bluuebunny/crossref_metadata_2025_split huggingface dataset:
A V3C2 MXene/graphene heterostructure as a sustainable electrode material for metal ion batteries
Authors: Dinda Partha Pratim , Meena Shweta 
Published: February 2021

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

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

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

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

Abstract
Trapped ion in the Lamb–Dicke (LD) regime with the LD parameter η ≪ 1 can be cooled down to its motional ground state using sideband cooling. Standard sideband cooling works in the weak sideband coupling (WSC) limit, where the sideband coupling strength is small compared to the natural linewidth γ of the internal excited state, with a cooling rate much less than γ. Here we consider cooling schemes in the strong sideband coupling (SSC) regime, where the sideband coupling strength is comparable or even greater than γ. We derive analytic expressions for the cooling rate and the average occupation of the motional steady state in this regime, based on which we show that one can reach a cooling rate which is proportional to γ, while at the same time the steady state occupation increases by a correction term proportional to η
2 compared to the WSC limit. We demonstrate with numerical simulations that our analytic expressions faithfully recover the exact dynamics in the SSC regime.
Modeling shape and volume transitions in liquid crystal elastomers
Authors: Egorov V I , Maksimova O G , Okumura M , Noro S , Koibuchi H 
Published: February 2021

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

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

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

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

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

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

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

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

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

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

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

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

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

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

AbstractSpectral clustering is one of the most popular clustering approaches. Despite its good performance and strong theoretical supports, it is limited to high complexity of the graph Laplacian similarity matrix construction and eigen-decomposition problems. Recently, deep learning has been successfully adopted in graph representation. In the paper, we jointly learn the manifold graph construction and non-linear low-dimension mapping of the graph. In addition, we theoretically proved that our model according with spectral clustering theory. Meanwhile, we use the proposed non-linear coders as the building blocks to formulate a deep structure to further refine features of layer wise fashion. Extensive experiments on clustering tasks demonstrate that our method performs well in terms of both clustering accuracy and normalized mutual information( NMI )