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Previous studies were mainly focused on profile designation of bearing rollers and lubrication performance without considering roller-races skidding. However, the width of round corner, load, rotational speed and some other parameters have significant effects on the roller-races sliding speed. This paper aims to investigate the effect of round corner on lubricating characteristics between the heavily loaded roller and inner race considering skidding and roughness.

A mixed elastohydrodynamic lubrication (EHL) model which is capable of handling practical cases with 3D machined roughness is combined with the skidding model to investigate the effect of round corner on lubricating characteristics between the heavily loaded roller and inner race.

The width of round corner and round corner radius have a desirable range under certain operating condition, within which the maximum pressure, stress and maximum flash temperature remain low. The optimized range is sensitive to the operating condition. Roughness and skidding narrow the optimized range of round corner radius. Roughness increases the pressure peak, Mises stress and friction coefficient. At the same time, skidding and roughness have obvious effects on film thickness at the contact center area if the round radius is small.

This paper uses the Harris skidding model that has a relatively bigger error, which is not accurate if the bearing load is less.

This paper unifies the skidding model and mixed EHL model which can be used as a tool for optimization design and lubricating performance analysis of cylindrical roller bearing.

Lubrication analyses for roller bearing are assumed to be pure rolling contact between roller and races in a previous study, which could not reflect the real contact characteristics. The skidding model is merged into a mixed EHL model which can be used as a dynamic tool to analyze the lubricating performance considering the round corner, skidding and roughness.

This study aims to investigate how firms of different sizes respond to competitive pressure from peers.

This study employs machine learning techniques to measure competitive pressure based on management discussion and analysis (MD&A) documents and then utilises the constructed pressure indicator to explore the relationship between competitive pressure and corporate risk-taking behaviours amongst firms of different sizes.

We find that firm sizes are positively associated with their risk-taking behaviours when firms respond to competitive pressure. Large firms are inclined to exhibit a high level of risk-taking behaviours, whereas small firms tend to make conservative decisions. Regional growth potential and institutional ownership moderate the relationships.

Utilising text mining techniques, this study constructs a novel quantitative indicator to measure competitive pressure perceived by focal firms and demonstrates the heterogeneous behaviour of firms of different sizes in response to competitive pressure from peers, advancing research on competitive market pressures.

This paper aims to establish a lattice Boltzmann (LB) method for solid-liquid phase transition (SLPT) from the pore scale to the representative elementary volume (REV) scale. By applying this method, detailed information about heat transfer and phase change processes within the pores can be obtained, while also enabling the calculation of larger-scale SLPT problems, such as shell-and-tube phase change heat storage systems.

Three-dimensional (3D) pore-scale enthalpy-based LB model is developed. The computational input parameters at the REV scale are derived from calculations at the pore scale, ensuring consistency between the two scales. The approaches to reconstruct the 3D porous structure and determine the REV of metal foam were discussed. The implementation of conjugate heat transfer between the solid matrix and the solid−liquid phase change material (SLPCM) for the proposed model is developed. A simple REV-scale LB model under the local thermal nonequilibrium condition is presented. The method of bridging the gap between the pore-scale and REV-scale enthalpy-based LB models by the REV is given.

This coupled method facilitates detailed simulations of flow, heat transfer and phase change within pores. The approach holds promise for multiscale calculations in latent heat storage devices with porous structures. The SLPT of the heat sinks for electronic device thermal control was simulated as a case, demonstrating the efficiency of the present models in designing and optimizing SLPT devices.

A coupled pore-scale and REV-scale LB method as a numerical tool for investigating phase change in porous materials was developed. This innovative approach allows for the capture of details within pores while addressing computations over a large domain. The LB method for simulating SLPT from the pore scale to the REV scale was given. The proposed method addresses the conjugate heat transfer between the SLPCM and the solid matrix in the enthalpy-based LB model.

