Coseismic deformation and fault slip distribution of the 2023 M_(W)7.8 and M_(W)7.6 earthquakes in Türkiye

On February 6,2023,a devastating earthquake with a moment magnitude of M_(W)7.8 struck the town of Pazarcik in south-central Türkiye,followed by another powerful earthquake with a moment magnitude of M_(W)7.6 that struck the nearby city of Elbistan 9 h later.To study the characteristics of surface deformation caused by this event and the influence of fault rupture,this study calculated the static coseismic deformation of 56 stations and dynamic displacement waveforms of 15 stations using data from the Turkish national fixed global navigation satellite system(GNSS)network.A maximum static coseismic displacement of 0.38 m for the M_(W)7.8 Kahramanmaras earthquake was observed at station ANTE,36 km from the epicenter,and a maximum dynamic coseismic displacement of 4.4 m for the M_(W)7.6 Elbistan earthquake was observed at station EKZ1,5 km from the epicenter.The rupture-slip distributions of the two earthquakes were inverted using GNSS coseismic deformation as a constraint.The results showed that the Kahramanmaras earthquake rupture segment was distinct and exposed on the ground,resulting in significant rupture slip along the Amanos and Pazarcik fault segments of the East Anatolian Fault.The maximum slip in the Pazarcik fault segment was 10.7 m,and rupture occurred at depths of 0–15 km.In the Cardak fault region,the Elbistan earthquake caused significant ruptures at depths of 0–12 km,with the largest amount of slip reaching 11.6 m.The Coulomb stress change caused by the Kahramanmaras earthquake rupture along the Cardak fault segment was approximately 2 bars,and the area of increased Coulomb stress corresponded to the subsequent rupture region of the M_(W)7.6 earthquake.Thus,it is likely that the M_(W)7.8 earthquake triggered or promoted the M_(W)7.6 earthquake.Based on the cumulative stress impact of the M_(W)7.8 and M_(W)7.6 events,the southwestern segment of the East Anatolian Fault,specifically the Amanos fault segment,experienced a Coulomb rupture stress change exceeding 2 bars,warranting further attention to assess its future seismic hazard risk.

Diameter distributions in Pinus sylvestris L.stands:evaluating modelling approaches including a machine learning technique

The diameter distribution of trees in a stand provides the basis for determining the stand’s ecological and economic value,its structure and stability and appropriate management practices.Scots pine(Pinus sylvestris L.)is one of the most common and important conifers in Turkey,so a well-planned management schedule is critical.Diameter distribution models to accurately describe the stand structure help improve management strategies,but developing reliable models requires a deep understanding of the growth,output and constraints of the forests.The most important information derived by diameter distribution models is primary data on horizontal stand structure for each diameter class of trees:basal area and volume per unit area.These predictions are required to estimate the range of products and predicted volume and yield from a forest stand.Here,to construct an accurate,reliable diameter distribution model for natural Scots pine stands in the Türkmen Mountain region,we used Johnson’s SBdistribution to represent the empirical diameter distributions of the stands using ground-based measurements from 55 sample plots that included1219 trees in natural distribution zones of the forests.As an alternative,nonparametric approach,which does not require any predefined function,an artificial intelligence model was constructed based on support vector machine methodology.An error index was calculated to evaluate the results.Overall,both Johnson’s SB probability density function with a three-parameter recovery approach and the support vector regression methodology provided reliable estimates of the diameter distribution of these stands.

Pricing European Options Based on a Logarithmic Truncated t-Distribution

The t-distribution has a “fat tail” feature, which is more suitable than the normal probability density function to describe the distribution characteristics of return on assets. The difficulty of using t-distribution to price European options is that a fat tail can lead to a deviation in one integral required for option pricing. We use a distribution called logarithmic truncated t-distribution to price European options. A risk neutral valuation method was used to obtain a European option pricing model with logarithmic truncated t-distribution.

