Systems, Volume 11, Issue 2 (February 2023) – 61 articles

Panic buying is now a frequent occurrence in many countries, leading to stockouts and supply chain disruptions. This paper highlights consumers’ panic buying behavior in different types of regions and the impact of different replenishment strategies after an emergency supply disruption. Panic buying behavior occurs when consumers try to mitigate the negative impact of a supply disruption. Therefore, this paper develops a consumer-based agency model to study the correlation between public opinion and panic buying and simulates the influence of consumers’ panic buying behavior under different situations in a complex network. The results show that the spread of panic feelings can lead to panic buying behavior among consumers, which then shocks the retailer market. The distribution of supplies according to the type of city and the number of people can have an impact on consumer panic buying behavior, and when the government adopts a restrictive strategy, implementing a quota policy or uniform rationing is very effective in reducing the number of consumers participating in panic buying. Full article

The sustainable development of the Yellow River Basin (YRB) is regarded as a national strategy for China. Previous literature has focused on the green efficiency measurement of YRB, ignoring its evolution process and influential mechanism. This paper tries to disclose the spatial-temporal evolution of green efficiency and its influential mechanism of the YRB region by proposing a novel integrated DEA-Tobit model to fill the gap. Based on the development path of the YRB region, the multi-period two-stage DEA model is adopted to evaluate the green development efficiency (GDE) from provincial and urban dimensions. In addition, the panel Tobit model is developed to investigate the influential factors of the GDE for the YRB region. The GDE in the YRB region shows an unbalanced state where the downstream is best, followed by the middle and upstream. The unbalanced development also exists within the province. Both Henan and Shandong Province achieved the optimal value, while cities in these two provinces show lower green efficiency. The results also show that economic development, technological innovation and foreign capital utilization obviously affect the GDE of the YRB region positively, while industrial structure, urbanization levels and environmental regulation have negative effects. Full article

With the increase in video surveillance data, techniques such as video synopsis are being used to construct small videos for analysis, thereby saving storage resources. The video synopsis framework applies in real-time environments, allowing for the creation of synopsis between multiple and single-view cameras; the same framework encompasses optimization, extraction, and object detection algorithms. Contemporary state-of-the-art synopsis frameworks are suitable only for particular scenarios. This paper aims to review the traditional state-of-the-art video synopsis techniques and understand the different methods incorporated in the methodology. A comprehensive review provides analysis of varying video synopsis frameworks and their components, along with insightful evidence for classifying these techniques. We primarily investigate studies based on single-view and multiview cameras, providing a synopsis and taxonomy based on their characteristics, then identifying and briefly discussing the most commonly used datasets and evaluation metrics. At each stage of the synopsis framework, we present new trends and open challenges based on the obtained insights. Finally, we evaluate the different components such as object detection, tracking, optimization, and stitching techniques on a publicly available dataset and identify the lacuna among the different algorithms based on experimental results. Full article

After different consecutive waves, the pandemic phase of Coronavirus disease 2019 does not look to be ending soon for most countries across the world. To slow the spread of the COVID-19 virus, several measures have been adopted since the start of the outbreak, including wearing face masks and maintaining social distancing. Ensuring safety in public areas of smart cities requires modern technologies, such as deep learning and deep transfer learning, and computer vision for automatic face mask detection and accurate control of whether people wear masks correctly. This paper reviews the progress in face mask detection research, emphasizing deep learning and deep transfer learning techniques. Existing face mask detection datasets are first described and discussed before presenting recent advances to all the related processing stages using a well-defined taxonomy, the nature of object detectors and Convolutional Neural Network architectures employed and their complexity, and the different deep learning techniques that have been applied so far. Moving on, benchmarking results are summarized, and discussions regarding the limitations of datasets and methodologies are provided. Last but not least, future research directions are discussed in detail. Full article

