Sustainability

Singapore is currently only producing 13% of its vegetable consumption. With little farming land available, Singapore relies heavily on imports from other countries. Due to increasing focus on food security, the alternative to solve land scarcity problem is to build greenhouses on concrete rooftop. Although concrete rooftop greenhouse are able to keep pests out, there is a signifcant heating problem which severely inhibits the growth of the vegetables. Therefore, there is a need for a rooftop greenhouse that is able to actively cool itself to avoid such problem. This technology offer is a modular rooftop greenhouse farming system (hydroponics) capable of producing vegetables on concrete roofs to meet the local demand while reducing over-reliance on imports. The design of the greenhouse farming system enables cooling and does not heat up, thus allowing the growth of pest-free vegetables. The system is approximately the size of a typical carpark lot (2.5 x 5 m). The production rate is 30 kg per month (2.5 x 5 m size) and requires minimal human intervention. The technology offer comprises both the farming system and its operation know-how. The modular rooftop greenhouse farming system can be set-up within 3 days or scaled-up when required with guaranteed vegetable growth. The break-even cost of one greenhouse is about 3 years. The technology owner is seeking to out-license their technology.

Manufacturing plants constantly seek opportunities to save energy, reduce cost, and be more environmentally sustainable. However, achieving these goals often requires heavy expenditure in the form of hiring teams of experienced engineers, who then perform cost-reduction tasks manually - this method is time-consuming, costly, and prone to inaccuracies due to the risk of human error.  This technology offer provides a no-code Artificial Intelligence (AI) powered platform that monitors energy consumption, carbon dioxide(CO2) emission, and operational expenditures (OPEX) in real-time. The AI engine builds a virtual cognitive model (digital twin) of a physical asset, e.g. a manufacturing plant or a piece of machinary. Simulations are carried out on the model to predict operational inefficiency i.e. high energy usage, equipment breakdown, etc. Upon detection of inefficiencies, the engine is able to suggest the best operating parameters to resolve the inefficiency.

Existing methods for load monitoring typically focus primarily on residential building data, while few look at the effectiveness of such systems for industrial or commercial buildings. Apart from the use of this technology for real-time supply-demand response, such methods can be extended for use in anomaly detection, small-scale load change detection, or an estimation of energy usage, without the associated high costs of sub-metering equipment. The proliferation of neural networks for such demanding tasks solves the computationally expensive problem of traditional methods like Hidden Markov Models (HMM) and fuzzy clustering algorithms. This technology offer is a neural network solution for residential and industrial energy management. It utilises a time-series forecasting tool to predict load, renewable energy generation, and electricity prices, without the need for costly sub-metering equipment. It is based on reinforcement learning algorithms which are trained by rewarding and penalising neural network algorithms for good or bad decisions respectively, the solution is a non-intrusive technique that helps residential and commercial end-users save on energy costs in the open energy market by scheduling their load demand for heating, ventilation, air conditioning (HVAC) systems, washing machines, and charging of their Electric Vehicles (EVs).

Different plant species have different nutrient requirements. The current practice of urban farming uses a generic hydroponic nutrient solution that is suitable to most plant types, and a crude sensing system that only measures total ion content in the solution. This approach often results in nutrients deficiency and/or overloading and hence requires consistently monitoring. Overloading of nutrients not only increases the input costs, it also results in greater quantities of contamination in effluent to be disposed after harvest.  A targeted hydroponic nutrient solution reduces the need to periodically adjust the nutrient. The technology provider has studied and formulated different nutrient recipes that had shown improved yield compared to commercial products. This ensures the best growth for each crop type. It also reduces common problem stemming from imbalanced nutrient, e.g. leaf chlorosis due to nutrient deficiency. All these translate to a better yield and a more marketable produce for the farmers. Formulations developed include Mizuna, Kale, Lettuce, Mustard, Kalian, and Caixin. The technology provider is seeking for licensing partners from the agriculture industry.

The presence of heavy metals in food or feed powders involves contamination of the food chain and potential harm to public health, as such, rapid detection is a time-critical issue. The uncertainty about food safety caused by the possible presence of heavy metals is of concern to consumers and regulatory authorities and this is typically addressed by increasing the testing frequency of food or feed samples. However, existing testing methods are often time-consuming and require highly skilled laboratory personnel to perform the testing. This technology employs spectroscopic imaging methods and machine learning techiniques to rapidly detect heavy metals in food or feed samples. The machine learning model can perform a multi-class differentiation of the various heavy metals based on spectroscopic measurements. It is also able to predict the concentration of heavy metals present in food or feed powders using spectroscopic measurements. Minimal sample preparation is required for this method, allowing for the rapid screening of food or feed powder samples. The technology owner is interested in collaboration with companies working with food powders, with an interest in heavy metal content within food powders.   

To date, the current primary feedstock for plastic production is oil, which accounts for more than 850 million metric tons of greenhouse gases emissions per year. Hence, there has been an increasing demand for green plastics, which are plastic materials produced from renewable sources. This technology offer is a synthesis method of green plastics from carbon dioxide (CO2) and renewable feedstock. The green plastics produced are non-isocyanate polyurethanes (NIPUs) and can be actively tuned to be anionic, cationic, oil-soluble and cross-linkable which enables a wide range of applications. These NIPUs are non-skin irritant, have high bio-content and can possibly be made to be bio-degradable. This technology owner is looking for partners in various industries such as personal and consumer care, coatings and lubricant additives (to name a few) for further co-development of the solution. The technology owner is keen to license this technology as well.

Conventional diagnostic imaging of the skin involves the use of dermatoscopes. Dermatoscopes use skin surface microscopy to examine dermal and sub-dermal tissues to diagnose skin problems. However, these devices can be costly and provide a limited view of the immediate skin surface. This limitation meant that dermatoscopes have to be used in direct contact with the patient's skin. Because of this, they can only be used to image patients in the same physical location as the clinician conducting the examination. The overall result is that only a tiny portion of the global dermatology patient-base can be reached cost-effectively and efficiently. Telemedicine and telehealth network operations are rapidly developing ways to address patients broadly and at lower costs for them and their care providers. Yet, such tools neither deliver desmatoscope-like functionality nor improved it in way that it allows patients' skins to be examined and analysed during an online medical consultation with a general practitioner. In order to facilitate remote skin disease diagnosis, the use of software is required to acquire and share images in real-time and ideally, by the patients themselves. This software enables patients to take their medical sub-skin images with their mobile, tablet or laptop cameras, and securely share it with doctors. Crucially, dermatoscopy images can also be used with the technology to improve diagnostic accuracy. This technology is intended to position itself as a technology which when scaled-up, could allow for products that can enable optical biopsy and phototherapy. 

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.

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.