Next Issue
Volume 12, December
Previous Issue
Volume 12, October
 
 

Systems, Volume 12, Issue 11 (November 2024) – 65 articles

Cover Story (view full-size image): The various controversies surrounding machine learning (ML) have provoked a deluge of applications for ML approaches in all scientific fields. In order to preserve the role of science in regard to generating novel knowledge by means of an implicit never-ending change in perspectives and paradigms, the proliferation of very precise (but often difficult to explain) ML applications must be prioritized. This paper, after demonstrating the impossibility of a completely theory-free approach, introduces the relational system theory paradigm as an efficient tool for transforming data-driven correlation structures into physically reliable interactions happening in the analyzed system. This approach allows us to regenerate the natural fusion of data-driven and theory-driven styles of reasoning in the ML era. View this paper
  • Issues are regarded as officially published after their release is announced to the table of contents alert mailing list.
  • You may sign up for e-mail alerts to receive table of contents of newly released issues.
  • PDF is the official format for papers published in both, html and pdf forms. To view the papers in pdf format, click on the "PDF Full-text" link, and use the free Adobe Reader to open them.
Order results
Result details
Section
Select all
Export citation of selected articles as:
18 pages, 3453 KiB  
Article
Development of a Blockchain-Based Food Safety System for Shared Kitchens
by Hyejin Jang, Daye Lee and Byungun Yoon
Systems 2024, 12(11), 509; https://doi.org/10.3390/systems12110509 - 20 Nov 2024
Viewed by 476
Abstract
With the recent growth of the sharing economy, businesses offering shared-kitchen services are expanding rapidly. Due to the communal nature of these kitchens, there is a heightened need for systematic food safety management. However, existing research on blockchain applications has largely overlooked shared [...] Read more.
With the recent growth of the sharing economy, businesses offering shared-kitchen services are expanding rapidly. Due to the communal nature of these kitchens, there is a heightened need for systematic food safety management. However, existing research on blockchain applications has largely overlooked shared kitchens, a complex setting with numerous stakeholders and sensitivity to real-time kitchen conditions. This study addresses this gap by proposing a blockchain-based food safety management system for shared kitchens. The system’s functional requirements were meticulously outlined based on guidelines from South Korea’s Ministry of Food and Drug Safety. Key participants were identified as system users, and use cases were crafted in alignment with their responsibilities and roles to ensure effective safety management. Additionally, the blockchain system’s mechanisms for enhancing safety in shared kitchens were substantiated through specific use cases and detailed data structures, addressing issues related to forgery, alteration, and management challenges. This study also offers practical insights that can facilitate more structured safety management in shared-kitchen environments. Full article
Show Figures

Figure 1

25 pages, 1677 KiB  
Article
Application of Mind Map and TRIZ to an Advanced Air Mobility System for Post-Disaster Response
by Olabode A. Olanipekun, Carlos J. Montalvo, Kari J. Lippert and John T. Wade
Systems 2024, 12(11), 508; https://doi.org/10.3390/systems12110508 - 20 Nov 2024
Viewed by 420
Abstract
In this article, an Advanced Air Mobility (AAM) platform focused on search and rescue applications is discussed and analyzed from a systems thinking perspective. By applying two systems thinking tools, namely Mind Map and TRIZ, the strong interactions within the constituent parts that [...] Read more.
In this article, an Advanced Air Mobility (AAM) platform focused on search and rescue applications is discussed and analyzed from a systems thinking perspective. By applying two systems thinking tools, namely Mind Map and TRIZ, the strong interactions within the constituent parts that make up the system’s whole are examined with the aim of providing a comprehensive roadmap for a proposed Advanced Air Mobility Post-Disaster Response (AAMPDR) system. Furthermore, two problems are discussed to demonstrate the application of the TRIZ technique. The first is in regards to a clause in the AGL rule that could present operational risks to the AAM’s airframe, while the second relates to a potential conflict ensuing from the advent of the 5G C-band and its effect on the AAM altimetry. The resulting solutions to resolve these conflicts using this same technique are also discussed, firstly by taking into account the mean sea/water level as a reference for vertical height within the provisions of the Federal Aviation Regulation requirements, and secondly by applying segmentation of the mission profile as well as a multi-stage frequency designation for each segment depending on a threshold vertical distance. Finally, this study demonstrated that Mind Map and TRIZ can be effective techniques in the early stages of conceptual model development for an AAM system applied to post–disaster response. Furthermore, that the contradictions tool of TRIZ can also be utilized in resolving those potential conflicts identified in relation to the system of interest. To this end, this paper proposes the amendment of the current Part 107 rule to include the term Above Mean Sea (or Water) Level (AMS/WL), a critical yet missing piece of the system requirements that engineers should take into account in future AAM system designs. Full article
(This article belongs to the Section Systems Engineering)
Show Figures

Figure 1

29 pages, 7296 KiB  
Article
Estimation of Arterial Path Flow Considering Flow Distribution Consistency: A Data-Driven Semi-Supervised Method
by Zhe Zhang, Qi Cao, Wenxie Lin, Jianhua Song, Weihan Chen and Gang Ren
Systems 2024, 12(11), 507; https://doi.org/10.3390/systems12110507 - 19 Nov 2024
Viewed by 448
Abstract
To implement fine-grained progression signal control on arterial, it is essential to have access to the time-varying distribution of the origin–destination (OD) flow of the arterial. However, due to the sparsity of automatic vehicle identification (AVI) devices and the low penetration of connected [...] Read more.
To implement fine-grained progression signal control on arterial, it is essential to have access to the time-varying distribution of the origin–destination (OD) flow of the arterial. However, due to the sparsity of automatic vehicle identification (AVI) devices and the low penetration of connected vehicles (CVs), it is difficult to directly obtain the distribution pattern of arterial OD flow (i.e., path flow). To solve this problem, this paper develops a semi-supervised arterial path flow estimation method considering the consistency of path flow distribution by combining the sparse AVI data and the low permeability CV data. Firstly, this paper proposes a semi-supervised arterial path flow estimation model based on multi-knowledge graphs. It utilizes graph neural networks to combine some arterial AVI OD flow observation information with CV trajectory information to infer the path flow of AVI unobserved OD pairs. Further, to ensure that the estimation results of the multi-knowledge graph path flow estimation model are consistent with the distribution of path flow in real situations, we introduce a generative adversarial network (GAN) architecture to correct the estimation results. The proposed model is extensively tested based on a real signalized arterial. The results show that the proposed model is still able to achieve reliable estimation results under low connected vehicle penetration and with less observed label data. Full article
Show Figures

