Journal Description
Systems
Systems
is an international, peer-reviewed, open access journal on systems theory in practice, including fields such as systems engineering management, systems based project planning in urban settings, health systems, environmental management and complex social systems, published monthly online by MDPI. The International Society for the Systems Sciences (ISSS) is affiliated with Systems and its members receive a discount on the article processing charges.
- Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
- High Visibility: indexed within Scopus, SSCI (Web of Science), dblp, and other databases.
- Journal Rank: JCR - Q2 (Social Sciences, Interdisciplinary) / CiteScore - Q2 (Modeling and Simulation)
- Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 16.8 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the second half of 2023).
- Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.
Impact Factor:
1.9 (2022);
5-Year Impact Factor:
2.5 (2022)
Latest Articles
Navigating Regional Airport System Economics: Insights from Central Europe and Croatia
Systems 2024, 12(5), 175; https://doi.org/10.3390/systems12050175 - 14 May 2024
Abstract
This paper delves into regional airport system economics in Central Europe, with a particular focus on Slovakia, Czechia, Poland, Hungary, and Croatia. This research aimed to identify key indicators that shape optimal business models for regional airport systems by analyzing data from 24
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This paper delves into regional airport system economics in Central Europe, with a particular focus on Slovakia, Czechia, Poland, Hungary, and Croatia. This research aimed to identify key indicators that shape optimal business models for regional airport systems by analyzing data from 24 airports between 2016 and 2019. Through cluster analysis, airports were categorized based on performance metrics, economic indicators, and ownership structures. The findings reveal distinct groupings among regional airports and shed light on critical factors influencing their operational and financial dynamics. By offering insights into the relationships between airport system characteristics and business model effectiveness, this paper aimed to provide valuable guidance for stakeholders, policymakers, and airport management teams. It facilitates informed decision-making and strategic planning for sustainable aviation infrastructure development in the region.
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Open AccessArticle
The Impact and Spatial Spillover Effects of Tourism Development on Urban Welfare: Empirical Evidence from the Yangtze River Delta in China
by
Gong Chen, Meijuan Hu, Zaijun Li and Lexin Kang
Systems 2024, 12(5), 174; https://doi.org/10.3390/systems12050174 - 13 May 2024
Abstract
The ultimate goal of China’s tourism industry is to create a flourishing sector that brings happiness. It is of immense theoretical and practical importance to investigate the impact of tourism development (TD) on urban welfare (UW) and uncover its spatial spillover characteristics from
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The ultimate goal of China’s tourism industry is to create a flourishing sector that brings happiness. It is of immense theoretical and practical importance to investigate the impact of tourism development (TD) on urban welfare (UW) and uncover its spatial spillover characteristics from a macro perspective. Utilizing panel data from 41 cities in the Yangtze River Delta region from 2000 to 2021, this study applies the spatial panel Durbin model to explore the direct and spillover effects of TD on UW. The results show that TD significantly boosts UW in both local and neighboring areas, with the spillover effects taking a dominant position in the total effects. Examining the sub-dimensions of UW, the local welfare effects of TD primarily stem from economic welfare, whereas the spillover effects are characterized by the “three-wheel drive” of economic, social, and environmental welfare. This study can provide practical insights into the coordinated and sustainable development of the regional tourism industry.
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(This article belongs to the Section Systems Practice in Social Science)
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Performance Evaluation of Carbon-Neutral Cities Based on Fuzzy AHP and HFS-VIKOR
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Chun-Ming Yang, Shiyao Li, Ding-Xuan Huang and Wei Lo
Systems 2024, 12(5), 173; https://doi.org/10.3390/systems12050173 - 13 May 2024
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Climate change threatens human survival and development. Cities, as the main gathering places for human production and life, serve as the focal points for the implementation of the policies related to energy efficiency, energy transition, and environmental protection. This study constructs an index
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Climate change threatens human survival and development. Cities, as the main gathering places for human production and life, serve as the focal points for the implementation of the policies related to energy efficiency, energy transition, and environmental protection. This study constructs an index system for the evaluation of carbon-neutral cities from the perspectives of carbon sources and carbon sinks. The system includes thirteen indicators under six dimensions. It combines objective and subjective data (i.e., statistical data and expert evaluations) by integrating two approaches: the fuzzy analytic hierarchy process (fuzzy AHP) and vise kriterijumska optimizacija i kompromisno resenje with hesitant fuzzy sets (HFS-VIKOR). We verify the efficacy of the proposed approach through a case study of thirteen low-carbon pilot cities in China. The results indicate that among these cities, Shenzhen performs the best, followed by Guangzhou and Hangzhou, while Kunming, Baoding, and Tianjin show poor performance in terms of carbon neutrality. Kunming and Baoding exhibit shortcomings mainly in carbon sources, while Tianjin faces deficiencies in both carbon sources and carbon sinks. Sensitivity analysis and comparative analysis show the availability and effectiveness of the proposed method. The proposed radar chart further highlights the improvement directions for each city to achieve carbon neutrality.
