Systems, Volume 11, Issue 8 (August 2023) – 57 articles

Data sharing with additional devices across wireless networks is made simple and advantageous by the Internet of Things (IoT), an emerging technology. However, IoT systems are more susceptible to cyberattacks because of their continued growth and technological advances, which could lead to powerful assaults. An intrusion detection system is one of the key defense mechanisms for information and communications technology. The primary shortcomings that plague current IoT security frameworks are their inability to detect intrusions properly, their substantial latency, and their prolonged processing time and delay. Therefore, this work develops a clever and innovative security architecture called Vectorization-Based Boost Quantized Network (VBQ-Net) for protecting IoT networks. Here, a Vector Space Bag of Words (VSBW) methodology is used to reduce the dimensionality of features and identify a key characteristic from the featured data. In addition, a brand-new classification technique, called Boosted Variance Quantization Neural Networks (BVQNNs), is used to classify the different types of intrusions using a weighted feature matrix. A Multi-Hunting Reptile Search Optimization (MH-RSO) algorithm is employed during categorization to calculate the probability value for selecting the right choices while anticipating intrusions. In this study, the most well-known and current datasets, such as IoTID-20, IoT-23, and CIDDS-001, are used to validate and evaluate the effectiveness of the proposed methodology. By evaluating the proposed approach on standard IoT datasets, the study seeks to address the limitations of current IoT security frameworks and provide a more effective defense mechanism against cyberattacks on IoT systems. Full article

Data sharing and service reuse in the health sector pose significant privacy and security challenges. The European Commission recognizes health data as a unique and cost-effective resource for research, while the OECD emphasizes the need for privacy-protecting data governance systems. In this paper, we propose a novel approach to health data access in a hospital environment, leveraging homomorphic encryption to ensure privacy and secure sharing of medical data among healthcare entities. Our framework establishes a secure environment that enforces GDPR adoption. We present an Information Sharing Infrastructure (ISI) framework that seamlessly integrates artificial intelligence (AI) capabilities for data analysis. Through our implementation, we demonstrate the ease of applying AI algorithms to treated health data within the ISI environment. Evaluating machine learning models, we achieve high accuracies of 96.88% with logistic regression and 97.62% with random forest. To address privacy concerns, our framework incorporates Data Sharing Agreements (DSAs). Data producers and consumers (prosumers) have the flexibility to express their prefearences for sharing and analytics operations. Data-centric policy enforcement mechanisms ensure compliance and privacy preservation. In summary, our comprehensive framework combines homomorphic encryption, secure data sharing, and AI-driven analytics. By fostering collaboration and knowledge creation in a secure environment, our approach contributes to the advancement of medical research and improves healthcare outcomes. A real case application was implemented between Portuguese hospitals and universities for this data sharing. Full article

The main task in visual object tracking is to track a moving object in an image sequence. In this process, the object’s trajectory and behavior can be described by calculating the object’s position, velocity, acceleration, and other parameters or by memorizing the position of the object in each frame of the corresponding video. Therefore, visual object tracking can complete many more advanced tasks, has great performance in relation to real scenes, and is widely used in automated driving, traffic monitoring, human–computer interaction, and so on. Siamese-network-based trackers have been receiving a great deal of attention from the tracking community, but they have many drawbacks. This paper analyzes the shortcomings of the Siamese network tracker in detail, uses the method of feature multi-scale fusion to improve the Siamese network tracker, and proposes a new target-tracking framework to address its shortcomings. In this paper, a feature map with low-resolution but strong semantic information and a feature map with high-resolution and rich spatial information are integrated to improve the model’s ability to depict an object, and the problem of scale change is solved by fusing features at different scales. Furthermore, we utilize the 3D Max Filtering module to suppress repeated predictions of features at different scales. Finally, our experiments conducted on the four tracking benchmarks OTB2015, VOT2016, VOT2018, and GOT10K show that the proposed algorithm effectively improves the tracking accuracy and robustness of the system. Full article

