Systems

22 pages, 1964 KB

Open AccessArticle

Performance Margin and Reliability Modeling Method for Multi-Level Redundant System

by Tianyu Yang, Ying Chen, Yujia Wang and Yaohui Guo

Systems 2026, 14(1), 117; https://doi.org/10.3390/systems14010117 - 22 Jan 2026

Viewed by 87

This study proposes a multi-level performance margin modeling and belief reliability framework for redundant systems. Starting from system performance, a “performance–margin–reliability” linkage is established by defining the performance and margin of multi-level redundant systems and deriving performance, margin, and metric equations that account [...] Read more.

This study proposes a multi-level performance margin modeling and belief reliability framework for redundant systems. Starting from system performance, a “performance–margin–reliability” linkage is established by defining the performance and margin of multi-level redundant systems and deriving performance, margin, and metric equations that account for failures. For complex redundant systems, a hierarchical Behavior Interaction Priority (BIP) modeling approach is developed to explicitly represent the normal and failure states of atomic component models. The effects of redundant components on the overall system are transformed into variations of performance parameters, enabling quantitative analysis of redundancy mechanisms. This paper proposes a boundary search algorithm for pruning optimization, which breaks through the computational bottleneck of non-analytic threshold sets in high-dimensional topological spaces. A case study on a power supply system with multi-level structural redundancy is conducted. Based on the proposed method, a performance-margin model of the redundant power supply system is constructed, critical states are analyzed, and system reliability is calculated. The results verify the effectiveness of the proposed margin-equation formulation and solution algorithm, offering practical guidance for reliability design of redundant systems. Full article

(This article belongs to the Topic Advanced Systems Engineering: Theory and Applications, 2nd Volume)

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29 pages, 651 KB

Open AccessArticle

Public Perceptions of Generative AI in Creative Industries: A Reddit-Based Text Mining Study

by Mitja Bervar, Mirjana Pejić Bach and Tine Bertoncel

Systems 2026, 14(1), 116; https://doi.org/10.3390/systems14010116 - 22 Jan 2026

Viewed by 282

Abstract

The integration of generative AI into creative industries is reshaping how content is produced, evaluated, and distributed. While recent advancements offer new opportunities for automation and innovation, they also raise questions about authorship, authenticity, and professional identity. This study examines public discourse on [...] Read more.

The integration of generative AI into creative industries is reshaping how content is produced, evaluated, and distributed. While recent advancements offer new opportunities for automation and innovation, they also raise questions about authorship, authenticity, and professional identity. This study examines public discourse on generative AI in creative domains through a text-mining analysis of nearly 4000 Reddit posts and comments. Drawing on six relevant subreddits from 2022 to 2025, the research investigates the structure of user engagement, interaction dynamics, and language patterns. It identifies dominant terms and phrases related to AI creativity, explores thematic clusters, and compares discussion styles across key tools such as Midjourney, ChatGPT, Stable Diffusion, and DALL·E. Additionally, it provides a sentiment overview based on automated classification and narrative interpretation. The findings show that Reddit users engage with generative AI not only as a set of technical tools but as a source of cultural, ethical, and creative negotiation. This study contributes to a deeper understanding of how digital transformation in creative industries is shaped by public perception, platform discourse, and evolving community norms. Full article

(This article belongs to the Special Issue Artificial Intelligence in Socio-Technical Systems: Human, Organizational, and Ethical Dimensions of Intelligent Transformation)

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23 pages, 1822 KB

Open AccessArticle

A System Model for Valuing Data Assets in Commercial Banks

by Hu Wang, Liangrong Song and Qingying Zong

Systems 2026, 14(1), 115; https://doi.org/10.3390/systems14010115 - 22 Jan 2026

Viewed by 104

Abstract

With the ongoing development of the digital economy, the productive function of data as an economic factor has become increasingly salient. Scientifically and rigorously assessing the value of data assets is essential for improving the national economic accounting system and promoting sustainable economic [...] Read more.

With the ongoing development of the digital economy, the productive function of data as an economic factor has become increasingly salient. Scientifically and rigorously assessing the value of data assets is essential for improving the national economic accounting system and promoting sustainable economic growth. In light of the limitations inherent in existing cost-based and market-based valuation approaches, this paper proposes a comprehensive valuation model that integrates the cost approach with the income approach and applies it to the commercial banking sector. Specifically, text analysis is employed to estimate human capital investment in data assets from the perspective of labor supply and demand, after which total costs are derived based on the proportion of human capital. An ARIMA model is used to forecast future cost inputs and net profits associated with data assets. Furthermore, the income-based approach is adopted to estimate the average present value of data assets, with the results of the two methods serving to validate each other. The comparison of estimation results under the cost approach and the income approach further validates the relationship between input and output in data assets. This also demonstrates that data assets follow the law of diminishing marginal utility, thereby contradicting the notion that data increases in value with greater usage. This study enriches the theoretical framework of data asset valuation, broadens its application scope, and provides meaningful guidance for advancing data asset accounting practices and related research. Full article

(This article belongs to the Special Issue Data-Driven Formation and Development of Business Ecosystems)

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20 pages, 326 KB

Open AccessArticle

Communication Skills of Female Entrepreneurs and Their Perceptions of Individual Entrepreneurship

by Remziye Terkan

Systems 2026, 14(1), 114; https://doi.org/10.3390/systems14010114 - 22 Jan 2026

Viewed by 146

Abstract

Effective communication skills are widely recognized as essential for entrepreneurial success, yet limited empirical research has explored their direct relationship with individual entrepreneurship perceptions, particularly among women entrepreneurs. This study addresses this gap by investigating how communication competencies correlate with entrepreneurial self-perception, while [...] Read more.

Effective communication skills are widely recognized as essential for entrepreneurial success, yet limited empirical research has explored their direct relationship with individual entrepreneurship perceptions, particularly among women entrepreneurs. This study addresses this gap by investigating how communication competencies correlate with entrepreneurial self-perception, while also examining whether these variables vary according to demographic and professional characteristics such as age, occupational field, business ownership, and job position. Employing a quantitative research design with a descriptive survey model, data were collected from 145 women entrepreneurs. Statistical analyses, including ANOVA, multiple regression analysis and correlation tests, were applied to explore differences and relationships among variables. Findings indicate that certain demographic factors, notably age and job position, significantly influence both communication skills and entrepreneurship perceptions. Furthermore, a strong positive correlation emerged between the communication skills and individual entrepreneurship perceptions of women entrepreneurs. In addition, the fact that the communication skills and entrepreneurship perceptions of branch managers were higher than those of other work statuses showed that the “manager position” served as an important node affecting both variables within the system. These results underscore the importance of enhancing communication capabilities as a strategic component in fostering entrepreneurial identity and potential among women in diverse professional contexts. Full article

(This article belongs to the Special Issue Agile Approaches to Organizational Governance: Towards a Resilient and Sustainable System)

21 pages, 831 KB

Open AccessArticle

Exploring the Roles of Age and Gender in User Satisfaction and Usage of AI-Driven Chatbots in Digital Health Services: A Multigroup Analysis

by Latifa Alzahrani and Vishanth Weerakkody

Systems 2026, 14(1), 113; https://doi.org/10.3390/systems14010113 - 22 Jan 2026

Viewed by 126

Abstract

As chatbot technology becomes increasingly prevalent across a wide range of industries, it is crucial to explore the factors that shape user satisfaction with this AI-driven innovation. This research provides insights into how age and gender impact user perceptions and engagement with AI-driven [...] Read more.

