bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of carbon nanotexture on the synthesis, initial growth mechanism and photoluminescence properties of SiC nanowires
Authors: Xie Xiangmin , Su Zhe’an , Huang Dong , Yang Cheng , Wang Yafeng , He Kejian , Huang Qizhong 
Published: November 2020

Abstract
Although the spontaneous vapor–solid growth of SiC nanowires is a well-established phenomenon, the exact mechanism by which nanowires grow on substrates is still poorly understood. Here, we studied the initial growth of SiC nanowires on carbon sources with different nanotextures via a catalyst-free vapor reaction between a polyacrylonitrile-based carbon fiber and a silicon powder. The results revealed that the SiC nanowires were preferentially formed on the carbon fiber with a higher degree of graphitization. Detailed analyses suggested that the growth behavior of the underlying SiC film formed on the carbon fibers, which is strongly affected by the microstructures of the carbon fibers, plays an important role in the formation of nanowires. In addition, the photoluminescence spectrum of SiC nanowires showed strong ultraviolet–visible emission peaks at an excitation wavelength of 250 nm, which may provide potential applications in the field of optoelectronic devices.
A nonexistence result for anisotropic problems
*
Authors: Le Phuong , Le Kim Anh T , Dinh Phuoc Vinh 
Published: November 2020

Abstract
We study the anisotropic problem

−

∑

i
=
1

N

∂

∂

x

i

∂
u

∂

x

i

p

i

−
2

∂
u

∂

x

i

=
w
f

(

u

)

in

Ω
,

u
=
0

on

∂
Ω
,

where p

i
⩾ 2, Ω is a domain of

R

N

, w is a nonnegative function and f is an increasing function. We prove that the problem has no nontrivial stable solution under various assumptions on p

i
, Ω, w and f.
Ab initio theory of the impact of grain boundaries and substitutional defects on superconducting Nb3Sn
Authors: Kelley Michelle M , Sitaraman Nathan S , Arias Tomás A 
Published: November 2020

Abstract
Grain boundaries play a critical role in superconducting applications of Nb3Sn: in dc applications, grain boundaries preserve the material’s inherently high critical current density by pinning flux, while in ac applications grain boundaries can provide weak points for flux entry leading to significant dissipation. We present the first ab initio study to investigate the physics of different grain boundary types in Nb3Sn and their impact on superconductivity using density-functional theory. We identify an energetically favorable selection of high-angle tilt and twist grain boundaries of distinct orientations. We find that clean grain boundaries free of point defects reduce the Fermi-level density of states by a factor of two, an effect that decays back to the bulk electronic structure ∼1–1.5 nm from the boundary. We further calculate the binding free-energies of tin substitutional defects to multiple boundaries, finding a strong electronic interaction that extends to a distance comparable to that of the reduction of density of states. Associated with this interaction, we discover a universal trend in defect electronic entropies near a boundary. We then probe the effects of defect segregation on grain boundary electronic structure and calculate the impact of substitutional impurities on the Fermi-level density of states in the vicinity of a grain boundary. We find that titanium and tantalum defects have little impact regardless of placement, whereas tin, copper, and niobium defects each have a significant impact but only on sites away from the boundary core. Finally, we introduce a model for a local superconducting transition temperature and consider how grain boundary composition affects

T

c

as a function of distance from the boundary plane. The methodology established in this manuscript can be applied to other A15 superconductors in addition to Nb3Sn.
Coupling between guided modes of two parallel nanofibers
Authors: Kien Fam Le , Ruks Lewis , Nic Chormaic Síle , Busch Thomas 
Published: November 2020

