Systems

Open AccessArticle

The Impacts of Payment Policy on Performance of Human Resource Market System: Agent-Based Modeling and Simulation of Growth-Oriented Firms

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Systems 2023, 11(6), 298; https://doi.org/10.3390/systems11060298 (registering DOI) - 10 Jun 2023

Abstract

The impact of human resource management (HRM) on corporate growth is a crucial research topic, especially for growth-oriented firms. This paper aims to study how different payment policies (such as recruitment and dismissal strategies and payment plans) affect the human resource market system. [...] Read more.

The impact of human resource management (HRM) on corporate growth is a crucial research topic, especially for growth-oriented firms. This paper aims to study how different payment policies (such as recruitment and dismissal strategies and payment plans) affect the human resource market system. Based on the HRM characteristics of growth-oriented firms, we develop an agent-based model to simulate the decision-making and interaction behaviors of firms and workers. The system performance is measured by six indicators: the average profit, the profit Gini coefficient, the average output of firms, the average payment, the payment Gini coefficient, and the employment rate of workers. According to the simulation results and statistical analysis, the recruitment plan is the only key factor that significantly impacts all performance indicators other than the employment rate, and companies should pay extra attention to such plans. This study also finds that the changing worker’s payment gap is influenced by industry growth and their abilities, and that the payment cap policy has a positive impact on the development of growth-oriented firms in the startup stage. Full article

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

Open AccessArticle

Conflicting Bundle Allocation with Preferences in Weighted Directed Acyclic Graphs: Application to Orbit Slot Allocation Problems

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Systems 2023, 11(6), 297; https://doi.org/10.3390/systems11060297 (registering DOI) - 09 Jun 2023

Abstract

We introduce resource allocation techniques for problems where (i) the agents express requests for obtaining item bundles as compact edge-weighted directed acyclic graphs (each path in such a graph is a bundle whose valuation is the sum of the weights of the traversed [...] Read more.

We introduce resource allocation techniques for problems where (i) the agents express requests for obtaining item bundles as compact edge-weighted directed acyclic graphs (each path in such a graph is a bundle whose valuation is the sum of the weights of the traversed edges), and (ii) the agents do not bid on the exact same items but may bid on conflicting items that cannot be both assigned or that require accessing a specific resource with limited capacity. This setting is motivated by real applications such as Earth observation slot allocation, virtual network functions, or multi-agent path finding. We model several directed path allocation problems (vertex-constrained and resource-constrained), investigate several solution methods (qualified as exact or approximate, and utilitarian or fair), and analyze their performances on an orbit slot ownership problem, for realistic requests and constellation configurations. Full article

(This article belongs to the Special Issue Frontiers in Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

Open AccessArticle

Deep Learning-Based Approach for Detecting DDoS Attack on Software-Defined Networking Controller

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Amran Mansoor

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Mohammed Anbar

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Abdullah Ahmed Bahashwan

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Basim Ahmad Alabsi

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Shaza Dawood Ahmed Rihan

Systems 2023, 11(6), 296; https://doi.org/10.3390/systems11060296 - 09 Jun 2023

Abstract

The rapid growth of cloud computing has led to the development of the Software-Defined Network (SDN), which is a network strategy that offers dynamic management and improved performance. However, security threats are a growing concern, particularly with the SDN controller becoming an attractive [...] Read more.

The rapid growth of cloud computing has led to the development of the Software-Defined Network (SDN), which is a network strategy that offers dynamic management and improved performance. However, security threats are a growing concern, particularly with the SDN controller becoming an attractive target for malicious actors and potential Distributed Denial of Service (DDoS) attacks. Many researchers have proposed different approaches to detecting DDoS attacks. However, those approaches suffer from high false positives, leading to low accuracy, and the main reason behind this is the use of non-qualified features and non-realistic datasets. Therefore, the deep learning (DL) algorithmic technique can be utilized to detect DDoS attacks on SDN controllers. Moreover, the proposed approach involves three stages, (1) data preprocessing, (2) cross-feature selection, which aims to identify important features for DDoS detection, and (3) detection using the Recurrent Neural Networks (RNNs) model. A benchmark dataset is employed to evaluate the proposed approach via standard evaluation metrics, including false positive rate and detection accuracy. The findings indicate that the recommended approach effectively detects DDoS attacks with average detection accuracy, average precision, average FPR, and average F1-measure of 94.186 %, 92.146%, 8.114%, and 94.276%, respectively. Full article

