bluuebunny/crossref_metadata_2025_split huggingface dataset:
Gap engineering effects on transport and tunneling magnetoresistance properties in phosphorene ferromagnetic/normal/ferromagnetic junction
Authors: Oubram O 
Published: February 2024

Abstract
Tuning the band gap is of utmost importance for the practicality of two-dimensional materials in the semiconductor industry. In this study, we investigate the ballistic transport and the tunneling magnetoresistance (TMR) properties within a modulated gap in a ferromagnetic/normal/ferromagnetic (F/N/F) phosphorene junction. The theoretical framework is established on a Dirac-like Hamiltonian, the transfer matrix method, and the Landauer–Büttiker formalism to characterize electron behavior and obtain transmittance, conductance and TMR. Our results reveal that a reduction in gap energy leads to an enhancement of conductance for both parallel and anti-parallel magnetization configurations. In contrast, a significant reduction and redshift in TMR have been observed. Notably, the application of an electrostatic field in a gapless phosphorene F/N/F junction induces a blueshift and a slight increase in TMR. Furthermore, we found that introducing an asymmetrically applied electrostatic field in this gapless junction results in a significant reduction and redshift in TMR. Additionally, intensifying the applied magnetic field leads to a substantial increase in TMR. These findings could be useful for designing and implementing practical applications that require precise control over the TMR properties of a phosphorene F/N/F junction with a modulated gap.
Electric properties of the twelve-fold vortex structure in hexagonal manganites
Authors: Lin Hongling , Yang Kunlun , Lin Lin , Yan Zhibo , Liu J-M 
Published: February 2024

Abstract
Hexagonal manganites, as a functional ferroelectric (FE) material, receive considerable attention due to their improper ferroelectricity and topological vortex structures. This family exhibits three low-symmetry states accompanied by distinct vortex domain structures. In addition to the FE P63
cm and anti-FE (AFE) P-3c1 states accompanied by dual six-fold vortex structures, there is another FE P3c1 state accompanied by a twelve-fold vortex structure. The responses of FE materials to external stimuli, such as external electric fields, are the core ingredients in the physics of FEs and are significant for technological applications. Under external electric fields, the responses of FE materials are determined by special FE domain structures. The electric properties of the FE P63
cm and AFE P-3c1 states are very different. However, the electric properties of the FE P3c1 state, which only stabilizes in Ga-substituted In(Mn, Ga)O3, are unclear. The present work studies the electric properties of the FE P3c1 state. The electric-field-driven transition of the FE P3c1 state is found to follow two sequences, i.e. (1) twelve-fold P3c1 → nine-fold P3c1 + P63
cm → three-fold P63
cm, and (2) twelve-fold P3c1 → six-fold P3c1 → three-fold P63
cm. The variation of average polarization with E for the FE P3c1 state with the second transition sequence manifests as an unusual triple-hysteresis loop, different from the usual single-hysteresis loop of FE materials. The results are related to the coexistence of the FE and non-FE domain walls in the FE P3c1 state. Furthermore, it is found that the FE P3c1 state at substitution concentration 0.39 exhibits the highest dielectric response. The results advance our understanding of topological vortex structures in hexagonal manganites.
An identification algorithm of lateral correction amount for the weeding components in paddy fields based on multi-sensor fusion
Authors: Wang Shanshan , Yu Shanshan , Wang Xingsong 
Published: February 2024

Abstract
In this paper, an identification algorithm of lateral correction amount for the weeding components in paddy fields based on multi-sensor fusion is proposed, which can accurately obtain the lateral deviation between the weeding components and the seedling rows under different soil hardness in paddy fields to avoid crushing seedlings. The proposed method first fuses the RGB images with depth images to obtain the three-dimensional point cloud of seedlings, establishes a visual calibration system to calibrate the positions of the weeding component at the limit positions in the camera coordinate system, then obtains the relative pose relationship between the camera coordinate system and the ground coordinate system based on the inertial measurement unit to solve the influence of the altitude change of the camera on the identification of the lateral correction amount, and finally calculates the lateral correction amount based on the lateral deviation model in the ground coordinate system. The experimental platforms for the visual calibration of the weeding components and the identification of the lateral correction amount were established. The experimental results showed that the mean positioning error of the weeding components was 2.766 mm, the mean identification error of the lateral correction amount did not exceed 0.22 cm, and the standard deviation of the identification error did not exceed 0.18 cm.
Discrete-coordinate crypto-Hermitian quantum system controlled by time-dependent Robin boundary conditions
Authors: Znojil Miloslav 
Published: February 2024

Abstract
A family of exactly solvable quantum square wells with discrete coordinates and with certain non-stationary Hermiticity-violating Robin boundary conditions is proposed and studied. Manifest non-Hermiticity of the model in conventional Hilbert space

