938 resultados para Accuracy.


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Cement hydration is a very complex process in which crystalline phases are dissolving in water and after supersaturation hydrated crystalline and amorphous phases precipitate. Great efforts are being made to develop analytical tools to accurately quantify these processes and X-ray Powder Diffraction (XRPD) combined with Rietveld methodology is a suitable tool to quantify these complex mixtures and their time evolutions. However, some problems/drawbacks should be overcome to fully apply it to cement pastes characterization in order to get accurate phase analyses. In order to tackle this issue, a comparison of the Rietveld quantitative phase analyses (RQPA) obtained using Cu-Kα1, Mo-Kα1, and synchrotron strictly monochromatic radiations of three set of mixtures with increasing amounts of a given phase (spiking-method) is presented. The main aim is to test a simple hypothesis: high energy Mo-radiation, combined with high resolution laboratory X-ray powder diffraction optics, could yield more accurate RQPA, for challenging samples, than well-established Cu-radiation procedure(s). Firstly, a series of crystalline inorganic phase mixtures with increasing amounts of an analyte was studied in order to determine if Mo-Kα1 methodology is as robust as the well-established Cu-Kα1 one. Secondly, a series of crystalline organic phase mixtures with increasing amounts of an organic compound was analyzed. This type of mixture can result in transparency problems in reflection and inhomogeneous loading in narrow capillaries for transmission studies. Finally, a third series with variable amorphous content was studied. Limit of detection in Cu-patterns, ~0.2 wt%, are slightly lower than those derived from Mo-patterns, ~0.3 wt%, for similar recording times and limit of quantification for a well crystallized inorganic phase using laboratory powder diffraction was established ~0.10 wt%. From the obtained results it is inferred that RQPA from Mo-Kα1 radiation have slightly better accuracies than those obtained from Cu-Kα1. The results obtained in the previous comparison have been taken into account to obtain accurate RQPA, including the amorphous component with internal standard methodology, of hydrating cement pastes. The final goal of this second study was understanding the early-stage hydration mechanisms of a variety of cementing systems (Ordinary Portland Cement or Belite Alite Ye’elimite cement) as a function of water content, superplasticizer additives and type and content of sulfate source. In order to do so, X-ray powder diffraction data were taken in-situ with the humidity chamber coupled to the Mo-Kα1 powder diffractometer. Some results of this ongoing investigation will be reported and discussed.

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Background: Cervicocephalic kinesthetic deficiencies have been demonstrated in patients with chronic neck pain (NP). On the other hand, authors emphasized the use of different motion speeds for assessing functional impairment of the cervical spine. Purpose: The objectives of this study were (1) to investigate the head repositioning accuracy in NP patients and control subjects and (2) to assess the influence of target distance, motion speed, motion direction and pain. Materials and methods: Seventy-one subjects (36 healthy subjects and 35 NP patients; age 30–55 years) performed the head repositioning test (HRT) at two different speeds for horizontal and vertical movements and at two different distances. For each condition, six consecutive trials were sampled. Results: The study showed the validity and reproducibility of the HRT, confirming a dysfunctional threshold of 4.5°. Normative values of head repositioning error up to 3.6° and 7.1° were identified for healthy and NP subjects, respectively. A distance of 180 cm from the target and a natural motion speed increased HRT accuracy. Repositioning after extension movement showed a significantly larger error in both groups. Intensity, duration of pain as well as pain level did not significantly alter head repositioning error. Conclusions: The assessment of proprioceptive performance in healthy and NP subjects allowed the validation of the HRT. The HRT is a simple, not expensive and fast test, easily implementable in daily practice to assess and monitor treatment and evolution of proprioceptive cervical deficits.

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In our research we investigate the output accuracy of discrete event simulation models and agent based simulation models when studying human centric complex systems. In this paper we focus on human reactive behaviour as it is possible in both modelling approaches to implement human reactive behaviour in the model by using standard methods. As a case study we have chosen the retail sector, and here in particular the operations of the fitting room in the women wear department of a large UK department store. In our case study we looked at ways of determining the efficiency of implementing new management policies for the fitting room operation through modelling the reactive behaviour of staff and customers of the department. First, we have carried out a validation experiment in which we compared the results from our models to the performance of the real system. This experiment also allowed us to establish differences in output accuracy between the two modelling methods. In a second step a multi-scenario experiment was carried out to study the behaviour of the models when they are used for the purpose of operational improvement. Overall we have found that for our case study example both, discrete event simulation and agent based simulation have the same potential to support the investigation into the efficiency of implementing new management policies.

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In this paper, we investigate output accuracy for a Discrete Event Simulation (DES) model and Agent Based Simulation (ABS) model. The purpose of this investigation is to find out which of these simulation techniques is the best one for modelling human reactive behaviour in the retail sector. In order to study the output accuracy in both models, we have carried out a validation experiment in which we compared the results from our simulation models to the performance of a real system. Our experiment was carried out using a large UK department store as a case study. We had to determine an efficient implementation of management policy in the store’s fitting room using DES and ABS. Overall, we have found that both simulation models were a good representation of the real system when modelling human reactive behaviour.

