Neutrino mass and charged lepton flavor violation in an extended left-right symmetric model

We consider an $U(1)_{L_\mu -L_\tau}$ extended left-right symmetric gauge theory where the neutrino masses are generated through inverse seesaw mechanism. In this model the muon $(g-2)$ anomaly is accounted for by the mediation of $Z_{\mu\tau}$, the gauge boson of $U(1)_{L_\mu - L_\tau}$ symmetry. The symmetries of the model require the light neutrino mass matrix to have a particular two-zero texture, which leads to non-trivial constraints in the minimum neutrino mass. In addition, the model predicts observable charged lepton flavor violation in $\mu-\tau$ sector.Comment: 21 pages, 2 figures, 2 tables, Version accepted in Nucl. Phys.

Multiple generation of Bengali static signatures

Handwritten signature datasets are really necessary for the purpose of developing and training automatic signature verification systems. It is desired that all samples in a signature dataset should exhibit both inter-personal and intra-personal variability. A possibility to model this reality seems to be obtained through the synthesis of signatures. In this paper we propose a method based on motor equivalence model theory to generate static Bengali signatures. This theory divides the human action to write mainly into cognitive and motor levels. Due to difference between scripts, we have redesigned our previous synthesizer [1,2], which generates static Western signatures. The experiments assess whether this method can approach the intra and inter-personal variability of the Bengali-100 Static Signature DB from a performance-based validation. The similarities reported in the experimental results proof the ability of the synthesizer to generate signature images in this script

Dark matter in the Alternative Left Right model

Abstract The Alternative Left-Right Model is an attractive variation of the usual Left-Right Symmetric Model because it avoids flavour-changing neutral currents, thus allowing the additional Higgs bosons in the model to be light. We show here that the model predicts several dark matter candidates naturally, through introduction of an R-parity similar to the one in supersymmetry, under which some of the new particles are odd, while all the SM particles are even. Dark matter candidates can be fermionic or bosonic. We present a comprehensive investigation of all possibilities. We analyze and restrict the parameter space where relic density, direct and indirect detection bounds are satisfied, and investigate the possibility of observing fermionic and bosonic dark matter signals at the LHC. Both the bosonic and fermionic candidates provide promising signals, the first in LHC at 300 fb −1, the second at higher luminosity, 3000 fb −1. Signals from bosonic candidates are indicative of the presence of exotic d′ quarks, while fermionic candidates imply the existence of charged Higgs bosons, all with masses in the TeV region

Conventional and advanced detection techniques of foodborne pathogens: A comprehensive review

Foodborne pathogens are a major public health concern and have a significant economic impact globally. From harvesting to consumption stages, food is generally contaminated by viruses, parasites, and bacteria, which causes foodborne diseases such as hemorrhagic colitis, hemolytic uremic syndrome (HUS), typhoid, acute, gastroenteritis, diarrhea, and thrombotic thrombocytopenic purpura (TTP). Hence, early detection of foodborne pathogenic microbes is essential to ensure a safe food supply and to prevent foodborne diseases. The identification of foodborne pathogens is associated with conventional (e.g., culture-based, biochemical test-based, immunological-based, and nucleic acid-based methods) and advances (e.g., hybridization-based, array-based, spectroscopy-based, and biosensor-based process) techniques. For industrial food applications, detection methods could meet parameters such as accuracy level, efficiency, quickness, specificity, sensitivity, and non-labor intensive. This review provides an overview of conventional and advanced techniques used to detect foodborne pathogens over the years. Therefore, the scientific community, policymakers, and food and agriculture industries can choose an appropriate method for better results