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Currently, lubrication analysis of piston ring is generally done under engine rated operating condition. However, the engine (such as the vehicle engine) does not always operate in rated operating condition, and its operating condition changes frequently in actual use. In addition, the lubrication status of piston ring is generally assumed as the flooded lubrication or a certain form of poor lubrication in most of the lubrication analysis.

In this paper, based on the equations about the flow rate of lubricating oil and the variation of control volume, the flow model of lubricating oil in the piston ring-cylinder liner conjunction is established. The lubrication analysis of piston ring for a four-stroke engine under different engine operating conditions is done, in which the lubricating oil at the inlet of piston ring is considered as the lubricating oil attached on the relevant location of cylinder wall after the piston ring moves over at the previous stroke.

There is remarkable difference for the lubrication characteristics of the piston ring under different engine operating conditions. The worst lubrication status of piston ring may not take place under engine rated operating condition.

In this paper, based on the measured engine cylinder pressure, the lubrication analysis of piston ring for a four-stroke engine under different engine operating conditions is done in which the lubricating oil supply condition at the inlet of piston ring is considered. The results of this paper are helpful for the design and research of engine piston ring-cylinder liner conjunction.

The purpose of this paper is to facilitate understanding of how to convert free players to paid consumers in free-to-play games.

Drawing on the consumption value framework and affordance theory, the present study argues that in-game purchase behaviors are determined by multiple consumption values of in-game items. The perceptions of consumption values were influenced by game affordances. The model was tested, using data from an empirical survey with 2,006 free-to-play game players.

Monetary, enjoyment and social values of in-game items positively predict purchase behaviors in free-to-play games. Game fairness and balance of challenges and skills significantly influence perceived enjoyment value.

The findings of this study provide operable implications to facilitate in-game consumption. The model was tested within the context of free-to-play multiplayer online battle arena (MOBA) games; however, caution is advisable when generalizing the findings to other subgenre of games.

This study extended and thus validated the consumption value framework in the context of free-to-play MOBA games. This study explored the antecedents of consumption values from the perspective of game affordance.

Improving the performance of recommender systems (RSs) has always been a major challenge in the area of e-commerce because the systems face issues such as cold start, sparsity, scalability and interest drift that affect their performance. Despite the efforts made to solve these problems, there is still no RS that can solve or reduce all the problems simultaneously. Therefore, the purpose of this study is to provide an effective and comprehensive RS to solve or reduce all of the above issues, which uses a combination of basic customer information as well as big data techniques.

The most important steps in the proposed RS are: (1) collecting demographic and behavioral data of customers from an e-clothing store; (2) assessing customer personality traits; (3) creating a new user-item matrix based on customer/user interest; (4) calculating the similarity between customers with efficient k-nearest neighbor (EKNN) algorithm based on locality-sensitive hashing (LSH) approach and (5) defining a new similarity function based on a combination of personality traits, demographic characteristics and time-based purchasing behavior that are the key incentives for customers' purchases.

The proposed method was compared with different baselines (matrix factorization and ensemble). The results showed that the proposed method in terms of all evaluation measures led to a significant improvement in traditional collaborative filtering (CF) performance, and with a significant difference (more than 40%), performed better than all baselines. According to the results, we find that our proposed method, which uses a combination of personality information and demographics, as well as tracking the recent interests and needs of the customer with the LSH approach, helps to improve the effectiveness of the recommendations more than the baselines. This is due to the fact that this method, which uses the above information in conjunction with the LSH technique, is more effective and more accurate in solving problems of cold start, scalability, sparsity and interest drift.

The research data were limited to only one e-clothing store.

In order to achieve an accurate and real-time RS in e-commerce, it is essential to use a combination of customer information with efficient techniques. In this regard, according to the results of the research, the use of personality traits and demographic characteristics lead to a more accurate knowledge of customers' interests and thus better identification of similar customers. Therefore, this information should be considered as a solution to reduce the problems of cold start and sparsity. Also, a better judgment can be made about customers' interests by considering their recent purchases; therefore, in order to solve the problems of interest drifts, different weights should be assigned to purchases and launch time of products/items at different times (the more recent, the more weight). Finally, the LSH technique is used to increase the RS scalability in e-commerce. In total, a combination of personality traits, demographics and customer purchasing behavior over time with the LSH technique should be used to achieve an ideal RS. Using the RS proposed in this research, it is possible to create a comfortable and enjoyable shopping experience for customers by providing real-time recommendations that match customers' preferences and can result in an increase in the profitability of e-shops.

