Systems, Volume 12, Issue 2 (February 2024) – 29 articles

This paper aims to tackle the problem of low accuracy in predicting financial distress in Chinese industrial enterprises, attributable to data imbalance and insufficient information. It utilizes annual data on systemic risk indicators and financial metrics of Chinese industrial enterprises listed on the China’s A-share market between 2008 and 2022 to construct the adaptive weighted XGBoost-Bagging model for corporate financial distress prediction. Empirical findings demonstrate that systemic risk indicators possess predictive potential independent of traditional financial information, rendering them valuable non-financial early warning indicators for China’s industrial sector; moreover, they help to enhance the predictive accuracy of various comparative models. The adaptive weighted XGBoost-Bagging model incorporating systemic risk indicators effectively addresses challenges arising from data imbalance and information scarcity, significantly improving the accuracy of financial distress prediction in Chinese industrial enterprises under the 2015 Chinese stock market crash, the Sino-US trade friction, and the COVID-19 epidemic; as such, it can be used as an efficient risk early warning tool for China’s industrial sector. Full article

This paper considers the agency selling channel in a supply chain under platform service investment. We construct Stackelberg game models to study the impact of the manufacturer’s encroachment strategy on supply chain members. Research results indicate that the encroachment strategy always has a positive impact at the levels of the manufacturer and platform service, which should dynamically change in response to the manufacturer’s action; the platform may actively implement a service strategy without encroachment, while the platform should be cautious in providing services to avoid backlash when encroachment occurs; the high commission rate may prompt the platform to increase the service effort and hinder manufacturer encroachment; when the channel substitution rate is high, both the manufacturer and platform may suffer from it and hence they should slow down their strategy implementation and consider cooperation; when the elasticity coefficient is large and the service cost is high, it may hinder the platform from providing services and the manufacturer may take the opportunity to encroach and thus seize the market. Full article

With the promotion of the “carbon neutrality” and “carbon peak” initiatives, green credit plays an important role in helping enterprises to change their high-pollution, high-energy-consumption production methods and establishing a sound green, low-carbon, and circular economic system. This study used spatial correlation analysis and a fixed effects SDM model to examine the spatiotemporal and causal relationship between green credit levels and enterprise green technology innovation in 271 prefecture level cities in China from 2013 to 2021. It found that (1) green credit and green technology innovation levels are both highest in the eastern region, followed by the central region, and exhibit spatial correlation characteristics. The main types of agglomeration are high–high and low–low agglomeration. (2) Green credit has a significant enhancing effect on green technology innovation in enterprises, and this conclusion still holds after robustness and endogeneity tests. (3) There is significant regional heterogeneity in the impact of green credit on green technology innovation, mainly concentrated in the central and western regions. (4) Green credit can significantly increase enterprise R&D investment and enhance the level of green technology innovation through this channel. Finally, some policy implications are provided to the decision-making departments that can be used for reference. Full article

In response to Boschma’s concern that the implications of relatedness- and unrelatedness-based diversification strategies lack empirical evidence at disaggregated levels and in the context of the Global South, this study generates a unique dataset at the city level and explores how these smart specialization strategies (S3) may explain digital industry innovations within a specific regional innovation system, i.e., the Yangtze River Delta, China. The findings reveal that both relatedness density and knowledge complexity play a positive role in explaining digital industry innovations. However, the relationship between relatedness and knowledge complexity and its interactive effects on innovation performance are less straightforward. In our study, we found that efficient cooperation between relatedness and complexity can only be achieved if the level of government intervention is moderate. Therefore, the discussion of S3 focuses on more than the dichotomous argument between relatedness and unrelatedness. Many socio-economic factors also impact the effectiveness of these theoretical components within different innovation systems, which are largely overlooked by present studies. Full article

