bluuebunny/crossref_metadata_2025_split huggingface dataset:
An investigation of flare emissions at multiple wavelengths
Authors: Li Dong , Warmuth Alexander , Lu Lei , Ning Zongjun 
Published: April 2021

Abstract
We report multi-wavelength observations of four solar flares on 2014 July 07. We firstly select these flares according to the soft X-ray (SXR) and extreme ultraviolet (EUV) emissions recorded by the Extreme Ultraviolet Variability Experiment and Geostationary Orbiting Environmental Satellites. Then their locations and geometries are identified from the full-disk images measured by the Atmospheric Imaging Assembly (AIA), and the time delays among the light curves in different channels are identified. The electron number densities are estimated using the differential emission measure method. We find that three of four flares show strong emissions in SXR channels and high temperature (>6 MK) EUV wavelengths during the impulsive phase, i.e., AIA 131 Å and 94 Å, and then they emit peak radiation subsequently in the middle temperature (∼0.6−3 MK) EUV channels. Moreover, they last for a long time and have smaller electron densities, which are probably driven by the interaction of hot diffuse flare loops. Only one flare emits radiation at almost the same time in all the observed wavelengths, lasts for a relatively short time, and has a larger electron density. It is also accompanied by a type III radio burst. The bright emission at the EUV channel could be corresponding to the associated erupting filament.
Longitudinal analysis of local field potentials recorded from directional deep brain stimulation lead implants in the subthalamic nucleus
Authors: Brinda AnneMarie K , Doyle Alex M , Blumenfeld Madeline , Krieg Jordan , Alisch Joseph S R , Spencer Chelsea , Lecy Emily , Wilmerding Lucius K , DeNicola Adele , Johnson Luke A , Vitek Jerrold L , Johnson Matthew D 
Published: April 2021

Abstract

Objective. The electrode–tissue interface surrounding a deep brain stimulation (DBS) lead is known to be highly dynamic following implantation, which may have implications on the interpretation of intraoperatively recorded local field potentials (LFPs). We characterized beta-band LFP dynamics following implantation of a directional DBS lead in the sensorimotor subthalamic nucleus (STN), which is a primary target for treating Parkinson’s disease. Approach. Directional STN-DBS leads were implanted in four healthy, non-human primates. LFPs were recorded over two weeks and again 1–4 months after implantation. Impedance was measured for two weeks post-implant without stimulation to compare the reactive tissue response to changes in LFP oscillations. Beta-band (12–30 Hz) peak power was calculated from the LFP power spectra using both common average referencing (CAR) and intra-row bipolar referencing (IRBR). Results. Resting-state LFPs in two of four subjects revealed a steady increase of beta power over the initial two weeks post-implant whereas the other two subjects showed variable changes over time. Beta power variance across days was significantly larger in the first two weeks compared to 1–4 months post-implant in all three long-term subjects. Further, spatial maps of beta power several hours after implantation did not correlate with those measured two weeks or 1–4 months post-implant. CAR and IRBR beta power correlated across short- and long-term time points. However, depending on the time period, subjects showed a significant bias towards larger beta power using one referencing scheme over the other. Lastly, electrode–tissue impedance increased over the two weeks post-implant but showed no significant correlation to beta power. Significance. These results suggest that beta power in the STN may undergo significant changes following DBS lead implantation. DBS lead diameter and electrode recording configurations can affect the post-implant interpretation of oscillatory features. Such insights will be important for extrapolating results from intraoperative and externalized LFP recordings.
Transcription factor binding site detection using convolutional neural networks with a functional group-based data representation
Authors: Pap Gergely , Zoltán Györgypál , Ádám Krisztián , Tóth László , Hegedűs Zoltán 
Published: April 2021