基于混合t Location-Scale分布模型的光伏功率随机性分量波动性分析

光伏波动性是影响光伏功率稳定并网的关键问题之一。为了更准确地对光伏功率随机性分量进行定量描述,文章首先利用周期图法提取功率的周期性分量,然后提出了混合t Location-Scale分布模型,并对光伏功率随机性分量进行拟合。验证了文章所提模型在拟合光伏功率随机性分量波动性上的优越性,讨论该模型在不同采样时间间隔、不同地区的有效性和适用性。并以某地区光伏电站群实测数据为例,验证了混合t Location-Scale分布模型对随机性分量波动性拟合效果的优势。

含T接逆变型分布式电源配电网的自适应距离保护

大量逆变型分布式电源(Inverter Interfaced Distributed Generation,IIDG)接入配电网,传统继电保护技术的可靠性无法满足。为了提高配电网供电可靠性,适应可再生能源大量渗透的新型电力系统,针对含T形接入IIDG的配电网,文章提出了一种自适应性的距离保护方案。分析了故障发生在不同位置对保护的影响,根据IIDG在系统发生故障时的输出特性,通过BP神经网络利用保护本地电气信息计算IIDG的输出电流,实时整定动作值。该方法由于无需与远方通信,动作速度快,并且减少了通信系统的投资。最后在MATLAB中建立了含T形接入IIDG的10 kV配电网模型,并与传统距离保护进行了对比,验证了该保护方法的优越性。

径流预报误差的混合t Location-Scale分布模型及应用

基于混合t Location-Scale分布良好的自适应性,利用k均值聚类法挖掘数据隐含结构、良好的聚类效果特点,建立了不同预见期径流预报误差混合t Location-Scale分布模型。将模型应用于雅砻江流域官地水库,对预见期分别为6、12、18 h和24 h的区间径流预报误差进行了分析与建模,结果发现混合t Location-Scale分布模型弥补了单一分布难以描述径流预报误差特征多样性的局限,能更准确地描述不同预见期径流预报误差的统计特征,误差分布与实测径流预报误差的变化规律一致,可为水库水电站的径流预报和调度运行提供更加准确、可靠的来水数据。

融合CNN和ViT的声信号轴承故障诊断方法

针对轴承故障诊断任务数据量少、故障信号非平稳等特点,提出一种短时傅里叶变换、卷积神经网络和视觉转换器相结合的轴承故障诊断方法。首先,利用短时傅里叶变换将原始声信号转换为包含时序信息和频率信息的时频图像。其次,将时频图像作为卷积神经网络的输入,用于隐式提取图像的深层特征,其输出作为视觉转换器的输入。视觉转换器用于提取信号的时间序列信息。并在输出层利用Softmax函数实现故障模式的识别。试验结果表明,该方法对于轴承故障诊断准确率较高。为了更好解释和优化提出的轴承故障诊断方法,利用t-分布领域嵌入算法对分类特征进行了可视化展示。

基于改进SCSO算法的光伏MPPT研究

在解决光伏阵列在局部遮挡时发电效率降低的问题时,传统最大功率点追踪(MPPT)方法容易追踪失败。为此,提出一种改进沙猫群优化算法的最大功率点追踪方法。该算法在标准沙猫群算法的基础上,引入了精英反向学习和自适应t分布,同时优化沙猫群算法(SCSO)的局部搜索并融合Jaya算法。通过对4种典型单峰、多峰函数的测试,证明该算法具有极高的收敛速度,容易跳出局部最优值。将算法应用于MPPT控制中,仿真结果表明:在静态遮荫情况下,所提方法的搜索最大功率点的时间更少;在动态遮荫条件下,重新搜寻到最大功率点的响应时间平均为0.2 s。实验表明所提算法可以适应动态变化的天气,解决了传统算法收敛速度和防止陷入局部最优等问题。

基于改进INFO-Bi-LSTM模型的SO_(2)排放质量浓度预测

针对火电机组SO_(2)排放质量浓度的影响因素众多,难以准确预测的问题,提出一种改进向量加权平均(weighted mean of vectors,INFO)算法与双向长短期记忆(bi-directional long short term memory,Bi-LSTM)神经网络相结合的预测模型(改进INFO-Bi-LSTM模型)。采用Circle混沌映射和反向学习产生高质量初始化种群,引入自适应t分布提升INFO算法跳出局部最优解和全局搜索的能力。选取改进INFO-Bi-LSTM模型和多种预测模型对炉内外联合脱硫过程中4种典型工况下的SO_(2)排放质量浓度进行预测,将预测结果进行验证对比。结果表明:改进INFO算法的寻优能力得到提升,并且改进INFO-Bi-LSTM模型精度更高,更加适用于SO_(2)排放质量浓度的预测,可为变工况下的脱硫控制提供控制理论支撑。

Species abundance distribution models of Toona ciliata communities in Hubei Province,China

