplinary collaboration for personalised care. Full article
(This article belongs to the Special Issue Theoretical Issues on Systems Science)
19 pages, 6127 KiB  
Article
Offset Optimization Model for Signalized Intersections Considering the Optimal Location Planning of Bus Stops
Systems 2023, 11(7), 366; https://doi.org/10.3390/systems11070366 - 18 Jul 2023
Viewed by 781
Abstract
Existing offset optimization methods for signalized intersections are mainly focused on regular traffic flow, which cannot accommodate cars and public transit (e.g., Bus Rapid Transit (BRT)) simultaneously. This study proposes a delay prediction model to formulate the signal delay of BRT at intersections. [...] Read more.
Existing offset optimization methods for signalized intersections are mainly focused on regular traffic flow, which cannot accommodate cars and public transit (e.g., Bus Rapid Transit (BRT)) simultaneously. This study proposes a delay prediction model to formulate the signal delay of BRT at intersections. The relation among the green wave bandwidth, signal timing plans, speed of the BRT vehicles, distance between the intersections, and the offset is also modeled. A combinatorial optimization model is then established, which takes the location planning of BRT stops and the offset of intersections at both directions along the artery as the decision variables. The proposed model is programmed with Mathematical Programming Language (AMPL) and solved efficiently by the Gurobi solver. The proposed optimization method is compared with seven different methods. The results show that the average BRT travel time is reduced by at least 19% and the green wave bandwidth is increased by around 30.2%. The importance of considering location planning of BRT stops when optimizing the offset is thereby verified. Full article
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17 pages, 3150 KiB  
Article
Research on Incentive and Coordination Strategy of Fresh Products’ Supply Chain with Delivery Time under New Retail
Systems 2023, 11(7), 365; https://doi.org/10.3390/systems11070365 - 18 Jul 2023
Viewed by 895
Abstract
The new retail focuses on the high integration between online and offline channels. The main problems faced by the development of the new retail are the interest balance of all decision subjects, the pricing strategy, and the coordination of online and offline channels. [...] Read more.
The new retail focuses on the high integration between online and offline channels. The main problems faced by the development of the new retail are the interest balance of all decision subjects, the pricing strategy, and the coordination of online and offline channels. This paper considers the effect of the new retail firms’ delivery time and establishes a two-part tariff contract to study the decision-making and coordination of the new retail fresh products supply chain. This paper constructs cooperative and non-cooperative models and employs the cooperative model as the benchmark case to realize the coordination. It is found that when the delivery time has little effect on the market demand, the offline store often should pay more fixed charges to the new retail firm. With the increased impact of delivery time on market demand, the fixed charges paid by the offline store become smaller. Under the coordination decision model, the offline store pays fixed charges to compensate for the new retail firm’s early delivery costs, but its interests still increase compared with the decentralized decision model. This study models the time-dependent demand for fresh products and proposes an incentive mechanism to coordinate the new retail fresh products’ supply chain; further, it demonstrates that the prices can be significantly decreased with the designed contract, and all the supply chain members can benefit from Pareto improvement. Full article
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14 pages, 2374 KiB  
Article
Designing Effective Instructional Feedback Using a Diagnostic and Visualization System: Evidence from a High School Biology Class
Systems 2023, 11(7), 364; https://doi.org/10.3390/systems11070364 - 17 Jul 2023
Cited by 2 | Viewed by 959
Abstract
Although instructional feedback plays an essential role in regulating learning and improving performance, few studies have systematically investigated the needs of teachers and students for instructional feedback systems or developed designs and experiments, especially at the high school level. To address this research [...] Read more.
Although instructional feedback plays an essential role in regulating learning and improving performance, few studies have systematically investigated the needs of teachers and students for instructional feedback systems or developed designs and experiments, especially at the high school level. To address this research need, the present study investigated the needs of selected students and teachers in a high school in Hubei Province, China, and designed and developed a diagnostic visual feedback system for an experimental study with 125 students from a 10th-grade biology class in the same high school. The results showed that this diagnostic visual feedback report improved student performance (ES = 0.37) and that functions such as misconception location, knowledge diagnosis, and knowledge alert were well received by students. These findings have multiple implications for facilitating the design and development of diagnostic visual feedback systems. Full article
(This article belongs to the Special Issue Human–AI Teaming: Synergy, Decision-Making and Interdependency)
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17 pages, 2342 KiB  
Article
Evolutionary Game Analysis of Data Resale Governance in Data Trading
Systems 2023, 11(7), 363; https://doi.org/10.3390/systems11070363 - 17 Jul 2023
Cited by 1 | Viewed by 1087
Abstract
Data trading is important for optimizing the allocation of data elements. However, data can be easily copied, disseminated, or resold, leading to disorderly development in the data trading market, and raising the issue of data governance. Data trading involves various participants, while existing [...] Read more.