As chatbot technology becomes increasingly prevalent across a wide range of industries, it is crucial to explore the factors that shape user satisfaction with this AI-driven innovation. This research provides insights into how age and gender impact user perceptions and engagement with AI-driven health technologies in Saudi Arabia. The information systems success model has been utilised to determine the effect of age and gender on user satisfaction. A self-administered questionnaire was distributed in two hospitals in Makkah City, Saudi Arabia, and 527 responses were collected from chatbot users. Structural equation modelling via analysis of moment structures validated the model constructs. The findings revealed that the privacy issue on user satisfaction has been significantly greater with males than with females. However, the correlation between user satisfaction and continuance usage intention, as well as net benefits, has been much higher among the females. Also, notable differences were found between user satisfaction and net benefits and continuance usage intention and net benefits, especially when comparing younger and older participants. Across all age groups, user satisfaction consistently emerged as a central driver of continuance usage intention and net benefits, underscoring the importance of fostering satisfaction to enhance the effectiveness of AI-driven chatbots in digital health services. This study can serve as a guide to highlight the importance of chatbot user satisfaction and provide implications, limitations, and future research opportunities. Full article

(This article belongs to the Section Artificial Intelligence and Digital Systems Engineering)

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15 pages, 458 KB

Open AccessArticle

Feedback Structures Generating Policy Exposure, Gatekeeping, and Care Disruption in Transgender and Gender Expansive Healthcare

by Braveheart Gillani, Rem Martin, Augustus Klein, Meagan Ray-Novak, Alyssa Roberts, Dana Prince, Laura Mintz and Scott Emory Moore

Systems 2026, 14(1), 112; https://doi.org/10.3390/systems14010112 - 21 Jan 2026

Viewed by 196

Abstract

Transgender and gender-expansive (TGE) communities face persistent health inequities that are reproduced through everyday administrative and clinical encounters across care systems. A feedback-focused lens can clarify how those inequities are generated and sustained. Objective: To identify and validate feedback loops that create policy [...] Read more.

Transgender and gender-expansive (TGE) communities face persistent health inequities that are reproduced through everyday administrative and clinical encounters across care systems. A feedback-focused lens can clarify how those inequities are generated and sustained. Objective: To identify and validate feedback loops that create policy exposure and institutional gatekeeping in TGE healthcare and to surface leverage points to stabilize their continuity of care. Methods: Two facilitated, Zoom-based Group Model Building (GMB) sessions were conducted in March 2021 with eight TGE participants (mean age 38 years; range 22–63; transfeminine and transmasculine identities; multiracial, White, and SWANA racial identities) recruited through a Lesbian Gay Bisexual and Transgender (LGBT) community center, followed by a participant member-checking session to validate loop structure, causal direction, and interpretive accuracy. Analysis focused explicitly on identifying reinforcing and balancing feedback structures, rather than isolated barriers, to explain how policy exposure and institutional gatekeeping are generated over time. Results: Participants co-constructed a nine-variable Causal Loop Diagram (CLD) with six feedback structures, four reinforcing and two balancing that interact dynamically to amplify or dampen policy exposure, institutional gatekeeping, and continuity of care, which were organized across structural, institutional/clinical, and individual/community tiers. Reinforcing dynamics linked structural stigma, exclusion from formal employment, institutionalized provider bias, and enacted stigma to degraded care experience, increased trauma and distrust, and disrupted continuity, manifesting as policy exposure (e.g., coverage volatility, denials) and gatekeeping (e.g., discretionary documentation, referral hurdles). Community-based supports and peer/elder navigation functioned as balancing loops that reduced trauma, improved continuity and encounters, and, over time, dampened provider bias. A salient theme was the visibility/invisibility paradox: symbolic inclusion without workflow redesign can inadvertently increase exposure and reinforce harmful loops. Full article

(This article belongs to the Section Systems Practice in Social Science)

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28 pages, 2273 KB

Open AccessArticle

Enhancing Reinforcement Learning-Based Crypto Asset Trading: Focusing on the Korean Venue Share Indicator

by Deok Han and YoungJun Kim

Systems 2026, 14(1), 111; https://doi.org/10.3390/systems14010111 - 21 Jan 2026

Viewed by 251

Abstract

Crypto asset markets are often described as globally integrated. However, empirical evidence suggests that they remain segmented across exchanges and jurisdictions. One notable example is the Korean premium (i.e., Kimchi premium), which refers to persistent price gaps between Korean exchanges and offshore venues. [...] Read more.

Crypto asset markets are often described as globally integrated. However, empirical evidence suggests that they remain segmented across exchanges and jurisdictions. One notable example is the Korean premium (i.e., Kimchi premium), which refers to persistent price gaps between Korean exchanges and offshore venues. The Korean market accounts for a substantial share of global crypto trading activity. Therefore, this segmentation can affect price discovery and create opportunities for systematic trading. Motivated by the Korean premium, this study introduces the Korean Venue Share Indicator (KVSI). Based on the price discovery literature, KVSI is an interpretable venue-level indicator that uses the relative trading volume share between Korean and global exchanges. This study integrates KVSI into the state space of multiple reinforcement learning algorithms to evaluate whether venue-level information improves trading decisions. The results show that the proposed model with KVSI achieves statistically significant improvements in cumulative return (CR), Sharpe ratio (SR), and maximum drawdown (MDD) compared to the baseline model without KVSI. It also achieves higher CR and mixed effects on risk metrics (SR, MDD) relative to benchmark strategies. Additional analyses indicate that the performance gains from KVSI are market-regime-dependent. Overall, the findings have practical implications for developing cross-market systematic trading strategies by leveraging a venue-level indicator as a proxy for market segmentation. Full article

(This article belongs to the Special Issue Data-Driven Modeling and Predictive Analysis in Business, Social, Economic, Education, and Engineering Applications (2nd Edition))

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15 pages, 3185 KB

Open AccessArticle

A Systems-Thinking Framework for Embedding Planetary Boundaries into Chemical Engineering Curriculum

by Yazeed M. Aleissa

Systems 2026, 14(1), 110; https://doi.org/10.3390/systems14010110 - 21 Jan 2026

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Abstract

The integration of complex system concepts and sustainability in chemical engineering education is often limited to elective or separate courses rather than their integration into the core curriculum. This pedagogical gap can lead to graduates who lack a holistic understanding of the intricate [...] Read more.