Investors frequently make judgments and decisions in the presence of affect (i.e., mood or emotion). Investors' moods may influence the extent to which they incorporate available financial information in their investment judgments. I propose that investors interpret their moods as signals of the extent to which financial information should be processed to make investment judgments, but only when other, more direct signals regarding the need for in-depth processing are unavailable. Consistent with research in psychology, my experimental results suggest that investors experiencing positive mood exert less effort to process available financial information than investors experiencing negative mood. Consequently, positive mood results in lower-quality financial judgments in my setting. However, when investors receive cues suggesting that initially received information is subjective, the effect of mood on effort to process financial information is mitigated. Overall, my results suggest that factors associated with positive investor mood (e.g., positive market sentiment) reduce the depth of investor analysis and lower judgment quality absent signals regarding the subjectivity of financial information.

Recently, sharing economy is gradually accepted by people, and it has expanded from life to knowledge. It is important to encourage people to produce high quality content in knowledge sharing area, and knowledge payment is one of the most effective ways to achieve it. Therefore, the knowledge payment has been regarded as a huge business opportunity, and it is of great meaning to study the development trend and feasibility of knowledge payment. This chapter, through big data methods, analyzes the business model of Zhihu (a Chinese platform of knowledge sharing) after it introduced knowledge payment projects, such as Zhihu Live and Pay Consultation. According to data of Zhihu users’ Q&A, concerned fields and others, this chapter tries to outline its user profile to find out the target groups of different topics, the proper form of knowledge payment and the hot topics of Zhihu Live. Through the analysis of knowledge graph, this chapter finds that Zhihu Live is expected to be the mainstream knowledge payment form in the future, and the most potential topics are mainly focused on science, law, and business. Meanwhile, it establishes a pricing model for Zhihu Live, and provides suggestions for the development of knowledge payment.

This is a companion paper to our previous study in Cao and Wei (2005) on stock market temperature anomaly for eight international stock markets. The temperature anomaly is characterized by a negative relationship between stock market returns and temperature. This line of work relies on the impact of environmental variables, such as temperature, on mood and behavior changes. In this paper, we expand the sample in Cao and Wei (2005) to include 19 additional financial markets. Our evidence confirms the identified negative relationship for the expanded sample. More importantly, our nonparametric tests, as opposite to the parametric or semi-parametric approaches used by previous related studies, demonstrate that this negative relationship is robust to distributional assumptions. Based on the sub-sample analysis, we find that this negative relationship is stable over time. Furthermore, we consider temperature deviation and demonstrate that this negative relationship is not just a level effect.

The aim of this paper is to examine the relationship between weather (temperature) and stock market returns using daily data from Portugal; also, to examine whether the temperature is driven by calendar‐related anomalies such as the January and trading month effects.

Daily financial and weather data from Lisbon Stock Exchange (PSI 20 index) and Lisbon capital for the period 1995‐2007 are considered. The paper employs an AR(1)‐TGARCH(1,1) model under several distributional assumptions (Normal, Student's‐t and GED) for the errors.

Empirical results show that temperature affects negatively the PSI20 stock returns in Portugal. Moreover, temperature is dependent of both January and trading month effects. Stock returns were found to be positive in January and higher over the first fortnight of the month. Lower temperature in January leads to higher stock returns due to investors' aggressive risk taking.

Further research should investigate the impact of other meteorological variables (humidity, amount of sunshine) and other calendar anomalies on the course and behaviour of major international stock indices using data before and after the recent crisis.

The findings are helpful to financial managers, investors and traders dealing with the Portuguese stock market.

The contribution of this paper is to provide evidence on the empirical linkages between temperature and stock market returns using GARCH models. To better understand the relationship between the temperature and stock market returns, the paper also examines whether the returns are higher in winter (January effect) and during the first or second fortnight of the month (trading month effect). To the best of the author's knowledge, this is the first empirical investigation on weather and stock market returns relationship for Portugal.

