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The formation of unmanned aerial vehicle (UAV) swarm plays a critical role in numerous applications, such as unmanned agriculture, environmental monitoring and cooperative fencing. Meanwhile, the self-organized swarm model exhibits excellent performance in amorphous formation flight, and its collective motion pattern displays great potential in dense obstacle avoidance. The paper aims to realize the formation maintenance of UAVs while combining the advantage of the self-organized swarm model in avoiding dense obstacles. Thereby enhancing the flexibility, adaptability and safety of UAV swarms in dense and unpredictable scenarios.

In this paper, a self-organized formation (SOF) swarm model with a constrained coordination mechanism is proposed. A global information-based formation rule is designed to flexibly maintain the formation. A constraint coordination mechanism is designed to resolve the problem of constraint conflicts between formation rules and self-organized behavior rules. The model introduces a new obstacle avoidance rule to prevent deadlocks. Extensive experiments including simulations, real flights and comparative experiments are conducted to evaluate the performance of the model.

The simulation results show that SOF swarm enables the formation elastically to dense obstacles. Compared to the Vasarhelyi model, swarm performance metrics are improved. For example, the task completion time of SOF swarm is reduced by 16%, 28% and 39% across the three obstacle densities, and the order of SOF swarm is improved by 4%, 13% and 18%, respectively. The proposed model is also validated with a swarm of seven quadcopters that can successfully navigate and maintain formation in a real-world indoor environment with dense obstacles. Video at: https://youtu.be/V8hYgOHxWls.

The proposed formation rule is based on global information construction, which presents challenges in terms of communication overhead in distributed systems.

An SOF swarm model is proposed, which achieves formation maintenance by incorporating formation rule and constraint coordination mechanism and improves obstacle avoidance performance by introducing a new obstacle avoidance rule. After real UAVs verification, the model is feasible for practical deployment and provides a new solution to the formation flight and formation maintenance problems encountered in dense environments.

Given that a prerequisite for COVID-19 transmission is the interaction between individuals, it is reasonable to suspect that transportation networks may have contributed to the spread of COVID-19. This study uses the air transportation network to quantify the risk of COVID-19 spread in the United States. The proposed model is applied at the county level and identifies the risk of importing COVID-19-infected passengers into a given county. We also undertake an examination of the factors influencing the spread of COVID-19 in relation to air travel. Utilizing an extensive dataset encompassing various socioeconomic, demographic, and healthcare-related variables, our results indicate a positive relationship between these factors and the relative risk of COVID-19 spread, highlighting the pronounced impact of population density, air travel volume, and larger household sizes on increasing travel-related risk. Conversely, greater healthcare capacity, particularly in terms of hospital and intensive care unit (ICU) beds, is associated with reduced risk. We provide estimates of expected relative risk for each county and a ranking that can be useful for informing public health policies to stem the spread of the virus by devoting resources such as screening and enhanced travel protocols to airports located in at-risk counties.

This study explores the crucial role of competitive intelligence (CI) in the tourism sector’s strategic decision-making. CI has significantly transformed the tourism sector through new insights and sophistication in data analysis and strategic planning. The rise in tourism-related competition, due to new destinations, varied tourist preferences and sustainability emphasis, makes competitive intelligence essential for understanding future market trends and making informed strategic choices.

Utilising PRISMA techniques for bibliometric analysis, the study examines literature from 1998 to 2023 (WoS), focusing on service innovation, customer experience management and sustainable strategies. It presents an analysis of the evolution of CI in tourism, its impact, influential works and future research directions.

Findings show that the multidisciplinary nature of CI in tourism is further evidenced by studies on quality cues, travellers’ information needs and the utilisation of big data. Future studies need to understand both global trends and regional specifics, as shown in investigations of spatial-temporal tourism dynamics.

This study represents a novel contribution to the field of tourism research by offering a comprehensive bibliometric analysis of CI literature from 1998 to 2023. It uniquely integrates service innovation, customer experience management and sustainable strategies within the context of CI, highlighting its multidisciplinary impacts and evolution. These insights collectively emphasise the need for future innovation and a comprehensive understanding of the global-local nexus to inform future tourism research and practice.