The integration of complex system concepts and sustainability in chemical engineering education is often limited to elective or separate courses rather than their integration into the core curriculum. This pedagogical gap can lead to graduates who lack a holistic understanding of the intricate interplay between industrial processes and the Earth’s ecological limits, and the feedback loops required to address complex global challenges. This paper presents a transformative approach to close this gap by embedding the Planetary Boundaries framework and system thinking across core chemical engineering courses, such as Material and Energy Balances, Reaction Engineering, and Process Design, and extending this integration to capstone projects. The framework treats the curriculum itself as an interconnected learning system in which key systems concepts are revisited and deepened through contextualized examples and digital modeling tools, including process simulators and life-cycle assessment. We map each boundary to illustrative process examples and learning activities and discuss practical implementation issues such as curriculum crowding, educator readiness, and data availability. This approach aligns with outcome-based education goals by making system thinking and absolute sustainability explicit learning outcomes, preparing future chemical engineers to design processes that respect planetary limits while balancing technical performance, economic feasibility, and societal needs. Full article

(This article belongs to the Special Issue Systems Thinking in Education: Learning, Design and Technology)

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22 pages, 5614 KB

Open AccessArticle

Modeling China’s Urban Network Structure: Unraveling the Drivers from a Population Mobility Perspective

by Haowei Duan and Kai Liu

Systems 2026, 14(1), 109; https://doi.org/10.3390/systems14010109 - 20 Jan 2026

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Abstract

Intercity population flows are playing an increasingly pivotal role in shaping the spatial evolution and structural dynamics of urban networks. Drawing upon Amap Migration Data (2018–2023), this study maps China’s urban networks using social network analysis and identifies their key drivers using a [...] Read more.

Intercity population flows are playing an increasingly pivotal role in shaping the spatial evolution and structural dynamics of urban networks. Drawing upon Amap Migration Data (2018–2023), this study maps China’s urban networks using social network analysis and identifies their key drivers using a temporal exponential random graph model. The findings reveal three primary insights: First, the overall network exhibits “high connectivity and strong clustering” traits. Enhanced efficiency in intercity resource allocation fosters cross-regional factor flows, resulting in multi-tiered connectivity corridors. Industrial linkages and policy interventions drive the development of a polycentric and clustered configuration. Second, the individual city network exhibits a core–periphery dynamic structure. A diamond-shaped framework dominated by hub cities in the national strategic regions directs factor flows. Development of strategic corridors enables peripheral cities to evolve into secondary hubs by leveraging structural hole advantages, reflecting the continuous interplay between network structure and geo-economic factors. Third, driving factors involve nonlinear interactions within a multi-layered system. Path dependence in topology, gradient potential from nodal attributes, spatial counterbalance between geographic decay laws and multidimensional proximity, and adaptive self-organization are collectively associated with the transition of the urban network toward a multi-tiered synergistic pattern. By revealing the dynamic interplay between network topology and multidimensional driving factors, this study deepens and advances the theoretical connotations of the “Space of Flows” theory, providing an empirical foundation for optimizing regional governance strategies and promoting high-quality coordinated development of Chinese cities. Full article

(This article belongs to the Special Issue Data-Driven Urban Mobility Modeling)

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34 pages, 718 KB

Open AccessArticle

The Extent of Digital Transformation, Dual Agency Problems, and the Choice of Industry Specialist Audits in Family Businesses: Evidence from China

by Zhening Tang and Chan Lyu

Systems 2026, 14(1), 108; https://doi.org/10.3390/systems14010108 - 20 Jan 2026

Viewed by 130

Abstract

Existing research often regards a family business’s choice of auditor as a simple and isolated decision. In contrast, this paper presents a complex adaptive system in a broader definition of family businesses, and shows audit supervision emerging dynamically from the interplay of digital [...] Read more.

Existing research often regards a family business’s choice of auditor as a simple and isolated decision. In contrast, this paper presents a complex adaptive system in a broader definition of family businesses, and shows audit supervision emerging dynamically from the interplay of digital transformation, governance, and family generational succession. This paper finds that a larger extent of digital transformation makes family businesses more likely to adopt industry specialist audits. This relationship happens through agency problems within the system. Crucially, the involvement of a digitally literate second generation rebalances these forces by alleviating agency issues, thereby strengthening the push toward industry specialist audits. Our study provides a perspective, revealing that audit governance is not a discrete choice but a system-level outcome of internal tensions and adaptations. This framework offers clear guidance: (1) for family owners, fostering digital literacy in the next generation is key to improving governance during technological change; (2) for auditors, it highlights the evolving risk and demand landscape in digitalizing family firms; (3) for policymakers, it encourages the importance of initiatives that support both digital transformation and governance development in family businesses. Full article

(This article belongs to the Section Systems Practice in Social Science)

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21 pages, 345 KB

Open AccessArticle

How Artificial Intelligence Technology Enables Renewable Energy Development: Heterogeneity Constraints on Environmental and Climate Policies

by Xian Zhao and Jincheng Liu

Systems 2026, 14(1), 107; https://doi.org/10.3390/systems14010107 - 20 Jan 2026

Viewed by 251

Abstract

The emergence of artificial intelligence as a transformative force in the field of information technology has exerted a significant impact on the development of renewable energy. In-depth analysis of the impact of AI on renewable energy development is crucial for promoting energy transition [...] Read more.

The emergence of artificial intelligence as a transformative force in the field of information technology has exerted a significant impact on the development of renewable energy. In-depth analysis of the impact of AI on renewable energy development is crucial for promoting energy transition and facilitating sustainable development. This research utilizes a dataset comprising 30 provincial panels spanning from 2010 to 2023. This study found that AI technology can promote renewable energy development, a conclusion that still holds after robustness and endogeneity tests. An examination of the mechanism reveals that AI technology facilitates the advancement of renewable energy through the enhancement of trade openness and the concentration of manufacturing activities. The analysis of the moderating effect indicates that environmental regulation and environmental protection expenditures positively moderated the relationship between AI technology and renewable energy development and climate policy uncertainty negatively moderated the relationship between AI technology and renewable energy development. Further analysis revealed that AI technology has the potential to substantially improve the development of local renewable energy resources while also facilitating the advancement of renewable energy in adjacent areas, exhibiting spatial spillover effects. This study verifies the positive effects of AI technology on renewable energy development and enriches existing research perspectives in the field of energy economics. Full article

(This article belongs to the Topic Recent Applications of Artificial Intelligence in Economy and Society)

23 pages, 870 KB

Open AccessArticle

Unraveling the Connection Between AI Adoption and E-Commerce Performance in the European Union: A Cross-Country Study

by Claudiu George Bocean

Systems 2026, 14(1), 106; https://doi.org/10.3390/systems14010106 - 19 Jan 2026

Viewed by 149

Abstract

The integration of artificial intelligence (AI) into marketing and sales has significantly reshaped the European digital economy, altering how companies engage with consumers and create online value. This research examines the impact of AI adoption on e-commerce performance across the 27 EU member [...] Read more.

