bluuebunny/crossref_metadata_2025_split huggingface dataset:
Superconductivity of disordered NbN films deposited on magnesium oxide and oxidized silicon substrates
Authors: Zhang Lu , You Lixing , Chen Lei , Peng Wei , Wang Zhen 
Published: August 2022

Abstract
This study investigates the evolution of superconducting properties as a function of disorder in epitaxial and polycrystalline niobium nitride (NbN) thin films grown on MgO and SiO2/Si substrates, respectively. By reducing the film thickness from 100 to 4 nm, the effective disorder in these two sets of films encompasses a large range, with the Ioffe–Regel parameter in the range of 5.7–9.3 and 1.5–3.0, respectively. Moreover, the density of states at the Fermi level [N(0)] of epitaxial NbN films decreases from 1.91 × 1028 to 1.46 × 1028 states eV−1m−3 with the superconducting transition temperature (Tc
) ranging from 16.0 to 11.8 K. In contrast, the N(0) of polycrystalline NbN films decreases from 2.18 × 1028 to 1.80 × 1028 states eV−1m−3 with Tc
ranging from 14.9 to 2.56 K. The experimental results reveal that the Tc
of NbN films with different thicknesses is mainly controlled by N(0), and the greater Coulomb pseudopotential (μ*) in the polycrystalline films leads to lower Tc
and larger resistivity. The changes in N(0) and μ* should collectively account for the reduction of Tc
with disorder in NbN thin films.
A general scenario of tunneling time in different energy regimes
Authors: Li Sheng-Chang 
Published: August 2022

Abstract
We theoretically study the tunneling time by investigating a wave packet of Bose-condensed atoms passing through a square barrier. We find that the tunneling time exhibits different scaling laws in different energy regimes. For negative incident energy of the wave packet, counterintuitively, the tunneling time decreases very rapidly with decreasing incident velocity. In contrast, for positive incident energy smaller than the barrier height, the tunneling time increases slowly and then reaches a maximum, which is in agreement with the Larmor clock experiments. The effect of the barrier width related to the uncertainty principle on the maximum tunneling time is also addressed. Our work provides a general scenario of tunneling time that can be used to understand and explain the controversy over tunneling time.
Thermal local Fisher information and quantum uncertainty in Heisenberg model
Authors: Mohamed A-B A , Eleuch H 
Published: August 2022

Abstract
This study explores the spontaneous emergence of local Fisher information and local quantum uncertainty beyond entanglement in a noisy two-qubit Heisenberg XXX model with Dzyaloshinskii-Moria (DM) and Kaplan-Shekhtman-Entin-Wohlman-Aharony (KSEA) interactions. Local quantum Fisher information, local quantum uncertainty, and logarithmic negativity are used to examine two-spin non-locality. We find that by employing strong two-spin, Dzyaloshinskii- Moriya, and Kaplan-Shekhtman-Entin-Wohlman-Aharony interactions, we can prevent noise-induced losses in local Fisher information, local quantum uncertainty, and logarithmic negativity. The robustness of the two-spin non-localities is greater for a strong spin’s coupling strength than for the strong DM and KSEA interactions. Local Fisher information and local quantum uncertainty, as well as logarithmic-negativity, display asymmetric behaviors with respect to two-spin coupling and symmetric behaviors with respect to the Dzyaloshinskii-Moriya and Kaplan-Shekhtman-Entin-Wohlman-Aharony interactions. At high bath temperatures, the phenomenon of sudden-death two-spin entanglement emerges. Furthermore, by combining the parameters of the strong two-spin, DM, and KSEA interactions, the robustness of the produced two-spin non-localities can be improved.
Role of coordination site in governing the structural, electronic and optical properties of Ca-doped strontium barium niobate
Authors: Ahmad Tahir , Tomar Monika , Jindal Kajal , Jha Pradip K 
Published: August 2022

