bluuebunny/crossref_metadata_2025_split huggingface dataset:
A phenomenological model for interfacial water near hydrophilic polymers
Authors: Earls A , Calderer M-C , Desroches M , Zarnescu A , Rodrigues S 
Published: June 2022

Abstract
We propose a minimalist phenomenological model for the ‘interfacial water’ phenomenon that occurs near hydrophilic polymeric surfaces. We achieve this by combining a Ginzburg–Landau approach with Maxwell’s equations which leads us to a well-posed model providing a macroscopic interpretation of experimental observations. From the derived governing equations, we estimate the unknown parameters using experimental measurements from the literature. The resulting profiles of the polarization and electric potential show exponential decay near the surface, in qualitative agreement with experiments. Furthermore, the model’s quantitative prediction of the electric potential at the hydrophilic surface is in excellent agreement with experiments. The proposed model is a first step towards a more complete parsimonious macroscopic model that will, for example, help to elucidate the effects of interfacial water on cells (e.g. neuronal excitability), the effects of infrared neural stimulation or the effects of drugs mediated by interfacial water.
A denoising and multiscale residual deep network for soft sensor modeling of industrial processes
Authors: Gao Renzhi , Zhu Hegong , Wang Gang , Wu Zhangjun 
Published: June 2022

Abstract
Deep learning plays an important role in soft sensors of industrial processes for the timely measurement of key quality variables. However, since sensors are often operated under noisy and nonstationary industrial conditions, the collected industrial process data exhibit extreme complexity, which severely restricts the learning capacity and measurement accuracy of deep learning methods. In this paper, a novel denoising and multiscale residual deep network (DMRDN) is proposed for soft sensor modeling. Firstly, a stacked denoising autoencoder with level-aware attention is developed to denoise the process data, in which denoised features on different levels are learned and fused. Secondly, the denoised features are fed into multiscale residual convolutional neural network with scale-aware attention, which is designed to capture and fuse deep dynamic features from different scales. Finally, experiments were conducted on an industrial debutanizer column. The experimental results demonstrate that the proposed DMRDN greatly strengthens the learning ability and achieves better prediction performance compared with other methods.
Porous polyvinyl alcohol/graphene oxide composite film for strain sensing and energy-storage applications
Authors: Cui Xu , Guo Jia , Araby Sherif , Abbassi Fethi , Zhang Chunyan , Diaby Abdullatif Lacina , Meng Qingshi 
Published: June 2022

Abstract
In this study, a flexible porous polyvinyl alcohol (PVA)/graphene oxide (GO) composite film was developed and tested for flexible strain sensing and energy-storage applications. Morphology and mechanical properties were studied; tensile strength and Young’s modulus increased by 225% and 86.88%, respectively, at 0.5 wt% GO. The PVA/GO film possesses exceptional sensing ability to various mechanical strains, such as tension, compression, bending, and torsion. For example, the gauge factor of the PVA/GO film as a tensile-strain sensor was measured as 2.46 (246%). Under compression loads, the PVA/GO composite film showed piezoresistive and capacitive strain-sensing characteristics. Under 5 kPa of compression load, the relative resistance increased by 81% with a 100 msec response time; the relative capacitance increased by 160% with a 120 msec response time. The PVA/GO strain sensor exhibited high durability and reliability over 20 × 103 cycles of tensile strain and bending at 3.33 Hz. Moreover, the PVA/GO composite film showed good electrochemical properties due to its porous structure; the maximum capacitance was 124.7 F g−1 at 0.5 wt% GO. After 20 × 103 charging–discharging cycles, the capacitance retention rate was 94.45%, representing high stable capacitance performance. The results show that electrically conductive porous PVA nanocomposite films are promising candidates for strain sensing and energy-storage devices.
Anomalous self-similar solutions of exponential type for the subcritical fast diffusion equation with weighted reaction
Authors: Iagar Razvan Gabriel , Sánchez Ariel 
Published: June 2022

Abstract
We prove existence and uniqueness of the branch of the so-called anomalous eternal solutions in exponential self-similar form for the subcritical fast-diffusion equation with a weighted reaction term

∂

t

u

=

Δ

u

m

+
|
x

|

σ

u

p

,

posed in

R

N

with N ⩾ 3, where

0
<
m
<

m

c

=

N
−
2

N

,
p
>
1
,

and the critical value for the weight

σ
=

2

(

p
−
1

)

1
−
m

.

