Conventional Sampling Approaches Research Articles

Precision enhancement in variance estimation for complex environmental populations using adaptive cluster sampling

Estimating dispersion in populations that are extremely rare, hidden, geographically clustered, and hard to access is a well-known challenge. Conventional sampling approaches tend to overestimate the variance, even though it should be genuinely reduced. In this environment, adaptive cluster sampling is considered to be the most efficient sampling technique as it provides generally a lower variance than the other conventional probability sampling designs for the assessment of rare and geographically gathered population parameters like mean, total, variance, etc. The use of auxiliary data is very common to obtain the precise estimates of the estimators by taking advantage of the correlation between the survey variable and the auxiliary data. In this article, we introduced a generalized estimator for estimating the variance of populations that are rare, hidden, geographically clustered and hard-to-reached. The proposed estimator leverages both actual and transformed auxiliary data through adaptive cluster sampling. The expressions of approximate bias and mean square error of the proposed estimator are derived up to the first-order approximation using Taylor expansion. Some special cases are also obtained using the known parameters associated with the auxiliary variable. The proposed class of estimators is compared with available estimators using simulation and real data applications.

A cost-effective skip-lot sampling scheme using loss-based capability index for product acceptance determination

Acceptance sampling plans are essential for quality control and assurance, empowering businesses to make well-informed decisions regarding lot disposition. Among these plans, the skip-lot sampling plan (SkSP) stands out as a cost-effective strategy for suppliers with consistently excellent product quality. In this article, we present an enhanced version of the SkSP, termed Cpm-based SkSP-RGSP, which incorporates a repetitive group sampling plan (RGSP) using the Taguchi capability index Cpm as its reference plan. We develop an optimization model to ensure that the proposed plan meets a two-point condition in its operating characteristic function and effectively minimizes the average sample number (ASN). Through investigations of two critical performance measures, ASN and discriminatory power, we demonstrate the superiority of our plan over conventional sampling approaches. Detailed plan parameters, including required sample size and lot sentencing criteria, are provided to assist examiners in practical implementation. Additionally, we showcase the plan's applicability and practicality through a case study. This research contributes to the field of quality control by offering an efficient and cost-effective sampling scheme for lot disposition.

Optimized uncertainty propagation across high fidelity taylor anvil simulation

In computational materials research, uncertainty analysis (more specifically, uncertainty propagation, UP) in the outcomes of model predictions is essential in order to establish confidence in the models as well as to validate them against the ground truth (experiments or higher fidelity simulations). Unfortunately, conventional UP models relying on exhaustive sampling from the distributions of input parameters may be impractical, particularly when the models are computationally expensive. In these cases, investigators must sacrifice accuracy in the propagated uncertainty by down-sampling the input distribution. Recently, a method was developed to correct for these inaccuracies by re-weighing the input distributions to create more statistically representative samples. In this work, the method is applied to computational models for the response of materials under high strain rates. The method is shown to effectively approximate converged output distributions at a lower cost than using conventional sampling approaches.

Adsorption of SARS-CoV-2 onto granular activated carbon (GAC) in wastewater: Implications for improvements in passive sampling