Figure 1

25 pages, 26130 KiB  
Article
Origin-Destination Spatial-Temporal Patterns of Dockless Shared Bikes Based on Shopping Activities and Its Application in Urban Planning: The Case of Nanjing
by Yufei Quan, Xiao Wu, Zijie Zhu and Congyu Liu
Systems 2024, 12(11), 506; https://doi.org/10.3390/systems12110506 - 19 Nov 2024
Viewed by 489
Abstract
The utilization of dockless shared bikes for shopping purposes has become increasingly prevalent. This research seeks to optimize the configuration of facilities and transportation policies for shared bike travel by analyzing the spatiotemporal patterns of shopping trips from the perspectives of destination (D), [...] Read more.
The utilization of dockless shared bikes for shopping purposes has become increasingly prevalent. This research seeks to optimize the configuration of facilities and transportation policies for shared bike travel by analyzing the spatiotemporal patterns of shopping trips from the perspectives of destination (D), origin (O), and O-D correlation in Nanjing’s main city area. As the second-largest commercial center in East China, Nanjing offers a significant context for this research. First, we introduce the “cycling intensity” indicator to analyze the patterns of shared bicycle trips with shopping facilities as destinations at both the subdistrict and road section scales. Second, we utilize spatial autocorrelation analysis and k-means clustering to explore the outflow patterns of shared bicycle trips originating from shopping facilities. Finally, we employ grey correlation analysis to investigate the dynamic flow correlations of shared bicycle O-D trips around various grades of shopping facilities at both subdistrict and road section levels. Concurrently, we endeavored to delineate the practical transformation and application of the research findings. Our results indicate the following: (1) There is a high concentration of cycling intensity around shopping facilities on east–west and north–south roads, with community shopping facilities primarily associated with north–south roads. (2) The outflow of shared bikes from shopping areas can be categorized into four distinct modes. (3) The inflow and outflow of shopping trips exhibit significant synchronicity, particularly on the branch routes. These findings can provide valuable insights for zoning planning, construction of bicycle infrastructure, and formulation of sustainable urban transportation policies. Full article
Show Figures

Figure 1

17 pages, 363 KiB  
Article
Protocol for Identifying and Retaining Critical Knowledge in a Public Health Administration
by Núria Arimany-Serrat, Maria Antentas-Peraile and Elisenda Tarrats-Pons
Systems 2024, 12(11), 505; https://doi.org/10.3390/systems12110505 - 19 Nov 2024
Viewed by 411
Abstract
The Secretary of Public Health (SSP) faces a looming skills gap due to retirements and rotations of civil service staff. Critical knowledge retention is crucial across all generational cohorts due to the retirement and turnover of workers. This study develops a protocol that [...] Read more.
The Secretary of Public Health (SSP) faces a looming skills gap due to retirements and rotations of civil service staff. Critical knowledge retention is crucial across all generational cohorts due to the retirement and turnover of workers. This study develops a protocol that addresses the knowledge retention needs of the four generations (Baby Boomers, X, Y, Z) that coexist in the workforce to ensure the continuity of the Public Health Secretariat. The objective of the study is to develop a protocol for the management, transfer, and retention of critical knowledge. A scoping review is conducted in Scopus and Web of Science to develop the protocol, to identify critical knowledge workers through tool scores. The instrument developed in this research includes two pilots on Baby Boomer and Millennial workers. Both workers had critical and essential knowledge for the continuity of the organisation. The Baby Boomer worker presented a higher amount of tacit, operational, and individually owned knowledge, while the Millennial worker showed a predominance of tacit technological knowledge. This protocol provides a practical and adaptable approach to identifying and prioritising critical knowledge holders, allowing organisations to map and determine the amount of essential knowledge within the workforce. An important limitation of the study is the small sample of workers who participated in the pilot test of the protocol. Further research is therefore recommended in other public administrations and across all generations in employment. Full article
16 pages, 3630 KiB  
Article
Leveraging Digital Technologies for Public Health Education in High-Density Community Spaces: A Geospatial Analysis
by Ting Liu, Yiming Luo, Patrick Cheong-Iao Pang and Yuanze Xia
Systems 2024, 12(11), 504; https://doi.org/10.3390/systems12110504 - 19 Nov 2024
Viewed by 487
Abstract
Public health education (PHE) plays a crucial role in mitigating the impact of public health crises, particularly in the context of high-density and aging populations. This study aims to address the challenges posed by these demographic trends in community public service spaces (CPSSs) [...] Read more.
Public health education (PHE) plays a crucial role in mitigating the impact of public health crises, particularly in the context of high-density and aging populations. This study aims to address the challenges posed by these demographic trends in community public service spaces (CPSSs) by integrating geospatial and population data. Using bivariate spatial autocorrelation analysis, this research investigates the relationship between PHE and social determinants of health across 40 CPSSs in Macao. Additionally, it highlights the underutilization of digital technologies (DTs) in PHE. A non-participatory, short-term field survey and observational study were conducted to analyze PHE data quantitatively and descriptively in Macao’s CPSSs. Moran’s I and LISA index were used to test spatial autocorrelation at 90% and 99% confidence levels. The results revealed significant positive spatial correlations between the distribution of community public service institutions (CPSIs) and the population in southern Macao, as well as between the elderly population and PHE themes and formats. PHE topics predominantly focus on health/fitness, geriatrics, chronic diseases, and mental health. Despite this, PHE remains heavily reliant on offline formats, with limited integration of DTs. Challenges such as low digital literacy and limited acceptance of DTs among community workers and the public hinder their broader adoption. This study provides valuable insights for optimizing the allocation of health education resources in densely populated and aging urban areas, offering both practical recommendations and theoretical support for health policy making and implementation. Full article
Show Figures

Figure 1

21 pages, 4654 KiB  
Article
System Dynamics Modeling: Technological Solution to Evaluating Cold-Chain Meat Packaging Scenarios
by Ernesto A. Lagarda-Leyva, Luis E. Hernández-Valdez and Alfredo Bueno-Solano
Systems 2024, 12(11), 503; https://doi.org/10.3390/systems12110503 - 19 Nov 2024
Viewed by 511
Abstract
A cold-chain meat packaging project was developed for a meat product company in northwestern Mexico that moves high volumes of fresh meat into national and international markets. The objective of the present research is to evaluate the supply process for three types of [...] Read more.
A cold-chain meat packaging project was developed for a meat product company in northwestern Mexico that moves high volumes of fresh meat into national and international markets. The objective of the present research is to evaluate the supply process for three types of thermo-shrinkable polyethylene bags to provide a technological solution for high-volume meat packaging based on a graphical user interface. A system dynamics (SD) methodology is developed in seven stages to generate a technological solution: (1) system mapping; (2) causal diagram construction; (3) stock, flow modeling, and equations; (4) model simulation; (5) model validation; (6) scenarios and multicriteria analysis; and (7) graphical user interface development. The main result for the company was a technological solution that could communicate with decision-makers and the proposed graphical user interface. Future optimistic and pessimistic scenarios were self-evaluated based on the current situation related to three thermo-shrinkable bags used for selling high volumes of fresh meat. In these solutions, previously simulated costs and savings can be implemented in a real situation. Quantitative graphical user interface data can be observed to adequately manage box and bag inventories and minimize costs. Using SD enables the development of technological solutions in complex environments with robust simulations and models that offer data to people interested in the system under study. Full article
(This article belongs to the Special Issue The Systems Thinking Approach to Strategic Management)
Show Figures

