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Understandings of house prices and their interrelationships have undoubtedly drawn a great amount of attention from various market participants. This study aims to investigate the monthly newly-built residential house price indices of seventy Chinese cities during a 10-year period spanning January 2011–December 2020 for understandings of issues related to their interdependence and synchronizations.

Analysis here is facilitated through network analysis together with topological and hierarchical characterizations of price comovements.

This study determines eight sectoral groups of cities whose house price indices are directly connected and the price synchronization within each group is higher than that at the national level, although each shows rather idiosyncratic patterns. Degrees of house price comovements are generally lower starting from 2018 at the national level and for the eight sectoral groups. Similarly, this study finds that the synchronization intensity associated with the house price index of each city generally switches to a lower level starting from early 2019.

Results here should be of use to policy design and analysis aiming at housing market evaluations and monitoring.

Density-based spatial clustering of applications with noise (DBSCAN) is the most commonly used density-based clustering algorithm, while it cannot be directly applied to the railway investment risk assessment. To overcome the shortcomings of calculation method and parameter limits of DBSCAN, this paper proposes a new algorithm called Improved Multiple Density-based Spatial clustering of Applications with Noise (IM-DBSCAN) based on the DBSCAN and rough set theory.

First, the authors develop an improved affinity propagation (AP) algorithm, which is then combined with the DBSCAN (hereinafter referred to as AP-DBSCAN for short) to improve the parameter setting and efficiency of the DBSCAN. Second, the IM-DBSCAN algorithm, which consists of the AP-DBSCAN and a modified rough set, is designed to investigate the railway investment risk. Finally, the IM-DBSCAN algorithm is tested on the China–Laos railway's investment risk assessment, and its performance is compared with other related algorithms.

The IM-DBSCAN algorithm is implemented on China–Laos railway's investment risk assessment and compares with other related algorithms. The clustering results validate that the AP-DBSCAN algorithm is feasible and efficient in terms of clustering accuracy and operating time. In addition, the experimental results also indicate that the IM-DBSCAN algorithm can be used as an effective method for the prospective risk assessment in railway investment.

This study proposes IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set to study the railway investment risk. Different from the existing clustering algorithms, AP-DBSCAN put forward the density calculation method to simplify the process of optimizing DBSCAN parameters. Instead of using Euclidean distance approach, the cutoff distance method is introduced to improve the similarity measure for optimizing the parameters. The developed AP-DBSCAN is used to classify the China–Laos railway's investment risk indicators more accurately. Combined with a modified rough set, the IM-DBSCAN algorithm is proposed to analyze the railway investment risk assessment. The contributions of this study can be summarized as follows: (1) Based on AP, DBSCAN, an integrated methodology AP-DBSCAN, which considers improving the parameter setting and efficiency, is proposed to classify railway risk indicators. (2) As AP-DBSCAN is a risk classification model rather than a risk calculation model, an IM-DBSCAN algorithm that consists of the AP-DBSCAN and a modified rough set is proposed to assess the railway investment risk. (3) Taking the China–Laos railway as a real-life case study, the effectiveness and superiority of the proposed IM-DBSCAN algorithm are verified through a set of experiments compared with other state-of-the-art algorithms.

Facial expression recognition by human observers is affected by subjective components. Indeed there is no ground truth. We have developed Discrete Choice Models (DCM) to capture the human perception of facial expressions. In a first step, the static case is treated, that is modelling perception of facial images. Image information is extracted using a computer vision tool called Active Appearance Model (AAM). DCMs attributes are based on the Facial Action Coding System (FACS), Expression Descriptive Units (EDUs) and outputs of AAM. Some behavioural data have been collected using an Internet survey, where respondents are asked to label facial images from the Cohn–Kanade database with expressions. Different models were estimated by likelihood maximization using the obtained data. In a second step, the proposed static discrete choice framework is extended to the dynamic case, which considers facial video instead of images. The model theory is described and another Internet survey is currently conducted in order to obtain expressions labels on videos. In this second Internet survey, videos come from the Cohn–Kanade database and the Facial Expressions and Emotions Database (FEED).

The purpose of this paper is to systematically investigate the dynamic strain aging (DSA) effect in solid solution treated IN718 at different temperatures through experiments and simulations to gain an understanding of the inelastic deformation mechanisms.

In the present work, uniaxial tensile tests have been carried out in conjunction with finite element (FE) simulations to investigate the behaviour of the solid solution treated Inconel 718 superalloy at different temperatures and strain rates. Dynamic strain aging (DSA) effects, which manifested during the tests in the form of a negative strain rate sensitivity and stress serrations, are investigated. The most significant DSA effect occurs at 500°C and at a strain rate of 10–4 s-1. In a newly proposed rate-dependent constitutive formulation, the DSA model, proposed by McCormick, Kubin and Estrin, was introduced into slip-assisted solute hardening, and an activation energy-dependent exponential flow rule was adopted.