An effective combination of resilience and efficiency will help to promote the high-quality development of the digital economy. In this paper, we use the entropy method, the Super-SBM model, and the Haken model to measure the digital economy resilience, digital economy efficiency, and their synergistic evolution mechanisms in 31 Chinese provinces and autonomous regions from 2013 to 2020, respectively. The results show that: (1) The resilience of the digital economy is the order parameter leading synergistic development, playing a prominent role in promoting the development of the digital economy from disorder to order. (2) Synergistic evolution has evolved from low-level to high-level, but it is in the primary stage as a whole, and the fluctuation of its development is not obvious, showing a unipolar development pattern. (3) There are significant regional differences in synergistic evolution, showing a polarization pattern, with the “Matthew effect” that the stronger the start the stronger the development. (4) Overall, the resilience and efficiency of the digital economy show a positive synergistic effect, but some provinces show a negative feedback mechanism that inhibits the orderly development of the system, there is a bias effect, and the development levels of the resilience and efficiency of the digital economy are mismatched. (5) The four paths that drive the synergistic evolution of resilience and efficiency in a high digital economy are the “Resilience Dominates Driven Path”, the “Basic Driven Path”, the “Innovation Driven Path”, and the “Balanced Driven Path”, respectively. In short, the synergistic evolution of the resilience and efficiency of the digital economy shows that the future development of the digital economy needs to cultivate endogenous momentum, pay attention to strengthening resilience in the process of continuing to build a diversified industrial system, and continuously improve the operational efficiency of the digital economy, in order to promote its high-quality development. Full article

Many combined heat and power (CHP) energy hubs work within the following heat load mode in the wintertime to supply the request for heat, and renewable energy has been often restricted in the unified energy network (UEN) markets. The power Internet of Things (PIoTs) has enabled UEN to transmit data increasingly frequently. As a result of flexible connections among various UEN networks, renewable energy increases its accommodation capacity considerably. Thus, the purpose of the study is to optimize UEN within the backdrop of PIoTs. According to the impact of PIoTs on UEN, this paper develops the combined demand response (DR) process and the layout of the important parts of UEN. Afterward, this study develops a bi-level economic dispatching process based on the cyber-physical systems of PIoTs and UEN. In the dispatching process, the higher level optimizes the total UEN function; the lower level optimizes the demand-side equipment output and combined DR. Then, the gray wolf optimization scheme is used to solve the bi-level dispatch. Lastly, the standard UEN and the practical network have been used to verify the efficiency of the suggested process. Full article

The automobile is an important part of transportation systems. Accurate prediction of sales prospects of different power vehicles can provide an important reference for national scientific decision making, flexible operation of enterprises and rational purchases of consumers. Considering that China has achieved the goal of 20% sales of new energy vehicles ahead of schedule in 2025, in order to accurately judge the competition pattern of new and old kinetic energy vehicles in the future, the automobile market is divided into three types according to power types: traditional fuel vehicles, new energy vehicles and plug-in hybrid vehicles. Based on the monthly sales data of automobiles from March 2016 to March 2023, the prediction effects of multiple models are compared from the perspective of univariate prediction. Secondly, based on the perspective of multivariate prediction, combined with the data of economic, social and technical factors, a multivariate prediction model with high prediction accuracy is selected. On this basis, the sales volume of various power vehicles from April 2023 to December 2025 is predicted. Univariate prediction results show that in 2025, the penetration rates of three types of vehicles will reach 43.8%, 44.4% and 11.8%, respectively, and multivariate prediction results show that the penetration rates will reach 51.0%, 37.9% and 11.1%, respectively. Full article

Information on short-term rental platforms plays an important role in consumer purchase behavior. However, information asymmetry between host and guest has been identified as a problem in sharing economy contexts. In this paper, to fill this gap, the authors develop six hypotheses about the focal impacts of self-presentation strategy and the moderating effects of third-party certification. Based on data from Airbnb, the authors first examine how the tactics of self-presentation strategy influence consumer purchase behavior. The results show that different self-presentation tactics affect consumer purchase behavior differently. The authors also found that the third-party certification level weakens the influence of self-presentation strategy interactions on consumer purchase behavior. Full article

