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This chapter provides a review of the Chinese government policies that promote the internationalization of the Chinese currency, the renminbi or RMB, which include the RMB swap arrangements between the central banks, trading of the RMB across different markets, and establishment of the dim sum bond market. In particular, we update the development of the dim sum bond market in terms of the size, amount of the issues, coupon and tenor characteristics, issuers, and investment bankers of dim sum bond issues. The dim sum bond market appears to be a promising global asset class for investors.

Identifying the dimensionality of a construct and selecting appropriate items for measuring the dimensions are important elements of marketing scale development. Scales for measuring marketing constructs such as service quality, brand equity, and marketing orientation have typically been developed using the influential classical test theory paradigm (Churchill, 1979), or some variant thereof. Users of the paradigm typically assume, albeit implicitly, that items and respondents are the only sources of variance and respondents are the objects of measurement. Yet, marketers need scales for other important managerial purposes, such as benchmarking, tracking, and perceptual mapping, each of which requires a scaling of objects other than respondents such as products, brands, retail stores, websites, firms, advertisements, or social media content. Scales that are developed without such objects in mind might not perform as expected. Finn and Kayande (2005) proposed a multivariate multiple objective random effects methodology (referred to here as M-MORE) could be used to identify construct dimensionality and select appropriate items for multiple objects of measurement. This chapter applies M-MORE to multivariate generalizability theory data collected to assess online retailer websites in the early 2000s to identify the dimensionality of and to select appropriate items for scaling website quality. The results are compared with those produced by traditional methods.

The Dim Sum bond market in Hong Kong, which allows China to regulate the amount of offshore yuans that flow back into the mainland, has grown steadily since its inception in 2007 and is expected to surpass in 2013 the threshold level that would attract insurers and long-term issuers to the market. Yet, the market has not matured sufficiently relative to the yuan deposit market in Hong Kong that has grown at a much faster pace on account of trade liberalization and the use of yuans in China’s international trade settlements. Even though Hong Kong has fulfilled its role as an offshore currency center for the yuan, it is being challenged by Taiwan, Singapore, and London in terms of being the premier location for the issuance of yuan-denominated bonds outside of Mainland China.

The intangible nature of tourism means that variables such as destination image (DIM) or intention to recommend (IR) are crucial for tourists, as they are reliable sources of information. However, since these are dynamic variables, they have been affected by Covid-19 and need to be updated. Based on the above, the purpose of this study is to examine how the unlearning (UL) process helps tourists to become aware of the new situation, relinquish old habits and relearn, thus, influencing DIM and the IR destinations. Furthermore, the relationship between DIM and IR is analysed.

To analyse the proposed model, an empirical analysis was carried out through an online survey obtaining a valid sample of 457 Spanish domestic travellers. The data were analysed using partial least square structural equation modelling (PLS-SEM).

Results show a positive effect between the UL outcomes and IR a destination, as well as a mediator effect of DIM on this relationship.

To the best of the authors’ knowledge, this study analyses for the first time UL in the tourism sector as an individual process carried out by tourists and shows how it influences their decision-making process.

La naturaleza intangible del turismo hace que variables como la imagen del destino o la intención de recomendarlo sean decisivas para los turistas, al considerarlas fuentes de información fiables. Dado su carácter dinámico, estas variables se han visto afectadas por la pandemia del Covid-19 y necesitan ser actualizadas. En base a lo anterior, el objetivo de este estudio es analizar cómo el proceso de desaprendizaje ayuda a los turistas a ser conscientes de la nueva situación, a abandonar los viejos hábitos y a reaprender, influyendo así en la imagen del destino y en la intención de recomendarlos. Asimismo, se analiza la relación entre la imagen del destino y la intención de recomendarlo.

Para analizar el modelo propuesto se llevó a cabo un estudio empírico basado en una encuesta online, obteniendo una muestra válida de 457 viajeros nacionales españoles. Los datos se han analizado mediante PLS-SEM.

Los resultados del estudio muestran un efecto positivo entre los resultados del desaprendizaje y la intención de recomendar destinos, así como un efecto mediador de la imagen de destino en esta relación.

Este estudio analiza por primera vez el desaprendizaje en el sector turístico como un proceso individual llevado a cabo por turistas y muestra cómo influye en el proceso de toma de decisiones de estos.

This paper aims to explore the development and application of place crowd safety management tools for areas of public assembly and major events, from a practitioner perspective.

The crowd safety risk assessment model is known as design, information, management-ingress, circulation, egress (DIM-ICE) (Still, 2009) is implemented to optimise crowd safety and potentially throughput. Three contrasting case studies represent examples of some of the world’s largest and most challenging crowd safety projects.

The paper provides some insight into how the DIM-ICE model can be used to aid strategic planning at major events, assess potential crowd risks and to avoid potential crowd safety issues.

It provides further clarity to what effective place management practice is. Evidence-based on the case studies demonstrates that the application of the DIM-ICE model is useful for recognising potential place crowd safety issues and identifying areas for require improvement.

Crowd science is an emerging field of research, which is primarily motivated by place crowd safety issues in congested places; the application and reporting of an evidence-based model (i.e. DIM-ICE model) add to this. The paper addresses a research gap related to the implementation of analytic tools in characterising place crowd dynamics.

