TECH OFFERS

Early life complications and chronic health conditions among children continue to rise over the years. Accumulating evidence shows that microbes residing in the human body play important roles in infant development and  immunity system maturation, starting from the first 1000 days and transcendence in later life. The development of healthy gut microbiota during early life, with a predominance of Bifidobacterium spp., offers an extraordinary window of opportunity for neonatal health development and disease prevention. Aberrant bifidobacterial colonization affected by factors including gestational age, delivery modes, and feeding types may lead to gut disorders, allergies, and obesity later in life. Consumer recognition of the need to maintain healthy gut microbiota during early life for lifelong well-being is getting stronger and they are seeking probiotics to support infant health. Infant nutrition products are increasingly fortified with probiotics, including bifidobacteria, which are gaining traction worldwide. However, not all bifidobacteria are the same. A leading Japanese dairy company has developed a premium line of probiotic strains that are highly compatible with the human gut. These strains are natural inhabitants of the human intestines and more superior in physiological functions critical for infant health. Among them, the proprietary strain of Bifidobacterium longum is a well-documented probiotics with proven clinical benefits to infant's and children’s health. The company is seeking collaborations with infant nutrition, dietary supplement and functional food manufacturers that are interested in enhancing their product offerings in forms of R&D collaborations or cocreations to develop novel products incorporated with this proprietary B. logum strain.

Consumers today are much more proactive in preserving their health with natural approaches. They have turned their interests into functional food and beverage as they opt for prevention over treatment. Moreover, the COVID-19 pandemic has transformed consumer attitudes towards health and immunity, hence the impact on health consciousness is long-term. With growing awareness, consumers are connecting the dots between gut health and immune health. As such, appetite is growing for products such as functional food and beverage that contain probiotic ingredients to support the gut and immune system. However, not all probiotics are able to withstand the rigors of the manufacturing process for a wide range of functional food and beverage formats. A leading Japanese dairy company has developed a postbiotic ingredient that resolves formulation challenges. The proprietary postbiotic ingredient is a clinically proven heat-killed strain of Lactobacillus paracasei that possesses excellent immune-enhancing activity. It can be incorporated into a wide variety of products irrespective of their processing method or form. Since it is an inactivated strain, it is easy to use in accomplishing innovative formulations. The company is seeking collaborations with food and beverage product manufacturers that are interested in enhancing their product offerings with additional health benefits in forms of R&D collaborations or cocreations to develop novel functional food and supplement products incorporated with this proprietary L. paracasei strain.

While interest and demand for blockchain-related technologies continue to gain popularity and spurs the exponential growth in adoption of blockchain in areas such as payment, documents and digital identities, not just in finance, but in industries such as logistics, supply chain as well. Many emerging areas that rely on blockchain as a core technology lack the manpower needed to sustain budding development, this lack of technical skillset required for blockchain development is the primary hurdle to successful blockchain application development - less than 1% of the tech workforce is skilled or competent in blockchain-related development. The characteristic of blockchain technology, which enables a permanent record of digital information such that it cannot be modified by any single entity renders it well suited as a digital ledger of online transactions. As such, blockchain is a core technology for many emerging areas such as Decentralised Finance (DeFi) and Decentralised Autonomous Organisation (DAO). This technology offer consists of a platform tool and a set of zero-configuration REST APIs that abstract away the complexity of blockchain technology and enables any developer to easily build blockchain-based applications or integrate blockchain functionality into their existing systems. Intended as a low-code platform, it addresses the skills gaps traditionally required for blockchain development and deployment and allows companies to realise their blockchain ideas, enhance business operations and expand solution offerings.  

The current clean meat technologies grow lab meat with conventional 2D cell culture. However, the conventional cell culture technique has an overall low yield of cells, as the cells are restricted to growth on surface areas.  A new 3D scaffolding method has been developed to overcome this problem with the use of microcarrier beads that provide cells with additional surface area to attach onto and proliferate. The microcarrier beads are suspended in the cell culture thus maximizing the 3D volume of the cell culture, leading to an increased yield. A microcarrier type has been identified to yield the highest number of porcine cells. The conditions of the cell culturing process have been optimised to improve the cell viability in a 3D environment Companies interested in cell-cultured meat development could consider using this method to grow cell-cultured meat at a larger scale with a potentially lower cost of production. The technology developer is seeking companies that are keen to scale up lab-grown meat applications. 

