Systems

28 pages, 1206 KB

Open AccessArticle

Integrated Subject–Action–Object and Bayesian Models of Intelligent Word Semantic Similarity Measures

by Siping Zeng, Xiaodong Liu, Wenguang Lin, Vasantha Gokula and Renbin Xiao

Systems 2025, 13(10), 902; https://doi.org/10.3390/systems13100902 (registering DOI) - 13 Oct 2025

Synonym similarity judgments based on semantic distance calculation play a vital role in supporting applications in the field of Natural Language Processing (NLP). However, existing semantic computing methods excessively rely on low-efficiency human supervision or high-quality datasets, which limits their further application. For [...] Read more.

Synonym similarity judgments based on semantic distance calculation play a vital role in supporting applications in the field of Natural Language Processing (NLP). However, existing semantic computing methods excessively rely on low-efficiency human supervision or high-quality datasets, which limits their further application. For these reasons, this paper proposes an automatic and intelligent method for calculating semantic similarity that integrates Subject–Action–Object (SAO) and WordNet to combine knowledge-based semantic similarity and corpus-based semantic similarity. First, the SAO structure is extracted from the Wikipedia dataset, and the statistics of SAO similarity are obtained by calculating co-occurrences of words in SAO. Second, the semantic similarity parameters of words are obtained based on WordNet, and the semantic similarity parameters are adjusted by Laplace Smoothing (LS). Finally, the semantic similarity can be obtained by the Bayesian Model (BM), which combines the semantic similarity parameter and the SAO similarity statistics. The experimental results from well-known word similarity datasets show that the proposed method outperforms traditional methods and even Large Language Models (LLM) in terms of accuracy. The Pearson, Spearman, and Kendall indices were introduced to prove the superiority of the proposed algorithm between model scores and human judgements. Full article

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18 pages, 2230 KB

Open AccessArticle

Capacity Matching Study of Different Functional Lanes at Signalized Intersections

by Jiao Yao, Chenke Zhu, Yin Wang, Yihang Liao and Yan Peng

Systems 2025, 13(10), 901; https://doi.org/10.3390/systems13100901 (registering DOI) - 13 Oct 2025

Abstract

The widening of entrance lanes at urban intersections improves the capacity. However, limited by length, vehicles queuing in different functional lanes often interfere with each other, causing wasted green time. This study analyses turning demand, lane division, and signal timing at short-lane intersections, [...] Read more.

The widening of entrance lanes at urban intersections improves the capacity. However, limited by length, vehicles queuing in different functional lanes often interfere with each other, causing wasted green time. This study analyses turning demand, lane division, and signal timing at short-lane intersections, identifying four types of blockages: left-turn queues overflow blocking straight-ahead, straight-ahead blocking left-turn, right-turn queues overflow blocking straight-ahead, and straight-ahead blocking right-turn. Then, various strategies, including signal timing adjustment, phase sequence, and variable lane functions, are considered. The lane capacity matching rate is calculated, and a model for matching the capacity of different functional lanes at signal-controlled intersections is established. The results show that the matching effect of left-turn is significant, with an improvement of 8.0%, followed by a 7.0% increase in right-turn. The corresponding lane delays are also improved, which demonstrates the effectiveness of the model. Full article

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27 pages, 1026 KB

Open AccessArticle

Ethical Dilemmas in Performance-Oriented Management: A Dual-Path Systems Model

by Jigan Wang, Qing Jia, Tianfeng Dong, Xiaochan Yang and Haodong Jiang

Systems 2025, 13(10), 900; https://doi.org/10.3390/systems13100900 (registering DOI) - 12 Oct 2025

Abstract

Background: High-performance work systems (HPWSs), while designed to boost corporate performance, can inadvertently create a core organizational paradox, triggering a negative feedback loop. Specifically, their intense focus on performance outcomes can create a climate conducive to unethical pro-organizational behavior (UPB), as employees navigate [...] Read more.

Background: High-performance work systems (HPWSs), while designed to boost corporate performance, can inadvertently create a core organizational paradox, triggering a negative feedback loop. Specifically, their intense focus on performance outcomes can create a climate conducive to unethical pro-organizational behavior (UPB), as employees navigate the pressures and perceived obligations, ultimately undermining the organization’s long-term sustainability and viability. While prior research has identified important singular pathways, the mechanisms through which HPWSs simultaneously generate both perceived obligations and performance pressures remain ambiguous. Methods: Drawing on the Job Demands–Resources (JD-R) model, we propose and test a moderated dual-mediation framework. Using survey data from 473 employees, we examine psychological contract fulfillment and bottom-line mentality as parallel mediators, with moral identity as a moderator, in the HPWS-UPB relationship. Results: The analysis demonstrated that HPWSs influence UPB through two distinct and paradoxical pathways: a pressure-driven path via an increased bottom-line mentality, and a reciprocity-driven path via enhanced psychological contract fulfillment. Moral identity emerged as a crucial, albeit asymmetrical, buffer, with its buffering role being particularly consequential for the pressure-driven pathway, as moral identity also significantly weakened the indirect effect of HPWSs on UPB channeled through bottom-line mentality. Conclusions: These findings offer a holistic, systems-based understanding of the performance-ethics paradox. The validation of a dual-pathway model provides a new blueprint for how a single management system produces contradictory outcomes through competing mechanisms. The identification of key intervention points (e.g., fostering moral identity) offers practical strategies for managers to foster systems that support both high productivity and a sustainable ethical climate. Full article

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

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28 pages, 3458 KB

Open AccessArticle

The AI Annotator: Large Language Models’ Potential in Scoring Sustainability Reports

by Yue Wu, Peng Hu and Derek D. Wang

Systems 2025, 13(10), 899; https://doi.org/10.3390/systems13100899 (registering DOI) - 11 Oct 2025

Abstract

To explore the potential of Large Language Models (LLMs) as AI Annotators in the domain of sustainability reporting, this study establishes a systematic evaluation methodology. We use the specific case of European football clubs, quantifying their sustainability reports based on the sport Positive [...] Read more.

To explore the potential of Large Language Models (LLMs) as AI Annotators in the domain of sustainability reporting, this study establishes a systematic evaluation methodology. We use the specific case of European football clubs, quantifying their sustainability reports based on the sport Positive matrix as a benchmark to compare the performance of three state-of-the-art models (i.e., GPT-4o, Qwen-2-72b-instruct, and Llama-3-70b-instruct) against human expert scores. The evaluation is benchmarked on dimensions including accuracy, mean absolute error (MAE), and hallucination rates. The results indicate that GPT-4o is the top performer, yet its average accuracy of approximately 56% shows it cannot fully replace human experts at present. The study also reveals significant issues with overconfidence and factual hallucinations in models like Qwen-2-72b-instructon. Critically, we find that by implementing further data processing, specifically a Chain-of-Verification (CoVe) self-correction method, GPT-4o’s initial hallucination rate is successfully reduced from 16% to 10%, while accuracy improved to 58%. In conclusion, while LLMs demonstrate immense potential to streamline and democratize sustainability ratings, inherent risks like hallucinations remain a primary obstacle. Adopting verification strategies such as CoVe is a crucial pathway to enhancing model reliability and advancing their effective application in this field. Full article

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17 pages, 542 KB

Open AccessArticle

Professional Determinants in ESG Reporting for Sustainable Financial Assessment

by Alina-Iuliana Tăbîrcă, Valentin Radu, Angela-Nicoleta Cozorici, Loredana-Cristina Tanase and Florin Radu

Systems 2025, 13(10), 898; https://doi.org/10.3390/systems13100898 (registering DOI) - 11 Oct 2025

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Abstract

This paper explores the key professional and institutional factors that influence the integration of environmental, social, and governance (ESG) considerations into financial evaluation and auditing processes. The study investigates the impact of legal familiarity, ESG experience, professional qualifications, and digital competencies on ESG [...] Read more.

