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This paper aims to analyze the temperature field and the heat transfer performance of the counter rotating dual rotor bearings (CRDRB) based on the air phase flow field at different speeds to provide effective support for the lubrication and the thermal design of CRDRB.

In this study, taking H7006C angular contact ball bearing as an example, based on the flow visualization technique and the thermal analysis methods, the effects of outer ring speed on the air phase flow field, the temperature field and the heat transfer in bearing cavity were investigated.

Results indicated that there were more complex turbulent air vortices in CRDRB cavity. Turbulent cyclones in critical contact zone reduced the heat dissipation capacity of air. Compared with single rotor bearing with a static outer ring, the average heat transfer coefficient reduced by 11.78% and the average temperature raised by 3.06 K inside CRDRB cavity. Under the influence of outer ring rotation, the high temperature area in ball-inner raceway contact zone and pocket raised and reduced by 160.13% and 30.48%, respectively. The outer ring rotation had opposite effect on the heat dissipation of raceway contact zone and pocket.

The air phase flow field characteristics and the heat transfer performance of CRDRB were revealed and analyzed from the mechanism. An area quantification method was presented as an auxiliary mean of the thermal analysis to evaluate the heat transfer performance of bearing.

Crack detection of pavement is a critical task in the periodic survey. Efficient, effective and consistent tracking of the road conditions by identifying and locating crack contributes to establishing an appropriate road maintenance and repair strategy from the promptly informed managers but still remaining a significant challenge. This research seeks to propose practical solutions for targeting the automatic crack detection from images with efficient productivity and cost-effectiveness, thereby improving the pavement performance.

This research applies a novel deep learning method named TransUnet for crack detection, which is structured based on Transformer, combined with convolutional neural networks as encoder by leveraging a global self-attention mechanism to better extract features for enhancing automatic identification. Afterward, the detected cracks are used to quantify morphological features from five indicators, such as length, mean width, maximum width, area and ratio. Those analyses can provide valuable information for engineers to assess the pavement condition with efficient productivity.

In the training process, the TransUnet is fed by a crack dataset generated by the data augmentation with a resolution of 224 × 224 pixels. Subsequently, a test set containing 80 new images is used for crack detection task based on the best selected TransUnet with a learning rate of 0.01 and a batch size of 1, achieving an accuracy of 0.8927, a precision of 0.8813, a recall of 0.8904, an F1-measure and dice of 0.8813, and a Mean Intersection over Union of 0.8082, respectively. Comparisons with several state-of-the-art methods indicate that the developed approach in this research outperforms with greater efficiency and higher reliability.

The developed approach combines TransUnet with an integrated quantification algorithm for crack detection and quantification, performing excellently in terms of comparisons and evaluation metrics, which can provide solutions with potentially serving as the basis for an automated, cost-effective pavement condition assessment scheme.

Wear greatly influences the machine lifetime, performance and reliability and its quantification is very important. This paper aims to propose a modified bearing area curve method by combining the theory of the bearing area curve, and the relocation technique to calculate wear accurately and efficiently.

H13 steel was chosen as the material of wear pair, and the wear experiments were carried out at 50 N, 60 r/min for 20 min. The surface was measured before and after wear experiments. The relocation was made by comparing the mean lines (planes) of the unworn and worn surface profiles. The calculated results using the proposed method were compared with that of the surface profile method for a two-dimensional surface to validate its accuracy. The method was then applied for a three-dimensional (3D) wear analysis.

The worn surface shows clearly displacement compared to the unworn surface and implies the importance of including relocation in the bearing area curve method. The results from the proposed method are 98 per cent close to that from the surface profile method, indicating that the method is accurate for wear evaluation.

As no feature point or relocation mark is needed to calculate the relocation value using the proposed method, the method can be applied to mild to severe wear. Also, as the deviation of different scans does not affect the relocation calculation, and no matching and stitching is required, this method can be easily applied to a wide wear area and 3D surface wear analysis.

With a focus on the evolution of human resource management (HRM) quantification over 2000–2020, this study addresses the following questions: (1) What are the data sources used to quantify HRM? (2) What are the methods used to quantify HRM? (3) What are the objectives of HRM quantification? (4) What are the representations of quantification in HRM?

This study is based on an integrative synthesis of 94 published peer-reviewed empirical and non-empirical articles on the use of quantification in HRM. It uses the theoretical framework of the sociology of quantification.

The analysis shows that there have been several changes in HRM quantification over 2000–2020 in terms of data sources, methods and objectives. Meanwhile, representations of quantification have evolved relatively little; it is still considered as a tool, and this ignores the possible conflicts and subjectivity associated with the use of quantification.

