This exhibition is an atmospheric physical-chemical grid model for prediction of changes in air quality, utilizing data from the U.S. NCEP global weather forecast and regional air pollutant emissions. Local scientific investigation data has been used to adjust and calibrate the model and thereby largely improve the accuracy of the air quality forecast over Taiwan. The 3D display system enables us to understand the transmission processes of cross-border and local air pollution. The system is capable of simulating the changes in air quality over the next three days (72 hours) in Taiwan, with a temporal resolution of 1 hour and a spatial resolution of 3 km. The simulated data can be integrated with air quality data from the Civil IoT Taiwan, providing air quality forecasts every three hours (totaling 72 hours) for six metropolitan areas This system offers regulatory authorities up-to-date information on air quality trends in Taiwan, as well as critical data for formulating control strategies.

The EUP Fleet Management System integrates ESG management, providing visualized reports and big data analysis to accelerate communication between the supply chain and transporters, ensuring effective ESG management and achieving carbon reduction goals. Through AI big data analysis, it automatically provides green ratings and compares carbon emissions with industry peers, enhancing market competitiveness and brand image. Additionally, cloud-based, digital, and automated management enables enterprises to easily monitor fleet emissions and develop carbon reduction strategies.

Through Sensor Node, including water level sensor, rain gauge, data recorder and LPWAN transmission module, the system provides the information factors including water level , rainfall, inundation height inside and outside the water gate. Then transmit data to the back end system. The members in emergency center could do the best practices based on these crucial information

E.SUN provides sustainable financing advisory services, acting as an underwriter to assist companies in issuing sustainable bonds to raise funds for sustainable actions in the capital market. Since issuing the first green bond in Taiwan in 2017, E.SUN has participated in the issuance of sustainable bonds annually and ranks first among Taiwan's domestic banks in terms of the sustainable bonds issuance scale with a total issuance amount of approximately NTD 23.4 billion by the end of 2023. E.SUN continuously channeling funds to industries and projects that are friendly to the society and environment and providing customers with diverse financing options and responsible investment targets.

Enables on-premise training of medical AI models, including deep-learning and generative AI machine learning, from healthcare providers' own data lakes while ensuring data privacy, ownership, robustness, and cost-effectiveness.

LanCore Intelligent High-efficiency Aerator is used in aquaculture and has smart control and energy-saving functions. Its features are: (1) adopting Brushless DC motor or PMSM, and comes with a FRP reducer. (2) it is suitable for high-speed operation and able to support long-distance wiring (300 meters). (3) It is of high quality and has obtained 2020 energy-saving certification, which can save 40–45% of total energy consumption. (4) Smart fish farming, users can monitor and control remotely through a mobile phone to manage operation status and set schedules. (5) General fish farmers can easily import this product to achieve intelligent control and energy-saving benefits.

FET has its own AI development team and long-term experiences in cooperation with various county and city governments. We launch one-stop customized solutions for traffic management with AI license plate recognition technology. FET AI Traffic Management provides flexible programs according to application scenarios, effectively reduces violations/accidents and enhances traffic safety. Features * Intelligent traffic management systems powered by AI, machine learning, and computer vision * Offering serval scenarios such as running a red light, unsafe lane changes, trucks prohibited and pedestrian signals

By the integration and utilization of IoT engineering information and sensor data about water resources management, air quality monitoring, disasters prevention and warnings…, we introduce end-to-end enterprise private networks to provide custom infrastructure systems and services for SMBs and public sectors.

LiftMind is a non-invasive AIoT elevator add-on system that enables rapid upgrades to smart functionality without modifying existing elevator control systems. The system leverages advanced external sensors and AI technology to deliver features such as passenger flow monitoring, emergency incident alerts, user behavior analysis, elevator operation optimization, and enhanced, user-friendly riding experiences. Its open architecture seamlessly integrates with Building Management Systems (BMS) and other IoT devices, significantly improving elevator management efficiency and user satisfaction. LiftMind supports a wide range of elevator brands and models, offering flexible adaptability to various application scenarios, making it the ideal solution for smart building upgrades.

【Smart Medical - Smart Cloud Rescue System】:Online pathology data helps emergency ambulance services and professional care in hospitals , it also break through geographical restrictions and share medical resources.

OpView is the largest Social Listening platform in Taiwan, offering a comprehensive data analysis tool that enables real-time public opinion monitoring. Leveraging cutting-edge AI-powered Semantic Analysis and Search Engine technologies, OpView delves into a vast pool of 10 billion Chinese characters collected daily to extract meaningful social insights. Our platform excels at capturing trends and community dynamics, delivering enterprises with valuable information to stay ahead of the curve.

Currently, the prognosis of critically ill patients in the intensive care unit (ICU) is often assessed using the APACHE or SOFA scoring systems to predict mortality rates. However, these systems have been in use for over 20 years, and the data they are based on is outdated. Today, clinical data is more abundant and precise, and advancements in machine analysis capabilities have made traditional scoring systems insufficient for meeting the demands of accurate predictions and personalized medicine. To address this, our team has developed a survival prediction device for critically ill patients. Using an AI algorithm model, it utilizes clinical data such as physiological, laboratory, and examination results to predict the patient's survival rates for the next 30, 60, and 90 days, providing a more accurate reference for prognosis assessment and medical resource allocation.