Application of the Conditional Nonlinear Local Lyapunov Exponent to Second-Kind Predictability

In order to quantify the influence of external forcings on the predictability limit using observational data,the author introduced an algorithm of the conditional nonlinear local Lyapunov exponent(CNLLE)method.The effectiveness of this algorithm is validated and compared with the nonlinear local Lyapunov exponent(NLLE)and signal-to-noise ratio methods using a coupled Lorenz model.The results show that the CNLLE method is able to capture the slow error growth constrained by external forcings,therefore,it can quantify the predictability limit induced by the external forcings.On this basis,a preliminary attempt was made to apply this method to measure the influence of ENSO on the predictability limit for both atmospheric and oceanic variable fields.The spatial distribution of the predictability limit induced by ENSO is similar to that arising from the initial conditions calculated by the NLLE method.This similarity supports ENSO as the major predictable signal for weather and climate prediction.In addition,a ratio of predictability limit(RPL)calculated by the CNLLE method to that calculated by the NLLE method was proposed.The RPL larger than 1 indicates that the external forcings can significantly benefit the long-term predictability limit.For instance,ENSO can effectively extend the predictability limit arising from the initial conditions of sea surface temperature over the tropical Indian Ocean by approximately four months,as well as the predictability limit of sea level pressure over the eastern and western Pacific Ocean.Moreover,the impact of ENSO on the geopotential height predictability limit is primarily confined to the troposphere.

Conditional Generative Adversarial Network Enabled Localized Stress Recovery of Periodic Composites

Structural damage in heterogeneousmaterials typically originates frommicrostructures where stress concentration occurs.Therefore,evaluating the magnitude and location of localized stress distributions within microstructures under external loading is crucial.Repeating unit cells(RUCs)are commonly used to represent microstructural details and homogenize the effective response of composites.This work develops a machine learning-based micromechanics tool to accurately predict the stress distributions of extracted RUCs.The locally exact homogenization theory efficiently generates the microstructural stresses of RUCs with a wide range of parameters,including volume fraction,fiber/matrix property ratio,fiber shapes,and loading direction.Subsequently,the conditional generative adversarial network(cGAN)is employed and constructed as a surrogate model to establish the statistical correlation between these parameters and the corresponding localized stresses.The stresses predicted by cGAN are validated against the remaining true data not used for training,showing good agreement.This work demonstrates that the cGAN-based micromechanics tool effectively captures the local responses of composite RUCs.It can be used for predicting potential crack initiations starting from microstructures and evaluating the effective behavior of periodic composites.

Censored Composite Conditional Quantile Screening for High-Dimensional Survival Data

In this paper,we introduce the censored composite conditional quantile coefficient(cC-CQC)to rank the relative importance of each predictor in high-dimensional censored regression.The cCCQC takes advantage of all useful information across quantiles and can detect nonlinear effects including interactions and heterogeneity,effectively.Furthermore,the proposed screening method based on cCCQC is robust to the existence of outliers and enjoys the sure screening property.Simulation results demonstrate that the proposed method performs competitively on survival datasets of high-dimensional predictors,particularly when the variables are highly correlated.

Mixed D-vine copula-based conditional quantile model for stochastic monthly streamflow simulation

Copula functions have been widely used in stochastic simulation and prediction of streamflow.However,existing models are usually limited to single two-dimensional or three-dimensional copulas with the same bivariate block for all months.To address this limitation,this study developed a mixed D-vine copula-based conditional quantile model that can capture temporal correlations.This model can generate streamflow by selecting different historical streamflow variables as the conditions for different months and by exploiting the conditional quantile functions of streamflows in different months with mixed D-vine copulas.The up-to-down sequential method,which couples the maximum weight approach with the Akaike information criteria and the maximum likelihood approach,was used to determine the structures of multivariate Dvine copulas.The developed model was used in a case study to synthesize the monthly streamflow at the Tangnaihai hydrological station,the inflow control station of the Longyangxia Reservoir in the Yellow River Basin.The results showed that the developed model outperformed the commonly used bivariate copula model in terms of the performance in simulating the seasonality and interannual variability of streamflow.This model provides useful information for water-related natural hazard risk assessment and integrated water resources management and utilization.

