bluuebunny/crossref_metadata_2025 huggingface dataset:
Reducing antibiotic use in the control of mastitis in dairy herds
Authors: Breen James E. , Bradley Andrew J. , Down Peter M. , Hyde Robert M. , Leach Katharine A. , Green Martin J.
Published: February 2021

The control of mastitis remains a focus of attention for dairy farmers, veterinary surgeons and advisors due to its impact on cow health and welfare, milk quality, sustainable production, and the financial costs associated with treatment, prevention and ongoing control. In addition, the focus on the unnecessary use of antibiotics in agriculture has meant that mastitis control in dairy herds has received renewed interest, particularly around prevention of new infection and alternative treatment strategies. The latter includes the selective use of intra-mammary antibiotic for infected cows at drying-off, reserving parenteral antibiotic for clinical cases where the cow is ill, and selecting intra-mammary antibiotic treatment for clinical mastitis based on culture results. Treating clinical mastitis caused by Gram-positive pathogens such as
Streptococcus
spp. with intra-mammary antibiotic remains important to optimise chance of cure and reduce risk of transmission of infection, although antibiotics may not be required for clinical mastitis infections caused by other pathogens, particularly
E. coli
. The long-term reduction and rationalisation of antibiotic use in mastitis control is achieved through improved management to prevent new infections and avoid the need to treat mastitis. This comes through understanding the predominant epidemiological ‘pattern’ of infection in the herd and targeted implementation of well-specified interventions to reduce the rate of new infection, either in lactation or during the dry period. For most dairy herds, environmental mastitis pathogens predominate and therefore management and hygiene of housed and pastured environments is a key component of mastitis control plans designed to reduce the need to use antibiotics in mastitis control.
Shock-free ion transmission in a skimmer-based MEMS mass spectrometer vacuum interface
Authors: Wright S , Syms R R A
Published: February 2021

Shock-free ion transmission from atmospheric pressure to a microelectromechanical system (MEMS)-based mass spectrometer has been achieved using micro-engineered nickel skimmers. The signal level has increased 70-fold compared with a previous configuration in which the skimmer did not sample the supersonic flow. The skimmers are formed by electroplating internal surfaces of anisotropically etched, pyramidal holes in (100) silicon. Etching from the reverse of the wafer exposes free-standing, open-ended skimmers supported by remaining silicon. High-resolution schlieren imaging has been used to visualise gas flow within the interface. Signal enhancement and increased gas throughput are observed when the skimmer attaches to the supersonic gas expansion via oblique shocks. The silicon back wall interacts with the flow field, causing the free jet Mach disc to evolve into a bowl-shaped surface shock whose position asymptotically approaches a stand-off separation as the interface pressure decreases. Ideally, the skimmer entrance should be located approximately midway between the inlet and the back wall. This development should allow a sensitivity increase in MEMS mass spectrometers using pumps of moderate capacity.
Electromagnetic wave-absorbing performance of carbons, carbides, oxides, ferrites and sulfides: review and perspective
Authors: Liu Jiaolong , Zhang Limin , Wu Hongjing
Published: February 2021

The development of electromagnetic wave-absorbing materials (EMWAMs) offers a promising way to address the ever-increasing issue of electromagnetic pollution. Up to now, significant efforts have been made to explore superior EMWAMs featuring strong absorption intensity, broad bandwidth, low density, and small thicknesses as well as those with exceptional performance. Therefore, in this paper, we offer a a comprehensive review summarizing the recent inspiring advancements in various EMWAMs, including those based on carbon, carbides, oxides, ferrites and sulfides. We begin by presenting diverse lossy materials, such as dielectric loss materials, magnetic loss materials and dielectric/magnetic loss materials. In parallel, we discuss the current difficulties with the materials themselves and the corresponding composite strategies for incorporating other dielectric or magnetic components. Finally, we outline the primary problems and bottlenecks, and more importantly, the prospective research directions of these materials. Overall, this work will present a brief but systematic overview of up-to-date progress in the EMW attenuation abilities of various EMWAMs.
Creation of azimuthally and radially directed laser-induced periodic structures on large tantalum surface
Authors: Boltaev G S , Alghabra M S , Iqbal M , Ganeev R A , Alnaser A S
Published: February 2021

The formation of well-controlled nano/micrometer-sized structures on metallic surfaces enables the modification of their optical and wetting properties. Forming such structures on the surface of biocompatible materials, in particular, can expand their applications in various areas of science and technology. Here we present results on covering tantalum (Ta), a biocompatible material, with complex nanosized structures comprising azimuthally- and radially-directed laser-induced periodic surface structures (LIPSS) by rotating the metallic sample with respect to the polarization direction of the irradiating laser pulses. For the first time, we use a high-repetition rate (150 kHz) fiber-based laser with 37 fs ablating pulses and a central wavelength of 1030 nm to form ripples that are directed both parallel and perpendicular to the laser polarization direction on the surface of Ta. Rotating the target during ablation led to forming two distinct zones of structures. The first zone, around the circumference of the target, consisted of both high- and low-spatial-frequency LIPSS, while in the second zone, at the center of the target, was covered by nanoparticles redeposition. We demonstrate how the formation of such complex structures significantly alters the optical reflectance and wetting characteristics of Ta.
Spin polarization in an ac-driven magnetic material with vanishing net magnetization: a new proposal
Authors: Dey Moumita , Sarkar Manik , Maiti Santanu K
Published: February 2021