Abstract
Transcription factors (TFs) play an essential role in molecular biology by regulating gene expression. The binding sites of TFs can vary by a large amount and the numerous possible binding locations make their detection a challenging issue. Recently, several machine learning approaches using nucleotide sequence data were applied to classify DNA sequences regarding Transcription Factor Binding Sites (TFBS). We propose a novel training strategy without the traditional 1D nucleotide-based DNA sequence representation by instead using a 2D topological matrix of sub-nucleotide chemical functional groups substantially defining the protein binding ability of DNA fragments. We train convolutional neural networks using this novel Functional Group DNA Representation (FGDR) to solve a TFBS classification task. We compare our results with the efficiency of previous nucleotide-based training approaches and show that learning from an FGDR data sequence has several benefits regarding TFBS classification. Moreover, we reason that learning deep neural networks from the FGDR representation produces competitive results while only introducing a pre-processing conversion step. Finally, we show that employing an ensemble of models from the nucleotide and FGDR representations for network training results in higher classification performance than any of the single input approaches.
Research on the Mechanism of Multi-resource Mapping Matching and Transmission of Road System Based on Meme Theory
Authors: Mao Chengyuan , Wang Qin , Feng Kangli , Yang Shengde , Bao Lewen 
Published: April 2021

Abstract
Because of the inefficiency and unscientific use of existing urban road traffic facilities, this paper sorts out the problems and optimization methods of domestic and foreign road traffic system, establishes the “set of problems” and “set of countermeasures”. Put forward the replication factor and mutation factor of optimization methods based on Meme theory, carry out the matching and transmission of multiple resource mapping, and feedback the implementation effect of the plan through simulation application and evaluation, so as to improve the mechanism of multi-resource mapping matching and transmission model. Cases study proved that Meme theory can be better applied to the automatic matching and selection of urban road traffic system optimization schemes and guided the optimization of urban road traffic systems more efficiently.
Automatic Finite Element Mesh Generation on 3D Surfaces Based on STEP
Authors: Jia Hong 
Published: April 2021

Abstract
Computer Aided Engineering (CAE) have a vital value in die and mould manufacturing. For the purpose of enhancing the modelling ability of the existing CAE analysis system and improving the mesh generation quality and working efficiency, a system for automatic finite element mesh generation on 3D surfaces is designed and implemented. Applying the neutral mechanism for product information sharing provided by STEP, product model import and reconstruction process is first implemented. And then product model information extraction module is developed aims at preparing geometrical and topological information for finite element mesh generation. An improved strategy for automatic triangular mesh generation over 3D surface is adopted to realize the mesh generation of the system. Implemented results indicate that the presented system helps to improve the mesh quality and at the same time increase the process efficiency and reduce the time cost.
Features of the deposition of photonic crystal films of polystyrene and silica
Authors: Panfilova E.V. , Shramko D.Y. , Ibragimov A.R. , Mozer K.V. 
Published: April 2021

Abstract
The paper presents the results of research in the field of deposition technology for self-organizing colloidal films. Such structures can be used in the manufacture of controlled photonic crystal devices. Therefore, it is important to determine methods and modes of obtaining high-quality ordered periodic opal structures. We compare the methods of vertical deposition and electrophoresis and discuss the features of their application in the deposition of films formed from colloidal particles of polystyrene and silica. We have found that vertical film deposition when the substrate is pulled out of the colloidal suspension is the most technologically convenient and suitable for both materials. The electrophoresis method is more difficult to implement, but it allows you to control the deposition process in dynamics. n this article, we present the identified rational modes of obtaining photonic crystal films by both methods.
Magneto-luminescent nanostructures for biomedicine
Authors: Matiushkina A A , Bazhenova A S , Litvinov I K , Belyaeva T N , Kornilova E S , Dubavik A Y , Orlova A O 
Published: April 2021

