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To solve the small sample problem of biometric identification, this paper investigates the limiting case of the problem, i.e., the recognition of a single training sample, and proposes a single sample discriminant analysis method based on Gabor wavelet and KPCA-RBF (KPRC) classifier (kernel principal component analysis-radial basis function). The proposed method performs pixel-level fusion of face and palmprint images. Firstly, a face image and a palmprint image were subject to two-dimensional (2D) Gabor wavelet transform. The resulting Gabor face image and Gabor palmprint image were fused on the pixel level into a new fused image. Next, a new classifier called KPCA-RBF was designed to extract nonlinear discriminative features by KPCA, and classify objects with RBF. Based on AR database, FERET database, and palmprint database, the single sample discriminant analysis method was realized based on Gabor transform and KPCA-RBF classifier. Experimental results show that multimodal recognition methods clearly outshine single-modal recognition methods, and the GABOR-KPRC with pixel-level fusion achieves better recognition effect than other fusion methods. It was also demonstrated that Gabor transform and KPRC classifier can effectively improve the fusion effect, whether for pixel-level fusion or decision-level fusion.

This research aims to know the effect of Interval Training on VO2 Max in the extracurricularPencak Silat Brotherhood Faithful Heart Terate Rayon Special smkn 1 Nanga Pinoh.This research includes pre-experiment, with the sample not separate. The experimental methodwith the sample does not mean that the research has a single sample group, measured twice, thefirst measurement is done before the subject ispretests,then treatment,which is finally closed witha second measurement (posttest). The design used in this study was "The One Group PretestPosttest Design" or the absence of a control group.The results of the previous study anddiscussion obtained obtained the calculated T value (2.630) t Table (1.812), and the value ofsig (2-tailed) (0.000) of 0.05, the result indicates that the value of T count is greater than in Ttable. The result is interpreted Ha: accepted and Ho: rejected. If Ha accepted interpreted theeffect of Interval Training on VO2 Max on the extra-curricular Pencak Silat Brotherhood ofFaithful Heart Terate Rayon Special SMKN 1 Nanga Pinoh.Keywords: Interval Training, VO2 Max, Pencak Silat.Abstrak: Penelitian ini bertujuan ingin mengetahui Pengaruh Interval Training Terhadap VO2Max Pada Ekstra Kurikuler Pencak Silat Persaudaraan Setia Hati Terate Rayon Khusus SMKN1 Nanga Pinoh. Penelitian ini termasuk pre-experiment, dengan sampel tidak terpisah. Metodeeksperimen dengan sampel tidak terpisah maksudnya penelitianya memiliki satu kelompoksampel saja,yang diukur dua kali, pengukuran pertama dilakukan sebelum subjek diberiperlakuan (pretest), kemudian perlakuan (treatment),yang akhirnya ditutup dengan pengukurankedua (posttest). Desain yang digunakan dalam penelitian ini adalah“The One Group PretestPosttest Design” atau tidak adanya grup kontrol. Hasil penelitian dan pembahasan sebelumnyadiperoleh nilai t hitung (2.630)t Tabel (1.812), dan nilai sig (2-tailed) (0,000) dari 0,05, hasiltersebut menunjukkan bahwa nilai t hitung lebih besar dari pada t tabel. Hasil tersebut diartikanHa: diterima dan Ho: ditolak. Jika Ha diterima diartikan ada Pengaruh Interval TrainingTerhadap VO2 Max Pada Ekstra Kurikuler Pencak Silat Persaudaraan Setia Hati Terate RayonKhusus SMKN 1 Nanga Pinoh.Kata kunci : Interval Training, VO2 Max, Pencak Silat.

The one-sample log-rank test allows to compare the survival of a single sample with a prefixed reference survival curve. It naturally applies in single-arm phase IIa trials with time-to-event endpoint. Several authors have described that the original one-sample log-rank test is conservative when sample size is small and have proposed strategies to correct the conservativeness. Here, we propose an alternative approach to improve the one-sample log-rank test. Our new one-sample log-rank statistic is based on the unique transformation of the underlying counting process martingale such that the moments of the limiting normal distribution have no shared parameters. Simulation results show that the new one-sample log-rank test gives type I error rate and power close to the nominal levels also when sample size is small, while relevantly reducing the required sample size to achieve the desired power as compared to current approaches to design studies to compare the survival outcome of a sample with a reference.

The purpose of this study was to determine the effectiveness of the model to Make a Match in mathematics learning 6th-grade students of Elementary School Marioriwawo Sub-District. This research is a quantitative experimental research. The population in the study were all 6th-grade students of Elementary School Marioriwawo Sub-District from 50 school. Determination of the sample was done by random sampling. Data analysis techniques used is descriptive statistics and inferential statistics using a single sample test. The results showed that the application of effective models in learning mathematics 6th-grade students of Elementary School Marioriwawo Sub-District in terms of the average learning outcomes 71.9, 80.4% activity and and the response to the application of the model 93.7%.

This protocol describes the use of freely available software for the reference-independent analysis of RADseq data produced from a single sample. It is useful to perform a pilot experiment on a single sample for species lacking a reference genome sequence in order to facilitate proper experimental design for a larger-scale experiment. The example data used in this protocol is from a wild Berberis darwinii plant growing in Chile. The protocol produces data that provides an estimation of the number of unique base pairs recovered from a size selection of restriction endonuclease digested genomic DNA, and also an estimation of the frequency of heterozygous polymorphisms in the plant. Evaluation of sequence quality also provides a validation of the methodologies used for tissue collection, DNA extraction, restriction digestion, and size selection (see https://dx.doi.org/10.17504/protocols.io.bdg9i3z6). In addition, the discovered heterozyygous polymorphisms serve as markers to evaluate natural propagation (e.g. self-fertilization versus outcrossing versus clonal propagation) and also for use in tracking the success of human-directed crosses for breeding.

