Evolution of superconductivity in K2-xFe4+ySe5: Spectroscopic studies of X-ray absorption and emission.

This study investigates the evolution of superconductivity in K2-xFe4+ySe5 using temperature-dependent X-ray absorption and resonant inelastic X-ray scattering techniques. Magnetization measurements show that polycrystalline superconducting (SC) K1.9Fe4.2Se5 has a critical temperature (T c) of ∼31 K with a varying superconducting volume fraction, which strongly depends on its synthesis temperature. An increase in Fe-structural/vacancy disorder in SC samples with more Fe atoms occupying vacant 4d sites is found to be closely related to the decrease in the spin magnetic moment of Fe. Moreover, the nearest-neighbor Fe-Se bond length in SC samples exceeds that in the non-SC (NS) sample, K2Fe4Se5, which indicates a weaker hybridization between the Fe 3d and Se 4p states in SC samples. These results clearly demonstrate the correlations among the local electronic and atomic structures and the magnetic properties of K2-xFe4+ySe5 superconductors, providing deeper insight into the electron pairing mechanisms of superconductivity

Discovering granger-causal features from deep learning networks

© Springer Nature Switzerland AG 2018. In this research, we propose deep networks that discover Granger causes from multivariate temporal data generated in financial markets. We introduce a Deep Neural Network (DNN) and a Recurrent Neural Network (RNN) that discover Granger-causal features for bivariate regression on bivariate time series data distributions. These features are subsequently used to discover Granger-causal graphs for multivariate regression on multivariate time series data distributions. Our supervised feature learning process in proposed deep regression networks has favourable F-tests for feature selection and t-tests for model comparisons. The experiments, minimizing root mean squared errors in the regression analysis on real stock market data obtained from Yahoo Finance, demonstrate that our causal features significantly improve the existing deep learning regression models

A novel osmosis membrane bioreactor-membrane distillation hybrid system for wastewater treatment and reuse

© 2016 . A novel approach was designed to simultaneously enhance nutrient removal and reduce membrane fouling for wastewater treatment using an attached growth biofilm (AGB) integrated with an osmosis membrane bioreactor (OsMBR) system for the first time. In this study, a highly charged organic compound (HEDTA3-) was employed as a novel draw solution in the AGB-OsMBR system to obtain a low reverse salt flux, maintain a healthy environment for the microorganisms. The AGB-OsMBR system achieved a stable water flux of 3.62 L/m2 h, high nutrient removal of 99% and less fouling during a 60-day operation. Furthermore, the high salinity of diluted draw solution could be effectively recovered by membrane distillation (MD) process with salt rejection of 99.7%. The diluted draw solution was re-concentrated to its initial status (56.1 mS/cm) at recovery of 9.8% after 6 h. The work demonstrated that novel multi-barrier systems could produce high quality potable water from impaired streams

The effect of Matmo typhoon on mixed zone between the Yellow sea and Bohai sea

The results of remote sensing, buoy and profile based on measured data indicate that the wind speed, H-1/3 and salinity increased, sea surface temperature declined, and wind direction changed greatly during the transit of the Matmo typhoon on July 25. It was found that the typhoon transport the Yellow Sea Cold Water Mass into the the Yellow and Bohai seas mixed zone

A THEORETICAL ANALYSIS ON THE MODEL OF POROUS GAS DIFFUSION ELECTRODE

A theoretical discussion on the polarization of porous gas diffusion electrode considering the flooded catalytic agglomerates covered with nonuniform liquid film is presented. Electrochemical reaction, diffusion in gaseous phase, diffusion through liquid film and diffusion in agglomerates are considered simultaneously.The performances of the electrode can be predicted as functions of measurable electrode parameters—characteristic transport currents. Analytical solutions and digital simulations are given and compared with experimental results

Repair Wind Field of Oil Spill Regional Using SAR Data

In this paper, we compared the normalized radar cross section (NRCS) of the synthetic aperture radar in the cases of oil spill and clean sea areas with image samples and determined their thresholds of the NRCS of SAR. we used the NRCS of clean water from the adjacent patches spill area to replace NRCS of oil spill area and retrieval wind field by CMOD5.N and comparison of wind velocity mending of oil spill with Model data the root mean square of wind speed and wind direction inversion are 0.89m/s and 20.26 satisfactory results, respectively. Therefore, after the occurrence not large scale oil spill, the real wind field could be restored by this method.&nbsp