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Research and Application of the Simulation Method for Product Development Process Based on System Dynamics
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Fupeng Yin, Qi Gao and Jiakun Sun
Systems 2024, 12(5), 172; https://doi.org/10.3390/systems12050172 - 12 May 2024
Abstract
Product development is a complex process involving intricate components, dynamics and constantly evolving internal and external environments, as well as numerous influencing factors. In order to accurately simulate and predict the effectiveness of the development process, this paper proposes a system dynamics simulation
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Product development is a complex process involving intricate components, dynamics and constantly evolving internal and external environments, as well as numerous influencing factors. In order to accurately simulate and predict the effectiveness of the development process, this paper proposes a system dynamics simulation method based on information maturity. Different types of development processes are simulated, and the discussion includes the impact of activity information correlation, information evolution coefficient, start time, and other parameters on the dynamic behavior of the process. This study examines a specific mold development process as a case study to validate the method’s feasibility, accurately predicting the duration and cost of the process. It also investigates dynamic fluctuations resulting from uncertain events such as changes in customer demand and resource shortages. The method provides support for process optimization and resource scheduling.
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Open AccessArticle
Data-Driven Strategies for Complex System Forecasts: The Role of Textual Big Data and State-Space Transformers in Decision Support
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Huairong Huo, Wanxin Guo, Ruining Yang, Xuran Liu, Jingyi Xue, Qingmiao Peng, Yiwei Deng, Xinyi Sun and Chunli Lv
Systems 2024, 12(5), 171; https://doi.org/10.3390/systems12050171 - 10 May 2024
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In this research, an innovative state space-based Transformer model is proposed to address the challenges of complex system prediction tasks. By integrating state space theory, the model aims to enhance the capability to capture dynamic changes in complex data, thereby improving the accuracy
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In this research, an innovative state space-based Transformer model is proposed to address the challenges of complex system prediction tasks. By integrating state space theory, the model aims to enhance the capability to capture dynamic changes in complex data, thereby improving the accuracy and robustness of prediction tasks. Extensive experimental validations were conducted on three representative tasks, including legal case judgment, legal case translation, and financial data analysis to assess the performance and application potential of the model. The experimental results demonstrate significant performance improvements of the proposed model over traditional Transformer models and other advanced variants such as Bidirectional Encoder Representation from Transformers (BERT) and Finsformer across all evaluated tasks. Specifically, in the task of legal case judgment, the proposed model exhibited a precision of 0.93, a recall of 0.90, and an accuracy of 0.91, significantly surpassing the traditional Transformer model (with precision of 0.78, recall of 0.73, accuracy of 0.76) and performances of other comparative models. In the task of legal case translation, the precision of the proposed model reached 0.95, with a recall of 0.91 and an accuracy of 0.93, also outperforming other models. Likewise, in the task of financial data analysis, the proposed model also demonstrated excellent performance, with a precision of 0.94, recall of 0.90, and accuracy of 0.92. The state space-based Transformer model proposed not only theoretically expands the research boundaries of deep learning models in complex system prediction but also validates its efficiency and broad application prospects through experiments. These achievements provide new insights and directions for future research and development of deep learning models, especially in tasks requiring the understanding and prediction of complex system dynamics.
