N_CityU119/24
Functional Reconstruction: A "Scar-Free" Strategy for Transplanting Human Neural progenitor Cells after Spinal Cord Injury

Hong Kong Project Coordinator: Prof Jessica Aijia Liu (City University of Hong Kong)
Mainland Project Coordinator: Prof Yi Li (Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences)

Traumatic spinal cord injury (SCI) causes irreversible neuronal loss and axonal damage, leading to defective locomotion and somatosensory function. Currently, there are no effective treatments for SCI patients, leaving them with lifelong disabilities. Transplantation of human neural progenitors(hNPCs) derived from human pluripotent stem cells(hPSCs) at SCI sites holds promise to compensate for the loss of spinal neurons and to restore neural circuits. However, the therapeutic effects of grafted hNPCs are hindered by the hostile microenvironment and the lack of growth factors support in the injured spinal cord. Previous work from the team has revealed the importance of non-neural cells, microglia, in modulating the injury niche. These cells play critical roles in promoting the scarless healing process by reducing excessive inflammation and fibrosis in an SCI mouse model, which significantly contributes to host axonal regeneration and homeostatic reconstruction of the injured spinal cord. Meanwhile, team findings also revealed the feasibility of modifying hNPCs genetically to enhance the adaptability in deleterious niches after injury and differentiation capacity in generating therapeutic cells spontaneously (neurons and oligodendrocytes) for SCI repair. By leveraging the expertise from both sides, we aim to maximize the therapeutic outcomes in treating severe contusive SCI by co-grafting human microglia and genetically modified hNPCs. We anticipate the coordinated actions of two different cell types could exert distinct beneficial roles in improving injury niches and restoring damaged neural circuits effectively, leading to significant functional recovery after SCI. The successful implementation of this research will also have broader applications, providing new insights into "co-grafting" strategies for various spinal cord disorders.

 

N_CityU124/24
Research on basic scientific issues and applied technologies for electric-field coupled wireless power transfer systems

Hong Kong Project Coordinator: Prof Chi-kong Tse (City University of Hong Kong)
Mainland Project Coordinator: Prof Chunbo Zhu (Harbin Institute of Technology)

The project aims to overcome key challenges in Capacitive Power Transfer (CPT) for aerospace applications, including power limitations from capacitive couplers, inefficiencies in power converters, and the lack of accurate modeling and design techniques. It develops analytical and simulation models to account for material, structural, and environmental factors, as well as load fluctuations. Innovative multilayer coupler designs and advanced dielectric materials are proposed to improve power levels and insulation. The project also designs high-frequency, high-power resonant converters and efficient control methods for stability. To validate these advancements, a high-power density CPT prototype will be constructed for aerospace applications. By addressing these limitations, the project advances CPT theory and technology, offering compact, lightweight, and interference-resistant solutions that could revolutionize power transfer in the aerospace industry.

 

N_CityU133/24
Efficient, Trustworthy and Safe Learning-Based Controller Design and Analysis

Hong Kong Project Coordinator: Prof Jie Chen (City University of Hong Kong)
Mainland Project Coordinator: Dr Yilin Mo (Tsinghua University)

Recent advances in control theory and artificial intelligence have fostered the growth of learning-based intelligent control technology, to which substantial research effort has been devoted. Learning-based control has been widely applied to industrial production, autonomous driving, and unmanned systems, promising enhanced performance, efficiency, and adaptability in complex environments and demonstrating great potential toward driving national economic growth and promoting industrial transformation. This project seeks to provide answers to limitations facing the current learning-based control. Our overarching goal is to develop a rigorous and explainable theory for the analysis and design of data-efficient, trustworthy, and safe learning-based controllers. We aim to (1) develop system knowledge-guided modelling and identification algorithms, which can identify close-to-optimal system models in real time with only a small amount of data, thus significantly improving the data efficiency of learning-based control; (2) propose a novel structured learning-based controller, which emulates the first-order fixed-point iteration of a parameterized and learnable optimization problem and enables the verification of the constraint satisfaction and the stability of the closed-loop system, in order to replace the traditional general-purpose neural network based controller, which is hardly explainable and verifiable; (3) design a safe deployment and online learning strategy for the learning-based controller, and provide safety guarantees for its real-time operation; (4) conduct real-world experiments to validate the controller developed in this project on unmanned ground vehicles and surface vessels, on which its performance, robustness, data efficiency, trustworthiness, and safety will be evaluated. The results generated from this project will potentially lead to new insights, better understanding, and improved design methods of learning-based control, thus contributing to the development of new-generation control technologies that will meet challenges in large-scale, complex engineering systems.

 

N_CityU193/24
Enabling carbon-neutral sewage treatment: A/O-MABR intensifying autotrophic nitrogen removal and mitigating greenhouse gas emissions

Hong Kong Project Coordinator: Prof Zhiguo Yuan (City University of Hong Kong)
Mainland Project Coordinator: Dr Rui Du (Beijing University of Technology)

The Chinese Government and Hong Kong SAR Government have pledged carbon neutrality by 2060 and 2050, respectively. Tremendous efforts will be made by all sectors to substantially reduce their greenhouse gas (GHG) emissions in the coming decades, with the urban water sector being no exception.

Urban water utilities emit GHGs both indirectly via power consumption and directly via fugitive emissions of nitrous oxide (N2O) and methane. In this project, an innovative yet simple technology for carbon-neutral sewage treatment will be developed, which can be easily implemented in existing sewage treatment plants (STP) via minor retrofitting, without major capital expenditure.

The technology features (1) maximised bioenergy recovery from sewage, (2) minimised energy consumption for sewage treatment, and (3) minimised direct emissions of fugitive greenhouse gases, particularly N2O, from the treatment processes. The bioenergy recovered is expected to be significantly higher than the energy consumed, enabling STP to become a net energy exporter. The carbon credit thus generated can potentially completely offset the fugitive emissions so that the treatment process becomes carbon neutral.

Equally importantly, the technology augments an existing STP, enabling it to handle a higher load without an increased physical footprint. This is particularly important for urban areas with projected population increase.

We will achieve these goals by submerging gas-permeable membranes in both the non-aerated (A) and aerated (O) zones in a traditional treatment process, forming a hybrid A/O-MABR (Membrane-Aerated Biofilm Reactor) system. In addition to the conventional bubbling aeration to the aerated zone, compressed air will be supplied via these bubbleless membranes to both zones. The air pressure will be controlled in such a way that all oxygen thus provided is completely consumed by the biofilms growing on membranes. The stratified biofilms, caused by gradients of dissolved oxygen and nitrogen species along the biofilm depth, host different functional groups of microorganisms at different depths. These consortia comprising, among others, aerobic ammonia oxidising bacteria, anammox bacteria, and denitrifiers, work in synergy with those in activated sludge to remove nitrogen with less energy consumption, less N2O emission, and much-reduced demand for organics. The latter spares organics in sewage for a multi-fold increase in bioenergy recovery.

We will develop and demonstrate the technology in both Hong Kong and Beijing, taking the distinctive sewage compositions into consideration. The proposed process represents a revolutionary change to the 100-year-old activated sludge process, and will have global implications for the “water utilities of the future”.

 

N_CityU198/24
Towards Intelligent Reconstruction and Transmission of 3D Cultural Heritage for Human and Machine Perception

Hong Kong Project Coordinator: Prof Shiqi Wang (City University of Hong Kong)
Mainland Project Coordinator: Prof Xinfeng Zhang (University of Chinese Academy of Science)

Cultural heritage holds immense historical and cultural significance. Fueled by technological advancements, the digitalization of cultural heritage has recently surged, revolutionizing how we preserve and interact with historical artifacts. These digital collections function as dynamic and globally accessible cultural repositories, which not only enable enhanced preservation, restoration, and backups of historical artifacts but also facilitate machine intelligence in their retrieval and analysis. Nevertheless, the unique characteristics, damaged structures, and vast volumes of 3D heritage data still present significant challenges, impeding the practical applications of digital cultural heritage. The challenges in digital heritage mainly arise in two folds: (1) High-precision modeling with substantial data volume: Heritage data typically entail huge volumes due to the demand for high-precision modeling with the distinction from heritage replicas. The management of such data requires robust solutions integrating effective processing and compression methodologies, while the lack of specifically designed methods raises barriers to the widespread accessibility of digital heritage initiatives. (2) Complicated heritage structures due to excavation damage: Limited excavation techniques frequently leave cultural heritage damaged and incomplete, complicating the reconstruction of multi-dimensional information such as geometric positions, shapes, colors, and even material properties. To address these two challenges, in this proposed project, we aim to develop a comprehensive system for the intelligent reconstruction and transmission of 3D digital cultural heritage data through four main tasks. First, we will develop a high-fidelity 3D heritage data reconstruction scheme with illumination-adaptive multi-modal texture mapping. This scheme caters to the fidelity of damaged heritage structures, ultimately enhancing their utility. Second, we will incorporate an immersive 3D heritage enhancement method for geometric and color restoration, rendering an improved authenticity of digital presentation. Third, we will develop an advanced compression framework for 3D digital cultural heritage data based on geometry and texture characteristics. Fourth, we will develop an online analysis scheme for heritage data, forming a robust foundation for analysis. This project is designed to be scalable, allowing its application to build a wide spectrum of digital museums, thereby broadening its impact and reach.

 

N_HKBU214/24
Research on Key Technology for Modeling Cross-Domain Sequential Behaviors in Recommender Systems

Hong Kong Project Coordinator: Prof Li Chen (Hong Kong Baptist University)
Mainland Project Coordinator: Prof Weike Pan (Shenzhen University)

In the era of artificial intelligence, recommender systems have become core modules and standard configurations of many online service platforms (e.g., e-commerce, digital media, education, healthcare, travel, social entertainment, and more) for providing users with personalized services and suggestions. In recent years, modeling users’ sequential behavior data and non-behavioral information has attracted extensive attention from academia and industry, because they can be exploited to more effectively learn users’ real interests and recent preferences for items, thereby enabling the system to provide more accurate personalized recommendation services.

However, most of the research on users’ sequential behavior modeling only considers the case of a single domain, and there are few modeling methods and recommendation algorithms designed specifically for cross-domain sequential behavior data. In a real recommender system (such as an online shopping platform), user behaviors not only have a sequential nature but also have cross-domain characteristics (such as browsing and purchasing behaviors of books and movie tickets in one day).

Therefore, effectively modeling cross-domain sequential behaviors (CDSB) can be crucial to deeply explore and learn user behavior data, further improving the performance of personalized services of recommender systems, which is in demand in many practical application scenarios. Unlike general single-domain sequential behavior modeling, modeling cross-domain sequential behaviors faces more prominent challenges, including (i) the diversity of users’ preferences across domains, (ii) the dependency of users’ behaviors across domains, (iii) the transferability of preference knowledge across domains, (iv) the cross-domain fairness issues, (v) the cross-domain biased dependency in user behaviors, and (vi) the transfer of sensitive information in cross-domain knowledge.

This project will center on exploring innovative Transfer Learning (TL) techniques for modeling CDSB. Our objective is to develop an Unbiased Learning (UL) framework for TL and recommendation algorithms, enabling learning user preferences from CDSB in an accurate and fairness-aware manner. It is important to highlight that the introduction of TL and UL paradigms and related technologies will offer fresh perspectives and novel technical approaches to address the above-mentioned challenges, constituting the primary contribution of this project.

 

N_HKBU236/24
Elucidating the molecular basis of sugar-mediated PTMs of NLP7 in regulating carbon-nitrogen metabolic coupling in plants

Hong Kong Project Coordinator: Dr Yang Bi (Hong Kong Baptist University)
Mainland Project Coordinator: Dr Mingyi Bai (Shandong University)

Nitrogen is an indispensable nutrient for plant growth, essential for synthesizing proteins, nucleic acids, and chlorophyll. It is often the rate-limiting macronutrient in agricultural practices. Applications of nitrogen fertilizers effectively boost crop growth and yield. However, overuse of nitrogen fertilizers raises farming costs and poses a great threat to the environment, causing issues like soil acidification, compaction, and eutrophication. Improving nitrogen use efficiency (NUE) is vital for sustainable agriculture. Nitrate, the primary form of nitrogen in soil, not only serves as a macronutrient but also acts as a signaling molecule that regulates nitrogen uptake, metabolism, growth, and many developmental processes. Thus, elucidating the regulatory mechanisms of nitrate signaling is critical for improving NUE in crops and paves the way for greener farming practices.

