Organic electronics is emerging for large-area applications such as photovoltaic cells, rollable displays or electronic paper. Its future development and integration will require a simple, low-power ...organic memory, that can be written, erased and readout electrically. Here we demonstrate a non-volatile memory in which the ferroelectric polarisation state of an organic tunnel barrier encodes the stored information and sets the readout tunnel current. We use high-sensitivity piezoresponse force microscopy to show that films as thin as one or two layers of ferroelectric poly(vinylidene fluoride) remain switchable with low voltages. Submicron junctions based on these films display tunnel electroresistance reaching 1,000% at room temperature that is driven by ferroelectric switching and explained by electrostatic effects in a direct tunnelling regime. Our findings provide a path to develop low-cost, large-scale arrays of organic ferroelectric tunnel junctions on silicon or flexible substrates.
Subsurface flow and transport problems usually involve some degree of uncertainty. Polynomial chaos expansion can be used as surrogate of physical models for uncertainty quantification. However, a ...global model can hardly be found for model responses with strong nonlinearity or irregularity. In this study, we propose a novel approach by use of the classification method in machine learning, that is, supported vector machine, to cope with such nonlinearity/irregularity. Piecewise surrogate models are constructed in relatively smooth subdomains separated by the supported vector machine hyperplanes. We demonstrate the effectiveness of using the trained piecewise surrogate model in solute transport and two‐phase flow problems in homogeneous and heterogeneous porous media. The numerical results are compared with standard global polynomial chaos expansion results and the Monte Carlo benchmark. The proposed nonintrusive approach is able to accurately quantify uncertainty, with much smaller computational efforts.
Key Points
By constructing surrogate models, computational efforts for uncertainty quantification can be significantly alleviated
Classification method in machine learning can be used to cope with strong nonlinearity/irregularity in uncertainty quantification
The piecewise surrogate model consists of a trained classifier and submodels in different categories
The Relativistic Configuration-interaction Density functional (ReCD) theory that combines the advantages of large-scale configuration-interaction shell model and relativistic density functional ...theory is extended to study nuclear chiral rotation. The energy spectra and transition probabilities of the chiral doublet bands are reproduced satisfactorily without any free parameters. By analyzing the probability amplitudes of the wavefunctions, the significant roles of configuration mixing and four quasiparticle states to the chiral doublets are revealed. The evolution from chiral vibration to static chirality is clearly illustrated by the K plot and azimuthal plot. The present investigation provides both microscopic and quantal descriptions for nuclear chirality for the first time and demonstrates the robustness of chiral geometry against the configuration mixing as well as the four quasiparticle states.
In this expository article we give an overview of some statistical methods for the monitoring of social networks. We discuss the advantages and limitations of various methods as well as some relevant ...issues. One of our primary contributions is to give the relationships between network monitoring methods and monitoring methods in engineering statistics and public health surveillance. We encourage researchers in the industrial process monitoring area to work on developing and comparing the performance of social network monitoring methods. We also discuss some of the issues in social network monitoring and give a number of research ideas.
A reflection-asymmetric triaxial particle rotor model (RAT-PRM) with a quasi-proton and a quasi-neutron coupled with a reflection-asymmetric triaxial rotor is developed and applied to investigate the ...multiple chiral doublet (MχD) bands candidates with octupole correlations in 78Br. The calculated excited energies, energy staggering parameters, and B(M1)/B(E2) ratios are in a reasonable agreement with the data of the chiral doublet bands with positive- and negative-parity. It is found that both the triaxial deformation γ and octupole deformation β3 influence the calculated B(E1) values. The chiral geometry based on the angular momenta for the rotor, the valence proton and valence neutron is discussed in details.
The ground-state properties of nuclei with 8⩽Z⩽120 from the proton drip line to the neutron drip line have been investigated using the spherical relativistic continuum Hartree–Bogoliubov (RCHB) ...theory with the relativistic density functional PC-PK1. With the effects of the continuum included, there are totally 9035 nuclei predicted to be bound, which largely extends the existing nuclear landscapes predicted with other methods. The calculated binding energies, separation energies, neutron and proton Fermi surfaces, root-mean-square (rms) radii of neutron, proton, matter, and charge distributions, ground-state spins and parities are tabulated. The extension of the nuclear landscape obtained with RCHB is discussed in detail, in particular for the neutron-rich side, in comparison with the relativistic mean field calculations without pairing correlations and also other predicted landscapes. It is found that the coupling between the bound states and the continuum due to the pairing correlations plays an essential role in extending the nuclear landscape. The systematics of the separation energies, radii, densities, potentials and pairing energies of the RCHB calculations are also discussed. In addition, the α-decay energies and proton emitters based on the RCHB calculations are investigated.
Social networks have become ubiquitous in modern society, which makes social network monitoring a research area of significant practical importance. Social network data consist of social interactions ...between pairs of individuals that are temporally aggregated over a certain interval of time, and the level of such temporal aggregation can have substantial impact on social network monitoring. There have been several studies on the effect of temporal aggregation in the process monitoring literature, but no studies on the effect of temporal aggregation in social network monitoring. We use the degree corrected stochastic block model (DCSBM) to simulate social networks and network anomalies and analyze these networks in the context of both count and binary network data. In conjunction with this model, we use the Priebe scan method as the monitoring method. We demonstrate that temporal aggregation at high levels leads to a considerable decrease in the ability to detect an anomaly within a specified time period. Moreover, converting social network communication data from counts to binary indicators can result in a significant loss of information, hindering detection performance. Aggregation at an appropriate level with count data, however, can amplify the anomalous signal generated by network anomalies and improve detection performance. Our results provide both insights on the practical effects of temporal aggregation and a framework for the study of other combinations of network models, surveillance methods, and types of anomalies.
Ultrafast optical spectroscopy is used to study the antiferromagnetic f-electron system USb(2). We observe the opening of two charge gaps at low temperatures (</~45 K), arising from renormalization ...of the electronic structure. Analysis of our data indicates that one gap is due to hybridization between localized f-electron and conduction electron bands, while band renormalization involving magnons leads to the emergence of the second gap. These experiments thus enable us to shed light on the complex electronic structure emerging at the Fermi surface in f-electron systems.
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