TECH OFFERS

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.  The technology owner uses microcarrier beads, which are small (approx. 75 um diameter) polymer beads. They collectively provide a significantly larger surface area than standard tissue culture flasks for cell growth. Typically used in the pharmaceutical industry to scale up cells for protein expression, the use of microcarrier beads for the scale-up of cell-cultured meat is a relatively new application.   Some key features of the technology: Increases cell yield by 7-fold from lab-scale Microcarrier beads hold their shape throughout Made up of inert and food grade material  Different cell types require specific types of microcarrier beads to optimize growth The method is suitable for:   Clean meat industry / Cultured meat companies Pharmaceutical companies that apply such technologies towards protein manufacturing, drug discovery or antibody production. Other potential clean meat applications using other cell line sources. With the increasing demand for meat production for population consumption, there is a need to look at other more sustainable meat source. Lab grown ‘clean meat’ can be an alternative to livestock farming. The development of ‘clean-meat’ can help to reduce carbon footprint, improve animal welfare and prevent foodborne illnesses. The cultured meat market size was valued at $1.6 million in 2021 and is projected to reach $27 billion by 20301.   1Cultured meat market size, share - global industry report, 2022-2030. Allied Market Research. April 2021.   Increased yield of viable cells as compared to 2D cell culture Scalable Up to 7x lower in cost of production at a larger scale Pathogen-free source of meat produced High reproducibility Low usage of microcarrier beads: 10g microcarriers/20 litres of cell culture clean meat, microcarrier, dynamic culture, cultured, meat, scaffolding, 3d, scaffold Chemicals, Polymers, Foods, Ingredients, Processes

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  Machine-to-Machine communication protocol for Lidar Elevator Stand system (Client), remote autonomous trolley puller (Client) and Robot Command Centre (Server). The 2D Lidar sensor has a field-of-view in Lidar Elevator Stand (LES) system with detection range from 0.05m to 10m, and a horizontal aperture angle of 200 degree, which is suitable for deployment in the application at airport Ability to avoid false triggering requests for trolleys replenishment from LES System. LES system’s operations and processing are able to work under different lighting conditions. Replenishment of trolleys can be customised for multiple location points. Material handling with delivery in airports, supermarkets, warehouses, logistics, hotels and factories. Improve and simplify work operations. Free up existing manpower to take up new higher value-added tasks. Seamless communication between monitoring and replenishment of trolley. Safe mobility replenishment of trolleys, trolleys, material handling, autonomous trolley puller, trolley puller, trolley Infocomm, Networks & Communications, Mobility, Robotics & Automation, Logistics, Inventory Management, Transportation

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. For the Machine Learning (ML) model to calculate the stop duration, users will need to key in the input parameters such as building name, block number, road name, postal code, day of week, day of month and time. The system will predict the stop duration (job completion duration) in minutes. ML model enables prediction of ETC based on historical data and a small set of input parameters Highly correlated with ETA which can be easily obtained from API services such as Google or Onemap Takes in consideration of temporal data including hours of the day and day of the week. Can be integrated with existing web/mobile based solutions. Apart from being a productive tool for route planning systems, the software can also be used in various situations such as: Customer Services/Call Centres Fleet Management  Loading Bay Assignment This model can be used to improve the existing route planning systems as it provides additional job completion duration prediction on top of estimated ETA.  Low cost - the model is easy to implement and incorporate into other applications. Simple to use - only a small set of input parameters are required. Able to predict the stop duration (job completion duration) with reasonable accuracy. The technology owner is keen to out-license this technology to collaborators in the field of last mile-logistics, or collaborators who are providers of software for last-mile logistics. This ML model is for Singapore use only. Last Mile Delivery, Estimated Time of Completion (ETC) Infocomm, Artificial Intelligence, Logistics, Delivery & Distribution, Value-Added Services

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. The current practice of using electrocardiogram (ECG) to measure the electrical activities of the heart is mainly found in the clinical settings and not easily accessible by the general population due to high costs. The need for an accurate placement of ECG electrodes does not allow the continuous monitoring of the heart condition in a non-clinical setting. It is not easy for individuals to continuously monitor their stress levels at the workplace in daily life, especially for drivers. This technology offer enables continuous, real-time monitoring and is not limited to clinical settings. It is cost-effective without the need to purchase medical grade ECG devices. The solution can be easily customised and adapted to existing and future PPG wearables. It is portable and feasible for all-day monitoring of stress levels and heart conditions in diverse workplace environments. Essentially, it is an algorithm that extracts proprietary features to predict stress levels, with an unique approach, from heart beat signals customised to a given context in the workplace. This facilitates the creation of contextualised stress profiles for specified workplace situations ranging from physical combat conditions to air-conditioned office sedentary conditions. The technology owner is interested to out-license this technology offer. Potential application areas are: Public or private hospitals, clinics, community care centers. Individual drivers and any workers who might be exposed to workplace related stress. Sports sectors including sports training, research and competition. Medical devices, equipment suppliers and manufacturers. Potential products: Existing or future wearables with PPG, e.g., wristwatch, headset, t-shirt, etc. Hardware and platform independent, easy to be incorporated into existing and future wearables. Portable and feasible for 24/7 stress monitoring. Affordable and cost-saving without the need to purchase medical grade ECG devices Real-time monitoring and timely feedback customised for users stress monitoring, ai algorithm, ai system, heart monitoring, workplace stress, wearable technology Infocomm, Artificial Intelligence, Internet of Things, Wireless Technology, Sustainability, Sustainable Living

