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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

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

Paper packaging is a versatile material used for a wide range of products. Its widespread adoption is due to its renewable and relatively low-cost resource along with environmental benefits such as recyclability and biodegradability. While paper packaging offers several advantages, some drawbacks of the material include porosity and the lack of barrier properties against moisture, oil, and grease. To overcome these limitations, conventional coatings such as polyethylene (PE) or polyfluoroalkyl substances (PFAS) have been employed to impart the required barrier protection. However, during the paper recycling process, it is difficult to repulp the coated paper due to several factors and results in reduced recyclability of such packaging materials. The technology on offer is a water-based coating formulation that can be applied onto paper packaging surfaces to act as a barrier against grease, liquid water, and water vapour. The coating imparts barrier protection functionalities, improving the paper’s resistance to grease, liquid water, and water vapor significantly. Use of bio-sourced constituents in the coating also improves product sustainability. As the coating’s constituents are repulpable, recyclability of the paper packaging can be achieved. With increasing awareness of reducing packaging waste, the deployment of this technology will offer companies a recyclable paper packaging with notable barrier properties. The technology owner is seeking for R&D co-development, test bedding and IP out licensing opportunities of this technology with interested companies. The water-based barrier coating technology has the following features: Consists of bio-sourced constituents to improve product sustainability Enables repulping of coated paper, largely improving recyclability of such packaging materials Improved barrier to water vapour transmission (WVTR) - WVTR value as low as 100 g/m2.day (based on ASTM E96) Improved liquid water resistance - Cobb60 value as low as 10 g/m2 (based on TAPPI T441) Improved grease resistance - a KIT rating as high as 12 (based on TAPPI T559) Easily applied by standard coating equipment Potential applications include (but are not limited to): Paper-based food packaging Paper boards, bags, and shipping sacks Products requiring enhanced barrier paper packaging Improves paper-based product recyclability while improving barrier properties of the paper Utilisation of bio-sourced constituents in coating formulation increases product sustainability Offers an alternative to PE and PFAS coated paper that are difficult to repulp coating, barrier, packaging, paper, water-based, recycling, recyclable, pulp, sustainability, sustainable, circular economy Chemicals, Coatings & Paints, Foods, Packaging & Storage, Organic, Bio-based, Sustainability, Circular Economy

The Reconfigurable Workspace Soft Gripper (RWSG) is a bio-inspired, pneumatically actuated, shape morphing soft robotic gripper that is capable of rapid reconfigurability. It features passive retractable nails, bi-directional foldable petals, and a flexible palm to adapt to various grasping and manipulation tasks and requirements. The ability to rapidly reconfigure allows the RWSG to grasp a wide range of large, thin, hard, delicate, and deformable objects. These capabilities make the RWSG a uniquely advantageous tool for high mix low volume manipulation and packing scenarios such as food assembly, packaging of groceries, and packing of consumer electronics. The RWSG features retractable nails to help in precision grasping of small, thin, and high aspect ratio objects. An optimized bidirectional finger flap design allows its fingers to morph into scoop-like shapes to easily manipulate granular and semi liquid items such as grains, jelly, stews, curries or scrambled eggs. A multi-material palm design helps regulate the RWSG’s aperture to adapt for large or wide objects. The RWSG utilizes low, safe pressures (-80kPa to 60kPa) to switch between and operate the various grasping modes. High mix low volume manipulation tasks for consumer goods, logistics, and food industries can benefit from advanced robotics to meet evolving demands in productivity, safety, and sustainability. These sectors often require manipulation and grasping capabilities that cannot be achieved by conventional robotics using rigid grippers or end-effectors. The RWSG can provide reliable and safe robotic handling of a wider range of objects in these challenging scenarios using its adaptive capabilities. With the ability of handling a wider range of objects, RWSG automation setups can help reduce changeover times (less or no tool changes required), improve safety (humans are not required any longer for manipulation in hazardous environments), and even contribute towards sustainability (less overall resources required). The RWSG has a unique structure that allows robust and safe grasping of a wide range of large, thin, hard, delicate, granular, and deformable objects. Its structure is composed of food safe, hypoallergenic silicones that can tolerate both high and low temperatures. These unique features far surpass the capabilities of traditional rigid grippers and end-effectors. The RWSG can be seamlessly integrated with all major cooperative manipulators currently available in the market. Soft Robotics, End Effector, Robotics, 3D Printing Electronics, Actuators, Infocomm, Robotics & Automation

