Project No.: R1018-22
Project Title: A Study of Energy Harvesting and Fire Hazards Associated with Double-Skin Green Façades of Tall Green Buildings
Project Coordinator: Dr CHOW Cheuk-lun (CityU)
Abstract
Double-skin facades (DSFs) can provide passive building solar control. Putting in plants to create double-skin green façades (DSGFs) can reduce solar heat gain. A new energy harvesting system using appropriate plants can convert solar energy to chemical and electrical energy. This gives a new and clean energy source in second-generation green buildings. However, the fire hazard of DSGFs is a key concern in its wide application.
Fire-safe DSGFs with energy-harvesting design in tall green buildings will be investigated and developed in this project. Energy harvesting system will be studied on fundamentals of photosynthetic microbial fuel cells (MFCs); improvement of the MFCs energy harvesting efficiency; and optimization of DSGFs. Fundamentals on extracting photosynthetic energy in MFCs will be simulated by First-Principles Molecular Dynamics. Energy harvesting efficiency will be explored in DSGFs. It is envisioned that the photosynthetic MFCs with a maximum utilization of building envelop can provide a maximum power output of 65 mW/m2.
DSGFs will have combustibles on green plants for passive solar control; and energy harvesting system with MFC. Fire research focus will be on examination of the hazardous scenario created in the façade cavity by a window plume from a flashover fire in an adjacent room. Dry plants and other combustible components can be ignited easily. The subsequent conflagration would emit vast quantities of hot toxic gases and particulate matter that would be trapped in the façade cavity. Breaking the interior glass skin would spread smoke to upper rooms. Moreover, the stack effect of tall buildings would drive the movement of smoke and flames within the façade cavity, particularly in places with very low outdoor winter temperatures.
Bench-scale tests with a cone calorimeter will be carried out on three plants typically used for DSGFs. Those are the climbing plants Hedera helix, Matteuccia struthiopteris or Jia Guo Jue, and Cynodondactylon (Linn.) Pers. (bermudagrass). Green plants used in photoMFCs are reed mannagrass and rice plants are also tested. Physical scale model tests and numerical simulations for part of a DSGF with these plants will be conducted to study the smoke and flame in the façade cavity. Full-scale experiments will be carried out to examine stack effects in a cold area such as Harbin, China with a large differential between indoor and outdoor temperatures. Results will be applied to justify that the stack effect-triggered fire spreading phenomenon observed in scale models would also apply at full scale. Mathematical models of heat and mass transfers will be combined with cone data, physical experimental and computational fluid dynamics results to yield the integrated understanding necessary for the provision of fire-safe DSGFs with energy harvesting systems.
Project No.: R4020-22
Project Title: Robotic Endobronchial Multi-modal Biopsy of Pulmonary Nodules
Project Coordinator: Prof REN Hongliang (CUHK)
Abstract
Lung cancer is one of the most dangerous cancers worldwide. Early detection and early intervention are the best way to prevent and treat the disease. The main monitoring methods used presently are computed tomography imaging and mechanical biopsy, with which it is difficult to locate nodules in situ, and which involve extensive pain and additional cost and time. The cross-institutional team will study the key optical, mechanical and electrochemical principles in microsampling and bioassays for biomarker monitoring, and develop flexible microfluidic immune-sensing arrays that can enter narrow, curvy airway branches for multi-modal biopsy around small pulmonary nodules. This project will facilitate profiling of the lesion microenvironment in lungs under fine structural imaging guidance in situ, and contribute to addressing the unmet clinical needs mentioned above.
Project No.: R4030-22
Project Title: Translating Microbiome, Multi-omics and Dietary Innovations to Enhance Inflammatory Bowel Disease Diagnosis and Outcome
Project Coordinator: Prof NG Siew-chien (CUHK)
Abstract
Crohn’s disease (CD), a subtype of inflammatory bowel disease that causes chronic intestinal inflammation, is of increasing incidence around the world and is one of the leading causes of adolescent and adult disability worldwide. Diagnosis is challenging as presentation can mimic infectious diseases and cancers. Suspected cases frequently undergo invasive and expensive tests, leading to delayed diagnosis and substantial healthcare costs. A multi-institutional collaborative team led by Professor Ng Siew-chien, Department of Medicine and Therapeutics at CUHK Medicine, will develop a novel, universal bacteria marker panel to predict CD and its complications, and devise a multi-tier algorithm incorporating microbial markers and food additives to provide an innovative non-invasive approach to early diagnosis and prediction of CD complications. The team will work closely with industry collaborators to translate the technologies into cost-effective, convenient products that enhance the care of patients with CD globally and benefit society.