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Open AccessArticle
Waiting Strategy for the Dynamic Meal Delivery Routing Problem with Time-Sensitive Customers Using a Hybrid Adaptive Genetic Algorithm and Adaptive Large Neighborhood Search Algorithm
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Wenjie Wang and Shen Gao
Systems 2024, 12(5), 170; https://doi.org/10.3390/systems12050170 - 10 May 2024
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In this paper, we study the dynamic meal delivery routing problem (MDRP) with time-sensitive customers. The multi-objective MDRP optimization model is developed to maximize customer satisfaction and minimize delay penalty cost and riding cost. To solve the dynamic MDRP, a novel waiting strategy
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In this paper, we study the dynamic meal delivery routing problem (MDRP) with time-sensitive customers. The multi-objective MDRP optimization model is developed to maximize customer satisfaction and minimize delay penalty cost and riding cost. To solve the dynamic MDRP, a novel waiting strategy is proposed to divide the dynamic problem into a series of static subproblems. This waiting strategy utilizes the decision threshold to determine rerouting points based on the number of dynamic meal orders. Meanwhile, time-sensitive priority is introduced to accelerate assignment and routing decisions for orders from customers with high time sensitivity. For each static subproblem, a hybrid AGA–ALNS algorithm that incorporates the adaptive genetic algorithm and adaptive large neighborhood search is developed to improve both the global and local search capabilities of the genetic algorithm. We validate the performance of the proposed waiting strategy and the AGA–ALNS algorithm through numerical instances. In addition, managerial insights are obtained from sensitivity analysis experiments.
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Framework for Assessing the Sustainability Impacts of Truck Routing Strategies
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Haluk Laman, Marc Gregory and Amr Oloufa
Systems 2024, 12(5), 169; https://doi.org/10.3390/systems12050169 - 9 May 2024
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The impact of freight on the transportation system is accentuated by the fact that trucks consume a greater roadway capacity than other vehicles and therefore cause more significant problems including traffic congestion, traffic delays, crashes, and pavement damage. Evaluating the actual repercussions of
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The impact of freight on the transportation system is accentuated by the fact that trucks consume a greater roadway capacity than other vehicles and therefore cause more significant problems including traffic congestion, traffic delays, crashes, and pavement damage. Evaluating the actual repercussions of truck traffic becomes paramount in locales where roadway expansion is unfeasible. Trucks are vital to the economy, providing essential services to commerce and industry, and yet it is crucial that their operation does not contribute to the deterioration of infrastructural quality or compromise public safety. Currently, we lack methodologies in practice for the real-time management of traffic, specifically for truck routing, to minimize travel times and prevent delays due to non-recurrent congestion, such as traffic incidents. Accordingly, this study aimed to devise a truck routing strategy utilizing a traffic micro-simulation model (VISSIM) and to assess its effects on reducing travel delays. This involved the development of real-time truck re-routing simulation models that take into account non-recurrent congestion and the resulting travel delays and fuel consumption. The VISSIM model was applied to the I-75 corridor in Marion County, Florida, focusing on non-recurrent congestion effects on travel delays and fuel consumption. The initial findings suggest that the implementation of a dynamic truck re-routing system can significantly alleviate traffic congestion, resulting in a marked decrease in travel delays and fuel usage, demonstrating the potential for such strategies to enhance the overall efficiency of the transportation system.
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(This article belongs to the Special Issue Performance Analysis and Optimization in Transportation Systems)
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Talent Management Digitalization and Company Size as a Catalyst
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Pedro César Martínez-Morán, Fernando Díez, Josu Solabarrieta, José María Fernández-Rico and Elene Igoa-Iraola
Systems 2024, 12(5), 168; https://doi.org/10.3390/systems12050168 - 8 May 2024
Abstract
As companies increasingly undergo digital transformation, the role of talent management processes becomes pivotal in enhancing overall organizational performance. The objective of this research is to assess the extent to which greater digitalization in the talent management process is linked to company size.
