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:
Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery
Interventional Pulmonology (middletown, de.)
In recent times, the term ‘Big Data’ has been under the limelight due to its exponential increase in relevance and importance in small, medium, large and very large companies. Industries of divergent sectors such as education, health, agriculture and telecommunication are all leveraging on the power of Big Data to enhance business prospects along with improved customer experience. Testing such highly volatile data, which is unstructured data and generated from myriad sources such as web logs, radio frequency Id (RFID), sensors embedded in devices, which are quite challenging in order to derive maximum benefit in information processing and decision making from the use of big data, such data must be of acceptable quality and must be fairly usable in terms of interloper- ability, relevance, and accuracy. However, such data quality can only be guaranteed if systems from which these data are harvested are adequately tested to ensure that output data from such systems exhibits minimum big data quality standards and characteristics. The methods adopted include Test criterion and cases which consider the volatility of big data and its underlying characteristics which is not limited to, but include Volume, Velocity, Veracity and Variety. Testing such highly volatile data, which is unstructured. Hadoop and Map Reduce which are also tools for testing big data. One of the most challenging endeavour for a tester is how to keep pace with changing dynamics of the industry. This work discusses inherent challenges faced in big data testing and the respective best practices that can be adopted in big data testing to enhance big data quality and accuracy.
Wind energy is one of the promising alternative energy resources after solar and hydropower. Most of wind turbine technologies are designed at high speed, whereas, not effectively operated in low wind speed areas. An effective technology is required to enhance the possible use of wind energy at low wind speeds. Diffuser Augmented Wind Turbine (DAWT) has been used recently to improve the use of wind turbine in a low wind speed area by manipulating the wind speed. The main concept of this technology is the pressure difference between inside and outside of DAWT which is occurred, hence, it might enhance the wind velocity and the power is increased as well. In this paper, simulation using ANSYS was conducted to investigate the performance of Horizontal Axis Wind Turbine (HAWT) in low wind speed area applying DAWT by modifying the angle and the length of diffuser. The variation of the diffuser angle was in the range 4-16o at L=1.25D. The simulation results showed a good agreement with the reference literature which obtained the increased power around 1.4-2.9 times higher than the non-diffuser wind turbine. The parameter of diffuser length was also investigated at L=0.25D-2.5D, with the significant impacts are obtained until L=1.25D