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Background/aims: Few studies have validated the performance of guidelines for the prediction of choledocholithiasis (CL). Our objective was to prospectively assess the accuracy of the American Society for Gastrointestinal Endoscopy (ASGE) guidelines for the identification of CL. Methods: A two-year prospective evaluation of patients with suspected CL was performed. We evaluated the ASGE guidelines and its component variables in predicting CL. Results: A total of 256 patients with suspected CL were analyzed. Of the 208 patients with high-probability criteria for CL, 124 (59.6%) were found to have a stone/sludge at endoscopic retrograde cholangiopancreatography (ERCP). Among 48 patients with intermediate-probability criteria, 21 (43.8%) had a stone/sludge. The performance of ASGE high- and intermediate-probability criteria in our population had an accuracy of 59.0% (85.5% sensitivity, 24.3% specificity) and 41.0% (14.4% sensitivity, 75.6% specificity), respectively. The mean ERCP delay time was 6.1 days in the CL group and 6.4 days in the group without CL, p = 0.638. The presence of a common bile duct (CBD) > 6 mm (OR 2.21; 95% CI, 1.20-4.10), ascending cholangitis (OR 2.37; 95% CI, 1.01-5.55) and a CBD stone visualized on transabdominal US (OR 3.33; 95% CI, 1.48-7.52) were stronger predictors of CL. The occurrence of biliary pancreatitis was a strong protective factor for the presence of a retained CBD stone (OR 0.30; 95% CI, 0.17-0.55). Conclusions: Irrespective of a patient's ASGE probability for CL, the application of current guidelines in our population led to unnecessary performance of ERCPs in nearly half of cases.

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We provide a comprehensive study of out-of-sample forecasts for the EUR/USD exchange rate based on multivariate macroeconomic models and forecast combinations. We use profit maximization measures based on directional accuracy and trading strategies in addition to standard loss minimization measures. When comparing predictive accuracy and profit measures, data snooping bias free tests are used. The results indicate that forecast combinations, in particular those based on principal components of forecasts, help to improve over benchmark trading strategies, although the excess return per unit of deviation is limited.

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Predicting accurate bond length alternations (BLAs) in long conjugated oligomers has been a significant challenge for electronic-structure methods for many decades, made particularly important by the close relationships between BLA and the rich optoelectronic properties of π-delocalized systems. Here, we test the accuracy of recently developed, and increasingly popular, double hybrid (DH) functionals, positioned at the top of Jacobs Ladder of DFT methods of increasing sophistication, computational cost, and accuracy, due to incorporation of MP2 correlation energy. Our test systems comprise oligomeric series of polyacetylene, polymethineimine, and polysilaacetylene up to six units long. MP2 calculations reveal a pronounced shift in BLAs between the 6-31G(d) basis set used in many studies of BLA to date and the larger cc-pVTZ basis set, but only modest shifts between cc-pVTZ and aug-cc-pVQZ results. We hence perform new reference CCSD(T)/cc-pVTZ calculations for all three series of oligomers against which we assess the performance of several families of DH functionals based on BLYP, PBE, and TPSS, along with lower-rung relatives including global- and range-separated hybrids. Our results show that DH functionals systematically improve the accuracy of BLAs relative to single hybrid functionals. xDH-PBE0 (N4 scaling using SOS-MP2) emerges as a DH functional rivaling the BLA accuracy of SCS-MP2 (N5 scaling), which was found to offer the best compromise between computational cost and accuracy the last time the BLA accuracy of DFT- and wave function-based methods was systematically investigated. Interestingly, xDH-PBE0 (XYG3), which differs to other DHs in that its MP2 term uses PBE0 (B3LYP) orbitals that are not self-consistent with the DH functional, is an outlier of trends of decreasing average BLA errors with increasing fractions of MP2 correlation and HF exchange.

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Establishing the ground control point (GCP) network is a pre-requisite for georeferencing raw image data. Given current typical digital spatial database quality, much interest among users is about the accuracy of the geometric correction model that yields the final product. This paper reports an approach to optimizing GCP assembly using a genetic/evolution algorithm. The paper also suggests an optimal criterion for accuracy assessment through appraisal of global accuracy of the transformation, which is computed at each point of the image space. Experimental results demonstrate that the proposed approach has a great potential for selection of the best GCPs, and considerable improvement to the accuracy of geometric correction models can be expected when it is implemented.

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BACKGROUND: Self-reported anthropometric data are commonly used to estimate prevalence of obesity in population and community-based studies. We aim to: 1) Determine whether survey participants are able and willing to self-report height and weight; 2) Assess the accuracy of self-reported compared to measured anthropometric data in a community-based sample of young people.

METHODS: Participants (16-29 years) of a behaviour survey, recruited at a Melbourne music festival (January 2011), were asked to self-report height and weight; researchers independently weighed and measured a sub-sample. Body Mass Index was calculated and overweight/obesity classified as ≥25 kg/m². Differences between measured and self-reported values were assessed using paired t-test/Wilcoxon signed ranks test. Accurate report of height and weight were defined as <2 cm and <2 kg difference between self-report and measured values, respectively. Agreement between classification of overweight/obesity by self-report and measured values was assessed using McNemar's test.