The purpose of this paper is to study the importance of image data in hotel selection-recommendation using different types of cognitive features and to explore whether there are reinforcing effects among these cognitive features.

This study represents user-generated images “cognitive” in a knowledge graph through multidimensional (shallow, middle and deep) analysis. This approach highlights the clustering of hotel destination imagery.

This study develops a novel hotel selection-recommendation model based on image sentiment and attribute representation within the construction of a knowledge graph. Furthermore, the experimental results show an enhanced effect between different types of cognitive features and hotel selection-recommendation.

This study enhances hotel recommendation accuracy and user satisfaction by incorporating cognitive and emotional image attributes into knowledge graphs using advanced machine learning and computer vision techniques.

This study advances the understanding of user-generated images’ impact on hotel selection, helping users make better decisions and enabling marketers to understand users’ preferences and trends.

This research is one of the first to propose a new method for exploring the cognitive dimensions of hotel image data. Furthermore, multi-dimensional cognitive features can effectively enhance the selection-recommendation process, and the authors have proposed a novel hotel selection-recommendation model.

This study uses self-determination theory (SDT) to investigate the impact of limited-time deals and credit card usage on online impulse buying behavior, with a focus on hedonic motivation as a mediating variable.

A survey was conducted with 290 participants from northern India, specifically Punjab, Haryana, Delhi NCR and Chandigarh. The current analysis validates the conceptual framework using the partial least squares structural equation modeling (PLS-SEM) methodology.

The results indicate that limited-time deals and credit card usage have a favorable impact on both hedonic motivation and online impulse buying behavior. Additionally, hedonic motivation mediates the relationship between limited-time deals, credit card usage and online impulse buying behavior.

The study’s originality stems from its novel application of the SDT to investigate the effects of limited-time deals and credit card usage on online impulse buying. By exploring the mediating role of hedonic motivation, it sheds new light on the psychological dynamics of online consumer behavior.

In order to make the construction industry develop in the direction of greening, this paper analyzes whether the application of intelligent technology in prefabricated buildings can achieve carbon emission reduction, starting from the problems of weak technology and insufficient encouragement policies in the prefabricated building industry. It also designs dynamic and adjustable incentives for the smart transformation of prefabricated buildings and makes recommendations to facilitate the transformation of assembly manufacturers into “smart factories”.

This paper takes the intelligent technology for carbon reduction, energy efficiency and policy design in the prefabricated buildings industry as the starting point. Based on in-depth expert interviews and questionnaire survey data, a linear multiple regression model is used to establish an association network of intelligent technology in the production and transportation, construction, operation and maintenance, demolition and scrapping stages. On this basis, an evolutionary game theory is used to construct a smart transformation and carbon reduction utility game model between the government and manufacturers, and relevant suggestions for smart empowerment of green construction development technology combinations and policy settings are proposed.

An assembly manufacturing plant with smart empowerment is an important way to achieve green and sustainable development in the construction industry. Among them, BIM and IoT have made a greater impact on carbon emission reduction of prefabricated buildings in all stages of the whole life cycle. The government’s proposed energy efficiency incentives and environmental tax amount will effectively increase companies' motivation for smart transformation of prefabricated buildings. However, when the environmental tax amount is low, the government should strengthen the regulation of the industry in order to increase the speed of smart transformation of assembly manufacturers. Therefore, a reasonable setting of the environmental tax rate and energy-saving incentives and flexible adjustment of the regulatory efforts can maximize the functional utility of the government in the process of smart transformation.

This paper focuses on the impact of intelligent technologies on the overall carbon emissions of the industry and provides an evolutionary analysis of the strategic game between the government and assembly manufacturers, the main players in the smart transformation process of prefabricated buildings. However, smart technologies for different categories of assembly manufacturing plants and strategic options for a wider range of stakeholders have not been examined in depth.

