The cleaner the sorting of recyclables in the resource circulation process, the higher their reusable value. We leverage the most advanced AI vision recognition technology to introduce innovative applications into existing production lines. Our systems automatically inspect the targets to ensure the purity of sorting, thereby increasing the reusable value and maximizing profits for the factories. By collecting and training with actual production line image data, we continuously update the dataset to handle various compositions of recyclables. Our vision recognition equipment features low-power hardware that can be easily and simply installed onto existing production lines. Additionally, we can create diverse labeling data for various recyclables.

Our product combines acoustic technology with artificial intelligence to offer an innovative device focused on muscle quality assessment. Traditional muscle testing methods often rely on the subjective experience of physicians or expensive equipment such as CT scans and MRIs, which are not only costly but also cumbersome. Our device uses non-invasive acoustic signal analysis, combined with AI models, to provide fast and accurate diagnoses, significantly reducing medical costs and improving diagnostic efficiency. The product is lightweight and portable, allowing physicians to use it flexibly in clinics, communities, or home visits, making it suitable for diverse scenarios such as long-term chronic disease patients, athletes, and the elderly. With high-precision AI algorithms, the device can instantly provide analysis reports on muscle condition, such as strength, elasticity, and power, helping physicians make more scientifically-based clinical decisions.

Delta’s EV charging solution utilizes the self-developed DeltaGrid® EVM, an electric vehicle charging management system for smart charging, power allocation, and charging equipment management. To avoid exceeding the site’s power limit during simultaneous charging, which could result in fines or power outages, the depot adopted a smart charging management system to effectively address power overuse and charging allocation issues. Under limited power conditions, the system efficiently allocates charging power for the electric buses and provides flexibility for future fleet expansion. To address the challenges of charging commercial fleets, the system has dedicated features that were developed for electric buses and commercial fleet charging needs, including Vehicle Identification, Site Current Limitation, Real-Time Alerts, etc, effectively enhances charging efficiency, optimizes operational benefits for electric fleets, and successfully demonstrates the transformation to the electrification of transportation.

1. Integrating data of water quality and water consumption, the online system will automatically conduct continuous data monitoring, and can be applied for both clean water and sewage water measurement. 2. Real-time monitoring: Seize real-time water information to maintain the stability of water quality and instantly react when abnormal condition is detected, dramatically increasing management efficiency. 3. Smart Dosing Scenario: Integrating automatic dosing equipments with upper and lower alarm settings to ensure dynamic stability of water quality and decrease labor cost. 4. Data Analysis: Apply schedule planning to build up the trend of historical changes and to develop the situation management model. 5. Compliance with the regulations: Complete the full-time management mode, in compliance with the regulations of the competent authority and environmental protection standards. 6. The water quality sensor (specific model) is automatically cleaned without additional consumables and can be cleaned frequently.

In the past, human operation and judgment were required, but now technology assists people in handling water distribution work. Set up water level or flow sensing devices in important water supply channels and transmit the collected data to the information management platform. Using online information or mobile APPs, water stewards can instantly grasp the field water distribution to achieve the goals of saving manpower and accurately allocating water. Taking the research results of the Jianan Irrigation District as an example, water stewards management can save about 20 to 25% of water by allocating irrigation water. If it is combined with Smart irrigation system, it can save another 5% of water. Currently, Water Resources Department, MOEA continues to cooperate with National Science and Technology Council, Ministry of Agriculture to gradually expand to 3,000 hectares in the Jianan Irrigation District and 3,000 hectares in the Kaohsiung Irrigation District, and gradually promote Smart irrigation and water-saving management technology.

Leveraging system development technology and combining it with environmental engineering consulting expertise, EGIT provides comprehensive and convenient climate-change adaptation support systems for various entities, including government agencies, financial institutions, universities, and private organizations. The system encompasses features such as GHG inventory, climate change risk alerts, and asset depreciation assessment. Our expertise assists organizations in addressing climate risks.

Centrally manage all signing tasks online, allowing you to keep track of the contract signing process at any time. The JGB system supports mobile phones and computers, so you can sign contracts anytime and anywhere, eliminating the need for paper signings and making work time allocation more efficient. In compliance with Taiwan's "Electronic Signature Law", contract signing will strictly ensure the legal validity and security of electronic signatures.

P-Waver Inc. is the global leader of AI on-site earthquake early warning system and structural safety monitoring system, providing earthquake early warning before earthquake, intelligent disaster mitigation measures, and structural safety monitoring after earthquake.

To establish a civil participation mechanism to widely gather opinions from all sectors, the National Development Council has established the "Just Transition Commission." The committee follows a principle of equal representation between governmental agencies and civil society. Members serve a term of two years. The government agency representatives on the committee are deputy ministers from ministries responsible for the 12 key strategies of the 2050 net-zero transition and related issues of just transition. In addition, the members of the civil society include citizen groups focused on environmental, labor, and indigenous issues, as well as representatives from social enterprises and experts/scholars in the field of economic industries. The committee has four working groups: an academic group, a strategic review group, an issue identification group and an influence group. The public-private partnership will work together to promote a net-zero fair transition. Additionally, this year, multiple public consultation sessions are planned to gather current visions and expectations for a net-zero fair transition from all sectors, aiming to enhance public awareness of net-zero carbon emissions and fair transition.

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.

airaTrack is a comprehensive personnel tracking system that utilizes AI algorithms developed by our team to extract features from both the "face and figure." It generates unique biometric IDs with a recognition accuracy of up to 99%, all without the need for registering facial data, addressing privacy concerns. Capable of cross-camera searches and matching 10,000 faces per second, it significantly enhances the efficiency of traditional manual matching processes and increases search efficiency and accuracy by 97% enabling automatic path tracking of personnel in the field. The system also generates investigation reports for evidential preservation.

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.