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

For road safety, traffic efficiency, and energy savings, we adopt V2X and 5G C-V2X advanced technology to develop smart transportation including iDVR fleet management, driving assistant, AI sensors, RSU, OBU, etc., optimizing the driving and traffic operation.

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.

The award-winning service application: 【Smart Campus - Smart Library, Healthcare Management System】to realize the independent management of school library and health care, it also greatly reduces the load on the schools’ administration, achieve the goal of sharing library and healthcare resources between teachers and students, through the collection and analysis of the big data in the background system, it will be more accurate for students’ recording values, AI predictive models can also help the govermant authorities the effectively assess and make the timely decisions, so far, all schools have been deployed and put into operation, benefiting about 25,000 teachers and students from 125 schools, the system is also launched simultaneously in all the Digital Opportunity Center (DOC), implement community cultural construction and connection; In future, we will continue to improve our services as a core: friendship between teachers and students, feelings between parents".

The Track Safety Management System is primarily aimed at addressing safety concerns along the track. It provides real-time monitoring, statistics, and detection-triggered alerts for trackside environments including stations, level crossings, along the track, and slope areas. The goal of this management system is to establish early warning alerts and prevent accidents from causing damage. Through the intelligent AIOT recognition monitoring system, it detects and analyzes station pedestrian traffic, tracks pedestrians at level crossings, identifies passing vehicles and traffic flow, detects foreign objects at level crossings and activates alarms, monitors personnel lingering and debris along the track, conducts rockfall monitoring on slopes with a rain gauge, monitors the environmental conditions in equipment boxes, and oversees the operation status of level crossing equipment. The monitoring data and image information are transmitted to the management system platform for integrated management via fiber-optic networks, NBIOT, or mobile communication. Additionally, it provides central management personnel with real-time interpretation and remote control in case of anomalies.

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.