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
Setting the Public Sentiment: Examining the Relationship between Social Media and News Sentiments
Systems 2024, 12(3), 105; https://doi.org/10.3390/systems12030105 (registering DOI) - 19 Mar 2024
Abstract
This study investigates whether news sentiment plays a role in setting social media sentiment to explore the dynamics of sentiment develop and diffusion within the public agenda. Based on the agenda-setting theory, this study analyzed the public and media sentiments towards the 2016
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This study investigates whether news sentiment plays a role in setting social media sentiment to explore the dynamics of sentiment develop and diffusion within the public agenda. Based on the agenda-setting theory, this study analyzed the public and media sentiments towards the 2016 US election and the candidates using data from Twitter, CNN, and Fox News. Focusing on the Twitter messages created by the supporters of Hillary Clinton and Donald Trump, over 1.3 million Twitter messages were collected associated with the election, employing hashtags as indicators of support. The Granger causality test between social media and news sentiments revealed that there is a mutual influence between social media and news sentiments; CNN’s overall sentiment was influenced by the sentiment of Hillary Clinton’s supporters, whereas Trump supporters’ sentiment was influenced by Fox News’ negative sentiment. The results suggest that public sentiment is formed in response to public agenda and mass media, indicating that sentiment is a critical component in understanding public opinion. Implications for future studies and limitations are also discussed.
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(This article belongs to the Special Issue Communication for the Digital Media Age)
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A Subsidization Scheme for Maximizing Social Welfare in Mobile Communications Markets
by
Carlos Agualimpia-Arriaga, José Vuelvas, Carlos-Iván Páez-Rueda, Carlos Adrián Correa-Flórez and Arturo Fajardo
Systems 2024, 12(3), 104; https://doi.org/10.3390/systems12030104 - 19 Mar 2024
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In contemporary mobile communications markets, various agents or players interact to pursue welfare. Regulatory policies enacted by governments in certain markets aim to maximize social welfare. However, some countries, both least developed and developing, often adopt successful models from developed nations without local
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In contemporary mobile communications markets, various agents or players interact to pursue welfare. Regulatory policies enacted by governments in certain markets aim to maximize social welfare. However, some countries, both least developed and developing, often adopt successful models from developed nations without local market validation. Therefore, network economics serves as a pertinent framework for analyzing such policies. This paper introduces a novel scheme based on constrained optimization problems, where the constraints represent multilevel economic game equilibria within a system model involving three agents: the central planner, the mobile network operator, and the mobile data users. These agents strategically optimize their payoff functions by considering benefit factors and decision variables such as the subsidization factor, pricing, and data consumption. To this end, a three-stage dynamic game is proposed to model the players’ interactions, employing the backward induction method to ascertain the subgame perfect equilibrium from the Nash equilibrium. A case study is presented, demonstrating a 31.16% increase in social welfare between scenarios involving no adoption of the subsidization factor and its adoption at the optimal value when the central planner enacts it to other players in the game, even if they do not necessarily attain maximum payoff values. In countries aligning with this proposed model, social welfare is maximized through a subsidization scheme.