Shared autonomous electric vehicles (SAEVs) can offer safer, more efficient, and more environmentally friendly real-time mobility services with advanced autonomous driving technologies. In this study, a multi-agent-based simulation model considering SAEVs’ vehicle range and charging behavior is proposed. Based on real-world datasets from the Luohu District in Shenzhen, China, various scenarios with different fleet sizes, charging rates, and vehicle ranges are established to evaluate the impact of these parameters on parking demand, charging demand, vehicle miles traveled (VMT), and response time in the era of SAEVs. The results show there would be much more charging demand than parking demand. Moreover, a larger fleet size and longer vehicle range would lead to more parking demand, more charging demand, and more VMT while increasing the charging rate can dramatically reduce the charging demand and VMT. Average response time can be reduced by increasing the fleet size or the charging rate, and a larger vehicle range leads to longer response time due to the longer time spent recharging. It is worth noting that the VMT generated from relocating from the previous request destination to the origin of the upcoming request accounts for nearly 90% of the total VMT, which should be addressed properly with appropriate scheduling. A charging policy considering current requests and the availability of charging stations was proposed and verified in terms of reducing the response time by 2.5% to 18.9%. Full article

Although previous studies have predominantly dealt with innovation ambidexterity, they have only focused on a single innovation activity and overlooked the interaction of innovation activities. Drawing on organizational ambidexterity theory, this study established four types of innovation configurations: dual exploration (technology exploration and business model exploration), business model leveraging (technology exploration and business model exploitation), technology leveraging (technology exploitation and business model exploration), and dual exploitation (technology exploitation and business model exploitation). Using the panel data of 613 listed manufacturing firms in China, this study examined whether and how configurations of ambidextrous innovation affect firm performance in the context of digital transformation. Empirical results provide evidence that a dual exploration and technology leveraging strategy has a positive impact on firm performance, while a dual exploitation and business model leveraging strategy has the opposite effect and is subject to the moderating influence of the level of digitalization. Under high levels of digitalization, the positive effect of the dual exploration strategy on firm performance becomes more significant, while the effects of others are weakened. This study contributes to the organizational ambidexterity literature by providing a finer-grained understanding of the effect of ambidextrous innovation from a configurational perspective. This study also contributes to the digitalization transformation literature by revealing the moderating role of digitalization. Full article

Through the examination of the ecological consequences of human actions, policymakers are able to distinguish certain areas in which resource use can be increased and the generation of waste diminished. This study examines the effects of foreign direct investment, gross domestic product, industrialization, renewable energy consumption, and urban population on the ecological footprints in 131 countries between 1997 and 2020. The objective of this study is to establish a thorough understanding of the relationship between these variables and ecological footprints while considering temporal changes from economic and environmental aspects. The analysis of a substantial dataset encompassing many countries aims to uncover recurring patterns and trends that can provide valuable information for the formulation of policies and strategies pertaining to sustainable development on a global level. The study fills a significant gap in the knowledge on the ecological impact of different variables, providing a nuanced understanding of the interdependencies among these factors, thus guiding sustainable development strategies, and promoting global sustainability. The study utilizes quantile regression analysis, a nonparametric estimator, to estimate consistent coefficients. The statistical analysis reveals that FDI, urbanization, and GDP have statistically significant and positive effects on ecological footprints. Industrialization and renewable energy consumption show significant and negative relationships with ecological footprints. The findings of this study contribute to the understanding of the relationships among these variables and provide insight to inform policy and decision-making efforts focused on reducing ecological consequences and advancing sustainable development goals. Full article

Embedding collaboration networks in the context of open innovation can facilitate firm innovation. Previous studies have not considered the impact of multilevel network structural embedding on firm innovation. In this study, organizational collaboration networks, knowledge networks, and urban collaboration networks are viewed as systems to explore their impact on innovation quantity and innovation quality. We validate the research hypotheses using data from Chinese high-tech firms in the field of artificial intelligence and intelligent manufacturing equipment. The results indicate that structural holes occupied by firms in organizational collaboration networks can increase the innovation quantity and have a U-shaped effect on innovation quality. Knowledge network structural holes and urban collaboration network structural holes moderate the relationship between organizational collaboration network structural holes and innovation quantity and quality. Our findings will help firms to efficiently utilize the advantages of multilevel network structural holes to improve the innovation quantity and innovation quality. Full article

Drug distribution performed through hospital pharmacies facilitates public expenditure savings but incurs higher social costs for patients and caregivers. The widespread presence of community pharmacies could support patient access while also improving drug distribution. The implementation of prescriptive data analyses as constrained optimization to achieve specific objectives, could be also applied with good results in the healthcare context. Assuming the perspective of the Italian National Healthcare Service, the present study, built upon existing research in this field, proposes a decision support tool that is able to define which self-administered drugs for chronic diseases should be distributed by community pharmacies, answering to critical challenges in the case of future pandemics and healthcare emergencies, while also providing suggestions for the institutional decision-making process. Moreover, the tool aids in determining the optimal setup of the drug distribution network, comparing centralized (hospital pharmacies) and decentralized (community pharmacies) approaches, as well as their economic and social implications. Full article