Extant literature has pointed to organizational hybridity to lever sustainable business transformation. Moreover, some authors hold that there is a possible trade-off between sustainability and performance. However, there is still little empirical evidence on the impact that such sustainability-driven hybridization systems have on performance. Thus, the present study’s main goal is to fill this gap by providing empirical evidence on the impact of the implementation of the Economy for the Common Good, as a sustainability-driven organizational system, on business performance. To do so, the authors relied on a sample of 206 businesses from five European countries. Then, the authors followed a quantitative research approach based on a hierarchical regression analysis that allowed them to test for linear, curvilinear, and moderating effects. The authors found a positive relationship between the implementation of a sustainability-driven hybridization system and firm performance. Besides, they identified some curvilinear effects pointing to the existence of a “too much of a good thing” effect, along with some moderating effects derived from organizational size. Full article

Novel developments in artificial intelligence excel in regard to the abilities of rule-based agent-based models (ABMs), but are still limited in their representation of bounded rationality. The future state maximization (FSX) paradigm presents a promising methodology for describing the intelligent behavior of agents. FSX agents explore their future state space using “walkers” as virtual entities probing for a maximization of possible states. Recent studies have demonstrated the applicability of FSX to modeling the cooperative behavior of individuals. Applied to ABMs, the FSX principle should also represent non-cooperative behavior: for example, in microscopic traffic modeling, there is a need to model agents that do not fully adhere to the traffic rules. To examine non-cooperative behavior arising from FSX, we developed a road section model populated by agent-cars endowed with an augmented FSX decision making algorithm. Simulation experiments were conducted in four scenarios modeling various traffic settings. A sensitivity analysis showed that cooperation among the agents was the result of a balance between exploration and exploitation. We showed that our model reproduced several patterns observed in rule-based traffic models. We also demonstrated that agents acting according to FSX can stop cooperating. We concluded that FSX can be useful for studying irrational behavior in certain traffic settings, and that it is suitable for ABMs in general. Full article

Most elderly people choose to age in place, making neighborhood environments essential factors affecting their health status. The policies, economic status, and housing conditions of old neighborhoods have led many elderly people to live in skipped-generation households (SGHs), where they have gradually weakened physical functions and are responsible for raising grandchildren; this puts their health in a more fragile state than that of the average elderly person. Practical experience has shown that when faced with complex environmental renovation problems in old communities, many cases often adopt a one-step treatment strategy; however, many scholars have questioned the sustainability of such unsystematically evaluated renovation projects. Therefore, it is often valuable to explore the root causes of these old neighborhood problems and conduct targeted transformations and upgrades according to the interactive relationship between various influencing factors. This study attempted to establish a novel evaluation system to benefit the health of elderly families in old neighborhoods and develop an understanding of the impact relationship among the indicators, while avoiding any form of waste when collecting responses in regard to the future transformation of old neighborhoods. A questionnaire survey was conducted on the elderly in the Guangzhou Che Bei neighborhood in China, and by applying the rough set theory of the decision-making trial and evaluation laboratory model, we established a preliminary evaluation system, obtained key environmental factors affecting the health of elderly people living in SGHs, and clarified their mutual relationships. Finally, on this basis, we proposed corresponding neighborhood renewal suggestions. The results of this study provide a theoretical basis for future research, and our research model can be applied to similar aging research in the future. Full article

Emergencies such as machine breakdowns and rush orders greatly affect the production activities of manufacturing enterprises. How to deal with the rescheduling problem after emergencies have high practical value. Meanwhile, under the background of intelligent manufacturing, automatic guided vehicles are gradually emerging in enterprises. To deal with the disturbances in flexible job shop scheduling problem with automatic guided vehicle transportation, a mixed-integer linear programming model is established. According to the traits of this model, an improved NSGA-II is designed, aiming at minimizing makespan, energy consumption and machine workload deviation. To improve solution qualities, the local search operator based on a critical path is designed. In addition, an improved crowding distance calculation method is used to reduce the computation complexity of the algorithm. Finally, the validity of the improvement strategies is tested, and the robustness and superiority of the proposed algorithm are verified by comparing it with NSGA, NSGA-II and SPEA2. Full article

