Quality of Hybrid GNSS Sampling Methods

ObjectiveThe aim of this study is to evaluate the performance of different platelet counting methods (optical, impedance, fluorescence and hand counting) applied in different analysers by comparing with the international flow cytometric reference method (IRM).MethodsA total of 333 blood samples from different subgroups (168 cases with thrombocytopenia, 136 cases with normal platelet counts and 29 cases with thrombocytosis) were tested. Regarding IRM as the gold standard, we compared the accuracy and precision of different platelet count methods; i.e. LH780 (impedance), BC-6000 Plus (optical (O) and impedance (I)), Sysmex XN-9000 (optical (O), impedance (I), fluorescence (F)), and hand counting.ResultsSysmex XN-9000-F (r = 0.988) had the best correlation with IRM for thrombocytopenic samples; BC-6000 Plus-I (r = 0.966) was more relevant to IRM than any other method for samples with normal platelet counts. Correlation between Sysmex XN-9000-I (r = 0.960) and IRM was the highest among these methods for samples with thrombocytosis. For bias evaluation, the average bias of Sysmex XN-9000-F was -1.5 × 109/L (95% LA = -9.4 to +6.4) for samples with thrombocytopenia, compared with IRM. BC-6000 Plus-I had a small mean difference with IRM for samples with normal platelet counts or thrombocytosis. Moreover, all evaluated methods had acceptable sensitivity, specificity, and concordance rates as compared with IRM in the diagnosis of thrombocytopenia and thrombocytosis.ConclusionsPlatelet counting by Sysmex XN-9000-F is more accurate than other methods for thrombocytopenic samples. BC-6000 Plus-I has superior association and consistency for normal platelet counts. As for thrombocytosis patients, Sysmex XN-9000-I has the highest correlation with IRM while Sysmex XN-9000-O has the highest diagnosis efficacy.

Given the increasing concern about microplastics (MPs) and nanoplastics (NPs) in the atmosphere, numerous studies have been conducted on their presence and impacts. This review explores the occurrence, sources, physicochemical characteristics, and the sampling and analytical methods of MPs and NPs in ambient air. MPs are primarily found in the form of fibers and fragments, with sizes generally exceeding several micrometers, and have been detected in various environments, including urban, industrial, coastal, and remote areas. Currently, there are no standardized methods for sampling and analyzing MPs and NPs in the atmosphere. Existing studies predominantly employ passive sampling methods to collect depositable microplastics and active sampling methods for suspended MPs. In the laboratory, most samples are treated with H2O2 and then separated by density before being analyzed using microscopy and spectroscopic techniques such as FT-IR and Raman. However, the current sampling and analytical methods have proven insufficient for detecting the presence of NPs in the air. Therefore, there is a need to develop new sampling and analytical methods to determine them in the atmospheric environment.

Airborne biological agents have become prominent safety and health issues in agriculture, biotechnology, industrial settings, and the indoor environment. Each of these environments presents unique exposure concerns due to the nature of the encountered biological agent, the microbial concentrations, the modes of exposure, and the susceptibility of the exposed population. Acceptable levels of airborne microorganisms have not been established and the sampling methods and analytical techniques employed to assess airborne biocontaminants are varied and non-standardized. This paper reviews and compares the different air sampling methods for biological agents and classical analytical methods (i.e., culture and microscopy), analysis for specific microorganism constituents (i.e., ergosterol, muramic acid, glucans, allergens, mycotoxins, endotoxins) and molecular methods (i.e., polymerase chain reactions). Each of the described methods has distinct advantages and disadvantages. Selection of sampling and analytical methods depends upon the nature of the information that is sought; there is no one ideal sampling or analytical method. Combinations of sampling and analytical methods can provide a wide range of data that can be effectively tailored to many different environmental settings.

