Systems

20 pages, 711 KiB

Open AccessArticle

Unpacking the Complexities of Emotional Responses to External Feedback, Internal Feedback Orientation and Emotion Regulation in Higher Education: A Qualitative Exploration

by Lan Yang, Yiqi Wu, Yuan Liang and Min Yang

Systems 2023, 11(6), 315; https://doi.org/10.3390/systems11060315 - 20 Jun 2023

Cited by 5 | Viewed by 3833

Abstract

Research suggests that unpleasant emotions induced by feedback may reduce its efficiency in enhancing students’ performance, which is a crucial issue to address in education. In the context of Chinese language instruction in higher education, this study sought to investigate how students regulate [...] Read more.

Research suggests that unpleasant emotions induced by feedback may reduce its efficiency in enhancing students’ performance, which is a crucial issue to address in education. In the context of Chinese language instruction in higher education, this study sought to investigate how students regulate their emotions as a result of feedback through the lens of individuals’ feedback orientation. In light of the feedback orientation lens and its conceptual framework, we applied in-depth qualitative interviews to explore how students experienced feedback, the negative emotions they experienced, and the emotion regulation strategies they used. Eleven undergraduates across years one to five joined our in-depth interviews. Students reported negative emotions when they received feedback that did not live up to their expectations or was unrealistic for them to accept. However, students’ feedback orientation supported their emotion regulation techniques, which in turn supported students’ adaptive feedback processing to interpret and take action to use feedback for academic performance improvement. Students also actively sought further teacher feedback or peer support to deal with a wide range of negative emotions. These findings imply the significance of fostering in students a high level of feedback orientation and the necessity of additional empirical investigation into the relationships between feedback orientation and emotional well-being in higher education. By shedding light on how students regulate the emotions that external feedback causes in them, the study adds valuable qualitative findings to the existing literature on positive psychology research in terms of emotions and emotion regulation. It also emphasizes how crucial students’ personal feedback orientation is for improving emotional well-being in the context of feedback. Full article

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

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19 pages, 2531 KiB

Open AccessArticle

Gender Gaps in Mode Usage, Vehicle Ownership, and Spatial Mobility When Entering Parenthood: A Life Course Perspective

by Hung-Chia Yang, Ling Jin, Alina Lazar, Annika Todd-Blick, Alex Sim, Kesheng Wu, Qianmiao Chen and C. Anna Spurlock

Systems 2023, 11(6), 314; https://doi.org/10.3390/systems11060314 - 20 Jun 2023

Cited by 3 | Viewed by 2092

Abstract

Entry into parenthood is a major disruptive event to travel behavior, and gender gaps in mobility choices are often widened during parenthood. The exact timing of gender gap formation and their long-term effects on different subpopulations are less studied in the literature. Leveraging [...] Read more.

Entry into parenthood is a major disruptive event to travel behavior, and gender gaps in mobility choices are often widened during parenthood. The exact timing of gender gap formation and their long-term effects on different subpopulations are less studied in the literature. Leveraging a longitudinal dataset from the 2018 WholeTraveler Study, this paper examines the effects of parenthood on a diverse set of short- to long-term outcomes related to the three hierarchical domains of mobility biography: mode choice, vehicle ownership, spatial mobility, and career decisions. The progress of the effects is evaluated over a sequential set of parenting stages and differentiated across three subpopulations. We find that individuals classified as “Have-it-alls”, who start their careers, partner up, and have children concurrently and early, significantly increase their car uses two years prior to childbirth (“nesting period”), and they then relocate to less transit-accessible areas and consequently reduce their reliance on public transportation while they have children in the household. In contrast, individuals categorized as “Couples”, who start careers and partnerships early but delay parenthood, and “Singles”, who postpone partnership and parenthood, have less pronounced changes in travel behavior throughout the parenting stages. The cohort-level effects are found to be driven primarily by women, whose career development is on average more negatively impacted by parenting events than men, regardless of their life course trajectory. Early career decisions made by women upon entering parenthood contribute to gender gaps in mid- to longer-term mobility decisions, signifying the importance of early intervention. Full article

(This article belongs to the Special Issue Decision Making and Policy Analysis in Transportation Planning)

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25 pages, 3791 KiB

Open AccessArticle

Pricing Decision of the Dual-Channel Supply Chain with the Manufacturer’s Extended Warranty

by Chenbo Zhu, Jiwei Liang and Yaqian Liu

Systems 2023, 11(6), 313; https://doi.org/10.3390/systems11060313 - 20 Jun 2023

Cited by 2 | Viewed by 1523

Abstract

With the rapid development of the internet economy, many manufacturers have opened online direct sales channels and built multi-channel distribution systems. Meanwhile, both consumers and companies are paying more attention to extended warranty services. Considering a dual-channel supply chain with a manufacturer and [...] Read more.

