How do the landslide and non-landslide sampling strategies impact landslide susceptibility assessment? d A catchment-scale case study from China

The aim of this study is to investigate the impacts of the sampling strategy of landslide and non-landslide on the performance of landslide susceptibility assessment(LSA).The study area is the Feiyun catchment in Wenzhou City,Southeast China.Two types of landslides samples,combined with seven non-landslide sampling strategies,resulted in a total of 14 scenarios.The corresponding landslide susceptibility map(LSM)for each scenario was generated using the random forest model.The receiver operating characteristic(ROC)curve and statistical indicators were calculated and used to assess the impact of the dataset sampling strategy.The results showed that higher accuracies were achieved when using the landslide core as positive samples,combined with non-landslide sampling from the very low zone or buffer zone.The results reveal the influence of landslide and non-landslide sampling strategies on the accuracy of LSA,which provides a reference for subsequent researchers aiming to obtain a more reasonable LSM.

Sampling Strategy Within a Wild Soybean Population Based on Its Genetic Variation Detected by ISSR Markers

In order to determine an appropriate sampling strategy for the effective conservation of wild soybean (Glycine soja Sieb. et Zucc.) in China, a natural population from Jiangwan Airport in Shanghai was studied for its genetic diversity through the inter-simple sequence repeat (ISSR) marker analysis of a sample set consisting of 100 randomly collected individuals. A relatively large genetic diversity was detected among the samples based on estimation of DNA products amplified from 15 selected ISSR primers, with the similarity coefficient varying from 0.17 to 0.89. The mean expected heterozygosity (He) was 0.171 4 per locus, and Shannon index (1) was 0.271 4. The Principal Coordinate Analysis (PCA) further indicated that genetic diversity of the Jiangwan wild soybean population was not evenly distributed, instead, was presented by a mosaic or clustered distribution pattern. Correlation study between genetic diversity and number of samples demonstrated that genetic diversity increased dramatically with the increase of number of samples within 40 individuals, but the increase became slow and rapidly reached a plateau when more than 40 individuals were included in the analysis. It is concluded that (i) a sample set of approximately 35-45 individuals should be included to represent possibly high genetic diversity when conservation of a wild soybean population ex situ is undertaken; and (ii) collection of wild soybean samples should be spread out as wide as possible within a population, and a certain distance should be kept as intervals among individuals for sampling.

Sampling Strategies for Soil Available K and P at Field Scale

Field nutrient distribution maps obtained from the study on soil variations within fields are the basis of precision agriculture. The quality of these maps for management depends on the accuracy of the predicted values, which depends on the initial sampling. To produce reliable predictions efficiently the minimal sampling size and combination should be decided firstly, which could avoid the misspent funds for field sampling work. A 7.9 hectare silage field close to the Agricultural Research institute at Hillsborough, Northern Ireland, was selected for the study. Soil samples were collected from the field at 25 m intervals in a rectangular grid to provide a database of selected soil properties. Different data combinations were subsequently abstracted from this database for comparison purposes, and ordinary kriging used to produce interpolated soil maps. These predicted data groups were compared using least significant difference (LSD) test method. The results showed that the 62 sampling sizes of triangle arrangement for soil available K were sufficient to reach the required accuracy. The triangular sample combination proved to be superior to a rectangular one of similar sample size.

Comparison of different sampling strategies for debris flow susceptibility mapping: A case study using the centroids of the scarp area, flowing area and accumulation area of debris flow watersheds

The quality of debris flow susceptibility mapping varies with sampling strategies. This paper aims at comparing three sampling strategies and determining the optimal one to sample the debris flow watersheds. The three sampling strategies studied were the centroid of the scarp area(COSA), the centroid of the flowing area(COFA), and the centroid of the accumulation area(COAA) of debris flow watersheds. An inventory consisting of 150 debris flow watersheds and 12 conditioning factors were prepared for research. Firstly, the information gain ratio(IGR) method was used to analyze the predictive ability of the conditioning factors. Subsequently, 12 conditioning factors were involved in the modeling of artificial neural network(ANN), random forest(RF) and support vector machine(SVM). Then, the receiver operating characteristic curves(ROC) and the area under curves(AUC) were used to evaluate the model performance. Finally, a scoring system was used to score the quality of the debris flow susceptibility maps. Samples obtained from the accumulation area have the strongest predictive ability and can make the models achieve the best performance. The AUC values corresponding to the best model performance on the validation dataset were 0.861, 0.804 and 0.856 for SVM, ANN and RF respectively. The sampling strategy of the centroid of the scarp area is optimal with the highest quality of debris flow susceptibility maps having scores of 373470, 393241 and 362485 for SVM, ANN and RF respectively.

