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To make the robot that have real autonomous ability is always the goal of mobile robot research. For mobile robots, simultaneous localization and mapping (SLAM) research is no longer satisfied with enabling robots to build maps by remote control, more needs will focus on the autonomous exploration of unknown areas, which refer to the low light, complex spatial features and a series of unstructured environment, lick underground special space (dark and multiintersection). This study aims to propose a novel robot structure with mapping and autonomous exploration algorithms. The experiment proves the detection ability of the robot.

A small bio-inspired mobile robot suitable for underground special space (dark and multiintersection) is designed, and the control system is set up based on STM32 and Jetson Nano. The robot is equipped with double laser sensor and Ackerman chassis structure, which can adapt to the practical requirements of exploration in underground special space. Based on the graph optimization SLAM method, an optimization method for map construction is proposed. The Iterative Closest Point (ICP) algorithm is used to match two frames of laser to recalculate the relative pose of the robot, which improves the sensor utilization rate of the robot in underground space and also increase the synchronous positioning accuracy. Moreover, based on boundary cells and rapidly-exploring random tree (RRT) algorithm, a new Bio-RRT method for robot autonomous exploration is proposed in addition.

According to the experimental results, it can be seen that the upgraded SLAM method proposed in this paper achieves better results in map construction. At the same time, the algorithm presents good real-time performance as well as high accuracy and strong maintainability, particularly it can update the map continuously with the passing of time and ensure the positioning accuracy in the process of map updating. The Bio-RRT method fused with the firing excitation mechanism of boundary cells has a more purposeful random tree growth. The number of random tree expansion nodes is less, and the amount of information to be processed is reduced, which leads to the path planning time shorter and the efficiency higher. In addition, the target bias makes the random tree grow directly toward the target point with a certain probability, and the obtained path nodes are basically distributed on or on both sides of the line between the initial point and the target point, which makes the path length shorter and reduces the moving cost of the mobile robot. The final experimental results demonstrate that the proposed upgraded SLAM and Bio-RRT methods can better complete the underground special space exploration task.

Based on the background of robot autonomous exploration in underground special space, a new bio-inspired mobile robot structure with mapping and autonomous exploration algorithm is proposed in this paper. The robot structure is constructed, and the perceptual unit, control unit, driving unit and communication unit are described in detail. The robot can satisfy the practical requirements of exploring the underground dark and multiintersection space. Then, the upgraded graph optimization laser SLAM algorithm and interframe matching optimization method are proposed in this paper. The Bio-RRT independent exploration method is finally proposed, which takes shorter time in equally open space and the search strategy for multiintersection space is more efficient. The experimental results demonstrate that the proposed upgrade SLAM and Bio-RRT methods can better complete the underground space exploration task.

Social network group decision-making (SNGDM) has rapidly developed because of the impact of social relationships on decision-making behavior. However, not only do social relationships affect decision-making behavior, but decision-making behavior also affects social relationships. Such complicated interactions are rarely considered in current research. To bridge this gap, this study proposes an SNGDM model that considers the interaction between social trust relationships and opinion evolution.

First, the trust propagation and aggregation operators are improved to obtain a complete social trust relationship among decision-makers (DMs). Second, the evolution of preference information under the influence of trust relationships is measured, and the development of trust relationships during consensus interactions is predicted. Finally, the iteration of consensus interactions is simulated using an opinion dynamics model. A case study is used to verify the feasibility of the proposed model.

The proposed model can predict consensus achievement based on a group’s initial trust relationship and preference information and effectively captures the dynamic characteristics of opinion evolution in social networks.

This study proposes an SNGDM model that considers the interaction of trust and opinion. The proposed model improves trust propagation and aggregation operators, determines improved preference information based on the existing trust relationships and predicts the evolution of trust relationships in the consensus process. The dynamic interaction between the two accelerates DMs to reach a consensus.

The present paper aims to address challenges associated with path planning and obstacle avoidance in mobile robotics. It introduces a pioneering solution called the Bi-directional Adaptive Enhanced A* (BAEA*) algorithm, which uses a new bidirectional search strategy. This approach facilitates simultaneous exploration from both the starting and target nodes and improves the efficiency and effectiveness of the algorithm in navigation environments. By using the heuristic knowledge A*, the algorithm avoids unproductive blind exploration, helps to obtain more efficient data for identifying optimal solutions. The simulation results demonstrate the superior performance of the BAEA* algorithm in achieving rapid convergence towards an optimal action strategy compared to existing methods.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bidirectional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The paper adopts a careful design focusing on the development and evaluation of the BAEA* for mobile robot path planning, based on the reference [18]. The algorithm has remarkable adaptability to dynamically changing environments and ensures robust navigation in the context of environmental changes. Its scale further enhances its applicability in large and complex environments, which means it has flexibility for various practical applications. The rigorous evaluation of our proposed BAEA* algorithm with the Bi-directional adaptive A* (BAA*) algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm.