Nitrogen (N) and carbon (C) metabolism are tightly coupled during plant growth and development, and their balance is critical for crop productivity and environmental adaptation. Sugar produced by photosynthesis in the aerial parts of plants provides energy to the roots for assimilating and metabolizing nitrogen, which is often the limiting factor for building and supporting the photosynthetic apparatus. It has been proposed that the interactions between sugar and nitrate signaling pathways contribute to CN balance in plants by coupling carbon-nitrogen metabolism, but the molecular underpinnings of such interactions are not fully understood. In this project, my collaborator and I will analyse how conserved energy and sugar signaling pathways mediated by TOR, SnRK1, and SPY modulate the activity of key nitrate sensor NLP7 to facilitate the metabolic coupling of sugar and nitrogen. Our studies will not only shed light on the regulatory mechanisms underlying nitrate signaling, CN metabolic coupling, and CN homeostasis in plants, but could also provide guidance and genetic resources for improving crop NUE in diverse growth conditions.

 

N_CUHK410/24
Comparing the Regulomes of C4 Maize and C3 Tobacco to Investigate the Evolution of C4 Photosynthesis

Hong Kong Project Coordinator: Prof Silin Zhong (The Chinese University of Hong Kong)
Mainland Project Coordinator: Prof Xiaoyu Tu (Shanghai Jiao Tong University)

Most plants utilize C3 photosynthesis, where all the reactions occur within the mesophyll (MS) cells of the leaves. However, C4 plants, such as maize and sorghum, have evolved a different mechanism. They fix CO2 in the MS cells and transport it as a 4-carbon substrate to bundle sheath (BS) cells, where CO2 is released for the Calvin cycle. This spatial separation of photosynthesis in two cell-types serves to concentrate CO2, minimize photorespiration and increase photosynthetic efficiency.

Interestingly, the genes responsible for photosynthesis in C4 plants are not novel and they are also present in C3 plants. This suggests that one of the key aspects of the evolution of C4 photosynthesis lies in the control of gene expression in the MS and BS cells. Transcription factors (TFs) and cis-regulatory elements (CREs) are fundamental components of transcriptional regulation. Therefore, we propose a systematic approach aimed at reconstructing and comparing the regulomes of C4 maize and C3 tobacco leaves.

To identify TF binding sites, we will use agro-infiltration to express TFs in tobacco leaves for large-scale ChIP-seq. Protoplast transient expression will be used to study TFs in maize MS cells. We will also raise TF-specific antibodies and utilize transgenic plants to express TFs in maize BS cells, which cannot be transformed as protoplasts.

To address the challenges associated with CRE prediction, we will develop a Tn5-based ChIP-exo method. This technique will allow us to directly map TF-protected footprints, which harbor the CREs. Furthermore, we will employ single-cell ATAC-seq and RNA-seq to systematically map the chromatin accessibility of promoters and gene expression in a cell-specific manner.

Pilot experiments have been performed to test the feasibility of our proposed research. We have successfully cloned over 400 tobacco and maize TFs and performed nearly 200 ChIP-seq experiments. Additionally, we have utilized ChIP-exo to map the TF-protected footprints of an AP2/ERF TF, demonstrating its potential in improving the accuracy of CRE discovery. Furthermore, our single-cell ATAC-seq of C4 maize leaves has revealed MS and BS cell-specific promoter chromatin accessibility patterns in the core C4 genes.

Through the comprehensive analysis of the regulome in tobacco and maize, our research aims to enhance our understanding of the regulatory networks governing C3 and C4 photosynthesis, which could lay the foundation for future crop improvement.

 

N_CUHK414/24
Overcoming the Stability Barrier for High-performance All-perovskite Tandem Solar Cells

Hong Kong Project Coordinator: Prof Martin Stolterfoht (The Chinese University of Hong Kong)
Mainland Project Coordinator: Prof Dewei Zhao (Sichuan University)

Perovskite/perovskite (all-perovskite) tandem solar cells are a high-performance solar cell technology that is lightweight, roll-to-roll, and low-temperature processable, with a significantly lower carbon footprint than Si-based technologies. Nevertheless, the technology currently faces real challenges concerning device stability (T80 lifetime usually <1000h) which is the key to future commercialization. In fact, both subcells have their unique problems. While halide segregation is considered to be the primary reason for wide-bandgap perovskite degradation, for the low-bandgap cells, the oxidation of Sn2+ to Sn4+ limits the stability. Therefore, further improvements in stability require a simultaneous improvement of both subcells. While tackling these issues has always been a main priority, the community lacks experimental methodologies to quantitatively characterize the recombination losses during degradation to enable a more systematic optimization, highlighting the urgent need for more effective diagnostic tools. Moreover, understanding the key indicators for early device degradation would allow the development of highly effective measures to enhance stability, for example, through rapid stability assessment based on the ionic fingerprints of the devices. In this project, we will combine our expertise in the development of high-performance all-perovskite tandem cells at world record levels and tailored charge transport layers, with innovative advanced diagnostic tools, and fundamental research of ion migration to accelerate the development of highly stable all-perovskite tandem cells and push the stability of this technology beyond state-of-the-art.

 

N_CUHK419/24
Mechanistic Study of Nasopharyngeal Carcinoma-specific Intracellular Bacteria Mediating Host Epigenomic Reprogramming and Promoting Malignant Progression

Hong Kong Project Coordinator: Prof Lili Li (The Chinese University of Hong Kong)
Mainland Project Coordinator: Prof Na Liu (Sun Yat-sen University)

Tumor-resident microbiota, a recently identified critical component of the tumor microenvironment, is closely associated with cancer prognosis and therapeutic efficacy. In contrast to solid tumors, which are mainly characterized by genetic alterations such as mutations, insertions or deletions, nasopharyngeal carcinoma (NPC) exhibits aberrant epigenetic alterations characterized by high levels of genomic DNA methylation. However, the potential impact of intratumoral microbiota on epigenetic modifications in NPC remains unclear.

In collaboration with a mainland team led by Prof. Liu, we systematically collected NPC tissue specimens with high/low intratumoral bacterial load, and performed multi-omics analysis including microbiome sequencing, host transcriptome sequencing, methylation chip detection and culture omics, which allowed us to identify a specific set of intratumoral bacteria associated with elevated methylation levels in NPC. In vitro and in vivo experiments revealed the ability of these intratumoral bacteria to stimulate the accumulation of the methyl donor S-adenosylmethionine and promote tumor metastasis.

Based on these findings, we aim to determine whether NPC-specific intratumoral bacteria accelerate the synthesis of host methyl donors via metabolites or virulence proteins, triggering epigenomic remodeling characterized by hypermethylation and ultimately leading to malignant progression of NPC. In this proposal, we will identify NPC-specific intratumoral microbiota-induced DNA hypermethylation in the host dependent on S-adenosylmethionine (SAM) accumulation, investigate NPC-specific intratumoral bacterial effectors for SAM pathway activation, investigate the involvement of histone methylation and m6A modification in NPC-specific intratumoral microbiota-mediated metabolic reprogramming, and elucidate the impact of NPC-specific intratumoral microbiota on malignant progression and treatment resistance in NPC tumorigenesis.

The data collected from this project will elucidate the molecular mechanisms underlying NPC-specific intratumoral bacteria-mediated epigenomic remodeling effects on the host, which will help to refine the mechanisms by which intratumoral bacteria mediate NPC malignant progression and provide new perspectives for precision therapy of NPC.

 

N_CUHK422/24
The Impact of Early-life Exposure to Metabolic Abnormalities on Pubertal Development – from Epidemiological Analyses to Mechanisms

Hong Kong Project Coordinator: Prof Ronald Ching-wan Ma (The Chinese University of Hong Kong)
Mainland Project Coordinator: Prof Xiu Qiu (Guangzhou Medical University)

The early onset of puberty is an important health issue of global concern, and it has a tremendous impact on the child’s physiology, personality, cognition, behaviour and long-term health. Recent data suggests earlier onset of puberty over past decades, and that this may contribute towards long-term health consequences including increased risk of cardiovascular disease and hormone-dependent cancers. Previous studies suggest that overnutrition during critical periods (e.g. pregnancy and early childhood) are associated with early pubertal development of the offspring, though their interactive roles and mechanisms are unclear. Our research from Hong Kong and Guangzhou birth cohorts, with more than 10-years follow-up data, have highlighted the increasing burden of maternal hyperglycaemia and obesity on offspring growth and adiposity, and their contribution towards childhood obesity and the epidemic of non-communicable diseases (NCDs). Based on this, we hypothesize that the cumulative effects from multiple exposures to glycolipid metabolism-obesity in early life would result in developmental reprogramming of child sexual development through modulating the gut-brain-gonad (GBG) axis, via genomic DNA methylation modification, imbalance of gut microbiome and host co-metabolism, systemic inflammation and hence altered regulation of key genes for sexual development, including those in the GBG axis. Based on two unique and ready cohorts with complementary resources, the Born in Guangzhou Cohort Study (BIGCS) and the Hyperglycaemia and Adverse Pregnancy Outcome (HAPO) Hong Kong cohort, our study aims to track the initiation and progression of puberty development in Chinese children using a lifecourse perspective, and to examine the interactive effects of glucose and lipid metabolism-obesity during pregnancy and early childhood on puberty development. We will further examine the effects of these early-life exposure on the GBG axis during this key window of development by utilizing inflammasome detection, methylation analyses, gut microbiota sequencing and plasma metabolites profiling to identify mediating pathways to better elucidate the underlying mechanisms. Finally, we will develop a prediction model for early onset of puberty, incorporating modifiable maternal and childhood factors, as well as biomarkers, for clinical use in the Chinese population. Our proposal leverages on the extensive, multidisciplinary and complementary expertise and well-phenotyped cohorts from the Guangzhou and Hong Kong teams. The results from our research program will deliver important findings and insights to tackle early onset of puberty, provide tools to predict risk in early life, and translate key data for the formulation of public health policies across the lifecourse in order to address this emerging public health issue.

 

N_CUHK439/24
Mechanistic Study of the Mitochondrial-nuclear Interaction during Embryonic Development and Reproductive Isolation

Hong Kong Project Coordinator: Prof Hui Zhao (The Chinese University of Hong Kong)
Mainland Project Coordinator: Prof Qi Long (Guangzhou Medical University)

The interaction between the cytoplasm and the nucleus is essential for eukaryotic organisms. Mitochondria, found within eukaryotic cells, are organelles for energy production. However, recent studies indicated the mitochondria functions are far beyond the “powerhouse of the cell”. This organelle also plays essential roles in apoptosis, autophagy, and calcium homeostasis and even influences DNA methylation. However, the precise mechanisms through which mitochondria regulate nucleocytoplasmic interactions and embryonic development remain largely unknown because of the lack of a suitable embryonic model. In our recent work (Sci Adv 2023), we introduced a unique embryonic model using cross-species hybridization of Xenopus, wherein the genomic DNA was conserved while the mitochondria differed. This model has provided valuable insights into the mitochondrial-nucleus interactions. Our hypothesis is that mitochondrial-nuclear incompatibility exists in the te×ls Xenopus hybrid embryos, and we can use this unique model to study the interaction between the mitochondria and nucleus. To test our hypothesis, we want to perform the following study, 1) to further characterize mitochondrial-nucleus incompatibility using the Xenopus hybrid embryos; 2) To elucidate the regulatory mechanism as to how mitochondria mediation of nuclear-cytoplasmic incompatibility influences embryonic development and contributes to reproductive isolation; 3) To eliminate incompatible mitochondria using the Transcription Activator-Like Effector Nuclease (TALEN) method and rescue te×ls embryos. By unraveling the complex relationship between mitochondria and the nucleus, we will broaden our understanding of fundamental biological processes and pave the way for novel therapeutic interventions for a range of human diseases caused by mitochondria dysfunction.

