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Oxygen enrichment membrane technology is emerging as a promising, cost-effective, and energy-efficient method for producing oxygen-enriched gas (OEG) with oxygen purities of 30-45%. Traditional oxygen production methods, such as cryogenic distillation and pressure swing adsorption, are often costly, energy-intensive, and complex, making them less suitable for applications requiring moderate oxygen enrichment. This innovative technology addresses these challenges through a thin-film composite (TFC) hollow fiber membrane that incorporates a novel use of polydimethylsiloxane (PDMS) as a selective layer on a polyethersulfone (PES) substrate. The PDMS selective layer is applied using a flow coating technique, which is both simple and scalable, allowing for consistent production of high-performance membranes. The technology was upscaled to commercial-sized membrane modules producing 15-53 Nm³/h of OEG with oxygen purities between 31-38%. The membrane system operates at ambient temperatures and pressures, offering significant energy savings and reduced operational costs compared to traditional methods. The benefits of this technology are substantial, including improved cost-effectiveness, enhanced energy efficiency, and flexibility in scalability, making it suitable for a wide range of industrial applications.  The technology owner is seeking collaboration with membrane manufacturers to further scale up this innovative technology, and with end-users who have a demand for oxygen-enriched gas with 30-40% O₂ purity. Two-Piece Module Design: Features a two-piece configuration with central coupling, enhancing compatibility with the TFC membrane and PDMS coating for a uniform, defect-free selective layer. Simplified Maintenance: Allows replacement of only the affected half of the module, reducing maintenance costs. Prototype System: Comprises 20 modules in a containerized skid with an air compressor, wet air receiver, refrigerated air dryer, and scaffolds. Operational Efficiency: Operates at 5 bar, producing OEG at 15-53 Nm³/h with 31-38% oxygen purity. Integration with OEG Gasifier: Replaces part of the liquid oxygen in municipal solid waste gasification, achieving 34.5-45.2 Nm³/h flow rate and over 20% liquid oxygen replacement in a 7-day test. With the ability to generate oxygen-enriched gas (OEG) with oxygen purity levels between 30 to 45% at a low working pressure of 5 bar, the TFC hollow fiber membrane technology offers versatile commercial applications across various industries: Healthcare Sector: Suitable for medical uses that require oxygen purity levels of 30 to 40%, such as oxygen therapy and respiratory support. Wellness Industry: Applicable in nitrox diving, oxygen bars, and training rooms, where controlled oxygen environments can enhance user experience and performance. Combustion Manufacturing Sectors: Ideal for furnace combustion, wastewater incineration, and petrochemical processes that benefit from oxygen-enriched air with 25 to 35% oxygen purity, leading to improved combustion efficiency and reduced emissions. Aquaculture Industry: Used for aeration in recirculating aquaculture systems (RAS), enhancing oxygen levels in water to support healthier and more productive aquatic environments. Additionally, the technology produces a pressurized nitrogen-enriched retentate stream of nitrogen purity greater than 85%. This nitrogen-enriched gas stream can be utilized in: Chemical and Oil & Gas Industries: Employed as an inert purge gas to prevent combustion and oxidation reactions during various processes. Food and Refinery Industries: Used as a blanketing gas to protect sensitive products from oxidation, moisture, or contamination, ensuring product quality and safety.  These diverse applications highlight the technology's flexibility and potential to enhance operational efficiency, safety, and sustainability across multiple sectors. Cost-Competitive for Moderate O₂ Purity and Lower Flow Rates: Offers clear cost advantages for applications requiring OEG with 30-40% oxygen purity and flow rates below 1200 Nm³/h, making it ideal for retrofitting existing plant. Low Operating Pressure: Generates OEG at a lower pressure of just 5 bar, compared to 7-14 bar for existing technologies, enhancing safety and reducing operational costs. Easy Installation and Low Set-Up Costs: Simple to install with minimal upfront investment, reducing barriers to adoption. Quick Start-Up: Delivers oxygen-enriched gas of the required purity immediately upon start-up, improving operational efficiency and responsiveness. Modular and Flexible Design: The modular system allows customization to meet a wide range of OEG demands, providing flexibility in application across various industries. Low Maintenance and Easy Operation: Requires minimal maintenance, simplifying operations and reducing downtime. Portability: Can be designed as a portable system, enabling on-site oxygen generation for diverse applications. membrane, air separation, oxygen enriched gas, hollow fibres Chemicals, Polymers, Sustainability, Sustainable Living, Low Carbon Economy