H

friendly

is required to coexist with the unitarity of system in another, ad hoc Hilbert space

H

physical

. Thus, quantum mechanics in its non-Hermitian interaction picture (NIP) representation is to be used. We must construct the time-dependent states (say, ψ(t)) as well as the time-dependent observables (say, Λ(t)). Their evolution in time is generated by the operators denoted, here, by the respective symbols G(t) (a Schrödinger-equation generator) and Σ(t) (a Heisenberg-equation generator, a.k.a. quantum Coriolis force). The unitarity of evolution in

H

physical

is then guaranteed by the reality of spectrum of the energy observable alias Hamiltonian H(t) = G(t) + Σ(t). The applicability of these ideas is illustrated via an N by N matrix model. At N = 2, closed formulae are presented not only for the measurable instantaneous energy spectrum but also for all of the eligible time-dependent physical inner-product metrics Θ(N=2)(t), for the related Dyson maps Ω(N=2)(t), for the Coriolis force Σ(N=2)(t) as well as, in the very ultimate step of the construction, for the truly nontrivial Schrödinger-equation generator G
(N=2)(t).
Taming assembly bias for primordial non-Gaussianity
Authors: Fondi Emanuele , Verde Licia , Villaescusa-Navarro Francisco , Baldi Marco , Coulton William R. , Jung Gabriel , Karagiannis Dionysios , Liguori Michele , Ravenni Andrea , Wandelt Benjamin D. 
Published: February 2024

Abstract
Primordial non-Gaussianity of the local type induces a strong scale-dependent bias on the clustering of halos in the late-time Universe. This signature is particularly promising to provide constraints on the non-Gaussianity parameter f
NL from galaxy surveys, as the bias amplitude grows with scale and becomes important on large, linear scales. However, there is a well-known degeneracy between the real prize, the f
NL parameter, and the (non-Gaussian) assembly bias i.e., the halo formation history-dependent contribution to the amplitude of the signal, which could seriously compromise the ability of large-scale structure surveys to constrain f
NL. We show how the assembly bias can be modeled and constrained, thus almost completely recovering the power of galaxy surveys to competitively constrain primordial non-Gaussianity. In particular, studying hydrodynamical simulations, we find that a proxy for the halo properties that determine assembly bias can be constructed from photometric properties of galaxies. Using a prior on the assembly bias guided by this proxy degrades the statistical errors on f
NL only mildly compared to an ideal case where the assembly bias is perfectly known. The systematic error on f
NL that the proxy induces can be safely kept under control.
Model-based measurements in the monitoring tasks of high-power plants
Authors: Borovik S Y , Kuteynikova M M , Sekisov Y N 
Published: February 2024

Abstract
The tasks of the measuring information obtaining about the state of the blading section and structural elements of gas turbine engines, as typical representatives of high-power plants, in the process of their experimental research and testing are considered. Such measurements are usually characterized by extreme environmental conditions, complex shapes and multidimensional movements of the monitored surfaces, strict constraints on the number and location of primary transducers, etc. As a result, the required measurement information is obtained under the influence of a large number of interfering factors, which are not always available for direct measurements and consideration in the experimental data processing. The approach based on the modeling the immeasurable components counting the current parameters of the power plant mode and environmental conditions is considered as an alternative to the direct measurements. The use of online models allows (in addition to increasing the reliability of measurements) to evaluate the unique characteristics of the monitored object (e.g., changes in shape) and, in some cases, to predict its critical state. The examples of the practical implementation of the approach in specific measurement systems designed for bench testing of gas turbine engines are also given in the article.
Assessing the uncertainty of the means for determining the positioning accuracy of the machine
Authors: Koleva S , Enchev M , Velev K 
Published: February 2024

Abstract
Positioning deviations such as systematic and random errors lend a substantial influence to finish machining. In this article is carried a theoretical research of the uncertainty when determining the repeatability of positioning with standard methodology, regulated in ISO 230-2. It is carried out by simulating different levels of random deviation for the position reached and the number of points on the controlled axis. The laws of distribution are established on standard assessment of positioning repeatability and the relative share of uncertainty regarding the actual (set) random positional deviation.
Iris-Based Biometric Identification Using a Combination of the Right - Left Iris Statistical Features
Authors: Dincă Lăzărescu A M , Moldovanu S , Moraru L 
Published: February 2024

