Cornell China Center Awarded Grants Spring 2020
Shanghai Jiao Tong University-Cornell University Joint Seed Fund (5 awards)
Understanding trichome formation at the nexus of food, nutrition and human health
- Cornell PI: Jocelyn Rose, Professor, School of Integrative Plant Science, College of Agriculture and Life Sciences
- SJTU PI: Kexuan Tang, Professor, School of Agriculture and Biology
- Abstract: A significant challenge for food security worldwide is the large proportion of fresh produce that is wasted due to postharvest desiccation and spoilage during processing, transport and storage. However, the key factors that limit transpiration from plant surfaces are not well understood. This project seeks to test the hypothesis that in some fruits, large pores associated with hair-like trichome cells provide channels through the hydrophobic skin, or cuticle. Using tomato fruit as a model, a collaborative team has been assembled, with collective expertise in trichome development (SJTU) and tomato fruit biotechnology (Cornell). Specific project goals include: 1) functional evaluation of two transcription factors (TFs), HDZIP8 and HDZIP14, which have putative roles in regulating trichome formation; 2) characterization of trichome formation during tomato fruit development; 3) identification of additional candidate TFs through protein interaction studies; and 4) elucidation of the gene regulatory network associated with fruit trichome initiation.
Robust learning for distributed medical data analysis with scarce and imperfect measurements and communication constraints
- Cornell PI: Yudong Chen, Assistant Professor, School of Operations Research and Information Engineering, College of Engineering
- SJTU PI: Xiaolin Huang, Associate Professor, Electronics, Automation, Information and Electrical Engineering
- SJTU co-PI: JieYang, Professor in School of Electronics, Informa-tion and Electrical Engineering, Shikui Tu, Associate Professorin School of Electronics, Information and Electrical Engineering.
- Abstract: Artificial intelligence and machine learning have the potential of impacting medical practice by assisting doctors to give trend analysis, healthcare advices, and early warning. This project is motivated by the application of analyzing cardiovascular disease data measured by wearable monitoring devices. To successfully employ machine learning techniques in such applications, three challenges need to be addressed: 1) the amount of data and inter-device communication is insufficient for existing algorithms; 2) the measurements may contain corrupted data, missing values and outliers; 3) inconsistency of the measurement devices leads to unknown bias and data shift. We will develop robust learning algorithms that can cope with scarce and corrupted data under a distributed setting. Our approach will integrate techniques from robustness statistics, distributed learning, and adversarial training. The developed techniques will be tested on masked medical data in collaboration with local hospitals. Follow-up research will expand collaboration with other medical partners.
Towards automated eating activity recognition in the wild using a commodity smartwatch
- Cornell PI: Cheng Zhang, Assistant Professor, Information Science, Computing and Information Science
- SJTU PI: Junchi Yan, Associate Professor, Department of Computer Science and Engineering
- SJTU collaborators: Xiaoyon Pan, Assistant Professor, Department of Automation, Weichang Wu, PhD Student, Department of Electrical Engineering
- Abstract: Journaling eating-related activities can help to promote healthy lifestyles, making it a popular practice to combat unhealthy eating habits recommended by doctors, personal trainers, and other relevant stakeholders. The traditional approach for eating activities journaling usually requires the user to manually log eating activities (e.g., on paper or a smartphone app), which is known to be unsustainable. Because it relies on the user's self-motivation and determination. The high frequency can make manual recording tedious, laborious and distracting. In order to alleviate this problem, this project proposes a novel, practical, and intelligent wrist-mounted sensing system for eating monitoring using artificial intelligence and sensing technology. It aims to significantly improve the accuracy and resolution of eating activity recognition in the wild by recognizing eating activities from estimated 3D arm postures from the inertial measurement units (IMU) embedded in a wrist-mounted device (E.g., Smartwatch).
Energy-use behaviors and patterns in public buildings: A comparison between China and the USA
- Cornell PI: Michael Tomlan, Professor, Department of City and Regional Planning, College of Architecture, Art, and Planning
- SJTU PI: Liang Xin, Assistant Professor, School of International and Public Affairs
- Abstract: The building sector is responsible for the most energy consumption in the world. Public buildings represent the highest energy intensity. The occupants’ behaviors impact energy consumption significantly. Promoting energy-saving behaviors is a low-cost, highly efficient method for increasing the energy efficiency of buildings. However, studies of energy-use behavior patterns in buildings are still limited. This project aims to investigate energy-use behaviors and explore behavior-driven policies for energy efficiency in buildings. First, this study will utilize mixed methods to identify occupants’ characteristics and energy-use behaviors. Then, energy-use behavior patterns can be analyzed. Based on the identified patterns, this study will develop an agent-based platform to model and simulate the energy-use behavior. Finally, different policies can be examined, evaluated and the optimal policies will be proposed. The results of this project can strengthen the research methods employed in studying energy-us behavior and improve the incentive mechanisms of behavior-driven policies.