Open AccessArticle

ICT Use, Environmental Quality Perception and Farmers’ Participation in Domestic Waste Separation: Micro-Survey Data from China

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Systems 2023, 11(6), 295; https://doi.org/10.3390/systems11060295 - 09 Jun 2023

Abstract

Encouraging farmers to participate in domestic waste sorting is an important initiative to optimize rural habitats and build a beautiful countryside. Using data from a sample of 2126 farmers obtained from a Chinese micro-survey, this paper empirically investigates the impact of ICT use [...] Read more.

Encouraging farmers to participate in domestic waste sorting is an important initiative to optimize rural habitats and build a beautiful countryside. Using data from a sample of 2126 farmers obtained from a Chinese micro-survey, this paper empirically investigates the impact of ICT use on farmers’ domestic waste classification using OLS and ordered probability models and examines the mediating role of environmental quality perception. The study shows that ICT use has a significant negative effect on farmers’ environmental quality perceptions and a significant positive effect on farmers’ domestic waste sorting. Furthermore, the direct positive effect of ICT use on farmers’ domestic waste sorting is greater than its negative indirect effect through environmental quality perceptions. Finally, farmers with ICT use are more willing to participate in domestic waste sorting. This suggests that farmers may have a tendency to complain and express dissatisfaction on the internet but still behave in a way that is participatory in waste sorting. The results of the study still hold after a rigorous robustness test. In addition, there are significant differences in the impact of ICT use on different age and income groups, so policies should be tailored to different groups. More attention should be paid to the environmental welfare effects on older and lower-income groups. Full article

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Open AccessArticle

Measuring Patent Similarity Based on Text Mining and Image Recognition

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Wenguang Lin

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Wenqiang Yu

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Renbin Xiao

Systems 2023, 11(6), 294; https://doi.org/10.3390/systems11060294 - 08 Jun 2023

Abstract

Patent application is one of the important ways to protect innovation achievements that have great commercial value for enterprises; it is the initial step for enterprises to set the business development track, as well as a powerful means to protect their core competitiveness. [...] Read more.

Patent application is one of the important ways to protect innovation achievements that have great commercial value for enterprises; it is the initial step for enterprises to set the business development track, as well as a powerful means to protect their core competitiveness. The emergence of a large amount of patent data makes the effective detection of patent data difficult, and patent infringement cases occur frequently. Manual measurement in patent detection is slow, costly, and subjective, and can only play an auxiliary role in measuring the validity of patents. Protecting the inventive achievements of patent holders and realizing more accurate and effective patent detection were the issues explored by academics. There are five main methods to measure patent similarity: clustering-based method, vector space model (VSM)-based method, subject–action–object (SAO) structure-based method, deep learning-based method, and patent structure-based method. To solve this problem, this paper proposes a calculation method to fuse the similarity of patent text and image. Firstly, the SAO structure extraction technique is used for the patent text to obtain the effective content of the text, and the SAO structure is compared for similarity; secondly, the patent image information is extracted and compared; finally, the patent similarity is obtained by fusing the two aspects of information. The feasibility and effectiveness of the scheme are proven by studying a large number of patent similarity cases in the field of mechanical structures. Full article

(This article belongs to the Section Systems Practice in Engineering)

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Open AccessArticle

Addressing Environmental Protection Supplier Selection Issues in a Fuzzy Information Environment Using a Novel Soft Fuzzy AHP–TOPSIS Method

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Hsiang-Yu Chung

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Kuei-Hu Chang

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Jr-Cian Yao

Systems 2023, 11(6), 293; https://doi.org/10.3390/systems11060293 - 07 Jun 2023

Abstract

With the current heightened promotion of environmental awareness, issues related to environmental protection have become a critical component of economic development. The emergence of new environment-friendly materials and simple packaging, and other environmental awareness demands in recent years, have prompted manufacturers to pay [...] Read more.

