Number Of Reference Samples Research Articles

Accurately calibrated X-ray fluorescence core scanning (XRF-CS) record of Ti ∕ Al reveals Early Pleistocene aridity and humidity variability over North Africa and its close relationship to low-latitude insolation

Abstract. In eastern Mediterranean sediments, the titanium-to-aluminum ratio (Ti/Al) captures relative variability in eolian to river-derived material and predominantly integrates climate signals over the Saharan and Sahel regions. Long Ti/Al time series can, therefore, provide valuable records of North African humidity and aridity changes. X-ray fluorescence core scanning (XRF-CS) can generate near-continuous Ti/Al records with relatively modest effort and in an acceptable amount of time, provided that accurate Ti/Al values are acquired. Calibration of raw XRF-CS data to those of established analytical methods is an important pathway for obtaining the required accuracy. We assess how to obtain reliable XRF-CS Ti/Al calibration by using different calibration reference sample sets for a long sediment record from ODP Site 967 (eastern Mediterranean Sea). The accuracy of reference concentrations and the number of reference samples are important for reliable calibration. Our continuous Ti/Al record allows detailed time series analysis over the past 3 Myr. Near-direct control of low-latitude insolation on the timing and amplitude of North African aridity and humidity is observed from 3 to ∼ 1.2 Ma. In our Ti/Al record, most arid North African intervals (i.e., with the longest period and highest amplitude) occur after the mid-Pleistocene transition (MPT; ∼ 1.2–0.7 Ma), when ice ages intensified. We also observe a subdued relationship between low-latitude insolation and North African climate after the MPT. These findings support the growing consensus that African climate became more sensitive to remote high-latitude climate when a threshold ice volume was reached during the MPT.

Prediction of Remaining Useful Life of the Lithium-Ion Battery Based on Improved Particle Filtering

Remaining useful life (RUL) prediction of lithium-ion batteries plays an important role in battery failure prediction and health management (PHM). By accurately predicting the RUL of the battery, the battery can be replaced accordingly, thereby effectively avoiding the occurrence of an accident and ensuring the normal operation of the entire system. In the prediction of the remaining service life of lithium-ion batteries, it is difficult to ensure accuracy due to the problem of particle degradation and the influence of singular values in the particle filter algorithm. In view of these problems, this article introduces the unscented Kalman algorithm to improve the particle filter algorithm from the perspective of re-weighting the particles, so as to improve the accuracy of the prediction results of the remaining service life of lithium-ion batteries. The improved particle filter is simulated and verified using the battery sample data in the Arbin experimental test platform. Comparing the simulation results with the traditional particle filter method, when the number of reference samples is the same, the PDF width of the prediction results of the improved particle filter algorithm is slightly smaller than that of the particle filter algorithm, indicating that the fluctuation of the prediction result is more accurate. It is proved that the improved particle filter method proposed in this article can provide more accurate battery RUL prediction results and can effectively improve the accuracy and robustness of the remaining service life prediction of lithium-ion batteries.

A Comprehensive and Reproducible Comparison of Clustering and Optimization Rules in Wi-Fi Fingerprinting

Wi-Fi fingerprinting is a well-known technique used for indoor positioning. It relies on a pattern recognition method that compares the captured operational fingerprint with a set of previously collected reference samples (radio map) using a similarity function. The matching algorithms suffer from a scalability problem in large deployments with a huge density of fingerprints, where the number of reference samples in the radio map is prohibitively large. This paper presents a comprehensive comparative study of existing methods to reduce the complexity and size of the radio map used at the operational stage. Our empirical results show that most of the methods reduce the computational burden at the expense of a degraded accuracy. Among the studied methods, only <inline-formula><tex-math notation="LaTeX">$k$</tex-math></inline-formula> -means, affinity propagation, and the rules based on the strongest access point properly balance the positioning accuracy and computational time. In addition to the comparative results, this paper also introduces a new evaluation framework with multiple datasets, aiming at getting more general results and contributing to a better reproducibility of new proposed solutions in the future.

Landmark-based audio fingerprinting system applied to vehicle squeak and rattle noises

This article investigates whether the robust landmark-based audio fingerprinting method created for recognizing music can be applied to identify squeak and rattle (S&amp;R) types of sounds. The identification is performed by matching a query audio sample to the perceptually closest audio sample that is stored in a pre-developed database of S&amp;R audio sounds. The aim of the application of the method in the automotive industry is to facilitate the process S&amp;R experts go through during sound identification. The experimental results show that the algorithm can be used for identification of different types of S&amp;R sounds when the audio database contains a limited number of reference samples.

