Tropical forests are the largest terrestrial component of the global carbon cycle, storing about 250 Giga tons (Gt) biomass carbon in their woody vegetation and absorbing ~70 Gt CO2 per year through photosynthesis. Loss of forests could be devastating because not only the stored carbon stocks in biomass and soil are losing but also the function of sequestering atmospheric carbon.
Although widely used in our daily life, lithium (Li) -ion batteries fall short because the materials used are often scarce, toxic, and expensive. They also have safety issue in operation due to their organic based electrolytes. Beyond lithium-ion batteries, a low-cost magnesium (Mg) metal anode based aqueous Mg-ion battery has been developed first time by Professor Dennis Leung’s research team in the HKU Department of Mechanical Engineering. As Mg is the 5th most abundant metal element in the earth’s crust (three orders of magnitude more than Li), the advantages of low cost and non-toxicity make Mg a desirable alternative to Li as the anode material. The proposed battery shows a high discharge plateau of 2.4-2.0 V and an excellent rechargeability for over 700 stable cycles. This high operation voltage exceeds the counterpart of other multivalent-ion batteries, including zinc (Zn) metal and aluminum (Al) metal batteries. The mechanism behind was also revealed, where a conductive metallic oxide layer was facilitated by the chloride (Cl-) ions inside the water-in-salt electrolyte, providing ionic pathways for rechargeable battery operations. The team hopes that the chemical insights obtained in this work could inspire further optimization and bring attention to the overlooked development of rechargeable aqueous Mg metal batteries. This work uncovers the once dismissed possibility of aqueous Mg metal batteries and opens a new avenue in the field of post-lithium-ion batteries. Other project team members are Dr. Wending Pan (Research Assistant Professor) and Miss Sarah Leong (PhD student).
Laser scanning is used in advanced biological microscopy to deliver superior imaging contrast, resolution and sensitivity. However, it is challenging to scale up the scanning speed required for interrogating a large and heterogeneous population of biological specimens or capturing highly dynamic biological processes at high spatiotemporal resolution. Bypassing the speed limitation of traditional mechanical methods, free-space angular-chirp-enhanced delay (FACED) is an all-optical, passive and reconfigurable laser-scanning approach that has been successfully applied in different microscopy modalities at an ultrafast line-scan rate of 1-80 MHz. Optimal FACED imaging performance requires optimized experimental design and implementation to enable specific high-speed applications. In this protocol, we aim to disseminate information allowing FACED to be applied to a broader range of imaging modalities. We provide (i) a comprehensive guide and design specifications for the FACED hardware; (ii) step-by-step optical implementations of the FACED module including the key custom components; and (iii) the overall image acquisition and reconstruction pipeline. We illustrate two practical imaging configurations: multimodal FACED imaging flow cytometry (bright-field, fluorescence and second-harmonic generation) and kHz 2D two-photon fluorescence microscopy. Users with basic experience in optical microscope operation and software engineering should be able to complete the setup of the FACED imaging hardware and software in ~2-3 months.
The project is an integral part of a flagship research initiative being carried out under the auspices of the HKU Center for Water Technology and Policy. The Water Centre was jointly established by the Faculty of Engineering and the Faculty of Social Sciences to conduct cutting-edge research on water science, technology and policy issues that pertain to the broader urban sustainability agenda. We would like to acknowledge the contributions of the members of our inter-disciplinary project team. They come from the Department of Civil Engineering, Department of Electrical and Electronic Engineering, Department of Mechanical Engineering, Department of Politics and Public Administration and the Faculty of Social Sciences.
The introduction of the Environmental, Social and Governance (ESG) Reporting Guide (Guide) by HKEX in 2013, and the subsequent upgrade of the Guide’s reporting obligation to “comply or explain” in 2016, have significantly moved the dial for Hong Kong issuers’ ESG reporting. However, ESG reporting faces many bottlenecks, including data authenticity, consistency, and transparency. Professor Huang’s team developed an IoT- and blockchain-based platform to upgrade the ESG reporting industry.
The LiDAR Camera fusion captures 3D space in real-time, opening up the portal into the virtual world. The built model can be used in real scene games, virtual campus tour, metaverse, and more! However, existing products in the market are costly, and the working process is not only slow but also complicated.
Compared to current wearable devices that can only measure vital signals, PERfECT is able to detect molecular indicators in the body fluid for disease screening and monitoring. “HKU PERfECT” is the first wearable platform that can simultaneously address three challenges: 1. highly sensitive, 2. smallest and lightest and 3. energy efficient.
Undergraduate medical education has been severely affected by the COVID-19 outbreak. While lectures can be easily conducted online via Zoom, clinical bedside teachings, including training of history taking skills from patients, cannot be easily replaced. A novel chatbot mobile app for training of undergraduate medical students’ clinical history taking skills was developed.
MindPipe, the first 4D parallel training system for large DNN models, has the following objectives:
1. Greatly reducing load imbalance in GPU pipeline parallel stages; 2. Effectively resolving contention of the 3D parallel communication tasks; 3. Deterministically scheduling multiple subnets to be trained in supernet parallelism, a novel parallel dimension proposed by MindPipe; and 4. Automatic near-optimal 4D configuration of GPUs considering both DNN converging efficiency and GPU utilization.
Computer vision enables machines to “see”. The capability of machine vision based on cameras, however, is fundamentally limited to a certain field-of-view with good lighting conditions – they cannot see through any occlusions or in the dark.
Wireless sensing opens a new sense for machine perception to decipher the physical world, even in absolute darkness and through walls and obstacles.
It can capture human activities invisibly in a contactless and sensorless way.