The integration of artificial intelligence (AI) into marketing and sales has significantly reshaped the European digital economy, altering how companies engage with consumers and create online value. This research examines the impact of AI adoption on e-commerce performance across the 27 EU member states. Drawing on Eurostat data, it applies advanced statistical methods, including factor analysis, structural equation modeling (SEM), and cluster analysis, to examine the links among AI-powered business practices, digital engagement, and e-commerce outcomes. The results reveal a strong positive association between AI use in marketing and e-commerce sales, underscoring the mediating role of consumer digital behavior. Regional disparities are also evident: Northern and Western European economies lead in AI adoption and digital maturity, while Southern and Eastern nations show emerging potential for rapid growth. Overall, the study emphasizes that AI-driven marketing boosts e-commerce growth and digital competitiveness, aligning with the European Union’s broader goals of fostering innovation and technological integration. Full article

(This article belongs to the Special Issue Systems Approaches in E-Commerce and Business Intelligence: Innovations and Applications)

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30 pages, 1972 KB

Open AccessArticle

Employee Satisfaction, Crisis Resilience, and Corporate Innovation: Evidence from Employer Review Data in China

by Yujiao Shang, Yuhai Wu, Tuan Pan and Yuping Shang

Systems 2026, 14(1), 105; https://doi.org/10.3390/systems14010105 - 19 Jan 2026

Viewed by 154

Abstract

Employee satisfaction, as a critical form of organisational social capital, represents a significant interdisciplinary topic in management and finance. A key question is whether it can be transformed into sustainable innovation momentum for corporates amid extreme crisis shocks. This study examines Chinese A-share [...] Read more.

Employee satisfaction, as a critical form of organisational social capital, represents a significant interdisciplinary topic in management and finance. A key question is whether it can be transformed into sustainable innovation momentum for corporates amid extreme crisis shocks. This study examines Chinese A-share listed corporates, utilising large-scale anonymous employee evaluation data from the Chinese employer review platform ‘KanZhun.com’, to construct corporate-level employee satisfaction indicators. Through econometric modelling, it investigates the impact of employee satisfaction on corporate innovation output during major crises and its underlying mechanisms. Findings reveal that during crises, employee satisfaction significantly enhances overall corporate innovation levels, with a particularly pronounced effect on green innovation. Mechanism analysis indicates that high employee satisfaction primarily drives innovation, especially green innovation, through two channels. These channels include reducing internal governance costs and alleviating external financing constraints. Heterogeneity tests further reveal that this effect is particularly pronounced in high-tech industries, technology-intensive sectors, non-state-owned corporates, and corporates under strong external institutional constraints or with relatively weak innovation capabilities. This study expands the theoretical boundaries of employee satisfaction’s economic value from an innovation perspective. It further provides Chinese empirical evidence for corporates seeking to enhance innovation resilience in complex environments via employee feedback and quality labour relations. Full article

(This article belongs to the Section Systems Practice in Social Science)

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22 pages, 2561 KB

Open AccessArticle

Deciphering the Crash Mechanisms in Autonomous Vehicle Systems via Explainable AI

by Zhe Zhang, Wentao Wu, Qi Cao, Jianhua Song, Jingfeng Ma, Gang Ren and Changjian Wu

Systems 2026, 14(1), 104; https://doi.org/10.3390/systems14010104 - 19 Jan 2026

Viewed by 226

Abstract

The rapid advancement of autonomous vehicle systems (AVS) has introduced complex challenges to road safety. While some studies have investigated the contribution of factors influencing AV-involved crashes, few have focused on the impact of vehicle-specific factors within AVS on crash outcomes, a focus [...] Read more.

The rapid advancement of autonomous vehicle systems (AVS) has introduced complex challenges to road safety. While some studies have investigated the contribution of factors influencing AV-involved crashes, few have focused on the impact of vehicle-specific factors within AVS on crash outcomes, a focus that gains importance due to the absence of a human driver. To address this gap, the advanced machine learning algorithm, LightGBM (v4.4.0), is employed to quantify the potential effects of vehicle factors on crash severity and collision types based on the Autonomous Vehicle Operation Incident Dataset (AVOID). The joint effects of different vehicle factors and the interactive effects of vehicle factors and environmental factors are studied. Compared with other frequently utilized machine learning techniques, LightGBM demonstrates superior performance. Furthermore, the SHapley Additive exPlanation (SHAP) approach is employed to interpret the results of LightGBM. The analysis of crash severity revealed the importance of investigating the vehicle characteristics of AVs. Operator type is the most predictive factor. For road types, highways and streets show a positive association with the model’s prediction of serious crashes. Crashes involving vulnerable road users can be attributed to different factors. The road type is the most significant factor, followed by precrash speed and mileage. This study identifies key predictive associations for the development of safer AV systems and provides data-driven insights to support regulatory strategies for autonomous driving technologies. Full article

(This article belongs to the Section Artificial Intelligence and Digital Systems Engineering)

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23 pages, 770 KB

Open AccessArticle

Research on the Sustainability of Local Implicit Debt from the Perspective of Economic Growth: Evidence from China

by Shengyin Ouyang, Yanhong Feng and Zhi Zhang

Systems 2026, 14(1), 103; https://doi.org/10.3390/systems14010103 - 19 Jan 2026

Viewed by 276

Abstract

The sustainability of local implicit debt reflects its effect on promoting economic growth. By analyzing the sustainability of local implicit debt, valuable insights can be gained to support the high-quality economic development of relevant countries. This study, using provincial panel data from China [...] Read more.

The sustainability of local implicit debt reflects its effect on promoting economic growth. By analyzing the sustainability of local implicit debt, valuable insights can be gained to support the high-quality economic development of relevant countries. This study, using provincial panel data from China spanning 2006 to 2020, constructs a measurement method for local implicit debt using the MIMIC model and investigates the sustainability of local implicit debt from an economic growth perspective. The results show that local implicit debt has a rising trend but strong economic tournament pressure; an imperfect financial system and stricter financial regulation will affect the scale of local implicit debt. The economic effects of small-scale local implicit debt are not significant; however, when the scale of local implicit debt exceeds CNY 123.88 billion, it can have a significant stimulating effect on regional economic growth. Local implicit debt has a significant sustainability and can significantly drive regional economic growth, with the driving effect being more pronounced in the western regions and at higher thresholds. Full article

(This article belongs to the Section Systems Theory and Methodology)

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23 pages, 13094 KB

Open AccessArticle

PDR-STGCN: An Enhanced STGCN with Multi-Scale Periodic Fusion and a Dynamic Relational Graph for Traffic Forecasting

by Jie Hu, Bingbing Tang, Langsha Zhu, Yiting Li, Jianjun Hu and Guanci Yang

Systems 2026, 14(1), 102; https://doi.org/10.3390/systems14010102 - 18 Jan 2026

Viewed by 160

Abstract

Accurate traffic flow prediction is a core component of intelligent transportation systems, supporting proactive traffic management, resource optimization, and sustainable urban mobility. However, urban traffic networks exhibit heterogeneous multi-scale periodic patterns and time-varying spatial interactions among road segments, which are not sufficiently captured [...] Read more.