Abstract
Magneto-luminescent nanosystems can be widely applied in biomedicine, namely for disease theranostics, early tumor imaging, and as drug delivery and hyperthermia agents. The creation of such systems using superparamagnetic iron oxide nanoparticles (SPIONs) and semiconductor quantum dots (QDs) approach is popular, since SPIONs are characterized by excellent magnetic properties and zero coercive force, and QDs have advantages over the majority of organic phosphors. In this work, the alloyed (CdxZn1-xSeyS1-y)/ZnS quantum dots are used to reduce the probability of Förster Resonance Energy Transfer or electron transfer, that quench QD photoluminescence, due to a thick gradient semiconductor shell. Moreover, Magnetic Circular Dichroism (MCD) spectroscopy has been successfully applied as a method for monitoring both magnetic properties and colloidal stability of magnetic nanoparticles in a solution, which is crucial for their biomedical applications. The simplicity of SPION/QDs nanostructure formation due to the sequential addition of a SPION solution to a QD solution is a major advantage of this approach. It was demonstrated that these SPION/QDs nanostructures are stable, can be efficiently absorbed by the HeLa cells, and do not show high cytotoxicity at low concentrations (up to 25 nM).
Application of scanning probe energy loss spectroscopy to SERS-active metal nanostructures
Authors: Beshr M , Amarandei G , Meade A D , Palmer R E , Murphy S 
Published: April 2021

Abstract
Surface-enhanced Raman spectroscopy (SERS) relies on adsorbing target molecules onto metal nanostructures where the light can resonantly couple with localised surface plasmon resonances. These plasmons can be tuned by changing the nanostructure size, shape, spacing and composition, but this is a complex process. Therefore, having an experimental method that can directly map the plasmons would be extremely useful for developing SERS-active substrates. This paper investigates the possibility of applying a novel scanning probe method, scanning probe energy loss spectroscopy, to map the plasmonic behaviour of SERS-active metal nanostructures in order to optimise their enhancement factor and reproducibility.
Investigation of the influence of tungsten carbide nanopowder WC and the mixture of tungsten carbides and titanium carbides (WC, TiC) on the change of concrete performance properties
Authors: Gavrish V. , Chayka T. , Baranov G. , Oleynik A.Y. , Shagova Y.O. 
Published: April 2021

Abstract
The paper deals with the possibility of changing the operational properties of concrete (strength, water absorption, corrosion resistance) by adding WC tungsten carbide nanopowders and a mixture of WC tungsten carbide and TiC titanium carbides obtained from hard-alloy waste to the initial composition. As a result of the research, the concentrations of nanopowders were determined, at which the maximum change in strength is observed. Changes in water absorption at different time intervals and corrosion resistance of concrete samples at optimal concentrations were studied. The paper shows a positive effect on the performance of concrete mixture of WC, WC and TiC nanomodifiers.
Influence of tungsten nanopowders W, tungsten oxide WO3, tungsten carbide WC on changing radio protective properties of polymers
Authors: Gavrish B , Chaika T , Cherkashina N , Frolova M 
Published: April 2021

AbstractThe paper considers the possibility of improving radio protective properties of polymers by modifying with nanopowders: tungsten W, tungsten oxide WO3, tungsten carbide WC, obtained from hard-alloy waste. The study determined the coefficients of linear attenuation of gamma radiation in the energy range of 59.93 keV…1408 keV at various concentrations of nanopowders 20…80% of the mass of the material, with a step of 20%. The comparative characteristic of the obtained data is given. The developed composite materials can be used in the systems of passive protection, dosimetry, space technology, as well as greatly facilitate the weight of various structural products.
Investigation of the effect of tungsten carbide nanopowder additives on the strength properties of fiberglass, based on LAVESAN glass fabric and EPR 320 epoxy resin
Authors: Gavrish V. , Chayka T. , Baranov G. , Oleynik A.Y. 
Published: April 2021

Abstract
The paper presents the results of tests for modulus of elasticity and tensile strength of fiberglass samples consisting of glass fiber Lavesan (Italy) and epoxy resin binder EPR 320 with hardener EPH 162, modified with WC tungsten carbide nanopowder, obtained from carbide waste. The positive effect of WC nanopowder on tensile strength and modulus of elasticity is shown. With the addition of nanoadditives in the mass concentration of 1 … 4% of the resin mass, the increase in tensile strength was more than 1.5 … 2.5 times, with no significant change in material elasticity.
Relevance Feedback For Image Retrieval Using Transfer Learning and Improved MQHOA
Authors: Wang Huaqiu , Liu Qian 
Published: April 2021

