Real-time prediction of earthquake potential damage:A case study for the January 8,2022 M_(S) 6.9 Menyuan earthquake in Qinghai,China

It is critical to determine whether a site has potential damage in real-time after an earthquake occurs,which is a challenge in earthquake disaster reduction.Here,we propose a real-time Earthquake Potential Damage predictor(EPDor)based on predicting peak ground velocities(PGVs)of sites.The EPDor is composed of three parts:(1)predicting the magnitude of an earthquake and PGVs of triggered stations based on the machine learning prediction models;(2)predicting the PGVs at distant sites based on the empirical ground motion prediction equation;(3)generating the PGV map through predicting the PGV of each grid point based on an interpolation process of weighted average based on the predicted values in(1)and(2).We apply the EPDor to the 2022 M_(S) 6.9 Menyuan earthquake in Qinghai Province,China to predict its potential damage.Within the initial few seconds after the first station is triggered,the EPDor can determine directly whether there is potential damage for some sites to a certain degree.Hence,we infer that the EPDor has potential application for future earthquakes.Meanwhile,it also has potential in Chinese earthquake early warning system.

Modelling analysis embodies drastic transition among global potential natural vegetations in face of changing climate

Potential natural vegetation(PNV)is a valuable reference for ecosystem renovation and has garnered increasing attention worldwide.However,there is limited knowledge on the spatio-temporal distributions,transitional processes,and underlying mechanisms of global natural vegetation,particularly in the case of ongoing climate warming.In this study,we visualize the spatio-temporal pattern and inter-transition procedure of global PNV,analyse the shifting distances and directions of global PNV under the influence of climatic disturbance,and explore the mechanisms of global PNV in response to temperature and precipitation fluctuations.To achieve this,we utilize meteorological data,mainly temperature and precipitation,from six phases:the Last Inter-Glacial(LIG),the Last Glacial Maximum(LGM),the Mid Holocene(MH),the Present Day(PD),2030(20212040)and 2090(2081–2100),and employ a widely-accepted comprehensive and sequential classification sy–stem(CSCS)for global PNV classification.We find that the spatial patterns of five PNV groups(forest,shrubland,savanna,grassland and tundra)generally align with their respective ecotopes,although their distributions have shifted due to fluctuating temperature and precipitation.Notably,we observe an unexpected transition between tundra and savanna despite their geographical distance.The shifts in distance and direction of five PNV groups are mainly driven by temperature and precipitation,although there is heterogeneity among these shifts for each group.Indeed,the heterogeneity observed among different global PNV groups suggests that they may possess varying capacities to adjust to and withstand the impacts of changing climate.The spatio-temporal distributions,mutual transitions and shift tendencies of global PNV and its underlying mechanism in face of changing climate,as revealed in this study,can significantly contribute to the development of strategies for mitigating warming and promoting re-vegetation in degraded regions worldwide.

Geophysical prediction of organic matter abundance in source rocks based on geochemical analysis:A case study of southwestern Bozhong Sag,Bohai Sea,China

The Bozhong Sag is the largest petroliferous sag in the Bohai Bay Basin,and the source rocks of Paleogene Dongying and Shahejie Formations were buried deeply.Most of the drillings were located at the structural high,and there were few wells that met good quality source rocks,so it is difficult to evaluate the source rocks in the study area precisely by geochemical analysis only.Based on the Rock-Eval pyrolysis,total organic carbon(TOC)testing,the organic matter(OM)abundance of Paleogene source rocks in the southwestern Bozhong Sag were evaluated,including the lower of second member of Dongying Formation(E_(3)d2L),the third member of Dongying Formation(E_(3)d_(3)),the first and second members of Shahejie Formation(E_(2)s_(1+2)),the third member of Shahejie Formation(E_(2)s_(3)).The results indicate that the E_(2)s_(1+2)and E_(2)s_(3)have better hydrocarbon generative potentials with the highest OM abundance,the E_(3)d_(3)are of the second good quality,and the E_(3)d2L have poor to fair hydrocarbon generative potential.Furthermore,the well logs were applied to predict TOC and residual hydrocarbon generation potential(S_(2))based on the sedimentary facies classification,usingΔlogR,generalizedΔlogR,logging multiple linear regression and BP neural network methods.The various methods were compared,and the BP neural network method have relatively better prediction accuracy.Based on the pre-stack simultaneous inversion(P-wave impedance,P-wave velocity and density inversion results)and the post-stack seismic attributes,the three-dimensional(3D)seismic prediction of TOC and S_(2)was carried out.The results show that the seismic near well prediction results of TOC and S_(2)based on seismic multi-attributes analysis correspond well with the results of well logging methods,and the plane prediction results are identical with the sedimentary facies map in the study area.The TOC and S_(2)values of E_(2)s_(1+2)and E_(2)s_(3)are higher than those in E_(3)d_(3)and E_(3)d_(2)L,basically consistent with the geochemical analysis results.This method makes up the deficiency of geochemical methods,establishing the connection between geophysical information and geochemical data,and it is helpful to the 3D quantitative prediction and the evaluation of high-quality source rocks in the areas where the drillings are limited.