This paper explores the key professional and institutional factors that influence the integration of environmental, social, and governance (ESG) considerations into financial evaluation and auditing processes. The study investigates the impact of legal familiarity, ESG experience, professional qualifications, and digital competencies on ESG readiness among financial analysts, auditors, and economists. By integrating a structured review of academic literature with an in-depth analysis of European regulatory instruments, the research identifies how dual materiality principles, standardized ESG metrics, and taxonomy-aligned disclosures reshape professional practices. A structured, ethics-approved survey (10 items) was administered nationally, and 145 responses were retained for analysis across economists, analysts, and auditors. Descriptive statistics, Pearson correlations, and linear/multiple regressions were used to test three hypotheses regarding ESG experience, legislative familiarity, and multifactor effects. The results reveal that familiarity with EU legislation is the strongest predictor of ESG integration capacity, while ESG-related experience and digitalization also show moderate to strong influence. The multiple regression model confirms the multifactorial nature of ESG implementation, though not all professional predictors contribute equally. Residual analysis confirms the statistical robustness of the models. The study highlights the need for regulatory literacy, targeted training, and digital adaptation as critical components of ESG competency. Full article

(This article belongs to the Special Issue Ethical and Innovative Systems for High-Performing Sustainable Organizations)

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21 pages, 1111 KB

Open AccessArticle

Beyond Immediate Impact: A Systems Perspective on the Persistent Effects of Population Policy on Elderly Well-Being

by Haoxuan Cheng, Guang Yang, Zhaopeng Xu and Lufa Zhang

Systems 2025, 13(10), 897; https://doi.org/10.3390/systems13100897 (registering DOI) - 11 Oct 2025

Viewed by 45

Abstract

This study adopts a systems perspective to examine the persistent effects of China’s One-Child Policy (OCP) on the subjective well-being of older adults, emphasizing structural persistence, reinforcing feedback, and path-dependent lock-in in complex socio-technical systems. Using nationally representative data from the China Longitudinal [...] Read more.

This study adopts a systems perspective to examine the persistent effects of China’s One-Child Policy (OCP) on the subjective well-being of older adults, emphasizing structural persistence, reinforcing feedback, and path-dependent lock-in in complex socio-technical systems. Using nationally representative data from the China Longitudinal Aging Social Survey (CLASS-2014), we exploit the OCP’s formal rollout at the end of 1979—operationalized with a 1980 cutoff—as a quasi-natural experiment. A Fuzzy Regression Discontinuity (FRD) design identifies the Local Average Treatment Effect of being an only-child parent on late-life well-being, mitigating endogeneity from selection and omitted variables. Theoretically, we integrate three lenses—policy durability and lock-in, intergenerational support, and life course dynamics—to construct a cross-level transmission framework: macro-institutional environments shape substitution capacity and constraint sets; meso-level family restructuring reconfigures support network topology and intergenerational resource flows; micro-level life-course processes accumulate policy-induced adaptations through education, savings, occupation, and residence choices, with effects materializing in old age. Empirically, we find that the OCP significantly reduces subjective well-being among the first generation of affected parents decades later (2SLS estimate ≈ −0.23 on a 1–5 scale). The effects are heterogeneous: rural residents experience large negative impacts, urban effects are muted; men are more adversely affected than women; and individuals without spouses exhibit greater declines than those with spouses. Design validity is supported by a discontinuous shift in fertility at the threshold, smooth density and covariate balance around the cutoff, bandwidth insensitivity, “donut” RD robustness, and a placebo test among ethnic minorities exempt from strict enforcement. These results demonstrate how demographic policies generate lasting impacts on elderly well-being through transforming intergenerational support systems. Policy implications include strengthening rural pension and healthcare systems, expanding community-based eldercare services for spouseless elderly, and developing complementary support programs. Full article

(This article belongs to the Special Issue Advances in the Modeling and Analytics of Health and Healthcare Systems)

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24 pages, 587 KB

Open AccessArticle

Maximizing Shareholder Wealth Through Strategic M&A: The Impact of Target Firm Listing Status and Acquirer Size on Sustainable Business Models in Korean SMEs

by Sung-woo Cho and Jin-young Jung

Systems 2025, 13(10), 896; https://doi.org/10.3390/systems13100896 - 10 Oct 2025

Viewed by 117

Abstract

Strategic mergers and acquisitions (M&A) can support sustainable business models by enabling firms to adapt their capabilities and competitive positions as conditions change. This study examines how target listing status (public vs. private) and acquirer size shape short-term shareholder wealth in Korean SMEs [...] Read more.

Strategic mergers and acquisitions (M&A) can support sustainable business models by enabling firms to adapt their capabilities and competitive positions as conditions change. This study examines how target listing status (public vs. private) and acquirer size shape short-term shareholder wealth in Korean SMEs (Small- and medium-sized enterprise), and links announcement reactions to subsequent operating outcomes. Using an event study and multivariate regressions on 155 M&A announcements by KOSDAQ-listed SMEs (Korean Securities Dealers Automated Quotations) (2016–2020), we find that smaller acquirers earn significantly higher announcement-period cumulative abnormal returns (CAR)—i.e., smaller firm size is positively associated with superior market-adjusted performance around M&A events. Although acquisitions of privately held targets and diversifying deals show higher unadjusted means, their effects become statistically insignificant once firm fundamentals and size are controlled for. To connect M&A strategy with business-model sustainability, we operationalize sustainability as the alignment between short-term market expectations (CAR) and realized operating performance over 1–2 years, measured by return on operating cash flow (ROCF); medium-term checks indicate that the short-run “size effect” attenuates, underscoring the role of execution and scale in longer-run outcomes. Overall, the evidence highlights the primacy of firm-specific fundamentals, strategic fit, and integration capacity in guiding M&A decisions that advance both near-term performance and longer-term resilience. The Korean SME setting—marked by concentrated ownership, resource constraints, and a chaebol-influenced market and policy environment—provides a stringent context for these tests. Full article

(This article belongs to the Special Issue Decision-Making in Sustainable Business Models: Prediction and Modeling)

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18 pages, 768 KB

Open AccessArticle

What Influences the Public to Work as Crowdshippers Using Cargo Bikes? An Extended Theory of Planned Behavior

by Sunho Bang, Jiarong Chen, Kwangsup Shin and Woojung Kim

Systems 2025, 13(10), 895; https://doi.org/10.3390/systems13100895 - 10 Oct 2025

Viewed by 196

Abstract

Driven by the green and low-carbon transformation of urban logistics, the integration of crowdsourced delivery and green transportation is considered an important pathway to achieving sustainable last-mile delivery. This study focuses on urban crowdsourced delivery using cargo bikes and develops an extended behavioral [...] Read more.