 

N_CUHK447/24
Towards Shared-Autonomy: Collaborative Robotic Ultrasound Scanning with Safe and Anytime Intervention

Hong Kong Project Coordinator: Prof Zheng Li (The Chinese University of Hong Kong)
Mainland Project Coordinator: Prof Xiang Li (Tsinghua University)

Ultrasound (US) imaging is renowned for its portability, cost-effectiveness, and real-time capabilities, which makes it a popular choice for various medical examinations. However, US imaging poses a significant physical burden on clinicians and the imaging quality highly relies on the experience of ultrasonologists. As per the Chinese Healthcare report, in 2020, China conducted approximately 2 billion US examinations with only 200 thousand registered ultrasonologists available (10,000: 1). With the elderly population projected to increase from 210 million in 2022 to 400 million in 2033, the demand for ultrasonologists will grow massively, exacerbating the existing shortage. US robots are seen as a potential solution to this emerging clinical challenge. Among the three types of US robots, collaborative US robots are considered the most viable, as tele-operated US robots struggle to enhance the safety and accessibility of US scanning, and fully autonomous US robots encounter both technical and ethical difficulties. The main challenges for collaborative US robots include 1) achieving a balance between safety and efficiency in US scanning; 2) managing the co-existence of intended and unintended clinician/patient-robot contact; and 3) addressing the lack of an intuitive clinician-robot interface. This project aims to address these challenges by developing a novel collaborative US robot with shared autonomy, through imitating the practices of experienced ultrasonologists with advanced robot motion planning and control methods, evaluating US image quality with deep learning neural networks, and developing a safety-oriented multimodal human-robot interface. The anticipated contributions and benefits include 1) allowing clinicians to intervene in the US robot’s autonomous scanning at any time, thus balancing safety and efficiency; 2) incorporating collision classification to manage the co-existence of intended and unintended contacts; and 3) implementing an Augmented Reality (AR)-haptic mixed interface with bidirectional communication, facilitating intuitive clinician-robot collaboration. By addressing these key challenges through collaboration between teams from Hong Kong and mainland China, a collaborative US robot will be developed, tested with phantoms, and evaluated by experienced ultrasonologists. This would pave the way toward a truly clinically embraced US robot and enabling safe and efficient robotic US scanning to meet the growing demands amid ultrasonologists shortages and imaging quality inconsistencies. This could not only advance robotics technologies but also establish a paradigm for developing safe collaborative medical robots. The project could also help to enhance Hong Kong’s position as an innovation hub, both locally and globally.

 

N_CUHK448/24
Molecular Characterization of LKB1-AMPK in Cellular Plasticity and Therapy Resistance in Prostate Cancer

Hong Kong Project Coordinator: Prof Chi-fai Ng (The Chinese University of Hong Kong)
Mainland Project Coordinator: Prof Dong Gao (Center for Excellence in Molecular Cell Science, Chinese Academy of Sciences)

Prostate cancer is a common cancer affecting men, and the incidence is rapidly increasing in Mainland China and Hong Kong. Most cases rely on male hormones for growth, leading to treatment with androgen-deprivation therapy (ADT). While ADT is initially effective, many patients eventually develop a more aggressive form called castration-resistant prostate cancer (CRPC). This type can still depend on androgen signalling but often becomes resistant to treatments like new-generation anti-androgen.

Recent research highlights that some cancer cells can adapt and no longer rely on traditional androgen signalling, developing what is known as AR-low or null phenotypes. One specific form, double-negative prostate cancer (DNPC), lacks both androgen receptor activity and neuroendocrine features. DNPC has been increasingly identified in CRPC patients, especially due to the use of strong anti-androgen treatments, but unfortunately, effective therapies for DNPC are limited.

In this study, researchers analyzed prostate cancer samples before and after ADT and found that ADT can trigger a shift from typical androgen-dependent cancer to DNPC. They identified that inactivation of the LKB1/AMPK signalling pathway contributes to this transition, leading to changes in DNA methylation patterns. These findings suggest that targeting this pathway could provide new treatment strategies for resistant forms of prostate cancer.

 

N_CUHK452/24
Synthesis of Multifunctionalized Piperidines through Asymmetric Diborylation

Hong Kong Project Coordinator: Prof Hairong Lyu (The Chinese University of Hong Kong)
Mainland Project Coordinator: Prof Xiao-chen Wang (Nankai University)

Multisubstituted piperidines are foundational components widely prevalent in natural products and pharmaceuticals, playing critical roles due to their biological relevance and therapeutic potential. Despite their significance, the synthesis of these compounds, particularly in enantiomerically enriched forms, remains a considerable challenge in organic chemistry. Traditional approaches often necessitate complex, multistep syntheses for the required starting materials or suffer from significant limitations regarding efficiency and selectivity. This gap in the synthetic accessibility of functionalized piperidines has spurred a demand for a more universal and efficient synthetic methodology.

In response to this challenge, this collaborative research project aims to develop innovative synthetic methods to access structurally diverse multisubstituted chiral piperidines from the readily available raw material pyridines via a “dearomative-functionalization strategy. The initiative leverages key technologies including chiral borane Lewis acid catalysts (Angew. Chem. Int. Ed. 2019, 58, 4664) and the novel pyridine hydroboration reaction (J. Am. Chem. Soc. 2023, 145, 11789; 2022, 144, 14463; 2022, 144, 4810) developed by the Mainland team, alongside sp2-sp3 diboron reagents innovated by the Hong Kong team (Angew. Chem. Int. Ed. 2023, 62, e202312633). The project seeks not only to establish novel synthetic methods that could simplify the production of complex piperidine structures but also aims to elucidate the underlying mechanisms of interaction between sp2-sp3 diboron reagents and various catalysts, including transition metals and borane Lewis acids.

This research is anticipated to significantly advance the synthesis of chiral, multisubstituted piperidines by offering a more straightforward, efficient, and selective approach. The expected outcomes include the generation of new catalysts and reagents, development of a versatile toolkit for synthesizing piperidine derivatives, and a deeper understanding of the catalytic processes involved. These advancements could potentially lead to further innovations in synthetic organic chemistry and drug discovery.

 

N_CUHK459/24
Investigating Intermolecular Mechanisms and Regulation Strategies for Non-radiative Charge Recombination in High-performance Organic Solar Cells

Hong Kong Project Coordinator: Prof Xinhui Lu (The Chinese University of Hong Kong)
Mainland Project Coordinator: Prof Lijian Zuo (Zhejiang University)

Organic photovoltaic (OPV) technology presents unique advantages such as being lightweight, flexible, colorful, and translucent, making it a promising solution for various next-generation photovoltaic applications. However, the advancement of power conversion efficiency (PCE) in OPVs is significantly hindered by open-circuit voltage loss due to severe non-radiative charge recombination, which impedes their further industrialization. Non-radiative recombination involves multi-scale processes, from the molecule level to the device level. Unfortunately, there is currently a lack of systematic multi-scale exploration and quantitative theoretical models to explain the correlation between multi-scale structure and non-radiative recombination, which significantly hampers the development of effective strategies to mitigate this issue.

In this project, we will leverage the expertise of the Zhejiang University (ZJU) team in molecular design, synthesis and device physics, along with the Chinese University of Hong Kong (CUHK) team’s expertise in morphology and film formation kinetics characterization. Our focus will be on studying non-radiative charge recombination across different scales, from molecular to device-level phenomena. We aim to establish the correlation between non-radiative charge recombination and multi-scale structural factors, including molecular structure, local intermolecular aggregation, donor-acceptor phase separation, and device interfaces. Drawing inspiration from organic light-emitting diodes, we will develop highly luminescent D:A-structured molecules and conduct a detailed examination of the fluorescence quenching mechanism resulting from molecular aggregation and heterojunction formation. Our goal is to gain a comprehensive understanding of the microscopic mechanisms governing non-radiative recombination in high-performance organic solar cells (OSCs) through advanced photophysical and X-ray and neutron scattering-based morphological characterizations. Once we establish a robust link between structure, processing and performance, we will move on to designing novel active layer and interfacial layer materials and optimizing morphology and device structure to further suppress the non-radiative open-circuit voltage loss and enhance device performance.

We believe the synergistic efforts of both teams will lead to a systematic, multi-scale solution—from molecules to devices—to address the critical issue of non-radiative charge recombination in OPVs. This, in turn, will enable the development of high-performance OSCs with minimal voltage loss. Our project will provide insightful guidelines for further improving the PCEs of OSCs towards their theoretical limits, paving the way for their commercialization.

 

N_CUHK460/24
Distributed Compression and Network Secure Computation for Functions

Hong Kong Project Coordinator: Prof Raymond Wai-ho Yeung (The Chinese University of Hong Kong)
Mainland Project Coordinator: Prof Xuan Guang (Nankai University)

In the era of big data, the information of the processed data, instead the information of the massive original data, is required to be obtained at destinations in communication systems for more and more practical applications. Meanwhile, in the era of intelligence, the transmission and communication of information is not oriented toward humans only but oriented toward broader intelligent agents. Driven by these demands, this project aims to establish and develop a data-computation-oriented information theory, which focuses on the efficient and reliable computation of functions of the source messages at the destinations, rather than the traditional data-transmission-oriented information theory which only focuses on efficient and reliable message transmission. This data-computation-oriented information theory is essential for modern applications in Big Data, the Internet of Things (IoT), Artificial Intelligence (AI), and network integrated communication and computation. In particular, the traditional data-transmission-oriented information theory can be regarded as a special case of the data-computation-oriented information theory for computing identity functions at the destinations. The main objectives of this project are:

1. Establish the model and develop the theory of distributed function compression, which can be regarded as a generalization of the topic of source coding (data compression) from compressing the original messages to compressing functions of the messages. The research team will characterize the compression limits and develop coding approaches which go beyond traditional coding methods for data compression, and will develop the coding and decoding theory for function compression.
2. Establish the model and develop the theory of (information-theoretically) secure network function computation, which considers securely computing a target function of source messages in a communication network and at the same time protecting the key information, modeled by a security function of the source messages, from being leaked to the wiretapper. This is particularly relevant in an era where cyber threats are prevalent and data security is paramount.
3. Characterize the zero-error capacities of communication channels, especially those with memory, and develop methods to represent these channels using graph theory. This will provide insights on the fundamental limits of error-free communication.
4. Characterize entropy functions on 2- and 3-dimensional faces of polymatroidal region. This not only contribute to a deeper understanding of information-theoretic limits and inspire novel approaches to data compression and transmission, but also facilitate the investigations of a plenty of information theory problems.

 

N_CUHK464/24
Modulation of Endosperm Starch Composition to Improve Rice Quality Using SSIIa, SSIIIa and MS Superior Alleles

Hong Kong Project Coordinator: Prof Cheng Li (The Chinese University of Hong Kong)
Mainland Project Coordinator: Prof Changquan Zhang (Yangzhou University)

The world is currently facing a growing public health crisis due to diabetes, obesity and colorectal cancers. As starch is a primary energy source, slowly digestible starch and resistant starch (RS) are linked to stable postprandial glycemic responses and support a healthy gut, offering benefits in preventing and managing chronic diseases such as diabetes. However, rice, a staple food for a large global population, typically has rapidly digestible starch, resulting in a high glycemic index that adversely affects health. Therefore, a current challenge faced by the rice industry is to cultivate rice varieties rich in RS while maintaining good yield, appearance and taste quality.

