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In recent years, knowledge graph completion has gained increasing research focus and shown significant improvements. However, most existing models only use the structures of knowledge graph triples when obtaining the entity and relationship representations. In contrast, the integration of the entity description and the knowledge graph network structure has been ignored. This paper aims to investigate how to leverage both the entity description and the network structure to enhance the knowledge graph completion with a high generalization ability among different datasets.

The authors propose an entity-description augmented knowledge graph completion model (EDA-KGC), which incorporates the entity description and network structure. It consists of three modules, i.e. representation initialization, deep interaction and reasoning. The representation initialization module utilizes entity descriptions to obtain the pre-trained representation of entities. The deep interaction module acquires the features of the deep interaction between entities and relationships. The reasoning component performs matrix manipulations with the deep interaction feature vector and entity representation matrix, thus obtaining the probability distribution of target entities. The authors conduct intensive experiments on the FB15K, WN18, FB15K-237 and WN18RR data sets to validate the effect of the proposed model.

The experiments demonstrate that the proposed model outperforms the traditional structure-based knowledge graph completion model and the entity-description-enhanced knowledge graph completion model. The experiments also suggest that the model has greater feasibility in different scenarios such as sparse data, dynamic entities and limited training epochs. The study shows that the integration of entity description and network structure can significantly increase the effect of the knowledge graph completion task.

The research has a significant reference for completing the missing information in the knowledge graph and improving the application effect of the knowledge graph in information retrieval, question answering and other fields.

Considering the unmeasurable states of the systems and the previewed reference signal, a novel fuzzy observer-based preview controller, which is a mixed controller of the fuzzy observer-based controller, fuzzy integrator and preview controller, is considered to address the tracking control problem.

The authors employ an augmentation technique to construct an augmented error system for uncertain T-S fuzzy discrete-time systems with time-varying uncertainties. Additionally, the authors obtain the corresponding linear matrix inequality (LMI) conditions for designing the preview controller.

This paper discusses the preview tracking problem for nonlinear systems. First, considering the unmeasurable states of the systems and the previewed reference signal, a novel fuzzy observer-based preview controller, which is a mixed controller of the fuzzy observer-based controller, fuzzy integrator, and preview controller, is considered to address the tracking control problem. Then, using the fuzzy Lyapunov functional with the linear matrix inequality (LMI) technique, new sufficient conditions for the asymptotic stability of the augmented system are derived by applying the LMI technique. The preview controller and fuzzy observer can be designed in one step. Finally, a numerical example is used to illustrate the effectiveness of the results.

An augmented error system is successfully constructed by the state augmentation approach. A novel preview controller is designed to address the tracking control problem. The preview controller and fuzzy observer can be designed in one step.

This paper aims to present a catalogue of the influential aspects resulting from a bibliometric meta-analysis of political connection literature.

This study undertakes a bibliometric meta-analysis review of political connections literature, covering 138 research papers from 2000 to 2020 using the visualization of similarities viewer program.

The authors identify six research groups: the value of political connections; political connections and finance; political connections in banks; political connections and debt; management and political connections; and political connections and governance. This study discusses each stream through a cartographic analysis, including co-authorship, countries and time networks.

This study makes an important and novel contribution to political connection literature. So far, to the best of the authors’ knowledge, it is the only bibliometric study on political connections. This study is the first to use network analysis and community detection to understand social clustering and to identify main research steams in political connection literature.

This study aims to discuss the state-of-the-art digital factory (DF) development combining digital twins (DTs), sensing devices, laser additive manufacturing (LAM) and subtractive manufacturing (SM) processes. The current shortcomings and outlook of the DF also have been highlighted. A DF is a state-of-the-art manufacturing facility that uses innovative technologies, including automation, artificial intelligence (AI), the Internet of Things, additive manufacturing (AM), SM, hybrid manufacturing (HM), sensors for real-time feedback and control, and a DT, to streamline and improve manufacturing operations.

This study presents a novel perspective on DF development using laser-based AM, SM, sensors and DTs. Recent developments in laser-based AM, SM, sensors and DTs have been compiled. This study has been developed using systematic reviews and meta-analyses (PRISMA) guidelines, discussing literature on the DTs for laser-based AM, particularly laser powder bed fusion and direct energy deposition, in-situ monitoring and control equipment, SM and HM. The principal goal of this study is to highlight the aspects of DF and its development using existing techniques.

A comprehensive literature review finds a substantial lack of complete techniques that incorporate cyber-physical systems, advanced data analytics, AI, standardized interoperability, human–machine cooperation and scalable adaptability. The suggested DF effectively fills this void by integrating cyber-physical system components, including DT, AM, SM and sensors into the manufacturing process. Using sophisticated data analytics and AI algorithms, the DF facilitates real-time data analysis, predictive maintenance, quality control and optimal resource allocation. In addition, the suggested DF ensures interoperability between diverse devices and systems by emphasizing standardized communication protocols and interfaces. The modular and adaptable architecture of the DF enables scalability and adaptation, allowing for rapid reaction to market conditions.

Based on the need of DF, this review presents a comprehensive approach to DF development using DTs, sensing devices, LAM and SM processes and provides current progress in this domain.

Laser-based powder bed fusion (LPBF) is a new method for forming thin-walled parts, but large cooling rates and temperature gradients can lead to large residual stresses and deformations in the part. This study aims to reduce the residual stress and deformation of thin-walled parts by a specific laser rescanning strategy.

A three-dimensional transient finite element model is established to numerically simulate the LPBF forming process of multilayer and multitrack thin-walled parts. By changing the defocus amount, the laser in situ annealing process is designed, and the optimal rescanning parameters are obtained, which are verified by experiments.