The branch of exponential self-similar solutions behaves similarly as the well-established anomalous solutions to the pure fast diffusion equation, but without a finite time extinction or a finite time blow-up, and presenting instead a change of sign of both self-similar exponents at m = m
s = (N − 2)/(N + 2), leading to surprising qualitative differences. In this sense, the reaction term we consider realizes a perfect equilibrium in the competition between the fast diffusion and the reaction effects.
Integrable semi-discretisation of the Drinfel’d–Sokolov hierarchies
Authors: Yin Yue , Fu Wei 
Published: June 2022

Abstract
We propose a novel semi-discrete Kadomtsev–Petviashvili equation with two discrete and one continuous independent variables, which is integrable in the sense of having the standard and adjoint Lax pairs, from the direct linearisation framework. By performing reductions on the semi-discrete Kadomtsev–Petviashvili equation, new semi-discrete versions of the Drinfel’d–Sokolov hierarchies associated with Kac–Moody Lie algebras

A

r

(

1

)

,

A

2
r

(

2

)

,

C

r

(

1

)

and

D

r
+
1

(

2

)

are successfully constructed. A Lax pair involving the fraction of

Z

N

graded matrices is also found for each of the semi-discrete Drinfel’d–Sokolov equations. Furthermore, the direct linearisation construction guarantees the existence of exact solutions of all the semi-discrete equations discussed in the paper, providing another insight into their integrability in addition to the analysis of Lax pairs.
Comparing two- and three-dimensional models of scrape-off layer turbulent transport
Authors: Nicholas T E G , Omotani J , Riva F , Militello F , Dudson B 
Published: June 2022

Abstract
There exists a large body of previous work using reduced two-dimensional models of the scrape-off layer (SOL), which model fluctuations in the drift-plane but approximate parallel transport with effective loss terms. Full size three-dimensional simulations of SOL turbulence in experimental geometries are now possible, but are far more computationally expensive than 2D models. We therefore use a flux-tube geometry model of the SOL to compare the results of 2D simulations to 3D simulations with a similar setup, looking for systematic differences. Our setup mimics the outer SOL of a double-null plasma, with no neutrals and no shear, and is therefore likely more favourable to agreement. Overall, we do find good agreement in the basic radial profiles, probability distribution functions, and power spectra of fluctuations. However, the average temperature is over-predicted in 2D relative to 3D, and we explain the difference in terms of the effect of geometrical simplifications of devices at low power. Varying geometric parameters, we find that supersonic flow in the divertor leg, which occurs because our simulations do not include neutrals and so represent low-recycling conditions, means that the divertor leg length only has a weak effect on the output. Finally, we examine the effect of altering the magnitude of source and sink terms in 2D, concluding that they cannot easily be used to recreate both the density and temperature profiles observed in 3D simultaneously.
Thermonuclearizing the plasma focus—converting plasma focus fusion mechanism from beam-gas target to thermonuclear
Authors: Lee S 
Published: June 2022

Abstract
It has been reported that over a range of 3–280 kJ, neutron-optimized deuterium plasma focus machines operate with a near constant speed factor S = 89 ± 8 kA cm−1 per (torr)0.5. This near-constant value of S is consistent with a narrow range of peak axial speeds approximately 10 cm μs−1 and peak radial speeds 20–30 cm μs−1, resulting in focus pinch temperatures less than 0.5 keV and inductively generated high voltages, producing deuteron beams with energies from high tens to hundreds keV. The low pinch temperatures and high beam-deuteron energies result in fusion neutrons predominantly from a beam-target mechanism. A converging taper ending in radius a
e, is added to the end of the standard cylindrical anode of radius ‘a’. This taper converts the pinch from one with starting radius ‘a’ to one with a reduced starting radius a
e. This increases the value of S and consequently the pinch temperature. This study examines the scaling of the end taper and finds that a taper ending in a radius, which is 1/20 that of the before-taper section increases the pinch temperature by a factor approximately 200, to above 20 keV. This increases the thermonuclear cross-sections by up to 14 orders of magnitude. Numerical experiments using the Lee code confirm that the thermonuclear component of the fusion yield becomes predominant. Such a taper may be considered as a thermonuclear converter.
Dependence of latch-up and threshold voltages on channel length in single-gated feedback field-effect transistor
Authors: Woo Sola , Kim Sangsig 
Published: June 2022

