Lately, there was some attention for the Variance Based SEM (VB-SEM) against that of Covariance Based SEM (CB-SEM) from social science researches regarding the fitness indexes, sample size requirement, and normality assumption. Not many of them aware that VB-SEM is developed based on the non-parametric approach compared to the parametric approach of CB-SEM. In fact the fitness of a model should not be taken lightly since it reflects the behavior of data in relation to the proposed model for the study. Furthermore, the adequacy of sample size and the normality of data are among the main assumptions of parametric test itself. This study intended to clarify the ambiguities among the social science community by employing the data-set which do not meet the fitness requirements and normality assumptions to execute both CB-SEM and VB-SEM. The findings reveal that the result of CB-SEM with bootstrapping is almost similar to that of VB-SEM (bootstrapping as usual). Therefore, the failure to meet the fitness and normality requirements should not be the reason for employing Non-Parametric SEM.

Microwave absorption properties were systematically studied for double-layer carbon black/epoxy resin (cB) and ni 0.6 Zn 0.4 fe 2 o 4 /epoxy resin (F) nanocomposites in the frequency range of 8 to 18 GHz. The ni 0.6 Zn 0.4 fe 2 o 4 nanoparticles were synthesized via high energy ball milling with subsequent sintering while carbon black was commercially purchased. The materials were later incorporated into epoxy resin to fabricate double-layer composite structures with total thicknesses of 2 and 3 mm. The CB1/F1, in which carbon black as matching and ferrite as absorbing layer with each thickness of 1 mm, showed the highest microwave absorption of more than 99.9%, with minimum reflection loss of −33.8 dB but with an absorption bandwidth of only 2.7 GHz. Double layer absorbers with F1/CB1(ferrite as matching and carbon black as absorbing layer with each thickness of 1 mm) structure showed the best microwave absorption performance in which more than 99% microwave energy were absorbed, with promising minimum reflection loss of −24.0 dB, along with a wider bandwidth of 4.8 GHz and yet with a reduced thickness of only 2 mm.In order to address issues induced by high proliferation of electromagnetic interferences in both civil and military applications, efficient microwave absorbers are becoming highly desirable and necessary. For that reason, such material is required to effectively reduce the reflection of electromagnetic (EM) signals over a broad absorption bandwidth. In order to improve the performance of microwave absorption properties, microwave absorbers are designed to meet the specific requirements of simultaneously having strong absorption, wide frequency band, lightweight and small thickness. Improvements can certainly be made to the designs by physical assembling of different types of absorbents 1-5 , chemical decorated absorbents 6,7 as well as by designing multi-layer structures [8][9][10][11] .Microwave absorbers are produced using different kinds of materials including one dimensional (1D) materials such as carbon nanotubes 12-15 , two dimensional (2D) materials such as graphene 16,17 and bulk three dimensional (3D) materials such as ferrites 9,18-21 . The difference in the dimensional structure of the materials would largely affect the microwave absorption performances since different kinds of structures contribute to different www.nature.com/scientificreports www.nature.com/scientificreports/ the F1/CB1 sample showed the best all round performance, in which more than 99% microwave energy was absorbed, with a reflection loss of −24.0 dB and a widest bandwidth of 4.8 GHz at −10 dB, yet it is the thinnest among the three designs, having a total thickness of only 2 mm.

Abstract-This paper presents a system with experimental complement to a simulation work for early breast tumor detection. The experiments are conducted using commercial Ultrawide-Band (UWB) transceivers, Neural Network (NN) based Pattern Recognition (PR) software for imaging and proposed breast phantoms for homogenous and heterogeneous tissues. The proposed breast phantoms (homogeneous and heterogeneous) and tumor are constructed using available low cost materials and their mixtures with minimal effort. A specific glass is used as skin. All the materials and their mixtures are considered according to the ratio of the dielectric properties of the breast tissues. Experiments to detect tumor are performed in regular noisy room environment. The UWB signals are transmitted from one side of the breast phantom (for both cases) and received from opposite side diagonally repeatedly. Using discrete cosine transform (DCT) of these received signals, a Neural Network (NN) module is developed, trained and tested. The tumor existence, size and location detection rates for both cases are highly satisfactory, which are approximately: (i) 100%, 95.8% and 94.3% for homogeneous and (ii) 100%, 93.4% and 93.1% for heterogeneous cases respectively. This gives assurance of early detection and the practical usefulness of the developed system in near future.

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The aim of this research is to understand the effect of product quality, medical price and staff skill on patient's loyalty through cultural impact in medical tourism. In this study, three exogenous constructs; namely product quality, medical price, and staff skill constructs were adopted to find their effects on patient's loyalty. Meanwhile, the cultural impact is also adopted as a mediator construct. This study uses confirmatory approach as Covariance based Structural Equation Modeling (CBSEM) for testing the research hypotheses. The study explores inbound medical tourists loyalty from 324 respondents sampled comprised from different countries using stratified sampling. In terms of the direct effect, the method reveals that medical price and staff skill had positive significant effects on cultural impact and patient loyalty. In terms of indirect effect, the cultural impact mediates the relationships between product quality, medical price, and staff skills on patient's loyalty.