Interior Architecture Design Project for a Restaurant

Abstract

Interior Architecture Design Project: A Halal Restaurant Integrating the Culture of Songkhla, Thailand

Objective

สร้างเอกลักษณ์เด่นเเก่สงขลาทางด้านอาหาร

Other Innovations

คณะเทคโนโลยีการเกษตร

Improvement and Technical production of hot pepper (C. chinense) for high yield and high pungency

Capsicum chinense is a high-potential economic crop in the food and pharmaceutical industries due to its role as a primary source of capsaicin, a bioactive compound with significant physiological effects. However, capsaicin levels and fruit quality will be influenced by genetic factors, environmental conditions, and genetic-by-environment (G×E) interactions, leading to variability in capsaicin biosynthesis. This study will aim to analyze the impact of different environmental conditions on the growth, fruit quality, and capsaicin content of C. chinense ‘Scotch Bonnet’. The field experiments will be conducted at the demonstration plots of the Faculty of Agricultural Technology, King Mongkut’s Institute of Technology Ladkrabang, during two growing seasons: July–October (rainy season) and December–April (dry season). Four condition environments will be evaluated, and environmental parameters such as temperature, relative humidity, and light quality will be monitored to assess their effects on plant physiology and capsaicin biosynthesis. Additionally, an F1 hybrid breeding program will be established using six parental lines through a Half-diallel mating design, generating 15 hybrid combinations. The general combining ability (GCA) and specific combining ability (SCA) will be assessed to identify promising hybrid combinations with high and stable capsaicin content and yield. The findings from this study will be expected to provide valuable insights into optimizing cultivation conditions for high-pungency chili production and supporting the development of F1 hybrid seeds with commercial viability and consistent capsaicin levels.

คณะวิทยาศาสตร์

Investigation variable star classification through light curve analysis using machine learning approach

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.