Current genetic breeding efforts have primarily focused on single major starch biosynthetic enzymes. Although rice varieties with high amylose content and enriched RS have been developed, they often face challenges in adoption due to difficult gelatinization properties and unsatisfactory appearance and taste. We propose that pyramiding multiple superior genes, which minimally impact overall rice quality while significantly reduce its digestibility, is a promising strategy to cultivate rice that balances both digestion and other qualities. Soluble starch synthase is an important enzyme for the elongation of amylopectin chains, with different isoforms responsible for synthesizing amylopectin chains of varying lengths, a critical factor in determining starch digestibility. Our preliminary studies have shown that a single soluble starch synthase gene (ss3a) mutant significantly increased rice RS, while ss3b had no significant effects on rice quality traits, including digestibility. Notably, the ss3a/ss3b double mutant showed even higher RS compared to the ss3a single mutant, suggesting that ss3a may play a key role and can be synergistically combined with other starch biosynthetic genes to regulate rice digestibility. However, the mechanism remains unclear, as does the potential for combining ss3a with other superior genes to further improve rice quality.

In this project, we will create ss3a-based superior gene pyramiding rice lines, incorporating different members of soluble starch synthase genes, and the mannose synthase gene MS, another important factor in determining starch digestibility. We will then comprehensively investigate the genetic regulation and starch structural basis of these rice grain quality traits, exploring the relationship of rice “yield-appearance-digestion-taste” and evaluating the potential health benefits of the newly bred rice in treating diabetes. To the best of our knowledge, this project will be the first of its kinds to explore developing high quality rice varieties by pyramiding SSIIa, SSIIIa and MS superior alleles.

 

N_CUHK472/24
Shape-morphing Microrobots Fabricated by Femtosecond Laser Multi-Focus Parallel Processing Technology for Tissue Penetration in the Digestive Tract

Hong Kong Project Coordinator: Prof Li Zhang (The Chinese University of Hong Kong)
Mainland Project Coordinator: Prof Dong Wu (University of Science and Technology of China)

In recent years, microrobots have received widespread attention, showing important application prospects in the field of life health, such as targeted drug delivery and non-invasive surgery. Controllable and efficient three-dimensional (3D) structural manufacturing and tunable shape morphing are crucial for improving the environmental adaptability and functionality of microrobots. At present, although direct laser writing can manufacture microrobots with three-dimensional structures, the processing efficiency of this method is low, making it difficult to massively fabricate 3D structure-designable microrobots. In addition, non-smart materials and limited structures make it still difficult to apply in many complex environments. For example, digestive tracts filled with complex biofluids, mucus, and tissue mucosa pose challenges for biopsy and drug delivery. Therefore, the fabrication of microrobots with tissue-penetrating capabilities is essential in tackling digestive tract diseases. This project proposes to use spatial light field modulation technology to generate multi-focus femtosecond lasers (>100 beams), aiming to carry out parallel processing of shape-morphing microrobots with controllable size and geometry to enable them to achieve controllable movement and tissue penetration in the complex digestive environment. These functions are crucial for effective biopsy and drug delivery. The research content of this project includes (1) Synthesizing photopolymerizable materials with superior properties (high biocompatibility, degradability, and magnetic responsiveness) and designing bionic shape-morphing microrobot structures; (2) Developing a multi-focus beam generation algorithm and processing technology based on spatial light modulation technology for efficient batch manufacturing of shape-morphing microrobots; (3) Building automated magnetic driving equipment to achieve efficient and controllable manipulation of shape-morphing microrobots in complex biological fluid environments. (4) Verifying the controllable locomotion and tissue penetration of shape-morphing microrobots in the digestive tissue and completing tissue biopsy and on-demand drug delivery. The high biocompatibility hydrogel materials prepared in this project, the proposed femtosecond laser multi-focus parallel processing technology, and the application of developed shape-morphing microrobots in gastrointestinal tissue penetration will provide new technical tools and solutions for breaking actual medical barriers. The cooperative team has accumulated a rich foundation in the efficient fabrication of 3D microstructures, microrobot driving control, and gastrointestinal medical applications using femtosecond modulated light beams. The team members have maintained a cooperative relationship for five years, jointly publishing about ten high-level SCI journals. The matching and complementary background and expertise can ensure the smooth execution of the project.

 

N_CUHK482/24
Molecular Mechanisms underlying RabGDI-mediated Embryogenesis in Arabidopsis

Hong Kong Project Coordinator: Prof Zizhen Liang (The Chinese University of Hong Kong)
Mainland Project Coordinator: Prof Yan Zhang (Nankai University)

In angiosperms, the gametes, egg cell and sperm, fuse to form a zygote, which undergoes directed and orderly cell division and differentiation to develop into a mature embryo that produces a seed. This is not only the key to the reproduction of angiosperms, but also provides an excellent system for exploring important biological issues, such as cell polarity and cell fate determination. Rab GTPases, as molecular switches, regulate vesicle transport in all eukaryotic cells and are regulated by various upstream factors, among which RabGDIs have yet been functionally characterized in plants. Our recent studies revealed that the Arabidopsis Rab5 mediates vesicle transport and vacuolar biogenesis as well as regulates pollen tube growth. More recently, we performed genome-wide interaction screening of Rab interactors by using yeast two hybrid assays and identified two RabGDIs. Our preliminary results showed that RabGDI1/2 regulate the first asymmetric zygotic division and thus embryo development, whose mutations lead to embryo lethality.

Based on these new exciting findings, here we propose to study the biological functions and underlying mechanisms of RabGDI1/2 in Arabidopsis thaliana with three objectives: 1) To study RabGDI-dependent biological functions during embryo development; 2) To illustrate the mechanism of RabGDI functions in regulating Rab GTPases; and 3) To identify and characterize the key regulators of RabGDI during embryo development.

These goals can be achieved by a joint effort of two well-complementary groups in molecular and pollen biology (Mainland China) and cell biology and electron tomography (Hong Kong). This study will provide a better understanding of cellular and molecular mechanisms underlying early embryogenesis. Also, functional studies on RabGDIs in Arabidopsis thaliana will be instructive to those of other plant species.

 

N_CUHK483/24
Data-driven Cross Market Systemic Risk Analytics with Behavioral Considerations

Hong Kong Project Coordinator: Prof Hoi-ying Wong (The Chinese University of Hong Kong)
Mainland Project Coordinator: Prof Shushang Zhu (Sun Yat-sen University)

Systemic risk refers to the possibility that an event at the company level triggers the failure of the banking system or even the entire economy. The 2007 subprime mortgage crisis is a typical example, causing the bankruptcy of Lehman Brother, the innovative quantitative easing and bailout policy by the US government. The major concern is the chained effects among firms within the same network system. Policymakers have to ensure firms to have sufficient capital to withstand shocks by setting up appropriate regulation and determines prior rescue plans upon a triggering event. The rise of inflation rate, the hike of interest rates and the recent failure of small banks in the USA and other part of the world generate the concern of systemic risk in a broader sense to include cross economy failure. The topic is of particular interest of Hong Kong and Mainland China due to the implementation of ‘One Country Two Systems’. This research project is a joint effort between statistics and risk management professors from Hong Kong and financial economic, statistic, and system engineering professors in mainland China to collaboratively investigate systemic risk in a cross market perspective, leveraging on the unique feature between Hong Kong and the Mainland China markets. This group of scholars aims to develop a cross market systemic risk measurement and management at individual company level and at the policymakers’ levels. Due to different regulations and market constraints between Hong Kong and the Mainland economies, this project will (1) investigate the asymmetric risk information flow between the two economies; (2) propose novel systemic risk measure framework taking into account of network system and forward-looking source of information, including the chained effects in credit quality and bankruptcy risk; and (3) develop machine learning framework to systemic risk management through determining capital reserve levels and intervention plans, such as bailout policy, capital injection and interest rate policies.

 

N_CUHK498/24
Intelligent Motion Planning of Drone Swarms with Multimodal Large Language Models

Hong Kong Project Coordinator: Prof Hongsheng Li (The Chinese University of Hong Kong)
Mainland Project Coordinator: Prof Si Liu (Beihang University)

In this project, we aim at developing innovative multimodal large language models for motion planning of drone swarms to accomplish complex tasks under open-domain language instructions. The proposed MLLM takes as input airborne sensor data, visual sensor signals, and open-domain language instructions as inputs, and generates planned actions for individual drones or drone swarms. Such a learnable MLLM-based planning system for autonomous drones will be the first of its kind (existing work use off-the-shelf GPT-4). The project will tackle the following challenges.

Challenge #1: How to align the multimodal drone sensor data encoder with language instructions? The multi-sensor data encoder is responsible for properly understanding the drone and environment status from the multiple airborne sensors. Pretraining and aligning the encoder with language instructions lay the foundation to achieve accurate language-guided drone motion planning. We will develop the method for pretraining and aligning the drone multi-sensor data encoder.

Challenge #2: How to instruct autonomous drones to accomplish different drone missions with open-domain language instructions? Instructing drones with natural language has a wide range of applications. It can significantly lower the burden of human operators and lead to more possible applications of drone usage. There have been existing LLM-based planning methods. But they are mainly for gaming AI or robotic arms, and cannot be easily generalized to drone planning. We aim at developing the MLLM for planning drone actions with natural language instructions to accomplish a wide variety of tasks.

Challenge #3: How to realize complex tasks decomposition and distribution to drone swarms with multimodal large language model? Operating multiple drones cooperatively to finish complex tasks requires a capable planning system that can effectively conduct reasoning to achieve drone grouping, task decomposition, and task distribution. Recently, the general-purpose large language models have shown to be effective for multi-agent cooperative operations. However, they cannot be easily extended to drone swarms. We will develop a hierarchical multi-agent system, where different MLLM-based agents assume different duties, perform communication, and conduct reasoning to plan drone actions.

Challenge #4: How to create training datasets and evaluation benchmark for the open-domain language-guided drone planning systems? There is a lack of training data for open-domain language-guided drone motion planning. We will collect a large-scale training dataset and develop a benchmark based on simulation and realistic environments. Such a language-guided drone planning dataset and benchmark will be the first time proposed and benefit the community in the long run.

 

N_CUHK4104/24
RNA m1A Modification Regulates the Crosstalk between Treg and Cardiomyocytes during Heart Regeneration and Repair

Hong Kong Project Coordinator: Prof Kathy Oi-lan Lui (The Chinese University of Hong Kong)
Mainland Project Coordinator: Prof Huabing Li (Shanghai Jiao Tong University)

Cardiovascular disease is the leading cause of death globally. However, in adult mammals, the heart cannot regenerate after damage, leading to the progression of ischemic cardiovascular diseases and heart failure. Recently, investigating the regulatory mechanisms underlying cardiac regeneration and repair has been a holy grail of regenerative medicine targeting cardiovascular diseases. Our latest research findings reveal, for the first time, that Treg cells can promote vascular regeneration even in type 2 diabetic mice and induce cardiac repair after myocardial infarction through paracrine secretion. Further preliminary results indicate that the enzyme responsible for m1A modification in Treg cells has the highest expression ratio among various RNA modifications after myocardial infarction in neonatal mice, and its expression is significantly higher than that of splenic Treg cells. Based on this finding, we speculate that m1A modification is necessary for Treg cells of the cardiac microenvironment to exert their function in promoting cardiac tissue regeneration and repair after cardiac injury. This project aims to elucidate the dynamic changes in the RNA epigenetic modification enzymes of immune cells during the process of cardiac regeneration and repair; to reveal the regulatory role of m1A modification in the interaction between Treg cells and myocardial cells of the cardiac microenvironment; to explore the molecular mechanisms underlying the regulation of m1A modification in the interaction between Treg cells and myocardial cells, and to target intervention of m1A modification and its downstream key genes to regulate the therapeutic effects of Treg cells in cardiac regeneration and repair.

 

N_PolyU511/24
Spatio-temporal Evolution of EV Loads and Flexible Interaction with Urban Power Distribution Systems

Hong Kong Project Coordinator: Prof Jerry Yan (The Hong Kong Polytechnic University)
Mainland Project Coordinator: Prof Peng Li (Tianjin University)

With electric vehicles (EVs) becoming more common in cities, our project focuses on understanding and managing how they affect the power grids in urban areas. We aim to develop methods to study how EVs charge and impact city power systems to make them more flexible and resilient. The project involves four main parts:

1. Investigating how EV charging patterns and urban demographics influence the power grid to guide improvements and policies.
2. Creating private predictive models to forecast EV charging needs accurately while protecting data privacy.
3. Testing new technologies to manage the power grid better when EVs charge, maximizing the use of renewable energy.
4. Developing strategies to make the power system more reliable during extreme situations by using EVs as energy sources.

