Effect Of Sampling Strategy Research Articles

Effects of sampling strategy in rivers on load estimation for Nitrate-Nitrogen and total Phosphorus in a lowland agricultural area

The transport of nutrients into water bodies is one of the main causes of water eutrophication. It is therefore important to estimate the loads of nutrients. Discharge and nutrient concentrations are the fundamental elements to estimate the loads of nutrients, the latter can be affected by sampling strategies. As conducting sampling campaign and laboratory analysis are both expensive, it is necessary to find the best effective sampling strategy. The aim of this paper is to show how autocorrelation and standard statistical methods can be used to test the effects of different sampling strategies on the nutrient load estimation and to find the optimal sampling strategy. The data set in this study is from the 50 km² Kielstau catchment, a UNESCO demo site for ecohydrology in Northern Germany and consists of 14 years daily values of climate, hydrology, and water quality from 2006 to 2019. We calculated the autocorrelation (AC) of discharge (Q), precipitation, Nitrate-Nitrogen (NO3-N) and total Phosphorus (Ptot). Then we tested the effects of sampling intervals from 7 to 56 days (1–8 weeks) on the nutrient loads. Our results showed a high AC of Q and NO3-N for a long period, but the AC of Ptot and precipitation decreased very fast. An increase of the sampling interval (less frequent) increased the error of estimating the concentrations and loads. Consequently, we recommend that (1) the optimal sampling strategy for nutrient load estimation in an agriculture-dominant catchment should be continuously monitoring discharge combined with periodic grabbed samples; (2) the sampling frequency for NO3-N is suggested to be monthly (every 28 days) and for Ptot weekly (every 7 days). The information will help those tasked with catchment monitoring to design appropriate sampling strategy to ensure adequate data for nutrients load estimation in lowland rivers.

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 1 000 and 2 000 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.

Rainfall retrieval using commercial microwave links: Effect of sampling strategy on retrieval accuracy

This study presents the first evaluation of using commercial microwave link (CML) data for rainfall measurements in Australia, with the test site being the greater Melbourne Metropolitan area. More than 100 CMLs with microwave frequency ranging between 10 and 40 GHz have been used for the rainfall retrieval. The 15-minute received signal levels (RSLs) for each CML based on two sampling strategies (average and minimum/maximum) collected for 2 years provided a unique dataset to compare performances of rainfall retrievals. The open source algorithm RAINLINK was used for deriving rainfall from the 15-minute RSL data. From two years of data, a subset of 30 rainy days distributed across this period were used for calibrating the RAINLINK parameters, with the remaining data used for validation. For this study, only path-averaged rainfall intensities were validated based on a gauge-adjusted radar product serving as the reference. The result of the wet-dry classification showed that the minimum and maximum RSL data performed better, with lower probability of false detection and higher Matthews correlation coefficient than average RSL data. For the rainfall retrieval, both datasets showed similar correlation with the gauge adjusted radar product. However, based on other statistics (RMSE, bias and CV) minimum and maximum RSL data outperformed average for the rainfall retrieval. Overall, this study highlights the robust accuracy of commercial microwave links for rainfall retrieval while using only minimum and maximum RSL data.

Currently favored sampling practices for tumor sequencing can produce optimal results in the clinical setting

Tumor heterogeneity is a consequence of clonal evolution, resulting in a fractal-like architecture with spatially separated main clones, sub-clones and single-cells. As sequencing an entire tumor is not feasible, we ask the question whether there is an optimal clinical sampling strategy that can handle heterogeneity and hypermutations? Here, we tested the effect of sample size, pooling strategy as well as sequencing depth using whole-exome sequencing of ovarian tumor specimens paired with normal blood samples. Our study has an emphasis on clinical application—hence we compared single biopsy, combined local biopsies and combined multi-regional biopsies. Our results show that sequencing from spatially neighboring regions show similar genetic compositions, with few private mutations. Pooling samples from multiple distinct regions of the primary tumor did not increase the overall number of identified mutations but may increase the robustness of detecting clonal mutations. Hypermutating tumors are a special case, since increasing sample size can easily dilute sub-clonal private mutations below detection thresholds. In summary, we compared the effects of sampling strategies (single biopsy, multiple local samples, pooled global sample) on mutation detection by next generation sequencing. In view of the limitations of present tools and technologies, only one sequencing run per sample combined with high coverage (100–300 ×) sequencing is affordable and practical, regardless of the number of samples taken from the same patient.

