Application of closed-vessel microwave digestion method for the determination of multi-elements in environmental samples by sequential ICP-AES

A closed-vessel microwave digestion method is described for the rapid dissolution of environmental samples such as foods, soils and sediments. Depending on the sample type, 0.1-0.2 g sample was decomposed with HNO3/H2O2 or HNO3 / H2O2/HF acid mixture in a PTFE digestion vessel by using microwave heating for 2-3 min at 500W of microwave power. The solution, or to which 0.5 g of boric acid was added, was diluted to 25-50 ml and directly determined by sequential ICP-AES. The accuracy of the procedure was validated by the analysis of six standard reference materials for 10 elements. Ail results were in a good agreements with the certified values.

Sequential Approximation of Functions in Sobolev Spaces Using Random Samples

We present an iterative algorithm for approximating an unknown function sequentially using random samples of the function values and gradients. This is an extension of the recently developed sequential approximation (SA) method, which approximates a target function using samples of function values only. The current paper extends the development of the SA methods to the Sobolev space and allows the use of gradient information naturally. The algorithm is easy to implement, as it requires only vector operations and does not involve any matrices. We present tight error bound of the algorithm, and derive an optimal sampling probability measure that results in fastest error convergence. Numerical examples are provided to verify the theoretical error analysis and the effectiveness of the proposed SA algorithm.

On the Confidence Limits of Bernoulli Parameter in Any Sequential Case

Let x 1,x 2,… be independent identically distributed (i.i.d.) random variables, in which x n=0 or 1 and the probability of ｛x n=1｝ is p. Here p is unknown. Let τ be any finite stopping time for (x n,n1). For any sequential sample (x 1,x 2,…,x τ ) and γ∈(0,1), we have given an optimal confidence limit of p with confidence level γ . Some related problems are also discussed.

On Spatial Distribution Pattern of Broccoli Clubroot Disease and and the Sampling Technique

[Objective] To further improve the prediction and forecast and continuous control ability of broccoli clubroot disease. [Methods] The spatial distribution pattern of diseased or infected plants was analyzed using the least square method, fre- quency distribution, aggregation index, m＊-m regression analysis and Taylor＇s pow- er law model. [Result] The field distribution of broccoli plants with clubroot disease tended to be aggregated distribution, m＇-m regression analysis showed that the el- ementary composition of the spatial distribution of diseased or infected plants was individual colony, the individuals attracted each other; the disease had obvious dis- ease focus in the field, and the individual colony showed uniform distribution pattern in the field. Taylor＇s power law showed that the spatial pattern of individual dis- eased or infected plant with clubroot disease tended to be uniform distribution with the increase of the density. On the basis of this, Iwao optimal theoretical sampling model and sequential sampling model were established, namely N =273.954 1/m- 59.698 5, To （N）=0.368 4N±1.926 8√N, respectively, it meant that when surveying N plants, if the accumulative incidence rate exceeded upper bound, the field can be set as control object; if the accumulative incidence rate didn＇t reach lower bound, it can be set as uncontrol field; if the accumulative incidence rate was between upper bound and lower bound, it should be surveyed continuously until the maximum sample size （mo=0.368 4） appeared, that was, the disease incidence was 15%, so the sampling number should be 684 plants. [Conclusion] The research results had very important instructive meaning for disease control.

On Robustness of a Sequential Test for Scale Parameter of Gamma and Exponential Distributions

The main aim of the present paper is to study the robustness of the developed sequential probability ratio test (SPRT) for testing the hypothesis about scale parameter of gamma distribution with known shape parameter and exponential distribution with location parameter. The robustness of the SPRT for scale parameter of gamma distribution is studied when the shape parameter has undergone a change. The similar study is conducted for the scale parameter of exponential distribution when the location parameter has undergone a change. The expressions for operating characteristic and average sample number functions are derived. It is found in both the cases that the SPRT is robust only when there is a slight variation in the shape and location parameter in the respective distributions.