Project No.: R4035-22
Project Title: Battling Sedentarism in Children with Special Educational Needs through Inclusive Physical Activity
Project Coordinator: Prof SIT Cindy Hui-ping (CUHK)
Abstract
During the COVID-19 pandemic, the global trend of “sedentarism” has become more serious. Sedentarism refers to physical inactivity and increased sitting time or sedentary behaviour, which is associated with premature deaths and non-communicable diseases such as cardiovascular disease, as well as with poor physical and mental health. The World Health Organization (WHO) recommended that children and adolescents engage in on average 60 minutes of moderate-to-vigorous physical activity while reducing the time they spend being sedentary. Yet, over 80% of children with special educational needs (SEN) do not meet the WHO recommendations. The project aims to examine the effects of an IPA programme on physical activity, sedentary behaviour, and physical and mental health in children with SEN. Children will be cluster randomly allocated either to a 3-month IPA condition or to a wait-list control condition. Measurements will be conducted at baseline and at two follow-ups. It will be the first project to put the WHO recommendations on both physical activity and sedentary behaviour into practice.
Project No.: R4040-22
Project Title: Increasing the Resilience to the Health Impacts of Extreme Cold Weather on the Older Population under Future Climate Change
Project Coordinator: Prof NG Edward Yan-yung (CUHK)
Abstract
In 2018, the study team initiated a RIF project (project no. R4046-18) (Phase 1) to work on the inter-relationship among climate change, the built environment, and older population living. The study focus was identified to be the increasingly hotter climate in the future due to climate change.
As the study progressed, the data obtained indicates that the future weather will fluctuate more; that is to say, the hot days will get hotter and the cold days will get colder. The mitigation and adaptation action plans developed in Phase 1 of the study can only address the summer issues. It would be more useful if the study team can further the work in order to provide a “year-round” holistic understanding and action plans, and hence, leading to the Phase 2 of this study. There is a lack of:
(1) data for understanding the extreme cold weather under global warming in our city. Hong Kong is a high-density city with a complex urban environment. There are large variations in the intra-urban temperature, so high-resolution spatial and temporal data are required for the assessment of exposure and vulnerability of extreme cold weather.
(2) evidence-based mitigation action plans. Building and neighbourhood design are important to both the livelihood of citizens and the mitigation to climate change. There is an urgent need for better understanding of the impacts of extreme cold weather on the living environment, and hence design strategies for the year-round climatic conditions.
(3) evidence-based adaptation response plans. Timely responses during extreme cold weather are important in safeguarding the health and well-being of older citizens. It requires a comprehensive understanding of the health impacts due to extreme cold weather so that effective adaptation plans can be developed for different seasons in future climates.
Combined with the extreme heat understanding of Phase 1 of the study, this study aims to contribute by: (1) establishing the frequency and intensity of the extreme cold and damp events, and evaluate their health effects to the older population on a year-round basis; (2) developing a more holistic mitigation action plan with better urban planning and building design under hot and cold extreme weather; and (3) developing a more holistic adaptation action plan for supporting services to increase the resilience of the older population to hot and cold extreme weather. This study will provide a methodological framework for incorporating the scientific knowledge of both extreme hot and cold weather and their associated impacts on the elderly health and well-being into a comprehensive plan for response actions.
The impact of the study will include the following:
(1) The findings of the study will help the Hong Kong Observatory transform the current simple weather information system into a more comprehensive one that is capable of reflecting timely conditions of different districts.
(2) Guidance will be developed for urban planners, architects, developers and other professionals in the field to help the industry fully unleash its potential in building towards a sustainable and healthy city under the vision of Hong Kong 2030+ and Hong Kong’s Climate Action Plan 2050.
(3) Current services will be improved by incorporating information provided by the new weather warning system. Housing protocols will be developed to better cater to the elderly’s need under extreme weather conditions (i.e., hot, cold and damp). Social workers and volunteers will be trained to take better care of the elderly under such conditions.