RESULTS: Of 1405 survey participants, 82% of males and 72% of females self-reported their height and weight. Among 67 participants who were also independently measured, self-reported height and weight were significantly less than measured height (p=0.01) and weight (p<0.01) among females, but no differences were detected among males. Overall, 52% accurately self-reported height, 30% under-reported, and 18% over-reported; 34% accurately self-reported weight, 52% under-reported and 13% over-reported. More females (70%) than males (35%) under-reported weight (p=0.01). Prevalence of overweight/obesity was 33% based on self-report data and 39% based on measured data (p=0.16).

CONCLUSIONS: Self-reported measurements may underestimate weight but accurately identified overweight/obesity in the majority of this sample of young people.

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Utility companies provide electricity to a large number of consumers. These companies need to have an accurate forecast of the next day electricity demand. Any forecast errors will result in either reliability issues or increased costs for the company. Because of the widespread roll-out of smart meters, a large amount of high resolution consumption data is now accessible which was not available in the past. This new data can be used to improve the load forecast and as a result increase the reliability and decrease the expenses of electricity providers. In this paper, a number of methods for improving load forecast using smart meter data are discussed. In these methods, consumers are first divided into a number of clusters. Then a neural network is trained for each cluster and forecasts of these networks are added together in order to form the prediction for the aggregated load. In this paper, it is demonstrated that clustering increases the forecast accuracy significantly. Criteria used for grouping consumers play an important role in this process. In this work, three different feature selection methods for clustering consumers are explained and the effect of feature extraction methods on forecast error is investigated.

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An initially wet contact filter paper test (IW-CFPT) and an initially dry contact filter paper test (ID-CFPT) were used to examine the wetting paths of geosynthetic clay liners, including non-contact filter paper tests for comparative purposes. The CFPTs were applied to both geosynthetic clay liner faces to examine the effect of geotextile type on capillary contact. The non-woven geotextile face was found to be more likely to cause capillary breaks than the woven geotextile face. Both IW- and ID-CFPTs were found to be applicable to geosynthetic clay liners within their accurate upper matric suction measurement limits of 146 kPa and 66 kPa, respectively.

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The effect of secondary (anticlastic) curvature and the stress state on the measurement of material properties in a free bending test is studied in order to improve the accuracy of the test. Experiments and numerical analysis are conducted on a medium strength 304L stainless steel and high strength dual-phase steels, DP780 and DP1000. The dependence of the secondary curvature on sample geometry is analysed and correction factors are introduced to improve the accuracy of the calculation of material properties when using plane strain or uniaxial stress two-dimensional assumptions. A free bending test procedure is proposed to characterize material behaviour close to yield. This will allow the quick and simple analysis of material properties for bending-dominated forming processes such as roll forming.

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Biological detectors, such as canines, are valuable tools used for the rapid identification of illicit materials. However, recent increased scrutiny over the reliability, field accuracy, and the capabilities of each detection canine is currently being evaluated in the legal system. For example, the Supreme Court case, State of Florida v. Harris, discussed the need for continuous monitoring of canine abilities, thresholds, and search capabilities. As a result, the fallibility of canines for detection was brought to light, as well as a need for further research and understanding of canine detection. This study is two-fold, as it looks to not only create new training aids for canines that can be manipulated for dissipation control, but also investigates canine field accuracy to objects with similar odors to illicit materials. ^ It was the goal of this research to improve upon current canine training aid mimics. Sol-gel polymer training aids, imprinted with the active odor of cocaine, were developed. This novel training aid improved upon the longevity of currently existing training aids, while also provided a way to manipulate the polymer network to alter the dissipation rate of the imprinted active odors. The manipulation of the polymer network could allow handlers to control the abundance of odors presented to their canines, familiarizing themselves to their canine’s capabilities and thresholds, thereby increasing the canines’ strength in court.^ The field accuracy of detection canines was recently called into question during the Supreme Court case, State of Florida v. Jardines, where it was argued that if cocaine’s active odor, methyl benzoate, was found to be produced by the popular landscaping flower, snapdragons, canines will false alert to said flowers. Therefore, snapdragon flowers were grown and tested both in the laboratory and in the field to determine the odors produced by snapdragon flowers; the persistence of these odors once flowers have been cut; and whether detection canines will alert to both growing and cut flowers during a blind search scenario. Results revealed that although methyl benzoate is produced by snapdragon flowers, certified narcotics detection canines can distinguish cocaine’s odor profile from that of snapdragon flowers and will not alert.^

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The hot strength of austenitic steels of different carbon contents was modelled using an artificial neural network (ANN) model with optimum training data. As training data employed in a traditional neural network model were randomly selected from experimental data, they were not representative and the prediction accuracy and efficiency were therefore significantly affected. In this work, only representatively experimental data were used for training and during the procedure, one tenth of the training data extracted from experiment were used for testing the training model and terminating the modelling. The effects of the carbon con tent on flow stress, peak strains and peak stresses observed from the experiment for both training and test data were accurately represented with the ANN scheme reported in this work.