Effects of sampling strategies and DNA extraction methods on eDNA metabarcoding: A case study of estuarine fish diversity monitoring

Environmental DNA(eDNA)integrated with metabarcoding is a promising and powerful tool for species composition and biodiversity assessment in aquatic ecosystems and is increasingly applied to evaluate fish diversity.To date,however,no standardized eDNA-based protocol has been established to monitor fish diversity.In this study,we investigated and compared two filtration methods and three DNA extraction methods using three filtration water volumes to determine a suitable approach for eDNA-based fish diversity monitoring in the Pearl River Estuary(PRE),a highly anthropogenically disturbed estuarine ecosystem.Compared to filtration-based precipitation,direct filtration was a more suitable method for eDNA metabarcoding in the PRE.The combined use of DNeasy Blood and Tissue Kit(BT)and traditional phenol/chloroform(PC)extraction produced higher DNA yields,amplicon sequence variants(ASVs),and Shannon diversity indices,and generated more homogeneous and consistent community composition among replicates.Compared to the other combined protocols,the PC and BT methods obtained better species detection,higher fish diversity,and greater consistency for the filtration water volumes of 1000 and 2000 mL,respectively.All eDNA metabarcoding protocols were more sensitive than bottom trawling in the PRE fish surveys and combining two techniques yielded greater taxonomic diversity.Furthermore,combining traditional methods with eDNA analysis enhanced accuracy.These results indicate that methodological decisions related to eDNA metabarcoding should be made with caution for fish community monitoring in estuarine ecosystems.

Evaluation of sampling strategies to estimate crown biomass

Background：Depending on tree and site characteristics crown biomass accounts for a significant portion of the total aboveground biomass in the tree.Crown biomass estimation is useful for different purposes including evaluating the economic feasibility of crown utilization for energy production or forest products,fuel load assessments and fire management strategies,and wildfire modeling.However,crown biomass is difficult to predict because of the variability within and among species and sites.Thus the allometric equations used for predicting crown biomass should be based on data collected with precise and unbiased sampling strategies.In this study,we evaluate the performance different sampling strategies to estimate crown biomass and to evaluate the effect of sample size in estimating crown biomass.Methods：Using data collected from 20 destructively sampled trees,we evaluated 11 different sampling strategies using six evaluation statistics：bias,relative bias,root mean square error（RMSE）,relative RMSE,amount of biomass sampled,and relative biomass sampled.We also evaluated the performance of the selected sampling strategies when different numbers of branches（3,6,9,and 12）are selected from each tree.Tree specific log linear model with branch diameter and branch length as covariates was used to obtain individual branch biomass.Results：Compared to all other methods stratified sampling with probability proportional to size estimation technique produced better results when three or six branches per tree were sampled.However,the systematic sampling with ratio estimation technique was the best when at least nine branches per tree were sampled.Under the stratified sampling strategy,selecting unequal number of branches per stratum produced approximately similar results to simple random sampling,but it further decreased RMSE when information on branch diameter is used in the design and estimation phases.Conclusions：Use of auxiliary information in design or estimation phase reduces the RMSE produced by a sampling strategy.However,this is attained by having to sample larger amount of biomass.Based on our finding we would recommend sampling nine branches per tree to be reasonably efficient and limit the amount of fieldwork.

A study on sampling strategies in the figure cognitive process

This study was aimed at investigating the sampling strategies for 2 types of figures: 3-D cubes and human faces. The research was focused on: (a) from where the sampling process started; (b) in what order the figures' features were sampled. The study consisted of 2 experiments: (a) sampling strategies for 3-D cubes; (b) sampling strategies for human faces. The results showed that: (a), for 3-D cubes, the first sampling was mostly located at the outline parts, rarely at the center part; while for human faces, the first sampling was mostly located at the hair and outline parts, rarely at the mouth or cheek parts, in most cases, the first sampling-position had no significant effects on cognitive performance and that (b), the sampling order, both for 3-D cubes and for human faces, was determined by the degree of difference among the sampled-features.

