CP violating supersymmetric contributions to the electroweak ρ\rho parameter

Effects of CP violation on the supersymmetric electroweak correction to the $\rho$ parameter are investigated. To avoid the EDM constraints, we require that arg$(\mu)<10^{-2}$ and the non-universal trilinear couplings $A_f=(0,0,A_0)$ and also assume that gluinos are heavier than 400 GeV. The CP phase $\phi_t=$ arg($A_0$) leads to large enhancement of the relative mass splittings between $\tilde{t}_2$ and $\tilde{b}_L(\tilde{t}_1)$, which in turn reduces the one-loop contribution of the stop and sbottom to $\Delta \rho$. For small $\tan \beta$, such a CP violating effect is prominent. We also study how much the two-loop gluon and gluino contributions are affected by the CP phase. Possible contributions to the $\rho$ parameter arising from the Higgs sector with CP violation are discussed.Comment: 14 pages, Revtex, 4 eps figures, to appear in Phys. Rev. D (Rapid Comm.

Prediction of a time-to-event trait using genome wide SNP data

BACKGROUND: A popular objective of many high-throughput genome projects is to discover various genomic markers associated with traits and develop statistical models to predict traits of future patients based on marker values. RESULTS: In this paper, we present a prediction method for time-to-event traits using genome-wide single-nucleotide polymorphisms (SNPs). We also propose a MaxTest associating between a time-to-event trait and a SNP accounting for its possible genetic models. The proposed MaxTest can help screen out nonprognostic SNPs and identify genetic models of prognostic SNPs. The performance of the proposed method is evaluated through simulations. CONCLUSIONS: In conjunction with the MaxTest, the proposed method provides more parsimonious prediction models but includes more prognostic SNPs than some naive prediction methods. The proposed method is demonstrated with real GWAS data

Restructuring TCAD System: Teaching Traditional TCAD New Tricks

Traditional TCAD simulation has succeeded in predicting and optimizing the device performance; however, it still faces a massive challenge - a high computational cost. There have been many attempts to replace TCAD with deep learning, but it has not yet been completely replaced. This paper presents a novel algorithm restructuring the traditional TCAD system. The proposed algorithm predicts three-dimensional (3-D) TCAD simulation in real-time while capturing a variance, enables deep learning and TCAD to complement each other, and fully resolves convergence errors.Comment: In Proceedings of 2021 IEEE International Electron Devices Meeting (IEDM

The Mechanism Underlying the Antibacterial Activity of Shikonin against Methicillin-Resistant Staphylococcus aureus

Shikonin (SKN), a highly liposoluble naphthoquinone pigment isolated from the roots of Lithospermum erythrorhizon, is known to exert antibacterial, wound-healing, anti-inflammatory, antithrombotic, and antitumor effects. The aim of this study was to examine SKN antibacterial activity against methicillin-resistant Staphylococcus aureus (MRSA). The SKN was analyzed in combination with membrane-permeabilizing agents Tris and Triton X-100, ATPase inhibitors sodium azide and N,N′-dicyclohexylcarbodiimide, and S. aureus-derived peptidoglycan; the effects on MRSA viability were evaluated by the broth microdilution method, time-kill test, and transmission electron microscopy. Addition of membrane-permeabilizing agents or ATPase inhibitors together with a low dose of SKN potentiated SKN anti-MRSA activity, as evidenced by the reduction of MRSA cell density by 75% compared to that observed when SKN was used alone; in contrast, addition of peptidoglycan blocked the antibacterial activity of SKN. The results indicate that the anti-MRSA effect of SKN is associated with its affinity to peptidoglycan, the permeability of the cytoplasmic membrane, and the activity of ATP-binding cassette (ABC) transporters. This study revealed the potential of SKN as an effective natural antibiotic and of its possible use to substantially reduce the use of existing antibiotic may also be important for understanding the mechanism underlying the antibacterial activity of natural compounds

An intelligent method for pregnancy diagnosis in breeding sows according to ultrasonography algorithms

Pig breeding management directly contributes to the profitability of pig farms, and pregnancy diagnosis is an important factor in breeding management. Therefore, the need to diagnose pregnancy in sows is emphasized, and various studies have been conducted in this area. We propose a computer-aided diagnosis system to assist livestock farmers to diagnose sow pregnancy through ultrasound. Methods for diagnosing pregnancy in sows through ultrasound include the Doppler method, which measures the heart rate and pulse status, and the echo method, which diagnoses by amplitude depth technique. We propose a method that uses deep learning algorithms on ultrasonography, which is part of the echo method. As deep learning-based classification algorithms, Inception-v4, Xception, and EfficientNetV2 were used and compared to find the optimal algorithm for pregnancy diagnosis in sows. Gaussian and speckle noises were added to the ultrasound images according to the characteristics of the ultrasonography, which is easily affected by noise from the surrounding environments. Both the original and noise added ultrasound images of sows were tested together to determine the suitability of the proposed method on farms. The pregnancy diagnosis performance on the original ultrasound images achieved 0.99 in accuracy in the highest case and on the ultrasound images with noises, the performance achieved 0.98 in accuracy. The diagnosis performance achieved 0.96 in accuracy even when the intensity of noise was strong, proving its robustness against noise