Air pollution severely threatens people’s health and sustainable economic development. In the era of the digital economy, modern information technology is profoundly changing the way governments govern, the production mode of enterprises, and the living behavior of residents. Whether digital technology can bring ecological welfare needs to be further studied. Based on panel data from 269 Chinese cities from 2006 to 2021, this study empirically examines the impact of digital technology on air pollution by using the two-way fixed effect model. The results show that digital technology will significantly reduce the concentration of fine particles in the air and help protect the atmospheric environment. The results are still valid after using the interactive fixed effect model and the two-stage least square method after the robustness test and causality identification. Digital technology can also reduce the air pollution by promoting green innovation, improving energy efficiency, and easing market segmentation. The effect of digital technology on reducing the concentration of fine particles in the air is heterogeneous. Digital technology plays a more substantial role in reducing pollution in resource-based cities and areas with a high degree of modernization of the commodity supply chain. The positive effect of digital technology in reducing air pollution is affected by the amount of air pollutants emitted. When the concentration of PM_2.5 in the air is high, the role of digital technology in protecting the atmosphere will be strongly highlighted. This research is a beneficial exploration of protecting the atmospheric environment by using digital technology while building an ecological civilization society. The conclusion will help urban managers, the public, and business operators entirely use modern equipment such as 5G, remote sensing, and the Internet of Things in their respective fields to protect the atmospheric environment. Full article

In psychotherapy research, the first applications of dynamical systems research (DSR) date back to the 1990s. Over time, DSR has developed three main lines of research: the study of oscillations in synchronization; the study of oscillations between stability and flexibility of process variables (S–F oscillations); the mathematical modeling to analyze the evolution of psychotherapy process. However, the connections among the empirical results and their implications for psychotherapy practice are unclear. For this reason, for the first time in the literature, this work carries out a comprehensive review of all three lines of research, including the main scientific contributions from the 1990s to the present day. For each line of research, the work critically analyzes the results, proposes future developments, and underlines the connections between empirical results and implications for psychotherapy practice. Furthermore, the work highlights the model of change that emerges from the empirical results, and its clinical correlates. In the conclusions, the author summarizes the results and the evolution of psychotherapy process in accordance with the DSR. Full article

High-tech manufacturing enterprises, as innovative entities, are a key focus of national attention. Currently, such enterprises are facing both internal governance pressure and external institutional pressure. Unlike traditional studies that mostly use regression equations, this article uses the fuzzy-set qualitative comparative analysis method to examine how high-tech manufacturing enterprises can coordinate their internal governance mechanisms and external institutional pressures to achieve optimal innovation. This improves the complex mechanism of the multiple factors jointly explaining corporate innovation, and also helps to elucidate the nonlinear relationship between internal governance factors, external institutional factors, and corporate innovation, effectively enriching research methods and results. However, there has not been any research on the issue of enterprise innovation from the perspective of coordinating the two, which urgently needs to be addressed. This article examines how high-tech manufacturing enterprises can reconcile their internal governance mechanisms with external institutional pressures to achieve optimal innovation. The results showed that (1) a single factor cannot constitute the necessary conditions for innovation in high-tech manufacturing enterprises, but executive and shareholder governance have universality in the innovation in high-tech manufacturing enterprises; (2) in the absence of political advantages, high-tech manufacturing enterprises should focus on the coordinated development of internal governance, making board, executive, and shareholder governance the core conditions for innovative development; (3) with political advantages as the main focus and market attention as a supplement, high-tech manufacturing enterprises promote innovative development by combining executive and shareholder governance. This finding indicates a significant substitution effect between government legitimacy and board governance, and confirms that the importance of obtaining government legitimacy for high-tech manufacturing innovation is higher than market legitimacy. This article enriches the research on enterprise innovation by linking internal corporate governance with external institutional pressure, expands the research on the coordination relationship between institutional pressure and corporate governance, and has enlightening significance in revealing the collaborative path for innovation in high-tech manufacturing enterprises. Full article