In this work, we address the fundamental question of whether a magnetic material having zero net magnetization can produce polarized spin current from a completely unpolarized one. Common wisdom suggests that this is not possible, but if we break the symmetry in hopping integrals in different segments of the magnetic sample, then a finite possibility of getting polarized spin current is established. To substantiate this fact, we consider a one-dimensional magnetic chain with vanishing net magnetization where one part of the chain is subjected to an ac electric field, keeping the other part free. The ac field, introduced through Peierls substitution, modulates the hopping integrals yielding a misalignment of up and down spin channels, which is the primary requirement to get finite spin polarization. Simulating the system within a tight-binding framework, we compute spin-dependent transmission probabilities using the well-known Green’s function prescriptionand determine junction currents following the Landauer–Büttiker formalism. Our analysis may shed some light on designing spin-polarized devices using driven magnetic materials with vanishing net magnetization.
Drag effect induced large anisotropic damping behavior in magnetic thin films with strong magnetic anisotropy
Authors: Li Yang , Li Yan , Sun Rui , Liu Jia-nan , Li Na , Yang Xu , Gong Zi-zhao , Xie Zong-kai , He Wei , Zhang Xiang-qun , Cheng Zhao-hua
Published: February 2021

The determination of intrinsic Gilbert damping is one of the central interests in the field of spintronics. However, some external factors in magnetic films tend to play a remarkable role in the magnetization dynamics. Here, we present a comprehensive study of the magnetic relaxation in ferromagnetic films with various in-plane magnetic anisotropy via ferromagnetic resonance technique. We find that the magnetic drag effect can result in the resonant linewidth broadening and the nonlinear dependence of linewidth on frequency stemming from field-magnetization misalignment. As a result, this could lead to the imprecise extraction of the key dynamic parameter-Gilbert damping and cause the confusing behaviors of ultra-low and anisotropic damping in thin films and multi-layers with high magnetic anisotropy. Our results provide a crucial way for the accurately quantitative estimation of the Gilbert damping in spintronics measurements.
A V3C2 MXene/graphene heterostructure as a sustainable electrode material for metal ion batteries
Authors: Dinda Partha Pratim , Meena Shweta
Published: February 2021

Individually, MXene and graphene based frameworks have been recognized as promising 2D electrode materials for metal ion batteries. Herein, we have engineered a heterostructure of V3C2 MXene and graphene using computational design. A comprehensive investigation of designed heterostructure has been reported in this work. Simulated heterostructure has been evaluated for various functionalities such as high performance of thermal stability, metal ion intercalation, diffusion energy using density functional theory method. Interestingly, simulation examinations and obtained calculations demonstrate the high storage capacity of Li and Ca (598.63 mAh g−1), and Na (555.87 mAh g−1) with the designed V3C2/graphene model. Promising diffusion energy barriers for Li (0.11 eV), Na (0.17 eV) and Ca (0.15 eV) ions are also investigated and have explained systematically in the present work. Moreover, we have achieved high capacity and fast charge/discharge rates of V3C2/graphene heterostructure indicating its promising electrode potential efficiency for ion batteries especially for Na ion battery. Thus, our investigation demonstrate the advantages of newly designed V3C2 MXene and graphene heterostructure for advance metal ion batteries.
Semiconducting MnB5 monolayer as a potential photovoltaic material
Authors: Han Fanjunjie , Yu Tong , Qu Xin , Bergara Aitor , Yang Guochun
Published: February 2021

Exploring new two-dimensional (2D) materials is of great significance for both basic research and practical applications. Although boron can form various 3D and 2D allotropes due to its ease of forming multi-center bonds, the coexistence of honeycomb and kagome boron structures has never been observed in any 2D material yet. In this article we apply first-principle swarm structural searches to predict the existence of a stable MnB5 structure, consisting of a sandwich of honeycomb and kagome borophenes. More interestingly, a MnB5 nanosheet is a semiconductor with a band gap of 1.07 eV and a high optical absorption in a broad band, which satisfies the requirements of a very good photovoltaic material. Upon moderate strain, MnB5 undergoes a conversion from an indirect to a direct band gap semiconductor. The power conversion efficiency of a heterostructure solar cell made of MnB5 is up to 18%. The MnB5 nanosheet shows a robust dynamical and thermal stability, stemming from the presence of intra- and interlayer multi-center σ and π bonds. These characteristics make MnB5 a promising photovoltaic material.
Fingerprint of chaos and quantum scars in kicked Dicke model: an out-of-time-order correlator study
Authors: Sinha Sudip , Ray Sayak , Sinha Subhasis
Published: February 2021

