Unified framework for B-anomalies, muon g − 2 and neutrino masses

We present a model of radiative neutrino masses which also resolves anomalies reported in $B$-meson decays, $R_{D^{(\star)}}$ and $R_{K^{(\star)}}$, as well as in muon $g-2$ measurement, $\Delta a_\mu$. Neutrino masses arise in the model through loop diagrams involving TeV-scale leptoquark (LQ) scalars $R_2$ and $S_3$. Fits to neutrino oscillation parameters are obtained satisfying all flavor constraints which also explain the anomalies in $R_{D^{(\star)}}$, $R_{K^{(\star)}}$ and $\Delta a_\mu$ within $1\, \sigma$. An isospin-3/2 Higgs quadruplet plays a crucial role in generating neutrino masses; we point out that the doubly-charged scalar contained therein can be produced in the decays of the $S_3$ LQ, which enhances its reach to 1.1 (6.2) TeV at $\sqrt s=14$ TeV high-luminosity LHC ($\sqrt s=100$ TeV FCC-hh). We also present flavor-dependent upper limits on the Yukawa couplings of the LQs to the first two family fermions, arising from non-resonant dilepton ($pp \rightarrow \ell^+ \ell^-$) processes mediated by $t$-channel LQ exchange, which for 1 TeV LQ mass, are found to be in the range $(0.15 - 0.36)$. These limits preclude any explanation of $R_{D^{(\star)}}$ through LQ-mediated $B$-meson decays involving $\nu_e$ or $\nu_\mu$ in the final state. We also find that the same Yukawa couplings responsible for the chirally-enhanced contribution to $\Delta a_\mu$ give rise to new contributions to the SM Higgs decays to muon and tau pairs, with the modifications to the corresponding branching ratios being at (2-6)% level, which could be tested at future hadron colliders, such as HL-LHC and FCC-hh

Investigating Electronic, Optical, and Phononic Properties of Bulk γ-M<inf>2</inf>ON<inf>2</inf>and β-M<inf>7</inf>O<inf>8</inf>N<inf>4</inf>(M = Hf and Zr) Insulators Using Density Functional Theory

Hafnium and zirconium oxynitrides have similar properties, yet a consolidated investigation of their intrinsic properties has not been carried out. In this paper, we perform first-principles density functional theory calculations of γ- and β-phase hafnium and zirconium oxynitrides, which show that the γ-M2ON2(M = Hf and Zr) is an indirect band-gap (Eg) insulator, while the β-M7O8N4has a "pseudo-direct" type of Eg. β-phase has higher Egthan γ-phase, with concomitant disappearance of the conduction band tail. Optical properties in γ-M2ON2show that the anisotropy is negligible, and the optical constant values are in the range of other superhard materials. Phonon calculations present peculiar characteristics such as a small phonon band gap in γ-Hf2ON2and imaginary phonon frequencies in β-phases relating to lattice instability. The phononic properties are unfavorable for their potential use as an absorber material of the hot carrier solar cell-an emerging photovoltaic concept

RIVER ECOLOGICAL STUDY: BUILDING THE KNOWLEDGE BASE FOR VARIETY OF ASSESSMENTS SUCH AS CLIMATE CHANGE IN NEPAL

Climate change is now universally acknowledged to be taking place across the globe. It is generally presumed that the impacts of climate change would be more severe in the country like Nepal due to its location, physiography, poverty and lack of preparedness to cope with the changes. The last reason is mainly associated with knowledge, information and ability to use technologies based on science.The main objective of this research is to analyze and evaluate the effects of climate change by taking fish as an indicator. However, an even more important outcome is to prepare a solid foundation of fish-based information, which could be used in the future as a reference for a variety of purposes including the study of climate change. Two sets of examples, one in the tributaries of a glacial river and another in the tributary of a rain -fed river are compared in terms of fish ecological attributes to test for effects of climate change. In addition to fish-based information, this research also studies physico-chemical parameters and benthic fauna so as to build up an ecological profile of the rivers

Developing a scalable training model in global mental health: pilot study of a video-assisted training Program for Generalist Clinicians in Rural Nepal.

