Pseudorapidity dependence of short-range correlations from a multi-phase transport model

Using a multi-phase transport model（AMPT） that includes both initial partonic and hadronic interactions, we study neighboring bin multiplicity correlations as a function of pseudorapidity in Au＋Au collisions at √sNN= 7.7- 62.4 GeV.It is observed that for √sNN〈19.6 GeV Au＋Au collisions, the short-range correlations of final particles have a trough at central pseudorapidity, while for √sNN 〉19.6 GeV AuAu collisions,the short-range correlations of final particles have a peak at central pseudorapidity. Our findings indicate that the pseudorapidity dependence of short-range correlations should contain some new physical information, and are not a simple result of the pseudorapidity distribution of final particles. The AMPT results with and without hadronic scattering are compared. It is found that hadron scattering can only increase the short-range correlations to some level, but is not responsible for the different correlation shapes for different energies. Further study shows that the different pseudorapidity dependence of short-range correlations are mainly due to partonic evolution and the following hadronization scheme.

Dynamics of Non-Markovianity, Quantum Correlations and Information Scrambling of Three Qubits Systems Interacting via Rashba Interaction

The behavior of the quantum correlations, information scrambling and the non-Markovianity of three entangling qubits systems via Rashba is discussed. The results showed that, the three physical quantities oscillate between their upper and lower bounds, where the number of oscillations increases as the Rashba interaction strength increases. The exchanging rate of these three quantities depends on the Rashba strength, and whether the entangled state is generated via direct/indirect interaction. Moreover, the coherence parameter can be used as a control parameter to maximize or minimize the three physical quantities.

Evaluation of frictional pressure drop correlations for air-water and air-oil two-phase flow in pipeline-riser system

Accurate prediction of the frictional pressure drop is important for the design and operation of subsea oil and gas transporting system considering the length of the pipeline. The applicability of the correlations to pipeline-riser flow needs evaluation since the flow condition in pipeline-riser is quite different from the original data where they were derived from. In the present study, a comprehensive evaluation of 24prevailing correlation in predicting frictional pressure drop is carried out based on experimentally measured data of air-water and air-oil two-phase flows in pipeline-riser. Experiments are performed in a system having different configuration of pipeline-riser with the inclination of the downcomer varied from-2°to-5°to investigated the effect of the elbow on the frictional pressure drop in the riser. The inlet gas velocity ranges from 0.03 to 6.2 m/s, and liquid velocity varies from 0.02 to 1.3 m/s. A total of885 experimental data points including 782 on air-water flows and 103 on air-oil flows are obtained and used to access the prediction ability of the correlations. Comparison of the predicted results with the measured data indicate that a majority of the investigated correlations under-predict the pressure drop on severe slugging. The result of this study highlights the requirement of new method considering the effect of pipe layout on the frictional pressure drop.

Quantum correlations and entanglement in coupled optomechanical resonators with photon hopping via Gaussian interferometric power analysis

Quantum correlations that surpass entanglement are of great importance in the realms of quantum information processing and quantum computation.Essentially,for quantum systems prepared in pure states,it is difficult to differentiate between quantum entanglement and quantum correlation.Nonetheless,this indistinguishability is no longer holds for mixed states.To contribute to a better understanding of this differentiation,we have explored a simple model for both generating and measuring these quantum correlations.Our study concerns two macroscopic mechanical resonators placed in separate Fabry–Pérot cavities,coupled through the photon hopping process.this system offers a comprehensively way to investigate and quantify quantum correlations beyond entanglement between these mechanical modes.The key ingredient in analyzing quantum correlation in this system is the global covariance matrix.It forms the basis for computing two essential metrics:the logarithmic negativity(E_(N)^(m))and the Gaussian interferometric power(P_(G)^(m)).These metrics provide the tools to measure the degree of quantum entanglement and quantum correlations,respectively.Our study reveals that the Gaussian interferometric power(P_(G)^(m))proves to be a more suitable metric for characterizing quantum correlations among the mechanical modes in an optomechanical quantum system,particularly in scenarios featuring resilient photon hopping.