Abstract
A combination between the information extracted for both right iris and left iris could increase the efficacy of the biometric recognition systems. In this paper, we propose a biometric identification method based on density of image patterns extracted from human iris images and the combination and comparison of the right iris and the left iris characteristics. The density of the patters approach for processed images can be a new biometric feature used to implement a biometric recognition system with high performance when a small feature dimension is used. In this way, we can maximize the retention of the effective biometric information. The experiments were conducted on the MMU Iris Database containing 225 images of the left eye and 225 images of the right eye. Two morphological Top-hat and Hit or Miss transforms were implemented to find out the particular pattern of pixels. They allow for the enhancement of detail in images. Then, a statistical feature extraction technique is employed to derive the density of the patterns in morphological transformed images. To assess the density of the patterns differences between the right and left iris data groups, the Pearson’s correlation coefficient (PCC) is computed. We reported very good results with a PCC of 0.6164 (strong and positive correlation) for Top-hat morphological operation whilst the Hit or Miss transform returns a PCC of 0.0127 so there is no relationship between the density of the patterns in the right and left irises.
2D multiple waves scattering for active detection of a dummy human body in a low frequency range and for various boundary conditions
Authors: Bibcu Dorin , Florescu Culea Anisia , Moldovanu Simona , Moraru Luminita 
Published: February 2024

Abstract
This paper investigates the results of simulations devoted to the acoustic field variation caused by different boundary conditions applied to the backscattering of the acoustic wave in both low and moderately low frequency ranges. The goal is to analyse the object behaviour and the detection ability of the 2D acoustic field approximations when the simulation data of the scattered sound field is available. The plane waves are backscattered by a dummy human body, positioned in a cabin which has a defined size. The dummy human body is created by using clusters of finite and disjointed but tangent circular cylinders. Each cylinder is a scatterer that emulates various boundary conditions i.e., the scatterers can be sound-soft (Dirichlet boundary condition) and sound-hard scatterers (Neumann boundary condition). The cylinders are filled with biological tissue (i.e., fat). The exterior scattering problem is solved using the preconditioned Krylov subspace iterative solvers (GMRES), as a computationally cheaper alternative. We use the hypothesis that the mass density of the obstacle and wave speed in the obstacle are both constant. During the simulation experiments, we neglected the interactions among scatterers. A frequency range from 100 Hz to 1000 Hz is analysed. Both the Sound Pressure Level (SPL) and Radar Cross Section (RCS) are used to analyse the numerical simulation of the sound field.
Application of the method of moments for doublet and triplet analysis in the radiation spectra
Authors: Izosimov I N 
Published: February 2024

Abstract
When processing spectra, it is usually sufficient to determine only the positions of the peaks and their areas. To use the method of moments for doublet peaks analysis, it is necessary to first construct (calibrate) the dependence of the second and third moments for single peaks on the energy of the corresponding radiation. After such calibration overlapping peaks can be separated. To separate peaks in doublet using the method of moments, it is necessary to solve a quadratic equation. To separate peaks in triplet it is necessary to have an appropriate calibration curves already up to the fifth moment inclusive and solve a cubic equation. Examples of the separation of overlapping peaks in the analysis of gamma-ray spectra are presented.
Numerical experiments on paleoclimate modeling
Authors: Parkhomenko V P 
Published: February 2024

Abstract
The influence of climatic parameters (values of CO2 concentration in the atmosphere, solar constant), the configuration of continents, as well as the distribution of the depths of the World Ocean, characteristic of the time periods under consideration, on the climate is investigated. A global climate model is used, including a model of three-dimensional thermohaline circulation of the ocean, an energy-moisture balance model of the atmosphere and a model of the sea ice evolution. Numerical experiments have been carried out to model the climate corresponding to the periods 120.4 million years ago and 200 million years ago with sharply different parameters. The first interval was characterized by high CO2 concentrations and a continent lying in meridional direction. During the second interval a super continent was located in the Northern polar region extending to the South but not reaching the South Pole. Calculations show that the average global climatic characteristics achieve to a stationary regime during about 1500-2000 years. The main global and spatial climatic characteristics for the atmosphere, ocean, and sea ice are obtained. The estimated average global atmospheric temperature falls within the limits reconstructed from observations. The features of ocean circulation for the corresponding periods are studied.
Planar Walking of a Five-Link Biped Robot over a Stepped Surface with Obstacles of Different Heights and Lengths
Authors: Kasiyanchuk Dmitry A. , Fetisov Dmitry A. 
Published: February 2024

Abstract
A problem of modeling biped locomotion is an actual and complex problem of modern control theory. In the present paper, we deal with a special case when a five-link biped robot with knees moves along a given stepped surface. We describe the robot motion by a hybrid system which consists of a system of ordinary differential equations at the single support phase and an algebraic relation at the double support phase. The main contribution of this paper is the construction of a periodic motion that satisfies a set of conditions inherent in human walking. The results of the paper will be useful to those who are interested in control theory applications.
Statistical calculation of beta radiotherapy dose using I-131: analysis and simulation method.
Authors: Ahmad Muntaser S. , Mohammad Hjouj 
Published: February 2024