Abstract
We study the coupling between the fundamental guided modes of two identical parallel nanofibers analytically and numerically. We calculate the coefficients of directional coupling, butt coupling, and self coupling as functions of the fiber radius, the light wavelength, and the fiber separation distance. We show that, due to the symmetry of the system, a mode of a nanofiber with the principal quasilinear polarization aligned along the axis joining the nanofibers or the perpendicular axis is coupled only to the mode with the same corresponding principal polarization of the other nanofiber. We find that the effects of the butt coupling and the self coupling on the power transfer are significant when the fiber radius is small, the light wavelength is large, or the fiber separation distance is small. We show that the power transfer coefficient may achieve a local maximum or become zero as the fiber radius, the light wavelength, or the fiber separation distance varies.
Enhancing the EEG classification in RSVP task by combining interval model of ERPs with spatial and temporal regions of interest
Authors: Li Bowen , Lin Yanfei , Gao Xiaorong , Liu Zhiwen 
Published: November 2020

Abstract

Objective. Brain–computer interface (BCI) systemsdirectly translate human intentions to instructions for machines by decoding the neural signals. The rapid serial visual presentation (RSVP) task is a typical paradigm of BCIs, in which subjects can detect the targets in the high-speed serial images. There are still two main challenges in electroencephalography (EEG) classification for RSVP tasks: inter-trial variability of event-related potentials (ERPs) and limited trial number of EEG training data.
Approach.
This study proposed an algorithm of discriminant analysis and classification for interval ERPs (DACIE) in RSVP tasks. Firstly, an interval model of ERPs was exploited to solve the inter-trial variability problem. Secondly, a spatial structured sparsity regularization was utilized to reinforce the important channels, which provided a spatial region of interest (sROI). Meanwhile, a temporal auto-weighting technique was conducted to emphasize the important discriminant components, which obtained a temporal regions of interest (tROIs). Thirdly, classification features were obtained by the discriminant eigenvalue analysis to avoid the ill-conditioned estimation of covariance matrix caused by fewer training trials.
Main results.
EEG datasets of 12 subjects in RSVP tasks were analyzed to evaluate the classification performance of proposed algorithm. The average accuracy rate, true positive rate, false positive rate and AUC value are 96.9%, 81.6%, 2.8% and 0.938, respectively. Compared with several state-of-the-art algorithms, the proposed algorithm can provide significantly better classification performance.
Significance.
The interval model of ERPs was exploited in a spatial linear discriminant framework to overcome the inter-trial variability. The sROIs and tROIs were explored to reinforce the pivotal channels and temporal components. And the proposed algorithm can provide good performance with fewer training trials.
Predicting PTSD severity using longitudinal magnetoencephalography with a multi-step learning framework
Authors: Zhang Jing , Wong Simeon M , Richardson J Don , Jetly Rakesh , Dunkley Benjamin T 
Published: November 2020

Abstract

Objective. The present study explores the effectiveness of incorporating temporal information in predicting post-traumatic stress disorder (PTSD) severity using magnetoencephalography (MEG) imaging data. The main objective was to assess the relationship between longitudinal MEG functional connectome data, measured across a variety of neural oscillatory frequencies and collected at two timepoints (Phase I and II), against PTSD severity captured at the later time point. Approach. We used an in-house developed informatics solution, featuring a two-step process featuring pre-learn feature selection (CV-SVR-rRF-FS, cross-validation with support vector regression (SVR) and recursive random forest feature selection) and deep learning (long-short term memory recurrent neural network, LSTM-RNN) techniques. Main results. The pre-learn step selected a small number of functional connections (or edges) from Phase I MEG data associated with Phase II PTSD severity, indexed using the PTSD CheckList (PCL) score. This strategy identified the functional edges affected by traumatic exposure and indexed disease severity, either permanently or evolving dynamically over time, for optimal predictive performance. Using the selected functional edges, LSTM modelling was used to incorporate the Phase II MEG data into longitudinal regression models. Single timepoint (Phase I and Phase II MEG data) SVR models were generated for comparison. Assessed with holdout test data, alpha and high gamma bands showed enhanced predictive performance with the longitudinal models comparing to the Phase I single timepoint models. The best predictive performance was observed for lower frequency ranges compared to the higher frequencies (low gamma), for both model types. Significance. This study identified the neural oscillatory signatures that benefited from additional temporal information when estimating the outcome of PTSD severity using MEG functional connectome data. Crucially, this approach can similarly be applied to any other mental health challenge, using this effective informatics foundation for longitudinal tracking of pathological brain states and predicting outcome with a MEG-based neurophysiology imaging system.
Intermittent resetting potentials
Authors: Mercado-Vásquez Gabriel , Boyer Denis , Majumdar Satya N , Schehr Grégory 
Published: November 2020