BackgroundIn low- and middle-income countries, mental health training often includes sending few generalist clinicians to specialist-led programs for several weeks. Our objective is to develop and test a video-assisted training model addressing the shortcomings of traditional programs that affect scalability: failing to train all clinicians, disrupting clinical services, and depending on specialists.MethodsWe implemented the program -video lectures and on-site skills training- for all clinicians at a rural Nepali hospital. We used Wilcoxon signed-rank tests to evaluate pre- and post-test change in knowledge (diagnostic criteria, differential diagnosis, and appropriate treatment). We used a series of 'Yes' or 'No' questions to assess attitudes about mental illness, and utilized exact McNemar's test to analyze the proportions of participants who held a specific belief before and after the training. We assessed acceptability and feasibility through key informant interviews and structured feedback.ResultsFor each topic except depression, there was a statistically significant increase (Δ) in median scores on knowledge questionnaires: Acute Stress Reaction (Δ = 20, p = 0.03), Depression (Δ = 11, p = 0.12), Grief (Δ = 40, p &lt; 0.01), Psychosis (Δ = 22, p = 0.01), and post-traumatic stress disorder (Δ = 20, p = 0.01). The training received high ratings; key informants shared examples and views about the training's positive impact and complementary nature of the program's components.ConclusionVideo lectures and on-site skills training can address the limitations of a conventional training model while being acceptable, feasible, and impactful toward improving knowledge and attitudes of the participants

Grain Yield Stability of Early Maize Genotypes

The objective of this study was to estimate grain yield stability of early maize genotypes. Five early maize genotypes namely Pool-17, Arun1EV, Arun-4, Arun-2 and Farmer's variety were evaluated using Randomized Complete Block Design along with three replications at four different locations namely Rampur, Rajahar, Pakhribas and Kabre districts of Nepal during summer seasons of three consecutive years from 2010 to 2012 under farmer's fields. Genotype and genotype × environment (GGE) biplot was used to identify superior genotype for grain yield and stability pattern. The genotypes Arun-1 EV and Arun-4 were better adapted for Kabre and Pakhribas where as pool-17 for Rajahar environments. The overall findings showed that Arun-1EV was more stable followed by Arun-2 therefore these two varieties can be recommended to farmers for cultivation in both environments

Development of an Augmented Reality Equipped Composites Bonded Assembly and Repair for Aerospace Applications

The prosperity of aircraft transportation together with revolutionary promotion of composite components used in commercial aircraft pose enormous challenges to the aircraft composite assembly and repair (especially uncertainties associated with polymer process parameters control, barely visible defects and non-destructive inspection capability to inspect zero-thickness defects). Therefore, an industry solution owning merits on reliability and repeatability of assembly process is at high demand. Augmented Reality (AR), a human computer interaction technology, possesses its exclusive superiority on its capability of inflicting digital mock-up into physical environment. The above property of AR provides colossal opportunities to be utilised into industrial applications to contribute the realisation of automated, efficient, streamlined and reliable process and assembly. The current ongoing research aims at developing an AR System integrated into aircraft composite bonded assembly and repair as a guidance tool to instruct technicians’ repairing operation, mitigate human errors, and reduce duration of repair and assembly. Upon the accomplishment of the System, the researchers would aim to investigate the incorporation of machine learning and deep network algorithms to enable and significantly improve the interactions between the multitude of process parameters involved in the composites assembly control procedures, solely relying upon the AR geometric data. This will ultimately lead to dramatic reduction of sensors in aircraft assembly, mitigation of in-process analysis time, reduction of process and post-process inspection time, and a higher quality assembly. Stepped scarf composite repair embedded with soft composite patches was selected as the archetype to be brought into effect though hard patches were partially examined as well. The AR System has focused on composite patches assembly and vacuum bagging process to address the predicament of miscellaneous steps and fibre directions