Figure 1

22 pages, 1813 KiB  
Article
Industrial Basic Capacity Research: Theory and Measurement
by Songling Wu and Mengjiao Ren
Systems 2024, 12(11), 502; https://doi.org/10.3390/systems12110502 - 19 Nov 2024
Viewed by 312
Abstract
This paper establishes a theoretical framework for understanding the connotations of industrial basic capacity. It employs models from economic growth theory to derive indices for assessing industrial basic capacity and exploring the economic correlations among its influencing factors. Additionally, it measures the industrial [...] Read more.
This paper establishes a theoretical framework for understanding the connotations of industrial basic capacity. It employs models from economic growth theory to derive indices for assessing industrial basic capacity and exploring the economic correlations among its influencing factors. Additionally, it measures the industrial basic capacity indices of 17 subsectors across 9 major industrial countries from 2000 to 2020 using OECD data. The findings reveal that from 2000 to 2020, the Chinese manufacturing industry has surpassed the United States, becoming the global leader. Specifically, within the 17 subsectors, 9 are globally ranked first, with 7 nearing advanced levels, and only 1 facing relative backwardness. Chinese manufacturing industry’s enhanced basic capacity is attributed to advantages in cost competitiveness and scale. However, significant disparities persist in technological input and industrial linkages with advanced nations. The decline in basic capacity among developed countries stems primarily from diminished value chain profitability due to inadequate investment. Sustainable improvement in industry basic capacity necessitates concurrent advancements in value chain profitability, fixed asset investment, technological levels, industrial linkages, and market scale. Overreliance on cost advantages or advanced technology poses substitution risks. Moreover, this paper underscores the limitations of exclusively relying on current data to assess global industrial basic capacity, advocating for a greater historical perspective. To strengthen the Chinese manufacturing industrial basic capacity within the global value chain, the Chinese manufacturing industry must enhance technological inputs, reduce the operational costs of enterprises, and elevate the degree of openness. Full article
Show Figures

Figure 1

23 pages, 12069 KiB  
Article
Priorities of Critical Success Factors for Lean Production Implementation of China’s Factories
by Ping-Yuan Kuo and Rong-Ho Lin
Systems 2024, 12(11), 501; https://doi.org/10.3390/systems12110501 - 18 Nov 2024
Viewed by 494
Abstract
As demonstrated by the existing literature, lean production and management can contribute to the improvement of firm performance. However, there are many companies that struggle to apply its ethos and practices. The key point is that lean production differs from traditional mass production [...] Read more.
As demonstrated by the existing literature, lean production and management can contribute to the improvement of firm performance. However, there are many companies that struggle to apply its ethos and practices. The key point is that lean production differs from traditional mass production in many ways. Other than that, numerous studies have shown that business management systems must take into account both soft power and hard power. The main purpose of this study is to use the Analytic Hierarchy Process (AHP) and Analytic Network Process (ANP) tools to find out the soft and hard power factors, rank their importance in identifying the key success factors for the introduction of a lean production system, and assist in making the company’s transformation smoother and more successful. The research results verify that a lean production system needs to take into account both soft power and hard power. Lean management in this study concludes the following priorities of critical factors: In hard power (technical dimension): (1) 5S, (2) seven major wastes, (3) solutions to lean production-related issues, (4) storage location management and warehouse management, (5) single minute exchange of die, and (6) total productive maintenance; In soft power (management dimension): (1) teamwork, (2) communication, (3) leadership, (4) culture, (5) initiative, and (6) employee training. The combination of soft power and hard power can improve the success rate of lean management system introduction. Full article
Show Figures

Figure 1

34 pages, 3609 KiB  
Article
The Spatial Effect of Digital Economy Enabling Common Prosperity—An Empirical Study of the Yellow River Basin
by Mu Yang, Qiguang An and Lin Zheng
Systems 2024, 12(11), 500; https://doi.org/10.3390/systems12110500 - 18 Nov 2024
Viewed by 412
Abstract
The digital economy enhances economic efficiency and improves economic structure, driving economic growth through transformations in efficiency, momentum, and quality. It has become a new driving force for advancing common prosperity. This study uses SDM, SDID, and SPSTR models to explore the impact [...] Read more.
The digital economy enhances economic efficiency and improves economic structure, driving economic growth through transformations in efficiency, momentum, and quality. It has become a new driving force for advancing common prosperity. This study uses SDM, SDID, and SPSTR models to explore the impact of digital economy on common prosperity, which constructs the index system to evaluate the common prosperity from process index and outcome index. According to the panel data of 76 cities in the Yellow River Basin from 2011 to 2021, and the findings are as follows: (1) The digital economy exhibits a development pattern characterized by high activity downstream and lower activity upstream, and the development trend is stable. The development pattern of common prosperity has changed from sporadic distribution to regional agglomeration, and the level of common prosperity in most cities has improved. (2) The digital economy has a significant positive spatial effect on common prosperity. And the findings are robust after introducing the “Big Data” exogenous policy impact, dynamic SDM model, and other methods. Moreover, spatial heterogeneity exists. The promotion effect in the upper and lower reaches is stronger, while the middle reaches are weakly affected by the digital economy. (3) The spatial spillover effect of the digital economy on common prosperity has a boundary, and the positive spillover reaches a maximum value at 600–650 km. (4) Nonlinear analysis confirms that the digital economy provides momentum for common prosperity industrial structure optimization that can effectively stimulate the “endogenous” growth mechanism, strengthen the marginal increasing effect of the digital economy driving common prosperity and enhance the effect of “making a bigger pie”. The digital economy makes effective use of digital resources and technologies, promotes the equalization of public services, exerts a positive impact on the realization of common prosperity, and consolidates the effect of “dividing a better cake”. Full article
Show Figures

Figure 1

18 pages, 979 KiB  
Article
The Impact of Artificial Intelligence on ESG Performance of Manufacturing Firms: The Mediating Role of Ambidextrous Green Innovation
by Hao Jing and Shiyu Zhang
Systems 2024, 12(11), 499; https://doi.org/10.3390/systems12110499 - 18 Nov 2024
Viewed by 579
Abstract
In the context of the worldwide quest for green and sustainable development, there is a growing importance in enhancing the environmental, social, and governance (ESG) performance of manufacturing companies. With the rise of digital transformation and pressing environmental challenges, artificial intelligence (AI) has [...] Read more.
In the context of the worldwide quest for green and sustainable development, there is a growing importance in enhancing the environmental, social, and governance (ESG) performance of manufacturing companies. With the rise of digital transformation and pressing environmental challenges, artificial intelligence (AI) has emerged as a crucial tool for manufacturing organizations to gain a competitive edge in sustainability. While the role of digital technologies in driving ESG improvements has been widely discussed, there is limited scholarly exploration of the specific impact of AI on the ESG performance of manufacturing firms, as well as the underlying mechanisms at play from an AI perspective. Addressing this research gap, this study constructs a theoretical model of AI affecting manufacturing firms’ ESG performance using a sample of Chinese-listed manufacturing firms from 2012–2022. Additionally, this study examines the role of mediating mechanisms of ambidextrous green innovation as well as differences in the intrinsic mechanisms triggered by the equilibrium of ambidextrous green innovation and firm size. The findings indicate that the application of AI technology effectively promotes improvements in the ESG performance of manufacturing firms, with ambidextrous green innovation playing a positive mediating role. Furthermore, manufacturing companies with a strong balance of ambidextrous green innovation and larger scale exhibit enhanced effects of AI on ESG performance. This study serves to supplement existing literature on ESG performance enhancement, elucidate the theoretical rationale behind the non-economic performance of AI-enabled firms, and extend the application of organizational dualism theory to new contexts. Full article
(This article belongs to the Special Issue Strategic Management in Digital Transformation Era)
Show Figures