Attribute Reduction of Hybrid Decision Information Systems Based on Fuzzy Conditional Information Entropy

The presence of numerous uncertainties in hybrid decision information systems(HDISs)renders attribute reduction a formidable task.Currently available attribute reduction algorithms,including those based on Pawlak attribute importance,Skowron discernibility matrix,and information entropy,struggle to effectively manages multiple uncertainties simultaneously in HDISs like the precise measurement of disparities between nominal attribute values,and attributes with fuzzy boundaries and abnormal values.In order to address the aforementioned issues,this paper delves into the study of attribute reduction withinHDISs.First of all,a novel metric based on the decision attribute is introduced to solve the problem of accurately measuring the differences between nominal attribute values.The newly introduced distance metric has been christened the supervised distance that can effectively quantify the differences between the nominal attribute values.Then,based on the newly developed metric,a novel fuzzy relationship is defined from the perspective of“feedback on parity of attribute values to attribute sets”.This new fuzzy relationship serves as a valuable tool in addressing the challenges posed by abnormal attribute values.Furthermore,leveraging the newly introduced fuzzy relationship,the fuzzy conditional information entropy is defined as a solution to the challenges posed by fuzzy attributes.It effectively quantifies the uncertainty associated with fuzzy attribute values,thereby providing a robust framework for handling fuzzy information in hybrid information systems.Finally,an algorithm for attribute reduction utilizing the fuzzy conditional information entropy is presented.The experimental results on 12 datasets show that the average reduction rate of our algorithm reaches 84.04%,and the classification accuracy is improved by 3.91%compared to the original dataset,and by an average of 11.25%compared to the other 9 state-of-the-art reduction algorithms.The comprehensive analysis of these research results clearly indicates that our algorithm is highly effective in managing the intricate uncertainties inherent in hybrid data.

Data-Driven Structural Topology Optimization Method Using Conditional Wasserstein Generative Adversarial Networks with Gradient Penalty

Traditional topology optimization methods often suffer from the“dimension curse”problem,wherein the com-putation time increases exponentially with the degrees of freedom in the background grid.Overcoming this challenge,we introduce a real-time topology optimization approach leveraging Conditional Generative Adversarial Networks with Gradient Penalty(CGAN-GP).This innovative method allows for nearly instantaneous prediction of optimized structures.Given a specific boundary condition,the network can produce a unique optimized structure in a one-to-one manner.The process begins by establishing a dataset using simulation data generated through the Solid Isotropic Material with Penalization(SIMP)method.Subsequently,we design a conditional generative adversarial network and train it to generate optimized structures.To further enhance the quality of the optimized structures produced by CGAN-GP,we incorporate Pix2pixGAN.This augmentation results in sharper topologies,yielding structures with enhanced clarity,de-blurring,and edge smoothing.Our proposed method yields a significant reduction in computational time when compared to traditional topology optimization algorithms,all while maintaining an impressive accuracy rate of up to 85%,as demonstrated through numerical examples.

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.

Characterizing large-scale weak interlayer shear zones using conditional random field theory

The shear behavior of large-scale weak intercalation shear zones(WISZs)often governs the stability of foundations,rock slopes,and underground structures.However,due to their wide distribution,undulating morphology,complex fabrics,and varying degrees of contact states,characterizing the shear behavior of natural and complex large-scale WISZs precisely is challenging.This study proposes an analytical method to address this issue,based on geological fieldwork and relevant experimental results.The analytical method utilizes the random field theory and Kriging interpolation technique to simplify the spatial uncertainties of the structural and fabric features for WISZs into the spatial correlation and variability of their mechanical parameters.The Kriging conditional random field of the friction angle of WISZs is embedded in the discrete element software 3DEC,enabling activation analysis of WISZ C2 in the underground caverns of the Baihetan hydropower station.The results indicate that the activation scope of WISZ C2 induced by the excavation of underground caverns is approximately 0.5e1 times the main powerhouse span,showing local activation.Furthermore,the overall safety factor of WISZ C2 follows a normal distribution with an average value of 3.697.