Different from existing research, this study focuses on exploring the strategic game between the government and assembly manufacturers in the smart transformation of prefabricated buildings. It provides an innovative explanation of the connection between intelligent technology and carbon emissions. The study develops an evolutionary game model for both parties, addressing the research gap on the combined effects of policy incentives and intelligent technology on carbon reduction and efficiency improvement in the prefabricated buildings industry. This research not only offers practical reference for the government in designing incentive mechanisms and establishing regulatory systems but also provides feasible practical guidance for the smart transformation and carbon reduction efforts of assembly manufacturing plants.

Nanotechnology (NT) advancements in personal protective textiles (PPT) or personal protective equipment (PPE) have alleviated spread and transmission of this highly contagious viral disease, and enabled enhancement of PPE, thereby fortifying antiviral behavior.

Review of a series of state of the art research papers on the subject matter.

This paper expounds on novel nanotechnological advancements in polymeric textile composites, emerging applications and fight against COVID-19 pandemic.

As a panacea to “public droplet prevention,” textiles have proven to be potentially effective as environmental droplet barriers (EDBs).

PPT in form of healthcare materials including surgical face masks (SFMs), gloves, goggles, respirators, gowns, uniforms, scrub-suits and other apparels play critical role in hindering the spreading of COVID-19 and other “oral-respiratory droplet contamination” both within and outside hospitals.

When used as double-layers, textiles display effectiveness as SFMs or surgical-fabrics, which reduces droplet transmission to <10 cm, within circumference of ∼0.3%.

NT advancements in textiles through nanoparticles, and sensor integration within textile materials have enhanced versatile sensory capabilities, robotics, flame retardancy, self-cleaning, electrical conductivity, flexibility and comfort, thereby availing it for health, medical, sporting, advanced engineering, pharmaceuticals, aerospace, military, automobile, food and agricultural applications, and more. Therefore, this paper expounds on recently emerging trends in nanotechnological influence in textiles for engineering and fight against COVID-19 pandemic.

Digital transformation provides a new impetus for the development of declining firms. However, there is currently a lack of sufficient research on whether digital transformation is beneficial for the turnaround of declining firms. This paper aims to explore the relationship between digital transformation and the turnaround of declining firms.

Drawing on the theoretical foundations of the resource-based view and dynamic capabilities theory, this research uses a comprehensive dataset of Chinese A-share listed companies from 2010–2021 to explore the influence of digital transformation on the turnaround of declining firms.

The research findings show that digital transformation contributes to the turnaround of declining firms. Mechanism analyses demonstrate that digital transformation enhances dynamic capabilities and attracts more analysts, thereby facilitating the turnaround process. Moreover, the moderation analysis reveals that CEO equity incentives strengthen the positive correlation between digital transformation and the turnaround of declining firms. Heterogeneity analysis indicates that the association between digital transformation and the turnaround of declining firms is particularly significant for firms with low financing constraints and high-tech firms. Moreover, this research reveals that digital transformation can facilitate the turnaround of firms in deep and long-term decline.

This research contributes to the literature on the digital transformation of enterprises and provides important insights for the turnaround of declining firms.

The main objective of this study was to understand why employees engage in time theft behavior and what is the behavioral consequence of this deviant behavior. To do this, the conservation of resources theory helps to examine the role of organizational cronyism behind employee time theft behavior and decreased proactive behavior.

A three-wave self-administered employee survey was used for data collection. The data were collected through an adopted questionnaire from nurses working in the public sector hospitals of Pakistan. Structural equation modeling (SEM) was used to analyze data collected from 256 respondents.

The results of this three-wave study supported the hypotheses which are: (1) Organizational cronyism positively predicts employee involvement in time theft behavior. (2) Employee time theft behavior negatively impacts their proactive behavior. (3) Organizational cronyism is detrimental to employee proactive behavior. (4) The relationship between organizational cronyism and proactive behavior is mediated by time theft.

In the presence of organizational cronyism, employees use time theft as a dysfunctional coping strategy to conserve their valued resources rather than allowing the organization to consume them. Organizational leaders of public sector hospitals must promote merit-based HRM practices to discourage time theft behavior as well as to improve the proactive performances of the nurses.

This study is one of the initial attempts to extend the scant literature on the antecedents and consequences of time theft behavior and its dimensions in the South Asian context.