By studying how EVs interact with city power systems, we aim to improve predictions, increase flexibility, and strengthen the resilience of urban power grids. This project not only addresses the challenges of more EVs on the road but also provides valuable training for young researchers, preparing them for future careers.

 

N_PolyU567/24
High Throughput Optoelectrical Modeling and Fabrication of High-efficiency Perovskite/ Cu(Ga,In)Se2 Tandem Solar Cells

Hong Kong Project Coordinator: Prof Gang Li (The Hong Kong Polytechnic University)
Mainland Project Coordinator: Prof Yan Jiang (Beijing Institute of Technology)

Solar energy has become the key to the energy transformation and the realization of strategic goal of “carbon neutrality”. This project aims at promoting novel Perovskite/copper indium gallium selenide (Cu(Ga, In)Se2) monolithic tandem solar cells to significantly improve the efficiency limit and realize great potential in the fields of flexible/portable devices, building-integrated photovoltaics, and aerospace applications. So far, a large device efficiency gap still exists between state-of-the-art tandem solar cell devices and their theoretical limit. Therefore, the development of high-efficiency perovskite/Cu(Ga, In)Se2 monolithic tandem solar cells is of great importance.

This project uses high-throughput optoelectrical simulation methods to reveal the optical and electrical loss mechanisms of state-of-the-art perovskite/ Cu(Ga, In)Se2 tandem solar cells, and guide the precise design and preparation of essential materials in near-infrared transparent perovskite top cells. In addition, the project will also develop large area conformal coating technologies for tandem perovskite Cu(Ga, In)Se2 cells.

This project will provide theoretical foundation and technical support for the development of high-efficiency perovskite/ Cu(Ga, In)Se2 tandem solar cells from the materials and devices perspective.

 

N_PolyU597/24
Ultrafast Laser-enabled Three-dimensional Characterization of Stacked Coatings

Hong Kong Project Coordinator: Prof Zhongqing Su (The Hong Kong Polytechnic University)
Mainland Project Coordinator: Prof Lin Ye (South China University of Science and Technology)

Ultrathin film coatings are pivotal in various industrial fields owing to their unique properties which can significantly enhance the performance, durability and functionality of coated materials and structures. These ultrathin film coatings, only a few nanometers to micrometers thick, require precise control over their thickness and profiles during manufacturing to ensure performance. However, defects at the coating interfaces, though small, can potentially result in downgraded properties and even failure of the entire coated systems. This has necessitated sophisticated inspection techniques to guarantee the integrity of the coating systems. Despite the maturity of current nondestructive testing (NDT) and material characterization approaches, a clear technological gap still exists for inspection at the scales from micrometers to a few hundreds of nanometers, precluding existing NDT from depicting the micron-scale or even nano-scale interfacial details of the stacked ultrathin coating systems cost-effectively and accurately, in a quick and real-time manner.

To fill this gap, the proposed research aspires to a new material characterization framework facilitated by ultrafast laser technology, from fundamental theory to implementation details, for precise, intuitive and rapid imaging of micron-to nano-scale interfacial features of stacked ultrathin coatings, in a fully non-contact, three-dimensional and real-time manner. For scientific research, it is vital to overcome the critical bottlenecks when the current NDT approaches are extended to access the micron-to nano-scale interfacial features of stacked ultrathin coatings used in semiconductors, aircraft, medical and energy industries. For real-world engineering applications, it is imperative to enhance the system integrity of stacked coating systems. The proposed research targets the novelty in fundamental research on femtosecond-laser-induced ultrasound, troubleshoots an important industrial challenge, and advances current NDT capability used in semiconductors, aircraft, medical and energy industries. Addressing these, the significance and impact of this proposed research cannot be overemphasized.

 

N_PolyU5141/24
Structure-preserving Algorithms of Stochastic Partial Differential Equations on Graphs

Hong Kong Project Coordinator: Dr Jianbo Cui (The Hong Kong Polytechnic University)
Mainland Project Coordinator: Prof Jialin Hong (Academy of Mathematics and Systems Science, Chinese Academy of Sciences)

Stochastic partial differential equations (PDEs) are mathematical tools used to model systems that are influenced by random factors, such as the unpredictable behavior of financial markets or quantum mechanics. However, stochastic PDEs are complex and often cannot be solved exactly, which is why researchers rely on numerical methods to approximate their solutions. Despite fruitful numerical results for stochastic PDEs on continuous domains under Euclidean metrics, there remains a gap in the numerical analysis and scientific computing of stochastic PDEs defined on graphs. The underlying graphs possess a type of mathematical structure that represents connections between different points, like the links between web pages on the internet or the interactions between individuals in a social network. Stochastic PDEs on graphs are incredibly useful for modeling complex networks, and characterizing asymptotic and dynamical behaviors of the real-world phenomena. But they also present distinct challenges for numerical methods due to their discrete nature and the absence of traditional geometric properties found in more familiar Euclidean spaces.

Our research aims to develop new algorithms that can handle these challenges and accurately simulate the behavior of stochastic PDEs on graphs. These algorithms are designed to preserve the essential characteristics of the original equations, such as their geometric structures, statistical properties, and dynamical behaviors. By doing so, we can ensure that our numerical simulations remain reliable in the real-world phenomena, even over long periods. The impact of this work is broad and significant. For example, in quantum mechanics, our algorithms could help predict the behavior of particles at the smallest scales; in epidemiology, they could model the spread of diseases through populations; and in finance, they could improve the forecasting of market trends. Furthermore, our research has the potential to influence the design of control systems, which are used to transport the distributions and resources in a network. By collaborating and sharing our findings, we shall advance the field of numerical analysis and provide researchers and practitioners with powerful new tools to understand and predict complex systems affected by randomness.

 

N_PolyU5145/24
Onsager-principle based Modelling and Deep Learning Method for Complex Fluids

Hong Kong Project Coordinator: Prof Zhonghua Qiao (The Hong Kong Polytechnic University)
Mainland Project Coordinator: Prof Tao Zhou (Academy of Mathematics and Systems Science, Chinese Academy of Sciences)

The transport, heat, and mass transfer of multiphase and multicomponent complex fluids are critical in environmental, energy, and industrial applications. Understanding and accurately modeling these processes involve navigating the intricate interactions among liquid-solid reactions, phase interface generation and dynamics, and the multifaceted coupling of seepage, stress, and temperature. These factors collectively pose significant scientific challenges.

This research project aims to address these challenges by developing, analyzing, and implementing computational models specifically for non-isothermal complex fluids. Central to this endeavor will be the emphasis on advanced mathematical modeling techniques and model reduction approaches based on the Onsager variational principle. These methods are expected to provide a robust theoretical foundation for accurately capturing the complex behavior of such fluids. Furthermore, the project will focus on the development of innovative deep learning algorithms tailored to solve the nonlinear, time-evolving partial differential equations governing non-isothermal complex fluids. These algorithms will leverage the latest advancements in machine learning to improve the accuracy and efficiency of simulations, potentially revolutionizing how these complex systems are understood and predicted. The ultimate goal of this research is to establish general theories and computational algorithms capable of effectively addressing fundamental scientific and engineering problems associated with multiphase and multicomponent fluids. By achieving this, the project aims to provide valuable insights and practical solutions applicable across various fields, from environmental science and energy production to industrial processes.

In summary, this research will combine advanced mathematical techniques and cutting-edge deep learning algorithms to tackle the significant challenges posed by the transport, heat, and mass transfer of complex fluids. The outcomes are expected to contribute to the development of general theories and tools that can be applied to a wide range of scientific and engineering problems, thereby advancing our understanding and capability to manage these intricate systems.

 

N_PolyU5165/24
Self-sensing Artificial Muscles with High Work Density from Coiled Hollow Filaments for Children's Scoliosis Braces

Hong Kong Project Coordinator: Prof Bin Fei (The Hong Kong Polytechnic University)
Mainland Project Coordinator: Prof Zunfeng Liu (Nankai University)

Highly twisted and coiled filament actuators exhibit surprising performances, such as high loading force and large actuation stroke with fine structures and precise adjustability. As a significant new member of actuation family, they are expected to replace the traditional bulky and noisy pneumatic bladder actuators. However, wide application of coiling actuators is still limited by several intrinsic problems: the non-uniformity of twist distribution in fibers/yarns limits their maximum accessible twist density at surface and wastes their central material; the low thermal conductivity of common polymers suppresses their heating/cooling and actuation speed; their achievable loading and actuation stroke are still low at mild or skin-safe temperatures; and the coiled filament actuators tend to untwist and lose energy upon release. Despite attempts to address these limits by physical and chemical treatments, no satisfactory and systematic strategy has been developed. Although single parameter was improved in a specific research, comprehensive assembly work to achieve overall performance via cooperation and complementation has not been explored.

In this project, we will develop a relatively universal design strategy by considering both of material matching and structure coordination, in order to achieve a novel class of biomimetic hierarchical structures that simultaneously allow high strength, actuation stroke and speed at mild temperatures. The resultant smart actuation bundles will be properly inserted into scoliosis correction braces to investigate their assistance to active correction treatment, which may lead to next generation of intelligent and comfort medical braces.

 

N_PolyU5172/24
Solar Interfacial Evaporation-driven Water-electricity-hydrogen-fuel Co-generation: Materials and Devices Design

Hong Kong Project Coordinator: Prof Zuankai Wang (The Hong Kong Polytechnic University)
Mainland Project Coordinator: Prof Jia Zhu (Nanjing University)

The efficient utilization of solar energy plays a vital role in China's pursuit of "dual carbon" goals, aiming to reduce carbon emissions and promote sustainable development. Among various solar energy utilization technologies, interfacial photothermal evaporation has emerged as a promising solution due to its unique capabilities in thermal localization and gas-liquid separation. However, despite notable advancements, designing an interfacial photothermal evaporation system that achieves high conversion efficiency, generates valuable products, and ensures effective product separation remains a complex task. This challenge arises from the lack of well-established energy-mass correlation laws, guidelines for material selection, and strategies for seamless device integration. Overcoming these obstacles is crucial for advancing solar energy utilization and contributing to a greener future.

In this groundbreaking project, we propose an innovative strategy for simultaneous cogeneration of water, electricity, hydrogen, and fuel using solar-driven interfacial evaporation. We will employ advanced calculation and simulation methods to comprehensively investigate this phenomenon and gain insights into the generation and management of energy and mass transfer phenomena. Building upon these insights, we will develop a comprehensive theoretical model that facilitates broad-spectrum solar absorption, efficient photon energy management, and localized thermal effects. This model will serve as a guiding principle for material and device design, unlocking the full potential of synergistic co-generation at interfaces. To optimize performance, we will prioritize materials design and regulation for different water and energy harvesting scenarios. This will involve the development of specific material interfaces, such as super-slippery and superhydrophilic surfaces, as well as fluid or optical topology structures such as porous and hierarchical structures. Through these approaches, we aim to enhance the speed and efficiency of water-solid interactions in both spatial and temporal domains, ultimately maximizing energy conversion efficiency. Furthermore, we will design a comprehensive co-generation energy device system that integrates a water evaporator, evaporation/droplet kinetic-based generator, and photocatalytic hydrogen producer. To facilitate large-scale implementation, we will leverage integrated construction techniques and advanced processing methods to enable rapid and efficient fabrication of power generation materials and electrodes.

The outcomes of this project will significantly contribute to our understanding of solar energy conversion at interfaces, foster the development of innovative solar utilization technologies, and promote the integration of interface science and energy materials. Ultimately, this research will pave the way for a sustainable energy future, aligning China's efforts with global environmental goals.

 

N_HKUST612/24
Magnetic field sensing and energy gap measurements of high Tc superconductivity under high pressure

Hong Kong Project Coordinator: Prof Sen Yang (The Hong Kong University of Science and Technology)
Mainland Project Coordinator: Prof Jinlong Zhu (Southern University of Science and Technology)

Superconductors are amazing materials that can carry electricity without any loss, making them incredibly valuable for power systems and transportation. However, most of them only work at very cold temperatures, which limits their practical use. Our project aims to understand how we can make superconductors work at higher temperatures.