Project No.: R5008-22
Project Title: A novel approach to target cancer stemness using peptidic chimeric antigen receptor (pCAR) macrophages
Project Coordinator: Prof LEE Kin Wah Terence (PolyU)
Abstract
Hepatocellular carcinoma (HCC) is the third leading cause of cancer-related mortality in Southeast Asia and Hong Kong. For advanced-stage HCC patients who bear unresectable tumors, chemotherapy and molecular targeted therapies are the second-line treatment, but their therapeutic efficacies remain disappointing. Recently, a number of immune checkpoint therapies have been approved by the FDA for the treatment of advanced HCC patients, but the response rate is unsatisfactory. In HCC, there is now solid evidence showing that tumor growth is fueled by cancer stem cells (CSCs), which contribute to therapeutic resistance to the above treatments. Therefore, targeting stemness as an HCC vulnerability could be a promising therapeutic strategy for HCC patients. Our group identified several putative liver CSC markers, which concomitantly function as a “don’t eat me signal” to macrophages, suggesting a distinct property of liver CSCs to evade immune destruction through macrophage-induced 5. Based on this observation, we hypothesize that targeting the vulnerability of liver CSCs can be achieved by using a macrophage-targeted therapeutic approach.
In this proposal, we employ a novel and non-viral one-pot peptide cyclization and cell surface conjugation strategy to create peptidic chimeric antigen receptor macrophages (pCAR-Ms). Our pilot study demonstrated the specificity and targetability of pCAR-Ms against CSCs. Based on this encouraging data, we hypothesize that our pCAR-Ms can hamper cancer stemness via induction of phagocytosis of liver CSCs. In this proposed work, we aim to 1) identify the peptides that selectively bind to the surface of receptors of liver CSCs via mass spectrometry analysis; 2) design and establish a library of liver CSC-specific pCAR-Ms using our established one-pot peptide cyclization and cell surface modification approach; 3) functionally characterize and optimize pCAR-Ms in cell-based models; and 4) evaluate the efficacy of designed pCAR-Ms in animal models. Collectively, we employed a systematic approach to target liver CSCs using the pCAR-M strategy from discovery and chemical modification to biological verification. Our proposed study is founded on solid preliminary data and has a high potential for significant translational impact. We believe our pCAR-Ms will open new therapeutic avenue for treatment of HCC.
Project No.: R5019-22
Project Title: Flexible and Stretchable Batteries for Wearable Applications
Project Coordinator: Prof ZHENG Zijian (PolyU)
Abstract
The booming development of flexible electronics has opened up vast opportunities for the realization of wearable technologies, smart electronic textiles, electronic skins, soft robotics, bioelectronics, and the Internet of Things. These emerging applications have imposed unprecedented demand on flexible and stretchable batteries, which can not only be seamlessly integrated into the soft electronic systems to provide electric energy but also enable the paradigm shift of product designs. However, commercial batteries nowadays are not flexible because of the use of rigid components such as metal foils. In addition, the high flammability of organic solvents of commercial batteries imposes a serious concern on the safety issue. Despite the great effort in developing flexible and stretchable batteries in the past decade, the performance is still far from fulfilling industrial or market needs. The critical challenge is to tackle the trade-off between energy density, flexibility/stretchability, cycling stability, and to enable the flame-retardant ability of the battery for safety purpose.
This project aims to develop safe, high-performance, flexible and stretchable batteries through a 4-year collaborative effort among three research groups in Hong Kong and three industrial partners in the battery sector. This project will deliver two types of batteries, including 1) high-energy-density, flexible, flame-retardant lithium batteries using organic electrolyte; and 2) a stretchable, supersafe aqueous batteries with good energy density. The success of the project will revolutionize the form factors of battery designs for wearable technologies, from the embedment of rigid, heavy, and flammable battery components to intrinsically safe, flexible, and stretchable batteries that can realize a seamless integration with wearable electronics.
Project No.: R5031-22
Project Title: Achieving the Circular Economy in Construction through Deconstruction and Reuse Technologies for Steel and Composite Structures
Project Coordinator: Dr CHAN Tak Ming (PolyU)
Abstract
The development of new infrastructure for a liveable city will be the primary objective for the nations in the upcoming decades but a challenge for Civil Engineers. The government of HKSAR is also taking several infrastructure development initiatives such as Greater Bay Area, Lantau Tomorrow Vision, and Northern Metropolis which are opportunities for Hong Kong Construction Industries. However, as per the latest report by the Hong Kong Development Bureau titled “Construction 2.0”, the construction industries in Hong Kong are facing productivity issues due to a lack of application of innovation and a complex supply chain. The productivity issue is also reported globally in many countries including the UK, US, Australia, and Singapore. Therefore, it will be challenging to meet future construction needs without a fundamental change in resource management and construction technologies. In addition to the productivity issues, it will be challenging to find a solution for a material demand for a future mega project. Hence, this project proposes to develop a novel building technology with directly reusable structural components made of high-strength steel materials and strategies for sustainable material supply and marketing, to achieve the target of 50% reduction in upfront carbon emission during construction. A novel concept “Design for Deconstruction and Reuse” will be developed for the design of structural components and connections design.