With the current heightened promotion of environmental awareness, issues related to environmental protection have become a critical component of economic development. The emergence of new environment-friendly materials and simple packaging, and other environmental awareness demands in recent years, have prompted manufacturers to pay more attention to planning greener production and supply processes than before. Many scholars have been urged to investigate the issues related to environmental protection and the sustainable economy of green suppliers. However, many factors needed to be considered, such as the price, cost, benefit, reputation, and quality involved in the process of green supplier selection. These factors require quantitative and qualitative analysis information, making the issue of environmental protection a multi-criteria decision making (MDCM) problem. Traditional research methods are unable to effectively and objectively handle the MCDM problem of green supplier selection due to the problem’s complexity and the method’s inclination towards biased conclusions. To resolve the complicated problem of green supplier selection, this study combined the fuzzy analytic hierarchy process (AHP), the technique for order preference by similarity to ideal solution (TOPSIS), and the 2-tuple fuzzy linguistic model (2-tuple FLM) and corrected the ranking of the possible green suppliers. The computation results were also compared with the typical TOPSIS and AHP–TOPSIS methods. Through the numerical verification of the actual case for the green supplier, the test results suggested that the proposed method could perform an objective evaluation of expert-provided information while also retaining all their valuable insights. Full article

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Open AccessArticle

Exploring the Potential of Mixed Reality in Enhancing Student Learning Experience and Academic Performance: An Empirical Study

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Ahmad Almufarreh

Systems 2023, 11(6), 292; https://doi.org/10.3390/systems11060292 - 06 Jun 2023

Abstract

In recent years, mixed reality (MR) technology has emerged as a promising tool in the field of education, offering immersive and interactive learning experiences for students. However, there is a need to comprehensively understand the impact of MR technology on students’ academic performance. [...] Read more.

In recent years, mixed reality (MR) technology has emerged as a promising tool in the field of education, offering immersive and interactive learning experiences for students. However, there is a need to comprehensively understand the impact of MR technology on students’ academic performance. This research aims to examine the effect of mixed reality technology in the educational setting and understand its role in enhancing the student’s academic performance through the student’s novel learning experiences and satisfaction with the learning environment. The present research has employed a quantitative research design to undertake the research process. The survey questionnaire based upon the five-point Likert scale was used as the data collection instrument. There were 308 respondents studying at various educational institutes in Saudi Arabia, all of whom were using mixed reality as part of their educational delivery. The findings of the present research have indicated that the application of mixed reality by creating experiential learning, interactivity and enjoyment can significantly enhance the student’s novel experience, which can directly enhance students’ satisfaction with learning objects and the learning environment, as well as indirectly enhancing the student’s academic performance. The research offers various kinds of theoretical implications and policy implications to researchers and policymakers. Full article

(This article belongs to the Special Issue Advances in Virtual Reality, Augmented Reality and Augmented Virtuality)

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Open AccessArticle

Agricultural Green Ecological Efficiency Evaluation Using BP Neural Network–DEA Model

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Qiang Sun

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Yu-Jiao Sui

Systems 2023, 11(6), 291; https://doi.org/10.3390/systems11060291 - 05 Jun 2023

Abstract

The evaluation of agricultural green ecological efficiency can reflect the capacity of agriculture for sustainable development and reduce the endogenous pollution caused by agricultural waste in order to alleviate the weakening of agricultural ecosystems. Taking the agricultural green economy as the research object, [...] Read more.