We're combining two powerful measuring techniques in a novel way: quantum sensors that can "see" magnetic fields at a tiny scale, and a method that probes electrical properties at specific points. Think of it like having both a microscope and a thermometer to study these materials in detail. By applying extreme conditions - high pressure, very low temperatures, and strong magnetic fields - we can see how these materials change and behave.

We're studying several promising materials, including new metal compounds and complex materials containing copper or nickel. By understanding how different factors affect superconductivity, we hope to discover the recipe for creating better superconductors that work at higher temperatures. This research could lead to breakthroughs in how we transport and use electricity in the future, while also training the next generation of scientists in this important field.

 

N_HKUST613/24
Outlining Catalysis in Confined Space Assisted by Machine Learning

Hong Kong Project Coordinator: Prof Yangjian Quan (The Hong Kong University of Science and Technology)
Mainland Project Coordinator: Prof Cheng Wang (Xiamen University)

Catalysts operating in confined spaces exhibit different catalytic performance from those in open environments, a phenomenon denoted as “confinement effect”. This effect essentially arises from multiple factors involving host-guest interactions, mass transfer dynamics, local concentration changes, entropy effects, and electronic fields. While certain confined catalysis systems, such as enzyme catalysis, catalysis in nanoreactors, and reticular material-based catalysis, have demonstrated remarkable improvements in performance, their design often relies on trial and error, lacking a comprehensive understanding of the confinement effect. The complexity of these catalytic systems and their multifaceted attributes pose formidable challenges in defining their underlying principles.

Recent advancements in data science, particularly machine learning (ML), offer new avenues for exploring the confinement effect. Chemical structures can now be quantified using physicochemical descriptors, topological graphs, or as vectors in pretrained models. By mapping the structures of catalysts and reagents into a latent space, we can undertake innovative studies that merge data-driven screening with fundamental physical insights. However, the use of ML requires substantial accurate reference data to train models and ensure their validity in a high-dimensional space. Fortunately, advancements in automation technology have facilitated the rapid, cost-effective generation of experimental data, significantly reducing human intervention and enhancing productivity. The integration of automation and ML is expected to become a powerful tool to revolutionize our understanding, prediction, and engineering of confined catalysis.

 

N_HKUST616/24
Spin Relaxation Management of Chiral Perovskites towards Efficient Spin Light-Emitting Diodes

Hong Kong Project Coordinator: Prof Haipeng Lu (The Hong Kong University of Science and Technology)
Mainland Project Coordinator: Prof Ye Yang (Xiamen University)

Organic-inorganic hybrid perovskites have recently been investigated as promising candidates for spintronic applications and as ideal platforms for exploring and controlling spin properties. Our previous research had confirmed the presence of chiral-induced spin selectivity, which results in spin-polarized excitons and electrons in chiral perovskites. However, the fundamental origins and precise mechanisms of this phenomenon remain unclear.

This proposed project seeks to address these critical questions by focusing on the spin properties of metal-halide perovskites. We aim to modulate spin-related properties, including spin polarization and spin relaxation lifetime, through variations in inorganic dimensionality and organic chirality, thereby establishing the relationship between structure and spin properties. Our objective is to elucidate the fundamental mechanisms by which the chirality of organic components and the structure of the inorganic framework result in novel spin phenomena. We plan to employ advanced ultrafast spectroscopic techniques, such as transient absorption and Faraday rotation spectroscopy, to initialize, probe, and manipulate spin states, thereby demonstrating control over spin dynamics in excited states. Subsequently, we will utilize halide perovskites with superior spin properties to construct spintronic devices, such as spin light-emitting diodes (spin-LEDs).

Our research team comprises experts in inorganic and physical chemistry with a proven track record in synthesizing novel halide perovskites and characterizing their spin properties, uniquely positioning us to achieve the project objectives. The successful completion of this project will accelerate the development of new energy-efficient technologies based on spin manipulation and significantly reduce the costs of current technologies through the solution processability of hybrid semiconductors.

 

N_HKUST626/24
Unraveling the role of formation and transformation processes of highly reactive nitrogen compounds on photochemical air pollution

Hong Kong Project Coordinator: Prof Zhe Wang (The Hong Kong University of Science and Technology)
Mainland Project Coordinator: Prof Weigang Wang (Institute of Chemistry, Chinese Academy of Sciences)

Urbanization and increasing energy consumption have led to substantial emissions of nitrogen oxides (NOx) from anthropogenic processes, significantly altering the atmospheric composition. NOx undergoes complex reactions and forms various reactive and oxidized nitrogen compounds, such as dinitrogen pentoxide (N2O5), nitrous acid (HONO), peroxyacetyl nitrate (PAN), and other organic nitrogen (OrgN). These highly reactive nitrogen (HRN) compounds play a crucial role in tropospheric atmospheric chemistry, affecting atmospheric oxidative capability, nitrogen cycles, and contributing to regional air pollution issues like photochemical smog, haze, and acid deposition. Despite dedicated research, significant knowledge gaps remain in our understanding of the sources, formation mechanisms, chemical transformation, and environmental impacts of these HRN species, particularly HONO, organic nitro and nitrate compounds. Quantifying their sources and fates is essential for mitigating their adverse effects on human health, regional air quality, and climate change.

The North China Plain (NCP) and the Greater Bay Area (GBA) are two rapidly developing regions in China facing severe air pollution challenges, notably winter haze in the NCP and photochemical ozone pollution in the GBA. High levels of NOx and oxidants create favorable conditions for HRN formation in those urban regions, but their characteristics and formation mechanisms differ significantly under varied precursors and aerosol conditions, which remain poorly understood. The under-studied mechanisms would lead to significant uncertainties in air quality models, affecting the accuracy of simulations and forecasts of photochemical pollutants formation. By leveraging the complementary expertise and collaborations of two research teams, we propose an integrated study combining field observations, laboratory experiments, and modeling to investigate the formation and fate of inorganic and organic HRN compounds in two polluted megacities with different precursors and climatic conditions. We will examine and compare the atmospheric abundance and characteristics of HRN compounds in typical urban environments of Beijing and Hong Kong. Ambient-relevant laboratory experiments will explore the molecular-level formation and degradation mechanisms of HRN, and the conversion and cycling of different nitrogen species. Model simulations, constrained with field and laboratory data, will assess the roles of HRN chemistry in shaping photochemistry and O3 pollution in both regions. The results will contribute to a more comprehensive understanding of reactive nitrogen chemistry, and provide scientific support for developing effective pollution mitigation strategies.

 

N_HKUST648/24
Investigating the Mechanisms of Single Synaptic Vesicle Recycling Using Real-Time Three-dimensional Nanometer-accuracy Tracking Microscope and Cryo-Electron Tomography

Hong Kong Project Coordinator: Prof Hyokeun Park (The Hong Kong University of Science and Technology)
Mainland Project Coordinator: Prof Guoqiang Bi (University of Science and Technology of China )

Neurons communicate by releasing neurotransmitters stored inside synaptic vesicles in presynaptic terminals, subsequently activating postsynaptic receptors and generating action potentials in postsynaptic neurons. Neuronal communication is very efficient, allowing the fast transfer of information in the neuronal network. To support fast communication, synaptic vesicles undergo exocytosis and are recycled rapidly in presynaptic terminals. Altered synaptic vesicle recycling was reported in various neurological disorders including Alzheimer’s disease, Parkinson’s disease, and autism spectrum disorder (ASD). However, synaptic vesicle recycling in presynaptic terminals is not clearly understood. Particularly, the spatial distribution, dynamics and recycling mechanisms of single recycled synaptic vesicles are poorly understood.

Professor Hyokeun Park at HKUST and Professor Guoqiang Bi at University of Science and Technology of China (USTC) and Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences (SIAT-CAS) have collaborated to study the dynamics, spatial distribution and exocytosis of single synaptic vesicles with complementary methods for long time and published a joint paper about inhibitory synaptic vesicles in PNAS. The Park lab developed a real-time three-dimensional (3D) nanometer-accuracy tracking microscope and used this microscope to monitor the exocytosis and mobility of single synaptic vesicles in living neurons at high spatial resolution. The Bi lab has visualized the structure and organization of individual synaptic vesicles and organelles in synapses at molecular resolution using cellular cryo-electron tomography (cryo-ET). Furthermore, they set up MINFLUX microscopy (a super-resolution light microscopy) and tracked synaptic proteins at high spatiotemporal resolution. Using these complementary techniques, the Park lab and Bi lab will investigate the spatial distribution, dynamics, and recycling mechanisms of single recycled synaptic vesicles in neurons.

Based on their preliminary results, the Park lab and Bi lab will test a working hypothesis that the spatial distribution and dynamics of single recycled synaptic vesicles depend on its previous releasing mode. In this proposal, we will measure the spatial distribution and dynamics of single recycled synaptic vesicles by evoked or spontaneous release in rat hippocampal neurons using the real-time 3D nanometer-accuracy tracking microscope and cryo-ET. Furthermore, we will investigate the mechanisms of synaptic vesicle recycling using cellular cryo-ET and MINFLUX microscopy.

The results from this joint project will provide detailed information about spatial distribution and dynamics of single recycled synaptic vesicle in presynaptic terminals and their recycling mechanisms. Thus, this project will advance our understanding of synaptic transmission and facilitate the development of new therapeutic approaches for treating neurological disorders associated with altered synaptic vesicle recycling.

 

N_HKUST654/24
Development of the Fundamental Technologies of Multimodal Ultrasound-Based Large Foundation Models for Fetal Brain Developmental Abnormalities Diagnosis

Hong Kong Project Coordinator: Prof Xiaomeng Li (The Hong Kong University of Science and Technology)
Mainland Project Coordinator: Prof Kenli Li (Hunan University)

Currently, due to the dynamic nature and low standardization of ultrasound imaging, existing artificial intelligence-based diagnostic techniques often face issues of misdiagnosis and missed diagnosis, particularly in diagnosing fetal brain developmental abnormalities (FBDA), which accounts for about 75% of the causes of fetal deaths during pregnancy. These challenges include inadequate model generalization, weak interpretability, and a lack of positive cases, making it difficult to meet the high precision requirements in clinical settings. Therefore, this study aims to develop key technologies for a multimodal ultrasound-based large foundation model specifically targeting FBDA diagnosis. Firstly, a comprehensive multimodal database with expert knowledge repository will be constructed to provide rich and accurate data support and expert-level annotations for intelligent diagnostic algorithms. Secondly, the project will focus on addressing the challenge of real-time capturing of key diagnostic planes in ultrasound videos and 3D volume ultrasound, thereby improving the accuracy and timeliness of ultrasound diagnosis. Thirdly, the project will explore semantic controllable synthesis techniques for positive cases, effectively addressing the issue of scarce data in abnormal diagnosis. Finally, this project will combine edge computing and cloud computing to develop a multimodal language-image-based diagnostic large foundation model, aiming to break down data silos and achieve robust and generalizable ultrasound-assisted screening and real-time intelligent diagnosis. This research holds significant importance in enhancing China's international competitiveness in intelligent ultrasound, ensuring the health of the population, and improving the quality of newborns.

 

N_HKUST660/24
Mechanism of water migration in multiple media of soils under coupled influences of atmosphere and vegetation

Hong Kong Project Coordinator: Dr Pui San So (The Hong Kong University of Science and Technology)
Mainland Project Coordinator: Dr Qing Cheng (Nanjing University)

Water migration in soil is a key factor in triggering geological disasters, especially slope failure. When water moves into soil, the strength of soil decreases to cause instability of earthen structures. Although vegetation is always considered as a natural-based solution to reinforce soil, roots can act as preferential flow paths and shrink at drying to accelerate soil-water migration. The natural occurrence of soil crack formation and healing due to drying and wetting further complicates the water migration. However, the interplay among vegetation, atmospheric conditions and cracks on soil-water migration is generally ignored. More fundamentally, water migration is governed by the interactions between multiple soil media (i.e., pores, root-soil interface and cracks). Identifying the relevant mechanisms is a prerequisite to achieve mitigation and prevention of geological disasters.