Data trading is important for optimizing the allocation of data elements. However, data can be easily copied, disseminated, or resold, leading to disorderly development in the data trading market, and raising the issue of data governance. Data trading involves various participants, while existing research lacks an understanding of participant interactions and strategy adoption, as well as determination of optimal strategies for the participants. To address these gaps and provide insights for the governance of data trading platforms, this paper proposes an evolutionary game model for the governance of data trading involving three parties: data suppliers, demanders, and trading platforms. Our findings reveal that data trading platforms choosing to govern, data suppliers choosing to innovate positively, and data demanders choosing not to resell can be achieved under certain conditions. We also find that an increase in the price of data trading or the number of transactions can weaken the effectiveness of platform governance and make data trading more difficult to govern. Additionally, the incentives for data innovation provided by the trading platform can significantly promote data suppliers to innovate data positively. However, when these incentives are too high, the platform may weaken its level of governance or even move towards non-governance. Increasing penalties for data resale weakens data demanders’ motivation to resell data, and a higher probability of data resale being reported lowers their motivation to do so. By examining the role of different participants in data trading, the model proposes ways to improve the efficiency and robustness of the data market while better protecting the interests of data traders. Full article
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31 pages, 7822 KiB  
Article
Participatory Modeling with Discrete-Event Simulation: A Hybrid Approach to Inform Policy Development to Reduce Emergency Department Wait Times
Systems 2023, 11(7), 362; https://doi.org/10.3390/systems11070362 - 17 Jul 2023
Viewed by 1259
Abstract
We detail a case study using a participatory modeling approach in the development and use of discrete-event simulations to identify intervention strategies aimed at reducing emergency department (ED) wait times in a Canadian health policy setting. A four-stage participatory modeling approach specifically adapted [...] Read more.
We detail a case study using a participatory modeling approach in the development and use of discrete-event simulations to identify intervention strategies aimed at reducing emergency department (ED) wait times in a Canadian health policy setting. A four-stage participatory modeling approach specifically adapted to the local policy environment was developed to engage stakeholders throughout the modeling processes. The participatory approach enabled a provincial team to engage a broad range of stakeholders to examine and identify the causes and solutions to lengthy ED wait times in the studied hospitals from a whole-system perspective. Each stage of the approach was demonstrated through its application in the case study. A novel and key feature of the participatory modeling approach was the development and use of a multi-criteria framework to identify and prioritize interventions to reduce ED wait times. We conclude with a discussion on lessons learned, which provide insights into future development and applications of participatory modeling methods to facilitate policy development and build multi-stakeholder consensus. Full article
(This article belongs to the Special Issue Systems Thinking and Models in Public Health)
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18 pages, 2304 KiB  
Article
Towards Understanding the Causal Relationships in Proliferating SD Education—A System Dynamics Group Modelling Approach in China
Systems 2023, 11(7), 361; https://doi.org/10.3390/systems11070361 - 16 Jul 2023
Viewed by 856
Abstract
Given the growing importance of system dynamics (SD) in solving increasingly complex and dynamic problems in any country, we believe SD education will become an imperative leverage point in helping us deal with our uncertain future. This study tries to understand the causal [...] Read more.
Given the growing importance of system dynamics (SD) in solving increasingly complex and dynamic problems in any country, we believe SD education will become an imperative leverage point in helping us deal with our uncertain future. This study tries to understand the causal relationships in proliferating SD education by a system dynamics group modelling approach in China. Based on a questionnaire survey and a group model building (GMB) workshop, we aim to explore the interactions of feedback loops in the constructed causal loop diagram (CLD). This uncovers insights into what constitutes the growth of SD education in China and helps to guide the design and implementation of policies to achieve this growth. We conclude that it is important and relevant to find ways to improve, including the construction of an SD teaching platform to integrate normative resources, providing opportunities for teacher training, enhancing the availability and accessibility of SD education, and building networks with international partners. The results of our study may set the foundation for further research to extend the generalizability of our insights and methodological approaches to other countries. Full article
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25 pages, 3664 KiB  
Article
A Theory of Heterogeneous City Evolution with Heterogenous Agents
Systems 2023, 11(7), 360; https://doi.org/10.3390/systems11070360 - 16 Jul 2023
Viewed by 885
Abstract
This paper develops a new unified theoretical general equilibrium model in which the interactions between heterogeneous workers and firms influence heterogeneous city evolutions. Given the heterogeneous worker–firm–city framework, I study in depth the possible heterogenous city evolutions and the resulting implications on the [...] Read more.
This paper develops a new unified theoretical general equilibrium model in which the interactions between heterogeneous workers and firms influence heterogeneous city evolutions. Given the heterogeneous worker–firm–city framework, I study in depth the possible heterogenous city evolutions and the resulting implications on the labor market, as well as on overall productivity. In particular, it is shown that the same exogenous shocks may lead to completely different results depending on the relative dominance of the two countervailing effects of congestion and agglomeration. In an open economy setting, it is also shown that such relative dominance may affect the trading partner and generate the comovement of city evolution in each country. Full article
(This article belongs to the Special Issue Circular Economy Systems: Design, Use, and Innovation)
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29 pages, 4450 KiB  
Article
Post-COVID-19 Recovery: An Integrated Framework of Construction Project Performance Evaluation in China
Systems 2023, 11(7), 359; https://doi.org/10.3390/systems11070359 - 14 Jul 2023
Cited by 3 | Viewed by 1281
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 [...] Read more.
