bluuebunny/crossref_metadata_2025 huggingface dataset:
Demonstration of an x-ray Raman spectroscopy setup to study warm dense carbon at the high energy density instrument of European XFEL
Authors: Voigt K. , Zhang M. , Ramakrishna K. , Amouretti A. , Appel K. , Brambrink E. , Cerantola V. , Chekrygina D. , Döppner T. , Falcone R. W. , Falk K. , Fletcher L. B. , Gericke D. O. , Göde S. , Harmand M. , Hartley N. J. , Hau-Riege S. P. , Huang L. G. , Humphries O. S. , Lokamani M. , Makita M. , Pelka A. , Prescher C. , Schuster A. K. , Šmíd M. , Toncian T. , Vorberger J. , Zastrau U. , Preston T. R. , Kraus D.
Published: August 2021

We present a proof-of-principle study demonstrating x-ray Raman Spectroscopy (XRS) from carbon samples at ambient conditions in conjunction with other common diagnostics to study warm dense matter, performed at the high energy density scientific instrument of the European x-ray Free Electron Laser (European XFEL). We obtain sufficient spectral resolution to identify the local structure and chemical bonding of diamond and graphite samples, using highly annealed pyrolytic graphite spectrometers. Due to the high crystal reflectivity and XFEL brightness, we obtain signal strengths that will enable accurate XRS measurements in upcoming pump–probe experiments with a high repetition-rate, where the samples will be pumped with high-power lasers. Molecular dynamics simulations based on density functional theory together with XRS simulations demonstrate the potential of this technique and show predictions for high-energy-density conditions. Our setup allows simultaneous implementation of several different diagnostic methods to reduce ambiguities in the analysis of the experimental results, which, for warm dense matter, often relies on simplifying model assumptions. The promising capabilities demonstrated here provide unprecedented insights into chemical and structural dynamics in warm dense matter states of light elements, including conditions similar to the interiors of planets, low-mass stars, and other celestial bodies.
Homodyne interferometry using a phase rotator for calibration of sine–cosine phase detection of a 70 GHz probe beam through a plasma
Authors: Nozawa Y. , Uchida M. , Igami H. , Kajita R. , Tanaka H. , Maekawa T.
Published: August 2021

Homodyne interferometry using a motorized phase rotator for calibration of sine–cosine detection of the phase shift of a 70 GHz probe beam through a plasma has been developed. Four interferometers based on this interferometry have been installed on the low aspect ratio torus experiment (LATE) device with four horizontal probe beams on the mid-plane, which has measured the line-integrated electron densities with a time resolution of 10 μs and a resolution of line-integrated density of 5 × 1015 m−2.
Scaling theory of three-dimensional magnetic reconnection spreading
Authors: Arencibia Milton , Cassak P. A. , Shay M. A. , Priest E. R.
Published: August 2021

We develop a first-principles scaling theory of the spreading of three-dimensional (3D) magnetic reconnection of finite extent in the out of plane direction. This theory addresses systems with or without an out of plane (guide) magnetic field, and with or without Hall physics. The theory reproduces known spreading speeds and directions with and without guide fields, unifying previous knowledge in a single theory. New results include the following: (1) reconnection spreads in a particular direction if an x-line is induced at the interface between reconnecting and non-reconnecting regions, which is controlled by the out of plane gradient of the electric field in the outflow direction. (2) The spreading mechanism for anti-parallel collisionless reconnection is convection, as is known, but for guide field reconnection it is magnetic field bending. We confirm the theory using 3D two-fluid and resistive-magnetohydrodynamics simulations. (3) The theory explains why anti-parallel reconnection in resistive-magnetohydrodynamics does not spread. (4) The simulation domain aspect ratio, associated with the free magnetic energy, influences whether reconnection spreads or convects with a fixed x-line length. (5) We perform a simulation initiating anti-parallel collisionless reconnection with a pressure pulse instead of a magnetic perturbation, finding spreading is unchanged rather than spreading at the magnetosonic speed as previously suggested. The results provide a theoretical framework for understanding spreading beyond systems studied here and are important for applications including two-ribbon solar flares and reconnection in Earth's magnetosphere.
Forward modeling of pile-up events in liquid scintillator detectors for neutron emission spectroscopy
Authors: Sahlberg A. , Eriksson J. , Conroy S. , Ericsson G. , Hägg L. , Giacomelli L. , Belli F. ,   JET Contributors
Published: August 2021