The evaluation of agricultural green ecological efficiency can reflect the capacity of agriculture for sustainable development and reduce the endogenous pollution caused by agricultural waste in order to alleviate the weakening of agricultural ecosystems. Taking the agricultural green economy as the research object, an evaluation index system based on the theories of green economic efficiency and economic growth for agricultural green ecological efficiency was constructed, and the impact mechanisms of specific indicators on agricultural green ecological efficiency were empirically explored. In addition, based on the data envelopment analysis (DEA) model, the overall agricultural green ecological efficiency of China from 2002 to 2021 was evaluated and the efficiency characteristics were analyzed from multiple perspectives. Then, the indicators of policy, finance, communication, society and other aspects were added in order to construct a comprehensive evaluation model of agricultural green ecological efficiency using a combination of DEA and a BP neural network, and the feasibility of the model was verified. The results indicate that the agricultural green ecological efficiency increased from 0.7340 in 2002 to 0.8205 in 2021, an increase of 11.78%. Additionally, the technological efficiency of China’s agricultural green ecological system did not show a very obvious trend of divergence. The results of the BP neural network were consistent with those obtained using DEA, and the overall evolution trend of the calculated BP neural network and DEA were mutually verified and integrated. The effectiveness and accuracy of the BP neural network was verified via a comparison with DEA. Full article

(This article belongs to the Topic Sustainable Development and Coordinated Governance of Urban and Rural Areas under the Guidance of Ecological Wisdom)

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Open AccessArticle

Online News Media Analysis on Information Management of “G20 Summit” Based on Social Network Analysis

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Systems 2023, 11(6), 290; https://doi.org/10.3390/systems11060290 - 05 Jun 2023

Abstract

This paper contributes to the Special Issue on Communication for the Digital Media Age by investigating the factors that influence the management of political information on online news media platforms, specifically Twitter and Weibo. Using the recent “G20 Summit” as a case study, [...] Read more.

This paper contributes to the Special Issue on Communication for the Digital Media Age by investigating the factors that influence the management of political information on online news media platforms, specifically Twitter and Weibo. Using the recent “G20 Summit” as a case study, this study employs a mixed-methods approach that incorporates both deductive and inductive reasoning. Social network analysis (SNA) and graph theory are used to evaluate specific social relationships in the context of the G20 summit, while a combination of structured and content (semantic) analysis is performed. The findings indicate that individual power is becoming increasingly important in the age of online news media. Individuals contribute significantly to the diffusion of information and may play a decisive role in the future. The study also finds that the frequency of retweets increases as the reciprocity ratio increases, and mentions may be the most effective method for delivering political news on online news media platforms. Practical implications suggest strategies for managing information diffusion effectively. Additionally, this study provides insights into effective information diffusion on online news media platforms that can be utilized in health communication management during the COVID-19 era. This study expands theoretical understanding by investigating the role of individual power in the age of online news media and enriching the literature on online news media through the use of structured and content analysis based on social network analysis. Full article

(This article belongs to the Special Issue Communication for the Digital Media Age)

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Open AccessArticle

The System for Extracting Crime Elements and Predicting Excavation-Type Heritage Crimes Based on Deep Learning Models

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Systems 2023, 11(6), 289; https://doi.org/10.3390/systems11060289 - 05 Jun 2023

Abstract

Heritage crimes can result in the significant loss of cultural relics and predicting them is crucial. To address the issues of inconsistent textual information format and the challenge of preventing and combating heritage crimes, this paper develops a system that extracts crime elements [...] Read more.

Heritage crimes can result in the significant loss of cultural relics and predicting them is crucial. To address the issues of inconsistent textual information format and the challenge of preventing and combating heritage crimes, this paper develops a system that extracts crime elements and predict heritage crime occurrences. The system comprises two deep-learning models. The first model, Bi-LSTM + CRF, is constructed to automatically extract crime elements and perform spatio-temporal analysis of crimes based on them. By integrating routine activity theory, social disorder theory, and practical field experience, the research reveals that holidays and other special days (SD) perform a critical role as influential factors in heritage crimes. Building upon these findings, the second model, LSTM + SD, is constructed to predict excavation-type heritage crimes. The results demonstrate that the model with the introduction of the holiday factor improves the RMSE and MAE by 6.4% and 47.8%, respectively, when compared to the original LSTM model. This paper presents research aimed at extracting crime elements and predicting excavation-type heritage crimes. With the ongoing expansion of data volume, the practical significance of the proposed system is poised to escalate. The results of this study are expected to provide decision-making support for heritage protection departments and public security authorities in preventing and combating crimes. Full article