Pose and illumination variations are very challenging for face recognition with a single sample per person (SSPP). In this paper, we address this issue by a Pose-Aware Metric Learning (PAML) approach. Our primary idea is “ from one to many ”: Synthesizing many images of sufficient pose and illumination variability from the single training image, based on which metric learning approach is applied to reduce these “synthesized” variations at each quantified pose. For this purpose, given a single frontal training image, a multi-depth generic elastic model and an extended generic elastic model are developed to synthesize facial images of the target pose with varying 3D shape (depth) and illumination variations respectively. To reduce these “synthesized” variability, Pose-Aware Metric spaces are separately learnt by linear regression analysis at each quantized pose, and pose-invariant recognition is performed in the corresponding metric space. By preserving the detailed texture and reducing the shape variability, the PAML method achieves an 100% accuracy on the Multi-PIE database under the test setting across poses, which is significantly better than the traditional methods that use a large generic image ensemble to learn the cross-pose transformations. On the more challenging setting across both poses and illuminations, PAML outperforms the recent deep learning approaches by over 10% accuracy.

In this article, the estimation of finite population mean of a sensitive variable based on generalized optional scrambled responses collected using a randomization device is presented under single sample approach. The estimator is then improved by using non-sensitive auxiliary information. Some special cases and privacy protection for proposed model has also been discussed. Simulation studies are conducted to evaluate the proposed device.

Papaya grading is performed manually which may lead to misclassifications, resulting in fruit boxes with different maturity stages. The objective is to predict the ripening of the papaya fruit using digital imaging and random forests. A series of physical/chemical analyses are carried out and true maturity stage is derived from pulp firmness measurements. Imaging and image analysis provides hand-crafted color features computed from the peel and random decision forests are implemented to predict ripening stage. More specifically, a total of 114 samples from 57 fruits are used for the experiments, and classified into three stages of maturity. After image acquisition and analysis, twenty-one hand-crafted color features (comprising seven groups) that have low computational cost are extracted and evaluated. Random forests with two datasets (cross-validation and prediction set) are employed for the experiments. Concerning all image features, 94.3% classification performance is obtained over the cross-validation set. The prediction set obtained 94.7% misclassifying only a single sample. For the group comparisons, the normalized mean of the RGB (red, green, blue) color space achieved better performance (78.1%). Essentially, the technique can mature into an industrial application with the right integration framework.

ملخص البحث دراسة مطابقة بين طريقتين لحشو وتبطين المانيکان لتحقيق حجم وشکل الجسم البشرى هدف البحث إلى إعداد قوالب لشکل الأمامي و الخلفي کل على حدا تسترشد بها مفردات العينة أثنا حشو والتبطين المانيکان لمعرفة المقدار اللازم من رقائق القطن في الأماکن التي تحتاج إليها بهدف وصول المانيکان إلى شکل وهيئة مفردة العينة . وأسفرت أهم النتائج إلى : 1. وجود فروق دالة إحصائيا عند مستوى دلاله 0,01 لصالح المجموعة التجريبية لمطابقة المانيکان بعد عملية الحشو والتبطين مع جسم العينة (للأمام – الخلف ) . 2. وجود فروق دالة إحصائيا عند مستوى دلاله 0,01 لصالح المجموعة التجريبية لمطابقة المانيکان بعد عملية الحشو والتبطين مع جسم العينة(الشکل ). أهم التوصيات : 1. اجراء المزيد من الدراسات في تبسط طريقة إعداد المانيکان، وتقلل من وقت التعلم . Abstract FIT Study Between The Two Ways Of Padding Mannequin To Achieve The Size and Shape of The Human Body The research aims to develop templates to form the front and back of each end to guide the sample twelve filler and lining mannequin necessary to know the amount of cotton chips in the places that you need in order to access the mannequin to the shape and form of a single sample. And resulted in the most important results: 1. There are significant differences at the 0.01 level of significance for the experimental group to match the mannequin after the padding and lining process with the sample body (forward – back). 2. There are significant differences at the 0.01 level of significance for the experimental group to match the mannequin after the padding and lining process with the sample body (Figure). The most important recommendations: 1. conduct further studies in preparation mannequin simplifies the way, reduces learning time.

Purpose Irregularity accounts for interproximal contact point displacements and can be measured using a variety of techniques. The aim of this study was to evaluate the validity and reliability of three methods in relation to the “gold standard” of manual caliper measurements of plaster study casts. Materials and Methods Six mandibular study casts representing varying degrees of anterior crowding were measured by the same observer using four methods over the course of 5 weeks. Validity was statistically assessed with single sample statistical testing by the cast, method, and week (or combinations) and reliability was tested using intraclass reliability coefficient. Results The three noncaliper techniques demonstrated validity (P > 0.05) when the caliper method mean was used as the set value, but the three noncaliper methods produced means that were significantly greater when mean differences among techniques were compared to hypothetical zero. However, none of the differences were clinically significant (>0.5 mm). High reliability (reproducibility) was demonstrated (P > 0.05) with both aggregated and nonaggregated mean differences. Conclusions Reliability of measuring irregularity index with any of the four methods tested was high, but the validity of techniques compared to the “gold standard” method of manual caliper measurements of plaster study casts should continue to be questioned. Differences among the techniques were not clinically significant or important.

