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In this study, we analyze the influence of Non-Standard Interaction (NSI) on steering in three-flavor neutrino oscillations, with a focus on the NO$\nu$A and DUNE experimental setups. DUNE, having a longer baseline, exhibits a more pronounced deviation towards NSI in steering compared to NO$\nu$A. Within the energy range where DUNE's maximum flux appears, the steering value for DUNE shows a $21\%$ deviation from the Standard Model (SM) to NSI for normal ordering (NO), while for inverted ordering (IO), the steering value increases by approximately $15\%$ relative to the SM. We conduct a comparative analysis of nonlocality, steering, and entanglement. Additionally, we express steering in terms of three-flavor neutrino oscillation probabilities and explore the relationship between steering inequality and concurrence., Comment: 8 pages, 2 figures

In this study, we investigate the impact of new physics on different measures of tripartite entanglement within the context of three-flavor neutrino oscillations. These measures encompass concurrence, entanglement of formation, and negativity. We analyze the influence of new physics on these measures across a range of experimental setups involving both reactors and accelerators. Reactor experiments under consideration include Daya Bay, JUNO, and KamLAND setups, while accelerator experiments encompass T2K, MINOS, and DUNE. Our analysis reveals that accelerator experiments demonstrate greater sensitivity to NSI, with the most pronounced impact observed in the DUNE experiment. Negativity, while a weaker metric compared to EOF and concurrence, exhibits maximal sensitivity to NSI effects, particularly evident when neutrinos possess moderate to high energies. Conversely, reactor experiments demonstrate less sensitivity to NSI, with concurrence and EOF displaying more prominent effects.

The recent anomaly observed in NO$\nu$A and T2K experiments, in standard three-flavor neutrino oscillation could potentially signal physics extending beyond the standard model (SM). For the NSI parameters that can accommodate this anomaly, we explore the violation of Leggett-Garg type inequalities (LGtI) within the context of three-flavor neutrino oscillations. Our analysis focuses on LGtI violations in scenarios involving complex NSI with $\epsilon_{e\mu}$ or $\epsilon_{e\tau}$ coupling in long baseline accelerator experiments for normal and inverted mass ordering. LGtI violation is significantly enhanced in normal ordering (NO) for $\epsilon_{e\tau}$ scenario, whereas it suppresses for $\epsilon_{e\mu}$ scenario for T2K, NO$\nu$A, and DUNE experiment set-up. We find that for inverted ordering (IO), in the DUNE experimental set-up above $6$ GeV, the LGtI violation can be an indication of $\epsilon_{e\tau}$ new physics scenario., Comment: 6 pages, 1 figure

Astigmatic Defocus Sensor is a very simple and easy technique for measuring the amount of defocus (deviation from the focal plane) present in an optical beam. The approach uses a cylindrical lens to focus the beam and the measure of the intensity at the focal plane is used to find out the amount of defocus present in the beam. In this paper, we provide a theoretical discussion, on the Astigmatic Defocus Sensor, based on Fourier optics. The theoretical discussion is validated through simulation results. Two ways to optimize the performance of the astigmatic defocus sensor were also established. Further, the process of implementing the Astigmatic Defocus Sensor using computer-generated hologram is presented.

Skillful seasonal prediction of lightning is crucial over several global hotspot regions, as it causes severe damages to infrastructures and losses of human life. While major emphasis has been given for predicting rainfall, prediction of lightning in one season advance remained uncommon, owing to the nature of the problem, which is short-lived local phenomenon. Here we show that on the seasonal time scale, lightning over the major global hot-spot regions is strongly tied with slowly varying global predictors (e.g., El Nino and Southern Oscillation). Moreover, the sub-seasonal variance of lightning is highly correlated with global predictors, suggesting a seminal role played by the global climate mode in shaping the local land-atmosphere interactions, which eventually affects seasonal lightning variability. It is shown that the seasonal predictability of lightning over the hotspot is comparable to that of seasonal rainfall, which opens up an avenue for reliable seasonal forecasting of lightning for special awareness and preventive measures. Keywords: Lightning, Seasonal forecasting, SST, Global predictors

Skilful prediction of the seasonal Indian summer monsoon (ISM) rainfall (ISMR) at least one season in advance has great socio-economic value. It represents a lifeline for about a sixth of the world's population. The ISMR prediction remained a challenging problem with the sub-critical skills of the dynamical models attributable to limited understanding of the interaction among clouds, convection, and circulation. The variability of cloud hydrometeors (cloud ice and cloud water) in different time scales (3-7 days, 10-20 days and 30-60 days bands) are examined from re-analysis data during Indian summer monsoon (ISM). Here, we also show that the 'internal' variability of cloud hydrometeors (particularly cloud ice) associated with the ISM sub-seasonal (synoptic + intra-seasonal) fluctuations is partly predictable as they are found to be tied with slowly varying forcing (e.g., El Ni\~no and Southern Oscillation). The representation of deep convective clouds, which involve ice phase processes in a coupled climate model, strongly modulates ISMR variability in association with global predictors. The results from the two sensitivity simulations using coupled global climate model (CGCM) are provided to demonstrate the importance of the cloud hydrometeors on ISM rainfall predictability. Therefore, this study provides a scientific basis for improving the simulation of the seasonal ISMR by improving the physical processes of the cloud on a sub-seasonal time scale and motivating further research in this direction., Comment: 36 Pages, 14 figures