Accurate traffic flow prediction is a core component of intelligent transportation systems, supporting proactive traffic management, resource optimization, and sustainable urban mobility. However, urban traffic networks exhibit heterogeneous multi-scale periodic patterns and time-varying spatial interactions among road segments, which are not sufficiently captured by many existing spatio-temporal forecasting models. To address this limitation, this paper proposes PDR-STGCN (Periodicity-Aware Dynamic Relational Spatio-Temporal Graph Convolutional Network), an enhanced STGCN framework that jointly models multi-scale periodicity and dynamically evolving spatial dependencies for traffic flow prediction. Specifically, a periodicity-aware embedding module is designed to capture heterogeneous temporal cycles (e.g., daily and weekly patterns) and emphasize dominant social rhythms in traffic systems. In addition, a dynamic relational graph construction module adaptively learns time-varying spatial interactions among road nodes, enabling the model to reflect evolving traffic states. Spatio-temporal feature fusion and prediction are achieved through an attention-based Bidirectional Long Short-Term Memory (BiLSTM) network integrated with graph convolution operations. Extensive experiments are conducted on three datasets, including Metro Traffic Los Angeles (METR-LA), Performance Measurement System Bay Area (PEMS-BAY), and a real-world traffic dataset from Guizhou, China. Experimental results demonstrate that PDR-STGCN consistently outperforms state-of-the-art baseline models. For next-hour traffic forecasting, the proposed model achieves average reductions of 16.50% in RMSE, 9.00% in MAE, and 0.34% in MAPE compared with the second-best baseline. Beyond improved prediction accuracy, PDR-STGCN reveals latent spatio-temporal evolution patterns and dynamic interaction mechanisms, providing interpretable insights for traffic system analysis, simulation, and AI-driven decision-making in urban transportation networks. Full article

(This article belongs to the Special Issue AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility)

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20 pages, 593 KB

Open AccessArticle

Three-Sided Fuzzy Stable Matching Problem Based on Combination Preference

by Ruya Fan and Yan Chen

Systems 2026, 14(1), 101; https://doi.org/10.3390/systems14010101 - 17 Jan 2026

Viewed by 117

Abstract

Previous studies, constrained by the overly rigid stability requirements, often fail to adapt to complex systems and struggle to identify stable outcomes that align with the practical context of multi-agent resource allocation. To address the three-sided matching problem in complex socio-technical and business [...] Read more.

Previous studies, constrained by the overly rigid stability requirements, often fail to adapt to complex systems and struggle to identify stable outcomes that align with the practical context of multi-agent resource allocation. To address the three-sided matching problem in complex socio-technical and business management systems, this paper proposes a fuzzy stable matching method for three-sided agents under a framework of combinatorial preference relations, integrating network and decision theory. First, we construct a membership function to measure the degree of preference satisfaction between elements of different agents, and then define the concept of fuzzy stability. By incorporating preference satisfaction, we introduce the notion of fuzzy blocking strength and derive the generation conditions for blocking triples and fuzzy stability under the fuzzy stable criterion. Furthermore, we abstract the three-sided matching problem with combined preference relations into a shortest path problem. Second, we prove the equivalence between the shortest path solution and the stable matching outcome. We adopt Dijkstra’s algorithm for problem-solving and derive the time complexity of the algorithm under the pruning strategy. Finally, we apply the proposed model and algorithm to a case study of project assignment in software companies, thereby verifying the feasibility and effectiveness of this three-sided matching method. Compared with existing approaches, the fuzzy stable matching method developed in this study demonstrates distinct advantages in handling preference uncertainty and system complexity. It provides a more universal theoretical tool and computational approach for solving flexible resource allocation problems prevalent in real-world scenarios. Full article

(This article belongs to the Section Systems Theory and Methodology)

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23 pages, 2002 KB

Open AccessArticle

Risk Assessment of Coal Mine Ventilation System Based on Fuzzy Polymorphic Bayes: A Case Study of H Coal Mine

by Jin Zhao, Juan Shi and Jinhui Yang

Systems 2026, 14(1), 99; https://doi.org/10.3390/systems14010099 - 16 Jan 2026

Viewed by 235

Abstract

Coal mine ventilation systems face coupled and systemic risks characterized by structural interconnection and disaster chain propagation. In order to accurately quantify and evaluate this overall system risk, this study presents a new method of risk assessment of the coal mine ventilation system [...] Read more.

Coal mine ventilation systems face coupled and systemic risks characterized by structural interconnection and disaster chain propagation. In order to accurately quantify and evaluate this overall system risk, this study presents a new method of risk assessment of the coal mine ventilation system based on fuzzy polymorphic Bayesian networks. This method effectively addresses the shortcomings of traditional assessment approaches in the probabilistic quantification of risk. A Bayesian network with 44 nodes was established from five dimensions: ventilation power, ventilation network, ventilation facilities, human and management factors, and work environment. The risk states were divided into multiple states based on the As Low As Reasonably Practicable (ALARP) metric. The probabilities of evaluation-type root nodes were calculated using fuzzy evaluation, and the subjective bias was corrected by introducing a reliability coefficient. The concept of distance compensation is proposed to flexibly calculate the probabilities of quantitative-type root nodes. Through the verification of the ventilation system of H Coal Mine in Shanxi, China, it is concluded that the high risk of the ventilation system is 18%, and the high-risk probability of the ventilation system caused by the external air leakage of the mine is the largest. The evaluation results are consistent with real-world conditions. The results can provide a reference for improving the safety of the ventilation systems. Full article

(This article belongs to the Special Issue Advances in Reliability Engineering for Complex Systems)

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25 pages, 2027 KB

Open AccessArticle

Remanufacturing Mode Selection Considering Different Low-Carbon Preferences of Consumers

by Yang Lv, Haowei Zhang and Weiming Sun

Systems 2026, 14(1), 98; https://doi.org/10.3390/systems14010098 - 16 Jan 2026

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Abstract

In today’s increasingly serious environmental problems, a growing number of enterprises are upgrading remanufacturing as an important corporate strategy. This paper compares two third-party remanufacturing models: the entrusting and Authorizing Models, and introduces two different levels of consumer low-carbon preferences: medium and high. [...] Read more.

In today’s increasingly serious environmental problems, a growing number of enterprises are upgrading remanufacturing as an important corporate strategy. This paper compares two third-party remanufacturing models: the entrusting and Authorizing Models, and introduces two different levels of consumer low-carbon preferences: medium and high. By establishing game equations, we find the equilibrium solution of each model. The results reveal that in the basic model, OEM tends to choose the Authorizing Model when consumers have a pronounced quality bias against remanufactured products. Contrary to intuition, TRM always prefers the Entrusting Model. In scenarios where consumers possess medium low-carbon preferences, OEM tends to choose the Authorizing Model when consumers have a high bias against the quality of the remanufactured products or a low bias against the carbon emissions of the new products. Conversely, OEM tends to choose the entrusting remanufacturing model under the opposite conditions. In scenarios where consumers express high low-carbon preferences, the situation becomes the complete opposite. When consumers exhibit a low bias against remanufactured products’ quality or a high bias against carbon emissions from new products, OEM tends to choose the Authorizing Model. Conversely, OEM prefers the Entrusting Model when consumers’ biases differ. In addition, the consumer surplus and social welfare of the Entrusting Model are higher than those of the Authorizing Model, regardless of the research scenario. Full article

(This article belongs to the Special Issue Supply Chain Management towards Circular Economy)

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20 pages, 1272 KB

Open AccessArticle

Towards an Integrated Educational Practice: Application of Systems Thinking in STEM Disciplines

by Selene Castañeda-Burciaga, Omar Alejandro Guirette-Barbosa, Martha Angélica Ramírez-Salazar, José María Celaya-Padilla, Claudia Guadalupe Lara Torres, Hector Durán Muñoz, Oscar Cruz-Domínguez, María Hosanna Iraís Correa Aguado, José de Jesús Reyes-Sánchez, José de Jesús Velázquez-Macías and Martín de Jesús Cardoso Pérez

Systems 2026, 14(1), 97; https://doi.org/10.3390/systems14010097 - 16 Jan 2026

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Abstract

Systems thinking is not a static concept, but rather a dynamic and evolving paradigm that continually adapts to the challenges of its time, becoming more refined and applicable in different areas, such as education. The main objective of the study is to identify [...] Read more.