COVID-19: Impact on the hospitality industry
- Cornell PI: Peng Liu, Associate Professor, School of Hotel Administration, S. C. Johnson College of Business
- Cornell collaborators: Danmei Lin, SHA-JCB, Bob Yu, SHA-JCB, Joshua Sheinberg, Dyson school of applied economics and management.
- SJTU PI: Haitao Yin, Professor, Vice Dean, Economics, Antai College of Economics and Management
- Abstract: COVID-19 is becoming a global pandemic and will likely lead to an economic recession around the world. Among various economic sectors, the hospitality and service industry will be the foremost and arguably the most damaged sector. We plan to analyze the impact of COVID-19 to travel, hotel, restaurant and related sector using data from China, both earlier impact (year-over-year changes in consumptions, employment, and activities) and long-term impact (interruptions for investment and changes of consumers’ behaviors). This study would advance our understanding on the organizational, financial and technical factors that determine firms’ resilience to and recoverability after ambiguous catastrophic risks, particularly in the hospitality and service sector.
Zhejiang University-Cornell University Joint Seed Fund (4 awards)
Toward cleaner water in China: Policy analysis of agricultural non-point source pollution
- Cornell PI: Panle Jia Barwick, Associate Professor, Department of Economics, College of Arts and Sciences
- ZJU PI: Weiwen Zhang, Professor, Vice Dean, School of Public Affairs
- ZJU collaborators: Zhaoyingzi Dong, School of Public Affairs, Yingyi Jin, School of Public Affairs
- Abstract: Agricultural non-point source pollution (ANSP), which causes escalating water pollution and shortage, is one of the most pressing environmental, social and economic problems in China, as a result of decades-long extensive agriculture development. Using water quality information from the universe of monitoring stations in China, together with a large number of complementary datasets on water quality and economic activities, we aim to offer an integrated framework that incorporates water quality, ANSP, as well as anthropogenic non-point source pollutants into one unified eco-economic system. This project will conduct a comprehensive empirical evaluation on the effectiveness of different abatement policies. Our results will provide guidance for a better environmental policy design that facilitates the development of sustainable agriculture and at the same time achieves a more effective water resource management.
Multistimuli-responsive silk-elastin-like protein hydrogels for dynamic biomaterials
- Cornell PI: Jingjie Yeo, Assistant Professor, School of Mechanical and Aerospace Engineering, College of Engineering
- ZJU PI: Wenwen Huang, Assistant Professor, Zhejiang University School of Medicine, Zhejiang University-University of Edinburgh Institute
- Abstract: China is grappling with demographic shifts due to the global trend of rapid aging that exacerbates the burden of noncommunicable diseases and causes an urgent need to improve preventions and treatments. To address this, we are developing soft, adaptive, and responsive biomaterials using elastin: a major structural protein with high water content, tunable viscoelasticity, and biocompatibility. Using recombinant DNA technology, mechanically weak elastin can be fused with strong silk to adjust the mechanical properties of the dynamic elastin system, dubbed silk-elastin- like proteins (SELPs). We will design and synthesize reconfigurable, self-assembled SELP hydrogels with controllable properties to perform highly specific, pre-programmed functions. We will tightly integrate computational modelling and synthetic biology to design hydrogels that respond to combinatorial changes in temperature, pH, light, and electromagnetic fields. We embed computational modelling into the early stages of material synthesis to achieve time- and cost- efficiencies in generating specific targeted functions from molecular building blocks.
Multivalent Al3+ electrode reactions in rechargeable high-energy aqueous aluminum batteries
- Cornell PI: Lynden Archer, Professor, School of Chemical and Bimolecular Engineering, College of Engineering
- ZJU PI: Yingying Lu, Principal Investigator, College of Chemical and Biological Engineering
- Abstract: Aluminum metal anodes based on the three-electron transfer reactions are considered as a promising energy storage candidate with high energy density due to its high abundance in the earth’s crust (8.21 wt%) and excellent specific capacity (2976 mAh g-1). However, aluminum metal batteries are facing huge challenges including the inactive anodes, irreversible cathodes and incompatible electrolytes. We plan to construct a new aqueous aluminum battery based on artificial solid electrolyte interphases modified aluminum anodes and Mn4 +/Mn2 + redox reversible cathodes. The two-electron transfer Mn2+/MnO2 deposition/dissolution reactions at high voltage and Al3+ insertion/extraction at relatively low voltage (MnO2 + nAl3+ + xe- ↔ AlnMnO2) can provide higher specific capacity at the cathode side. The energy density of this new battery system can be significantly improved. Through in-situ characterization and density functional theory calculations, we will conduct in-depth study on the structural changes of the anodes and the cathode reaction mechanism.