Abstract
The structural, electronic and optical properties of Ca doped SBN60 are studied via first principle calculations based on density functional theory. Influence of Sr site substitution by Ca at square and pentagonal sites has been studied. The bandgap of Ca incorporated SBN is found to depend largely on the coordination of site of doping, where the bandgap increases for the square site substitution and decreases for the pentagonal site substitution. The formation energy calculations reveal that the Ca is more favorable to occupy the pentagonal site which is supported by the UV-Visible spectra study of the thin films of doped and undoped SBN60 grown using PLD technique in which a decrease in the bandgap can be seen in Ca doped SBN. A detailed analysis is provided for the changes in structural parameters upon doping at square and pentagonal sites. Dependence of the optical properties like dielectric constant, refractive index, reflectance and absorption of doped and undoped SBN60 on different polarization directions ‘(100), (010) and (001)’ are reported. The anisotropy in the optical properties can be observed which makes Ca doped SBN a promising material for optoelectronic device applications.
Dark sector domain walls could explain the observed planes of satellites
Authors: Naik Aneesh P. , Burrage Clare 
Published: August 2022

AbstractThe observed 'planes of satellites' around the Milky Way and other nearby galaxies are notoriously difficult to explain under the ΛCDM paradigm. Here, we propose an alternative solution: domain walls arising in theories with symmetry-breaking scalar fields coupled to matter. Because of the matter coupling, satellite galaxies experience fifth forces as they pass through domain walls, leading to a subset of satellites with orbits confined to the domain wall plane. We demonstrate this effect using simple simulations of a toy model comprising point-like satellites and an infinite domain wall, and explore the efficacy of various planarity metrics in detecting this effect. We believe this is the first potential 'new physics' explanation for the observed planes of satellites which does not do away with dark matter.
The effect of multiple features on the power spectrum in two-field inflation
Authors: Boutivas K. , Dalianis I. , Kodaxis G.P. , Tetradis N. 
Published: August 2022

Abstract
We extend our previous work
on the enhancement of the curvature spectrum during inflation to the two-field case.
We identify the slow-roll parameter η as the quantity
that can trigger the rapid growth of perturbations.
Its two components, η
∥ along the background trajectory
and η
⊥ perpendicular to it, remain small during most of the evolution,
apart from short intervals during which they take large, positive or negative, values.
The typical reason for the appearance of strong features in η
∥ is
sharp steps or inflection points in the inflaton potential,
while η
⊥ grows large during sharp turns in
field space. We focus on the additive effect of several features
leading to the resonant growth of the curvature spectrum.
Three or four features in the evolution of
η are sufficient in order to induce an enhancement of the
power spectrum by six or seven orders of magnitude, which can
lead to the significant production of primordial black holes and stochastic
gravitational waves.
A big part of our study focuses on understanding the evolution
of the perturbations and the resulting spectra through analytic means.
The presence of multiple features in
the background evolution points to a more complex inflationary paradigm,
which is also more natural in the multi-field case.
The critical examination of this possibility is within
the reach of experiment.
Coherent manipulation of perfect optical vortex via inelastic four-wave mixing in a cold five-level atomic system
Authors: Zhang Tong , Hu Die , Shui Tao , Jiang Li-Bo , Li Ling , Yang Wen-Xing , Liao Yingqi 
Published: August 2022

Abstract
We propose an efficient scheme to manipulate the perfect optical vortex (POV) beam in a cold atomic ensemble with a five-level configuration. The orbital angular momentum can be completely transferred from a POV control beam to the generated mixing field via inelastic four-wave mixing (IFWM) process. It is found that the intensity and helical phase of the generated vortex IFWM field can be coherently controlled by adjusting the relevant parameters, such as the atomic population, and the intensity and detuning of the pumping field. The obtained results can be reasonably explained via the combination of inherent and modal dispersion relation. Our result may find potential applications in POV-based large-particle manipulation and optical communication.
Electric Vehicle Charging Station Load Prediction Based on IWOA-LSSVM Combined Model with Improved Variational Mode Decomposition
Authors: Ru Feng , Yang Xiaoqian , Zou Haoquan , Zhang Lina , Xu Xianfeng 
Published: August 2022