Abstract
Image retrieval is a challenging technology in multimedia applications where meeting the users’ subjective retrieval needs while achieving high retrieval performance is insufficient for existing methods. In this work, a related feedback image retrieval algorithm based on deep learning and optimization algorithm (CAMQHOA-RF) is proposed. Transfer learning based on the deep convolutional neural network is applied to extract deeper image features to reduce the semantic gap. The multi-scale quantum harmonic oscillator algorithm improved by the idea of “aggregation” is introduced to search the feature space effectively. The covariance matrix is used to strengthen the relationship between feature points at different scales to guide feature points to approach ideal query points faster. Moreover, the query point is reselected based on the feedback information to explore more potential users’ interest areas. Experiments have shown that compared with other algorithms, the proposed algorithm has fewer parameters that need to be set, but higher retrieval accuracy, faster retrieval speed, and stronger robustness are obtained, which can meet users better.
An Occlusion Handling Evaluation Criterion for Deep Learning Object Segmentation
Authors: Yang C , Chong P H J , Lam P 
Published: April 2021

Abstract
This paper introduces a novel evaluation criterion to occlusion handling for deep learning object segmentation. The occlusion is defined as objects blocking each other on an image. It affects deep learning object segmentation. More and more researches focus on occlusion handling for object segmentation. However, these researches do not clearly show the evaluation of their occlusion handling method, because there is no suitable evaluation criterion. Traditionally, people just use images results or use the entire object boundary accuracy to show the occlusion handling of their methods. Conversely, these ideas cannot give a numerical evaluation focusing on occlusion handling. This research reports an evaluation criterion to measure occlusion handling performance. This evaluation criterion uses the shortest distances between the pixels from the ground truth occlusion edges and the segmentation (result) shape. The shortest distances are segmentation errors. Then, the average value of these errors is the final parameter for occlusion handling evaluation criterion. The experiment uses a deep learning based segmentation model as an example. It shows that this criterion (or method) successfully measures the occlusion handling for deep learning based object segmentation.
Dynamic Degradation Quantification of Wind Turbine High Speed Shaft Bearing Based on Oscillation Based Sparsity Indices
Authors: Noman Khandaker , He Qingbo , Peng Zhike , Wang Dong 
Published: April 2021

Abstract
Wind turbine (WT) high speed shaft (HSS) bearing fault is important due to its significant number of failures. However, due to its non-stationary operation under varying speed condition, it is a challenging issue to quantify the degradation of WT-HSS bearing by conventional bearing indices. Aiming at the aforementioned issue, in this paper, tunable Q factor wavelet transform (TQWT) preprocessed sparsity indices are proposed to achieve the dynamic degradation quantification of WT-HSS bearing. Firstly, based on the ability of TQWT in dynamic extraction of fault component of a bearing signal under varying speed condition, low oscillatory transient signal component is separated from a noisy commercial WT-HSS bearing signal by continuously adjustable tunable Q factor wavelet transform (TQWT). Then, considering the suitability of sparsity indices in quantifying extracted fault component based on its energy concentration, four representative sparsity indices namely kurtosis, gini index, negative entropy and reciprocal smoothness index are used to quantify the separated low oscillatory signal component as a measure of WT-HSS bearing health. The proposed indices show a better performance than original sparsity indices and an improved version of sparsity indices based on adaptive weighted signal preprocessing in dynamic degradation quantification of a commercial WT-HSS bearing.
Automated Biometric Identification using Dorsal Hand Images and Convolutional Neural Networks
Authors: Mohaghegh Mahsa , Payne Ash 
Published: April 2021