Nowadays, geopolitical, extreme weather and other emergencies have exacerbated the global energy crisis, and thus, have increased the urgency of the world’s transition to sustainable energy. Sustainable energy policies play an important role in the process of sustainable energy transformation. The research on sustainable energy policy is mainly carried out through conventional qualitative and quantitative methods, in which bibliometrics and meta-analysis methods are paid attention to; however, the mining and analysis of the semantics of the relationships between policies are ignored. This paper uses knowledge graph technology to build a knowledge graph of China’s sustainable energy policy by using 10,815 open official documents of sustainable energy policy issued by China from 1981 to 2022. It forms the relevant policy archive storage and details related organizations. The legal source can be traced through the graph database, where the powerful synergy can be seen, and the policy focus can be monitored. In terms of structural data, this paper uses graph algorithms to identify key policy nodes at different stages, to identify the key government departments for policy issuance and cluster policy issuance departments, and it investigates China’s policy evolution in the issue of sustainable energy policies, the evolution of policy issuance departments, and the power co-evolution process between policy issuance departments. The research found that: (1) China’s sustainable energy policy was initiated in environmental protection, and the relevant policies on collecting pollution charges has continued to play an important policy node. Additionally, the three versions of the Environmental Protection Law of the People’s Republic of China have successively become the main legal source of other sustainable energy transformation policies. (2) The prominent feature of China’s sustainable energy policy transformation has involved transforming the process where the issuance of policies came from a single department to the joint issuance of documents by multiple departments. The joint exercise of government functions and powers by multiple departments jointly promotes sustainable energy policies’ implementation and play. (3) In the future, when formulating sustainable energy policies, the Chinese government should focus on the strategic and systematic aspects of the policies, so that the sustainable energy policies can meet both short-term and long-term development goals. At the same time, the synergy of various policies and measures should be fully played in implementing sustainable energy policies. The establishment of the policy knowledge graph based on publicly-open official documents can facilitate the analysis and visualization of sustainable energy policies, providing new ideas for policy research. This paper introduces the knowledge graph, graph machine learning algorithms and big data technology, which can deepen the depth and breadth of people’s research on sustainable energy policy. This study will help the public policy formulation work in the future and has a positive reference value for the evaluation of the implementation effect of policy objectives. Full article

Nation Wide Digital Twin is an emerging paradigm that pushes the context of a classical Digital Twin to a whole country. Under this perspective, models, which are central for digital twins, will play a key role for the design and implementation of such a specific digital twin. However, to achieve a nation wide digital twin vision, a whole set of problems related to models have to be solved. In this paper, we detailed the notion of nation wide digital twin with respect to well known digital twin from a model point of view and discuss the problems the community is facing in this context. As a result, from the identified challenges, we propose a research road-map paving the way for future scientific contributions. Full article

Today, frameworks and models are critical for enabling organizations to identify their current sustainability integration into business and to follow up on these initiatives over time. In this context, the maturity models offer a structured way of analyzing how a supply chain meets specific sustainability requirements and which areas demand attention to reach maturity levels. This study proposes a five-level maturity model to help supply chains managers identify their level of engagement with sustainability practices combining three perspectives: the intra- and inter-organizational sustainability practices, the triple-bottom-line approach and the critical areas for sustainability. All the steps followed in constructing the maturity model were based on a literature review, and case studies supported its improvement, application, and testing. The proposed model presents many advantages, such as being used as a self-assessment tool, a roadmap for sustainability behaviors improvement, and a benchmarking tool to evaluate and compare standards and best practices among organizations and supply chains. Full article

