基于Gaussian、ECOSAR模型的紫外/次氯酸体系降解含卤阻燃剂的产物预测与毒性评估

含卤阻燃剂广泛应用于各类电子产品的生产,难降解且具有生物毒性.在生产和使用过程中,部分含卤阻燃剂会残留在水体并排放到水环境造成累积污染,威胁水环境安全,亟需探寻有效的降解去毒方法.本研究通过Gaussian与ECOSAR模型预测了四氯双酚A(TCBPA)、四溴双酚A(TBBPA)、十溴二苯乙烷(DBDPE)等3种典型含卤阻燃剂在紫外/次氯酸(UV/Cl)体系中的光催氧化降解路径与产物毒性.结果表明,UV/Cl体系中的含氯自由基(RCS)与羟基自由基(·OH)易攻击阻燃剂分子结构上键能较低、Fukui指数较高的位点,促使C—Cl键、C—Br键、C—C键等因为受到攻击而断裂,进而降解阻燃剂.同时,利用ECOSAR模型评估发现降解产物的急性毒性LC_(50)-96 h均低于100 mg·L^(-1),佐证了UV/Cl体系对含卤阻燃剂降解的有效性,并降低其环境危害.因此,采用Gaussian计算、ECOSAR模型相结合的分析方法,能够更加便捷地预测阻燃剂降解路径与产物毒性特征,为深入揭示UV/Cl体系光催氧化降解含卤阻燃剂机理提供新思路.

Bose–Einstein distribution temperature features of quasiparticles around magnetopolaron in Gaussian quantum wells of alkali halogen ions

We have applied strong coupling unitary transformation method combined with Bose–Einstein statistical law to investigate magnetopolaron energy level temperature effects in halogen ion crystal quantum wells.The obtained results showed that under magnetic field effect,magnetopolaron quasiparticle was formed through the interaction of electrons and surrounding phonons.At the same time,magnetopolaron was influenced by phonon temperature statistical law and important energy level shifts down and binding energy increases.This revealed that lattice temperature and magnetic field could easily affect magnetopolaron and the above results could play key roles in exploring thermoelectric conversion and conductivity of crystal materials.

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.

Reliable calculations of nuclear binding energies by the Gaussian process of machine learning

Reliable calculations of nuclear binding energies are crucial for advancing the research of nuclear physics. Machine learning provides an innovative approach to exploring complex physical problems. In this study, the nuclear binding energies are modeled directly using a machine-learning method called the Gaussian process. First, the binding energies for 2238 nuclei with Z > 20 and N > 20 are calculated using the Gaussian process in a physically motivated feature space, yielding an average deviation of 0.046 MeV and a standard deviation of 0.066 MeV. The results show the good learning ability of the Gaussian process in the studies of binding energies. Then, the predictive power of the Gaussian process is studied by calculating the binding energies for 108 nuclei newly included in AME2020. The theoretical results are in good agreement with the experimental data, reflecting the good predictive power of the Gaussian process. Moreover, the α-decay energies for 1169 nuclei with 50 ≤ Z ≤ 110 are derived from the theoretical binding energies calculated using the Gaussian process. The average deviation and the standard deviation are, respectively, 0.047 MeV and 0.070 MeV. Noticeably, the calculated α-decay energies for the two new isotopes ^ (204 )Ac(Huang et al. Phys Lett B 834, 137484(2022)) and ^ (207) Th(Yang et al. Phys Rev C 105, L051302(2022)) agree well with the latest experimental data. These results demonstrate that the Gaussian process is reliable for the calculations of nuclear binding energies. Finally, the α-decay properties of some unknown actinide nuclei are predicted using the Gaussian process. The predicted results can be useful guides for future research on binding energies and α-decay properties.

MULTIPLE INTERSECTIONS OF SPACE-TIME ANISOTROPIC GAUSSIAN FIELDS

Let X={X(t)∈R^(d),t∈R^(N)}be a centered space-time anisotropic Gaussian field with indices H=(H_(1),…,H_(N))∈(0,1)~N,where the components X_(i)(i=1,…,d)of X are independent,and the canonical metric√(E(X_(i)(t)-X_(i)(s))^(2))^(1/2)(i=1,…,d)is commensurate with■for s=(s_(1),…,s_(N)),t=(t_(1),…,t_(N))∈R~N,α_(i)∈(0,1],and with the continuous functionγ(·)satisfying certain conditions.First,the upper and lower bounds of the hitting probabilities of X can be derived from the corresponding generalized Hausdorff measure and capacity,which are based on the kernel functions depending explicitly onγ(·).Furthermore,the multiple intersections of the sample paths of two independent centered space-time anisotropic Gaussian fields with different distributions are considered.Our results extend the corresponding results for anisotropic Gaussian fields to a large class of space-time anisotropic Gaussian fields.