Under the existing single sample condition, the fatigue detection method of an automobile driver has some problems, such as an improper camera calibration method, image denoising beyond the controllable range, low fatigue detection accuracy, and unsatisfactory effect. A fatigue detection method for drivers based on face recognition under a single sample condition is proposed. Firstly, the camera is calibrated by Zhang Zhengyou’s calibration method. The optimal camera parameters were calculated by linear simulation analysis, and the image was nonlinear refined by the maximum likelihood method. Then, the corrected image effect is enhanced, and the scale parameter gap in the MSRCR image enhancement method is adjusted to the minimum. The detection efficiency is improved by a symmetric algorithm. Finally, the texture mapping technology is used to enhance the authenticity of the enhanced image, and the face image recognition is carried out. The constraint conditions of fatigue detection are established, and the fatigue detection of car drivers under the condition of a single sample is completed. Experimental results show that the proposed method has a good overall detection effect: the fatigue detection accuracy is 20% higher than that of the traditional method, and the average detection time is over 30%. Compared with the traditional fatigue detection methods, this method has obvious advantages, can effectively extract more useful information from the image, and has strong applicability.

The aim of this study was to apply Bayesian models to the Fourier-transform infrared spectroscopy spectra of individual sheep milk samples to derive calibration equations to predict traditional and modeled milk coagulation properties (MCP), and to assess the repeatability of MCP measures and their predictions. Data consisted of 1,002 individual milk samples collected from Sarda ewes reared in 22 farms in the region of Sardinia (Italy) for which MCP and modeled curd-firming parameters were available. Two milk samples were taken from 87 ewes and analyzed with the aim of estimating repeatability, whereas a single sample was taken from the other 915 ewes. Therefore, a total of 1,089 analyses were performed. For each sample, 2 spectra in the infrared region 5,011 to 925 cm −1 were available and averaged before data analysis. BayesB models were used to calibrate equations for each of the traits. Prediction accuracy was estimated for each trait and model using 20 replicates of a training-testing validation procedure. The repeatability of MCP measures and their predictions were also compared. The correlations between measured and predicted traits, in the external validation, were always higher than 0.5 (0.88 for rennet coagulation time). We confirmed that the most important element for finding the prediction accuracy is the repeatability of the gold standard analyses used for building calibration equations. Repeatability measures of the predicted traits were generally high (≥95%), even for those traits with moderate analytical repeatability. Our results show that Bayesian models applied to Fourier-transform infrared spectra are powerful tools for cheap and rapid prediction of important traits in ovine milk and, compared with other methods, could help in the interpretation of results.

Copy number variations (CNVs) play an important role in many types of cancer. With the rapid development of next generation sequencing (NGS) techniques, many methods for detecting CNVs of a single sample have emerged: (i) require genome-wide data of both case and control samples, (ii) depend on sequencing depth and GC content correction algorithm, (iii) rely on statistical models built on CNV positive and negative sample datasets. These make them costly in the data analysis and ineffective in the targeted sequencing data. In this study, we developed a novel alignment-free method called DL-CNV to call CNV from the target sequencing data of a single sample. Specifically, we collected two sets of samples. The first set consists of 1301 samples, in which 272 have CNVs in ERBB2 and the second set is composed of 1148 samples with 63 samples containing CNVs in MET. Finally, we found that a testing AUC of 0.9454 for ERBB2 and 0.9220 for MET. Furthermore, we hope to make the CNV detection could be more accurate with clinical "gold standard" (e.g. FISH) information and provide a new research direction, which can be used as the supplement to the existing NGS methods.

Data samples from $\mathbb{R}^{d}$ with a common support of size $k$ are accessed through $m$ random linear projections (measurements) per sample. It is well-known that roughly $k$ measurements from a single sample are sufficient to recover the support. In the multiple sample setting, do $k$ overall measurements still suffice when only $m$ measurements per sample are allowed, with $m, Comment: A preliminary version of this paper appeared at IEEE International Symposium on Information Theory 2019

Swab samples were obtained from the surfaces of randomly selected beef carcasses passing through a high-speed dressing process. A single sample was obtained from a randomly selected site on the surface of each selected carcass. Fifty such samples were collected at each of four stages in the process. The aerobic bacteria, coliforms, and Escherichia coli recovered from each sample were enumerated. Values for the mean log units and standard deviations of each set of 50 log values were calculated on the assumption that the log values were normally distributed. The log of the arithmetic mean was estimated from the mean log and standard deviation values for each set. The results show that the average numbers of E. coli , coliforms, and aerobic bacteria which are deposited on carcasses during skinning and evisceration are not reduced by trimming, and that washing approximately halves the average numbers of those bacteria on carcasses. It is concluded that commercial trimming and washing operations are not effective means of decontaminating beef carcasses.

Este trabalho teve como objetivo sugerir diretrizes para melhor mapear usos da terra usando o complemento Semi-automatic Classification Plugin (SCP) para QGIS, destacando-se quais os melhores conjuntos de dados, classificadores e estratégias amostrais para treinamento. Foram combinados quatro conjuntos de dados derivados de imagem Sentinel 2A, três classificadores disponíveis no SCP, e duas estratégias amostrais: amostras de treinamento (ROI’s) separadas ou dissolvidas em uma única amostra, obtendo-se 24 tratamentos. Os tratamentos foram avaliados quanto à acurácia (coeficiente Kappa), qualidade visual do mapa final e tempo de processamento. Os resultados mostraram que: (1) o SCP é adequado para mapear usos da terra; (2) quanto maior o conjunto de dados, melhor o desempenho do classificador; e (3) a utilização de ROI’s dissolvidas sempre diminui o tempo de processamento, mas apresenta efeito ambíguo sobre os diferentes classificadores. Para melhores resultados, recomenda-se a aplicação do classificador Maximum Likelihood sobre o maior conjunto de dados disponível, utilizando-se amostras de treinamento coletadas contemplando todas as variações intraclasse, e posteriormente dissolvidas em uma única ROI.Palavras-chave: sensoriamento remoto, amostras de treinamento, QGIS, Sentinel 2A,MAPPING LAND USES/COVERS WITH SEMI-AUTOMATIC CLASSIFICATION PLUGIN: WHICH DATA SET, CLASSIFIER AND SAMPLING DESIGN? ABSTRACT: This paper aimed to suggest guidelines to better map land uses using the Semi-automatic Classification Plugin (SCP) for QGIS, highlighting which the best data sets, classifiers and training sampling designs. Four data sets from a Sentinel 2A image were combined with three classifiers available in the SCP, and two sampling designs: separate or dissolved training samples (ROI's) in a single sample, obtaining 24 treatments. The treatments were evaluated regarding the accuracy (Kappa coefficient), visual quality of the final map and processing time. The results suggest that: (1) the SCP is suitable to map land uses; (2) the larger the data set, the better the classifier performance; and (3) the use of dissolved ROI always decreases processing time, but has an ambiguous effect on the different classifiers. In order to get better results, we recommend to apply the Maximum Likelihood classifier on the largest data set available, using training samples that cover all possible intraclass variations, subsequently dissolved in a single ROI.Keywords: remote sensing, training samples, QGIS, Sentinel 2A.