The aim of the study is twofold: to assess the usability of a virtuality (VR) interaction designed for nonliterate users in accordance with ISO-Standard 9241-11 and to compare the feasibility of two interaction modalities (motion controllers and real hands) considering the impact of VR sickness. To accomplish these goals, two levels were designed for a VR prototype application. The system usability scale (SUS) was used for self-reported satisfaction, while effectiveness and efficiency were measured based on observations and logged data. These measures were then analyzed using exploratory factor analysis, and the ones with high factor loading were selected. For this purpose, two studies were conducted. The first study investigated the effects of three independent variables on the interaction performance of a VR system, i.e., “User Type,” “Interaction Modality,” and “Use of New Technology.” The SUS results suggest that all the participants were satisfied with the application. The results of one-way ANOVA tests showed that there were no significant differences in the use of the VR application among the three selected user types. However, some measures, such as task completion time in level one, showed significant differences between user types, suggesting that nonliterate users had difficulty with the grab-and-move interaction. The results of the multivariate analysis using statistically significant variables from both ANOVA tests were also reported to verify the effect of modern technology on interactivity. The second study evaluated the interaction performance of nonliterate adults in a VR application using two independent variables: “Interaction Modality” and “Years of Technological Experience.” The results of the study showed a high level of satisfaction with the VR application, with an average satisfaction score of 90.75. The one sample T-tests indicated that the nonliterate users had difficulty using their hands as the interaction modality. The study also revealed that nonliterates may struggle with the poses and gestures required for hand interaction. The results suggest that until advancements in hand-tracking technology are made, controllers may be easier for nonliterate adults to use compared to using their hands. The results underline the importance of designing VR applications that are usable and accessible for nonliterate adults and can be used as guidelines for creating VR learning experiences for nonliterate adults. Full article

Optimizing and predicting the energy consumption of industrial manufacturing can increase its cost efficiency. The interaction of different aspects and components is necessary. An overarching framework is currently still missing, and establishing such is the central research approach in this paper. This paper provides an overview of the current demands on the manufacturing industry from the perspective of digitalization and sustainability. On the basis of the developed fundamentals and parameters, a superordinate framework is proposed that allows the modelling and simulation of energy-specific properties on several product and process levels. A detailed description of the individual methods concludes this work and demonstrates their application potential in an industrial context. As a result, this integrated conceptual framework offers the possibility of optimizing the production system, in relation to different energy flexibility criteria. Full article

This study examines the impact of female education and other control variables such as trade openness, per capita GDP, urbanization, and male employment on women’s employment opportunities in South Asian countries. The annual data from 1990 to 2020 were evaluated. After determining the existence of slope heterogeneity, cross-sectional dependence, and mixed order stationary in the panel data, the paper applied the Cross-Sectional Autoregressive Distributive Lag (CS-ARDL) model to estimate long and short-run impacts. At the same time, AMG, MG, and CCEMG models have been utilized for checking robustness and validating the findings. According to CS-ARDL findings, female education and trade openness have a significant positive impact on female employment in the short and long term. In contrast, GDP per capita and urbanization are diminishing female employment in the targeted countries in the long run. The AMG, MG, and CCEMG results support the CS-ARDL findings. This shows that these governments should incorporate trade and education for women into their labor strategies. The key contribution of this study is in the field of labor market opportunity for female employment and shows the relative importance of education in determining female employment in South Asia. Full article

The study of academic title differences in universities helps to promote researchers’ enthusiasm and is critical to the efficiency of university scientific research. This study examines the impact of academic title differences on the research efficiency of universities and explores its mechanism. Based on the perspective of production types, the scientific and technological innovation achievements of universities are divided into academic output and economic output. By using the stochastic frontier model, this paper evaluates the influence of different academic titles on the academic and economic production efficiency of scientific research innovation in universities. The research results show that academic output efficiency increases over time, while the economic output efficiency decreases over time. Researchers with associate professor titles are more efficient in academic research production, and researchers with lecturer titles are more efficient in economic research production. Regional economy is positively correlated with the economic output of universities and negatively correlated with academic output. The production and development of academic and economic research in different regions are not coordinated. Full article