With the rapid development of the internet economy, many manufacturers have opened online direct sales channels and built multi-channel distribution systems. Meanwhile, both consumers and companies are paying more attention to extended warranty services. Considering a dual-channel supply chain with a manufacturer and a retailer, we assume the manufacturer provides an extended warranty in the online direct channel and investigates the decision making of the supply chain players. We develop three game models to study this problem, and they are the basic model without extended warranty (Model B), the decentralized decision model with the manufacturer’s extended warranty (Model M), and the centralized decision model with the manufacturer’s extended warranty (Model C). The Stackelberg game method is used to solve the established model, the influence of relevant parameters on the solution result is analyzed, and different models are compared. Compared with Model B, we find that the whole supply chain always be better, but the retailer would be worse in Model M. Compared with Model M, we find that the entire supply chain always performs better in Model C. Finally, we do some sensitivity analysis. Full article

(This article belongs to the Section Supply Chain Management)

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21 pages, 3261 KiB

Open AccessArticle

How Explainable Machine Learning Enhances Intelligence in Explaining Consumer Purchase Behavior: A Random Forest Model with Anchoring Effects

by Yanjun Chen, Hongwei Liu, Zhanming Wen and Weizhen Lin

Systems 2023, 11(6), 312; https://doi.org/10.3390/systems11060312 - 19 Jun 2023

Viewed by 2083

Abstract

This study proposes a random forest model to address the limited explanation of consumer purchase behavior in search advertising, considering the influence of anchoring effects on rational consumer behavior. The model comprises two components: prediction and explanation. The prediction part employs various algorithms, [...] Read more.

This study proposes a random forest model to address the limited explanation of consumer purchase behavior in search advertising, considering the influence of anchoring effects on rational consumer behavior. The model comprises two components: prediction and explanation. The prediction part employs various algorithms, including logistic regression (LR), adaptive boosting (ADA), extreme gradient boosting (XGB), multilayer perceptron (MLP), naive bayes (NB), and random forest (RF), for optimal prediction. The explanation part utilizes the SHAP explainable framework to identify significant indicators and reveal key factors influencing consumer purchase behavior and their relative importance. Our results show that (1) the explainable machine learning model based on the random forest algorithm performed optimally (F1 = 0.8586), making it suitable for analyzing and predicting consumer purchase behavior. (2) The dimension of product information is the most crucial attribute influencing consumer purchase behavior, with features such as sales level, display priority, granularity, and price significantly influencing consumer perceptions. These attributes can be considered by merchants to develop appropriate tactics for improving the user experience. (3) Consumers’ purchase intentions vary based on the presented anchor point. Specifically, high anchor information related to product quality ratings increases the likelihood of purchase, while price anchors prompted consumers to compare similar products and opt for the most economical option. Our findings provide guidance for optimizing marketing strategies and improving user experience while also contributing to a deeper understanding of the decision−making mechanisms and pathways in online consumer purchase behavior. Full article

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24 pages, 1442 KiB

Open AccessArticle

A Universality–Distinction Mechanism-Based Multi-Step Sales Forecasting for Sales Prediction and Inventory Optimization

by Daifeng Li, Xin Li, Fengyun Gu, Ziyang Pan, Dingquan Chen and Andrew Madden

Systems 2023, 11(6), 311; https://doi.org/10.3390/systems11060311 - 19 Jun 2023

Cited by 1 | Viewed by 1716

Abstract

Sales forecasting is a highly practical application of time series prediction. It is used to help enterprises identify and utilize information to reduce costs and maximize profits. For example, in numerous manufacturing enterprises, sales forecasting serves as a key indicator for inventory optimization [...] Read more.