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Open AccessArticle

Preparedness Indicator System for Education 4.0 with FUCOM and Rough Sets

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Joerabell Lourdes Aro

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Samantha Shane Evangelista

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Systems 2023, 11(6), 288; https://doi.org/10.3390/systems11060288 - 05 Jun 2023

Abstract

In view of the recent education sectoral transition to Education 4.0 (EDUC4), evaluating the preparedness of higher education institutions (HEIs) for EDUC4 implementation remains a gap in the current literature. Through a comprehensive review, seven criteria were evaluated, namely, human resources, infrastructure, financial, [...] Read more.

In view of the recent education sectoral transition to Education 4.0 (EDUC4), evaluating the preparedness of higher education institutions (HEIs) for EDUC4 implementation remains a gap in the current literature. Through a comprehensive review, seven criteria were evaluated, namely, human resources, infrastructure, financial, linkages, educational management, learners, and health and environment. This work offers two crucial contributions: (1) the development of an EDUC4 preparedness indicator system and (2) the design of a computational structure that evaluates each indicator and computes an aggregate preparedness level for an HEI. Using the full consistency method (FUCOM) to assign the priority weights of EDUC4 criteria and the rough set theory to capture the ambiguity and imprecision inherent in the measurement, this study offers an aggregate EDUC4 preparedness index to holistically capture the overall preparedness index of an HEI towards EDUC4. An actual case study is presented to demonstrate the applicability of the proposed indicator system. After a thorough evaluation, the results indicate that human resources were the most critical criterion, while health and environment ranked last. Insights obtained from the study provide HEIs with salient information necessary for decision making in various aspects, including the design of targeted policies and the allocation of resources conducive to implementing EDUC4 initiatives. The proposed indicator system can be a valuable tool to guide HEIs in pursuing EDUC4, resulting in a more effective and efficient implementation of this educational paradigm. Full article

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Open AccessArticle

Data-Driven Futuristic Scenarios: Smart Home Service Experience Foresight Based on Social Media Data

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Yu Cheng

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Sanghun Sul

Systems 2023, 11(6), 287; https://doi.org/10.3390/systems11060287 - 03 Jun 2023

Abstract

Exploring future scenarios can consider future generations and society from a long-term perspective. A Futures Triangle is an approach used for mapping future scenarios. In general, the Futures Triangle collects weak signals using qualitative research methods. However, collecting weak signals qualitatively is limited [...] Read more.

Exploring future scenarios can consider future generations and society from a long-term perspective. A Futures Triangle is an approach used for mapping future scenarios. In general, the Futures Triangle collects weak signals using qualitative research methods. However, collecting weak signals qualitatively is limited by its small data size and manual data analysis errors. To overcome those limitations, this study proposes the data-driven futuristic scenario approach. This approach analyzes a large number of social perceptions existing in social networks as weak signals via semantic network analysis. Using our proposed data-driven approach, researchers can quantitatively collect weak signals for a Futures Triangle. To verify the applicability of the proposed method, we conducted a case study on the Chinese smart home service experience. The dataset consists of 2421 posts containing the keyword “smart home experience” on the Chinese social media platform Weibo. Three future scenarios were constructed using the proposed method. The results demonstrate the feasibility of the proposed methodology. The data-driven futuristic scenario approach has the advantage of quantitatively analyzing a large amount of stakeholder data to provide weak signals for the Futures Triangle. We suggest that the data-driven futuristic scenario approach serves as a supplementary method, combined with the traditional Futures Triangle approach, to comprehensively explore future scenarios. Full article