Faced with the increasing levels of carbon dioxide, carbon capture, utilisation, and storage (CCUS) technologies have garnered significant attention. However, as most CCUS technologies rely heavily on various forms of monetary support from governments and faced numerous technical and scalability challenges, most of the CCUS facilities developed are unable to achieve financial profitability or even achieve a net reduction of carbon dioxide (CO2) emissions. The technology proposed herein relates to an electrochemical-based CO2 reduction reaction process, which can directly capture and convert CO2 to carbon nanotubes (CNTs), a high-value material that exhibits unique electrical and thermal properties suited for applications in various sectors, including electronics, energy storage, sensors and medical uses. In contrast to synthesis methods that involve complex reactions and expensive catalysts, the proposed method uses a molten salt chemistry that can convert CO2 to cathodic solid carbon nanotubes (CNTs) via the electrochemical process. Although high reaction temperature (about 760 degC) is required, this method is highly controllable and uses cost-effective pure iron catalyst, producing high quality CNTs at a relatively high production rate. Based on preliminary process modeling and technoeconomic analysis, this technology has the potential to be completely CO2-negative without re-emission, is more scalable, and profitable with high quality CNT materials. The technology owner is seeking to collaborate with industry partners and research institutions for joint R&D to advance the lab scale technology to pilot or event industrial production scale, as well as to explore applications for the CNTs produced. Upon further development, the system has the potential to be integrated with existing carbon capture systems to improve their financial viability and achieve carbon negative objective. The molten salt CO2 reduction reaction enables CO2 conversion into high value nanostructured CNTs, which captures carbon as a solid and stable material, complementing other processes that convert CO2 into combustible fuels. Provides a highly controllable production method, using cost-effective pure iron (Fe) as a catalyst and lithium carbonate (Li2CO3) based electrolyte. The electro-reduction reaction and CNTs produced exhibits good graphitization degree (0.24 ID/IG intensity ratio), high Faradaic efficiency (~80%), with a high production rate (~58 gCNTs gFe-1 h-1). Based on a preliminary process modeling and technoeconomic analysis, the system may potentially achieve a profitable CO2 utilisation, subject to further scale up and detailed studies. Energy Storage: The high-quality CNTs produced could be utilised in next-generation batteries and supercapacitors, enhancing energy storage capacity and charging speeds. Aerospace and Automotive: Lightweight, strong CNT composites could be developed for use in aircraft and vehicle manufacturing, improving fuel efficiency. Construction: CNT-reinforced materials (such as CNT-reinforced concrete) could lead to stronger, lighter building materials with improved durability and insulation properties. Environmental Remediation: The technology itself serves as a carbon capture solution, potentially deployable near industrial CO2 emission sources. Textiles: CNTs could be incorporated into smart textiles for wearable technology applications. Water Purification: CNT-based filters could be developed for advanced water treatment systems. The carbon nanotube (CNT) market is projected to grow from USD 1.1 Billion in 2023 to USD 2.3 Billion by 2028, at a CAGR of 14.6% between 2023 and 2028. This proprietary electro-reduction process has the potential to achieve a net reduction of CO2 emissions without re-emission, offering an efficient and scalable CCUS solution, while producing high value CNTs material for various industrial uses. The process allows for CNTs to be produced with higher purity and quality than was previously possible from CO2. CCUS, CNTs Sustainability, Low Carbon Economy