Figure 1

19 pages, 4321 KiB  
Article
Bitcoin Trend Prediction with Attention-Based Deep Learning Models and Technical Indicators
by Ming-Che Lee
Systems 2024, 12(11), 498; https://doi.org/10.3390/systems12110498 - 18 Nov 2024
Viewed by 691
Abstract
This study presents a comparative analysis of two advanced attention-based deep learning models—Attention-LSTM and Attention-GRU—for predicting Bitcoin price movements. The significance of this research lies in integrating moving average technical indicators with deep learning models to enhance sensitivity to market momentum, and in [...] Read more.
This study presents a comparative analysis of two advanced attention-based deep learning models—Attention-LSTM and Attention-GRU—for predicting Bitcoin price movements. The significance of this research lies in integrating moving average technical indicators with deep learning models to enhance sensitivity to market momentum, and in normalizing these indicators to accurately reflect market trends and reversals. Utilizing historical OHLCV data along with four key technical indicators (SMA, EMA, TEMA, and MACD), the models classify trends into uptrend, downtrend, and neutral categories. Experimental results demonstrate that the inclusion of technical indicators, particularly MACD, significantly improves prediction accuracy. Furthermore, the Attention-GRU model offers computational efficiency suitable for real-time applications, while the Attention-LSTM model excels in capturing long-term dependencies. These findings contribute valuable insights for financial forecasting, providing practical tools for cryptocurrency traders and investors. Full article
Show Figures

Figure 1

26 pages, 1563 KiB  
Article
Unlocking the Potential of Construction Governance: Developing Participants’ Capability Scale
by Zhizhe Zheng, Yikun Su and Junhao Liu
Systems 2024, 12(11), 497; https://doi.org/10.3390/systems12110497 - 18 Nov 2024
Viewed by 397
Abstract
There is a consistent lack of consensus on critical elements in the study of construction governance. To advance the practice and theoretical development of project governance, this paper aims to construct a scale for the governance capabilities of participants in construction projects. By [...] Read more.
There is a consistent lack of consensus on critical elements in the study of construction governance. To advance the practice and theoretical development of project governance, this paper aims to construct a scale for the governance capabilities of participants in construction projects. By employing agency theory, stakeholder theory, resource dependence theory, and transaction cost economics, this study examines the governance capabilities of participants in construction projects and conceptualizes a comprehensive framework for governance capabilities. Based on post-positivism, the triangulation method was used to collect data, and the Governance Capability Scale was developed through a pre-survey and formal research. The research findings identify governance capabilities across eight dimensions and 47 measurement items, encompassing business, finance, human resources, learning and innovation, marketing, organizational management, project management, and procurement. The scale has satisfactory applicability. Among these constructs, only organizational management is negatively correlated with the other constructs. The findings significantly clarify capability constructs in construction governance, aiding project managers in achieving refined management during construction. Essentially, this study advances the knowledge base of project governance. This contribution not only supports the theoretical development of governance practices but also promotes high-quality development in the construction industry. Full article
Show Figures

Figure 1

17 pages, 858 KiB  
Article
The Antecedents of Courier Continuance Participation Intention: A Dyadic Analysis of Courier–Customer Interactions in Crowdsourcing Delivery
by Wenjie Wang and Yulu Yin
Systems 2024, 12(11), 496; https://doi.org/10.3390/systems12110496 - 18 Nov 2024
Viewed by 358
Abstract
Crowdsourcing delivery has emerged as an innovative solution for last-mile delivery in the sharing economy era. However, enhancing courier continuance participation intention is an increasing challenge for crowdsourcing delivery platforms due to the independence of crowdsourced couriers. Given that couriers and customers are [...] Read more.
Crowdsourcing delivery has emerged as an innovative solution for last-mile delivery in the sharing economy era. However, enhancing courier continuance participation intention is an increasing challenge for crowdsourcing delivery platforms due to the independence of crowdsourced couriers. Given that couriers and customers are subject to interdependencies and may influence each other in delivery service systems, this paper utilizes a dyadic analysis to examine how courier–customer interactions may influence the continuance participation intention of couriers. Specifically, we investigate the influence mechanism of customer satisfaction and courier job satisfaction, as well as the mediating role of courier pay satisfaction, drawing upon the balance theory and distributive justice theory. The empirical results of 261 courier–customer dyads indicate that the customer side impacts the courier side in both direct and indirect ways. There is a direct satisfaction transmission in courier–customer delivery service interactions. Meanwhile, satisfied customers indirectly enhance courier job satisfaction via the mediator of courier pay satisfaction, which in turn strengthens courier continuance participation intention. Our study offers novel insights for administrators on the influence of courier–customer interactions and pay satisfaction on courier continuance participation intention, contributing to decreasing couriers’ turnover in the fluid crowdsourcing labor market. Full article
(This article belongs to the Section Systems Practice in Social Science)
Show Figures

Figure 1

21 pages, 1050 KiB  
Article
The Impact of Climate Policy Uncertainty on the ESG Performance of Enterprises
by Zhi Zhang, Yanhong Feng, Hongwei Zhou, Liming Chen and Yi Liu
Systems 2024, 12(11), 495; https://doi.org/10.3390/systems12110495 - 16 Nov 2024
Viewed by 481
Abstract
In the context of addressing climate change, the uncertainty of climate policies has intensified the environmental and regulatory risks faced by enterprises, forcing them to adjust their strategies for fulfilling ESG responsibilities in pursuit of sustainable development. This paper uses panel data from [...] Read more.
In the context of addressing climate change, the uncertainty of climate policies has intensified the environmental and regulatory risks faced by enterprises, forcing them to adjust their strategies for fulfilling ESG responsibilities in pursuit of sustainable development. This paper uses panel data from listed non-financial enterprises on China’s Shanghai and Shenzhen A-share markets from 2011 to 2022, employing a fixed-effects panel model to examine the impact of climate policy uncertainty on corporate ESG performance. The findings indicate that climate policy uncertainty significantly hampers the ESG performance of enterprises. The mechanism analysis reveals that climate policy uncertainty negatively affects ESG performance by deepening corporate financing constraints and increasing short-term financial performance. The heterogeneity analysis shows that in terms of ownership structure, the negative impact of climate policy uncertainty on the ESG performance of state-owned enterprises is relatively weaker. In terms of industry heterogeneity, climate policy uncertainty suppresses the ESG performance of enterprises in technology-intensive industries. From a regional perspective, climate policy uncertainty has a stronger inhibitory effect on the ESG performance of enterprises in eastern China. This study provides valuable insights for both national climate policy formulation and corporate efforts to enhance ESG performance. Full article
(This article belongs to the Special Issue Systems Analysis of Enterprise Sustainability)
Show Figures