Background Measurement errors occurring during glomerular filtration rate (GFR) studies propagate to an error in the calculated GFR. Previous work has modelled measurement errors for slope-intercept (SI-GFR), single-sample (SS-GFR) and slope-only (SO-GFR) methods. In this study, we have extended these models. The primary aims were to (i) compare measurement errors in two-sample SI-GFR, three-sample SI-GFR, SS-GFR and SO-GFR, and (ii) determine the sensitivity of GFR to errors arising from different measurements. Patients and methods This study expanded on previous models of GFR measurement error incorporating biological data from 786 patients and realistic measurement errors. GFR median absolute error and the coefficient of variation (CV) were calculated for each method. A sensitivity analysis was carried out for individual measurement errors. Results The median absolute error ranged between 1.2 and 2.3 ml/min/1.73 m, lowest for SS-GFR (4 h) and highest for SO-GFR. At higher rates of clearance, CV was less than 5% for all methods. CV increased rapidly when GFR decreased below a threshold ranging between 34 and 56 ml/min/1.73 m, lowest for three-point SI-GFR and highest for SO-GFR. SI-GFR and SS-GFR are most sensitive to injected activity quantification, but less sensitive to other measurement errors. Conclusion Measurement errors are probably insignificant relative to biological variation for GFR of more than 60 ml/min/1.73 m, but become significant irrespective of biological variation at lower GFR, particularly in serial studies when GFR less than 25 ml/min/1.73 m. Limits of precision recommended in the 2018 British Nuclear Medicine Society guideline are appropriate for once-off GFR measurement, whereas slightly more stringent limits are proposed for serial studies.

Background An adaptive neuro-fuzzy inference system (ANFIS) was employed to predict the oxidative stability of virgin olive oil (VOO) during storage as a function of time, storage temperature, total polyphenol, α-tocopherol, fatty acid profile, ultraviolet (UV) extinction coefficient (K268 ), and diacylglycerols (DAGs). Results The mean total quantities of polyphenols and DAGs were 1.1 and 1.9 times lower in VOOs stored at 25 °C than in the initial samples, and the mean total quantities of polyphenols and DAGs were 1.3 and 2.26 times lower in VOOs stored at 37 °C than in the initial samples, respectively. In a single sample, α-tocopherol was reduced by between 0.52 and 0.91 times during storage, regardless of the storage temperature. The mean specific UV extinction coefficients (K268 ) for VOO stored at 25 and 37 °C were reported as 0.15 (ranging between 0.06-0.39) and 0.13 (ranging between 0.06-0.35), respectively. The ANFIS model created a multi-dimensional correlation function, which used compositional variables and environmental conditions to assess the quality of VOO. The ANFIS model, with a generalized bell-shaped membership function and a hybrid learning algorithm (R2 = 0.98; MSE = 0.0001), provided more precise predictions than other algorithms. Conclusion Minor constituents were found to be the most important factors influencing the preservation status and freshness of VOO during storage. Relative changes (increases and reductions) in DAGs were good indicators of oil oxidative stability. The observed effectiveness of ANFIS for modeling oxidative stability parameters confirmed its potential use as a supplemental tool in the predictive quality assessment of VOO. © 2019 Society of Chemical Industry.

Stock status for many medium- and small-scale fisheries is unknown due, for example, to a lack of catch data and the absence of scientific observer programs. However, length-frequency data are often available for such fisheries because they are the cheapest and easiest data to obtain. Various stock assessment methods have been developed that use length-frequency data and make equilibrium assumptions regarding both recruitment and fishing mortality. These assumptions raise questions regarding the reliability of the results, particularly when the method is applied to a single sample of length-frequency. We developed a Length-Based Pseudo-cohort Analysis (LBPA) model whose parameters can be estimated using multiple length frequencies and penalized maximum likelihood, under the assumption that using more than one length-frequency sample reduces the effects of the equilibrium conditions assumed in the model. We explored the performance of LBPA using simulations that examined the implications for estimation of the spawning potential rate (SPR) and the relative fishing mortality (F/FMSY). These simulations considered scenarios related to exploitation rate, steepness, numbers of years and sample sizes for length-frequency, and data weights. The performance of LBPA was compared to that of length-based spawning potential ratio (LBSPR). LBPA performed better with additional length-frequencies than with increased sample size, irrespective of how the data were weighted, and generally outperformed LBSPR. Estimates were more accurate and less biased when exploitation rates were high. This work provides guidelines that should be considered when using length-based pseudo-cohort models for data-poor fisheries.