In recent years, green innovation has gained substantial attention and popularity from the manufacturing industry around the world. As an essential part of the manufacturing industry, small and medium-sized manufacturing enterprises (SMMEs) are vital participants that promote green innovation to realize sustainable development. However, how green innovation evolves in SMMEs is unclear, which hinders SMMEs from implementing or even adopting green innovation. This study attempted to essentially reveal the evolution of green innovation in SMMEs based on complex systems theory. First, this study divided green innovation into green product innovation and green process innovation, defined the state variables of the two components, and dissected the symbiotic interactions between them. This study then designed a nonlinear dynamic model followed by extensive simulations to theoretically and visually describe how green innovation evolves. This study found that green innovation with non-zero levels in both dimensions is desired and determines the evolutionary paths with corresponding measures that can guide SMMEs to realize green innovation at desired stable states. Besides, symmetric mutualism is discovered to be the optimal symbiotic interaction. Based on these findings, regulatory subjects and SMMEs can duly adjust the inputs on green innovation and the symbiotic interactions within green innovation to better manage green innovation practices. Full article

Inefficiencies in the construction and demolition waste (CDW) recycling supply chain constrain green innovation in the construction industry. However, existing studies have not analyzed the innovation behavior of recyclers in CDW recycling public-private partnership (PPP) projects from the perspective of innovation diffusion theory. To reveal the mechanism of recyclers’ innovation behavior in CDW recycling PPP projects in which recyclers and remanufacturers jointly participate in the operation stage, this study uses a Stackelberg game to analyze the optimal innovation strategy choice and total profit of the CDW recycling supply chain among the two innovation paths of green independent innovation and green imitation innovation under the combined effects of technology spillover, consumer green sensitivity, and government price subsidies to consumers. The main conclusions are as follows. (1) Remanufacturers and recyclers can improve their own innovation level and profit through technology spillover. (2) The total profit of the CDW recycling supply chain changes dynamically with the level of spillover. (3) The government price subsidy to consumers does not always improve the total profit of the CDW recycling supply chain. (4) The effect of consumers’ green sensitivity on the total profit of the CDW recycling supply chain shows heterogeneity with the innovation path of recyclers and the level of technological spillover. This study not only enriches the theoretical study of the green supply chain but also provides a basis for decision-making for recyclers and governments in practice. Full article

The diffusion of carsharing in cities can potentially support the transition towards a sustainable mobility system and help build a circular economy. Since urban transportation is a complex system due to the involvement of various stakeholders, including travelers, suppliers, manufacturers, and the government, a holistic approach based on systems thinking is essential to capture this complexity and its causalities. In this regard, the current research aims at identifying cause-and-effect relationships in the diffusion of carsharing services within the urban transport systems. To do so, a causal loop diagram (CLD) is developed to identify and capture the causalities of carsharing adoption. On this basis, the main four players within the carsharing domain in urban transportation were scrutinized and their causes and effects were visualized, including (i) the characteristics, behavior, and dynamics of the society population; (ii) transportation system and urban planning; (iii) the car manufacturing industry; and (iv) environmental pollution. The developed CLD can support decision-makers in the field of urban transport to gain a holistic and systemic approach to analyzing the issues within the transport sector due to their complexity. Moreover, they can help regulators and policymakers in intensifying the diffusion of more sustainable modes of transport by highlighting the role of population, car manufacturing, the transportation system, and environmental pollution. Full article

“Systemic structure” is an oft-used term in Systems Thinking. However, different authors use different, sometimes conflicting definitions of “systemic structure,” many of which are nebulous, and therefore its meaning is not clear. In this paper, we review the various definitions and interpretations and develop a logical, practical definition that may be applied to develop a deep understanding of system behavior: in Systems Thinking, “structure” is the cause-and-effect manner in which system components interrelate to yield system behavior; and the rules, laws, protocols, procedures, policies, and incentives/rewards that govern those interactions. Full article

Estimating the project effort remains a challenge for project managers and effort estimators. In the early phases of a project, having a high level of uncertainty and lack of experience cause poor estimation of the required work. Especially for projects that produce a highly customized unique product for each customer, it is challenging to make estimations. Project effort estimation has been studied mainly for software projects in the literature. Currently, there has been no study on estimating effort in customized machine development projects to the best of our knowledge. This study aims to fill this gap in the literature regarding project effort estimation for customized machine development projects. Additionally, this study focused on a single phase of a project, the automation phase, in which the machine is automated according to customer-specific requirements. Therefore, the effort estimation of this phase is crucial. In some cases, this is the first time that the company has experienced the requirements specific to the customer. For this purpose, this study proposed a model to estimate how much work is required to automate a machine. Insufficient effort estimation is one of the main reasons behind project failures, and nowadays, researchers prefer more objective approaches such as machine learning over expert-based ones. This study also proposed an artificial neural network (ANN) model for this purpose. Data from past projects were used to train the proposed ANN model. The proposed model was tested on 11 real-life projects and showed promising results with acceptable prediction accuracy. Additionally, a desktop application was developed to make this system easier to use for project managers. Full article