Comparison of Microwave Imaging Algorithms for Short-Range Scenarios

Three dimensional(3-D)imaging algorithms with irregular planar multiple-input-multiple-output(MIMO)arrays are discussed and compared with each other.Based on the same MIMO array,a modified back projection algorithm(MBPA)is accordingly proposed and four imaging algorithms are used for comparison,back-projection method(BP),back-projection one in time domain(BP-TD),modified back-projection one and fast Fourier transform(FFT)-based MIMO range migration algorithm(FFT-based MIMO RMA).All of the algorithms have been implemented in practical application scenarios by use of the proposed imaging system.Back to the practical applications,MIMO array-based imaging system with wide-bandwidth properties provides an efficient tool to detect objects hidden behind a wall.An MIMO imaging radar system,composed of a vector network analyzer(VNA),a set of switches,and an array of Vivaldi antennas,have been designed,fabricated,and tested.Then,these algorithms have been applied to measured data collected in different scenarios constituted by five metallic spheres in the absence and in the presence of a wall between the antennas and the targets in simulation and pliers in free space for experimental test.Finally,the focusing properties and time consumption of the above algorithms are compared.

Spatial correlations in ground motion intensity measuring from the 2023 Türkiye earthquake

When evaluating an area's seismic risk or resilience,it is necessary to use the spatial correlation to analyze the ground motion parameters of multiple sites together in an earthquake.These two large earthquakes in Türkiye provided the possibility for spatial correlation analysis of ground motion intensity measurements in this area.Based on the strong motion records provided by The Disaster and Emergency Management Authority of Türkiye(AFAD),this study uses the local ground motion prediction equation in Türkiye to give spatial correlation analysis of Intensity Measurements.This study gives an exponential model based on a semivariogram and compares it with the correlation model obtained from previous studies.

Generalization properties of restricted Boltzmann machine for short-range order

A biased sampling algorithm for the restricted Boltzmann machine(RBM) is proposed, which allows generating configurations with a conserved quantity. To validate the method, a study of the short-range order in binary alloys with positive and negative exchange interactions is carried out. The network is trained on the data collected by Monte–Carlo simulations for a simple Ising-like binary alloy model and used to calculate the Warren–Cowley short-range order parameter and other thermodynamic properties. We demonstrate that the proposed method allows us not only to correctly reproduce the order parameters for the alloy concentration at which the network was trained, but can also predict them for any other concentrations.

Channel Correlation Based User Grouping Algorithm for Nonlinear Precoding Satellite Communication System

Low Earth Orbit(LEO)multibeam satellites will be widely used in the next generation of satellite communication systems,whose inter-beam interference will inevitably limit the performance of the whole system.Nonlinear precoding such as Tomlinson-Harashima precoding(THP)algorithm has been proved to be a promising technology to solve this problem,which has smaller noise amplification effect compared with linear precoding.However,the similarity of different user channels(defined as channel correlation)will degrade the performance of THP algorithm.In this paper,we qualitatively analyze the inter-beam interference in the whole process of LEO satellite over a specific coverage area,and the impact of channel correlation on Signal-to-Noise Ratio(SNR)of receivers when THP is applied.One user grouping algorithm is proposed based on the analysis of channel correlation,which could decrease the number of users with high channel correlation in each precoding group,thus improve the performance of THP.Furthermore,our algorithm is designed under the premise of co-frequency deployment and orthogonal frequency division multiplexing(OFDM),which leads to more users under severe inter-beam interference compared to the existing research on geostationary orbit satellites broadcasting systems.Simulation results show that the proposed user grouping algorithm possesses higher channel capacity and better bit error rate(BER)performance in high SNR conditions relative to existing works.

Space-time correlations of passive scalar in Kraichnan model

We consider the two-point,two-time(space-time)correlation of passive scalar R(r,τ)in the Kraichnan model under the assumption of homogeneity and isotropy.Using the fine-gird PDF method,we find that R(r,τ)satisfies a diffusion equation with constant diffusion coefficient determined by velocity variance and molecular diffusion.Itssolution can be expressed in terms of the two-point,one time correlation of passive scalar,i.e.,R(r,0).Moreover,the decorrelation o R(k,τ),which is the Fourier transform of R(r,τ),is determined byR(k,0)and a diffusion kernal.