An Analysis of Conditional Survival Rates for Ewing Sarcoma Patients

Using data from the Surveillance, Epidemiology, and End Results (SEER) Program based at the National Cancer Institute in the US, conditional survival rates are reported for 1,988 Ewing Sarcoma patients diagnosed during the period 2000-2015. These patients represent the experience of 26.5% of the US population. Specifically, 5-year conditional relative survival rates are calculated for these patients for the first eight years subsequent to diagnosis of their cancer by Extent of Disease (EOD) (Localized, Regional, and Distant as coded by the SEER Program), gender, and age (<18, 18 - 34, and 35+). Findings include showing how the conditional survival rate patterns improve over time and that there are differences by gender, age, and EOD.

Conditional survival probability of distant-metastatic hepatocellular carcinoma: A population-based study

BACKGROUND The prognosis of many patients with distant metastatic hepatocellular carcinoma(HCC)improved after they survived for several months.Compared with tradi-tional survival analysis,conditional survival(CS)which takes into account changes in survival risk could be used to describe dynamic survival probabilities.AIM To evaluate CS of distant metastatic HCC patients.METHODS Patients diagnosed with distant metastatic HCC between 2010 and 2015 were extracted from the Surveillance,Epidemiology and End Results database.Univariate and multivariate Cox regression analysis were used to identify factors for overall survival(OS),while competing risk model was used to identify risk factors for cancer-specific survival(CSS).Six-month CS was used to calculate the probability of survival for an additional 6 mo at a specific time after initial diagnosis,and standardized difference(d)was used to evaluate the survival differences between subgroups.Nomograms were constructed to predict CS.Positiveα-fetoprotein expression,higher T stage(T3 and T4),N1 stage,non-primary site surgery,non-chemotherapy,non-radiotherapy,and lung metastasis were independent risk factors for actual OS and CSS through univariate and multivariate analysis.Actual survival rates decreased over time,while CS rates gradually increased.As for the 6-month CS,the survival difference caused by chemotherapy and radiotherapy gradually disappeared over time,and the survival difference caused by lung metastasis reversed.Moreover,the influence of age and gender on survival gradually appeared.Nomograms were fitted for patients who have lived for 2,4 and 6 mo to predict 6-month conditional OS and CSS,respectively.The area under the curve(AUC)of nomograms for conditional OS decreased as time passed,and the AUC for conditional CSS gradually increased.CONCLUSION CS for distant metastatic HCC patients substantially increased over time.With dynamic risk factors,nomograms constructed at a specific time could predict more accurate survival rates.

超长柔性悬挂固体充填刮板输送机异常工况表征及自主调控方法

固体充填刮板输送机是固体充填开采技术的关键装备,其高效智能化的程度制约着固体智能充填开采技术的进步,暂难突破超大采长、大功率、高可靠性的瓶颈,运行状态受地质条件、充填工艺等主控因素影响显著,异常工况自主调控问题亟待解决。总结了固体充填刮板输送机特征;构建了固体充填刮板输送机位姿形态与运输状态表征方法;通过分析异常工况的形成机理,选取异常工况判别指标,结合位姿与运输状态表征方法,给出了其空间位姿、牵引状态等异常工况判别准则与典型异常工况的调控路径,揭示了多工况状态下固体充填刮板输送机运载调控机制,提出了实时调控充填材料运输量、直线及水平程度组合的异常工况自主调控方法。全国首个260 m工作面长固体充填工程案例表明:通过异常工况自主调控,平均最大水平偏移距每减少100 mm,刮板链牵引力可减少85 kN,输送功率损耗可减少55 kW;平均最大垂直偏移距每减小100 mm时,输送功率损耗可减少7.2 kW;单位长度货载运量每减少100 kN/m,刮板链牵引力可减少31.9 kN,输送功率损耗可减少38.2 kW。所提出的异常工况自主调控方法可显著提升固体充填刮板输送机的输送效能,可助力实现固体智能充填开采。