This project investigates water migration across multiple soil media in vegetated soil with cracks under different atmospheric conditions. State-of-the-art technologies utilising computer vision, machine learning-based CT analysis and electrical resistivity tomography will be first developed to quantify the soil media. These technologies will be implemented to determine the effects of atmospheric conditions on the evolution of root morphology and multiple media in vegetated soil. Both laboratory and field tests will also be conducted to examine the strength and hydrological behaviour of vegetated soil with cracks. The new findings will be incorporated into theoretical models to create an integrated framework for evaluating the evolution of multiple soil media and hence the water migration.

An interdisciplinary team with expertise in unsaturated soil mechanics, eco-geotechnics and engineering geology has been assembled from the Mainland and Hong Kong to collaboratively investigate the soil-vegetation-atmosphere interactions. The collaborative project would achieve several deliverables: 1) Improvement of fundamental understanding on water migration in vegetated soil with cracks to facilitate mitigation and prevention of geological disasters under climate change; 2) Development of scientific bases and theoretical tools to establish the first design guideline for vegetated earthen structures with cracks against climate change; 3) Provision of an integrated approach to quantify multiple soil media for field monitoring and advancing the performance of existing earthen structures.

 

N_HKUST664/24
Nucleation and growth of ice under nanoconfinement: a combined in-situ liquid cell TEM and ab initio molecular dynamics study

Hong Kong Project Coordinator: Prof Ding Pan (The Hong Kong University of Science and Technology)
Mainland Project Coordinator: Prof Lifen Wang (Institute of Physics, Chinese Academy of Sciences)

Water in the natural world does not solely exist in the bulk phase. Aqueous solutions can also be trapped within nanometer-scale cavities or adsorbed as thin liquid films at the nanoscale. In biological systems, water may be confined by proteins and DNA. Similarly, in deep Earth, aqueous solutions are often confined within nanopores, grain boundaries, and fractures in geological minerals. Engineered water purification systems can also give rise to nanoconfined aqueous solutions. When bulk water reaches temperatures around 0 °C at ambient pressure, the molecules stick together to form solid ice, typically at the liquid-solid interface through a process known as heterogeneous ice nucleation. While heterogeneous ice nucleation in the bulk phase has been extensively studied, our understanding of water freezing under nanoconfinement remains very limited. Spatial confinement and the influence of liquid-solid interfaces can dramatically alter the physical and chemical properties of water compared to the bulk state, suggesting that the freezing process may also behave very differently in nanoconfined environments.

This study aims to elucidate the nucleation and growth of ice under nanoconfinement by integrating in-situ liquid cell transmission electron microscopy (TEM) and first-principles computational simulations. We will develop and apply first-principles methods accelerated by data-driven machine learning techniques to study the water freezing process in nanoconfined settings. The theoretical models will be validated against the experimental TEM observations, and will be used to interpret experimental data at the atomistic scale. The molecular dynamics trajectories generated by machine learning molecular dynamics will be used to develop Markov state models (MSMs) through unsupervised learning techniques. These MSMs can provide comprehensive insights into the complex nucleation kinetics of nanoconfined water. Furthermore, the combination of neural network force fields and dipole models will allow us to obtain the vibrational and dielectric spectra, which can be leveraged to validate our theoretical approaches, as well as analyze the hydrogen bonding network and dynamics in nanoconfined water systems. This joint experimental and theoretical study holds significant importance for the field of water science, with far-reaching implications for diverse domains such as biology, Earth science, and engineering.

 

N_HKUST675/24
The Spin Dynamics in Noncollinear Magnetic Materials and Emerging Spintronic Applications

Hong Kong Project Coordinator: Prof Shiming Lei (The Hong Kong University of Science and Technology)
Mainland Project Coordinator: Prof Peng Li (University of Science and Technology of China)

Magnons and topological spin textures have emerged as highly promising research directions in the fields of condensed matter physics and quantum materials, due to their rich physics and their immense potential in applications such as information storage, transmission, and processing. Although there has been active research progress on the development of theoretical frameworks and some device designing concepts involving magnons, excitons, and their interactions with microwave photons on conventional ferromagnetic materials, the spin dynamics of materials with noncollinear spin texture and their potentials in novel computation devices remain largely unexplored. The goal of our current project is to address this issue by combining our complementary expertise on the development of single crystalline magnetic materials with noncollinear spin texture, investigation of spin dynamics by magnon-photon coupling, and development of new computational devices. In particular, we will focus on two materials families with large chemical tunability, aiming to synthesize high-quality single crystals and map their magnetic phase diagrams, including noncollinear phases. Subsequently, we will explore magnon properties (dispersion, damping, and resonance), their coupling with skyrmions and microwave photons, and memory and nonlinear response characteristics within the physical reservoir computing framework. Combining materials growth, characterization, spectroscopy, device fabrication, and theoretical modeling, this project aims to address key challenges in quantum materials development, propelling innovation in the fields of magnonics for information storage and processing and laying the foundation for future quantum information technologies and computing.

 

N_HKUST677/24
Cognitive Mobile Sensing Network for Coastal Bay Coral Reef Environment

Hong Kong Project Coordinator: Prof Fumin Zhang (The Hong Kong University of Science and Technology)
Mainland Project Coordinator: Prof Feng Tong (Xiamen University)

A cognitive mobile sensing network composed of micro autonomous underwater vehicles (AUVs) will be established to provide an effective way for the protection, management, and sustainable utilization of coral reef marine ecosystems in the Greater Bay Area. The proposed research addresses great research challenges that include: the complex and time-varying multipath of the underwater acoustic channel in the coral reef environment, the highly complicated temporal-spatial environmental factors, as well as the severely limited capabilities of micro AUVs.

This proposal overcomes these challenges by performing a series of collaborative research efforts. Firstly, the stable and time-varying multipath components of the shallow water acoustic channel in the coral reef area are dynamically decoupled, and a discriminative processing mechanism is designed to improve the underwater acoustic communication performance. Furthermore, combining with the control algorithms for micro AUVs, a dynamic optimization network access scheme is proposed to address weak underwater acoustic connectivity. Then a resource scheduling method is designed, which considers environmental factors of coral reefs, node characteristics, and link quality of the network. A framework for analyzing and processing environmental sensing data collected by the cognitive mobile sensing networks will be established. Finally, for conducting experiments in the sea, an experimental sensing network will be constructed by integrating the proposed underwater acoustic networking systems and micro AUVs. This proposal brings innovations and breakthroughs for both theoretical research and practical underwater systems to achieve an in-situ, real-time, and three-dimensional observation network for the coral reef environment in the Greater Bay Area.

 

N_HKU702/24
Graph neural network-assisted multiscale framework for strain hardening prediction: the case of age-hardened aluminum alloys

Hong Kong Project Coordinator: Prof Alfonso Hing Wan Ngan (The University of Hong Kong)
Mainland Project Coordinator: Prof Wei Xu (Northeastern University)

For age-hardened aluminum alloys used in key sectors, complex precipitates affect the gliding and storage mechanisms of dislocations, thus affecting the strain hardening behavior and overall mechanical properties of the alloys. It is very challenging to link the individual dislocation motion with the macroscopic plastic flow of polycrystals, and traditional small-scale methods are difficult to achieve this goal. Therefore, this research aims to establish a new hierarchical multiscale framework that can predict the strain hardening behavior of age-hardened aluminum alloys based on dislocation-precipitate interactions. By using a deep graph neural network to transform the dislocation-precipitate interactions into a graph inference problem, and based on this, a graph neural network-assisted multiscale framework is proposed to predict the strain hardening behavior of age-hardened aluminum alloys. Continuous and discrete dislocation dynamics models are developed for age-hardened aluminum alloys, respectively, to accurately simulate the dislocation-precipitate interactions, and provide training data for the subsequent graph neural network; the trained graph neural network can be used to efficiently predict the dislocation evolution; crystal plasticity finite element model, using the graph neural network returned dislocation evolution to accurately simulate the crystal plasticity problem; in order to verify the rationality of the model and establish the model parameters, carry out macro/micro mechanical testing and microstructural characterization experiments. Our multiscale framework can serve as a demonstration for the simulation of other alloys and mechanical properties.

 

N_HKU705/24
Modelling, Perception, Learning and Reaction of Human Feedback for Open-World Mixed Autonomy

Hong Kong Project Coordinator: Dr Jia Pan (The University of Hong Kong)
Mainland Project Coordinator: Prof Yong-jin Liu (Tsinghua University)

Robots are becoming more common in our daily lives, from self-driving cars to personal home assistants. However, making robots work smoothly alongside humans is difficult. For example, autonomous cars sometimes crash, and personal robots might move things too close to people. This happens because humans are complex and can think, feel, and act unpredictably, which robots find hard to understand and predict. To solve such problems, this project focuses on several key areas:

1. Understanding Human Behavior: We will create models to predict how people perceive and react to robots based on past behaviors, which is crucial for understanding human intentions and emotions.
2. Perception of Human Actions: Robots need to understand human reactions in various situations. We will develop methods to help robots better perceive human behaviors, even those that are unusual or unexpected.
3. Intelligent Inferences: Even with good models and perception, robots need to infer human intentions and work collaboratively. The project aims to make robots not just safe and efficient, but also able to communicate their intentions clearly to humans.
4. Collecting Human Feedback: Current methods of gathering feedback are costly and not always accurate. The project will find scalable ways to collect data on human-robot collaboration to improve robot performance.
5. Real-World Testing: Most current research is done in labs. This project will test robots in real-world industrial tasks to develop better benchmarks and criteria for evaluating robot performance in everyday settings.

In summary, the project aims to make robots better at understanding, predicting, and working with humans by improving their ability to perceive human behavior, infer intentions, and gather feedback, all tested in real-world scenarios.

 

N_HKU711/24
Dynamic intercity ridesharing: Models, algorithms, and policy insights

Hong Kong Project Coordinator: Prof Wai-yuen Szeto (The University of Hong Kong)
Mainland Project Coordinator: Prof Jiancheng Long (Heifei University of Technology)

Intercity ridesharing is one of the emerging mobility services for intercity trips, which can provide a high-quality, comfortable, and door-to-door service together with flexible routes and schedules to customers. Thus, intercity ridesharing may become an alternative travel mode to public transport. As a new mobility service, intercity ridesharing has many unique features different from traditional ridesharing including the routes comprising multiple round trips, the schedules considering the break of drivers due to long-distance trips, and customized travel requirements from passengers (e.g., the first to board). Therefore, existing ridesharing methodologies cannot be directly applied to tackle operational and planning challenges such as unprofitable fares, an insufficient/excessive fleet size, customer complaints about long waiting times, frequent unsuccessful matching, and long/excessive empty trips. However, the literature has rarely developed methodologies to address these challenges. To address these challenges, this proposed study will propose novel methodologies for time-varying demand estimation, strategic fare setting and fleet size planning, and operation optimization for intercity ridesharing. Specifically, we will first collect multi-type travel demand data (e.g., ride-hailing data, new energy vehicle user data, and mobile phone data) to perform data fusion and develop deep-learning-based methods for intercity ridesharing demand prediction. The optimization models and algorithms for vehicle route determination will then be developed, which will aim to improve the operational efficiency under different operation modes (i.e., holiday and non-holiday modes) and practical constraints (e.g., pick-up and drop-off sequences, break of drivers, and travel time uncertainty). The optimization models and algorithms will also be extended to the scenarios with multi-type vehicles (e.g., more comfortable vehicles vs less comfortable vehicles) and multiple cities. Finally, we will strategically determine optimal flag fares and fleet sizes by considering both the single-platform scenario and two-platform competition under travel time uncertainty. Case studies will be conducted to draw managerial and regulatory policy insights for platforms and government agencies, respectively.