Figure 1

22 pages, 4190 KiB  
Article
A Consensus Reaching Process for Product Design Decision-Making by Integrating Intuitionistic Fuzzy Sets and Trust Network
by Yanpu Yang, Kai Zhang and Zijing Lei
Systems 2024, 12(11), 494; https://doi.org/10.3390/systems12110494 - 15 Nov 2024
Viewed by 581
Abstract
In the process of product design decision-making (PDDM), decision-makers (DMs) conventionally engage in discussions to evaluate design alternatives. Achieving a consistent result is essential for selecting optimal product design schemes, as it helps eliminate preference conflicts. However, uncertainties and ambiguities, along with the [...] Read more.
In the process of product design decision-making (PDDM), decision-makers (DMs) conventionally engage in discussions to evaluate design alternatives. Achieving a consistent result is essential for selecting optimal product design schemes, as it helps eliminate preference conflicts. However, uncertainties and ambiguities, along with the interrelationships among DMs, make it challenging to attain an acceptable consensus level in PDDM. To address this issue, intuitionistic fuzzy sets (IFSs) are introduced to capture DMs’ preferences regarding product design schemes, and a trust network is integrated to analyze DMs’ interrelationships. A double hierarchy linguistic term set (LTS) is employed to assess DMs’ relationships, and an incomplete trust network is supplemented by leveraging the transitivity principle, thereby determining DMs’ weights. By establishing a consensus measurement model, DMs contributing less to consensus are identified, and consensus optimization is achieved through the modification of DMs’ preferences or the calibration of their trust relationships. A consensus reaching process (CRP) for PDDM is proposed, and the technique for order preference by similarity to ideal solution (TOPSIS) is utilized to rank product design schemes after consensus is reached. A case study involving the decision-making process for a specific household disinfection machine design illustrates the efficacy of our method in achieving consensus by integrating vague PDDM data. Full article
Show Figures

Figure 1

28 pages, 14799 KiB  
Article
Improving Elementary Students’ Geometric Understanding Through Augmented Reality and Its Performance Evaluation
by Wernhuar Tarng, Jen-Kai Huang and Kuo-Liang Ou
Systems 2024, 12(11), 493; https://doi.org/10.3390/systems12110493 - 15 Nov 2024
Viewed by 554
Abstract
Augmented reality (AR) technology provides context-aware experiences by overlaying digital information onto the real world to enhance learning effectiveness and reduce cognitive load. This study aimed to develop an AR Mobile Learning System (ARMLS) to address the limitations of traditional teaching materials and [...] Read more.
Augmented reality (AR) technology provides context-aware experiences by overlaying digital information onto the real world to enhance learning effectiveness and reduce cognitive load. This study aimed to develop an AR Mobile Learning System (ARMLS) to address the limitations of traditional teaching materials and help elementary-school students learn geometric concepts. The ARMLS was designed based on the fifth-grade mathematics curriculum, covering topics such as definitions, geometric properties, different views of prisms and pyramids, and their relationships. A teaching experiment was conducted to compare students’ learning achievement, motivation, and cognitive load when using the ARMLS versus traditional teaching materials. This study adopted a quasi-experimental design, where four fifth-grade classes were selected from an elementary school in northern Taiwan as experimental subjects. A total of 66 students participated in the experiment, divided into two groups: 32 students from two classes as the experimental group (using the ARMLS) and 34 students from the other two classes as the control group (using traditional teaching materials). In the teaching experiment, data were collected through pre-tests, post-tests, and questionnaires. Achievement tests assessed learning effectiveness, while learning motivation and cognitive load were measured with standardized scales. System satisfaction was evaluated using a questionnaire. The Johnson–Neyman method determined the regions of significance in the analysis of covariance. Independent-sample t-tests evaluated differences in learning motivation and cognitive load between the groups, and descriptive statistics summarized system satisfaction responses. The results indicated that (1) the ARMLS enhanced the learning achievement among low- and moderate-achieving students, (2) there was no significant difference in learning motivation between the two groups, (3) the ARMLS helped reduce students’ cognitive load, and (4) most students expressed satisfaction with the ARMLS according to the questionnaire results. The ARMLS enhances engagement and deepens understanding by making abstract geometry topics more accessible. It effectively overcomes the limitations of traditional teaching materials, providing elementary students with an interactive, hands-on approach to learning geometric concepts. Full article
(This article belongs to the Special Issue Information Systems: Discipline, Critical Research and Education)
Show Figures

Figure 1

29 pages, 4783 KiB  
Article
How Does the Digitalization Strategy Affect Bank Efficiency in Industry 4.0? A Bibliometric Analysis
by Claudia Gherțescu, Alina Georgiana Manta, Roxana Maria Bădîrcea and Liviu Florin Manta
Systems 2024, 12(11), 492; https://doi.org/10.3390/systems12110492 - 15 Nov 2024
Viewed by 707
Abstract
This study conducts a detailed bibliometric analysis of the concept of bank efficiency, investigating its evolution in the scientific literature between 2000 and 2024 in the context of the digital transformation specific to the Industry 4.0 era. Using recognized databases, such as Web [...] Read more.
This study conducts a detailed bibliometric analysis of the concept of bank efficiency, investigating its evolution in the scientific literature between 2000 and 2024 in the context of the digital transformation specific to the Industry 4.0 era. Using recognized databases, such as Web of Science and Scopus, the research explores the main trends and themes in the field, as well as the impact of emerging technologies on bank efficiency. Eight major thematic clusters are identified, including “risk”, “‘performance”, “efficiency”, “competition”, “corporate governance” and “banking”, highlighting key dimensions of recent research. The co-citation analysis highlighted central authors like Berger, Sufian, and Casu, along with distinct thematic and regional clusters, underscoring the diversity of research directions in banking efficiency. The co-citation analysis shows the influence of leading institutions and authors, including “University Putra Malaysia”, “World Bank”, and “NBER, United States”, which have contributed significantly to the development of the literature. The results indicate that bank efficiency research is dynamic, multifunctional, and ever-expanding, providing an important foundation for future studies that will explore the challenges and opportunities for banks in the era of digitalization and sustainable development. Full article
(This article belongs to the Special Issue Strategic Management in Digital Transformation Era)
Show Figures