In this study, by considering a combination of personality traits, demographic characteristics and time-based purchasing behavior of customers along with the LSH technique, we were able for the first time to simultaneously solve the basic problems of CF, namely cold start, scalability, sparsity and interest drift, which led to a decrease in significant errors of recommendations and an increase in the accuracy of CF. The average errors of the recommendations provided to users based on the proposed model is only about 13%, and the accuracy and compliance of these recommendations with the interests of customers is about 92%. In addition, a 40% difference between the accuracy of the proposed method and the traditional CF method has been observed. This level of accuracy in RSs is very significant and special, which is certainly welcomed by e-business owners. This is also a new scientific finding that is very useful for programmers, users and researchers. In general, the main contributions of this research are: 1) proposing an accurate RS using personality traits, demographic characteristics and time-based purchasing behavior; 2) proposing an effective and comprehensive RS for a “clothing” online store; 3) improving the RS performance by solving the cold start issue using personality traits and demographic characteristics; 4) improving the scalability issue in RS through efficient k-nearest neighbors; 5) Mitigating the sparsity issue by using personality traits and demographic characteristics and also by densifying the user-item matrix and 6) improving the RS accuracy by solving the interest drift issue through developing a time-based user-item matrix.

Gives a bibliographical review of the finite element methods (FEMs) applied for the linear and nonlinear, static and dynamic analyses of basic structural elements from the theoretical as well as practical points of view. The range of applications of FEMs in this area is wide and cannot be presented in a single paper; therefore aims to give the reader an encyclopaedic view on the subject. The bibliography at the end of the paper contains 2,025 references to papers, conference proceedings and theses/dissertations dealing with the analysis of beams, columns, rods, bars, cables, discs, blades, shafts, membranes, plates and shells that were published in 1992‐1995.

This study aims to examine the scientific publications related to leadership research in hospitality from 2000 to 2021 by conducting a systematic review (qualitative) and to discuss implications for future research.

For the qualitative approach, the authors conduct an in-depth critique of major leadership theories using 167 articles indexed in the Web of Science Core Collection.

The findings show that transformational leadership, leader–member exchange and servant leadership are the most prominent leadership topics studied from 2000 to 2021, followed by abusive supervision, empowering leadership, ethical leadership and authentic leadership. A framework is presented highlighting the mediators, moderators, outcomes, sample and research designs used in each of these lines of leadership research. Moreover, 16 areas for further research are identified and discussed.

This review uncovers scholars’ general lack of regard for how the study of leadership might benefit from examining hospitality as a special and challenging context for leadership and business performance.

This study reviews and critically analyzes leadership research in hospitality using qualitative methods. Therefore, the authors believe this review is of great value to academics and practitioners because it synthesizes and analyzes the field and identifies important research opportunities.

Augmented reality (AR) is transforming the business and interactive marketing landscape. This research aims to investigate consumers' degree of involvement and if a feeling of immersion and presence influences AR shopping satisfaction, comparing high- and low-immersive AR experiences.

This paper uses a quantitative approach. Two studies were carried out: a high-immersive AR experiment with 173 participants and a low-immersive AR experience with 222 participants. Findings were analyzed using partial least square structural equation modeling with SmartPLS.

Results indicate the antecedents of immersion and presence differ when it comes to different immersive AR levels. In a high-immersive AR experience, flow, information seeking and novelty are attributes related to immersion, while enjoyment and personalization are related to presence. Contrastingly, in a low-immersive AR experience, only flow is related to immersion, while information seeking, novelty and personalization are related to presence. These results highlight the role of immersion and presence as mediators for AR shopping satisfaction experience.

This study's originality lies in the use of a rival model for analysis. Findings suggest a contingent perspective of AR experience, depending on high- or low-immersion experience, so companies must pay attention for how to measure AR experiences to increase involvement and satisfaction.

Intuitionistic linguistic fuzzy information (ILFI), characterized by linguistic terms and intuitionistic fuzzy sets (IFSs), can easily express the fuzzy information in the process of muticriteria decision making (MCDM) and muticriteria group decision making (MCGDM) problems. The purpose of this paper is to provide an overview of aggregation operators (AOs) and applications of ILFI.