Abstract
We study the non-equilibrium steady states (NESS) and first passage properties of a Brownian particle with position X subject to an external confining potential of the form V(X) = μ|X|, and that is switched on and off stochastically. Applying the potential intermittently generates a physically realistic diffusion process with stochastic resetting toward the origin, a topic which has recently attracted a considerable interest in a variety of theoretical contexts but has remained challenging to implement in lab experiments. The present system exhibits rich features, not observed in previous resetting models. The mean time needed by a particle starting from the potential minimum to reach an absorbing target located at a certain distance can be minimized with respect to the switch-on and switch-off rates. The optimal rates undergo continuous or discontinuous transitions as the potential strength μ is varied across non-trivial values. A discontinuous transition with metastable behavior is also observed for the optimal strength at fixed rates.
Prediction of learning behavior based on improved random forest algorithm
Authors: Xia Xiaona 
Published: November 2020

Abstract
The research of learning behaviors is the premise of improving the learning process and results. The scale, composition and quality of the corresponding dataset will have the key impacts on research methods. Based on big dataset of learning behaviors, this study demonstrates the rules of learning behaviors, uses intelligent learning algorithms to analyze data and predict trends. Based on data characteristics, this paper evolves random forest algorithm and constructs the data structure. Taking the assessment results of learners as the test targets, the data samples are divided into two parts: train set and test set. Through the train set, we design the data model. With the help of the test set, the optimal model is finally selected, that is, the learning interaction activities are predicted and estimated. The research shows that learning behaviors have predictability and guidance, transferability and adjustability, groupness and personality. Based on this conclusion, the data analysis scheme of learning platforms is designed, which will help to improve the platform construction and learning behaviors.
One target classification method based on target feature distribution fusion
Authors: Luan Zhuzheng , Gu Bing , Wang Jinfeng , Yu Chenglong , Xu Lei 
Published: November 2020

Abstract
Target classification technology is an important method to improve the operational performance of radar in complex electromagnetic environment, as well as an important means to reduce radar false alarm. In this paper, according to the randomness of target plot parameters, a feature prior model is established to fuse the features distribution of true/false targets based on Bayesian estimation so as to achieve intra-feature information fusion. Under the guidance of the classification hyper-plane, feature dimension reduction is conducted to multi-dimension Gauss distribution after fusion, so as to achieve inter-feature information fusion and obtain 1-D Gauss probability distribution of target features. The target probability is obtained according to the vector distance integral of the target, thus the target classification is completed according to the probability threshold. This paper makes analysis, calculation and simulation to target classification algorithm under the features of 1-D, 2-D, 3-D and multi-dimension so as to validate the effectiveness of the proposed method.
An improved priority heuristic for the fixed guillotine rectangular packing problem
Authors: Shang Zhengyang , Pan Mingming , Pan Jiabao 
Published: November 2020

Abstract
An improved priority heuristic (IPH) is presented for the guillotine rectangular packing problem with a fixed orientation constraint. This algorithm can continuously divide the remaining space into blocks and recursively layout the entire space by filling each current block. IPH inherits the placement method of PH and adopts an improved space partitioning rule. The partitioning rule comprehensively considers the effects of the newly generated block on subsequent item placement. Thus, the remaining space can be filled as much as possible from a probability perspective. Given the introduction and utilization of different solving parameters, the worst-case time complexity is O(40n
2). Computational results on benchmark datasets show that IPH outperforms existing heuristics on average. For large-scale instances, several perfect layouts can be obtained for the first time.
Single-phase ground fault identification method for distribution network based on inception model and sample expansion
Authors: Zhu Jiran , Chen Kai , Peng Zhonghan , Zhang Hailong , Wan Xingchen , Zhu Daoge 
Published: November 2020