Even big data is not enough: need for a novel reference modelling for forensic document authentication

With the emergence of big data, deep learning (DL) approaches are becoming quite popular in many branches of science. Forensic science is no longer an exception. However, there are certain problems in forensic science where the solutions would hardly benefit from the recent advances in DL algorithms. Document authentication is one such problem where we can have many reference samples, and with the big data scenario probably we would have even more number of reference samples but number of defective or forged samples will remain an issue. Experts often encounter situations where there is no or hardly a scanty number of forged samples available. In such situation, employment of data-hungry algorithms would be inefficient as they will not be able to learn the forged samples properly. This paper addresses this problem and proposes a novel reference modelling framework for forensic document authentication. The approach is based on Mahalanobis space. Two questioned document examination problems have been studied to show the effectiveness of our reference modelling algorithm which has also been compared to a commonly used learning approach, namely neural network-based classification.

Performance and characteristics of the BILBY time-of-flight small-angle neutron scattering instrument

BILBY is a recently constructed and commissioned time-of-flight small-angle neutron scattering instrument, operated by the Australian Centre for Neutron Scattering at the Australian Nuclear Science and Technology Organisation (ANSTO). BILBY provides a wide accessible q range (q ≃ 1.0 × 10−3 Å−1 to ∼1.8 Å−1) and variable wavelength resolution (Δλ/λ ≃ 3–30%) to complement the other small-angle and ultra-small-angle neutron scattering capabilities available at ANSTO. Since its construction, BILBY has been used to study samples from a wide range of scientific disciplines, including biology, chemistry, physics and materials science. This article describes the BILBY design and components, and shows data collected from a number of reference samples.

CHOOSING OF OPTIMAL REFERENCE SAMPLES FOR BOREAL LAKE CHLOROPHYLL A CONCENTRATION MODELING USING AERIAL HYPERSPECTRAL DATA

Abstract. Optical remote sensing has potential to overcome the limitations of point estimations of lake water quality by providing spatial and temporal information. In open ocean waters the optical properties are dominated by phytoplankton density, while the relationship between color and the constituents is more complicated in inland waters varying regionally and seasonally. Concerning the difficulties relating to comprehensive modeling of complex inland and coastal waters, the alternative approach is considered in this paper: the raw digital numbers (DN) recorded using aerial remote hyperspectral sensing are used without corrections and derived by means of regression modeling to predict Chlorophyll a (Chl-a) concentrations using in situ reference measurements. The target of this study is to estimate which number of local reference measurements is adequate for producing reliable statistical model to predict Chl-a concentration in complex lake water ecosystem. Based on the data collected from boreal lake Lohjanjärvi, the effect of standard deviation of Chl-a concentration of reference samples and their local clustering on predictability of model increases when number of reference samples or bands used as model variables decreases. However, the 2 or 3 band models are beneficial and more cost efficient when compared to 5 or 7 band models when the standard deviation of Chl-a concentration of reference samples is over certain level. The simple empirical approach combining remote sensing and traditional sampling may be feasible for regional and seasonal retrieval of Chl-a concentration distributions in complex ecosystems, where the comprehensive models are difficult or even impossible to derive.

Predictive long-range allele-specific mapping of regulatory variants and target transcripts.

Genome-wide association studies (GWASs) have identified a large number of noncoding associations, calling for systematic mapping to causal regulatory variants and their distal target genes. A widely used method, quantitative trait loci (QTL) mapping for chromatin or expression traits, suffers from sample-to-sample experimental variation and trans-acting or environmental effects. Instead, alleles at heterozygous loci can be compared within a sample, thereby controlling for those confounding factors. Here we introduce a method for chromatin structure-based allele-specific pairing of regulatory variants and target transcripts. With phased genotypes, much of allele-specific expression could be explained by paired allelic cis-regulation across a long range. This approach showed approximately two times greater sensitivity than QTL mapping. There are cases in which allele imbalance cannot be tested because heterozygotes are not available among reference samples. Therefore, we employed a machine learning method to predict missing positive cases based on various features shared by observed allele-specific pairs. We showed that only 10 reference samples are sufficient to achieve high prediction accuracy with a low sampling variation. In conclusion, our method enables highly sensitive fine mapping and target identification for trait-associated variants based on a small number of reference samples.

Polyurethane coatings in twentieth century outdoor painted sculptures. Part II: comparative study of four systems by means of Py-GC/MS

Because PU coatings offer a compromise between aesthetic and performance expectations, unachievable with other types of industrial paints, they are currently recognized as the most appropriate option to coat sculptures intended for an outdoor setting. However, the PU class includes various systems, such as two package solvent-borne, two package water-borne, one package water-borne and fluoropolymer polyurethanes, which possess very different properties. 115 reference samples of PU coatings were investigated by means of Py-GC/MS, in order to outline the differences and the similarities existing, in terms of composition, between the major PU systems used for creating as well as for conserving modern painted outdoor sculptures. The Py-GC/MS study of an extended number of reference samples showed that the composition of equivalent PU systems strongly varies depending on the product line and the manufacturer. Furthermore the comparison of all the produced pyrograms allowed defining characteristic marker compounds helpful to discriminate specific PU paint systems.