MII: A Novel Text Classification Model Combining Deep Active Learning with BERT

Active learning has been widely utilized to reduce the labeling cost of supervised learning. By selecting specific instances to train the model, the performance of the model was improved within limited steps. However, rare work paid attention to the effectiveness of active learning on it. In this paper, we proposed a deep active learning model with bidirectional encoder representations from transformers (BERT) for text classification. BERT takes advantage of the self-attention mechanism to integrate contextual information, which is beneficial to accelerate the convergence of training. As for the process of active learning, we design an instance selection strategy based on posterior probabilities Margin, Intra-correlation and Inter-correlation (MII). Selected instances are characterized by small margin, low intra-cohesion and high inter-cohesion. We conduct extensive experiments and analytics with our methods. The effect of learner is compared while the effect of sampling strategy and text classification is assessed from three real datasets. The results show that our method outperforms the baselines in terms of accuracy.

Effects of sampling strategy and DNA extraction on human skin microbiome investigations

The human skin is colonized by a wide array of microorganisms playing a role in skin disorders. Studying the skin microbiome provides unique obstacles such as low microbial biomass. The objective of this study was to establish methodology for skin microbiome analyses, focusing on sampling technique and DNA extraction. Skin swabs and scrapes were collected from 9 healthy adult subjects, and DNA extracted using 12 commercial kits. All 165 samples were sequenced using the 16S rRNA gene. Comparing the populations captured by eSwabs and scrapes, 99.3% of sequences overlapped. Using eSwabs yielded higher consistency. The success rate of library preparation applying different DNA extraction kits ranged from 39% to 100%. Some kits had higher Shannon alpha-diversity. Metagenomic shotgun analyses were performed on a subset of samples (N = 12). These data indicate that a reduction of human DNA from 90% to 57% is feasible without lowering the success of 16S rRNA library preparation and without introducing taxonomic bias. Using swabs is a reliable technique to investigate the skin microbiome. DNA extraction methodology is crucial for success of sequencing and adds a substantial amount of variation in microbiome analyses. Reduction of host DNA is recommended for interventional studies applying metagenomics.

Local Image Descriptor Based Phishing Web Page Recognition as an Open-Set Problem

With the advent of e-commerce, digital services and social media, scammers have changed their way to gain illegal benefits in various forms such as capturing the credit card information or exploiting personal cloud accounts which is termed as phishing. For this reason, against this cyber crime, last two decades have witnessed a variety of combatting methodologies like HTML content based similarity analysis, URL based classification and recently visual similarity based matching since phishing web pages visually mimic to their legitimate counterparts in order to create an illusion to deceive innocent users. To this end, in this study, we propose a computer vision and machine learning based approach in order to classify whether a suspicious web page is phishing and further recognize its original brand name. In this regard, we have utilized and investigated two different local image descriptors namely Scale Invariant Feature Transform (SIFT) and DAISY. Apart from their common properties such as scale invariance, the aforementioned descriptors have apparent differences such that in addition to rotational invariance, SIFT employs key-point based sampling whereas DAISY applies dense sampling by default. Therefore, we first aimed to investigate the feasibility of these two local image descriptors in addition to revealing the effects of sampling strategy and rotational invariance in problem domain. Furthermore, in order to create a discriminative representation of a web page, we followed the bag of visual words (BOVW) approach having different vocabulary sizes such as 50, 100, 200 and 400. In order to evaluate the proposed approach, we have utilized a publicly available phishing dataset including snapshots of webpages sampled from both 14 different highly phished brands and ordinary legitimate web pages yielding a challenging open-set problem. The aforementioned dataset involves 1313 training and 1539 testing image samples in total. The visual features extracted via SIFT and DAISY were first transformed to a BOVW histogram and fed to three different machine learning methods such as SVM, Random Forest and XGBoost. According to the conducted experiments, based on a 400-D visual vocabulary, SIFT descriptor along with XGBoost has been found as the best descriptor-learner configuration having reached up to 89.34% validation accuracy with 0.76% false positive rate. Moreover, SIFT has outperformed DAISY descriptor in all settings. As a result, it has been shown that SIFT descriptors equipped with BOVW representation can be effectively used for brand identification of phishing web pages.