Abstract
A single-phase ground fault identification method based on inception model and sample expansion was proposed in this paper. Based on the basic characteristics of the inception model, a hybrid inception model structure is constructed to enhance the width and depth of the network and reduce network parameters. Combined with the training sample expansion method, the model is fully trained to adapt to the single-phase ground-fault current characteristics of the distribution network. The experimental results show that the accuracy for the hybrid inception model structure to the single-phase ground-fault current of the distribution network is 94.21%. After the sample is expanded, the recognition accuracy rate can be further improved by 97.52%.
An empirical study on the evaluation of the open sharing capability of meteorological data based on the data of 5 provincial meteorological departments
Authors: Shen Danna , He Jieying , Li Yan , Lu Yicen 
Published: November 2020

Abstract
Data sharing benefit evaluation is an important guarantee for the continued stable construction and application of data sharing projects and an important basis for making data application policies. Although meteorological data is an essential part of the basic strategic resources of national big data, through domestic and foreign research, there is no comprehensive system research on the comprehensive capabilities of data sharing services, especially on the meteorological industry. Therefore, writers of this paper establish an evaluation model for open sharing of meteorological data, select the open sharing capability of meteorological data from five representative provincial meteorological departments in China for pilot evaluation and empirical analysis, and then propose the applicability of the meteorological data sharing policy, unified meteorological data sharing service platform, overall planning and allocation of meteorological data resources and other policy recommendations.
Research on stable control communication network platform for multi-source big data fusion
Authors: Gao Jian , Song Guorui , Xu Yujie , He Jinhong 
Published: November 2020

Abstract
With the rapid development of large power grid, the demand for real-time, efficient and reliable applications of different services is also increasing. In this paper, based on the architecture of control and protection private network, we studied the new stable control test system and communication network scheme, proposed the multi-source data real-time transmission and interactive processing architecture of machine side, network side and load side. And we built a digital analog hybrid simulation test platform to verify its overall architecture, functions and key technologies in the test environment. Finally, we tested the reliability and rapidity indexes of communication network transmission protocol, interface and information transmission, at the same time gave some feasible suggestions for engineering application in actual power grid by taking business flow simulation as an example. It lays a foundation for engineering application and popularization.
In vitro investigation of an intracranial flow diverter with a fibrin-based, hemostasis mimicking, nanocoating
Authors: Link Antonia , Michel Tatjana , Schaller Martin , Tronser Tina , Krajewski Stefanie , Cattaneo Giorgio 
Published: November 2020

Abstract
Flow diversion aims at treatment of intracranial aneurysms via vessel remodeling mechanisms, avoiding the implantation of foreign materials into the aneurysm sack. However, complex implantation procedure, high metal surface and hemodynamic disturbance still pose a risk for thromboembolic complications in the clinical praxis. A novel fibrin and heparin based nano coating considered as a hemocompatible scaffold for neointimal formation was investigated regarding thrombogenicity and endothelialization. The fibrin-heparin coating was compared to a bare metal as well as fibrin- or heparin-coated flow diverters. The implants were tested separately in regard to inflammation and coagulation markers in two different in vitro hemocompatibility models conducted with human whole blood (n = 5). Endothelialization was investigated through a novel dynamic in vitro cell seeding model containing primary human cells with subsequent viability assay. It was demonstrated that platelet loss and platelet activation triggered by presence of a bare metal stent could be significantly reduced by applying the fibrin-heparin, fibrin and heparin coating. Viability of endothelial cells after proliferation was similar in fibrin-heparin compared to bare metal implants, with a slight, non-significant improvement observed in the fibrin-heparin group. The results suggest that the presented nanocoating has the potential to reduce thromboembolic complications in a clinical setting. Though the new model allowed for endothelial cell proliferation under flow conditions, a higher number of samples is required to assess a possible effect of the coating.
Distribution of the coalescence times in a system of diffusion-aggregation of particle clusters in one dimension
Authors: Fortin Jean-Yves , Choi MooYoung 
Published: November 2020