Figure 1

29 pages, 4009 KiB  
Article
An Inspiration Recommendation System for Automotive Styling Design Based on User Behavior Data and Group Preferences
by Wanxin Cai, Mingqing Yang and Li Lin
Systems 2024, 12(11), 491; https://doi.org/10.3390/systems12110491 - 14 Nov 2024
Viewed by 639
Abstract
Group preferences are crucial for Inspirational Solutions of Automotive Design (ISAD). However, sparse individual purchase behavior hinders the identification of group preferences. Therefore, a novel inspiration recommendation (IR) system based on multi-level mining of user behavior data is proposed. Firstly, the K-means algorithm [...] Read more.
Group preferences are crucial for Inspirational Solutions of Automotive Design (ISAD). However, sparse individual purchase behavior hinders the identification of group preferences. Therefore, a novel inspiration recommendation (IR) system based on multi-level mining of user behavior data is proposed. Firstly, the K-means algorithm is employed to cluster users based on a variety of features. The fixed association rule is then applied to filter and identify relevant subsets, forming the foundational basis for constructing a user portrait. The Nonlinear Bayesian Personalized Ranking (NBPR) is constructed to explore common preferences using explicit feedback. Finally, the item preference matrix is enriched with implicit feedback to compile a comprehensive recommendation list that caters to group preferences. Using a multi-user joint evaluation approach, we compare the performance of IR with baseline models across multiple metrics. This comparison demonstrates the robust reliability of the IR system and its ability to prioritize ISAD with preference-aligned groups. Our research overcomes data sparsity in the automotive recommendation system, providing a new method for embedding human elements in decision support systems. Full article
Show Figures

Figure 1

21 pages, 780 KiB  
Article
Enhancing Cybersecurity: Hybrid Deep Learning Approaches to Smishing Attack Detection
by Tanjim Mahmud, Md. Alif Hossen Prince, Md. Hasan Ali, Mohammad Shahadat Hossain and Karl Andersson
Systems 2024, 12(11), 490; https://doi.org/10.3390/systems12110490 - 14 Nov 2024
Viewed by 843
Abstract
Smishing attacks, a sophisticated form of cybersecurity threats conducted via Short Message Service (SMS), have escalated in complexity with the widespread adoption of mobile devices, making it increasingly challenging for individuals to distinguish between legitimate and malicious messages. Traditional phishing detection methods, such [...] Read more.
Smishing attacks, a sophisticated form of cybersecurity threats conducted via Short Message Service (SMS), have escalated in complexity with the widespread adoption of mobile devices, making it increasingly challenging for individuals to distinguish between legitimate and malicious messages. Traditional phishing detection methods, such as feature-based, rule-based, heuristic, and blacklist approaches, have struggled to keep pace with the rapidly evolving tactics employed by attackers. To enhance cybersecurity and address these challenges, this paper proposes a hybrid deep learning approach that combines Bidirectional Gated Recurrent Units (Bi-GRUs) and Convolutional Neural Networks (CNNs), referred to as CNN-Bi-GRU, for the accurate identification and classification of smishing attacks. The SMS Phishing Collection dataset was used, with a preparatory procedure involving the transformation of unstructured text data into numerical representations and the training of Word2Vec on preprocessed text. Experimental results demonstrate that the proposed CNN-Bi-GRU model outperforms existing approaches, achieving an overall highest accuracy of 99.82% in detecting SMS phishing messages. This study provides an empirical analysis of the effectiveness of hybrid deep learning techniques for SMS phishing detection, offering a more precise and efficient solution to enhance cybersecurity in mobile communications. Full article
Show Figures

Figure 1

19 pages, 1202 KiB  
Article
Human Resource Management in Complex Environments: A Viable Model Based on Systems Thinking
by Mario Aguilar-Fernández, Graciela Salgado-Escobar and Andrés David Barragán-Hernández
Systems 2024, 12(11), 489; https://doi.org/10.3390/systems12110489 - 14 Nov 2024
Viewed by 468
Abstract
Developing the company’s capacity to deal with changing environments means ceasing to see processes as a traditional and linear model. Therefore, the objective of this research is to apply VSM to HRM to show its complexity. It is qualitative research, which is carried [...] Read more.
Developing the company’s capacity to deal with changing environments means ceasing to see processes as a traditional and linear model. Therefore, the objective of this research is to apply VSM to HRM to show its complexity. It is qualitative research, which is carried out in two moments. The first consists of a literature review in the WoS, and the second, is the design of the model “MV-HRM”, based on the approach of complex adaptive systems, viable system model, soft system methodology, and holistic theory. The MV-HRM consists of five systems: (S1) HRM processes, (S2) information system (S4) operational control, (S4) strategic planning and (S5) governance. The model emphasizes the relationships and interactions it has with its immediate and future environment. Finally, the contribution of the research is to show another look and understanding of the functioning of HRM, in addition to awakening the interest of strategists to develop best practices that allow them to respond in an agile way to the dynamic and complex environment. Full article
Show Figures

Figure 1

24 pages, 927 KiB  
Article
Research on Citizens’ Intentions for Continued Usage of Mobile Government Services from the PPM Perspective
by Huiying Zhang and Zijian Zhu
Systems 2024, 12(11), 488; https://doi.org/10.3390/systems12110488 - 14 Nov 2024
Viewed by 417
Abstract
Provincial mobile government service platforms, represented by ‘Zheliban’ and ‘Yueshengshi’, have transformed the traditional way governments provide public services to citizens. Maintaining user engagement with these platforms has become a critical challenge in promoting the digitalization of public services. Despite the widespread adoption [...] Read more.
Provincial mobile government service platforms, represented by ‘Zheliban’ and ‘Yueshengshi’, have transformed the traditional way governments provide public services to citizens. Maintaining user engagement with these platforms has become a critical challenge in promoting the digitalization of public services. Despite the widespread adoption of mobile services, the characteristics influencing citizens’ intentions for continued usage of mobile government service platforms have not received sufficient attention in the academic literature. This study, based on the Information Systems Success Model (IS Theory) and Expectation Confirmation Theory (ECT), constructs a Push-Pull-Mooring (PPM) model from a dynamic perspective to examine factors influencing citizens’ continued usage intentions. The research findings indicate that the quality of mobile government service platforms has a significant positive impact on citizens’ continued usage intentions, with citizen satisfaction mediating the relationship between platform quality and continued usage intention. Furthermore, digital exclusion and platform user stickiness negatively moderate the mediating role of satisfaction. This study provides a comprehensive framework for explaining the pathways influencing citizens’ continued usage of mobile government service platforms, extends the theoretical boundaries of the PPM model, and contributes to research in related fields. The findings offer valuable insights for the government in optimizing and promoting mobile government services. Full article
Show Figures