The study of plant species abundance distribution(SAD)in natural communities is of considerable importance to understand the processes and ecological rules of community assembly.With the distribution of tree,shrub and herb layers of eight natural communities of Toona ciliata as research targets,three diff erent ecological niche models were used:broken stick model,overlapping niche model and niche preemption model,as well as three statistical models:log-series distribution model,log-normal distribution model and Weibull distribution model,to fi t SAD of the diff erent vegetation layers based on data collected.Goodness-of-fi t was compared with Chi square test,Kolmogorov–Smirnov(K–S)test and Akaike Information Criterion(AIC).The results show:(1)based on the criteria of the lowest AIC value,Chi square value and K–S value with no signifi cant diff erence(p>0.05)between theoretic and observed SADs.The suitability and goodness-of-fi t of the broken stick model was the best of three ecological niche models.The log-series distribution model did not accept the fi tted results of most vegetation layers and had the lowest goodness-of-fi t.The Weibull distribution model had the best goodness-of-fi t for SADs.Overall,the statistical SADs performed better than the ecological ones.(2)T.ciliata was the dominant species in all the communities;species richness and diversity of herbs were the highest of the vegetation layers,while the diversities of the tree layers were slightly higher than the shrub layers;there were fewer common species and more rare species in the eight communities.The herb layers had the highest community evenness,followed by the shrub and the tree layers.Due to the complexity and habitat diversity of the diff erent T.ciliata communities,comprehensive analyses of a variety of SADs and tests for optimal models together with management,are practical steps to enhance understanding of ecological processes and mechanisms of T.ciliata communities,to detect disturbances,and to facilitate biodiversity and species conservation.

Carbon Accumulation and Distribution in Ecosystems of Taiwania flousiana Plantation and Successive Rotation Plantation of Cunninghamia lanceolata

The 23-year-old T. flousiana plantation and successive rotation plantation of C. lanceolata at Shankou Forest Farm of Nandan County, Guangxi were tested by the method of plot investigation combining with biomass measurement. Carbon storage and spatial distribution of different components in the two forests were analyzed. The results indicated that carbon content in different organs of T. flousiana ranged from 426.0 to 503.9 g/kg, and the order was bark 〉stem 〉 root 〉 branch 〉 leaf. The carbon contents in shrub, herb and litter layers were452.9 , 408.0 and 428.9 g/kg, respectively. Carbon content in the soil （0 -80 cm）was 16.59 g/kg. The carbon content in different organs of C. lanceolata ranged from 464.5 to 508.9 g/kg, and the order was bark 〉 stem 〉 branch 〉 root 〉 leaf. The carbon contents in shrub, herb and litter layers were 456.2, 416.3 and 468.1 g/kg, respectively. Carbon content in the soil （0 -80 cm）was 15.77 g/kg. Total carbon storage amounts of T. flousiana plantation and successive rotation plantation of C. lanceolata were 245.83 and 213.52 t/hm^2, respectively. The carbon storage order of different structure layers from T. flousiana plantation and successive rotation plantation of C. lanceolata was soil layer （0 -8 0 cm） 〉 vegetation layer 〉 litter layer. Annual net productivity in the arbor layers of T. flousiana plantation and successive rotation plantation of C. lanceolata plantations was 10.75 and 17.13 t/（hm^2 · a） ; annual net carbon fixation amounts were estimated as 5.05 and 3.47 t /（hm^2 · a） ; annual net absorption C02 amounts were 18.53 and 12.73 t/（hm^2 · a） respectively.

Modeling and forecasting exchange rate volatility in Bangladesh using GARCH models:a comparison based on normal and Student's t-error distribution

Background:Modeling exchange rate volatility has remained crucially important because of its diverse implications.This study aimed to address the issue of error distribution assumption in modeling and forecasting exchange rate volatility between the Bangladeshi taka(BDT)and the US dollar($).Methods:Using daily exchange rates for 7 years(January 1,2008,to April 30,2015),this study attempted to model dynamics following generalized autoregressive conditional heteroscedastic(GARCH),asymmetric power ARCH(APARCH),exponential generalized autoregressive conditional heteroscedstic(EGARCH),threshold generalized autoregressive conditional heteroscedstic(TGARCH),and integrated generalized autoregressive conditional heteroscedstic(IGARCH)processes under both normal and Student’s t-distribution assumptions for errors.Results and Conclusions:It was found that,in contrast with the normal distribution,the application of Student’s t-distribution for errors helped the models satisfy the diagnostic tests and show improved forecasting accuracy.With such error distribution for out-of-sample volatility forecasting,AR(2)–GARCH(1,1)is considered the best.