Sales forecasting is a highly practical application of time series prediction. It is used to help enterprises identify and utilize information to reduce costs and maximize profits. For example, in numerous manufacturing enterprises, sales forecasting serves as a key indicator for inventory optimization and directly influences the level of cost savings. However, existing research methods mainly focus on detecting sequences and local correlations from multivariate time series (MTS), but seldom consider modeling the distinct information among the time series within MTS. The prediction accuracy of sales time series is significantly influenced by the dynamic and complex environment, so identifying the distinct signals between different time series within a sales MTS is more important. In order to extract more valuable information from sales series and to enhance the accuracy of sales prediction, we devised a universality–distinction mechanism (UDM) framework that can predict future multi-step sales. Universality represents the instinctive features of sequences and correlation patterns of sales with similar contexts. Distinction corresponds to the fluctuations in a specific time series due to complex or unobserved influencing factors. In the mechanism, a query-sparsity measurement (QSM)-based attention calculation method is proposed to improve the efficiency of the proposed model in processing large-scale sales MTS. In addition, to improve the specific decision-making scenario of inventory optimization and ensure stable accuracy in multi-step prediction, we use a joint Pin-DTW (Pinball loss and Dynamic Time Warping) loss function. Through experiments on the public Cainiao dataset, and via our cooperation with Galanz, we are able to demonstrate the effectiveness and practical value of the model. Compared with the best baseline, the improvements are 57.27%, 50.68%, and 35.26% on the Galanz dataset and 16.58%, 6.07%, and 5.27% on the Cainiao dataset, in terms of the MAE (Mean Absolute Error), MAPE (Mean Absolute Percentage Error), and RMSE (Root Mean Squared Error). Full article

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19 pages, 1108 KiB

Open AccessArticle

Research on the Mechanism of the Role of Big Data Analytic Capabilities on the Growth Performance of Start-Up Enterprises: The Mediating Role of Entrepreneurial Opportunity Recognition and Exploitation

by Xinqiang Chen, Weijun Chen and Jiangjie Chen

Systems 2023, 11(6), 310; https://doi.org/10.3390/systems11060310 - 19 Jun 2023

Cited by 1 | Viewed by 1774

Abstract

With the advent of the era of big data, the application of big data analytics in entrepreneurial activities has become increasingly prevalent. However, research on the relationship between big data analytic capabilities and entrepreneurial activities is still in its infancy, and the mechanism [...] Read more.

With the advent of the era of big data, the application of big data analytics in entrepreneurial activities has become increasingly prevalent. However, research on the relationship between big data analytic capabilities and entrepreneurial activities is still in its infancy, and the mechanism by which the two interact remains unclear. Drawing on resource-based theory and entrepreneurial process theory, this research examines the impact mechanism of big data analytic capabilities on the growth performance of start-up enterprises and explores the mediating role of entrepreneurial opportunity recognition and entrepreneurial opportunity exploitation. Empirical analysis reveals that big data analytic capabilities have a significant positive impact on the growth performance of start-up enterprises; entrepreneurial opportunity exploitation plays a mediating role in the relationship between big data analytic capabilities and the growth performance of start-up enterprises, but entrepreneurial opportunity recognition does not show a significant mediating effect between the two; and entrepreneurial opportunity recognition and entrepreneurial opportunity exploitation play a chain-mediated role in the relationship between big data analytic capabilities and the growth performance of start-up enterprises. These research findings enrich the study of digital entrepreneurship and provide valuable references for the entrepreneurial practice of start-up enterprises. Full article

(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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21 pages, 2234 KiB

Open AccessSystematic Review

Pharmaceutical Communication in Spain around the COVID-19 Crisis: A Scoping Review

by Ana Ibáñez-Hernández, Natalia Papí-Gálvez and Carmen Carretón-Ballester

Systems 2023, 11(6), 309; https://doi.org/10.3390/systems11060309 - 16 Jun 2023

Viewed by 1547

Abstract

This paper addresses the scientific production of pharmaceutical communication in Spain around the COVID-19 crisis, in which information overload, amplified by the digital media, evidenced the relevance of communication in the digital society. The research observes the evolution and characteristics of such studies, [...] Read more.

This paper addresses the scientific production of pharmaceutical communication in Spain around the COVID-19 crisis, in which information overload, amplified by the digital media, evidenced the relevance of communication in the digital society. The research observes the evolution and characteristics of such studies, identifying scientific fields and disciplines related to communication, thematic lines, agents and publics. To this end, it proposes an exploratory review study adjusted to the PRISMA protocol with a search strategy including three databases (Scopus, WOS and Dialnet) and whose filtration produced a final population of 56 publications on Spanish pharmaceutical communication between 2018 and 2022. The results point to a greater production of scientific papers around the year of the pandemic. These papers were published by university institutions in health sciences journals, although differences in authorship by gender were detected. Most of them are empirical papers, with a predominance of mixed content analyses. The field of public relations stands out, but terminological confusion was also detected. This leads to a reflection on its causes and solutions in favour of the transparency and accountability in pharmaceutical communication. Full article

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

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14 pages, 2540 KiB

Open AccessArticle

Machine Learning-Driven Ubiquitous Mobile Edge Computing as a Solution to Network Challenges in Next-Generation IoT

by Moteeb Al Moteri, Surbhi Bhatia Khan and Mohammed Alojail

Systems 2023, 11(6), 308; https://doi.org/10.3390/systems11060308 - 16 Jun 2023

Cited by 5 | Viewed by 1995

Abstract

Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of [...] Read more.

