Hong Kong Baptist University

About: Hong Kong Baptist University is a education organization based out in Hong Kong, China. It is known for research contribution in the topics: Population & China. The organization has 7811 authors who have published 18919 publications receiving 555274 citations. The organization is also known as: Hong Kong Baptist College & HKBU.

Plant-mediated biosynthesis of metallic nanoparticles: A review of literature, factors affecting synthesis, characterization techniques and applications

Abstract: Nanoparticles exhibit unique properties that enable them to find potential applications in various fields. Accordingly, significant research attention is being given to the development of novel strategies for the synthesis of nanoparticles. Among these, biological route of nanoparticle synthesis has been portrayed as an efficient, low-cost and environmental friendly technique. Biological materials such as bacteria, fungi, yeast, algae and plant have been reported to possess high bioreduction ability to synthesize various size and shape of metallic nanoparticles. Of these biomaterials, this review focuses on plant-mediated biosynthesis of metallic nanoparticles. The biomolecules present in the plants such as terpenoids, flavones, ketones, aldehydes, proteins, amino acids, vitamins, alkaloids, tannins, phenolics, saponins, and polysaccharides play a vital role in reduction of metals. A systematic comparison of literature, based on the bioreduction capacity of various plant biomass/extract towards various metals under different experimental conditions, is also provided. Various instrumental techniques utilized to characterize nanoparticles are also discussed. Finally, this review also highlights the application of biosynthesized nanoparticles in different fields such as medicine, agriculture, catalytic, cosmetic and food. Thus, this article reviews the achievements and current status of plant-mediated biosynthesis, and hopes to provide insights into this exciting research frontier.

Critiquing-based recommenders: survey and emerging trends

Abstract: Critiquing-based recommender systems elicit users' feedback, called critiques, which they made on the recommended items. This conversational style of interaction is in contract to the standard model where users receive recommendations in a single interaction. Through the use of the critiquing feedback, the recommender systems are able to more accurately learn the users' profiles, and therefore suggest better recommendations in the subsequent rounds. Critiquing-based recommenders have been widely studied in knowledge-, content-, and preference-based recommenders and are beginning to be tried in several online websites, such as MovieLens. This article examines the motivation and development of the subject area, and offers a detailed survey of the state of the art concerning the design of critiquing interfaces and development of algorithms for critiquing generation. With the help of categorization analysis, the survey reveals three principal branches of critiquing based recommender systems, using respectively natural language based, system-suggested, and user-initiated critiques. Representative example systems will be presented and analyzed for each branch, and their respective pros and cons will be discussed. Subsequently, a hybrid framework is developed to unify the advantages of different methods and overcome their respective limitations. Empirical findings from user studies are further presented, indicating how hybrid critiquing supports could effectively enable end-users to achieve more confident decisions. Finally, the article will point out several future trends to boost the advance of critiquing-based recommenders.

Retaining and motivating employees

Abstract: This paper reports two studies involving data collected from 583 participants in Hong Kong and 121 participants in the People’s Republic of China (PRC), and examines the most popular compensation components offered by organizations to employees and participants’ perceptions regarding the five most important compensation components to retain and motivate people in Hong Kong and PRC, respectively. Results suggested that in Hong Kong, base salary, merit pay, year‐end bonus, annual leave, mortgage loan, and profit sharing were the most important factors to retain and motivate employees. In China, base salary, merit pay, year‐end bonus, housing provision, cash allowance, overtime allowance, and individual bonus were the most important factors to retain and motivate employees. Results are discussed in light of economic, geographic, and culture‐related factors.

The Signaling Effect of Corporate Social Responsibility in Emerging Economies

Abstract: What signals do firms in emerging economies send to stakeholders when they adopt corporate social responsibility (CSR) practices? We argue that in emerging economies, firms that adopt CSR practices positively signal investors that their firms have superior capabilities for filling institutional voids. From an institution-based view, we hypothesize that the institutional environment moderates the signaling effect of CSR on a firm’s financial performance. Based on a sample of firms from ten Asian emerging economies, we find a positive relationship between CSR practices and financial performance. This positive relationship is stronger in the less developed capital market than in the more developed one. The financial benefits of CSR practices are also more salient in the low information diffusion market than in the high one. We emphasize that signaling theory and the institution-based view can jointly contribute to the CSR literature.

Reinforcement Learning in Healthcare: A Survey

Abstract: As a subfield of machine learning, reinforcement learning (RL) aims at empowering one's capabilities in behavioural decision making by using interaction experience with the world and an evaluative feedback. Unlike traditional supervised learning methods that usually rely on one-shot, exhaustive and supervised reward signals, RL tackles with sequential decision making problems with sampled, evaluative and delayed feedback simultaneously. Such distinctive features make RL technique a suitable candidate for developing powerful solutions in a variety of healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged and sequential procedure. This survey discusses the broad applications of RL techniques in healthcare domains, in order to provide the research community with systematic understanding of theoretical foundations, enabling methods and techniques, existing challenges, and new insights of this emerging paradigm. By first briefly examining theoretical foundations and key techniques in RL research from efficient and representational directions, we then provide an overview of RL applications in healthcare domains ranging from dynamic treatment regimes in chronic diseases and critical care, automated medical diagnosis from both unstructured and structured clinical data, as well as many other control or scheduling domains that have infiltrated many aspects of a healthcare system. Finally, we summarize the challenges and open issues in current research, and point out some potential solutions and directions for future research.