Skew t Distribution-Based Nonlinear Filter with Asymmetric Measurement Noise Using Variational Bayesian Inference

This paper is focused on the state estimation problem for nonlinear systems with unknown statistics of measurement noise.Based on the cubature Kalman filter,we propose a new nonlinear filtering algorithm that employs a skew t distribution to characterize the asymmetry of the measurement noise.The system states and the statistics of skew t noise distribution,including the shape matrix,the scale matrix,and the degree of freedom(DOF)are estimated jointly by employing variational Bayesian(VB)inference.The proposed method is validated in a target tracking example.Results of the simulation indicate that the proposed nonlinear filter can perform satisfactorily in the presence of unknown statistics of measurement noise and outperform than the existing state-of-the-art nonlinear filters.

不同HDAC水平的外周T细胞淋巴瘤患者预后复发的影响因素分析

目的:基于潜在类别分析不同组蛋白去乙酰化酶(HDAC)水平的外周T细胞淋巴瘤(PTCL)患者预后复发的影响因素分布特征。方法:选取2012年9月至2019年9月在本院就诊的PTCL患者112例,将患者分为复发组和未复发组,比较两组患者的临床资料。通过多因素Logistic回归分析影响患者预后复发的危险因素。采用潜在类别分析法比较复发高风险组与复发低风险组间预后复发影响因素的分布特征差异。结果:复发组患者87例(77.68%),未复发组患者25例(22.32%)。多因素Logistic回归分析结果显示,ECOG评分≥2分、Ann Arbor分期为III-IV期、IPI评分>2分、骨髓受累、血清β2-微球蛋白(β2-MG)水平升高、近期疗效未达到完全缓解(CR)和部分缓解(PR)、HDAC高表达为患者复发的独立危险因素(P<0.05)。HDAC高水平患者的复发率明显高于HDAC低水平患者(P<0.05)。聚类分析结果显示,预后复发风险呈明显聚集性,可将患者分为预后复发高风险组(HDAC>5分)和低风险组(HDAC≤5分)。潜在类别分析结果显示,与预后复发低风险组相比,预后复发高风险组中“危险因素分布较多型”占比较高(P<0.05)。结论:不同HDAC水平的PTCL患者的预后复发情况以及预后复发高风险与低风险人群中危险因素分布特征均有差异。

Global Distribution of Thermosteric Contribution to Sea Level Rising Trend

The sea level derived from TOPEX/Poseidon(T/P) altimetry data shows prominent long term trend and inter-annual variability.The global mean sea level rising rate during 1993-2003 was 2.9 mm a-1.The T/P sea level trend maps the geographical variability.In the Northern Hemisphere(15°-64°N),the sea level rise is very fast at the mid-latitude(20°-40°N) but much slower at the high-latitude,for example,only 0.5 mm a-1 in the latitude band 40°-50°N.In the Southern Hemisphere,the sea level shows high rising rate both in mid-latitude and high-latitude areas,for example,5.1 mm a-1 in the band 40°-50°S.The global thermosteric sea level(TSL) derived from Ishii temperature data was rising during 1993-2003 at a rate of 1.2 mm a-1 and accounted for more than 40% of the global T/P sea level rise.The contributions of the TSL distribution are not spatially uniform;for instance,the percentage is 67% for the Northern Hemisphere and only 29% for the Southern Hemisphere(15°-64°S) and the maximum thermosteric contribution appears in the Pacific Ocean,which contributes more than 60% of the global TSL.The sea level change trend in tropical ocean is mainly caused by the thermosteric effect,which is different from the case of seasonal variability in this area.The TSL variability dominates the T/P sea level rise in the North Atlantic,but it is small in other areas,and shows negative trend at the high-latitude area(40°-60°N,and 50°-60°S).The global TSL during 1945-2003 showed obvious rising trend with the rate of about 0.3 mm a-1 and striking inter-annual and decadal variability with period of 20 years.In the past 60 years,the Atlantic TSL was rising continuously and remarkably,contributing 38% to the global TSL rising.The TSL in the Pacific and Indian Ocean rose with significant inter-annual and decadal variability.The first EOF mode of the global TSL from Ishii temperature data was the ENSO mode in which the time series of the first mode showed steady rising trend.Among the three oceans,the first mode of the Pacific TSL presented the ENSO mode;there was relatively steady rising trend in the Atlantic Ocean,and no dominant mode in the Indian Ocean.

