Application of in-house method for determinationof radium isotopes in environmental samples usingthe Liquid Scintillation counting technique

A method for determination of 226Ra and 228Ra in environmental samples using the α-β coincidence liquid scintillation counting (LSC) has been developed. Radium were preconcentrated from environmental samples by coprecipitation with BaSO4, then purified from others radionuclide interferences using the cation column exchange (Bio-Rad AG 50 W-X4 resin with 200-400 mesh size and H+ form) and operating in warm temperature which is between 70-80oC. Then, the Ba(Ra)SO4 precipitate was filtered through the Millipore filter paper, dried and weighed to calculate chemical yield. The activity concentration of radium isotopes in mixture of liquid scintillation cocktails were measured using LSC after being stored for over 21 days to allow the growth of the progeny nuclides. The method has been validated with a certi-fied reference material supplied by the International Atomic Energy Agency and reliable results were obtained. The radiochemical yields for radium were 59% - 90% and recovery was 97% and 80% for 226Ra and 228Ra, respectively. Sixteen seawater and fish flesh samples collected in Kapar coastal water have been analyzed with the developed method. The obtained radium activity concentrations in seawater were in the range of 02.08 ± 0.82 mBq/L to 3.69 ± 1.29 mBq/L for 226Ra and 6.01 ± 3.05 mBq/L to 17.07 ± 6.62 mBq/L for 228Ra. Meanwhile, the activity concentrations of 226Ra and 228Ra in fish flesh were in the range of 11.82 ± 5.23 – 16.53 ± 6.53 Bq/kg dry wt. and 43.52 ± 16.34 – 53.57 ± 19.86 Bq/kg dry wt., respectively.

Sample Plot Change in the Seventh Review of Continuous National Forest Inventory in Zigui County

The following qualitative conclusions of forest resources in Zigui can be drawn by the research on 73 plots and 5 vegetation plots:forest area is increasing; forest growing stock is increasing; the adjustment of forest category structure is constantly improved; forest quality has been improving; stand structure is optimized continuously; biodiversity has initially appeared.

Factors Associated with Sample Rejection for CD4+/CD8+ T Cell Count Analyses at the Kenyatta National Hospital Comprehensive Care Center Laboratory, Kenya

Background: The appropriate time to initiate antiretroviral therapy (ART) in HIV/AIDS patients is determined by measurement of CD4+/CD8+ T cell count. The CD4/CD8+ T cell count is also useful, together with viral load, in monitoring disease progression and effectiveness treatment regimens. Several factors may contribute to sample rejection during the CD4+/CD8+ T cells count, resulting in negative effects on patient management. Objective: Evaluate the causes for CD4+CD8+ T cell count sample rejection at the Kenyatta National Hospital Comprehensive Care Center Laboratory. Method: A retrospective cross-sectional study was conducted between 2018 and 2020. Data was obtained from the “rejected samples” for PartecR FlowCyp flow cytometry file. Designed data collection sheet was used for data capture. A total of 3972 samples were submitted for CD4+/CD8+ T cell count during the study period. Causes for sample rejection were numbered 1 to 12, each representing a reason for sample rejection. Number 1 was sub-categorized into clotted, hemolyzed, short-draw and lipemic. Data was analyzed using excel, and presented using tables, graphs and pie charts. Approval to conduct the study was obtained from KNH/UoN ERC. Results: In the study period, 81/3972 (2.0%) samples were rejected. Samples submitted more than 48 hours after collection were mostly rejected. Other factors included improper collection technique, delayed testing, patient identification error and incorrect use of vacutainer. A combination of clotted samples, specimen submission more than 48 hours caused the most frequent sample rejection, followed with combination of specimen submission more than 48 hours, delayed testing and delayed specimen processing. Together, clotted samples, incorrect vacutainer and poor specimen label caused the least sample rejection. Conclusion: Sample rejection rate for CD4/CD8+ T cell count was relatively low, and multiple factors contributed to rejection. However, improved quality assurance will enable more benefit to patients who seek this test in the laboratory.

