Used cooking oil was undergoing trans-esterification reaction to produce biodiesel fuel. Method of production consisted of pretreatment steps, trans-esterification, separation, washing and drying. Trans-esterification of treated oils was studied at different operation conditions, the methanol to oil mole ratio were 6:1, 8:1, 10:1, and 12:1, at different temperature 30, 40, 50, and 60 ^º C, reaction time 40, 60, 80, and 120 minutes, amount of catalyst 0.5, 1, 1.5, and 2 wt.% based on oil and mixing speed 400 rpm. The maximum yield of biodiesel was 91.68 wt.% for treated oils obtained by trans-esterification reaction with 10:1 methanol to oil mole ratio, 60 ^º C reaction temperature, 80 minute reactio

Development and population expansion have the lion's share of driving up the fuel cost. Biodiesel has considerable attention as a renewable, ecologically friendly and alternative fuel source. In this study, CaO nanocatalyst is produced from mango leaves as a catalysis for the transesterification of waste cooking oil (WCO) to biodiesel. The mango tree is a perennial plant, and its fruit holds significant economic worth due to its abundance of vitamins and minerals. This plant has a wide geographical range and its leaves can be utilized without any negative impact on its growth and yield. An analysis was conducted to determine the calcium content in the fallen leaves, revealing a significant quantity of calcium that holds potential fo

Semantic segmentation is an exciting research topic in medical image analysis because it aims to detect objects in medical images. In recent years, approaches based on deep learning have shown a more reliable performance than traditional approaches in medical image segmentation. The U-Net network is one of the most successful end-to-end convolutional neural networks (CNNs) presented for medical image segmentation. This paper proposes a multiscale Residual Dilated convolution neural network (MSRD-UNet) based on U-Net. MSRD-UNet replaced the traditional convolution block with a novel deeper block that fuses multi-layer features using dilated and residual convolution. In addition, the squeeze and execution attention mechanism (SE) and the s