Ubiquitous mobile edge computing (MEC) using the internet of things (IoT) is a promising technology for providing low-latency and high-throughput services to end-users. Resource allocation and quality of service (QoS) optimization are critical challenges in MEC systems due to the large number of devices and applications involved. This results in poor latency with minimum throughput and energy consumption as well as a high delay rate. Therefore, this paper proposes a novel approach for resource allocation and QoS optimization in MEC using IoT by combining the hybrid kernel random Forest (HKRF) and ensemble support vector machine (ESVM) algorithms with crossover-based hunter–prey optimization (CHPO). The HKRF algorithm uses decision trees and kernel functions to capture the complex relationships between input features and output labels. The ESVM algorithm combines multiple SVM classifiers to improve the classification accuracy and robustness. The CHPO algorithm is a metaheuristic optimization algorithm that mimics the hunting behavior of predators and prey in nature. The proposed approach aims to optimize the parameters of the HKRF and ESVM algorithms and allocate resources to different applications running on the MEC network to improve the QoS metrics such as latency, throughput, and energy efficiency. The experimental results show that the proposed approach outperforms other algorithms in terms of QoS metrics and resource allocation efficiency. The throughput and the energy consumption attained by our proposed approach are 595 mbit/s and 9.4 mJ, respectively. Full article

(This article belongs to the Special Issue AI, IoT, and Edge Computing for Sustainable Smart Cities)

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21 pages, 1549 KiB

Open AccessArticle

Do Environmental Taxes Affect Carbon Dioxide Emissions in OECD Countries? Evidence from the Dynamic Panel Threshold Model

by Abdullah Sultan Al Shammre, Adel Benhamed, Ousama Ben-Salha and Zied Jaidi

Systems 2023, 11(6), 307; https://doi.org/10.3390/systems11060307 - 14 Jun 2023

Cited by 19 | Viewed by 3004

Abstract

The latest decades have been marked by rapid climate change and global warming due to the release of greenhouse gas emissions into the atmosphere. Environmental taxes have emerged as a cost-effective way to tackle environmental degradation. However, the effectiveness of environmental taxes in [...] Read more.

The latest decades have been marked by rapid climate change and global warming due to the release of greenhouse gas emissions into the atmosphere. Environmental taxes have emerged as a cost-effective way to tackle environmental degradation. However, the effectiveness of environmental taxes in reducing pollution remains a topic of ongoing debate. The purpose of this paper is to examine empirically the effects of various environmental tax categories (energy, pollution, resource and transport) on CO₂ emissions in 34 OECD countries between 1995 and 2019. The dynamic panel threshold regression developed by Seo and Shin (2016) is implemented to assess whether the impact of environmental taxes on CO₂ emissions depends on a given threshold level. The locally weighted scatterplot smoothing analysis provides evidence for a nonlinear association between environmental taxes and CO₂ emissions. The analysis indicates the existence of one significant threshold and two regimes (lower and upper) for all environmental tax categories. The dynamic panel threshold regression reveals that the total environmental tax, energy tax and pollution tax reduce CO₂ emissions in the upper regime, i.e., once a given threshold level is reached. The threshold levels are 3.002% of GDP for the total environmental tax, 1.991% for the energy tax and 0.377% for the pollution tax. Furthermore, implementing taxes on resource utilization may be effective but with limited environmental effects. Based on the research results, it is recommended that countries in the OECD implement specific environmental taxes to reduce greenhouse gas emissions. 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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13 pages, 2342 KiB

Open AccessArticle

Data-Driven Decision-Making (DDDM) for Higher Education Assessments: A Case Study

by Samuel Kaspi and Sitalakshmi Venkatraman

Systems 2023, 11(6), 306; https://doi.org/10.3390/systems11060306 - 13 Jun 2023

Cited by 2 | Viewed by 7493

Abstract

The higher education (HE) system is witnessing immense transformations to keep pace with the rapid advancements in digital technologies and due to the recent COVID-19 pandemic compelling educational institutions to completely switch to online teaching and assessments. Assessments are considered to play an [...] Read more.