This literature review addresses the use of quantification in HRM in general and is thus larger in scope than previous reviews. Notably, it brings forth new insights on possible differences between the main uses of quantification in HRM, as well as on artificial intelligence and algorithms in HRM.

This paper aims to establish if quantified self-data positively impact motivation in a goal pursuit across a broad cross-section of consumers and in multiple contexts; and to understand the underlying causal mechanism and identify boundary conditions.

Exploratory qualitative research helped direct the hypotheses development. Two quantitative experiments were then conducted via MTURK, involving 331 respondents, to test the hypotheses in two different personal goal areas (fitness and carbon footprint reduction).

Self-quantification has a significant and positive impact on anticipated motivation in both contexts studied. The mediated model provides insight into the psychological process underlying self-quantification’s motivational impact, which involves strengthening user perceptions regarding feedback meaningfulness, self-empowerment and goal focus. Age (>50) was found to be a boundary condition; however, distance to goal was not.

This paper focuses on initial (anticipated) motivation, which is the vital first step in behavior change. However, more work is needed to understand quantification’s long-term impact over the course of a behavior change process.

This research encourages firms to incorporate self-quantification features into products/services aimed at behavior change and helps firms better understand consumer-perceived benefits. It alerts firms regarding the extra effort needed to convince older consumers of these benefits.

This is the first study to confirm the “quantification effect” on motivation in multiple life areas and provide a causal model to explain how it works. It is also the first to highlight age as a boundary condition.

Water distribution networks (WDNs) must deliver water to its customers 24/7. Disruption of this important service after a strong seismic event impedes post-disaster activities and poses health and sanitation problems. Hence, WDNs must be able to quickly restore services after the occurrence of a major seismic event. This ability to return the water service can be a metric for resilience. The purpose of this paper is to quantify the resilience by developing a framework that translates various restoration strategies into an improved resilience measure for a multisource WDN.

This paper used a quantitative risk assessment method in developing the framework for the resilience quantification of WDN. Prim’s algorithm, Horn’s algorithm and maximum slope method are used for the restoration analysis conducted in this study.

This paper provides resilience indices of the WDN for each repair scenarios. Then, the resilience indices are used to determine the most efficient and optimized repair scenario to restore the hypothetically damaged WDN owing to Level 1 and Level 2 seismic events.

The developed framework of this study only focuses on the robustness, rapidity and resourcefulness properties of resilience.

This study aims to help the water district in the maintenance, repair and evaluation of WDN against seismic events. The results from the study can be used in preparing the disaster management plan of the local water district to repair possible pipelines. This study also serves as a starting point to more complex and comprehensive research about the resilience quantification of WDNs with the consideration of optimal restoration sequence in the future.

The developed framework in the resilience quantification of WDN is original, as it uses optimal restoration strategies to represent the rapidity property of resilience.

This study aims to explore the sociocultural appeal of SNS game content from the point of view of the human emotions aroused by the interaction between the players and the games.

The author's research methodology is based on Miryoku Engineering. The author interviewed 11 knowledgeable gamers by the evaluation grid method (EGM) and surveyed 321 players through a questionnaire in order to gather data about their preferences, which were then analysed using quantification theory type I.

The semantic structure of the appeal of the games was determined from interviews with knowledgeable gamers (EGM). This structure represents a hierarchy of the relationship between the types of appeal or appeal factors, the reasons for gamers’ preferences, and the specific characteristics of SNS games. According to the statistical analysis, the most important sociocultural appeal factors of SNS games are “practicable and realizable” and “innovative and unique”, and these are affected in varying degrees by particular reasons and characteristics.

The author has found that sociocultural factors influence the design of SNS games significantly. In addition, the issue of how to integrate the sociocultural factors into the design of games is critical to the success of the marketing strategies of SNS games in different areas or countries.

The author's findings provide important information for designers and researchers of SNS games. The study also contributes to the field of human‐computer interaction in cyberspace.

In the recent past, academic researchers have noted the quantity of food wasted in food service establishments in educational institutions. However, more granular inputs are required to counter the challenge posed. The purpose of this study is to undertake a review of the prior literature in the area to provide a platform for future research.

Towards this end, the authors used a robust search protocol to identify 88 congruent studies to review and critically synthesize. The research profiling of the selected studies revealed limited studies conducted on food service establishments in universities. The research is also less dispersed geographically, remaining largely focused on the USA. Thereafter, the authors performed content analysis to identify seven themes around which the findings of prior studies were organized.

