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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 amplify the retrieval and utilization of historical newspapers through the application of semantic organization, all from the vantage point of a fine-grained knowledge element perspective. This endeavor seeks to unlock the latent value embedded within newspaper contents while simultaneously furnishing invaluable guidance within methodological paradigms for research in the humanities domain.

According to the semantic organization process and knowledge element concept, this study proposes a holistic framework, including four pivotal stages: knowledge element description, extraction, association and application. Initially, a semantic description model dedicated to knowledge elements is devised. Subsequently, harnessing the advanced deep learning techniques, the study delves into the realm of entity recognition and relationship extraction. These techniques are instrumental in identifying entities within the historical newspaper contents and capturing the interdependencies that exist among them. Finally, an online platform based on Flask is developed to enable the recognition of entities and relationships within historical newspapers.

This article utilized the Shengjing Times·Changchun Compilation as the datasets for describing, extracting, associating and applying newspapers contents. Regarding knowledge element extraction, the BERT + BS consistently outperforms Bi-LSTM, CRF++ and even BERT in terms of Recall and F1 scores, making it a favorable choice for entity recognition in this context. Particularly noteworthy is the Bi-LSTM-Pro model, which stands out with the highest scores across all metrics, notably achieving an exceptional F1 score in knowledge element relationship recognition.

Historical newspapers transcend their status as mere artifacts, evolving into invaluable reservoirs safeguarding the societal and historical memory. Through semantic organization from a fine-grained knowledge element perspective, it can facilitate semantic retrieval, semantic association, information visualization and knowledge discovery services for historical newspapers. In practice, it can empower researchers to unearth profound insights within the historical and cultural context, broadening the landscape of digital humanities research and practical applications.

This paper aims to construct a spatio-temporal emotional framework (STEF) for digital humanities from a quantitative perspective, applying knowledge extraction and mining technology to promote innovation of humanities research paradigm and method.

The proposed STEF uses methods of information extraction, sentiment analysis and geographic information system to achieve knowledge extraction and mining. STEF integrates time, space and emotional elements to visualize the spatial and temporal evolution of emotions, which thus enriches the analytical paradigm in digital humanities.

The case study shows that STEF can effectively extract knowledge from unstructured texts in the field of Chinese Qing Dynasty novels. First, STEF introduces the knowledge extraction tools – MARKUS and DocuSky – to profile character entities and perform plots extraction. Second, STEF extracts the characters' emotional evolutionary trajectory from the temporal and spatial perspective. Finally, the study draws a spatio-temporal emotional path figure of the leading characters and integrates the corresponding plots to analyze the causes of emotion fluctuations.

The STEF is constructed based on the “spatio-temporal narrative theory” and “emotional narrative theory”. It is the first framework to integrate elements of time, space and emotion to analyze the emotional evolution trajectories of characters in novels. The execuability and operability of the framework is also verified with a case novel to suggest a new path for quantitative analysis of other novels.

This article presents an advanced method for managing knowledge using a modular design approach and applied the approach through an example with computer manufacturing. The benefits of modularity are discussed both within and between companies. We find that through modular design, knowledge management systems are simplified and communication between modules is greatly improved. In addition, modularity makes parallel innovation possible, as well as, sharing innovative resources through collaboration. Modularity leads to effective knowledge sharing and thus stimulates knowledge-based innovation. A case study of the computer industry further demonstrates the effects of modularity in an industry. Finally, we suggest that enterprises should take advantage of modularity when they carry out knowledge-based innovation and the measures are developed to implement successful modularity.

The purpose of this paper is to present a broadband parallel coupled‐line quadrature coupler implemented with lumped‐elements.

The even‐ and odd‐mode method is used to compute the proposed coupler, and the analysis and optimization are done using the computer‐aided design tools. A design example of a broadband tunable quadrature coupler is provided.

This novel structure is clearly increasing the conductor spacing and decreasing the whole structure size, at the same time, the bandwidth is much wider.