Manufacturing companies are always looking for ways to outperform their competitors. They are constantly trying to improve their efficiency and reduce costs. One method that improves efficiency and maximises the availability of production equipment is total productive maintenance (TPM), which is a lean optimisation philosophy tool that focuses on the optimisation of maintenance. Although TPM is known for improving maintenance, there are many obstacles to its successful implementation. Failure to properly implement TPM can result in additional costs and lost time, and it can have a negative impact on employees. For these reasons, a survey was prepared and conducted among several companies, each involved in a different field of work and having a different number of employees. The main findings of this research are the key factors that can negatively impact the implementation of TPM and lean tools in general, as well as suggestions for improvements that can ensure their successful implementation and sustainability. An analysis was conducted based on the size of each company as well as the job roles within them. The study covers issues that may arise during the implementation of TPM and other lean tools at all levels of the hierarchy in an enterprise and provides guidance on how to manage situations that may prevent the successful application of TPM. Full article

Furniture production is a specific industrial sector with a high human labor demand, a wide range of materials processed, and short production runs caused by high customization of end products. The difficulty of measuring the aesthetic requirements of customers is also specific to furniture. This review of academic papers identifies and explains effective quality management strategies in furniture production. The reviewed literature highlights a range of quality management methodologies, including concurrent engineering (CE), total quality management (TQM), lean manufacturing, lean six sigma, and kaizen. These strategies encompass a variety of pro-quality tools, such as 5S, statistical process control (SPC), quality function deployment (QFD), and failure mode and effects analysis (FMEA). The strengths of these quality management strategies lie in their ability to enhance efficiency, reduce waste, increase product diversity, and improve product quality. However, the weaknesses concern implementation challenges and the need for culture change within organizations. Successful quality management in furniture production requires tailoring strategies to the specific context of the furniture production industry. Additionally, the importance of sustainability in the furniture industry is emphasized, which entails incorporating circular economy principles and resource-efficient practices. The most important finding from the literature analysis is that early detection and correction of poor quality yields the most beneficial outcomes for the manufacturer. Therefore, it is essential to strengthen the rigor of quality testing and analysis during the early stages of product development. Consequently, a deep understanding of consumer perspectives on required furniture quality is crucial. The review identified two research gaps: (1) the impact of unnecessary product over-quality on the efficiency of furniture production and (2) the influence of replacing CAD drawings with a model-based definition (MBD) format on quality management in furniture production. Full article

Artificial intelligence, augmented, virtual, and mixed reality applications are improving business tools to increase their efficiency and ability to innovate. Technological innovation offers creative opportunities, but each generation values these advances differently. This study analysed the intergenerational differences and their leadership styles. The research questions are as follows: what are the main characteristics of each generation? And what leadership style is most appropriate for managing generational diversity in companies? Firstly, the main characteristics of each generation—Boomers, Generation X, Millennials, Generation Z, and Generation Alpha—were identified. Secondly, the most representative leadership styles of each generation were analysed. And thirdly, a proposal for a leadership style that can be used to better manage the intergenerational needs and technological demands of companies was presented. The development of leadership styles that take account of all generations can support economic growth and the creation of innovative and sustainable industries, as well as improve social welfare. Full article

Nowadays, the importance of logistics management has been increasingly realized in industry and society. However, current educational approaches in logistics management seem unable to effectively equip students with the necessary skills to cope with practical issues after graduation. Recently, contest-based education has attracted logistics management educators’ attention, but how it can be effectively utilized in this discipline is largely unclear. To fill this gap, this study followed a system approach and analyzed the factors influencing student performance in logistics management contests in China using interpretive structural modelling (ISM) and Matrice d’ Impacts Croisés-Multiplication Appliquée á un Classement (MICMAC). The results suggest that the driving forces for improving student performance in contests are the instructors’ encouragement and their previous experience in instructing contests. Also, the contestants’ previous experience in academic contests, team leadership, and effectiveness of communication between instructors and contestants are critical influencing factors. Based on the results, the educational strategies for effective utilization of contest-based education in logistics management are discussed. This study contributes to the existing literature by using a system modeling approach to clarify the mechanisms of contest-based education adoption in logistics management as well as informing university teachers and higher education institutes about strategies to improve their education quality. Full article