Driven by the green and low-carbon transformation of urban logistics, the integration of crowdsourced delivery and green transportation is considered an important pathway to achieving sustainable last-mile delivery. This study focuses on urban crowdsourced delivery using cargo bikes and develops an extended behavioral model based on the Theory of Planned Behavior (TPB). The model systematically examines the key factors influencing the public’s behavioral intention (BI) to participate as crowdshippers. While retaining the core structure of TPB, the model incorporates external variables—perceived risk (PR), policy support (PS), and infrastructure conditions (IC)—to improve its explanatory power and applicability to real-world delivery scenarios. A questionnaire survey was conducted in South Korea, yielding 600 valid responses. The results indicate that usage attitude and perceived behavioral control exert significant positive effects on BI. PR has a significant negative effect on both attitude and BI. PS indirectly enhances BI by improving attitudes, whereas IC primarily influences BI by strengthening the public’s sense of control. This study not only expands the theoretical explanatory power of the TPB model in the context of green crowdsourced delivery but also provides empirical evidence for policymakers and platform operators. Full article

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33 pages, 4768 KB

Open AccessArticle

Evaluating Potential E-Bike Routes in Valparaíso’s Historic Quarter, Chile: Comparative Human and AI Street Auditing and Local Scale Approaches

by Vicente Aprigliano, Mitsuyoshi Fukushi, Catalina Toro, Gonzalo Rojas, Emilio Bustos, Iván Bastías, Sebastián Seriani and Ualison Rébula de Oliveira

Systems 2025, 13(10), 894; https://doi.org/10.3390/systems13100894 (registering DOI) - 10 Oct 2025

Viewed by 97

Abstract

This study evaluates potential routes for electric bicycles (E-Bikes) in Valparaíso, Chile, using street audits performed by both humans and artificial intelligence (AI). Audit methods were compared to identify routes connecting the Puerto metro station with Avenida Alemania (a strategic city avenue), prioritizing [...] Read more.

This study evaluates potential routes for electric bicycles (E-Bikes) in Valparaíso, Chile, using street audits performed by both humans and artificial intelligence (AI). Audit methods were compared to identify routes connecting the Puerto metro station with Avenida Alemania (a strategic city avenue), prioritizing criteria such as street infrastructure, habitability, and street coexistence. The results show that the human audit gives higher scores in subjective variables, such as the perception of security and urban dynamism, while AI penalizes infrastructure deficiencies more severely, especially in areas with steep slopes and low tree cover. Despite these differences, both methods highlight the inadequacy of current infrastructure to promote the use of E-Bikes in the city. This work provides a novel perspective by evaluating human and AI-assisted methodologies, suggesting that an integration between the two could improve accuracy and reduce subjectivity in urban audits. In addition, the results underline the need for public policies that prioritize accessibility, safety, and equity in urban mobility, especially in vulnerable areas. Future research should explore training AI algorithms with human audit data to strengthen AI’s ability to interpret contextual variables and dynamics in complex urban environments. Full article

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

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18 pages, 357 KB

Open AccessArticle

The Roles of Technology Acceptance and Technology Use Frequency in Employees’ Quality of Work Life

by Natália Vraňaková and Zdenka Gyurák Babeľová

Systems 2025, 13(10), 893; https://doi.org/10.3390/systems13100893 - 10 Oct 2025

Viewed by 179

Abstract

The frequency of technology use is an important factor that can significantly influence employees’ well-being and the perceived quality of their work life in an ever-changing digital workplace. The introduction of new technologies affects the lives of employees. It is therefore important how [...] Read more.

The frequency of technology use is an important factor that can significantly influence employees’ well-being and the perceived quality of their work life in an ever-changing digital workplace. The introduction of new technologies affects the lives of employees. It is therefore important how employees themselves perceive new technologies and the need to digitalize their work tasks. Previous studies have focused more on technology adoption or quality of work life separately. The main aim of the article is to present the results of analyses on how the frequency of technology use is related to employees’ perception of digitalization in their workplace, as well as the impact these factors have on their perceived quality of work life. This study simultaneously examines the impact of perceptions of technological change and frequency of technology use on quality of work life in the context of medium-sized and large industrial enterprises in Slovakia. In this way, it is possible to better understand the connection between digitalization and employee well-being. The research tool was a questionnaire that focused on the perceived quality of work life of employees and questions related to the perception of digitalization and to the frequency of technology use. Hypothesis testing was processed using IBM SPSS version 25 software. Considering the results, it can be stated that a positive perception of technological changes and regular use of technology in the workplace are related to a higher level of quality of work life perceived by employees. The results can be used for multiple strategic and practical applications in organizational development and human-centered approaches to digital transformation. Full article

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

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18 pages, 3037 KB

Open AccessArticle

Stacked Ensemble Model with Enhanced TabNet for SME Supply Chain Financial Risk Prediction

by Wenjie Shan and Benhe Gao

Systems 2025, 13(10), 892; https://doi.org/10.3390/systems13100892 - 10 Oct 2025

Viewed by 145

Abstract

Small and medium-sized enterprises (SMEs) chronically face financing frictions. While supply chain finance (SCF) can help, reliable credit risk assessment in SCF is hindered by redundant features, heterogeneous data sources, small samples, and class imbalance. Using 360 A-share–listed SMEs from 2019–2023, we build [...] Read more.

Small and medium-sized enterprises (SMEs) chronically face financing frictions. While supply chain finance (SCF) can help, reliable credit risk assessment in SCF is hindered by redundant features, heterogeneous data sources, small samples, and class imbalance. Using 360 A-share–listed SMEs from 2019–2023, we build a 77-indicator, multidimensional system covering SME and core-firm financials, supply chain stability, and macroeconomic conditions. To reduce dimensionality and remove low-contribution variables, feature selection is performed via a genetic algorithm enhanced LightGBM (GA-LightGBM). To mitigate class imbalance, we employ TabDDPM for data augmentation, yielding consistent improvements in downstream performance. For modeling, we propose a two-stage predictive framework that integrates TabNet-based feature engineering with a stacking ensemble (TabNet-Stacking). In our experiments, TabNet-Stacking outperforms strong machine-learning baselines in accuracy, recall, F1 score, and AUC. Full article

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24 pages, 2296 KB

Open AccessArticle

Parking Choice Analysis of Automated Vehicle Users: Comparing Nested Logit and Random Forest Approaches

by Ying Zhang, Chu Zhang, He Zhang, Jun Chen, Shuhong Meng and Weidong Liu

Systems 2025, 13(10), 891; https://doi.org/10.3390/systems13100891 (registering DOI) - 10 Oct 2025

Viewed by 84

Abstract

Parking shortages and high costs in Chinese central business districts (CBDs) remain major urban challenges. Emerging automated vehicles (AVs) are expected to diversify parking options and mitigate these problems. However, AV users’ parking preferences and their influencing factors within existing urban zoning frameworks [...] Read more.

