CP Violation from Non Standard Models

1984 ◽  
pp. 637-653
Author(s):  
Gino Segrè
Keyword(s):  
Cp Violation ◽  
Standard Models

scholarly journals Supersymmetric alignment models for (g − 2)μ

2021 ◽  
Vol 2021 (10) ◽  
Author(s):  
Yuichiro Nakai ◽  
Matthew Reece ◽  
Motoo Suzuki
Keyword(s):  
Cp Violation ◽  
Electric Dipole ◽  
Dipole Moments ◽  
Mass Scale ◽  
Electric Dipole Moments ◽  
Viable Solution ◽  
Supersymmetric Models ◽  
Standard Models ◽  
Supersymmetric Particles ◽  
Electron Edm

Abstract Hierarchical masses of quarks and leptons are addressed by imposing horizontal symmetries. In supersymmetric Standard Models, the same symmetries play a role in suppressing flavor violating processes induced by supersymmetric particles. Combining the idea of spontaneous CP violation to control contributions to electric dipole moments (EDMs), the mass scale of supersymmetric particles can be lowered. We present supersymmetric models with U(1) horizontal symmetries and discuss CP and flavor constraints. Models with two U(1) symmetries are found to give a viable solution to the muon g − 2 anomaly. Interestingly, the parameter space to explain the anomaly will be probed by future electron EDM experiments.

The CP violation of theB→Kϕ decay at γ(4s) in the non-standard models

1990 ◽  
Vol 48 (1) ◽  
pp. 99-104 ◽  
Author(s):  
M. Tanimoto ◽  
K. Hirayama ◽  
T. Shinmoto ◽  
K. Senba
Keyword(s):  
Cp Violation ◽  
Standard Models

CP Violation

2009 ◽  
Author(s):  
I. I. Bigi ◽  
A. I. Sanda
Keyword(s):  
Cp Violation

Quantum Calculation of Proton and Other Charge Transfer Steps in Voltage Sensing in the Kv1.2 Channel

2019 ◽  
Author(s):  
Alisher M Kariev ◽  
Michael Green
Keyword(s):  
Charge Transfer ◽  
Energy Charge ◽  
Quantum Calculation ◽  
Quantum Calculations ◽  
Gating Current ◽  
Transmembrane Segment ◽  
Voltage Sensing ◽  
Standard Models ◽  
Charge Displacement ◽  
Voltage Sensing Domain

Quantum calculations on 976 atoms of the voltage sensing domain of the K<sub>v</sub>1.2 channel, with protons in several positions, give energy, charge transfer, and other properties. Motion of the S4 transmembrane segment that accounts for gating current in standard models is shown not to occur; there is H<sup>+ </sup>transfer instead. The potential at which two proton positions cross in energy approximately corresponds to the gating potential for the channel. The charge displacement seems approximately correct for the gating current. Two mutations are accounted for (Y266F, R300cit, cit =citrulline). The primary conclusion is that voltage sensing depends on H<sup>+</sup> transfer, not motion of arginine charges.

COVID-19 Outbreak Prediction with Machine Learning

2020 ◽  
Author(s):  
Sina Faizollahzadeh Ardabili ◽  
Amir Mosavi ◽  
Pedram Ghamisi ◽  
Filip Ferdinand ◽  
Annamaria R. Varkonyi-Koczy ◽  
...  
Keyword(s):  
Machine Learning ◽  
Prediction Models ◽  
Fuzzy Inference ◽  
Control Measures ◽  
Future Research ◽  
Complex Nature ◽  
Inference System ◽  
Wide Range ◽  
Standard Models ◽  
High Level

Several outbreak prediction models for COVID-19 are being used by officials around the world to make informed-decisions and enforce relevant control measures. Among the standard models for COVID-19 global pandemic prediction, simple epidemiological and statistical models have received more attention by authorities, and they are popular in the media. Due to a high level of uncertainty and lack of essential data, standard models have shown low accuracy for long-term prediction. Although the literature includes several attempts to address this issue, the essential generalization and robustness abilities of existing models needs to be improved. This paper presents a comparative analysis of machine learning and soft computing models to predict the COVID-19 outbreak as an alternative to SIR and SEIR models. Among a wide range of machine learning models investigated, two models showed promising results (i.e., multi-layered perceptron, MLP, and adaptive network-based fuzzy inference system, ANFIS). Based on the results reported here, and due to the highly complex nature of the COVID-19 outbreak and variation in its behavior from nation-to-nation, this study suggests machine learning as an effective tool to model the outbreak. This paper provides an initial benchmarking to demonstrate the potential of machine learning for future research. Paper further suggests that real novelty in outbreak prediction can be realized through integrating machine learning and SEIR models.

Boolean-valued structures

2018 ◽  
Author(s):  
Tim Button ◽  
Sean Walsh
Keyword(s):  
Model Theory ◽  
Order Logic ◽  
First Order Logic ◽  
Inference Rules ◽  
Mathematical Concepts ◽  
Semantic Framework ◽  
First Order ◽  
Standard Models ◽  
Boolean Valued Model ◽  
Semantic Values

Chapters 6-12 are driven by questions about the ability to pin down mathematical entities and to articulate mathematical concepts. This chapter is driven by similar questions about the ability to pin down the semantic frameworks of language. It transpires that there are not just non-standard models, but non-standard ways of doing model theory itself. In more detail: whilst we normally outline a two-valued semantics which makes sentences True or False in a model, the inference rules for first-order logic are compatible with a four-valued semantics; or a semantics with countably many values; or what-have-you. The appropriate level of generality here is that of a Boolean-valued model, which we introduce. And the plurality of possible semantic values gives rise to perhaps the ‘deepest’ level of indeterminacy questions: How can humans pin down the semantic framework for their languages? We consider three different ways for inferentialists to respond to this question.

Effective Field Theory in Particle Physics and Cosmology

2020 ◽  
Keyword(s):  
Field Theory ◽  
Effective Field Theory ◽  
Particle Physics ◽  
Large Scale ◽  
Effective Field ◽  
Effective Theory ◽  
Soft Collinear Effective Theory ◽  
Multiple Length Scales ◽  
Cosmological Observations ◽  
Standard Models

Effective field theory (EFT) is a general method for describing quantum systems with multiple-length scales in a tractable fashion. It allows us to perform precise calculations in established models (such as the standard models of particle physics and cosmology), as well as to concisely parametrize possible effects from physics beyond the standard models. EFTs have become key tools in the theoretical analysis of particle physics experiments and cosmological observations, despite being absent from many textbooks. This volume aims to provide a comprehensive introduction to many of the EFTs in use today, and covers topics that include large-scale structure, WIMPs, dark matter, heavy quark effective theory, flavour physics, soft-collinear effective theory, and more.

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