Mobile laser scanning (MLS) has been widely used in three-dimensional (3D) city modelling data collection, such as Google cars for Google Map/Earth. Building Information Modelling (BIM) has recently emerged and become prominent. 3D models of buildings are essential for BIM. Static laser scanning is usually used to generate 3D models for BIM, but this method is inefficient if a building is very large, or it has many turns and narrow corridors. This paper proposes using MLS for BIM 3D data collection. The positions and attitudes of the mobile laser scanner are important for the correct georeferencing of the 3D models. This paper proposes using three high-precision ultra-wide band (UWB) tags to determine the positions and attitudes of the mobile laser scanner. The accuracy of UWB-based MLS 3D models is assessed by comparing the coordinates of target points, as measured by static laser scanning and a total station survey.

Background Since 23^rd January 2020, the Chinese government has imposed restrictive measures including self-isolation, travel restrictions and lockdown of Wuhan city in order to prevent the incoming waves of COVID-19 outbreak in the country. However, the impact of mental health and stress concerns on relationship and sexuality amidst the COVID-19 lockdown was currently unclear. Aim The cross-sectional study was designed to determine the changes in health, relationship and sexuality among the Chinese couples who lived together amid the early stages of COVID-19 pandemic in China. Methods Participants of Chinese nationality aged ≥18 years were asked to complete a self-administered online questionnaire regarding sexuality behaviour and impact of event scale (IES) in March 2020. Non-random sampling was used for participant recruitment. Also assessed were sociodemographic data including sex, age, employment, region, sexual dysfunction, and whether participants tested positive for COVID-19. Outcomes IES score, frequency of sexual intercourse per week, quality of usual sex life, emotional bonding and duration of relationship were measured. Results A total of 1139 participants (i.e., 735 males and 404 females) were included in the study. Mean age and IES of participants was 33.6±9.5 years and 27.4±8.6, respectively. Being male was significantly associated with increased frequency of sexual intercourse amid the COVID-19 pandemic (P=0.012). Also, participants with an IES score <26 were more likely to report that they had increased frequency of sexual intercourse per week (P<0.001) and the COVID-19 pandemic had positively affected the quality of their usual sex lives (P<0.001). On the other hand, participants with IES score ≥26 were more likely to report that the COVID-19 pandemic had positively affected their emotional bonding (P<0.001). Clinical implications Frequency of sexual intercourse and quality of sex life in participants who experienced high stressful impact were more likely to be affected by the COVID-19 pandemic. Strengths and limitations This was one of the first studies to assess sexual behaviour during the COVID-19 pandemic in Chinese adults. Since participants were asked to self-report their sexual behaviour, this potentially introduced self-reporting and recall bias into our findings. Conclusions Our study reported that despite the moderate-to-severe stressful impact due to the COVID-19 pandemic, the majority reported no significant changes in the frequency of their sexual intercourse per week, quality of their usual sexual lives and emotional bonding.

The study intends to conduct a systematic mechanism for herding detection in the Hong Kong stock market. We take stocks from three market sectors as samples and investigate the existence of herding in the two periods: before and during the outbreak of COVID-19 in Hong Kong, from August 2019 to July 2020. We adopt CCK model-based OLS and quantile regression to examine herding in each observed period and employ HS model to measure the magnitude of herding during the time. The empirical results indicate the emergence of mild herding from August 2019 to January 2020, and the herding phenomenon is generally weakened between February and July 2020. Our study confirms the implication of the systematic herding detection mechanism that can improve the sensitivity of detection and capture the magnitude and variation of herding.

The constantly expanding losses caused by frequent natural disasters pose many challenges to the traditional catastrophe insurance market. The purpose of this paper is to develop an innovative and systemic trigger mechanism for pricing catastrophic bonds triggered by multiple events with an extreme dependence structure. Due to the bond’s low cashflow contingencies and the CAT bond’s high return, the multiple-event CAT bond may successfully transfer the catastrophe risk to the huge financial markets to meet the diversification of capital allocations for most potential investors. The designed hybrid trigger mechanism helps reduce the moral hazard and increase the bond’s attractiveness with a lower trigger likelihood, displaying the determinants of the wiped-off coupon and principal by both the magnitude and intensity of the natural disaster events involved. As the trigger indicators resulting from the potential catastrophic disaster might be associated with heavy-tailed margins, nested Archimedean copulas are introduced with marginal distributions modeled by a POT-GP distribution for excess data and common parametric models for moderate risks. To illustrate our theoretical pricing framework, we conduct an empirical analysis of pricing a three-event rainstorm CAT bond based on the resulting losses due to rainstorms in China during 2006–2020. Monte Carlo simulations are carried out to analyze the sensitivity of the rainstorm CAT bond price in trigger attachment levels, maturity date, catastrophe intensity, and numbers of trigger indicators.

In this paper, we explore a novel model for pricing Chinese convertible bonds that seamlessly integrates machine learning techniques with traditional models. The least squares Monte Carlo (LSM) method is effective in handling multiple state variables and complex path dependencies through simple regression analysis. In our approach, we incorporate machine learning techniques, specifically support vector regression (SVR) and random forest (RF). By employing Bayesian optimization to fine-tune the random forest, we achieve improved predictive performance. This integration is designed to enhance the precision and predictive capabilities of convertible bond pricing. Through the use of simulated data and real data from the Chinese convertible bond market, the results demonstrate the superiority of our proposed model over the classic LSM, confirming its effectiveness. The development of a pricing model incorporating machine learning techniques proves particularly effective in addressing the complex pricing system of Chinese convertible bonds. Our study contributes to the body of knowledge on convertible bond pricing and further deepens the application of machine learning in the field in an integrated and supportive manner.