基于Counting Bloom Filter的流抽样算法研究

为适应高速网络环境并实现对网络流量的准确测量,提出一种将计数型布隆过滤器结构与基于报文的流抽样技术相结合的网络流等概率抽样算法。利用4 bit的Counter向量识别是否有新流出现,通过实时调整抽样频率弥补新流判定中的错误率,从而对网络流进行等概率抽样并获取较真实的网络流分布情况。实验结果表明,该算法的测量结果与网络流真实值较接近,且具有可扩展性,可以满足当前复杂多变的高速网络环境下的流量测量需求。

Comparison of the local pivotal method and systematic sampling for national forest inventories

Background:The local pivotal method(LPM)utilizing auxiliary data in sample selection has recently been proposed as a sampling method for national forest inventories(NFIs).Its performance compared to simple random sampling(SRS)and LPM with geographical coordinates has produced promising results in simulation studies.In this simulation study we compared all these sampling methods to systematic sampling.The LPM samples were selected solely using the coordinates(LPMxy)or,in addition to that,auxiliary remote sensing-based forest variables(RS variables).We utilized field measurement data(NFI-field)and Multi-Source NFI(MS-NFI)maps as target data,and independent MS-NFI maps as auxiliary data.The designs were compared using relative efficiency(RE);a ratio of mean squared errors of the reference sampling design against the studied design.Applying a method in NFI also requires a proven estimator for the variance.Therefore,three different variance estimators were evaluated against the empirical variance of replications:1)an estimator corresponding to SRS;2)a Grafström-Schelin estimator repurposed for LPM;and 3)a Matérn estimator applied in the Finnish NFI for systematic sampling design.Results:The LPMxy was nearly comparable with the systematic design for the most target variables.The REs of the LPM designs utilizing auxiliary data compared to the systematic design varied between 0.74–1.18,according to the studied target variable.The SRS estimator for variance was expectedly the most biased and conservative estimator.Similarly,the Grafström-Schelin estimator gave overestimates in the case of LPMxy.When the RS variables were utilized as auxiliary data,the Grafström-Schelin estimates tended to underestimate the empirical variance.In systematic sampling the Matérn and Grafström-Schelin estimators performed for practical purposes equally.Conclusions:LPM optimized for a specific variable tended to be more efficient than systematic sampling,but all of the considered LPM designs were less efficient than the systematic sampling design for some target variables.The Grafström-Schelin estimator could be used as such with LPMxy or instead of the Matérn estimator in systematic sampling.Further studies of the variance estimators are needed if other auxiliary variables are to be used in LPM.

Reduction of the Beam Hardening Artifacts in the X-Ray Computer Tomography: Energy Discrimination with a Photon-Counting Detector

The material identification is a pressing requirement for the sensitive security applications. Dual-energy X-ray computer tomography (DXCT) has been investigated for material identification in the medical and security fields. It requires two tomographic images at sufficiently different energies. To discriminate dangerous materials of light elements such as plastic bombs in luggage, it is needed to measure accurately with several tens of kilo electron volts where such materials exhibit significant spectral differences. However, CT images in that energy region often include artifacts from beam hardening. To reduce these artifacts, a novel reconstruction method has been investigated. It is an extension of the Al-gebraic Reconstruction Technique and Total Variation (ART-TV) method that reduces the artifacts in a lower-energy CT image by referencing it to an image obtained at higher energy. The CT image of a titanium sample was recon-structed using this method in order to demonstrate the artifact reduction capability.

碳排放清单在天然气场站应用的不确定性研究

温室气体清单编制指南为有效推进节能减排措施提供了重要参考和政策支撑,但目前各清单标准不一、推荐缺省值的偏差是碳排放计算误差的主要来源。从计算方法的角度建立研究排放清单不确定性的模型,通过对比采用蒙特卡洛模拟法和拉丁超立方抽样法对不确定性因素进行模拟的结果;提出采用消减技术找到具有代表性典型数据的方法,改善仅采用平均值计算碳排放量带来的误差。将模型应用于阿克苏管理站2022年碳排放计算中,依据不同清单计算造成的碳排放不确定度在1%~4%,采用不确定性模型计算的碳排放结果比确定性模型计算的碳排放量少,更具代表性,有助于企业更早实现“双碳”目标。