Effects of sampling strategies and estimation algorithms on total nitrogen load determination in a small agricultural headwater watershed

Monitoring data collected from rivers are used to support assessments of the quality of the river environment and help decision makes formulate appropriate management plans. Therefore, accurate and precise estimates of constituent fluxes in streams and rivers are important. However, seasonal errors have not received enough attention and the published results have rarely been verified by other methods. In this study, the error associated with sampling frequencies (3 daily, weekly, 2 weekly, 4 weekly, 6 weekly, 8 weekly) and seven load estimation algorithms were examined. Seasonal and annual total N loads were estimated and wavelet coherence was used as a reference. In order to reduce the contingency of the results, three years data were used and total N (with 17.53% particulate N, 58.70% NO3−-N and 23.77% other dissolved N fractions) was chosen to reflect water quality parameters. Results indicated that 2 weekly sampling frequency and algorithm F (linear interpolation) are sufficient for evaluating the characters of annual total N load in Fengyu River Watershed with 5% RMSE (root-mean-square error). There was a large RMSE (3.97–44.66%) in summer with a high CV (coefficient variation) (35.2%) for total N concentration. These results can serve as a useful reference for decision makes who need to establish monitoring programmes in watersheds with similar characteristics.

The effects of sampling strategy on the quality of reconstruction of viral population dynamics using Bayesian skyline family coalescent methods: A simulation study.

The ongoing large-scale increase in the total amount of genetic data for viruses and other pathogens has led to a situation in which it is often not possible to include every available sequence in a phylogenetic analysis and expect the procedure to complete in reasonable computational time. This raises questions about how a set of sequences should be selected for analysis, particularly if the data are used to infer more than just the phylogenetic tree itself. The design of sampling strategies for molecular epidemiology has been a neglected field of research. This article describes a large-scale simulation exercise that was undertaken to select an appropriate strategy when using the GMRF skygrid, one of the Bayesian skyline family of coalescent methods, in order to reconstruct past population dynamics. The simulated scenarios were intended to represent sampling for the population of an endemic virus across multiple geographical locations. Large phylogenies were simulated under a coalescent or structured coalescent model and sequences simulated from these trees; the resulting datasets were then downsampled for analyses according to a variety of schemes. Variation in results between different replicates of the same scheme was not insignificant, and as a result, we recommend that where possible analyses are repeated with different datasets in order to establish that elements of a reconstruction are not simply the result of the particular set of samples selected. We show that an individual stochastic choice of sequences can introduce spurious behaviour in the median line of the skygrid plot and that even marginal likelihood estimation can suggest complicated dynamics that were not in fact present. We recommend that the median line should not be used to infer historical events on its own. Sampling sequences with uniform probability with respect to both time and spatial location (deme) never performed worse than sampling with probability proportional to the effective population size at that time and in that location and frequently was superior. As a result, we recommend this approach in the design of future studies. We also confirm that the inclusion of many recent sequences from a single geographical location in an analysis tends to result in a spurious bottleneck effect in the reconstruction and caution against interpreting this as genuine.

The effect of sampling strategies on assessment of water quality criteria attainment

Sample locations for large river studies affect the representativeness of data, and thus can alter decisions made regarding river conditions and the need for interventions to improve water quality. The present study evaluated three water-quality sampling programs for Total Dissolved Solid (TDS) assessment in the Monongahela River from 2008 to 2012. The sampling plans cover the same 145 km of river but differ in frequency, sample location and type (e.g., river water sample vs drinking water plant intake sample). Differences resulting from temporal and spatial variability in sampling lead to different conclusions regarding water quality in the river (including regulatory listing decisions), especially when low flow leads to concentrations at or near the water quality criteria (500mg/L TDS). Drinking water samples exceeded the criteria 82 out of 650 samples (12.6%), while river water samples exceeded the criteria 47 out of 464 samples (10.1%). Different water sample types could provide different pictures of water quality in the river and lead to different regulatory listing decisions.

