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This paper aims to present a new design for removable partial dentures (RPDs) for partially edentulous patients to improve the efficiency and quality of RPD manufacturing. Additive and subtractive manufacturing technologies and zirconium silicate micro-ceramic bonding in the aesthetic zone are used herein.

A case was presented. First, RPD digital definitive casts were acquired, and then digital frameworks with crown retainers and digital crowns were obtained by computer-aided design (CAD). The titanium alloy frameworks and resin crowns were fabricated by three-dimensional (3D) printing and computer-aided manufacturing (CAM) processes, respectively. The crowns adhered to the crown retainers. Ceramage bonding was used to reform the gingival anatomy in the aesthetic zone during the fabrication of the RPDs. The finished RPDs were assessed by a clinician and delivered to the patient.

The RPDs were conventionally assessed by a clinician, were deemed to be accurate and satisfied both the patient and clinician.

This novel method provides a way to fabricate RPDs with a combination of additive and subtractive manufacturing technologies. The design of the framework was different from that of a conventional framework because it contained the crown retainers, and the traditional base retainer no longer existed. Ceramage bonding was used to replicate the gingival anatomy in the aesthetic zone. The new RPDs provided accuracy and were less time-consuming to produce than those produced with the traditional method. The new method enables the digital manufacturing of nearly the entire RPDs.

The purpose of this paper is to identify the historical trends and status of the national development of artificial intelligence (AI) from a nationwide perspective and to enable governments at different administrative levels to promote AI development through policymaking.

This paper analyzed 248 Chinese AI policies (36 issued by the state agencies and 212 by the regional agencies). Policy bibliometrics, policy instruments and network analysis were used to reveal the AI policy patterns. Three aspects were analyzed: the spatiotemporal distribution of issued policies, the policy foci and instruments of policy contents and the cooperation and citation among policy-issuing agencies.

Results indicate that Chinese AI development is still in the initial phase. During the policymaking processes, the state and regional policy foci have strong consistency; however, the coordination among state and regional agencies is supposed to be strengthened. According to the issuing time of AI policies, Chinese AI development is in accordance with the global situation and has witnessed unprecedented growth in the last five years. And the coastal provinces have issued more targeted policies than the middle and western provinces. Governments at the state and regional levels have emphasized familiar policy foci and played the role of policymakers, along with regional governments that also functioned as policy executors as well. According to the three-dimension instruments coding, the authors found an uneven structure of policy instruments at both levels. Furthermore, weak cooperation appears at the state level, while little cooperation is found among regional agencies. Regional governments cite state policies, thus leading to the formation of top-down diffusion, lacking bottom-up diffusion.

The paper contributes to the literature by characterizing policy patterns from both external attributes and semantic contents, thus revealing features of policy distribution, contents and agencies. What is more, this research analyzes Chinese AI policies from a nationwide perspective, which contributes to clarifying the overall status and multi-level relationships of policies. The findings also benefit the coordinated development of governments during further policymaking processes.

Although interdisciplinary research is an increasing trend in scientific funding projects, they are suffering from a lower probability of being funded. The purpose of this paper is to analyze the current situation on successful case of funding application and provides suggestions on how libraries can expand services to help scientific funding application.

This paper utilizes the co-occurrences of disciplinary application codes to construct an interdisciplinary knowledge flow network. Based on 193517 sponsored projects of the National Natural Science Foundation of China, the authors study the interdisciplinary flow of knowledge and investigate the evolution of network structure using social network analysis.

Results show that the interdisciplinary knowledge flow network is not only a small-world network but also a scale-free network. Two main knowledge flow paths across scientific departments exist, showing the heterogeneity of knowledge distributions across scientific disciplines. The authors also find that if two disciplines in the same scientific department both have a wide influence to other disciplines, they are more prone to link together and create a knowledge chain.

Funding consultation currently has not occupied an advisory role either in library services or in the research team. This paper conducts a co-occurrences network analysis of interdisciplinary knowledge flow in scientific funding projects. Considering the complexity of funding application and the advantage of traditional library services on information collection, integration, and utilization, the authors conclude the possibility and necessity of embedding funding consultation in traditional library services.

Knowledge sharing, as a kind of social behavior that incorporates collective intelligence to achieve a certain goal, has become a remarkable developing trend in recent years. Under the context of traditional teaching, this study aims to explore the manner in which students become effective in sharing knowledge to help optimize course design and improve our existing education.

Among 195 university students taking an elective, the effects of different incentives on group performance in completing tasks is explored on the basis of a randomized experiment.

Results show that intra-group cooperation can be helpful to student performance, whereas intra-group competition neither improves nor worsens student performance. The former is mainly driven by reciprocity, especially for that stimulated by inter-group competition, whereas the latter is stimulated by egoism. Thus, proper reciprocity can promote student behavior to increase voluntary contribution. In addition, intra-group differences do not interfere with group performance, especially task-oriented groups.

Certain suggestions are proposed to improve the curriculum design in large classrooms. Forming groups is the best way to strengthen student knowledge sharing. Within task-oriented groups, the incentives of inter-group competition can encourage students to deepen intra-group cooperation and thus effectively improve group performance under the conditions of external competition.

Employees play a central role in firms’ knowledge transferal, but knowledge-sharing brings significant costs for employees. Thus, this study aims to explore the components of firms’ incentive systems and how these influence employees’ knowledge-sharing, and also to test whether employees’ knowledge-sharing intentions transform into better knowledge transfer performance at the firm level.