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An Empirical Evaluation of a Generative Artificial Intelligence Technology Adoption Model from Entrepreneurs’ Perspectives
by
Varun Gupta
Systems 2024, 12(3), 103; https://doi.org/10.3390/systems12030103 - 18 Mar 2024
Abstract
Technologies, such as Chat Generative Pre-Trained Transformer (ChatGPT, Smart PLS version 4), are prime examples of Generative Artificial Intelligence (AI), which is a constantly evolving area. SMEs, particularly startups, can obtain a competitive edge, innovate their business models, gain business value, and undergo
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Technologies, such as Chat Generative Pre-Trained Transformer (ChatGPT, Smart PLS version 4), are prime examples of Generative Artificial Intelligence (AI), which is a constantly evolving area. SMEs, particularly startups, can obtain a competitive edge, innovate their business models, gain business value, and undergo a digital transformation by implementing these technologies. Continuous but gradual experimentation with these technologies is the foundation for their adoption. The experience that comes from trying new technologies can help entrepreneurs adopt new technologies more strategically and experiment more with them. The urgent need for an in-depth investigation is highlighted by the paucity of previous research on ChatGPT uptake in the startup context, particularly from an entrepreneurial perspective. The objective of this research study is to empirically validate the Generative AI technology adoption model to establish the direction and strength of the correlations among the adoption factors from the perspectives of the entrepreneurs. The data are collected from 482 entrepreneurs who exhibit great diversity in their genders, the countries in which their startups are located, the industries their startups serve, their age, their educational levels, their work experience as entrepreneurs, and the length of time the startups have been on the market. Collected data are analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, which results in a statistical examination of the relationships between the adoption model’s factors. The results indicate that social influence, domain experience, technology familiarity, system quality, training and support, interaction convenience, and anthropomorphism are the factors that impact the pre-perception and perception phase of adoption. These factors motivate entrepreneurs to experiment more with the technology, thereby building perceptions of its usefulness, perceived ease of use, and perceived enjoyment, three factors that in turn affect emotions toward the technology and, finally, switching intentions. Control variables like age, gender, and educational attainment have no appreciable effect on switching intentions to alternatives of the Generative AI technology. Rather, the experience factor of running businesses shows itself to be a crucial one. The results have practical implications for entrepreneurs and other innovation ecosystem actors, including, for instance, technology providers, libraries, and policymakers. This research study enriches the Generative AI technology acceptance theory and extends the existing literature by introducing new adoption variables and stages specific to entrepreneurship.
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(This article belongs to the Special Issue Strategies to Build Startup Dynamic Capabilities for a Sustainable and Competitive Advantage: Engineering & Management Perspective)
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A Method for Inspiring Radical Innovative Design Based on Cross-Domain Knowledge Mining
by
Fei Yu, Xiuchuan Jia, Xiaowei Zhao and Jing Li
Systems 2024, 12(3), 102; https://doi.org/10.3390/systems12030102 - 17 Mar 2024
Abstract
The reasonable application of cross-domain knowledge tends to promote the generation of radical innovation. However, it is difficult to accurately capture the cross-domain knowledge needed for radical innovation. To solve this problem, this paper proposes a method for inspiring radical innovative design based
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The reasonable application of cross-domain knowledge tends to promote the generation of radical innovation. However, it is difficult to accurately capture the cross-domain knowledge needed for radical innovation. To solve this problem, this paper proposes a method for inspiring radical innovative design based on FOS and technological distance measurement. First, the functional analysis of the problem product is carried out to determine the target function. Second, the patent sets of problem domain and target domains are constructed based on FOS. Then, this study optimizes the method of technological distance measurement and uses it to determine the optimal target domain. After further categorizing and screening the patents contained in the optimal target domain, specific cross-domain knowledge is pushed to designers. This method can help firms select the most appropriate cross-domain knowledge to design solutions for different problems, thus increasing the possibility of generating radical innovation. In the end, the method is validated in the design of a stovetop cleaning device.
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(This article belongs to the Topic Advanced Paradigms, Systems and Enabling Technologies for Product Life Cycle)
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Open AccessEditorial
Systems Thinking and Models in Public Health
by
Philippe J. Giabbanelli and Andrew Page
Systems 2024, 12(3), 101; https://doi.org/10.3390/systems12030101 - 16 Mar 2024
Abstract
In responding to population health challenges, epidemiologists want to identify causal associations between an exposure (e [...]
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(This article belongs to the Special Issue Systems Thinking and Models in Public Health)
Open AccessArticle
Data Science Supporting Lean Production: Evidence from Manufacturing Companies
by
Rossella Pozzi, Violetta Giada Cannas and Tommaso Rossi
Systems 2024, 12(3), 100; https://doi.org/10.3390/systems12030100 - 15 Mar 2024
Abstract
Research in lean production has recently focused on linking lean production to Industry 4.0 by discussing the positive relationship between them. In the context of Industry 4.0, data science plays a fundamental role, and operations management research is dedicating particular attention to this
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Research in lean production has recently focused on linking lean production to Industry 4.0 by discussing the positive relationship between them. In the context of Industry 4.0, data science plays a fundamental role, and operations management research is dedicating particular attention to this field. However, the literature on the empirical implementation of data science to lean production is still under-investigated and details are lacking in most of the reported contributions. In this study, multiple case studies were conducted involving the Italian manufacturing sector to collect evidence of the application of data science to support lean production and to understand it. The results provide empirical proof of the link and examples of a variety of data science techniques and tools that can be combined to support lean production practices. The findings offer insights into the applications of the traditional lean plan–do–check–act cycle, supporting feedback on performance metrics, total productive maintenance, total quality management, statistical process control, root cause analysis for problem-solving, visual management, and Kaizen.