The new building technology proposed in this project is revolutionary and targets productivity and sustainable delivery for the construction industry. This project has the following four objectives: (1) to develop the fundamental basis for “Circular Economy in Construction” through Design for Deconstruction and Reuse (DfDR) technologies. More productive construction technologies to be developed for steel and composite structures through advanced engineering methods and management strategies; (2) to increase the productivity of the Hong Kong construction industries for quicker supply and delivery of buildings through digital fabrication, erection, and demounting, also to create knowledge, job, and business opportunities for the local and global levels through implementing the circular economy in construction; (3) to identify the reusable building components and establish evidence-based policies for reuse of building products, and also demonstrate the technology in prototype projects for enhancement and to develop digital-based rapid non-destructive testing for quick certification of reusable components; (4) to develop standards of practice for the development of DfDR technologies, policies for testing and certification by government-approved agencies, and managemental strategies for sustainable product delivery in construction through stakeholders’ integration.
The outcome of the project will be a paradigm shift in the construction process leading to more business opportunities for Hong Kong Construction industries.
Project No.: R5047-22
Project Title: Next Generation of In-situ Precision Three-dimensional Surface Metrology: A Smart Self-adaptive Multiscopic Approach for Industrial 4.0
Project Coordinator: Prof CHEUNG Chi-fai Benny (PolyU)
Abstract
With the rapidly growing demand for advanced products and high-precision components for various functional applications, the use of multi-scale complex three-dimensional (3D) surfaces in the design and manufacture of such products has become more widespread for various industries such as biomedical, optics, electronics, optometry, semiconductor, information technology, energy, aerospace, etc. These surfaces usually possess complex geometrical features which have posed a lot of challenges in their manufacturing and measuring processes in terms of higher accuracy, better surface finish, and higher geometrical complexity. However, the machine tools, manufacturing equipment and manipulators themselves (e.g. robot and motion slides) with closed machine interfaces and control systems are not accessible to users, which makes it difficult to use the motion axes of the machine tool, manufacturing equipment and manipulators for in-situ measurements. The lack of spatial position coordinate information in the measurement system and the inability to pro-actively acquire this information leads to uncertainty in the taking of measurements. Currently, there is a lack of generic in-situ precision metrology systems which provide high resolution, high repeatability, high speed, a large measurement range, and minimized repositioning errors. Moreover, there is a growing demand for flexible deployment of measurement systems and processes in the context of Industry 4.0. Industry 4.0 has created great demand for in-situ and/or in-process measurement in manufacturing environments when trying to maintain the position of the workpiece for further compensation processes to be run in order to improve the accuracy and efficiency of the precision manufacturing of complex 3D surfaces.
As a result, this project aims to explore, research and develop the next generation of in-situ precision 3D surface metrology which can simultaneously achieve a high dynamic range (HDR) and in-situ measurement of complex 3D surfaces with autonomous characteristics, is independent of the machine tool and manufacturing equipment being embedded and possesses self-adaptive capability to cater for different manufacturing and measuring environments. This is an interdisciplinary collaborative research project among competent researchers from The Hong Kong Polytechnic University (PolyU) and University of Hong Kong (HKU) as well as strategic partners and industrial partners from various industrial sectors. The project attempts to research and develop a smart self-adaptive multiscopic (SAMS) approach and hence the establishment of a customizable smart SAMS system for in-situ precision 3D surface measurement under different manufacturing environments and applications through the development and integration of building blocks of functional modules including a multiscopic optical (MSO) module to perform multiplex acquisition of raw 3D information, an integrated real-time position and status tracking (IRPST) module to determine the spatial position and state in the global coordinate system with high accuracy of the smart SAMS system; an artificial intelligence-based data processing and fusion (AIDPF) module for 3D surface reconstruction and measurement, as well as a machine learning-based self-adaption (MLSA) module that can adjust the measurement strategy under various measurement scenarios and environments. The smart SAMS system does not only perform in-situ precision 3D measurement with sub-micrometre measurement accuracy but also possesses the self-adaptive capability for measuring different types of complex 3D surfaces and is also able to be customized and embedded in different types of machine tools, manufacturing equipment and manipulators operating in different measurement environments in different industries including digital dentistry, optics, semiconductors, optometry, telecommunication, etc.