Abstract
This study demonstrates an optimal design method for the channel length in a p
+–i–p–n
+ structure of feedback field-effect transistors (FBFETs) for next-generation memory devices. We demonstrate the dependence of latch-up and threshold voltages on the channel length in single-gated FBFETs with silicon channels consisting of gated and non-gated regions. The operation principle of the latch-up phenomena related to the channel length using an equivalent circuit in an FBFET has been described. The abrupt increase in the drain current of the single-gated FBFETs at the latch-up (threshold) voltage in the sweep of the drain (gate) voltage was analyzed with current gains in an equivalent circuit. The current gain depends on the gated and non-gated channel lengths; thereby, the latch-up and threshold voltages too depend on the gated and non-gated channel lengths. The dependences of the latch-up and threshold voltages on the non-gated channel length were found to be 3.62 times and 1.68 times higher than that on the gated channel length, respectively.
Piezoelectric transducer design for simultaneous ultrasonic power transfer and backscatter communication
Authors: Allam Ahmed , Sabra Karim , Erturk Alper 
Published: June 2022

Abstract
Ultrasonic waves can transfer power and data to sensors and devices deployed to traditionally inaccessible locations, such as inside the human body or deep in the ocean, eliminating the need for battery replacement. In ultrasonic power and data transfer systems, a piezoelectric transducer converts incident ultrasonic waves to useful electric power while transmitting data by modulating its reflected signal through backscatter communication. Existing approaches rely on reflecting a portion of the incident power to communicate, reducing the harvested power. This work realizes uninterrupted power harvesting with simultaneous backscatter communication through frequency multiplexing. A piezoelectric transducer is first designed and tested experimentally for high sensitivity and high bandwidth operation through low-loss broadband acoustic and electrical impedance matching. The transducer achieved 70% bandwidth at 1 MHz with a 10 dB difference between reflecting and absorbing incident ultrasonic waves. A frequency multiplexing technique is then developed to separate power and data into different frequency bands achieving simultaneous operation. The technique extends the range and bandwidth of ultrasonically powered devices such as biomedical implants and ocean monitoring sensors.
Multi-physical modeling and fabrication of high-performance IPMC actuators with serrated interface
Authors: Rao Manting , Tang Fei , Li Yan , Chang Longfei , Zhu Zicai , Aabloo Alvo 
Published: June 2022

Abstract
Ionic Polymer–Metal Composite (IPMC) has been widely recognized as a promising and representative candidate of soft intelligent materials actuated under low voltage. In the last few years, the importance of the electrode/substrate interface has received growing attention for research on both the modeling of ion-based mass transport and practical performance of the manipulation of ionic electro-active actuators. In this paper, based on a macroscopic serrated interface morphology, the influences of the interface were revealed comprehensively by distinguishing the bending direction as well as the variation of interfacial area, excisional volume and moment of inertia. The offsetting interaction from different aspects were analyzed in detail. On this basis, an interesting result showed that, contrary to current understanding, an enlarged interface area did not necessarily lead to better deformation, which was primarily ascribed to the trade-off of influences from the increasing excisional volume and decreasing bending inertia moment. In addition, a corresponding fabrication process was established, which verified experimentally that IPMC with a super simple macroscopic serrated interface can present a high electro-active performance, providing a minimalist design strategy for ionic electroactive polymer structures.
Microstructure, pinning properties, and aging of CSD-grown SmBa2Cu3O7−δ
films with and without BaHfO3 nanoparticles
Authors: Hänisch Jens , Iida Kazumasa , Cayado Pablo , Erbe Manuela , Grünewald Lukas , Hatano Takafumi , Okada Tatsunori , Gerthsen Dagmar , Awaji Satoshi , Holzapfel Bernhard 
Published: June 2022

