Source parameters of the Gonghe,Qinghai Province,China,earthquake from inversion of digital broadband waveform data

An earthquake of M S=6.9 occurred in Gonghe County, Qinghai Province, China on April 26, 1990.This earthquake was followed by three larger aftershocks of M S=5.5 on May 7, 1990, M S=6.0 on Jan.3, 1994, and M S=5.7 on Feb.16, 1994, consecutively. The moment tensors of these earthquakes as function of time were obtained by the technique of moment tensor inversion in frequency domain . The results inverted indicate that these earthquakes had a very similar focal mechanism of predominantly reverse faulting on a plane striking NWW, dipping to SSW.The scalar seismic moments of these earthquakes are M 0=9.4×10 18 Nm for the M S=6.9 event, 8.0×10 16 Nm for the M S=5.5 event, 4.9×10 17 Nm for the M S =6.0 event and 2.9×10 17 Nm for the M S=5.7 event, respectively. The results inverted also show that the source processes of these events were significantly different. The main shock had a very complex process, consisting of two distinct sub events with comparable sizes. The first sub event occurred in the first 12s, having a seismic moment of 4.7×10 18 Nm, and the second one continued from 31s to 41s, having a seismic moment of 2.5×10 18 Nm. In addition, a much smaller sub event, having a seismic moment of about 2.1×10 18 Nm, may exist in the interval of 12 s and 31 s, In contrast, the source processes of the three aftershocks are quite simple. The source time function of each of aftershocks is a single impulse, suggestting that each of aftershocks consists of a mainly uninterrupted rupture. The rise times and total rupture durations are 4 s and 11 s for the M S=5.5 event, 6 s and 16 s for the M S= 6.0 event and 6 s and 13 s for the M S=5.7 event, respectively.

Global parameter estimation of the Cochlodinium polykrikoides model using bioassay data

Cochlodinium polykrikoides is a notoriously harmful algal species that inflicts severe damage on the aquacultures of the coastal seas of Korea and Japan. Information on their expected movement tracks and boundaries of influence is very useful and important for the effective establishment of a reduction plan. In general, the information is supported by a red-tide（a.k.a algal bloom） model. The performance of the model is highly dependent on the accuracy of parameters, which are the coefficients of functions approximating the biological growth and loss patterns of the C. polykrikoides. These parameters have been estimated using the bioassay data composed of growth-limiting factor and net growth rate value pairs. In the case of the C. polykrikoides, the parameters are different from each other in accordance with the used data because the bioassay data are sufficient compared to the other algal species. The parameters estimated by one specific dataset can be viewed as locally-optimized because they are adjusted only by that dataset. In cases where the other one data set is used, the estimation error might be considerable. In this study, the parameters are estimated by all available data sets without the use of only one specific data set and thus can be considered globally optimized. The cost function for the optimization is defined as the integrated mean squared estimation error, i.e., the difference between the values of the experimental and estimated rates. Based on quantitative error analysis, the root-mean squared errors of the global parameters show smaller values, approximately 25%–50%, than the values of the local parameters. In addition, bias is removed completely in the case of the globally estimated parameters. The parameter sets can be used as the reference default values of a red-tide model because they are optimal and representative. However, additional tuning of the parameters using the in-situ monitoring data is highly required.As opposed to the bioassay data, it is necessary because the bioassay data have limitations in terms of the in-situ coastal conditions.

A New Typhoon Bogus Data Assimilation and its Sampling Method:A Case Study

In this study,the authors introduce a new bogus data assimilation method based on the dimension-reduced projection 4-DVar,which can resolve the cost function directly in low-dimensional space.The authors also try a new method to improve the quality of samples,which are the base of dimension-reduced space projection bogus data assimilation (DRP-BDA).By running a number of numerical weather models with different model parameterization combinations on the typhoon Sinlaku,the authors obtained two groups of samples with different spreads and similarities.After DRP-BDA,the results show that,compared with the control runs,the simulated typhoon center pressure can be deepened by more than 20 hPa to 30 hPa and that the intensity can last as long as 60 hours.The mean track error is improved after DRP-BDA,and the structure of the typhoon is also improved.The wind near the typhoon center is enhanced dramatically,while the warm core is moderate.

