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Industrial organization, theory of the firm and boundaries of the firm are well established fields of study involved in the size, structure and scope of a corporate entity (i.e. firm) to the market. However, a key characteristic of corporate entities is that economic concerns (costs and profit) is the overriding or dominant factor. This paper attempts to apply the above mentioned concepts to organizations such as public institutions where economic concerns are secondary considerations, to seek a more objective analysis on what the structure and scope of such organizations should be

This paper examines the evolving trends in Chinese student mobility to Thailand, highlighting three distinct phases shaped by changes in the higher education: the dominance of Thai language programmes (1990–2010), the rise of business and international programmes (2010–2020), and the increasing preference for graduate studies (2020 onwards). By analysing the economic, cultural, and institutional factors facilitating these shifts, this paper positions Thailand as an emerging alternative study destination for Chinese students. It highlights the significance of this migration within the context of Thailand’s declining fertility rate and labour shortages, focusing on how Thai universities have adapted through active recruitment strategies targeting Chinese students. This paper also addresses the push and pull factors underpinning this migration and the pursuit of alternative educational pathways among Chinese youth. Additionally, it explores the strategic role of Sino-Thai collaborations under the BRI and their broader implications for educational mobility and economic ties.

Municipal Solid Waste (MSW) is associated with industrialization, urbanization, and a modern economic development, covering several origins such as households and similar waste streams from commerce and trade. Inappropriate waste management impacts human health and the environment negatively, but also the economy and society in general. Waste is today also seen more and more as resource itself. The world trend is to move from mere waste management to a consistent form of resource management within a circular economy, e.g. in form of an Integrated Waste Management System (IMWS). Concerning Macao, MSW is being transported to the Macao Refuse Incineration Plant for thermal treatment with energy recovery. For 2014 and 2015, the amount of waste transferred to the Macao Refuse Incineration Plant for treatment shows a strong yearly increase (11.3 %) being expected to reach or even exceed the maximum allowable waste handling capacity in near future. Alternative methods for waste treatment and valorization are necessary for an effective and sustainable waste management system in Macao. In this research, three case-studies were carried out to analysis real case scenarios that are considered examples of well-functioning MSW management. They were: 1) LIPOR (Portugal); 2) Resinorte (Portugal) and 3) Hong Kong. A questionnaire was prepared and distributed to Macao residents in order to understand their perceptions and views on the existing solid waste recycling in Macao. According to the results of the case-studies and questionnaire, based on the “Polluter Pays Principle” and “Producer Responsibility Scheme”, the main objective of this research is to suggest best practices for waste recycling and management in Macao for the Government, Company, Recycling Trade Participator and the Individual Level

Objective. As the preclinical stage of Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI) is characterized by hidden onset, which is difficult to detect early. Traditional neuropsychological scales are main tools used for assessing MCI. However, due to its strong subjectivity and the influence of many factors such as subjects’ educational background, language and hearing ability, and time cost, its accuracy as the standard of early screening is low. Therefore, the purpose of this paper is to propose a new key technology of fast digital early warning for MCI based on eye movement objective data analysis. Methodology. Firstly, four exploratory indexes (test durations, correlation degree, lengths of gaze trajectory, and drift rate) of MCI early warning are determined based on the relevant literature research and semistructured expert interview; secondly, the eye movement state is captured based on the eye tracker to realize the data extraction of four exploratory indexes. On this basis, the human-computer interactive 2.5-minute fast digital early warning paradigm for MCI is designed; thirdly, the rationality of the four early warning indexes proposed in this paper and their early warning effectiveness on MCI are verified. Results. Through the small sample test of human-computer interactive 2.5 fast digital early warning paradigm for MCI conducted by 32 elderly people aged 70–90 in a medical institution in Hangzhou, the two indexes of “correlation degree” and “drift rate” with statistical differences are selected. The experiment results show that AUC of this MCI early warning paradigm is 0.824. Conclusion. The key technology of human-computer interactive 2.5 fast digital early warning for MCI proposed in this paper overcomes the limitations of the existing MCI early warning tools, such as low objectification level, high dependence on professional doctors, long test time, requiring high educational level, and so on. The experiment results show that the early warning technology, as a new generation of objective and effective digital early warning tool, can realize 2.5-minute fast and high-precision preliminary screening and early warning for MCI in the elderly.

Association Rule Mining by Aprior method has been one of the popular data mining techniques for decades, where knowledge in the form of item-association rules is harvested from a dataset. The quality of item-association rules nevertheless depends on the concentration of frequent items from the input dataset. When the dataset becomes large, the items are scattered far apart. It is known from previous literature that clustering helps produce some data groups which are concentrated with frequent items. Among all the data clusters generated by a clustering algorithm, there must be one or more clusters which contain suitable and frequent items. In turn, the association rules that are mined from such clusters would be assured of better qualities in terms of high confidence than those mined from the whole dataset. However, it is not known in advance which cluster is the suitable one until all the clusters are tried by association rule mining. It is time consuming if they were to be tested by brute-force. In this paper, a statistical property called prior probability is investigated with respect to selecting the best out of many clusters by a clustering algorithm as a pre-processing step before association rule mining. Experiment results indicate that there is correlation between prior probability of the best cluster and the relatively high quality of association rules generated from that cluster. The results are significant as it is possible to know which cluster should be best used for association rule mining instead of testing them all out exhaustively.