Estimating the diversity of wood-decaying polypores in tropical lowland rain forests in Malaysia: the effect of sampling strategy

Unlike in temperate regions, few studies have examined the effect of sampling effort on the observed number of wood-decaying polypores in the tropics. We conducted field surveys at Pasoh Forest Reserve and Lambir Hills National Park, two primary lowland dipterocarp forests in Malaysia, to compare the numbers of observed and estimated species for each site. Field surveys included five occasions over 4 years on a 2-ha plot in Pasoh, and two occasions in 1 year in 12 0.1-ha transects in Lambir. From Pasoh, 140 polypore species were recorded and 188 species were estimated (using the Chao2 estimator). From Lambir, 90 species were recorded and 149 estimated. Thus, at both sites the number of species observed comprised <75 % of the estimated number of species. At Pasoh, more than 80 % of the total species were recorded by the end of the third sampling occasion. When sampling effort (area × number of sampling occasions) was constant, more species were recorded by field surveys conducted over larger areas with less frequent sampling than those conducted over smaller areas with more frequent sampling. The numbers of species recorded in the present work were greater than those identified in temperate and boreal regions in previous studies. The species richness of wood-decaying polypores in tropical regions may be largely underestimated without conducting field surveys on at least three occasions. If potential sampling effort is limited, surveying a larger area on fewer occasions is a reasonable compromise to ensure coverage of the majority of polypore species.

How does sampling strategy influence detection of morphological variation in orchids? A case-study of the epiphyte Oberonia hosseusii

Effects of sampling strategy on detection of morphological variation were studied in the epiphytic orchid Oberonia hosseusii in Thailand. We compared the different effects of local versus regional sampling on the one hand and the size of local samples on the other. A local data set was collected by sampling a natural population on Doi Inthanon, during which process a preservation experiment was made. A regional data set was established by measurement of 18 preserved specimens from 12 different localities. Based on the preservation experiment, data from the regional sampling were calibrated (whenever needed) to make the two data sets more directly comparable. Heat drying and spirit preservation significantly influenced measurements for two mutually exclusive sets of characters. Increasing the local sample size from 18 to 52 individuals had no statistically significant effect on the mean value of any character. In contrast, local versus regional sampling significantly influenced the mean value of every character, virtually irrespective of local sample size. In the regional data set only, a difference was found in overall variability in vegetative versus floral characters. Three cluster analyses with equal representation of individuals from the local and regional samples were performed using different clustering techniques. Only in the analysis using neighbour-joining did pure “local clusters” prove to be significantly larger than pure “regional clusters”, suggesting that this clustering technique is particularly sensitive to effects from sampling strategy.

Effect of sampling strategy on uncertainty and precision of flatness inspection studied by dynamic minimum deviation zone evaluation

Evaluation of geometric deviations for the purpose of determining compliance with specified form tolerances requires the acquisition of numerous measured data points and extensive computation to accurately characterize the inspected part’s geometry. If there are not enough data points measured or when the measured points are not distributed properly on the measured surface a high level of uncertainty in characterizing the inspected part’s geometry can be expected. However, increasing the number of data point also significantly increases the computational time and also increases computational uncertainty by adding to instability of the optimization process required to find the minimum deviation zone. Selections of number and location of the measured date points need to be performed by understanding the significance of these two sources of uncertainties. This paper discussed the effect of sampling procedure on uncertainty and precision of flatness inspection.

