An imputation/copula-based stochastic individual tree growth model for mixed species Acadian forests： a case study using the Nova Scotia permanent sample plot network

Background： A novel approach to modelling individual tree growth dynamics is proposed. The approach combines multiple imputation and copula sampling to produce a stochastic individual tree growth and yield projection system. Methods： The Nova Scotia, Canada permanent sample plot network is used as a case study to develop and test the modelling approach. Predictions from this model are compared to predictions from the Acadian variant of the Forest Vegetation Simulator, a widely used statistical individual tree growth and yield model. Results： Diameter and height growth rates were predicted with error rates consistent with those produced using statistical models. Mortality and ingrowth error rates were higher than those observed for diameter and height, but also were within the bounds produced by traditional approaches for predicting these rates. Ingrowth species composition was very poorly predicted. The model was capable of reproducing a wide range of stand dynamic trajectories and in some cases reproduced trajectories that the statistical model was incapable of reproducing. Conclusions： The model has potential to be used as a benchmarking tool for evaluating statistical and process models and may provide a mechanism to separate signal from noise and improve our ability to analyze and learn from large regional datasets that often have underlying flaws in sample design.

Experimental and numerical simulation of loading rate effects on failure and strain energy characteristics of coal-rock composite samples

The deformation and failure of coal and rock is energy-driving results according to thermodynamics.It is important to study the strain energy characteristics of coal-rock composite samples to better understand the deformation and failure mechanism of of coal-rock composite structures.In this research,laboratory tests and numerical simulation of uniaxial compressions of coal-rock composite samples were carried out with five different loading rates.The test results show that strength,deformation,acoustic emission(AE)and energy evolution of coal-rock composite sample all have obvious loading rate effects.The uniaxial compressive strength and elastic modulus increase with the increase of loading rate.And with the increase of loading rate,the AE energy at the peak strength of coal-rock composites increases first,then decreases,and then increases.With the increase of loading rate,the AE cumulative count first decreases and then increases.And the total absorption energy and dissipation energy of coal-rock composite samples show non-linear increasing trends,while release elastic strain energy increases first and then decreases.The laboratory experiments conducted on coal-rock composite samples were simulated numerically using the particle flow code(PFC).With careful selection of suitable material constitutive models for coal and rock,and accurate estimation and calibration of mechanical parameters of coal-rock composite sample,it was possible to obtain a good agreement between the laboratory experimental and numerical results.This research can provide references for understanding failure of underground coalrock composite structure by using energy related measuring methods.

Engineering behaviour of in situ cored deep cement mixed marine deposits subjected to undrained and drained shearing

The deep cement mixing(DCM)is used to improve the capacity and reduce the settlement of the soft ground by forming cemented clay columns.The investigation on the mechanical behaviour of the DCM samples is limited to either laboratory-prepared samples or in-situ samples under unconfined compression.In this study,a series of drained and undrained triaxial shearing tests was performed on the in-situ cored DCM samples with high cement content to assess their mechanical behaviours.It is found that the drainage condition affects significantly the stiffness,peak and residual strengths of the DCM samples,which is mainly due to the state of excess pore water pressure at different strain levels,i.e.being positive before the peak deviatoric stress and negative after the peak deviatoric stress,in the undrained tests.The slope of the failure envelope changes obviously with the confining pressures,being steeper at lower stress levels and flatter at higher stress levels.The strength parameters,effective cohesion and friction angle obtained from lower stress levels(c′0 andφ′0)are 400 kPa and 58°,respectively,which are deemed to be true for design in most DCM applications where the in-situ stress levels are normally at lower values of 50-200 kPa.Additionally,the computed tomography(CT)scanning system was adopted to visualize the internal structures of DCM samples.It is found that the clay pockets existing inside the DCM samples due to uneven mixing affect markedly their stress-strain behaviour,which is one of the main reasons for the high variability of the DCM samples.

Joint empirical likelihood confidence regions for a finite number of quantiles under strong mixing samples

In this paper, we obtain the joint empirical likelihood confidence regions for a finite number of quantiles under strong mixing samples. As an application of this result, the empirical likelihood confidence intervals for the difference of any two quantiles are also obtained.

