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The proprietary solution is a data acquisition and analytics system that employs non-intrusive clip-on current transformers which are easily installed at electrical distribution boards. This enables AI algorithms to detect subtle changes and patterns in the electrical signature of each connected asset or device. Monitoring electrical assets has traditionally been complex and costly, requiring multiple sensors and expensive systems. This has led to widespread under-monitoring,  resulting in expensive maintenance and significant energy inefficiencies. The solution extracts a proprietary set of deep energy data from electrical devices, assets, and machines, and can be easily installed on both new and existing electrical assets or building infrastructure. It offers real-time monitoring and reporting on important metrics such as real-time power usage effectiveness (PUE) and enables automation of sustainability reporting. The technology offers an industry-changing solution: a non-intrusive cost efficient AI-powered monitoring system that is easy to install. It generates a proprietary data set that fuels machine learning algorithms, enhancing efficiency and reducing total cost of ownership for all connected assets. The technology owner is seeking test-bedding partnerships with real estate businesses, data centre companies or service providers, facility management businesses.

Collagen is a structural protein prevalent in the connective tissues of all organisms, and is the building block of biomaterial that is essential in wound healing and tissue regeneration. Through a patented extraction method, a novel Type I Amphibian collagen has been valorised from discarded skins, an agrifood waste stream and processed into a medical grade collagen biomaterial. The extracted pristine native amphibian collagen possesses unique properties, combining attributes associated with aquatic and land-based collagen sources, giving the extracted collagen more versatility than conventional sources of collagen. The Type I Amphibian collagen possesses a higher biocompatibility and water solubility as compared to mammalian sources of collagen, with a better thermostability profile, than marine sources of collagen. The technology provider has demonstrated the medical application of this extracted collagen by developing a range of specialised wound dressings, specifically designed for the management of chronic wounds. These dressing will significantly improve clinical outcomes and increase the rate of chronic wound closure.  The technology provider is looking for partnerships or collaborations to transform this pristine collagen into medical products. Additionally, with a pristine collagen extract, hydrolysing them into smaller fragments (collagen peptides) that can be customised to the needs of the partnership or collaboration, for the medical/cosmeceutical/nutraceutical industry. 

Despite the remarkable achievements of large language models (LLMs) in various tasks, there remains a linguistic bias that favors high-resource languages, such as English, often at the expense of low-resource and regional languages. To address this imbalance, we introduce SeaLLMs, an innovative series of language models that specifically focuses on Southeast Asian(SEA) languages. SeaLLMs are built upon the Llama-2 model and further advanced through continued pre-training with an extended vocabulary, specialized instruction and alignment tuning to better capture the intricacies of regional languages. This allows them to respect and reflect local cultural norms, customs, stylistic preferences, and legal considerations. Highlights: The models' attunement to local norms and legal stipulations—validated by human evaluations—establishes SeaLLMs as not only a technical breakthrough but also a socially responsiveinnovation. SeaLLM-13b models exhibit superior performance across a wide spectrum of linguistic tasks and assistant-style instruction-following capabilities relative to comparable open-source models. SeaLLMs outperform mainstream commercialized models for some tasks in non-Latin languages spoken in the region, meanwhile, SeaLLMs are efficient, faster, and cost-effective compared to commercialized models.

As rapid global warming accelerates, the need for increased sustainability efforts has become a critical societal challenge. While individual lifestyle changes can contribute, their impact remains limited without broader systemic shifts. This places significant pressure on industries, particularly the fashion & textiles sector, a major contributor to climate change responsible for 10% of global greenhouse gas emissions. Decarbonising this industry is therefore crucial to achieving a sustainable future. This technology enables textiles and fabrics to remove carbon dioxide (CO2) from air. The patent-pending material functionalises textiles to capture CO2 present in air which is sequestered into a harmless mineral during the laundering process. The resultant mineral which is environmentally safe is then washed away, leaving the textile recharged to remove CO2 once more. With this technology, decarbonisation of the textiles industry can be achieved through the decentralised action of consumers utlising functionalised carbon removing products. The technology owner is interested in working with interested companies in the fashion industry value chain to test-bed this new material for carbon removing apparel and fabrics.