The use of learning analytics (LA) in real-world educational applications is growing very fast as academic institutions realize the positive potential that is possible if LA is integrated in decision making. Education in schools on public health need to evolve in response to the new knowledge and th...

The global living standard improved significantly in the last decades and China moved 800 million people out of poverty since 1980. However, production and consumption in their current forms are unsustainable in matters of resource and energy use and involved emissions with their significant ecological impact. The global human community committed itself in the COP21 Agreement of Paris 2015 to reduce Green House Gas (GHG) emissions based on National Determined Contributions (NDCs) in order to limit the increase of global average temperature below 2°C or better 1.5°C above pre industrial levels. This commitment entails a comprehensive transformation of the current social and economic system in view of decoupling economic growth from both resource extraction and GHG emissions, in view of fostering a resource sensitive and CO2 neutral Circular Economy (CE) based on Sustainable Development (SD). China submitted its first Intended Nationally Determined Contributions (INDCs) in 2015 with policies and measures affecting 15 major areas. In 2020, President Xi Jinping announced the commitment to peak China’s carbon dioxide emissions before 2030 and achieving carbon neutrality before 2060. Proper and Integrated Resource and Waste Management is central on the way to achieve the transformation into a CE. The State Council of the People’s Republic of China (PRC) released its plan “生活垃圾分类制度实施方案” to promote source separation of household waste on March 30, 2017 aiming at the recycling rate for household waste to reach 35% by 2020 for the selected cities. The first China’s Mandatory Waste Source Separation Law “廣州 市生活垃圾分類管理條例” was enforced by the city of Guangzhou on July 1st, 2018. One of the key strategic plans in China is the development of the Greater Bay Area (GBA) with its comprehensive development plan released on February 18, 2019. Following the INDCs by China, parts of the GBA Development Plan emphasize that the development of the CE systems and the implementation of extended producer responsibility (EPR), are effective principles to provide financial incentives in view of reducing embedded emissions in material and processes. The present research studied, analysed, and compared the MSW treatment strategies, rules, regulations, and retrievable data, which lead to MSW source separation and the reverse logistic of separated waste among 4 selected cities Guangzhou, Zhuhai, and the 2 S.A.R.s of Hong Kong and Macao. The experience and comparison from Guangzhou and Hong Kong revealed that a Top-down approach in environmental policy decision making is more efficient and is able to implement necessary policies faster. However, the experiences from Guangzhou and Zhuhai indicate, that also a more participatory implementation process is crucial, as it enables the involved stakeholders to express their experiences and opinions properly, which can lead to a higher level of policy feasibility and acceptance and a smoother operation accompanied with a higher effectiveness. For the two SARs, to achieve the objective to increase the recycling rate, the local Government must seek approval from China’s Central Government to allow locally generated recyclable material, in their original form, to enter mainland China for further treatment and to be turned into secondary raw material. Without such a proper support by a reverse resource logistic from the mainland, the CE schemes, such as the Mandatory Waste Source Separation, Producer Responsibility Scheme (PRS), Waste Charging Scheme, are not able to be implemented effectively. The current approach of the Local Government purchasing of recycling and exportation services of recyclable materials from private companies, and the sole reliance on existing market forces to handle, process, and export recyclable material out of the S.A.R.s cannot ensure a reliable and continuous operation in view of mitigating involved emissions. By way of a comparative analyses, the present investigation works out and distils suggestions for best practices of implementing the CE to comply with targets of emission reductions

Mining the sentiment of the user on the internet via the context plays a significant role in uncovering the human emotion and in determining the exactness of the underlying emotion in the context. An increasingly enormous number of user-generated content (UGC) in social media and online travel platforms lead to development of data-driven sentiment analysis (SA), and most extant SA in the domain of tourism is conducted using document-based SA (DBSA). However, DBSA cannot be used to examine what specific aspects need to be improved or disclose the unknown dimensions that affect the overall sentiment like aspect-based SA (ABSA). ABSA requires accurate identification of the aspects and sentiment orientation in the UGC. In this book chapter, we illustrate the contribution of data mining based on deep learning in sentiment and emotion detection.

In view of the complex marine environment of navigation, especially in the case of multiple static and dynamic obstacles, the traditional obstacle avoidance algorithms applied to unmanned surface vehicles (USV) are prone to fall into the trap of local optimization. Therefore, this paper proposes an improved artificial potential field (APF) algorithm, which uses 5G communication technology to communicate between the USV and the control center. The algorithm introduces the USV discrimination mechanism to avoid the USV falling into local optimization when the USV encounter different obstacles in different scenarios. Considering the various scenarios between the USV and other dynamic obstacles such as vessels in the process of performing tasks, the algorithm introduces the concept of dynamic artificial potential field. For the multiple obstacles encountered in the process of USV sailing, based on the International Regulations for Preventing Collisions at Sea (COLREGS), the USV determines whether the next step will fall into local optimization through the discrimination mechanism. The local potential field of the USV will dynamically adjust, and the reverse virtual gravitational potential field will be added to prevent it from falling into the local optimization and avoid collisions. The objective function and cost function are designed at the same time, so that the USV can smoothly switch between the global path and the local obstacle avoidance. The simulation results show that the improved APF algorithm proposed in this paper can successfully avoid various obstacles in the complex marine environment, and take navigation time and economic cost into account.