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PLoS ONE ◽  
2022 ◽  
Vol 17 (1) ◽  
pp. e0262465
Author(s):  
Sam Henry ◽  
Isabel Thielmann ◽  
Tom Booth ◽  
René Mõttus

Despite the widespread use of the HEXACO model as a descriptive taxonomy of personality traits, there remains limited information on the test-retest reliability of its commonly-used inventories. Studies typically report internal consistency estimates, such as alpha or omega, but there are good reasons to believe that these do not accurately assess reliability. We report 13-day test-retest correlations of the 100- and 60-item English HEXACO Personality Inventory-Revised (HEXACO-100 and HEXACO-60) domains, facets, and items. In order to test the validity of test-retest reliability, we then compare these estimates to correlations between self- and informant-reports (i.e., cross-rater agreement), a widely-used validity criterion. Median estimates of test-retest reliability were .88, .81, and .65 (N = 416) for domains, facets, and items, respectively. Facets’ and items’ test-retest reliabilities were highly correlated with their cross-rater agreement estimates, whereas internal consistencies were not. Overall, the HEXACO Personality Inventory-Revised demonstrates test-retest reliability similar to other contemporary measures. We recommend that short-term retest reliability should be routinely calculated to assess reliability.


2021 ◽  
Author(s):  
Mohomed Abraj ◽  
M. Helen Thompson ◽  
You-Gan Wang

Abstract In environmental monitoring, multiple measurements are often collected at many locations and these measurements depend on each other in complex ways, such as nonlinear dependence. In this research, a novel copula-based geostatistical modelling approach was developed to model multivariate continuous spatial random fields using mixture copulas that captures both spatial and joint dependence of multiple responses over two-dimensional locations. In a bivariate context, the mixture copulas were used to capture the joint spatial dependence of a bivariate random field and the spatial copula of the bivariate random field was constructed as the convex combination of mixture copulas. The proposed model was applied to real forest data and simulated nonlinear data. The performance of the novel method was compared with existing spatial methods, which included a univariate spatial pair-copula model, a multivariate spatial pair-copula model that utilises nonlinear principal component analysis (NLPCA), and conventional kriging. The results show that the proposed model outperforms the existing methods in the interpolation of individual responses and reproduction of their bivariate dependence.


Author(s):  
Chao Xing ◽  
Junhui Huang ◽  
Zhao Wang ◽  
Jianmin Gao

Abstract It is a challenge to improve the accuracy of 3D profile measurement based on binary coded structured light for complex surfaces. A new method of weighted fusion with multi-system is presented to reduce the measurement errors due to the stripe grayscale asymmetry, which is based on the analysis of stripe center deviation related to surface normal and the directions of incident and reflected rays. First, the stripe center deviation model is established according to the geometric relationship between the stripe center deviation, the incident and reflected angles at any measured point. The influence of each variable on stripe center deviation is analyzed, and three subsystems are formed by a binocular structured light framework to achieve multiple measurements based on the influence regularity. Then in order to improve the measurement accuracy, different weights are assigned to the measured point in different subsystems according to the stripe center deviation model and its relationship with measurement error, and the weighted data from different subsystems are fused. Experiments are carried out to validate the presented method, and the experimental results demonstrate that it effectively improves the measurement accuracy of complex surfaces and measurement accuracy is improved by about 27% compared with the conventional method.


2021 ◽  
Vol 104 (6) ◽  
Author(s):  
Bo-Fu Xie ◽  
Fei Ming ◽  
Dong Wang ◽  
Liu Ye ◽  
Jing-Ling Chen

Cells ◽  
2021 ◽  
Vol 10 (11) ◽  
pp. 3107
Author(s):  
Rosy Ghanem ◽  
Philippe Roquefort ◽  
Sophie Ramel ◽  
Véronique Laurent ◽  
Tanguy Haute ◽  
...  