Parking shortages and high costs in Chinese central business districts (CBDs) remain major urban challenges. Emerging automated vehicles (AVs) are expected to diversify parking options and mitigate these problems. However, AV users’ parking preferences and their influencing factors within existing urban zoning frameworks remain unclear. This study examines Nanjing as a representative case, proposing six distinct AV parking modes. Using survey data from 4644 responses collected from 1634 potential users, we employed nested logit models and random forest algorithms to analyze parking choice behavior. Results indicate that diversified AV parking modes would significantly reduce CBD parking demand. Users with medium- to long-term needs prefer home-parking, while short-term users favor CBD proximity. Key influencing factors include parking service satisfaction, duration, congestion time, AV punctuality, and individual characteristics, with satisfaction attributes showing the greatest impact across all modes. Comparative analysis reveals that random forest algorithms provide superior predictive accuracy for parking mode importance, while nested logit models better explain causal relationships between choices and influencing factors. This study establishes a dual analytical framework combining interpretability and predictive accuracy for urban AV parking research, providing valuable insights for transportation management and future metropolitan studies. Full article

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29 pages, 7442 KB

Open AccessArticle

Vulnerability Analysis of the Sea–Railway Cross-Border Intermodal Logistics Network Considering Inter-Layer Transshipment Under Cascading Failures

by Hairui Wei and Huixin Qi

Systems 2025, 13(10), 890; https://doi.org/10.3390/systems13100890 - 10 Oct 2025

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Abstract

Maritime logistics and railway logistics are crucial in cross-border logistics, and their integration forms a sea-rail cross-border intermodal logistics network. Against the backdrop of frequent unexpected events in today’s world, the normal operation of the sea-rail cross-border intermodal logistics network is under considerable [...] Read more.

Maritime logistics and railway logistics are crucial in cross-border logistics, and their integration forms a sea-rail cross-border intermodal logistics network. Against the backdrop of frequent unexpected events in today’s world, the normal operation of the sea-rail cross-border intermodal logistics network is under considerable threat. Therefore, researching the vulnerability of the intermodal network is extremely urgent. To this end, this paper first constructs a topological model of the sea-rail cross-border intermodal logistics network, designed to reflect the crucial process of “inter-layer transshipment” via transshipment nodes. Subsequently, a cascading failure model is developed to evaluate network vulnerability, featuring a load redistribution process that distinguishes between transshipment and non-transshipment nodes. The paper yields three primary findings. First, it identifies the optimal values for the capacity factor, overload factor, and inter-layer load transfer rate that most effectively mitigate the network’s vulnerability. Second, compared to a single sub-network (such as a maritime logistics network or a railway logistics network), the sea-rail cross-border intermodal network exhibits lower vulnerability when facing attacks. Third, it highlights the critical role of transshipment nodes, confirming that their failure will make the entire sea-rail cross-border intermodal logistics network more vulnerable. Full article

(This article belongs to the Section Supply Chain Management)

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23 pages, 1428 KB

Open AccessArticle

Digital Organizational Resilience in Latin American MSMEs: Entangled Socio-Technical Systems of People, Practices, and Data

by Alexander Sánchez-Rodríguez, Reyner Pérez-Campdesuñer, Gelmar García-Vidal, Yandi Fernández-Ochoa, Rodobaldo Martínez-Vivar and Freddy Ignacio Alvarez-Subía

Systems 2025, 13(10), 889; https://doi.org/10.3390/systems13100889 - 10 Oct 2025

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Abstract

This study develops a systemic framework to conceptualize digital organizational resilience in micro, small, and medium-sized enterprises (MSMEs) as an emergent property of entangled socio-technical systems. Building on theories of distributed cognition, sociomateriality, and resilience engineering, this paper argues that resilience does not [...] Read more.

This study develops a systemic framework to conceptualize digital organizational resilience in micro, small, and medium-sized enterprises (MSMEs) as an emergent property of entangled socio-technical systems. Building on theories of distributed cognition, sociomateriality, and resilience engineering, this paper argues that resilience does not reside in isolated elements—such as leadership, technologies, or procedures—but in their dynamic interplay. Four interdependent dimensions—human, technological, organizational, and institutional—are identified as constitutive of resilience capacities. The research design is conceptual and exploratory in nature. Two theory-driven conceptual statements are formulated: first, that natural language mediation in human–machine interaction enhances coordination and adaptability; and second, that distributed cognition and prototyping practices strengthen collective problem-solving and adaptive capacity. These conceptual statements are not statistically tested but serve as conceptual anchors for the model and as guiding directions for future empirical studies. Empirical illustrations from Ecuadorian MSMEs ground the framework in practice. The evidence highlights three insights: (1) structural fragility, as micro and small firms dominate the economy but face high mortality and financial vulnerability; (2) uneven digitalization, with limited adoption of BPM, ERP, and AI due to skill and resource constraints; and (3) disproportionate gains from modest interventions, such as optimization models or collaborative prototyping. This study contributes to organizational theory by positioning MSMEs as socio-technical ecosystems, providing a conceptual foundation for future empirical validation. Full article

(This article belongs to the Special Issue Cognitive and Practical Perspectives on Resilience, Organization and Entangled Systems)

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15 pages, 929 KB

Open AccessArticle

A Chaos-Driven Fuzzy Neural Approach for Modeling Customer Preferences with Self-Explanatory Nonlinearity

by Huimin Jiang and Farzad Sabetzadeh

Systems 2025, 13(10), 888; https://doi.org/10.3390/systems13100888 - 9 Oct 2025

Viewed by 113

Abstract

Online customer reviews contain rich sentimental expressions of customer preferences on products, which is valuable information for analyzing customer preferences in product design. The adaptive neuro fuzzy inference system (ANFIS) was applied to the establishment of customer preference models based on online reviews, [...] Read more.

