With the current cost of living situation in Thailand continuously rising, many recent graduates face challenges in managing their expenses in alignment with the increasing living costs. Food expenses, even for common street food, continue to surge with no sign of decreasing, despite improvements in raw material costs. Pay-Attention is a website platform designed to help recent graduates gain insights into managing and optimizing their food expenses effectively. It provides guidance on how to spend wisely, ensuring cost-effectiveness while maintaining adequate daily nutritional intake, without falling into monotonous eating habits.
ด้วยสถานการณ์ “ค่าครองชีพ” ของประเทศไทยในปัจจุบันมีแนวโน้มพุ่งขึ้นสูงเรื่อยๆ นับตั้งแต่ ยุคหลังโควิด-19 แต่ฐานเงินเดือนและรายได้ของนักศึกษาจบใหม่ ก็ยังคงมีฐานเริ่มต้นเหมือนเดิม ไม่ต่างจากหลายปีก่อน เป็นผลทำให้ประชากรที่พึ่งสำเร็จการศึกษา ในประเทศไทยจำนวนไม่น้อย ประสบกับปัญหาเรื่องการจัดการค่าใช้จ่าย ให้สอดคล้องกับค่าครองชีพอย่างเหมาะสมในปัจจุบัน ทั้งค่าใช้จ่ายคงที่ และค่าใช้จ่ายผันแปร ที่แปรเปลี่ยนไปตามวิถีชีวิตของแต่ละคน มากน้อย ตามความต้องการและรสนิยม ซึ่งเป็นค่าใช้จ่ายที่ต้องระวังและควบคุมการใช้จ่ายอย่างมีสติเสมอโดยเฉพาะค่าใช้จ่ายเรื่องอาหารที่แม้แต่ตามสั่งทั่วไปก็พุ่งสูงขึ้นเรื่อยเรื่อยไม่มีท่าทีจะลดลงแม้ต้นทุนวัตถุดิบจะปรับปรุงก็ตาม
คณะเทคโนโลยีการเกษตร
The extreme weathers according to PM 2.5 is a global problem with out any borders. This pollutant can directly attack human health. The objective of the study was aimed to develop medicinal plant essential oil emulsions in order to use to decrease PM 2.5 based on chemical characterization of water-soluble anions and cations. A mount of 31 medicinal plant essential oil emulsions were prepared and then initially careened and tested for their efficiency in reducing PM 2.5 under test chamber by spraying method. It was found that spraying for 1 hr with kaffir lime essential oil emulsion at 0.025% concentration could reduce PM 2.5 obtained from engine exhaust pipe effectively when PM 2.5 of 24.7 µg/m3 was detected within 6 hrs, followed by kaffir lime essential oil emulsion at 0.05% and Eucalyptus essential oil emulsion at 0.05% and 0.025% concentration resulting in 27.3, 30.0 and 95.3 µg/m3, respectively. Whereas, water (blank) and control group (water and carboxymethylcellulose, CMC 0.2%) showed high revels of PM 2.5 with 126.4 and 157.3 µg/m3, respectively. This kaffir lime essential oil emulsion at 0.025% concentration showed 3-6 time decline of PM 2.5 upward 2 hrs compared with control group. Field experiment was performed at 3 Bangkok parks, namely, Suantaweewanarom, Suanbankharepirom and Suanthonbureerom. There were many factors affecting the decline of PM 2.5 caused by this essential oil emulsion, particularly, the windy as well as temperature and humidity. PM 2.5 level tended to be decreased after the beginning of spraying. In general, PM 2.5 levels appeared at those 3 parks were decreased rapidly within 1 hr as by average of 21.8 (7.7-27.3) µg/m3, Whereas, decline of only 6.4 (5.0-8.0) µg/m3 was observed in control (water). Incase of calm wind, (10-20 km/hr) this plant essential oil emulsion could even reduce PM 2.5 at 37.0-44.0 µg/m3 and reached to 13.5-16.5 µg/m3 within 3 hrs. As high level of PM 2.5 as 98.0-101.0 µg/m3 , it could reduce PM 2.5 to be an average of 23.0-26.5 µg/m3 within 3 hrs, Whereas, the use of water performed low capacity of PM 2.5 reduction found with only 31.0-40.0 µg/m3. However, windy condition (15-35 km/hr), the efficacy of this essential oil emulsion seem to be lower but tended to work better than using water alone
คณะวิศวกรรมศาสตร์
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คณะวิศวกรรมศาสตร์
The evaluation of mango yield and consumer behavior reflects an increasing awareness of product origins, with a growing demand for traceability to understand how the produce has been cultivated and managed. This study explores the relationship between mango characteristics and cultivation practices before harvest, using location identification to provide insights into these processes. To achieve this, a model was developed to detect and locate mangoes using 2D images via a Deep Learning approach. The study also investigates techniques to determine the real-world coordinates of mangoes from 2D images. The YOLOv8 model was employed for object detection, integrated with camera calibration and triangulation techniques to estimate the 3D positions of detected mangoes. Experiments involved 125 trials with randomized mango positions and camera placements at varying yaw and pitch angles. Parameters extracted from sequential images were compared to derive the actual 3D positions of the mangoes. The YOLOv8 model demonstrated high performance with prediction metrics of Precision (0.928), Recall (0.901), mAP50 (0.965), mAP50-95 (0.785), and F1-Score (0.914). These results indicate sufficient accuracy for predicting mango positions, with an average positional error of approximately 38 centimeters.