First, some meaningful AOs for ILFI are summarized, and some extended MCDM approaches for intuitionistic uncertain linguistic variables (IULVs), such as extended TOPSIS, extended TODIM, extended VIKOR, are discussed. Then, the authors summarize and analyze the applications about the AOs of IULVs.

IULVs, characterized by linguistic terms and IFSs, can more detailed and comprehensively express the criteria values in the process of MCDM and MCGDM. Therefore, lots of researchers pay more and more attention to the MCDM or MCGDM methods with IULVs.

The authors summarize and analyze the applications about the AOs of IULVs Finally, the authors point out some possible directions for future research.

Burnout has been recognized and measured in the workplace since the 1970s, particularly in service industries. Libraries can be viewed as service providers. Burnout is the result of chronically high work demands combined with emotional exhaustion, depersonalization, and diminished personal accomplishment. Burnout components have been linked to physical, emotional, and behavioral consequences, and to high turnover and loss of engagement. Libraries can evaluate burnout levels among staff and address burnout on an individual, management, and organizational level. The Nurse-Experienced Time Pressure, Burnout, and Patient Interaction Questionnaire is modified to identify and quantify activities individuals might use to reduce burnout. The survey is administered to librarians and staff at an academic library and to self-chosen attendees at a conference session on avoiding burnout. Feedback is also solicited in terms of burnout avoidance strategies and possible library responses. Most respondents feel burned out but also committed to providing excellent service to patrons. Respondents have a genuine interest in making work less prone to burnout. Sample sizes were small but gave consistent responses. Burnout can be addressed on an institutional, management, and personal level, with each entity having equal responsibility. Leadership, management, communication, and support efforts can counteract burnout threats. Burnout causes disengagement at work and in personal lives. In terms of personality, neuroticism is a strong predictor of burnout. Making efforts to counteract burnout will lead to a healthier, balanced life. This book chapter is based on research done for a presentation at ER&L 2016 on Avoiding E-Burnout. Causes and counteractions to burnout have been expanded.

The purpose of this study is to design a wearable medical device as a human care platform and to introduce the design details, key technologies and practical implementation methods of the system.

A multi-channel data acquisition scheme based on PCI-E (rapid interconnection of peripheral components) was proposed. The flexible biosensor is integrated with the flexible data acquisition card with monitoring capability, and the embedded (device that can operate independently) chip STM32F103VET6 is used to realize the simultaneous processing of multi-channel human health parameters. The human health parameters were transferred to the upper computer LabVIEW by intelligent clothing through USB or wireless Bluetooth to complete the transmission and processing of clinical data, which facilitates the analysis of medical data.

The smart clothing provides a mobile medical cloud platform for wearable medical through cloud computing, which can continuously monitor the body's wrist movement, body temperature and perspiration for 24 h. The result shows that each channel is completely accurate to the top computer display, which can meet the expected requirements, and the wearable instant care system can be applied to healthcare.

The smart clothing in this study is based on the monitoring and diagnosis of textiles, and the electronic communication devices can cooperate and interact to form a wearable textile system that provides medical monitoring and prevention services to individuals in the fastest and most accurate way. Each channel of the system is precisely matched to the display screen of the host computer and meets the expected requirements. As a real-time human health protection platform technology, continuous monitoring of human vital signs can complete the application of human motion detection, medical health monitoring and human–computer interaction. Ultimately, such an intelligent garment will become an integral part of our everyday clothing.

As the hospitality industry is shifting its focus from service to experience, customers are becoming co-creators of the perceived value of a hospitality service because experiences customers obtained when consuming a hospitality service involve the participation of the customers. Thus, more research is needed to examine the relationships among consumer’s personal factors and their evaluations of hospitality services. This study developed and tested hypotheses that examined the effects of customers’ intrinsic characteristics on their evaluations of a restaurant service. Data were collected from college students in the United States (n = 220) and China (n = 254) using a scenario approach. Findings reveal that customers’ gender, personality, and cultural background had significant effects on their evaluations of a restaurant service. Specifically, female customers rated the same service higher than male customers on the reliability dimension of service quality and overall service quality; customers with personalities of extroversion, conscientiousness, agreeableness, and openness rated the service higher than customers with neuroticism personality on the responsiveness dimension; and customers in individualistic cultures rated the service higher than those in collectivistic cultures on most of the service evaluation measures. Implications of the study’s findings are discussed.