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當代中國經濟發展大致上可劃分為兩個重要時期，第一階段是1949至1978年為止﹔第二階段是1978年以後至今。兩者最大的區別在於經濟模式的轉變。在第一階段中國奉行社會主義計劃經濟，此模式直至1978年12月鄧小平提出「社會主義現代化」，實行改革開放政策為止。本章將探討第一階段在計劃經濟模式下的中國經濟發展概況。

當代中國經濟發展的轉捩點可謂在1978年。在歷經文化大革命，與及數次政治鬥爭以後，中共終於在1978年12月18日至22日期間舉行第十一屆中央委員會第三次全體會議，碓立了今後中國的發展方針。該會批判除了「兩個凡是」的方針，並停止了「以階級鬥爭為綱」的路線，並確立了其後的工作重點轉移到「社會主義現代化」的建設和經濟發展，亦決定了「對內改革，對外開放」的政策方向，這正是「改革開放」的由來，而這個政策的成效之一，是改變了中國長期以來對外封閉的情況，使中國大陸向世界開放。

In this chapter, we described how a gratitude intervention, which aims to increase student well-being, was integrated with Academic and Social Skills Building Seminar (ASSB), which aims to develop academic skills among students—in line with positive education’s goals of incorporating a well-being component to traditional education. In Part I, we provide an overview of the gratitude intervention and ASSB, and the basis for their integration. Part II describes how the gratitude intervention was integrated to ASSB, and presents the modules of the integrated program. Lastly, in Part III, we describe the pilot implementation of the integrated program, feedback from students and counselors, and insights gained from its initial implementation. Then, we present the recommendations for an improved program based on the findings of the pilot study and discuss future directions for program implementation and research.

In English and Chinese.

Construction practitioners make decisions about safety risks that can be subjective and prone to error. The trained computer object detection provides a standardised method to deal with this issue, but it typically relies on many photos to train one object, which is costly and time-consuming. This project proposes a new algorithm to train a computer to identify construction safety risks with fewer photos. In addition, holographic hybrid reality will be developed for safety training in the construction industry; Mercedes-Benz has used a similar approach to inform employees about collision zones. We will use the trained images to develop HoloLens hybrid reality to share construction site safety knowledge via wearable HoloLens for on-site safety risk detection. Lastly, although decision-making in various areas has been studied using neuroscience, how an individual’s brain makes decisions when different construction safety risks are perceived and the impact of holographic safety training on brain reaction and activities remains unknown. These issues will be studied via haemodynamic response and neuroimaging. In this research agenda, we plan to construct a photo library of 10,000 high-quality photos of various construction risks with different shading, size, and orientation by collecting Creative Commons construction photos and turning existing online Creative Commons videos into photos. To achieve this goal, we will input the online common creative photos into our image-based CAPTCHA system (similar to Google’s ReCaptcha). Each group will include 16 photos to allow construction practitioners to identify and click on those that include a safety risk. The identified photos with safety risks will be saved, and specific categories of safety risks will be uploaded to social media and sent via email. The trained figures will be deployed to the HoloLens in our laboratory. About 20 safety experts, including safety managers and trainers, will be invited to use the HoloLens for detecting hazards on-site and provide comments for improvement. Holographic hybrid reality will be built with Unity C# and HoloToolkit. The object detection results obtained will also be used so the research participants will see the real scene with not only hazards labelled by AI, but also some high-risk elements that cannot be included in ordinary safety training, such as open taps with water running into the ground and blasting. Four holographic hybrid reality training scenarios will be generated: a general construction site, and three specially designed scenarios for refurbishment, new building, and road construction settings. In the last stage, we will use functional near-infrared spectroscopy (fNIRS) to study construction practitioners’ brain responses when they see and identify various kinds of hazards. The first group will be exposed to holographic hybrid reality with some safety risks on the first day, and they will be asked on the tenth day to identify the safety risks. The control group will receive no safety training but will be asked to identify risks. All research participants will be monitored by fNIRS when they attempt to identify the safety risks. Haemodynamic response and neuroimaging tests will be used to study the effectiveness of the safety training.

In the literature, the relationship between stock price and economic variables was examinedin different countries. The findings confirmed the existence of fundamental variables in the Hong Kong stock market. This paper focuses on the case of Hong Kong and extends this issue to the Hang Seng sub-indexes, which include the Commerce and Industry Sub-index, the Finance Sub-index, the Properties Sub-index and the Utilities Sub-index, by applying the Vector Error Correction Model based on the sample from January 2004 to August 2014. ApplyingJohansen’s (1991)method, cointegration is found between each of the sub-indexes and different setsof economic variables, including price level, money supply, effective exchange rate, long-term interest rate, China stock market and industry-related variables. The results show that the long-run coefficients of some economic variables vary in size and sign in the cointegratingvectors in different sub-index models. Granger causality resultsconclude that all four sub-indexes are long-run Granger-caused by the economic variables with differentspeeds of adjustment. Thepaperalso finds that industry-specific variables, relative to the macroeconomic fundamentals, are playing modest roles in determining the long-run equilibrium of the stock indexes.In addition,impulse responsesand variance decomposition analysis are performed to show the relative strength of the causal chain between thesub-indexes and economicvariables.This paper could draw implications for investors in their decision-making process about how the stock performance in various sectors is affected by different economic fundamentals.

According to “Huang's law”, Artificial intelligence (AI)-related hardware increases in power 4–10 times per year. AI can benefit various stages of real estate development, from planning and construction to occupation and demolition. However, Hong Kong's legal system is currently behind when it comes to technological abilities, while the field of AI safety in built environments is still in its infancy. Negligent design and production processes, irresponsible data management, questionable deployment, algorithm training, sensor design and/or manufacture, unforeseen consequences from multiple data inputs, and erroneous AI operation based on sensor or remote data can all lead to accidents. Yet, determining how legal rules should apply to liability for losses caused by AI systems takes time. Traditional product liability laws can apply for some systems, meaning that the manufacturer will bear responsibility for a malfunctioning part. That said, more complex cases will undoubtedly have to come before the courts to determine whether something unsafe should be the manufacturer's fault or the individual's fault, as well as who should receive the subsequent financial and/or non-financial compensation, etc. Since AI adoption has an inevitable relationship with safety concerns, this project intends to shed light on responsible AI development and usage, with a specific focus on AI safety laws, policies, and people's perceptions. We will conduct a systematic literature review via the PRISMA approach to study the academic perspectives of AI safety policies and laws and data-mining publicly available content on social media platforms such as Twitter, YouTube, and Reddit to study societal concerns about AI safety in built environments. We will then research court cases and laws related to AI safety in 61 jurisdictions, in addition to policies that have been implemented globally. Two case studies on AI suppliers that sell AI hardware and software to users of built environment will also be included. Another two case studies will be conducted on built environment companies (a contractor and Hong Kong International Airport) that use AI safety tools. The results obtained from social media, court cases, legislation, and policies will be discussed with local and international experts via a workshop, then released to the public to provide the international community and Hong Kong with unique policy and legal orientations.