Online customer reviews contain rich sentimental expressions of customer preferences on products, which is valuable information for analyzing customer preferences in product design. The adaptive neuro fuzzy inference system (ANFIS) was applied to the establishment of customer preference models based on online reviews, which can address the fuzziness of customers’ emotional responses in comments and the nonlinearity of modeling. However, due to the black box problem in ANFIS, the nonlinearity of the modeling cannot be shown explicitly. To solve the above problems, a chaos-driven ANFIS approach is proposed to develop customer preference models using online comments. The model’s nonlinear relationships are represented transparently through the fuzzy rules obtained, which provide human-readable equations. In the proposed approach, online reviews are analyzed using sentiment analysis to extract the information that will be used as the data sets for modeling. After that, the chaos optimization algorithm (COA) is applied to determine the polynomial structure of the fuzzy rules in ANFIS to model the customer preferences. Using laptop products as a case study, several approaches are evaluated for validation, including fuzzy regression, fuzzy least-squares regression, ANFIS, ANFIS with subtractive cluster, and ANFIS with K-means. Compared to the other five approaches, the values of mean relative error, variance of error, and confidence interval of validation error are improved based on the proposed approach. Full article

(This article belongs to the Special Issue Systems Modeling and Analysis of Consumer Behavior and Business Management)

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17 pages, 2744 KB

Open AccessArticle

Adaptive Deployment of Fixed Traffic Detectors Based on Attention Mechanism

by Wenzhi Zhao, Ting Wang, Guojian Zou, Honggang Wang and Ye Li

Systems 2025, 13(10), 887; https://doi.org/10.3390/systems13100887 - 9 Oct 2025

Viewed by 156

Abstract

In urban intelligent transportation systems, the real-time acquisition of network-wide traffic states is constrained by limited sensor density and high deployment costs. To address this challenge, this paper proposes a learnable Detection Point Selection Module (DPSM), which adaptively determines the most informative observation [...] Read more.

In urban intelligent transportation systems, the real-time acquisition of network-wide traffic states is constrained by limited sensor density and high deployment costs. To address this challenge, this paper proposes a learnable Detection Point Selection Module (DPSM), which adaptively determines the most informative observation points through an end-to-end attention mechanism to support full-map traffic state estimation. Distinct from conventional fixed deployment strategies, DPSM provides an adaptive detector configuration that, under the same number of loop sensors, achieves significantly higher estimation accuracy by intelligently optimizing their placement. Specifically, the module takes normalized spatial and temporal information as input and generates an attention-based distribution to identify critical traffic flow readings, which are subsequently fed into various backbone prediction models, including fully connected networks, convolutional neural networks, and long short-term memory networks. Experiments on the real-world NGSIM-US101 dataset demonstrate that three variants—DPSM-NN, DPSM-CNN, and DPSM-LSTM—consistently outperform their corresponding baselines, with notable robustness under sparse observation scenarios. These results highlight the advantage of adaptive detector placement in maximizing the utility of limited sensors, effectively mitigating information loss from sparse deployments and offering a cost-efficient, scalable solution for urban traffic monitoring and control. Full article

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

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25 pages, 1405 KB

Open AccessArticle

Monetizing Food Waste and Loss Externalities in National Food Supply Chains: A Systems Analytics Framework

by Je-Liang Liou and Shu-Chun Mandy Huang

Systems 2025, 13(10), 886; https://doi.org/10.3390/systems13100886 - 9 Oct 2025

Viewed by 137

Abstract

Reducing food loss and waste (FLW) is a global priority under UN SDG 12.3, yet Taiwan has lacked stage-specific FLW data and systematic valuation of its environmental and economic implications. This study addresses these gaps by integrating localized FLW estimates from the APEC-FLOWS [...] Read more.

Reducing food loss and waste (FLW) is a global priority under UN SDG 12.3, yet Taiwan has lacked stage-specific FLW data and systematic valuation of its environmental and economic implications. This study addresses these gaps by integrating localized FLW estimates from the APEC-FLOWS database with an enhanced analytical framework—the Environmentally Extended Input–Output Valuation (EEIO-V) model. The EEIO-V extends conventional input–output analysis by monetizing multiple environmental burdens, including greenhouse gases, air pollutants, wastewater, and solid waste, thereby linking FLW reduction to tangible economic benefits and policy design. The simulations reveal substantial differences in environmental cost reductions across supply chain stages, with downstream interventions delivering the largest benefits, particularly in reducing air pollution and greenhouse gases. By contrast, upstream measures contribute relatively smaller improvements. These findings highlight the novelty of EEIO-V in bridging environmental valuation with system-level FLW analysis, and they provide actionable insights for designing cost-effective, stage-specific strategies that prioritize downstream interventions to advance Taiwan’s sustainability and policy goals. Full article

(This article belongs to the Special Issue Data Analytics for Social, Economic and Environmental Issues)

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22 pages, 3343 KB

Open AccessArticle

Spatio-Temporal Evolution and Synergistic Development of Urban Road Infrastructure and Urbanization: Evidence from 101 Chinese Cities

by Mengzhen Ding, Jun Cai, Jiaqi Xu, Qiyao Yang, Feiyang Chen and Yishuang Wu

Systems 2025, 13(10), 885; https://doi.org/10.3390/systems13100885 - 9 Oct 2025

Viewed by 216

Abstract

Balancing the development of urban road infrastructure (URI) with the pace of urbanization is crucial to supporting high-quality urban growth. This study constructed a comprehensive evaluation framework of URI and urbanization using data from 101 Chinese cities between 2002 and 2021. The spatio-temporal [...] Read more.

Balancing the development of urban road infrastructure (URI) with the pace of urbanization is crucial to supporting high-quality urban growth. This study constructed a comprehensive evaluation framework of URI and urbanization using data from 101 Chinese cities between 2002 and 2021. The spatio-temporal characteristics of URI and urbanization were assessed using the entropy weighting method and the relative development index (RDI). Key variables were identified through the obstacle degree model and further refined via relative importance analysis. To investigate the nonlinear interactions among the most influential factors, a random forest model was employed in combination with SHapley Additive exPlanations (SHAP). The results revealed three key findings: (1) both URI and urbanization levels exhibited overall upward trends during the study period, although notable disparities were observed across cities; (2) URI development generally outpaced urbanization, indicating a lack of synergy between the two systems; and (3) key determinants of this mismatch included road density, total road area, the number of streetlights per unit road length, resident population size, and educational human capital. By integrating multidimensional URI and urbanization metrics in a comprehensive evaluation framework, this study provides new insights into the spatial synergy mechanisms and supports the formulation of tier-specific urban planning strategies. Full article

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21 pages, 2203 KB

Open AccessArticle

LSTM-PPO-Based Dynamic Scheduling Optimization for High-Speed Railways Under Blizzard Conditions

by Na Wang, Zhiyuan Cai and Yinzhen Li

Systems 2025, 13(10), 884; https://doi.org/10.3390/systems13100884 - 9 Oct 2025

Viewed by 200

Abstract

Severe snowstorms pose multiple threats to high-speed rail systems, including sudden drops in track friction coefficients, icing of overhead contact lines, and reduced visibility. These conditions can trigger dynamic risks such as train speed restrictions, cascading delays, and operational disruptions. Addressing the limitations [...] Read more.