Systems thinking is not a static concept, but rather a dynamic and evolving paradigm that continually adapts to the challenges of its time, becoming more refined and applicable in different areas, such as education. The main objective of the study is to identify the relationship between academic performance and the pedagogical strategies used to promote systems thinking in undergraduate and graduate students in STEM disciplines (science, technology, engineering, and mathematics). The method used is quantitative research with a non-experimental cross-sectional design. For data collection, a 25-item Likert scale called “STEM Pedagogical Strategies” was used, with an overall Cronbach’s alpha coefficient of α = 0.985. The instrument measures students’ perceptions of the application of five key strategies: problem-based learning, thinking routines, system maps and visual diagrams, design thinking, and system dynamics. The sample consisted of 350 undergraduate and graduate students in STEM fields. The main results show that there is a significant correlation between students’ academic performance and the pedagogical strategies of thinking routines and design thinking. Likewise, the skills developed through systems thinking, as shown in the available literature, would be the basis for fostering collaboration, complex problem solving, and students’ ability to become “systems”. Full article

(This article belongs to the Special Issue Systems Thinking in STEM Education: Pedagogies and Applications)

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27 pages, 572 KB

Open AccessArticle

Digital Governance in Rural China and Social Participation Deprivation Among Rural Households: The Mediating Role of Public Service Access and the Moderating Effect of Digital Exclusion

by Mei Zhang and Zenghui Huo

Systems 2026, 14(1), 96; https://doi.org/10.3390/systems14010096 - 16 Jan 2026

Viewed by 269

Abstract

Promoting social participation is a core objective of digital inclusive development. Drawing on rural household survey data from five provinces in China and the Digital Governance Index developed by Peking University, this study systematically examines the impact of digital governance on rural households’ [...] Read more.

Promoting social participation is a core objective of digital inclusive development. Drawing on rural household survey data from five provinces in China and the Digital Governance Index developed by Peking University, this study systematically examines the impact of digital governance on rural households’ social participation deprivation. The benchmark regression results show that the effect of digital governance on rural households’ social participation deprivation follows an inverted U-shape, characterized by an initial increase followed by a subsequent decline. A series of robustness and endogeneity tests confirms the stability of these findings. Further heterogeneity analyses reveal pronounced regional differences. In the western region, the impact of digital governance on farmers’ social participation deprivation follows a U-shaped pattern, with deprivation initially decreasing and then increasing as digital governance deepens. By contrast, in the central and eastern regions, the inflection point of the inverted U-shaped relationship shifts further to the right relative to the full sample. Furthermore, digital governance exerts a significantly stronger mitigating effect on social participation deprivation among households experiencing higher levels of deprivation. Mechanism analysis shows that digital governance reduces farmers’ social participation deprivation by enhancing their perceived access to public services and improving their psychological well-being. However, moderation analysis shows that household-level digital exclusion and relative poverty significantly weaken these beneficial effects. Full article

(This article belongs to the Section Systems Practice in Social Science)

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21 pages, 772 KB

Open AccessArticle

Lead by Relationship: The Behaviors of Relational Leadership in Regional Collaborative Governance

by Hua Xing, Lin Luo and Bo Feng

Systems 2026, 14(1), 95; https://doi.org/10.3390/systems14010095 - 16 Jan 2026

Viewed by 256

Abstract

Leadership lies at the core of public administration, yet research on boundary-spanning leadership has paid limited attention to the micro-level behaviors through which regional collaboration is enacted. Drawing on empirical evidence from China and a mixed-methods research design, this study examines relational leadership [...] Read more.

Leadership lies at the core of public administration, yet research on boundary-spanning leadership has paid limited attention to the micro-level behaviors through which regional collaboration is enacted. Drawing on empirical evidence from China and a mixed-methods research design, this study examines relational leadership behaviors (RLBs) in regional collaborative governance (RCG). It identifies three types of collaborative leaders—leaders embedded in network administrative organizations, leaders within specialized collaborative departments, and leaders exchanged between regions—and four core RLBs: relational initiative, reconciliation, catalysis, and linkage. These behaviors enhance the perceived effectiveness of RCG by fostering trust, managing conflicts, and integrating diverse interests. The findings further show that RLBs are shaped by the collaborative context, including institutional arrangements, leader roles, task complexity, and the temporal dynamics of collaboration. By incorporating relational leadership into a process-oriented perspective, this study extends RCG theory and offers practical insights for improving governance effectiveness in RCG. Full article

(This article belongs to the Special Issue Public Value and Public Service: Reconsidering Humans in a Vuca-D Society)

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35 pages, 6908 KB

Open AccessArticle

Integrating Complexity and Risk Analysis for Selection of Management Approaches in Complex Projects: Application to UN Peacekeeping Missions

by Juan-Manuel Álvarez-Espada, Teresa Aguilar-Planet and Estela Peralta

Systems 2026, 14(1), 100; https://doi.org/10.3390/systems14010100 - 16 Jan 2026

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Abstract

The growing complexity and dynamism of industrial and organizational projects require management approaches that can effectively adapt to uncertainty and rapidly changing operational environments. In this context, this study proposes a methodology to identify the most suitable management approach—predictive, agile, or hybrid—in complex [...] Read more.

The growing complexity and dynamism of industrial and organizational projects require management approaches that can effectively adapt to uncertainty and rapidly changing operational environments. In this context, this study proposes a methodology to identify the most suitable management approach—predictive, agile, or hybrid—in complex projects. Building on the “Approach suitability tool” of the Project Management Institute’s (PMI), the methodology integrates quantitative assessments of complexity and systemic risk. This is achieved through the analysis of stakeholder and risk networks, using metrics such as cyclomatic complexity and the coevolution parameter g, which allow for a deeper understanding of interactions and the evolution of project elements. The methodology was validated in three peacekeeping missions of the United Nations: UNMISS in South Sudan, MONUSCO in the Democratic Republic of Congo, and MINUSTAH in Haiti. The results confirm that the methodology accurately identifies the most appropriate management approach, emphasizing the effectiveness of hybrid approaches in complex and volatile environments. The proposed methodology serves as a valuable tool for optimizing project management in diverse contexts, enabling a quantitative and systematic evaluation of complexity and risk. It is adaptable and applicable to a wide range of complex projects, improving decision-making and planning in uncertain settings. Furthermore, by incorporating resilience as a cross-cutting principle, the methodology strengthens the ability of projects and their teams to maintain functionality and sustain learning even in highly volatile environments, where continuous adaptation becomes a critical success factor. In this sense, resilience emerges as the property that allows projects to absorb disruptions, reorganize, and preserve their core purpose without losing cohesion or direction. Full article

(This article belongs to the Special Issue Strategic Management Towards Organisational Resilience)

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30 pages, 3292 KB

Open AccessArticle

AI-Based Demand Forecasting and Load Balancing for Optimising Energy Use in Healthcare Systems: A Real Case Study

by Isha Patel and Iman Rahimi

Systems 2026, 14(1), 94; https://doi.org/10.3390/systems14010094 - 15 Jan 2026

Viewed by 336

Abstract

This paper addresses the critical need for efficient energy management in healthcare facilities, where fluctuating energy demands pose challenges to both operational reliability and sustainability objectives. Traditional energy management approaches often fall short in healthcare settings, resulting in inefficiencies and increased operational costs. [...] Read more.