基于耦合推断的时间外延性辅助数据偏倚修正——以森林蓄积量估计为例

【目的】1)量化“过期”模型对推断总体参数(总体均值、方差)的影响;2)提出利用基于模型辅助的估计量修正模型保质期引起的推断偏倚;3)评估度量误差模型对推断偏倚的修正作用。【方法】在基于设计、基于模型和基于模型辅助3种推断框架下,应用度量误差模型,修正“过期”模型对总体参数(总体均值、方差)的影响。【结果】1)将二阶抽样的估计值作为参照,对比基于模型的统计推断估计值,无论是线性回归模型还是度量误差模型,其均值估计值与二阶抽样下总体均值估计值6.774 m^(3)·hm^(−2)接近,其方差估计值的平均值为0.117,远小于基于设计的方差估计值0.965,精度提升平均为87.93%;2)遥感数据“过期”引起模型失效,对总体均值估计产生较大偏差,总体均值估计值的偏移程度随着遥感数据获取时间的推移加剧;3)在基于模型辅助推断框架下,线性回归模型和度量误差模型的总体均值估计值均在6.5~6.8波动,波动范围较小,变化趋势相同,二者均值估计值差异较小,后者推断精度更高,精度提升范围为5.71%~22.50%,平均为13.34%。【结论】1)遥感数据作为辅助信息可有效提高估计精度;2)当遥感数据“过期”时模型失效,总体均值估计值偏倚增大,方差估计值被低估;3)基于模型辅助的统计推断可解决模型“过期”造成的推断偏倚问题,保持估计量近似无偏性,且精度与概率样本的样本量呈正相关;4)度量误差模型可降低总体方差估计值,但仅使用度量误差模型无法消除时间外延性遥感数据产生的推断偏倚。

针对大规模动态图流三角形计数的边哈希分布式抽样算法

三角形计数是大图分析的一个经典问题,近年的研究工作主要集中在针对静态流式图的三角形数量估计上,相关流式图抽样算法只能处理边的插入操作,无法处理边的删除操作;而现有的动态流式图抽样算法估计准确性又偏低.针对上述问题,提出了基于边哈希分配的分布式抽样(edge hashing assignmentbased distributed sampling,EHADS)算法,它是一个用于估计动态流式图中三角形数量的分布式流算法,可以快速准确地估计动态流式图中的全局三角形数量以及每个顶点的局部三角形数量.EHADS算法只对输入的图流进行1次处理,并在多台机器上对边进行抽样.与先进的单机流算法相比,EHADS算法具有2点优势:1)在相同样本容量的情况下,EHADS算法以更短的运行时间获得了更小的估计误差,估计全局三角形数量的误差平均降低了31.79%,估计局部三角形数量的误差平均降低了23.35%;2)EHADS算法能够提供流式图中三角形数量的无偏估计,并且严格的数学证明显示该无偏估计具有更小的方差.

常见商业化猪精子计数板使用效果与测试分析

精子计数板在常规精液品质检测过程中必不可少,其搭配常用的精液检测系统如Minitube AndroVision等,可快速、简便地对精液各项指标进行检测。但目前市场上精子计数板品牌众多,不同的品牌品质也有较大差距,具体表现在对精子活力、运动速度以及运动能力等精液品质相关指标的影响方面。本研究主要比对市面上常见的8款精子计数板对同一精液样品检测效果及加样后10 min各品牌精液品质变化情况,研究结果显示品牌A的精子计数板效果最好,其所测定的精子活力可达85.89%,品牌D的精子计数板效果最差,其所测定的精子活力仅51.63%,显著低于其他品牌所测结果。综上,不同品牌精子计数板品质存在差异,检测结果也存在差异。

神经网络在库存物资缺陷智能抽检中的应用

已有的库存物资缺陷智能抽检过程存在准确率低、耗时长的问题,为解决这些问题,提出一种将神经网络应用于库存物资缺陷智能抽检中的方法。在采集库存物资图像的基础上,利用小波阈值的方式对图像实施去噪处理,并通过量化处理以及小波逆变换实现图像重构;对像素进行相关性分析,提取目标对象及对应场景信息,描述库存物资缺陷图像的局部特征;通过图像增强扩充样本数据,利用以遗传算法优化后的卷积神经网络实现库存物资缺陷智能抽检。实验结果表明,神经网络可以快速准确地实现库存物资缺陷智能抽检。