The rigorous evaluation of our proposed BAEA* algorithm with the BAA* algorithm [18] in five different environments demonstrates the superiority of the BAEA* algorithm. The BAEA* algorithm consistently outperforms BAA*, demonstrating its ability to plan shorter and more stable paths and achieve higher success rates in all environments.

The originality of this paper lies in the introduction of the bidirectional adaptive enhancing A* algorithm (BAEA*) as a novel solution for path planning for mobile robots. This algorithm is characterized by its unique characteristics that distinguish it from others in this field. First, BAEA* uses a unique bidirectional search strategy, allowing to explore the same path from both the initial node and the target node. This approach significantly improves efficiency by quickly converging to the best paths and using A* heuristic knowledge. In particular, the algorithm shows remarkable capabilities to quickly recognize shorter and more stable paths while ensuring higher success rates, which is an important feature for time-sensitive applications. In addition, BAEA* shows adaptability and robustness in dynamically changing environments, not only avoiding obstacles but also respecting various constraints, ensuring safe path selection. Its scale further increases its versatility by seamlessly applying it to extensive and complex environments, making it a versatile solution for a wide range of practical applications. The rigorous assessment against established algorithms such as BAA* consistently shows the superior performance of BAEA* in planning shorter paths, achieving higher success rates in different environments and cementing its importance in complex and challenging environments. This originality marks BAEA* as a pioneering contribution, increasing the efficiency, adaptability and applicability of mobile robot path planning methods.

Fintechs are expected to develop rapidly as technologies that help improve the efficiency of the traditional financial system, but an examination of fintech subbranches shows different behaviors. In some sub-branches, the transition has been accompanied by a higher speed and more success, but in some other sub-branches, the opposite has been observed. The difference in the development of fintech sub-branches and its reasons have been paid less attention. Therefore, this article aims to identify the factors affecting the transition.

The use of new technologies in financial services at the international level has led to the provision of fast, customized and economical services, and the fact that these services are welcomed by the users has created opportunities for fintech's transition. This qualitative research follows the socio-technical phenomenon of fintech transition through narrative research. For its formulation, the transition process of fintech sub-branches was analyzed based on the multi-level analytical framework and Geels et al.’s transition path theory.

Transition is a change from one socio-technical regime to another. The findings of the research showed that these changes are influenced by the following factors: provision of infrastructure, the support of industry incumbents from innovative financial services, policy-making, citizen's welcoming, improving the knowledge and expertise of actors, legal adjustments as well as provision of innovative services.

The fintech transition has a special nature because the speed of developments in fintech is high and there is a series of innovations that are continuously replaced by subsequent innovations. Existing models have often focused on the long-term transition of a technology. This article presents a new approach for the analysis of changes in the short term in such a way that, based on the position of the actors in favor of or against the technological changes and institutional changes of the transition, it has analyzed and identified the factors affecting the transition. By focusing on these factors, policymakers can direct the way of fintech transition and help accelerate and facilitate fintech transition.

This study aims to propose an efficient dual-robot task collaboration strategy to address the issue of low work efficiency and inability to meet the production needs of a single robot during construction operations.

A hybrid task allocation method based on integer programming and auction algorithms, with the aim of achieving a balanced workload between two robots has been proposed. In addition, while ensuring reasonable workload allocation between the two robots, an improved dual ant colony algorithm was used to solve the dual traveling salesman problem, and the global path planning of the two robots was determined, resulting in an efficient and collision-free path for the dual robots to operate. Meanwhile, an improved fast Random tree rapidly-exploring random tree algorithm is introduced as a local obstacle avoidance strategy.

The proposed method combines randomization and iteration techniques to achieve an efficient task allocation strategy for two robots, ensuring the relative optimal global path of the two robots in cooperation and solving complex local obstacle avoidance problems.

This method is applied to the scene of steel bar tying in construction work, with the workload allocation and collaborative work between two robots as evaluation indicators. The experimental results show that this method can efficiently complete the steel bar banding operation, effectively reduce the interference between the two robots and minimize the interference of obstacles in the environment.

This paper aims to explore the determinants of maternal and infant health knowledge (M&IHK) adoption and sharing in the short video from an empathy theory perspective. We explore how to transfer users from free health knowledge to health-related product purchase intention, which is vital for platform knowledge management and service.

Focusing on the M&IHK, this study proposes four processes of health knowledge adoption and sharing – knowledge quality persuasion process; source credibility persuasion process; affective empathy emotion process; and cognitive empathy emotion process – to build a framework of M&IHK adoption and sharing. Furthermore, based on adoption and sharing, we explore whether they can promote health-related product purchase intentions. A theoretical model is constructed and tested via Smart PLS in 388 samples.

In a short video context, perceived knowledge quality and perceived source credibility are still two determinants of health knowledge adoption and sharing. On the contrary, perceived affective empathy and perceived cognitive empathy are two new determinants of health knowledge adoption, but not of health knowledge sharing. Adoption of M&IHK is more driven by both rational thinking and emotional thinking than sharing-only driven by emotional thinking. Adoption and sharing both contribute to health-related product purchase intention, but the female’s intention is more related to rational adoption than the male, which is only related to emotional sharing.