Acknowledging the importance of high-quality data, this project aims to revolutionize data lifecycle management in the AI to improve data accessibility, collaboration, and commercialization. The solution enables (i) efficiently clean, process and extract valuable data assets from high volumes of mass data, and (ii) contribute and commercialize high-quality data assets without disclosing the actual data. DataS comprises three pillars: (1) GLASSDB serves as an end-user database, including add-in tools for data cleaning, visualization, security, aiding data owners in preparing data for future transactions. (2) Apache SINGA offers a powerful machine learning library to allow users to efficiently apply or develop AI models on their data. (3) Falcon enables privacy-preserving federated learning. It allows multiple parties to develop AI applications using joint data without compromising privacy. This technology uses a zero trust, three-layer design to ensure security and flexibility in data handling and AI development: Falcon Federated Learning: Enables secure collaboration without sharing data. Supports various models (deep neural networks, LLMs, SVMs) and frameworks (TensorFlow, PyTorch, etc.). Handles structured, semi-structured, and unstructured data. Apache SINGA: Scalable deep learning for healthcare applications. Supports data visualization, cleaning, extraction, and distributed training. ForkBase: On-premise data storage with version control. Features data obfuscation tools (pseudonymization, anonymization, synthesization) for enhanced privacy. This solution is ideal for industries needing advanced AI with stringent data protection, especially healthcare. AI requires good quality data and representative data, but privacy and security are the concern. we help you to unlock the power of data and collaboration, in a privacy-preserving and compliant way. Our solution works for Data exchange activities in any industry. Now we focus on financial, medical and legal data. We are the first solution that integrate data extraction, AI application and data collaboration in a single database. It helps our clients to commercialize their data asset easier, cheaper and more secure. Infocomm, Artificial Intelligence

Monitoring for product or part surface defects and anomalies such as cracks and chips throughout the manufacturing process is vital for product quality assurance and control. Traditional inspection or machine vision systems often struggle with complex and nonlinear defect patterns, leading to false positives and missed defects. Deep Learning AI-based detection methods, particularly those using deep neural networks (DNNs), typically require a sufficiently large amount of labelled training data to be effective. However, gathering and labelling such data can be time-consuming and costly, especially for rare or specialized defects. The technology owner has developed a cost-effective system solution leveraging on neuromorphic AI to utilise the principles of human cognitive memory with machine learning to detect surface defects and classifies them reliably and accurately. The system solution includes their patent-pending neuromorphic AI framework architecture with complementary hardware modules, patented lighting system and proprietary software platform. Through the use of neuromorphic edge-AI chip, the proprietary AI model requires smaller training dataset supported incremental learning capabilities, resulting in a high precision, high accuracy system overtime. As the system is camera agnostic and customisable, it enables easy integration and retrofitting to various industrial applications. There are currently a few ongoing POC projects with industrial partners for automating and enhancing quality checks within their manufacturing line. They are seeking industrial collaboration opportunities who are open to explore surface defect detection for quality assurance or monitoring applications. The technology system solution includes: Licensed neuromorphic AI chip with 5508 neurons for edge-AI computing Neuromorphic vision module using their patent pending neuromorphic AI framework architecture Patented programmable LED lighting system for accentuating surface defects Proprietary image pre-processing AI algorithm and software library Proprietary software platform With the above components, the system solution has the capabilities to do the following: Edge-AI computing capabilities Identification of complex and nonlinear surface defect detection (such as chip and crack) even with high reflectivity and transparency Require smaller (10 to 20) dataset for training, hence reducing training time High accuracy rate of up to 95% Incremental learning capabilities to further improve accuracy and identification Quality Assurance (QA) Inspection: The technology solution is able to autonomous detect surface defects for products for QA checks in industries that require low tolerance in product quality, such as advanced manufacturing. With the system being adaptable to different lighting condition, the technology solution can be integrated along any production processes. Other Modes of QA Inspection: The technology system is adaptable to other inputs for QA inspections (e.g. x-ray imaging, acoustics) to accommodate for a larger variety of products for quality inspection. Equipment Condition Monitoring for Predictive