When using liquid scintillator detectors to measure the neutron emission spectrum from fusion plasmas, the problem of pile-up distortion can be significant. Because of the large neutron rates encountered in many fusion experiments, some pile-up distortion can remain even after applying traditional pile-up elimination methods, which alters the shape of the measured light-yield spectrum and influences the spectroscopic analysis. Particularly, pile-up events appear as a high-energy tail in the measured light-yield spectrum, which obfuscates the contribution that supra-thermal ions make to the energy spectrum. It is important to understand the behavior of such “fast ions” in fusion plasmas, and it is hence desirable to be able to measure their contribution to the neutron spectrum as accurately as possible. This paper presents a technique for incorporating distortion from undetected pile-up events into the analysis of the light-yield spectrum, hence compensating for pile-up distortion. The spectral contribution from undetected pile-up events is determined using Monte Carlo methods and is included in the spectroscopic study as a pile-up component. The method is applied to data from an NE213 scintillator detector at JET and validated by comparing with results from the time-of-flight spectrometer TOFOR, which is not susceptible to pile-up distortion. Based on the results, we conclude that the suggested analysis method helps counteract the problem of pile-up effects and improves the possibilities for extracting accurate fast-ion information from the light-yield spectrum.
Complex permittivity of common dielectrics in 20–110 GHz frequency range measured with a Fabry–Pérot open resonator
Authors: Salski Bartlomiej , Cuper Jerzy , Karpisz Tomasz , Kopyt Pawel , Krupka Jerzy
Published: August 2021

A Fabry–Pérot open resonator is a tool dedicated to broadband accurate microwave and mm-wave characterization of dielectric sheets. It has been applied in this paper to the measurement of the dielectric constant and loss tangent of various types of commonly used dielectric materials, such as semiconductors, electronic substrates, glasses, and plastics, in the 20–110 GHz range. The obtained results are in good agreement with the literature data, which are, however, often available only at discrete frequencies, thus making it difficult to recognize dispersive properties of the measured samples. It is shown in this paper that broadband measurement of high-resistivity and intrinsic silicon allows extracting their resistivity solely due to conduction losses. In the case of typical electronic substrates, it is shown that their dielectric constant is almost non-dispersive, whereas losses are, in general, linearly increasing with frequency. Glasses also exhibit almost non-dispersive dielectric constant, which is, however, larger than 3.8, whereas their losses can vary by over two orders of magnitude depending on the properties, like the hydroxyl content. The lowest dielectric constant is easily achievable in common plastics, spanning from ca. 2.03 for Teflon up to ca. 3.0 for PETG.
Odd-viscosity-induced instability of a thin film with variable density
Authors: Chattopadhyay Souradip
Published: August 2021

The stability of a two-dimensional gravity-driven thin viscous Newtonian fluid with broken time-reversal-symmetry draining down a uniformly heated inclined plane is discussed. The presence of the odd part of the Cauchy stress tensor with an odd viscosity coefficient brings new characteristics in fluid flow. A theoretical model is implemented, which captures the dependence of the surface tension on temperature, and the model also allows for variation in the density of the liquid with a thermal difference. The coupled effect of odd viscosity, variable density, and surface tension has been investigated both analytically and numerically. A nonlinear evolution equation of the free surface is derived by the method of systematic asymptotic expansion. A linear stability analysis is carried out, which yields the critical conditions for the onset of instability in long-wave perturbations. New interesting results illustrating how the critical Reynolds number depends on the odd viscosity as well as other various dimensionless parameters have been obtained. In addition, a weakly nonlinear stability analysis is performed based on the method of multiple scales from which a complex Ginzburg–Landau equation is obtained. It is observed that the film not only has supercritical stable and subcritical unstable zones, but also unconditional stable and explosive zones. It has been also shown that the spatial uniform solution corresponding to the sideband disturbance may be stable in the unstable region. Employing the Crank–Nicolson method in a periodic domain, the spatiotemporal evolution of the model has been analyzed numerically for different values of the odd viscosity as well as other flow parameters. Nonlinear simulations are found to be in good agreement with the linear and weakly nonlinear stability analysis. The results are conducive to the further development of related experiments.
A ring generator of two- and three-frequency quasiperiodic self-oscillations based on the van der Pol oscillator
Authors: Astakhov Sergey V. , Astakhov Oleg V. , Fadeeva Natalia S. , Astakhov Vladimir V.
Published: August 2021

In this work, we present a model of an autonomous three-mode ring generator based on the van der Pol oscillator, where periodic, two-frequency quasiperiodic, three-frequency quasiperiodic, and chaotic self-oscillations are observed. The transitions to chaos occur as a result of a sequence of torus doubling bifurcations. When the control parameters are varied, the resonant limit cycles appear on a two-dimensional torus, and two-dimensional tori appear on a three-dimensional torus as a result of synchronization. We used a time series of dynamic variables, projections of phase portraits, Poincaré sections, and spectra of Lyapunov characteristic exponents to study the dynamics of the ring generator.
High responsivity quantum cascade detectors with bound-to-miniband diagonal transition
Authors: Li Kun , Ren Fei , Liu Shu-Man , Liu Jun-Qi , Zhuo Ning , Zhu Yi-Xuan , Zhai Shen-Qiang , Zhang Jin-Chuan , Wang Li-Jun , Li Yuan , Liu Feng-Qi
Published: August 2021