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(This article belongs to the Special Issue Lean Manufacturing in Industry 4.0)
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The Impact of the Digital Economy on Total-Factor Carbon Emission Efficiency in the Yellow River Basin from the Perspectives of Mediating and Moderating Roles
by
Lei Nie, Xueli Bao, Shunfeng Song and Zhifang Wu
Systems 2024, 12(3), 99; https://doi.org/10.3390/systems12030099 - 15 Mar 2024
Abstract
China’s digital economy has been expanding rapidly in the past decade. This expansion is having a profound impact on the country’s economy. Using panel data on 97 prefecture-level cities in the Yellow River Basin from 2011 to 2020, this study investigates the multifaceted
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China’s digital economy has been expanding rapidly in the past decade. This expansion is having a profound impact on the country’s economy. Using panel data on 97 prefecture-level cities in the Yellow River Basin from 2011 to 2020, this study investigates the multifaceted relationship between the digital economy and total-factor carbon emission efficiency. The research yields three key findings: (1) The digital economy positively enhances overall carbon emission efficiency. This conclusion is drawn with robustness tests. (2) Green technology innovation serves as a partial mediator between the digital economy and total-factor carbon emission efficiency, and this mediation role is influenced by government intervention, which negatively moderates the relationship between the digital economy and green technology innovation but positively impacts the mediation role of green technology innovation between the digital economy and total-factor carbon emission efficiency. (3) The positive impact of the digital economy on total-factor carbon emission efficiency is more significant in the upper reaches, lower reaches, and resource-based cities of the Yellow River Basin. These findings provide new perspectives and empirical evidence for better understanding the relationship between digital economy development and total-factor carbon emission efficiency. They also provide policy recommendations for achieving strategic objectives, including digital economy development, carbon emission reduction, carbon peaking, and carbon neutrality.
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(This article belongs to the Section Systems Practice in Social Science)
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Effective Evolutionary Principles for System-of-Systems: Insights from Agent-Based Modeling in Vehicular Networks
by
Junjie Liu, Junxian Liu and Mengmeng Zhang
Systems 2024, 12(3), 98; https://doi.org/10.3390/systems12030098 - 15 Mar 2024
Abstract
System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution
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System-of-systems (SoS) evolution is a complex and unpredictable process. Although various principles to facilitate collaborative SoS evolution have been proposed, there is a lack of experimental data validating their effectiveness. To address these issues, we present an Agent-Based Model (ABM) for SoS evolution in the Internet of Vehicles (IoV), serving as a quantitative analysis tool for SoS research. By integrating multiple complex and rational behaviors of individuals, we aim to simulate real-world scenarios as accurately as possible. To simulate the SoS evolution process, our model employs multiple agents with autonomous interactions and incorporates external environmental variables. Furthermore, we propose three evaluation metrics: evolutionary time, degree of variation, and evolutionary cost, to assess the performance of SoS evolution. Our study demonstrates that enhanced information transparency significantly improves the evolutionary performance of distributed SoS. Conversely, the adoption of uniform standards only brings limited performance enhancement to distributed SoSs. Although our proposed model has limitations, it stands out from other approaches that utilize Agent-Based Modeling to analyze SoS theories. Our model focuses on realistic problem contexts and simulates realistic interaction behaviors. This study enhances the comprehension of SoS evolution processes and provides valuable insights for the formulation of effective evolutionary strategies.