This paper shows how a parallel coupled‐line quadrature coupler with lumped‐elements has been analyzed and designed, and proposes a simple and convenient method to calculate this structure. Compared with the normal parallel coupled‐line structure, the conductor spacing of this coupler is increased effectively, while the structure size is decreased. In addition, because of the tunable embedded capacitors, the bandwidth becomes much wider – a broadband up to 77 percent is achieved.

The purpose of this paper is to propose a new algorithm for independent navigation of unmanned aerial vehicle path planning with fast and stable performance, which is based on pigeon-inspired optimization (PIO) and quantum entanglement (QE) theory.

A biomimetic swarm intelligent optimization of PIO is inspired by the natural behavior of homing pigeons. In this paper, the model of QEPIO is devised according to the merging optimization of basic PIO algorithm and dynamics of QE in a two-qubit XXZ Heisenberg System.

Comparative experimental results with genetic algorithm, particle swarm optimization and traditional PIO algorithm are given to show the convergence velocity and robustness of our proposed QEPIO algorithm.

The QEPIO algorithm hold broad adoption prospects because of no reliance on INS, both on military affairs and market place.

This research is adopted to solve path planning problems with a new aspect of quantum effect applied in parameters designing for the model with the respective of unmanned aerial vehicle path planning.

While Hong Kong has exercised laissez-faire policy with regard to popular cultural industries, such broadcasting freedom may not fully accepted in countries where state control of the media is tight. This paper aims to have a review and brief discussion on music development and the phenomenon of popular music censorship under Chinese societies including China, Hong Kong and Singapore.

After a brief discussion on the popular music scene in Hong Kong, China and Singapore, this paper reviews on the possible reasons for popular music censorship issue in the Chinese societies.

It is found that Canto-pop can be freely broadcast in Hong Kong; however, such freedom is not accepted in China and Singapore due to censorship. While China is sensitive to pro-democratic content in popular music, Singapore's language policies hinder the development of Canto-pop since the 70s.

After reviewing the reasons for music censorship, in particular Cantonese popular music in the Chinese societies, the paper gives a prediction on the future development of Canto-pop.

The purpose of this paper is to determine if Chief Executive Officers (CEOs) are rewarded or punished in the corporate director labor market for engaging in corporate social responsibility (CSR) activities.

The authors empirically examine the relation between CEOs' CSR engagement and their corporate board appointments in retirement using logit, ordinary least squares (OLS) and Poisson regression models.

Results indicate that CSR engagement has significant director labor market consequences for retiring CEOs. Specifically, CSR engagement has a favorable impact on the ability of retired CEOs to obtain board seats and board seats at larger firms generally associated with higher pay, even after controlling for firm performance and other determinants previously documented to explain director selection. The authors also find evidence that CEOs of firms with high CSR engagement build up their firms' CSR scores over time as they approach retirement, which is consistent with the labor market for directors providing incentives to attract CEOs to board service in retirement.

By examining the relationship between a CEO's CSR engagement and their external corporate board directorships, this paper advances the understanding of the determinants of corporate board appointments. Further, while most prior research assesses the value of CSR engagement by looking at the relation between CSR engagement and that firm's performance, this is the first study to our knowledge to look outside the firm to determine if CSR engagement has value to the CEO.

This paper aims to give the guidance for the design of the bearing.

The finite element method, the multi-body dynamics method, the finite difference method and the tribology are combined to analyze the lubrication.

The performance parameters of crankshaft-bearing system such as the misalignment, the oil filling ratio and the oil groove are also investigated. Misalignment causes the pressure to incline on one side and the pressure increases obviously. Filling ratio has great relationship with pressure distribution; the factors influencing the filling ratio are also analyzed. Different oil groove models are investigated, as it can provide the theory for oil groove design, and three factors above are always combined to influence the lubrication characteristics.

The optimization of bearing system is conducted by orthogonal test and neural network, unlike the linear optimization theory. Neural network uses the nonlinear theory to optimize crankshaft-bearing system.