Widespreadadoption oflow-carbontechnologies such as electric vehicles,renewables, and heat pumps requires urgentadaptations of existing transportation andenergy infrastructures. However, questionsremain on the effectiveness and robustness ofvarious strategies for infrastructure expansionand demand management to decarbonizewhile incurring minimum disruptions tohuman activities. Addressing these questionscan be challenging because it requires aspatially and temporally explicit modeling ofthe variations and uncertainties in humantravel and energy-consuming behaviors,technology performance, and resourceavailability. This talk will discuss some of myrecent work on tackling this challenge around1) planning of charging stations and powergrid for personal electric vehicles, 2) demandmanagement of electric vehicle charging, 3)

Stem cells are controlled by the microenvironment termed the “niche” to control tissue development, renewal, and regeneration. How niche signals dictate stem cell behavior is a central question underlying the use of stem cells in regenerative medicine. Niche signals such as EGF instructs cells to proliferate, while Wnt proteins act to maintain the proliferative capacity of cells. However, how these niche signals control cell division during tissue growth and regeneration is not fully understood. In this talk, I will show my work that identified molecular mechanisms of how niche signals such as EGF and Wnt control cell division, and how they contribute to tissue growth and regeneration. More specifically, I will focus on the novel mechanisms for Wnt signaling in control of cell cycle progression in the liver.

lt is expected that commercial use of dronesin the near future will involve delivery serviceoperations by e-commerce companies. Weconsider relevantstrategic andtacticadecisions that these retailers will face indrone-based delivery operations, and derivepolicies on when to offer drone delivery, whatdelivery capacity to maintain, and whatamount to charge for such deliveries. To thisend, we develop a Markov decision processframework, andintroduce two(MDP)heuristic procedures, through which near-optimalclosed-formsolutions canbeobtained. The results are aimed at helpingonline retailers to determine in real timewhether and to what extent to offer drone-based delivery for given product categories indifferent service zones. ln addition, we studydelivery fee structures and identify drone

Mechanosensitive channels transduce physical force into electrochemical signaling and play essential roles in touch, hearing, and proprioception. Several classes of mechanosensitive channels have been identified, including the degenerin and epithelial Na+ channels (DEG/ENaCs), transient receptor potential (TRP) proteins, transmembrane channel-like (TMC) proteins, and Piezo ion channels. Mechanosensitive ion channels are not only expressed in sensory systems but also in various tissues throughout the body to regulate numerous developmental processes, such as vascular development and erythrocyte volume homeostasis. However, the involvement of mechanosensitive channels in neuronal development is not understood. In this talk, I will present our recent works about how DEG/ENaC mechanosensitive channels regulate the growth and branching of dendrites during neuronal development and what their roles are in the proprioceptive system in C. elegans.

As a key enabling technology of Industry 4.0, Digital Twin has been applied to various industrial domains covering different lifecycle phases to provide a comprehensive virtual description of products and systems. While the benefit of Digital Twin is undoubted, it faces challenges when dealing with certain complex industrial systems, which requires integrating all relevant data, information and knowledge involving multiple domains and across the entire lifecycle. Semantic technologies, such as ontology engineering and knowledge graphs, provide potential solutions by empowering Digital Twins with augmented interoperability and cognitive capabilities. The Model Based Systems Engineering (MBSE) technology facilitates the formalized application of modeling to support cross-domain and cross-lifecycle information representation. Enabled by these state-of-the-art technologies, the Cognitive Digital Twin (CDT) concept has been recently proposed which reveals a promising evolution of the current Digital Twin concept towards a more intelligent, comprehensive, and full lifecycle representation of complex systems. In this seminar, the concept of CDT and its key features will be presented, together with application cases developed in collaborative projects with EU industries.

10月12日下午，系统枢纽署理院长李世玮教授、生命科学与生物医学工程学域署理主任齐众教授、智能交通学域署理主任杨柳青教授、机器人与自主系统学域署理主任刘明教授、智能制造学域署理主任汤凯教授齐聚系统枢纽会议室，共同开启系统枢纽2023年博士招生宣讲会直播。此次直播共吸引超8000人在线收看，获得4461人按赞。

In recent years, with the implementation of thenational marine development strategy and theconstruction ofa number l of cross-seainfrastructures such as the Hong Kong-Zhuhai-Macao Bridge, the inadequacy of China’ s basicmarine engineeringresearch hasbecomeincreasingly apparent. In the long run, marineengineering is an important link to a maritimepower and an infrastructure for the developmentand utilization of marine resources. Enhancing thebasic theoretical research aroundmarineengineering has a bearing on the effectiveness ofmarine development and is very important to thelong-term development of China's undertakingsTo this end, the team of Prof. Quanke SU from theInstitute of Cross-sea Engineering and IntegratedTransportation of HKUST(GZ) organized a surveyand visited eight units in Nanjing, Tianjin, andQingdao from August 15 to 23,including

In recent years, along with rapid advancements inhigh-performance computing hardware andalgorithms,computer vision-based approacheshave been extensively explored and developed forthe analysis, modeling, and optimization of civiland transportation infrastructure systems. Thisseminar presents the research outcomes by DrZhou, which are focused on automated structuradefects identification. and condition assessmentthrough computer vision and deep learning, andLight Detection and Ranging (LiDAR)-basedapplications for vehicle recognition tasks

Robots play significant roles in reducing human labor costs, increasing work efficiency and improving product quality. As the speed of product renewal accelerates, the demand for flexible lines is becoming more and more evident for small batches or multi-types production. However, most current robots still have large gaps in mechanism, perception and control capabilities compared with humans, making it difficult to adapt to the varying scenarios or tasks. In contrast, humans are better at handling incomplete, contradictory and ambiguous information. Therefore, communion with humans and the environment is an important approach to solving the problem of insufficient robot intelligence.

Plastic waste poses an ecological challenge and enzymatic degradation offers a green and sustainable route for plastic waste recycling. Poly (ethylene terephthalate) (PET) accounts for 12% of global solid waste, and a circular carbon economy for PET is theoretically attainable through rapid enzymatic depolymerization followed by repolymerization or valorization into other products. However, the practical application of PET hydrolases has been hampered by their lack of robustness, slow reaction rates and inability to directly use untreated postconsumer plastics.

Herein, we report the use of a structure-based, machine learning algorithm to engineer a robust and active PET hydrolase. Our best resulting mutant (FAST-PETase: Functional, Active, Stable, and Tolerant PETase) exhibits superior PET-hydrolytic activity relative to both wild-type and engineered alternatives. We demonstrate that whole, untreated, post-consumer PET from 51 different plastic products can all be completely degraded by FAST-PETase within one week, and in as little as 24 hours. FAST-PETase can also depolymerize untreated, amorphous portions of a commercial water bottle and an entire thermally pretreated water bottle at 50 ºC. Moreover, we demonstrate a closed-loop PET recycling process by using FAST-PETase and resynthesizing PET from the recovered monomers. Collectively, our results demonstrate a viable route for enzymatic plastic recycling at the industrial scale.