Machine Learning (ML) is a sub-field of Artificial Intelligence (AI) where a machine is able to learn without being explicitly programmed. However, before a machine can effectively perform even the simplest AI tasks, e.g. differentiating between images containing an elephant or a tiger, it has to be trained on images containing both animals. To be useful in supervised learning, training data needs to be properly labelled or annotated by a human for the machine to extract the relevant features and produce an ML model that serves its intended purpose. This highlights the important role that data annotation plays in producing robust, accurate ML algorithms in video analytics, natural language processing, and audio processing. However, many organisations that want to embark on their supervised learning journey often face difficulties gaining access to high-quality labelled datasets, known as ground truth data, due to the abundance of low-quality, expensive and unstructured data. This technology offer is a mobile application-based data platform that enables companies to obtain high-quality annotated data. It de-centralises data collection and data annotation tasks into manageable bite-sized chunks for optimal annotation performance and crowd/out-sources the annotation task to a pool of data taggers via a mobile application. Labelling quality is established through a gamification system and a series of built-in verification procedures, including AI-assisted pre-filtering and collective human quality control.

This technology offer presents an autonomous materials handling system. The technology could reduce manual labor requirements and increase working efficiency. The solution consists of a Lidar Elevator Stand (LES) system, which will trigger the autonomous actuators (e.g., trolley puller, tray return robots) via the robot command center whenever no goods (e.g., trolley, tray, and stock) is detected in the designated area. Currently, the technology has been demonstrated in autonomous trolley return solutions. Generally, trolley replenishment requires deploying manual labour to monitor the available quantity at the trolley bay and replenishing it by physically operating an electric trolley puller to transport the new stack of trolleys. Therefore, the system was developed to solve the problem by triggering an autonomous trolley puller to replenish the new stack of trolleys whenever the trolley quantity is depleting. The system can be further customized and repositioned based on clients’ requirements.

The proliferation of e-commerce, ride-hailing and food-delivery services have fueled the need for more accurate and reliable estimation of delivery times. The current common estimation of delivery time is based on Estimated Time of Arrival (ETA) which relies on route distance that is calculated between the origin and the desired destination. It only considers the duration from pick up to drop off, and does not consider the additional time needed for preparing and offloading the goods. This technology offer is a Machine Learning (ML) model that is able to calculate the stop duration (job completion duration), which together with the ETA, provides the Estimated Time of Completion (ETC). This ML model is for Singapore use only.

There are 30,000 occupational drivers in Singapore, out of which 13,500 are 45 years old and above. The risk of acquiring cardiovascular disease increases with age and is potentially exacerbated by low physical activity and high emotional stress levels, which are two typical characteristics of occupational drivers arising from their work environment. Low level of physical activity and high stress levels have been shown to have significant relationship with heart rate variability, one of the indicators of cardiovascular disease. This technology is developed to help drivers to monitor their stress level, provide them with instantaneous feedback and the necessary alerts for a timely intervention. This technology offer presents a cross-platform AI system that estimates the stress levels continuously in real time, and can be easily integrated with commercially available photoplethysmography (PPG) wearables, e.g., a PPG wristwatch. In addition, this technology can be adapted for the monitoring of workplace stress with the aim of improving overall mental well-being.

One use of AI, including deep learning, is in prediction tasks, such as image scene understanding and medical image diagnosis. As deep learning models are complex, heatmaps are often used to help explain the AI’s prediction by highlighting pixels that were salient to the prediction. While existing heatmaps are effective on clean images, real-world images are frequently degraded or ‘biased’-such as camera blur or colour distortion under low light. Images may also be deliberately blurred for privacy reasons. As the level of image clarity decreases, the performance of the heatmaps decreases. These heatmap explanations of degraded images therefore deviate from both reality and user expectations.  This novel technology-Debiased-CAM-describes a method of training a convolutional neural network (CNN) to produce accurate and relatable heatmaps for degraded images. By pinpointing relevant targets on the images that align with user expectations, Debiased-CAMs increase transparency and user trust in the AI’s predictions.