TECH OFFERS

The human skin is the largest organ of the body, capable of extremely sensitive sensing ability and functional characteristics including elasticity, mechanical resistance and self-healing due to different mechano-receptors and sensory nerves. Electronic skin (e-skin) or synthetic skin, is a thin electronic material that stimulate the characteristics of the skin, making it possible for applications in prosthetics, robotics, wearables devices and percutaneous drug delivery systems. This patented technology is an e-skin with tactile, pain and temperature sensing, capable of differentiating various mechanical forces, sensory heat or moisture concurrently. It is a promising technology for healthcare applications. Currently, majority of the sensors in the market for healthcare are in rigid forms and for small application areas, which make it difficult for portable and wearable applications in large surface areas. This thin film flexible electronic skin can detect applied pressure and temperature on it. The skin’s electrical resistance varies with applied pressure and temperature. By measuring the skin’s electrical resistance, the applied pressure and temperature can be derived. The skin can be made stretchable to be covered on irregular curved surfaces. These features complement the drawbacks of rigid sensors for healthcare applications. The technology owner is looking for collaborators in the medical and robotics sectors and potential opportunities outside of healthcare such as beauty and cosmetics. Skin size, shape, density: customizable Pressure and temperature detection ranges: customizable (up to 5000KPa and 120°C) Single sensor repeatability: less than 10% Thickness: less than 1mm Communication port: via digital IO, UART, USB, Bluetooth, and Wi-Fi Data storage: SD card or other storage media Working voltage: DC 3-5V, or customizable The electronic skin can be: Embedded in insole for fall risk warning, fall detection, gait analysis, foot, and leg abnormality detection. Embedded in rehabilitation glove for finger gripping strength assessment. Embedded in surgical glove, robot end-effector and body for tactile sensing and force feedback control. Embedded in bed for bed sore prevention. Covered on artificial limb for pressure, temperature, and collision sensing. Deployed at shower room or bed side for fall detection. Used for teeth alignment and tongue muscle strength measurement. Used for training of doctor to operate surgical robot, under AR, MR, metaverse environment. Wearable electronic devices with skin-like properties will provide various applications for monitoring of human physiological signals such as body pressure, temperature, motion, and disease-related signals.  Low cost.  Customizable and durable electronic skins based on requirements. Compared with rigid sensors, these electronic skins have soft surfaces, can be made in large size, and covered on various flat and curved surfaces.  Possible to develop an interface to connect the e-skin to human neural brain or spinal cord. API under Windows, Linux, Android, and iOS to facilitate development of various applications.  Electronic skin, Tactile sensing, Pressure mapping, Temperature mapping Electronics, Sensors & Instrumentation, Personal Care, Cosmetics & Hair, Healthcare, Medical Devices, Infocomm, Internet of Things

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. Only a current transformer is required for each device, greatly reducing cost and increasing reliability. The proprietary current transformers are easily clipped onto electrical circuitry. The system can be installed into new or retrofitted into existing buildings and operates from its own independent network. Installation can be done by a locally qualified electrician. High-frequency electrical signature collection. The circuit transformer sensors are tethered to electrical circuits. These sensors acquire high-frequency electrical data, and the data is then fed into the intelligent monitoring system. The system has specialised machine learning algorithms specifically designed to provide valuable insight into the unique challenges of the built environment. Proprietary hardware/software platform to make data acquisition and installation as un-intrusive, easy and cost-effective as possible. Web console for easy data visualization and open API for integration with other systems. Growing knowledge base and algorithm library to add value to the unique building environment. Dedicated in-house data solutions team with exceptional data science expertise that can understand and solve the bepsoke challenges of specific buildings and assets. All data is also made available for direct download and local processing via a comprehensive Application Programming Interface (API). Opportunities provided by the system Electrical device condition monitoring for predictive maintenance Fault prediction and detection for maximising availability Energy optimisation, cost savings and carbon footprint reduction Arc detection capabilities for identification of fire hazards Power quality monitoring Real-time warning and notifications The system can be deployed in many different sectors and locations where electrical assets and infrastructure are not comprehensively monitored or understood. It has been deployed in sectors including data centres, key infrastructure such as wastewater, mining, manufacturing, leisure and office environments. Typically the system is best suited for real estate companies, companies with facility management responsibilities, building management businesses, carbon reduction companies or building owners who have reasonably sized property portfolios and require a proper insight into their electrical infrastructure. The solution gathers an unprecedented level of data, simultaneously monitoring thousands of different data points at any one time. The level of granularity provides a rich level of insight hitherto deployed at scale in most sectors. Typically alternative technologies, such as sensors can be costly, require regular configuration, and are not always part of a scalable solution where things such as condition-based monitoring have to be done on a site-by-site basis as opposed to a learn and deploy model. Net Zero, Condition-Based Monitoring, Carbon Reduction Technology, Digital Transformation, Data Acquisition Platform, Data Analysis Platform, Machine Learning Algorithms, AI technology, Digital Insights, Fault Prediction, Power Factor, ESG Reporting, Energy Optimisation, Power Quality Green Building, Sensor, Network, Building Control & Optimisation