Maintenance: The technology solution is able to adaptively learn operating condition status of equipment (e.g. anomaly vibration and acoustics, temperature) to execute predictive maintenance. Security and Surveillance Application: Facial-based and biometrics for security access control and surveillance ensures computing and storage is on the edge, providing security and tamper-proof. The technology system solution enables integration of an autonomous surface defect detection into any production line as a quality assurance solution. Being a customisable solution requiring only a much smaller dataset, the technology solution can easily be integrated with little downtime. By leveraging on their patent-pending neuromorphic AI framework architecture, patented lighting system and proprietary software platform, it enables the use of neuromorphic AI chips for edge-AI applications, provide incremental learning capabilities for enhanced accuracy and overcomes transparency and reflectivity issues in conventional machine vision. Surface Defect, Small Dataset, Neuromorphic AI Chip, Real-time Incremental Learning, Anomaly Detection Electronics, Sensors & Instrumentation, Infocomm, Video/Image Analysis & Computer Vision, Robotics & Automation

The Edge AI-based Drone System for Pipe Inspection and Monitoring addresses the need for efficient, accurate, and real-time pipeline infrastructure inspections. It leverages edge AI processing to detect defects, offering a significant advantage over traditional methods. Unlike conventional systems that rely solely on optical cameras, this solution integrates both optical and thermal imaging, enhancing the detection of various pipeline-related issues. The system’s unique value proposition lies in its ability to process data locally on the drone, ensuring immediate issue detection and minimizing data breach risks by reducing the reliance on cloud processing. The technology owner is seeking collaboration with SMEs specializing in drone manufacturing, AI and machine learning, thermal imaging, industrial inspection services, telecommunications and IoT, and data security, which offer complementary expertise for the development and commercialization of the technology. The system comprises four major components: Notification system Object detection system Data storage system Network system It utilizes an embedded Graphics Processing Unit (GPU) with YOLOv3 for real-time object detection. The cameras capture images at a resolution of 1024x768, operating at 8 frames per second. Notifications are sent via a Telegram bot, and detected objects are tagged with unique IDs to manage redundant alerts. The system supports remote management and can handle both optical and thermal imaging inputs, which are processed alternately to optimize resource use. Additionally, it features auto-zoom capabilities for detailed inspections and comprehensive data storage to safeguard information in case of drone malfunctions. This technology can be employed across various industries, primarily in infrastructure maintenance and inspection. It is particularly suited for pipeline monitoring, where it can detect defects and help prevent potential hazards. The system can also be adapted for other industrial visual inspections, such as monitoring construction sites, agricultural fields, and security surveillance. Marketable products based on this technology include advanced inspection drones for utility companies, automated surveillance systems for critical infrastructure, and comprehensive monitoring solutions for industrial facilities. The market size for industrial inspection drones is growing rapidly, driven by increasing demand for advanced inspection solutions in utilities and public infrastructure maintenance. This IP is attractive due to its real-time detection capabilities, comprehensive data storage, and enhanced privacy features. The ability to provide immediate notifications and support remote management makes it a valuable tool for reducing inspection costs and improving maintenance efficiency. Enhanced Inspection Capabilities: Integrates optical and thermal imaging with edge AI processing for superior defect detection. Real-Time Notifications: Immediate alerts enable faster response to maintenance issues. Improved Efficiency and Accuracy: Provides detailed, real-time defect detection, significantly enhancing maintenance efficiency. Local Data Processing: Ensures enhanced privacy and security by minimizing data transmission to external servers. Cost Reduction: Lowers operational costs by reducing the need for manual inspections and enabling proactive maintenance. Minimized Downtime: Proactive detection helps prevent system failures, reducing downtime. Remote Management: Offers flexibility and control over inspection operations, improving overall efficiency.     Edge AI, Real-Time Processing, Drone, Pipe Inspection, Optical Camera, Thermal Camera, Notification System, Object Detection, Remote Management, Data Storage Infocomm, Security & Privacy, Video/Image Processing, Artificial Intelligence, Electronics, Embedded Systems