Based on recent studies, passive sampling is a promising method for detecting SARS-CoV-2 in wastewater surveillance (WWS) applications. Passive sampling has many advantages over conventional sampling approaches. However, the potential benefits of passive sampling are also coupled with apparent limitations. We established a passive sampling technique for detecting severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in wastewater using electronegative filters. Though, it was evident that the adsorption capacity of the filters constrained their use. This work intends to demonstrate an optimized passive sampling technique for SARS-CoV-2 in wastewater using granular activated carbon (GAC). Through bench-scale batch-adsorption studies and sewershed deployments, we established the adsorption characteristics of SARS-CoV-2 and two human feacal viruses (PMMoV and CrAssphage) onto GAC. A pseudo-second-order model best-described adsorption kinetics for SARS-CoV-2 in either deionized (DI) water and SARS-CoV-2, CrAssphage, and PMMoV in wastewater. In both laboratory batch-adsorption experiments and in-situ sewershed deployments, the maximum amount of SARS-CoV-2 adsorbed by GAC occurred at ~60 h in wastewater. In wastewater, the maximum adsorption of PMMoV and CrAssphage by GAC occurred at ~60 h. In contrast, the adsorption capacity was reached in DI water seeded with SARS-CoV-2 after ~35 h. The equilibrium assay modeled the maximum adsorption quantity (qmax) in wastewater with spiked SARS-CoV-2 concentrations using a Hybrid Langmuir-Freundlich equation, a qmax of 2.5 × 109 GU/g was calculated. In paired sewershed deployments, it was found that GAC adsorbs SARS-CoV-2 in wastewater more effectively than electronegative filters. Based on the anticipated viral loading in wastewater, bi-weekly sampling intervals with deployments up to ~96 h are highly feasible without reaching adsorption capacity with GAC. GAC offers improved sensitivity and reproducibility to capture SARS-CoV-2 RNA in wastewater, promoting a scalable and convenient alternative for capturing viral pathogens in wastewater.

Batch active learning for accelerating the development of interatomic potentials

Classical molecular dynamics (MD) has been widely used to study atomistic mechanisms and emergent behavior in materials at length and time scales beyond the capabilities of first-principles approaches. The success of classical MD simulations relies on the ability of classical interatomic potentials to accurately map complex many-body interacting systems of electrons and nuclei into effective few-body interacting systems of atoms. In practice, the development of interatomic potentials is a nontrivial process and requires considerable amount of effort. Recently, machine learning has become a promising approach to accelerate interatomic potential development. However, these machine learning approaches are often computation and data intense, as they require a large amount of training data from first-principles calculations, such as total energies, atomic forces, and stress tensors of many atomistic structures. Here we propose an active learning approach combined with first-principles theory calculations to expedite the development of machine learning interatomic potentials. In particular, we develop a batch active learning method which combines both energy uncertainty and structure similarity metrics to efficiently sample the highly uncertain structures that are difficult to predict. This active sampling approach maximizes the utility of the dataset in each batch and generates interatomic potential with highly accurate and robust model coefficients which are difficult to achieve with conventional sampling approaches. To demonstrate this batch active learning method, we develop an active learning potential for monolayer GeSe, a two-dimensional ferroelectric-ferroelastic material, and compare the quality and robustness of the active learning potential with the potential obtained from random sampling. Batch active learning method opens up avenues for accelerating the development of robust and accurate machine learning potential using a small set of atomistic structures which will be valuable for computational materials, physics, and chemistry community.

Spatial Variability and Hotspots of Methane Concentrations in a Large Temperate River

Rivers are significant sources of greenhouse gases (GHGs; e.g., CH4 and CO2); however, our understanding of the large-scale longitudinal patterns of GHG emissions from rivers remains incomplete, representing a major challenge in upscaling. Local hotspots and moderate heterogeneities may be overlooked by conventional sampling schemes. In August 2020 and for the first time, we performed continuous (once per minute) CH4 measurements of surface water during a 584-km-long river cruise along the German Elbe to explore heterogeneities in CH4 concentration at different spatial scales and identify CH4 hotspots along the river. The median concentration of dissolved CH4 in the Elbe was 112 nmol L−1, ranging from 40 to 1,456 nmol L−1 The highest CH4 concentrations were recorded at known potential hotspots, such as weirs and harbors. These hotspots were also notable in terms of atmospheric CH4 concentrations, indicating that measurements in the atmosphere above the water are useful for hotspot detection. The median atmospheric CH4 concentration was 2,033 ppb, ranging from 1,821 to 2,796 ppb. We observed only moderate changes and fluctuations in values along the river. Tributaries did not obviously affect CH4 concentrations in the main river. The median CH4 emission was 251 μmol m−2 d−1, resulting in a total of 28,640 mol d−1 from the entire German Elbe. Similar numbers were obtained using a conventional sampling approach, indicating that continuous measurements are not essential for a large-scale budget. However, we observed considerable lateral heterogeneity, with significantly higher concentrations near the shore only in reaches with groins. Sedimentation and organic matter mineralization in groin fields evidently increase CH4 concentrations in the river, leading to considerable lateral heterogeneity. Thus, river morphology and structures determine the variability of dissolved CH4 in large rivers, resulting in smooth concentrations at the beginning of the Elbe versus a strong variability in its lower parts. In conclusion, groin construction is an additional anthropogenic modification following dam building that can significantly increase GHG emissions from rivers.

