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A sensitivity increase of two orders of magnitude in proton (1H) and carbon (13C) spins via dynamic nuclear polarization (DNP) has been accomplished recently using a compact benchtop DNP polarizer operating at 1 T and 77 K. However the DNP mechanisms at play at such low magnetic field and high operating temperature are still not elucidated. A deeper understanding of the dominant polarization transfer mechanisms between electrons and 1H and 13C spins at these unconventional benchtop conditions is therefore required if one wants to devise strategies to boost sensitivity further. In this study, we found that DNP is generally dominated by solid effect for narrow electron paramagnetic resonance (EPR) line radicals (15 mM trityl OX063) and cross effect for broad EPR line radicals (50 mM TEMPOL). For both radicals, the dominant DNP mechanisms were investigated varying the microwave frequency and measuring the 1H and 13C DNP enhancement factors to obtain 1H and 13C DNP spectra. The impact of varying the microwave power on the 1H DNP buildup times and the 1H nuclear spin relaxation times were important as well to distinguish between solid effect and cross effect DNP. Finally, time-resolved electron saturation simulations under continuous microwave irradiation could replicate the experimental 1H and 13C DNP spectra at 1 T and 77 K for both radicals considering their electron relaxation properties. Only for trityl OX063, the 13C DNP spectra showed additional DNP maxima compared to the simulations. This has been attributed to methyl rotor induced 1H-13C heteronuclear cross relaxation in [1-13C] acetate present at 1 T and 77 K., Comment: Main text, 15 pages, 6 figures. Supporting information, 7 pages, 5 figures

In this work, coalescence of phase-space holes of collision-less, one-dimensional plasmas is studied using kinetic simulation techniques. Phase-space holes are well-known Bernstein-Greene-Kruskal waves known for exhibiting coalescence, are numerically simulated and their coalescence is observed. Relations between the hole speed, potential, phase-space vorticity and phase-space depth are then obtained using the simulation data. This study involves the study of electron phase-space hole coalescence in a cylindrically wave-guided plasma. Using the recently developed phase-space hydrodynamic analogy, it is shown that the coalescence phenomena can be explored in terms of the fluid-analogous vortical nature of the phase-space holes. Coalescence occurs due to the interaction of the phase-space velocity fields associated with these phase-space vortices. Results obtained from the study describes various parametric relations between the coalesced hole characteristics and the characteristics of the colliding holes.

In this work, the recently introduced fluid-like treatment of the phase-space has been further extended and some interesting outcomes have been presented. A modified form of the Vlasov equation has been presented which describes the diffusion of the phase-space density. This anisotropic diffusion is analysed and the flow of the phase-space probability field is shown. Growth of phase-space vortices is then shown due to increased turbulent-like flow, which is marked by the dominating inertial flow above the diffusive flow. The nature of this flow is judged by using a parameter for the phase-space. It is then shown that the formation of phase-space vortices is due to growth of turbulent-like flow in the phase-space. On the bases of the diffusion parameters, the vorticity field transport of the hydrodynamic phase-space is studied and a Schamel-KdV form of the vorticity transport equation is derived, suggesting solitary modes of the phase-space vorticity waves.

In recent years, solid state magnetic cooling based on magnetocaloric effect (MCE) have drawn attention worldwide as a promising alternative potential candidate to the conventional gas compression-expansion cooling technique. In this chapter, the current developments of MCE in pyrochlore oxide materials is presented. The interaction between magnetic frustration, strong spin-orbit coupling, electronic correlations and crystal electric field lead to various complex magnetic states in pyrochlore oxides gives a broad template to explore these materials in MCE point of view. Pyrochlore oxides show strong frustration due to its crystal structure. Thus, the study of MCE in pyrochlore oxides could be fascinating not only from the an applications point of view but as well as from fundamental point of view. Among all the compounds, cubic pyrochlore oxide ErMnO exhibit largest conventional MCE value at change of field 5 T. On the other hand, YIrCrO series show coexistence of conventional MCE and inverse MCE which is absent in whole reported family of pyrochlore oxides. It is worth mention that depending on synthesis condition A2Mn2O7 series crystallizes in either cubic pyrochlore phase (Fd-3m) or monoclinic layered perovskite (P2M). It suggests the need of more materials to be explore experimentally and theoretically to establish the connection between these two phases.

