This research investigates active packaging films made from polyvinyl alcohol (PVA) and nanocellulose fibers (NFC), incorporating silver nanoparticles (AgNPs) synthesized from Terminalia chebula extract, which possesses antibacterial and antifungal properties. The developed films were tested for their mechanical properties, microbial inhibition, and biodegradability. The results showed that the addition of AgNPs from Terminalia chebula enhanced product protection and effectively extended the shelf life of strawberries while being environmentally friendly.
บรรจุภัณฑ์แอคทีฟถูกพัฒนาเพื่อปกป้องผลิตภัณฑ์และยืดอายุการเก็บรักษา โดยสามารถแลกเปลี่ยนแก๊สได้อย่างมีประสิทธิภาพ ฟิล์มที่ผลิตจากพอลิไวนิลแอลกอฮอล์ (PVA) มีความยืดหยุ่น แข็งแรง และสามารถละลายน้ำได้ อีกทั้งยังสามารถเติมอนุภาคซิลเวอร์นาโน (AgNPs) เพื่อเพิ่มคุณสมบัติในการยับยั้งจุลินทรีย์ สมอไทย (Terminalia chebula) ซึ่งอุดมไปด้วยสารต้านเชื้อแบคทีเรียและเชื้อรา ถูกนำมาใช้ในการสังเคราะห์ AgNPs เพื่อนำไปประยุกต์ใช้กับฟิล์มบรรจุภัณฑ์แอคทีฟ งานวิจัยนี้จึงศึกษาคุณสมบัติของฟิล์มที่พัฒนาขึ้นสำหรับการเคลือบสตรอเบอร์รีเพื่อยืดอายุการเก็บรักษาอย่างมีประสิทธิภาพ
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.
วิทยาลัยเทคโนโลยีและนวัตกรรมวัสดุ
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คณะวิศวกรรมศาสตร์
The designing of mosquitoes counting system instrument is presented in this work. The mosquitoes that were counted died in order not to measure duplicate counting data. As soon as the input source counting machine can detect the mosquito, the single trigger signal is transmitted to the IOT system to interrupt the server immediately. The number of real mosquito is not transmitting to the IOT but only a signal to interrupt the server. The server records the number of the interrupt signal with real-time clock. Then the interrupt information will be further handled. The front end counting machine consist of the high voltage generate with the suitable voltage value and electrode distance for the required mosquitoes size. The low trigger pulse signals of the mosquitoes killed by high voltage are sending to the controller unit. Immediately, interrupt counting signal of the number of mosquitoes is sent to the big stream data collection on IOT system by the time stamp technique. Form the measurement results, 10 live sample mosquitoes in a limited space box to fly though the counting machine show that the count results are 100% correct count.