沥青混凝土心墙堆石坝心墙拱效应研究

心墙拱效应是影响土石坝安全稳定运行的重要因素,心墙力学特性复杂且受众多因素的影响,复杂地形地质条件对心墙拱效应具有重要影响。首先对比了直心墙、斜心墙、下直上斜式心墙拱效应的差异,在此基础上选取岸坡坡度、河谷宽度、覆盖层厚度以表征坝体所处的地形地质条件。基于数值计算定量研究复杂地质条件对沥青混凝土心墙拱效应的影响规律。结果表明:心墙应力拱效应主要集中在心墙中部3/4坝高附近及靠近岸坡处;斜心墙应力拱效应相对较小,可以很好地改善心墙的整体受力状况;岸坡变陡,斜心墙整体的拱效应强度增加,应力传递的核心区域由心墙中部拓宽至心墙两岸坡;随着河谷宽度的增加,应力传递的重心逐渐由底部转移到心墙两岸及心墙中上部;斜心墙整体的应力拱效应并非随着河谷宽度的增加单调变化,当坝轴线长度与坝高比值增加到3~4时,河谷产生的河谷效应对斜心墙变形及拱效应的影响大幅下降;随着覆盖层厚度的增加,斜心墙底部的拱效应明显增强,底部的拱效应系数分布逐渐集中化、区域化,容易产生局部破坏。

凝固剂及加工条件对豆腐凝胶形成及品质影响研究进展

豆腐的本质是一种蛋白质凝胶体,是大豆蛋白(主要是7S和11S球蛋白)在热处理下发生变性使蛋白质分子结构展开,再通过盐离子、氢离子或酶等凝固剂作用下发生聚集,形成致密均匀且具有网状结构蛋白凝胶。豆腐凝胶的形成及豆腐品质受多种因素的影响。本文从凝固剂种类(盐类、酸类和酶类凝固剂)以及加工条件(加工原料、制浆方法、豆浆体系pH、热处理条件)角度出发,阐明凝固剂及加工条件对豆腐凝胶形成以及品质的影响研究进展。以期为豆腐制品的工业化生产提供理论指导,为豆腐食品的研究开发与品质调控提供科学依据。

深、浅部煤层气地质条件差异性及其形成机制

深部煤层气资源丰富、开发前景广阔,但对其与浅部煤层气地质条件的内在联系研究尚不够深入。从煤层形成演化角度出发,以鄂尔多斯盆地上古生界煤层为例,总结了煤层深埋深藏型、深埋浅藏型及浅埋浅藏型3种埋深演化模式。系统分析了深部和浅部煤层在温压条件与含气性、地应力与渗透率特征、变质程度与含水性等方面的差异性及其形成机制。研究指出,受埋深与演化过程影响,深部和浅部煤储层温度最多相差100℃以上,储层压力最大相差40 MPa左右,导致由浅部向深部,气体赋存状态以吸附气为主转变为吸附气与游离气共存,地应力场由水平应力主导转化为垂向应力主导,煤储层孔隙率、渗透率及含水性逐渐降低。明确了深部煤层气的典型特点,即:在高温高压条件下,以吸附态和游离态共存于一定深度以下煤储层中的甲烷气体,该类煤储层在垂向应力为主导的作用下,孔裂隙空间极度压缩,含水极少且矿化度极高,内生微裂隙为主要渗流通道。基于含气性临界深度和地应力场转换深度的不一致性,指出浅部向深部煤层演化过程中存在过渡区,该区内呈现出非典型深部煤层气的特点,或深部煤层气和浅部煤层气地质条件共存的情况,在勘探开发过程中,应具体分析,制定针对性开发方案,以实现浅部与深部煤层气的高效协同开发。