Abstract
Accurately predicting the electric vehicle charging station load is the basis for the optimal design of power supply planning. Aiming at the problem that the load data of electric vehicle charging station has strong randomness and volatility due to the interaction of various factors. The electric vehicle charging load prediction model is constructed, which considers the highest temperature, day type and weather type factors to select similar days. Firstly, an algorithm for optimizing variational modal decomposition parameters with relative entropy is proposed to decompose the extracted similar daily charging load data into multiple sub-sequences. Then, the decomposed data are respectively input into the combined prediction model of the least squares support vector machine optimized by the whale algorithm improved by the adaptive probability threshold and the random difference mutation strategy, and finally the predicted value of each subsequence is superimposed and summed to obtain the final predicted value. Based on the measured charging load data and weather forecast data of an electric vehicle charging station in Shanghai, simulation experiments are carried out to compare and verify the accuracy and effectiveness of the model in this paper.
Lightning Transient Analysis Comparison and Wind Turbine Models Using PSCAD/EMTDC Circuit Simulator
Authors: Chinges Tserensambuu , Li Qingmin , Zhao Jiyao 
Published: August 2022

Abstract
This paper aims to compute the lightning electromagnetic transient overvoltage and current on the wind turbine using the PSCAD/EMTDC simulation program. The double-peaked lightning current, tower, grounding system models, and dangerous situation of the grounding system are investigated by the PSCAD simple circuit branch segment codes and defining components. A comparison analysis has been performed to see the influence of lightning current wave shapes and grounding characteristics. The simulation results show that 43 kA and 31 kA lightning currents produce 200 kV overvoltage on the turbine grounding in case of 1000 Ω·m soil resistivity. However, current amplitudes on the equipotential grounding system become 28 kA and 20 kA. The influencing factors of the surge protection device are determined using good accuracy computer simulation.
Detection of Self-explosive Insulators in Aerial Images Based on Improved YOLO v4
Authors: Yi Li , Jinguo Li , Yongjie Zhai , Ping Meng 
Published: August 2022

Abstract
In view of the low detection efficiency of traditional aerial image-based self-explosive insulators and the need to manually extract the features of self-explosive insulators, a self-explosive insulator detection algorithm based on the improved YOLO v4 is proposed: We use the hybrid data augmentation method to increase the number of defective samples of self-explosive insulators and increase the diversity of self-explosive insulators to improve the detection accuracy of the algorithm. In addition, the channel attention mechanism is combined with the YOLO v4 algorithm to improve the feature extraction capability of algorithms to self-explosion insulators. The proposed algorithm is verified using the expanded self-explosive insulator dataset and the traffic signs dataset TT100K. The experimental results show that the proposed algorithm has a mAP of 92.0 % on the self-explosive insulator dataset and has an F1-score of 78.70% on TT100K dataset, which has positive significance for the research on self-explosive insulator detection.
Hybrid Model Compression for Multi-Task Network
Authors: Xie Min , Sun Weize , Huang Lei 
Published: August 2022

Abstract
In recent years, deep neural networks have made significant achievements in various fields in science and engineering. To achieve outstanding performance, neural network models with very deep structures and an extensive number of parameters are usually used, leading to huge memory storage and computational resources requirements of the hardware systems, and thus preventing them from being deployed in devices with hardware limits. In particular, such problem will be further amplified when multiple neural network models for several tasks are to be integrated into one single platform at the same time. In this paper, a hybrid joint-network optimization model is proposed to solve this multi-task multi-model compression problem. The idea of hard parameter sharing in multi-task learning is adopted. Specifically, we turn the sub-network of the same structure of multiple models into a hybrid network model, in which the same parameters are shared for all the tasks. In addition, a unique sparse matrix for each task is added to each task independently. Experimental results show that the proposed compression methods can not only achieve a high compression ratio but also get performance improvement for some tasks.
Photovoltaic Output Prediction Method Based on Weather Forecast and Machine Learning
Authors: Zhang Kaikai , Zou Guibin 
Published: August 2022