The mucus obstructing the airways of Cystic Fibrosis (CF) patients is a yield stress fluid. Linear and non-linear rheological analyses of CF sputa can provide relevant biophysical markers, which could be used for the management of this disease. Sputa were collected from CF patients either without any induction or following an aerosol treatment with the recombinant human DNAse (rhDNAse, Pulmozyme®). Several sample preparations were considered and multiple measurements were performed in order to assess both the repeatability and the robustness of the rheological measurements. The linear and non-linear rheological properties of all CF sputa were characterized. While no correlation between oscillatory shear linear viscoelastic properties and clinical data was observed, the steady shear flow data showed that the apparent yield stress of sputum from CF patients previously treated with rhDNAse was approximately one decade lower than that of non-treated CF patients. Similar results were obtained with sputa from non-induced CF patients subjected ex vivo to a Pulmozyme® aerosol treatment. The results demonstrate that the apparent yield stress of patient sputa is a relevant predictive/prognostic biomarker in CF patients and could help in the development of new mucolytic agents.


2021 ◽  
Vol 29 (6) ◽  
pp. 0-0

Inducing more and higher-quality answers to questions is essential to sustainable development of Social Question-and-Answer (SQA) websites. Previous research has studied factors affecting question success and user motivation in answering questions, but how a question’s own characteristics affect the question’s answer outcome on SQA websites remains unknown. This study examines the impact of the characteristics of a question, namely readability, emotionality, additional descriptions, and question type, on the question’s answer outcome as measured by number of answers, average answer length, and number of “likes” received by answers to the question. Regression analyses reveal that readability, additional descriptions, and question type have significant impact on multiple measurements of answer outcome, while emotionality only affects the average answer length. This study provides insights to SQA website builders as they instruct users on question construction. It also provides insights to SQA website users on how to induce more and higher-quality answers to their questions.


2021 ◽  
Vol 29 (6) ◽  
pp. 0-0

Inducing more and higher-quality answers to questions is essential to sustainable development of Social Question-and-Answer (SQA) websites. Previous research has studied factors affecting question success and user motivation in answering questions, but how a question’s own characteristics affect the question’s answer outcome on SQA websites remains unknown. This study examines the impact of the characteristics of a question, namely readability, emotionality, additional descriptions, and question type, on the question’s answer outcome as measured by number of answers, average answer length, and number of “likes” received by answers to the question. Regression analyses reveal that readability, additional descriptions, and question type have significant impact on multiple measurements of answer outcome, while emotionality only affects the average answer length. This study provides insights to SQA website builders as they instruct users on question construction. It also provides insights to SQA website users on how to induce more and higher-quality answers to their questions.


