Structure and Two-dimensional Correlation Infrared Spectroscopy Study of a New One-dimensional Chain Compound: (4,4’-Hbpy)_3[NaMo_8O_(26)](4,4’- bpy)_2(H_2O)_4 (bpy = Bipydine)

A novel compound, （4,4＇-Hbpy）3[NaMo8O26]（4,4＇-bpy）2（H2O）4 1 （bpy=bipydine）, was synthesized by the hydrothermal method. Single-crystal X-ray diffraction shows that compound 1 belongs to the monoclinic system, space group C2/m with a=19.1921（5）, b=18.6931（6）, c=9.3821（3） A° β=104.8020（11）°, V=3254.22（17）A°^3 C50H51Mo8N10NaO30, Mr=2062.52, Z=2, F（000）=2016, μ=1.591 mm^-1 and Dc=2.105 g/cm^3. The final R=0.0283 and wR=0.0912 for 3118 observed reflections （I〉20（I））. Compound 1 contains the β-[Mo8O26]^4-anion, sodium ion, 4,4＇-bpy and lattice crystalline water molecules. The β-[MosO26] units link the sodium ion to form a chain structure. The infinitechains of [Na（Mo8O26）]^3- blocks are surrounded by protonized 4,4＇-bpy cations, 4,4＇-bpy and lattice crystalline water molecules. The 2D-IR correlation spectroscopy study indicates that the stretching vibrations of Mo=O occur more preferentially due to the thermal effect. The TGA analysis shows that compound 1 has high thermal stability.

Application of Two-Dimensional Correlation UV-Vis Spectroscopy in Chinese Liquor Moutai Discrimination

Chinese liquor Moutai is the “National alcoholic drink” in China and plays a very important role of social activities in Chinese people’s life. In pursuit of high profits, some illegal counterfeit Moutai liquors have begun to appear in the market. Therefore, it is an urgent need for new techniques to discriminate the genuine and counterfeit Moutai liquor. In this work, the conventional Ultraviolet-Visible (UV-Vis) spectroscopy and two-dimensional correlation UV-Vis spectroscopy are applied to obtain the UV-Vis characteristic of Moutai liquor and counterfeit one, respectively. The experimental results reveal that the conventional UV-Vis spectra of the genuine and counterfeit Moutai liquor are similar. However, the two-dimensional correlation UV-Vis spectra of them are different and this method would be applied to differentiate the counterfeit Moutai liquor from the genuine Moutai liquor. Compared with conventional methods, this novel method has the advantages of easy operation, simple instrumentation and direct recognition, which make it a potential tool in the fields of food safety.

Monogamy quantum correlation near the quantum phase transitions in the two-dimensional XY spin systems

We investigate the role of quantum correlation around the quantum phase transitions by using quantum renormalization group theory. Numerical analysis indicates that quantum correlation as well as quantum nonlocality can efficiently detect the quantum critical point in the two-dimensional XY systems. The nonanalytic behavior of the first derivative of quantum correlation is observed at the critical point as the size of the model increases. Furthermore, we discuss the quantum correlation distribution in this system based on the square of concurrence（SC） and square of quantum discord（SQD）. The monogamous properties of SC and SQD are obtained. Particularly, we prove that the quantum critical point can also be achieved by monogamy score.

Evolution of Two-Dimensional Correlation Spectroscopy

A number of useful techniques associated with two-dimensional correlation spectroscopy(2DCOS)to improve its performance and utility have been developed in the last 30years.Evolution of these 2DCOS techniques,including some of the very recent developments,is reviewed with examples.Topics include merged or modified asynchronous 2Dcorrelation spectrum,two-dimensional codistribution spectroscopy(2DCDS),Pareto scaling,and null-space projection treatment of spectral dataset.

Thermal Behavior and Crystal Transition of Biodegradable Poly(butylenes adipate)Revealed by Two-Dimensional Correlation Spectroscopy

In the present study,thermal behavior and crystal transition of pure poly(butylene adipate)(PBA)upon heating process were investigated by FTIR spectroscopy.To gain further insight into the thermal behavior alteration and the phase transition of PBA,we performed two-dimensional(2D)correlation analysis.We found thatβ-form PBA crystal undergoes not only the melting process but also crystal transition upon the heating process.