In an era where environmental challenges are escalating, the need for precise and timely flood monitoring has never been more critical. Addressing this pressing issue is a state-of-the-art flood detection system that offers unparalleled accuracy in water level detection, down to the centimetre, and in real-time. Designed to resist environmental disturbances, this technology ensures consistent and reliable performance. Its self-sufficiency is highlighted by its connectivity via mobile networks and an ultra-efficient power system, which includes a solar panel ensuring sustained operation even in less-than-ideal sunlight conditions. The primary beneficiaries of this technology are government agencies and enterprises involved in environmental infrastructure projects. Additionally, businesses facing environmental challenges and seeking robust solutions will find this invention invaluable. By providing instant alerts on potential flood threats and integrating seamlessly with third-party management systems, this technology addresses a significant gap in the marketplace, ensuring safety, reducing potential damages, and saving lives. Multiple high precision pressure sensors for accurate sensing pressure change due to rising water
 Built-in SIM and included IoT network connectivity works anywhere independently by support LTE-M, NB IoT and GPRS mobile networks
 Unlimited battery life
Ultra efficient power system with built-in 750mAH rechargeable battery and solar panel, 3-months battery life without Sun Auto alerts for sudden flood
support push notification, email, sms or custom alerts
 Enterprise Ready
Cloud based Enterprise Dashboard and ready API for integration
 Compact size and easy to install
8cm x 8cm x 7cm (inclusive screw-on mounting for standard 3” PVC pipe)
 Weather proof
rain resistant and operates from -20℃ to 60℃ The flood detection system's advanced capabilities make it a versatile tool with applications spanning multiple industries: Urban Planning & Infrastructure: Municipalities can integrate the system into city planning, placing sensors in flood-prone zones, underpasses, and near water bodies. This aids in timely evacuation and infrastructure protection during heavy rainfall or sudden water level rises. Agriculture: Farmers can deploy the system in fields to monitor water levels, ensuring optimal irrigation and preventing crop damage from unexpected flooding. Real Estate & Construction: Developers can use the technology to assess flood risks in potential construction sites, ensuring the safety and longevity of structures. Environmental Research: Research institutions can utilize the system for studying climate change effects, water table fluctuations, and the impact of deforestation on water levels. Insurance: Insurance companies can integrate the technology to assess flood risks in specific areas, aiding in policy formulation and claims verification. Disaster Management: Emergency response teams can deploy the system in regions prone to natural disasters, ensuring rapid response during floods. Tourism & Recreation: Resorts and recreational areas near water bodies can use the system to ensure guest safety, especially in regions with unpredictable weathers. Transportation: The system can be installed near roads, railways, and bridges to monitor water levels, ensuring safe transit and timely maintenance. This technology enables marketing various products, including smart city flood management systems, agricultural water management kits, construction site safety tools, and environmental research equipment. Its adaptability meets diverse industry water monitoring needs. The flood detection system's market size is influenced by key factors: Urbanization: Expanding urban areas increase flood risks due to inadequate drainage. With 68% projected to live in cities by 2050, urban flood detection systems are crucial. Climate Change: Environmental changes cause more extreme weather events and rising sea levels. This has escalated flood risks in previously safe areas, emphasizing the importance of flood detection systems. Agricultural Dependency: Agriculture employs over 26% of the global population, creating a significant market for flood detection in farming. Infrastructure Development: Growing infrastructure projects demand protection from environmental threats like floods. Considering these factors, the global market for flood detection systems is expected to be worth billions, with steady growth. The technology's attractiveness to the market lies in: Real-time Detection: Its centimeter-precise real-time water level detection is invaluable for damage prevention. Low Maintenance: Self-sufficient power and weather-resistant design reduce long-term maintenance costs. Versatility: Easy installation and compact design suit various settings, from urban areas to remote agriculture. Integration: Compatibility with third-party systems and diverse alert mechanisms adapts it to different industries. Environmental Focus: Growing awareness of climate change and its impact drives demand for technologies addressing flooding and rising sea levels. In summary, the technology's precision, adaptability, and response to global environmental challenges make it highly attractive to the market. This innovative flood detection and environmental monitoring technology offers several unique value propositions (UVPs) that set it apart from the current "State-of-the-Art" systems. Precision in real-time flood detection is unparalleled. It can detect water level changes down to the centimeter within minutes, providing stakeholders with timely and accurate data. Self-sufficiency power and connectivity. With a 750mAH rechargeable battery and solar panels, it operates independently of external power sources. It also supports multiple mobile networks for seamless connectivity, even in remote areas. Environmental robustness ensures it remains operational in challenging conditions, overcoming a limitation of many existing systems. Versatile alert mechanisms, including push notifications, emails, SMS, and custom alerts, ensuring critical flood warnings reach recipients promptly. Compact and user-friendly design simplifies installation, contrasting with bulky and complex systems. Holistic enterprise solution with a cloud-based Enterprise Dashboard and API integration capabilities, making it easy for businesses and government agencies to incorporate into existing infrastructure and management systems. In essence, the UVP of this technology lies in its precision, self-sufficiency, robustness, and user-centric design. It not only addresses the limitations of the current "State-of-the-Art" but also anticipates the evolving needs of a world grappling with environmental challenges, making it a future-ready solution in flood detection and management. Real-time flood detection and alerts, Pressure sensing for water level monitoring, Mobile network connectivity, Solar-powered self-sufficient monitoring, Enterprise environmental tools, Disaster prevention Infocomm, Internet of Things, Wireless Technology, Environment, Clean Air & Water, Sensor, Network, Monitoring & Quality Control Systems