Investigating the use of quartile ranked set samples for estimating the population mean

Computational comparison for ML estimator of quadratic functions of the Bernoulli parameter in IS and FSS methods

A virtual study of the physical and chemical behaviour of particles in the energy space is referred to as computer simulation. The interaction of biomolecules and atoms during conformational changes is studied through molecular dynamics (MD) simulation. MD simulation complements the experimental results by providing a theoretical perspective of the real-time environment. However, the sampling of configuration is limited to a definite timescale due to free energy barriers. This free energy barrier arises due to the energy gap between initial and closing entropy in biomolecular structural transition. To deal with this biophysical problem, various enhanced sampling methods have been developed that are classified into collective variable-based and collective variable-free approaches based on the algorithm of the sampling method. This chapter discusses the numerical aspects of sampling methods, followed by a review of some of the most commonly used techniques in MD simulation and enhanced sampling. Lastly, a combined enhanced sampling method has been discussed.

Cetacean skin microbiome samples are traditionally collected by swabbing the skin of captured animals or using skin tissue from remote biopsies. The effects of different sampling methods are not well understood, and factors such as swab type and storage methods may affect the microbiome. The objective of this study was to determine if samples collected by swabbing biopsied skin from bottlenose dolphins yield sufficient microbial DNA for gene sequencing, while testing if commonly used sampling and storage methods affect the skin microbiome. Skin swab samples were collected during remote biopsy surveys with either a nylon flocked swab and flash frozen with liquid nitrogen or with a cotton swab and stored in RNAlater. Sequencing of the V3–V4 region of the 16S rRNA gene revealed swabs from dolphin skin biopsies yielded sufficient DNA to characterize the microbiome. We found no significant difference between the two sampling and storage methods tested, suggesting that studies using different techniques may be comparable. This work also demonstrates a novel technique to collect samples for interpretation of the skin microbiome of free‐ranging animals. Benefits of this method are that it can be done without capture and preserves valuable and typically scarce biopsy skin samples for other analyses.

The surrogate model-based sampling method has been an active area of research due to its evident efficiency over direct sampling method. The fusion of the adaptive kriging (AK) model with the adaptive radial-based importance sampling (ARBIS) method is developed as an improvement of the AK model combined with Monte Carlo simulation (MCS) method especially for estimating the small failure probabilities. In the AK–ARBIS method, the number of candidate samples in each updating process of the kriging model is reduced through partitioning the whole MCS sample domain into different subdomains and removing the samples inside the last subdomain from the participating candidate sampling pool, which can avoid the time-consuming matrix operation involved in kriging predictions in order to find the best training sample in each updating step. In the existing AK–ARBIS method, kriging model stops updating according to the learning function-based stopping criterion, which cannot directly reflect the error of the estimated failure probability. In this regard, this paper proposes the error-based stopping criterion (ESC) enhanced AK–ARBIS method, where the processes of finding the optimal hypersphere and accurately identifying the sign of candidate samples are decoupled in order to successfully embed the ESC. In addition to embedding the ESC into the AK–ARBIS method, two target-oriented candidate sampling reduction strategies are investigated, respectively, to improve the efficiency of finding the optimal hypersphere and accurately identifying the sign of the participating candidate samples. Based on the candidate sampling pool reduction strategies and ESC, both the computational time and the required number of calls to the real limit state function are reduced over the existing AK–ARBIS method. Three case studies are analyzed to demonstrate the accuracy and efficiency of the proposed enhanced AK–ARBIS method, especially for estimating the small failure probabilities.

Contrary to other non-probability sampling methods in which researchers actively recruit potential participants, respondent-driven sampling (RDS) relies on connection and trust within social networks to access hidden or hard-to-reach populations through a peer-to-peer recruitment process. These interdependency relations aligned with Indonesian communal culture. Personal network size calculation in the RDS method makes this innovative sampling method approximate random sampling methods, where findings from the sample could be generalized to the target population. Considering its superiority, the RDS method could be applied in social psychology research in Indonesia to explore current sensitive social issues among hidden or hard-to-reach Indonesian sub-populations, for instance, radical religious groups. The current article aimed to concisely describe the RDS method; discuss ethical considerations, strengths, and weaknesses of the RDS method; and outline the potential use of the RDS method in improving the contribution of social psychology research in Indonesia, for example, by advancing strategies for social intervention programs. It is followed by a brief step-by-step process to conduct a study using the RDS method.