Sequential Shrinkage Estimate for COX Regression Models with Uncertain Number of Effective Variables

In the applications of COX regression models, we always encounter data sets that contain too many variables that only a few of them contribute to the model. Therefore, it will waste much more samples to estimate the “noneffective” variables in the inference. In this paper, we use a sequential procedure for constructing the fixed size confidence set for the “effective” parameters to the model based on an adaptive shrinkage estimate such that the “effective” coefficients can be efficiently identified with the minimum sample size. Fixed design is considered for numerical simulation. The strong consistency, asymptotic distributions and convergence rates of estimates under the fixed design are obtained. In addition, the sequential procedure is shown to be asymptotically optimal in the sense of Chow and Robbins (1965).

Investigation on Fe,Mn,Zn,Cu,Pb and Cd fractions in the natural surface coating samples and surficial sediments in the Songhua River,China

Natural surface coating samples （NSCSs） from the surface of shingles and surficial sediments （SSs） in the Songhua River, China were employed to investigate the relationship between NSCSs and SSs in fractions of heavy metals （Fe, Mn, Zn, Cu, Pb, and Cd） using the modified sequential extraction procedure （MSEP）. The results show that the differences between NSCSs and SSs in Fe fi＇actions were insignificant and Fe was dominantly present as residual phase （76.22% for NSCSs and 80.88% for SSs） and Fe-oxides phase （20.33% for NSCSs and 16.15% for SSs）. Significant variation of Mn distribution patterns between NSCSs and SSs was observed with Mn in NSCSs mainly present in Mn-oxides phase （48.27%） and that in SSs present as residual phase （45.44%）. Zn, Cu, Pb and Cd were found dominantly in residual fractions （〉48%）, and next in solid oxides/hydroxides for Zn, Pb and Cd and in easily oxidizable solids/compounds form for Cu, respectively. The heavy metal distribution patterns implied that Fe/Mn oxides both in NSCSs and SSs were more important sinks for binding and adsorption of Zn, Pb and Cd than organic matter （OM）, and inversely, higher affinity of Cu to OM than Fe/Mn oxides in NSCSs and SSs was obtained. Meanwhile, it was found that the distributions of heavy metals in NSCSs and SSs were similar to each other and the pseudo-total concentrations of Zn, Cu, Pb and Cd in NSCSs were greater than those in SSs, highlighting the more importance for NSCSs than SSs in controlling behaviours of heavy metals in aquatic environments.

Sequential stratified sampling belief propagation for multiple targets tracking

Rather than the difficulties of highly non-linear and non-Gaussian observation process and the state distribution in single target tracking, the presence of a large, varying number of targets and their interactions place more challenge on visual tracking. To overcome these difficulties, we formulate multiple targets tracking problem in a dynamic Markov network which consists of three coupled Markov random fields that model the following： a field for joint state of multi-target, one binary process for existence of individual target, and another binary process for occlusion of dual adjacent targets. By introducing two robust functions, we eliminate the two binary processes, and then apply a novel version of belief propagation called sequential stratified sampling belief propagation algorithm to obtain the maximum a posteriori （MAP） estimation in the dynamic Markov network, By using stratified sampler, we incorporate bottom-up information provided by a learned detector （e.g. SVM classifier） and belief information for the messages updating. Other low-level visual cues （e.g. color and shape） can be easily incorporated in our multi-target tracking model to obtain better tracking results. Experimental results suggest that our method is comparable to the state-of-the-art multiple targets tracking methods in several test cases.

Two-stage sequential sampling for two rare species in western Inner Mongolia of China

To evaluate the efficiencies of different sampling methods for a rare and clustered population, we investigated the sampling effects for the two species Tamarix chinensis (Salt cedar) and Elaeagnus angustifolia (Russian olive) in western Inner Mongolia with two-stage sequential sampling, which is a new sampling method, traditional simple random sampling and two-stage sampling. Based on two-stage sequential sampling and two-stage sampling, each population was partitioned into four primary sampling units, and then two of them were randomly selected. Sampling designs were simulated based on the conditions of five secondary sampling unit areas, two criterion values, five initial secondary sampling units and two sequential secondary sampling units in 1000 repetitions. To evaluate the performance of the sampling designs for each method, the variance and relative error of the density estimates were used. The relative sampling efficiencies of the three sampling methods were compared using the same final sampling sizes. We analyzed the sampling efficiency generated by two-stage sequential sampling and found that it yielded smaller variances than those of simple random sampling and two-stage sampling in all sampling designs, and that two-stage sampling was more efficient than simple random sampling. Density estimates from the two-stage sequential sampling were very close to the true values. We also determined the optimum secondary sampling unit areas for the two species in the two-stage sequential sampling. It was best for Tamarix chinensis and Elaeagnus angustifolia when the secondary sampling unit areas were 200 and 100 m2 , respectively.