We investigate the onset of chaos in a periodically kicked Dicke model (KDM), using the out-of-time-order correlator (OTOC) as a diagnostic tool, in both the oscillator and the spin subspaces. In the large spin limit, the classical Hamiltonian map is constructed, which allows us to investigate the corresponding phase space dynamics and to compute the Lyapunov exponent. We show that the growth rate of the OTOC for the canonically conjugate coordinates of the oscillator is able to capture the Lyapunov exponent in the chaotic regime. The onset of chaos is further investigated using the saturation value of the OTOC, that can serve as an alternate indicator of chaos in a generic interacting quantum system. This is also supported by a system independent effective random matrix model. We further identify the quantum scars in KDM and detect their dynamical signature by using the OTOC dynamics. The relevance of the present study in the context of ongoing cold atom experiments is also discussed.
Fabrication and application of nanoporous anodic aluminum oxide: a review
Authors: Liu Sixiang , Tian Junlong , Zhang Wang
Published: February 2021

Due to the unique optical and electrochemical properties, large surface area, tunable properties, and high thermal stability, nanoporous anodic aluminum oxide (AAO) has become one of the most popular materials with a large potential to develop emerging applications in numerous areas, including biosensors, desalination, high-risk pollutants detection, capacitors, solar cell devices, photonic crystals, template-assisted fabrication of nanostructures, and so on. This review covers the mechanism of AAO formation, manufacturing technology, the relationship between the properties of AAO and fabrication conditions, and applications of AAO. Properties of AAO, like pore diameter, interpore distance, wall thickness, and anodized aluminum layer thickness, can be fully controlled by fabrication conditions, including electrolyte, applied voltage, anodizing and widening time. Generally speaking, the pore diameter of AAO will affect its specific application to a large extent. Moreover, manufacturing technology like one/two/multi step anodization, nanoimprint lithography anodization, and pulse/cyclic anodization also have a major impact on overall array arrangement. The review aims to provide a perspective overview of the relationship between applications and their corresponding AAO pore sizes, systematically. And the review also focuses on the strategies by which the structures and functions of AAO can be utilized.
Fast cooling of trapped ion in strong sideband coupling regime
Authors: Zhang Shuo , Zhang Jian-Qi , Wu Wei , Bao Wan-Su , Guo Chu
Published: February 2021

Trapped ion in the Lamb–Dicke (LD) regime with the LD parameter η ≪ 1 can be cooled down to its motional ground state using sideband cooling. Standard sideband cooling works in the weak sideband coupling (WSC) limit, where the sideband coupling strength is small compared to the natural linewidth γ of the internal excited state, with a cooling rate much less than γ. Here we consider cooling schemes in the strong sideband coupling (SSC) regime, where the sideband coupling strength is comparable or even greater than γ. We derive analytic expressions for the cooling rate and the average occupation of the motional steady state in this regime, based on which we show that one can reach a cooling rate which is proportional to γ, while at the same time the steady state occupation increases by a correction term proportional to η
2 compared to the WSC limit. We demonstrate with numerical simulations that our analytic expressions faithfully recover the exact dynamics in the SSC regime.
Realization of arbitrary two-qubit quantum gates based on chiral Majorana fermions*
Authors: Yan Qing , Sun Qing-Feng
Published: February 2021

Quantum computers are in hot-spot with the potential to handle more complex problems than classical computers can. Realizing the quantum computation requires the universal quantum gate set {T, H, CNOT} so as to perform any unitary transformation with arbitrary accuracy. Here we first briefly review the Majorana fermions and then propose the realization of arbitrary two-qubit quantum gates based on chiral Majorana fermions. Elementary cells consist of a quantum anomalous Hall insulator surrounded by a topological superconductor with electric gates and quantum-dot structures, which enable the braiding operation and the partial exchange operation. After defining a qubit by four chiral Majorana fermions, the single-qubit T and H quantum gates are realized via one partial exchange operation and three braiding operations, respectively. The entangled CNOT quantum gate is performed by braiding six chiral Majorana fermions. Besides, we design a powerful device with which arbitrary two-qubit quantum gates can be realized and take the quantum Fourier transform as an example to show that several quantum operations can be performed with this space-limited device. Thus, our proposal could inspire further utilization of mobile chiral Majorana edge states for faster quantum computation.
Micro-scale photon source in a hybrid cQED system*
Authors: Chen Ming-Bo , Wang Bao-Chuan , Gu Si-Si , Lin Ting , Li Hai-Ou , Cao Gang , Guo Guo-Ping
Published: February 2021

Coherent photon source is an important element that has been widely used in spectroscopy, imaging, detection, and teleportation in quantum optics. However, it is still a challenge to realize micro-scale coherent emitters in semiconductor systems. We report the observation of gain in a cavity-coupled GaAs double quantum dot system with a voltage bias across the device. By characterizing and analyzing the cavity responses to different quantum dot behaviors, we distinguish the microwave photon emission from the signal gain. This study provides a possibility to realize micro-scale amplifiers or coherent microwave photon sources in circuit quantum electrodynamics (cQED) hybrid systems.
Modeling shape and volume transitions in liquid crystal elastomers
Authors: Egorov V I , Maksimova O G , Okumura M , Noro S , Koibuchi H
Published: February 2021