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(This article belongs to the Topic Agents and Multi-Agent Systems)
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Sustainable Business Practices and the Role of Digital Technologies: A Cross-Regional Analysis
by
Samuel Plečko and Barbara Bradač Hojnik
Systems 2024, 12(3), 97; https://doi.org/10.3390/systems12030097 - 14 Mar 2024
Abstract
This study examines the relationship between digital transformation and sustainable practices within enterprises against the backdrop of global transformative forces framed within the holistic paradigm of systems thinking. It examines the extent to which digital advances either facilitate or impede the sustainable development
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This study examines the relationship between digital transformation and sustainable practices within enterprises against the backdrop of global transformative forces framed within the holistic paradigm of systems thinking. It examines the extent to which digital advances either facilitate or impede the sustainable development of companies, while also considering the systemic impact of demographic variables (such as gender, age, education), national income levels, and geographical regions on business sustainability. Using data from the Global Entrepreneurship Monitor (GEM), which encompasses 26,790 entrepreneurs in 47 countries, this research uses multinomial regression to assess how these factors influence companies’ commitment to social and environmental goals. A key finding is that the strategic use of digital technologies in sales processes significantly increases the likelihood that entrepreneurs will integrate social and environmental considerations into their decision-making. Notably, this conscientious approach to business is most prevalent among entrepreneurs in Latin America and the Caribbean. Our findings underscore the central role of digital technologies in driving sustainable business transformation while also highlighting the significant influence of regional socio-environmental contexts on business sustainability orientations.
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(This article belongs to the Special Issue Harnessing Systems Thinking in Entrepreneurship: Interdisciplinary Perspectives and Emerging Trends)
Open AccessArticle
Comparing the Complexity and Efficiency of Composable Modeling Techniques for Multi-Scale and Multi-Domain Complex System Modeling and Simulation Applications: A Probabilistic Analysis
by
Neal Wagner
Systems 2024, 12(3), 96; https://doi.org/10.3390/systems12030096 - 14 Mar 2024
Abstract
Modeling and simulation of complex systems frequently requires capturing probabilistic dynamics across multiple scales and/or multiple domains. Cyber–physical, cyber–social, socio–technical, and cyber–physical–social systems are common examples. Modeling and simulating such systems via a single, all-encompassing model is often infeasible, and thus composable modeling
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Modeling and simulation of complex systems frequently requires capturing probabilistic dynamics across multiple scales and/or multiple domains. Cyber–physical, cyber–social, socio–technical, and cyber–physical–social systems are common examples. Modeling and simulating such systems via a single, all-encompassing model is often infeasible, and thus composable modeling techniques are sought. Co-simulation and closure modeling are two prevalent composable modeling techniques that divide a multi-scale/multi-domain system into sub-systems, use smaller component models to capture each sub-system, and coordinate data transfer between component models. While the two techniques have similar goals, differences in their methods lead to differences in the complexity and computational efficiency of a simulation model built using one technique or the other. This paper presents a probabilistic analysis of the complexity and computational efficiency of these two composable modeling techniques for multi-scale/multi-domain complex system modeling and simulation applications. The aim is twofold: to promote awareness of these two composable modeling approaches and to facilitate complex system model design by identifying circumstances that are amenable to either approach.