(This article belongs to the Special Issue Futures Thinking in Design Systems and Social Transformation)

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Open AccessArticle

Spatiotemporal Hybrid Air Pollution Early Warning System of Urban Agglomeration Based on Adaptive Feature Extraction and Hesitant Fuzzy Cognitive Maps

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Xiaoyang Gu

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Hongmin Li

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Henghao Fan

Systems 2023, 11(6), 286; https://doi.org/10.3390/systems11060286 - 02 Jun 2023

Abstract

Long-term exposure to air pollution will pose a serious threat to human health. Accurate prediction can help people reduce exposure risks and promote environmental pollution control. However, most previous studies have ignored the spatial spillover of air pollution, i.e., that the current region’s [...] Read more.

Long-term exposure to air pollution will pose a serious threat to human health. Accurate prediction can help people reduce exposure risks and promote environmental pollution control. However, most previous studies have ignored the spatial spillover of air pollution, i.e., that the current region’s air quality is also correlated with that of geographically adjacent areas. Therefore, this paper proposes an innovative spatiotemporal hybrid early warning system based on adaptive feature extraction and improved fuzzy cognition maps. Firstly, a spatial spillover analysis model based on the Moran index and local gravitational clustering was proposed to capture the diffusion and concentration characteristics of air pollution between regions. Then, an adaptive feature extraction model based on an optimized Hampel filter was put forward to process and correct the outliers in the original series. Finally, a hesitant fuzzy information optimized fuzzy cognitive maps model was proposed to forecast the air quality of urban agglomeration. The experimental results show that the air quality forecasting accuracy of urban agglomerations can be significantly improved when the geographical conditions and other interactions among cities are comprehensively considered, and the proposed model outperformed other benchmarks and can be used as a powerful analytical tool during urban agglomeration air quality management. Full article

(This article belongs to the Special Issue Recent Advances and Applications of Forecasting and Evaluation Techniques in Energy, Environment and Economy Management)

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Open AccessArticle

Forecasting per Capita Energy Consumption in China Using a Spatial Discrete Grey Prediction Model

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Huiping Wang

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Zhun Zhang

Systems 2023, 11(6), 285; https://doi.org/10.3390/systems11060285 - 02 Jun 2023

Abstract

To overcome the limitations of the present grey models in spatial data analysis, a spatial weight matrix is incorporated into the grey discrete model to create the SDGM(1,1,m) model, and the L1-SDGM(1,1,m) model is proposed, considering the time lag effect to realize the [...] Read more.

To overcome the limitations of the present grey models in spatial data analysis, a spatial weight matrix is incorporated into the grey discrete model to create the SDGM(1,1,m) model, and the L1-SDGM(1,1,m) model is proposed, considering the time lag effect to realize the simultaneous forecasting of spatial data. The validation of the SDGM(1,1,m) and L1-SDGM(1,1,m) models is achieved, and finally, the per capita energy consumption levels (PCECs) of 30 provinces in China from 2020 to 2025 is predicted using SDGM(1,1,m) with a metabolic mechanism. We draw the following conclusions. First, the SDGM(1,1,m) and L1-SDGM(1,1,m) models established in this paper are reasonable and improve forecasting accuracy while supporting interactive regional forecasting. Second, although SDGM(1,1,m) resembles the DGM(1,n) model, their modeling conditions and targets are different. Third, the SDGM(1,1,m) and L1-SDGM(1,1,m) models can be used to effectively analyze the spatial spillover effects within the selected modeling interval while achieving accurate predictions; notably, from 2010 to 2017, the PCECs of Inner Mongolia and Qinghai were most affected by spatial factors, while the PCECs of Jilin, Jiangxi, and other provinces were influenced little by spatial factors. Fourth, predictions indicate that the PCECs of most Chinese provinces will increase under the current grey conditions, while the PCECs of provinces such as Beijing are expected to decrease. Full article

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Open AccessArticle

Leading Role of Big Data Analytic Capability in Innovation Performance: Role of Organizational Readiness and Digital Orientation

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Abdelmohsen A. Nassani

Systems 2023, 11(6), 284; https://doi.org/10.3390/systems11060284 - 01 Jun 2023

Abstract

The advancement of technology offers various opportunities for business organizations to achieve sustainable growth. Through emerging technologies, business organizations are able to collect and analyze essential information vital for the acceleration of innovation. Therefore, this study investigated how big data contribute to the [...] Read more.

