B meson rare decays in the TNMSSM

We investigate the two loop electroweak corrections to B meson rare decays $\bar B\rightarrow X_s\gamma$ and $B_s^0\rightarrow \mu^+\mu^-$ in the minimal supersymmetry standard model (MSSM) extension with two triplets and one singlet (TNMSSM). The new particle contents and interactions in the TNMSSM can affect the theoretical predictions of the branching ratios ${\rm Br}(\bar B\rightarrow X_s\gamma)$ and ${\rm Br}(B_s^0\rightarrow \mu^+\mu^-)$, and the corrections from two loop diagrams to the process $\bar B\rightarrow X_s\gamma$ can reach around $4\%$. Considering the latest experimental measurements, the numerical results of ${\rm Br}(\bar B\rightarrow X_s\gamma)$ and ${\rm Br}(B_s^0\rightarrow \mu^+\mu^-)$ in the TNMSSM are presented and analyzed. It is found that the results in the TNMSSM can fit the updated experimental data well and the new parameters $T_{\lambda},\;\kappa,\;\lambda$ affect the theoretical predictions of ${\rm Br}(\bar B\rightarrow X_s\gamma)$ and ${\rm Br}(B_s^0\rightarrow \mu^+\mu^-)$ obviously

Consumers’ Kansei Needs Clustering Method for Product Emotional Design Based on Numerical Design Structure Matrix and Genetic Algorithms

Consumers’ Kansei needs reflect their perception about a product and always consist of a large number of adjectives. Reducing the dimension complexity of these needs to extract primary words not only enables the target product to be explicitly positioned, but also provides a convenient design basis for designers engaging in design work. Accordingly, this study employs a numerical design structure matrix (NDSM) by parameterizing a conventional DSM and integrating genetic algorithms to find optimum Kansei clusters. A four-point scale method is applied to assign link weights of every two Kansei adjectives as values of cells when constructing an NDSM. Genetic algorithms are used to cluster the Kansei NDSM and find optimum clusters. Furthermore, the process of the proposed method is presented. The details of the proposed approach are illustrated using an example of electronic scooter for Kansei needs clustering. The case study reveals that the proposed method is promising for clustering Kansei needs adjectives in product emotional design