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.  A unique pristine Type 1 collagen of amphibian origin, in its native triple helix form. Relatively high denaturation temperature of ~43°C, to withstand the average human body temperature of 37°C, thus retaining its functionality better in human body as compared to marine collagen. Can serve as a matrix or carrier for bioactives such as anti-microbials or anti-inflammatory drugs or compounds that confer additional specific therapeutic benefits. Reduced risk of adverse reactions or rejection compared to traditional biomolecules, thereby increasing clinical safety. Can be easily chemically cross-linked to form a microporous scaffold that facilitates tissue regeneration and accelerates the rate of re-epithelialization. Proven to inhibit/deactivate matrix metalloproteinase (MMPs), producing an optimal healing environment for the wound. Exists as Nano Fibres that are 20–25 nm in diameter with a length of 200–400 nm, enhancing cell-material interactions and better supporting fundamental cellular processes. Highly absorbable and thus able to remove wound exudate, allowing for a reduction in inflammation and oedema at the wound site.  Amphibian collagen can be used widely for biomedical applications, nutraceutical products, as well as cosmetics. Well known for its biocompatibility in human tissue, collagen is widely used in clinical practice for accelerated wound healing, post debridement. The main clinical usage of this technology allows collagen to act as support matrices for the repair of matrix-rich tissues that have been damaged and replacing scaffolds for tissue filling.  In the cosmetic front, collagen is extremely suitable for the care of dry, UV-exposed, and environmentally stressed skin as well as ageing skin. It is one of the main constituents of cosmetic formulations due to its moisturising, regenerating, and film-forming properties. However, the technology provider is also keen to collaborate with partners to explore beyond the applications stated above. With increasing consumer awareness of skincare and beauty products, the collagen market is expected to have a continued upward trend. Due to a greater emphasis being placed on developing products that are environmentally friendly and sustainable, the approach of upcycling amphibian skins that would otherwise be discarded as waste, will be embraced by the consumer fraternity. The global collagen market was valued at USD 9.66 billion in 2022 and is expected to expand to USD 19.98 billion in 2030 at a CAGR of 9.36 % during the forecast period of 2023-2030. Though intense competition, with many established brands and new entrants in the extraction of collagen, this technology is unique in the resources used – amphibians. There is no commercial available amphibian collagen in the market and the technology provider is the first to have demonstrated the use of this in wound dressing and cosmetics. Amphibian collagen is expected to be widely embraced as there are no religious restrictions, unlike other traditional sources of collagen.  A medical grade collagen with intact native triple helix structure. A special mechano-chemical method of extraction that form a sustainable waste valorisation process. Maintains unique properties of both aquatic and land-based collagen, unlike current sources of collagen. Nano-collagen fibres that are of 175-187% thinner than those of mammalian collagen. Is easily processed into gelatin and easily hydrolysed to form collagen peptides. Collaborations can be medical or cosmetic related. White labeling options available. All products conform to ISO10993 and can be ETO sterilized. Amphibian Collagen, Wound Healing, Cosmetic, Environmental Sustainability, Biocompatibility Personal Care, Cosmetics & Hair, Healthcare, Medical Devices, Pharmaceuticals & Therapeutics, Waste Management & Recycling, Food & Agriculture Waste Management