The advancements in human-centered artificial intelligence (HCAI) systems for Industry 5.0 is a new phase of industrialization that places the worker at the center of the production process and uses new technologies to increase prosperity beyond jobs and growth. HCAI presents new objectives that were unreachable by either humans or machines alone, but this also comes with a new set of challenges. Our proposed method accomplishes this through the knowlEdge architecture, which enables human operators to implement AI solutions using a zero-touch framework. It relies on containerized AI model training and execution, supported by a robust data pipeline and rounded off with human feedback and evaluation interfaces. The result is a platform built from a number of components, spanning all major areas of the AI lifecycle. We outline both the architectural concepts and implementation guidelines and explain how they advance HCAI systems and Industry 5.0. In this article, we address the problems we encountered while implementing the ideas within the edge-to-cloud continuum. Further improvements to our approach may enhance the use of AI in Industry 5.0 and strengthen trust in AI systems. Full article

Artificial intelligence (AI) is advancing across technology domains including healthcare, commerce, the economy, the environment, cybersecurity, transportation, etc. AI will transform healthcare systems, bringing profound changes to diagnosis, treatment, patient care, data, medicines, devices, etc. However, AI in healthcare introduces entirely new categories of risk for assessment, management, and communication. For this topic, the framing of conventional risk and decision analyses is ongoing. This paper introduces a method to quantify risk as the disruption of the order of AI initiatives in healthcare systems, aiming to find the scenarios that are most and least disruptive to system order. This novel approach addresses scenarios that bring about a re-ordering of initiatives in each of the following three characteristic layers: purpose, structure, and function. In each layer, the following model elements are identified: 1. Typical research and development initiatives in healthcare. 2. The ordering criteria of the initiatives. 3. Emergent conditions and scenarios that could influence the ordering of the AI initiatives. This approach is a manifold accounting of the scenarios that could contribute to the risk associated with AI in healthcare. Recognizing the context-specific nature of risks and highlighting the role of human in the loop, this study identifies scenario s.06—non-interpretable AI and lack of human–AI communications—as the most disruptive across all three layers of healthcare systems. This finding suggests that AI transparency solutions primarily target domain experts, a reasonable inclination given the significance of “high-stakes” AI systems, particularly in healthcare. Future work should connect this approach with decision analysis and quantifying the value of information. Future work will explore the disruptions of system order in additional layers of the healthcare system, including the environment, boundary, interconnections, workforce, facilities, supply chains, and others. Full article

Research focuses mainly on the impact of automation on employment and wages but pays little attention to its impact on employee job satisfaction, especially in the context of the Global South. Using survey data from China, this article investigates the impact of automation on employee job satisfaction due to the effects of job improvement and position replacement stress. The results indicate that automation can improve the job satisfaction of individual employees but reduces the job satisfaction of employees with a position that can be replaced easily by automation. The improvement and replacement effects coexist within the impact of automation. Through a structural equation model, this article finds that the improvement effect arises from an increase in job income, safety, and ability, whereas replacement stress is produced through the mediating effect of job stress and boredom. The heterogeneity analysis shows that the improvement effect is present in young employees with low job skills, position competency, and experience requirements, while replacement stress occurs in middle-aged and elderly employees with high job skills and high position competency and experience requirements. Our study provides evidence for the construction of an internal labor market in enterprises and labor policy interventions in the digital age. Full article

Despite a profusion of literature on complex adaptive system (CAS) definitions, it is still challenging to definitely answer whether a given system is or is not a CAS. The challenge generally lies in deciding where the boundaries lie between a complex system (CS) and a CAS. In this work, we propose a novel definition for CASs in the form of a concise, robust, and scientific algorithmic framework. The definition allows a two-stage evaluation of a system to first determine whether it meets complexity-related attributes before exploring a series of attributes related to adaptivity, including autonomy, memory, self-organisation, and emergence. We demonstrate the appropriateness of the definition by applying it to two case studies in the medical and supply chain domains. We envision that the proposed algorithmic approach can provide an efficient auditing tool to determine whether a system is a CAS, also providing insights for the relevant communities to optimise their processes and organisational structures. Full article