This technology delivers physical climate risk analytics for any asset or portfolio. It combines climate hazard with consequence models, offering richer insights than typical climate risk screening tools. Outputs detail financial repercussions from damages, projected downtime, portfolio risk correlation, increased climate-induced risks, and various other actionable risk metrics. The technology has global coverage, uses high-resolution input data (30x30m), validated computations, and proper uncertainty quantification. Models integrate climate dynamics, providing these same risk metrics for future climate. Stochastic event simulations underpin all the models, which uniquely enables the computation of climate risk correlation across portfolios. Outputs: Climate risk analysis results for any asset or portfolio of assets. API or dashboard for users to access the physical climate risk model. Technical features: Multi-hazard climate risk analytics (coastal and riverine floods, hurricanes, heat, tornadoes and more). Global coverage at high-resolution. Projections for current and future climate scenarios. Compliant with data-standards used by the insurance sector. Based on millions of stochastics event simulations, which enables proper calculation of correlated portfolio losses, full probabilistic analysis, or extraction of specific simulations for stress-testing. Output in quantified and actionable metrics: expected losses for all recurrence levels, average annual loss, projected downtime, portfolio risk correlation, increased climate-induced risks and more. Underpinned by validated methods, high-resolution datasets, efficient computation, and leading scientific research. Proper uncertainty quantification and auditable results. Ideal collaboration partners include consulting firms, ESG SaaS enterprises, auditing firms, financial data aggregators and insurance firms. The product can be used for three main areas of application: Structuring risk financing products: can be used to develop new insurance products, insurance products in new geographies, set triggers for parametric insurance and develop other risk transfer products. Physical climate risk disclosures: support quantification and disclosure of climate risk, meet increasing regulatory requirements, promote ESG strategy, increase investor confidence in demonstrated climate-resilient entities, etc. Climate risk management: support climate-informed investments, strategies for protecting at-risk assets, acquisitions of new assets, develop climate-resilient loans, test and improve supply-chain resilience, screening for physical or nature-based protection potential, and much more. It is estimated that the global market for the use of this technology is approximately US$2.5 billion and growing, including customers in the Insurance, Investment & Risk Management, and Financial Disclosure industries. A recent acquisition of RMS – the leading global provider of climate and natural hazard risk modelling and analytics – by Moody’s Corporation for approximately US$2 billion is a clear signal of the growing interest and need for such services in the market. The technology offers high quality and high-resolution climate risk information for the entire world, quantified in financial and operational metrics. A key value proposition is the ability to compute proper portfolio risk across the globe. This is possible due to the unique computational architecture: generating millions of stochastic extreme events, each of which propagates to complex hazard intensity footprints, which propagate further to impacts on physical exposures. These simulations can capture impacts to multiple assets by the same events, as well as risk correlation and clustering across portfolios. Tests have shown that alternative methods overestimate portfolio risks by up to 400%. Other solutions which utilize event sets also have limited geographic coverage (eg: USA). This product also considers the impact of climate change. No other known product provides the comprehensive coverage which includes risk from future climate change scenarios. The technology is underpinned by machine-learning enhanced datasets. The digital-elevation model (DEM) used is high-resolution (30x30m) and is the lowest average and standard error among known global DEMs. The risk outputs from the models are precisely attributable. Unlike black-box models in the market, this product can pin-point the specific sources of risk and uncertainties. For instance, it can determine whether a building's flood risk stems mainly from flood intensity or the building's inherent vulnerability. The sources of uncertainty can be queried and areas where data-refinement can reduce the uncertainty can be identified. This is useful for purposes of auditing results, or for better targeting strategies for risk reduction. Infocomm, Big Data, Data Analytics, Data Mining & Data Visualisation, Artificial Intelligence, Computer Simulation & Modeling, Geoinformatics & Location-based Services, Financial Technology