Cross-classes domain inference with network sampling for natural resource inventory

There are two distinct types of domains,design-and cross-classes domains,with the former extensively studied under the topic of small-area estimation.In natural resource inventory,however,most classes listed in the condition tables of national inventory programs are characterized as cross-classes domains,such as vegetation type,productivity class,and age class.To date,challenges remain active for inventorying cross-classes domains because these domains are usually of unknown sampling frame and spatial distribution with the result that inference relies on population-level as opposed to domain-level sampling.Multiple challenges are noteworthy:(1)efficient sampling strategies are difficult to develop because of little priori information about the target domain;(2)domain inference relies on a sample designed for the population,so within-domain sample sizes could be too small to support a precise estimation;and(3)increasing sample size for the population does not ensure an increase to the domain,so actual sample size for a target domain remains highly uncertain,particularly for small domains.In this paper,we introduce a design-based generalized systematic adaptive cluster sampling(GSACS)for inventorying cross-classes domains.Design-unbiased Hansen-Hurwitz and Horvitz-Thompson estimators are derived for domain totals and compared within GSACS and with systematic sampling(SYS).Comprehensive Monte Carlo simulations show that(1)GSACS Hansen-Hurwitz and Horvitz-Thompson estimators are unbiased and equally efficient,whereas thelatter outperforms the former for supporting a sample of size one;(2)SYS is a special case of GSACS while the latter outperforms the former in terms of increased efficiency and reduced intensity;(3)GSACS Horvitz-Thompson variance estimator is design-unbiased for a single SYS sample;and(4)rules-ofthumb summarized with respect to sampling design and spatial effect improve precision.Because inventorying a mini domain is analogous to inventorying a rare variable,alternative network sampling procedures are also readily available for inventorying cross-classes domains.

A note on the estimation of variance for big BAF sampling

Background:The double sampling method known as“big BAF sampling”has been advocated as a way to reduce sampling effort while still maintaining a reasonably precise estimate of volume.A well-known method for variance determination,Bruce’s method,is customarily used because the volume estimator takes the form of a product of random variables.However,the genesis of Bruce’s method is not known to most foresters who use the method in practice.Methods:We establish that the Taylor series approximation known as the Delta method provides a plausible explanation for the origins of Bruce’s method.Simulations were conducted on two different tree populations to ascertain the similarities of the Delta method to the exact variance of a product.Additionally,two alternative estimators for the variance of individual tree volume-basal area ratios,which are part of the estimation process,were compared within the overall variance estimation procedure.Results:The simulation results demonstrate that Bruce’s method provides a robust method for estimating the variance of inventories conducted with the big BAF method.The simulations also demonstrate that the variance of the mean volume-basal area ratios can be computed using either the usual sample variance of the mean or the ratio variance estimators with equal accuracy,which had not been shown previously for Big BAF sampling.Conclusions:A plausible explanation for the origins of Bruce’s method has been set forth both historically and mathematically in the Delta Method.In most settings,there is evidently no practical difference between applying the exact variance of a product or the Delta method—either can be used.A caution is articulated concerning the aggregation of tree-wise attributes into point-wise summaries in order to test the correlation between the two as a possible indicator of the need for further covariance augmentation.

Interval sampling methods in Zagros forests using GIS

In this study, for accuracy and cost an optimal inventory method was examined and introduced to obtain information about Zagros forests, Iran. For this purpose,three distance sampling methods(compound, order distance and random-pairs) in 5 inventory networks(100 m × 100 m, 100 m × 150 m, 100 m × 200 m,150 m × 150 m, 200 m × 200 m) were implemented in GIS environment, and the related statistical analyses were carried out. Average tree density and canopy cover in hectare with 100% inventory were compared to each other.All the studied methods were implemented in 30 inventory points, and the implementation time of each was recorded.According to the results, the best inventory methods for estimating density and canopy cover were compound150 m × 150 m and 100 m × 100 m methods, respectively. The minimum amount of product inventory time per second(T), and(E%)2 square percent of inventory error of sampling for the compound 150 m × 150 m method regarding density in hectare was 691.8, and for the compound 100 m × 100 m method regarding canopy of 12,089 ha. It can be concluded that compound method is the best for estimating density and canopy features of the forests area.