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As companies increasingly undergo digital transformation, the role of talent management processes becomes pivotal in enhancing overall organizational performance. The objective of this research is to assess the extent to which greater digitalization in the talent management process is linked to company size. The research has addressed four research questions in order to explore the significance of talent management in corporate digital transformation, examining whether variations in the digitalization of these processes can be attributed to company size. A qualitative approach was employed, utilizing a questionnaire, and collecting responses from 202 organizations across diverse sectors. The findings reveal disparities in digitalization throughout the talent management process, with pronounced presence in the attracting, selecting, and rewarding phases, but diminishing in deployment and development, and further declining in planning. A positive correlation between company size and the adoption of specific digital platforms was observed. Larger enterprises exhibit greater utilization of digital platforms in talent deployment and development. Moreover, corporate communication tools are consistently utilized in the rewarding phase, irrespective of company size. These findings offer practical insights for organizations aiming to optimize their digitalization strategies based on their scale, thereby contributing to more effective and tailored digitalization endeavours. The uniqueness of this research lies in its exploration of the influence of company size on the digitalization of talent management processes and its potential to explain variations across different stages of these processes.
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(This article belongs to the Special Issue Strategic Management in Digital Transformation Era)
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Assessing the Impact of Risk Factors on Vaccination Uptake Policy Decisions Using a Bayesian Network (BN) Approach
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Hafiz Waqar Abbas, Zaman Sajid and Uyen Dao
Systems 2024, 12(5), 167; https://doi.org/10.3390/systems12050167 - 8 May 2024
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This study evaluates the propagation impact of three risk categories (hazard and exposure, socio-economic vulnerability, and lack of coping capacity) and their associated factors on vaccination uptake policy decisions in Pakistan. This study proposed Bayesian influence diagrams using expert elicitation and data-driven approaches.
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This study evaluates the propagation impact of three risk categories (hazard and exposure, socio-economic vulnerability, and lack of coping capacity) and their associated factors on vaccination uptake policy decisions in Pakistan. This study proposed Bayesian influence diagrams using expert elicitation and data-driven approaches. The Bayesian network (BN) approach uses the best policy algorithm to determine the expected utility of decisions. The study found that the government’s firm vaccine uptake decisions had a positive effect in Pakistan. The findings on hazard and exposure-related factors show that people living in rural areas were more susceptible to COVID-19 than people living in urban areas. Among socio-economic vulnerability factors, household characteristics were affected due to household economic situations, fear of using health facilities due to the spread of COVID-19, lack of public transportation services, food insecurity, a temporary halt in education, and weak governance, which affected the vaccination uptake decision. The factors linked with coping capacity show that the government’s financial assistance and development of digital platforms raised digital health literacy and increased vaccine uptake decision utility. The proposed methodology and results of this study can be used to develop contingency planning for any future potential pandemic situations.
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Exploring Ecological Management Plans for Typical Systems in Arid Areas from the Perspective of Ecosystem Service Value Evolution
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Litang Yao, Xuebin Zhang, Jiale Yu, Yanni Liu, Hucheng Du and Xuehong Li
Systems 2024, 12(5), 166; https://doi.org/10.3390/systems12050166 - 8 May 2024
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Ecological management zoning plays a significant role in optimizing resource utilization, improving ecosystem service function, and promoting coordinated regional development. Taking Hexi Corridor as a representative region of the Mountain–Oasis–Desert composite system in arid regions of Asia, this study analyzed the spatial and
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Ecological management zoning plays a significant role in optimizing resource utilization, improving ecosystem service function, and promoting coordinated regional development. Taking Hexi Corridor as a representative region of the Mountain–Oasis–Desert composite system in arid regions of Asia, this study analyzed the spatial and temporal evolution of ecosystem service values and explored the influencing mechanism based on the optimal parameters-based geographical detector model. We have comprehensively divided ecological management zones and proposed corresponding control strategies. The results show that (1) the Hexi Corridor is characterized by regional differentiation, which is composed of three systems: The southern mountain system, central oasis system, and northern desert system. The mountain system is mainly composed of forestland and grassland, the oasis system is mainly composed of cropland, and the desert system is mainly composed of unused land. The conversion of land use mainly involves the conversion of unused land to cropland and grassland, while grassland is mainly converted to cropland. (2) The ecosystem service value of the Hexi Corridor increased significantly and demonstrated agglomeration characteristics in space. The highest value areas are mainly distributed in the southern mountain, with higher value and medium areas mainly distributed in the central oasis, and the lowest value areas are mainly located in the northern desert. (3) Socio-economic factors greatly influence the spatial differentiation of ecosystem service values in the Hexi Corridor, with natural environmental factors having less impact. Additionally, the internal interaction of natural environmental factors is the most significant. (4) The Hexi Corridor is divided into three ecological management zones: Ecological function protection zone, ecological and agricultural coordinated development zone, and ecological and urbanization coordinated development zone. This research has important reference value for global ecological management in arid regions.