We demonstrate a long-wavelength infrared quantum cascade detector (QCD) that operates at a wavelength of 10 μm, in which the active diagonal transition for the electrons occurs from a fundamental energy level into a miniband in adjacent coupled quantum wells. When compared with a bound-to-bound diagonal transition-based QCD, the use of a miniband as the final transition state increases the absorption strength and enhances the electron extraction efficiency of the device, leading to QCD responsivities of 75 at 180 and 30 mA/W at 300 K for 10.55 μm wavelength operation.
Lyapunov exponents for Hamiltonian systems under small Lévy-type perturbations
Authors: Chao Ying , Wei Pingyuan , Duan Jinqiao
Published: August 2021

This work is to investigate the (top) Lyapunov exponent for a class of Hamiltonian systems under small non-Gaussian Lévy-type noise with bounded jumps. In a suitable moving frame, the linearization of such a system can be regarded as a small perturbation of a nilpotent linear system. The Lyapunov exponent is then estimated by taking a Pinsky–Wihstutz transformation and applying the Khas’minskii formula, under appropriate assumptions on smoothness, ergodicity, and integrability. Finally, two examples are presented to illustrate our results.
Role of Fe3+ in altering the degrees of freedom in ZnAl2O4 spinel
Authors: Jain Megha , Manju  , Kumar Manish , Lee Hyun Hwi , Won Sung Ok , Chae Keun Hwa , Gupta Govind , Vij Ankush , Thakur Anup
Published: August 2021

Distribution of Zn2+, Al3+, and Fe3+ ions on tetrahedral and octahedral sites in Fe3+-doped ZnAl2O4 nanocrystals is studied through detailed x-ray diffraction (XRD) analysis. Significant changes in the intensity ratio of (220), (311), (400), and (422) diffraction peaks upon Fe addition were perceived, whose correlation with lattice inversion, Fe3+ ions distribution, and ionic vacancies is probed through simulations and Rietveld refinement. Fe3+ ions were found to be occupying tetrahedral sites in increasing proportion with the doping concentration, leading to decreased spinel inversion. However, different orders of defects, viz., cation and anion vacancies were also found to be playing their part in the statistical distribution of cations on octahedral/tetrahedral interstices. Fe L2,3-edge x-ray absorption near edge structure spectroscopy indicated Fe3+ ions being present on tetrahedral as well octahedral interstices and distortion in cation polyhedra, while O K-edge inferred formation of different orders of ionic vacancies upon Fe addition, thereby corroborating the results from XRD. The photoluminescence study illustrated the quenched emission intensity upon Fe doping, with almost no change in emission color, and signatures of tetrahedral/octahedral Fe3+ occupancy in the lattice. Thus, this study highlights the variation in degrees of freedom associated with atomic arrangements in spinel ZnAl2O4 lattice upon Fe3+ addition.
Coherent thermal transport in nano-phononic crystals: An overview
Authors: Zhang Zhongwei , Guo Yangyu , Bescond Marc , Chen Jie , Nomura Masahiro , Volz Sebastian
Published: August 2021

Nano-phononic crystals have attracted a great deal of research interest in the field of nanoscale thermal transport due to their unique coherent thermal transport behavior. So far, there have been many advances in the theory and simulation studies of coherent thermal transport in nano-phononic crystals. In this paper, we summarize the state-of-the-art studies in this field from the perspective of coherent thermal transport at low temperatures, minimum thermal conductivity, Anderson localization, in various nanosystems, and in the frame of machine learning driven studies. Each part is specifically presented under different simulation methodologies, in which the background theories are also summarized. Accordingly, the controversies between different methodologies in describing wave-like/coherent and particle-like/incoherent thermal phonons are discussed. Various effects on coherent thermal transport are reviewed, including interface roughness, mass disorder, structural randomness, aperiodic ordering, and temperature effect. Finally, an outlook on the future research on coherent thermal transport in nano-phononic crystals is given. This overview provides fundamental and advanced knowledge to understand the coherent thermal transport in nano-phononic crystals, which will be beneficial to the further understanding of the physical picture of thermal phonons and heat transfer related applications.
Interface stability of compressible fluids in porous media
Authors: Le Thi Thai , Koch Thorsten
Published: August 2021

The stability of flows in porous media plays a vital role in transiting energy supply from natural gas to hydrogen, especially for estimating the usability of existing underground gas storage infrastructures. Due to mixed gases inside storage, the interface stability of tangential-velocity discontinuity provides us the exact velocity rate in injecting and withdrawing natural gases effectively. Thus, this research aims to analyze the interface stability of the tangential-velocity discontinuity between two compressible gases by using Darcy's model to include the porosity effect. The results shown in this research will be a basis for considering whether underground gas storage in porous material can store hydrogen. We show the relation between the Mach number M, the viscosity μ, and the porosity ε on the stability of the interface. This interface stability affects gases' withdrawal and injection processes, thus will help us to determine the velocity with which gas can be extracted and injected into the storage effectively. By imposing solid walls along the flow direction, the critical values of these parameters regarding the stability of the interface are smaller than when considering no walls. The consideration of bounded flows approaches the problem more realistically. In particular, this analysis plays a vital role when considering two-dimensional gas flows in storages and pipes.
Disparate regulation of IMD signaling drives sex differences in infection pathology in
Drosophila melanogaster
Authors: Vincent Crystal M. , Dionne Marc S.
Published: August 2021