The purity of street cocaine is an important parameter in drug research and for forensic purposes, as it can be used to group samples, determining their origin, and to assess the monetary value of a drug sample. Current methods require batch processing of samples, as calibration curves need to be run. In this paper, a method is presented for the quantification of cocaine by single injection, avoiding the need for a calibration curve by using multiple, differently isotopically labeled internal standards. Using this fast method, results can be reported immediately after analysis and fully open-access mass spectrometric analysis becomes possible. The method was fully validated, with recoveries compared to certified standards of 99–101 % and to an accredited method from an independent laboratory of 86–117 %. Precision was tested both interday and intraday on three levels and all relative standard deviations were lower than 6.1 %. A linear response was found down to a purity of 1.3 %. The total analysis time for a single sample was approximately 30 min. The method was applied to 106 cocaine samples collected from a large UK music festival. Cocaine purity ranged from 1.3 to 78.8 %, with a mean of 43.1 %. This was comparable to other studies of UK cocaine samples. Our new approach has the potential to be applied to simple quantification of a variety of analytes in biological and non-biological samples.

Estimating effective population size (N e) using linkage disequilibrium (LD) information (N e( LD )) has the operational advantage of using a single sample. However, N e( LD ) estimates assume discrete generations and its performance are constrained by demographic issues. However, such concerns have received little empirical attention so far. The pedigree of the endangered Gochu Asturcelta pig breed includes individuals classified into discrete filial generations and individuals with generations overlap. Up to 780 individuals were typed with a set of 17 microsatellites. Performance of N e( LD ) was compared with N e estimates obtained using genealogical information, molecular coancestry (N e(M)) and a temporal (two‐sample) method (N e( JR )). Molecular‐based estimates of N e exceeded those obtained using pedigree data. Estimates of N e( LD ) for filial generations F3 and F4 (17.0 and 17.3, respectively) were lower and steadier than those obtained using yearly or biannual samplings. N e( LD ) estimated for samples including generations overlap could only be compared with those obtained for the discrete filial generations when sampling span approached a generation interval and demographic correction for bias was applied. Single‐sample N e(M) estimates were lower than their N e( LD ) counterparts. N e(M) estimates are likely to partially reflect the number of founders rather than population size. In any case, estimates of LD and molecular coancestry tend to covary and, therefore, N e(M) and N e( LD ) can hardly be considered independent. Demographically adjusted estimates of N e( JR ) and N e( LD ) took comparable values when: (1) the two samples used for the former were separated by one equivalent to discrete generations in the pedigree and (2) sampling span used for the latter approached a generation interval. Overall, the empirical evidence given in this study suggested that the advantage of using single‐sample methods to obtain molecular‐based estimates of N e is not clear in operational terms. Estimates of N e obtained using methods based in molecular information should be interpreted with caution.

The research was conducted with the aims as the operation describing of garlic market, garlic price and determining the duration of time required to restore its normal price. The type of data was secondaries data with applying a deststudy collection method. Data were analyzed descriptively equipped with a single sample Chi-Square non-parametric statistical test. The results of the study showed that The operation of the Garlic engaged several department e.g. Jambi Province Industry and Trade Service (Disperindag), Ministry of Trade of the Republic of Indonesia, Jambi Province Food Security Service, and Indonesian onion and tuber vegetable business association (Bahrindo). A total of 3.2 tons of imported garlic were distributed to the wholesale level at a price of Rp. 22,000 per kg, so that the price at the consumer level was Rp. 32,000 per Kg. The lowest average daily price was Rp.18,000 per Kg (in January) and the highest average price was Rp.46,419 per Kg (in May). In the period January to May, the average price increase reached 157.8%, while the decline in May-December was smaller than the previous increase of only 65.9% (Average price in December was Rp.25,800 per Kg). The price of garlic after market operations continued to show an increase not only due to the presence of Ramadan and Idul Fitri, but also the supply of garlic imports was too slow. The highest price fluctuation occurred in May where the price range was Rp.70,000 per kg (Rp. 30,000 to Rp.100,000 per kg). The time required to return the price of garlic in the field to the normal price set by the government was 50 days or nearly two months from the started periode of market operations.

This paper proposes a novel method for estimating the evolutionary power spectral density (EPSD) of a nonstationary process based on a single sample. In the proposed method, a sample of a nonstationary process is decomposed into several components with a new binomial fitting decomposition (BFD). The EPSD of each component can be estimated using a newly proposed time-varying standard deviation estimation method and short-time Thomson multiple-window spectrum estimation method. The EPSD of the analyzed nonstationary sample is obtained by combining the EPSDs of all components. Via a comprehensive numerical study, the applicability of the proposed EPSD estimation method (for estimating the EPSD of a nonstationary process) is analyzed and compared with those by the Priestley method and wavelet-based method. The numerical results indicate that the estimated EPSD by the proposed method is more consistent with the corresponding theoretical one than those by the other two methods. Finally, the EPSDs of Storm Ampil, measured atop the Shanghai World Financial Center, are analyzed by the proposed method.

