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:
TThere are several variables that determine in the process of impregnation include the flow of water into the well, the value of shape factors and in situ field permeability.This research aim is to investigate the shape factor of flat base recharge wells values of the three models whose upper walls are made of concrete buis with diameters of 0.6 m, 0.8 m, and 1.0 m.The high porous wall with var iations: 0.3 m, 0.7 m and 1.0 m. The shape factor is the coefficient of dimension planning which takes into accou nt the perimeter and area of the recharge well, volume and water level. The Permeability coefficient is very important. Experiments in the field are three recharges well models in a flat base impregnation on the porous wall (L) test and well radius (R The value of permeability of soil obtained by direct drilling of the research area in Sukolilo Village, Malang Indonesia. Field permeability was obtained by using auger boring falling Head method with a soil depth of 00.0 m to 2.65 m.he result showed that shap e fa ctor simultaneously influenced the high of porous wall (F = 23.71 > F table = 5.32; P < 0.05). However, the positive correlation between the changes of shape factor and high of porous wall in flat base on the recharge wells (R2 = 0.88)
The kernel estimator especially the univariate type often needs one smoothing parameter as against more smoothing parameters demanded by greater dimensional estimators though it all depends on kind of smoothing parameterizations employed. The smoothing parameter(s) of kernels with higher dimension may be called smoothing matrices. Kernels of higher dimensions have three kinds of parameterizations as estimators viz: constant, diagonal and full parameterizations. Unlike the full parameterization, the diagonal parameterization exhibits some levels of restrictions. This study investigates the efficiency of kernel estimators to which smoothing parameterizations are applied. The asymptotic mean-integrated squared error is employed as a criterion function with emphasis on bivariate case only. With real data, the results show that full smoothing parameterization outperformed the constant and diagonal parameterizations in respect of the asymptotic mean-integrated squared error’s value and the kernel estimate’s ability to retain the true characteristics of the affected distribution