Application of Gaussian Beam Summation Migration in Reflected In-seam Wave Imaging

The geological conditions for coal mining in China are complex,with various structural issues such as faults and collapsed columns seriously compromising the safety of coal mine production.In-seam wave exploration is an effective technique for acquiring detailed information on geological structures in coal seam working faces.However,the existing reflected in-seam wave imaging technique can no longer meet the exploration precision requirements,making it imperative to develop a new reflected in-seam wave imaging technique.This study applies the Gaussian beam summation(GBS)migration method to imaging coal seams'reflected in-seam wave data.Firstly,with regard to the characteristics of the reflected in-seam wave data,methods such as wavefield removal and enveloped superposition are employed for the corresponding wavefield separation,wave train compression and other processing of reflected in-seam waves.Thereafter,imaging is performed using the GBS migration technique.The feasibility and effectiveness of the proposed method for reflected in-seam wave imaging are validated by conducting GBS migration tests on 3D coal-seam fault models with different dip angles and throws.By applying the method to reflected in-seam wave data for an actual coal seam working face,accurate imaging of a fault structure is obtained,thereby validating its practicality.

Bessel–Gaussian beam-based orbital angular momentum holography

Orbital angular momentum(OAM), as a new degree of freedom, has recently been applied in holography technology.Due to the infinite helical mode index of OAM mode, a large number of holographic images can be reconstructed from an OAM-multiplexing hologram. However, the traditional design of an OAM hologram is constrained by the helical mode index of the selected OAM mode, for a larger helical mode index OAM mode has a bigger sampling distance, and the crosstalk is produced for different sampling distances for different OAM modes. In this paper, we present the design of the OAM hologram based on a Bessel–Gaussian beam, which is non-diffractive and has a self-healing property during its propagation. The Fourier transform of the Bessel–Gaussian beam is the perfect vortex mode that has the fixed ring radius for different OAM modes. The results of simulation and experiment have demonstrated the feasibility of the generation of the OAM hologram with the Bessel–Gaussian beam. The quality of the reconstructed holographic image is increased, and the security is enhanced. Additionally, the anti-interference property is improved owing to its self-healing property of the Bessel-OAM holography.

Persymmetric adaptive polarimetric detection of subspace range-spread targets in compound Gaussian sea clutter

This paper focuses on the adaptive detection of range and Doppler dual-spread targets in non-homogeneous and nonGaussian sea clutter.The sea clutter from two polarimetric channels is modeled as a compound-Gaussian model with different parameters,and the target is modeled as a subspace rangespread target model.The persymmetric structure is used to model the clutter covariance matrix,in order to reduce the reliance on secondary data of the designed detectors.Three adaptive polarimetric persymmetric detectors are designed based on the generalized likelihood ratio test(GLRT),Rao test,and Wald test.All the proposed detectors have constant falsealarm rate property with respect to the clutter texture,the speckle covariance matrix.Experimental results on simulated and measured data show that three adaptive detectors outperform the competitors in different clutter environments,and the proposed GLRT detector has the best detection performance under different parameters.

MAXIMAL FUNCTION CHARACTERIZATIONS OF HARDY SPACES ASSOCIATED WITH BOTH NON-NEGATIVE SELF-ADJOINT OPERATORS SATISFYING GAUSSIAN ESTIMATES AND BALL QUASI-BANACH FUNCTION SPACES

Assume that L is a non-negative self-adjoint operator on L^(2)(ℝ^(n))with its heat kernels satisfying the so-called Gaussian upper bound estimate and that X is a ball quasi-Banach function space onℝ^(n) satisfying some mild assumptions.Let HX,L(ℝ^(n))be the Hardy space associated with both X and L,which is defined by the Lusin area function related to the semigroup generated by L.In this article,the authors establish various maximal function characterizations of the Hardy space HX,L(ℝ^(n))and then apply these characterizations to obtain the solvability of the related Cauchy problem.These results have a wide range of generality and,in particular,the specific spaces X to which these results can be applied include the weighted space,the variable space,the mixed-norm space,the Orlicz space,the Orlicz-slice space,and the Morrey space.Moreover,the obtained maximal function characterizations of the mixed-norm Hardy space,the Orlicz-slice Hardy space,and the Morrey-Hardy space associated with L are completely new.