Figure 1

19 pages, 3317 KiB  
Article
Multi-Step Parking Demand Prediction Model Based on Multi-Graph Convolutional Transformer
by Yixiong Zhou, Xiaofei Ye, Xingchen Yan, Tao Wang and Jun Chen
Systems 2024, 12(11), 487; https://doi.org/10.3390/systems12110487 - 13 Nov 2024
Viewed by 654
Abstract
The increase in motorized vehicles in cities and the inefficient use of parking spaces have exacerbated parking difficulties in cities. To effectively improve the utilization rate of parking spaces, it is necessary to accurately predict future parking demand. This paper proposes a deep [...] Read more.
The increase in motorized vehicles in cities and the inefficient use of parking spaces have exacerbated parking difficulties in cities. To effectively improve the utilization rate of parking spaces, it is necessary to accurately predict future parking demand. This paper proposes a deep learning model based on multi-graph convolutional Transformer, which captures geographic spatial features through a Multi-Graph Convolutional Network (MGCN) module and mines temporal feature patterns using a Transformer module to accurately predict future multi-step parking demand. The model was validated using historical parking transaction volume data from all on-street parking lots in Nanshan District, Shenzhen, from September 2018 to March 2019, and its superiority was verified through comparative experiments with benchmark models. The results show that the MGCN–Transformer model has a MAE, RMSE, and R2 error index of 0.26, 0.42, and 95.93%, respectively, in the multi-step prediction task of parking demand, demonstrating its superior predictive accuracy compared to other benchmark models. Full article
(This article belongs to the Section Artificial Intelligence and Digital Systems Engineering)
Show Figures

Figure 1

22 pages, 4889 KiB  
Article
Research on Forecasting Sales of Pure Electric Vehicles in China Based on the Seasonal Autoregressive Integrated Moving Average–Gray Relational Analysis–Support Vector Regression Model
by Ru Yu, Xiaoli Wang, Xiaojun Xu and Zhiwen Zhang
Systems 2024, 12(11), 486; https://doi.org/10.3390/systems12110486 - 13 Nov 2024
Viewed by 425
Abstract
Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive integrated moving average (SARIMA) model was [...] Read more.
Aiming to address the complexity and challenges of predicting pure electric vehicle (EV) sales, this paper integrates a time series model, support vector machine and combined model to forecast EV sales in China. Firstly, a seasonal autoregressive integrated moving average (SARIMA) model was constructed using historical EV sales data, and the model was trained on sales statistics to obtain forecasting results. Secondly, variables that were highly correlated with sales were analyzed using gray relational analysis (GRA) and utilized as input parameters for the support vector regression (SVR) model, which was constructed to optimize sales predictions for EVs. Finally, a combined model incorporating different algorithms was verified against market sales data to explore the optimal sales prediction approach. The results indicate that the SARIMA-GRA-SVR model with the squared prediction error and inverse method achieved the best predictive performance, with MAPE, MAE and RMSE values of 12%, 1.45 and 2.08, respectively. This empirical study validates the effectiveness and superiority of the SARIMA-GRA-SVR model in forecasting EV sales. Full article
(This article belongs to the Topic Data-Driven Group Decision-Making)
Show Figures

Figure 1

17 pages, 1489 KiB  
Review
Industrial Organizations Adapting to the Novel EU Taxonomy While Developing Socio-Technical Systems: A Literature Review
by Henri Giudici, Fabio Bento and Kristin Falk
Systems 2024, 12(11), 485; https://doi.org/10.3390/systems12110485 - 13 Nov 2024
Viewed by 603
Abstract
The EU taxonomy defines criteria for economic activities committed to a net zero emission by 2050, and other environmental goals. Its overall goal is to direct investments to economic activities aligned with the European Green Deal and, thereby, classified as sustainable. For industrial [...] Read more.
The EU taxonomy defines criteria for economic activities committed to a net zero emission by 2050, and other environmental goals. Its overall goal is to direct investments to economic activities aligned with the European Green Deal and, thereby, classified as sustainable. For industrial organizations, there is an urgent need to develop adaptive capabilities to meet the requirements set by the new taxonomy. The present scoping review contributes by analyzing academic publications on this topic through the lens of a complex science and systems approach. It analyzes academic publications on the EU taxonomy, related to industrial organizations and their development of socio-technical systems. At the initial stage, 526 scientific publications were retrieved from three web repositories (Scopus, World of Science, and Scholar). Only eight publications matched the selection criteria and constitute the data of this study. Results indicate that there is a lack of holistic perspectives and an unbalanced system response to the EU taxonomy. Adopting a systems approach can help industrial organizations navigate the intricate dynamics of a sustainable transition, striking a balance between adhering to the EU taxonomy and maintaining market competitiveness. The findings emphasize the importance of trans-disciplinary approaches and the need for agile and adaptive ways of learning inside industrial organizations. Full article
Show Figures

Figure 1

14 pages, 980 KiB  
Article
A Regional Efficiency Assessment of Long-Term Care Services in Taiwan
by Ming-Chung Chang, Jin-Li Hu and Chih-Wei Liu
Systems 2024, 12(11), 484; https://doi.org/10.3390/systems12110484 - 13 Nov 2024
Viewed by 382
Abstract
Taiwan is currently an aging society and will be a super-aging society in the near future. The purpose of this research is to use two models of data envelopment analysis (DEA)—the slacks-based measurement (SBM) model and the dynamic slacks-based measurement (DSBM) model—to analyze [...] Read more.
Taiwan is currently an aging society and will be a super-aging society in the near future. The purpose of this research is to use two models of data envelopment analysis (DEA)—the slacks-based measurement (SBM) model and the dynamic slacks-based measurement (DSBM) model—to analyze the efficiency of long-term care (LTC) in Taiwan. This analysis aims to explore the current situation of LTC in Taiwan and provide policy recommendations for LTC. The computation empirical result on the LTC efficiency score presents that the DSBM model exhibits higher efficiency than the SBM model after considering the carry-over variable in the former model. The result from the SBM model indicates that Taiwan’s outlying islands display the worst LTC efficiency, but this result does not appear in the DSBM model. Lastly, these two models both indicate that the number of elderly people being serviced in institutions exhibits higher efficiency and lower slack than those serviced in homes in 2017 and 2018. This paper concludes that the DEA approach is a viable method for examining the performance of the LTC services system as Taiwan approaches a super-aged society. Full article
Show Figures