Automatic determination of stellar atmospheric parameters and construction of stellar spectral templates of the Guoshoujing Telescope (LAMOST)

A number of spectroscopic surveys have been carried out or are planned to study the origin of the Milky Way. Their exploitation requires reliable automated methods and softwares to measure the fundamental parameters of the stars. Adopting the ULySS package, we have tested the effect of different resolutions and signal-to- noise ratios （SNR） on the measurement of the stellar atmospheric parameters （effective temperature Teff, surface gravity log g, and metaUicity [Fe/H]）. We show that ULySS is reliable for determining these parameters with medium-resolution spectra （R ～2000）. Then, we applied the method to measure the parameters of 771 stars selected in the commissioning database of the Guoshoujing Telescope （LAMOST）. The results were compared with the SDSS/SEGUE Stellar Parameter Pipeline （SSPP）, and we derived precisions of 167 K, 0.34dex, and 0.16dex for Teff, logg and [Fe/H] respectively. Furthermore, 120 of these stars are selected to construct the primary stellar spectral template library （Version 1.0） of LAMOST, and will be deployed as basic ingredients for the LAMOST automated parametrization pipeline.

Validation of LAMOST stellar parameters with the PASTEL catalog

The Large Sky Area Multi-Object Fiber Spectroscopic Telescope （LAMOST） published its first data release （DR1） in 2013, which is currently the largest dataset of stellar spectra in the world. We combine the PASTEL catalog and SIMBAD radial velocities as a testing standard to validate stellar parameters （effec- tive temperature Tefr, surface gravity log g, metallicity [Fe/H] and radial velocity Vr） derived from DR1. Through cross-identification of the DR1 catalogs and the PASTEL catalog, we obtain a preliminary sample of 422 stars. After removal of stellar param- eter measurements from problematic spectra and applying effective temperature con- straints to the sample, we compare the stellar parameters from DR1 with those from PASTEL and SIMBAD to demonstrate that the DR1 results are reliable in restricted ranges of Tefr. We derive standard deviations of 110 K, 0.19 dex and 0.11 dex for Tell, log 9 and [Fe/H] respectively when Teff〈 8000 K, and 4.91 km s-1 for Vr when Teff 〈 10 000 K. Systematic errors are negligible except for those of Vr. In addition, metallicities in DR1 are systematically higher than those in PASTEL, in the range of PASTEL [Fe/H] 〈 -1.5.

Estimating stellar atmospheric parameters based on Lasso features

With the rapid development of large scale sky surveys like the Sloan Digital Sky Survey （SDSS）, GAIA and LAMOST （Guoshoujing telescope）, stellar spectra can be obtained on an ever-increasing scale. Therefore, it is necessary to estimate stel- lar atmospheric parameters such as Teff, log g and [Fe/H] automatically to achieve the scientific goals and make full use of the potential value of these observations. Feature selection plays a key role in the automatic measurement of atmospheric parameters. We propose to use the least absolute shrinkage selection operator （Lasso） algorithm to select features from stellar spectra. Feature selection can reduce redundancy in spectra, alleviate the influence of noise, improve calculation speed and enhance the robustness of the estimation system. Based on the extracted features, stellar atmospheric param- eters are estimated by the support vector regression model. Three typical schemes are evaluated on spectral data from both the ELODIE library and SDSS. Experimental results show the potential performance to a certain degree. In addition, results show that our method is stable when applied to different spectra.

