Magnetic domain formation in La1-xSrxMnO3 nanowires studied with resonant soft x-ray scattering

Phase separation and nanoscale fluctuation in strongly correlated systems are known to exist around their phase transitions. They are directly connected to the ordering mechanisms that cause magnetic orders, density waves, or superconductivity. These orders likely have their origins rooted in the differences in the correlation lengths of the underlying competing orders. Therefore studying materials in size that is comparable to these fluctuations can disentangle the complexity of the mechanism. To serve this purpose, we studied magnetic domain formation in La(1-x)Sr(x)MnO3 (LSMO) nanowires. In theory, a 1D ferromagnetic wire is not capable of forming a single domain without an applied field. Therefore, it is meaningful to study how the spatial confinement contributes towards magnetic domain formation. In particular, how its phase transition differs from that of the bulk, how magnetization density distributes inside the nanowires, and what the domain sizes are inside the nanowires. For this purpose, we fabricated arrays of nanowires 30nm tall, 80nm wide from LSMO thin films using e-beam lithography. Magnetization measurements performed on these wires showed an anomalous increase in the magnetization at temperatures far below the Curie point of the bulk material. Around this temperature, coexisting phase separated domains were observed with transport measurements. To understand these observations, resonant soft x-ray scattering studies were performed on Mn L-absorption-edge with an applied field and varying polarization at different temperatures. Our results suggest nontrivial magnetic domain formation inside the nanowires that may be phase separated at low temperature. In the end, we suggest a phase retrieval model to reconstruct the real space evolution of the magnetization density in nanowires to better understand the magnetic systems measured with resonant soft x-ray scattering

Prediction of Fishing Ground based on RBF Neural Network

AbstractThis study tested Radial Basis Function Neural Network (RBFN) as an intelligent method to fulfill the prediction of fishery forecasting in Southwest Atlantic on Illex argentines. Due to the existing drawback of fuzzy C-means (FCM) RBF which is time consuming, we used symmetry-based Fuzzy C-means (SFCM) to improve the effectiveness of RBF. Altogether Six marine environmental factors are considered which are months, longitude and latitude, sea surface temperature (SST), Sea surface Height (SSH) and chlorophyll for predicting the Habitat Suitability Index (HSI). The traditional calculation methods of HSI are statistical ways such as multiple linear regressions. The results obtained from the SFCM/RBF model were compared with Multiple Linear Regressions in terms of accuracy criterions MSE, RMSE. Through the prototype system, it is shown that the intelligent model has high predictive ability and better goodness of fit compared with statistical models

Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning

In this paper we demonstrate that reservoir computing can be used to learn the dynamics of the shallow-water equations. In particular, while most previous applications of reservoir computing have required training on a particular trajectory to further predict the evolution along that trajectory alone, we show the capability of reservoir computing to predict trajectories of the shallow-water equations with initial conditions not seen in the training process. However, in this setting, we find that the performance of the network deteriorates for initial conditions with ambient conditions (such as total water height and average velocity) that are different from those in the training dataset. To circumvent this deficiency, we introduce a transfer learning approach wherein a small additional training step with the relevant ambient conditions is used to improve the predictions