Innovation is an important factor to improve the quality of economic growth, and amplifying the innovation spatial spillover effect is an important measure to support the development of innovation. Scholars have carried out diversified research on the innovation spatial spillover effect, but there is still practical significance for deepening the research on the spatial spillover effect. In particular, the multi-angle comparative study in different regions still has research value, especially for the field of urban agglomeration integration. The spatial econometric model is a common method to measure spatial spillover effect. In order to carry out a multi-angle comparative study of innovation spatial spillover effects in different regions, this study takes two typical integrated urban agglomerations of the Yangtze River Delta and the Pearl River Delta in China as the object, and conducts a comparative study of the evolutionary characteristics of innovation spatial spillover effects based on urban scale data and the spatial econometric model. Differently from previous studies, invention patents are adopted to characterize the innovation level. The results show that there are significant positive innovation spatial spillover effects in the Yangtze River Delta and the Pearl River Delta, and the spatial spillover effect in the Yangtze River Delta is stronger. The spatial spillover effect exhibits significant spatiotemporal heterogeneity. For example, the spatial spillover effect in the core region and the fringe region of the urban agglomeration exhibits a positive effect, but the Yangtze River Delta is stronger than the Pearl River Delta. As an important innovation, the spatial spillover effects both exhibit the evolutionary characteristics of “inverted U-shape” based on the changes in geographical distance, and the spatial spillover effect of the Yangtze River Delta is always larger. Based on the empirical research, we propose promoting high-quality development by strengthening urban agglomeration cooperation, realizing urban agglomeration expansion in an orderly way, and improving the macro-political system. Full article

With the rapid development of science and technology, the ability to creative thinking has become an essential criterion for measuring talents. Current creative thinking courses for college students are affected by COVID-19 and are challenging to conduct. This study aimed to explore practical ways to teach creative thinking knowledge online and explored design opportunities for working on this teaching activity online. Through qualitative interviews, we found that the factors that influenced the design of the online virtual simulation course platform were focused on five dimensions: information presentation, platform characteristics, course assessment, instruction design, and presentation format. Through the analysis of user requirements, we obtained six corresponding design guidelines. Based on the knowledge system of design thinking, we set up eight modules in the course platform and developed a prototype including 100 user interfaces. We invited three experts and 30 users to conduct cognitive walk-through sessions and made design iterations based on the feedback. After user evaluation, dimensions of attractiveness, efficiency, dependability, and novelty reached excellent rating and were recognized by users. Full article

Integrated technology management in building smart ports or intelligent ports is a crucial concern for global sustainable development, especially when human societies are facing increasing risks from climate change, sea-levels rising, and supply chain disruptions. By mapping the knowledge base of 103 papers on intelligent ports, retrieved in late December 2022 from the Web of Science, this study conducted a roadmapping exercise using knowledge mapping findings, assisted by Bibliometrix, VoSviewer, and customized Python scripts. The three structural (intellectual, social, and conceptual) aspects of knowledge structure reveal the significance of the internet of things (IoT), the fourth industrial revolution (Industry 4.0), digitalization and supply chains, and the need for digital transformation alignment across various stakeholders with Industry 4.0 practices. Furthermore, an even geographical distribution and institutional representation was observed across major continents. The results of the analysis of the conceptual structure demonstrated the existence of several established and emerging clusters of research, namely (1) industry data, IoT, and ICT, (2) industry 4.0, (3) smart airports, (4) automation; and (5) protocol and security. The overall empirical findings revealed the underlying technology and innovation management issues of digital transformation alignment across stakeholders in IoT, Industry 4.0, 5G, Big Data, and AI integrated solutions. In relation to roadmapping, this study proposed a socio-technical transition framework for prototyping ecosystem innovations surrounding smart sustainable ports, focusing on contributing to valuable carbon or greenhouse gas emission data governance, management, and services in global value chains. Full article