With the continuous development of digital technology, the widespread use of virtual spokespersons to promote city images is becoming increasingly prevalent. This study responds to this trend by employing a factor analysis and entropy weight methodology to explore the different dimensions and priorities in shaping the image of virtual city spokespersons in China. The aim is to offer insights into the design strategies and directions for shaping the image of virtual city spokespersons. For the research, we first conducted a literature review and semi-structured interviews to investigate the requirements of users in mainland China and Hong Kong regarding the image shaping of virtual city spokespersons. Building upon this groundwork, a questionnaire was designed and distributed, and it successfully gathered 512 valid responses. Subsequently, a factor analysis was utilized to identify eight key dimensions in shaping the images of Chinese virtual city spokespersons: “Design elements”, “Anthropomorphism”, “Evolutionary”, “Emotionalization”, “Narrativity”, “Culturalism”, “Interactivity”, and “Reliability”. Then, the entropy weighting method was applied to analyze the weights of each indicator within these dimensions. The results revealed that “Design elements” have the highest priority in shaping the image of virtual city spokespersons, followed by “Anthropomorphism”, “Emotionalization”, “Evolutionary”, “Culturalism”, “Narrativity”, “Reliability”, and “Interactivity”. Based on these findings, a series of design optimization strategies are proposed, including but not limited to shaping visually appealing images aligned with user perceptions, establishing emotional connections with users, and meeting the functional experience needs of users. These strategies not only contribute to the image shaping of virtual city spokespersons, but also provide vital guidance for innovative directions in promoting the publicity and marketing of Chinese cities. Full article

When confronted with rainstorms and flood disturbances, the operational processes of urban metro systems demonstrate vulnerabilities to attacks, inadequate resistance, and sluggish recovery characteristics. The flood resilience of UMS operational processes requires urgent enhancements. This paper aims to enhance the flood resilience of urban metro operation processes by proposing a three-stage PEL resilience enhancement framework: prevention resilience, response resilience, and learning resilience. Additionally, it summarizes the influencing factors on UMS flood resilience from five dimensions: natural-physical-social-management-economic (NPSME). By employing system dynamics as a simulation tool, this study elucidates the logical interconnections among these influential factors. Furthermore, by utilizing economic change conditions as an illustrative example, it effectively simulates the response characteristics of both standardized benchmark scenarios and economic change scenarios. Based on these simulation results, corresponding strategies for flood resilience enhancement are proposed to offer valuable insights for metro operation management. The Nanjing metro system was taken as a case study, where relevant historical data were collected and strategies were simulated for different development scenarios to validate the effectiveness and rationality of the proposed method for enhancing resilience. The simulation results demonstrate that changes in economic conditions and population structure are the primary factors influencing the enhancement of flood resilience in UMS operations. Full article

The following work aims to show how a combination of continuous improvement (CI) and Lean tools can reduce waste and process variability along an air-conditioned pipe production line (PL), calculate its capacity, and improve its efficiency to achieve the expected productivity. A variability study focused on the PL’s balancing was conducted to identify and reduce possible bottlenecks, as well as to evaluate the line’s real capacity. Several layout improvements were made to upgrade the line’s operational conditions and reduce unnecessary movements from the workers. The Constant Work-In-Progress (CONWIP) methodology was also applied to ease the component’s production management in the preparation stage. Additional modifications were implemented to support production and to contribute to the increases in efficiency, quality, and safety on the line. The results revealed an increase in the line’s capacity, associated with an efficiency rise from 28.81% to 47.21% from February to June 2023. The overall equipment effectiveness (OEE) in the same period increased by 18%. This demonstrates that, by interactively applying a mix of tools and methodologies, it is possible to achieve better performance of production lines. This knowledge can help scholars and practitioners to apply the same set of tools to solve usual problems in cell and production lines with performance below expectations. Full article

We adopt a multilayer networks approach to assess how network structural embeddedness affects corporate technological innovation. Our findings indicate an annual increase in both single-layer and multilayer networks, although adoption of the latter by Chinese listed companies is comparatively low. We found that structural embeddedness of multilayer networks positively impacts corporate technological innovation. By reducing uncertainty within the internal environment, these networks bolster technological innovation. Moreover, such embeddedness notably spurs innovation in non-state-owned companies and those with greater internal transparency and robust external oversight. Our analysis reveals an intermediate effect where structural embeddedness in multilayer networks influences innovation. Our work provides new insights into enhancing innovation capacity via network embeddedness and supplies empirical data on utilizing network resources for innovation. We also offer actionable guidance and policy advice for managers, investors, and policymakers, especially relevant amidst economic transformation and pursuit of technological self-reliance of China. Full article

