Top Quark Rare Decays via Loop-Induced FCNC Interactions in Extended Mirror Fermion Model

Flavor changing neutral current (FCNC) interactions for a top quark $t$ decays into $Xq$ with $X$ represents a neutral gauge or Higgs boson, and $q$ a up- or charm-quark are highly suppressed in the Standard Model (SM) due to the Glashow-Iliopoulos-Miami mechanism. Whilst current limits on the branching ratios of these processes have been established at the order of $10^{-4}$ from the Large Hadron Collider experiments, SM predictions are at least nine orders of magnitude below. In this work, we study some of these FCNC processes in the context of an extended mirror fermion model, originally proposed to implement the electroweak scale seesaw mechanism for non-sterile right-handed neutrinos. We show that one can probe the process $t \to Zc$ for a wide range of parameter space with branching ratios varying from $10^{-6}$ to $10^{-8}$, comparable with various new physics models including the general two Higgs doublet model with or without flavor violations at tree level, minimal supersymmetric standard model with or without $R$-parity, and extra dimension model.Comment: 30 pages, 8 figures, 2 tables and 1 appendix. Version to appear in NP

Predicting Students Performance Based on Their Reading Behaviors

E-learning systems can support students in the on-line classroom environment by providing different learning materials. However, recent studies find that students may misuse such systems with a variety of strategies. One particular misused strategy, gaming the system, has repeatedly been found to negatively affect the students’ learning results. Unfortunately, methods to quantitatively capture such behavior are poorly developed, making it difficult to predict students learning outcomes. In this work, we tackle this problem based on a study of the 567,193 records of the 71 students’ reading behaviors from two classes in the academic year 2016. We first quantify the extent to which students misused the system and then predict their class performance based on the quantified results. Our results demonstrated that such misbehavior in the E-learning system can be quantified as a probability and then further used as a significant factor to predict students class learning outcomes with high accuracy