T7T_7 Flavor Symmetry gym: The Key to Unlocking the Neutrino Mass Puzzle

Recent research has indicated that the Standard Model (SM), while historically highly effective, is found to be insufficient due to its prediction of zero mass for neutrinos. With the exception of a few, the majority of the parameters related to neutrinos have been determined by neutrino oscillation experiments with excellent precision. Experiments on neutrino oscillation and neutrino mixing have shown that neutrinos are massive. To fill in gaps, discrete symmetries are becoming more common alongside continuous symmetries while describing the observed pattern of neutrino mixing. Here, we present a $T_7$ flavor symmetry to explain the masses of charged leptons and neutrinos. The light neutrino mass matrix is derived using seesaw mechanism of type I, which involves the Dirac neutrino mass matrix as well as the right-handed neutrino mass matrix. We estimate the Pontecorvo-Maki-Nakagawa-Sakata matrix ($U_{PMNS}$), three mixing angles, $\theta_{12}$, $\theta_{23}$ and $\theta_{13}$, which are strongly correlated with the recent experimental results. The extent of $CP$ violation in neutrino oscillations is obtained by calculating Jarskog invariant $(J_{CP})$ on the behalf of $U_{PMNS}$. We also find the masses of three neutrinos and Effective Majorana neutrino mass parameter $\langle m_{ee} \rangle$ which is $1.0960$ $meV$ and $10.9217$ $meV$ for normal and inverted hierarchy, respectively.Comment: 23 Page

On the Parameter of the Burr Type X under Bayesian Principles

A comprehensive Bayesian analysis has been carried out in the context of informative and non-informative priors for the shape parameter of the Burr type X distribution under different symmetric and asymmetric loss functions. Elicitation of hyperparameter through prior predictive approach is also discussed. Also we derive the expression for posterior predictive distributions, predictive intervals and the credible Intervals. As an illustration, comparisons of these estimators are made through simulation study

17-(Pyrimidin-2-yl)-8,16-dioxa-17-aza­tetra­cyclo­[7.7.1.02,7.010,15]hepta­deca-2,4,6,10,12,14-hexa­ene

In the title compound, C18H13N3O2, the benzene rings form a dihedral angle of 78.49 (9)°. The dihedral angles between the benzene rings and the pyrimidine ring are 76.53 (10) and 27.73 (11)°. The two cis-fused six-membered heterocyclic rings adopt half-chair confirmations. In the crystal, mol­ecules are linked by C—H⋯O hydrogen bonds, forming chains parallel to the b axis

{2-[(3-Bromo­benzyl­idene)amino]-5-chloro­phen­yl}(phen­yl)methanone

In the title compound, C20H13BrClNO, the azomethine double bond [C=N = 1.246 (4) Å] adopts an E conformation. The bromo- and chlorophenyl rings are inclined to one another by 13.70 (11)°, and form dihedral angles of 76.68 (10) and 74.24 (7)°, respectively, with the phenyl ring. In the crystal, mol­ecules are linked by C—H⋯O hydrogen bonds to form double stranded chains propagating along the b-axis direction

Diagnosis and monitoring of Alzheimer's patients using classical and deep learning techniques

Machine based analysis and prediction systems are widely used for diagnosis of Alzheimer's Disease (AD). However, lower accuracy of existing techniques and lack of post diagnosis monitoring systems limit the scope of such studies. In this paper, a novel machine learning based diagnosis and monitoring of AD-like diseases is proposed. The AD-like diseases diagnosis process is accomplished by analysing the magnetic resonance imaging (MRI) scans using deep learning and is followed by an activity monitoring framework to monitor the subjects’ activities of daily living using body worn inertial sensors. The activity monitoring provides an assistive framework in daily life activities and evaluates vulnerability of the patients based on the activity level. The AD diagnosis results show up to 82% improvement in comparison to well-known existing techniques. Moreover, above 95% accuracy is achieved to classify the activities of daily living which is quite encouraging in terms of monitoring the activity profile of the subject

Telehealth technology: Potentials, challenges and research directions for developing countries

Telehealth has been developed and successfully applied in clinical practices, gained a strong interest and demonstrated its usefulness for medical diagnosis, treatments and rehabilitation worldwide. The advent of high speed communication technology and complex signal processing techniques, and recent advancements in cloud and cognitive computing, has created a new wave of opportunities for delivering remote healthcare applications and services, where the cost-effective diagnosis and treatment solutions as well as healthcare services are important and need to be deployed widely. Nevertheless, there is still a significant challenge in fully adopting this technology due to asymmetry among the healthcare centers, hospitals and the user-ends, especially in developing countries. This paper provides an overview of the telehealth, then to addresses the possible telehealth technologies and applications that could be applied to improve the healthcare service performance, with the focus on the developing countries. The incorporation of different technologies in telehealth including, Internet of Things (IoT), cloud and cognitive computing, medical image processing and effective encoding is introduced and discussed. Finally, the possible research directions, challenges for the efficient telehealth, and potential research and technology collaborations are outlined