The observed negative strain rate sensitivity and stress serrations are well predicted by a 3 D FE. The FE results indicate that the equivalent plastic strain rate distribution in the specimen gauge length is as highly inhomogeneous as in the other materials exhibiting DSA effects such as aluminium and titanium alloy. During inelastic deformation, propagating high strain rate bands can be closely correlated to the stress serrations.

For the DSA effect in solid solution treated IN718, the existing researching mainly focuses on the mechanical properties experiment and microstructure observation. In this study, a constitutive formulation, combined with the DSA model, has been proposed, and the mechanical behaviors, including the DSA effect, have been well predicted by a finite element model.

This study proposes a theoretical model for measuring the greenness factors of a firm. We develop the multifactor utility function and find that the proportion of investment in green bonds is higher if greenness factors account for by a firm and vice versa. Moreover, we further develop the global aspects of greenness measures which identify how much level of greenness is maintained by a firm to make the environment green. In terms of reduction in emissions based on global measures, we report that the proportion of investment in green bonds is higher. This study argues that the difference between firm-related and global measures of greenness refers to distortion in portfolio allocation. Lastly, we compare the results of five Asian countries and report that Japanese firms are appropriately following the greenness measures while the firms operating in developing countries including Indonesia, Malaysia, Pakistan, and Thailand are far behind in implementing the greenness measures.

Purpose: Pandemics lead to a sudden decline in the level of economic activities. Lending institutions reduce credit supply to businesses due to fears of rising bad debts during a pandemic. This chapter highlights some approach to financial regulation and bank supervision during a pandemic such as the SARS and COVID-19 pandemic.

Methodology: This chapter uses discourse analysis based on the literature on banking regulation and supervision.

Findings: The author shows that financial regulation during a pandemic can be enhanced by diversifying the financial system, maintaining adequate liquidity in the financial system, stimulating financial institutions to provide more credit, delaying the recognition of significant increase in credit risk, lowering the reference interest rate to encourage more lending, and providing stimulus packages to financial institutions in the economy. The author also suggest measures to improve bank supervision during a pandemic which include adopting a flexible supervisory framework, modifying bank supervisory examinations, using ad hoc stress tests, releasing the countercyclical capital buffer to banks, and increase the use of regulatory forbearance.

Implications: The implication of these approaches to coping with a pandemic is that these measures can help to ensure the survival of small and large businesses and financial institutions. It can also help to preserve jobs and help to reduce the long-term damage to the economy caused by the pandemic.

Originality: Prior studies have not examined the effect of COVID-19 pandemic on bank supervision and financial regulation.

The purpose of the study is to analyse the impact of COVID-19 on the hospitality industry during the rise of worldwide pandemic crises using Twitter analysis. The study is based on 57,794 English-language tweets mined from Twitter from 1 April 2020 to 15 October 2020. Based on thematic and sentiment analysis, the study found that overall sentiments expressed on Twitter were negative. This chapter contributes to existing knowledge about the COVID-19 crisis and broadens the respondents’ understanding of the potential impacts of the crisis on the most vulnerable tourism and hospitality industry. This research emphasises the sustainable revival of the hospitality industry.

This chapter develops a framework for quantile regression in binary longitudinal data settings. A novel Markov chain Monte Carlo (MCMC) method is designed to fit the model and its computational efficiency is demonstrated in a simulation study. The proposed approach is flexible in that it can account for common and individual-specific parameters, as well as multivariate heterogeneity associated with several covariates. The methodology is applied to study female labor force participation and home ownership in the United States. The results offer new insights at the various quantiles, which are of interest to policymakers and researchers alike.

On March 11, 2020, the World Health Organization declared the COVID-19 outbreak a global pandemic. The policy responses to this outbreak have caused many impacts on our daily life and economic activities around the world. Also, this outbreak has affected financial reporting practices of the firms. Thorough financial reporting in such uncertain times plays an essential role in providing qualitative financial information. It is very important for a firm to increase transparency and report on the impacts of possible risks in times of uncertainty. Furthermore, stock markets may react to COVID-19 as they have already responded to major events like disasters, news, environmental and political events. This study investigates whether and how firms’ providing information about COVID-19 pandemic in their financial reports plays a role in explaining the stock market reactions. For this, annual reports of companies included in BIST-100 index in Turkey is used to analyze the capacity for detecting risks early and determine whether there is an association between early reporting about COVID-19 and the stock risk.