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Transforming Cybersecurity into Critical Energy Infrastructure: A Study on the Effectiveness of Artificial Intelligence
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Jaime Govea, Walter Gaibor-Naranjo and William Villegas-Ch
Systems 2024, 12(5), 165; https://doi.org/10.3390/systems12050165 - 5 May 2024
Abstract
This work explores the integration and effectiveness of artificial intelligence in improving the security of critical energy infrastructure, highlighting its potential to transform cybersecurity practices in the sector. The ability of artificial intelligence solutions to detect and respond to cyber threats in critical
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This work explores the integration and effectiveness of artificial intelligence in improving the security of critical energy infrastructure, highlighting its potential to transform cybersecurity practices in the sector. The ability of artificial intelligence solutions to detect and respond to cyber threats in critical energy infrastructure environments was evaluated through a methodology that combines empirical analysis and artificial intelligence modeling. The results indicate a significant increase in the threat detection rate, reaching 98%, and a reduction in incident response time by more than 70%, demonstrating the effectiveness of artificial intelligence in identifying and mitigating cyber risks quickly and accurately. In addition, implementing machine learning algorithms has allowed for the early prediction of failures and cyber-attacks, significantly improving proactivity and security management in energy infrastructure. This study highlights the importance of integrating artificial intelligence into energy infrastructure security strategies, proposing a paradigmatic change in cybersecurity management that increases operational efficiency and strengthens the resilience and sustainability of the energy sector against cyber threats.
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(This article belongs to the Special Issue Cyber Security Challenges in Complex Systems)
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Digitalization as a Factor of Production in China and the Impact on Total Factor Productivity (TFP)
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Pei Li, Jinyi Liu, Xiangyi Lu, Yao Xie and Ziguo Wang
Systems 2024, 12(5), 164; https://doi.org/10.3390/systems12050164 - 5 May 2024
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In the digital transformation era, digitalization integrates deeply into production, bolstering output efficiency and economic value. Through stochastic frontier analysis (SFA), this research positions digitalization as an input in the production function, dissecting its elasticity impact on capital, labor, and output. The effect
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In the digital transformation era, digitalization integrates deeply into production, bolstering output efficiency and economic value. Through stochastic frontier analysis (SFA), this research positions digitalization as an input in the production function, dissecting its elasticity impact on capital, labor, and output. The effect of digitalization on total factor productivity change (TFPC) is explained by comparing TFPC with and without digitalization. Findings reveal that digitalization’s integration into economic growth displays a U-shaped trajectory, with initial productivity setbacks transitioning to long-term benefits as industries adapt. The periodic complementarity and substitution between digitalization and labor, along with a weak substitution relationship with capital, illustrate that, as a production factor, digitalization dynamically interacts with other factors, both complementing and substituting them. This dynamic interplay highlights the intricate role that digitalization plays within the production function. Furthermore, digitalization has played a crucial role in China’s TFP growth, which also highlights the lack of other technological progress. Meanwhile, the pace of digital transformation presents scalability challenges, evident in the fluctuating scale efficiency change (SEC). Policymakers are advised to address these early stage challenges through supportive measures, ensuring smoother digital transitions. Concurrently, industries should embrace this non-linear transformation, emphasizing adaptability to maximize digitalization’s long-term advantages.