A Fast and Robust Feature-Based Scan-Matching Method in 3D SLAM and the Effect of Sampling Strategies

Simultaneous localization and mapping (SLAM) plays an important role in fully autonomous systems when a GNSS (global navigation satellite system) is not available. Studies in both 2D indoor and 3D outdoor SLAM are based on the appearance of environments and utilize scan-matching methods to find rigid body transformation parameters between two consecutive scans. In this study, a fast and robust scan-matching method based on feature extraction is introduced. Since the method is based on the matching of certain geometric structures, like plane segments, the outliers and noise in the point cloud are considerably eliminated. Therefore, the proposed scan-matching algorithm is more robust than conventional methods. Besides, the registration time and the number of iterations are significantly reduced, since the number of matching points is efficiently decreased. As a scan-matching framework, an improved version of the normal distribution transform (NDT) is used. The probability density functions (PDFs) of the reference scan are generated as in the traditional NDT, and the feature extraction - based on stochastic plane detection - is applied to the only input scan. By using experimental dataset belongs to an outdoor environment like a university campus, we obtained satisfactory performance results. Moreover, the feature extraction part of the algorithm is considered as a special sampling strategy for scan-matching and compared to other sampling strategies, such as random sampling and grid-based sampling, the latter of which is first used in the NDT. Thus, this study also shows the effect of the subsampling on the performance of the NDT.

Archival quality and long-term preservation: a research framework for validating the usefulness of digital surrogates

Digital archives accept and preserve digital content for long-term use. Increasingly, stakeholders are creating large-scale digital repositories to ingest surrogates of archival resources or digitized books whose intellectual value as surrogates may exceed that of the original sources themselves. Although digital repository developers have expended significant effort to establish the trustworthiness of repository procedures and infrastructures, relatively little attention has been paid to the quality and usefulness of the preserved content itself. In situations where digital content has been created by third-party firms, content quality (or its absence in the form of unacceptable error) may directly influence repository trustworthiness. This article establishes a conceptual foundation for the association of archival quality and information quality research. It outlines a research project that is designed to develop and test measures of quality for digital content preserved in HathiTrust, a large-scale preservation repository. The research establishes methods of measuring error in digitized books at the data, page, and volume level and applies the measures to statistically valid samples of digitized books, adjusting for inter-coder inconsistencies and the effects of sampling strategies. The research findings are then validated with users who conform to one of four use-case scenarios: reading online, printing on demand, data mining, and print collection management. The paper concludes with comments on the implications of assessing archival quality within a digital preservation context.

On the sampling of three-dimensional polycrystalline microstructures for distribution determination

How to sample three-dimensional microstructure and effectively reduce experimental error is a challenging problem. Taking seven single-phase polycrystalline structures generated by 400×400×400 Potts Monte Carlo simulation as the study object, effects of sampling strategy on the determination of various characteristic parameters of the grain size distribution and grain topology distribution are studied. The mean voxel value (or volume) of individual grains in the three-dimensional simulated microstructure varies from 4.56×10(4) to 1.0×10(3) , and the number of grains contained in the structure varies from 63 901 to 1403. The results show that, a minimum of 200 sampled grains can ensure the relative error to be less than 5% for determination of the mean grain volume, the mean grain face number and the coefficient of variance of the distribution of grain size and the grain face number. Whereas for the coefficient of the skewness and the kurtosis of grain size distribution or grain face number distribution, a minimum of 800 sampled grains are required for the same error level. However, if some exceptional big grains appear, e.g. a grain larger than with eight multiples of mean grain volume and/or three multiples of mean grain face number, abnormal values of the two parameters would be resulted, even the number of examined grains is over 1000.

Sensitivity analysis of permeability parameters of bovine nucleus pulposus obtained through inverse fitting of the nonlinear biphasic equation: effect of sampling strategy

Permeability controls the fluid flow into and out of soft tissue, and plays an important role in maintaining the health status of such tissue. Accurate determination of the parameters that define permeability is important for the interpretation of models that incorporate such processes. This paper describes the determination of strain-dependent permeability parameters from the nonlinear biphasic equation from experimental data of different sampling frequencies using the Nelder–Mead simplex method. The ability of this method to determine the global optimum was assessed by constructing the whole manifold arising from possible parameter combinations. Many parameter combinations yielded similar fits with the Nelder–Mead algorithm able to identify the global maximum within the resolution of the manifold. Furthermore, the sampling strategy affected the optimum values of the permeability parameters. Therefore, permeability parameter estimations arising from inverse methods should be utilised with the knowledge that they come with large confidence intervals.