Implementing a multispecies size-spectrum model in a datapoor ecosystem

Multispecies ecological models have been used for predicting the effects of fishing activity and evaluating the performance of management strategies. Size-spectrum models are one type of physiologically-structured ecological model that provide a feasible approach to describing fish communities in terms of individual dietary variation and ontogenetic niche shift. Despite the potential of ecological models in improving our understanding of ecosystems, their application is usually limited for data-poor fisheries. As a first step in implementing ecosystem-based fisheries management（EBFM）, this study built a size-spectrum model for the fish community in the Haizhou Bay, China. We describe data collection procedures and model parameterization to facilitate the implementation of such size-spectrum models for future studies of data-poor ecosystems. The effects of fishing on the ecosystem were exemplified with a range of fishing effort and were monitored with a set of ecological indicators. Total community biomass, biodiversity index, W-statistic, LFI（Large fish index）, Mean W（mean body weight） and Slope（slope of community size spectra） showed a strong non-linear pattern in response to fishing pressure, and largest fishing effort did not generate the most drastic responses in certain scenarios. We emphasize the value and feasibility of developing size-spectrum models to capture ecological dynamics and suggest limitations as well as potential for model improvement. This study aims to promote a wide use of this type of model in support of EBFM.

Estimation of Stellar Atmospheric Parameters from LAMOST DR8 Low-resolution Spectra with 20 ≤ S/N < 30

The accuracy of the estimated stellar atmospheric parameter evidently decreases with the decreasing of spectral signal-to-noise ratio(S/N)and there are a huge amount of this kind observations,especially in case of S/N<30.Therefore,it is helpful to improve the parameter estimation performance for these spectra and this work studied the(T_(eff),log g,[Fe/H])estimation problem for LAMOST DR8 low-resolution spectra with 20≤S/N<30.We proposed a data-driven method based on machine learning techniques.First,this scheme detected stellar atmospheric parameter-sensitive features from spectra by the Least Absolute Shrinkage and Selection Operator(LASSO),rejected ineffective data components and irrelevant data.Second,a Multi-layer Perceptron(MLP)method was used to estimate stellar atmospheric parameters from the LASSO features.Finally,the performance of the LASSO-MLP was evaluated by computing and analyzing the consistency between its estimation and the reference from the Apache Point Observatory Galactic Evolution Experiment high-resolution spectra.Experiments show that the Mean Absolute Errors of T_(eff),log g,[Fe/H]are reduced from the LASP(137.6 K,0.195,0.091 dex)to LASSO-MLP(84.32 K,0.137,0.063 dex),which indicate evident improvements on stellar atmospheric parameter estimation.In addition,this work estimated the stellar atmospheric parameters for 1,162,760 lowresolution spectra with 20≤S/N<30 from LAMOST DR8 using LASSO-MLP,and released the estimation catalog,learned model,experimental code,trained model,training data and test data for scientific exploration and algorithm study.

Analysis of Extreme Rainfall Event with Different Microphysics and Parameterization Schemes in WRF Model

In the present study, the imitation of heavy rainfall event which occurred over Jharkhand during 18 August 2016 was taken as a case study. Weather Research and Forecasting (WRF) model has been utilized for this study. National Centers for Environmental Prediction (NCEP) analysis data is compared with GSMaP data with different combination of physical parameterization scheme like microphysics (MP) and cumulus parameterization (CP). In the present study, three MP schemes: Kessler scheme, Lin et al. scheme and WRF Single-moment 6-class scheme with combination of three CP schemes: Betts-Miller-Janjic scheme, Multi-scale Kain-Fritsch scheme and New simplified Arakawa-Schubert scheme have been used. The model predicted humidity, temperature and precipitation were compared with the GSMaP product. The model nicely depicted the cloud pattern and recognized the rain event spatially. The obtained result shows that the model overestimates the precipitation for all the schemes.

Parameterization based on maximum curvature minimization model

Parameterization is one of the key problems in the construction of a curve to interpolate a set of ordered points. We propose a new local parameterization method based on the curvature model in this paper. The new method determines the knots by mi- nimizing the maximum curvature of quadratic curve. When the knots by the new method are used to construct interpolation curve, the constructed curve have good precision. We also give some comparisons of the new method with existing methods, and our method can perform better in interpolation error, and the interpolated curve is more fairing.