Self-Starting Monitoring Scheme for Poisson Count Data With Varying Population Sizes

In this article, we consider the problem of monitoring Poisson rates when the population sizes are time-varying and the nominal value of the process parameter is unavailable. Almost all previous control schemes for the detection of increases in the Poisson rate in Phase II are constructed based on assumed knowledge of the process parameters, for example, the expectation of the count of a rare event when the process of interest is in control. In practice, however, this parameter is usually unknown and not able to be estimated with a sufficiently large number of reference samples. A self-starting exponentially weighted moving average (EWMA) control scheme based on a parametric bootstrap method is proposed. The success of the proposed method lies in the use of probability control limits, which are determined based on the observations during rather than before monitoring. Simulation studies show that our proposed scheme has good in-control and out-of-control performance under various situations. In particular, our proposed scheme is useful in rare event studies during the start-up stage of a monitoring process. Supplementary materials for this article are available online.

Calibration independent generalized cavity method for microwave characterization of powdered materials.

A generalized cavity method relaxing the major assumptions of conventional cavity perturbation technique is presented for characterization of the powdered sample in the microwave frequency range. The unified method, which is based on the inverse optimization technique, eliminates the complexity of measurement caused due to the existence of sample holder and produces an accurate result. In this paper, an attractive numerical calibration approach is proposed in lieu of the practical calibration technique which usually requires either a set of standards or a number of reference samples. The sample holder especially made of borosilicate glass is designed to contain the powdered samples, and the X-band rectangular cavity is fabricated. For verification of the proposed technique, the pulverized alumina and polyethylene oxide with various packing fractions are measured using the fabricated cavity and the vector network analyzer. The dielectric constant of these samples is extracted using the proposed unified approach which is found to be in good agreement with the theoretical data obtained by Landau-Lifshitz and Looyenga model. The accuracy of the proposed generalized cavity method for powdered samples is found to be better than the conventional resonator methods available in the literature.

Biochemia Medica's editorial policy on authorship.

Computer- intensive resampling/bootstrap methods are feasible when calculating reference intervals from non-Gaussian or small reference samples. Microsoft Excel® in version 2010 or later includes natural functions, which lend themselves well to this purpose including recommended interpolation procedures for estimating 2.5 and 97.5 percentiles. The purpose of this paper is to introduce the reader to resampling estimation techniques in general and in using Microsoft Excel® 2010 for the purpose of estimating reference intervals in particular. Parametric methods are preferable to resampling methods when the distributions of observations in the reference samples is Gaussian or can transformed to that distribution even when the number of reference samples is less than 120. Resampling methods are appropriate when the distribution of data from the reference samples is non-Gaussian and in case the number of reference individuals and corresponding samples are in the order of 40. At least 500-1000 random samples with replacement should be taken from the results of measurement of the reference samples.

Effect of the number of reference samples on the accuracy in X-ray fluorescence determination of the macroscopic composition of As-S-Se glasses

This paper examines the effect of the number of reference samples on the accuracy in X-ray fluorescence determination of the macroscopic composition of As-S-Se glasses. We use empirical calibration and the method of virtual relative X-ray intensities.

Performance of genotype imputation for rare variants identified in exons and flanking regions of genes.

Genotype imputation has the potential to assess human genetic variation at a lower cost than assaying the variants using laboratory techniques. The performance of imputation for rare variants has not been comprehensively studied. We utilized 8865 human samples with high depth resequencing data for the exons and flanking regions of 202 genes and Genome-Wide Association Study (GWAS) data to characterize the performance of genotype imputation for rare variants. We evaluated reference sets ranging from 100 to 3713 subjects for imputing into samples typed for the Affymetrix (500K and 6.0) and Illumina 550K GWAS panels. The proportion of variants that could be well imputed (true r2>0.7) with a reference panel of 3713 individuals was: 31% (Illumina 550K) or 25% (Affymetrix 500K) with MAF (Minor Allele Frequency) less than or equal 0.001, 48% or 35% with 0.001<MAF< = 0.005, 54% or 38% with 0.005<MAF< = 0.01, 78% or 57% with 0.01<MAF< = 0.05, and 97% or 86% with MAF>0.05. The performance for common SNPs (MAF>0.05) within exons and flanking regions is comparable to imputation of more uniformly distributed SNPs. The performance for rare SNPs (0.01<MAF< = 0.05) was much more dependent on the GWAS panel and the number of reference samples. These results suggest routine use of genotype imputation for extending the assessment of common variants identified in humans via targeted exon resequencing into additional samples with GWAS data, but imputation of very rare variants (MAF< = 0.005) will require reference panels with thousands of subjects.