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. The SeaLLMs went supervised finetuning (SFT) and specialized self-preferencing alignment usinga mix of public instruction data and a small number of queries used by SEA language native speakers in natural settings, which adapt to the local cultural norms, customs, styles and laws inthese areas. 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. Moreover, they also outperform other mainstream commercialized models in tasks involving very low-resource non-Latin languages spoken in the region, such as Thai, Khmer, Lao,and Burmese. Training Process Our pre-training data consists of more balanced mix of unlabeled free-text data across all SEA languages. We conduct pre-training in multiple stages. Each stage serves a different specific objective and involves dynamic control of (unsupervised and supervised) data mixture, as well as data specification and categorization. We also employ novel sequence construction and masking techniques during these stages.Our supervised finetuning (SFT) data consists of many categories. The largest and most dominantof them are public and open-source. As the aforementioned are English only, we employed several established automatic techniques to gather more instruction data for SEA languages through synthetic means. For a small number of SFT data, we engaged native speakers to vet, verify and modify SFT responses so that they adapt to the local cultural customs, norms, and laws. We also adopted safety tuning with data for each of these SEA countries, which helps to address many culturally and legally sensitive topics more appropriately - such tuning data tend to be ignored, or may even appear in conflict with the safety-tuning data of other mainstream models. Therefore, we believe that our models are more local-friendly and abide by local rules to a higher degree. We conduct SFT with a relatively balanced mix of SFT data from different categories. We make use of the system prompt during training, as we found it helps induce a prior which conditions the model to a behavioral distribution that focuses on safety and usefulness.   Through rigorous pre-training enhancements and culturally tailored fine-tuning processes,SeaLLMs have demonstrated exceptional proficiency in language understanding and generation tasks, challenging the performance of dominant commercial players in SEA languages, especially non-Latin ones. The models’ attunement to local norms and legal stipulations—validated by human evaluations—establishes SeaLLMs as not only a technical breakthrough but a socially responsive innovation, poised to democratize access to high-quality AI language tools across linguistically diverse regions. This work lays a foundation for further research into language models that respect and uphold the rich tapestry of human languages and cultures, ultimately driving the AI community towards a more inclusive future. One of the most reliable ways to compare chatbot models is peer comparison. With the help ofnative speakers, we built an instruction test set, called Sea-bench that focuses on various aspects expected in a user-facing chatbot, namely: (1) task-solving (e.g. translation & comprehension), (2)math-reasoning (e.g., math and logical reasoning questions), (3) general-instruction (e.g.,instructions in general domains), (4) natural-questions (e.g., questions about local context often written informally), and (5) safety- related questions. The test set also covers all languages that we are concerned with. AI model candidates' responses to the test set's instructions may be judged and compared by human evaluators or more powerful large and commercialized AI models to derive a reliable performance metric. Through this process, we demonstrate that our SeaLLM-13b model is able to perform on-par or supasses other open-source or private state-of-the-art models across many linguistic and writing tasks. Infocomm, Artificial Intelligence

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. The technology is formulated and provided in a liquid formulation, to be a drop-in process where it is embedded in textiles during the “finishing stage” (last step) of a textile mill. Some features of the carbon removing technology include: Continual recharging of functionalised textiles through normal laundering process Forms a stable and environmentally friendly mineral upon sequestration of CO2 by regular detergent Lasts at least 10 washing cycles Can be embedded with standard finishing equipment (particularly at the padding and stenting steps) Currently optimised for cellulose based textiles but proof of concept has demonstrated polyester, polyamide, wool and blends thereof This technology has been designed for textiles – both for apparel and functional fabrics. It can also be considered for non-woven materials as well as for other applications such as coatings. Facing immense pressure to reduce its environmental impact, the fashion and textiles industry, a major contributor to global warming, seeks sustainable solutions that don't disrupt its fast-paced production. With an addressable global market of US$227 billion for textiles, this innovative technology offers a solution to textile manufacturers to reduce the industry’s carbon footprint. This empowers consumers to become active participants in combating climate change, simply by choosing clothes made with this technology. Offers a proprietary, environmentally safe carbon removal solution for textile industry Continual usage of the functionalised textiles – textiles are rechargagle to remove CO2 multiple times Does not require the adoption of new machinery or processes for its implementation carbon dioxide removal, textile, additive, carbon removal, fabric, decarbonisation, fashion, clothing, materials, mineral, functionalisation, sustainable, sustainability, apparel Materials, Nano Materials, Chemicals, Inorganic, Additives, Sustainability, Low Carbon Economy

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 is compatible with multiple third party scanning solutions and also provides some native options - - mm level one shot scan using stationary laser scanner with a scan time of a few minutes. - cm level mobile LiDAR based scan. - cm level TOF flight based fast scanning. BIM conversion time is dependent on the kind of scan perfomed. For benchmarking, a test done using  THETA and BLK2Go where BLK2Go was used to walk around and scan the site while THETA was used to take necessary pictures, required conversion time of ~10 days including manual intervention, for a unit 2500 sqm in size at LOD 100. A further scanning option, using a handheld scanner requiring scans 5m apart and with a scan time of 1 second is also planned. This aims to remove the need of specialized personnel to conduct the scans. The solution also provides a model hosting option with GUI which gets automatically updated with new scan data for the same location regardless of the technology used. The previous data is still preserved and is available for review. Possible applications for the solution include - - Faster and more accurate site surveys with lesser manpower requirement. - Identification of available space at planned construction site and simulation of delivery routes, assesment of safety and workability. - Progress monitoring by using data in comparison to BIM along with assessing material requirements. - Intuitive management analysis and reduction of building lifecycle costs by monitoring parameters like human flow, energy requirements, equipment use amongst others. (Digital Twin) - Simulation of processes and optimization of production line efficiency. The technology uses AI to replace manual alignment of point cloud data and generate spacial linkages automatically thus saving substantial amount of time over current processes. The technology also provides option for an end to end solution encompassing scanning, data processing, digital twin generation and utilization. The generated results can also be hosted on a web platform which allows extension of use cases such as additional AI based solutions by third party collaborators. Infocomm, Artificial Intelligence, Green Building, Sensor, Network, Building Control & Optimisation, Smart Cities