Author(s):  
К.В. Шаталов

Разработаны новые робастные алгоритмы обработки результатов многократных измерений состава и свойств нефтепродуктов, учитывающие тот факт, что эмпирическая функция распределения результатов измерений состава и свойств нефтепродуктов представляет собой смесь двух нормальных распределений с разными значениями параметров положения и масштаба. В случае измерений состава и свойств нефтепродуктов в качестве робастных оценок параметра положения и параметра масштаба выборки предложено использовать М-оценки с предварительным масштабированием на основе модифицированной функции Хампеля. Для нахождения М-оценки предложены два итеративных способа вычисления на основе средневзвешенного метода наименьших квадратов, отличающиеся процедурами расчета начальных оценок параметров положения и масштаба выборки. При числе результатов в выборке более двадцати в качестве начальных значений параметров положения и масштаба целесообразно использовать α‑урезанное среднее и α‑урезанное стандартное отклонение с долей усечения 0,05. При числе результатов в выборке менее двадцати в качестве начальных значений параметра положения и параметра масштаба обоснованно использование робастных оценок, не требующих удаления части данных. В качестве начальной оценки параметра положения предложено использовать оценку Ходжеса – Лемана; в качестве параметра масштаба – медианы абсолютных разностей. Предложенные робастные алгоритмы могут быть использованы при обработке результатов эксперимента по определению показателей прецизионности, правильности и точности методик измерений состава и свойств нефтепродуктов, итогов межлабораторных сравнительных испытаний нефтепродуктов, расчете аттестованного значения стандартных образцов состава и свойств нефтепродуктов, а также в других случаях многократных наблюдений. New robust algorithms of treatment of the results of multiple measurements of composition and properties of petroleum products were developed in respect that empirical distribution function of the results of measurements of composition and properties of petroleum products are the mixture of two normal distributions with different values of position and scale parameters. In case of measurements of composition and properties of petroleum products it has been proposed to use M-estimator with pre-scaling based on modified Hampel function as robust estimators of position and scale parameters. To calculation M-estimator two iterative methods based on weighted average method of least squares were suggested which differs by procedures of initial estimators of position and scale parameters of sample. In case of more than twenty results in sample, it is expedient to apply α-truncated mean and α-truncated standard deviation with 0,05 truncation share as initial values of position and scale parameters. In case of less than twenty results in sample, it is reasonable to apply robust estimators as initial values of position and scale parameters, which don’t require removal of some part of the data. It was proposed to use Hodges-Lehmann estimator as an initial value of position parameter and median of absolute differences as a scale parameter. The proposed robust algorithms can be used in treatment of experiment results on determination of indexes of precision, trueness and accuracy of the methods of measurement of composition and properties of petroleum products; results of interlaboratory comparison tests of petroleum products; calculation of certified value of standard samples of composition and properties of petroleum products and in other cases of multiple observations.


2021 ◽  
Author(s):  
Alisson Alencar ◽  
César Mattos ◽  
João Gomes ◽  
Diego Mesquita

Multilateration (MLAT) is the de facto standard to localize points of interest (POIs) in navigation and surveillance systems. Despite sensors being inherently noisy, most existing techniques i) are oblivious to noise patterns in sensor measurements; and ii) only provide point estimates of the POI’s location. This often results in unreliable estimates with high variance, i.e., that are highly sensitive to measurement noise. To overcome this caveat, we advocate the use of Bayesian modeling. Using Bayesian statistics, we provide a comprehensive guide to handle uncertainties in MLAT. We provide principled choices for the likelihood function and the prior distributions. Inference within the resulting model follows standard MCMC techniques. Besides coping with unreliable measurements, our framework can also deal with sensors whose location is not completely known, which is an asset in mobile systems. The proposed solution also naturally incorporates multiple measurements per reference point, a common practical situation that is usually not handled directly by other approaches. Comprehensive experiments with both synthetic and real-world data indicate that our Bayesian approach to the MLAT task provides better position estimation and uncertainty quantification when compared to the available alternatives.


2021 ◽  
Author(s):  
Alisson Alencar ◽  
César Mattos ◽  
João Gomes ◽  
Diego Mesquita

Multilateration (MLAT) is the de facto standard to localize points of interest (POIs) in navigation and surveillance systems. Despite sensors being inherently noisy, most existing techniques i) are oblivious to noise patterns in sensor measurements; and ii) only provide point estimates of the POI’s location. This often results in unreliable estimates with high variance, i.e., that are highly sensitive to measurement noise. To overcome this caveat, we advocate the use of Bayesian modeling. Using Bayesian statistics, we provide a comprehensive guide to handle uncertainties in MLAT. We provide principled choices for the likelihood function and the prior distributions. Inference within the resulting model follows standard MCMC techniques. Besides coping with unreliable measurements, our framework can also deal with sensors whose location is not completely known, which is an asset in mobile systems. The proposed solution also naturally incorporates multiple measurements per reference point, a common practical situation that is usually not handled directly by other approaches. Comprehensive experiments with both synthetic and real-world data indicate that our Bayesian approach to the MLAT task provides better position estimation and uncertainty quantification when compared to the available alternatives.


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