A Amostragem Não Destrutiva (AND) permite uma caracterização eficiente, simples e segura das propriedades químicas do vegetal, como o Coeficiente de Utilização Biológico (CUB). A associação da AND com a técnica de Redes Neurais Artificiais (RNA) pode ser uma alternativa potencial em substituição às equações de regressão e aos métodos tradicionais de interpolação. Portanto, o presente trabalho objetivou avaliar a eficiência da RNA e da amostragem não destrutiva para estimar a eficiência de uso de nutrientes no tronco. O experimento foi instalado em blocos ao acaso, sendo estudado, em três blocos, o efeito de cinco espaçamentos de plantio: T1 – 3,0 m x 0,5 m; T2 – 3,0 m x 1,0 m; T3 – 3,0 m x 1,5 m; T4 – 3,0 m x 2,0 m e T5 – 3,0 m x 3,0 m. Uma árvore-amostra foi abatida para realizar a cubagem rigorosa e quantificar a matéria seca de casca e lenho por unidade experimental, totalizando-se 15 árvores. As árvores-amostras foram pesadas no campo e subamostras de casca e lenho foram coletadas ao longo do fuste para compor uma amostra composta por árvore. Também foi retirada uma amostra simples de cada componente obtidas com auxílio de um formão e martelo na região do DAP nas mesmas árvores-amostras. As amostras foram secas a 65ºC até peso constante. O material vegetal foi moído e submetido à análise química. Ajustaram-se modelos de regressão e aplicação de RNA para estimação do CUBTronco a partir do CUBDAP Casca e CUBDAP Lenho. As RNA apresentaram maior precisão e confiabilidade do que a regressão. A modelagem por redes neurais artificiais utilizando-se apenas uma amostra da casca na região do DAP demonstrou ser adequada para a estimativa do coeficiente de utilização biológico do tronco.

The Poisson-lognormal distribution was suggested as a model for commonness of species by Preston. The analysis leading him to suggest this distribution was as follows: Suppose that the number of each species caught in a trap is assumed to be a single sample from a Poisson distribution with mean λ. R. S. Uhler and P. G. Bradley proposed two models to describe a phenomenon in petroleum exploration. These two models used the sum of lognormal random variables as a compounder of Poisson and negative binomial distributions. P. Holgate noted that a clear distinction between the P-LN and the lognormal has not always been maintained. R. M. Cassie indicated that for most plankton populations, samples with μ = 100 would not depart significantly from a lognormal distribution.

Background Type II diabetes mellitus causes many complications, and its prevalence continues to increase in Korea. Accurate measurement of glycated Hb (HbA1c) is important because of its usefulness in diagnosis, follow-up, and prediction of prognosis. We tested the analytical performance of the HLC-723 G11 Variant Mode (G11vr; Tosoh Bioscience, Inc., Tokyo, Japan), recently introduced to Korea, in detecting HbA1c. Methods We evaluated precision, linearity, carry-over, and turnaround time. Using 208 samples, including 108 flagged samples, we compared HbA1c concentrations from four analyzers through correlation analysis: G11vr, HLC-723 G8 Variant Mode (G8vr, Tosoh Bioscience), HLC-723 G11 Standard Mode (G11st, Tosoh Bioscience), and HLC-723 G8 Standard Mode (G8st, Tosoh Bioscience). We used HPLC mass spectrometry (MS) and capillary electrophoresis (CE) to confirm the HbA1c concentrations of 15 additional known Hb variant samples. Results Repeatability (% CV) in measuring low- and high-concentration controls was 0.57% and 0.35%, respectively; within-laboratory precision was 0.86% and 0.69%, respectively. In a linearity test, the coefficient of determination was 0.9999 (measurement range: 3.64% to 18.59%) for HbA1c. The correlations between G11vr and other analyzers were weaker for flagged samples than for non-flagged samples. The carry-over effect was less than 0.4%. Turnaround time for a single sample was lower in G11vr (one minute) than in G8vr (1.6 minutes). For 15 samples with Hb variants, G11vr HbA1c results were more similar than those of other analyzers to HPLC-MS and CE results. Conclusions G11vr showed adequate performance and rapid turnaround time in measuring HbA1c.

Hi-C data is commonly normalized using single sample processing methods, with focus on comparisons between regions within a given contact map. Here, we aim to compare contact maps across different samples. We demonstrate that unwanted variation, of likely technical origin, is present in Hi-C data with replicates from different individuals, and that properties of this unwanted variation changes across the contact map. We present BNBC, a method for normalization and batch correction of Hi-C data and show that it substantially improves comparisons across samples, including in a QTL analysis as well as differential enrichment across cell types.

Microarray batch effect (BE) has been the primary bottleneck for large-scale integration of data from multiple experiments. Current BE correction methods either need known batch identities (ComBat)or have the potential to overcorrect, by removing true but unknown biological differences (SVA).Even though the effects of technical differences on measured expression have been published, there are no BE correction algorithms that take the approach of predicting technical effects from parameters computed from a fixed reference sample set. We show that a set of signatures, each of which is a vector the length of the number of probes, calculated on a Reference set of microarray samples can predict much of the batch effect in other Validation sets. We present a rationale of selecting a Reference set of samples designed to estimate technical differences without removing biological differences. Putting both together, we introduce the Batch Effect Signature Correction (BESC) algorithm that uses the BES calculated on the Reference set to efficiently predict and remove BE. Using two independent Validation sets, we show that BESC is capable of removing batch effect without removing unknown but true biological differences. Much of the variations due to batch effect is shared between different microarray datasets. That shared information can be used to predict signatures (i.e. directions of perturbation) due to batch effect in new datasets. The correction is blind (without needing to re-compute the parameters on new samples to be corrected), single sample, (each sample is corrected independently of each other) and conservative (only those perturbations known to be likely to be due to technical differences are removed ensuring that unknown but important biological differences are maintained). Those three characteristics make it ideal for high-throughput correction of samples for a microarray data repository. An R Package besc implementing the algorithm is available from http://explainbio.com.

Most methods to estimate creatinine clearance (CCr) such as Cockroft–Gault (CG) and modification of diet in renal disease (MORD) use only a single sample of serum creatinine (SCr) on which to base their estimate. This only works when the patient is in a stable steady state. When renal function is changing and SCr is either rising or falling, SCr has not yet reached such a stable steady state. Because of this, the change in CCr estimated by methods using only a single SCr is always false and is always late in being recognized. What is needed is to estimate CCr from a pair of changing SCr samples. The one with most clinical use is described in this chapter. It uses a pair of SCr samples taken at known times, and calculates the CCr that makes SCr go from that initial value to the second one in the stated time period in a patient of the stated age, gender, height, and weight. When SCr is stable, its results are close to CG. When changing, it provides superior ability to track CCr in intensive care unit patients, for example. While such estimates may be biased due to systematic errors in estimation of creatinine production, the method nevertheless provides a direct link between the changes in a patient’s SCr the possibly biased estimate of glomerular filtration rate, whatever it may beand dry behaviour, with direct information of the relationship between SCr and drug elimination.