Abstract
We consider the stochastic dynamics of a system of diffusing clusters of particles on a finite periodic chain. A given cluster of particles can diffuse to the right or left as a whole and merge with other clusters; this process continues until all the clusters coalesce. We examine the distribution of the cluster numbers evolving in time, by means of a general time-dependent master equation based on a Smoluchowski equation for local coagulation and diffusion processes. Further, the limit distribution of the coalescence times is evaluated when only one cluster survives.
Geometric-phase polarimetry
Authors: Garza-Soto Luis , De-Luna-Pamanes Alejandra , Melendez-Montoya Israel , Sanchez-Soria Natalia , Gonzalez-Hernandez Diana , Lopez-Mago Dorilian 
Published: November 2020

Abstract
This paper describes polarimetric strategies based on measuring the light’s geometric phase, which results from the evolution of the polarisation state while traversing an optical system. The system in question is described by a homogeneous Jones matrix, which by definition, contains mutually perpendicular eigenpolarisations. Our leading theory links the system’s Jones matrix parameters (eigenvalues and eigenvectors) with the input polarisation state and the geometric phase. We demonstrate two interferometric techniques. The first one measures the geometric phase based on the relative lateral fringe displacement between the interference pattern of two mutually-orthogonal polarisation states. The second technique uses the visibility of the interference fringes to determine the eigenpolarisations of the system. We present proof-of-principle experiments for both techniques.
Synergetic effect of growth factor and topography on fibroblast proliferation
Authors: Guo Zhenzhao , Genlong Jiao , Huang Zhiqiang , Li Hong , Ge Yao , Wu Zhe , Yu Pei , Li Zhizhong 
Published: November 2020

Abstract
An innovative basic fibroblast growth factor (bFGF)-loaded polycaprolactone (PCL) fibrous membrane with highly aligned structure is developed for guided tissue regeneration (GTR). The aligned membrane is fabricated by electrospinning. In order to make efficient use of bFGF, PCL electrospun fibrous membrane is firstly surface-coated by self-polymerization of dopamine, and followed by immobilization of heparin via covalent conjugation to the polydopamine (PDA) layer. Subsequently, bFGF is loaded by binding to heparin. The loading yield of bFGF on heparin-immobilized PDA-coated PCL membrane significantly increases to around 7 times as compared with that of pure PCL membrane. NIH-3T3 cells show an enhanced proliferation and exhibit a stretched morphology aligned along the direction of the fibers on the aligned membranes. However, aligned bFGF-loaded PCL membrane exhibit a similar morphology but a highest cell density prolonged till 9 days. The synergetic effect of growth factor and topography would effectively regulate cell proliferation.
Graded index multimode fibre as saturable absorber induced by nonlinear multimodal interference for ultrafast photonics
Authors: Wang Zhaokun , Wang D N , Zhu Tianyu , Chen Jikai , Chang Shuo 
Published: November 2020

Abstract
Graded index multimode fibre (GIMF) has emerged as a promising platform for two- and three-dimensional nonlinear optics. Based on the nonlinear multimodal interference technique, GIMF has demonstrated the saturable absorption effect and applied for fibre-based-laser short pulse generation as versatile, wideband ultrafast optical switches. Herein, this review presents the basic principles and the optical properties of the GIMF-based saturable absorber (SA). With this proposed GIMF-based SA device, mode-locking fibre lasers in the wavelength range of 1, 1.55, and 2 μm are realized. Particular focus is on the tunable and multi-wavelength mode-locked fibre lasers, various kinds of soliton generation and large energy soliton generation and a detailed summary of the current advances are given.