Potential-Decomposition Strategy in Markov Chain Monte Carlo Sampling Algorithms

We introduce the potential-decomposition strategy （PDS）, which can be used in Markov chain Monte Carlo sampling algorithms. PDS can be designed to make particles move in a modified potential that favors diffusion in phase space, then, by rejecting some trial samples, the target distributions can be sampled in an unbiased manner. Furthermore, if the accepted trial samples are insumcient, they can be recycled as initial states to form more unbiased samples. This strategy can greatly improve efficiency when the original potential has multiple metastable states separated by large barriers. We apply PDS to the 2d Ising model and a double-well potential model with a large barrier, demonstrating in these two representative examples that convergence is accelerated by orders of magnitude.

Research of matrix converter based on asymmetric regular sampling method SPWM control strategy

Based on the topological analysis of three-phase matrix AC to AC conversion circuit, an AC to AC nine-switch matrix isequivalent to rectification part and conversion part. The Matrix converter can be viewed as AC-DC-AC converter, the asymmetricregular sampling method SPWM(Sine Pulse Width Modulation) is studied and applied in the three-phase matrix AC to AC converter,Based on Matlab/simulink the simulation of the matrix converter with such strategy is carried out. Inductive load simulation is carriedout on the matrix converter prototype. The simulation results verify the workability of the asymmetric regular sampling method SPWMstrategy for matrix converter.

A simulation study of sampling time point designing for therapeutic drug monitoring based on a population pharmacokinetic model

Aim To develop a method to estimate population pharmacokinetic parameters with the limited sampling time points provided clinically during therapeutic drug monitoring. Methods Various simulations were attempted using a one-compartment open model with the first order absorption to determine PK parameter estimates with different sampling strategies as a validation of the method. The estimated parameters were further verified by comparing to the observed values. Results The samples collected at the single time point close to the non-informative sampling time point designed by this method led to bias and inaccurate parameter estimations. Furthermore, the relationship between the estimated non-informative sampling time points and the values of the parameter was examined. The non-informative sampling time points have been developed under some typical occasions and the results were plotted to show the tendency. As a result, one non-informative time point was demonstrated to be appropriate for clearance and two for both volume of distribution and constant of absorption in the present study. It was found that the estimates of the non-informative sampling time points developed in the method increase with increases of volume of distribution and the decrease of clearance and constant of absorption. Conclusion A rational sampling strategy during therapeutic drug monitoring can be established using the method present in the study.

Estimating the macrobenthic species richness with an optimized sampling design in the intertidal zone of Changjiang Estuary

It is a challenge in the field sampling to face conflict between the statistical requirements and the logistical constraints when explicitly estimating the macrobenthos species richness in the heterogeneous intertidal wetlands. To solve this problem, this study tried to design an optimal, efficient and practical sampling strategy by comprehensively focusing on the three main parts of the entire process(to optimize the sampling method, to determine the minimum sampling effort and to explore the proper sampling interval) in a typical intertidal wetland of the Changjiang(Yangtze) Estuary, China. Transect sampling was selected and optimized by stratification based on pronounced habitat types(tidal flat, tidal creek, salt marsh vegetation). This type of sampling is also termed within-transect stratification sampling. The optimal sampling intervals and the minimum sample effort were determined by two beneficial numerical methods: Monte Carlo simulations and accumulative species curves. The results show that the within-transect stratification sampling with typical habitat types was effective for encompassing 81% of the species, suggesting that this type of sampling design can largely reduce the sampling effort and labor. The optimal sampling intervals and minimum sampling efforts for three habitats were determined: sampling effort must exceed 1.8 m^2 by 10 m intervals in the salt marsh vegetation, 2 m^2 by 10 m intervals in the tidal flat, and 3 m^2 by 1 m intervals in the tidal creek habitat. It was suggested that the differences were influenced by the mobility range of the dominant species and the habitats' physical differences(e.g., tidal water, substrate, vegetation cover). The optimized sampling strategy could provide good precision in the richness estimation of macrobenthos and balance the sampling effort. Moreover, the conclusions presented here provide a reference for recommendations to consider before macrobenthic surveys take place in estuarine wetlands. The sampling strategy, focusing on the three key parts of the sampling design, had a good operational effect and could be used as a guide for field sampling for habitat management or ecosystem assessment.