Utility of Adaptive Sample Size Designs and A Review Example

Sample size can be a key design feature that not only affects the probability of a trial＇s success but also determines the duration and feasibility of a trial. If an investigational drug is expected to be effective and address unmet medical needs of an orphan disease, where the accrual period may require many years with a large sample size to detect a minimal clinically relevant treatment effect, a minimum sample size may be set to maintain nominal power. In limited situations such as this, there may be a need for flexibility in the initial and final sample sizes; thus, it is useful to consider the utility of adaptive sample size designs that use sample size re-estimation or group sequential design. In this paper, we propose a new adaptive performance measure to consider the utility of an adaptive sample size design in a trial simulation. Considering that previously proposed sample size re-estimation methods do not take into account errors in estimation based on interim results, we propose Bayesian sample size re-estimation criteria that take into account prior information on treatment effect, and then, we assess its operating characteristics in a simulation study. We also present a review example of sample size re-estimation mainly based on published paper and review report in Pharmaceuticals and Medical Devices Agency （PMDA）.

基于傅里叶变换与近端采样的序列推荐算法

传统推荐算法比较注重于模型本身对于推荐效果的提升,实际上数据质量对于算法的影响更为重要,但目前在推荐算法领域对于数据的科学处理方法比较零散,没有形成一个系统的框架。针对以上问题,基于傅里叶变换与近端序列采样的数据预处理,结合SASRec提出可以普遍应用的序列推荐框架FTRRec。首先通过傅里叶变换将序列数据在时域和频域中相互转换,并根据序列数据的特点,过滤序列中的无用信息,其次使用近端序列采样替换传统的滑动窗口采样法,加速样本采样的同时,提升模型对于序列的特征捕获能力。通过在5个公开数据集上的实验,将框架应用于三个不同的主流推荐算法时,每种模型均有3%-5%的提升。

不均衡小样本下多特征优化选择的生命体触电故障识别方法

针对现有的剩余电流保护装置无法有效识别触电事故的问题,该文提出了一种不均衡小样本下多特征优化选择的生命体触电故障识别方法。首先通过变分自编码器(VAE)对实验收集到的生命体触电小样本数据进行增殖以实现正负样本均衡;然后在时域上提取能够反映波形动态变化特性的23个特征量,并利用高斯核Fisher判别分析(GKFDA)与最大信息系数(MIC)法从中选择最优表达特征组;最后,提出基于遗忘因子的在线顺序极限学习机(FOS-ELM)算法实现生命体触电行为的鉴别。实验结果表明,所提方法利用不均衡小样本触电数据集就可以训练出一个优秀的分类模型,诊断准确率可达98.75%,诊断时间仅为1.33 ms。其优良的性能结合在线增量式学习分类器设计,使得模型具备新知识学习能力,具有极好的工程应用前景。

基于顺序等效采样的DAC测试方法设计

当前数模转换器(DAC)测试面临不能兼顾采样精度与采样速率的问题,测试时通常只能二者选其一,随着DAC器件的发展,常规测试系统不能满足当前测试领域需求。针对采样速率提升导致系统噪声提高,最终使采样系统有效分辨率恶化的问题,在分析对比了当前采样技术和系统噪声影响的基础上,提出了基于顺序等效采样的DAC测试方法。该方法将高频的原始信号在时间域上展开,其核心是制造采样周期与输入信号的周期的固定频差以进行分周期采样。对采样时钟抖动对系统有效位数的影响进行分析,确定了系统的最大时钟抖动,根据被测信号周期特征设计了测试系统的采样结构与周期。并对一款16bit、500MHz输出的DAC进行了测试,其结果与传统外挂频谱仪的测试结果进行对比。对比结果表明两次实验获得的频谱特征一致,采用本文设计的测试系统获得的测试结果得到了约4dBc无杂散范围提升,约3dBc双音交调参数提升,证明基于顺序等效采样DAC测试方法的有效性。