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Sustainability under Active Inference
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Mahault Albarracin, Maxwell Ramstead, Riddhi J. Pitliya, Ines Hipolito, Lancelot Da Costa, Maria Raffa, Axel Constant and Sarah Grace Manski
Systems 2024, 12(5), 163; https://doi.org/10.3390/systems12050163 - 4 May 2024
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In this paper, we explore the known connection among sustainability, resilience, and well-being within the framework of active inference. Initially, we revisit how the notions of well-being and resilience intersect within active inference before defining sustainability. We adopt a holistic concept of sustainability
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In this paper, we explore the known connection among sustainability, resilience, and well-being within the framework of active inference. Initially, we revisit how the notions of well-being and resilience intersect within active inference before defining sustainability. We adopt a holistic concept of sustainability denoting the enduring capacity to meet needs over time without depleting crucial resources. It extends beyond material wealth to encompass community networks, labor, and knowledge. Using the free energy principle, we can emphasize the role of fostering resource renewal, harmonious system–entity exchanges, and practices that encourage self-organization and resilience as pathways to achieving sustainability both as an agent and as a part of a collective. We start by connecting active inference with well-being, building on existing work. We then attempt to link resilience with sustainability, asserting that resilience alone is insufficient for sustainable outcomes. While crucial for absorbing shocks and stresses, resilience must be intrinsically linked with sustainability to ensure that adaptive capacities do not merely perpetuate existing vulnerabilities. Rather, it should facilitate transformative processes that address the root causes of unsustainability. Sustainability, therefore, must manifest across extended timescales and all system strata, from individual components to the broader system, to uphold ecological integrity, economic stability, and social well-being. We explain how sustainability manifests at the level of an agent and then at the level of collectives and systems. To model and quantify the interdependencies between resources and their impact on overall system sustainability, we introduce the application of network theory and dynamical systems theory. We emphasize the optimization of precision or learning rates through the active inference framework, advocating for an approach that fosters the elastic and plastic resilience necessary for long-term sustainability and abundance.
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(This article belongs to the Special Issue Developing Resilient Systems: Engineering Solutions for a Changing World)
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Digital Transformation, Gender Discrimination, and Female Employment
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Rendao Ye and Xinya Cai
Systems 2024, 12(5), 162; https://doi.org/10.3390/systems12050162 - 4 May 2024
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With the demographic dividend disappearing, the key to achieving high-quality development in China is to promote full employment of the workforce. Women are a significant group in the job market, but they frequently face greater pressure and higher employment thresholds. Ensuring high-quality employment
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With the demographic dividend disappearing, the key to achieving high-quality development in China is to promote full employment of the workforce. Women are a significant group in the job market, but they frequently face greater pressure and higher employment thresholds. Ensuring high-quality employment for women will be one of the most important tasks in the future. Based on the China Family Panel Studies data, this paper uses two-way fixed effects models, causal stepwise regression analysis, and structural equation models to study the impact of digital transformation of households on female employment and how it works. The empirical results show that digital transformation of households significantly promotes female employment. For low-security employment and high-security employment, the promotion effect of digital transformation is significant. Further mechanism analysis shows that digital transformation of households mainly increases women’s human capital, improves their search for information, and stimulates improvements in social skills, thus effectively eliminating employment-related gender discrimination and ultimately promoting women’s employment. This paper can provide a significant reference for alleviating female employment pressure, promoting full employment, and achieving high-quality development in the context of digital transformation.
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Open AccessArticle
Do Cross-Border Mergers and Acquisitions by Emerging Market Enterprises Enhance Long-Term Productivity? The Host Country Market Size Effect Moderated by Technological Absorption Efforts
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Xia Liu, Jiaqi Fang, Xin Hu and Yiwei Lv
Systems 2024, 12(5), 161; https://doi.org/10.3390/systems12050161 - 2 May 2024
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The purpose of this study was to establish a spatial structural framework to explore how cross-border mergers and acquisitions (M&As) in emerging markets can enhance long-term productivity and select the appropriate host country market structures. Utilizing cross-border M&A data from Chinese companies from
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The purpose of this study was to establish a spatial structural framework to explore how cross-border mergers and acquisitions (M&As) in emerging markets can enhance long-term productivity and select the appropriate host country market structures. Utilizing cross-border M&A data from Chinese companies from 2008 to 2016, we developed a moderated U-shaped mediation model. Employing Two-Stage Least Squares and the Generalized Method of Moments for endogeneity analysis, we offer robust empirical insights. Our findings illustrate that enterprise productivity progression from cross-border M&As is significantly influenced by a U-shaped mediation of the host country’s market size effect, which is further moderated by the technological distance between the home and host countries. A high technological distance intensifies the U-shaped mediation of market size effects on enterprise productivity, while low technological distances result in an inverted U-shaped curve, indicating that such markets may boost short-term productivity but limit long-term growth. Conversely, larger markets with greater technological distances better support sustained productivity increases, even requiring persistent technological absorption efforts. This study underscores the necessity of selecting appropriate host country market structures and effectively managing the acquisition timeline to positively impact both short- and long-term productivity. By conceptualizing firm-level technological absorption efforts as the technological gap between the home and host countries, this study highlights the crucial moderating role that the technological gap plays in influencing long-term productivity at the macro level, providing new insights into the economic geographic strategic decisions and spatial planning for emerging market enterprises in cross-border acquisitions.