This paper is arguably the first study to examine how short videos impact the mechanisms of M&IHK adoption, sharing and health-related products' purchase intention. It’s perhaps the first study to integrate empathy theory into health knowledge management.

This study aims to explore the dissemination and evolutionary path of online public opinion from a crisis management perspective. By clarifying the influencing factors and dynamic mechanisms of online public opinion dissemination, this study provides insights into attenuating the negative impact of online public opinion and creating a favorable ecological space for online public opinion.

This research employs bibliometric analysis and CiteSpace software to analyze 302 Chinese articles published from 2006 to 2023 in the China National Knowledge Infrastructure (CNKI) database and 276 English articles published from 1994 to 2023 in the Web of Science core set database. Through literature keyword clustering, co-citation analysis and burst terms analysis, this paper summarizes the core scientific research institutions, scholars, hot topics and evolutionary paths of online public opinion crisis management research from both Chinese and international academic communities.

The results show that the study of online public opinion crisis management in China and internationally is centered on the life cycle theory, which integrates knowledge from information, computer and system sciences. Although there are differences in political interaction and stage evolution, the overall evolutionary path is similar, and it develops dynamically in the “benign conflict” between the expansion of the research perspective and the gradual refinement of research granularity.

This study summarizes the research results of online public opinion crisis management from China and the international academic community and identifies current research hotspots and theoretical evolution paths. Future research can focus on deepening the basic theories of public opinion crisis management under the influence of frontier technologies, exploring the subjectivity and emotionality of web users using fine algorithms and promoting the international development of network public opinion crisis management theory through transnational comparison and international cooperation.

Empirical attempts to recommend enabling mechanisms for organizational unlearning are sparse and have almost neglected the vital role of leadership in transforming organizations through unlearning. Based on the tenets of persistence theories like path-dependence and imprinting theory, this study examines the relationship between transformational leadership and unlearning with the mediating role of knowledge sharing, transparent internal communication and intrapreneurship.

To analyze the hypothesized relationship between these constructs, data were collected from 452 faculty members working in Centrally Funded Technical Institutions (CFTIs) in India. The data were analyzed using Process macro (Hayes, 2022).

The results show a significant effect of transformational leadership on organizational unlearning. This effect is mediated by transparent internal communication and intrapreneurship. However, knowledge sharing did not mediate the relationship between transformational leadership and organizational unlearning.

The Fourth Industrial Revolution, Covid-19, the rise of generative artificial intelligence tools like ChatGPT and policy reforms have pushed higher educational institutions to transform by unlearning old practices and experimenting with new ones. This paper informs how educational institutions can initiate and sustain the unlearning process.

Persistence theories like path-dependence and imprinting theory suggest that organizations often stick with proven success formulas and find it challenging to adopt new practices. Moreover, path dependence theorists advocate the role of an external intervening mechanism to break away from rigid and inefficient routines (or paths). This paper argues that in addition to external events (e.g. crisis, etc.), transformational leaders combined with organizational processes also help in unlearning obsolete knowledge and routines.

This study aims to explore the different effects of inter-community group networks and intra-community group networks on group innovation.

The authors used a pooled panel dataset of 12,111 self-organizing innovation groups in 463 game product creative workshop communities from Steam support to test the hypothesis. The pooled ordinary least squares (OLS) model is used for analyzing the data.

The results show that network constraint is negatively associated with the innovation performance of online groups. The average path length of the inter-community group network negatively moderates the relationship between network constraint and group innovation, while the average path length of the intra-community group network positively moderates the relationship between network constraint and group innovation. In addition, both the network density of inter-community group networks and intra-community group networks can negatively moderate the negative relationship between network constraint and group innovation.

The findings of this study suggest that network structural characteristics of inter-community networks and intra-community networks have different effects on online groups’ product innovation, and therefore, group members should consider their inter- and intra-community connections when choosing other groups to form a collaborative innovation relationship.

The purpose of this paper is to provide rich, qualitative insights around internal communication in strategic communication agencies, addressing the evolutions in expectations and best practices for agency leadership through COVID-19.

Qualitative interview study with 18 US-based leaders of public relations and advertising agencies to examine their experiences of leading and managing strategic communication teams during COVID-19.

Synthesized findings around changes in leadership values and important facets of ongoing internal crisis communication led to the development of the following five categories—Improvisation and Flexibility, Transparency and Trust, Ownership and Embodiment, Care and Empathy, Relationships and Resilience.

Using a high-value sample, the study is the first (to the best of the authors' knowledge) to focus on the crucial context of agencies and internal communication around COVID-19; diversity, equity, and inclusion (DEI); and other pandemic-era challenges. It provides theoretical implications around ongoing, internal crisis communication and practical implications for agency leaders in crisis.