A spatially-explicit count data regression for modeling the density of forest cockchafer(Melolontha hippocastani) larvae in the Hessian Ried(Germany)

Background： In this paper, a regression model for predicting the spatial distribution of forest cockchafer larvae in the Hessian Ried region （Germany） is presented. The forest cockchafer, a native biotic pest, is a major cause of damage in forests in this region particularly during the regeneration phase. The model developed in this study is based on a systematic sample inventory of forest cockchafer larvae by excavation across the Hessian Ried. These forest cockchafer larvae data were characterized by excess zeros and overdispersion. Methods： Using specific generalized additive regression models, different discrete distributions, including the Poisson, negative binomial and zero-inflated Poisson distributions, were compared. The methodology employed allowed the simultaneous estimation of non-linear model effects of causal covariates and, to account for spatial autocorrelation, of a 2-dimensional spatial trend function. In the validation of the models, both the Akaike information criterion （AIC） and more detailed graphical procedures based on randomized quantile residuals were used. Results： The negative binomial distribution was superior to the Poisson and the zero-inflated Poisson distributions, providing a near perfect fit to the data, which was proven in an extensive validation process. The causal predictors found to affect the density of larvae significantly were distance to water table and percentage of pure clay layer in the soil to a depth of I m. Model predictions showed that larva density increased with an increase in distance to the water table up to almost 4 m, after which it remained constant, and with a reduction in the percentage of pure clay layer. However this latter correlation was weak and requires further investigation. The 2-dimensional trend function indicated a strong spatial effect, and thus explained by far the highest proportion of variation in larva density. Conclusions： As such the model can be used to support forest practitioners in their decision making for regeneration and forest protection planning in the Hessian predicting future spatial patterns of the larva density is still comparatively weak. Ried. However, the application of the model for somewhat limited because the causal effects are

An approximate point-based alternative for the estimation of variance under big BAF sampling

Background:A new variance estimator is derived and tested for big BAF(Basal Area Factor)sampling which is a forest inventory system that utilizes Bitterlich sampling(point sampling)with two BAF sizes,a small BAF for tree counts and a larger BAF on which tree measurements are made usually including DBHs and heights needed for volume estimation.Methods:The new estimator is derived using the Delta method from an existing formulation of the big BAF estimator as consisting of three sample means.The new formula is compared to existing big BAF estimators including a popular estimator based on Bruce’s formula.Results:Several computer simulation studies were conducted comparing the new variance estimator to all known variance estimators for big BAF currently in the forest inventory literature.In simulations the new estimator performed well and comparably to existing variance formulas.Conclusions:A possible advantage of the new estimator is that it does not require the assumption of negligible correlation between basal area counts on the small BAF factor and volume-basal area ratios based on the large BAF factor selection trees,an assumption required by all previous big BAF variance estimation formulas.Although this correlation was negligible on the simulation stands used in this study,it is conceivable that the correlation could be significant in some forest types,such as those in which the DBH-height relationship can be affected substantially by density perhaps through competition.We derived a formula that can be used to estimate the covariance between estimates of mean basal area and the ratio of estimates of mean volume and mean basal area.We also mathematically derived expressions for bias in the big BAF estimator that can be used to show the bias approaches zero in large samples on the order of 1n where n is the number of sample points.

Influence of sampling intensity on performance of two-phase forest inventory using airborne laser scanning

