果蠅緯度相關晝夜節律特徵：穩定性、活動量分佈與演化意義
Latitude-Dependent Circadian Traits in Drosophila: Stability, Activity Peaks, and Evolutionary Implications
生物時鐘可對生物體的行為與生理造成影響，在探討晝夜節律特徵的差異時，過去研究常侷限於北美大陸的品系，缺少赤道及南半球品系的晝夜節律特徵探討。有鑒於黑腹果蠅在全球各大洲的廣泛分佈，因此我們以黑腹果蠅（近赤道與中高緯度品系）為材料，研究果蠅是否因緯度而有相異的晝夜節律特徵？結果顯示不同緯度的果蠅品系展現出相異的晝夜節律特徵。赤報品系在全暗狀態下仍維持原本光暗12小時的穩定節律，而南北半球的中高緯度品系則具有相似節律特徵，即在全暗狀態下的節律不對齊原本正常光源的穩定節律，其他如活動量、週期、及節律強度等皆有著品系間的差異。更進一步比對實驗中各個品系基因序列，研究發現per和tim在調控區段有許多SNP變異，顯示其與晝夜節律特徵的關係，有助於後續尋找更多造成晝夜節律特徵差異的可能遺傳變異並探討。
TEST & SAVE
Electricity has become an essential part of modern life, powering homes, businesses, and industries. However, the misuse of electricity or malfunctioning electrical systems can lead to hazardous situations such as electrical fires, shocks, and significant energy wastage. This project focuses on creating a Comprehensive Electrical Security System to protect users and properties from the risks associated with electricity. The system is designed to prevent electrical malfunctions, ensure safety in various scenarios, and monitor energy consumption effectively. It integrates a variety of sensors and safety mechanisms to detect dangers and take preemptive action
Revolutionizing Potato Agriculture: Harnessing Machine Learning Techniques for Disease Detection and Management
Aim: The aim of this study is to make a disease-predicting model trained on data from weather stations and API using machine learning that gives the farmer the ability to predict crop diseases before they set in, allowing them to take timely preventative measures and reduce wastage. Materials and Methods: In this study the Internet of Things (IoT) sensors throughout agricultural fields of potato crops in Jafferabad, Depalpur Punjab. The sensors collect real-time data on environmental conditions, such as precipitation, air temperature, relative humidity, wind speed, and direction, Dew Point, VPD, and the Delta T values, to identify subtle disease indicators and patterns within the environmental data. Our novel machine-learning program makes use of the data collected by the weather station and analyses them. Results: Using the data, one predictive statistical method using Python 3.8.0 was created which uses the data from the weather station which can predict diseases before they set in.