Control of Facies and Potential on Jurassic Hydrocarbon Accumulation and Prediction of Favorable Targets in the Hinterland Region of the Junggar Basin

Exploration practices show that the Jurassic System in the hinterland region of the Junggar Basin has a low degree of exploration but huge potential, however the oil/gas accumulation rule is very complicated, and it is difficult to predict hydrocarbon-bearing properties. The research indicates that the oil and gas is controlled by structure facies belt and sedimentary system distribution macroscopically, and hydrocarbon-bearing properties of sand bodies are controlled by lithofacies and petrophysical facies microscopically. Controlled by ancient and current tectonic frameworks, most of the discovered oil and gas are distributed in the delta front sedimentary system of a palaeo-tectonic belt and an ancient slope belt. Subaqueous branch channels and estuary dams mainly with medium and fine sandstone are the main reservoirs and oil production layers, and sand bodies of high porosity and high permeability have good hydrocarbon-bearing properties; the facies controlling effect shows a reservoir controlling geologic model of relatively high porosity and permeability. The hydrocarbon distribution is also controlled by relatively low potential energy at the high points of local structure macroscopically, while most of the successful wells are distributed at the high points of local structure, and the hydrocarbon-bearing property is good at the place of relatively low potential energy; the hydrocarbon distribution is in close connection with faults, and the reservoirs near the fault in the region of relatively low pressure have good oil and gas shows; the distribution of lithologic reservoirs at the depression slope is controlled by the distribution of sand bodies at positions of relatively high porosity and permeability. The formation of the reservoir of the Jurassic in the Junggar Basin shows characteristics of favorable facies and low-potential coupling control, and among the currenffy discovered reservoirs and industrial hydrocarbon production wells, more than 90% are developed within the scope of facies- potential index FPI〉0.5, while the FPI and oil saturation of the discovered reservoir and unascertained traps have relatively good linear correlation. By establishing the relation model between hydrocarbon- bearing properties of traps and FPI, totally 43 favorable targets are predicted in four main target series of strata and mainly distributed in the Badaowan Formation and the Sangonghe Formation, and the most favorable targets include the north and east of the Shinan Sag, the middle and south of the Mobei Uplift, Cai-35 well area of the Cainan Oilfield, and North-74 well area of the Zhangbei fault-fold zone.

Control of facies and fluid potential on hydrocarbon accumulation and prediction of favorable Silurian targets in the Tazhong Uplift,Tarim Basin,China

Exploration practices show that the Silurian hydrocarbon accumulation in the Tazhong Uplift is extremely complicated.Our research indicates that the oil and gas accumulation is controlled by favorable facies and low fluid potential.At the macro level,hydrocarbon distribution in this uplift is controlled by structural zones and sedimentary systems.At the micro level,oil occurrences are dominated by lithofacies and petrophysical facies.The control of facies is embodied in high porosity and permeability controlling hydrocarbon accumulation.Besides,the macro oil and gas distribution in the uplift is also influenced by the relatively low fluid potential at local highs,where most successful wells are located.These wells are also closely related to the adjacent fractures.Therefore,the Silurian hydrocarbon accumulation mechanism in the Tazhong Uplift can be described as follows.Induced by structures,the deep and overpressured fluids migrated through faults into the sand bodies with relatively low potential and high porosity and permeability.The released overpressure expelled the oil and gas into the normal-pressured zones,and the hydrocarbon was preserved by the overlying caprock of poorly compacted Carboniferous and Permian mudstones.Such a mechanism reflects favorable facies and low potential controlling hydrocarbon accumulation.Based on the statistical analysis of the reservoirs and commercial wells in the uplift,a relationship between oil-bearing property in traps and the facies-potential index was established,and a prediction of two favorable targets was made.