A machine intelligence approach to quantify climate change impact on rainfed and irrigated corn production
- Cornell PI: Fengqi You, Professor, School of Chemical and Biomolecular Engineering, College of Engineering
- ZJU PI: Xi Chen, Professor, College of Control Science and Engineering
- ZJU collaborators: Yibin Ying, College of Biosystems Engineering and Food Science
- Abstract: Mechanism-based methods are commonly used to model and optimize process systems. When dealing with complex systems, the models established by the mechanism-based methods are often difficult to solve in a limited time. To address these issues, deep learning is considered in this project. Deep learning are data-driven methods that can effectively take many factors in the actual process into the scope of modelling. Furthermore, the strong fitting capability of deep learning models is conducive to the establishment of the model. With a highly parallel structure, deep model can obtain ideal results quickly and stably. However, the current deep learning methods are developed for computer science area such as image processing. But when applied to process systems engineering (PSE) problems, most of current methods cannot perform well. In this project, we aim to design deep learning methods for PSE applications, including both the process modelling and optimization.
Cornell China Center-Cornell East Asia Program Research Fund (4 awards)
China, Aging and the COVID-19 Response
- Cornell PI: Mildred E Warner, Professor; Department of City and Regional Planning; College of Art, Architecture and Planning
- Chinese collaborators: Dr. Zhilin Liu, Associate Professor, School of Public Policy & Management at Tsinghua University, Director of the Public Policy Institute. Dr. Tao Ma, Professor and Assistant Dean, School of Management at Harbin Institute of Technology. Dr. Lina Zhao, Assistant Professor of the Department of Public Affairs Management at Jiangsu University of Science and Technology.
- Abstract: The outbreak of COVID-19 in 2019/20 has raised the important role of community institutions in addressing the public health needs of seniors. This research will build a collaboration between Cornell and Chinese scholars to explore strategies used in Chinese urban and rural communities as a response to COVID-19. Community based responses – for contact tracing, and for providing health and community support needs – have been critical to the response. As an aging society, China gave special attention to outreach to older residents. We will build a team of scholars to share insights and discuss strategies to reach the needs of older adults in a public health crisis. This grant will build a collaboration between Cornell and Chinese scholars to develop comparative research that will enable us to apply for future external funding.
Understanding cross-cultural social perceptions towards interacting with an on-skin display for displaying health and environmental data
- Cornell PI: Cindy Kao, Assistant Professor, Department of Design + Environmental Analysis, College of Human Ecology
- Taiwan collaborator: Chuang-Wen You, Assistant Professor, National Tsing-Hua University
- Abstract: On-skin displays have emerged as a seamless wearable form factor for displaying information. However, the non-traditional form factor of these on-skin displays may raise concerns for public wear. These perceptions will impact whether a device is eventually adopted or rejected by society. Therefore, it is critical for researchers to consider the societal facets of device design. Further, there are often significant cultural variations which impact device usage. In this project, we study social perceptions towards interacting with an on-skin display for visualizing health and environmental information, with a large-scale study deployed across the US and Taiwan to examine cross-cultural attitudes. The results of this structured examination offer insight into the design of on-skin displays for everyday use. On-skin displays are projected to have a significant impact for elderly care, pervasive healthcare to environmental data monitoring, all which are pressing issues concerning the greater China region and the US.
The Roots of Female Underrepresentation in Academia in China: Exploring the Development of Gender Stereotypes about Intelligence
- Cornell PI: Lin Bian, Assistant Professor, Department of Human Development, College of Human Ecology
- Chinese collaborator: Qingfen Hu, Professor, Department of Psychology, Beijing Normal University
- Abstract: In China, female scientists are consistently underrepresented in certain sectors of academia. Here, we focus on the developmental roots of the gender imbalance across the Chinese academic spectrum. Past research with American samples has suggested that women may encounter additional obstacles when pursuing professions that are believed to require intellectual giftedness because they are stereotyped as less intelligent than men. This same barrier may exist and even be more pronounced for Chinese women. To test this, we will conduct three studies to examine: (1) When do Chinese children start believing that males are more likely to be intellectually gifted than females? (2) How early are Chinese girls discouraged by messages about brilliance? Investigating the developmental course of these processes will be informative both with respect to both the magnitude of the cumulative toll they might have on Chinese women’s educational and career choices and the timing of potential interventions to block their adverse effects.
The Making and Remaking of China’s Cities: Demolition, Redevelopment, and Expansion
- Cornell PI: Jeremy Wallace, Associate Professor, Department of Government, College of Arts and Sciences
- Cornell collaborators: Shiqi Ma and Jiwon Baik, Government Department
- Abstract: China’s dual urbanizations, of land and of people, are crucial factors in China’s evolving political economy and occur at a scale affecting the entire globe. Recent efforts to redevelop parts of Chinese cities show the need to better understand the connections between demolition, redevelopment, and expansion into greenfield sites. We propose to marry in person observations, interviews and other traditional sources with big data—especially satellite imagery—to improve analysis comparing across China’s many cities.