Self-localization of an underwater robot using global positioning sensor and other radio positioning systems is not possible, as an alternative onboard sensor-based self-location estimation provides another possible solution. However, the dynamic and unstructured nature of the sea environment and highly noise effected sensory information makes the underwater robot self-localization a challenging research topic. The state-of-art multi-sensor fusion algorithms are deficient in dealing of multi-sensor data, e.g. Kalman filter cannot deal with non-Gaussian noise, while parametric filter such as Monte Carlo localization has high computational cost. An optimal fusion policy with low computational cost is an important research question for underwater robot localization.

In this paper, the authors proposed a novel predictive coding-biased competition/divisive input modulation (PC/BC-DIM) neural network-based multi-sensor fusion approach, which has the capability to fuse and approximate noisy sensory information in an optimal way.

Results of low mean localization error (i.e. 1.2704 m) and computation cost (i.e. 2.2 ms) show that the proposed method performs better than existing previous techniques in such dynamic and unstructured environments.

To the best of the authors’ knowledge, this work provides a novel multisensory fusion approach to overcome the existing problems of non-Gaussian noise removal, higher self-localization estimation accuracy and reduced computational cost.

The purpose of this study is to design a more flexible and larger range of the dimming circuit that achieves the independence of multiple LED strings drive and can time-multiplex the power circuit.

The state-space method is used to model the BUCK circuit working in Pseudo continuous conduction mode, analyze the frequency characteristics of the system transfer function and design the compensation network. Build a simulation platform on the Orcad PSPICE platform and verify the function of the designed circuit through the simulation results. Use Altium Designer 16 to draw the printed circuit board, complete the welding of various components and use the oscilloscope, direct current (DC) power supply and a signal generator to verify the circuit function.

A prototype of the proposed LED driver is fabricated and tested. The measurement results show that the switching frequency can be increased to 1 MHz, Power inductance is 2.2 µH, which is smaller than current research. The dimming ratio can be set from 10% to 100%. The proposed LED driver can output more than 48 W and achieve a peak conversion efficiency of 91%.

The proposed LED driver adopts pulse width modulation (PWM) dimming at a lower dimming ratio and adopts DC dimming at a larger dimming ratio to realize switching PWM dimming to analog dimming. The control strategy can be more precise and have a wide range of dimming.

Reviews the Chinese dish, dim sum, in the light of healthy eating recommendations, especially cooking methods, use of ingredients and the fat and sugar intake. Notes in conclusion that, with over 100 varieties of dim sum, many modifications are still required.

Outlines the special characteristics of the Athens stock exchange which may cause misspecification in the simple market model and make Dimson type models more appropriate. Refers to previous research on then and nonsynchronous trading, discusses the methodological issues involved and applies both simple and Dimson type models to 1993‐1997 data for 22 Greek shares. Finds the latter “in many ways more useful” than the simple model, summarizes the main conclusions and suggests that they may be particularly suited to emerging markets in bullish periods.

This paper aims to investigate the intervalling effect bias in ETFs' systematic risk expressed by beta. The authors' findings reveal the existence of a significant intervalling effect on ETFs' beta obtained by the ordinary least squares method (OLS). Also investigated is the impact of ETFs' capitalization on beta. Results provide evidence that small cap ETFs have greater betas than large cap ETFs. Results also reveal that the OLS beta of all ETFs increases when the return interval is lengthened regardless of capitalization. The impact of ETFs' trading activity on systematic risk is assessed too. Findings give evidence that the OLS betas of the ETFs that trade infrequently are biased downwards while the beta of the frequently traded ETFs is biased upwards. Finally, the paper reveals a strong intervalling effect on ETFs' tracking error.

The authors employ a sample of 40 broad‐based ETFs listed on Nasdaq Stock Exchange to test whether beta estimates change when the return interval measurement changes. Their data cover a maximum period of ten years starting from September 16, 1998 using daily, weekly and monthly return data. The authors estimate beta applying three alternative methods: the market model applied with the OLS method, the Scholes and Williams model (SW beta) and the Dimson model (Dim beta).

Results indicate that the average beta of ETFs derived by the OLS method increases when the return interval increases. The differences among the daily, weekly and monthly OLS betas are statistically significant at the 1 per cent level. This finding implies a strong intervalling effect bias in ETFs' OLS beta. On the other hand, the authors did not find any statistically significant differences in daily, weekly and monthly Scholes and Williams and Dimson betas. Moreover, results show that the daily and weekly OLS and Scholes and Williams betas and weekly OLS and Dimson betas are significantly different from each other.

In this paper using a sample of 40 broad‐based ETFs listed on Nasdaq Stock Exchange, the authors have examined various issues concerning: the intervalling effect bias in ETFs' systematic risk, the relation between beta and capitalization of ETFs, the relation between beta and trading frequency of ETFs and, finally, the intervalling effect bias in ETFs' tracking error. While the literature on intervalling effect on securities' beta and the relation between systematic risk and market value and trading activity is voluminous, this is the first attempt to examine these issues with respect to ETFs.