The advancement of technology offers various opportunities for business organizations to achieve sustainable growth. Through emerging technologies, business organizations are able to collect and analyze essential information vital for the acceleration of innovation. Therefore, this study investigated how big data contribute to the innovation activities of manufacturing entrepreneurs in terms of big data analytic capability (BDAC). The aim of this study was to relate BDAC to organizational readiness and innovation performance (IP). Moreover, we examined the mediating role of organizational readiness between BDAC and IP. We also examined the strengthening role of digital orientation. To collect the study data, we approached 494 frontline managers of the manufacturing sector of Saudi Arabia. The collected data were analyzed using statistical techniques such as descriptive, correlation, and hierarchical regression techniques. We found that BDAC plays a vital role in developing organizational readiness and IP. The findings also proved that organizational readiness has a significant effect on IP. The results revealed that organizational readiness mediates between BDAC and IP. Full article

(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

An Assessment of the Effects of Food Districts on Sustainable Management of Land: The Case of Lombardia, Italy

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Felicetta Carillo

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Roberto Henke

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Alberto Sturla

Systems 2023, 11(6), 283; https://doi.org/10.3390/systems11060283 - 01 Jun 2023

Abstract

The article aims to analyze whether a larger diffusion of institutional–private co-operation in farming systems, such as Italian food districts (FDs), is helpful in pursuing goals of sustainable land use in agriculture. The paper focuses on the case of Lombardia in Italy, a [...] Read more.

The article aims to analyze whether a larger diffusion of institutional–private co-operation in farming systems, such as Italian food districts (FDs), is helpful in pursuing goals of sustainable land use in agriculture. The paper focuses on the case of Lombardia in Italy, a region where this form of public–private partnership is widespread throughout the regional territory. Combining differences-in-differences (DiD) and propensity score matching (PSM) methods to reduce the estimation bias, we assessed and quantified a “district effect” on the sustainable management of lands. Specifically, using several land-use and land-use change proxies as outcome measures, we verified whether there are significant differences in such outcomes between two different groups of municipalities: those involved in FDs and those not. Our analysis shows that there is an “FD effect” on the persistence of agricultural activity and, although this does not necessarily translate into more landscape diversity, it can at least counteract detrimental tendencies such as the loss of natural elements, the loss of landscape diversity due to intensive farming, and land abandonment. Full article

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Open AccessArticle

Research on the Synergistic Effect of the Composite System for High-Quality Development of the Marine Economy in China

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Zhenzhen Sun

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Hongjun Guan

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Aiwu Zhao

Systems 2023, 11(6), 282; https://doi.org/10.3390/systems11060282 - 01 Jun 2023

Abstract

The key to achieving the high-quality development of the marine economy (HDME) is to facilitate synergy through the coordination of marine elements. By adopting a perspective concerning complex systems, this paper presents a comprehensive framework—the composite system for high-quality development of the marine [...] Read more.