Human factors play a huge role in road traffic safety. Research has found that a huge proportion of traffic crashes occur due to some form of human error. Improving road user behavior has been the major strategy that has been emphasized for improving road traffic safety. Meanwhile, despite the training efforts, and testing for drivers, the global status of road traffic safety is alarming. This research highlights the seriousness of human factors on road traffic safety and provides actionable strategies to greatly reduce the negative impact of human factors on road traffic safety. Motor vehicle safety data that were made available online by the U.S. Bureau of Transportation Statistics were reviewed to evaluate the severity of traffic collisions. To evaluate the extent of human factors in motor vehicle traffic fatalities, data for Canadian motor vehicle traffic collision statistics were reviewed. The study confirms that human factors (such as driver distraction, fatigue, driving under the influence of drugs and alcohol etc.) play a huge role in road traffic fatalities. The need for a reasonable degree of automation to help reduce the impacts of human factors on road safety and recommendations aimed at providing widespread support for a reasonable degree of automation systems in driving tasks are presented. Actionable strategies that can be implemented by policymakers to reduce global road traffic fatalities are also presented. Full article

Timely short-term spatial air quality forecasting is essential for monitoring and prevention in urban agglomerations, providing a new perspective on joint air pollution prevention. However, a single model on air pollution forecasting or spatial correlation analysis is insufficient to meet the strong demand. Thus, this paper proposed a complex real-time monitoring and decision-making assistance system, using a hybrid forecasting module and social network analysis. Firstly, before an accurate forecasting module was constructed, text sentiment analysis and a strategy based on multiple feature selection methods and result fusion were introduced to data preprocessing. Subsequently, CNN-D-LSTM was proposed to improve the feature capture ability to make forecasting more accurate. Then, social network analysis was utilized to explore the spatial transporting characteristics, which could provide solutions to joint prevention and control in urban agglomerations. For experiment simulation, two comparative experiments were constructed for individual models and city cluster forecasting, in which the mean absolute error decreases to 7.8692 and the Pearson correlation coefficient is 0.9816. For overall spatial cluster forecasting, related experiments demonstrated that with appropriate cluster division, the Pearson correlation coefficient could be improved to nearly 0.99. Full article

In the context of Industry 4.0 and smart manufacturing, production factories are increasingly focusing on process optimization, high product customization, quality improvement, cost reduction, and energy saving by implementing a new type of digital solutions that are mainly driven by Internet of Things (IoT), artificial intelligence, big data, and cloud computing. By the adoption of the cyber–physical systems (CPSs) concept, today’s factories are gaining in synergy between the physical and the cyber worlds. As a fast-spreading concept, a digital twin is considered today as a robust solution for decision-making support and optimization. Alongside these benefits, sectors are still working to adopt this technology because of the complexity of modeling manufacturing operations as digital twins. In addition, attempting to use a digital twin for fully automatic decision-making adds yet another layer of complexity. This paper presents our framework for the implementation of a full-duplex (data and decisions) specific-purpose digital twin system for autonomous process control, with plastic injection molding as a practical use-case. Our approach is based on a combination of supervised learning and deep reinforcement learning models that allows for an automated updating of the virtual representation of the system, in addition to an intelligent decision-making process for operational metrics optimization. The suggested method allows for improvements in the product quality while lowering costs. The outcomes demonstrate how the suggested structure can produce high-quality output with the least amount of human involvement. This study shows how the digital twin technology can improve the productivity and effectiveness of production processes and advances the use of the technology in the industrial sector. Full article

This research aims to study a real-world example of the unrelated parallel machine scheduling problem (UPMSP), considering job-splitting, inventories, shortage, and resource constraints. Since the nature of the studied optimization problem is NP-hard, we applied a metaheuristic algorithm named Grey Wolf Optimizer (GWO). The novelty of this study is fourfold. First, the model tackles the inventory problem along with the shortage amount to avoid the late fee. Second, due to the popularity of minimizing completion time (Makespan), each job is divided into small parts to be operated on various machines. Third, renewable resources are included to ensure the feasibility of the production process. Fourth, a mixed-integer linear programming formulation and the solution methodology are developed. To feed the metaheuristic algorithm with an initial viable solution, a heuristic algorithm is also fabricated. Also, the discrete version of the GWO algorithm for this specific problem is proposed to obtain the results. Our results confirmed that our proposed discrete GWO algorithm could efficiently solve a real case study in a timely manner. Finally, future research threads are suggested for academic and industrial communities. Full article