Severe snowstorms pose multiple threats to high-speed rail systems, including sudden drops in track friction coefficients, icing of overhead contact lines, and reduced visibility. These conditions can trigger dynamic risks such as train speed restrictions, cascading delays, and operational disruptions. Addressing the limitations of traditional scheduling methods in spatio-temporal modeling during blizzards, real-time multi-objective trade-offs, and high-dimensional constraint solving efficiency, this paper proposes a collaborative optimization approach integrating temporal forecasting with deep reinforcement learning. A dual-module LSTM-PPO model is constructed using LSTM (Long Short-Term Memory) and PPO (Proximal Policy Optimization) algorithms, coupled with a composite reward function. This design collaboratively optimizes punctuality and scheduling stability, enabling efficient schedule adjustments. To validate the proposed method’s effectiveness, a simulation environment based on the Lanzhou-Xinjiang High-Speed Railway line was constructed. Experiments employing a three-stage blizzard evolution mechanism demonstrated that this approach effectively achieves a dynamic equilibrium among safety, punctuality, and scheduling stability during severe snowstorms. This provides crucial decision support for intelligent scheduling of high-speed rail systems under extreme weather conditions. Full article

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39 pages, 2713 KB

Open AccessArticle

An Exact Algorithm for Continuous Ship Unloading Based on Vehicle Routing

by Toygar Emre and Rızvan Erol

Systems 2025, 13(10), 883; https://doi.org/10.3390/systems13100883 - 9 Oct 2025

Viewed by 178

Abstract

Port operations involving ship unloading have traditionally posed significant complexity and have proven difficult to solve optimally using exact methods. This study investigates the long continuous unloading of ships carrying liquid products, where transportation is carried out using full truckload deliveries. For the [...] Read more.

Port operations involving ship unloading have traditionally posed significant complexity and have proven difficult to solve optimally using exact methods. This study investigates the long continuous unloading of ships carrying liquid products, where transportation is carried out using full truckload deliveries. For the first time, this work integrates the problem of liquid-based ship unloading with full truckload vehicle routing and truck driver scheduling. The primary objective is to minimize the total transportation costs during the continuous unloading process, while satisfying extra constraints such as driver rest–break–drive regulations, time windows, a heterogeneous fleet structure, and port-specific constraints such as maintaining a minimum number of backup vehicles at the port during unloading. To address this complex problem, a route-based insertion heuristic is employed as an initial step in a column generation framework designed for exact optimization. The approach incorporates a nested label setting algorithm for column generation, enhanced with acceleration techniques involving multi-search strategies, and refined selection methods. Performance analysis, based on artificial datasets closely resembling real-world scenarios and consisting of 112 instances, demonstrates that optimality gaps below 1% can be achieved within computational times considered reasonable in the context of the existing literature, while the total number of customer nodes and the minimum number of required vehicles at the port are at most 100 and 5, respectively. Full article

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18 pages, 1693 KB

Open AccessArticle

Debunk Lists as External Knowledge Structures for Health Misinformation Detection with Generative AI

by Melika Rostami and Suliman Hawamdeh

Systems 2025, 13(10), 882; https://doi.org/10.3390/systems13100882 - 9 Oct 2025

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Abstract

The rapid dissemination of health misinformation on the Internet and social media has become a growing challenge for public health, particularly in terms of health information credibility. Promising efforts have been made to detect misinformation using generative AI and large language models (LLMs). [...] Read more.

The rapid dissemination of health misinformation on the Internet and social media has become a growing challenge for public health, particularly in terms of health information credibility. Promising efforts have been made to detect misinformation using generative AI and large language models (LLMs). However, such tools still lack domain-specific knowledge that limits their performance. In this study, we examine the use of predefined knowledge data structures in the forms of debunk lists to augment existing LLMs’ capabilities. We evaluate five different LLMs, including Llama-3.1-8B-instruct, Mistral-large, GPT-4o-mini, Claude-3.5-haiku, and Gemini-1.5-flash, under three experimental settings: zero-shot and debunk-augmented (50 and 100 entities). Results show that external knowledge, in the form of debunk lists, can notably improve LLMs’ performance in detecting misinformation. While Llama shows minimal benefit, the F1 score improvement ranges from 2.63% (GPT-4o) to 11% (Claude). In addition, analysis of model justifications shows that frequent use of debunk lists does not necessarily relate to accurate predictions. This highlights the importance of a model’s ability in effectively using the debunk list rather than reporting superficial integration of external knowledge. Moreover, the proposed framework is generalizable to other misinformation domains and provides key insights for applying external knowledge and evaluating LLMs’ reasoning reliability. Full article

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35 pages, 8300 KB

Open AccessArticle

Modelling and Forecasting Passenger Rail Demand in Slovakia Under Crisis Conditions with NARX Neural Networks

by Anna Dolinayová, Zdenka Bulková, Jozef Gašparík and Igor Dӧmény

Systems 2025, 13(10), 881; https://doi.org/10.3390/systems13100881 - 8 Oct 2025

Viewed by 308

Abstract

Transportation systems are particularly vulnerable to disruptions such as pandemics, which create significant challenges for maintaining efficiency, safety, and service quality. This study focuses on rail passenger transport in the Slovak Republic and develops a simulation framework to evaluate system performance under crisis [...] Read more.

Transportation systems are particularly vulnerable to disruptions such as pandemics, which create significant challenges for maintaining efficiency, safety, and service quality. This study focuses on rail passenger transport in the Slovak Republic and develops a simulation framework to evaluate system performance under crisis conditions. Weekly data from the national rail operator for the period 2019–2021 were combined with information on governmental restrictions, standardized into a five-level framework. A nonlinear autoregressive model with exogenous inputs (NARX), implemented and validated in MATLAB R2021b (MathWorks, Natick, MA, USA), was applied to simulate the impact of restrictive measures on passenger demand. The results revealed a strong relationship between the severity of measures and ridership levels, with the most significant effects observed in education, workplace access, movement limitations, and retail. For instance, during complete school closures, passenger volumes declined by up to 75% relative to the pre-pandemic baseline. Based on the simulation outcomes, recommendations were formulated for adapting railway operations, including dynamic adjustments of transport capacity (10–40%) according to restriction levels. The proposed modelling and simulation approach offers transport authorities a cost-effective tool for scenario testing, disruption management, and the design of resilient passenger rail systems capable of adapting to crises and uncertainties. Full article

(This article belongs to the Special Issue Modelling and Simulation of Transportation Systems)

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19 pages, 426 KB

Open AccessArticle

Internal Dynamics and External Contexts: Evaluating Performance in U.S. Continuum of Care Homelessness Networks

by Jenisa R C and Hee Soun Jang

Systems 2025, 13(10), 880; https://doi.org/10.3390/systems13100880 - 8 Oct 2025

Viewed by 518

Abstract

Understanding public service performance remains a persistent challenge, particularly when services are delivered through complex interorganizational networks. This difficulty is amplified in contexts addressing wicked problems such as homelessness, where needs are multifaceted, solutions are interdependent, and outcomes are hard to measure. In [...] Read more.