Abstract
In order to improve the electrical transport properties of REBa2Cu3O7−δ
nanocomposite films, SmBa2Cu3O7−δ
films with and without BaHfO3 nanoparticles were grown by chemical solution deposition, and their microstructural and transport properties were investigated in a detailed study using transmission electron microscopy and transport measurements in magnetic fields up to 24 T. The optimization process of the crystallization step (temperature and oxygen partial pressure) as well as an aging effect, which is due to the release of trapped fluorine, are described. Critical temperature and critical current densities surprisingly improve initially during the aging. Due to the complex microstructure, the additional BaHfO3 nanoparticles have only a positive effect at low magnetic fields for our samples.
Absence of stationary states and non-Boltzmann distributions of fractional Brownian motion in shallow external potentials
Authors: Guggenberger Tobias , Chechkin Aleksei , Metzler Ralf 
Published: June 2022

Abstract
We study the diffusive motion of a particle in a subharmonic potential of the form U(x) = |x|
c
(0 < c < 2) driven by long-range correlated, stationary fractional Gaussian noise ξ

α
(t) with 0 < α ⩽ 2. In the absence of the potential the particle exhibits free fractional Brownian motion with anomalous diffusion exponent α. While for an harmonic external potential the dynamics converges to a Gaussian stationary state, from extensive numerical analysis we here demonstrate that stationary states for shallower than harmonic potentials exist only as long as the relation c > 2(1 − 1/α) holds. We analyse the motion in terms of the mean squared displacement and (when it exists) the stationary probability density function. Moreover we discuss analogies of non-stationarity of Lévy flights in shallow external potentials.
Study of radiation effect and bismuth content on the physical properties of some chalcogenides based on selenium
Authors: Abd El-Raheem M M , Solieman A H M , Wakkad M M , Ali H M , Mohammed S K , Mohamed H F 
Published: June 2022

Abstract
A series of Ge18BixSe82−x (x = 0.5, 2, 4, 6 & 8) thin films were prepared using thermal evaporation on glass substrates. The structural, optical and electrical properties of the prepared samples were investigated. The samples were exposed to γ-ray of 60Co source with gradually increased doses up to (150 kGy). The changes of the properties of the irradiated samples have been studied. Single oscillator and Drude models were used to describe the dispersion region. The changes of the optical parameters such as optical energy gap (Eop), Urbach energy (Eu), and the plasma frequency

(

ω

p

)

due to gamma irradiation were studied. The results obtained were explained in terms of the interaction of radiation with matter. The electrical conductivity was studied for both the as-prepared and irradiated films revealing negative differential conductance behavior.
Vacuum energy density measured from cosmological data
Authors: Prat J. , Hogan C. , Chang C. , Frieman J. 
Published: June 2022

AbstractWithin the ΛCDM cosmological model, the absolute value of Einstein's cosmological constant Λ, sometimes expressed as the gravitating mass-energy densityρΛof the physical vacuum, is a fundamental constant of nature, whose accurate measurement plays a central role in testing some proposed theories of quantum gravity. Several combinations of currently public cosmological data and an assumed flat ΛCDM cosmological model are used here to make a joint Bayesian inference on the combination of conventional parameters ΩΛh2that corresponds to the absolute physical densityρΛ. In physical units, we obtainρΛ= (60.3 ± 1.3)× 10-31g/cm3, the most accurate constraint to date, with an absolute calibration of cosmological measurements based on CMB temperature. Significantly different values are obtained with calibrations that use a local distance scale, mainly connected to systematic differences in the value of the Hubble constant. It is suggested that future comprehensive cosmological parameter studies assuming the ΛCDM model include constraints on the vacuum density.
Multiframe Correction Blind Deconvolution for Solar Image Restoration
Authors: Wang Shuai , Rong Huiqin , He Chunyuan , Zhong Libo , Rao Changhui 
Published: June 2022