Assessing the Impact of Atrazine on the Availability of Arsenic in Soils Using DGT Technique.

Arsenic (As) has been observed to co-exist with atrazine (ATR) in soils worldwide. ATR, as an organic chemical, may affect the availability of As and further influence its uptake by organisms. Here we used a novel passive sampling technique, DGT (diffusive gradients in thin-films), to compare with other two conventional sampling approaches (soil solution extraction and 'Olsen As' measurement) to investigate the influence of ATR addition (normal recommended level and contaminated level) on the availability of As in soils, to further interpret the potential risk of As in soil environment. The effect of adding ATR on the behaviour of As in soils was limited. When the concentration of ATR was much higher, the availability of As in soils was supressed, the labile pool size was also affected, but the R value did not change much. The properties of the soils also played an important role by affecting the states of the compounds.

Nuclear Dynamical Correlation Effects in X-ray Spectroscopy from a Theoretical Time-Domain Perspective.

To date X-ray spectroscopy has become a routine tool that can reveal highly local and element-specific information on the electronic structure of atoms in complex environments. Here, we focus on nuclear dynamical correlation effects in X-ray spectra and develop a rigorous time-correlation function method employing ground state classical molecular dynamics simulations. The importance of nuclear correlation phenomena is demonstrated by comparison against the results from the conventional sampling approach performed on the same data set for gas phase water. In contrast to the first-order absorption, second-order resonant inelastic scattering spectra exhibit pronounced fingerprints of nuclear motions. The developed methodology is not biased to a particular electronic structure method and, owing to its generality, can be applied to, e.g., X-ray photoelectron and Auger spectroscopies.

Utility of Compound‐Specific Isotope Analysis for Vapor Intrusion Investigations

AbstractDifferent types of data can be collected to evaluate whether or not vapor intrusion is a concern at sites impacted with volatile organic compound (VOC) contamination in the subsurface. Typically, groundwater, soil gas, or indoor air samples are collected to determine VOC concentrations in the different media. Sample results are evaluated using a “multiple lines of evidence” approach to interpret whether vapor intrusion is occurring. Data interpretation is often not straightforward because of many complicating factors, particularly in the evaluation of indoor air. More often than not, indoor air sample results are affected by indoor or other background sources making interpretation of concentration‐based data difficult using conventional sampling approaches. In this study, we explored the practicality of compound‐specific isotope analysis (CSIA) as an additional type of evidence to distinguish between indoor sources and subsurface sources (i.e., vapor intrusion). We developed a guide for decision‐making to facilitate data interpretation and applied the guidelines at four different test buildings. To evaluate the effectiveness of the CSIA method for vapor intrusion applications, we compared the interpretation from CSIA to interpretations based on data from two different investigation approaches: conventional sampling and on‐site GC/MS analysis. Interpretations using CSIA were found to be generally consistent with the other approaches. In one case, CSIA provided the strongest line of evidence that vapor intrusion was not occurring and that a VOC source located inside the building was the source of VOCs in indoor air.

Ll-ACHRB: a scalable algorithm for sampling the feasible solution space of metabolic networks.