Emergence of correlations in twisted monolayer-trilayer graphene heterostructures

Twisted bilayer graphene heterostructures have recently emerged as a well-established platform for studying strongly correlated phases,such as correlated insulating,superconducting,and topological states.Extending this notion to twisted multilayer graphene heterostructures has exhibited more diverse correlated phases,as some fundamental properties related to symmetry and band structures are correspondingly modified.Here,we report the observations of correlated states in twisted monolayer-trilayer(Bernal stacked)graphene heterostructures.Correlated phases at integer fillings of the moire unit cell are revealed at a high displacement field and stabilized with a moderate magnetic field on the electron-doping side at a twist angle of 1.45°,where the lift of degeneracy at the integer fillings is observed in the Landau fan diagram.Our results demonstrate the effectiveness of moire engineering in an extended structure and provide insights into electric-field tunable correlated phases.

Dominant factors in MiniCone, CPT and pile correlations: A data‐based approach

The cone penetration test(CPT)contributes to the design and analysis of piles regarding geometry,installation effect,and pile capacity(shaft and toe resistance).MiniCone,as an alternative to CPT sounding,has been used to carry outfield and laboratory investigations by physical modeling.More tests can be practically carried out through light equipment and small soil mass,involving fewer errors caused by boundary conditions.Furthermore,it can be used for in situ testing,such as quality control,assessment of ground improvement,and subgrade characterization.A database comprising MiniCone and CPT records infield and physical modeling is proposed with a variety of cone diameters.The case study records in the database have been obtained from 140 tests compiled from data from 26 sources.The sources include the results of 20 physical modelings andfield data from six sites in 10 countries.The data comprise MiniCone and CPT cone tip resistance(qc),and sleeve friction(fs).The different cones are used in sandy,silty sand,and clayey soils via simple chambers(1 g),calibration chambers,and frustum confining vessels.In addition,correlations were found in penetration records in terms of physical modeling types,cone diameters,penetration rates,and soil densities.Moreover,qc and fs are related to capacities of pile toes and shafts using proper correlation coefficients less than unity,respectively.Correlations and dominant factors in geotechnical practice between MiniCone,CPT,and pile have been reviewed and discussed.

Assessment of Dependent Performance Shaping Factors in SPAR-H Based on Pearson Correlation Coefficient

With the improvement of equipment reliability,human factors have become the most uncertain part in the system.The standardized Plant Analysis of Risk-Human Reliability Analysis(SPAR-H)method is a reliable method in the field of human reliability analysis(HRA)to evaluate human reliability and assess risk in large complex systems.However,the classical SPAR-H method does not consider the dependencies among performance shaping factors(PSFs),whichmay cause overestimation or underestimation of the risk of the actual situation.To address this issue,this paper proposes a new method to deal with the dependencies among PSFs in SPAR-H based on the Pearson correlation coefficient.First,the dependence between every two PSFs is measured by the Pearson correlation coefficient.Second,the weights of the PSFs are obtained by considering the total dependence degree.Finally,PSFs’multipliers are modified based on the weights of corresponding PSFs,and then used in the calculating of human error probability(HEP).A case study is used to illustrate the procedure and effectiveness of the proposed method.

Pressure drop of structured packing in pilot column and comparison to common correlations

Packed columns are widely used in the chemical industry such as absorption,stripping,distillation,and extraction in the production of e.g.organic chemicals,and pharmaceuticals.Pressure loss and pressure drop correlations are of special interest when it comes to the hydrodynamic properties of a column.The pressure loss across the column is of interest in the design phase when the size of the blower to drive the gas stream through the column has to be decided.The loading point and flooding point are also influenced by the pressure loss and the area of operation is determined from these points.This work examines four different correlations on pressure drop.The correlations are(i)Ergun’s equation(1952),(ii)an improved version of Ergun’s equation by Stichlmair,Bravo,and Fair(1989),(iii)an equation developed by Billet and Schultes(1999),and(iv)an equation by Rocha,Bravo,and Fair(1993).The complexity of the correlations is increasing in the mentioned order,Ergun’s equation being the simplest one.This study investigates if the more complicated correlations give better predictions to pressure drop in packed columns.This is determined by comparing the correlations to experimental data for pressure drop in a packed column with 8.2 m of structured packing using water as the liquid and atmospheric air as the gas.Seven experiments were carried out for determining the pressure drop in the column with liquid flows varying from 0 to 500 kg·h^(-1).At constant liquid flow,the gas flow was varied from approximately 10 to 70 kg·h^(-1).The pressure drop across the non-wetted column was best described by the correlation by Rocha et al.while the pressure drop for liquid flows from 100 to 500 kg·h^(-1)was,in general,best described by Stichlmair’s equation.For an irrigated column,the highest deviation was a predicted pressure drop 69.6%lower than measured.The best prediction was 0.1%higher than the measured.This study shows,surprisingly,that for a system of water and atmospheric air,complicated correlations on pressure drop determination do not provide better estimates than simple equations.