Spawning rings of exceptional points out of Dirac cones

The Dirac cone underlies many unique electronic properties of graphene and topological insulators, and its band structure--two conical bands touching at a single point--has also been realized for photons in waveguide arrays, atoms in optical lattices, and through accidental degeneracy. Deformations of the Dirac cone often reveal intriguing properties; an example is the quantum Hall effect, where a constant magnetic field breaks the Dirac cone into isolated Landau levels. A seemingly unrelated phenomenon is the exceptional point, also known as the parity-time symmetry breaking point, where two resonances coincide in both their positions and widths. Exceptional points lead to counter-intuitive phenomena such as loss-induced transparency, unidirectional transmission or reflection, and lasers with reversed pump dependence or single-mode operation. These two fields of research are in fact connected: here we discover the ability of a Dirac cone to evolve into a ring of exceptional points, which we call an "exceptional ring." We experimentally demonstrate this concept in a photonic crystal slab. Angle-resolved reflection measurements of the photonic crystal slab reveal that the peaks of reflectivity follow the conical band structure of a Dirac cone from accidental degeneracy, whereas the complex eigenvalues of the system are deformed into a two-dimensional flat band enclosed by an exceptional ring. This deformation arises from the dissimilar radiation rates of dipole and quadrupole resonances, which play a role analogous to the loss and gain in parity-time symmetric systems. Our results indicate that the radiation that exists in any open system can fundamentally alter its physical properties in ways previously expected only in the presence of material loss and gain

Video Fragmentation and Reverse Search on the Web

This chapter is focused on methods and tools for video fragmentation and reverse search on the web. These technologies can assist journalists when they are dealing with fake news—which nowadays are being rapidly spread via social media platforms—that rely on the reuse of a previously posted video from a past event with the intention to mislead the viewers about a contemporary event. The fragmentation of a video into visually and temporally coherent parts and the extraction of a representative keyframe for each defined fragment enables the provision of a complete and concise keyframe-based summary of the video. Contrary to straightforward approaches that sample video frames with a constant step, the generated summary through video fragmentation and keyframe extraction is considerably more effective for discovering the video content and performing a fragment-level search for the video on the web. This chapter starts by explaining the nature and characteristics of this type of reuse-based fake news in its introductory part, and continues with an overview of existing approaches for temporal fragmentation of single-shot videos into sub-shots (the most appropriate level of temporal granularity when dealing with user-generated videos) and tools for performing reverse search of a video on the web. Subsequently, it describes two state-of-the-art methods for video sub-shot fragmentation—one relying on the assessment of the visual coherence over sequences of frames, and another one that is based on the identification of camera activity during the video recording—and presents the InVID web application that enables the fine-grained (at the fragment-level) reverse search for near-duplicates of a given video on the web. In the sequel, the chapter reports the findings of a series of experimental evaluations regarding the efficiency of the above-mentioned technologies, which indicate their competence to generate a concise and complete keyframe-based summary of the video content, and the use of this fragment-level representation for fine-grained reverse video search on the web. Finally, it draws conclusions about the effectiveness of the presented technologies and outlines our future plans for further advancing them

Deep Convolutional Neural Networks for Breast Cancer Histology Image Analysis

Breast cancer is one of the main causes of cancer death worldwide. Early diagnostics significantly increases the chances of correct treatment and survival, but this process is tedious and often leads to a disagreement between pathologists. Computer-aided diagnosis systems showed potential for improving the diagnostic accuracy. In this work, we develop the computational approach based on deep convolution neural networks for breast cancer histology image classification. Hematoxylin and eosin stained breast histology microscopy image dataset is provided as a part of the ICIAR 2018 Grand Challenge on Breast Cancer Histology Images. Our approach utilizes several deep neural network architectures and gradient boosted trees classifier. For 4-class classification task, we report 87.2% accuracy. For 2-class classification task to detect carcinomas we report 93.8% accuracy, AUC 97.3%, and sensitivity/specificity 96.5/88.0% at the high-sensitivity operating point. To our knowledge, this approach outperforms other common methods in automated histopathological image classification. The source code for our approach is made publicly available at https://github.com/alexander-rakhlin/ICIAR2018Comment: 8 pages, 4 figure

Structure of hadron resonances with a nearby zero of the amplitude

We discuss the relation between the analytic structure of the scattering amplitude and the origin of an eigenstate represented by a pole of the amplitude.If the eigenstate is not dynamically generated by the interaction in the channel of interest, the residue of the pole vanishes in the zero coupling limit. Based on the topological nature of the phase of the scattering amplitude, we show that the pole must encounter with the Castillejo-Dalitz-Dyson (CDD) zero in this limit. It is concluded that the dynamical component of the eigenstate is small if a CDD zero exists near the eigenstate pole. We show that the line shape of the resonance is distorted from the Breit-Wigner form as an observable consequence of the nearby CDD zero. Finally, studying the positions of poles and CDD zeros of the KbarN-piSigma amplitude, we discuss the origin of the eigenstates in the Lambda(1405) region.Comment: 7 pages, 3 figures, v2: published versio