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(This article belongs to the Section Complex Systems)
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Navigating Resource Challenges in Health Emergencies: The Role of Information Diffusion and Virus Spread in Demand Dynamics
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Yizhuo Zhou, Jianjun Zhang and Yundan Yang
Systems 2024, 12(3), 95; https://doi.org/10.3390/systems12030095 - 13 Mar 2024
Abstract
The dynamics of medical resource demand during public health crises pose significant challenges to emergency supply chain management, particularly within an evolving and complex social environment. To explore this, the interactive effects of information diffusion and virus spreading on medical resource demand are
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The dynamics of medical resource demand during public health crises pose significant challenges to emergency supply chain management, particularly within an evolving and complex social environment. To explore this, the interactive effects of information diffusion and virus spreading on medical resource demand are investigated using a novel three-layer coevolution “information–epidemic–resource” model through Markov process simulations. The study firstly identifies eight factors influencing demand fluctuations in terms of some city characteristics, such as media exposure, consistency of public opinion, self-protection level, and restrictive protection level, while categorizing resources into individual holdings and centralized storage. Then, extensive simulations are examined to elucidate the impact of these factors. The results reveal that various city characteristics can affect fluctuation in demand for both individual holdings and centralized storage. Inaccurate media information tends to inflate fluctuations, while higher public opinion consistency can reduce it. Reinforcing self-protection decreases the demand fluctuations of individuals, and effective restrictive protections can reduce fluctuations in centralized resource storage. Moreover, an analytical simulation of various city scenarios, underpinned by statistical data from selected Chinese and German cities, demonstrates that distinct city characteristics significantly influence medical resource demand changes during epidemics. This underscores the importance of tailoring emergency medical supply strategies to the specific developmental traits of different countries and cities. This study provides valuable insights to researchers, governments, and enterprises, enhancing their preparedness and response for emergency supply chain disruptions.
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(This article belongs to the Topic Risk Management in Public Sector)
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Leveraging Business Intelligence Systems for Enhanced Corporate Competitiveness: Strategy and Evolution
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Montserrat Jiménez-Partearroyo and Ana Medina-López
Systems 2024, 12(3), 94; https://doi.org/10.3390/systems12030094 - 13 Mar 2024
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This study contextualizes the transformative role of Business Intelligence (BI) over the past two decades, emphasizing its impact on business strategy and competitive advantage. Employing a dual-method approach, it integrates a bibliometric analysis using SciMAT with a qualitative examination of six key articles
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This study contextualizes the transformative role of Business Intelligence (BI) over the past two decades, emphasizing its impact on business strategy and competitive advantage. Employing a dual-method approach, it integrates a bibliometric analysis using SciMAT with a qualitative examination of six key articles from the Web of Science (WoS), analyzed through the Gioia methodology, focusing on BI and competitiveness. The aim is to examine the metamorphosis of Business Intelligence (BI) and how it has evolved from a traditionally supporting role to a central strategic player in shaping corporate strategy and business competitive advantage over the past two decades. It discusses the overall transformation of BI and provides an in-depth examination of the specific ways in which Business Intelligence tools have redefined the landscape in contemporary business practices. Key findings reveal BI’s pivotal role in enhancing knowledge management, innovation, and marketing capabilities. Challenges in BI implementation, such as the necessity for skilled personnel and adaptability to swift technological shifts, are also highlighted. Results advocate for a dynamic BI approach, adaptable to market trends and technological evolutions. The research demonstrates that BI tools, especially when integrated with technologies like AI, IoT, and machine learning, significantly enhances decision making and efficiency in socio–technical and management systems, leading to a paradigm shift in handling complex systems and adapting to changing environments.
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(This article belongs to the Special Issue Business Intelligence as a Tool for Business Competitiveness)
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Risk Perception-Based Project Contingency Management Framework
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Filippo Maria Ottaviani, Alberto De Marco, Carlo Rafele and Gabriel Castelblanco
Systems 2024, 12(3), 93; https://doi.org/10.3390/systems12030093 - 13 Mar 2024
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Project risk management (PRM) involves identifying risks, assessing their impact, and developing a contingency plan. A structured contingency management (CM) approach prevents subjective biases in analyzing risks and developing responses. Previous studies have either focused on improving the accuracy of risk estimates or
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Project risk management (PRM) involves identifying risks, assessing their impact, and developing a contingency plan. A structured contingency management (CM) approach prevents subjective biases in analyzing risks and developing responses. Previous studies have either focused on improving the accuracy of risk estimates or analyzed, from a qualitative perspective, the relationships between perceived risk and project performance. This study aimed to improve PRM by providing a risk-perception-based contingency management framework (CMF). The CMF guides contingency depletion based on two short- and long-term cost overrun indicators and their respective thresholds. Thresholds and the initial contingency reserve amount are determined by applying the Monte Carlo method to a stochastic, discrete-event, finite-horizon, dynamic project simulation model. The study developed the CMF through a structured approach, validating the simulation model on eight specific project configurations. The results prove that the framework can be applied to any project, shaping the risk response strategy. This study contributes to PRM by explaining the relationships between risk perception and risk responses and providing a prescriptive CM tool.
