Neutrino oscillations in matter offer a novel path to investigate new physics. One of the main goals of neutrino experiments is to determine the CP phase, and the presence of new physics can alter the scenario. We assume that the observed difference, if any, in the CP phase is due to the possible non-standard interactions. We derive the relevant coupling strengths using the results of NO$$\nu $$
ฮฝ
A and T2K and study their effects in the next generation of long-baseline experiments: T2HK and DUNE. Our analysis reveals a significant impact on the sensitivity of atmospheric mixing angle $$\theta _{23}$$
ฮธ
23
in the normal and inverted orderings. Furthermore, we observe discernible differences in probabilities for both experiments when non-standard interaction from $$e-\mu $$
e
-
ฮผ
and $$e-\tau $$
e
-
ฯ
sectors are included.
Neutrino oscillations in matter offer a novel path to investigate new physics. One of the main goals of neutrino experiments is to determine the CP phase and the presence of new physics can alter the scenario. We assume that the observed difference, if any, in the CP phase is due to the possible non-standard interactions. We derive the relevant coupling strengths using the simulated data sets of NO๐A and T2K and study their effects in the next generation long-baseline experiments: T2HK and DUNE. Our analysis show significant impact on the sensitivity of atmospheric mixing angle ๐ 23 in the normal as well as inverted orderings and also exhibits appreciable difference in probabilities for both the experiments with inclusion of non-standard interaction arising from ๐ โ ๐ as well as ๐ โ ๐ sectors.
Cognitive-inspired Computational Computing systems play a crucial role in designing intelligent health monitoring systems which help both patients and hospitals. It also helps in early and consistent decision-making for various health issues including human psychological health. Water fountains built in parks and public spaces are used as decorative instruments which not only give appealing visuals but also it provides a relaxing environment to the visitors. These natural sounds have a direct effect on the psychological health of visitors. Very few research works are reported on developing the relationship between water sounds and their corresponding psychological impact. This assessment needs trained manpower and a lot of experimental time which is costly and may not be always available. In this paper to access the human health-friendly water fountain sounds from the pleasantness, a Perceptually Weighted functional link artificial neural network (P-FLANN) model is developed. To reduce the computational complexity of training and for faster convergence, swam intelligence-based optimization algorithm is used for updating the weights. It is observed from the comparative simulation results that the proposed P-FLANN model can effectively perform prediction tasks which is not only cost-effective but also 95% accurate and can play a crucial role in designing human health-friendly water fountains in smart cities.
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