Significance

Sex differences in infection outcome are a widely observed phenomenon. While it is known that biological sex can influence an animal’s response to infection, the mechanisms through which these differences emerge are less clear. Here, we describe a mechanism through which heightened regulation of the IMD signaling pathway by female—but not male—
Drosophila melanogaster
reduces the cost of immune activity at the expense of resistance to bacterial infection. Through the masculinization of the main organ responsible for antimicrobial peptide activity in the fly (fat body), this work demonstrates that this heightened immune regulation is mediated by sex-determining pathways.
Volatile chemical product emissions enhance ozone and modulate urban chemistry
Authors: Coggon Matthew M. , Gkatzelis Georgios I. , McDonald Brian C. , Gilman Jessica B. , Schwantes Rebecca H. , Abuhassan Nader , Aikin Kenneth C. , Arend Mark F. , Berkoff Timothy A. , Brown Steven S. , Campos Teresa L. , Dickerson Russell R. , Gronoff Guillaume , Hurley James F. , Isaacman-VanWertz Gabriel , Koss Abigail R. , Li Meng , McKeen Stuart A. , Moshary Fred , Peischl Jeff , Pospisilova Veronika , Ren Xinrong , Wilson Anna , Wu Yonghua , Trainer Michael , Warneke Carsten
Published: August 2021

Significance

Recent work in Los Angeles has shown that urban volatile organic compound (VOC) emissions from consumer and industrial products—termed volatile chemical products (VCPs)—are now an important source of ozone precursors. Using advancements in VOC instrumentation, we show that VCP emissions are ubiquitous in urban regions and can be identified via unique VOC fingerprints. Through detailed modeling, we show that VCPs are as important to ozone production as fossil fuel VOCs and that the chemistry of VCPs can have significant impacts on model simulations of key atmospheric processes. Consequently, air quality models must be updated to account for both the emissions and atmospheric chemistry of VCPs in order to capture their full impact on urban air quality.
Footprint of greenhouse forcing in daily temperature variability
Authors: Kotz Maximilian , Wenz Leonie , Levermann Anders
Published: August 2021

Significance

Understanding how the variability of daily temperature may change with greenhouse gas emissions is important because it has been identified as a key factor in societal and economic well-being. Assessing historical changes to daily temperature variability in comparison with those from state-of-the-art climate models, we show that variability has changed with distinct global patterns over the past 65 years, changes which are attributable to rising concentrations of greenhouse gases. If these rises continue, temperature variability is projected to increase by up to 100% at low latitudes and decrease by 40% at northern high latitudes by the end of the century. We further show that these changes would be limited by mitigating emissions and will depend on the equilibrium climate sensitivity.
In vitro turnover numbers do not reflect in vivo activities of yeast enzymes
Authors: Chen Yu , Nielsen Jens
Published: August 2021

Turnover numbers (
k
cat
values) quantitatively represent the activity of enzymes, which are mostly measured in vitro. While a few studies have reported in vivo catalytic rates (
k
app
values) in bacteria, a large-scale estimation of
k
app
in eukaryotes is lacking. Here, we estimated
k
app
of the yeast
Saccharomyces cerevisiae
under diverse conditions. By comparing the maximum
k
app
across conditions with in vitro
k
cat
we found a weak correlation in log scale of
R
2
= 0.28, which is lower than for
Escherichia coli
(
R
2
= 0.62). The weak correlation is caused by the fact that many in vitro
k
cat
values were measured for enzymes obtained through heterologous expression. Removal of these enzymes improved the correlation to
R
2
= 0.41 but still not as good as for
E. coli
, suggesting considerable deviations between in vitro and in vivo enzyme activities in yeast. By parameterizing an enzyme-constrained metabolic model with our
k
app
dataset we observed better performance than the default model with in vitro
k
cat
in predicting proteomics data, demonstrating the strength of using the dataset generated here.
On conceptualizing grammatical change in a Darwinian framework
Authors: Breyl Michael , Leiss Elisabeth
Published: August 2021

