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:
Kongzhi yu Juece/Control and Decision
Azerbaijan Medical Journal
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery
Zhenkong Kexue yu Jishu Xuebao/Journal of Vacuum Science and Technology
Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering)
Zhonghua yi shi za zhi (Beijing, China : 1980)
Changjiang Liuyu Ziyuan Yu Huanjing/Resources and Environment in the Yangtze Valley
Tobacco Science and Technology
Shenyang Jianzhu Daxue Xuebao (Ziran Kexue Ban)/Journal of Shenyang Jianzhu University (Natural Science)
General Medicine (ISSN:1311-1817)
Carbon foam is lightweight material that reveals many unique properties which is good thermal conductivity and high mechanical strength. This study is to generate carbon foam from sucrose and was derived from 10g of sucrose and 10 wt% of boric acid. The process was continued with the foaming process in the oven at 120oC for 48 hours. The solid organic foam was continued to dehydrating process at 250oC for 16 hours with rate of 2oC/min in a box furnace. The carbonization temperature was 600oC, 700oC, 800oC and 1000oC for 2 hours with the similar heating rate of 2oC/min. The purpose for carbonization process is to create a high quality structure and properties of carbon foam where the carbon content in the material increases while the precursor is carbonized. Finding shows the density and porosity value of 1000oC carbonization temperature is the higher which is 0.600 g/cm3 and 50.08% respectively. Scanning Electron Microscope (SEM) shows the surface structure of temperature 1000oC has more pores than other temperatures during the carbonization process. Energy Dispersive Spectroscopy (EDS) analysis shows oxygen content in the range of 2.08 to 11.73 wt% in the carbon foams prepared in the range of 600oC to 1000oC by the carbonization process. The highest carbonization temperature of 1000oC reveals the mechanical properties which is 2.307 N/mm2 as the highest compressive strength. It is concluding that the higher the carbonization temperature gives the higher compressive strength and the density due to the increase in the material structure of the pore
Corn is one of the leading food that produces carbohydrates in Indonesia. It can grow well in hot and cold areas with sufficient rainfall and irrigation. However, each part of the corn is sensitive to several diseases, and it can reduce the quantity and quality of the corn result production. Damage of corn plant that is caused by the disease can be conducted by the disturbing process into the plant and make the plant died. The diseases can undermine corn plants by disrupting the processes inside the plant and make the plant died. Therefore, this study aims to design a system for detecting diseases and pests in corn plants using Certainty Factor and Fuzzy Sugeno methods. The Fuzzy Sugeno method is employed to identify diseases and pests in corn plants based on the degree of trust in the diseases of the corn plants. The degree of confidence in the disease can be obtained from the certainty level of the base system built by the Certainty Factor method. The experiments have been carried out to determine the accuracy of the Certainty Factor and Fuzzy Sugeno methods. Therefore, the detection system can work effectively and efficiently as well as minimize the amount of damaged corn production. We collected 15 diseases or pests and 48 symptoms, and the experiment results have obtained an accuracy of 85.16%