This study collected data in China, and 219 usable questionnaires were collected. Then, this study used a structure equation model by LISREL for hypotheses testing.

This study finds that positive economic incentives, positive relational incentives and negative relational incentives all increase employees’ knowledge-sharing intentions, contributing to firms’ improved knowledge-transfer performance. Thus, both positive and negative incentives and both economic and relational incentives exert influences on employees’ knowledge-sharing activities.

Because employees have both material and emotional needs and always want to approach good things and avoid bad things, firms should take measures to make their incentive systems more comprehensive. Then, employees can be motivated to share their knowledge effectively.

Existing studies have mainly explored the effects of positive economic incentives on knowledge transferal. Because individuals have both a promotion self-regulatory focus associated with an approach motivation and a prevention self-regulatory focus associated with an avoidance motivation, and because they have both material and emotional needs, this study classifies incentives into three types and confirms their effectiveness for motivating employees to share knowledge.

The purpose of this paper is to explore the effect of ZnO on the structure and properties of micro-arc oxidation (MAO) coating on rare earth magnesium alloy under large concentration gradient.

The macroscopic and microscopic morphology, thickness, surface roughness, chemical composition and structure of the coating were characterized by different characterization methods. The corrosion resistance of the film was studied by electrochemical and scanning Kelvin probe force microscopy. The results show that the addition of ZnO can significantly improve the compactness and corrosion resistance of the MAO coating, but the high concentration of ZnO will cause microcracks, which will reduce the corrosion resistance to a certain extent.

When the concentration of zinc oxide is 8 g/L, the compactness and corrosion resistance of the coating are the best, and the thickness of the coating is positively correlated with the concentration of ZnO.

Too high concentration of ZnO reduces the performance of MAO coating.

The MAO coating prepared by adding ZnO has good corrosion resistance. Combined with organic coatings, it can be applied in corrosive marine environments, such as ship parts and hulls. To a certain extent, it can reduce the economic loss caused by corrosion.

The effect of ZnO on the corrosion resistance of MAO coating in electrolyte solution was studied systematically, and the conclusion was new to the common knowledge.

The tribological properties of muscovite and its thermal-treated products as lubricant additives in lithium grease were investigated. The effect of thermal temperature on the crystal structure and tribological properties of muscovite was studied. This study aims to explore the tribological mechanism of muscovite and optimize a proper thermal activation temperature, thus further improving the tribological properties.

The crystal structure of muscovite samples was characterized by SEM, TG-DSC, XRD and FTIR. The tribological properties of grease samples were investigated using a four-ball tribotester and the worn surface was analyzed by SEM and EDS.

The excellent tribological properties of muscovite can be ascribed to the layer structure and lubricant film formed on the worn surface. Thermal temperature at 500-600°C increases the surface activity and oxygen releasing capability, and thus favors the formation of lubricant film and accordingly further improves the tribological properties. However, the layer structure is destroyed and hard phases such as alumina and amorphous appear after thermal temperature activated beyond 1000°C, as it results in the aggravation of friction and wear.

To the authors’ knowledge, it is the first to study the effect of thermal temperature on the crystal structure and tribological properties of muscovite. The tribological mechanism of muscovite particle and its thermal-treated products was disclosed.

The COVID-19 outbreak has disrupted the habits of customers as well as their shopping behavior. This study aims to critically examine the associated benefits and challenges of online shopping from the perspective of customers in the COVID-19 pandemic.

A systematic review of the relevant literature published between 2020 and 2022 was conducted via performing comprehensive search query in leading scholarly databases “Scopus and Web of Science” with the restriction of their predefined subject category of “Business.” Overall, 30 research studies were selected for the review and a significant number of studies were published in 2021 (n = 15).

The research findings revealed that customers are motivated to shop online because of perceived benefits such as time-saving, convenience, 24/7 accessibility, interactive services without physical boundaries, trust, website attractiveness and cost-saving. However, challenging factors such as financial scams, privacy concerns, poor quality of products and services, fake promotions and reduced social interaction have hindered the growth of online shopping. The recommendations regarding designing marketing strategies, secured transaction, multiple payment options, trust building, protection of privacy, promotion via social media, effective mechanism to secure and timely delivery of product are helpful to improve the service quality of online shopping.

The outcomes of this research are valuable to online retailers and policymakers, as it highlights how the benefits can enhance customers’ shopping intentions and minimize the impact of associated challenges. This study also recommends the redesigning of user-friendly interfaces of online shopping websites and ensures their privacy, security and performance on a regular basis.

The aim of this study is to propose an efficient rule extraction and integration approach for identifying phishing websites. The proposed approach can elucidate patterns of phishing websites and identify them accurately.

Hyperlink indicators along with URL-based features are used to build the identification model. In the proposed approach, very simple rules are first extracted based on individual features to provide meaningful and easy-to-understand rules. Then, the F-measure score is used to select high-quality rules for identifying phishing websites. To construct a reliable and promising phishing website identification model, the selected rules are integrated using a simple neural network model.

Experiments conducted using self-collected and benchmark data sets show that the proposed approach outperforms 16 commonly used classifiers (including seven non–rule-based and four rule-based classifiers as well as five deep learning models) in terms of interpretability and identification performance.

Investigating patterns of phishing websites based on hyperlink indicators using the efficient rule-based approach is innovative. It is not only helpful for identifying phishing websites, but also beneficial for extracting simple and understandable rules.