In the backdrop of a revolution in weather prediction by Numerical Weather Prediction (NWP) models, quantitative prediction of intensity of heavy rainfall events and associated disasters has remained a challenge. Encouraged by compelling evidence of electrical influences on cloud/rain microphysical processes, here we propose a hypothesis that modification of raindrop size distribution (RDSD) towards larger drop sizes through enhanced collision-coalescence facilitated by cloud electric fields could be one of the factors responsible for intensity errors in weather/climate models. The robustness of the hypothesis is confirmed through a series of simulations of strongly electrified (SE) rain events and weakly electrified (WE) events with a convection-permitting weather prediction model incorporating the electrically modified RDSD parameters in the model physics. Our results indicate a possible roadmap for improving hazard prediction associated with extreme rainfall events in weather prediction models and climatological dry bias of precipitation simulation in many climate models.

In this paper we propose and experimentally demonstrate information transfer through free-space using a laser beam encoded with multiple orthogonal aberration modes in its phase profile. We use Zernike polynomials which forms a complete set of basis functions to represent the aberration modes. The user information is encoded as amplitudes of multiple Zernike modes whose linear combination is used to define the wavefront of the laser beam in the transmission station. It therefore requires a single phase modulating device since the multiplexing is done digitally providing flexibility over the number and type of modes used. The receiving station uses a wavefront sensor that gives a direct measure of all the aberration modes from a single measurement which is decoded to retrieve the user information. The transmission mechanism enables the use of multiple modes and multiple strengths of each mode to encode the user information and external perturbation compensation, utilizing the completeness of the orthogonal modes.

We explore the influence of demagnetization interaction on magnetic memory effect by varying organization geometry of anisotropic ZnFe$_2$O$_4$ nanoparticles in an ensemble. The static and dynamic behaviour of two differently organized ensembles, compact ensemble (CE) and hollow core ensemble (HCE), are extensively studied by both dc and ac susceptibility, magnetic memory effect and spin relaxation. The frequency-dependence peak shifting of freezing temperature in both the systems is analyzed properly with the help of two dynamic scaling models: Vogel-Fulcher law and power law. Presence of cluster spin-glass phase is reflected from Vogel-Fulcher temperature $T_0$ $\simeq$ 142.58 K for CE, $\simeq$ 97 K for HCE and characteristic time constant $\tau_0$ $\simeq$ $8.85\times10^{-9}$ s for CE, $\simeq$ $3.8\times10^{-10}$ s for HCE; along with $\delta$T$_{Th}$ $\sim$ 0.1 for CE and 0.2 for HCE. The power law fitting with dynamic exponent $zv'$ = 6.2 $\pm$ 1.1 for CE, 6.3 $\pm$ 0.5 for HCE and single spin flip $\tau^*$ $\simeq$ $7.7\times10^{-11}$ s for CE, $\simeq$ $1.3\times10^{-10}$ s for HCE provide firm confirmation of cluster spin-glass phase. The progressive spin freezing across multiple metastable states with prominent memory effects is reflected in both the systems via nonequilibrium dynamics study. The hollow core geometry with anisotropic nanoparticles on surface with closer proximity leads to complex anisotropy energy landscape with enhanced demagnetizing field resulting highly frustrated surface spins. As a consequence, more prominent magnetic memory effect is observed in HCE with higher activation energy, reduced blocking temperature and enhanced coercivity than that of CE., Comment: 13 pages, 10 figures

CA-Polar codes have been selected for all control channel communications in 5G NR, but accurate, computationally feasible decoders are still subject to development. Here we report the performance of a recently proposed class of optimally precise Maximum Likelihood (ML) decoders, GRAND, that can be used with any block-code. As published theoretical results indicate that GRAND is computationally efficient for short-length, high-rate codes and 5G CA-Polar codes are in that class, here we consider GRAND's utility for decoding them. Simulation results indicate that decoding of 5G CA-Polar codes by GRAND, and a simple soft detection variant, is a practical possibility.

READ transactions that read data distributed across servers dominate the workloads of real-world distributed storage systems. The SNOW Theorem stated that ideal READ transactions that have optimal latency and the strongest guarantees, i.e. "SNOW" READ transactions, are impossible in one specific setting that requires three or more clients: at least two readers and one writer. However, it left many open questions. We close all of these open questions with new impossibility results and new algorithms. First, we prove rigorously the result from the The SNOW Theorem paper saying that it is impossible to have a READ transactions system that satisfies SNOW properties with three or more clients. The insight we gained from this proof led to teasing out the implicit assumptions that are required to state the results and also, resolving the open question regarding the possibility of SNOW with two clients. We show that it is possible to design an algorithm, where SNOW is possible in a multi-writer, single-reader (MWSR) setting when a client can send messages to other clients; on the other hand, we prove it is impossible to implement SNOW in a multi-writer, single-reader (MWSR) setting, which is more general than the two-client setting, when client-to-client communication is disallowed. We also correct the previous claim in The SNOW Theorem paper that incorrectly identified one existing system, Eiger, as supporting the strongest guarantees (SW) and whose read-only transactions had bounded latency. Thus, there were no previous algorithms that provided the strongest guarantees and had bounded latency. Finally, we introduce the first two algorithms to provide the strongest guarantees with bounded latency.