Adaptive Radial Based Sampling for Reliability and Reliability Sensitivity Analysis Involving Truncated Correlated Normal Variables

The reliability and reliability sensitivity （ RS ） models are presented for the engineering problem involving truncated correlated normal variables （CNV）, and in the case an adaptive radial based sampling is used to analyze the reliability and the RS. In the presented models, the truncated CNV is transformed to general CNV, and the value domains of the truncated CNV are treated as multiple failure modes, then the reliability and the RS with the truncated CNV are transformed to the general cases, on which an e^cient radial based sampling is used to analyze the trans- formed reliability and RS. An adaptive strategy is employed to search for the optimal radial in the sampling, by which the robustness of the method is improved. After the model concepts and the detailed implementation are given, several examples are presented to demonstrate the feasibility of the model and the efficiency of the solutions.

Climate Change Perceptions , Impacts and Adaptation Strategies of F arm Households in Potohar Region of Punjab, Pakistan

Climate change has become a global phenomenon and is adversely affecting agricultural development across the globe.Developing countries like Pakistan where 18.9%of the GDP(gross domestic product)came from the agriculture sector and also 42%of the labor force involved in agriculture.They are directly and indirectly affected by climate change due to an increase in the frequency and intensity of climatic extreme events such as floods,droughts and extreme weather events.In this paper,we have focused on the impact of climate change on farm households and their adaptation strategies to cope up the climatic extremes.For this purpose,we have selected farm households by using multistage stratified random sampling from four districts of the Potohar region i.e.Attock,Rawalpindi,Jhelum and Chakwal.These districts were selected by dividing the Potohar region into rain-fed areas.We have employed logistic regression to assess the determinants of adaptation to climate change and its impact.We have also calculated the marginal effect of each independent variable of the logistic regression to measure the immediate rate of change in the model.In order to check the significance of our suggested model,we have used hypothesis testing.

基于邻域采样的多任务图推荐算法

近年来,图神经网络(GNN)成为解决协同过滤的主流方法之一。它通过构建用户-物品图,模拟用户与物品的交互关系,并用GNN学习它们的特征表示。尽管现有在模型结构上的研究已取得了较大进展,但如何在图结构上更有效地进行负采样仍未有效解决。为此,提出一种基于邻域采样的多任务图推荐算法。该算法提出了一种基于GNN的邻域采样策略,该策略以每个用户为中心构建子图,将次高阶物品作为用户邻域采样的负样本,可以更有效地挖掘强负样本并提高采样质量。通过GNN对图结点进行信息聚合与特征提取,得到结点的最终嵌入表示。设计一种余弦边际损失来过滤部分冗余负样本,以有效减少采样过程中的噪声数据。同时,该算法引入了多任务策略对模型进行联合优化,以增强模型的泛化能力。在3个公开数据集上进行的大量实验表明,该算法在大多数情况下明显优于其他主流算法。

基于改进Faster RCNN的微操作空间目标检测算法

将Faster RCNN引入微操作系统的目标检测之中。针对微操作空间下待检测目标存在尺度变化和在显微镜放大倍数较小时,待检测目标尺度过小、特征不明显的问题,提出了一种基于改进Faster RCNN的微操作空间目标检测算法。使用在图像分类任务中性能优越的深度残差网络提取图像的特征。引入递归特征金字塔网络,对特征进行融合。改进区域建议网络的采样策略,对损失函数进行优化。实验结果表明:这种改进的Faster RCNN算法能有效解决由于目标尺度变化和目标尺度过小带来的问题。相比通用的目标检测算法,该算法的准确度更高,速度更快,具有实际应用价值。