Shape and volume phase transitions of liquid crystal elastomers (LCEs) immersed in a solvent are studied within the framework of the Finsler geometry (FG). In the FG model of an LCE, an internal variable is introduced to describe the directional degrees of freedom of liquid crystals. We also introduce an Ising-like variable to effectively describe the swelling behavior of the LCE. From the results of Monte Carlo simulations, we find that the model successfully reproduces changes in the swelling degree as well as the smooth change in the shape anisotropy with increasing temperature. Moreover, the maximal value of the shape anisotropy is close to the experimentally observed value.
Modelling the optical control of electron spin dynamics in a quantum dot near a two-dimensional semiconductor
Authors: Stefanatos Dionisis , Karanikolas Vasilios , Paspalakis Emmanuel
Published: February 2021

A quantum dot in the Voigt configuration is a basic system with important potential applications in quantum technologies. The spin states of this quantum dot can act as a prototype qubit. By applying laser pulses the dynamics of the quantum dot spin can be controlled and the necessary quantum gates can be achieved. An important problem is the initialization, i.e., the creation of one of the two electron spin states starting from the natural initial state of the system, which is an equal incoherent mixture of the two spin states. The initialization process can be achieved by proper interaction of the quantum dot with laser pulses. Also, it has been realized that the integration of the quantum dot with photonic structures that give preferential Purcell-enhanced decay rate towards the target spin state increases the fidelity of spin initialization. Here, we propose a new coupled quantum dot - nanophotonic structure that may give high initialization fidelity in short times by coupling the quantum dot with a tungsten disulfide (WS2) monolayer. For the modelling of the spin dynamics we combine quantum dynamics calculations with electromagnetic calculations. Specifically, we model the interaction of the quantum dot with the applied laser field with density matrix equations. Also, the spontaneous decay rates that enter in the density matrix equations are obtained by electromagnetic calculations based on the electromagnetic Green’s tensor, which is calculated with the scattering superposition method. We first show that the spontaneous decay rates for the quantum dot near a WS2 monolayer are enhanced by the Purcell effect and are anisotropic for quantum dot dipole moments parallel and perpendicular to the layer. We then use the most common method for initialization, optical pumping and show that a preferential Purcell-enhanced decay rate towards the target spin state increases the fidelity of spin initialization in short times, in comparison to the case that the quantum dot is placed in an isotropic photonic environment.
Space-time chaos in the nonlinear Schrödinger equation
Authors: Magnitskii Nikolai
Published: February 2021

The paper provides an analytical and numerical analysis of the transition to space-time chaos in the generalized nonlinear Schrödinger equation with complex, in the general case, parameters. Equation describes, in particular, the wave amplitude of a surface plasmon polariton propagating over the contact surface of a metal with a dielectric. It is proved that considered equation has an infinite number of different stable wave solutions running along the spatial axis with arbitrary velocities, as well as an infinite number of different modes of space-time chaos in full accordance with the universal Feigenbaum-Sharkovsky-Magnitskii bifurcation theory. In this case, the bifurcation parameter is the value of the speed of propagation of traveling waves along the spatial axis, which is clearly not included in the original equation.
Divergence decision tree classification with Kolmogorov kernel smoothing in high energy physics
Authors: Kus Vaclav , Jaruskova Kristina
Published: February 2021

The binary classification of a given dataset is a task of assigning one of the two possible classes to each observation. This can be achieved by many machine learning techniques, e.g. logistic regression, decision trees, neural networks. The supervised divergence decision tree (SDDT) is our own binary classification algorithm in favour of the Rényi divergence, which incorporates multi-dimensional kernel density estimates (KDEs) as the main part of the splitting process in its tree nodes. However, the KDE needs an efficient smoothing in order to obtain quite satisfactory classification results. In this paper, the D-discrepancy method for selecting the bandwidth was applied. It is based on an evaluation of divergences, or distances, between two estimated distributions. The Kolmogorov metric distance on probability space is used and the performance of such a novel technique is compared to standard smoothing techniques. The final goal is to perform a binary classification and achieve the best possible results with respect to the AUC value (area under ROC curve) on a given high energy physics (HEP) dataset, specifically for d+Au heavy ions decay data. This HEP dataset is described and the main structure of the used SDDT is outlined. Final classification results are presented for KDE under Kolmogorov D-method of smoothing in SDDT algorithm.
Modeling the influence of the Earth rotation axis position on the global climate variations
Authors: Parkhomenko V P
Published: February 2021

