Inferring seismic hazard in Sichuan-Yunnan region of China based on the modern earthquake catalogue (1980-2019)

Based on the modern earthquake catalogue,the incomplete centroidal voronoi tessellation(ICVT)method was used in this study to estimate the seismic hazard in Sichuan-Yunnan region of China.We calculated spatial distributions of the total seismic hazard and background seismic hazard in this area.The Bayesian delaunay tessellation smoothing method put forward by Ogata was used to calculate the spatial distributions of b-value.The results show that seismic hazards in Sichuan-Yunan region are high,and areas with relatively high hazard values are distributed along the main faults,while seismic hazards in Sichuan basin are relatively low.

Discrete Circular Distributions with Applications to Shared Orthologs of Paired Circular Genomes

For structural comparisons of paired prokaryotic genomes,an important topic in synthetic and evolutionary biology,the locations of shared orthologous genes(henceforth orthologs)are observed as binned data.This and other data,e.g.,wind directions recorded at monitoring sites and intensive care unit arrival times on the 24-hour clock,are counted in binned circular arcs,thus modeling them by discrete circular distributions(DCDs)is required.We propose a novel method to construct a DCD from a base continuous circular distribution(CCD).The probability mass function is defined to take the normalized values of the probability density function at some pre-fixed equidistant points on the circle.Five families of constructed DCDs which have normalizing constants in closed form are presented.Simulation studies show that DCDs outperform the corresponding CCDs in modeling grouped(discrete)circular data,and minimum chi-square estimation outperforms maximum likelihood estimation for parameters.We apply the constructed DCDs,invariant wrapped Poisson and wrapped discrete skew Laplace to compare the structures of paired bacterial genomes.Specifically,discrete four-parameter wrapped Cauchy(nonnegative trigonometric sums)distribution models multi-modal shared orthologs in Clostridium(Sulfolobus)better than the others considered,in terms of AIC and Freedman’s goodness-of-fit test.The result that different DCDs fit the shared orthologs is consistent with the fact they belong to two kingdoms.Nevertheless,these prokaryotes have a common favored site around 70°on the unit circle;this finding is important for building synthetic prokaryotic genomes in synthetic biology.These DCDs can also be applied to other binned circular data.

Machine-learning-assisted discovery of polymers with high thermal conductivity using a molecular design algorithm

The use of machine learning in computational molecular design has great potential to accelerate the discovery of innovative materials.However,its practical benefits still remain unproven in real-world applications,particularly in polymer science.We demonstrate the successful discovery of new polymers with high thermal conductivity,inspired by machine-learning-assisted polymer chemistry.This discovery was made by the interplay between machine intelligence trained on a substantially limited amount of polymeric properties data,expertise from laboratory synthesis and advanced technologies for thermophysical property measurements.Using a molecular design algorithm trained to recognize quantitative structure—property relationships with respect to thermal conductivity and other targeted polymeric properties,we identified thousands of promising hypothetical polymers.From these candidates,three were selected for monomer synthesis and polymerization because of their synthetic accessibility and their potential for ease of processing in further applications.The synthesized polymers reached thermal conductivities of 0.18–0.41 W/mK,which are comparable to those of state-of-the-art polymers in non-composite thermo-plastics.

Application and discussion of statistical seismology in probabilistic seismic hazard assessment studies

Earthquakes are one of the natural disasters that pose a major threat to human lives and property. Earthquake prediction propels the construction and development of modern seismology;however, current deterministic earthquake prediction is limited by numerous difficulties. Identifying the temporal and spatial statistical characteristics of earthquake occurrences and constructing earthquake risk statistical prediction models have become significant;particularly for evaluating earthquake risks and addressing seismic planning requirements such as the design of cities and lifeline projects based on the obtained insight. Since the 21 st century, the occurrence of a series of strong earthquakes represented by the Wenchuan M8 earthquake in 2008 in certain low-risk prediction areas has caused seismologists to reflect on traditional seismic hazard assessment globally. This article briefly reviews the development of statistical seismology, emphatically analyzes the research results and existing problems of statistical seismology in seismic hazard assessment, and discusses the direction of its development. The analysis shows that the seismic hazard assessment based on modern earthquake catalogues in most regions should be effective. Particularly, the application of seismic hazard assessment based on ETAS(epidemic type aftershock sequence)should be the easiest and most effective method for the compilation of seismic hazard maps in large urban agglomeration areas and low seismic hazard areas with thick sedimentary zones.