We describe sampling methods and provide software to accurately and efficiently estimate snag and tree densities at desired scales to meet a variety of research and management objectives. The methods optimize sampling effort by choosing a plot size appropriate for the specified forest conditions and sampling goals. Plot selection and data analyses are supported by SnagPRO, a software program designed specifically to serve our sampling methods. We present two sampling methods to estimate density and associated characteristics of snags and trees. The first method requires sampling until a desired precision is obtained for a density estimate. The second method compares estimated densities with target densities, such as target snag densities specified under a land management plan. Our methods of snag and tree sampling are compatible with recently developed methods of log sampling, thereby improving efficiencies by enabling the simultaneous collection of all three habitat components—snags, large trees, and logs—to meet research or management objectives for a variety of resource disciplines, including wildlife, silviculture, fuels, and soils. Recently developed methods of log sampling also use SnagPRO for data collection and analysis. Our methods and software are particularly relevant to forest management, given that nearly all federal land use plans require monitoring of snag and tree densities in relation to management direction for wildlife. Staffing and budgets available to estimate snag and tree densities, however, are extremely limited, and thus require efficient methods to achieve acceptable accuracy. Our methods are an efficient approach for estimating snag and tree densities, particularly when combined with use of the supporting SnagPRO software.

ABSTRACTIn this study, a novel sampling method was established to study changes in the activity of lactic acid bacteria during spray drying. Before spray drying, a sampling device with a refrigerant and a magnet was pre-cooled and positioned in a spray-drying tower. Carbonyl iron powder (CIP) was added to the drying media to increase the sampling mass by the magnetic effect. During drying, a temperature sensor measured the particle contact temperature. After drying, the sample in the sampling device was taken out to analyze the characteristics, including the mass, water content, and bacteria livability in the sample. The sampling device and method were optimized with a sampling cup with a depth of 3.5 cm (for contact temperatures below 79.1°C) or 5 cm (for contact temperatures below 89.1°C). The ratio of CIP to reconstituted skim milk (RSM) was 1:10, and RSM concentration was not lower than 20% (w/w). This method was then applied to spray drying of Lactobacillus bulgaricus sp1.1. Furthermore, it was demonstrated that 2 mL of sterile water placed in the sampling cup prior to drying resulted in a more accurate measurement of cell viability. This sampling method can be used in practical spray drying applications for biological materials without using a special instrument or drying tower.

This experiment compares the (a) rank ordering of cognitive differentiation scores by observers and actors, and (b) methods of construct sampling based on instructional set. Results indicate that observer and actor codings are inconsistent but that two different methods of sampling are consistent. Additionally, data suggest that the particular score of the RCQ assigned by observers using the established procedures may not reflect the actual construct system of the individual (actor).

The first part of the article reports on a study, undertaken at a small college library, to determine the accuracy of the statistical sampling techniques used for determining the number of books missing from library collections, com pared with taking a full inventory. A full inventory of circulating stock was performed involving a total of 5742 books (4000 general non fiction and 1742 specialist titles). The interval method of statistical sampling was used as a quick method of determining losses and five methods of sampling are described and discussed, those of: Bommer and Ford; Miller and Sorum; Clark; Niland and Kurth; and Griffith. Results for book losses determined via sampling methods were found to be very close to actual inventoried results, with a particularly acceptable result obtained when only one per cent of the collection was checked. The Bommer and Ford method came closest with a loss figure of 8.06 per cent compared with the inventory figure of 8.20 per cent. The second part of the article considers different ways of allocating a monetary value on replacing the lost material. The costs obtained (varying between 5 000 and £20 000) are compared and used as the basis for a cost benefit analysis of the introduction of library theft detection systems. Concludes that such secu rity systems would be justified, both in the short term or over a five year peri od. Provided the average price of replacing each book is greater than or equal to £13.56, the initial cost of the theft detection system and its first year's operating costs will be met by savings in replacement costs.