Abstract
The identification of perpetrators, present in Child Sexual Abuse Imagery (CSAI), is a significant challenge due to the use of anonymisation techniques that mask their identities. Consequently, researchers have investigated the use of uncommon biometric identifiers such as knuckle patterns, palmprints and the dorsal side of the hand. This research proposes a Convolutional Neural Network (CNN) based, fully automated approach to biometric identification using dorsal hand images. The identification performance of three different CNN architectures, AlexNet, ResNet50 and ResNet152, is experimentally determined against two similar datasets, the 11k Hands and IITD dorsal hand databases. A transfer learning approach is used and the final output layers of the CNNs are modified to match the number of classes present in the datasets. The results showed that ResNet CNNs achieved identification accuracies greater than 99.9% on both datasets, whereas the AlexNet CNN achieved between 80.1% and 93.7%. These results demonstrate that it is feasible to use deep, off-the-shelf CNNs, such as ResNets, for automated biometric identification using dorsal hand images. This highlights the potential of using dorsal hand images to identify perpetrators of child sexual abuse from CSAI.
Research on HMM-based Exoskeleton Robot Falling Prediction Algorithm
Authors: Gao Leilei , Deng Keyong , Zhang Rui , Wang Meng , Wang Hao 
Published: April 2021

Abstract
Exoskeleton robot is essentially a wearable robot. In recent years, the research of exoskeleton robots has become a new hot spot, and has gradually been widely used in military, medical and civilian fields. However, the balance and safety issues of power-assisted exoskeleton robots need to be solved urgently. At present, although there are many researches on exoskeleton robots at home and abroad, the research on the falling problem of assisted lower limb exoskeleton robots is not in-depth. Existing studies on falls are mainly focused on the detection of falls, while the prediction of falls is relatively small, and these studies rely more on a single sensor, and its accuracy needs to be improved. In response to the status quo, This paper proposes a fall prediction algorithm based on HMM to study the fall behaviour of the exoskeleton human-machine system in advance, it provides a reference for the anti-fall measures of the exoskeleton robot and aims to effectively reduce the damage of the human-machine system.
A Object detection Method for Missile-borne Images Based on Improved YOLOv3
Authors: Wang Shaobo , Zhang Cheng , Su Di , Sun Tianqi 
Published: April 2021

Abstract
Detecting small objects in complex circumstances is an important topic in the research of today’ s object detection [1], especially in military, which needs more reliable, stable and accurate detection results. In order to improve the detection of small objects, we improved the structure of the YOLOv3 network by replacing the convolution module in the original network with multi-branch scale convolution, increasing the adaptability of the network to different sizes of objectss and reducing the number of network layers to balance the depth and width of the network, while also improving the feature extraction and representation capabilities. And based on the premise of a small number of data sets, we simulate some complex environments, which are composed of different weather, illumination, motion and rotational blur. We also enhance and extend the data in the network learning. Through the system simulation experiment, small objects can be recognized in such complex environments, which provides a reference for object detection of missile-borne images.
Attacking Sequential Learning Models with Style Transfer Based Adversarial Examples
Authors: Zhang Zhilu , Yang Xi , Huang Kaizhu 
Published: April 2021

Abstract
In the field of deep neural network security, it has been recently found that non-sequential networks are vulnerable to adversarial examples. There are however few studies to investigate the adversarial attack on sequential tasks. To this end, in this paper, we propose a novel method to generate adversarial examples for sequential tasks. Specifically, an image style transfer method is used to generate for a Scene Text Recognition (STR) network adversarial examples, which are only different from the original image on the style. While they will not interfere with the recognition of image information by human vision, the adversarial examples would significantly mislead the recognition results of sequential networks. Moreover, based on a black-box attack, both in digital and physical environments, we show that the proposed method can use cross text shape information and attack successfully the TPS-ResNet-BiLSTM-Attention (TRBA) and Convolutional Recurrent Neural Network (CRNN) models. Finally, we demonstrate further that physical adversarial examples can easily mislead commercial recognition algorithms, e.g. iFLYTEK and Youdao, suggesting that STR models are also highly vulnerable to attacks from adversarial examples.