Site selective 5f electronic correlations inβ-uranium

We investigate the electronic structure ofβ-uranium,which has five nonequivalent atomic sites in its unit cell,by means of the density functional theory plus Hubbard-U correction with U from linear response calculation.It is found that the 5f electronic correlations inβ-uranium are moderate.More interestingly,their strengths are site selective,depending on the local atomic environment of the present uranium atom.As a consequence,the occupation matrices and partial 5f density of states ofβ-uranium manifest site dependence.In addition,the complicate experimental structure ofβ-uranium could be well reproduced within this theoretical framework.

Correlations among core species corresponding to the clinical staging of periodontitis

The correlation between microbiota plays a vital role in the progression of periodontal disease.This study investigated the in situ interaction networks between periodontal pathogens in periodontal and peri-implant disease.We used quantitative real-time polymerase chain reaction and Pearson’s correlation coefficients to quantify the copy numbers and correlations of four oral core species—Fusobacterium nucleatum,Porphyromonas gingivalis,Prevotella intermedia,and Streptococcus gordonii—from 80 subgingival sites(healthy and with periodontitis or gingivitis)in patients with periodontitis,and 68 subgingival sites(healthy and with periodontitis,gingivitis,peri-implantitis,or peri-implant mucositis)in patients with implants.The highest bacterial counts were observed for Porphyromonas gingivalis and Prevotella intermedia at all the sites.Within the same cohorts,the bacterial loads were greater at diseased sites than at healthy sites.Bacterial counts did not differ among clinical sites in the same group(P>0.05)but differed between periodontitis and peri-implant mucositis sites in the two groups.Porphyromonas gingivalis,F.nucleatum,and Prevotella intermedia had strong correlations at gingivitis and healthy sites and moderate correlations at periodontitis sites in patients with periodontitis.In patients with implants,Prevotella intermedia,F.nucleatum,and S.gordonii had strong correlations only at peri-implantitis sites.Also,based on metagenomic analysis,F.nucleatum and Prevotella intermedia were significantly correlated at the subgingival plaque in peri-implantitis and periodontitis samples.Our results suggest that variations in microbe-microbe interactions in subgingival plaque reflect changes in the progression of periodontal disease,providing a new perspective for understanding the mechanisms of periodontitis and peri-implantitis.

Maintenance Optimization for EMU Trains Considering Environmental Impacts and Correlations

The complexity of the actual operating environment of EMU trains and the interaction between the reliability of system components have become a huge challenge for the maintenance scheduling of EMU trains. In response to these problems, the evolution of reliability and failure rate under the influence of environmental factors, failure correlations and economy correlations is analyzed. We assume bogie systems form the EMU train in series. The failure correlation matrix of the bogie systems is modeled. With the lowest total maintenance cost as the optimization objective, a decision-making model for EMU train maintenance is established. A dynamic maintenance strategy is proposed for the model, which can improve maintenance plans efficiently. Artificial bee colony algorithm is applied to further iteratively optimize the threshold parameters in the strategy. The results are calculated and verified by a numerical example. The results show the effectiveness of the maintenance decision model. The dynamic maintenance strategy in this paper is compared with the traditional opportunistic maintenance strategy. The proposed maintenance strategy outperforms the traditional opportunistic maintenance strategy in the numerical example.

Vacuum Correlations, Detector Efficiency Fluctuations and Classical Dynamic Violations of CH-Inequality

We study in this paper the possible influence of vacuum fluctuations on photo detection and its background noise in Bell tests. We analyze its consequences on the standard statistical analysis of data showing that it is not fulfilled anymore the conventional hypothesis of a Poisson like probability density distribution of single photodetection events. We assume that vacuum fluctuations are due to real and measurable fluctuating fields, as recently confirmed experimentally, and that their non null correlations outside the light cone contribute to photon coincidence rates making them time dependent. We introduce a generalized Bell like correlation function which contains a new term due to supposed vacuum induced photon counting events. We deduce then a generalization of CH-inequality which takes in account the effect of these vacuum electric fields on detector efficiency. We predict an apparatus temperature fluctuations during photon detection which we suggest could be observed by looking for colored noise thermal emission of the photodetectors, generalizing the standard white noise prediction of C.S.L. models on wave function collapse postulate. We discuss an experimental test of this prediction, based on the idea of inducing a thermal wave on the whole quantum detectors, aimed to observe time dependent deviations from standard stationary statistical predictions of Quantum Mechanics.