The results show that the annealing effect is related to the average surface temperature and scan time. When the laser power is 30 W and the scanning speed is 20 mm/s, the overall residual stress and deformation of the thin-walled parts are the smallest, and the in situ annealing effect is the best. When the annealing frequency is reduced to once every three layers, the total annealing time can be reduced by more than 60%.

The research results can help better understand the influence mechanism of laser in situ annealing process on residual stress and deformation in LPBF and provide guidance for reducing residual stress and deformation of LPBF thin-walled parts.

Based on the socio-emotional wealth and agency theories, this study empirically investigates the impact of family ownership and management on green innovation (GI) in family businesses, as well as the moderating effects of institutional environmental support factors, namely, the technological achievement marketisation index and the market-rule-of law index.

This study empirically tests the hypotheses based on a sample of listed Chinese family companies with A-shares in 14 heavily polluting industries from 2009 to 2019.

There is a U-shaped relationship between the percentage of family ownership and GI, and an inverted U-shaped relationship between the degree of family management and GI. Additionally, different institutional environmental support factors affect these relationships in different ways. As the technological achievement marketisation index increases, the U-shaped relationship between the percentage of family ownership and GI becomes steeper, while the inverted U-shaped relationship between the degree of family management and GI becomes smoother. The market rule-of-law index weakens the U-shaped relationship between family ownership and GI.

First, the authors enrich the research on the driving factors of GI from the perspective of the most essential heterogeneity of family businesses. This study shows nonlinear and opposite effects of family ownership and management on GI in family firms. Second, this study contributes to the literature on family firm innovation. GI, not considered by researchers, is regarded as an important deficiency in research on innovation in family businesses. Therefore, this study fills that gap. Third, the study expands research on moderating effects in the literature on GI from the perspective of institutional environmental support factors.

This study explores challenges facing the applicability of deep learning (DL) in software-defined networks (SDN) based campus networks. The study intensively explains the automation problem that exists in traditional campus networks and how SDN and DL can provide mitigating solutions. It further highlights some challenges which need to be addressed in order to successfully implement SDN and DL in campus networks to make them better than traditional networks.

The study uses a systematic literature review. Studies on DL relevant to campus networks have been presented for different use cases. Their limitations are given out for further research.

Following the analysis of the selected studies, it showed that the availability of specific training datasets for campus networks, SDN and DL interfacing and integration in production networks are key issues that must be addressed to successfully deploy DL in SDN-enabled campus networks.

This study reports on challenges associated with implementation of SDN and DL models in campus networks. It contributes towards further thinking and architecting of proposed SDN-based DL solutions for campus networks. It highlights that single problem-based solutions are harder to implement and unlikely to be adopted in production networks.

Understanding employees’ multi-dimensional motivations is at the core of realizing the potential of a well-designed human resource (HR) system. This study aims to investigate whether the effects of HR practices on employee motivations, and their performance would be dependent on the service orientation of HR department.

The authors collected data in two surveys: a pilot survey and a main survey with a two-wave design. The pilot survey with 93 respondents was to verify the newly developed HR service orientation scale. In the main survey, a total of 276 supervisor-subordinate pairs from 48 companies were valid for analysis.

The authors find support for their hypothesis that promotion-oriented motivation mediates the relationship between discretionary HR practices and employee outcomes [in-role performance and organizational citizenship behavior (OCB)]. Furthermore, the indirect effect of discretionary HR practices on employee outcomes is stronger when the HR service orientation is higher. Transactional HR practices, however, are not evidenced to relate to employee prevention-focused motivation and outcomes.

The findings illustrate a comprehensive process of HR practices on employees’ multi-dimensional motivations. High service skills of HR professionals in handling internal employees’ needs could amplify employees’ promotion-focused motives, which in turn increase their in-role performance and OCB.

In sum, the authors' study contributes to both human resource management (HRM) and employee motivation literature by demonstrating the different impacts of discretionary and transactional HR practices on employees’ motivations. In addition, by revealing HR service orientation as an important contingency factor, the authors shed greater light on when and how HR practices can motivate employees.

This paper employs human resources (HR) analytics to investigate the pathways through which high-potential managers ascend to C-suite positions, and how different developmental paths influence turnover among executives.

By combining job analysis and competency assessment with sequence analysis, the authors utilize HR analytics to analyze the work experiences of 53 general managers spanning 57 years (n = 2,742), encompassing various roles, job requirements, and 20 executive competencies attached to over 1,000 positions.

This study's findings reveal three distinct developmental paths that lead to the C-suite, characterized by differences in the content, context, timing, and complexity of work experience. Furthermore, the authors identify that a more complex developmental path tends to reinforce executives' competency in self-awareness while inhibiting their development of technical competency, ultimately resulting in reduced executive turnover.

By employing HR analytics to analyze empirical data embedded in job and organizational contexts, this study sheds light on the critical role of timing and complexity of work experiences in executive development. It also offers practical implications for firms seeking to optimize their leadership pipeline and reduce executive turnover by leveraging HR analytics effectively.

This study aims to elucidate the process and internal mechanism of place identity construction in traditional villages under the impact of tourism by taking Cuandixia village as a case. The research methods comprise participatory observation and in-depth interviews with the residents. The main results are as follows: the impact of tourism on traditional villages is mainly reflected in space reconstruction, livelihood change, social relations restructuring and culture change; under the impact of tourism, the representation of residents’ identity construction shows complexity, with positive and negative effects; and the place identity construction of residents affects their perception of and attitudes toward tourism. Moreover, self-esteem and self-efficacy principles play a key role in their perception of tourism. This study provides some reference for further investigation of the tourism development model and the mental mechanism of residents in traditional villages.