The aim of the study is to identify the interrelations and interdependencies of systemic risk formation in the banking sector under the influence of the COVID-19 pandemic. The analysis of theoretical sources resulted in the main hypotheses of this study: (H1) The number of COVID-19 cases contributes to the formation of systemic risk in the banking sector through an increase in household debt; (H2) the number of COVID-19 cases contributes to the formation of systemic risk in the banking sector through an increase in overdue loans; (H3) the number of COVID-19 cases contributes to the formation of systemic risk in the banking sector through changes in the liquidity of the capital of banking institutions; (H4) the number of fatal COVID-19 cases contributes to the formation of systemic risk in the banking sector, through an increase in household debt; (H5) the number of fatal COVID-19 cases does not have a significant impact on the formation of systemic risk in the banking sector through an increase in overdue loans; (H6) the number of fatal COVID-19 cases does not have a significant impact on the formation of systemic risk in the banking sector through changes in the liquidity of the capital of banking institutions; (H7) the COVID-19 pandemic has a significant impact on the formation of systemic risk in the banking sector with an increase in the number of cases. The research methodology was based on a quantitative approach. The methodological basis of the study was the time-series model, analyzed using a complex of econometric and economic-statistical methods. The proposed methodological approach was tested on the example of China. As a result of the conducted research, polynomial mathematical models of the selected indicators were developed, and sustainable relations and correlations between individual indicators of the systemic risk formation in the banking sector and indicators of the COVID-19 pandemic were revealed, on the basis of which Hypotheses H1, H4, H6, and H7 were proved and Hypotheses H2 and H5 were refuted. At the same time, the H3 hypothesis was proved with a remark about the need for an individual approach since the negative effect manifests itself primarily in the medium and long term. The results of the study can be used by bank managers to implement measures that prevent the formation of systemic risk. In addition, the results of this study may be of interest to subsequent studies, including in terms of forming promising directions for future research. Full article

Agile project management (APM) can be defined as an iterative approach that promotes satisfying customer requirements, adjusts to change, and develops a working product in rapidly changing environments. Managers usually apply agile management as the project management approach in projects requiring extraordinary speed and flexibility in their processes. Earned value management (EVM) is a fundamental part of project management to establish practical measures. Often, managers use a task board to visually represent the work on a project and the path to completion. Still, managing an agile project can be a challenging endeavor. In this paper, we propose an agent-based model describing the management of tasks within a project using earned value assessment and a task board. Our model illustrates how EVM yields an efficient method to measure a project’s performance by comparing actual progress against planned activities, thus facilitating the formulation of more accurate predicted estimations. As proof of concept, we leverage our implementation to calculate EVM performance indexes according to a performance measurement baseline (PMB) in a task board fashion. Full article

This paper describes a new information system developed as part of the Portuguese R&D project DECIdE. The project aimed at the development of a platform for infrastructure asset management tailored to Portuguese water utilities. The platform allows the integration of different data from several information systems of the water utilities and includes several tools for the performance assessment of the water supply systems in terms of water losses, energy efficiency and quality of service (i.e., water and energy balances and key performance indicators). The developed platform was tested with data from five small to medium size Portuguese water utilities with different maturity levels in terms of technological and human resources. The obtained results are very promising because the platform allows for periodic system performance assessment which constitutes an important part of the infrastructure asset management for small and medium-sized water utilities. Full article

