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The structural and magnetic properties of Ge0.5Mn0.5Co2O4 (GMCO) have been investigated in detail utilizing neutron powder diffraction (NPD), x-ray diffraction (XRD), DC magnetometry, and heat capacity analysis and compared with GeCo2O4. Despite both compounds exhibiting a cubic structure at room temperature, a substantial difference on low temperature structural properties have been observed for GMCO, indicating the effect of Mn substitution on crystal structure. The magnetic and heat capacity data reveal a ferrimagnetic ordering around 108 K in GMCO. A minor secondary phase is confirmed which undergoes long range AFM ordering at further lower temperatures. This secondary phase remains undetected in XRD due to identical lattice parameters. Furthermore, the analysis of heat capacity data indicates a broadening of the high-temperature transition, attributing to the short-range correlation persisting up to higher temperatures. The estimated magnetic entropy amounts is 63% of the value expected for GMCO. The missing entropy is likely linked with the short-range magnetic correlations persisting well above the transition temperature. Cation distribution at the A and B sites has been estimated in GMCO using NPD. Magnetic structures are also confirmed in the main phase as well as in the secondary phase using NPD analysis. The high-temperature transition corresponds to the ferrimagnetic ordering of A and B site cations in the main phase. A collinear ferrimagnetic arrangement of A and B site spins aligned parallel to c axis is observed. The average values of A and B site moments in the ferrimagnetic phase at 5 K are 2.31(3) and 1.82(3)mB, respectively, with the temperature dependence of moments following the expected power law behavior. The low-temperature ordering is attributed to the antiferromagnetic ordering of B site ions associated with the secondary phase, something similar to GeCo2O4., Comment: 16 pages, 9 figures

Cloud microphysics has important consequences for climate and weather phenomena, and inaccurate representations can limit forecast accuracy. While atmospheric models increasingly resolve storms and clouds, the accuracy of the underlying microphysics remains limited by computationally expedient bulk moment schemes based on simplifying assumptions. Droplet-based Lagrangian schemes are more accurate but are underutilized due to their large computational overhead. Machine learning (ML) based schemes can bridge this gap by learning from vast droplet-based simulation datasets, but have so far struggled to match the accuracy and stability of bulk moment schemes. To address this challenge, we developed SuperdropNet, an ML-based emulator of the Lagrangian superdroplet simulations. To improve accuracy and stability, we employ multi-step autoregressive prediction during training, impose physical constraints, and carefully control stochasticity in the training data. Superdropnet predicted hydrometeor states and cloud-to-rain transition times more accurately than previous ML emulators, and matched or outperformed bulk moment schemes in many cases. We further carried out detailed analyses to reveal how multistep autoregressive training improves performance, and how the performance of SuperdropNet and other microphysical schemes hydrometeors' mass, number and size distribution. Together our results suggest that ML models can effectively emulate cloud microphysics, in a manner consistent with droplet-based simulations.

We present a detailed investigation of the stoichiometric CeRh$_4$Al$_{15}$ single crystal compound using the temperature dependence of the heat capacity [$C_{\text{P}}$($T$)], electrical resistivity [$\rho$($T$)], magnetic susceptibility [$\chi$($T$)], and magnetization [$M$($H$)] measurements for a magnetic field ($H$) applied in the basal plane and along the $c$-axis. The low temperature power-law behavior of $C$/$T$ $\propto$ $\chi$ $\propto$ $T^{-1+\alpha}$, the isotherm magnetization, $M \sim H^{\alpha}$ with the exponent $\alpha$ = 0.45 - 0.55, and the $T$-linear resistivity $\Delta \rho$ $\sim$ $T^\epsilon$ with $\epsilon \sim$ 1 are found to be consistent with the formation of quantum Griffiths singularities in the non-Fermi-liquid (NFL) regime. We further investigated the spin dynamics of a polycrystalline sample of CeRh$_4$Al$_{15}$, using zero-field (ZF) and longitudinal-field (LF) muon spin relaxation ($\mu$SR) measurements. ZF-$\mu$SR measurements do not reveal any sign of long-range magnetic ordering down to 70~mK. The electronic relaxation rate ($\lambda$) below 0.5~K increases rapidly and shows a thermal activation-like characteristic [$T$log($\lambda$)$\sim$ $T$] over the entire measured temperature range between 70~mK to 4~K, indicating the presence of low energy spin fluctuations in CeRh$_4$Al$_{15}$. LF-$\mu$SR measurements show a time-field ($t/H^{\eta}$) scaling of the $\mu$SR asymmetry indicating a quantum critical behavior of this compound. Furthermore, inelastic neutron scattering study on the polycrystalline sample reveals two crystal field excitations near 19 and 33~meV. These features collectively provide strong evidence of NFL behavior in CeRh$_4$Al$_{15}$ due to the formation of Griffiths phase close to a $T$ $\rightarrow$ 0~K quantum critical point., Comment: 12 pages, 5 figures

Cybersecurity is very essential for Mobile Transactions to complete seamlessly. Mobile Commerce (Mcom.) is the very basic transaction type, which is very commonly used (2 in 5 people uses mobile as transaction medium), To secure this there are various technologies used by this research. The four factors formally known as Multi-Factor-Authentication are: two of them are Traditional methods (User Login-password and One Time Password (aka OTP)) with addition of Geolocation and Facial Recognition. All the data is converted to a text file, which is hidden in an image (using Babushka algorithm). The end-point then decrypts the image using same algorithm.