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:
The study aimed to establish chess influence on learner’s performance in mathematics. It adopted a quantitative approach and followed a descriptive research design. 70 Grade 9 learners from seven secondary schools participated in the study. Cluster sampling technique was employed with 70 learners being surveyed using a questionnaire. A close-ended questionnaire was used to gather data. The study found that the largest group of learners in school that did not offer chess could not understand or explain En Passant. It also emerged that in schools that offered chess, all learners agreed that they could explain En Passant. Slightly less than 50% of the participants strongly disagreed that they can algebraically notate the game whereas 17.1% disagreed. In non-chess schools, only 4% (N=2) indicated that they can algebraically notate whereas all 20 learners (100%) in chess schools confirmed that they can notate the game. The study concludes that the learners in those schools that offered chess had ideas/could explain the chess terms and vice versa. The study recommends that since the influence of chess training may have positive impact, chess training can be introduced to schools to enhance the mathematics performance of learners.
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.