Approaching language change within a Darwinian framework constitutes a long-standing tradition within the
literature of diachronic linguistics. However, many publications remain vague, omitting conceptual details or missing necessary
terminology. For example, phylogenetic trees of language families are regularly compared to biological speciation, but definitions
on mechanisms of inheritance, i.e. how linguistic information is transferred between individuals and cohorts, or on the linguistic
correlates to genotype and phenotype are often missing or lacking. In light of this, Haider’s attempts to develop this
approach into a theoretically more precise position, closely mirroring principles of Darwinian natural selection in the dimension
of diachronic grammatical change, but contrasting this with non-Darwinian lexical change. He draws a comparison to viral
replication, essentially positing that grammar variants act as mental viruses, competing for replication in new hosts, i.e.
children during critical periods of language acquisition. Haider proposes that in light of this competition for replication, the
unconscious fixation of an individual’s grammar leads to diachronic grammatical change largely mirroring Darwinian natural
selection. Despite the intuitive appeal this mode of reasoning may feature, the following response paper identifies and discusses
a suit of shortcomings to this conceptualization. Some problems arise from underspecified theoretical notions, others due to the
incomplete or inaccurate adoption of biological principles, and yet more through a partial incompatibility with empirical data.
These criticisms do not amount to a dismissal of the Darwinian framework Haider is following, but to a rejection of Haider’s
current position. Albeit it remains unclear if a truly Darwinian approach to language change can be reached, suggestions on how
Haider’s theoretical notions could be further developed are made and pertinent efforts may ultimately lead to a productive
theory.
Janus-faced presidents: extroverted and introverted politics in oil-age Niger
Authors: Schritt Jannik
Published: August 2021

This article analyses political speeches and practices of three Nigerien presidents between 2008 and 2011. It argues that politics in Niger are characterised by a logic of code-switching between an extroverted rhetoric to gain access to international aid, and an introverted rhetoric that critiques this very international system. This analysis makes a case for studies of African states that do not completely adhere to a perspective of either neocolonial dependency or neopatrimonialism. Rather, as leaders of a Janus-faced state, Nigerien presidents walk a tightrope to manoeuvre between external and internal demands in order to acquire resources and legitimacy in both spheres.
Drift orbit islands of energetic particles due to 3D fields in ITER
Authors: Liu Yueqiang , Li L. , Loarte A. , Pinches S.D. , Polevoi A.
Published: August 2021

In this paper, we report a comparative study of the orbit islands formed by the guiding center drift motion of test energetic particles (EPs), including both high-energy deuterium ions and fusion-born α-particles, with respect to the magnetic islands formed by tracing the field lines, for an ITER plasma representing the 15 MA baseline scenario. The particle drift orbit is modified by the presence of static resonant magnetic perturbations (RMPs) of different toroidal mode numbers n (n = 1 and 3 in this study), forming orbit islands in the Poincaré plane by passing EPs. The key findings are as follows. (i) With an n = 1 RMP field, the size of the orbit islands combined with the total RMP field, including the plasma response, is about three times smaller than that obtained by assuming the corresponding vacuum field. (ii) The orbit island size is not sensitive to the EP energy, and is comparable to those of the magnetic islands. (iii) Passing EPs with outward radial orbital drift form orbit islands that shift inward with respect to the corresponding magnetic islands, and vice versa. This radial shift, ΔΨ, measured in the normalized equilibrium poloidal flux, is quantified as ΔΨ ≃ 0.03X for the ITER baseline plasma and assuming n = 1 RMPs, with

X
≡

(

M
/

M

p

)

1
/
2

(

E

[

M
e
V

]

)

1
/
2

Z

−
1

, where M is the particle mass, M
p is the proton mass, E is the particle energy, and Z is the particle charge number. (iv) Trapped EPs do not form drift orbit islands, even in the presence of the 3D fields produced by the 90 kAt ELM control coil current in ITER, and thus possess good confinement properties. Nevertheless, the deformation of the trapped particle drift orbits in the Poincaré plane shows that the canonical toroidal angular momentum of EPs is no longer conserved in the presence of the RMP fields.
Continuum limit of lattice quasielectron wavefunctions
Authors: Patra Aniket , Hillebrecht Birgit , Nielsen Anne E B
Published: August 2021

Trial states describing anyonic quasiholes in the Laughlin state were found early on, and it is therefore natural to expect that one should also be able to create anyonic quasielectrons. Nevertheless, the existing trial wavefunctions for quasielectrons show behaviors that are not compatible with the expected topological properties or their construction involves ad hoc elements. It was shown, however, that for lattice fractional quantum Hall systems, it is possible to find a relatively simple quasielectron wavefunction that has all the expected properties (2018 New J. Phys.
20 033029). This naturally poses the question: what happens to this wavefunction in the continuum limit? Here we demonstrate that, although one obtains a finite continuum wavefunction when the quasielectron is on top of a lattice site, such a limit of the lattice quasielectron does not exist in general. In particular, if the quasielectron is put anywhere else than on a lattice site, the lattice wavefunction diverges when the continuum limit is approached. The divergence can be removed by projecting the state on the lowest Landau level, but we find that the projected state does also not have the properties expected for anyonic quasielectrons. We hence conclude that the lattice quasielectron wavefunction does not solve the difficulty of finding trial states for anyonic quasielectrons in the continuum.
Research on Anti-lock Braking System of Electro-mechanical Braking Vehicle Based on Feature Extraction
Authors: Liu Xuebin , Ma Chenbin , Jiang Yu , Zhang Lingli , Xue Yangang
Published: August 2021