Species composition and minimum sampling area of a riparian mixed broadleaved-Korean pine forest in Changbai Mountain Nature Reserve

Riparian areas are unique although often small component of the overall watershed landscape. The structure of riparian forests along Erdaobai River on the north slope of Changbai Mountain were investigated by using field data collected from eight sampling transects perpendicular to the Erdaobai River channel. Two kinds of species-area saturation curves were used to examine the relationship between species number and minimum sampling area. The results showed that riparian gymnosperms accounted for a high proportion of all gymnosperms in the Changbai Mountain Nature Reserve while riparian ferns and angiosperms accounted for a relatively low proportion. The average minimum sampling areas of riparian forest that included 60%, 80%, and 90% of the community species pool were about 85, 185, and 328 m 2 , respectively; while those for nonriparian forest were about 275, 390, and 514 m 2 , correspondingly.

Strong Consistency of M Estimator in Linear Model for φ-mixing Samples

The strong consistency of M estimator of regression parameter in linear model for φ-mixing samples is discussed by using the classic Rosenthal type inequality. We get the strong consistency of M estimator under lower moment condition, which generalizes and improves the corresponding ones for independent sequences.

应用Mixed Logit模型估计非同质旅行时间价值

采用计算机模拟的方法,假设时间和费用的参数服从正态分布、对数正态分布、确定性常数等多种形式,得出了上海黄浦江水上巴士调查的旅行时间价值分布及数字特征,并根据时间价值分布函数进行方式分担,和Logit模型进行了对比。考虑不同出行者对时间和费用的不同敏感性,即具有不同的时间价值,可以更加准确地预测交通方式分担比以及分析交通定价政策对交通行为的影响。

Posterior propriety in nonparametric mixed efects model

It is well known that spline smoothing estimator relates to the Bayesian estimate under partially informative normal prior. In this paper, we derive the conditions for the pro- priety of the posterior in the nonparametric mixed effects model under this class of partially informative normal prior for fixed effect with inverse gamma priors on the variance compo- nents and hierarchical priors for covariance matrix of random effect, then we explore the Gibbs sampling procedure.

Size and Shape of Sample Units in Native Forests and Plantations

The size and form of sampling units-SU have always been variables considered in planning and structuring forest inventories, being performed in forests or in plantations. The experimental work outlined to deal with the problem was conducted in an area of araucaria forest, in Sao Joao de Triunfo, PR, Brazil. The forms of sampling units circle, square and rectangular were evaluated, whose areas ranged from 200 m2 to 1000 m2. Time was recorded using a stopwatch and computed separately for locomotion and measurement. The power model was used to adjust the relations of the times of locomotion and measurement, as well as a hyperbolic one for the coefficient of variation, all taking as function of the SUs sizes. To achieve the analytical solution for the optimum size of the SU, it was necessary simulating the behavior of the three functions until the size of 10,000 m2. By taking the derivative of the combined function it was found the maximum point, which allowed optimizing the size of the SU in 600 m2 for the structured experimental conditions. This result proved the formulated hypothesis, performing even a critical analysis of the inclusion of other relevant variables, such as size of the area to be inventoried, number of SUs performed in one day of work, average distance between the SUs, average speeds for locomotion between SUs and for taking all respective measurements.

MIXED-GRAINED CMOS FIELD PROGRAMMABLE ANALOG ARRAY FOR SMART SENSORY APPLICATIONS

The drive towards shorter design cycles for analog integrated circuits has given impetus to the development of Field Programmable Analog Arrays(FPAAs),which are the analogue counterparts of Field Programmable Gate Arrays(FPGAs).In this paper,we present a new design methodology which using FPAA as a powerful analog front-end processing platform in the smart sensory microsystem.The proposed FPAA contains 16 homogeneous mixed-grained Configurable Analog Blocks(CABs) which house a variety of processing elements especially the proposed fine-grained Core Configurable Amplifiers(CCAs).The high flexible CABs allow the FPAA operating in both continuous-time and discrete-time approaches suitable to support variety of sensors.To reduce the nonideal parasitic effects and save area,the fat-tree interconnection network is adopted in this FPAA.The functionality of this FPAA is demonstrated through embedding of voltage and capacitive sensor signal readout circuits and a configurable band pass filter.The minimal detectable voltage and capacitor achieves 38 uV and 8.3 aF respectively within 100 Hz sensor bandwidth.The power consumption comparison of CCA in three applications shows that the FPAA has high power efficiency.And the simulation results also show that the FPAA has good tolerance with wide PVT variations.