Abstract
A series of short-exposure images are often used for rich, small-scale structure, high-quality, and high-resolution astronomical observations. Postprocessing of the closed-loop adaptive optics (AO) image using ground-based astronomical telescopes plays an important role in astronomical observations due to it further improving image quality after AO processing. These images show several main characteristics: random spatial variation blur kernel, unclear model after AO correction, unclear physical characteristics of observation objects, etc. Our goal is to propose a multiframe correction blind deconvolution (MFCBD) algorithm to restore AO closed-loop solar images. MFCBD introduces a denoiser and corrector to help estimate the intermediate latent image and proposes using an L

q
norm of the kernel as the sparse constraint to acquire a compact blur kernel. MFCBD also uses the half-quadratic splitting strategy to optimize the objective function, which makes the algorithm not only simple to solve, but also easy to adapt to different fidelity terms and prior terms. In tests on three data sets observed from the photosphere and chromosphere of the Sun, MFCBD not only restored clearer and more detailed images, but also converged smoothly and monotonically in terms of the peak signal-to-noise ratio (PSNR) and structural similarity (SSIM) after a few iterations. Taking the speckle-reconstructed image as a reference, the clear image restored by our method performs best both in PSNR and SSIM compared with the state-of-the-art traditional methods OBD and BATUD.
On the Determination of the Evolutionary Status of Supernova Remnants from Radio Observation Data
Authors: Urošević Dejan 
Published: June 2022

Abstract
This paper aims to give a brief review of a new concept for the preliminary determination of the evolutionary status of supernova remnants (SNRs). Data obtained by radio observations in continuum are used. There are three different methods underlying the new concept: The first one is based on the location of the observationally obtained radio surface brightness and the corresponding diameter of an SNR in theoretically derived Σ–D tracks, the second one is based on the forms of radio spectra, and the third one is based on the magnetic field strengths that are estimated through the equipartition (eqp) calculation. Using a combination of these methods, developed over the last two decades by the Belgrade SNR Research Group, we can estimate the evolutionary status of SNRs. This concept helps radio observers to determine preliminarily the stage of the evolution of an SNR observed in the radio domain. Additionally, this concept was applied to several SNRs, observed by the Australia Telescope Compact Array, and the corresponding results are reviewed here. Moreover, some of the results are revised in this review to reflect the recently published updated Σ–D and eqp analyses.
GA-MKB:A Multi-kernel Boosting Learning Method based on Normalized Kernel Target Alignment and Kernel Difference
Authors: Chen Linlin , Wang Mei , Zhang Qiang , Hou Nan 
Published: June 2022

Abstract
Concentrates on the problem that the traditional kernel target alignment(KTA) is not invariance under data translation in the feature space, a cosine matrix alignment method is proposed for kernel selection, which is called normalized kernel target alignment(NKTA). On the basis of normalized kernel target alignment and kernel difference, we propose a new multi-kernel boosting. Firstly, the value of NKTA is taken as the election rarget of the kernel function in each iteration of algorithm, which leads to a selective kernel fusion. Secondly, the kernel difference measure is used to construct the combination coefficient to increase the diversity of weak classifiers, and then improve the generalization performance of integrated strong classifiers. Finally, among the 6 data sets, the GA-MKB performed better than MKBoost-D1 under the accuracy of classification, and can improve the generalization performance of the integrated classifier compared with MKBoost-D2.
Intelligent Recognition of UAV Pilot Training Actions Based on Dynamic Bayesian Network
Authors: Yin Wenqiang , An Ran , Zhao Qun 
Published: June 2022

Abstract
The recognition of UAV pilot training actions using artificial intelligence methods can effectively improve the intelligence level of UAV pilot training ability assessment. Based on the flight characteristics of UAV systems, this paper constructs an intelligent recognition model of flight training actions based on dynamic Bayesian network. Combined with the training tasks, the model recognition effect was verified. The results show that the model can accurately recognize different flight phases and typical training actions of UAV, have the advantages of high recognition accuracy and good real-time performance, and effectively improve the training effect of pilots.
A Two-stage Speech Enhancement Method Based on Deep Learning Network
Authors: Ye Moujia , Wan Hongjie 
Published: June 2022

Abstract
The conventional single-channel speech enhancement algorithms require only a small amount of computation to implement, but have problems with “musical noise” and speech distortion. The learning-based enhancement method achieves a relatively good performance, but it requires a right training data set and a large computational load. This paper proposed a method to combine the two types of algorithms. This method includes two stages. In the first stage, the Berouti spectral subtraction was used to subtract part of noise from the noisy speech. In the second stage, a deep neural network was built to remove residual noise and improve the clarity of speech obtained in the first stage. The experimental results indicate that, training with the same data set, the method proposed in this paper clearly outperforms the baseline model in PESQ and FWSegSNR.