The project, if successful, will not only contribute significantly to a better scientific understanding of measurement science and technology for in-situ precision measurement of complex 3D surfaces, but also provide a new generation of self-adaptive multiscopic approach for the development of a smart in-situ precision 3D measurement system which can be embedded in different machine tools, manufacturing equipment, and portable measurement devices to conduct versatile in-situ measurement tasks under various manufacturing or measurement scenarios for Industry 4.0. The success of the project will provide an excellent foundation for the research and development of a smart in-situ 3D measurement system and 3D machine vision sensors for different industries such as a new digital dental intraoral (DDIO) scanner for digital dentistry and novel endoscopic SAMS system for non-line-of-sight in-situ precision 3D measurement, with a profound and lasting impact on the advancement of measurement science and technology ready for Industry 4.0 which will contribute significantly to the advanced manufacturing and high-tech industries in Hong Kong and the Greater Bay Area in the re-industrialization process and enhance their industrial status in the global era.
Project No.: R6003-22
Project Title: Develop a multimodal artificial intelligence platform integrating computational pathology and multi-omics data from large numbers of patients to advance precision medicine in neurooncology
Project Coordinator: Prof Jiguang WANG (HKUST)
Abstract
A major challenge that prevents the optimal management of aggressive cancers is the lack of a robust and precise approach for diagnosis and for clinically-relevant disease classification. Conventional cancer treatment plans require sophisticated collaboration among radiologists, pathologists, oncologists, surgeons, and geneticists. Histopathological evaluation is routinely performed to grade tumors based on how the cancerous cells look under a microscope, but such manual examination is labor-intensive, time-consuming, difficult to standardize between physicians, and frequently fails to predict either the patient prognosis or treatment outcomes of innovative therapeutic options. This project focuses on the study of adult diffuse gliomas and aims to develop an AI platform of precision neuro-oncology trained on high-quality, well-annotated multimodal data, which would not only increase the efficiency and accuracy of cancer diagnosis but also lead to better medical decision-making for improved treatment outcomes. Specifically, four technological developments will be achieved in this proposal: (1) Computer-aided diagnostic system that extracts and annotates local and global diagnostic features; (2) Innovative modality fusion methods to integrate imaging and multi-omics data for identifying genotype-phenotype associations and evaluating patient prognosis; (3) Drug repurposing framework targeting cancer dynamics for personalized drug recommendation; (4) An integrated computer system for brain cancer management which will be tested and developed through collaboration with hospitals and companies. The success of this project will benefit brain cancer patients by providing a more efficient and standardized procedure for diagnosis, prognosis, and intervention. Once established, the system can be easily extended to many other diseases.
Project No.: R7007-22
Project Title: Horizon scanning of medium- to long-term burden of chronic diseases and care needs to 2030 in Hong Kong (SCAN-2030)
Project Coordinator: Dr LI Xue (HKU)
Abstract
As innovative technologies emerge, policymakers and healthcare providers often face an everexpanding choice of products and services that are expected to meet the health needs of the population. Given the limited resources for healthcare, and the burgeoning demand for effective yet high-cost innovative medicines, there is an urgent need to balance drug cost investment and health benefit to optimise patient management, especially for chronic noncommunicable diseases (NCDs) which collectively generate long-term fatal and non-fatal burden in the healthcare system and society. Horizon Scanning and Health Technology Assessment (HTA) are useful processes to assess unmet needs, identify the pipeline of innovative medicines, evaluate economic performance and guide evidence-based decision making for resource prioritisation to maximise health equity and value-for-money within the healthcare system. Establishing and strengthening HTA in decision-making processes and the feasibility of using healthcare big data for horizon scanning are expanding globally but still under development in Hong Kong.
SCAN-2030 aims to utilise the unique population-based electronic medical records in Hong Kong to understand the status quo of disease burden and unmet needs and nowcasting the medium-long-term burden and care needs to guide systematic health policy planning. SCAN- 2030 will develop ready-to-use decision assistance toolkits to illustrate the recent and next 10-year disease burden, pipeline innovative medicines for NCDs and their cost-effectiveness, return-on-investment, and budget impact to the health system. The vision is for SCAN-2030 to initiate the HTA ecosystem in Hong Kong to improve access, delivery, and introduce innovative and cost-effective medications using an evidence-based and transparent evaluation mechanism. SCAN-2030 also collaborates with researchers and key opinion leaders from local and overseas universities, pharmaceutical companies, and health authorities; the project team will leverage the expertise within each organization to provide the full range of technical skills necessary to strengthen capacity in SCAN-2030.