Background: Renewable natural resources, such as forests, have played an important role in human life since the past.The principled and sustainable use of forests requires technical planning.Fundamental and technical planning requires data collection and analysis and obtaining useful quantitative and qualitative information.A data collection method in forest populations is 100% statistics, which is rarely implemented in large forest areas due to technical and economic reasons, and it is used in most cases to compare different sampling methods.Sampling methods with a fixed area are mostly used in the preparation of plans and studies of northern forests.Today, different forest sampling methods have been designed and presented, each of which has its own advantages and merits.The use of these methods requires examining and determining the most suitable ones in terms of ease of implementation, time, and accuracy.On the other hand, the industrial forests in the north of Iran have currently become heterogeneous stands due to exploitation in the past.For this type of forest, using the stratification method combined with sampling methods will be useful and effective.To plan to increase efficiency, speed of operation, and reduce costs, it is necessary to examine and use different sampling methods with fixed and variable areas, including multi-tree sampling.This study is aimed at the effect of forest classification and its combined effect with the fixed area sampling method and the PRODAN six tree variable area sampling method and comparing the accuracy of estimators.Methods: This study was conducted in the Safaroud forests with an area of 176 hectares in Mazandaran Province.The selected areas were measured and recorded using the statistical method of 100% of all the trees in the area (counting limit, a diameter at the breast of more than 7.5 cm).To avoid mistakes and make it easier to perform 100% statistics, the studied area was divided into square pieces with dimensions of 50 50 m, and 704 plots were placed in the field.Then, the trees in each of these parts were measured separately.Prodan's six-tree method was carried out in two stages.The first stage was measured in an unclassified area of 176 hectares with a grid size of 100 100 m.The second stage of the investigated forest area using the "Neyman" stratification method into three strata in terms of volume inventory, including less than 200 m 3 , 200-250 m 3 , and more than 250 m 3 per hectare based on Prodan's six-tree method, was performed using new grid dimensions for each stratum.In this study, the effect of segmentation on accuracy and time was investigated using the criterion (E% 2 T).For this purpose, the time required to unload the sample plots for the necessary measurements inside the sample plot and the time required to travel the distance between the adjacent sample plots were also calculated and recorded using a stopwatch.Results: The results of the comparison of the statistical test between the average of the variables calculated with the 100% statistical method (true mean) and six-tree sampling (Prodan) in the first stage without stratification and in the second stage with the implementation of the "Neyman" stratification method showed a significant difference at the level of 0.05%.The results of this study in terms of accuracy and the cost index (E% 2 T) show that the error and statistical error of the measurement characteristics in the standing forest with the six-tree sampling method with stratification was lower than the sampling method without grafting.This amount was calculated for the number of trees per hectare as 8811.81 and 5677.52,respectively.The estimated average of the examined characteristics was 35.56% closer to the real average and the error rate was less than the statistical standard with the forest stratification and Prodan's six-tree sampling implementation.The percentage of error reduction in sampling after seeding was calculated at

espanolExact tests, fuzzy p-values, Gibbs sampling, iterval censoring, linear extensions, linear rank statistics, perfect MCMC, proportional hazard model, topological sorting EnglishWe discuss distribution-free exact rank tests from partially ordered data that arise in various biological and other applications where the primary objective is to conduct testing of significance to assess the linear dependence or to compare different groups. The tests here are obtained by treating the usual rank statistics, based on the completely ordered data as “latent” or missing, and conceptualizing the “latent” p-value as the random probability under the null hypothesis of a test statistic that is as extreme, or more extreme, than the latent test statistics based on the completely ordered data. The latent p-value is then predicted by sampling linear extensions or the complete orderings that are consistent with the observed partially ordered data. The sampling methods explored here include importance sampling methods based on randomized topological sorting algorithms, Gibbs sampling methods, random-walk based Metropolis–Hasting sampling methods and random-walk based modern perfect Markov chain Monte Carlo sampling methods. We discuss running times of these sampling methods and their strength and weaknesses. A simulation experiment and three data examples are given. The simulation experiment illustrates how the exact rank tests from partially ordered data work when the desired result is known. The first data example concerns the light preference behavior of fruit flies and tests whether heterogeneity observed in average light-preference behavior can be explained by manipulations in serotonin signaling. The second one is a reanalysis of the lead absorption data in children of employees who worked in a lead battery factory and consolidates the results reported in Rosenbaum [Ann. Statist. 19 (1991) 1091–1097]. The third one reexamines the breast cosmesis data from Finkelstein [Biometrics 42 (1986) 845–854].