Understanding public service performance remains a persistent challenge, particularly when services are delivered through complex interorganizational networks. This difficulty is amplified in contexts addressing wicked problems such as homelessness, where needs are multifaceted, solutions are interdependent, and outcomes are hard to measure. In the United States, the Continuum of Care (CoC) system represents a federally mandated and HUD-funded network model designed to coordinate local responses to homelessness through collaborative governance. Despite its standardized structure and federal oversight, CoC’s performance varies significantly across regions. This study investigates the conditions that influence the CoC network’s performance, focusing on the delivery of Permanent Supportive Housing (PSH) services, a critical intervention for addressing chronic homelessness. It applies to a theoretical framework that combines Ansell and Gash’s collaborative governance model with Emerson et al.’s integrative framework. This approach allows for a comprehensive assessment of internal network factors such as board size, nonprofit leadership, and federal funding, as well as external system contexts including political orientation, income levels, and rent affordability. Drawing on regression analysis of data from 343 CoCs across the United States, the study shows that federal funding, favorable political climates, and larger board size are significant predictors of PSH availability, while nonprofit leadership and income levels are not. Findings highlight the importance of aligning internal governance and external context to improve network outcomes. Full article

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20 pages, 1725 KB

Open AccessArticle

Optimization of Semi-Finished Inventory Management in Process Manufacturing: A Multi-Period Delayed Production Model

by Changxiang Lu, Yong Ye and Zhiming Shi

Systems 2025, 13(10), 879; https://doi.org/10.3390/systems13100879 - 8 Oct 2025

Viewed by 255

Abstract

This study investigates how process manufacturing enterprises can optimize semi-finished inventory (SFI) distribution in delayed production models, with particular attention to differences in cost volatility between single- and multi-period planning scenarios. To address this research gap, we develop a mixed-integer programming model that [...] Read more.

This study investigates how process manufacturing enterprises can optimize semi-finished inventory (SFI) distribution in delayed production models, with particular attention to differences in cost volatility between single- and multi-period planning scenarios. To address this research gap, we develop a mixed-integer programming model that determines optimal customer order decoupling point (CODP)/product differentiation point (PDP) positions and SFI quantities (both generic and dedicated) for each production period, employing particle swarm optimization for solution derivation and validating findings through a comprehensive case study of a steel manufacturer with characteristic long-period production processes. The analysis yields two significant findings: (1) single-period operations demonstrate marked cost sensitivity to service level requirements and delay penalties, necessitating end-stage inventory buffers, and (2) multi-period optimization generates a distinctive cost-smoothing effect through strategic order deferrals and cross-period inventory reuse, resulting in remarkably stable total costs (≤2% variation observed). The study makes seminal theoretical contributions by revealing the convex cost sensitivity of short-term inventory decisions versus the near-flat cost trajectories achievable through multi-period planning, while establishing practical guidelines for process industries through its empirically validated two-period threshold for optimal order deferral and inventory positioning strategies. Full article

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29 pages, 456 KB

Open AccessArticle

Exploring the Relationship Between Corporate Social Responsibility and Organizational Resilience

by Rongbin Ruan and Zuping Zhu

Systems 2025, 13(10), 878; https://doi.org/10.3390/systems13100878 - 7 Oct 2025

Viewed by 322

Abstract

This study constructs a conceptual model based on the relationship between corporate social responsibility (CSR) and organizational resilience based on stakeholder theory, resource dependence theory, information asymmetry theory, and signaling theory, and it uses the panel data of Shanghai and Shenzhen [...] Read more.

This study constructs a conceptual model based on the relationship between corporate social responsibility (CSR) and organizational resilience based on stakeholder theory, resource dependence theory, information asymmetry theory, and signaling theory, and it uses the panel data of Shanghai and Shenzhen A-share listed enterprises in the period of 2010–2021 to conduct empirical research. The results show that (1) corporate social responsibility helps to reduce financial volatility and promote performance growth, which, in turn, contributes to organizational resilience; (2) CSR shapes the enhancement of organizational resilience mainly through three aspects: improving the corporate information environment, easing corporate financing constraints, and improving technological innovation; (3) the effect of CSR on organizational resilience varies according to the degree of board diversity within the enterprise and the degree of regional marketization outside the enterprise, and the enhancement effect of CSR on organizational resilience is more pronounced when the degree of board diversity and the degree of regional marketization are higher. This study provides theoretical support for CSR-enabled organizational resilience in the era of high-quality development, as well as suggestions for strengthening the level of organizational resilience. Full article

(This article belongs to the Special Issue Enterprise Sustainability: From Management System to Corporate Social Responsibility)

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23 pages, 360 KB

Open AccessArticle

Knowledge Recombination Reveals the Nonlinear Influence of Team Scale on Technological Breakthroughs

by Le Song, Shan Chen, Jinqiao Liang and Xiao Yin

Systems 2025, 13(10), 877; https://doi.org/10.3390/systems13100877 - 7 Oct 2025

Viewed by 314

Abstract

In the knowledge economy era, optimizing R&D team size is crucial for breakthrough innovation. Breakthrough technologies rely more on knowledge restructuring and technological leaps than general technologies do. However, it remains unclear whether breakthrough technology formation follows a simple “more people, more power” [...] Read more.

In the knowledge economy era, optimizing R&D team size is crucial for breakthrough innovation. Breakthrough technologies rely more on knowledge restructuring and technological leaps than general technologies do. However, it remains unclear whether breakthrough technology formation follows a simple “more people, more power” logic within technological systems. This work examines 35,955 patents in recommendation system technology to propose a relationship model between collaboration scale and breakthrough technological innovation based on patent data from the recommendation system field. It aims to elucidate how collaboration scale influences breakthrough technological innovation through knowledge restructuring, thereby providing theoretical support and practical guidance for enterprises, institutions, and governments in innovation activities to advance technological innovation. The findings reveal three key points: (1) The relationship between collaboration scale and breakthrough innovation is not linear but follows an inverted U-shaped curve; (2) Knowledge recombination significantly mediates this relationship, also exhibiting an inverted U-shaped pattern with collaboration scale; (3) The inverted U-shaped effect of collaboration scale on breakthrough innovation varies by country. The optimal thresholds are 14.058 entities for China, 57.151 entities for the United States, and 4.801 entities for Russia. This work breaks through the limitations of the traditional theoretical framework and constructs a three-dimensional analysis framework of “collaboration scale → knowledge recombination → breakthrough technological innovation”. By introducing the mediating variable of knowledge recombination, this paper reveals the mechanism of R&D team size on radical innovation. It provides a theoretical basis for the construction of an innovation team and provides a theoretical basis for enterprises, governments, and institutions. Full article

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

32 pages, 2590 KB

Open AccessArticle

Model for Innovation Project Selection Supported by Multi-Criteria Methods Considering Sustainability Parameters

by Jamile Eleutério Delesposte, Luís Alberto Duncan Rangel, Marcelo Jasmim Meiriño, Carlos Manuel dos Santos Ferreira, Rui Jorge Ferreira Soares Borges Lopes and Ramon Baptista Narcizo

Systems 2025, 13(10), 876; https://doi.org/10.3390/systems13100876 - 7 Oct 2025

Viewed by 173

Abstract

Innovation projects with sustainable characteristics are increasingly seen as strategic drivers for organizations to expand market share and retain customers. Yet, firms face limited resources while dealing with many potential projects. To address this challenge, an integrated framework for evaluating and ranking innovation [...] Read more.