In a trend towards digital continuity, model-based systems engineering is becoming widely adopted for the design of complex systems, supporting system development from the very first stages. A narrow panel of methods and tools are available on the market; they offer different scopes and approaches, are more or less intuitive to follow, and are sometimes supported by tools. Among them, the Architecture Analysis & Design Integrated Approach (ARCADIA) is becoming popular and is gradually spreading in different industrial fields to model a wide variety of systems at different stages of their development and from different points of view. It is implemented using an open-source tool called Capella. Few feedback on its use in industrial settings have been published, while other feedback remains confidential. The goal of this paper is to analyze the interests and limitations of ARCADIA/Capella. To reach this goal, we experimented with ARCADIA/Capella in several projects and chose one to explain how the method and tool proceeded. In addition, we conducted a survey to obtain industrial feedback. As a result, the paper gives an overview of the relevance of ARCADIA/Capella in projects and of its usefulness, effectiveness, and adaptability in modeling different types of systems. It also provides some perspectives for the evolution of the method and the tool according to industrial feedback. Full article

Natural gas is playing an important role in the reconstruction of the energy system of China. Natural gas supply and consumption indicators forecasting is an important decision-making support for the government and energy companies, which has attracted considerable interest from researchers in recent years. In order to deal with the more complex features of the natural gas datasets in China, a Grey Wavelet Support Vector Regressor is proposed in this work. This model integrates the primary framework of the grey system model with the kernel representation employed in the support vector regression model. Through a series of mathematical transformations, the parameter optimization problem can be solved using the sequential minimal optimization algorithm. The Grey Wolf Optimizer is used to optimize its hyperparameters with the nested cross-validation scheme, and a complete computational algorithm is built. The case studies are conducted with real-world datasets from 2003–2020 in China using the proposed model and 15 other models. The results show that the proposed model presents a significantly higher performance in out-of-sample forecasting than all the other models, indicating the high potential of the proposed model in forecasting the natural gas supply and consumption in China. Full article

Green and low carbon automated production has become a research hotspot. In this paper, the AGV transport resource constraint, machine layout and job setup time have been integrated into the background of a flexible job shop. From a whole life-cycle perspective, the AGV allocation strategy has been formulated by simulating multiple scenarios within the production system. Aimed at makespan, carbon footprint, and machine load, a green low-carbon flexible job shop scheduling model with multiple transport equipment (GFJSP-MT) has been constructed. To address this problem, a relevant case dataset was formed, and a heuristic strategy NSGA-II using a real number encoded embedded cycle to replace repeated individuals was proposed. Through longitudinal and horizontal comparisons, the effectiveness of the AGV allocation strategy has been verified and the optimum number of AGVs in the case determined. Finally the quality and diversity of the Pareto frontier solutions are compared and the scheduling scheme for each sub-objective are discussed. The results show that the model and algorithm constructed in this paper can effectively achieve the optimal scheduling of green flexible shop production. Full article

This study focuses on the equity of spatial layout for public service facilities targeting different age groups and proposes a systematic analytical approach. The method mainly includes the following steps: (1) collecting spatial distribution data of population and various types of public service facilities for each age group; (2) establishing demand matrices for different groups regarding different types of public service facilities and calculating supply-demand density; (3) calculating the level of public service accessibility for different population groups at any spatial location; (4) introducing the Gini coefficient to assess the spatial equity of public service accessibility for different age groups; (5) evaluating whether the Gini coefficient meets the planning objectives and identifying areas with inadequate public service accessibility for optimization of facility layout. Choosing Hongkou District in Shanghai as a case study, the analysis process and results indicate the technical feasibility of the proposed method and its supportive role in public service facility planning. Furthermore, the article discusses the importance of a systematic analysis perspective, the applicability of the methodology in planning decisions, and the enhancement of facility supply levels in weak areas. Full article