Abstract
This paper conducts prediction research based on the historical power generation data and meteorological data of a photovoltaic power station. The obtained data is firstly classified by season, and the correlation analysis of meteorological factors is carried out. The data closely related to PV generation is selected for prediction, which improves the prediction efficiency. Then the Kmeans algorithm is used to cluster the historical data, and data are divided into four types: sunny, partly cloudy, cloudy, and rainy, further each type of data is separately trained to obtain four training models. The classified data is decomposed by EEMD, and a set of frequency-distributed IMF components and a monotonic distribution of residual components are obtained. Use support vector machine to predict the dimension-reduced components, and reconstruct the result to obtain the final predicted value. After obtaining the future weather forecast data, select the same type of historical data set for model training. The results show that the prediction accuracy will be significantly improved after signal processing in cloudy and rainy weather.
Systematic review: Analysis of the use of D-limonene to Reduce the Environmental Impact of Discarded Expanded Polystyrene (EPS).
Authors: Mollehuara MA , Cuadrado AR , Vidal VL , Camargo SD 
Published: August 2022

Abstract
The article presents a systematic review on the use of D-limonene with the aim of analyzing its efficiency to reduce the environmental impact of expanded polystyrene (EPS). Initially, an analysis of the life cycle of the EPS was carried out in a general way to identify the stage where the greatest impact on the environment is evident. Subsequently, primary sources were examined that raise the issue of reducing EPS using this green solvent, in view of the fact that it generates sustainability through recycling, optimization and application of various methods that could allow the circular economy of this waste in order to be applied in the industrial field and contribute to the socio-environmental aspect. Finally, the importance of this solvent was demonstrated according to the analysis and evaluation of each EPS recycling examining country.
Revealing heterogeneity in correlation times of EGFP encapsulated in complex coacervate core micelles by analysis of fluorescence anisotropies
Authors: Skakun Victor , Digris Anatoli , Nolles Antsje , Borst Jan Willem , Visser Antonie 
Published: August 2022

Abstract
Encapsulation of enhanced green fluorescent protein (EGFP) in complex coacervate core micelles (C3Ms) can be established by mixing EGFP with diblock polymers at equal charge ratio. It has previously been shown that this encapsulation system is highly dynamic, implying existence of different populations; GFP free in solution or complexed with polymers (small complexes) and EGFP encapsulated in C3Ms. We performed time resolved fluorescence anisotropy experiments to determine the relative populations of EGFP encapsulated in C3Ms using three different fluorescence anisotropy decay analysis methods. First, Maximum Entropy Method (MEM) data analysis was employed for five different EGFP concentrations in C3Ms that were mixed with dark fluorescent proteins (10, 20, 30, 40 and 50% EGFP, respectively). In all cases, correlation-time distributions between 0.1 and 100 ns (on a logarithmic timescale) are clearly visible showing bimodal distribution. The distribution between 0.1 and 2.0 ns is due to homo-FRET between EGFP molecules packed in micelles and the distribution between 8 and 30 ns coincides with the correlation-time distribution of free EGFP in solution. The fraction of homo-FRET distribution linearly increases with increase of relative micellar EGFP concentrations. These MEM results were corroborated by two different analysis methods: global population analysis of all five fluorescence anisotropy decays arising from EGFP in micelles together with the one of free EGFP (direct analysis of anisotropies) and global associative population analysis of anisotropies by fitting parallel and perpendicular fluorescence decay components. In contrast to global analyses approaches, the MEM method directly reveals distributions of correlation times without any prior information about the sample. However, global associative analysis of anisotropies by fitting parallel and perpendicular fluorescence decay components is the only method that allows to estimate accurately fractions of free fluorophores in solution and encapsulated fluorophores.
Microstructure and texture evolution of pure niobium in cold-drawn Mg+B/Nb/Cu wires
Authors: Park Sang-Yong , Lee Jong-Beom , Jeong Ha-Guk 
Published: August 2022