Lead chalcogenides are the promising thermoelectric (TE) materials having narrow band gap. The present work investigates the TE behaviour of PbSe in the temperature range 300-500 K. The transport properties of the sample have been studied using the Abinit and BoltzTrap code. The experimentally observed value of \textit{S} at 300 and 500 K is found to be $\sim$ 198 and 266 $\mu$V K$^{-1}$, respectively. The rate of increase in \emph{S} from 300 to 460 (460 to 500) K is found to be $\sim$ 0.4 (0.09). The temperature dependent electrical conductivity \textit{($\sigma$)} shows the increasing trend, with values of $\sim $ 0.35 $\times $ 10$^{3}$ and $\sim$ 0.58 $\times$ 10$^{3}$ $\Omega$$^{-1}$ m$^{-1}$ at 300 and 500 K, respectively. Further, the value of thermal conductivity \textit{($\kappa$)} at 300 (500) K is found to be 0.74 (1.07) W m$^{-1}$ K$^{-1}$. The value of \textit{$\kappa$} is found to be increasing upto 460 K and then starts decreasing. The dispersion plot indicates that PbSe is a direct band gap semiconductor with band gap value of 0.16 (0.27) eV considering spin-orbit coupling (without SOC). The partial density of states (PDOS) plot shows that Pb 6p and Se 4p states have a major contribution in the transport properties. The observed and calculated values of \textit{S} gives a good match for SOC case. The calculated \textit{$\sigma$} and electronic part of thermal conductivity (\textit{$\kappa{_e}$}) gives good match with the experimental data. The maximum power factor (PF) value of $\sim$ 4.3 $\times$ 10$^{-5}$ W/mK$^{2}$ is observed at 500 K. This work helps in understanding the TE behaviour of PbSe through a novel and insightful alliance of experimental measurements and DFT approach.

In this research, an effort is made to address microgrid systems' operational challenges, characterized by power oscillations that eventually contribute to grid instability. An integrated strategy is proposed, leveraging the strengths of convolutional and Gated Recurrent Unit (GRU) layers. This approach is aimed at effectively extracting temporal data from energy datasets to improve the precision of microgrid behavior forecasts. Additionally, an attention layer is employed to underscore significant features within the time-series data, optimizing the forecasting process. The framework is anchored by a Multi-Layer Perceptron (MLP) model, which is tasked with comprehensive load forecasting and the identification of abnormal grid behaviors. Our methodology underwent rigorous evaluation using the Micro-grid Tariff Assessment Tool dataset, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (r2-score) serving as the primary metrics. The approach demonstrated exemplary performance, evidenced by a MAE of 0.39, RMSE of 0.28, and an r2-score of 98.89\% in load forecasting, along with near-perfect zero state prediction accuracy (approximately 99.9\%). Significantly outperforming conventional machine learning models such as support vector regression and random forest regression, our model's streamlined architecture is particularly suitable for real-time applications, thereby facilitating more effective and reliable microgrid management., Comment: 2024 11th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE)

The kinetic analyses of many-particle soft matter often employ many simulation studies of various physical phenomena which supplement the experimental limitations or compliment the theoretical findings of the study. Such simulations are generally conducted by the numerical integration techniques of the governing equations. In the typical case of collisionless electrostatic systems such as electrostatic plasmas, the Vlasov-Poisson (VP) equation system governs the dynamical evolution of the particle phase-space. The one-dimensional position-velocity (1D-1V) particle phase-space, on the other hand, is known to exhibit direct analogy with ordinary two-dimensional fluids, wherein the Vlasov equation resembles the fluid continuity equation of an in-compressible fluid. On the basis of this fluid-analogy, we present, in this work, a new numerical integration scheme which treats the 1D-1V phase-space as a two-dimensional fluid vector space. We then perform and present analyses of numerical accuracy of this scheme and compare its speed and accuracy with the well-known finite splitting scheme, which is a standardised technique for the numerical Vlasov-Poisson integration. Finally, we show some simulation results of the 1D collisionless electrostatic plasma which highlight the higher speed and accuracy of the new scheme. This work presents a fast and sufficiently accurate numerical integration technique of the VP system which can be directly employed in various simulation studies of many particle systems, including plasmas.