Electronic sports show significant user churn caused by a toxic gaming atmosphere, and current GUI-based interventions are insufficient to address the issue. Based on the theoretical framework of Perceiving and Experiencing Fictional Characters, a new hybrid interaction interface and paradigm combined with tangibles is proposed to counter negative mood. To support the frustrated users of Massive Online Battle Arena (MOBA) games, we added AI teammates for better personal performance and social robots for the disclosure of negative mood. We hypothesized that AI teammates’ invisibility and anonymity would mitigate negative emotions; an effect amplified by the presence of social robots. A comparative experiment was conducted with 111 participants. Social robots for emotion-oriented coping improved user mood but AI teammates for problem-oriented coping did so better, although their higher levels of experienced anonymity may not have been preferred. Unexpectedly, conversing with a robot after playing with an AI teammate brought the mood back to that experienced when talking to a robot alone, while increasing the distancing tendencies. With this in mind, AI and social robots can counter the negative atmosphere in MOBA games, positively contributing to game design and empathic human–computer interaction. Full article

Efficient monitoring and achievement of the Sustainable Development Goals (SDGs) has increased the need for a variety of data and statistics. The massive increase in data gathering through social networks, traditional business systems, and Internet of Things (IoT)-based sensor devices raises real questions regarding the capacity of national statistical systems (NSS) for utilizing big data sources. Further, in this current era, big data is captured through sensor-based systems in public sector organizations. To gauge the capacity of public sector institutions in this regard, this work provides an indicator to monitor the processing capacity of the public sector organizations within the country (Pakistan). Some of the indicators related to measuring the capacity of the NSS were captured through a census-based survey. At the same time, convex logistic principal component analysis was used to develop scores and relative capacity indicators. The findings show that most organizations hesitate to disseminate data due to concerns about data privacy and that public sector organizations’ IT personnel are unable to deal with big data sources to generate official statistics. Artificial intelligence (AI) techniques can be used to overcome these challenges, such as automating data processing, improving data privacy and security, and enhancing the capabilities of IT human resources. This research helps to design capacity-building initiatives for public sector organizations in weak dimensions, focusing on leveraging AI to enhance the production of quality and reliable statistics. Full article

Uncertain yield and demand often result in unbalanced revenue and risk sharing among agriculture supply chain (ASC) members. Agricultural cooperatives help smallholder farmers enhance their bargaining power via revenue and risk sharing and are a significant bridge between farmers and retailers. Therefore, this study aimed to design a contractual mechanism considering a cooperative’s participation in the coordination of ASCs that face random yield and demand in pursuit of risk and benefit equivalence. We first compared two- and three-echelon decentralized ASCs with a centralized system, and the results indicate that the cooperative’s participation is conducive to redistributing profit between the retailer and farmer. As a verified revenue-sharing (RS) contract cannot coordinate a two-echelon ASC under uncertain yield and demand, we developed a double revenue-sharing (DRS) contract, which has been demonstrated to coordinate an ASC system with the cooperative’s participation. A numerical analysis was conducted to show that the cooperative can influence profit distribution among the upstream and downstream members via wholesale prices. Therefore, equivalence of risk and benefit is realized and fairly allocated among the members under the DRS contract, which is of great significance to the ASC system’s stable and sustainable development. Full article

Background: Brucellosis is a serious public health problem distributed worldwide. Jordan has one of the highest incidences of brucellosis globally, mostly caused by infection with Brucella melitensis, which is associated with small, ruminant animals such as sheep. Sheep are an important livestock species in Jordan where there is a high value is placed on keeping animals at home. The incidence of human brucellosis rapidly increased following the Syrian conflict, in association with the resettlement of displaced people. Methods: A systems thinking (ST) approach was applied to develop a model to explain the dynamics of brucellosis in Jordan. A causal loop diagram (CLD) was developed to visualize and conceptualize interactions and feedback between several factors involved in sheep husbandry, animal and human health, and livestock trading systems. The CLD was constructed using information from published literature, historical governmental reports, policy documents and media coverage regarding brucellosis in Jordan. Semi-structured, in-depth interviews with diverse stakeholders were conducted to elicit information on their understanding of critical factors associated with brucellosis. Results: The CLD demonstrated that brucellosis transmission in Jordan is driven by sheep husbandry practices like livestock movement/trading and the management of symptomatic animals (those suffering abortion). Five dimensions were identified to be associated with brucellosis transmission: human infection; consumers; traders and markets; and livestock infection and control. Conclusion: The development of a CLD to explain brucellosis transmission in Jordan provides a better understanding of and reveals the multi-sectoral nature of the problem. The need for a multi-sectoral approach for effective brucellosis management in Jordan is clear from the five dimensions identified. Full article