Earthquakes pose a significant threat to infrastructure systems. However, improving the seismic resilience of infrastructure systems in earthquake-prone regions is fraught with obstacles. First, this article reviews the current status of earthquake resilience research, points out the gaps of existing research, and then focuses on the adaptability in resilience. Secondly, five groups of influencing factors of infrastructure system adaptability are identified and clustered through literature review and expert knowledge. Thirdly, the structure and conditional probability table of the Bayesian network model are given in detail, and the evaluation model of Bayesian network adaptability is created. A Chinese earthquake-prone county was used to verify the applicability of the model. The research uses forward propagation analysis to calculate the adaptability of the case and obtains the probability of the case’s adaptability. The backward propagation to obtain the ranking of the influence degree of the critical influencing factors on the adaptability and the top three factors are respectively earthquake history, relevant information and contingency mechanisms. Finally, the research suggests measures to improve adaptability. Full article

To more effectively solve the complex optimization problems that exist in nonlinear, high-dimensional, large-sample and complex systems, many intelligent optimization methods have been proposed. Among these algorithms, the particle swarm optimization (PSO) algorithm has attracted scholars’ attention. However, the traditional PSO can easily become an individual optimal solution, leading to the transition of the optimization process from global exploration to local development. To solve this problem, in this paper, we propose a Hybrid Reinforcement Learning Particle Swarm Algorithm (HRLPSO) based on the theory of reinforcement learning in psychology. First, we used the reinforcement learning strategy to optimize the initial population in the population initialization stage; then, chaotic adaptive weights and adaptive learning factors were used to balance the global exploration and local development process, and the individual optimal solution and the global optimal solution were obtained using dimension learning. Finally, the improved reinforcement learning strategy and mutation strategy were applied to the traditional PSO to improve the quality of the individual optimal solution and the global optimal solution. The HRLPSO algorithm was tested by optimizing the solution of 12 benchmarks as well as the CEC2013 test suite, and the results show it can balance the individual learning ability and social learning ability, verifying its effectiveness. Full article

With the cyclical development of emerging technologies, in reality, the evolution dynamics of their innovation networks will inevitably show obvious time attributes. Numerous network analyses of real complex systems usually focus on static networks; however, it is difficult to describe that most real networks undergo topological evolutions over time. Temporal networks, which incorporate time attributes into traditional static network models, can more accurately depict the temporal features of network evolution. Here, we introduced the time attribute of the life cycle of emerging technology into the evolution dynamics of its innovation network, constructed an emerging technology temporal innovation network from a temporal network perspective, and established its evolution model in combination with the life cycle and key attributes of emerging technology. Based on this model, we took 5G technology as an example to conduct network evolution simulation, verified the rationality of the above model building, and analyzed the cyclical evolution dynamics of this network in various topological structures. The results show that the life cycle of emerging technology, as well as multiple knowledge attributes based on the key attributes of emerging technology, are important factors that affect network evolution by acting on node behaviors. Within this study, we provide a more realistic framework to describe the internal mechanism of the cyclical evolution of emerging technology innovation network, which can extend the research on innovation network evolution from the single topological dynamics to the topological–temporal dynamics containing time attributes and enrich the research dimensions of innovation network evolution from the perspective of temporal evolution. Full article

The Artificial Intelligence Recommender System has emerged as a significant research interest. It aims at helping users find things online by offering recommendations that closely fit their interests. Recommenders for research papers have appeared over the last decade to make it easier to find publications associated with the field of researchers’ interests. However, due to several issues, such as copyright constraints, these methodologies assume that the recommended articles’ contents are entirely openly accessible, which is not necessarily the case. This work demonstrates an efficient model, known as RPRSCA: Research Paper Recommendation System Using Effective Collaborative Approach, to address these uncertain systems for the recommendation of quality research papers. We make use of contextual metadata that are publicly available to gather hidden relationships between research papers in order to personalize recommendations by exploiting the advantages of collaborative filtering. The proposed system, RPRSCA, is unique and gives personalized recommendations irrespective of the research subject. Thus, a novel collaborative approach is proposed that provides better performance. Using a publicly available dataset, we found that our proposed method outperformed previous uncertain methods in terms of overall performance and the capacity to return relevant, valuable, and quality publications at the top of the recommendation list. Furthermore, our proposed strategy includes personalized suggestions and customer expertise, in addition to addressing multi-disciplinary concerns. Full article