Pre-Training Physics-Informed Neural Network with Mixed Sampling and Its Application in High-Dimensional Systems

Recently,the physics-informed neural network shows remarkable ability in the context of solving the low-dimensional nonlinear partial differential equations.However,for some cases of high-dimensional systems,such technique may be time-consuming and inaccurate.In this paper,the authors put forward a pre-training physics-informed neural network with mixed sampling(pPINN)to address these issues.Just based on the initial and boundary conditions,the authors design the pre-training stage to filter out the set of the misfitting points,which is regarded as part of the training points in the next stage.The authors further take the parameters of the neural network in Stage 1 as the initialization in Stage 2.The advantage of the proposed approach is that it takes less time to transfer the valuable information from the first stage to the second one to improve the calculation accuracy,especially for the high-dimensional systems.To verify the performance of the pPINN algorithm,the authors first focus on the growing-and-decaying mode of line rogue wave in the Davey-Stewartson I equation.Another case is the accelerated motion of lump in the inhomogeneous Kadomtsev-Petviashvili equation,which admits a more complex evolution than the uniform equation.The exact solution provides a perfect sample for data experiments,and can also be used as a reference frame to identify the performance of the algorithm.The experiments confirm that the pPINN algorithm can improve the prediction accuracy and training efficiency well,and reduce the training time to a large extent for simulating nonlinear waves of high-dimensional equations.

The identification of individuals with hatchery and natural origin in a mixed sample of Amur River chum salmon by Otolith microchemistry

We estimated the proportion of hatchery and natural fall spawning chum salmon returning to the Amur River using chemical markers specific to hatchery-origin fry.We used otolith microchemistry technique to identify fish with artificial origin among returning spawners.First,we found that juveniles of artificial origin had higher values of the Sr:Ca molar ratio of the otoliths’edge zone compared with juveniles of natural origin,what can be related to the use of rearing feed produced from raw materials of marine origin rich in strontium.Then we observed that most of the spawners from Anyuisky Hatchery and from the Amur River mouth at the start of the spawning migration has also the higher value of Sr:Ca molar ratio of the juvenile zone of otoliths.Also,adults with higher values of the Sr:Ca molar ratio are characterized by a skewed right in the peak of the age distribution.Both,the age structure and phenological shift in the time of spawning migration of individuals with higher value of the used chemical marker corresponds to results of studies on hatchery-produced chum salmon completed at different parts on Northern Pacific.The results of this study will be used in the management of Amur fall chum salmon fisheries,and also demonstrates the necessity of the development of specific measures for increasing the survival of juvenile anadromous salmonids released at large rivers and exposed to prolonged freshwater migration to the ocean.As a further application of the methodology,we plan to identify the markers specific to each of the hatcheries and main spawning tributaries belonging to Amur River catchments.This will be an important step in the evaluation of the contribution of different stocks in mixed fisheries and also in the estimation of the effect of hatchery releases on naturally spawning stocks of Amur fall chum.Following to,our results may indicate the applicability of this approach for the determination of artificial-origin fish in a mixed sample of the Amur fall chum salmon.

STRONG UNIFORM CONSISTENCY OF KERNEL DENSITY ESTIMATORS ON φ-MIXED SAMPLES

Suppose {Xn} be a sequence of stationary and φ-mixed random variables （for example, see Ref. [1]）, the common unknown probability density of Xn′s is f（x）. For each n≥1, the kernel estimators of f（x）, based on X1′,X2′…… XN′, are defined as:

THE LINEAR SAMPLING METHOD FOR RECONSTRUCTING A PENETRABLE CAVITY WITH UNKNOWN EXTERNAL OBSTACLES

We consider the interior inverse scattering problem for recovering the shape of a penetrable partially coated cavity with external obstacles from the knowledge of measured scattered waves due to point sources.In the first part,we obtain the well-posedness of the direct scattering problem by the variational method.In the second part,we establish the mathematical basis of the linear sampling method to recover both the shape of the cavity,and the shape of the external obstacle,however the exterior transmission eigenvalue problem also plays a key role in the discussion of this paper.