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(This article belongs to the Special Issue Strategic Management in Digital Transformation Era)
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Potential Spatial Accessibility to Cardiovascular Hospitals in Romania
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Alexandra Cioclu, Liliana Dumitrache, Mariana Nae and Alina Mareci
Systems 2024, 12(5), 160; https://doi.org/10.3390/systems12050160 - 2 May 2024
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Cardiovascular diseases (CVDs) represent the leading cause of death globally. Romania recorded the highest mortality rate due to CVDs in the EU in 2022, with 162,984 deaths, while the number of registered patients with CVDs surpassed 4 million. This study aims to measure
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Cardiovascular diseases (CVDs) represent the leading cause of death globally. Romania recorded the highest mortality rate due to CVDs in the EU in 2022, with 162,984 deaths, while the number of registered patients with CVDs surpassed 4 million. This study aims to measure the population’s potential spatial accessibility to cardiovascular hospitals in Romania, as timely access to such healthcare facilities is crucial to minimise avoidable mortality due to CVDs. Although distance is an essential parameter of spatial accessibility, time-based analysis is more reflective of real-world scenarios due to the unpredictability of travel. The potential spatial accessibility was measured using the Application Program Interface (API) offered through the Google Maps platform and a personal car as the transportation mode. The country’s cardiovascular hospital network comprises 161 units, of which 84 can provide complex care. Because all of them are located in urban areas, three different time slots were considered to distinguish between high and low traffic congestion situations. We created hierarchies of ten-minute and five km intervals for travel time and distance, respectively, to emphasize the population percentages with better or low potential spatial accessibility. Results showed that only 15% of the population can reach the nearest cardiovascular hospital in less than 20 min, and 23% must travel for over 60 min, while 45.7% live farther than 20 km from a cardiovascular hospital. Inhabitants living in remote areas, especially rural ones, are the most vulnerable, having to travel for the longest time and distance. Actions like improving the existing transport infrastructure and upgrading healthcare facilities and equipment are needed to ensure better medical care and an adequate response to population needs. This study can support local authorities in optimising spatial accessibility to cardiovascular care by identifying the most burdened hospitals in the context of low medical specialised staff and large catchment areas.
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(This article belongs to the Special Issue Systems Science Addressing Health Disparities: Thinking, Modelling & Practice)
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Institutional Approaches for Studying System-Oriented Networks
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Cody Taylor and Branda Nowell
Systems 2024, 12(5), 159; https://doi.org/10.3390/systems12050159 - 1 May 2024
Abstract
Institutional, policy, and management scholars and practitioners are increasingly interested in leveraging network perspectives, methods, and data to understand complex social phenomena, including the various stages of the policy process, community mobilization, and coupled natural and human systems. Viewing these phenomena through the
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Institutional, policy, and management scholars and practitioners are increasingly interested in leveraging network perspectives, methods, and data to understand complex social phenomena, including the various stages of the policy process, community mobilization, and coupled natural and human systems. Viewing these phenomena through the lens of system-oriented networks can be valuable for understanding and intervening within complex policy arenas. However, currently, there is no clear consensus on who and what constitutes a relevant actor in a system-oriented network. Furthermore, numerous conceptual and methodological traditions for conceptualizing, measuring, and analyzing system-oriented networks have arisen, and each is linked to different disciplinary traditions. In this paper, we showcase six approaches from the public policy and public management literature for conceptualizing and analyzing system-oriented networks. We offer a conceptual framework for characterizing different approaches which considers differences in their focal system of interest, analytical focus, theoretical orientation, and approach for determining network boundaries. We review these elements with an eye toward helping scholars and practitioners interested in system-oriented networks to make informed decisions about the array of available approaches.