With the increase of vehicle speed, the increase of road traffic density and people’s higher requirements for vehicle safety, the Anti-lock Braking System (ABS) has become an important safety device on vehicles. It not only has the braking function of the common braking system, but also can prevent the wheels from locking. Automobile ABS is an important device that affects the safety of automobile braking system and driving safety. It can shorten braking distance and improve directional stability and operability during braking. Electro-mechanical braking control system adopts advanced electro-mechanical braking technology and is applied to automobile braking control system together with anti-lock braking system. Emb (electro-mechanical brake) uses electric energy as the energy source of the braking system, and uses the motor to drive the actuator to generate braking force, which responds quickly and can effectively improve the active safety of automobiles. In this paper, based on feature extraction algorithm, the brake pressure estimation method of EMB actuator is proposed, and the anti-lock braking control algorithm of automobile is analyzed.
Research on Optimization Method of Computer Network Service Quality Based on Feature Matching Algorithm
Authors: Lu Haifeng , Li Manman , Zhang Yajuan
Published: August 2021

On the whole, the main basis for strengthening the quality of network service is the construction of network system, reasonable arrangement of network and adjustment of information resources. In order to enhance the quality of network services, we should minimize the problems. In order to improve the quality of network, we should study more new network technologies and make up for network loopholes in time. In this study, in order to solve the efficiency problem brought by mass candidate services to quality-aware service selection, a service selection strategy based on feature matching algorithm is proposed. Based on statistical method, a probability matrix describing the mapping relationship between quality demand pattern and quality-oriented service pattern is established, and the service selection algorithm is improved by using the probability matrix. A series of experiments on artificial simulated data sets generated from real service data sets prove the feasibility and effectiveness of the proposed method.
Ultimate strength analysis of longitudinal bending of hull girder based on image analysis
Authors: Xu Zhenhao
Published: August 2021

In the traditional code, the allowable stress method is used to check the total longitudinal strength of hull girder, which has the characteristics of simple and quick calculation. However, this method is based on the assumption of elastic materials, and can not truly reflect the actual bearing capacity of the structure. Based on image analysis, a practical method for calculating the ultimate longitudinal strength of ship hull is studied. The average stress-strain relationship of the elements constituting the cross section of the hull girder under axial pressure is given, and the progressive collapse behavior of the hull girder in longitudinal bending is analyzed. Finally, the reliability analysis method for the ultimate strength of the hull girder in longitudinal bending is put forward, and a real ship example is given. The research results show that the proposed method is not only suitable for the safety assessment of ship structures in the conceptual design stage, but also meets the requirements of rapid prediction of real ship wave loads and structural strength reliability analysis.
Research and Application of Analysis System in Material Field by Digital Audit and Artificial Intelligence
Authors: Fan Ruixia , Song Junguo
Published: August 2021

In the material management process of large power companies, there are frequent withdrawals from the library to modify the age of the warehouse, resulting in a substantial backlog of materials. In view of the huge amount of data and multiple analysis dimensions in the process of auditing and screening such problems, the research and development is based on digitalization. The automatic analysis system in the field of audit materials, through the comprehensive cross-analysis of engineering information, financial data, and material data, can quickly determine the data of the false profit pool, and significantly improve the quality and efficiency of the audit. According to the screening results, the material management department is urged to deploy the stockpiled materials in the library to promote the efficient turnover of materials.
Research on Test Method of Smart Anti-Towed Radar Active Decoy in Complex Electromagnetic Environment
Authors: Zhao Bo , Wang Duo
Published: August 2021

As an effective jamming method, towed radar active decoy, poses a great threat to monopulse radar seekers, and has attracted more and more attention from various countries. This article analyzes the interference mechanism and main parameters of towed radar active decoys, and provides a theoretical basis for the use of towed decoys. In view of its technical characteristics, combined with technical and tactical countermeasure strategies and technical approaches, research the test range to carry out countermeasures against towed radar active decoys, and provide theoretical basis and reference value for the use of towed radar active decoy countermeasures in the range.
New Artificial Intelligence Technology Applied in Automobile Lithium Battery Manufacturing
Authors: Wang Zhaiyi
Published: August 2021

In order to improve the estimation accuracy of the state of charge (SOC) of electric vehicle power batteries, this paper is based on artificial intelligence technology for lithium-ion battery model and parameter identification algorithm, adaptive unscented Kalman filter algorithm and SOC estimation based on battery model fusion Algorithm for research. The simulation results show that the SOC error estimated by the artificial intelligence adaptive Kalman filter method is less than 2.4%, which effectively reduces the impact of unknown interference noise on the battery management system when the electric vehicle is driving. The SOC estimation accuracy is higher than that of the extended Kalman method, and has good robustness.
Research on Intelligent Evaluation System of Influence Model Using Cosine Similarity
Authors: Li Zhitao , Wang Xin , Li Xiaoling
Published: August 2021