A total of 12 papers in this volume represent some current research on important topics in finance. The contributions include analyses of issues relating to the recent reforms on corporate governance, the behavior of stock returns, the option pricing models, the financial regulation and banking theory, and the international finance. Kensinger and Poe argue that the legal status of corporations and the higher costs of Sarbanes-Oxley will accelerate further hollowing of public corporations. Using stock price reactions to the information in joint venture announcements, Keown, et al. find that the market considers relevant information for valuing the firms apart from the joint venture itself. Based upon data from a large sample of global financial markets, Cao and Wei find empirical evidence strongly supporting the negative relationship between stock returns and temperature. Kang and Ding find differential results of stock returns on the financial signals in Asian financial markets. Chen et al. develop a binomial-tree pricing model for Stock Participation Accreting Redemption Quarterly pay Securities (SPARQS), and show the pricing performance of the model. Lai and Soumare analyze the investment incentives in project finance in the presence of government financial guarantees. Chang et al. develop option-pricing models with price limits and market illiquidity and show that both of these market imperfections have significant impact on the option values.

According to extensive analysis, employee agility is influenced by teamwork, coordination and the organizational environment. However, less consideration has been given to the role of work stressors (challenge, hindrance) in influencing employee agility. To address this research gap, this study sheds light on how the use of enterprise social media (ESM) for social and work purposes influences employee agility through work stressors.

This research also explores how ESM visibility enhances the interaction between work stressors and employee agility by using primary data obtained from Chinese workers. A total of 377 entries were analyzed using AMOS 24.10 tools. All the hypotheses were tested using structural equation modeling (SEM).

The findings revealed that ESM use (social and work) negatively impacts challenge and hindrance work stressors. The results also reflect that challenge stressors have a significant impact on employee agility, whereas hindrance stressors are negatively related to it. Furthermore, the outcome also indicated that increased ESM visibility reinforces the connection between challenge stressors and employee agility. However, ESM visibility did not indicate a significant moderating impact on the link between hindrance stressors and employee agility.

This study describes how ESM usage effects agility of stressed employees. This research also explores how ESM visibility improves the interaction between work stressors and employee agility. The study results contribute to growing research on social media and employee agility and suggest several points of guidance for managers.

This paper aims to demonstrate how to make emergency decision when decision makers face a complex and turbulent environment that needs quite different decision-making processes from conventional ones. Traditional decision techniques cannot meet the demands of today’s social stability and security.

The main work is to develop an instance-driven classifier for the emergency categories based upon three fuzzy measures: features for an instance, solution for the instance and effect evaluation of the outcome. First, the information collected from the past emergency events is encodes into a prototype model. Second, a three-dimensional space that describes the locations and mutual distance relationships of the emergency events in different emergency prototypes is formulated. Third, for any new emergency event to be classified, the nearest emergency prototype is identified in the three-dimensional space and is classified into that category.

An instance-driven classifier based on prototype theory helps decision makers to describe emergency concept more clearly. The maximizing deviation model is constructed to determine the optimal relative weights of features according to the characteristics of the new instance, such that every customized feature space maximizes the influence of features shared by members of the category. Comparisons and discusses of the proposed method with other existing methods are given.

To reduce the affection to economic development, more and more countries have recognized the importance of emergency response solutions as an indispensable activity. In a new emergency instance, it is very challengeable for a decision maker to form a rational and feasible humanitarian aids scheme under the time pressure. After selecting a most suitable prototype, decision makers can learn most relevant experience and lessons in the emergency profile database and generate plan for the new instance. The proposed approach is to effectively make full use of inhomogeneous information in different types of resources and optimize resource allocation.

The combination of instances can reflect different aspects of a prototype. This feature solves the problem of insufficient learning data, which is a significant characteristic of emergency decision-making. It can be seen as a customized classification mechanism, while the previous classifiers always assume key features of a category.