High-risk industrial sectors, represented by the chemical industries, are prone to experience production safety accidents. When these incidents occur, the consequences can be severe. Traditional risk management methods, often rely on manual processes, have limitations such as insufficient oversight, incomplete management, and ineffective control. These methods also struggle to provide timely pre-incident warnings, active interventions during incidents, and reliable post-incident evidence collection. To address these challenges, the technology owner has developed an intelligent industry solution leveraging cutting-edge artificial intelligence (AI) technologies, such as computer vision, the Internet of Things (IoT), and big data. By integrating enterprise camera systems with algorithms on server platforms, it establishes an advanced risk detection and management platform based on intelligent video analysis. This platform enhances the safety management through comprehensive risk perception and control, proactive hazard identification, predictive warnings, and visual decision-making assistance. Ultimately, it realizes comprehensive safety and intelligent management capabilities for high-risk industrial enterprises. The technology owner seeks collaboration with industrial partners interested in artificial intelligence, such as companies in chemical and energy sectors, as well as hardware providers, such as manufacturers of surveillance cameras, to co-develop and implement this technology to meet specific needs.   The solution integrates AI technology with safety management of operations in high-risk industries, independently developing over 300 proprietary algorithms to ensure comprehensive monitoring of the four key elements in industrial safety operations: personnel, equipment, environment, and operational procedures. Additionally, it incorporates industrial large language models and knowledge graphs to enhance data-driven decision-making support. The technology exhibits the following key features: Small Sample Detection and Recognition Based on Siamese Network: Utilizing a proprietary Siamese Network structure, the solution leverages large volumes of normal data and small amounts of abnormal data to train the neural network. By designing a classification network based on compressed sensing optimization, the system achieves over 90% recognition accuracy in high-risk scenarios, even with small sample sizes. Sequence Standard Action Verification Based on Transformer: In actual chemical production scenarios, standard actions often occur between people and equipment. By designing a Transformer network structure trained on sequential video action dataset, the solution robustly extracts action features and verifies continuous actions in real time. It achieves over 95% detection accuracy in high-risk scenarios like filling platforms and other operations. Decision Support Based on Industrial Large Language Models and Knowledge Graphs: By combining large language models with knowledge graphs, the solution enables intelligent decision-making support, improving efficiency and accuracy in industrial safety management. The solution enables significant advancements in safety management and operational optimisation, helping companies reduce property damage and personal injury while enhancing efficiency and productivity. It also increases industrial automation, reduces costs, and lowers energy consumption. Applicable across various high-risk industries, such as chemical, oil fields, mining, power, steel, construction sites, energy, and ports, etc., including: Supervision of Production Safety: Monitors standard operating procedures (SOPs), temporary operation supervision, and personnel PPE compliance, etc. Intelligent Monitoring of Equipment Status: Inspects vehicles, equipment leaks and drips, instrument data reading, spark inspection, etc. Additionally, this technology is applicable to industrial quality inspection, to identify and classify product defects, and to perform fine-grained inspections of product surface appearances, ultimately helping companies reduce costs and improve efficiency. The solution integrates computer vision with high-risk industrial safety, offering a lightweight "Active Safe Workplace" deployment. It is the only real-time safety information system integrating multiple computer vision algorithms, such as video stream overlay, posture recognition, classification, and traditional security hardware. The AI, equipped with learning capabilities, ensures 24/7 uninterrupted supervision across all cameras Monitors both fixed operational