“混合学街”的样态表征、实践价值与政策贡献

“混合学街”是温州市第二职业中等专业学校首创的职业院校混合所有制办学新模式,其样态表征体现在三个方面:基础在“混合”、关键在“治理”、核心在“育人”。对“混合学街”的实践探索深化了产教融合、优化了产教资源的合理配置、推动了职业学校高质量发展,不仅为混合所有制办学政策创新提供了典型模式、路径方法,也为混合所有制办学政策创新增添了勇气信心。

基于新课标的中小学生计算思维量表构建研究

对学生计算思维的培养已成为数字时代的核心议题,科学、精准的评价则是培养学生计算思维能力的基础。然而,针对义务教育阶段学生的计算思维测评,目前国内尚无依托相关课程标准制定的本土化的、权威的量表。为此,研究首次依托《义务教育信息科技课程标准(2022年版)》(以下简称“新课标”)中的计算思维定义及培养要求,共以12920名(N1=1029,N2=1458,N3=10433)小学生和初中生为研究样本,运用收敛混合方法构建一个面向我国教育实际的、经严格论证的中小学生计算思维量表。结果显示:经过两轮的收敛混合分析,修改后的计算思维量表共包含5个因子及15个题项,具有良好的内容效度;经过大样本实证检验分析,量表具有良好的信效度,且具有跨性别、年级和地区测量等值性,可以用来测量中小学生的计算思维水平。

考虑训练样本分布不均衡的超短期风电功率概率预测

提出一种考虑训练样本分布不均衡的超短期风电概率预测方法。首先构建深度信念混合密度网络,通过深度信念网络独特的预训练和微调机制提取输入变量的隐特征,利用Beta混合概率分布的有界性准确表征风电预测功率的概率分布,实现隐特征与预测功率概率分布参数之间的非线性映射;然后引入训练样本分布平滑策略,其中特征分布平滑技术用于校准输入特征,标签分布平滑技术用于对各样本误差赋予差异化权重,从输入和输出两方面改善训练样本分布不均衡现象对预测结果的不利影响。实际算例结果表明,与常见风电功率概率预测模型相比,所提模型在点预测和概率预测方面均能获得较高的预测精度,尤其能有效提高低密度样本区域的预测精度。

基于联合熵的非平衡数据边界混合重采样

为了克服在数据平衡处理过程中单一重采样方法易生成冗余样本及误删重要样本信息的局限,本文提出一种基于联合熵的非平衡数据边界混合重采样算法。该算法首先通过引入边界因子对边界集和非边界集进行有效的区分,进一步构建一个联合熵指标体系以判断出边界集中少数类样本的重要程度,并根据其重要程度对细分后的少数类样本点设置不同的过采样方法和采样数量,最后使用NearMiss-2算法对非边界集中多数类样本点进行筛选并删除,从而实现数据的相对平衡。通过对9组UCI数据集进行对比实验,实验结果表明:该算法在F1-Score、G-mean及AUC这3个指标上均有提升,验证了其有效性,有较好的非平衡数据分类性能表现。

基于逆高斯随机过程的混凝土梁桥抗力退化模型

为准确体现混凝土梁桥抗力退化的时变性与随机性,运用逆高斯(IG)随机过程建立了结构抗力退化模型.基于现场检测数据,采用贝叶斯更新理论对IG随机过程进行实时更新.针对IG随机过程中的参数因先验分布与后验分布非共轭而难以采样估计的问题,提出了混合Gibbs采样法.结合数值案例论证了该方法的可行性,并将其应用于某一混凝土梁桥的抗力预测分析.研究结果表明:IG随机过程可利用现场检测信息对桥梁抗力退化过程实时更新;混合Gibbs采样法可解决IG随机过程的高维参数估计问题,克服了形函数中指数q值的经验性给定缺陷;随着服役年限的增大,桥梁结构的累积退化量逐步增大,当桥梁服役年限为60 a时,其累积退化量为服役年限为30 a时的3.48倍.与Gamma随机过程相比,IG随机过程避免了部分初始参数的假定,可得到更为精确的桥梁抗力退化模型.

药品网络抽检存在的问题及对策分析

目的:在网络销售渠道开展药品抽检工作存在挑战,需改进抽检工作的实际操作和完善理论研究,以适应药品监管的新形势。方法:在药品网络抽检试点工作中,依据网售药品的特点,制定抽检方案并执行。分析抽查检验程序、处方开具及审核、药品储运等方面的困难及风险点,并提出对策和建议。结果与结论:建议修订和完善相关法律规章中关于网络抽检的内容,对抽样凭证和处方获取等作统一要求;核实样品真实性,优化混批样品的处置流程,设立动态检验机制。