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.