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. Debiased-CAMs are effective in helping users identify relevant targets even on images affected by different clarity levels and multiple issues such as camera blur, poor lighting conditions and colour distortion. The AI’s prediction also becomes more accurate. As the model is trained using self-supervised learning, no additional data is needed to train it.  The training for Debiased-CAM is generalisable, and thus applicable to other types of degraded or corrupted data and other prediction tasks such as image captioning and human activity recognition. Used to train 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. It also increases the ability of meeting regulatory standards to deploy CNN models in the following applications, where explainable AI is required. Healthcare, eg. Radiology Autonomous Vehicles   Produces accurate, robust and interpretable heatmaps for degraded images Works on images with multiple degradation levels and types such as blurring and improper white balance Agnostic to degradation level, so that enhancement can be applied even when the level is unknown Perceived by users to be more truthful and helpful as compared to current heatmaps distorted due to image degradation Method of training can be applied to other degradation types and prediction tasks Explainable AI Infocomm, Video/Image Analysis & Computer Vision, Artificial Intelligence

Lists are an indispensable part of the online experience, often used to show many results, such as products, web pages, and food dishes. These items can be neatly sorted by a desired attribute like price, relevance, or healthiness. Listed items often have multiple attributes. However, instead of being able to sort multiple attributes simultaneously, consumers are currently limited to sorting only one attribute at a time. This makes searching for the desired item tedious and confusing. Imma Sort supports interpretable and multi-attribute sorting. Sorting for two or more attributes is possible. In contrast to existing search technology, Imma Sort trades off the smoothness of the sorted trend for the main attribute to increase ease of prediction for other attributes, by sorting them more approximately. Results for specific attributes can be made smoother by setting higher importance weights. Provides intuitively sorted results sorted by two or more attributes to improve decision-making and user experience Results can be customised by allocating higher weightage for selected attributes Enables users to perform multi-attribute sorting in any existing list interface without requiring sophisticated spreadsheets or data visualisations Can be integrated into search and recommendation systems across a wide range of applications Can also be incorporated into various search and recommendation systems for more effective search results. Examples of possible applications: Food dishes can be sorted by healthiness and tastiness Hotels can be sorted by price and distance Sorting by price and rating would generate results that generally trend in one direction for both attributes. This makes it easy for users to anticipate the values of multiple attributes as they move down the list, without having to construct a mental list for the secondary attribute. By decreasing users’ mental effort, this will improve decision-making and increase satisfaction. Multi-Attribute Sorting, e-commerce, algorithm Infocomm, eCommerce & ePayment, Enterprise & Productivity

Cadaveric bones are used to carry out medical training for surgeons and trainees. However, such bones are limited in supply, difficult to store, inconsistent in terms of quality and costly to use for repeated training. As such, it is necessary to create an alternative to cadaveric bones that is equally realistic while being more cost effective and easier to obtain. This technology can resolve the limitations of cadaveric bones by offering the formulation and processing method to produce a Fused Deposition Modelling (FDM) system-agnostic bone-like 3D printing filaments for surgical models printing. Printed anatomical bone models developed from this technology will have the look and feel of the real bone. The technology presents an affordable and readily available alternative that minimises the demand for cadaveric bones while still providing realistic training to medical professionals. The technology owner is seeking for collaborations with companies interested to scale-up the manufacture of the filaments and/or licensing of the technology.   The technology offers the formulation and processing method to produce bone-like filaments that allows users to 3D print realistic and relatively lower cost cadaver bone models for use in medical training of surgeons and trainees. These bone-like filaments have the following features: Compatible with all FDM 3D printers Achieves good mechanical properties  (~80% improvement in tensile strength) compared to the commercial bone-like filament. Printed bone models using this technology feel realistic to drill and cut and screws can be tightened nicely, just like real cadaveric bones. This technology is primarily targeted for use in the healthcare industry (hospitals, medical schools, and biomedical companies) to obtain realistic anatomical bone models for surgical training and workshops at a more affordable price compared to cadaveric bones. Surgeons can also create 3D printed bone structures to help them plan surgical procedures before operating on the patient to better understand the procedure. Significantly lowers the cost for realistic medical trainings and workshops as compared to using cadaveric bones Fabricated bone models are equal in quality with actual cadaveric bones cadeveric bones, cadever, cadevers, 3d printing, fdm, medical training, surgery, surgeon, medicine, bone models, additive manufacturing, fused deposition modelling, printed anatomical bone models, surgical models, printing, bone-like Materials, Composites, Manufacturing, Additive Manufacturing, Moulding, Sintering, Casting & Nanoimprinting, Life Sciences, Industrial Biotech Methods & Processes