Real-time rear-end crash potential prediction on freeways

This study develops new real-time freeway rear-end crash potential predictors using support vector machine(SVM) technique. The relationship between rear-end crash occurrences and traffic conditions were explored using historical loop detector data from Interstate-894 in Milwaukee, Wisconsin, USA. The extracted loop detection data were aggregated over different stations and time intervals to produce explanatory features. A feature selection process, which addresses the interaction between SVM classifiers and explanatory features, was adopted to identify the features that significantly influence rear-end crashes. Afterwards, the identified significant explanatory features over three separate time levels were used to train three SVM models. In the end, the multi-layer perceptron(MLP) artificial neural network models were used as benchmarks to evaluate the performance of SVM models. The results show that the proposed feature selection procedure greatly enhances the accuracy and generalization capability of SVM models. Moreover, the optimal SVM classifier achieves 81.1% overall prediction precision rate. In comparison with MLP artificial neural networks, SVM models provide better results in terms of crash prediction accuracy and false positive rate, which confirms the superior performance of SVM technique in rear-end crash potential prediction analysis.

The SSA-BP-based potential threat prediction for aerial target considering commander emotion

The target's threat prediction is an essential procedure for the situation analysis in an aerial defense system.However,the traditional threat prediction methods mostly ignore the effect of commander's emotion.They only predict a target's present threat from the target's features itself,which leads to their poor ability in a complex situation.To aerial targets,this paper proposes a method for its potential threat prediction considering commander emotion(PTP-CE)that uses the Bi-directional LSTM(BiLSTM)network and the backpropagation neural network(BP)optimized by the sparrow search algorithm(SSA).Furthermore,we use the BiLSTM to predict the target's future state from real-time series data,and then adopt the SSA-BP to combine the target's state with the commander's emotion to establish a threat prediction model.Therefore,the target's potential threat level can be obtained by this threat prediction model from the predicted future state and the recognized emotion.The experimental results show that the PTP-CE is efficient for aerial target's state prediction and threat prediction,regardless of commander's emotional effect.

The Potential Predictability of the South China Sea Summer Monsoon in a Dynamical Seasonal Prediction System

The potential predictability of climatological mean circulation and the interannual variation of the South China Sea summer monsoon (SCSSM) were investigated using hindcast results from the Institute of Atmospheric Physics Dynamical Seasonal Prediction System (IAP DCP),along with the National Centers for Environmental Prediction (NCEP) reanalysis data from the period of 1980-2000.The large-scale characteristics of the SCSSM monthly and seasonal mean low-level circulation have been well reproduced by IAP DCP,especially for the zonal wind at 850 hPa;furthermore,the hindcast variability also agrees quite well with observations.By introducing the South China Sea summer monsoon index,the potential predictability of IAP DCP for the intensity of the SCSSM has been evaluated.IAP DCP showed skill in predicting the interannual variation of SCSSM intensity.The result is highly encouraging;the correlation between the hindcasted and observed SCSSM Index was 0.58,which passes the 95% significance test.The result for the seasonal mean June-July-August SCSSM Index was better than that for the monthly mean,suggesting that seasonal forecasts are more reliable than monthly forecasts.