This paper deals with global sensitivity analysis of computer model output. Given an independent input sample and associated model output vector with possibly the vector of output derivatives with respect to the input variables, we show that it is possible to evaluate the following global sensitivity measures: (i) the Sobol indices, (ii) the Borgonovo's density-based sensitivity measure, and (iii) the derivative-based global sensitivity measure of Sobol and Kucherenko. We compare the efficiency of the different methods to address factors fixing setting, an important issue in global sensitivity analysis. First, global sensitivity analysis of the Ishigami function is performed with the different methods. Then, they are applied to two different responses of a soil drainage model. The results show that the polynomial chaos expansion for estimating Sobol indices is the most efficient approach. We compare several methods for global sensitivity analysis of model output.All the methods only require one single sample with input variables independently distributed.We study their efficiency to address factors fixing setting.The different methods are tested on the Ishigami function and a soil drainage model.Sobol indices via sparse PCE are found to be the most efficient approach.

Combining similar data provides larger data sets through conducting test for homogeneity of several samples under various production processes or samples from different LOTs. The test for homogeneity has been applied to either variable or attribute data, and for variable data set physical homogeneity has been tested without consideration of the specification to the set. This paper proposes a method for test of homogeneity based on quality level through using both variable data and the specification. Quality-based test for homogeneity as a way of combining data is implemented by test for coefficient of variation in the proposed method. The method was verified through the application to the data set in open literature. And possibility to combine performance data for various types of thermal battery was discussed in order to estimate operation reliability.Key Words : Combining Data(데이터 통합, 단일 표본화), Coefficient of Variation(변동계수), Test for Homogeneity(동질성 검정), Thermal Battery(열전지), Reliability(신뢰성)

The single sample per person problem (SSPP) is quite common in real-world face recognition applications. In such circumstance, the lack of enough training samples often results in poor generalization ability for majority of the existing state-of-the-art methods. To address this problem, in this paper, a fairly simple but effective approach, called adaptive linear regression classifier (ALRC), is presented based on the simple observation that similar subjects have similar intra-personal variations. ALRC is a linear model representing a probe image as a linear combination of the single class-specific gallery and the intra-personal variations adaptively pulled from his/her kNNs in an auxiliary generic training set with multiple samples per person. ALRC can be easily employed with a regularized least square estimator and the decision is ruled in favor of the class with the minimum reconstruction error. Experimental results on AR and FERET face datasets show that ALRC outperforms several state-of-the-art approaches and demonstrates promising abilities against variations including expression, illumination and disguise.

In this paper, like a magician can show several birds flying out of an empty basket, we show that at least seven parameters of interest to a social scientist can be estimated from a single sample and one response from each respondent. A real survey data application is given.

This paper presents a basic formula for performance gradient estimation of semi-Markov decision processes (SMDPs) under average-reward criterion. This formula directly follows from a sensitivity equation in perturbation analysis. With this formula, we develop three sample-path-based gradient estimation algorithms by using a single sample path. These algorithms naturally extend many gradient estimation algorithms for discrete-time Markov systems to continuous time semi-Markov models. In particular, they require less storage than the algorithm in the literature.

In this paper, we propose a robust probability based sparse method to solve single sample face recognition, which harvests the advantages of both local and global representation. Different from previous sparse representation methods that generate sparse coefficients by 𝑙1, we produce sparse class probability distribution by proposing a multi-phase sparse probability (MSP) framework. To create class probability distribution, we divide each face image into many local blocks and vote based on the classification results of all blocks. For classifying each block, we propose local similarity assumption that makes many conventional methods feasible to SSPP problem. Moreover, we also propose a heuristic multiphase class selection scheme to solve the entropy minimization problem, which finally provides a higher classification confidence from the global perspective. Experimental results on three popular databases show that our approach not only generalizes well to SSPP problem but also has strong robustness to expression, illumination, occlusion and time variation.

Objectives: To develop a theoretical model in order to understand how to recognize acquainted faces and the relationship between face processing acknowledgement with other aspects in face. Greater Performance is achieved in face recognition by local appearance based methods. Methods: The Centre Symmetric local binary pattern with gabor magnitude phase have been proposed in this paper to provide an expression, illumination and pose invariant in a single sample problem for face recognition approach with local spatial, scale and directional discriminate and low dimensional face representation based on features. The proposed methodology was compared with PCA and LBP. Findings: Gabor magnitude and Gabor phase tracks the texture boundaries of textured regions accurately. The evaluated Face features from CS-LBP and gabor magnitude phase has better performance. The Photometric descriptors are used in recent years, proven successful for computing regions which are in interest. In this approach the strength of SIFT descriptors are used in combination with LBP texture operator collectively called CS-LBP descriptor. This nullified illumination changes, strengthening flat image areas, and proficiency in computation. Improvements: For images with severe illumination variations SIFT descriptor is outperformed by CSLBP descriptor this was proved experimentally. The face recognition rate is increased by selective local texture feature Gabor Magnitude and Phase CS-LBP when compared with LBP method and Gabor filter.

