In raising crickets for meat consumption, the growth rate and growth period of crickets are important data used to identify the number of crickets per breeding area at each age. Therefore, the researcher has an idea to create a system for monitoring the growth rate of crickets in a closed system using an infrared camera combined with computer image processing to study the growth and identify the growth period of crickets at each age in order to obtain knowledge that can be disseminated to farmers to improve the breeding process for maximum efficiency.
ปัจจุบันจิ้งหรีดถือได้ว่าเป็นสัตว์เศรษฐกิจชนิดใหม่ของประเทศไทยซึ่งทางภาครัฐโดยเฉพาะกรมปศุสัตว์ได้เริ่มมีการส่งเสริมให้ภาคการเกษตรได้เพาะเลี้ยงจิ้งหรีดเพื่อการบริโภคสำหรับการส่งออก และเป็นการตอบรับกับเทรนด์อุตสาหกรรมอาหารใหม่ (Novel food) ตามแนวทางขององค์การอาหาร และเกษตรแห่งสหประชาชาติ (FAO : Food and Agriculture Organization) ซึ่งคาดการณ์เอาไว้ว่าจำนวนประชากรโลกจะเพิ่มขึ้นอย่างต่อเนื่องทำให้ความต้องการแหล่งโปรตีนมีมากขึ้นตามไปด้วย คณะผู้วิจัยจึงมีแนวความคิดที่จะหาสร้างระบบเลี้ยงจิ้งหรีดที่มีประสิทธิภาพ
คณะอุตสาหกรรมอาหาร
Bio-calcium powders were extracted from Asian sea bass bone by heat-treated alkaline with fat removal and bleaching supplementary method. Cereal bars (CBs) were fortified with produced bio-calcium at 3 levels: (1) increased calcium (IS-Ca; calcium ≥10% Thai RDI), (2) good source of calcium (GS-Ca; calcium ≥15% Thai RDI), and (3) high calcium (H-Ca; calcium ≥30% Thai RDI) which were consistent with the notification of the Ministry of Public Health, Thailand: No. 445; Nutrition claim issued in B.E. 2023. Moisture content, water activity, color, calcium content and FTIR analysis of bio-calcium powders were measured. Dimension, color, water activity, pH and texture of fortified CBs were determined. Produced bio-calcium could be classified as a dried food with light yellow-white color. Calcium contents in bio-calcium powder was 23.4% (w/w). Dimension, weight and color except b* and ΔE* values of fortified CBs were not different (P > 0.05) from those of the control. Fortifying of bio-calcium resulted in harder texture CBs. An increase of fortified bio-calcium amounts decreased carbohydrate and fat but increased of protein, ash and calcium in the fortified CBs. Shelf life of CBs was to be shorten by fortification of bio-calcium powder because of the increment of moisture, water activity and pH. Yield of bio-calcium production was 40.30%. Production cost of bio-calcium was approximately 7,416 Bth/kg while cost of fortified CBs increased almost 2-3 times compared to the control. Calcium contents in IS-Ca (921.12 mg/100g), GS-Ca (1,287.10 mg/100g) and H-Ca (2,639.70 mg/100g) cereal bars could be claimed as increased calcium, good source of calcium and high calcium, respectively. In conclusion, production of cereal bar fortified with Asian sea bass bone bio-calcium powder as a fortified food was possible. However, checking the remained hazardous reagents in bio-calcium powder must be carried out before using in food products and analysis of calcium bioavailability, sensory acceptance and shelf life of the developed products should be determined in further studies.
คณะวิศวกรรมศาสตร์
This project objectives are 1) investigate the utilization of coconut husk and rubber latex in construction applications, 2) determine the optimal ratio of coconut husk and rubber latex mixtures, and 3) test the properties of ceiling panels made from coconut husk and rubber latex composite under Thai Industrial Standard (TIS) 219-2552 for gypsum ceiling boards. The methodology involves the following steps: 1) planning the project, 2) designing the mixture for the coconut husk and rubber latex composite ceiling panels, 3) producing the composite ceiling panels, 4) testing the product for properties according to TIS 219-2552 for gypsum ceiling boards, and 5) summarizing the test results.
คณะวิทยาศาสตร์
With the development of space technology, wide-field sky surveys using telescopes have expanded the range of new data available for time-domain astronomical research. Traditional data analysis methods can no longer respond quickly and accurately enough to the growing volume of data. Thus, classifying time-series data, such as light curves, has become a significant challenge in the era of big data. In modern times, analyzing light curves has become essential for using machine learning techniques to handle and filter through massive amounts of data. Machine learning algorithms can be divided into two categories: shallow learning and deep learning. Numerous researchers have proposed and developed a variety of algorithms for light curve classification. In this study, we experimented with Support Vector Machine (SVM) and XGBoost, which are shallow machine learning algorithms, as well as 1D-CNN and Long Short-Term Memory (LSTM), which are deep learning algorithms, which are branches of deep machine learning, to classify variable stars. The training and testing data used in this study were from the Optical Gravitational Lensing Experiment-III (OGLE-III), consisting of variable star data from the Large Magellanic Cloud (LMC), categorized into five main classes: Classical Cepheids, δ Scutis, eclipsing binaries, RR Lyrae stars, and Long-period variables. The results demonstrate the performance analysis of each machine learning algorithm type applied to light curve data, while also highlighting the accuracy and statistical metrics of the algorithms used in the experiments.