Overall, the proposed study will focus on the practical intercity ridesharing problem and develop various new optimization models and algorithms for the problem. The proposed study will make theoretical advances to ridesharing problems, and practical contributions to the development of intercity ridesharing services in major cities like Hong Kong, Shenzhen, and Macao. The results and policy analysis enable not only policymakers to achieve an in-depth understanding of intercity ridesharing and regulate the market effectively but also the intercity ridesharing platform to increase the level of customer satisfaction, service quality, and profitability.

 

N_HKU715/24
Developing strategies to boost resilience of metro systems to extreme flooding in a changing climate

Hong Kong Project Coordinator: Dr Mingfu Guan (The University of Hong Kong)
Mainland Project Coordinator: Prof Jie Yin (East China Normal University )

Extreme flooding is a recurring issue in coastal cities, including Hong Kong, as climatic extremes become more frequent, resulting in significant socioeconomic impacts. Recent evidence has highlighted the vulnerability of metro systems to surface water flood intrusion caused by heavy rainfall, storm surges, or a combination of both. Notably, in September 2023, Hong Kong and Shenzhen experienced record-breaking rainfall, leading to severe waterlogging at four stations and necessitating the closure of exits at four others. Similar events have occurred in other coastal megacities like New York, and Shanghai. The flood intrusion process into metro systems is complex, posing significant challenges in capturing, modeling, and replicating the entire flood dynamic process. Conducting a comprehensive evaluation of flood vulnerability and resilience in metro systems at a city scale is crucial for developing effective disaster plans, including preparation, emergency response strategies, and timely implementation of mitigation measures. Hence, this project aims to develop strategies to enhance the resilience of metro systems to extreme flooding under the impact of climate change and sea-level rise. This will be achieved through local scale dynamic modelling of flood processes in a metro station. The developed local flood dynamic model will be then upscaled to regional and city scale quantification and evaluation of flood vulnerability and resilience of metro stations. The impact of climate change and sea-level rise on metro system flood risks will be also evaluated. We will then develop and systematically assess both structural and non-structural strategies to improve the resilience of metro systems in the face of climate change. Research tasks will be conducted in both Hong Kong and Shanghai, considering their distinct metro system structures, designs, and operational characteristics.

 

N_HKU729/24
Precise Thickness Control for 2D Crystal Growth: Theoretical Insight and Experimental Realization

Hong Kong Project Coordinator: Prof Lain-jong Li (The University of Hong Kong)
Mainland Project Coordinator: Prof Feng Ding (Shenzhen Institute of Advanced Technology, Chinese Academy of Science)

This project focuses on improving the way we grow futuristic materials called two-dimensional (2D) materials, which are as thin as a few atoms and have unique properties that could revolutionize future electronics, energy-efficient devices, and even quantum computing. For example, slightly thicker versions of one 2D material, MoS2, can have higher performance, show unusual electrical effects, or have properties that change based on how the layers are stacked. The challenge lies in growing these materials in a way that is precise, scalable, and controllable. The project starts by investigating how the surface of the underlying material (the substrate) interacts with these 2D materials during growth. Special layers, called atomic buffer layers, form on the substrate during this process and can greatly affect the quality of the 2D material. However, scientists don’t yet fully understand how this happens. By tweaking the surface with things like sulfur or salt, we aim to control how the 2D materials form, layer by layer. We will use advanced computer simulations and artificial intelligence to predict the best ways to grow these materials and test their ideas in experiments. The ultimate goal is to develop a method for growing high-quality 2D materials that are uniform, scalable, and tailored for specific uses. Starting with a material called MoS2, the project will later expand to other important materials like WSe2 (a type of semiconductor) and hBN (an insulator).

In simple terms, this project seeks to unlock the secrets of how to grow these futuristic materials reliably, paving the way for better electronics, advanced sensors, and innovative computing technologies.

 

N_HKU753/24
Boron and Nitrogen Doped Chiral Organic Semiconductors

Hong Kong Project Coordinator: Dr Junzhi Liu (The University of Hong Kong)
Mainland Project Coordinator: Prof Zhaohui Wang (Tsinghua University)

The design, synthesis, and self-assembly of organic chiral materials, and the fabrication of opto-electronic devices based upon them are still in an embryonic stage. However, they have great potential for scientific innovation. Especially in recent years, although the helicenes and their derivatives have been booming, most of them are limited to synthesis, the relationship between chirality and devices has not been effectively developed. In this joint project, coordinated by Prof. Dr. Zhaohui Wang from Tsinghua University and Dr. Junzhi Liu from The University of Hong Kong, we target the rational development of multi-dimensional chiral π-functional materials based on BN-doped and imide-based helicenes. This topic is highly multi-disciplinary lying at the interface of materials science, organic chemistry, theoretical chemistry, and semiconductor physics. Our integral approach includes the precise construction of single chiral π-functional molecules and aims to unravel the mechanism by which chirality propagates in supramolecular and aggregates, and to establish the relations between chiral effects and functions.

A series of chiral functional materials will be developed via tailored synthesis of pure π-functional enantiomers. To boost the chiroptical response of the materials, experimental synthesis will be guided by computational screening based on quantum-chemical calculations with the aim of pre-identifying compounds with high luminescence dissymmetry factors. Furthermore, we will focus on fine-tuning multi-level self-assembled nanostructures and understanding the mechanism of chirality propagation. Also here, computer simulations will play an important role in the chemical design by predicting the conformations of chiral polymers and aggregates and their corresponding chiroptical properties. Finally, experimental and theoretical investigations of transport and photo-modulation properties of these materials will serve as a basis for selecting suitable candidates for the eventual construction of new functional chiral molecular devices.

 

N_HKU784/24
Molecular Mechanism and Functional Study of XLF Lactylation in Non-Homologous End Joining Repair and Radio-Chemo Resistance in Cancer

Hong Kong Project Coordinator: Prof Shikang Liang (The University of Hong Kong)
Mainland Project Coordinator: Prof Jian Yuan (Tongji University)

DNA is the main carrier of human genetic information and must be delivered undamaged to the next generation to sustain hereditary and health. However, DNA damage is inevitable due to a series of factors (e.g., radiation, chemical reagents etc.).There are different types of DNA damage and DNA double-strand break (DSB) is the most lethal one, which can easily result in cell death. In humans, a pathway known as Non-Homologous End Joining (NHEJ) is responsible for fixing most of the DSBs.

Cancer cells are fast-dividing cells without proper physiological functions. Many well-established, widely-used, and economical cancer treatments, including chemo- and radiotherapy, kill cancer cells via inducing high loads of DNA damage, especially DSBs. However, successful cancer therapy remains a major challenge, particularly in chemo- and radioresistant patients. There are urgent and growing needs for better understanding of the resistance.

One hallmark of cancer is deregulating cellular metabolism. Cancer cells have high level of lactate production. It is known as the Warburg effect, proposed by Otto Warburg in 1920s. Lactate has been long recognised as a metabolic by-product and energy source with no other physiological roles. It was not discovered until 2019 that lactate can be used to modify and regulate proteins, a phenomenon known as protein lactylation. However, our understanding of protein lactylation has been very limited.

As part of our ongoing efforts in understanding human DNA Damage Response and Repair (DDR&R), we discovered that XLF, a key NHEJ protein, is regulated by lactate modification and that XLF lactylation is also related to the resistance of cancer cells to radio- and chemotherapy. However, the molecular mechanisms of XLF lactylation and its physiological roles remain unclear. This project will investigate the molecular mechanism and functional study of XLF lactylation in NHEJ using different approaches including Molecular Biology, Biochemistry, Biophysics, Structural Biology, and Cellular Biology. This project will provide us with better understanding and potential therapeutic strategies of targeting XLF lactylation to treat cancer resistance to radio- and chemotherapy.

 

N_HKU7107/24
Quantum sensors with optimized complexity for the NISQ era

Hong Kong Project Coordinator: Dr Yuxiang Yang (The University of Hong Kong)
Mainland Project Coordinator: Prof Lijian Zhang (Nanjing University)

Quantum sensing provides the precision and resolution beyond its classical counterpart, and therefore may find important applications in both fundamental science and practical tasks. For instance, quantum-enhanced lasers can serve as clocks and rulers that are much more accurate, allowing ultra-precise global positioning and detection of ultra-weak signals like gravitational waves. As quantum technologies are advancing, quantum sensors are being employed in more complex situations, where the overall high complexity (i.e., the number of operations needed to be applied) of sensing schemes becomes the limiting factor of quantum sensing. In this project, we develop quantum sensing protocols from a unique angle that unifies quantum sensing and complexity theory. Under the constraint of near-term quantum technologies, we will establish the optimal tradeoff between the precision of quantum sensors and their complexity and use an algorithmic approach to design quantum sensors that attain the optimal learning of physical processes. Besides the theoretical framework, we will also demonstrate our sensing algorithms via a photonic platform, leading to primitive low-complexity, high-efficiency quantum sensing systems. Unlike conventional approaches to quantum sensing, we take a novel algorithmic point of view and investigate the role that circuit complexity applies in quantum sensing. Our approach is particularly meaningful for the implementation of quantum sensing and for its practical applications in the near-term future, as it will yield a series of algorithms and benchmarks tailored for near-term quantum technologies.

 

N_HKU7120/24
Exploring the role of anaphylatoxins in the pathogenesis of diabetic kidney disease

Hong Kong Project Coordinator: Prof Sydney Chi-wai Tang (The University of Hong Kong)
Mainland Project Coordinator: Prof Min Chen (Peking University First Hospital)

Diabetic kidney disease (DKD), one of the most common complications of diabetes mellitus, is characterized by a progressive increase in urine protein excretion or decrease in glomerular filtration rate (a measure of kidney function) or both in approximately 40% of patients with diabetes and is the leading cause of chronic kidney disease and end-stage kidney disease worldwide. Apart from abnormal blood glucose levels, it has long been known that dysregulation of lipid metabolism is associated with DKD progression, however, the underlying mechanisms remain unexplored. The complement system is an essential part of natural or innate immunity that triggers inflammatory responses as a means of host defense. Our previous studies have revealed that the potent complement anaphylatoxins C3a and C5a were upregulated in patients with DKD and played a critical role in kidney inflammation, kidney injury and ultimately fibrosis in experimental animals. Intriguingly, we discovered that complement activation modulated lipid metabolism in DKD, suggesting a metabolic reprogramming process during the progression of the disease that aligns with the observation of a high prevalence of abnormal lipid or cholesterol levels in diabetic patients. In this study, we aim to further explore how complement anaphylatoxins are related to DKD by (i) exploring the role of C5a/C5a receptor (C5aR) interaction in lipid metabolism in DKD, (ii) dissecting the mechanisms of C5a-mediated DKD and (iii) evaluating the efficacy of pharmacological inhibition of C5a/C5aR interaction in alleviating DKD. Findings from this study will provide insights into a new mechanism of DKD and open a new paradigm for the development of novel therapeutic targets of the complement system in DKD.

 

N_HKU7132/24
Conserved mechanisms that underlie motile cilia function

Hong Kong Project Coordinator: Prof Mu He (The University of Hong Kong)
Mainland Project Coordinator: Prof Junmin Pan (Tsinghua University)

Motile cilia are eukaryotic organelles with essential chemo- and mechano-sensing functions that span evolution, from single-cell organisms to humans. Motile cilia within the mammalian nervous, respiratory, and reproductive systems are characterized by unique motility proteins that generate fluid flow, crucial for transporting metabolites and removing mucus. These cilia are characterized by radial spokes, dynein arms, and a central pair apparatus, all of which are essential for driving motility. The molecular mechanism of motile cilia biogenesis remains unknown. In this proposal, we aim to identify the molecular function of a highly conserved protein complex, involving STK36-KIF27, in regulating cilia assembly and motility. To identify conserved molecular pathways involved in ciliogenesis, we will perform comparative transcriptomic analyses for genes associated with cilia and flagella in Chlamydomonas and mammalian ciliated cells, followed by functional validations using respective model organisms. This collaborative project will shed light on the unity and divergence in cilia formation and provide insight to better understand the molecular basis underlying motile ciliopathies.