Study on the Hydrogen Bond Interaction Between Soy Protein Isolate and Glycerol Using Two-Dimensional Correlation Fourier-Transform Infrared Spectroscopy

A series of soy protein isolate(SPI)films plasticized by glycerol(Gly)were studied using attenuated total reflectance-Fourier transform infrared spectroscopy(ATR/FTIR).Perturbation-correlation movingwindow two-dimensional(PCMW2D)and two-dimensional correlation(2DCOS)analyses were applied to the amideⅠband and thus the hydrogen bond interaction between SPI and Gly was systematically investigated.When Gly concentrations were in the range 0~35%,the hydrogen bond amongβ-sheets was replaced by the one between SPI chain and Gly molecule,which caused these protein chains being changed toα-helix.However,the transformation ofβ-sheet toα-helix was saturated and both of them tend to change to random coil when Gly concentrations were in the range 35%~60%.

LONG-RANGE CORRELATIONS IN DNA SEQUENCES USING TWO-DIMENSIONAL DNA WALKS

The characterization of long-range correlations and fractal properties of DNA sequences has proved to be adifficult though rewarding task mainly due to the mosaic character of DNA consisting of many patches of various lengthswith different nucleotide constitutions.In this paper we investigate statistical correlations among different positions in DNAsequences using the two-dimensional DNA walk.The root-mean-square fluctuation F(l)is described by a power law.Theautocorrelation function C(l),which is used to measure the linear dependence and periodicity,exists a power law ofC(l)-l^(-μ).We also calculate the mean-square distance<R^2(l)>along the DNA chain,and it may be expressed as<R^2(l)>-l^(?)with 2>γ>1.Our investigations can provide some insights into long-range correlations in DNA sequences.

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.

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.

First results of turbulence investigation on Globus-M2 using radial correlation Doppler reflectometry

The first results of investigation of the turbulence structure using Doppler backscattering(DBS)on the Globus-M2 tokamak are presented.A one-channel DBS system with a variable probing frequency within the 18–26 GHz range was installed to investigate the edge plasma at normalized minor radiiρ=0.9–1.1.Radial correlation Doppler reflectometry was used to study the changes in turbulence eddies after the LH transition.Correlation analysis was applied to the phase derivative of complex in-phase and quadrature(IQ)signals of the DBS diagnostic as it contains information about the poloidal plasma rotation velocity.In L-mode,the radial correlation length L_(r)is estimated to be 3 cm and after transition to H-mode reduces to approximately 2 cm.Gyrokinetic modelling in a linear local approximation using code GENE indicates that the instability with positive growth rate at the normalized minor radiusρ=0.75 in L-mode and H-mode on Globus-M2 was the ion temperature gradient(ITG)mode.

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.

Study of baryon number transport dynamics and strangeness conservation effects using Ω-hadron correlations

In nuclear collisions at RHIC energies, an excess of Ω hyperons over ■ is observed, indicating that Ω has a net baryon number despite s and s quarks being produced in pairs. The baryon number in Ω may have been transported from the incident nuclei and/or produced in the baryon-pair production of Ω with other types of anti-hyperons such as Ξ. To investigate these two scenarios, we propose to measure the correlations between Ω and K and between Ω and anti-hyperons. We use two versions, the default and string-melting, of a multiphase transport(AMPT) model to illustrate the method for measuring the correlation and to demonstrate the general shape of the correlation. We present the Ω-hadron correlations from simulated Au+Au collisions at ■ =7.7 and 14.6 Ge V and discuss the dependence on the collision energy and on the hadronization scheme in these two AMPT versions. These correlations can be used to explore the mechanism of baryon number transport and the effects of baryon number and strangeness conservation on nuclear collisions.

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.

Anomalous Hall effect and electronic correlation in a spin-reoriented kagome antiferromagnet LuFe_(6)Sn_(6)

The kagome lattice system has been identified as a fertile ground for the emergence of a number of new quantumstates,including superconductivity,quantum spin liquids,and topological electronic states.This has attracted significantinterest within the field of condensed matter physics.Here,we present the observation of an anomalous Hall effect in aniron-based kagome antiferromagnet LuFe_(6)Sn_(6),which implies a non-zero Berry curvature in this compound.By means ofextensive magnetic measurements,a high Neel temperature,T_(N)=552 K,and a spin reorientation behavior were identifiedand a simple temperature-field phase diagram was constructed.Furthermore,this compound was found to exhibit a largeSommerfeld coefficient ofγ=87 mJ·mol^(-1)·K^(-2),suggesting the presence of a strong electronic correlation effect.Ourresearch indicates that LuFe_(6)Sn_(6)is an intriguing compound that may exhibit magnetism,strong correlation,and topologicalstates.