Nowadays, music plays an increasingly important role in social development. As time goes by, music influences music, artists, and the times. The purpose of this paper is to create a model to quantify the influence of previously created music on new music and music artists. Firstly, this paper constructs two music influence networks based on artists and genres and defines and explains some meaningful parameters of music influence networks. The parameters of this paper describe the influence of communication between artists, and then this paper calculates and explains the parameters in the subnet. Next, this paper uses cosine similarity to measure similarity between two music samples and innovatively uses variance as the weight of music features to identify differences between music. Then the similarity measure is constructed based on matrix norm. Finally, this paper concludes that artists within schools are more similar than artists between schools.
Evaluation of Systems Current Status by PCA-RBF Neural Network and Novel Fuzzy Intelligence Method
Authors: Li Zimu , Luo Lan , Lu Yuqing
Published: August 2021

The health and the sustainability of a higher education system are crucial to the prosperity of a nation. It is crucial to effectively evaluate a higher education system in various aspects and promptly adjust the corresponding educational policies. Firstly, 23 higher education quality indicators with respect to more than 1,000 universities are carefully collected and converted to the corresponding indicators of 40 countries/regions. Moreover, such indicators are normalized by the range transformation method in order to facilitate subsequent analyses. Secondly, a novel fuzzy intelligence (F&I) method is proposed to model the health states of different higher education systems based on radial basis function (RBF) neural networks. This method firstly reduces the input indicators’ dimension by the principal component analysis (PCA) technique. Using the PCA results as the input layer, the RBF neural network is carefully trained and the F&I score of each country/region is therefore obtained. Next, the hierarchical cluster analysis is carried out to depict the health state of each county/region. 1, 5, 5 and 29 countries/regions are categorized as healthy, good, general and unhealthy, respectively.
A Text Category Detection and Information Extraction Algorithm with Deep Learning
Authors: Wu Xiaohan , Wu Zejun , Feng Yuqi
Published: August 2021

In order to solve the problem that the text classification model based on neural network is easy to over-fit and ignore the key words in sentences in the training process, a Bi-GRU Chinese text classification model based on hierarchical Attention mechanism is proposed. The model introduces the idea of layering, uses bi-directional gated cyclic neural network to learn the text representation at word level and sentence level, uses Self-Attention hierarchical model to obtain the information of the influence of words and sentences on text classification, shares the weight between embedded layer and softmax layer by binding, and uses AMSBound optimization method to obtain the optimal weight matrix quickly and effectively while reducing the parameters in the model. Two commonly used Chinese data sets, FudanSet and THUCNews, are tested on the long Chinese text classification data set FudanSet. The experimental results show that the accuracy, recall rate and F-score of this model are better than Text-CNN model, Attention-BiLSTM model and Bi-GRU_CNN model, and the accuracy, recall rate and F-score index are improved by 5.9%, 5.8% and 4.6%, respectively.
Assessment and Information System Establishment of the COVID-19 Impacts and countermeasures: Gray Prediction Model Applied in Analysis and Prediction
Authors: Liu Yushan
Published: August 2021

The outbreak of COVID-19 has had a huge impact on China’s economic and social development, among which the tertiary industry has been severely impacted. As the epidemic prevention and control in China has achieved initial success and entered the normal prevention and control stage, it is very necessary to analyze the damage situation of industries directly affected by the epidemic. According to the historical data of various industries in China in the past five years, a grey prediction model was established to predict the normal development law of some economic indicators without an epidemic situation. Compared with the actual values in the first two quarters of 2020, we can estimate the economic and social losses caused by the COVID-19 epidemic. The epidemic has had the most serious impact on the tertiary industry, with retail, tourism, and catering sectors were hit hard. With the effective control of the epidemic, China’s overall economic performance in the second quarter rose steadily. Many enterprises in the comprehensive service sector have been upgraded and transformed during the epidemic. From the current perspective, the epidemic will not have a serious impact on economic development throughout the year.
Analysis and innovation prospect of intelligent ship visual tracking and recognition system
Authors: Yu Shiguan , Zhang Hongshuo , Mu Shilong , Liu Shizhong , Ding Haojie
Published: August 2021

In recent years, due to the rise of the concept of intelligent ship and the development of the intelligent ship, the ship intelligent has become a global shipping the trend of The Times, with the rapid development of maritime shipping industry, sea lanes and port ship intensity increasing, shipping safety risk increasingly improve, intelligent traffic regulation becomes increasingly important task, shipping information acquisition is more accurate. Ship tracking and identification system is not only the core of shipping safety supervision system, but also has an effective role in the collection of ship navigation information and intelligent supervision of navigation traffic. Computer vision is a technology developed rapidly in recent years, as the current research hotspot, with its access to large amount of information, and the advantages of fast response speed, strong anti-jamming capability compared with traditional of AIS and radar easily affected by obstacles, affected by the operator subjective behavior of communication system device, it can well realize the target ship recognition and tracking, to the modern channel intelligent regulation of ship sailing information collection, transportation, ship navigation safety are very important significance. This article mainly from the principle of development background significance, system characteristics, the visual technology, tracking algorithm for visual identification and tracking system to do a detailed description of principle, on the basis of AIS and radar, based on traditional visual algorithm combined with some data and AIS system on personal improvement ideas on the application of the system innovation, and finally puts forward personal has been developed out of a visual tracking innovative device of the ship.
Tire Defect Detection Using Adaptive Dictionary
Authors: Xiang Yuanyuan , Li Shengbao , Yin Chuanming
Published: August 2021