Prediction of Potential Sorghum Suitability Distribution in China Based on Maxent Model

It is increasingly relevant to study the effects of climate change on species habitats. Using a maximum entropy model, 22 environmental factors with significant effects on sorghum habitat distribution in China were selected to predict the potential habitat distribution of sorghum in China. The potential distribution of sorghum under baseline climate conditions and future climate conditions (2050s and 2070s) under two climate change scenarios, RCP4.5 and RCP8.5, were simulated, and the receiver operating curve under the accuracy of the model was evaluated using the area under the receiver operating curve (AUC). The results showed that the maximum entropy model predicted the potential sorghum habitat distribution with high accuracy, with Bio2 (monthly mean diurnal temperature difference), Bio6 (minimum temperature in the coldest month), and Bio13 (rainfall in the wettest month) as the main climatic factors affecting sorghum distribution among the 22 environmental factors. Under the baseline climate conditions, potential sorghum habitats are mainly distributed in the southwest, central, and east China. Over time, the potential sorghum habitat expanded into northern and southern China, with significant additions and negligible decreases in potential sorghum habitat in the study area, and a significant increase in total area, with the RCP8.5 scenario adding much more area than the RCP4.5 scenario.

Prediction of Potential Distribution of Quadrastichus erythrinae Kim in China

Based on Maxent niche model and combined with ArcGIS,the suitable area range for Quadrastichus erythrinae Kim in China was predicted in the paper.The results showed that high suitable area for Q. erythrinae in China included most northeast coastal areas of Hainan Island,partial southern coastal area of Guangdong Province,partial northwestern coastal area and partial southeast coastal area of Taiwan Island; moderate suitable area included partial area of Hainan,some contiguous areas of Guangxi and Guangdong,most areas of Guangdong,partial area of Fujian and Taiwan; low suitable area included partial area from northwestern coast to inland of Hainan Island,west coastal area of Taiwan Island,most area in Guangxi,partial areas in Guangdong,Fujian and Yunnan.

Bayesian prediction of potential depressions in the Erlian Basin based on integrated geophysical parameters

In this study,we analyzed the geological,gravity,magnetic,and electrical characteristics of depressions in the Erlian Basin.Based on the results of these analyses,we could identify four combined feature parameters showing strong correlations and sensibilities to the reservoir oil-bearing conditions:the average residual gravity anomaly,the average magnetic anomaly,the average depth of the conductive key layer,and the average elevation of the depressions.The feature parameters of the 65 depressions distributed in the whole basin were statistically analyzed:each of them showed a Gaussian distribution and had the basis of Bayesian theory.Our Bayesian predictions allowed the defi nition of a formula to calculate the posterior probability of oil occurrence in the depressions based on the combined characteristic parameters.The feasibility of this prediction method was verifi ed by considering the results obtained for the 22 drilled depressions.Subsequently,we were able to determine the oilbearing threshold of hydrocarbon potential for the depressions in the Erlian Basin,which can be used as a standard for quantitative optimizations.Finally,the proposed prediction method was used to calculate the probability of hydrocarbons in the other 43 depressions.Based on this probability and on the oil-bearing threshold,the fi ve depressions with the highest potential were selected as targets for future seismic explorations and drilling.We conclude that the proposed method,which makes full use of massive gravity,magnetic,electric,and geological data,is fast,eff ective,and allows quantitative optimizations;hence,it will be of great value for the comprehensive geophysical evaluation of oil and gas in basins with depression group characteristics.

Predicting differentiation potential of human pluripotent stem cells:Possibilities and challenges

The capability of human pluripotent stem cell(hPSC)lines to propagate indefinitely and differentiate into derivatives of three embryonic germ layers makes these cells be powerful tools for basic scientific research and promising agents for translational medicine.However,variations in differentiation tendency and efficiency as well as pluripotency maintenance necessitate the selection of hPSC lines for the intended applications to save time and cost.To screen the qualified cell lines and exclude problematic cell lines,their pluripotency must be confirmed initially by traditional methods such as teratoma formation or by highthroughput gene expression profiling assay.Additionally,their differentiation potential,particularly the lineage-specific differentiation propensities of hPSC lines,should be predicted in an early stage.As a complement to the teratoma assay,RNA sequencing data provide a quantitative estimate of the differentiation ability of hPSCs in vivo.Moreover,multiple scorecards have been developed based on selected gene sets for predicting the differentiation potential into three germ layers or the desired cell type many days before terminal differentiation.For clinical application of hPSCs,the malignant potential of the cells must also be evaluated.A combination of histologic examination of teratoma with quantitation of gene expression data derived from teratoma tissue provides safety-related predictive information by detecting immature teratomas,malignancy marker expression,and other parameters.Although various prediction methods are available,distinct limitations remain such as the discordance of results between different assays and requirement of a long time and high labor and cost,restricting their wide applications in routine studies.Therefore,simpler and more rapid detection assays with high specificity and sensitivity that can be used to monitor the status of hPSCs at any time and fewer targeted markers that are more specific for a given desired cell type are urgently needed.