The higher education (HE) system is witnessing immense transformations to keep pace with the rapid advancements in digital technologies and due to the recent COVID-19 pandemic compelling educational institutions to completely switch to online teaching and assessments. Assessments are considered to play an important and powerful role in students’ educational experience and evaluation of their academic abilities. However, there are many stigmas associated with both “traditional” and alternative assessment methods. Rethinking assessments is increasingly happening worldwide to keep up with the shift in current teaching and learning paradigms due to new possibilities of using digital technologies and a continuous improvement of student engagement. Many educational decisions such as a change in assessment from traditional summative exams to alternate methods require appropriate rationale and justification. In this paper, we adopt data-driven decision-making (DDDM) as a process for rethinking assessment methods and implementing assessment transformations innovatively in an HE environment. We make use of student performance data to make an informed decision for moving from exam-based assessments to nonexam assessment methods. We demonstrate the application of the DDDM approach for an educational institute by analyzing the impact of transforming the assessments of 13 out of 27 subjects offered in a Bachelor of Information Technology (BIT) program as a case study. A comparison of data analysis performed before, during, and after the COVID-19 pandemic using different student learning measures such as failure rates and mean marks provides meaningful insights into the impact of assessment transformations. Our implementation of the DDDM model along with examining the influencing factors of student learning through assessment transformations in an HE environment is the first of its kind. With many HE providers facing several challenges due to the adoption of blended learning, this pilot study based on a DDDM approach encourages innovation in classroom teaching and assessment redesign. In addition, it opens further research in implementing such evidence-based practices for future classroom innovations and assessment transformations towards achieving higher levels of educational quality. Full article

(This article belongs to the Topic Data-Driven Group Decision-Making)

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14 pages, 680 KiB

Open AccessArticle

Credit Card Fraud Detection Based on Unsupervised Attentional Anomaly Detection Network

by Shanshan Jiang, Ruiting Dong, Jie Wang and Min Xia

Systems 2023, 11(6), 305; https://doi.org/10.3390/systems11060305 - 13 Jun 2023

Cited by 21 | Viewed by 5533

Abstract

In recent years, with the rapid development of Internet technology, the number of credit card users has increased significantly. Subsequently, credit card fraud has caused a large amount of economic losses to individual users and related financial enterprises. At present, traditional machine learning [...] Read more.

In recent years, with the rapid development of Internet technology, the number of credit card users has increased significantly. Subsequently, credit card fraud has caused a large amount of economic losses to individual users and related financial enterprises. At present, traditional machine learning methods (such as SVM, random forest, Markov model, etc.) have been widely studied in credit card fraud detection, but these methods are often have difficulty in demonstrating their effectiveness when faced with unknown attack patterns. In this paper, a new Unsupervised Attentional Anomaly Detection Network-based Credit Card Fraud Detection framework (UAAD-FDNet) is proposed. Among them, fraudulent transactions are regarded as abnormal samples, and autoencoders with Feature Attention and GANs are used to effectively separate them from massive transaction data. Extensive experimental results on Kaggle Credit Card Fraud Detection Dataset and IEEE-CIS Fraud Detection Dataset demonstrate that the proposed method outperforms existing fraud detection methods. 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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26 pages, 7643 KiB

Open AccessArticle

Iot-Based Privacy-Preserving Anomaly Detection Model for Smart Agriculture

by Keerthi Kethineni and Pradeepini Gera

Systems 2023, 11(6), 304; https://doi.org/10.3390/systems11060304 - 13 Jun 2023

Cited by 12 | Viewed by 3274

Abstract

Internet of Things (IoT) technology has been incorporated into the majority of people’s everyday lives and places of employment due to the quick development in information technology. Modern agricultural techniques increasingly use the well-known and superior approach of managing a farm known as [...] Read more.

Internet of Things (IoT) technology has been incorporated into the majority of people’s everyday lives and places of employment due to the quick development in information technology. Modern agricultural techniques increasingly use the well-known and superior approach of managing a farm known as “smart farming”. Utilizing a variety of information and agricultural technologies, crops are observed for their general health and productivity. This requires monitoring the condition of field crops and looking at many other indicators. The goal of smart agriculture is to reduce the amount of money spent on agricultural inputs while keeping the quality of the final product constant. The Internet of Things (IoT) has made smart agriculture possible through data collection and storage techniques. For example, modern irrigation systems use effective sensor networks to collect field data for the best plant irrigation. Smart agriculture will become more susceptible to cyber-attacks as its reliance on the IoT ecosystem grows, because IoT networks have a large number of nodes but limited resources, which makes security a difficult issue. Hence, it is crucial to have an intrusion detection system (IDS) that can address such challenges. In this manuscript, an IoT-based privacy-preserving anomaly detection model for smart agriculture has been proposed. The motivation behind this work is twofold. Firstly, ensuring data privacy in IoT-based agriculture is of the utmost importance due to the large volumes of sensitive information collected by IoT devices, including on environmental conditions, crop health, and resource utilization data. Secondly, the timely detection of anomalies in smart agriculture systems is critical to enable proactive interventions, such as preventing crop damage, optimizing resource allocation, and ensuring sustainable farming practices. In this paper, we propose a privacy-encoding-based enhanced deep learning framework for the difficulty of data encryption and intrusion detection. In terms of data encoding, a novel method of a sparse capsule-auto encoder (SCAE) is proposed along with feature selection, feature mapping, and feature normalization. An SCAE is used to convert information into a new encrypted format in order to prevent deduction attacks. An attention-based gated recurrent unit neural network model is proposed to detect the intrusion. An AGRU is an advanced version of a GRU which is enhanced by an attention mechanism. In the results section, the proposed model is compared with existing deep learning models using two public datasets. Parameters such as recall, precision, accuracy, and F1-score are considered. The proposed model has accuracy, recall, precision, and F1-score of 99.9%, 99.7%, 99.9%, and 99.8%, respectively. The proposed method is compared using a variety of machine learning techniques such as the deep neural network (DNN), convolutional neural network (CNN), recurrent neural network (RNN), and long short-term memory (LSTM). Full article