Background: Forest inventories have always been a primary information source concerning the forest ecosystem state. Various applied survey approaches arise from the numerous important factors during sampling scheme planning. Paramount aspects include the survey goal and scale, target population inherent variation and patterns,and available resources. The last factor commonly inhibits the goal, and compromises have to be made. Airborne laser scanning(ALS) has been intensively tested as a cost-effective option for forest inventories. Despite existing foundations, research has provided disparate results. Environmental conditions are one of the factors greatly influencing inventory performance. Therefore, a need for site-related sampling optimization is well founded.Moreover, as stands are the basic operational unit of managed forest holdings, few related studies have presented stand-level results. As such, herein, we tested the sampling intensity influence on the performance of the ALSenhanced stand-level inventory.Results: Distributions of possible errors were plotted by comparing ALS model estimates, with reference values derived from field surveys of 3300 sample plots and more than 300 control stands located in 5 forest districts. No improvement in results was observed due to the scanning density. The variance in obtained errors stabilized in the interval of 200–300 sample plots, maintaining the bias within +/-5% and the precision above 80%. The sample plot area affected scores mostly when transitioning from 100 to 200 m2. Only a slight gain was observed when bigger plots were used.Conclusions: ALS-enhanced inventories effectively address the demand for comprehensive and detailed information on the structure of single stands over vast areas. Knowledge of the relation between the sampling intensity and accuracy of ALS estimates allows the determination of certain sampling intensity thresholds. This should be useful when matching the required sample size and accuracy with available resources. Site optimization may be necessary, as certain errors may occur due to the sampling scheme, estimator type or forest site, making these factors worth further consideration.

Two Contemporary and Efficient Two-Stage Sampling Methods for Estimating the Volume of Forest Stands: A Brief Overview and Unified Mathematical Description

Big Basal Area Factor (Big BAF) and Point-3P are two-stage sampling methods. In the first stage the sampling units, in both methods, are Bitterlich points where the selection of the trees is proportional to their basal area. In the second stage, sampling units are trees which are a subset of the first stage trees. In the Big BAF method, the probability of selecting trees in the second stage is made proportional to the two BAFs’ ratio, with a basal area factor larger than that of the first stage. In the Point-3P method the probability of selecting trees, in the second stage, is based on the height prediction and use of a specific random number table. Estimates of the forest stands’ volume and their sampling errors are based on the theory of the product of two random variables. The increasing error in the second stage is small, but the total cost of measuring the trees is much smaller than simply using the first stage, with all the trees measured. In general, the two sampling methods are modern and cost-effective approaches that can be applied in forest stand inventories for forest management purposes and are receiving the growing interest of researchers in the current decade.

Optimizing Forest Sampling by Using Lagrange Multipliers

In two-phase sampling, or double sampling, from a population with size N we take one, relatively large, sample size n. From this relatively large sample we take a small sub-sample size m, which usually costs more per sample unit than the first one. In double sampling with regression estimators, the sample of the first phase n is used for the estimation of the average of an auxiliary variable X, which should be strongly related to the main variable Y (which is estimated from the sub-sample m). Sampling optimization can be achieved by minimizing cost C with fixed var Y, or by finding a minimum var Y for fixed C. In this paper we optimize sampling with use of Lagrange multipliers, either by minimizing variance of Y and having predetermined cost, or by minimizing cost and having predetermined variance of Y.

智能化库房建设在医用耗材管理模式优化中的价值研究

目的:探讨智能化库房建设在医用耗材管理模式优化中的应用价值。方法:设计智能化库房管理系统,构建数据采集、传输、存储和终端显示的拓扑结构,实现系统信息管理、医用耗材计入、物资信息管理、计入数据核对及导出等功能,改善医用耗材库房管理方式。选取医院临床常规使用的1949种医用耗材,根据库房管理模式不同将其分为对照组(963种)和观察组(986种),对照组库房管理采用半手工方式,观察组采用智能化库房管理方式,对比两种方式的库房管理效率和管理质量。结果:观察组医用耗材的入库、出库、信息核对和查阅统计用时分别为(1.29±0.51)h、(0.64±0.25)h、(0.36±0.08)h和(0.84±0.38)h,均少于对照组,差异有统计学意义(t=6.205,t=3.966,t=11.440,t=7.324;P<0.05);观察组Ⅰ级、Ⅱ级和Ⅲ级医用耗材的库存剩余量和临时采购频次分别为(224.98±94.93)件和(3.15±1.56)次、(218.13±96.58)件和(3.62±1.22)次、(259.51±68.02)件和(1.52±0.89)次,均低于对照组,差异有统计学意义(t=5.094,t=5.126,t=5.461,t=4.336,t=3.254,t=2.970;P<0.05)。结论:智能化库房管理模式可减少医用耗材入库出库时间,提高数据采集的准确性,降低库存管理压力。