支持体参数化模型表达的翼面数据结构

为了高效、简洁地存储体参数化模型中的信息,提出支持体参数化模型表达的翼面数据结构.首先,提取体参数化模型的几何信息、拓扑信息和参数化信息,其中,几何信息主要包括体参数化模型的节点矢量、阶数与控制点,拓扑信息主要包括体参数化模型子片之间的曲面共享信息,参数化信息主要包括子片的参数方向.然后,使用类似边界表达模型的翼边数据结构存储所提取的信息,形成支撑体参数化模型的翼面数据结构,基于该数据结构实现体参数化模型的查找、插入、细化与删除等操作.利用所开发的体参数化建模软件构建多个体参数化模型实例,展示翼面数据结构的存储与操作过程,结果表明该数据结构能够高效地实现查找、细化等操作,并且基于该数据结构的模型最大数据压缩量达到47.9%,为复杂体参数化模型的构建及应用提供了良好的支撑.

一种解决三区间数回归的混合PM方法

为了解决现有PM方法应用于三区间数回归模型时难以保证数学一致性及回归区间和观测区间之间存在的相交性问题,文章构建一种用于回归区间上下界的PM方法和用于回归最有可能值点非参数化方法的混合回归模型,通过添加“回归区间上界≥回归最有可能值≥回归区间下界”这一约束条件来确保区间数据的数学一致性,并通过限制“回归区间上界≥观测区间下界且观测区间上界≥回归区间下界”来确保区间相交性。基于K-T方程组讨论回归系数的解析表达式。蒙特卡洛模拟结果表明:与CCRM+模型相比,混合回归模型具有更好的回归效果,PCO平均提高了121%,N0平均降低了89%。在观测数据波动更大的多元回归中,混合回归模型也具有更好的回归效果。

基于多源数据的古建筑数字化建模技术研究与应用

古建筑是历史文化瑰宝,如何将其保护传承,一直是值得探究的热点问题,数字信息技术的高速发展也为古建筑保护提供了多样化的手段。通过梳理古建筑构造逻辑,结合现代先进技术,从古建筑基础数据归集和精细化模型建立两方面阐述古建筑保护中数字化技术的运用,建立标准化、参数化的构件库和完善的建模体系。轻量化的数字模型可结合虚拟现实技术通过网络形象、逼真地展示古建筑构造细节,从而使古建筑脱离地域限制,实现文化遗产共享。

Phantom cosmologies from the simplest parameterization of the DE model using observational data in a BI type universe

To scrutinize the nature of dark energy,many equations of state have been proposed.In this context,we examine the simplest parameterization of the equation of state parameter of dark energy in an anisotropic Bianchi type I universe compared with theΛCDM model.Using different combinations of data samples,including Pantheon and Pantheon+H(z),alongside applying the minimization of theχ^(2)function of the distance modulus of data samples,we obtain the constrained values of cosmographic parameters in the parameterization of the dark energy scenario.One condition of the phantom barrier crossing is acquired.Several physical properties of the universe are discussed by considering the anisotropy effect and different observational data points.One should note that the deductions of the cosmological parameter verify recent observational data.

基于实时参数化数据的应答器报文智能管理系统

为提升目前应答器设备配套使用的维护手段,改进应答器报文系统管理的以人工操作为主的方法,不断提高管理效率,对应答器报文管理工作进行研究,简要说明应答器报文管理系统的主要功能和作用;分析应答器报文管理系统的总体需求,提出总体设计方案,并详细阐述应答器报文管理系统的网络结构设计和功能模块划分;描述实现该软件的相关技术和具体的数据结构、模块功能、操作界面等内容。通过设计基于实时参数化数据的应答器报文智能管理系统,实现以铁路局统一管理,各级电务人员在“云-边”协同架构下进行应答器报文动态运用管理的全流程应答器报文安全管理体系,为高速铁路列车安全运行提供可靠保证。