Random sampling of the solution space has emerged as a popular tool to explore and infer properties of large metabolic networks. However, conventional sampling approaches commonly used do not eliminate thermodynamically unfeasible loops. In order to overcome this limitation, we developed an efficient sampling algorithm called loopless Artificially Centered Hit-and-Run on a Box (ll-ACHRB). This algorithm is inspired by the Hit-and-Run on a Box algorithm for uniform sampling from general regions, but employs the directions of choice approach of Artificially Centered Hit-and-Run. A novel strategy for generating feasible warmup points improved both sampling efficiency and mixing. ll-ACHRB shows overall better performance than current strategies to generate feasible flux samples across several models. Furthermore, we demonstrate that a failure to eliminate unfeasible loops greatly affects sample statistics, in particular the correlation structure. Finally, we discuss recommendations for the interpretation of sampling results and possible algorithmic improvements. Source code for MATLAB and OCTAVE including examples are freely available for download at http://www.aibn.uq.edu.au/cssb-resources under Software. Optimization runs can use Gurobi Optimizer (by default if available) or GLPK (included with the algorithm). lars.nielsen@uq.edu.au Supplementary data are available at Bioinformatics online.

Association Between Malaria and Invasive Nontyphoidal Salmonella Infection in a Hospital Study: Accounting for Berkson's Bias.

There is growing evidence for a positive association between malaria and invasive nontyphoidal Salmonella (iNTS) disease. However, case-control studies conducted within healthcare facilities also report inverse associations. This may be due to Berkson's bias, a selection bias that acts when both exposure and outcome are associated with hospital attendance and study participants are selected among attendees only. This study describes the effect of Berkson's bias on the malaria-iNTS association and provides a less biased effect estimate. Data collected in 2 Ghanaian hospitals were analyzed using 2 case-control approaches. In both approaches, cases were defined as iNTS-positive children, and concomitant malaria infection was the exposure of interest. In the first conventional sampling approach, children without any febrile bloodstream infection served as controls. In the second control-disease approach, children with non-iNTS bacteremia were used as controls. Data from 6746 children were suitable for the analyses. One hundred sixty children with iNTS infection were study cases. In the conventional case-control approach 6301 children were controls, and in the control-disease approach 285 children were controls. In the conventional case-control study, malaria was estimated to protect against iNTS disease (odds ratio [OR], 0.4; 95% confidence interval [CI], .3-.7), whereas in the control-disease approach, malaria was identified to be a risk factor for iNTS disease (OR, 1.9; 95% CI, 1.1-3.3). The study highlights how a selection bias may reverse results if an unsuitable control group is used and adds further evidence on the malaria-iNTS disease association.

Diffusion related isotopic fractionation effects with one-dimensional advective–dispersive transport

Aqueous phase diffusion-related isotope fractionation (DRIF) for carbon isotopes was investigated for common groundwater contaminants in systems in which transport could be considered to be one-dimensional. This paper focuses not only on theoretically observable DRIF effects in these systems but introduces the important concept of constraining “observable” DRIF based on constraints imposed by the scale of measurements in the field, and on standard limits of detection and analytical uncertainty. Specifically, constraints for the detection of DRIF were determined in terms of the diffusive fractionation factor, the initial concentration of contaminants (C0), the method detection limit (MDL) for isotopic analysis, the transport time, and the ratio of the longitudinal mechanical dispersion coefficient to effective molecular diffusion coefficient (Dmech/Deff). The results allow a determination of field conditions under which DRIF may be an important factor in the use of stable carbon isotope measurements for evaluation of contaminant transport and transformation for one-dimensional advective–dispersive transport. This study demonstrates that for diffusion-dominated transport of BTEX, MTBE, and chlorinated ethenes, DRIF effects are only detectable for the smaller molar mass compounds such as vinyl chloride for C0/MDL ratios of 50 or higher. Much larger C0/MDL ratios, corresponding to higher source concentrations or lower detection limits, are necessary for DRIF to be detectable for the higher molar mass compounds. The distance over which DRIF is observable for VC is small (less than 1m) for a relatively young diffusive plume (<100years), and DRIF will not easily be detected by using the conventional sampling approach with “typical” well spacing (at least several meters). With contaminant transport by advection, mechanical dispersion, and molecular diffusion this study suggests that in field sites where Dmech/Deff is larger than 10, DRIF effects will likely not be observable for common groundwater contaminants. Importantly, under most field conditions, Dmech/Deff≥10 is usually satisfied in the longitudinal direction, suggesting that DRIF is not likely to be observable in most groundwater systems in which contaminant transport is predominantly one-dimensional. Given the importance in the MDL it is recommended that MDL should always be explicitly reported in both modeling and field studies.

