In a world increasingly focused on sustainability and reducing environmental impact, DreamHigh is pioneering an innovative approach to packaging solutions using mycelium—a natural, biodegradable, and renewable material derived from fungi. Our mission is to revolutionize the packaging industry by offering eco-friendly alternatives that not only reduce waste but also align with global efforts to combat climate change. Mycelium packaging offers a compelling alternative to traditional plastic and Styrofoam packaging, which contribute significantly to environmental pollution. It is fully biodegradable, compostable, and capable of breaking down in natural environments within weeks, leaving no toxic residues behind. Additionally, mycelium-based products are lightweight, durable, and customizable, making them suitable for a wide range of applications, from consumer goods packaging to protective shipping materials. DreamHigh’s business plan outlines a scalable production process leveraging advanced mycelium cultivation techniques and partnerships with local agricultural sectors to utilize agricultural waste as a key raw material. This not only ensures cost-efficiency but also supports a circular economy by repurposing waste that would otherwise be discarded.
เนื่องจากเราเล็งเห็นถึงปัญหาของการทิ้งโฟมหรือพลาสติกกันกระแทก ที่ใช้เวลาในการย่อยสลายนาน เราจึงนำตัวไมซีเลียมที่ใช้เวลาย่อยสลายไม่นานอีกทั้งยังเป็นมิตรต่อธรรมชาติ
คณะวิทยาศาสตร์
This special problem aims to study and compare the performance of predicting the air quality index (AQI) using five ensemble machine learning methods: random forest, XGBoost, CatBoost, stacking ensemble of random forest and XGBoost, and stacking ensemble of random forest, SVR, and MLP. The study uses a dataset from the Central Pollution Control Board of India (CPCB), which includes fifteen pollutants and nine meteorological variables collected between January, 2021 and December, 2023. In this study, there were 1,024,920 records. The performance is measured using three methods: root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination. The study found that the random forest and XGBoost stacking ensemble had the best performance measures among the three methods, with the minimum RMSE of 0.1040, the minimum MAE of 0.0675, and the maximum of 0.8128. SHAP-based model interpretation method for five machine learning methods. All methods reached the same conclusion: the two variables that most significantly impacted the global prediction were PM2.5 and PM10, respectively.
คณะเทคโนโลยีการเกษตร
This study aimed to develop a website for collecting and organizing data on Young Smart Farmers in Chanthaburi Province. Data were collected through structured interviews with a sample of 30 participants. The information obtained was categorized and utilized to develop the website, which was subsequently disseminated to farmers and other stakeholders. The study also assessed user satisfaction with the website through a questionnaire, with data analyzed using descriptive statistics, including frequency, percentage, mean, and standard deviation.The results indicated that the sample comprised an equal proportion of male and female participants, with the majority (50.00%) aged between 36 and 40 years. Most respondents were Young Smart Farmers from the districts of Khlung, Laem Sing, and Kaeng Hang Maeo, each representing 13.33% of the sample. The majority of participants had attained a bachelor’s degree or equivalent (60.00%) and were primarily engaged in agricultural occupations (73.33%). The findings on user satisfaction with the website revealed a high level of satisfaction across all dimensions, ranked as follows 1) Website usability (Mean 4.97), 2) Overall satisfaction (Mean 4.93), 3) Content quality (Mean 4.91), 4) Practical benefits and applicability (Mean 4.87), and 5) Design and layout (Mean 4.85).
คณะเทคโนโลยีสารสนเทศ
This research presents a deep learning method for generating automatic captions from the segmentation of car part damage. It analyzes car images using a Unified Framework to accurately and quickly identify and describe the damage. The development is based on the research "GRiT: A Generative Region-to-text Transformer for Object Understanding," which has been adapted for car image analysis. The improvement aims to make the model generate precise descriptions for different areas of the car, from damaged parts to identifying various components. The researchers focuses on developing deep learning techniques for automatic caption generation and damage segmentation in car damage analysis. The aim is to enable precise identification and description of damages on vehicles, there by increasing speed and reducing the work load of experts in damage assessment. Traditionally, damage assessment relies solely on expert evaluations, which are costly and time-consuming. To address this issue, we propose utilizing data generation for training, automatic caption creation, and damage segmentation using an integrated framework. The researchers created a new dataset from CarDD, which is specifically designed for cardamage detection. This dataset includes labeled damages on vehicles, and the researchers have used it to feed into models for segmenting car parts and accurately labeling each part and damage category. Preliminary results from the model demonstrate its capability in automatic caption generation and damage segmentation for car damage analysis to be satisfactory. With these results, the model serves as an essential foundation for future development. This advancement aims not only to enhance performance in damage segmentation and caption generation but also to improve the model’s adaptability to a diversity of damages occurring on various surfaces and parts of vehicles. This will allow the system to be applied more broadly to different vehicle types and conditions of damage inthe future