This paper proposes a new algorithm for detecting tire defects, which is based on principal component analysis and adaptive dictionary learning. Because the defects on the side of the tire are very small and difficult to detect, the dictionary is directly obtained from the test image itself instead of the reference image, thereby improving the soul of adapting to changes in lighting intensity and texture. When using the learned dictionary to represent the test image, patches involving abnormalities in the test image may have larger reconstruction errors than normal errors. Through threshold operation, the defect area can be segmented from the residual image. Compared with the wavelet-based method and the component decomposition method, the experimental results of test images with defects show that the algorithm can adapt to changing light intensity and texture, and also shows more accurate defect detection results.
Attention Generating Target Images with Labels for Unsupervised Domain Adaptation
Authors: Fu Shuai , Ye YiFan , Chen Jing
Published: August 2021

Domain adaptation has recently become a popular and successful field of solving the dependence of the deep learning model on the training set. Previous works proposed unsupervised learning methods based on Generative Adversarial Networks (GANs), which solved the mixed target domain labels problem caused by the neglect of class-level matching, by generating target pairs. And these well-designed models have successfully achieved great performance on different datasets. Moreover, due to the recent widespread use of attention mechanism, we intuitively introduce attention mechanism to the previous model. Our new structure is based on GANs containing attention mechanism and well-designed upampling module, which make our model more robust. Demonstrated by extensive experiments, our new model outperforms the previous works on several standard domain adaption datasets.
Research on Forecast Model Based on BP Neural Network Algorithm
Authors: Xu Qinfeng , Kong Wenju
Published: August 2021

Back propagation Neural Network is a well-known method of prediction. Due to the development of the neural network technology, there has been an increasing amount of neural network application research in the last decade, with a majority of application focusing on financial decisions. Firstly, this paper demonstrates the algorithm of back propagation Neural Network. Then, the application of Back propagation algorithm in prediction sector is explained in detail through the design of pre-warning model of financial distress for real estate industry in China. Meanwhile Z-score model is utilized to produce the only output nodes of Back propagation Neural Network. The research can provide evidence of forecast performance based on BP Neural network technique.
Adaptive Teacher Finetune: Towards high-performance knowledge distillation through adaptive fine-tuning
Authors: Hou Zhenyan , Fan Wenxuan
Published: August 2021

Knowledge distillation is a widely used method to transfer knowledge from a large model to a small model. Traditional methods use pre-trained large models to supervise the training of small models, called Offline Knowledge Distillation, However, the structural gap between teachers and students limits its performance. After that, Oneline Knowledge Distillation retrained the teacher-student network from the beginning and the method of echo teaching greatly improved the performance. But there is very little work to explore the difference between the two. In this paper, we first point out that the essential difference between Offline and Oneline Knowledge Distillation is actually whether the weight of the teacher-student network has a process of mutual adaptation. If they adopt the teacher network and the student network jointly train to implement Offline Knowledge Distillation, there is no obvious difference in the final performance, no matter whether it is a joint distillation training. This shows that teacher-student network adaptation is important for Knowledge Distillation. Then, we propose an Adaptive Teacher Finetune (ATF) to adapt the teacher model to the student network. It will use student model information for Tinetune during the Offline Knowledge Distillation process. With normalized logical distribution and alpha-divergence, the performance improvement of ATF clearly exceeds the existing Offline and Oneline Knowledge Distillation method. Extensive experiments conducted on cifar and ImageNet support our aforementioned analysis and conclusions. With the newly introduced ATF, we obtained state-of-the-art performance on ResNet 18 on ImageNet.
Adaptive trading system based on LSTM neural network
Authors: Li Yueqiu , You Chunming
Published: August 2021

With the continuous development of statistics and computer technology, the methods of technical analysis are also constantly expanding. A large number of statistical and mathematical methods have been applied to the analysis of stocks, which greatly expands the tools available for stock analysis. With the rapid development of deep learning and the great progress of computer hardware technology, statistical learning algorithms have been widely used in big data processing. Including convolutional neural network model and cyclic neural network model, have shown great advantages in the processing of time series such as images, speech and text. In today’s financial market, due to the existence of a large number of assets and fast information flow, people are more and more inclined to use quantitative investment. Quantitative investment as a means of financial sector, the New Deal, which is mainly composed of fundamental analysis and technical analysis from the traditional investment method, its core is to use mathematical model set up trading strategy, and with the help of computer technology on the financial data for quantitative analysis, and judgment, found that financial market rules, eliminating dependence on the experience of the people in the course of investment and emotional impact, so as to guide investment decisions. In order to improve the prediction ability of LSTM neural network model, this paper chooses the stack LSTM neural network model, and combines the stack LSTM neural network model with ADAM algorithm to achieve better prediction results. The empirical results show that the prediction effect of ADAM-based stack LSTM neural network is better than that of ADAM-based LSTM neural network.