The airline sector is critical in today’s globalized society, supporting the efficient movement of people and products worldwide. Airlines continuously seek methods to enhance their operational performance to remain competitive in the face of increased competition. This study focuses on the application of multi-criteria decision making (MCDM) techniques to improve the ground operational performance of low-cost carriers (LCCs), also known as low-cost (budget) airlines. In recent years, MCDM techniques have gained considerable attention in addressing complex decision problems with complex goals. This research aims to bridge this gap by proposing a comprehensive framework combining MCDM techniques to enhance airline operational strategies and increase performance. The study utilizes qualitative and quantitative data, drawing on previously published materials on MCDM techniques in the aviation sector. It utilizes a fuzzy Analytic Hierarchy Process (AHP) and a fuzzy Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodologies. A case study is conducted to evaluate the ground operational performance of three airline companies based in Türkiye, considering five main criteria and eighteen sub-criteria. The findings of this research will contribute to a comprehensive understanding of operational performance in the airline sector. The study’s findings show that five primary criteria’s estimated weights are examined; it is seen that “Flight Schedule and Routes (FSR)” has the highest importance weight of 0.30. With a weight value of 0.26, “Counter Services (CS)” has the second most significant impact. “Ticketing (T)”, which ranks third in terms of its impact on the solution, has a weight value of 0.19. Upon reviewing the analysis’s findings, it can be seen that the third alternative is relatively prominent among the others. Airline_3 ranked first with a weight value of 0.361, while Airline_2 ranked second with a weight value of 0.331. Airline_1 ranked last with an actual weight of 0.308. The study provides highlights of the implications and limitations of the research and suggests future research directions. Full article

Establishing appropriate inter-organizational partnership in disaster response is of great help to the improvement of disaster relief performance. However, the selection of proper disaster response organizations for government agencies to build partnerships among the numerous relief organizations remains to be addressed. To fill the gap, this paper adopted a complex network modeling method to explain the formation mechanism of inter-organizational partnership in disaster response. Then, based on a numerical simulation method, the paper compared different inter-organizational partnering strategies, and explored the optimal inter-organizational partnering strategy in the disaster response process. Results demonstrate that the optimal partnering strategy is contingent on disaster response conditions, with accurate disaster information and abundant relief materials to choose the material difference principle, and vague disaster information to choose the organizational influence principle. At the same time, frequent information communication and material cooperation allocation can be transformed into low-frequency information communication and government-led material allocation. This study provides practical guidance for disaster relief organizations to choose inter-organizational partnering strategies, and enriches the disaster organization management theory. Full article

The aim of this research is to explore the antecedents of usefulness in the technology acceptance model for a food delivery application system and to confirm the accountability of the technology acceptance model in the area of food delivery application systems. The determinants of usefulness are information quality, swiftness, and food quality. For data collection, this research employs Amazon Mechanical Turk. The number of valid observations is 379. For the data analysis, confirmatory factor analysis and structural equation modeling were primarily implemented. The results indicate that usefulness is positively impacted by information quality, swiftness, and ease of use. Additionally, it is found that attitude is positively influenced by usefulness. Moreover, intention to use is positively affected by usefulness and attitude. This research is worthwhile in that it provides service providers with information for constructing better systems. Full article

Government policy is crucial to control air pollution, while industrial structure upgrading and green technology progress are needed to optimize air pollution control performance (APCP). Meanwhile, policy spillovers from one region to another affect the APCP. This study applied systems theory to explain the mechanisms that drive both environmental policy spillover and APCP. We evaluated the APCPs of 41 cities in the Yangtze River Delta region from 2006 to 2020 using a super-efficiency SBM-DEA model. We then analyzed the paths by which industry and technology drive APCP using a spatial Durbin model (SDM) and investigated heterogeneity across different regional governance groups. The effects generated by the regulatory spillover of air pollutants were decomposed into four subsystems: chain transmission effect, vibration effect, ripple effect, and halo effect. The results show the following: (1) Throughout the study period, the APCP of most of the regional governance groups in the Yangtze River Delta region showed a fluctuating trend with continuous improvement. The APCP was higher and more stable in the Zhejiang Province in the southeast, and lower and more drastic in the Jiangsu and Anhui Provinces in the north, and shows a significant positive spatial correlation. (2) Industrial structure upgrading and green technology progress had different impact paths on the APCP. Industrial structure upgrading had a significant indirect contribution to the APCP, but the direct effect was not significant. Green technology progress had a significant direct inhibitory effect and an indirect promoting effect on the APCP. (3) In the optimization path of the APCP, industrial structure upgrading played a more important role than green technology progress, but they did not reinforce each other’s enhancement of the APCP. (4) There was regional heterogeneity in the impacts of industry and technology on the APCP. The paths and actual effects of industry and technology on the APCP varied greatly among different regional governance groups. Full article