The CALYPSO methodology for structure prediction

Structure prediction methods have been widely used as a state-of-the-art tool for structure searches and materials discovery, leading to many theory-driven breakthroughs on discoveries of new materials. These methods generally involve the exploration of the potential energy surfaces of materials through various structure sampling techniques and optimization algorithms in conjunction with quantum mechanical calculations. By taking advantage of the general feature of materials potential energy surface and swarm-intelligence-based global optimization algorithms, we have developed the CALYPSO method for structure prediction, which has been widely used in fields as diverse as computational physics, chemistry, and materials science. In this review, we provide the basic theory of the CALYPSO method, placing particular emphasis on the principles of its various structure dealing methods. We also survey the current challenges faced by structure prediction methods and include an outlook on the future developments of CALYPSO in the conclusions.

Lane-Exchanging Driving Strategy for Autonomous Vehicle via Trajectory Prediction and Model Predictive Control

The cooperation between an autonomous vehicle and a nearby vehicle is critical to ensure driving safety in the laneexchanging scenario.The nearby vehicle trajectory needs to be predicted,from which the autonomous vehicle is controlled to prevent possible collisions.This paper proposes a lane-exchanging driving strategy for the autonomous vehicle to cooperate with the nearby vehicle by integrating vehicle trajectory prediction and motion control.A trajectory prediction method is developed to anticipate the nearby vehicle trajectory.The Gaussian mixture model(GMM),together with the vehicle kinematic model,are synthesized to predict the nearby vehicle trajectory.A potential-feldbased model predictive control(MPC)approach is utilized by the autonomous vehicle to conduct the lane-exchanging maneuver.The potential feld of the nearby vehicle is considered in the controller design for collision avoidance.On-road driving data verifcation shows that the nearby vehicle trajectory can be predicted by the proposed method.CarSim®simulations validate that the autonomous vehicle can perform the lane-exchanging maneuver and avoid the nearby vehicle using the proposed driving strategy.The autonomous vehicle can thus safely perform the laneexchanging maneuver and avoid the nearby vehicle.

Computational prediction of RNA tertiary structures using machine learning methods

RNAs play crucial and versatile roles in biological processes. Computational prediction approaches can help to understand RNA structures and their stabilizing factors, thus providing information on their functions, and facilitating the design of new RNAs. Machine learning (ML) techniques have made tremendous progress in many fields in the past few years. Although their usage in protein-related fields has a long history, the use of ML methods in predicting RNA tertiary structures is new and rare. Here, we review the recent advances of using ML methods on RNA structure predictions and discuss the advantages and limitation, the difficulties and potentials of these approaches when applied in the field.

Studies on Y- Potential of Positive Sol of Magnesium Aluminum Hydroxide

ＩｎｔｒｏｄｕｃｔｉｏｎＴｈｅｅｌｅｃｔｒｉｃｐｏｔｅｎｔｉａｌａｔｔｈｅｓｈｅａｒｐｌａｎｅｂｅｔｗｅｅｎｔｈｅｃｈａｒｇｅｄｓｕｒｆａｃｅａｎｄｅｌｅｃｔｒｏｌｙｔｅｓｏｌｕｔｉｏｎｉｓｃａｌｅｄζ－ｐｏｔｅｎｔｉａｌ．Ｗｈｅｎζ＝０，ｔｈｅｐＨ...