Innovation projects with sustainable characteristics are increasingly seen as strategic drivers for organizations to expand market share and retain customers. Yet, firms face limited resources while dealing with many potential projects. To address this challenge, an integrated framework for evaluating and ranking innovation projects using sustainability-related factors can support more consistent decision-making. Although several models for project selection exist in the literature, few provide a comprehensive approach that incorporates sustainability criteria. This study proposes a model for selecting innovation projects by explicitly considering sustainability aspects, supported by multi-criteria decision support methods. The methodological approach followed the Design Cycle method, grounded in Design Science Research. The main result is a novel, customizable model for evaluating, ranking, and managing innovation projects within a sustainability-oriented context. The model was validated through application in two high-performance organizations recognized for their innovation and sustainability practices. Additionally, this research offered reflections on how sustainability-driven innovation can be implemented in practice. Overall, the findings demonstrated that the proposed model is adaptable to different organizational realities, sectors, and sizes, enhancing the capacity to assess and understand the role of sustainability in innovation projects more effectively. Full article

(This article belongs to the Special Issue Decision-Making in Sustainable Business Models: Prediction and Modeling)

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37 pages, 20433 KB

Open AccessArticle

Change Point Detection in Financial Market Using Topological Data Analysis

by Jian Yao, Jingyan Li, Jie Wu, Mengxi Yang and Xiaoxi Wang

Systems 2025, 13(10), 875; https://doi.org/10.3390/systems13100875 - 6 Oct 2025

Viewed by 498

Abstract

Change points caused by extreme events in global economic markets have been widely studied in the literature. However, existing techniques to identify change points rely on subjective judgments and lack robust methodologies. The objective of this paper is to generalize a novel approach [...] Read more.

Change points caused by extreme events in global economic markets have been widely studied in the literature. However, existing techniques to identify change points rely on subjective judgments and lack robust methodologies. The objective of this paper is to generalize a novel approach that leverages topological data analysis (TDA) to extract topological features from time series data using persistent homology. In this approach, we use Taken’s embedding and sliding window techniques to transform the initial time series data into a high-dimensional topological space. Then, in this topological space, persistent homology is used to extract topological features which can give important information related to change points. As a case study, we analyzed 26 stocks over the last 12 years by using this method and found that there were two financial market volatility indicators derived from our method, denoted as

L_{1}

and

L_{2}

. They serve as effective indicators of long-term and short-term financial market fluctuations, respectively. Moreover, significant differences are observed across markets in different regions and sectors by using these indicators. By setting a significance threshold of 98 % for the two indicators, we found that the detected change points correspond exactly to four major financial extreme events in the past twelve years: the intensification of the European debt crisis in 2011, Brexit in 2016, the outbreak of the COVID-19 pandemic in 2020, and the energy crisis triggered by the Russia–Ukraine war in 2022. Furthermore, benchmark comparisons with established univariate and multivariate CPD methods confirm that the TDA-based indicators consistently achieve superior F1 scores across different tolerance windows, particularly in capturing widely recognized consensus events. Full article

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

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24 pages, 4683 KB

Open AccessArticle

Quantifying Tail Risk Spillovers in Chinese Petroleum Supply Chain Enterprises: A Neural-Network-Inspired Multi-Layer Machine Learning Framework

by Xin Zheng, Lei Wang, Tingqiang Chen and Tao Xu

Systems 2025, 13(10), 874; https://doi.org/10.3390/systems13100874 - 6 Oct 2025

Viewed by 172

Abstract

This study constructs a neural-network-inspired multi-layer machine learning model (

R Q L N e t

) to measure and analyze the effects of tail risk spillover and its associated sensitivities to macroeconomic factors among petroleum supply chain enterprises. On this basis, the [...] Read more.

This study constructs a neural-network-inspired multi-layer machine learning model (

R Q L N e t

) to measure and analyze the effects of tail risk spillover and its associated sensitivities to macroeconomic factors among petroleum supply chain enterprises. On this basis, the study constructs a tail risk spillover network and analyzes its network-level structural features. The results show the following: (1) The proposed model improves the accuracy of tail risk measurement while addressing the issue of excessive penalization in spillover weights, offering enhanced interpretability and structural stability and making it particularly suitable for high-dimensional tail risk estimation. (2) Tail risk spillovers propagate from up- and midstream to downstream and ultimately to end enterprises. Structurally, the up- and midstream are the main sources, whereas the downstream and end enterprises are the primary recipients. (3) The tail risk sensitivities of Chinese petroleum supply chain enterprises exhibit significant differences across macroeconomic factors and across types of enterprises. Overall, the sensitivities to CIMV and LS are higher. (4) The network evolves in stages: during trade frictions, spillovers accelerate and core nodes strengthen; during public-health events, intra-community cohesion increases and cross-community spillovers decline; in the recovery phase, cross-community links resume and concentrate on core nodes; and during geopolitical conflicts, spillovers are core-dominated and cross-community transmission accelerates. Full article

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

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29 pages, 2376 KB

Open AccessSystematic Review

Manufacturing Supply Chain Resilience Amid Global Value Chain Reconfiguration: An Enhanced Bibliometric–Systematic Literature Review

by Yan Li, Xinxin Xia, Cong Wang and Qingbo Huang

Systems 2025, 13(10), 873; https://doi.org/10.3390/systems13100873 - 5 Oct 2025

Viewed by 523

Abstract

Global Value Chains (GVCs) have driven the worldwide dispersion of manufacturing but remain highly vulnerable to macro-level shocks, including financial crises, geopolitical tensions, and the COVID-19 pandemic. These shocks expose manufacturing supply chains (MSCs) to systemic risks, but limited research has explored how [...] Read more.

Global Value Chains (GVCs) have driven the worldwide dispersion of manufacturing but remain highly vulnerable to macro-level shocks, including financial crises, geopolitical tensions, and the COVID-19 pandemic. These shocks expose manufacturing supply chains (MSCs) to systemic risks, but limited research has explored how GVC reconfiguration mediates their impact on manufacturing supply chain resilience (MSCR). To address this gap, this study conducts an enhanced bibliometric–systematic literature review (B-SLR) of 120 peer-reviewed articles. The findings reveal that macro-level shocks induce GVC reconfigurations along geographical, value, and governance dimensions, which in turn trigger MSCR through node- and link-level mechanisms. MSCR represents a manufacturer-centered capability that enables MSCs to preserve, realign, and enhance value amid shocks. Building on these insights, this research proposes a multi-tier strategy encompassing firm-level practices, inter-firm collaborations, and policy interventions. This study outlines three key contributions. First, at the theoretical level, it embeds MSCR within a GVC framework, clarifying how GVC reconfiguration mediates SCR under macro-level shocks. Second, at the methodological level, it ensures corpus completeness through snowballing and refines bibliometric mapping with multi-dimensional visualization. Third, at the managerial level, it provides actionable guidance for firms, industry alliances, and policymakers to align MSCR strategies with the dynamics of global production networks. Full article

(This article belongs to the Section Supply Chain Management)

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