Hong Kong is a mega city and is developed on the mountains and hillside slopes and nearby the sea. Hence, slope engineering is very important to Hong Kong sustainability and development against landslide hazards in Hong Kong. The University of Hong Kong has developed a system called Drilling Process Monitoring (DPM) technique.
The diamond has been well known as the gem stones in jewellery market, and the same material with various atomic defects, i.e., fluorescent impurities in diamond lattice, shows unique quantum behaviors even at ambient conditions. A diamond, not just a best friend of ladies, but also the best friend of scientists. Due to their unique quantum properties, these atomic defects has been demonstrated to achieve nanometric measurement of various physical quantities such as electromagnetic fields, temperature and etc. with unprecedented precision. Here, I will firstly review the development of diamond-based science and technology, and discuss its potential applications in diverse fields. Specifically, I will introduce the on-going research activities in my group, mainly including the high figure-of-merit diamond materials synthesis, advanced quantum diamond microscope development and diamond quantum sensing in single living cells. In addition, I will also share my journey in exploring beyond academics, e.g., we apply quantum diamond microscope for authenticity identification in local jewellery industry.
Stainless steel (SS) is one of the most extensively used materials in many public areas and hygiene facilities but has no inherent antimicrobial properties. Additionally, the SARS-CoV-2 exhibits strong stability on regular SS surfaces, with viable viruses detected even after three days. Undoubtedly, this has created a high possibility of virus transmission among people using these areas and facilities. Here, this talk presents the inactivation of pathogen microbes (especially the SARS-CoV-2) on SS surface by tuning the chemical composition and microstructure of regular SS. It is discovered that Pathogen viruses like H1N1 and SARS-CoV-2 exhibit good stability on the surface of pure Ag and Cu-contained SS of low Cu content, but are rapidly inactivated on the surface of pure Cu and Cu-contained SS of high Cu content. Significantly, the developed anti-pathogen SS with 20 wt% Cu can distinctly reduce 99.75% and 99.99% of viable SARS-CoV-2 on its surface within 3 and 6 h, respectively. Lift buttons made of the present anti-pathogen SS are produced using mature powder metallurgy technique, demonstrating its potential applications in public areas and fighting the transmission of SARS-CoV-2 and other pathogens via surface touching.
Membrane separation technology is increasingly used for water and energy related applications. Pressure-driven membrane processes, such as microfiltration (MF), ultrafiltration (UF), nanofiltration (NF), and reverse osmosis (RO), have received great attention, fueled by the increasing needs for water purification, wastewater treatment and reclamation, and seawater desalination. In parallel, many novel membranes and membrane processes are being developed. In this TechTalk, Prof. Chuyang Tang will share his personal journey in the amazing membrane world. He will highlight some of his previous and ongoing research works covering topics on water reuse, seawater desalination, resource recovery, energy production, and beyond.
Distributed training using multiple devices (i.e., GPU servers) has been widely adopted for learning DNN models over large datasets. However, the performance of large-scale distributed training tends to be far from linear speed-up in practice. Given the complexity of distributed systems, it is challenging to identify the root cause(s) of inefficiency and exercise effective performance optimizations when unexpected low training speed occurs. To date, there exists no software tool which diagnoses performance issues and helps expedite distributed DNN training, while the training can be run using different machine learning frameworks. This paper proposes dPRO, a toolkit that includes: (1) an efficient profiler that collects runtime traces of distributed DNN training across multiple frameworks, especially fine-grained communication traces, and constructs global data flow graphs including detailed communication operations for accurate replay; (2) an optimizer that effectively identifies performance bottlenecks and explores optimization strategies (from computation, communication and memory aspects) for training acceleration. We implement dPRO on multiple deep learning frameworks (PyTorch, TensorFlow, MXNet) and representative communication schemes (AllReduce and Parameter Server architecture). Extensive experiments show that dPRO predicts performance of distributed training in various settings with<5% errors in most cases and finds optimization strategies with up to87.1%speed-up over the baselines.