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. Full article
(This article belongs to the Section Project Management)
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27 pages, 1811 KiB  
Article
To Compete or to Collaborate? Logistics Service Sharing and Retailers’ Resale in Competitive Online Channels
Systems 2023, 11(7), 358; https://doi.org/10.3390/systems11070358 - 13 Jul 2023
Cited by 2 | Viewed by 1220
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 [...] Read more.
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. Full article
(This article belongs to the Section Systems Engineering)
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15 pages, 1378 KiB  
Article
Decentralized Inventory Transshipments with Quantal Response Equilibrium
Systems 2023, 11(7), 357; https://doi.org/10.3390/systems11070357 - 12 Jul 2023
Viewed by 743
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Manufacturing and Service Systems for Industry 4.0/5.0)
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17 pages, 684 KiB  
Article
Artificial Intelligence and Green Total Factor Productivity: The Moderating Effect of Slack Resources
Systems 2023, 11(7), 356; https://doi.org/10.3390/systems11070356 - 11 Jul 2023
Viewed by 1450
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 [...] Read more.
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. Full article
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23 pages, 644 KiB  
Article
How Does Digital Transformation Increase Corporate Sustainability? The Moderating Role of Top Management Teams
Systems 2023, 11(7), 355; https://doi.org/10.3390/systems11070355 - 11 Jul 2023
Cited by 3 | Viewed by 4335
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 [...] Read more.
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. Full article
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20 pages, 4609 KiB  
Article
The Innovative Research on Sustainable Microgrid Artwork Design Based on Regression Analysis and Multi-Objective Optimization
Systems 2023, 11(7), 354; https://doi.org/10.3390/systems11070354 - 11 Jul 2023
Viewed by 691
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 [...] Read more.
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%. Full article
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20 pages, 1010 KiB  
Article
Automatically Detecting Incoherent Written Math Answers of Fourth-Graders
Systems 2023, 11(7), 353; https://doi.org/10.3390/systems11070353 - 10 Jul 2023
Cited by 2 | Viewed by 1018
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 [...] Read more.
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. Full article
(This article belongs to the Topic Methods for Data Labelling for Intelligent Systems)
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28 pages, 5701 KiB  
Article
Agile Methodology for the Standardization of Engineering Requirements Using Large Language Models
Systems 2023, 11(7), 352; https://doi.org/10.3390/systems11070352 - 10 Jul 2023
Cited by 2 | Viewed by 2517
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 [...] Read more.
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). Full article
(This article belongs to the Section Systems Engineering)
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37 pages, 18116 KiB  
Article
Harnessing the Power of ChatGPT for Automating Systematic Review Process: Methodology, Case Study, Limitations, and Future Directions
Systems 2023, 11(7), 351; https://doi.org/10.3390/systems11070351 - 09 Jul 2023
Cited by 11 | Viewed by 7709
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 [...] Read more.
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. Full article
(This article belongs to the Special Issue Human–AI Teaming: Synergy, Decision-Making and Interdependency)
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15 pages, 2267 KiB  
Article
Reinforcement Learning for Optimizing Can-Order Policy with the Rolling Horizon Method
Systems 2023, 11(7), 350; https://doi.org/10.3390/systems11070350 - 07 Jul 2023
Viewed by 969
Abstract
This study presents a novel approach to a mixed-integer linear programming (MILP) model for periodic inventory management that combines reinforcement learning algorithms. The rolling horizon method (RHM) is a multi-period optimization approach that is applied to handle new information in updated markets. The [...] Read more.
This study presents a novel approach to a mixed-integer linear programming (MILP) model for periodic inventory management that combines reinforcement learning algorithms. The rolling horizon method (RHM) is a multi-period optimization approach that is applied to handle new information in updated markets. The RHM faces a limitation in easily determining a prediction horizon; to overcome this, a dynamic RHM is developed in which RL algorithms optimize the prediction horizon of the RHM. The state vector consisted of the order-up-to-level, real demand, total cost, holding cost, and backorder cost, whereas the action included the prediction horizon and forecasting demand for the next time step. The performance of the proposed model was validated through two experiments conducted in cases with stable and uncertain demand patterns. The results showed the effectiveness of the proposed approach in inventory management, particularly when the proximal policy optimization (PPO) algorithm was used for training compared with other reinforcement learning algorithms. This study signifies important advancements in both the theoretical and practical aspects of multi-item inventory management. Full article
(This article belongs to the Special Issue Manufacturing and Service Systems for Industry 4.0/5.0)
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