Technology Reports of Kansai University (ISSN: 04532198) is a monthly peer-reviewed and open-access international Journal. It was first built in 1959 and officially in 1975 till now by kansai university, japan. The journal covers all sort of engineering topic, mathematics and physics. Technology Reports of Kansai University (TRKU) was closed access journal until 2017. After that TRKU became open access journal. TRKU is a scopus indexed journal and directly run by faculty of engineering, kansai university.
Technology Reports of Kansai University (ISSN: 04532198) is a peer-reviewed journal. The journal covers all sort of engineering topic as well as mathematics and physics. the journal's scopes are
in the following fields but not limited to:
In the present research, an attempt had been made to utilize durian shell as a carbon source for catalyst support in transesterification of palm oil (PO). The objective of this research was to explore durian shell as a carbon source for catalyst support, its modification with KOH, as well as its characterization and application in transesterification of PO. Prior to usage, durian shell was calcined at 600℃ for 2 h in a furnace. Carbon formed was impregnated in KOH solution, followed by drying and calcination. The resulting activated carbon was characterized by SEM-EDX, FTIR, and BET. The activated carbon was then utilized as a solid catalyst in biodiesel formation. Transesterification was conducted at 55 to 70℃, methanol to PO molar ratio (MPR) of 12:1, catalyst load of 2-5%, and reaction time of 90-150 min. The highest biodiesel yield of 97.3% was reached using 3% catalyst load at 60℃ for 90 min. Biodiesel obtained in this work was then characterized for its chemical physical properties. The biodiesel properties met the European standard (EN-14214). The results suggested that durian shell based activated carbon is applicable as a solid catalyst for biodiesel synthesis
Electroencephalogram (EEG) signal interpretation has been developed for various purposes such as brain health examination, brain detection, brain trauma, emotional condition, and even predict the response that will occur. The complex form of EEG signals will complicate one's interpretation visually so that it requires neurologists to deduce it. One of the brain disorders that are of concern and can be detected through EEG is epilepsy. EEG signal patterns can be identified through excessive brain cell activity before or after a person experiences seizures without cause. In this study, we proposed an EEG epilepsy signal recognition using Wavelet Time Entropy (WTE) as the main modality to obtain signal features. 300 EEG signal consisting of 3 classes (normal, interictal, seizure) has been tested with the highest accuracy result of 86.3% generated by Db 2 with decomposition level 2 or 3 using cubic Support Vector Machine (SVM)