This paper addresses the critical need for efficient energy management in healthcare facilities, where fluctuating energy demands pose challenges to both operational reliability and sustainability objectives. Traditional energy management approaches often fall short in healthcare settings, resulting in inefficiencies and increased operational costs. To address this gap, the paper explores AI-driven methods for demand forecasting and load balancing and proposes an integrated framework combining Long Short-Term Memory (LSTM) networks, a genetic algorithm (GA), and SHAP (Shapley Additive Explanations), specifically tailored for healthcare energy management. While LSTM has been widely applied in time-series forecasting, its use for healthcare energy demand prediction remains relatively underexplored. In this study, LSTM is shown to significantly outperform conventional forecasting models, including ARIMA and Prophet, in capturing complex and non-linear demand patterns. Experimental results demonstrate that the LSTM model achieved a Mean Absolute Error (MAE) of 21.69, a Root Mean Square Error (RMSE) of 29.96, and an R² of approximately 0.98, compared to Prophet (MAE: 59.78, RMSE: 81.22, R² ≈ 0.86) and ARIMA (MAE: 87.73, RMSE: 125.22, R² ≈ 0.66), confirming its superior predictive performance. The genetic algorithm is employed both to support forecasting optimisation and to enhance load balancing strategies, enabling adaptive energy allocation under dynamic operating conditions. Furthermore, SHAP analysis is used to provide interpretable, within-model insights into feature contributions, improving transparency and trust in AI-driven energy decision-making. Overall, the proposed LSTM–GA–SHAP framework improves forecasting accuracy, supports efficient energy utilisation, and contributes to sustainability in healthcare environments. Future work will explore real-time deployment and further integration with reinforcement learning to enable continuous optimisation. Full article

(This article belongs to the Section Artificial Intelligence and Digital Systems Engineering)

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30 pages, 5277 KB

Open AccessArticle

Critical Systemic Risks in Multilayer Automotive Supply Networks: Static and Dynamic Network Perspectives

by Xiongping Yue and Qin Zhong

Systems 2026, 14(1), 93; https://doi.org/10.3390/systems14010093 - 15 Jan 2026

Viewed by 141

Abstract

Current research on automotive supply networks predominantly examines single-type entities connected through one relationship type, resulting in oversimplified, single-layer network structures. This conventional approach fails to capture the complex interdependencies that exist among mineral resources, intermediate components, and finished products throughout the automotive [...] Read more.

Current research on automotive supply networks predominantly examines single-type entities connected through one relationship type, resulting in oversimplified, single-layer network structures. This conventional approach fails to capture the complex interdependencies that exist among mineral resources, intermediate components, and finished products throughout the automotive industry. To overcome these analytical limitations, this study implements a multilayer network framework for examining global automotive supply chains spanning 2017 to 2023. The research particularly emphasizes the identification of critical risk sources through both static and dynamic analytical perspectives. The static analysis employs multilayer degree and strength centralities to illuminate the pivotal roles that countries such as China, the United States, and Germany play within these multilayer automotive supply networks. Conversely, the dynamic risk propagation model uncovers significant cascade effects; a disruption in a major upstream supplier can propagate through intermediary layers, ultimately impacting over 85% of countries in the finished automotive layer within a short temporal threshold. Furthermore, this study investigates how individual nations’ anti-risk capabilities influence the overall resilience of multilayer automotive supply networks. These insights offer valuable guidance for policymakers, enabling strategic topological modifications during disruption events and enhanced protection of the most vulnerable risk sources. Full article

(This article belongs to the Section Systems Practice in Social Science)

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24 pages, 2760 KB

Open AccessArticle

Optimizing Calibration Processes in Automotive Component Manufacturing

by Jana Karaskova, Ales Sliva, Mahalingam Nainaragaram Ramasamy, Ivana Olivkova, Petr Besta and Jan Dizo

Systems 2026, 14(1), 92; https://doi.org/10.3390/systems14010092 - 15 Jan 2026

Viewed by 259

Abstract

High-precision calibration of inertial measurement units for automotive safety systems combines fixed automated chamber cycles with semi-manual loading, alignment, and transfer. Motion waste and ergonomic constraints can therefore dominate throughput and cycle time stability. This study redesigns a production calibration workstation using time-and-motion [...] Read more.

High-precision calibration of inertial measurement units for automotive safety systems combines fixed automated chamber cycles with semi-manual loading, alignment, and transfer. Motion waste and ergonomic constraints can therefore dominate throughput and cycle time stability. This study redesigns a production calibration workstation using time-and-motion analysis, operator observation, and structured root-cause analysis based on the Ishikawa diagram and the five whys. Three interventions were implemented and validated with pre- and post-measurements: bundled handling that consolidates full-set transfers and reduces non-value-adding motions; a fixture and material handling redesign with a manual lifting aid to reduce physical load and enable reliable single-operator operation; and a modular workstation layout that supports the phased addition of chambers. Total cycle time decreased from 4475 s to 1230 s, a 72 percent reduction, and weekly output rose from 800 to 4500 units without additional staffing or significant automation investment. Overall equipment efficiency improved from 75.3 percent to 85.2 percent, while the quality rate remained at 98.8 percent. Full article

(This article belongs to the Section Systems Engineering)

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22 pages, 1943 KB

Open AccessArticle

Repairing the Urban Metabolism: A Dynamic Life-Cycle and HJB Optimization Model for Resolving Spatio-Temporal Conflicts in Shared Parking Systems

by Jiangfeng Li, Jianlong Xiang, Fujian Chen, Longxin Zeng, Haiquan Wang, Yujie Li and Zhongyi Zhai

Systems 2026, 14(1), 91; https://doi.org/10.3390/systems14010091 - 14 Jan 2026

Viewed by 148

Abstract

Urban shared parking systems represent a complex socio-technical challenge. Despite vast potential, utilization remains persistently low (<15%), revealing a critical policy failure. To address this, this study develops a dynamic system framework based on Life-Cycle Cost (LCC) and Hamilton-Jacobi-Bellman (HJB) optimization to analyze [...] Read more.