Projects are undertaken in all science, engineering, and technology fields to achieve strategic and tactical goals. It is evident from the literature that projects are becoming more complex day by day, making project complexity a domain for current research. The objective of this study is to evaluate project complexity using a systematic, comprehensive, and widely accepted definition that can capture the multidimensional nature of project complexity and its impact on project success. Therefore, an integrative systemic framework has been selected to define project complexity considering seven key dimensions: context, size, diversity, autonomy, connectivity, emergence, and belonging. The study employed structural equation modeling to analyze project complexity, its dimensions and their relationship with project success for complex engineering projects. After an extensive literature review, a validated questionnaire was developed and used to obtain responses from different countries (Pakistan, China, UAE, UK, USA, and others) in the engineering fields of aerospace, design, manufacturing, oil and gas, IT, and construction. The work shows that project complexity has a negative impact on project success for complex engineering projects. Further, analyses examined the relationship between project success and the seven dimensions of project complexity. The significance of this study lies in its evaluation of project complexity using a systematic and comprehensive definition which is different from previous studies and brings more clarity and understanding of the underlying mechanisms and causal relationships between project complexity, project success and their related factors. The findings suggest that careful consideration of these dimensions and their factors can help project managers better understand and navigate project complexity and ultimately improve project success rates. Full article

Digital transformation and sustainability are both at the forefront of current supply chain developments. However, the specific mechanisms of how digital transformation and green supply chain development interact still need to be clarified, which can help supply chain business operators to enhance supply chain sustainability more effectively. This paper focuses on how the companies’ organization structure and the socio-economic environment interact with digital technologies under the process of green supply chain development. Based on the “Technology–Organization–Environment” (TOE) framework, this paper analyze how digital transformation can drive green supply chain development. To test the TOE theoretical analysis framework, this paper calculates the digital transformation and green supply chain development index at the provincial level in China and conducts an empirical study. The main findings and implications of this paper can be summarized in the following aspects: First, according to the TOE theory, the external environment dimensions, such as the market and policy environments, affect the role of digital technology in promoting GSC development. Second, in the organizational dimensions, labor–capital relations, company size, and ownership factors can all affect the contribution of digital transformation to green supply chains. Third, there are differences in the impact of different types of digitization technologies on GSC development. Full article

The transformation of resource-based cities has a crucial impact on the development of local manufacturing enterprises. This study aims to assess the impact of the Resource-Exhausted City Program (RECP) on the performance of manufacturing enterprises, with a particular focus on the role of policy measures. Using data on manufacturing enterprises from 2004 to 2014, this study employs multiple difference-in-difference methods to investigate the impact of the RECP on corporate performance and its potential mechanisms. The results show that (1) the RECP can significantly improve the performance of firms in the pilot areas; (2) the implementation of RECP contributes to the improvement of manufacturing enterprises’ performance by using policy measures, mainly including subsidies, tax incentives and access to low-interest loans; and (3) the supportive effects of the RECP are more pronounced in outward-oriented, state-owned and larger manufacturing enterprises. This study provides new insights into the relationship between resource-exhausted city policies and corporate performance. Full article