Automated estimation of materials parameter from X-ray absorption and electron energy-loss spectra with similarity measures

Materials informatics has significantly accelerated the discovery and analysis of materials in the past decade.One of the key contributors to accelerated materials discovery is the use of on-the-fly data analysis with high-throughput experiments,which has given rise to the need for accelerated and accurate automated estimation of the properties of materials.In this regard,spectroscopic data are widely used for materials discovery because these data include essential information about materials.An important requirement for the realisation of the automated estimation of materials parameters is the selection of a similarity measure,or kernel function.The required measure should be robust in terms of peak shifting,peak broadening,and noise.However,the determination of appropriate similarity measures for spectra and the automated estimation of materials parameters from these spectra currently remain unresolved.We examined major similarity measures to evaluate the similarity of both X-ray absorption and electron energy-loss spectra.The similarity measures show good correspondence with the materials parameter,that is,the crystal-field parameter,in all measures.The Pearson's correlation coefficient was the highest for the robustness against noise and peak broadening.We obtained the regression model for the crystal-field parameter 10 Dq from the similarity of the spectra.The regression model enabled the materials parameter,that is,10 Dq,to be automatically estimated from the spectra.With regard to research progress in similarity measures,this methodology would make it possible to extract the materials parameter from a large-scale dataset of experimental data.

Estimating effective reproduction number revisited

Accurately estimating the effective reproduction number is crucial for characterizing the transmissibility of infectious diseases to optimize interventions and responses during epidemic outbreaks.In this study,we improve the estimation of the effective reproduction number through two main approaches.First,we derive a discrete model to represent a time series of case counts and propose an estimation method based on this framework.We also conduct numerical experiments to demonstrate the effectiveness of the proposed discretization scheme.By doing so,we enhance the accuracy of approximating the underlying epidemic process compared to previous methods,even when the counting period is similar to the mean generation time of an infectious disease.Second,we employ a negative binomial distribution to model the variability of count data to accommodate overdispersion.Specifically,given that observed incidence counts follow a negative binomial distribution,the posterior distribution of secondary infections is obtained as a Dirichlet multinomial distribution.With this formulation,we establish posterior uncertainty bounds for the effective reproduction number.Finally,we demonstrate the effectiveness of the proposed method using incidence data from the COVID-19 pandemic.

统计地震学在地震危险性概率预测方法研究中的应用与讨论

地震是给人民生命财产造成重大威胁的自然灾害之一,地震预测是推动现代地震学学科建设与发展的原动力.在地震的确定性预测迄今仍面临诸多困难的情况下,寻找地震发生的时空统计特征,构建地震危险性统计预测模型成为了评估地震风险并应用于城市与生命线工程等抗震规划设计实际需求的重要途径. 21世纪以来,以2008年汶川8级地震为代表的系列强震在一些低风险预测区发生,引起了世界各国地震学家对传统的地震危险性预测方法的反思.文章对统计地震学的发展进行了简要综述,着重分析了统计地震学在地震危险性预测中的研究成果及存在的问题,对其发展方向进行了讨论.分析指出:在大多数地区以现代地震目录为基础资料的地震危险性预测是有效的,尤其是在大型城市群地区及厚沉积、弱震区的地震危险性区划图的编制中,引入基于传染型余震序列模型的地震危险性概率预测应该是最为简便和有效的方法.

RadonPy:automated physical property calculation using all-atom classical molecular dynamics simulations for polymer informatics

The spread of data-driven materials research has increased the need for systematically designed materials property databases.However,the development of polymer databases has lagged far behind other material systems.We present RadonPy,an open-source library that can automate the complete process of all-atom classical molecular dynamics(MD)simulations applicable to a wide variety of polymeric materials.Herein,15 different properties were calculated for more than 1000 amorphous polymers.The MD-calculated properties were systematically compared with experimental data to validate the calculation conditions;the bias and variance in the MD-calculated properties were successfully calibrated by a machine learning technique.During the high-throughput data production,we identified eight amorphous polymers with extremely high thermal conductivity(>0.4 W∙m^(–1)∙K^(–1))and their underlying mechanisms.Similar to the advancement of materials informatics since the advent of computational property databases for inorganic crystals,database construction using RadonPy will promote the development of polymer informatics.

Automated stopping criterion for spectral measurements with active learning

The automated stopping of a spectral measurement with active learning is proposed.The optimal stopping of the measurement is realised with a stopping criterion based on the upper bound of the posterior average of the generalisation error of the Gaussian process regression.It is revealed that the automated stopping criterion of the spectral measurement gives an approximated X-ray absorption spectrum with sufficient accuracy and reduced data size.The proposed method is not only a proof-of-concept of the optimal stopping problem in active learning but also the key to enhancing the efficiency of spectral measurements for highthroughput experiments in the era of materials informatics.

Data augmentation in microscopic images for material data mining

Recent progress in material data mining has been driven by high-capacity models trained on large datasets.However,collecting experimental data(real data)has been extremely costly owing to the amount of human effort and expertise required.Here,we develop a novel transfer learning strategy to address problems of small or insufficient data.This strategy realizes the fusion of real and simulated data and the augmentation of training data in a data mining procedure.For a specific task of grain instance image segmentation,this strategy aims to generate synthetic data by fusing the images obtained from simulating the physical mechanism of grain formation and the“image style”information in real images.The results show that the model trained with the acquired synthetic data and only 35%of the real data can already achieve competitive segmentation performance of a model trained on all of the real data.Because the time required to perform grain simulation and to generate synthetic data are almost negligible as compared to the effort for obtaining real data,our proposed strategy is able to exploit the strong prediction power of deep learning without significantly increasing the experimental burden of training data preparation.