一种压阻式传感器信号调理电路的设计

由于压阻式MEMS传感器输出信号极其微弱,极易受到测试环境的噪声干扰,还需要外接调理电路,为了提升信号的采集精度,提出一种压阻式传感器信号ASIC调理电路的设计方案,实现输出信号的放大、滤波功能。测试结果表明,调理电路可以在10~200倍的范围内调节增益,-0.5 dB截止频率调节范围覆盖0.1~20 kHz;低频范围内,芯片的等效噪声为45 nV/√Hz。将调理电路与传感器相结合,改进的测试系统响应速度快,滤波效果更好,测试精度优于5%,传感器非线性为5.4%。同时,调理电路的大小和体积也很小,功耗低,对后续继续研究传感器测试系统有重要意义。

氦气资源形成地质条件、成因机理与富集规律

借鉴含油气系统思路及方法,基于典型富氦气田解剖,并利用地球化学成藏方法技术,研究天然气中氦气资源形成的地质条件、成因机理与富集规律。结果表明:①氦气“生-运-聚”机理与天然气有显著差异性,氦气主要为基底富U、Th元素缓慢α衰变或深部壳幔氦释放,沿岩石圈复合输导体系运移至天然气成藏系统,依附适宜载体气聚集成藏。②氦运移输导主要受“岩石圈断裂、基底断裂、沉积层断裂、有效输导层”复合输导体系控制,基于地下流体中“氦-气-水”相平衡及相-势耦合综合分析,提出氦气运聚过程中具有“水溶相、气溶相、游离相”3种主要赋存状态,存在氦气“集流、渗流、扩散”3种运移方式。③富氦气藏形成和氦气富集通常受控于“优质氦源、高效输导、适宜载体”3大主控要素,具有“脱溶汇聚、浮力驱动、压差驱替”3种聚集成藏动力类型,已发现富氦气藏具有相对“近氦源、邻断裂、低势区、高部位”的分布规律和成藏模式。④氦气富集区勘探和评价需要依托天然气兼探/并探,在评价落实氦气“源-运-聚”要素与天然气“生-储-盖”条件耦合匹配性、局部相对低势高部位有利圈闭载体气区的基础上,综合评价优选“通源连圈、低势高位、气氦适配”的有利氦气富集区。

准噶尔盆地东部石钱滩凹陷石炭系石钱滩组烃源岩特征及其生烃潜力分析

石钱滩组烃源岩为准噶尔盆地石钱滩凹陷石炭系的主力生烃层位。在充分调研其空间展布的基础上,选取最新发现的双井子地质探槽剖面及石钱1井、石钱2井与钱1井进行系统的烃源岩总有机碳、热解、族组分组成、有机显微组分、镜质体反射率及饱和烃色质等有机地球化学分析测试。结果表明,石钱滩组烃源岩为一套典型的海湾泻湖相沉积,呈NW–SE向展布于石钱滩凹陷,局部地区厚度巨大,可达370 m,沉积厚度中心位于凹陷东南隅。富有机质层段主要集中在石钱滩组下部,次为上部。整体具有较高的有机质丰度,可达到好等级及以上,有机质类型以Ⅱ2-Ⅲ为主,且已进入到高成熟生气阶段。综合研究认为,石炭系石钱滩组烃源岩具有良好的生烃条件,能够为石钱滩凹陷提供充足的油气源。

