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With the development of data mining technology, diverse and broader domain knowledge can be extracted automatically. However, the research on applying knowledge mapping and data visualization techniques to genealogical data is limited. This paper aims to fill this research gap by providing a systematic framework and process guidance for practitioners seeking to uncover hidden knowledge from genealogy.

Based on a literature review of genealogy's current knowledge reasoning research, the authors constructed an integrated framework for knowledge inference and visualization application using a knowledge graph. Additionally, the authors applied this framework in a case study using “Manchu Clan Genealogy” as the data source.

The case study shows that the proposed framework can effectively decompose and reconstruct genealogy. It demonstrates the reasoning, discovery, and web visualization application process of implicit information in genealogy. It enhances the effective utilization of Manchu genealogy resources by highlighting the intricate relationships among people, places, and time entities.

This study proposed a framework for genealogy knowledge reasoning and visual analysis utilizing a knowledge graph, including five dimensions: the target layer, the resource layer, the data layer, the inference layer, and the application layer. It helps to gather the scattered genealogy information and establish a data network with semantic correlations while establishing reasoning rules to enable inference discovery and visualization of hidden relationships.

This paper aims to study individuals’ information service satisfaction during the COVID-19 pandemic lockdown in China’s urban communities.

The researchers analyse people’s uncertainties during the pandemic and argue that uncertainties caused by the lockdown can negatively affect people. By reducing people’s uncertainty during the pandemic, community staff members can improve individuals’ information service satisfaction and social order. This study constructs a conceptual model that includes key transparency and self-disclosure constructs and their relationships that can contribute to the trust and satisfaction of the community information service phenomenon. The researchers collected 489 responses to test their hypothesis from an online survey of Chinese residents in areas where the strict lockdown policy was implemented.

The empirical results show that policy and goods information transparency significantly affect information service satisfaction in a positive way, with goods information transparency having the highest impact. Second, self-disclosure of community staff members is also an effective way to increase information service satisfaction. Finally, trust plays a mediating role in the influence of information transparency and self-disclosure on information service satisfaction.

This paper innovatively uses uncertainty reduction theory to examine the effects of information transparency and self-disclosure on satisfaction with community information services. It expands the research in the field of information service satisfaction and extends the scope of the research subjects of self-disclosure.

This study aims to examine how different hospitals utilize social media to communicate risk information about COVID-19 with the communities they serve, and how hospitals' social media messaging (firm-generated content and their local community's responses (user-generated content) evolved with the COVID-19 outbreak progression.

This research proposes a healthcare-specific social media analytics framework and studied 68,136 tweets posted from November 2019 to November 2020 from a geographically diverse set of ten leading hospitals' social media messaging on COVID-19 and the public responses by using social media analytics techniques and the health belief model (HBM).

The study found correlations between some of the HBM variables and COVID-19 outbreak progression. The findings provide actionable insight for hospitals regarding risk communication, decision making, pandemic awareness and education campaigns and social media messaging strategy during a pandemic and help the public to be more prepared for information seeking in the case of future pandemics.

For hospitals, the results provide valuable insights for risk communication practitioners and inform the way hospitals or health agencies manage crisis communication during the pandemic For patients and local community members, they are recommended to check out local hospital's social media sites for updates and advice.

The study demonstrates the role of social media analytics and health behavior models, such as the HBM, in identifying important and useful data and knowledge for public health risk communication, emergency responses and planning during a pandemic.

Despite rapid growth in blockchains, there was limited discussion about non-technical and technical factors on blockchain governance in the extant literature. This study aims to contribute new knowledge to the literature on potential factors affecting the adoption, governance and scale-up of blockchain technologies in the health-care and energy sectors, presented in a holistic framework.

This study adopts the qualitative case study research methodology to research blockchain governance in practice. The authors contacted a national blockchain consortium to conduct their research on the governance issue of blockchain. Two leading case organizations, one from the health-care industry and another from the energy industry, were deliberately selected for their study for their active role and reputation in the consortium and practical experience in blockchain governance.