Project No.: R7030-22
Project Title: MindPipe: High-performance and Carbonefficient Four-dimensional Parallel Training System for Large AI Models
Project Coordinator: Dr CUI Heming (HKU)
Abstract
This MindPipe project will develop a new software training system that can manage hundreds or thousands of GPUs for training one general large AI model (e.g., Transformer, GPT, and Pan-Gu) containing enormous neuron parameters (e.g., one GPT model developed by Microsoft contains hundreds of billions of parameters). The MindPipe system is designed to achieve both high training performance and low carbon emission generated from the massive number of GPUs. This project is expected to launch new commercial releases of one of the world’s fastest software training systems for large AI models with global leading IT enterprises. Preliminary results of this project include the publication of parallel training systems in international best-tiered computer system venues (i.e., China Computer Federation’s A-class journals or conferences), including [vPipe IEEE TPDS 2021], [NASPipe ACM ASPLOS 2022], and [Rog ACM MICRO 2022].
In the long term, the proposed research objectives and the open-source software artifacts developed from this MindPipe project may promote the designing, training, customizing, and revolution of large AI models among broad academia and industries (e.g., enterprises relevant to AI, Fintech, cloud, or chips).
Project No.: R7036-22
Project Title: Industry 4.0 Smart Prefabrication Yard for Modular Construction Fitout
Project Coordinator: Prof HUANG Guo-quan George (PolyU)
Abstract
Housing production and supply have been among the top priorities in Hong Kong with direct impacts on both the economy and people's livelihood. This has been reflected in Chief Executive’s Policy Address almost every year. Hong Kong construction industry council (CIC) has conducted an empirical study on the costs and benefits as well as government policy implications of setting up a prefabrication yard (PY) on the Hong Kong side near cross-border land ports. The PY serves multi-folded functions. One is as a warehouse shared by construction module producers and users as well as their logistics service providers. The second is that the PY uses its space to enable Just-In-Time (JIT) deliveries of construction modules through two legs of cross-border logistics. The third is the opportunity of conducting off-site fit-out operations while the modules are waiting in the PY. The fourth function is that the PY can be used as workshops for prefabricating construction modules. The scope of this project is limited to the first three functions without dealing with the fourth function. However, insights and lessons gained from pioneering initiatives worldwide, and especially those from Singapore, the PY may not be able to fully deliver its power to hedge risks and flexibilities, to simplify module logistics planning and scheduling, and to achieve the economy of scale without the supports of advanced technologies including BIM (Building Information Modelling) and smart construction with IoT (Internet of Things) / Digital Twins (DTs).
This project is aimed at innovating Industry 4.0-compliant smart prefabrication yard technologies and solutions. First, smart digitization technologies will be innovated to elevate the traditional prefabrication yard (PY) into a smart prefabrication yard (SPY) as a cyber-physical system (CPS). Real-time visibility and traceability are developed and employed to facilitate SPY’s inbound / outbound operations, inventory management and fit-out operations. Second, the SPY provides a new opportunity for transforming construction module logistics from traditionally m2m (many producers to many sites) delivery mode into a new mode of two legs with the first leg in between multiple module producers and the SPY, and the second leg between the SPY and multiple construction sites. This new mode not only facilitates cross-docking operations, but also allows stakeholders to share logistics resources and services while achieving Just In Time (JIT) delivery performances. Third, the SPY provides a new business model of carrying out off-site fit-out construction operations in addition to those at the module production factories and construction sites. This is particularly important when modules are produced by different factories but share similar fitout operations and materials from the same suppliers. The positive risk and resource pooling effects can then be fully utilized. Finally, the developed smart technologies and solutions will be demonstrated, tested and further refined through our industrial collaborator and its business partners. Project deliverables will be integrated to form a lab testbed SPY platform with “what you see is what you get” facilities. Key components will be demonstrated and evaluated impact pilot implementations within industrial collaborators. Insights and lessons will be used to compile handbooks on technology roadmap, implementation guidelines, and good practice case for substantiating wider research impacts.