(This article belongs to the Special Issue Intelligent Information Technologies for Quality and Security Assurance)

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20 pages, 1757 KiB

Open AccessArticle

Dynamic Optimization of Emergency Logistics for Major Epidemic Considering Demand Urgency

by Jianjun Zhang, Jingru Huang, Tianhao Wang and Jin Zhao

Systems 2023, 11(6), 303; https://doi.org/10.3390/systems11060303 - 13 Jun 2023

Cited by 2 | Viewed by 1911

Abstract

In recent years, epidemic disasters broke through frequently around the world, posing a huge threat to economic and social development, as well as human health. A fair and accurate distribution of emergency supplies during an epidemic is vital for improving emergency rescue efficiency [...] Read more.

In recent years, epidemic disasters broke through frequently around the world, posing a huge threat to economic and social development, as well as human health. A fair and accurate distribution of emergency supplies during an epidemic is vital for improving emergency rescue efficiency and reducing economic losses. However, traditional emergency material allocation models often focus on meeting the amount of materials requested, and ignore the differences in the importance of different emergency materials and the subjective urgency demand of the disaster victims. As a result, it is difficult for the system to fairly and reasonably match different scarce materials to the corresponding areas of greatest need. Consequently, this paper proposes a material shortage adjustment coefficient based on the entropy weight method, which includes indicators such as material consumption rate, material reproduction rate, durability, degree of danger to life, and degree of irreplaceability, to enlarge and narrow the actual shortage of material supply according to the demand urgency. Due to the fact that emergency materials are not dispatched in one go during epidemic periods, a multi-period integer programming model was established to minimize the adjusted total material shortage based on the above function. Taking the cases of Wuhan and Shanghai during the lockdown and static management period, the quantitative analysis based on material distribution reflected that the model established in this paper was effective in different scenarios where there were significant differences in the quantity and structure of material demand. At the same time, the model could significantly adjust the shortage of emergency materials with higher importance and improve the satisfaction rate. Full article

(This article belongs to the Special Issue Systems Thinking and Models in Public Health)

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22 pages, 10505 KiB

Open AccessArticle

Venture Capital Syndication Network Structure of Public Companies: Robustness and Dynamic Evolution, China

by Xinyuan Luo, Jian Yin, Hongtao Jiang, Danqi Wei, Ruici Xia and Yi Ding

Systems 2023, 11(6), 302; https://doi.org/10.3390/systems11060302 - 13 Jun 2023

Cited by 3 | Viewed by 2159

Abstract

Venture capital plays a vital role in boosting economic growth by providing an inexhaustible impetus for economic innovation and development. We use all the joint venture capital events of Chinese listed companies in the past 10 years to describe the characteristics of the [...] Read more.

Venture capital plays a vital role in boosting economic growth by providing an inexhaustible impetus for economic innovation and development. We use all the joint venture capital events of Chinese listed companies in the past 10 years to describe the characteristics of the joint venture capital network structure, identify the dynamic evolution characteristics of the community, and introduce random attacks and deliberate attacks to explore the resilience of joint venture capital cooperation. The study finds that the joint venture capital network in China has expanded in scale, with an increasing number of participants and a diversified investment industry. However, the connection between members within the network remains relatively loose, indicating fragmentation and a need to improve network quality. The community structure of core members is significant, with evident differences in scale. The network exhibits weak robustness, relying heavily on key enterprises and demonstrating a poor ability to resist external interference. The study proposes countermeasures and suggestions for optimizing the network structure of joint venture capital, aiming to enhance the environment and performance of joint venture capital and promote the high-quality development of China’s joint venture capital market. Full article