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(This article belongs to the Special Issue Managing Complexity: A Practitioner's Guide)
Open AccessArticle
Minimization of Costs with Picking and Storage Operations
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Cristina Lopes and Ana Oliveira
Systems 2024, 12(5), 158; https://doi.org/10.3390/systems12050158 - 1 May 2024
Abstract
This work presents two mixed-integer programming models that intend to minimize the costs of the picking and storage operation through better planning and organization of the places occupied by the products in the warehouse. A large customer that stores frozen goods in a
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This work presents two mixed-integer programming models that intend to minimize the costs of the picking and storage operation through better planning and organization of the places occupied by the products in the warehouse. A large customer that stores frozen goods in a Portuguese cold chain logistics company was selected for the analysis of the allocation of the products in the warehouse and of the corresponding outbound movements. Data with 8525 movements that occurred during 2021 were collected for 228 different product references. For this case study, the products that had a picking place in the initial scenario now have pallets with all the goods in the reserve area, and vice versa. The mathematical models were permitted to obtain savings for the logistics operator costs of around 30.9%. The proposed models can, in the future, be applied in other warehouse scenarios to companies in completely different sectors of activity.
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(This article belongs to the Special Issue Digital Transformation and Processes Innovation)
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Labor Mobility Networks and Green Total Factor Productivity
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Jiajia He and Zhenghui Li
Systems 2024, 12(5), 157; https://doi.org/10.3390/systems12050157 - 1 May 2024
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Population migration continues to reshape the spatial pattern of China’s population and regional economic development. During this internal migration process, production and consumption patterns often change, ultimately leading to changes in green total factor productivity. This paper, based on the Chinese population census
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Population migration continues to reshape the spatial pattern of China’s population and regional economic development. During this internal migration process, production and consumption patterns often change, ultimately leading to changes in green total factor productivity. This paper, based on the Chinese population census data and 1% sampling survey data from 2005 to 2015, utilizes social network analysis methods to measure the labor mobility network indicators of 284 prefecture-level cities. Further, this paper analyzes the impact and mechanisms of regional network status on green total factor productivity using a panel fixed effects model. We find that as network density increases, the interpersonal connections between regions become closer, and the network exhibits a clear pattern of “concentrated inflows” and “dispersed outflows”, with the trend of forming strong alliances becoming increasingly apparent. Regions positioned centrally either in terms of network in-degree or out-degree exhibit higher green total factor productivity. Among these, the labor mobility network plays a crucial role in enhancing green total factor productivity through the channel of technology diffusion effects, which improve investment efficiency via knowledge exchange and material capital accumulation. The promotive effect of labor network status on green total factor productivity is more pronounced in the eastern regions, where talent quality is higher, and in areas with fewer restrictions from the household registration system.
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(This article belongs to the Section Systems Practice in Social Science)
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The Promises and Challenges toward Mass Customization of Healthcare Services
by
Shuang Ma, Xiaojin Zhang and Songlin Chen
Systems 2024, 12(5), 156; https://doi.org/10.3390/systems12050156 - 1 May 2024
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The healthcare industry is confronted with the challenge to offer an increasing variety of healthcare services while in the meantime controlling rapidly increasing healthcare costs. Mass customization has been proven to be an effective strategy to fulfill customers’ individual specific needs with high
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The healthcare industry is confronted with the challenge to offer an increasing variety of healthcare services while in the meantime controlling rapidly increasing healthcare costs. Mass customization has been proven to be an effective strategy to fulfill customers’ individual specific needs with high efficiency and low cost in the manufacturing industry. This paper investigates the theoretical feasibility and practical applicability of adopting mass customization as a conceptual framework for designing a healthcare service delivery system. The nature of healthcare delivery systems and their evolution are discussed relative to those of manufacturing systems. Recent research in personalized medicine, consumer-driven healthcare, consumer healthcare informatics, and integrated healthcare delivery is reviewed as enabling technologies towards mass customization of healthcare services. By synthesizing these scattered efforts in different streams of literature, this paper concludes that mass customization can contribute to the redesign of healthcare service systems, and delineates a roadmap for future research.
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