Growing pseudo-eigenmodes and positive logarithmic norms in rotating shear flows

Rotating shear flows, when angular momentum increases and angular velocity decreases as functions of radiation coordinate, are hydrodynamically stable under linear perturbation. The Keplerian flow is an example of such systems which appears in astrophysical context. Although decaying eigenmodes exhibit large transient energy growth of perturbation which could govern nonlinearity into the system, the feedback of inherent instability to generate turbulence seems questionable. We show that such systems exhibiting growing pseudo-eigenmodes easily reach an upper bound of growth rate in terms of the logarithmic norm of the involved nonnormal operators, thus exhibiting feedback of inherent instability. This supports the existence of turbulence of hydrodynamic origin in the Keplerian accretion disc in astrophysics. Hence, this enlightens the mismatch between the linear theory and experimental/observed data and helps in resolving the outstanding question of origin of turbulence therein.Comment: 12 pages including 4 figures; to appear in New Journal of Physic

Flavour equilibration in quark-gluon plasma

Within the framework of a dynamical and physically transparent model developed earlier, we study the time evolution of various quark flavours in the baryon-free region in ultrarelativistic heavy ion collisions. We show that even under optimistic conditions, the quark-gluon system fails to achieve chemical equilibrium

Overexpression of the Synthetic Chimeric Native-T-Phylloplanin-GFP Genes Optimized for Monocot and Dicot Plants Renders Enhanced Resistance to Blue Mold Disease in Tobacco (\u3cem\u3eN. Tabacum L.\u3c/em\u3e)

To enhance the natural plant resistance and to evaluate the antimicrobial properties of phylloplanin against blue mold, we have expressed a synthetic chimeric native-phylloplanin-GFP protein fusion in transgenic Nicotiana tabacum cv. KY14, a cultivar that is highly susceptible to infection by Peronospora tabacina. The coding sequence of the tobacco phylloplanin gene along with its native signal peptide was fused with GFP at the carboxy terminus. The synthetic chimeric gene (native-phylloplanin-GFP) was placed between the modified Mirabilis mosaic virus full-length transcript promoter with duplicated enhancer domains and the terminator sequence from the rbcSE9 gene. The chimeric gene, expressed in transgenic tobacco, was stably inherited in successive plant generations as shown by molecular characterization, GFP quantification, and confocal fluorescent microscopy. Transgenic plants were morphologically similar to wild-type plants and showed no deleterious effects due to transgene expression. Blue mold-sensitivity assays of tobacco lines were performed by applying P. tabacina sporangia to the upper leaf surface. Transgenic lines expressing the fused synthetic native-phyllopanin-GFP gene in the leaf apoplast showed resistance to infection. Our results demonstrate that in vivo expression of a synthetic fused native-phylloplanin-GFP gene in plants can potentially achieve natural protection against microbial plant pathogens, including P. tabacina in tobacco

Ensemble Machine Learning Model for Phishing Intrusion Detection and Classification from URLs

Phishing sounds like fishing (which means to cash fish) is a term used for an attempt to commit financial fraud on the internet. An e-mail scam is carried out on individuals or corporate organizations in an attempt to defraud them by falsely obtaining their sensitive details such as usernames, passwords, credit card information, and account numbers. For example, an email may be sent to an individual and appears with a link to click, such as “click me” showing that the recipient has won a certain amount of money, and thereafter requesting him to provide account information for verification. Unfortunately, the credentials are actually transmitted to a phisher who may exploit the person's account when the receiver sends the account details for validation. This research’s focus is to utilize different machine learning classification models to predict whether a given URL is legitimate or a phishing URL. A legitimate URL directs users to a benign authentic webpage and typically serves the user’s request. In contrast, a phishing URL directs users to a fraudulent website, usually impersonating another entity, luring visitors to believe otherwise, and eventually allowing the attacker to perform limitless post-exploitation attacks. Given the little-to-no internet safety awareness of average individuals, this paper aims to take an adaptive approach to detect phishing URLs on the client-side, which can significantly protect users from falling victims to cyber-attacks such as stealing important personal credentials. The proposed approach is to build a machine-learning powered tool that can help individuals stay safe and assist security researchers in identifying patterns and relations that correlate to these attacks, which will help maintain high-security standards for everyday internet users