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.

Variational data encoding and correlations in quantum-enhanced machine learning

Leveraging the extraordinary phenomena of quantum superposition and quantum correlation,quantum computing offers unprecedented potential for addressing challenges beyond the reach of classical computers.This paper tackles two pivotal challenges in the realm of quantum computing:firstly,the development of an effective encoding protocol for translating classical data into quantum states,a critical step for any quantum computation.Different encoding strategies can significantly influence quantum computer performance.Secondly,we address the need to counteract the inevitable noise that can hinder quantum acceleration.Our primary contribution is the introduction of a novel variational data encoding method,grounded in quantum regression algorithm models.By adapting the learning concept from machine learning,we render data encoding a learnable process.This allowed us to study the role of quantum correlation in data encoding.Through numerical simulations of various regression tasks,we demonstrate the efficacy of our variational data encoding,particularly post-learning from instructional data.Moreover,we delve into the role of quantum correlation in enhancing task performance,especially in noisy environments.Our findings underscore the critical role of quantum correlation in not only bolstering performance but also in mitigating noise interference,thus advancing the frontier of quantum computing.

Correlation Study on Grain Size Characteristics and Geotechnical Properties of Surface Sediments in Qingdao Offshore Area

The comprehension of sediment grain size parameters and the corresponding sedimentary environment holds paramount importance in elucidating the engineering geological attributes of the subaqueous seabed.This study delineated the sedimentary environment zoning in the northern sea area of Qingdao through cluster analysis of grain size parameters derived from 123 surface sediment samples.The study analyzed the correlation between sediment geotechnical indices and grain size parameters across diverse sedimentary environments.A correlation equation was established for samples exhibiting a strong correlation.The study found four distinct sedimentary environments in the study area:coastal,transitional,shallow sea,and residual.Within the same sedimentary environment,the average grain size and sorting coefficient exhibit significant correlations with geotechnical indices such as water content,density,shear strength,plastic limit,liquid limit,and plastic index.However,notable disparities in the correlation between grain size parameters and geotechnical indices emerge across different sedimentary environments.

SFGA-CPA: A Novel Screening Correlation Power Analysis Framework Based on Genetic Algorithm

Correlation power analysis(CPA)combined with genetic algorithms(GA)now achieves greater attack efficiency and can recover all subkeys simultaneously.However,two issues in GA-based CPA still need to be addressed:key degeneration and slow evolution within populations.These challenges significantly hinder key recovery efforts.This paper proposes a screening correlation power analysis framework combined with a genetic algorithm,named SFGA-CPA,to address these issues.SFGA-CPA introduces three operations designed to exploit CPA characteris-tics:propagative operation,constrained crossover,and constrained mutation.Firstly,the propagative operation accelerates population evolution by maximizing the number of correct bytes in each individual.Secondly,the constrained crossover and mutation operations effectively address key degeneration by preventing the compromise of correct bytes.Finally,an intelligent search method is proposed to identify optimal parameters,further improving attack efficiency.Experiments were conducted on both simulated environments and real power traces collected from the SAKURA-G platform.In the case of simulation,SFGA-CPA reduces the number of traces by 27.3%and 60%compared to CPA based on multiple screening methods(MS-CPA)and CPA based on simple GA method(SGA-CPA)when the success rate reaches 90%.Moreover,real experimental results on the SAKURA-G platform demonstrate that our approach outperforms other methods.

Longitudinal investigation of mineral composition in human milk and its correlation with infant anthropometric outcomes among Tibetan mother-infant dyads during the first 6 months postpartum

Nutrients in human milk,including minerals,relate growth and development of breast-fed infants.Tibetan mother-infant dyads possess unique characteristics on early nutrition due to their featured long-lasting lifestyle.This study longitudinally investigated the relationship between the mineral composition in human milk and the Z-scores of infants among Tibetan mother-infant dyads during their first 6 months postpartum through a prospective cohort study.The results show that the minerals of Na,Mg,K,Ca,Cu,Zn,and Se were of higher levels in colostrum than other lactation stages.Several minerals were below the recommended values for infants according to Chinese dietary guidelines.Besides,a large proportion of infant Z-scores were below-2 as lactation period continued.Multivariate statistical analysis revealed that classifications and correlations in varying degrees were observed between minerals in human milk and infant Z-scores.These findings will be advantageous for research upon Chinese early nutrition and progress of tailor-made infant formula.