This study presents the results of numerical experiments to determine the Earth’s climate when its rotation axis is displaced without changing the axis tilt to the ecliptic plane. There is some evidence of the possibility of this shift in the past. The calculations were carried out using a hydrodynamic three-dimensional global climate model, including blocks of atmosphere, thermohaline large-scale ocean circulation and sea ice. Numerical experiments demonstrate a significant temperature changes throughout the world. A large area of Antarctica warmed up to temperatures above 15 ° C. This is reason of intense melting of glaciers for a long time. Significant warming of the Arctic Ocean will lead to sea ice melting in the Arctic. Strong changes in temperature and ice cover lead to significant changes in horizontal ocean circulation. A procedure is proposed for calculating wind speed in atmosphere energy - moisture balance model. It is based on the geostrophic approach, taking into account the thermal component of the wind, and introducing the mechanism of friction on the underlying surface. A technique has been developed for the formation of the necessary maps and the relationships between them when turning the Earth rotation axis or using new cartographic data.
Disconnected stationary solutions for 2D Kolmogorov flow problem in periodic domain
Authors: Evstigneev Nikolay M.
Published: February 2021

The classical A.N. Kolmogorov’s flow problem for the stationary 2D Navier-Stokes equations on a stretched torus for velocity vector function is considered. The problem is analysed by the construction of the solution curves in the parameter-phase space and analysis of disconnected solutions is performed. Disconnected solutions from the main solution branch are found.
MODELLING AN EXPERIMENT TO MEASURE THE SPEED OF GRAVITY IN SHORT DISTANCES USING ROTATING MASSES
Authors: Fabricio Junior C. A. , Sanchez E , Frajuca C , Bortoli F. S. , Magalhaes N. S. , Da Silva W. C. , Da Silva D. M. , Souza R. C.
Published: February 2021

An experiment to measure the speed of gravitational signals using rotating masses in short distances has been developed with the intention to study its behaviour when a medium different from air is allocated between the emitter and the detection and check if the speed of the interaction changes. The experiment is composed of two masses rotating at incredible rotation (the goal is rotate the masses at rotation speed higher than 600,000 RPM, that excites a saphire bar called the detector. The amplitude of the central device (detector) is monitored by an ultralow phase noise microwave signal using resonance in the whispering gallery modes and cooled down at 4.2 K. Between the rotating masses and the detector, a different medium will be placed, and then the speed is measured and compared with the case where the medium is pure air. The modelling of the experiment is made assuming the detector as a spring-mass system. The results show that the detection is achievable
Movement characteristics of a non-smooth model with a closed curve equilibrium
Authors: Nagyová Judita Buchlovská
Published: February 2021

The main aim of this paper is to analyze the dynamical properties of a model with a closed curve equilibrium. The corresponding three-variable model is given as a set of nonlinear ordinary differential equations containing non-smooth functions. The dynamics of the model are studied depending on three parameters. For this purpose, new methods, as the 0-1 test for chaos and approximate entropy, are applied. Using these tools, the dynamics are quantified and qualified. It is shown that depending on the system’s parameters, the system exhibits both irregular (chaotic) and regular (periodic) character.
Estimation of Uncertainty of a Volumetric Reservoir Model and Optimization of Numerical Inversion in Well Log Data Interpretation
Authors: Khismatullina E R , Indrupskiy I M , Kovalenko K V , Samokhvalov N I
Published: February 2021

This study considers an approach to joint inversion of logging data in oil and gas wells for finding the volume fractions of reservoir components. The inversion is reduced to a linear least squares problem with additional iterations to account for physical constraints. To analyse the quality of inversion, uncertainty evaluation tools are developed for both the model responses and inversion results – volume fractions of the reservoir components. The tools include algorithms for construction of depth-related “confidence corridors” for individual properties and standard deviation ellipses to analyse cross-correlations. An optimization problem of improving the quality of inversion by tuning specific readings of logging methods for individual reservoir layers (or facies/lithotypes) is also posed and solved by minimizing a mismatch criterion between the actual and simulated log curves. Thus, the developed toolkit provides a means for optimized joint inversion of logging data with visual control of uncertainty and cross-correlation in the inversion results.
On the Estimation of Some Relevant Parameters in the COVID-19 Pandemic
Authors: De La Sen M. , Garrido A. J. , Ibeas A.
Published: February 2021

This paper gives simple rules to calculate the transmission rate and some other parameters in COVID- 19 from recorded data on the infection, recovery and death through several SIR-based epidemic models. In particular, it is emphasized how the transmission rate is highly dependent on the quarantine or confinement interventions. The proposed rules estimate the relevant time-derivative of some of the subpopulations of the model by using the standard known discretization rules.
Wind and Temperature Effect on the Performance of a Mobile e-nose platform for Real Time Victim Localization
Authors: Blionas S.
Published: February 2021