Operational optimization of copper flotation process based on the weighted Gaussian process regression and index-oriented adaptive differential evolution algorithm

Concentrate copper grade(CCG)is one of the important production indicators of copper flotation processes,and keeping the CCG at the set value is of great significance to the economic benefit of copper flotation industrial processes.This paper addresses the fluctuation problem of CCG through an operational optimization method.Firstly,a density-based affinity propagationalgorithm is proposed so that more ideal working condition categories can be obtained for the complex raw ore properties.Next,a Bayesian network(BN)is applied to explore the relationship between the operational variables and the CCG.Based on the analysis results of BN,a weighted Gaussian process regression model is constructed to predict the CCG that a higher prediction accuracy can be obtained.To ensure the predicted CCG is close to the set value with a smaller magnitude of the operation adjustments and a smaller uncertainty of the prediction results,an index-oriented adaptive differential evolution(IOADE)algorithm is proposed,and the convergence performance of IOADE is superior to the traditional differential evolution and adaptive differential evolution methods.Finally,the effectiveness and feasibility of the proposed methods are verified by the experiments on a copper flotation industrial process.

A Gaussian Noise-Based Algorithm for Enhancing Backdoor Attacks

Deep Neural Networks(DNNs)are integral to various aspects of modern life,enhancing work efficiency.Nonethe-less,their susceptibility to diverse attack methods,including backdoor attacks,raises security concerns.We aim to investigate backdoor attack methods for image categorization tasks,to promote the development of DNN towards higher security.Research on backdoor attacks currently faces significant challenges due to the distinct and abnormal data patterns of malicious samples,and the meticulous data screening by developers,hindering practical attack implementation.To overcome these challenges,this study proposes a Gaussian Noise-Targeted Universal Adversarial Perturbation(GN-TUAP)algorithm.This approach restricts the direction of perturbations and normalizes abnormal pixel values,ensuring that perturbations progress as much as possible in a direction perpendicular to the decision hyperplane in linear problems.This limits anomalies within the perturbations improves their visual stealthiness,and makes them more challenging for defense methods to detect.To verify the effectiveness,stealthiness,and robustness of GN-TUAP,we proposed a comprehensive threat model.Based on this model,extensive experiments were conducted using the CIFAR-10,CIFAR-100,GTSRB,and MNIST datasets,comparing our method with existing state-of-the-art attack methods.We also tested our perturbation triggers using various defense methods and further experimented on the robustness of the triggers against noise filtering techniques.The experimental outcomes demonstrate that backdoor attacks leveraging perturbations generated via our algorithm exhibit cross-model attack effectiveness and superior stealthiness.Furthermore,they possess robust anti-detection capabilities and maintain commendable performance when subjected to noise-filtering methods.

Optimization of Generator Based on Gaussian Process Regression Model with Conditional Likelihood Lower Bound Search

The noise that comes from finite element simulation often causes the model to fall into the local optimal solution and over fitting during optimization of generator.Thus,this paper proposes a Gaussian Process Regression(GPR)model based on Conditional Likelihood Lower Bound Search(CLLBS)to optimize the design of the generator,which can filter the noise in the data and search for global optimization by combining the Conditional Likelihood Lower Bound Search method.Taking the efficiency optimization of 15 kW Permanent Magnet Synchronous Motor as an example.Firstly,this method uses the elementary effect analysis to choose the sensitive variables,combining the evolutionary algorithm to design the super Latin cube sampling plan;Then the generator-converter system is simulated by establishing a co-simulation platform to obtain data.A Gaussian process regression model combing the method of the conditional likelihood lower bound search is established,which combined the chi-square test to optimize the accuracy of the model globally.Secondly,after the model reaches the accuracy,the Pareto frontier is obtained through the NSGA-II algorithm by considering the maximum output torque as a constraint.Last,the constrained optimization is transformed into an unconstrained optimizing problem by introducing maximum constrained improvement expectation(CEI)optimization method based on the re-interpolation model,which cross-validated the optimization results of the Gaussian process regression model.The above method increase the efficiency of generator by 0.76%and 0.5%respectively;And this method can be used for rapid modeling and multi-objective optimization of generator systems.

State of health prediction for lithium-ion batteries based on ensemble Gaussian process regression

The performance of lithium-ion batteries(LIBs)gradually declines over time,making it critical to predict the battery’s state of health(SOH)in real-time.This paper presents a model that incorporates health indicators and ensemble Gaussian process regression(EGPR)to predict the SOH of LIBs.Firstly,the degradation process of an LIB is analyzed through indirect health indicators(HIs)derived from voltage and temperature during discharge.Next,the parameters in the EGPR model are optimized using the gannet optimization algorithm(GOA),and the EGPR is employed to estimate the SOH of LIBs.Finally,the proposed model is tested under various experimental scenarios and compared with other machine learning models.The effectiveness of EGPR model is demonstrated using the National Aeronautics and Space Administration(NASA)LIB.The root mean square error(RMSE)is maintained within 0.20%,and the mean absolute error(MAE)is below 0.16%,illustrating the proposed approach’s excellent predictive accuracy and wide applicability.