While machine learning algorithms hold promise for personalised medicine, their clinical adoption remains limited, partly due to biases that can compromise the reliability of predictions. In this paper, we focus on sample selection bias (SSB), a specific type of bias where the study population is less representative of the target population, leading to biased and potentially harmful decisions. Despite being well-known in the literature, SSB remains scarcely studied in machine learning for healthcare. Moreover, the existing machine learning techniques try to correct the bias mostly by balancing distributions between the study and the target populations, which may result in a loss of predictive performance. To address these problems, our study illustrates the potential risks associated with SSB by examining SSB's impact on the performance of machine learning algorithms. Most importantly, we propose a new research direction for addressing SSB, based on the target population identification rather than the bias correction. Specifically, we propose two independent networks(T-Net) and a multitasking network (MT-Net) for addressing SSB, where one network/task identifies the target subpopulation which is representative of the study population and the second makes predictions for the identified subpopulation. Our empirical results with synthetic and semi-synthetic datasets highlight that SSB can lead to a large drop in the performance of an algorithm for the target population as compared with the study population, as well as a substantial difference in the performance for the target subpopulations that are representative of the selected and the non-selected patients from the study population. Furthermore, our proposed techniques demonstrate robustness across various settings, including different dataset sizes, event rates, and selection rates, outperforming the existing bias correction techniques., Comment: 21 pages and 11 figures (under review)

We have explored a transitioning cosmic model, depicting late-time accelerated expansion in $f(R,T^{\phi})$ theory of gravity for an isotropic and homogeneous universe, where the trace of energy-momentum tensor $T^{\phi}$ is the function of the self-interacting scalar field $\phi$. We have proposed an explicit solution to the derived model by utilizing a scale factor of the hybrid form $a(t) = t^{\alpha} e^{\beta t}$, where $\alpha$ and $\beta$ are constants. To evaluate the best-fit values of free parameters of the suggested model, the statistical analysis based on the Markov Chain Monte Carlo (MCMC) method has been employed on 57 OHD points. We have described the dynamical features of the model like energy density, cosmic pressure, and equation of state parameter in the context of scalar field $\phi$. We have also described the potential and behavior of the scalar field for quintessence and phantom scenarios. The deceleration parameter depicts a transitioning universe with signature flipping at $z_t = 0.82$ with the present value of deceleration parameter $q_0=-0.41$. The violation of SEC for the derived model indicates the cosmic expansion at a faster rate. We have used statefinders to diagnose the model. The findings for our theoretical model indicate that the derived model agrees with observed findings within a particular range of limitations., Comment: 18 pages, 13 figures

Graph Neural Networks (GNN) have emerged as a popular and standard approach for learning from graph-structured data. The literature on GNN highlights the potential of this evolving research area and its widespread adoption in real-life applications. However, most of the approaches are either new in concept or derived from specific techniques. Therefore, the potential of more than one approach in hybrid form has not been studied extensively, which can be well utilized for sequenced data or static data together. We derive a hybrid approach based on two established techniques as generalized aggregation networks and topology adaptive graph convolution networks that solve our purpose to apply on both types of sequenced and static nature of data, effectively. The proposed method applies to both node and graph classification. Our empirical analysis reveals that the results are at par with literature results and better for handwritten strokes as sequenced data, where graph structures have not been explored., Comment: 2 figures, 3 tables and 26 pages