SOPs and temporary operations Features over 300 proprietary, customisable algorithm that can be infinitely combined based on specific conditions and business logic Owns extensive chemical industry data resources (PB-level) for algorithm optimization, achieving superior algorithm performance with over 90% accuracy Proven experience in chemical industry projects enables rapid deployment in new scenarios, based on mature algorithms and strong generalization capabilities Integrates a specialized large language model tailed for industrial safety, leveraging accumulated expertise, data resources, and empirical knowledge, to provide scientifically informed, superior domain-specific decision-making for chemical sector safety AI Intelligent Monitoring, Industrial Safety, Computer Vision Infocomm, Artificial Intelligence

The global heating industry is undergoing significant transformation as the demand for energy-efficient and eco-friendly solutions increases. Traditional wire heating systems are inefficient with limitations such as uneven heat distribution, high power consumption, and restricted flexibility and high carbon emission. These challenges necessitate the development of advanced heating materials that can offer higher efficiency, lower energy consumption, greater flexibility, and adaptability to various applications. In response, the technology owner has developed an innovative flexible intelligent electrothermal film made from carbon fibers, representing a novel electrothermal conversion material. Through independent research and development (R&D) in large-scale preparation and modification, this technology has achieved low-voltage and high-efficiency energy conversion. By incorporating intelligent power management, this electrothermal film overcomes many limitations of traditional resistance wire heating and offers unique surface radiation benefits, delivering more than 30% energy savings. Its scalable production and unique performance advantages position it as a key technology to address the heating needs of the future. The technology owner has expertise and large-scale production capabilities and is actively seeking R&D collaboration opportunities with industrial partners to explore potential applications. Compared to traditional electric heating, it offers the following key features: Uniform Surface Heating: Maintains a temperature difference of only +/- 2°C for consistent performance Rapid Temperature Rise: Capable of reaching 300°C within seconds, reducing preheating time by two-thirds High Electrothermal Conversion Efficiency (up to 99%): Achieves over 40% power savings compared to resistance wire heating coils, significantly conserving electricity. Sensorless Intelligent Temperature Control: Allows for precise temperature regulation of the heated object Ultra-thin and Ultra-light: Thickness ranges from 30 to 200μm and the density is 1/12 of copper Safe and Reliable Design: Features a flame-retardant and heat-resistant construction with no open flames This technology is designed for innovative products in areas such as new energy vehicles, smart furniture, industrial energy conservation, physiotherapy, and military applications. Potential applications include, but are not limited to: Anti-icing systems for wind turbine blades Smart thermal insulation jackets for Li-ion batteries in electric vehicles (EV) Electric heating elements for cold start of LED screens Rapid electric heating for smart temperature-controlled alloy panels Constant temperature control for cultivation of agricultural seedlings for breeding purposes Revolutionary Electrothermal Conversion Material: Offers hsigh thermal efficiency and significant energy savings Intelligent Self-Temperature Control: Provides precise heat regulation without the need for additional sensors Lightweight and Compact Design: Applicable for a wide range of applications across various sectors High Safety and Reliability: Lasts up to 10 times longer than conventional films due to enhanced resistance to deformation, ensuring long-time safety with no open flames Materials, Composites, Electronics, Power Management, Infocomm, Internet of Things