(This article belongs to the Special Issue Frontiers in Complex Network Theory and Its Applications)

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24 pages, 1102 KiB

Open AccessArticle

An Improved User Requirements Notation (URN) Models’ Construction Approach

by Cyrille Dongmo and John Andrew Van der Poll

Systems 2023, 11(6), 301; https://doi.org/10.3390/systems11060301 - 11 Jun 2023

Cited by 1 | Viewed by 1575

Abstract

Semi-formal software techniques have been very successful in industry, government institutions and other areas such as academia. Arguably, they owe a large part of their success to their graphical notation, which is more human-oriented than their counterpart text-based and formal notation techniques. However, [...] Read more.

Semi-formal software techniques have been very successful in industry, government institutions and other areas such as academia. Arguably, they owe a large part of their success to their graphical notation, which is more human-oriented than their counterpart text-based and formal notation techniques. However, ensuring the consistency between two or more models is one of the known challenges of these techniques. This work looks closely at the specific case of the User Requirements Notation (URN) technique. Although the abstract model of URN provides for link elements to ensure the consistency between its two main components, namely, Goal-Oriented Requirement Language (GRL) and Use Case Maps (UCM), the effective implementation of such links is yet to be fully addressed. This paper performs a detailed analysis of the existing URN models construction process and proposes an improved process with some guidelines to ensure, by construction, the correctness and consistency of the GRL and UCM models. A case study is used throughout the paper to illustrate the suggested solution. Full article

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21 pages, 2156 KiB

Open AccessArticle

Research on Intelligent Emergency Resource Allocation Mechanism for Public Health Emergencies: A Case Study on the Prevention and Control of COVID-19 in China

by Ruhao Ma, Fansheng Meng and Haiwen Du

Systems 2023, 11(6), 300; https://doi.org/10.3390/systems11060300 - 11 Jun 2023

Cited by 3 | Viewed by 2679

Abstract

The outbreak of COVID-19 posed a significant challenge to the emergency management system for public health emergencies, especially in China, where the epidemic began. As intelligent technology has injected new vitality into emergency management, applying intelligent technology to optimize emergency resource allocation (ERA) [...] Read more.

The outbreak of COVID-19 posed a significant challenge to the emergency management system for public health emergencies, especially in China, where the epidemic began. As intelligent technology has injected new vitality into emergency management, applying intelligent technology to optimize emergency resource allocation (ERA) has become a focus of research in the post-epidemic era. Based on China’s experience in preventing and controlling COVID-19, this paper first analyzes the characteristics and process of ERA in public health emergencies, and then synthesizes the relevant Chinese studies in recent years to identify the intelligent technologies affecting ERA in China using word frequency analysis technology. We also construct an intelligent emergency resource allocation mechanism in four areas: medical intelligence, management intelligence, decision-making intelligence, and supervision intelligence. Finally, we use the entropy-TOPSIS method to evaluate the impact of intelligent technologies on ERA, and we rank the criticality of intelligent technologies. The experimental results show that (i.) medical intelligence and management intelligence are the keys to developing intelligent ERA, and (ii.) among the identified essential intelligent technologies, artificial intelligence (AI), and big data technology have a more significant and critical role in emergency resource intelligence allocation. Full article

(This article belongs to the Special Issue Systems Thinking and Models in Public Health)

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25 pages, 2434 KiB

Open AccessArticle

The Effect of Blockchain Technology on Supply Chain Collaboration: A Case Study of Lenovo

by Jianting Xia, Haohua Li and Zhou He

Systems 2023, 11(6), 299; https://doi.org/10.3390/systems11060299 - 10 Jun 2023

Cited by 19 | Viewed by 33784

Abstract

Blockchain technology, as a revolutionary technology that has emerged in recent years, holds significant potential for application in supply chain operations. This paper provides a systematic review of blockchain-based supply chain case studies. The existing literature primarily focuses on the food, agriculture, and [...] Read more.

