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Composites of ferromagnetic and ferroelectric phases are of interest for studies on mechanical strain-mediated coupling between the two phases and for a variety of applications in sensors, energy harvesting, and high-frequency devices. Nanocomposites are of particular importance since their surface area-to-volume ratio, a key factor that determines the strength of magneto-electric (ME) coupling, is much higher than for bulk or thin-film composites. Core–shell nano- and microcomposites of the ferroic phases are the preferred structures, since they are free of any clamping due to substrates that are present in nanobilayers or nanopillars on a substrate. This review concerns recent efforts on ME coupling in coaxial fibers of spinel or hexagonal ferrites for the magnetic phase and PZT or barium titanate for the ferroelectric phase. Several recent studies on the synthesis and ME measurements of fibers with nickel ferrite, nickel zinc ferrite, or cobalt ferrite for the spinel ferrite and M-, Y-, and W-types for the hexagonal ferrites were considered. Fibers synthesized by electrospinning were found to be free of impurity phases and had uniform core and shell structures. Piezo force microscopy (PFM) and scanning microwave microscopy (SMM) measurements of strengths of direct and converse ME effects on individual fibers showed evidence for strong coupling. Results of low-frequency ME voltage coefficient and magneto-dielectric effects on 2D and 3D films of the fibers assembled in a magnetic field, however, were indicative of ME couplings that were weaker than in bulk or thick-film composites. A strong ME interaction was only evident from data on magnetic field-induced variations in the remnant ferroelectric polarization in the discs of the fibers. Follow-up efforts aimed at further enhancement in the strengths of ME coupling in core–shell composites are also discussed in this review. 

There are many studies of approximations using deep neural networks. In this paper, the authors provide yet another proof that deep neural networks are universal approximators. In their earlier work, the authors showed that an arbitrary binary target function can be effectively rewritten in terms of a set of strings, or a set of subsets, and that a single hidden neuron can identify and only identify a single string or a single subset. Therefore, an arbitrary binary target function can be effectively rewritten in the form of a neural network with one hidden layer. In this study, the authors imposed locality on the deep neural network, and will show here that an arbitrary binary target function can be effectively rewritten in the form of a locally connected deep neural network that can have many hidden layers. Although it will increase the neural network size, as a neural network is localized, it will generally increase the speed of training for large networks 

This study developed a hybrid model for predicting dissolved oxygen (DO) using real-time sensor data for thirteen parameters. This novel hybrid model integrated one-dimensional convolutional neural networks (CNN) and long short-term memory (LSTM) to improve the accuracy of prediction for DO in water. The hybrid CNNLSTM model predicted DO concentration in water using soft sensor data. The primary input parameters to the model were temperature, pH, specific conductivity, salinity, density, chlorophyll, and blue-green algae. The model used 38,681 water quality data for training and testing the hybrid deep learning network. The training procedure for the model was successful. The training and test losses were both nearly zero and within a similar range. With a coefficient of determination (R2) of 0.94 and a mean squared error (MSE) of 0.12, the hybrid model indicated higher performance compared to the classical models. The normal distribution of residual errors confirmed the reliability of the DO predictions by the hybrid CNN-LSTM model. Feature importance analysis indicated pH as the most significant predictor and temperature as the second important predictor. The feature importance scores based on extreme gradient boosting (XGBoost) for the pH and temperature were 0.76 and 0.12, respectively. This study indicated that the hybrid model can outperform the classical machine learning models in the real-time prediction of DO concentration. 

Symmetry properties of the order parameter are among the most fundamental characteristics of a superconductor. UTe₂, which was found to feature an exceedingly large upper critical field and striking reentrant behavior at low temperatures, is widely believed to possess a spin-triplet pairing symmetry. However, unambiguous evidence for such a pairing symmetry is still lacking, especially at zero and low magnetic fields. The presence of an inversion crystalline symmetry in UTe₂requires that, if it is indeed a spin-triplet superconductor, the order parameter must be of odd parity. We report here phase-sensitive measurements of the symmetry of the orbital part of the order parameter using the Josephson effect. The selection rule in the orientation dependence of the Josephson coupling between In, ans-wave superconductor, and UTe₂suggests strongly that UTe₂possesses the odd-parity pairing state of B₁usymmetry near zero magnetic field, making it a spin-triplet superconductor. We also report the apparent formation of Andreev surface bound states on the (1−10) surface of UTe₂. 

Abstract We report the growth of InSe films on semi-insulating GaAs(111)B substrates by molecular beam epitaxy (MBE). Excellent nucleation behavior resulted in the growth of smooth, single-phase InSe films. The dominant polytype was the targeted γ-InSe. Transmission electron microscopy revealed the presence of three bulk polytypes β, γ, and ε-InSe arranged in nanosized domains, which can be interpreted as sequences of stacking faults and rotational twin boundaries of γ-InSe. Additionally, a centrosymmetric Se-In-In-Se layer polymorph with$$P\bar{3}m$$ $P\bar{3}m$symmetry was identified as typically not present in bulk. Sizeable differences in their electronic properties were found, which resulted in sizeable electronic disorder arising from the nanoscale polytype arrangement that dominated the electronic transport properties. While MBE is a viable synthesis route towards stabilization of InSe polytypes not present in the bulk, an improved understanding to form the targeted polymorph is required to ultimately inscribe a layer sequence on demand utilizing bottom-up synthesis approaches.