The Smart Energy Management Platform (SEMP) is designed for homeowners and building owners/operators facing rising energy costs due to fluctuating energy prices and the impact of climate change. As an all-in-one solution, SEMP integrates distributed energy resources such as solar photovoltaic (PV) systems, electric vehicle (EV) chargers, and battery energy storage systems (BESS) into a single, user-friendly platform. Unlike standalone systems that offer limited monitoring, SEMP employs advanced AI algorithms to optimize energy use, reduce electricity bills, and maximize savings. Additionally, the platform enables Peer-to-Peer (P2P) energy trading, allowing users to trade excess renewable energy within a decentralized network. SEMP also tracks and aggregates carbon credits, helping users contribute to sustainability goals. With its holistic approach, SEMP not only simplifies energy management but also provides users with a seamless way to participate in the renewable energy market, improving efficiency and lowering overall energy costs. The technology owner is seeking partnerships with renewable energy companies, EV manufacturers, utilities, real estate developers, facility managers, government agencies, automation firms, and carbon credit agencies. The SEMP designed to simplify and enhance energy management. Key features include: An all-in-one platform with centralized, multi-scale monitoring of energy generation, consumption, savings, and aggregated carbon credits. Advanced AI-driven time-series prediction and optimization to maximize energy savings and reduce electricity bills. User-friendly mobile app enabling real-time monitoring and control of energy resources. Support for key renewable energy sources such as Solar PV panels, Battery Energy Storage Systems (BESS), and Electric Vehicle Supply Equipment (EVSE) for each residential prosumer site. Aggregation of energy data from multiple prosumers under the same corporate entity, allowing seamless integration. Compliance with Renewable Energy Certificate (REC) standards, with issuance available when aggregated renewable energy exceeds 1 MWh. Automatic inclusion of residential sites in Peer-to-Peer (P2P) energy trading or grid exporting, ensuring minimum effort participation in energy markets. Detailed insights into energy flow and personalized energy-saving advice, empowering prosumers to optimize their renewable energy use. For homeowners, it provides a user-friendly solution to manage renewable energy, optimize electricity savings, and enable seamless EV charging. Homeowners can also trade excess energy with neighbors through Peer-to-Peer (P2P) energy trading. For commercial buildings and factories, SEMP serves as a comprehensive platform that integrates solar PV, EV chargers, and BESS, allowing centralized monitoring, AI-driven energy optimization, and carbon credit calculation. Its multi-site dashboard simplifies energy management across multiple locations, while enabling energy trading and improving overall efficiency. In response to global warming, there is a growing shift toward eco-conscious living, with individuals and businesses embracing green initiatives to protect the planet from the effects of climate change. Governments worldwide are implementing policies to reduce carbon emissions, encouraging sustainable practices in daily life and work. As a result, energy sectors in many countries are moving away from coal-based power and adopting greener alternatives, such as hydrogen. The widespread adoption of electric vehicles (EVs) and hydrogen fuel cell vehicles (HFCVs) is expected to phase out internal combustion engine (ICE) vehicles. Innovative technologies like Solar Photovoltaics (PV) panels, Battery Energy Storage Systems (BESS), and Electric Vehicle Supply Equipment (EVSE) are transforming residential and commercial sectors into virtual power plants (VPPs). These systems enable energy self-sufficiency while significantly reducing carbon footprints. To accelerate this green transition, developers and property owners require systems that streamline access to Renewable Energy Certificates (RECs) and facilitate carbon credit trading. Additionally, enabling Peer-to-Peer (P2P) energy trading will empower users. An AI-powered platform that predicts energy usage, offers traceability, and personalizes energy insights is essential to meet these evolving demands. (183 words) Lower Electricity Bills and Reduced Carbon Emissions: Optimize energy use with advanced AI to significantly cut electricity costs and lower your carbon footprint. Additional Revenue Streams: Generate extra income by trading excess energy and participating in carbon credit trading, capitalizing on your renewable energy assets. Comprehensive and User-Friendly Platform: Enjoy an easy-to-use platform for monitoring and managing distributed energy resources (Solar PV, EV chargers, BESS), unlike standalone Solar PV or EV charger systems that only offer energy monitoring. Green Initiatives, Energy Sustainability, Renewable Energy Certificates, Carbon Credit Trading, Peer-To-Peer Energy Trading, Energy Traceability and Personalization Infocomm, Artificial Intelligence, Energy, Sensor, Network, Power Conversion, Power Quality & Energy Management, Green ICT, Smart Cities, Sustainability, Sustainable Living