The developed framework helps identify potential research gaps or concerns on adopting a blockchain as well as assessing blockchain implementation and governance in other industries. Depending on the circumstances, some of the factors can be either drivers or obstacles to further blockchain development. The different forces may also be more or less evident over time as blockchains develop. The two real-world case studies contribute to the information technology governance literature on blockchain governance.

The results of this case studies will be beneficial for developing theories and empirical models to determine antecedents for achieving consensus and trust in blockchain and testing the relationship between these factors and blockchain governance at different levels. As a result, theories related to the governance of blockchain technologies could be further developed.

The coronavirus disease 2019 (COVID-19) pandemic caused significant disruption to the global labor market, resulting in a rapid transition toward remote work, e-commerce and workforce automation. This shift has sparked a considerable amount of public discussion. This study aims to explore the online public's sentiment toward remote work amid the pandemic.

Based on justice theory, this paper examines user-generated content on social media platforms, particularly Twitter, to gain insight into public opinion and discourse surrounding remote work during the COVID-19 pandemic. Employing content analysis techniques such as sentiment analysis, text clustering and evolutionary analysis, this study aims to identify prevalent topics, temporal patterns and instances of sentiment polarization in tweets.

Results show that people with positive opinions focus mainly on personal interests, while others focus on the interests of the company and society; people's subjectivities are higher when they express extremely negative or extremely positive emotions. Distributive justice and interactional justice are distinguishable with a high degree of differentiation in the cluster map.

Previous research has inadequately addressed public apprehensions about remote work during emergencies, particularly from a justice-based perspective. This study seeks to fill this gap by examining how justice theory can shed light on the public's views regarding corporate policy-making during emergencies. The results of this study provide valuable insights and guidance for managing public opinion during such events.

The purpose of this study is to design a more flexible and larger range of the dimming circuit that achieves the independence of multiple LED strings drive and can time-multiplex the power circuit.

The state-space method is used to model the BUCK circuit working in Pseudo continuous conduction mode, analyze the frequency characteristics of the system transfer function and design the compensation network. Build a simulation platform on the Orcad PSPICE platform and verify the function of the designed circuit through the simulation results. Use Altium Designer 16 to draw the printed circuit board, complete the welding of various components and use the oscilloscope, direct current (DC) power supply and a signal generator to verify the circuit function.

A prototype of the proposed LED driver is fabricated and tested. The measurement results show that the switching frequency can be increased to 1 MHz, Power inductance is 2.2 µH, which is smaller than current research. The dimming ratio can be set from 10% to 100%. The proposed LED driver can output more than 48 W and achieve a peak conversion efficiency of 91%.

The proposed LED driver adopts pulse width modulation (PWM) dimming at a lower dimming ratio and adopts DC dimming at a larger dimming ratio to realize switching PWM dimming to analog dimming. The control strategy can be more precise and have a wide range of dimming.

This paper aims to solve the problem of the inability to apply learning methods for robot motion skills based on dynamic movement primitives (DMPs) in tasks with explicit environmental constraints, while ensuring the reliability of the robot system.

The authors propose a novel DMP that takes into account environmental constraints to enhance the generality of the robot motion skill learning method. First, based on the real-time state of the robot and environmental constraints, the task space is divided into different regions and different control strategies are used in each region. Second, to ensure the effectiveness of the generalized skills (trajectories), the control barrier function is extended to DMP to enforce constraint conditions. Finally, a skill modeling and learning algorithm flow is proposed that takes into account environmental constraints within DMPs.

By designing numerical simulation and prototype demonstration experiments to study skill learning and generalization under constrained environments. The experimental results demonstrate that the proposed method is capable of generating motion skills that satisfy environmental constraints. It ensures that robots remain in a safe position throughout the execution of generation skills, thereby avoiding any adverse impact on the surrounding environment.

This paper explores further applications of generalized motion skill learning methods on robots, enhancing the efficiency of robot operations in constrained environments, particularly in non-point-constrained environments. The improved methods are applicable to different types of robots.