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 trained AI model recognizes terms and entities from scanned medical certificates. This includes but is not limited to - Clinic name Clinic address Clinic telephone number Patient name Start date of medical leave End date of medical leave Duration of medical leave The Name Entity Recognizer (NER) model is trained based on an open-source library and can be integrated with the existing workflow or system to automate the extraction of information for approval or recording purposes. The model, in its current state, is trained on a diverse dataset of medical certificates issued in Singapore and is suitable for application in systems providing Document Management and Human Resource solutions. The application of the model will particularly be useful for - Companies looking to automate their medical leave processing or application workflow. Insurance providers. Vendors specialising in Document Management, HR software solution, Payroll, and Attendance solutions.  The model can be integrated into their existing solution to value add in the processing of medicate certificates. The model is implemented using Natural Language Processing and deals with the domain of Named Entity Recognition. It has been trained using a diverse dataset of medical certificates issued in Singapore and is able to recognize entries of interest automatically from a scanned copy of the document. The model is able to take in the variation of formats, prints and naming of the entries and provide a recognizable input to the software systems making use of it. Medical Leave, Documentation Management, Named Entity Model, Human Resource Software, Medical Leave Automation Infocomm, Artificial Intelligence

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. The system comprises a base bioreactor unit with multiple add-on modules, including a multitude of environmental sensors, linear peristaltic pumps, compressed gas flow regulators, and novel stirrers & boosters. It is also controlled by a propietary cloud-based software, which provides a number of benefits for device management. This allows the user to access the bioreactor from anywhere, monitor experiments in real-time, and receive alerts for any errors. The modularity provides flexibility in the co-development of various bio-manufacturing applications, especially in streamlining production.      This technology is applicable in industries ranging from bio-medical to bio-pharma to food tech to environment tech. It serves as a foundation for products like vaccines, metabolites, cultured meats, fermented foods, biofuels, adjuvants, microbial inoculants, etc. It is especially relevant for R&D departments and educational programs focused on biotechnology and life sciences. The technology surpasses current market offerings with its modular design, ease of use, flexibility and affordability, enabling users to customize their setup according to their needs without extensive training or investment, thus democratizing advanced bioprocessing. Bioprocessing, Modular Bioreactor, Cell Culture, Microbial Cultivation, Synthetic Biology Culture, Scalable Biotechnology, Biotech Education, Advanced Biomanufacturing, Cultivated Meats, Alternative Proteins, Bio-Medical, Bio-Pharma, Fermentation, Precision Fermentation, Traditional Fermentation Foods, Processes

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. Compared to conventional near-infrared LED light sources, this NIR light source has a broader spectrum width, making it more suitable for spectroscopic measurements, especially those using multiple wavelengths. Key features of this technology are: Wideband spectrum: 450nm to 1000nm (over 1000nm is under development) Output: approximately 3W per module Adjustable spectral shape and light distribution angle to meet different needs Easy light focusing control and uniform wavelength intensity on the irradiation surfaces Long product life: 40,000 hours (LED chip guaranteed time), 40 times longer than typical lifespan of halogen lamps Minimal accuracy loss due to temperature changes: output drops by 8% only when the temperature increases from 25°C to 75°C, compared to a 27% drop of other products This unique near-infrared light source can be widely applied to night vision, non-contact vital sensing, food analysis, medical diagnosis, agricultural analysis, and other fields. Potential applications include (but are not limited to): Night vision: surveillance camera, traffic monitoring system, etc. Non-contact vital sensing: health monitoring (heart rate, oxygen saturation), self-health care, etc. NIR spectroscopy: foreign matter inspection, fruit and vegetable analysis, internal quality check, fresh food quality control, etc. Fluorescence imaging: endoscope, fundus camera, etc. Wide wavelength range and adjustable spectral shape No heat generation: enable degradation-free analysis Long product life: reduce maintenance frequency and costs Enhance the consistency and accuracy of sensing and analysis near-infrared spectroscopy, vital sensing, night vision, Light source Electronics, Lasers, Optics & Photonics, Healthcare, Diagnostics, Foods, Quality & Safety