Prestack Gaussian beam depth migration under complex surface conditions

In areas with a complex surface,the acquisition and processing of seismic data is a great challenge.Although elevation-static corrections can be used to eliminate the influences of topography,the distortions of seismic wavefields caused by simple vertical time shifts still greatly degrade the quality of the migrated images.Ray-based migration methods which can extrapolate and image the wavefields directly from the rugged topography are efficient ways to solve the problems mentioned above.In this paper,we carry out a study of prestack Gaussian beam depth migration under complex surface conditions.We modify the slant stack formula in order to contain the information of surface elevations and get an improved method with more accuracy by compositing local plane-wave components directly from the complex surface.First,we introduce the basic rules and computational procedures of conventional Gaussian beam migration.Then,we give the original method of Gaussian beam migration under complex surface conditions and an improved method in this paper.Finally,we validate the effectiveness of the improved method with trials of model and real data.

结合Gaussian分布和LK光流法的视频对象分割算法

为了提高视频对象分割的性能,在传统光流法的基础上,提出了一种改进算法.该算法首先对序列图像中的帧进行预处理,平滑序列图像;然后用Gaussian金字塔降低图像的分辨率,使运动对象的速度降低;再使用结合Gaussian分布的LK光流法进行分割,得到运动对象;最后使用帧相"与"运算做进一步的分割.实验结果表明:该算法减少了运算时间,能更精确地分割出运动对象,取得较好的效果.

Gaussian软件在红外光谱学教学中的应用

探讨了Gaussian软件在红外光谱学教学中的具体应用,帮助学生在课堂上对分子的三维空间结构、振动光谱的理解、振动模式的指认与归属等方面有更加感性的认识,从而提高课堂趣味性,使得教学内容更为形象生动.

基于Magnus-Gaussian截断的铣削系统稳定性的半离散分析法

机床铣削系统的动力学可以通过一微分差分方程组来描述。对该动力学模型进行稳定性分析,可以确定稳定的铣削参数域及稳定性图。半离散法作为一种有效的半解析稳定性分析法,在对铣削系统动力学模型进行零阶半离散化时的误差级数较大,往往需要进行多步长的运算才能获得比较精确的稳定性分析结果。通过引入Magnus-Gaussian截断法,推导出了基于Magnus-Gaussian截断的零阶半离散稳定性分析法。用该方法对铣床切削稳定性进行了分析,并获得了各种工况下的铣床稳定性图。结果表明该方法比原来的零阶半离散稳定性分析法能够更快的收敛于稳定解,从而节省了稳定性分析的运算时间。

基于Gaussian Copula与t-Copula的沪深股指相关性分析

针对沪深股指,讨论了Gaussian Copula与t-Copula的密度函数,并进行相关性建模,采用二步估计法对所建模型进行参数估计并给出了相关性指标。最后,通过Monte Carlo模拟的方法比较了Copula关联结构之间的差异。

Gaussian 09软件在电子圆二色谱教学中的应用

利用Gaussian 09软件优化并计算了手性农药R-丁烯氟虫腈和S-丁烯氟虫腈的电子圆二色谱,讨论了其在立体化学（电子圆二色谱）教学中的具体应用,帮助学生对手性分子的三维空间结构、电子圆二色谱的理解及归属等方面有更加感性的认识,使教学内容形象生动,提高了学生的学习兴趣及积极性。

基于多维Gaussian Copula的岩土体设计参数概率转换模型构建方法

岩土工程中经常需要基于直接测量参数去预测设计参数,概率转换模型是确定设计参数真实分布范围的有效工具。常用的基于多维正态分布的概率转换模型构建方法容易引起误差且受边缘分布类型限制。为此,提出了基于多维Gaussian Copula的岩土体设计参数概率转换模型构建方法。以全球黏土数据库CLAY/6/535为例验证了所提方法的有效性,分别推导了不排水抗剪强度和超固结比与孔压静力触探试验指标之间的概率转换模型。结果表明:基于多维Gaussian Copula的概率转换模型构建方法可以将相关结构与边缘分布分开构建,该方法不受岩土体参数的边缘分布类型限制,有效地避免了误差由边缘分布向相关结构的传播。在构建的概率转换模型中,岩土体设计参数的不确定性、相关性与直接测量参数的数目以及直接测量参数与设计参数之间的相关性成反比。