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(This article belongs to the Section Project Management)
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Methods for Coordinating Optimization of Urban Building Clusters and District Energy Systems
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Peng Wu and Yisheng Liu
Systems 2024, 12(3), 92; https://doi.org/10.3390/systems12030092 - 12 Mar 2024
Abstract
In the face of increasingly severe global climate change, achieving zero-carbon development goals has gradually become a consensus across various industries. Enhancing the electrification level of building energy use and increasing the proportion of renewable energy applications are primary means to achieve zero-carbon
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In the face of increasingly severe global climate change, achieving zero-carbon development goals has gradually become a consensus across various industries. Enhancing the electrification level of building energy use and increasing the proportion of renewable energy applications are primary means to achieve zero-carbon development in the construction sector, which also imposes higher demands on energy system planning and operation. This study focuses on urban building clusters and district energy systems, proposing coordinated optimization methods for energy supply and demand. On the demand side, strategies such as utilizing energy storage from electric vehicles are applied to enhance the flexibility of building energy use, along with methods to improve building load leveling rates and increase renewable energy penetration rates. On the supply side, a dual-layer planning method is proposed for the optimal configuration and operation of district energy systems considering the construction of shared energy storage stations. Results indicate that the optimization methods for urban building clusters significantly improve the flexibility of building energy use, and different functional compositions of building clusters can enhance load leveling and renewable energy penetration rates to a certain extent. The dual-layer optimization method for district energy systems can further exploit the potential of building energy flexibility, thereby achieving a balance between economic and environmental benefits.
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(This article belongs to the Topic SDGs 2030 in Buildings and Infrastructure)
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Research on the Decision Making of Value Chain Reconstruction of Chinese Port Enterprises under the Background of Free Trade Zone Policy
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Min Wan, Haibo Kuang, Peng Jia and Sue Zhao
Systems 2024, 12(3), 91; https://doi.org/10.3390/systems12030091 - 12 Mar 2024
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This paper aims to solve the decision-making problem of value chain reconstruction of Chinese port enterprises under the background of the Free Trade Zone policy. Based on value chain theory and system dynamics method, this paper constructs a simulation model that can simulate
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This paper aims to solve the decision-making problem of value chain reconstruction of Chinese port enterprises under the background of the Free Trade Zone policy. Based on value chain theory and system dynamics method, this paper constructs a simulation model that can simulate the value-added change process of port enterprises under different combination input conditions. Furthermore, it conducts simulation case studies. The research indicates that the Free Trade Zone policy has a significant promoting effect on the restructuring of port enterprise value chains and the transformation and upgrading of enterprises. Moreover, considering the impact of the Free Trade Zone policy and limited resources, the overall benefits to port enterprises from combined investments are superior to those from single-factor investments. According to the value chain theory, the business segments of a port are decomposed into ancillary value activities, basic value activities, and external value activities. The investments in these three types of value activities play roles, respectively, in enhancing the operational efficiency of port enterprises, expanding the business scope of port enterprises, and strengthening the core competitiveness of port enterprises. From the overall perspective of the system, Shanghai Port can obtain the maximum operating profit when the endogenous factor input rate is 13%, the basic factor input rate is 4%, and the exogenous factor input rate is 13%. The findings of this research provide a decision-making reference for Chinese port enterprises to realize value reconstruction, transformation, and upgrading in the context of the Free Trade Zone policy.