Abstract
In this study, Mg+B wires were prepared by powder in tube method using Nb and Cu tubes as barrier and sheath, respectively, followed by cold drawing. Microstructural, textural, and mechanical properties of the Nb barrier at different drawing strains (ε
d) were investigated. The results showed that the Nb barrier demonstrated a saturation hardness of 159.4 HV. The microstructure of the Nb barrier became elongated along the drawing direction increasing ε
d. Sub-grains existing inside the deformed grains rotated from low-angle grain boundaries to high-angle grain boundaries and developed into new grains. The main textural components of the Nb barrier were {111} γ-fiber and {hkl}〈110 〉 α-fiber. Recrystallized grains exhibited a low maximum orientation distribution function intensity, weak {100}〈110 〉 α-fibers, and strong {111}〈110 〉 γ-fibers as compared to those of the deformed grains. The relationship between the microstructure evolution and mechanical properties of the Nb barrier and the changes in the cross-sectional area fractions of the materials constituting the Mg+B composite wire are discussed. The current study provides details about the misorientation profile inside deformed grains and continuous dynamic recrystallization mechanism of the cold-drawn Nb barrier.
GameRank: R package for feature selection and construction
Authors: Henneges Carsten , Paulson Joseph N 
Published: August 2022

Abstract

Motivation
Building calibrated and discriminating predictive models can be developed through the direct optimization of model performance metrics with combinatorial search algorithms. Often, predictive algorithms are desired in clinical settings to identify patients that may be high and low risk. However, due to the large combinatorial search space, these algorithms are slow and do not guarantee the global optimality of their selection.

Results
Here, we present a novel and quick maximum likelihood-based feature selection algorithm, named GameRank. The method is implemented into an R package composed of additional functions to build calibrated and discriminative predictive models.

Availability and implementation
GameRank is available at https://github.com/Genentech/GameRank and released under the MIT License.
Pathways to Drug Dealing in the Middle and Upper Classes: Early Marginalization, Relative Disadvantage and Countercultural Opposition
Authors: Berger Eirik Jerven , Pedersen Willy , Sandberg Sveinung 
Published: August 2022

Abstract
Drug dealing is widespread in all sectors of society but is still studied predominantly in disadvantaged urban areas. We identify three main pathways to drug dealing based on qualitative interviews with middle- and upper-class individuals in Oslo, Norway. First, problems in the family and school and a lack of belonging in affluent neighbourhoods intersected with drug use and eventually led to recruitment into the illegal drug economy. Second, criminal entrepreneurship developed among relatively disadvantaged people who dealt drugs in an affluent low-risk context. Third, dealing emerged from involvement in drug liberalization and medical marihuana countercultures. The first pathway is similar to trajectories in disadvantaged urban areas, while the others reveal the importance of studying drug dealing in the upper layers of society.
Elevated expression of the membrane-anchored serine protease TMPRSS11E in NSCLC progression
Authors: Li Shufeng , Chen Zhenfa , Zhang Wei , Wang Ting , Wang Xihua , Wang Chao , Chao Jie , Liu Ling 
Published: August 2022

Abstract
TMPRSS11E was found to be upregulated in human nonsmall cell lung cancer samples (NSCLC) and cell lines, and high expression was associated with poor survival of NSCLC patients. The results of in vitro and in vivo experiments showed that overexpressing TMPRSS11E resulted in A549 cell proliferation and migration promotion, while the TMPRSS11E S372A mutant with the mutated catalytic domain lost the promoting function. In addition, in mouse xenograft models, silencing TMPRSS11E expression inhibited the growth of 95D cell-derived tumors. To explore the mechanism of marked upregulation of TMPRSS11E in NSCLC cells, promoter analysis, EMSA, and ChIP assays were performed. STAT3 was identified as the transcription factor responsible for TMPRSS11E transcription. Moreover, the purified recombinant TMPRSS11E catalytic domain exhibited enzymatic activity for the proteolytic cleavage of PAR2. Recombinant TMPRSS11E catalytic domain incubation further activated the PAR2-EGFR-STAT3 pathway. These findings established a mechanism of TMPRSS11E-PAR2-EGFR-STAT3 positive feedback, and the oncogenic role of TMPRSS11E as a PAR2 modulator in NSCLC was revealed.