The extensive application of algorithm recommendation technology not only meets the information needs of network users but also leads to the emergence of “information cocoons”. On the basis of summarizing three generating mechanisms, namely, the theory of technological innovation, the theory of interest-driven, and the theory of emotional identity, this paper constructs a game model of bilateral evolution between information platforms and network users and simulates the influence path of key factors on the evolution of both parties’ main strategies. The research shows that algorithm recommendation technology is the root of “information cocoons” in the algorithm era. As the algorithm technology matures day by day, the cost of using algorithm recommendations on information platforms and the loss cost of accepting algorithm recommendations by network users are constantly decreasing, which causes the information platforms and network users’ strategy choice for algorithm recommendation to evolve from {give up and conflict} to {use and accept}, and finally leads to the long-term existence of “information cocoons”. Full article

The melting of ice creates favorable conditions for the development of the Arctic by elevating the Arctic’s geostrategic value and stoking national competition. As a result, many states, both within and beyond the Arctic region, have recently released Arctic strategies or updated existing ones, clarifying their roles, interests, priorities, and policies for Arctic international cooperation. In this study, a database is generated with a collection of Arctic strategies from 16 states, and then grounded theory is applied to code the texts in these strategy documents. Using this code, word-frequency analysis, cross-tabulation, correlation analysis, and cooperative-network analysis are implemented to explore competition and collaboration in the Arctic among these 16 states. The results indicate that Arctic states are significantly concerned with governance, economic and social development, and security and stability, while environmental protection and scientific research are given more attention by non-Arctic states. In addition, the highest value of the Pearson correlation coefficient is identified as 0.85 for collaboration and Arctic public security affairs, which indicates that collaboration on Arctic security will dominate the development of Arctic affairs in the near future. Full article

Inspired by long-distance road transport in industrial logistics in China, this paper studies a simultaneous loading scheduling and vehicle routing problem over a multi-workday planning horizon. Industrial cargo often requires specialized facilities, and these facilities vary in performance and quantity and are subject to available time constraints. Consequently, achieving coordinated optimization of vehicle routing and loading scheduling becomes a significant challenge in practice. We describe the studied problem as a multi-trip vehicle routing problem with time windows and resource synchronization on heterogeneous facilities. First, we develop a mixed integer programming model in a multi-workday setting to minimize the total travel distance and the number of vehicles. Moreover, a three-phase heuristic approach is developed. An initial solution is constructed using a sequential strategy in the first phase, and then an adaptive large neighbourhood search and a post-optimization procedure based on ejection chains are, respectively, designed to optimize the two hierarchical objective functions. Finally, extensive computational experiments are conducted to demonstrate the effectiveness of the proposed method. Specifically, the research results indicate that in long-distance road transport in industrial scenarios, expanding the planning horizon from a single workday to a multi-workday period could significantly reduce logistics operational costs and improve service quality. Full article

Online exercise communities play an important role in their users’ self-health management. The willingness of users to interact and create user-generated content in online communities reflects the vitality of the online exercise community and the positive impact it has on offline users’ health performance. Therefore, based on social capital theory, we study the relationship between three types of social capital and users’ offline exercise behaviors and add off-topics in the community in the model. We select the KEEP health community user group as the research setting and conduct the regression analysis. The results show that owned centrality and reciprocity have a significant positive relationship with users’ exercise behaviors; accessed centrality and trust have a significant negative relationship with users’ exercise behaviors; and common topics and off-topics show a partly significant correlation. As a moderating variable, off-topics have a negative moderating effect on owned centrality and betweenness centrality, but a positive moderating effect on reciprocity and trust among group members. The results enrich and expand social capital theory, deepen the research on users’ exercise behaviors in the online exercise community, and provide a good reference for online exercise community management. Full article

The COVID-19 pandemic has affected smart city operations and planning. Smart cities, where digital technologies are concentrated and implemented, face new challenges in becoming sustainable from social, ecological, and economic perspectives. Using text mining methodologies of topic modeling and network analysis, this study aims to identify keywords in the field of smart cities after the pandemic and provide a future-oriented perspective on the direction of smart cities. A corpus of 1882 papers was collected from the Web of Science and Scopus databases from December 2019 to November 2022. We identified six categories of potential issues in smart cities using topic modeling: “supply chain”, “resilience”, “culture and tourism”, “population density”, “mobility”, and “zero carbon emission”. This study differs from previous research because it is a quantitative study based on text mining analysis and deals with smart cities, given the prevalence of COVID-19. This study also provides insights into the development of smart city policies and strategies to improve urban resilience during the pandemic by anticipating and addressing related issues. The findings of this study will assist researchers, policymakers, and planners in developing smart city strategies and decision-making in socioeconomic, environmental, and technological areas. Full article