In this paper it is presented the influence of weather conditions on the performance of the sensors of a mobile e-nose system that measures the atmospheric air, gas components’ concentrations (actually CO2 and O2), into a rubble void, where humans are entrapped, after a disastrous event that caused the collapsing of a building/construction. In this case, the entrapped humans in the void, they affect the air composition and temperature into the void. Additionally to the human(s) created CO2 source(s), O2 sink(s), and heat source(s) (from body temperature), rubble openings (possibly too many), create sources of fresh atmospheric air and sinks for letting the air from the inside of the cavity to go out of it and vice-versa. The existence of the aforementioned sources/sinks constantly affect the air composition and temperature into the void and makes gas components’ concentrations dynamic rather than static. There will be studied the fresh atmospheric air sources into a cavity taking into account also the outside weather conditions. The final goal is to estimate the concentrations of CO2 and O2 into the cavity as a function of time, having as parameters, the external weather conditions, the openings of the void and the number of entrapped humans. Estimating the concentrations it will enable the operator of a mobile e-nose system with sensors of a specific sensitivity and specificity, to determine the possibilities of detecting the presence of humans in a rubble void.
A refined version of the inverse decomposition theorem for modular multiplicative inverse operators
Authors: Cortés–Vega Luis A.
Published: February 2021

Recently, we started in the IOP Conference Series [1, 2] and in [3] the study of modular multiplicative inverse operators in an algorithmic functional context on (ℤ/ϱℤ)* with ϱ > 3, where it is possible to distinguish different formulas that break up these operators over (ℤ/ϱℤ)*, all this thanks to the so-called “inverse decomposition theorem (IDT)”. In this paper, we will study these issues a little deeper. Though we mainly focus the paper on derived for these operators a refined version of the (IDT), also appear on the scene some additional contributions. Finally, in a brief overview, we compare the differences that exist among the new result here established and the old ones. However, along with this, there are still plenty of questions to be answered in this research field.
Motions of the human cardiac cell electrophysiology model
Authors: Halfar Radek
Published: February 2021

One of the many processes in the human body on which our lives depend is the proper propagation of the electrical signal in the heart tissue. This propagation is dependent on the work of each heart cell, and even small variations in the synchronous work of these cells can lead to life-threatening conditions. A proper understanding of cardiac electrophysiology is therefore essential to understanding heart function and treating heart disease. In this work, cardiac electrophysiology is investigated using a mathematical model of a human ventricular cell (Bueno-Orovio-Cherry-Fenton model). This model is paced by regular stimulation impulses, and its responses to this stimulation are analyzed in terms of their dynamic properties, and the dependence of its dynamic parameters for the frequency and amplitude of stimulation. For this analysis, classical and modern tools from the field of dynamic systems theory (e.g. entropy measures, Fourier spectra, the 0-1 test for chaos) are used.
CNN with residual learning extensions in neutrino high energy physics
Authors: Kubu Miroslav , Bour Petr
Published: February 2021

As many reconstruction steps in neutrino high energy physics (HEP) are similar to image pattern recognition tasks, we explore the potential of Convolutional Neural Networks (CNN) combined with residual machine learning algorithm. Characteristic features from neutrino track image pixelmaps are extracted at different scales and these features are used for classification of the type of neutrino interaction. In this contribution, we sumarize observed performance of the residual neural networks (ResNet) for neutrino charged current (CC) interaction detections using image-like Monte Carlo simulated data for muon and electron neutrinos. The two topologies depicted at the neutrino detectors differ, muon neutrino CC interaction is dominated by a slowly ionizing muon, while electron neutrino CC interaction is usually recorded as a wide shower. For the ResNet performance evaluation, we use area under ROC curve (AUC) as the evaluation metric. We observe an improvement while using residual learning compared to general CNN architecture, which is caused by a more stable training with lesser vulnerability to the vanishing gradient of the ResNets. Moreover, stacking other hidden layers within our ResNet model greatly increased the AUC value on the test neutrino dataset without the signs of unstable training or overfitting.
Similarity Graph Learning and Non-linear Deep Representations for Spectral Clusterings
Authors: Li Ni , Peng Manman , Cao Buwen , Li Kenli , Li Keqin
Published: February 2021

Spectral clustering is one of the most popular clustering approaches. Despite its good performance and strong theoretical supports, it is limited to high complexity of the graph Laplacian similarity matrix construction and eigen-decomposition problems. Recently, deep learning has been successfully adopted in graph representation. In the paper, we jointly learn the manifold graph construction and non-linear low-dimension mapping of the graph. In addition, we theoretically proved that our model according with spectral clustering theory. Meanwhile, we use the proposed non-linear coders as the building blocks to formulate a deep structure to further refine features of layer wise fashion. Extensive experiments on clustering tasks demonstrate that our method performs well in terms of both clustering accuracy and normalized mutual information( NMI )
Research on Hardness Detection Method of Crisped Grass Carp Based on Visible - Near Infrared Hyperspectral Technology
Authors: Wang Qing Xiu , Su Li Heng , Zou Juan , Chen Ning Xia , Wu Ting , Yang Ling
Published: February 2021