3D scanning by employing technologies like LiDAR, laser scans, TOF cameras and photogrammetry is an essential step in the process of making digital twins for the construction and built space sectors. This data is then meticulously processed, often manually, to form the 3D models for integration with Building Information Modeling (BIM) platforms and creation of accurate digital twins. The 3D models, by themselves or in conjunction with the realised Digital Twin, also help in different planning and monitoring tasks during the entire lifecycle of a building from construction to maintenance.  Obtaining the 3D cloud data from scanning and its conversion to a model is an involved process. The technology presented here eases both these processes by providing diverse options for scanning and enabling AI assisted conversion of the data obtained  to a 3D model capable of being used with BIM.

The technology presented is an Artificial Intelligence (AI) model developed to extract essential information from scanned medical certificates. The trained model can extract pertinent details from medical certificates issued locally in Singapore and can help companies streamline their medical leave management  process by automating the approval of medical leave requests. The extracted details can also help in seamless integration with a company's existing workflow. The technology enables prompt and precise handling of leave requests and thus reduces administrative workload, processing time and errors introduced due to manual entry.

The advanced green building performance assessment platform, developed by a Singapore-based technology enterprise, addresses the critical challenge of inefficiencies in green building projects due to limited data transparency and coordination among industry stakeholders. While numerous tools offer building performance evaluations, this platform distinguishes itself through its AI-driven, comprehensive, and user-friendly approach. Standard energy assessment software takes a long time (typically 6-8 weeks) to complete similar assessment, requires special skillsets to use the software and is very costly. These are among the reasons that brings down the motivation of building owners. The proposed platform empowers stakeholders to access instant assessments and solutions, significantly reducing transaction times, marketing costs, and investment risks. Its AI-powered cloud-based platform facilitates the assessment and analysis of building performance. It aggregates diverse building data sources, applies physics-based simulations and machine-learning analytics, and creates digital twins. The platform serves as a catalyst for sustainable development through digitalization, catering to the urgent need for decarbonization strategies worldwide. The technology owner is seeking co-development and test-bedding partnerships with real estate owners including commercial building and residential building owners, as well as smart city planners.

This biotechnology pertains to a modular cloud-based bioprocessing system designed to streamline and enhance the cultivation and analysis of biological cultures. Addressing the complexities and constraints of traditional bioprocessing, this technology simplifies operations, making advanced bioprocessing tools accessible to a broader range of users. It has shown its versatility across various segments including educational institutions, research labs, biotech and bio-manufacturing companies and even within the food service industry, providing an efficient, flexible, affordable and scalable solution for growing biological cultures.

Near-infrared (NIR) light, part of the electromagnetic spectrum just beyond visible light, has various applications, particularly in vital sensing and food analysis. However, existing technologies for generating NIR light present certain limitations. Traditional halogen lamps can emit a continuous spectrum from visible to NIR wavelengths but pose challenges such as considerable heat generation, short lifetime, and difficulties in light distribution control. As a modern alternative, near-infrared LED arrays offer advantages such as no heat radiation and long lifespan. However, they are not suitable for applications requiring a wide wavelength range due to a lack of continuous output across the entire NIR spectrum. The wavelength intensity variation of NIR-LED arrays also affected the consistency of sensing and analysis. To overcome these challenges, the technology owner has developed a unique NIR phosphor as a heat-free light source with a wide spectrum range, enabling degradation-free analysis. Especially in food analysis, prolonged exposure to a halogen lamp may damage food. In addition, the long lifetime of this NIR source reduced the need for frequent replacements, leading to cost savings. Moreover, it can irradiate broadband NIR light from a single source, enabling easy light distribution control and wavelength axis alignment and reducing wavelength intensity variation within the irradiation plane. These advantages ensure consistency and accuracy in sensing and analytical applications. The technology owner is seeking R&D collaborations with industrial partners interested in integrating this advanced NIR light source into their applications.