Abstract
Radioiodine-131 (I-131) treats hyperthyroidism and differentiated thyroid carcinoma. In I-131, beta radiation (β−) is utilized for treatment and gamma radiation (γ) is used for diagnostic. This research sought to determine if a patient would be treated by establishing an equation for beta-thyroid cell interaction. The prospective study included 35 thyroid cancer patients receiving I-131 treatment. Beta and gamma readings were taken at different distances and sent to the statistical shop to find the coefficients of change on which the beta reaction depends and the equation that depends on it to find the beta range involved in healing. The strongest equation was R square (98.1%). To measure beta at 1 m, use the equation with a 99% association between variables and independent variables, and improve with ANOVA with a p-value of 0.00 0.05. The equation is: β_1m= -123.893+(0.947*δ) +(0.123*ε) -(0.002*π) -(2.11*Log Gamma). The discrepancy between true beta readings (mean = 1040) and the equation (mean = 1087, p-value = 0.411) is more than 0.05. That indicates the values are same. The equation that measures beta during iodine therapy has been achieved, which is the first step to improved thyroid cancer treatment.
An advanced approach to reconstruct CT images from limited-angle projections, reducing radiation dose and tube load.
Authors: BaniOdeh Doaa , Hjouj Mohammad 
Published: February 2024

Abstract
The process of reconstructing CT scan images from limited angle projections is critical and requires strict adherence to the ALARA principle. This principle is designed to minimize radiation exposure while maintaining image quality. Our study utilized filter back-projection (FBP) and algebraic iterative reconstruction (IR) algorithms to reconstruct brain CT images from 200 projection lines and a 100 × 100 matrix size. By combining the results of a MATLAB function with the insights of a radiologist, we can produce high-quality images that decrease radiation dose and tube load. Our findings reveal that the algebraic method is superior to the filter back-projection in preserving image quality when utilizing limited-angle projections.
A Developed Multi-Level Deep Learning Model for Network Slicing Classification in 5G Network
Authors: Hamza Hawraa S. , Ebady Manaa Mehdi 
Published: February 2024

Abstract
5G is considered as a key contributor and infrastructure supplier in the communication technology industry, capable of supporting a wide range of services such as virtual reality, driverless automobiles, e-health, and a variety of intelligent applications. Network slicing is designed to support the diversity of services applications with increased performance and flexibility needs by dividing the physical network into many logical networks. Service classification allows 5G service providers to accurately select the network slices for each service. We propose a Network Slicing classifier that uses a Multi-level Deep learning Model. First, we created a dataset of 5G network slicing that contain attributes connected with various network services. Next, we performed a multi-level model that consist of a set of Machine learning and deep learning model (such as deep Neural Network, Random Forest and Decision Tree) as a first level followed by next level that which is represent Attentive Interpretable Tabular Learning model. The outcomes of the experiment showed that the proposed model was able to exceed the normal models with high performance results.
QESRL: Exploring Selfish Reinforcement Learning for Repeated Quantum Games
Authors: Silva Agustin , Gustavo Zabaleta Omar , Miguel Arizmendi Constancio 
Published: February 2024

Abstract
A novel unified learning algorithm that seamlessly applies to both classical and quantum non-zero sum games is presented. Building upon the exploring selfish reinforcement learning (ESRL) framework previously proposed in the context of classical games, we extend this approach to handle quantum games with imperfect information. A comparison is made between performance and fairness among agents learning using plain QRL vs. QESRL. The latter enables agents to explore and learn periodic policy strategies in quantum games, leveraging the quantization of games to uncover fairer results. By addressing the challenges posed by the expanded strategy space in quantum games, we test the algorithm’s scalability by increasing the number of agents. Empirical evidence is provided to showcase its performance and to compare classical and quantum game scenarios. The proposed learning algorithm represents a significant step towards understanding the convergence and optimality of strategies in non-zero sum games across classical and quantum settings, bringing us closer to harnessing the potential of independent reinforcement learning and quantum computing in game theory applications.
Design of unbiased reduced order filter for stochastic systems with sampled measurements
Authors: Kamenshchikov Mikhail 
Published: February 2024

Abstract
The problem of synthesizing reduced order filter, that forms an unbiased estimate of linear functional from an unknown phase vector of a continuous time stochastic system with sampled measurements and additive noises, is considered in this work. The H
2 norm of the weighted transfer function from input noises to estimation error is used as a performance criterion. The method for reduced order filter design is based on the expression of the explicit dependence of performance criterion on unknown parameters in the canonical basis and the application of the non-convex optimization technique. Numerical simulations are performed to illustrate the proposed approach.