云南山区旱地稻田蛴螬组成结构及空间分布特征

【目的】明确云南省山区旱地稻田蛴螬种类组成、种群空间分布型及抽样方法,为云南山区旱地稻田蛴螬类害虫的预测预报及综合防控提供参考依据。【方法】选择云南省普洱市澜沧县竹塘乡蒿枝坝村、哈果马村和诺国村3个主要旱地稻种植区为试验地点,于2022和2023年采用五点取样法对旱地稻田中的蛴螬种类进行系统调查及鉴定;通过聚集度指标法、Iwao回归分析、Taylor幂函数法及种群聚集均数分析不同试验点旱地稻田蛴螬空间分布型和聚集原因,并基于空间分布结果,拟合建立理论抽样和序贯抽样模型。【结果】云南省普洱市澜沧县3个试验地点的旱地稻田共发现9种蛴螬,隶属于3科6属,其中黑阿鳃金龟(Apogonia cupreoviridis)和东方绢金龟(Maladera orientalis)是主要的优势种,相对丰度分别为30.91%和24.18%。聚集度指标法、Iwao回归分析及Taylor幂法结果显示,2022和2023年蒿枝坝村、哈国马村和诺国村的蛴螬空间分布型均表现为聚集分布(α<0,β>1);聚集均数λ值均大于2,表明昆虫本身的生活习性和环境气候、土壤湿度、植株生长状况等因素是影响旱地稻田蛴螬个体聚集的主要因素。通过建立理论抽样和序贯抽样模型可知,当允许误差D为0.3,蒿枝坝村、哈国马村和诺国村的虫口密度均为2头/m^(2)时,2022年所需的理论抽样数分别为353、393和155 m^(2),2023年所需抽样数为76、44和49 m^(2)。【结论】云南省普洱市澜沧县竹塘乡3个村的旱地稻种植田蛴螬共有9种,主要优势种为黑阿鳃金龟和东方绢金龟。蛴螬种群在田间呈聚集分布,引起聚集的原因是昆虫本身的生活习性和环境气候、土壤湿度、植株生长状况等因素。

旱地优质稻田稻象甲成虫发生动态及空间分布型

【目的】明确旱地优质稻田稻象甲成虫的发生动态、空间分布型和种群抽样技术,为旱地优质稻稻象甲成虫的调查取样、预测预报及综合防控提供科学参考。【方法】以云南省普洱市澜沧县竹塘乡蒿枝坝村、哈果马村和诺国村旱地优质稻种植区为试验地点,于2022和2023年采用连片调查的方法对旱地优质稻田稻象甲成虫发生动态及空间分布型进行调查;采用聚集度指标法、Iwao回归分析法、Taylor幂函数法及种群聚集均数分析不同地区旱地优质稻田稻象甲成虫的空间分布型和聚集原因,并基于空间分布结果,拟合形成理论抽样模型和序贯抽样模型。【结果】2022和2023年各调查点旱地优质稻田稻象甲成虫发生动态均为单峰型,始见于5月中旬,于8月中旬后仅零星发生,7月是其发生高峰期,并于7月中下旬达到最高峰。聚集度指标法、Iwao回归分析法及Taylor幂函数法分析表明2022、2023年3个调查点的旱地优质稻田稻象甲成虫在田间均呈聚集分布,引起聚集的原因是昆虫本身的生活习性及气候、土壤湿度、植株生长状况等环境因素。2023年蒿枝坝村和诺国村旱地优质稻田稻象甲成虫基于空间分布的理论抽样模型分别为N=t^(2)/D^(2)(2.170/m+0.214)、N=t^(2)/D^(2)(2.599/m+0.228),基于稻象甲成虫临界防治指标的序贯抽样模型分别为T_(0)(n)=2n±2.279√n、T_(0)(n)=2n±2.472√n。【结论】云南省普洱市澜沧县竹塘乡旱地优质稻田稻象甲成虫发生动态为单峰型,其在田间均呈聚集分布,引起聚集的原因是昆虫本身的生活习性及气候、土壤湿度、植株生长状况等环境因素。