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Open AccessArticle
Using a Metadata Approach to Extend the Functional Resonance Analysis Method to Model Quantitatively, Emergent Behaviours in Complex Systems
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Rees Hill and David Slater
Systems 2024, 12(3), 90; https://doi.org/10.3390/systems12030090 - 12 Mar 2024
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In an increasingly complex world there is a real, urgent need for methodologies to enable engineers to model complex sociotechnical systems, as these now seem to describe the majority of systems in use today. This is, of course, exacerbated by the increasing involvement
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In an increasingly complex world there is a real, urgent need for methodologies to enable engineers to model complex sociotechnical systems, as these now seem to describe the majority of systems in use today. This is, of course, exacerbated by the increasing involvement and augmentation with “black box” AI contributions. Hollnagel produced a methodology (FRAM) which did allow the analyst insights into these systems’ behaviour, but the model-based system engineering applications demand numbers and a quantitative approach. In the last 10 years, this original approach, developed to model systems as sets of interactive, interdependent “functions” (abstracted from agent or component details), has been further developed to the point where it can take the basic data and structures from the current component-focussed system engineering “models”, and can pull them all together into dynamic models (as opposed to the static, fixed System Theoretic Process Accimaps) from which analysts can discern how they really work in practice, and predict the emergent behaviours characteristic of complex systems. This paper describes how the FRAM methodology has now been extended to provide these extra, essential attributes. It also describes its implementation using an open-source software, freely available for use and verification on the GitHub site.
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Open AccessArticle
Impact of Static Urban Traffic Flow-Based Traffic Weighted Multi-Maps Routing Strategies on Pollutant Emissions
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Alvaro Paricio-Garcia and Miguel A. Lopez-Carmona
Systems 2024, 12(3), 89; https://doi.org/10.3390/systems12030089 - 12 Mar 2024
Abstract
Addressing urban traffic congestion is a pressing issue requiring efficient solutions that need to be analyzed regarding travel time and pollutant emissions. The traffic weighted multi-maps (TWM) method has been proposed as an efficient mechanism for congestion mitigation that enables differential traffic routing
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Addressing urban traffic congestion is a pressing issue requiring efficient solutions that need to be analyzed regarding travel time and pollutant emissions. The traffic weighted multi-maps (TWM) method has been proposed as an efficient mechanism for congestion mitigation that enables differential traffic routing and path diversity by strategically distributing different network views (maps) to the drivers. Previous works have focused on TWM generation by creating optimal edge weights, but the complexity exponentially increases with the network size and traffic group diversity. This work describes how congestion and emissions can be addressed using TWM maps based on the k-shortest paths for the traffic flows (instead of individuals) that are optimally assigned and distributed to the components of the traffic flow. The map allocation strategies optimal TWM (OTV), optimal TWM per path flow with linear constraints (LCTV), and its variant unconstrained optimal TWM per path flow (UCTV) are described. They use maps generated from the k-shortest paths of the traffic flows (kSP-TWM). The heuristic solution obtained is compared with the theoretical static traffic assignment estimation baseline with different configurations, regarding congestion reduction, total travel time enhancement, and pollutant emissions. Experiments are developed using a synthetic traffic grid network scenario with a mesoscopic simulation. They show that the solution provided is adequate for its proximity to the theoretical equilibrium solutions and can generate minimum emissions patterns. The presented solution opens new possibilities for further congestion and pollutant management studies and seamless integration with existing traffic management frameworks.
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(This article belongs to the Special Issue Advancements in Practical Applications of Agents, Multi-Agent Systems and Simulating Cognitive Mimetics)
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Open AccessReview
The Integration of Complex Systems Science and Community-Based Research: A Scoping Review
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Travis R. Moore, Nicholas Cardamone, Helena VonVille and Robert W. S. Coulter
Systems 2024, 12(3), 88; https://doi.org/10.3390/systems12030088 - 09 Mar 2024
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Complex systems science (CSS) and community-based research (CBR) have emerged over the past 50 years as complementary disciplines. However, there is a gap in understanding what has driven the recent proliferation of integrating these two disciplines to study complex and relevant issues. In
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Complex systems science (CSS) and community-based research (CBR) have emerged over the past 50 years as complementary disciplines. However, there is a gap in understanding what has driven the recent proliferation of integrating these two disciplines to study complex and relevant issues. In this review, we report on the results of a scoping review of articles that utilized both disciplines. After two levels of reviewing articles using DistillerSR, a web-based platform designed to streamline and facilitate the process of conducting systematic reviews, we used two forms of natural language processing to extract data. We developed a novel named entity recognition model to extract descriptive information from the corpus of articles. We also conducted dynamic topic modeling to deductively examine in tandem the development of CSS and CBR and to inductively discover the specific topics that may be driving their use in research and practice. We find that among the CSS and CBR papers, CBR topic frequency has grown at a faster pace than CSS, with CBR using CSS concepts and techniques more often. Four topics that may be driving this trend are collaboration within social systems, business management, food and land use and knowledge, and water shed management. We conclude by discussing the implications of this work for researchers and practitioners who are interested in studying and solving complex social, economic, and health-related issues.
