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.

两图运算后Sombor指数的上界

Sombor指数是基于顶点度引入的一种新的化学拓扑指数。本文研究了两个有限简单的连通图经过连接运算、笛卡尔积运算、冠运算、字典序积运算、对称差运算后的Sombor指数,并且刻画了其极图。首先,对每种运算后表达式的边进行了分类。然后,利用顶点的最大度并结合不等式放缩,给出了各运算图Sombor指数上界的估值不等式。最后,证明了取得Sombor指数上界的条件为两图都是正则图。

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.

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.

一类具有正初始能量的双重退化方程解的爆破和爆破时间上界估计

研究了具有齐次Dirichlet边界条件的双重退化抛物方程u t=div(|Δu^(m)|^(p-2)Δu^(m))+f(u),x∈Ω,t≥0解的爆破问题.利用构造凸函数的方法,证明了f在适当的条件下,r>p>2,m≥1,初始能量为正时,解在有限时刻爆破,并给出了爆破时间的上界估计.该结论推广了已有结果.

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.

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.

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.

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.

图的Nirmala能量新上界

设G是一个简单无向连通图,定义图G的Nirmala能量为图的Nirmala矩阵的特征值的绝对值之和.利用数学分析和不等式方法,给出了图的Nirmala能量的几个新上界.

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.

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.

采用异步指纹图联合上界预测法对地下矿坑涌水进行溯源及预测

地下矿坑水来源复杂,难于准确溯源及预测,易造成安全事故。为了对矿坑涌水准确溯源和水量预测,根据矿坑涌水来源不同时与大气降水之间的关系差异,在指纹图法中考虑异步因素首创出异步指纹图法。利用异步指纹图法对大气降水与矿坑涌水之间的影响时间滞后特性进行对比分析,以对矿坑涌水进行准确溯源;又根据矿坑涌水的前期波动特征,提取出其上界数据,利用上界包络预测对矿坑涌水的最大涌水量进行了分析预测。经某铝土矿山实例分析,异步指纹图法确定矿山矿坑涌水来源于大气降水,大气降水5天后影响矿坑涌水;上界包络预测矿山矿坑最大涌水量为10218 m3/d。据此对矿山设计了针对性的截流排水措施后,突水事故数减少了68%,水害经济损失减少了91%。说明异步指纹图联合上界包络预测能够准确地溯源和预测矿坑涌水。

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.

基于深度学习的Hermite-Gaussian光束模式分析方法研究

提出了一种基于深度学习的Hermite-Gaussian(HG)光束模式分解方案,利用仿真生成的具有多个模式的模拟样本训练卷积神经网络,并通过训练好的模式识别网络采用迁移学习技术加速模式分解网络的训练过程。通过单次发射的强度图像,训练后的神经网络能够对四模以内所有模式的HG光束实现高效准确的模式分解,获取HG光束的模式振幅和相位信息。实验结果表明,该方法在不同模式数量的识别和分解任务中均表现出较高的精度和稳定性,有望在光通信、光学成像、激光加工等领域得到广泛应用。

基于网络演算的无线Mesh网时延上界

无线Mesh网是一种新型宽带接入通信网络,端到端时延上界分析的准确性会直接影响网络的QoS保障。针对无线Mesh网传统时延边界分析方法没有考虑当前节点处理时延对后一个节点到达曲线的影响,造成时延上界计算不紧致的问题,利用(ρ,σ)模型作为数据流到达曲线,延迟-速率函数LR作为数据流服务曲线,同时考虑前一个节点的处理时延对后一个节点到达曲线的影响,推导出无线Mesh网中单节点、单路径和多路径传输时延上界,并利用数值分析方法得到网络服务速率、流量分配方式、多路径数目等因素对时延上界的影响。仿真结果表明,与传统方法相比,以上计算得到的无线Mesh网时延上界更接近实际仿真值。

Gaussian和Multiwfn软件在计算制冷剂介电常数中的应用

物理化学的主要研究内容之一是化学体系的微观结构和性质。介电常数是物质的重要物理量,与物质的电子结构紧密相关。结合物理化学分析方法,利用Gaussian和Multiwfn软件对制冷剂材料的介电常数进行了探究,并与相应的实验值进行了对比分析。通过结合分子可视化和量子化学计算软件,提高了化学教学的生动性,将该研究方法用于课堂教学,激发学生的研究兴趣,有利于学生对相关知识的理解。