This innovative wearable device, integrating an actuator and a sensor, addresses a pressing issue in the field of neuromuscular disease diagnosis and management. By enabling in vivo measurements of muscular elasticity and employing machine learning models for disease severity evaluation, it offers an objective and accessible solution. The wearable conforms to the human body, facilitating quantitative assessments by correlating elastic moduli with voltage amplitude, thereby eliminating the subjectivity of traditional assessments. It significantly enhances accessibility, breaking down barriers to muscle assessment, and introduces a remote monitoring capability that allows continuous tracking of muscle health during rapid joint stretches. This technology serves medical professionals, patients with neuromuscular diseases, and rehabilitation centers by providing a reliable tool for improved diagnosis and personalized treatment plans. In summary, this wearable device represents a transformative approach to assessment of muscle-related pathophysiological conditions, offering objectivity, accessibility, and remote monitoring, ultimately enhancing the quality of care and treatment outcomes. The core components of the system include a pneumatic actuator for controlled mechanical force generation, a piezoelectric sensor to measure muscle response, and integrated machine learning models for disease severity evaluation. The system is designed to seamlessly conform to the human body, its wearability ensures patient comfort and enables a point of care continuous monitoring of muscle health, a groundbreaking advancement in the field of muscular biomechanics assessment.  This technology offers a wide array of potential applications spanning various industries. In the healthcare sector, it can help with the diagnosis and treatment of neuromuscular diseases and find use in tele-rehabilitation programs. Athletes and sports professionals can benefit from improved performance and injury prevention. This technology could help in creating customized rehabilitation equipment designed for specific patient needs and conditions, making the recovery process more effective and personalized. Routine muscle health assessments can be realized, promoting proactive healthcare management across the board. The booming wearable market and recent advances in material science has led to the rapid development of the various wearable sensors, actuators, and devices that can be worn, embedded in fabric, accessorized, or tattooed directly onto the skin. Wearable actuators, a subcategory of wearable technology, have attracted enormous interest and many wearable actuators and devices have been developed in the past few decades to assist and improve people’s everyday lives. In addition, The global diagnostic wearable medical devices market size is estimated to grow by USD 7,333.3 million at a CAGR of 15.2% between 2022 and 2027. (Source: Technavio). The system provides a substantial improvement over the current state-of-the-art in muscular biomechanics assessment. Unlike existing methods that are either subjective and qualitative or hindered by bulky, stationary instruments, this system introduces a precise, objective, and patient-friendly solution. Its wearability, facilitated by a soft textile-based cuff, enables point-of-care assessments and home-based monitoring, dramatically enhancing accessibility and convenience. Furthermore, with dynamic movement analysis it can providing valuable insights into muscle behaviour during real-world activities, a dimension largely unexplored by current techniques. These position the system as a transformative technology, poised to revolutionize the diagnosis and management of neuromuscular diseases and expand the horizons of muscular biomechanics assessment.  soft robotics, sensor, wearable, soft actuator, muscle, muscle assessment, neuromuscular assessment, biomechanics, neuromuscular diseases, actuator, wearable sensor, skin sensor Electronics, Sensors & Instrumentation, Actuators, Healthcare, Diagnostics, Medical Devices

Anti-corrosion is important for piping systems because corrosion can lead to several problems including reduced flow capacity, leaks and ruptures, contamination, increased maintenance costs and reduced lifespan. While there are several approaches to mitigate these problems, a possible approach is to utilise thermoplastic materials which are lightweight, durable, and resistant to corrosion. This technology is a thermoplastic piping system lined with HDPE/LDPE linings that is corrosion-resistant, do not generate any waste (waste material can be recycled) and has a reduced carbon footprint. The piping system is easy to assemble and install, providing long service lives due to the high-quality thermoplastic materials being deployed in the system. By laying these thermoplastic pipes underground using native soil without sand-bedding, a reduction in CO2 is achieved and offers users a sustainable piping solution against conventional piping materials. In combination with proprietary welding technologies, the technology has the lowest rate of leakages with high guarantee of preservation of drinking water quality when used in water piping systems. The technology owner is seeking for co-development and test-bedding opportunities with asset owners to integrate the technology into their infrastructure, particularly with hydrogen producing and transporting companies. The technology is a thermoplastic piping system that exhibits the following features: Efficient corrosion protection against aggressive media Excellent product properties (static puncture resistance) Long service life (minimum service life is 50 years, up to 100 years) Maintenance free – pipework is homogenous, longitudinally force-locked and leak-tight Reduced carbon footprint compared to conventional piping materials Easy to install using permanently leak-tight welding technologies Suitable for clean and efficient trenchless installation Black piping and fitting are resistant to UV and corrosion free against chemicals The technology is a thermoplastic piping system that has been successfully deployed in several industries. Possible applications include (but are not limited to): Hydrogen Plant Hydrogen transport (or transportation of natural gas) Semiconductor Photovoltaic Life Science Water and Wastewater Chemical Processing Oil & Gas Mining Power Plant Municipal Shipbuilding Environmental Engineering Irrigation Long life expectancy (up to 100 years) Maintenance free Simple and economical installation Toxic free and recyclable hdpe, high density polyethylene, thermoplastic, piping systems, anti-corrosion, corrosion resistance, low leakage, polymers, hydrogen gas pipe Materials, Plastics & Elastomers, Chemicals, Polymers, Environment, Clean Air & Water, Mechanical Systems, Sustainability, Circular Economy