Urban shared parking systems represent a complex socio-technical challenge. Despite vast potential, utilization remains persistently low (<15%), revealing a critical policy failure. To address this, this study develops a dynamic system framework based on Life-Cycle Cost (LCC) and Hamilton-Jacobi-Bellman (HJB) optimization to analyze and calibrate the key policy levers influencing owner participation timing (T*). The model, resolved using finite difference methods, captures the system’s non-linear threshold effects by simulating critical system parameters, including system instability (price volatility,

σ_{p}

), internal friction (management fee,

w_{g} g_{t}

), and demand signals (transaction ratio, Q). Simulations reveal extreme non-linear system responses: a 100% increase in system instability (

σ_{p}

) delays participation by 325.5%. More critically, a 100% surge in internal friction (management fees) delays T* by 492% and triggers a 95% revenue collapse—demonstrating the risk of systemic collapse. Conversely, a 20% rise in the demand signal (Q) advances T* by 100% (immediate participation), indicating the system can be rapidly shifted to a new equilibrium by activating positive feedback loops. These findings support a sequenced calibration strategy: regulators must first manage instability via price stabilization, then counteract high friction with subsidies (e.g., 60%), and amplify demand loops. The LCC framework provides a novel dynamic decision support system for calibrating complex urban transportation systems, offering policymakers a tool for scenario testing to accelerate policy adoption and alleviate urban congestion. Full article

(This article belongs to the Section Complex Systems and Cybernetics)

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32 pages, 2252 KB

Open AccessArticle

Digitalization and Industrial Chain Resilience: Evidence from Chinese Manufacturing Enterprises

by Hua Feng and Yewen He

Systems 2026, 14(1), 90; https://doi.org/10.3390/systems14010090 - 14 Jan 2026

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Abstract

(1) Background. The rapid development of the digital economy provides a new perspective for enhancing industrial chain resilience. This study examines how manufacturing firms’ digitalization affects their industrial chain resilience, drawing on resource dependence and dynamic capability theories, and explores spillover effects on [...] Read more.

(1) Background. The rapid development of the digital economy provides a new perspective for enhancing industrial chain resilience. This study examines how manufacturing firms’ digitalization affects their industrial chain resilience, drawing on resource dependence and dynamic capability theories, and explores spillover effects on upstream and downstream enterprises. (2) Data and Methods. Using panel data from Chinese listed manufacturing firms (2011–2023), we employ ordinary least squares (OLS) models to analyze the relationship, its mechanisms, and heterogeneity. We further match firms with their suppliers and customers to identify spillover effects. (3) Results. Digitalization significantly improves resilience, particularly by enhancing supply–demand matching and competitive capabilities. Effects are stronger for small, labor-intensive, and high-environment, social and governance (ESG) firms. Bargaining power and governance capability are key channels. Spillover effects are heterogeneous, with a stronger impact on downstream customers. (4) Discussion. The positive impact of digitalization varies by firm characteristics, and spillovers differ across the chain. These findings offer precise insights and policy implications for leveraging digitalization to strengthen industrial chain resilience. Full article

(This article belongs to the Topic Digital Technologies in Supply Chain Risk Management)

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24 pages, 5067 KB

Open AccessArticle

Collision Avoidance Strategy by Utilizing Safety Envelope for Automated Driving System: Hazardous Situation Case

by Mingwei Gao and Hidekazu Nishimura

Systems 2026, 14(1), 89; https://doi.org/10.3390/systems14010089 - 14 Jan 2026

Viewed by 234

Abstract

Autonomous vehicles (AVs) must dynamically maintain sufficient safe distances from surrounding vehicles to ensure safety. Many existing studies have focused on collisions avoidance, such as the safety ranges in a rectangular shape that consider only longitudinal safe distance. A safety envelope is proposed [...] Read more.

Autonomous vehicles (AVs) must dynamically maintain sufficient safe distances from surrounding vehicles to ensure safety. Many existing studies have focused on collisions avoidance, such as the safety ranges in a rectangular shape that consider only longitudinal safe distance. A safety envelope is proposed herein, which is geometrically constructed from four quarter ellipses that account for longitudinal and lateral safe distances. The origin of the safety envelope is placed at the AV’s center of gravity. Using the safety envelope, a potential collision is identified when any surrounding vehicle enters it. To sustain the safety envelope even under hazardous situations, a collision avoidance strategy is introduced. In this strategy, the AV dynamically adjusts its velocity or changes lanes with velocity adjusting by assessing the risk level, complexity level, and riding comfort. For the lane-changing maneuvers, a virtual vehicle is introduced to be placed in the target lane to guide the AV’s movement. The efficacy of this strategy is verified via a simulation under a hazardous situation involving an AV and six human-driven vehicles driving on a highway. Results show that the proposed collision avoidance strategy utilizing safety envelope effectively ensures the safety of AV and surrounding vehicles, even under hazardous situations. Full article

(This article belongs to the Special Issue Application of the Safe System Approach to Transportation)

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25 pages, 991 KB

Open AccessArticle

Sustainable Development Performances Assessment in Upper-Middle Income Developing Countries: A Novel Hybrid Evaluation System in Fuzzy and Non-Fuzzy Environments

by Nazli Tekman Ordu and Muhammed Ordu

Systems 2026, 14(1), 88; https://doi.org/10.3390/systems14010088 - 13 Jan 2026

Viewed by 157

Abstract

Advancing the Sustainable Development Goals (SDGs)—framed around social, environmental, and governance dimensions—offers societies across the world the possibility of achieving long-term prosperity and ensuring that future generations enjoy a high quality of life. Governments pursue the 17 SDGs in accordance with their own [...] Read more.

Advancing the Sustainable Development Goals (SDGs)—framed around social, environmental, and governance dimensions—offers societies across the world the possibility of achieving long-term prosperity and ensuring that future generations enjoy a high quality of life. Governments pursue the 17 SDGs in accordance with their own socioeconomic and cultural contexts, institutional capacities, and available resources. Because countries differ substantially in structure and capability, their progress toward these goals varies, making the systematic measurement and analysis of SDG performance essential for appropriate timing and efficient resource allocation. This study proposes a hybrid assessment system to evaluate the sustainable development performance of upper-middle-income developing countries under both fuzzy and non-fuzzy environments. This integrated evaluation system consists of four main stages. In the first stage, evaluation criteria and alternative countries are specified, relevant data are obtained, and an initial decision matrix is developed. In the second stage, an efficiency analysis is conducted to identify countries that are efficient and those that are not. In the third stage, evaluation criteria are weighted using AHP and Fuzzy AHP methods. In the final stage, the TOPSIS and Fuzzy TOPSIS methods are used to rank efficient countries depending on sustainable development performance criteria. As a result, six countries were identified as inefficient countries based on sustainable development: China, Kazakhstan, Mongolia, Paraguay, Namibia and Turkmenistan. The AHP and Fuzzy AHP methods produced similar criterion weight values compared to each other. The criteria were prioritized from most important to least one as follows: Life expectancy, expected years of schooling, mean years of schooling, gross national income per capita, CO₂ emissions per capita, and material footprint per capita. While some countries achieved similar rankings using the TOPSIS and Fuzzy TOPSIS methods, most countries achieved different rankings because of the multidimensional nature of sustainable development. When the rankings obtained from the fuzzy and non-fuzzy approaches were compared, a noticeable level of overlap was observed, with a Spearman’s rank correlation coefficient of 68.73%. However, the fuzzy TOPSIS method is considered more reliable for assessing sustainable development performance due to its ability to handle data uncertainty, imprecision, and the multidimensional nature of SDG indicators. The results of this study demonstrate that analyses related to sustainable development, which may not contain precise and clear values and have a complex structure encompassing many areas such as social, environmental, and governance, should preferably be conducted within a fuzzy logic framework to ensure more robust and credible evaluations. Full article

(This article belongs to the Section Systems Practice in Social Science)

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