Blockchain technology, as a revolutionary technology that has emerged in recent years, holds significant potential for application in supply chain operations. This paper provides a systematic review of blockchain-based supply chain case studies. The existing literature primarily focuses on the food, agriculture, and pharmaceutical sectors, highlighting the advantages of blockchain technology in terms of traceability and transparency. However, there is a limited number of studies addressing the improvement of collaboration efficiency in supply chains, particularly within the realm of information technology enterprises. By conducting semi-structured interviews, we present a case study of Lenovo, a leading enterprise utilizing blockchain technology, to elucidate the advantages of using blockchain technology. Subsequently, it proposes a conceptual model for a blockchain-based information collaboration system and discusses the potential applications of blockchain technology in supply chain collaboration. Our study contributes to the existing work on blockchain applications to enhance supply chain collaboration. Full article

(This article belongs to the Special Issue Enablers and Capabilities for the Digital Supply Chain)

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21 pages, 718 KiB

Open AccessArticle

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

by Jian Yang, Jichang Dong, Qi Song, Yulia S. Otmakhova and Zhou He

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

Viewed by 2102

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)

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31 pages, 1007 KiB

Open AccessArticle

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

by Stéphanie Roussel, Gauthier Picard, Cédric Pralet and Sara Maqrot

Systems 2023, 11(6), 297; https://doi.org/10.3390/systems11060297 - 9 Jun 2023

Viewed by 1314

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 Advancements in the Practical Applications of Agents, Multi-Agent Systems and Simulating Complex Systems)

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21 pages, 7006 KiB

Open AccessArticle

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

by Amran Mansoor, Mohammed Anbar, Abdullah Ahmed Bahashwan, Basim Ahmad Alabsi and Shaza Dawood Ahmed Rihan

Systems 2023, 11(6), 296; https://doi.org/10.3390/systems11060296 - 9 Jun 2023

Cited by 8 | Viewed by 3805

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

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19 pages, 576 KiB

Open AccessArticle

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

by Fan Chen, Jianyi Jiao, Zhongan Wu and Can Zhang

Systems 2023, 11(6), 295; https://doi.org/10.3390/systems11060295 - 9 Jun 2023

Cited by 1 | Viewed by 1458

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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21 pages, 6016 KiB

Open AccessArticle

Measuring Patent Similarity Based on Text Mining and Image Recognition

by Wenguang Lin, Wenqiang Yu and Renbin Xiao

Systems 2023, 11(6), 294; https://doi.org/10.3390/systems11060294 - 8 Jun 2023

Cited by 7 | Viewed by 3299

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 Engineering)

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19 pages, 1501 KiB

Open AccessArticle

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

by Hsiang-Yu Chung, Kuei-Hu Chang and Jr-Cian Yao

Systems 2023, 11(6), 293; https://doi.org/10.3390/systems11060293 - 7 Jun 2023

Cited by 4 | Viewed by 1927

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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18 pages, 945 KiB

Open AccessArticle

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

by Ahmad Almufarreh

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

Cited by 10 | Viewed by 5297

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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20 pages, 2311 KiB

Open AccessArticle

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

by Qiang Sun and Yu-Jiao Sui

Systems 2023, 11(6), 291; https://doi.org/10.3390/systems11060291 - 5 Jun 2023

Cited by 2 | Viewed by 2173

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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17 pages, 4187 KiB

Open AccessArticle

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

by Xiaohong Zhang, Yuting Pan, Yanbo Wang, Cheng Xu and Yanqi Sun

Systems 2023, 11(6), 290; https://doi.org/10.3390/systems11060290 - 5 Jun 2023

Cited by 1 | Viewed by 2079

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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19 pages, 3595 KiB

Open AccessArticle

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

by Hongyu Lv, Ning Ding, Yiming Zhai, Yingjie Du and Feng Xie

Systems 2023, 11(6), 289; https://doi.org/10.3390/systems11060289 - 5 Jun 2023

Viewed by 1994

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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31 pages, 1530 KiB

Open AccessArticle

Preparedness Indicator System for Education 4.0 with FUCOM and Rough Sets

by Rose Mary Almacen, Delfa Castilla, Gamaliel Gonzales, Roselyn Gonzales, Felix Costan, Emily Costan, Lynne Enriquez, Jannen Batoon, Rica Villarosa, Joerabell Lourdes Aro, Samantha Shane Evangelista, Fatima Maturan, Charldy Wenceslao, Nadine May Atibing and Lanndon Ocampo

Systems 2023, 11(6), 288; https://doi.org/10.3390/systems11060288 - 5 Jun 2023

Cited by 1 | Viewed by 2823

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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27 pages, 4756 KiB

Open AccessArticle

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

by Yu Cheng and Sanghun Sul

Systems 2023, 11(6), 287; https://doi.org/10.3390/systems11060287 - 3 Jun 2023

Viewed by 2009

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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27 pages, 3604 KiB

Open AccessArticle

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

by Xiaoyang Gu, Hongmin Li and Henghao Fan

Systems 2023, 11(6), 286; https://doi.org/10.3390/systems11060286 - 2 Jun 2023

Cited by 2 | Viewed by 1589

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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