The increasing African population and economic growth leading to urbanisation continues to increase the need to redevelop brownfields as a strategy of encouraging sustainable development of cities, in particular in Ethiopia. However, the adoption of brownfield redevelopment in Ethiopia is at initial stage. Thus, the purpose of this paper is to highlight the framework based on grey-incidence decision-making approach to manage brownfields in African countries by taking Ethiopia as case example. The grey-incidence decision-making model integrates multiple factors such as economic, social, environmental, technical and associated risks and provides an effective decision-making and management tool for environmental practitioners and government agencies.

Questionnaires were used to collect data on terms and definitions of brownfield. The questions were prepared on the basis of currently used definitions developed by a number of developed countries. Moreover, this study utilises a grey-incidence decision-making approach to help in management and decision-making for the implementation of brownfield redevelopment projects (BRPs) in the remediated sites.

Standard definition of brownfield and essential guidelines for brownfield redevelopment is proposed for Ethiopian context. The research findings were tested and verified using literature data and survey from major stakeholders. In addition, the grey-incidence decision-making approach is applied for the evaluation of BRPs in the remediated sites. A framework is proposed to control future brownfields for African countries by taking Ethiopia as a case example.

This research stresses the significance of an urban structure to address sustainable development, and the need to consider redevelopment of brownfields and identify the potential for a specific government policy framework. This research provides the best opportunity for Ethiopia by devising an urban land policy and create a strategy to contribute social, economic, financial and environmental benefits. It also provides a foundation to solve environmental issues by involving all major stakeholders, including community citizens, environmentalists and government agencies, and it also serves as guidelines to transform brownfields into Greenfields; and finally, it contributes to achieve the 2030 UN global goals.

Construction safety resilience is gradually gaining attention in the field of engineering construction as a new management concept and way to improve safety performance. However, how to cope with the dilemma of the unclear relationship of construction safety resilience elements at the practice level and promote the harmonization of construction safety goals and resilience enhancement paths has become an urgent challenge for safe construction.

This study analyzes the components of construction safety resilience elements. A relationship network model of construction safety resilience elements is developed by using the social network analysis method. The location and influence of each element in the network and the interrelationships among the elements are explored in depth.

The findings reveal a robust interconnection among the elements of safety resilience in the construction industry. Key components such as safety behavior, risk prevention and control mechanisms, disaster prevention and mitigation technologies as well as information technology, are positioned at the core of the network. Notably, safety behavior exerts the most significant influence over the other elements, serving as the linchpin of safety management in the construction industry. Moreover, the interplay among safety resilience elements in the construction sector can alter the structure of the relationship network.

This study adopts the social network approach to solve the problem that it is difficult to quantitatively analyze the elements of construction safety resilience and their interrelationships and to clarify the interactions among the core elements, which can help to further assist the construction project manager to continuously optimize safety resilience and improve construction safety.

Inter-organizational collaboration is the organizational guarantee and key link to achieve the goals of megaproject management. Project governance has always played an important role in the construction of megaprojects, but the relationship between project governance and organizational collaboration is unclear. The purpose of this study is to explore the role paths of different project governance mechanisms in influencing the collaborative behaviors of stakeholders and collaborative performance and to elucidate the mechanism of project governance on inter-organizational collaboration.

A conceptual framework was developed based on a comprehensive literature review, termed the structural equation model (SEM). The hypotheses of the model were tested based on data obtained from a questionnaire survey of 235 experts with experience in megaprojects within the construction industry in China.

The results show that project governance positively contributes to the collaborative behavior of megaproject stakeholders and the collaborative performance of the project team. Collaborative behavior acts as a partial mediator between project governance and the collaborative performance of the megaproject inter-organization alliance. The complexity of the project modulates the relationship between the governance mechanism of the project and the collaborative behavior of the stakeholders, which affects the collaborative performance of the megaproject inter-organization alliance.

The findings provide theoretical and practical implications for promoting positive collaborative behavior among stakeholders in megaproject selection and improving the collaborative performance of megaproject inter-organization alliances.