The key themes of the reviewed studies are the drivers of food waste, quantitative assessment of food waste, assessment of the behavioural aspects of food waste, operational strategies for reducing food waste, interventions for inducing behavioural changes to mitigate food waste, food diversion and food waste disposal processes and barriers to the implementation of food waste reduction strategies.

This study has key theoretical and practical implications. From the perspective of research, the study revealed various gaps in the extant findings and suggested potential areas that can be examined by academic researchers from the perspective of the hospitality sector. From the perspective of practice, the study recommended actionable strategies to help managers mitigate food waste.

The authors have made a novel contribution to the research on food waste reduction by identifying theme-based research gaps, suggesting potential research questions and proposing a framework based on the open-systems approach to set the future research agenda.

The primary purpose of this exploratory paper is to propose a novel analytical framework for examining corruption from a behavioral perspective by highlighting multiple issues associated with quantification culture.

This paper is part of the author’s exploratory study into the complex corruption phenomenon in Indonesia from a variety of viewpoints to obtain a better understanding of its nature and dynamics. By building on previous literature on quantification culture, audit culture and the corruption phenomenon, this paper explores the different issues related to overreliance on numbers and how they may have led to increased corruption, particularly in Indonesia. Using the Fraud Decision Scale framework, this paper also demonstrates how the quantification culture affects the cost-benefit analysis of corruption offenders. To develop the best anticorruption strategy and lay the groundwork for future anticorruption studies, this paper presents a fresh perspective on how to analyze and evaluate the corruption issue.

The author established that despite the benefits that quantification practices – such as indicators, scores, ratings and rankings – bring to societies and organizations, there is evidence that these practices can have unfavorable effects when used excessively or incorrectly. One of the adverse effects of quantification practices is over-quantification, which manifests as various inappropriate behaviors, including an excessive desire to acquire riches and material belongings. Additionally, the worship of ranks and status derived from quantitative evaluation processes has led many people to prioritize short-term objectives above long-term improvements. Eventually, these will cause organizations to lose productivity and make them susceptible to fraud and corruption. Future studies are needed to determine the most effective approach for mitigating over-quantification issues. In this exploratory paper, the author proposes balancing quantitative practices with qualitative knowledge to gradually alter people’s behavior by broadening their perspectives in perceiving various phenomena in the world, not only by describing and explaining them but also by comprehending their underlying meanings.

This exploratory study is self-funded and relies primarily on documentary analysis to explore the corruption phenomenon in Indonesia. Future studies will benefit from in-depth interviews with former corruption offenders and investigators.

This exploratory paper contributes to the development of a sound corruption prevention approach by presenting a novel analytical framework for examining various behavioral problems linked with quantification culture that may lead to the escalation of corruption.

This study emphasizes the significance of understanding the structure and dynamics of quantification culture and their negative behavioral impacts on people to comprehend the corruption phenomenon better.

Defects in concrete surfaces are inevitably recurring during construction, which needs to be checked and accepted during construction and completion. Traditional manual inspection of surface defects requires inspectors to judge, evaluate and make decisions, which requires sufficient experience and is time-consuming and labor-intensive, and the expertise cannot be effectively preserved and transferred. In addition, the evaluation standards of different inspectors are not identical, which may lead to cause discrepancies in inspection results. Although computer vision can achieve defect recognition, there is a gap between the low-level semantics acquired by computer vision and the high-level semantics that humans understand from images. Therefore, computer vision and ontology are combined to achieve intelligent evaluation and decision-making and to bridge the above gap.

Combining ontology and computer vision, this paper establishes an evaluation and decision-making framework for concrete surface quality. By establishing concrete surface quality ontology model and defect identification quantification model, ontology reasoning technology is used to realize concrete surface quality evaluation and decision-making.

Computer vision can identify and quantify defects, obtain low-level image semantics, and ontology can structurally express expert knowledge in the field of defects. This proposed framework can automatically identify and quantify defects, and infer the causes, responsibility, severity and repair methods of defects. Through case analysis of various scenarios, the proposed evaluation and decision-making framework is feasible.

This paper establishes an evaluation and decision-making framework for concrete surface quality, so as to improve the standardization and intelligence of surface defect inspection and potentially provide reusable knowledge for inspecting concrete surface quality. The research results in this paper can be used to detect the concrete surface quality, reduce the subjectivity of evaluation and improve the inspection efficiency. In addition, the proposed framework enriches the application scenarios of ontology and computer vision, and to a certain extent bridges the gap between the image features extracted by computer vision and the information that people obtain from images.