TECH OFFERS

This technology is suitable for companies looking for a probiotics delivery system with increased probiotics viability. Spray-dried probiotic powder derived from this technology can be used as dietary supplements or functional food additives for human and animal consumption. Conventional probiotics often lose viability during shelf storage and upon ingestion, especially during their transit through the gastric region. Our industrially scalable encapsulation technology can improve probiotics’ shelf life and maintain viability during their passage through the human upper gastrointestinal tract. The encapsulated probiotic product achieves qualities of gastroprotection and targeted release in the intestinal region, overall boosting the beneficial effects of probiotics on gut health. Probiotics represent a US$ 58 billion market with immense growth potential, as global consumers are increasingly invested in digestive health and means to enhance the gut microbiome. Our patented technology of encapsulating probiotics involves a modified spray-drying process and is a high-throughput, food-grade, and inexpensive technique applicable to pharmaceutical, food and animal feed sectors.

Grasping technology, often associated with robotics and automation, addresses the challenge of manipulating and handling objects in various environments. The primary problem solved by grasping technology is the ability to securely and accurately pick up, hold, move, and release objects with different shapes, sizes, and materials. This technology is especially crucial in situations where human intervention may be difficult, dangerous, or inefficient. Before the deployment of new models and algorithms in the real world, it would be great to test the algorithm in a realistic simulation environment first. 

Autonomous driving technologies hold promise of substantial manpower savings, but the technology is still not mature enough to remove the driver from the vehicle. This also hinders the deployment of autonomous systems for many business applications as the ROI (Return on Investment) is not justifiable. There are also multiple scenarios, such as firefighting or waste processing, that require the agility offered by a human operator but have worksites that can be harmful. The technology presented here offers a high-fidelity teleoperation solution platform which can control many kinds of vehicles and machinery with high quality video feed at low latency. This technology is particularly useful for autonomous vehicle or machinery related companies that want to release their fleet to the market and have the option to remove the requirement for a safety driver onboard. It is also useful for companies providing heavy machinery, or end users of heavy machinery who seek to remove operators from harmful worksites.

In the mid-infrared region, gases exhibit absorption spectral features that are typically two orders of magnitude stronger compared to the near-infrared region. This makes the mid-infrared quantum cascade laser (QCL) a highly suitable choice for gas spectroscopy applications. QCLs offer several advantages, including broadband spectral coverage ranging from 3 to 25μm, narrow linewidth, compact size, and robustness, which have contributed to their popularity in various spectroscopic applications. In this context, a portable gas sensor has been developed utilizing self-developed QCL arrays, covering two specific wavelength regimes: 9-10 μm and 13-14 μm. To further enhance the detection sensitivity, an artificial intelligence (AI) algorithm has been integrated into the gas sensor. The incorporation of a hollow-core fiber as a miniaturized gas cell contributes to the overall compactness of the system. By leveraging the capabilities of QCLs, this gas sensor overcomes critical weaknesses associated with existing approaches, particularly their lack of selectivity and inability to differentiate mixtures of gases effectively. We anticipate that this technological innovation will accelerate scientific research progress and prove valuable across various industry sectors.

Fast-moving consumer goods (FMCG) and other product components come in a wide variety of shapes, sizes and packaging configurations. During the manufacture of such products, a key challenge for automation is to effectively handle and manipulate such diverse products during production or logistical processes. Users planning to automate their production lines typically have to take into consideration the use of either multiple grippers for different product types, or incorporate an automated tool changer with added complexity and cost. To address this challenge, a Singapore start-up has developed a universal soft robotic gripper designed to manipulate a wider range of product sizes by incorporating a resizeable gripper base. Gripper adjustment is automatically carried out via an integrated computer vision system thus minimizing the need for human intervention during pick-and-place processes. The gripper's soft fingers also minimize damage to products during the gripping process.

Current state-of-the-art lab-scale methods for fabricating superhydrophobic membranes for membrane distillation often involve complex surface modifications or the use of nanomaterials. However, these methods are difficult to scale up. This technology relates to a pure rheological spray-assisted non-solvent induced phase separation (SANIPS) approach to fabricate superhydrophobic polyvinylidene fluoride (PVDF) membranes. The resulting membranes have high porosity, superhydrophobicity, high liquid entry pressure, and hierarchical micro/nanostructures. They can also be easily scaled up. The spraying step caused local distortion of the membrane surface, which induced a two-stage phase inversion. This led to the formation of multilevel polymeric crystal structures. The morphological structures and other membrane properties (e.g., mechanical strength and liquid entry pressure) could be tuned by applying spraying materials with different physicochemical properties. This facile fabrication method will pave the way for the large-scale production of superhydrophobic membranes for membrane distillation.

Osteoporosis is a significant global public health concern affecting approximately 500 million people. The condition is associated with high mortality and disability rates due to osteoporotic fractures. The management of osteoporotic fractures comes at a considerable cost of SGD 11K per patient in Singapore, placing a growing burden on healthcare budgets as the aging population increases. Currently, osteoporosis is assessed by measuring bone mineral density (BMD) using dual energy X-ray absorptiometry (DXA). However, the availability of DXA machines, particularly in developing countries, is limited. Consequently, DXA examinations are not routinely ordered, resulting in orthopaedists often lacking DXA results during examinations. Therefore, an alternative method for estimating and screening osteoporosis is necessary. To address this, an automated AI system that can predict a patient's osteoporotic score by evaluating the CTI (cortical thickness index) from a plain femur X-ray scan is designed and developed. This system would provide a preliminary assessment and enable mass screening for osteoporosis.

This technology offers an automated diagnostic solution for retinal health based on fundus image and deep learning technology. The network automatically classifies fundus images of age-related macular degeneration (AMD), diabetic retinopathy (DR), glaucoma and normal into abnormal and normal classes. The network also can be run on any computing platform, delivering instant results for clinicians and patients.

Diabetes is associated with macrovascular and microvascular complications, including Diabetic Foot Ulcers (DFU). To identify and manage DFU risk, diabetic patients are recommended to go for a regular foot assessment. Patients who are at‐risk diabetic foot should undergo regular podiatry evaluation, however specialised diabetes centers are currently facing high rates of ulcer recurrence. Frequent visits to these centers can strain an already overwhelmed healthcare system. The technology developer has invented an Artificial Intelligence (AI) model that is able to detect pre-ulceration. By detecting feet at risk of developing DFU, the model is able to refer patients for timely intervention before it becomes a DFU. Users only need to submit photos of their feet from different angles and an anomaly score will be calculated.