With the fish of crisped grass carp as the research object, this paper analyzes the hardness-hyperspectral data of crisped grass carp with a texture analyzer and a visible - near infrared hyperspectral imaging system (400-1100nm), and then proposes a method to quickly detect the hardness of crisped grass carp. In this study, the hardness of 15 fish crisped grass carp was analyzed with a texture analyzer, and it was found that there was a difference in hardness between different fish with a significance P<0.05.Four spectral bands affecting the hardness of crisped grass carp were found by using the random forest algorithm, which indicated that there was a connection between hardness and hyperspectrum. Four pretreatment methods were used, among which SNV pretreatment method had the best effect, R2was 0.80. PLS, SVM and BP-NN prediction models were established respectively, and R2and RMSEP of the models were used as evaluation indexes to compare the advantages and disadvantages. The results are as follows: R2is 0.8033, 0.8564, 0.8814 and RMSEP are 3.6417, 2.6891 and 2.6128 respectively, among which BP-NN prediction model R2is 0.8814 and RMSEP is 2.6128, which has the most significant effect. Therefore, BP-NN prediction model is more suitable for nondestructive testing of crisped grass carp hardness.
EIT-4LDNN: A Novel Neural Network For Electrical Impedance Tomography
Authors: Zhang Xinyu , Chen Xiaoyan , Wang Zichen , Zhang Miao
Published: February 2021

EIT (Electrical Impedance Tomography) is a non-invasive dynamic image detection technology that can well reflect the distribution of objects in a uniform field. But the inverse problem of EIT is a non-linear, highly ill-conditioned, and ill-posed problem. Images reconstructed by traditional iterative algorithms and non-iterative algorithms have too many uneliminated artifacts and low spatial resolution. This paper proposes a deep neural network containing a multi-layer fully connected network so that predicting the conductivity distribution of different samples through the excellent nonlinear fitting ability of the neural network. Compared with the CG(Conjugate Gradient) algorithm and TR ( Tikhonov Regularization) algorithm, the quality of images is improved and the noise is reduced, but the generalization ability and prediction accuracy of the network need to be further improved.
Implementation of A New Speech Negative Emotion Recognition System
Authors: Wang Yuqiong , Zhao Zehui , Huang Zhiwei
Published: February 2021

A new emotion recognition system based on speech is constructed to improve the ability of recognizing negative emotions. Multi-dimensional acoustic characteristics were tested and among them, short-term energy and Mel-frequency cepstral coefficients (MFCC) were selected to be used as parameters for recognition. The system consists two modes: single recognition and group recognition. Single recognition adopts BP neural network model based on MFCC, while group recognition adds support vector machine model based on short-term energy on the basis of single recognition which the group recognition rate of 20 speech can reach 97%. With the increase of the number of speech in each group, the recognition accuracy of negative emotion tends to 100%.
Multimodal Gesture Recognition Based on Attention Slow-Fast Fusion Networks
Authors: Zhang Xunlei , Tie Yun , Qi Lin
Published: February 2021

Gestures serve as the best alternative to traditional human-computer interaction (HCI), but there is still a great challenge to apply gestures to practical operations. Faced with the problem of generally low recognition accuracy in dynamic gesture recognition, we propose a fusion network with a slow-fast structure based on an attention mechanism to improve the recognition accuracy of dynamic gestures. The slow pathway acquires the temporal information of the input dynamic gesture, the fast pathway acquires the semantic information of the gesture in the input video, and suppresses the influence of non-gesture regions on the gesture features as much as possible through the attention mechanism, and finally performs the fusion operation according to the strategy of score fusion to obtain the recognition accuracy of the input dynamic gesture. We validate our proposed method on the ChaLearn large-scale gesture challenge gesture dataset IsoGD, and the experimental results are obtained to verify the effectiveness of our proposed structure by comparing it with the previous experimental results.
Swarm Security Intelligent Dynamic Networking Algorithm Based on Multiple Constraints
Authors: He Sixi , Yang Xiaohua , Wan Ya Ping , Liu Zhenghai , Zeng Tiejun
Published: February 2021

To reduce the security risks and guide management pressure of “mountain-type” scenic areas with steep and changeable topography, this paper combines the idea of swarm security intelligence and proposes a swarm security intelligent dynamic networking algorithm based on multiple constraints to ensure the swarm safety of the tour group and relieve the management pressure of tour guide through the dynamic group and the self-discovery after leaving the group. Based on multiple constraints such as signal strength, power consumption and fault probability etc, the algorithm optimizes the network topology in real-time, which can effectively reduce the high false-positive rate o f leaving the group due to node failure. Secondly, the algorithm constructs the Spf constrained fitness function and introduces the ant colony optimization algorithm to avoid the situation that the optimal routing path falls into the local optimal solution. Finally, the algorithm converts the adjacency matrix composed of routing table and node connection signals into areachable matrix to judge whether the member has left the group and reduce the false-negative rate of group loss in the case of multi-node connection. Experimental results show that the algorithm has good stability and robustness in mobile wireless adhoc networks
Automatic Classification of Japanese Question Intention Based on Deep Learning
Authors: Jin Lan , Su Yancong , Li Jianxun
Published: February 2021