不放回样本追加下基于永久随机数抽样技术的多层次推断方法及应用研究

抽样调查如何满足多层次推断的需要是我国政府统计调查在实践应用过程中面临的主要难题之一。本文根据我国现阶段抽样调查工作的实际情况,提出一套完整的适用于多层次推断的抽样调查方法。首先,使用永久随机数对现有抽样框进行更新维护,打破现有方法假设抽样框不变的局限性;其次,采用基于列表–序贯算法的条件Poisson抽样实现多层次推断,消除了固定样本量抽样技术仅适用于一次性抽样调查的限制,同时解决了随机样本量抽样技术样本量不固定的问题;此外,分别使用Sen-Yates-Grundy方法和Deville方法进行方差估计,以提高估计精度;最后,通过数值模拟和应用研究验证所提出方法的有效性。该方法在不显著增加调查经费的情况下,可广泛适用于不同数据类型的目标总体,在企业、住户等经济领域调查中具有可推广性的同时,也可为大数据背景下各级政府在实际解决多层次推断问题方面提供有价值的参考。

基于FPGA的时基设计和时基非线性补偿

为提高宽带时域反射计的等效采样率和时基分辨率,设计一种基于现场可编程门阵列(field programma-ble gate array,FPGA)的时域反射计数据采集模块的时基方案,并设计一种基于查找表的时基校准方法对时基非线性进行补偿。该方法基于等精度测频法,使用FPGA测量该方波信号的频率,得到准确的延迟时间以实现查找表的建立。并利用采样芯片的微调引脚实现1 ps步进的顺序等效采样和时基非线性实时补偿。实测数据表明:设计的时基采样方案实现了1 TSa/s等效采样率,时基补偿方案使时基非线性减小至-0.8~1 ps。

Confidence limits for the mean of exponential distribution in any time-sequential samples

We present the general results determining confidence limits for the mean of exponential distribution in any time-sequential samples, which are obtained in any sequential life tests with replacement or without replacement. Especially, we give the best lower confidence limits in the case of no failure data.

卫星监测任务仿真的序贯试验设计方法研究

针对卫星监测任务仿真试验在复杂试验空间中无法兼顾样本点数量和模型精度的问题,提出一种基于点密度和局部非线性度的卫星仿真试验混合序贯试验设计方法。使用Voronoi划分描述离散点分布密度,借助泰勒展开式及样本点邻域梯度信息衡量非线性度,将两者组合计算综合指标,对样本点进行排序,进而添加新的样本点,直到满足停止准则。仿真结果表明:相比均匀设计和拉丁超立方设计,序贯试验设计方法在函数算例上可以在较少样本点的情况下达到停止准则。在仿真系统数据上,误差指标RMSE分别提高44.88%和38.77%,MAPE分别提高47.07%和37.4%。序贯试验设计方法相较于传统试验设计在样本点采集上可以有效降低采样数量,提升模型精度。

On-line solid-phase extraction using alizarin violet functionalized silica gel for determination of trace lead in environmental samples and wastewater by sequential injection spectrophotometry

Trace of Pb(Ⅱ) has been on-line separated and enriched from environmental samples and wastewater by using the self-made alizarin violet functionalized silica gel micro-column coupling with a sequential injection sampling technology. The determination is based on the color reaction of Pb(Ⅱ) with iodide and crystal violet to form an ionic association complex in the presence of polyvinyl alcohol and hydrochloric acid. The use of the microcolumn can prevent the interference of most familiar metal ions, and therefore improve the selectivity and sensitivity of this analytical technique. The proposed method was used for the determination of Pb(Ⅱ) in environmental samples and wastewater. No statistically significant difference was observed between the results determined by the present method and atomic absorption spectrometry.