With the global population rapidly aging, the prevalence of dementia is becoming an increasing concern. As more elderly individuals suffer from cognitive decline, including memory loss and impaired daily functioning, the need for effective interventions is urgent. Dementia, a condition affecting many worldwide, not only diminishes the quality of life for patients but also places a heavy burden on caregivers and healthcare systems.  In response to these challenges, this solution offers a novel method to help dementia patients maintain cognitive function through engaging therapeutic activities. The solution is a cognitive stimulation system that leverages motion tracking and multi-sensory feedback to help users recall familiar actions from their daily lives. By recreating activities such as ironing or spreading jam, the device taps into procedural memory, allowing users to re-enact tasks they once performed regularly. This process stimulates recollection, helping to evoke personal memories and improve cognitive agility. The technology owner is interested to work with:  (1) companies to co-develop/ out-license the technology to help serve more patients and elderly; (2) healthcare facilities such as hospitals or nursing homes for validations and trials.  This solution integrates motor skill exercises with multi-sensory feedback to engage dementia patients through familiar activities, tapping into their procedural memory, and potentially slowing cognitive decline. Key features include: Handheld Motion-Tracking Tool: The device is equipped with a motion-tracking tool that detects and tracks gestures during therapy. Users interact with the tool by performing actions such as ironing or pounding peanuts. Accelerometer & Gesture Recognition: The system uses an accelerometer paired with machine learning algorithms to recognize different hand movements. This ensures real-time tracking and accurate interpretation of the user's gestures, helping simulate everyday activities. Visual & Audio Feedback: As users re-enact familiar activities, the system provides real-time visual and audio feedback on a paired device, such as a tablet. This feedback is designed to stimulate the senses, reinforcing memory recall and promoting greater cognitive engagement. Stimulating Procedural Memory: By focusing on routine tasks, the tool taps into procedural memory, which is responsible for motor skills and habit formation. This stimulation aids in improving recollection and mental agility over time. By offering repeated gestures and personalized feedback, it exercises mental agility. Sensorial Interaction: The combination of gesture-based exercises and multi-sensory stimulation (sight and sound) helps to improve alertness, tranquillity, and motivation during therapy, supporting overall mental and emotional well-being. This solution provides a non-invasive, engaging therapy option for dementia patients, offering caregivers and healthcare providers a simple yet effective solution for cognitive rehabilitation. This solution has broad applications across various domains, particularly in elder care, dementia therapy, and healthcare: Dementia and Elderly Care: The primary application of this technology is in elderly care facilities, nursing homes, and hospitals that specialize in treating dementia patients. By facilitating autonomous cognitive therapy, it reduces the dependency on one-on-one interactions with therapists, allowing for more frequent and consistent therapy sessions that can be managed by caregivers or nurses. Home-Based Care for Seniors: As the aging population grows, home-based care becomes an important part of healthcare delivery. This tool enables families or home caregivers to provide engaging, therapeutic activities for dementia patients in their own homes, without the need for constant supervision by professional therapists. This platform can also be customised by therapists or healthcare professionals for various conditions and improve the well-being of other patients. This technology offers a unique value by fostering collaborative wellness between generations. It allows family members to actively participate in the therapy process through family-initiated activities and personalized scenarios, enhancing emotional connection and engagement with elderly relatives through gamification. This fun and interactive sessions are both therapeutic and enjoyable for the patient, bridging the gap between different generations. Additionally, the platform’s customization potential for therapists and allied health professionals enables it to be adapted for various conditions, making it a versatile solution for broader rehabilitation needs. By integrating motion-tracking and feedback technologies, the solution makes technology more accessible to the elderly, thereby increasing their comfort and interaction with modern tools.   Dementia, Healthcare, Cognitive Engagement, Gestures Based, Geriatric, Cognitive Impairment, Therapy, Elderly Infocomm, Interactive Digital Media & Multimedia, Healthcare, Telehealth, Medical Software & Imaging, Healthcare ICT