Estimating density of ant nests using distance sampling

The quantification of ant nest densities is a use- ful but challenging task given the group's high abundance and diversity of nesting sites. We present a new application of a distance-sampling method which follows standard distance analytical procedures, but introduces a sampling innovation that is particularly useful for ants; instead of having an observer look for ants we let ants find a bait station and measure the distances covered between nest and station. We test this method by estimating the density of epigaeic ant nests in an Amazon tropical forest site near Manaus, Brazil. We distributed 220 baits of canned sardine mixed with cassava flour among 10, 210-m long transects in old-growth upland forest. Forty-five minutes after baiting, we followed the ants' trails and measured the linear distance between the bait and each nest's entrance. We then used the freely available program DISTANCE to estimate the num- ber of nests per unit area while accounting for the effect of distance on the probability that a colony will find a bait. There were found 38 species nesting in 287 different colo- nies, with an estimated 2.66 nests/m 2 . This estimate fell within the 95 % confidence bounds of nest density predicted for a similar number of species based on a literature survey of ant species richness and nest density. Our sampling solution, however, takes less than 30 % of the time used by conventional sampling approaches for a similar area, with the advantage that it produces not only a point estimate but also a quantification of uncertainty about density.

Spatially Complex Distribution of Dissolved Manganese in a Fjord as Revealed by High-Resolution in Situ Sensing Using the Autonomous Underwater Vehicle Autosub

Loch Etive is a fjordic system on the west coast of Scotland. The deep waters of the upper basin are periodically isolated, and during these periods oxygen is lost through benthic respiration and concentrations of dissolved manganese increase. In April 2000 the autonomous underwater vehicle (AUV) Autosub was fitted with an in situ dissolved manganese analyzer and was used to study the spatial variability of this element together with oxygen, salinity, and temperature throughout the basin. Six along-loch transects were completed at either constant height above the seafloor or at constant depth below the surface. The ca. 4000 in situ 10-s-average dissolved Mn (Mnd) data points obtained provide a new quasi-synoptic and highly detailed view of the distribution of manganese in this fjordic environment not possible using conventional (water bottle) sampling. There is substantial variability in concentrations (<25 to >600 nM) and distributions of Mnd. Surface waters are characteristically low in Mnd reflecting mixing of riverine and marine end-member waters, both of which are low in Mnd. The deeper waters are enriched in Mnd, and as the water column always contains some oxygen, this must reflect primarily benthic inputs of reduced dissolved Mn. However, this enrichment of Mnd is spatially very variable, presumably as a result of variability in release of Mn coupled with mixing of water in the loch and removal processes. This work demonstrates how AUVs coupled with chemical sensors can reveal substantial small-scale variability of distributions of chemical species in coastal environments that would not be resolved by conventional sampling approaches. Such information is essential if we are to improve our understanding of the nature and significance of the underlying processes leading to this variability.

Direct design of multichannel fiber Bragg grating with discrete layer-peeling algorithm

The conventional sampling approach fails when the seed grating contains phase shifts because this approach is based on the resonant coupling principle, which explains the multichannel spectrum formation but does not provide an accurate solution. We use the discrete layer-peeling algorithm for directly synthesizing the multichannel grating. The new method restores the channel distortion in the sampled grating, resulting in uniform reflection and dispersion spectrum channels. We introduce further an optimized set of channel phases, such that the maximum reflective index modulation required for N channels is only /spl radic/N times that for the single channel.