The key to achieving the high-quality development of the marine economy (HDME) is to facilitate synergy through the coordination of marine elements. By adopting a perspective concerning complex systems, this paper presents a comprehensive framework—the composite system for high-quality development of the marine economy (CSHDME)—that synergistically integrates the five dimensions of “economy—technology—ecological—society—culture” in the marine domain. Building upon this framework, in this paper, we construct a CSHDME synergy evaluation index system using relevant data from 11 coastal provinces in China from 2010 to 2020. We utilize the subsystem orderliness degree model and composite system synergy degree model to calculate the degree of subsystem orderliness and degree of composite system synergy for the CSHDME as well as analyze the development status of the CSHDME subsystem and the degree of CSHDME synergy. The results indicate that: (1) the five-dimensional subsystem of the CSHDME exhibits an overall trend of year-on-year growth, with significant development disparities; (2) the degree of the synergy of the CSHDME is characterized by strong regional differences, strong fluctuations, instability, and low synergy; (3) the degree of the synergy of the CSHDME demonstrates the spatial evolution characteristics of low synergy, strong differences, strong fluctuations, and high agglomeration; and (4) the degree of orderliness and development speed of subsystems affect the degree of the synergy of the CSHDME. Therefore, measures such as the rational allocation of marine resources, accurate improvement of shortcomings, optimization of the spatial layout, and strengthening of regional cooperation should be considered to further promote the synergistic development of the CSHDME. Full article

(This article belongs to the Section Complex Systems)

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Open AccessArticle

Scheduling Complex Cyber-Physical Systems with Mixed-Criticality Components

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Jaewoo Lee

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Keumseok Koh

Systems 2023, 11(6), 281; https://doi.org/10.3390/systems11060281 - 01 Jun 2023

Abstract

Two emerging trends for designing a complex, cyber-physical systems are the component-based and mixed-criticality (MC) approaches. A component-based approach independently develops individual components and subsequently integrates them to reduce system complexity. This approach provides strong isolation among components but incurs resource inefficiency. Alternatively, [...] Read more.

Two emerging trends for designing a complex, cyber-physical systems are the component-based and mixed-criticality (MC) approaches. A component-based approach independently develops individual components and subsequently integrates them to reduce system complexity. This approach provides strong isolation among components but incurs resource inefficiency. Alternatively, an MC approach integrates components of different criticality with different levels of guarantee for resource efficiency, while components are not isolated. To leverage MC and component-based approaches, we investigate how to balance component isolation and resource efficiency under component-based MC systems. We introduce the concept of component-MC schedulability, where isolated tasks are protected from external events outside the component, and shared tasks may be suspended for the critical events of other components. Under component-MC schedulability, we propose a component-based mixed-criticality scheduling framework with dynamic resource allocation (CMC-DRA), which suspends low-criticality tasks differently depending on internal or external component behavior. We also develop scheduling semantics and analyze the schedulability for CMC-DRA. Through simulation on synthetic workloads, we demonstrate that CMC-DRA has up to 88.3% higher schedulability than existing approaches and reduces the deadline miss ratio by up to 47.7%. Full article

(This article belongs to the Section Complex Systems)

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Open AccessArticle

A Two-Stage Investment Decision-Making Model for Urban Rail Transit Drainage Renovation

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Systems 2023, 11(6), 280; https://doi.org/10.3390/systems11060280 - 01 Jun 2023

Abstract

Climate change is the main cause of frequent extreme weather and natural disasters. Therefore, effective climate adaptation strategies for urban rail transit (URT) should be adopted to cope with extreme precipitation events (EPEs). This study proposes a decision-making model based on climate change [...] Read more.

Climate change is the main cause of frequent extreme weather and natural disasters. Therefore, effective climate adaptation strategies for urban rail transit (URT) should be adopted to cope with extreme precipitation events (EPEs). This study proposes a decision-making model based on climate change for drainage renovation, which consists of an optimal renovation sequence model and an optimal investment timing model. This study analyzes the inundation risk of each station and its node importance in the URT network and then uses a multi-attribute decision analysis (MADA) to determine the optimal renovation sequence. This study also uses a real options pricing approach to calculate the value of an option in order to defer the renovation project and determine the optimal investment timing. Then, the Beijing Urban Rail Transit (BURT) is taken as an example to conduct an empirical analysis of the proposed model. Considering the uncertainty of climate change and the complexity of the URT network, the model can obtain the optimal renovation sequence and the investment timing of each station, which is expected to provide a decision-making tool for urban governments to formulate an optimal plan that strengthens the prevention of flooding disasters. Full article

(This article belongs to the Topic SDGs 2030 in Buildings and Infrastructure)

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