土壤中硒元素来源和迁移作用研究现状

自然过程和人为因素可以活化和迁移硒进入和离开土壤。查明土壤中硒的来源和迁移作用对富硒土地管理和富硒产业开发具有科学上和实践上的重要意义。岩石圈和大气干湿沉降是土壤硒的最基本来源。地表水径流、地下水淋滤、农作物收割、植物和微生物的挥发是土壤硒丢失的主要途径。土壤中硒的迁移作用可归因于水动力迁移作用、固相吸附作用和动植物循环作用。土壤组成和物理化学条件是控制土壤中硒分布和迁移的直接因素,气候、地形和农业活动通过改变土壤组成和物理化学条件间接地影响土壤中硒的迁移和重分布。在区域尺度(如大洲、国家和省)上,地质背景、气候和地形因素对硒的分布至关重要,而在局部尺度(如县、乡和农田)上,人类活动特别是农业耕作的影响更为显著。基于硒在土壤中的行为特性,提出了一系列硒资源管理策略:1)生物强化和修复,通过动植物循环作用来调节土壤中硒分布和生物有效性,它被认为是一种生态富硒手段;2)灌溉模式,通过改变水动力条件来调整土壤的理化条件,进而改变土壤的固相吸附作用和动植物循环作用,其中漫水灌溉和有氧灌溉结合的水管理模式被认为可以提升土壤硒的有效性;3)农艺措施,如施加硒肥、磷肥、硫肥和撒石灰,通过直接改变土壤组成和理化条件调整土壤硒的分布和有效性,这是传统的富硒农艺措施,但也可能产生一定的生态风险。

磁性生物炭合成及其对重金属吸附机制的研究进展

磁性生物炭(Magnetic biochar,MBC)因其磁分离能力和广阔应用前景而受到研究者广泛关注。MBC中碳基结构特征(如形貌、比表面积、官能团等)和铁氧化物形态及分布受多因素影响,如原料来源、热解温度、合成方法等。然而,MBC特性与合成条件的关联性以及MBC对重金属的吸附机制有待进一步研究。本文通过阐述合成条件对MBC特性的影响及其吸附重金属机制,提出关于未来MBC吸附重金属研究的一些科学问题,这将为认识MBC的环境效应提供重要的基础信息。

花岗质岩石中黑云母成分区域性变化对深部物质示踪及成矿的约束:以秦岭地区为例

秦岭造山带早中生代花岗质岩浆及成矿作用非常发育,是探讨地壳深部物质组成及成矿关系的重要地区之一,学者已从岩石学、矿床学和地球化学等角度开展了众多研究,但对成矿作用与花岗岩和地壳深部物质组成的认识仍不太清楚。黑云母是中酸性岩中常见的暗色矿物,也是指示成矿可能性的重要矿物之一。因此,本文对秦岭该期花岗质岩石中黑云母成分开展研究,为解决该问题探索新途径。对秦岭早中生代花岗质岩石中黑云母成分的研究结果显示,北秦岭和南秦岭两个构造单元中黑云母成分和形成物理条件存在系统差异,揭示其岩浆物源有别,进而制约了其成矿种类特征。北秦岭早中生代花岗质岩石中黑云母的成分和形成条件变化范围小,以MgO(8%~13%)、TiO_(2)(3%~5%)和Cl(0.02%~0.6%)含量较高,F(0.2%~0.4%)含量较低,以及较高的氧逸度(log f O_(2)值为-16.96~-14.62)和温度(682~771℃)为特征;而南秦岭的成分和形成条件总体变化范围较大,具有MgO(3%~15%)、TiO_(2)(2%~4.5%)、Cl(0.01%~0.18%)含量较低,F(0.1%~1.6%)含量较高,以及较低的氧逸度(log f O_(2)值为-20.88~-15.08)和温度(536~754℃)。此外,研究还显示,黑云母的形成压力与岩浆演化程度和矿物组合相关,当岩石中出现黑云母+白云母±石榴子石组合时,压力较高。秦岭两个构造单元中黑云母成分和形成条件的差异,特别是氧逸度和Cl含量的明显不同,揭示各自的成矿种类和成矿潜力不同,如北秦岭较高的氧逸度和Cl含量,形成铜矿的潜力比南秦岭大。同时,两个构造单元中黑云母的成分差异,还揭示了岩浆物源及深部物质组成的不同,即北秦岭比南秦岭具有更为年轻的地壳,这与区域同位素填图示踪的深部物质组成差异基本一致。由此可见,对区域上同时代花岗质岩石中黑云母成分的研究,不仅可以揭示岩浆演化、岩浆结晶过程物理化学条件等,还可示踪深部物质组成的空间变化与差异及成矿种类和潜力,有望成为探测深部物质组成的新方法和了解区域成矿背景和潜力的新途径。