Figure 1

21 pages, 4031 KiB  
Article
Barriers Hindering the Successful Deployment of GLSS in Organizations: Findings from an Empirical Study
by Monserrat Perez-Burgoin, Jorge Limon-Romero, Diego Tlapa, Armando Perez-Sanchez, Guilherme Tortorella and Yolanda Baez-Lopez
Systems 2024, 12(11), 483; https://doi.org/10.3390/systems12110483 - 13 Nov 2024
Viewed by 526
Abstract
Given the growing interest in manufacturing’s environmental effects, understanding the impact of improvement strategies on sustainability is critical. This study focuses on the implementation of Green Lean Six Sigma (GLSS) in the Mexican manufacturing sector, an area where research is limited, particularly concerning [...] Read more.
Given the growing interest in manufacturing’s environmental effects, understanding the impact of improvement strategies on sustainability is critical. This study focuses on the implementation of Green Lean Six Sigma (GLSS) in the Mexican manufacturing sector, an area where research is limited, particularly concerning the barriers to successful outcomes. Through the development of a structural equation model using SPSS software,,version 25, 64-bit edition, key barriers such as lack of investment, insufficient participation, ineffective infrastructure, and lack of training were identified as the primary challenges to GLSS implementation. This study also highlights the interrelationships between these barriers and their influence on project success, emphasizing the role of insufficient participation as a critical mediating factor. This insight allows for a deeper understanding of how these obstacles affect the benefits of GLSS initiatives. The findings offer valuable guidance for researchers and practitioners, providing tools to enhance the effectiveness of GLSS implementation in the manufacturing industry. The study’s marginal contribution lies in its identification of key barriers and the introduction of insufficient participation as a mediating factor, offering a more comprehensive understanding of their impact on project success and contributing to improved GLSS adoption strategies. Full article
Show Figures

Figure 1

28 pages, 6286 KiB  
Article
An Evolutionary Game Analysis of China’s Power Battery Export Strategies Under Carbon Barriers
by Chunsheng Li, Xuanyu Ji, Kangye Tan, Yumeng Wu and Fang Xu
Systems 2024, 12(11), 482; https://doi.org/10.3390/systems12110482 - 12 Nov 2024
Viewed by 669
Abstract
With the continuous evolution of international trade, the global market has been steadily expanding while also facing increasing challenges, particularly in relation to the introduction of environmental policies such as carbon barriers. Our research explores how China’s power battery manufacturers can adapt their [...] Read more.
With the continuous evolution of international trade, the global market has been steadily expanding while also facing increasing challenges, particularly in relation to the introduction of environmental policies such as carbon barriers. Our research explores how China’s power battery manufacturers can adapt their export strategies to the EU’s carbon barrier policies. Additionally, we examine the roles of government regulations, research institutions, and manufacturers in either facilitating or hindering compliance with carbon reduction objectives. Using evolutionary game theory, we construct models involving government entities, manufacturers, and research institutions to systematically analyze market evolution, strategic interactions, and outcomes among these stakeholders. Our analysis focuses on understanding the competitive dynamics faced by exporting countries under stringent environmental policies and provides strategic insights to guide export strategies. Taking the EU’s carbon barrier policy as a case study, we explore Chinese battery manufacturers’ adaptive strategies and decision-making processes as they respond to shifting market demands and regulatory environments. The findings not only offer valuable insights into exporting countries but also provide policymakers with information on international trade and industrial policy design. Furthermore, we validate our model through numerical simulations and conduct sensitivity analyses on key parameters. The results underscore the importance of governmental adoption of punitive and incentive policies, revealing their substantial impact on stakeholder behavior. Additionally, the study highlights how participants’ pre-cooperation losses and post-cooperation gains influence participation rates and the speed at which stakeholder consensus is reached. By offering a novel approach with which to address carbon barrier challenges, this research contributes valuable perspectives on environmental regulations’ strategic and policy implications in global trade. Full article
(This article belongs to the Special Issue New Trends in Sustainable Operations and Supply Chain Management)
Show Figures

Figure 1

34 pages, 9001 KiB  
Article
Advanced System for Optimizing Electricity Trading and Flow Redirection in Internet of Vehicles Networks Using Flow-DNET and Taylor Social Optimization
by Radhika Somakumar, Padmanathan Kasinathan, Rajvikram Madurai Elavarasan and G. M. Shafiullah
Systems 2024, 12(11), 481; https://doi.org/10.3390/systems12110481 - 12 Nov 2024
Viewed by 692
Abstract
The transportation system has a big impact on daily lifestyle and it is essential to energy transition and decarbonization initiatives. Stabilizing the grid and incorporating sustainable energy sources require technologies like the Internet of Energy (IoE) and Internet of Vehicles (IoV). Electric vehicles [...] Read more.
The transportation system has a big impact on daily lifestyle and it is essential to energy transition and decarbonization initiatives. Stabilizing the grid and incorporating sustainable energy sources require technologies like the Internet of Energy (IoE) and Internet of Vehicles (IoV). Electric vehicles (EVs) are essential for cutting emissions and reliance on fossil fuels. According to research on flexible charging methods, allowing EVs to trade electricity can maximize travel distances and efficiently reduce traffic. In order to improve grid efficiency and vehicle coordination, this study suggests an ideal method for energy trading in the Internet of Vehicles (IoV) in which EVs bid for electricity and Road Side Units (RSUs) act as buyers. The Taylor Social Optimization Algorithm (TSOA) is employed for this auction process, focusing on energy and pricing to select the best Charging Station (CS). The TSOA integrates the Taylor series and Social Optimization Algorithm (SOA) to facilitate flow redirection post-trading, evaluating each RSU’s redirection factor to identify overloaded or underloaded CSs. The Flow-DNET model determines redirection policies for overloaded CSs. The TSOA + Flow-DNET approach achieved a pricing improvement of 0.816% and a redirection success rate of 0.918, demonstrating its effectiveness in optimizing electricity trading and flow management within the IoV framework. Full article
Show Figures

Figure 1

17 pages, 7758 KiB  
Article
An Autotuning Hybrid Method with Bayesian Optimization for Road Edge Extraction in Highway Systems from Point Clouds
by Jingxu Chen, Qiru Cao, Mingzhuang Hua, Jinyang Liu, Jie Ma, Di Wang and Aoxiang Liu
Systems 2024, 12(11), 480; https://doi.org/10.3390/systems12110480 - 11 Nov 2024
Viewed by 615
Abstract
In transportation infrastructure systems, feature images and spatial characteristics are generally utilized as complementary elements derived from point clouds for road edge extraction, but the involvement of one or more hyperparameters in each makes the extraction complicated. This study proposes an autotuning hybrid [...] Read more.
In transportation infrastructure systems, feature images and spatial characteristics are generally utilized as complementary elements derived from point clouds for road edge extraction, but the involvement of one or more hyperparameters in each makes the extraction complicated. This study proposes an autotuning hybrid method with Bayesian optimization for road edge extraction in highway systems. The hybrid method combines the strengths of 2D feature images and 3D spatial characteristics while also automatically tuning the hyperparameter combination using Bayesian optimization. The hyperparameters encompass high and low pixel gradient thresholds, neighborhood radius, and normal vector threshold. Later, the point cloud dataset of national highways in Henan Province, China, is taken as the case study to evaluate the performance of the proposed method against three benchmark methods in two typical road scenarios: straight and curved edges. Experimental results show that the proposed method outperforms the benchmarks in detection quality and accuracy. It can serve as a decision-making tool to complement traditional manual road surveying, enabling efficient and automated road edge extraction in highway systems. Full article
Show Figures

Figure 1

Previous Issue
Next Issue
Back to TopTop