In this paper, weighted Support Vector Machine was introduced. And the weighted Support Vector Machine was improved. Some methods of determining weight were introduced, and the comprehensive method of determining weight was adopted. Simulation results indicated that the total accuracy rating by improved weighted Support Vector Machine is higher than C-SVM. It’s able to improve the distribution accuracy rating with improved Supported Vector Machine effectively. Weighted Support Vector Machine In standard Support Vector Machine, it’s equal to one in each kinds of sample weight. But some samples is bigger used in determining classified ultra-plane, and some is smaller, or another exceeds some else classified soundly. Therefore, considering conditions of some samples classified, it adjusts weight to Support Vector Machine. Each single sample-point gets different penalized coefficient, to gain much more exact classifications, and lastly it produces Weighted Support Vector Machine, among representing optimized the problems. The initial problem is: l i b x w y t s s C w w

In this study, the authors propose a novel face recognition algorithm based on geometric features to alleviate the one sample per subject problem, called the robust estimation system. Our application adopts both local and global information for robust estimation. The authors utilise the original images from the ORL and Yale databases for evaluation. The images of the FERET database are pre-processed to extract the pure face region and execute the affine transformation. The authors roughly divide the face images into four block images that are most significant for the face: left eye, right eye, nose and mouth. The feature extraction using magnitude of first-order gradients, based on geometric features, is ideal for estimating a single sample. While conducting the classification stage, local features are putatively matched before processing or the global random sample consensus robust estimation features, with the aim of identifying the fundamental matrix between two matched face images. Finally, similarity scores are calculated, and the candidate awarded the highest score is designated the correct subject. Experiments were implemented using the FERET, ORL and Yale databases to demonstrate the efficiency of the proposed method. The experimental results show that our algorithm greatly improves recognition performance compared with the existing methods.

Different random or purposive allocations of items to parcels within a single sample are thought not to alter structural parameter estimates as long as items are unidimensional and congeneric. If, additionally, numbers of items per parcel and parcels per factor are held fixed across allocations, different allocations of items to parcels within a single sample are thought not to meaningfully alter model fit-at least when items are normally distributed. We show analytically that, although these statements hold in the population, they do not necessarily hold in the sample. We show via a simulation that, even under these conservative conditions, the magnitude of within-sample item-to-parcel-allocation variability in structural parameter estimates and model fit can alter substantive conclusions when sampling error is high (e.g., low N, low item communalities, few items per few parcels). We supply a software tool that facilitates reporting and ameliorating the consequences of item-to-parcel-allocation variability. The tool's utility is demonstrated on an empirical example involving the Neuroticism-Extroversion-Openness (NEO) Personality Inventory and the Computer Assisted Panel Study data set.

In a number of situations such as industrial quality control experiments the only observations are record-breaking data. In this paper, two sampling schemes are used to collect record data: single sample and multisample. The aim of this paper is to investigate which one of them is more efficient in the sense of Shannon information. Several general results are established and it is shown that there is a connection between some reliability properties of the parent distribution and the considered comparison criterion. A number of examples illustrating the results are given.

The American Society of Agricultural and Biological Engineers (ASABE) publishes a standard for identifying particle size by sieving (ASABE S319.4). However, this standard includes a number of options that allow the test to be conducted differently, and different laboratories may analyze a single sample with different results. Options include the type of sieve shaker used, the use of sieve agitators, the use of a dispersion agent, and the sieving time. A small study was conducted to examine the effect of varying these methods on the calculated geometric mean diameter by weight (dgw) and geometric standard deviation by weight (sgw). Results indicated that large differences existed depending on the methods used, with dgw varying by as much as 100 microns, and sgw varying by as much as 0.42 simply by altering one option. When compounding the differences in methods, the variations can be even larger. These discrepancies demonstrate that, for particle size analysis by sieving to be used as an effective tool, the same methodology must be used to compare samples. Additionally, the data demonstrate that unless the methods in the current standard are better defined, dgw and sgw should be used only as relative values for comparison.

Visou-se, neste trabalho, avaliar a variabilidade de características químicas do solo, definir o número de amostras simples para formar uma amostra composta e avaliar se a média aritmética das amostras simples é igual ao resultado da análise química da amostra composta. Realizaram-se três séries de amostragens constituídas de 36 amostras simples por série, em um Latossolo Amarelo. Os locais de retirada das amostras da primeira série se distanciaram 15 m um do outro, os da segunda série, 2,5 m, e os da terceira série, 0,42 m. Com as amostras de solo da primeira série, foram formuladas amostras compostas mediante a mistura homogênea de 4, 8, 14, 20, 28 e 36 amostras simples, utilizando-se três repetições. A variabilidade das características químicas do solo praticamente não variou quando o tamanho da área amostrada variou de 8.100 m² para 225 m². Para recomendação de calagem e adubação, as doses de calcário e fertilizante são, em geral, as mesmas, independentemente se a fertilidade média do solo é avaliada a partir da análise química da amostra composta ou a partir da média aritmética dos resultados das análises químicas das amostras simples. A coleta de pelo menos oito amostras simples de solo foi suficiente para formar uma amostra composta representativa. The aim of this study was to evaluate the variability of chemical attributes of soil, to define the number of single samples required to form a composite sample and to evaluate if the mean fertility values obtained from the composite sample is the same as obtained from the arithmetic mean of single samples. Three series of sampling, each one with 36 samples collected in an Oxisol, were carried out. Samples of the first series were taken 15 m apart from each other, the second one 2.5 m, and the third one 0.42 m. Composite samples were formulated with samples of first series by a homogenous mixture of 4, 8, 14, 20, 28 e 36 single samples, with three replications. The variability of soil chemical attributes did not vary when the area size decreased from 8,100 m² to 225 m². Doses of lime and fertilizer may have the same recommendation, independently if soil fertility was evaluated by chemical analysis of a composite sample or obtained from the arithmetic mean of single sampling. At least eight single samples were necessary to compose a representative sample of the area.