This technology offer helps to address the problems of global warming, food security crisis and energy crisis. With the increase in human population and rapid urbanisation, the change in weather patterns and increase in food demand has been inevitable. One of the major concerns faced in Singapore, due to global warming, is the urban heat island effect. This occurs when urban areas in cities have a higher air, surface and soil temperature than rural areas. Initiatives for high-rise greenery has been put in place to help solve the problem. However, there has been problems with limited space and high maintenance cost for these greeneries. Rooftop hydroponics farming is a possible solution to offset the running costs of rooftop greeneries or even generate profits for rooftop greeneries as it produces fresh produce, while simultaneously reducing the urban heat island effect. The reduction in urban heat island is due to a combination of green and blue body acting as a thermal buffer and contributing to the building sustainability (due to reducing in cooling costs). This initiative addresses the constraints of limited land, as solar energy generators require large areas for photovoltaic panels to be laid. This technology offer aims to provide an integrated solution to this economic challenge for environmentally sustainable urban planning. This Technology Offer is a luminescent solar concentrator that enables both power generation by photovoltaic modules, as well as efficient urban rooftop farming. In rooftops where solar panels are used for power generation, real estate would be taken up by the solar panels, so farming cannot be done. If crops are placed under the solar cells to be grown, the growth rate will not be optimal due to the obstruction of sunlight by the solar panels. This technology features a luminescent solar concentrator (LSC) film with organic dye which converts wavelengths not used in photosynthesis (green and yellow) to the red wavelength used for photosynthesis. This film is placed under the matrix of solar cells, and can be used to optimise the growth rates of crops placed under the solar cell matrix, so that solar power generation and rooftop farming can co-exist together. Know-how is also available to optimise the solar cell arrangement to maximise both solar cell power output and plant growth. The existing configuration has solar cell coverage of 54.1% which is able to maximise the amount of energy that could be harvested by the solar cells, and yet still ensure adequate light passing through the spacings between the solar cells to maximise the growth rate of the plants placed under the solar cells. This complete system is known as Wavelength Selective Photovoltaic (WSPV) system. The wavelength-selective solar photovoltaic system technology is suited for the following: Urban food production with simultaneous solar power generation Controlled plants R&D Industries that are interested to improve crops growth   Customer benefits includes: Optimise crop’s yield, yet achieving solar power generation Space saving Energy saving We are seeking industry partners to scale up, and to out-license the technology. Energy, Solar, Life Sciences, Agriculture & Aquaculture, Sustainability, Food Security

This Technology Offer is an Internet of Things ( IoT) based platform designed to assist the modern-day farmers in monitoring the entire farm seamlessly. It can be customized to suit each farm depending on the type of sensors, machine vision camera, cloud storage, etc., and is equipped with detailed data tracking and analytics to provide the most accurate growth process from start to finish. The software architecture used in this technology offer addresses a decentralized framework to provide the ability to exchange data between IoT devices autonomously without any centralized server. In recent years, the development of IoT applications has become increasingly complex. Thus, this technology addresses this problem by providing the ability to simplify the streaming of data to the IoT platforms over the web. This design can be customized for other applications. The software has a light-weight runtime, taking full advantage of its event driven, non-blocking model. This makes it ideal to run at the edge of the network on low-cost hardware such as Raspberry Pi as well as in the cloud. Real-time data can be easily imported and exported for sharing with others. Plug & Play solutions make it easy to connect various sensors. This system has good fault tolerance as each node runs a distinct component of the web server application software and identical copies of each other. Upon a node failure, the application software can be replaced by another good node easily. The failure and restoration processes of the hardware and software are highly dependent on the status of other components as well as the sequence of failure events. Data can be downloaded from each individual node or from the consolidated database in the cloud. This IoT platform architecture can be applied to the following segments of the market: Outdoor farming Indoor farming Laboratories/research/education institutions Home Hobbyist It can also be applied in the following segments of the market: Office Building, Home, Hotel: Fire alarm system Electrical system This technology is a low-cost functioning system, easy-to-install and is compatible with most standard Internet of Things (IoT) sensors, switches, and gateways. This Technology Offer allows modern-day farmers to monitor the entire farm seamlessly. It can be customized to suit each farm depending on the type of sensors, machine vision camera, cloud storage, etc., and is equipped with detailed data tracking and analytics to provide the most accurate growth process from start to finish. Urban Farming, IoT Farming, Indoor Farming, Outdoor Farming Electronics, Sensors & Instrumentation, Life Sciences, Agriculture & Aquaculture