This project aims to explore the implications of Dhaka's upcoming mass rapid transit (MRT) system and first line, MRT-6, on the distribution of socioeconomic activity and mobility within the city. Even though large-scale mass transit projects like the MRT system affect travel conditions in the entire city and are important for sustainable development, a comprehensive assessment of an infrastructure project is rarely undertaken. Data such as simulated and actual trips, property prices, and job availability will be collected both before and after the start of the line, as well as within and outside the areas directly affected by MRT-6. Analysis involving recent advances in econometric and spatial modelling techniques will be conducted to estimate the general equilibrium impacts of MRT-6 and understand the distribution of welfare gains across space and different socioeconomic groups, as well as the channels through which these gains transpire.
This is a project under the AI Singapore 100 Experiments Programme. It tackles the research challenge of generating good logistics plans and schedules for parcel delivery using AI. uParcel faces the challenges that the number of deliveries daily are in the thousands and the number of drivers delivering a day is in hundreds, which makes it very challenging to match jobs to drivers and encourage job acceptance. Using reinforcement learning coupled with large-scale optimization methods, the research team will develop route optimization, dynamic recommendation, and logistics marketplace matching algorithms for improving operational efficiency. This will also greatly improve city logistics by reducing trips and congestion.
Many experts agree that even if populations can be immunized against particular viruses using drugs or vaccines, they must be prepared to live with infectious diseases because of the interrelations between infection agents and climate change. The management of epidemics therefore requires a paradigmatic shift in disease control. To achieve sustainable responses to health challenges, it is critical that local communities and urban stakeholders be regarded as active players in the production of knowledge, surveillance, and responses to epidemics. The SPACE project builds on this premise to develop a dynamic, adaptive approach to urban sustainability. The project draws upon analyses of the risk factors and sociospatial patterns that drive dengue transmission in Singapore, as well as the social and technical skills developed by individuals, community groups and state actors in response to disease propagation. The project will use the concept of “adaptive capacity” (AC) to explore the potential of community-based “latent social capital” as key assets for adaptive responses to health challenges related to dengue in its interplay with COVID-19 in the context of Singapore’s Smart Nation initiative. Based on the AC approach, the project targets four outcomes: a) improve the current spatiotemporal forecasting framework for dengue and Covid 19 outbreaks in Singapore using an Agent-Based Model; b) develop innovative policy ideas to enhance disease prevention and mitigation in Singapore’s built and green space; c) improve governmental communication strategies towards epidemic mitigation and control, and; d) assist in reshaping or building urban configurations at various scales so as to achieve an “antivirus-built environment”.
This proposal adopts an asset-based approach to enabling civic engagement among older adults in Singapore by positioning them as both the drivers and beneficiaries of ground-up initiatives aimed at keeping older adults integrated in their communities. It is hypothesized that such an intervention will improve overall well-being for older adults based on findings from the Singapore Life Panel® (SLP). A pilot program is proposed to assess the efficacy of such an approach. 50 older adult residents of an estate will participate by developing and implementing various initiatives in their community under the broad themes of ‘rediscovering Singapore’, ‘cultural exchanges’, ‘volunteering’, ‘physical activities’, and ‘collective purchasing initiatives’. The themes are targeted at specific quadrants of well-being (economic, social, psychological, and physical). The team will conduct pre and post intervention surveys to track the overall well-being. If successful, the project will be proposed to be run in additional neighbourhoods. The particular intervention model can be easily replicated in different communities across Singapore, largely due to the fact that it is a community driven initiative and can be easily adapted.
The project will provide state of art guidance for ensuring that research and innovation using technological applications for controlling COVID-19 is legally compliant, in so far as these present challenges to rights and liberties. Focusing on AI-assisted technology, the research will address COVID-19 control strategies in the pandemic and post pandemic phases.
This project aims to provide a cross-jurisdictional comparison of the way in and extent to which commercial litigation funding is used in the insolvency context in Australia, England, Ireland, Canada, New Zealand, Singapore, the Netherlands, Germany, USA and South Africa.
Equitable access is an important means to reducing socioeconomic status (SES) gaps in higher education. However, even in countries with universal access to education, high-SES individuals were still more likely to complete tertiary education than low-SES individuals. This project investigates why socioeconomic gaps continue to emerge even with lower economic barriers to higher education. We propose that low-SES students may experience higher levels of chronic stress and respond more negatively to stress in highly competitive university environments than high-SES students. We also propose to examine if the negative effect of chronic stress may further impact low-SES students’ cognitive capacity, academic performance, persistence, and general outlook on academic success. Overall, this project will potentially highlight the multiple challenges faced by low-SES students as they strive for social mobility, and underscore the need to address any potential psychological disadvantage encountered by low-SES students in higher education settings.
With the advent of e-commerce, users are presented with numerous alternatives to satisfy their everyday needs. Choosing from the available options generally entails the consideration of multiple, often conflicting aspects, the tradeoff among which is assessed differently by different users.
This project proposes PERFLEXO, a new methodology for multi-objective querying centred around three hard requirements, i.e., personalization, flexibility in the preference input, and output-size control. Past approaches have considered these requirements individually, but no existing work satisfies all three of them. On the technical side, the main contributions of the project will centre on PERFLEXO’s ability to process large option-sets (i.e., scalability) and produce shortlists in reasonable time (i.e., responsiveness).
This research project aims to improve understanding of Singapore consumers’ perception of alternative protein and subsequently, foster their acceptance of this novel food technology. The term “alternative proteins” refers to animal-free protein alternatives that can be organised into three distinct categories: “plant-based proteins, edible insects, and a group referred to as ‘cellular agriculture’. This latter group encompasses products commonly referred to as ‘cultured’ or ‘clean’ meat, milk and other animal products, created either through culturing stem cells outside (in vitro) animal bodies, or through the genetic modification and fermentation of yeast cells. More specifically, this study aims to answer the following research questions:
- What are Singapore consumers’ perception of alternative proteins?
- What message frames are most effective in fostering consumers’ acceptance of alternative proteins?
- Which type/s of social media influencers (SMI) are most effective in influencing consumer acceptance of alternative proteins?
- What are the effects of the message frames on Singapore consumers’ attitudes and behavioural intentions (or behavior) in relation to alternative proteins?
AI models trained offline rely on the accessibility of all classes in training data. When they are updated online to learn new incoming data, they often bias to the patterns of new classes, and thus forget old ones. The problem is known as catastrophic forgetting. This project aims to tackle this issue by task-specific data augmentation. The augmentation for old classes is achieved by distilling from new or open-set data that contain the knowledge of old classes, e.g., shared contexts and sub-parts.
