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"Huang, Liang"
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The impact of ChatGPT on L2 writing and expected responses: Voice from doctoral students
Despite the growing popularity of ChatGPT and chatbot-assisted writing, research on the use of ChatGPT in second language (L2) writing classrooms remains insufficient. Using reflection papers and focus group interviews, the qualitative study examined doctoral students’ views on the impact of using ChatGPT on L2 writing and their expected responses. Thematic analysis revealed that ChatGPT could support writers at the pre-writing, during-writing and post-writing stages and serve as a self-learning tool for writing and thinking development with its human and non-human features. Nonetheless, its generative nature also gave rise to concerns for learning loss, authorial voice, unintelligent texts, academic integrity as well as social and safety risks. Based on the benefits and drawbacks, the doctoral students expected the education sector to make concerted efforts for the effective, ethical and responsible use of ChatGPT in L2 writing. Suggestions are accordingly provided for future considerations in teaching and research to leverage ChatGPT for L2 writing.
Journal Article
Electrochemical Reduction of CO2 to CO over Transition Metal/N‐Doped Carbon Catalysts: The Active Sites and Reaction Mechanism
Electrochemical CO2 reduction to value‐added chemicals/fuels provides a promising way to mitigate CO2 emission and alleviate energy shortage. CO2‐to‐CO conversion involves only two‐electron/proton transfer and thus is kinetically fast. Among the various developed CO2‐to‐CO reduction electrocatalysts, transition metal/N‐doped carbon (M‐N‐C) catalysts are attractive due to their low cost and high activity. In this work, recent progress on the development of M‐N‐C catalysts for electrochemical CO2‐to‐CO conversion is reviewed in detail. The regulation of the active sites in M‐N‐C catalysts and their related adjustable electrocatalytic CO2 reduction performance is discussed. A visual performance comparison of M‐N‐C catalysts for CO2 reduction reaction (CO2RR) reported over the recent years is given, which suggests that Ni and Fe‐N‐C catalysts are the most promising candidates for large‐scale reduction of CO2 to produce CO. Finally, outlooks and challenges are proposed for future research of CO2‐to‐CO conversion. The activity of M‐N‐C catalysts on CO2 reduction reaction (CO2RR) can be adjusted through regulation of active center and local atomic environments, including tuning the central metal atom and its neighbored coordinated atoms structure, or doping other heteroatoms and forming dual‐atoms sites. The role of nonmetal moieties and metal nanoparticles in M‐N‐C is also included.
Journal Article
Tai ji dancing for kids
\"With simple and evocative words, calligraphy, and home photos of the words in action, grandfather and granddaughter team Chungliang and Sylvie bring to life the spirit of Tai Ji. This book is the perfect introduction to Tai Ji for kids. Written and conceived by Master Chungliang Al Huang, in collaboration with his granddaughter Sylvie, it brings to life the five elements underpinning Chinese thought, Earth, Fire, Water, Wood, and Metal, and how they can be simply and instinctively expressed through the body. Making Tai Ji fun and simple, with the possibility of learning through repetition, the book offers a wonderful foundation for developing intuitive understanding and is a great way of keeping kids active, and improving their wellbeing and mindfulness.\" -- Provided by publisher.
Book
Lanthanide-doped MoS2 with enhanced oxygen reduction activity and biperiodic chemical trends
Molybdenum disulfide has broad applications in catalysis, optoelectronics, and solid lubrication, where lanthanide (Ln) doping can be used to tune its physicochemical properties. The reduction of oxygen is an electrochemical process important in determining fuel cell efficiency, or a possible environmental-degradation mechanism for nanodevices and coatings consisting of Ln-doped MoS
2
. Here, by combining density-functional theory calculations and current-potential polarization curve simulations, we show that the dopant-induced high oxygen reduction activity at Ln-MoS
2
/water interfaces scales as a biperiodic function of Ln type. A defect-state pairing mechanism, which selectively stabilizes the hydroxyl and hydroperoxyl adsorbates on Ln-MoS
2
, is proposed for the activity enhancement, and the biperiodic chemical trend in activity is found originating from the similar trends in intraatomic 4
f
–5
d
6
s
orbital hybridization and interatomic Ln–S bonding. A generic orbital-chemistry mechanism is described for explaining the simultaneous biperiodic trends observed in many electronic, thermodynamic, and kinetic properties.
Oxygen reduction reaction plays a key role in many applications of MoS
2
-based materials. Here, using first-principles simulations, the authors find the enhanced oxygen-reduction activity with a biperiodic chemical trend on the lanthanide-doped MoS
2
.
Journal Article
A documentary history of public health in Hong Kong
This book chronicles the history of public health in Hong Kong from 1842 to 1980. The editors provide a framework to understand important events, policies, institutions, and advances in technology related to health developments in colonial Hong Kong. Colonial health policies before and after the world wars differed markedly as postwar conditions offered new challenges and opportunities
Book
CO2 Geological Storage and Utilization
With increasing greenhouse gas emissions caused by human activities, climate change is affecting the survival and development of human society [...]
Journal Article
Distributed Deep Learning-based Offloading for Mobile Edge Computing Networks
This paper studies mobile edge computing (MEC) networks where multiple wireless devices (WDs) choose to offload their computation tasks to an edge server. To conserve energy and maintain quality of service for WDs, the optimization of joint offloading decision and bandwidth allocation is formulated as a mixed integer programming problem. However, the problem is computationally limited by the curse of dimensionality, which cannot be solved by general optimization tools in an effective and efficient way, especially for large-scale WDs. In this paper, we propose a distributed deep learning-based offloading (DDLO) algorithm for MEC networks, where multiple parallel DNNs are used to generate offloading decisions. We adopt a shared replay memory to store newly generated offloading decisions which are further to train and improve all DNNs. Extensive numerical results show that the proposed DDLO algorithm can generate near-optimal offloading decisions in less than one second.
Journal Article