Prefabricated construction is an emerging construction approach to produce prefabricated components in the off-site factory and transport them to the construction site for assembly, which provides enhanced quality, productivity, and sustainability. On-site assembly is an uncertain and complex stage in prefabrication projects, due to high variability of outside conditions, organization of multi-contractors, and geographic dispersion of activities. Information technology is adopted for the management of precast on-site assembly, such as Internet-of-Things (IoT), Cyber-Physical Internet (CPS), and cloud computing, which generate massive digital twins of construction resources and activities. This tech talk introduces a digital twin-enabled real-time synchronization system (DT-SYNC) with a robotic testbed demonstration for smart prefabricated on-site assembly. On-site resources are converted into Smart Construction Objects (SCOs) attaching with UWB and RFID devices to collect and integrate real-time nD data (e.g. identity, location, cost, and construction progress). Through smart mobile gateway, various on-site resources and activities could be real-timely interoperated with their corresponding digital twins. Cloud-based services are provided for real-time monitoring through high-fidelity virtual models, and robotic control with automatic navigations and alerts. Supported by the cyber-physical visibility and traceability provided by digital twins, a real-time synchronization model is designed to organize and coordinate operations and resources with simplicity and resilience, which guarantees that the appropriate resources are spatiotemporally allocated to the appropriate activities.
Complex microbial communities, e.g., biofilms residing in our oral cavity, have recognized clinical significance, as they are typically the main cause for infections. Diamond nanoparticles, namely, nanodiamonds (NDs) have been demonstrated to work as an effective antibacterial agent against planktonic cells (free-floating state) due to their many promising physico-chemical properties. However, little is known about the behaviors of NDs against biofilms (sessile state).
Balance ability is human’s basic physiological ability that ensures stable standing and walking. Falls are major threats to the health and independent living of the elderly. 10% of the falls in the elderly are associated with fractures, and some can lead to head injuries and deaths. A highly effective human balance sensor is invented, which can capture high-resolution dynamic pressure distribution under human feet. The variation of the pressure distribution can be used to solve the dynamics of human motion while standing on the balance sensor. The algorithms using artificial intelligence are developed to assess the risk of falling for elderly people to help them prevent falls. Furthermore, this sensor can be used to measure balance ability of athletes, such as weightlifting, golf and gymnastics. In medical diagnosis, balance ability tests can provide important information for diagnostics of neural disease. It can also be used to identify drunk driving.
Debate and scientific inquiries regarding airborne transmission of respiratory infections such as COVID-19 and influenza continue. Exposure was investigated under a face-to-face scenario, where people experience the highest risk of respiratory infection. The short-range airborne route was found to dominate exposure during close contact. Based on the fact that most of the outbreaks occurred in indoor environments, we built the link between long-range airborne transmission and short-range airborne route. Results suggest that effective environmental prevention strategies for respiratory infections require appropriate increases in the ventilation rate while maintaining a sufficiently low occupancy.
The application of stainless steel (SS) as an alternative construction material has been developing in the last decade. SS construction has an outstanding structural performance, excellent corrosion resistance, and long-term durability. Moreover, the SS construction has a relatively low maintenance cost with possibility of a longer occupancy period, and thus it promotes sustainability in the construction industry. In combination with the cold-forming technique during the fabrication process, SS structures offer additional strength and a faster construction speed. However, the available international design standards for cold-formed stainless steel (CFSS) structures have not been developed thoroughly, specifically on the strength prediction of a member under concentrated bearing loads, which causes a web crippling. Therefore, a series of laboratory testing and computational simulations were conducted in this research to evaluate the existing design standards. The reliability analysis shows that the available strength predictions in the design standards are not safely used even though they are conservative. This research proposes new strength predictions that are safe and conservative, and it can be used for an improvement of the design standards.