Effect of sampling strategy on estimation of fine-scale spatial genetic structure in Androsace tapete (Primulaceae), an alpine plant endemic to Qinghai-Tibetan Plateau

The fine-scale spatial genetic structure (SGS) of alpine plants is receiving increasing attention, from which seed and pollen dispersal can be inferred. However, estimation of SGS may depend strongly on the sampling strategy, including the sample size and spatial sampling scheme. Here, we examined the effects of sample size and three spatial schemes, simple-random, line-transect, and random-cluster sampling, on the estimation of SGS in Androsace tapete, an alpine cushion plant endemic to Qinghai-Tibetan Plateau. Using both real data and simulated data of dominant molecular markers, we show that: (i) SGS is highly sensitive to sample strategy especially when the sample size is small (e.g., below 100); (ii) the commonly used SGS parameter (the intercept of the autocorrelogram) is more susceptible to sample error than a newly developed Sp statistic; and (iii) the random-cluster scheme is susceptible to obvious bias in parameter estimation even when the sample size is relatively large (e.g., above 200). Overall, the line-transect scheme is recommendable, in that it performs slightly better than the simple-random scheme in parameter estimation and is more efficient to encompass broad spatial scales. The consistency between simulated data and real data implies that these findings might hold true in other alpine plants and more species should be examined in future work.

The effect of sampling strategy on the estimated prevalence of welfare outcome measures on finishing pig farms

The effect of sampling strategy on the estimates of prevalence of some welfare outcome measures was investigated as part of a larger project examining the feasibility and benefits of incorporating some on-farm welfare outcome assessments into UK Pig Farm Assurance Schemes. All finishing pigs (>40 kg) on 6 farms that had pens of 25 pigs or less were assessed for dirtiness, body lesions, tail lesions, bursae, lameness, oral behaviour and pigs requiring hospitalisation. A statistical sampling methodology was applied to the data to generate 10,000 random samples for each measure, on each farm and for every possible number of sampled pens on that farm. The 95% confidence interval range, the proportion of samples outside a ‘tolerance’ range and the proportion of samples that would wrongly classify the farm with respect to a ‘threshold’ were calculated for each measure on each farm for every possible sample size. It was found that even large sample sizes were unable to accurately reflect the whole farm for measures with very low prevalences (lameness and pigs requiring hospitalisation). For the other measures, as the number of pens sampled increased, the confidence interval range and the proportion of samples outside the ‘tolerance’ range reduced. The differing inter-pen variation in prevalence on a farm gave rise to differences in accuracy of prevalence estimates between the farms. Those farms with a true farm prevalence close to the threshold required more pens to be sampled to achieve the same level of accuracy of classification as other farms with similar confidence intervals but a more extreme prevalence. The effect of sampling on the estimate of prevalence should be considered when an accurate prevalence of the whole population is required.

Effects of sampling strategy on image quality in noncontact panoramic fluorescence diffuse optical tomography for small animal imaging

Fluorescence diffuse optical tomography is an emerging technology for molecular imaging with recent technological advances in biomarkers and photonics. The introduction of noncontact imaging methods enables very large-scale data acquisition that is orders of magnitude larger than that from earlier systems. In this study, the effects of sampling strategy on image quality were investigated using an imaging phantom mimicking small animals and further analyzed using singular value analysis (SVA). The sampling strategy was represented in terms of a number of key acquisition parameters, namely the numbers of sources, detectors, and imaging angles. A number of metrics were defined to quantitatively evaluate image quality. The effects of acquisition parameters on image quality were subsequently studied by varying each of the parameters within a reasonable range while maintaining the other parameters constant, a method analogue to partial derivative in mathematical analysis. It was found that image quality improves at a much slower rate if the acquisition parameters are above certain critical values (approximately 5 sources, approximately 15 detectors, and approximately 20 angles for our system). These critical values remain virtually the same even if other acquisition parameters are doubled. It was also found that increasing different acquisition parameters improves image quality with different efficiencies in terms of the number of measurements: for a system characterized by a smaller threshold in SVA (less than 10(-5) in our study), the number of sources is the most efficient, followed by the number of detectors and subsequently the number of imaging angles. However, for systems characterized by a larger threshold, the numbers of sources and angles are equally more efficient than the number of detectors.