Cluster modeling of the short-range correlation of acoustically emitted scattering signals

As a widely used measurement technique in rock mechanics,spatial correlation modeling of acoustic emission(AE)scattering signals is attracting increasing focus for describing mechanical behavior quantitatively.Unlike the statistical description of the spatial distribution of randomly generated AE signals,spatial correlation modeling is based mainly on short-range correlation considering the interrelationship of adjacent signals.As a new idea from percolation models,the covering strategy is used to build the most representative cube cluster,which corresponds to the critical scale at peak stress.Its modeling process of critical cube cluster depends strongly on the full connection of the main fracture network,and the corresponding cube for coverage is termed the critical cube.The criticality pertains to not only the transition of local-to-whole connection of the fracture network but also the increasing-to-decreasing transition of the deviatoric stress with an obvious stress drop in the brittle failure of granite.Determining a reasonable critical cube guarantees the best observation scale for investigating the failure process.Besides,the topological connection induces the geometric criticality of three descriptors,namely anisotropy,pore fraction,and specific surface area,which are evaluated separately and effectively.The results show that cluster modeling based on the critical cube is effective and has criticality in both topology and geometry,as well as the triaxial behavior.Furthermore,the critical cube length presents a high confidence probability of being correlated to the mineral particle size.Besides,its pore fraction of cube cluster is influenced strongly by the critical cube length and confining pressure.

A Hand Features Based Fusion Recognition Network with Enhancing Multi-Modal Correlation

Fusing hand-based features in multi-modal biometric recognition enhances anti-spoofing capabilities.Additionally,it leverages inter-modal correlation to enhance recognition performance.Concurrently,the robustness and recognition performance of the system can be enhanced through judiciously leveraging the correlation among multimodal features.Nevertheless,two issues persist in multi-modal feature fusion recognition:Firstly,the enhancement of recognition performance in fusion recognition has not comprehensively considered the inter-modality correlations among distinct modalities.Secondly,during modal fusion,improper weight selection diminishes the salience of crucial modal features,thereby diminishing the overall recognition performance.To address these two issues,we introduce an enhanced DenseNet multimodal recognition network founded on feature-level fusion.The information from the three modalities is fused akin to RGB,and the input network augments the correlation between modes through channel correlation.Within the enhanced DenseNet network,the Efficient Channel Attention Network(ECA-Net)dynamically adjusts the weight of each channel to amplify the salience of crucial information in each modal feature.Depthwise separable convolution markedly reduces the training parameters and further enhances the feature correlation.Experimental evaluations were conducted on four multimodal databases,comprising six unimodal databases,including multispectral palmprint and palm vein databases from the Chinese Academy of Sciences.The Equal Error Rates(EER)values were 0.0149%,0.0150%,0.0099%,and 0.0050%,correspondingly.In comparison to other network methods for palmprint,palm vein,and finger vein fusion recognition,this approach substantially enhances recognition performance,rendering it suitable for high-security environments with practical applicability.The experiments in this article utilized amodest sample database comprising 200 individuals.The subsequent phase involves preparing for the extension of the method to larger databases.

Track correlation algorithm based on CNN-LSTM for swarm targets

The rapid development of unmanned aerial vehicle(UAV) swarm, a new type of aerial threat target, has brought great pressure to the air defense early warning system. At present, most of the track correlation algorithms only use part of the target location, speed, and other information for correlation.In this paper, the artificial neural network method is used to establish the corresponding intelligent track correlation model and method according to the characteristics of swarm targets.Precisely, a route correlation method based on convolutional neural networks (CNN) and long short-term memory (LSTM)Neural network is designed. In this model, the CNN is used to extract the formation characteristics of UAV swarm and the spatial position characteristics of single UAV track in the formation,while the LSTM is used to extract the time characteristics of UAV swarm. Experimental results show that compared with the traditional algorithms, the algorithm based on CNN-LSTM neural network can make full use of multiple feature information of the target, and has better robustness and accuracy for swarm targets.