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Open AccessArticle
Spatial Characteristics and Influencing Factors of Intercity Innovative Competition Relations in China
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Xinyu Yang, Lizhen Shen, Xia Wang and Xiao Qin
Systems 2024, 12(3), 87; https://doi.org/10.3390/systems12030087 - 07 Mar 2024
Abstract
In the knowledge economy era, innovation has become a key emphasis for urban competitions. This paper constructs a theoretical research framework that integrates the basic understandings, influencing factors and ensuing results of intercity innovative competition relations. On the basis of data from the
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In the knowledge economy era, innovation has become a key emphasis for urban competitions. This paper constructs a theoretical research framework that integrates the basic understandings, influencing factors and ensuing results of intercity innovative competition relations. On the basis of data from the general programs of the National Natural Science Foundation of China from 2005 to 2019, this paper constructs intercity innovative competition relations in China, analyses their spatial distribution and quantitative characteristics, and quantitatively investigates the impact of urban innovation capacity and multidimensional proximity (e.g., geographical proximity, institutional proximity and cognitive proximity) on intercity innovative competition relations through a negative binomial model. The study obtained the following findings: (1) In terms of the overall intercity innovative competition relations, the intensity of China’s intercity innovative competition relations gradually increased from 2005 to 2019, with a spatial clustering towards cities with high administrative ranks (e.g., municipalities directly under the central government, sub-provincial cities and provincial capitals); Beijing is always at the centre of innovative competition relations, but its standing has slightly slipped in recent years. (2) From the perspective of disciplines, cities can become benchmarks in particular fields of innovative competitions by competing according to their disciplinary strengths; intercity innovative competition relations in China vary across various academic disciplines. (3) In terms of influencing factors, urban innovation capacity has significant positive effects on intercity innovative competition relations; geographical proximity, institutional proximity and cognitive proximity all have significant positive effects on innovative competition relations; and interactions occur between multidimensional proximities, including a complementary effect between geographical proximity and institutional proximity, a substitutive effect between cognitive proximity and geographical proximity, and a substitutive effect between cognitive proximity and institutional proximity.
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(This article belongs to the Special Issue Innovation, Digital Transformation and Process Improvement Towards a Better Efficiency on Industrial and Management Systems)
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Open AccessArticle
A Dynamic Collision Risk Assessment Model for the Traffic Flow on Expressways in Urban Agglomerations in North China
by
Bing Li, Xiaoduan Sun, Yulong He and Meng Zhang
Systems 2024, 12(3), 86; https://doi.org/10.3390/systems12030086 - 06 Mar 2024
Abstract
Expressways in urban agglomerations are important in connecting cities, thus attracting great attention from researchers in the expressways risk assessment. However, there is a lack of safety assessment models suitable for the characteristics of expressways in Chinese urban agglomerations, and the nature and
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Expressways in urban agglomerations are important in connecting cities, thus attracting great attention from researchers in the expressways risk assessment. However, there is a lack of safety assessment models suitable for the characteristics of expressways in Chinese urban agglomerations, and the nature and mode of dynamic risks on Chinese highways are still unclear. Therefore, this study adopts the Adaptive Neural Fuzzy Inference System (ANFIS) and the method of decision tree, combined with data from the Beijing section of the Beijing Harbin Expressway, to model the risk of accident-prone highways in urban agglomerations. To determine the optimal model, we evaluated the model’s bias at different time intervals. In addition, key factors affecting highway safety were analyzed, providing scientific support for the risk prevention of highways in urban agglomerations in China.
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(This article belongs to the Special Issue Application of System Engineering and Complex Theory in Transportation)
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