Prediction of Crystal Structure and Density for Benzotrifuroxan(BTF) by AAPM

因苯并三氧化呋咱（ＢＴＦ）是具有广泛应用的很重要的化合物，且其晶体结构和性能的信息缺乏，故本文基于原子原子势方法（ＡＡＰＭ）预示了它的晶体结构和密度．分析了静电相互作用对ＢＴＦ晶体结构的影响．ＢＴＦ的非共面结构不能简单地不考虑电荷模型而加以预测．运用ａｂｉｎｉｔｉｏ计算求得的净原子电荷计算了它的结构参数和密度．结果发现，ＭＫＳ电荷能提供结构和密度预示与实测之间的最佳对应．由整体探索求得的分子堆积给出了合理成功的ＢＴＦ的结构和密度预测．密度预测的非精确度不超过０．０２ｇ·ｃｍ－３．

Investigating the ENSO prediction skills of the Beijing Climate Center climate prediction system version 2

The El Niño-Southern Oscillation(ENSO)ensemble prediction skills of the Beijing Climate Center(BCC)climate prediction system version 2(BCC-CPS2)are examined for the period from 1991 to 2018.The upper-limit ENSO predictability of this system is quantified by measuring its“potential”predictability using information-based metrics,whereas the actual prediction skill is evaluated using deterministic and probabilistic skill measures.Results show that:(1)In general,the current operational BCC model achieves an effective 10-month lead predictability for ENSO.Moreover,prediction skills are up to 10–11 months for the warm and cold ENSO phases,while the normal phase has a prediction skill of just 6 months.(2)Similar to previous results of the intermediate coupled models,the relative entropy(RE)with a dominating ENSO signal component can more effectively quantify correlation-based prediction skills compared to the predictive information(PI)and the predictive power(PP).(3)An evaluation of the signal-dependent feature of the prediction skill scores suggests the relationship between the“Spring predictability barrier(SPB)”of ENSO prediction and the weak ENSO signal phase during boreal spring and early summer.

Geochemical Evaluation for the Hydrocarbon Potential of Source Rocks in the Anza Basin

Anza basin is located in the extensional arm of the central African rift system in the North-Eastern part of Kenya. Cretaceous sedimentary rocks were sampled from the four wells namely, Chalbi-3, Sirius-1, Ndovu-1 and Kaisut-1. Anza basin occurs on a fault block within a Paleocene-Cretaceous rift basin. The methodological approach used for the evaluation of source rocks included petrophysical and geochemical methods to ascertain their potential. Well sections with a higher shale-volume ratio were sampled for geochemical screening to determine the organic richness and thermal maturity of potential source rocks, respectively. Source rock with organic richness ≥ 0.5% were evaluated further for their petroleum potential using Rock-Eval pyrolysis to determine their thermal maturity, organo-facies and in-situ generated hydrocarbons present in sedimentary facies. The geochemical evaluation of rock samples from the drilled wells’ sections of Chalbi-3 and Sirius-1 confirmed both oil and gas potential. Gas Chromatography and Mass Spectrometry (GCMS) were used to characterize the biomarker signatures and oil-oil correlation of Sirius-1 samples. A predictive model was developed to integrate the petrophysical and geochemical data to reveal hydrocarbons’ potential in the Anza basin.

AN AIR POLLUTION PREDICTION TECHNIQUE FOR URBAN DISTRICTS BASED ON MESO-SCALE NUMERICAL MODEL

Taking Shenzhen city as an example, the statistical and physical relationship between the density of pollutants and various atmospheric parameters are analyzed in detail, and a space-partitioned city air pollution potential prediction scheme is established based on it. The scheme considers quantitatively more than ten factors at the surface and planetary boundary layer (PBL), especially the effects of anisotropy of geographical environment, and treats wind direction as an independent impact factor. While the scheme treats the prediction equation respectively for different pollutants according to their differences in dilute properties, it considers as well the possible differences in dilute properties at different districts of the city under the same atmospheric condition, treating predictions respectively for different districts. Finally, the temporally and spatially high resolution predictions for the atmospheric factors are made with a high resolution numerical model, and further the space-partitioned and time-variational city pollution potential predictions are made. The scheme is objective and quantitative, and with clear physical meaning, so it is suitable to use in making high resolution air pollution predictions.