肾移植患者霉酚酸暴露量预测公式比较

目的比较目前已报道的12个基于有限采样策略的霉酚酸暴露量预测公式在我院肾移植患者霉酚酸暴露量中的预测效果。方法入组20例口服吗替麦考酚酯分散片的肾移植患者,采集不同时间的全血样本,使用二维液相色谱测定霉酚酸血药浓度。采用WinNonlin软件计算霉酚酸的药代动力学参数。运用12个预测公式预测霉酚酸AUC0~12h,计算预测值与实测值的相关系数(R2)值、预测AUC0~12h平均相对预测误差和理想预测AUC0~12h占比。结果公式8的预测AUC0~12h与MPA-AUC0~12h相关性较好,R2=0.923,平均预测误差绝对值最小(约为12.00),预测AUC0~12h在理想预测范围内的占比最高(80%)。结论预测公式8在估算我院肾移植患者霉酚酸暴露量中有良好的预测效果。

自适应压缩频谱感知的采样率动态调整策略

受奈奎斯特-香农采样定理的限制,宽带频谱感知(wideband spectrum sensing,WBSS)的首要难点是采集宽带信号。根据变步长自适应滤波算法原理,基于自适应压缩频谱感知(adaptive compressed spectrum sensing,ACSS)提出了一种动态调整采样率的压缩频谱感知方法(dynamically adjust sampling rate ACSS,DASR-ACSS)。采用调制宽带转换器结构,针对传统ACSS固定步长的问题设计了步长动态调整策略。针对原始信号难以获取的问题,通过对信道占用状态相关性和检测率的变化趋势进行数据统计分析,设计了利用信道占用状态相关性的停止准则;通过对停止准则的实验数据分析,以sigmoid函数为基础设计了分段函数作为步长调整策略,利用步长补偿因子充分提升性能。仿真结果表明,与传统ACSS相比,DASR-ACSS能更快达到高检测率,能够平衡算法的实时性能和采样率压缩比性能,当信噪比在-30~30 dB时,DASR-ACSS表现出更好的检测性能。

基于多维空间离散基准的目标检测网络设计

针对目标检测网络中检测头的设计缺少依据,不能对不同数据集进行相应优化的问题,用一至四维的空间离散基准和正样本点(组)分配冲突率模型,提出了相应的设计依据。即基于位置的分配原则中,针对特定数据集的正样本点在4个回归量所组成的空间维度中的分布特点,在不高于指定的分配冲突率的条件下,以减少正负样本不均衡度和系统计算资源为目的,设计出具有最少点数检测头的规律。其本质是一个回归基准编码和正负样本分配设计的过程,进而实现检测性能与资源消耗之间的平衡。得出基于卷积目标检测网络检测头的回归基准可以采用4个回归中的任意组合,但需结合样本分配策略与数据集的正样本分配冲突率进行综合设计的结论。

基于二阶价值梯度模型强化学习的工业过程控制方法

为了实现对高延时、非线性和强耦合的复杂工业过程稳定准确的连续控制,提出了一种基于二阶价值梯度模型强化学习的控制方法。首先,该方法在模型训练过程中加入了状态价值函数的二阶梯度信息,具备更精确的函数逼近能力和更高的鲁棒性,学习迭代效率更高;其次,通过采用新的状态采样策略,可以更高效地利用模型进行策略学习。最后,通过在OpenAI的Gym公共实验环境和两个工业场景的仿真环境的实验表明:基于二阶价值梯度模型对比传统的基于最大似然估计模型,环境模型预测误差显著降低;基于二阶价值梯度模型的强化学习方法学习效率优于现有的基于模型的策略优化方法,具备更好的控制性能,并减小了控制过程中的振荡现象。可见该方法能有效地提升训练效率,同时提高工业过程控制的稳定性和准确性。

基于距离感知的金属缺陷样本标签分配算法

针对金属表面缺陷检测模型在训练过程中正负样本分配时不考虑金属表面缺陷的宽高比、对目标分布的定位能力差等问题,提出了距离感知的动态标签分配(DDA)算法。DDA不改变原有检测模型的结构也不增加计算开支,根据真实框的几何特性提出新的距离损失计算范式,以优化宽高比悬殊的回归问题,将迭代过程中的回归偏移量解码为预测框坐标,最后计算预测框、锚框和真实框三者之间综合交并比信息,动态地选择正负样本以提高训练精度。在武汉某钢厂冷轧带钢表面缺陷检测中进行了验证,并引入公开的热轧带钢表面缺陷数据集进行了泛化试验,检测效果均有显著改善,对金属表面质量规范有实际应用价值。