In order to improve the effect of deep learning, this paper puts forward the method of deep learning of Japanese. Before classification, we need to preprocess the word segmentation and delete the termination word in the question, and then use the feature vector to represent it. By learning to store the information in the network, we use heuristic rules to classify the question intention, extract the feature vectors representing different types of questions, and then make statistical analysis on the corpus of actual marked questions, establish a classification system, and bind features based on word packets, Realization intention classification. The experimental results show that the classification accuracy and influence parameters are relatively higher after deep learning, and the automatic classification method of Japanese question intention should be unique.
Research on Identification Algorithm Based on ECG Signal and Improved Convolutional Neural Network
Authors: Wan Dechao , Zhang Luqiao , Bai Yunxiang , Xie Yu
Published: February 2021

In the research of ECG signal identity recognition, most of them adopt the method of feature extraction and recognition model separation, extract the time domain features, transform domain features of the original signal, or combine the features with the cross domain. Then the model is used to complete the recognition and classification. In this paper, an advanced improved convolutional neural network model is proposed, which integrates feature extraction and classification to complete identity recognition. ECG data selected from the ecg-id database and MIT-BIH arrhythmia database are directly sent to the model for automatic sign extraction after hierarchical denoising with wavelet tools and then identified. This method achieves the highest recognition rate of 98.49% on ecg-id database and 99.35% on ECG data of MIT-BIH arrhythmia database. The high reliability of the algorithm and the universality of wireless sensors in mobile devices make this research has high commercial value.
Research on Making Orthophotos of Mountainous Areas Based on WorldView-2 Images
Authors: Xing Zhu Rong , Feng You Gui , Xie Ji Ying , Wang Lei , Xu Yan Ming , Wei Yequn
Published: February 2021

Combining the RPC rational polynomial coefficient model, using WorldView-2 remote sensing images, combined with field surveyed control points, survey area digital elevation model DEM and other data to produce the mountain orthophoto map, combined with the detection points to analyze the accuracy of the results, solve the problem Topographic maps are difficult to measure and are subject to flight restrictions.
Classification of Chinese Hotel Reviews Based on GCN
Authors: Lin Zhiyu , Zhang Jingxuan , Zhang Haoxi
Published: February 2021

Text classification is a fundamental part of natural language processing and can help with many downstream tasks, such as emotion analysis, question and answer systems, and recommendation systems. The graph convolution neural network has the natural superiority in the non - Euclidean space data. For Chinese text data, there is a lot of correlation between the data, using the graph convolutional neural network for text classification can achieve good results. In our experiment, we use a simple one-hot encoding of the word vector to process our words and use of the graph convolutional neural network can achieve 94.24% accuracy in our data set.
Research on Phishing Websitedetection Based on R-SVM algorithm
Authors: Hu Shuting , Li Jiawei , Liu Mingmeng , Pan Bin
Published: February 2021

The Internet has brought us convenience, at the same timeimportant personal information has been exposed. Once the information is leaked, it may cause huge economic losses to individuals. Therefore, it is urgent to realize network security. The traditional method of detecting phishing websites is aimed at big data. Due to the relatively large amount of data, it indirectly causes low efficiency and often fails to solve user needs in a short time. In the detection, the operation is actually performed on the small data stream. If the small data is fundamentally classified, the efficiency will be greatly improved. In summary, based on the realization that we can identify phishing websites, we mainly solve the problems of speed and accuracy. Therefore, this paper uses the R-SVM algorithm to study the data to reduce the time spent on detecting samples and improve efficiency.
Research of Network Behavior Pattern Recognition Based on Power Monitoring System
Authors: Wang Yang Yingfu , Shi Junxia , Li Huixun , Chen Yongwei , Shen Jianqiang , Kan Min , Liang Ye
Published: February 2021

Based on the network security protection of the power monitoring system, this paper proposes a pattern recognition model for the operation behavior of the operation and maintenance personnel of power monitoring system. Through the technical research of the alarm pattern recognition of the network security management platform, and carries out the operation type data of the network security management platform Clustering analysis uses historical data to intelligently train the clustering model. Through training, the risk level classification of the operation behavior is obtained, and then real-time data is introduced into the model for detection, which can judge the degree of risk of the operation event in real time. With the improvement of the K-means clustering algorithm and the application of big data analysis, it further improves the he intelligence level of the platform and provides technical support for the intelligent protection of network.
Quality Prediction of Discrete Manufacturing Process Based on CGAN&Catboost Hybrid Model
Authors: Xu Peng , Ren Zhichen , Shen Yili , Yu Wendi , He Lianghua
Published: February 2021

Accurate prediction of product quality can be used to control the online production process of workpieces, an appropriate prediction model can effectively improve the yield of industrial production. In order to obtain missing data, the classic method is to regress them one by one. However, the traditional regression prediction method destroys the correlation of the data and cannot use the quality inspection index efficiently. This paper presents an improved hybrid model. The essence lies in a novel easily pluggable CGAN module that refines the previous step of regression data. compared with the regression method, the CGAN module can learn the joint d istribution of data. The experiment of real data result shows that our method has high accuracy compared with the existing ones