The dynamic and complex interactions between the urban freight-transportation system and population, economy, traffic flow, fuel consumption, and environmental pollution, make policymaking in this system one of the fundamental challenges of urban management. In this regard, a systemic approach in urban freight-transportation system modelling should be considered to solve the problems of the system. One of the main problems of this system is the mismatch between the freight-transportation capacity and the total freight-transportation demand. Considering the lack of sufficient studies in the field of macro and quantitative modeling of this system, the main goal of this article is to model the urban freight-transportation system in order to identify the factors affecting the urban freight-transportation demand and capacity. The main focus of the research is to develop quantitative scenarios which balance the freight-transportation capacity and freight-transportation demand. The urban freight-transportation system is modelled by the System Dynamics (SD) approach and their basic behaviors; as well as this the results of some policy-making scenarios are simulated. The model is validated by the real data of Shiraz. Five quantitative scenarios are designed with two approaches of managing the freight-transportation demand and freight-transportation-capacity sectors. The scenarios are based on four control variables, including the distribution coefficient, trip numbers, vehicle capacity, and vehicle numbers. The simulation results show that the total gap between freight-transportation capacity and freight-transportation demand will decrease by optimizing each of the control variables. However, the combined scenario is the most applicable policy in order to maintain the balance between freight-transportation capacity and demand. Generally, the proposed model can be used to design different quantitative scenarios in order to optimize the freight-transportation system’s performance. This study can also help policymakers to manage the urban freight-transportation system more efficiently. Full article

Owners and managers of small and medium-sized enterprises (SMEs) have changed their perspective on risk management due to the current global negative threats in the business environment. If they want to be successful and ensure the financial performance of their business, they must adopt a proactive approach to reducing strategic risks in connection with operational risks. The aim of the article is to identify and quantify the differences in operational risk management and its effect on the financial management of SMEs between four Central European countries. The statistical sample of SMEs consisted of 1090 owners and top managers. Data collection was carried out through a questionnaire in the period December 2022–January 2023. Statistical hypotheses were evaluated with the application of correlation analysis and linear regression modelling. Empirical findings have confirmed that operational risk management has a significant positive impact on improving the profitability of the enterprise and on reducing the enterprise’s indebtedness, as well as in the context of reducing the enterprise’s inability to pay its obligations. However, there are disparities in the perception of this influence between owners/managers with regard to the country in which they carry out their business activities. The findings are important for both national and multinational organizations and entities dealing with risk management in the business environment of small and medium-sized enterprises. Full article

Social activities have a significant impact on the rainstorm flood disaster risk. It is crucial to explore the dynamic changes of urban rainstorm flood disaster risk caused by crowd activities. In this study, a risk simulation method of urban rainstorm flood disasters is proposed, composed of an urban rainstorm flood model based on SWMM and LISFLOOD-FP and a crowd activities model based on ABM. Taking the Futian District of Shenzhen as an example, the temporal and spatial changes in rainstorm flood disaster risk for buildings and roads are analyzed under three scenarios: midnight, morning peak, and evening peak. The results show that: (1) Although the overall risk of urban rainstorm flood disasters increases as the inundation area expands, the average risks of roads and buildings increase rapidly and then stabilize during the morning peak due to commuting activities, while the average risk of roads remains high level during the evening peak due to commuting activities, while; (2) The risk of urban rainstorm flood varies significantly at different time periods. The average risk of buildings is the largest during the morning peak, about twice that during the evening peak. The number of high-risk roads during the evening peak is much higher than in the morning peak, and both buildings and roads have the least risk during midnight; (3) The spatial distribution of urban rainstorm flood disaster risk changes with the crowd activities, shifting from residential areas to industrial areas, schools, shopping malls, etc., during the morning peak, while the evening peak shows the reverse. Full article