FLOCK: a method for quick mapping of admixture without source samples

Identifying and estimating individual and/or population admixture is a very common objective in evolution and conservation biology. There are many situations where samples from one or many of the putatively hybridizing entities are not available or easily identified. Here we describe FLOCK, a new method especially designed to provide spatial and/or temporal admixture maps in the absence of one or several source samples. FLOCK is a non-Bayesian method and therefore differs substantially from previous clustering algorithms. Its working principle is repeated re-allocation of all collected specimens (total sample) to the k subsamples, each re-allocation being more effective than the previous one in attracting genetically similar individuals. This snowball effect, more formally referred to as a positive feedback mechanism, makes FLOCK an efficient and quick sorting process. The usage of FLOCK is illustrated with two empirical situations which have been thoroughly analysed previously with other approaches. A number of simulations were run to better assess the power of the FLOCK algorithm. Performance comparisons were made between the FLOCK and Structure algorithms. When non-negligible numbers of pure genotypes were present, the two performed equally well. However, FLOCK proved significantly more powerful in the absence of pure genotypes. Moreover, FLOCK showed more potential for fast processing. Run times were shown to increase linearly with size of total sample and with size of k, the number of reference samples from which admixture mapping is performed.

A fast analysis system for forensic DNA reference samples

On January 1st, 2006, the Swedish legislation on obtaining DNA reference samples from suspects and the recording of DNA profiles in databases was changed. As a result the number of samples analysed at the Swedish National Laboratory of Forensic Science (SKL) increased from about 4500 in 2005 to more than 25,000 in 2006. To meet this challenge, SKL launched a new analysis system to create an unbroken chain, from sampling to incorporation of a profile in the national DNA database and subsequent automatic generation of digitally signed hit reports. The system integrates logistics, digital data transfer, new functions in LIMS (ForumDNA Version 4, Ida Infront AB) and laboratory automation. Buccal swab samples are secured on a FTA ® card attached to an identity form, which is barcoded with a unique sample ID. After sampling, the police officer sends a digital request to SKL. The sample is automatically registered in LIMS and processed on delivery. The resulting DNA profiles are automatically classified according to quality using a custom-made expert system. Building the evaluation around mathematical rules makes it reproducible, standardised and minimises manual work and clerk errors. All samples are run in duplicate and the two profiles are compared within LIMS before incorporation in the database. In the first year of operation, the median time for completion of an analysis was 3 days, measured from delivery of the sample to incorporation of the profile in the national DNA database. In spite of the dramatic increase in the number of reference samples there was no backlog.

A study of digital camera colorimetric characterization based on polynomial modeling

The digital camera is a powerful tool to capture images for use in image processing and colour communication. However, the RGB signals generated by a digital camera are device-dependent, i.e., different digital cameras produce different RGB responses for the same scene. Furthermore, they are not colorimetric, i.e., the output RGB signals do not directly correspond to the device-independent tristimulus values based on the CIE standard colorimetric observer. One approach for deriving a colorimetric mapping between camera RGB signals and CIE tristimulus values uses polynomial modeling and is described here. The least squares fitting technique was used to derive the coefficients of 3 × n polynomial transfer matrices, yielding a modeling accuracy typically averaging 1 ΔE units in CMC(1:1) when a 3 × 11 matrix is used. Experiments were carried out to investigate the repeatability of the digitizing system, characterization performance when different polynomials were used, modeling accuracy when 8-bit and 12-bit RGB data were used for characterization, and the number of reference samples needed to achieve a reasonable degree of modeling accuracy. Choice of characterization target and media and their effect on metamerism have been examined. It is demonstrated that a model is dependent upon both media and colorant, and applying a model to other media/colorants can lead to serious eye–camera metamerism problems. © 2001 John Wiley & Sons, Inc. Col Res Appl, 26, 76–84, 2001

Sample introduction for inductively coupled plasma atomic emission spectrometry using an inductively heated vaporizer

A sample introduction system for inductively coupled plasmas (ICPs) which utilizes an induction coil for vaporizing analyte has been developed. This device uses HCl(g) in the carrier gas to prevent arcing in the induction furnace. Adding HCl has the additional benefit of acting as a physical carrier which can enhance analyte transport and allow the vaporization of relatively non-volatile elements. Experiments were carried out to investigate some of the performance characteristics of this vaporizer. Absolute detection limits using inductively coupled plasma atomic emission spectrometry (ICP-AES) for a number of elements ranged from 1.5 to 30 ng corresponding to relative detection limits which ranged from 0.04 to 0.7 ng ml −1. Compared with solution nebulization, improvements in detection limits for these elements was 2–3 orders of magnitude. Analysis of Zn, Mn and Cu in a number of reference samples, including river water, lobster tissue and river sediment, demonstrated the applicability of this vaporizer for a variety of matrices.