基于t-SNE的多参数岩体结构面分步聚类方法

结构面聚类是进行岩体稳定性评价的重要步骤。常用聚类方法多以产状作为分组依据,忽略了结构面物理特性指标对岩体稳定性的影响。针对分组依据单一化的不足,综合考虑结构面倾向、倾角、迹长、张开度、填充状态和粗糙度的影响,提出一种基于学生分布随机邻近嵌入(student-distributed stochastic neighbor embedding,简称t-SNE)的多参数岩体结构面分步聚类方法。首先,利用t-SNE算法对除产状外的结构面特征进行数据降维;进而利用模拟退火算法搜索K-means算法的全局最优初始值,并采用分步聚类思想完成聚类。研究表明:所提方法有效地解决了高维空间样本稀疏的问题,同时保留了数据的局部结构与全局结构。新方法相比于传统方法能对空间分布相似区内结构面的物理特性进行精确划分,分组精度更高,且在避免复杂权重值计算的条件下,能有效区分产状与物理特性参数对岩体稳定性的影响差异。最后,将所提方法应用于中国新疆某露天矿坡结构面实测数据分析中,所得分组结果合理可靠,进一步证明该方法在实际工程中的有效性。研究方法可为多参数岩体结构面的分步聚类提供参考。

An Extended Bivariate T-Distribution Type Symmetry Model for Square Contingency Tables

The purpose of this paper is to propose a new model of asymmetry for square contingency tables with ordered categories. The new model may be appropriate for a square contingency table if it is reasonable to assume an underlying bivariate t-distribution with different marginal variances having any degrees of freedom. As the degrees of freedom becomes larger, the proposed model approaches the extended linear diagonals-parameter symmetry model, which may be appropriate for a square table if it is reasonable to assume an underlying bivariate normal distribution. The simulation study based on bivariate t-distribution is given. An example is given.

非中心t分布合格判定方法中k值计算

非中心t分布是批量产品电磁兼容符合性评定的重要统计技术之一,k值是非中心t分布合格判定方法中的关键参数,在GB/Z 6113.403-2020中仅给出了80%/80%准则的有限样本下的k值,且未给出具体计算方法。本文结合电磁兼容试验统计技术实际应用情况,从非中心t分布分位数表中有对应分位数概率(P)值而无置信度(α)值、无P值且无α值两方面进行计算方法研究,提出了适用于不同准则和不同样本的k值计算方法,对不同计算方法进行了对比研究分析,指出基于线性插值和指数逼近拟合算法的准确性、与标准的一致性较高,最后给出了两种典型的k参数计算值。旨在为电磁兼容非中心t分布试验符合性统计评定提供工程指导和参考。

基于冗余矢量的T型三电平双向变换器中点电位平衡模型预测控制

T型三电平双向变换器因其具有损耗小、输出电能质量高等优势适合应用于低压交直流混合配电网互联等场景。针对T型三电平双向变换器存在直流侧中点电位不平衡等问题,模型预测控制因其易于实现多目标优化的特点具有良好的应用价值。为了解决传统有限集模型预测控制(FCS-MPC)权重因子整定困难,中点电位控制效果不佳的问题,提出一种基于冗余矢量的中点电位平衡模型预测控制,利用冗余小矢量的特性实现对中点电位平衡的控制,避免了权重因子的选择。在此基础上,利用代价函数计算矢量的作用时间并预测输出最优开关序列,提升系统的稳态性能。最后,通过实验测试验证了所提策略的有效性。

Gaussian-Student's t mixture distribution PHD robust filtering algorithm based on variational Bayesian inference

Aiming at the problem of filtering precision degradation caused by the random outliers of process noise and measurement noise in multi-target tracking(MTT) system,a new Gaussian-Student’s t mixture distribution probability hypothesis density(PHD) robust filtering algorithm based on variational Bayesian inference(GST-vbPHD) is proposed.Firstly,since it can accurately describe the heavy-tailed characteristics of noise with outliers,Gaussian-Student’s t mixture distribution is employed to model process noise and measurement noise respectively.Then Bernoulli random variable is introduced to correct the likelihood distribution of the mixture probability,leading hierarchical Gaussian distribution constructed by the Gaussian-Student’s t mixture distribution suitable to model non-stationary noise.Finally,the approximate solutions including target weights,measurement noise covariance and state estimation error covariance are obtained according to variational Bayesian inference approach.The simulation results show that,in the heavy-tailed noise environment,the proposed algorithm leads to strong improvements over the traditional PHD filter and the Student’s t distribution PHD filter.