依据轨迹数据的船舶交通密度计算方法

研究基于轨迹数据的船舶交通密度计算方法,利用精准的船舶交通密度计算结果提升海上交通规划水平。利用AIS设备采集船舶航行轨迹数据,利用均匀参数化方法对所采集的航行轨迹数据重采样处理。将通过重采样处理获取的航行轨迹数据,划分为静止轨迹数据点以及移动轨迹数据点,依据数据点间的欧式距离,以及船舶航行方向、航行速度的相似性,选取基于密度的DBSCAN聚类算法完成轨迹数据聚类。依据船舶航行轨迹数据聚类结果,选取多维密度方法,通过更新船舶经过总数、船舶经过总时间等参数,计算船舶交通密度。实验结果表明,该方法可以依据船舶航行轨迹数据,精准计算船舶交通密度,为海上交通规划提供有效支撑。

基于角色的参数化访问控制模型

文章针对基于角色的访问控制(RBAC)模型在细粒度权限控制上存在的不足,对RBAC模型进行了扩展,提出了一种将权限划分为功能权限和数据权限的改进模型。该模型将数据权限从权限中独立并抽象出来,使功能权限和数据权限共同决定主体对客体的访问权限,从而实现细粒度的访问控制,改善了RBAC模型权限管理模式。同时,为提高角色授权效率,提出了一种参数化角色管理方法,增加了新建角色的默认指派,使得角色的授权过程由映射关系的逐个分配转变为对部分映射关系的修改和完善,降低了授权管理的复杂性,增强了权限分配的灵活性。

Software Metric Analysis of Open-Source Business Software

Over the past decade, open-source software use has grown. Today, many companies including Google, Microsoft, Meta, RedHat, MongoDB, and Apache are major participants of open-source contributions. With the increased use of open-source software or integration of open-source software into custom-developed software, the quality of this software component increases in importance. This study examined a sample of open-source applications from GitHub. Static software analytics were conducted, and each application was classified for its risk level. In the analyzed applications, it was found that 90% of the applications were classified as low risk or moderate low risk indicating a high level of quality for open-source applications.

我国自主研制的全球/区域一体化数值天气预报系统GRAPES的应用与展望

介绍了中国气象局自主研制的新一代全球与区域一体化数值天气预报系统(GRAPES),着重讨论了该系统的全可压/非静力平衡动力框架,全球模式/区域模式一体化设计,半隐式-半拉格朗日差分方案,标准化、模块化、并行化、模式程序软件体系等核心技术特点。GRAPES系统已在国家级、区域级气象业务中心,以及一些大学和研究所得到应用,并在不断地完善和发展。

GRAPES_Meso V4.0主要技术改进和预报效果检验

针对GRAPES_Meso V3.0存在的降水量偏大、模式运行不稳定、近地面温度预报偏差较大、可同化资料偏少以及分辨率偏低等问题,开展了多方面的改进工作:引入变分质量控制以及探空湿度的偏差订正,实现了GPS/PW资料、FY-2E云导风资料以及无线电掩星资料的同化应用,提高了模式分辨率,引入四阶水平扩散方案,调整了微物理参数化方案与动力框架的耦合方案,完善了地面辐射能量平衡方程以及优化了后处理雷达组合反射率因子的诊断方案,并集成所有改进成果形成新的业务化GRAPES_Meso V4.0。批量试验结果表明:GRAPES_Meso V4.0降水ETS评分普遍提高,同时预报偏差明显降低,月平均降水更接近实况,且能够较好地刻画雨带细节;2 m温度预报偏差有较为显著的改善,大部分地区24 h预报有1~2℃左右的降低,有些地区有3~5℃的降低;GRAPES_Meso V4.0对高度场、温度场和风场的改进效果比较显著,500 hPa的温度、风速、位势高度场的相关系数均有显著提高,850 hPa的均方根误差也明显降低,整体性能明显高于GRAPES_Meso V3.0。