Relativistic rise measurements with very fine sampling intervals

International Nuclear Information System (INIS)

Ludlam, T.; Platner, E.D.; Polychronakos, V.A.; Lindenbaum, S.J.; Kramer, M.A.; Teramoto, Y.

1980-01-01

The motivation of this work was to determine whether the technique of charged particle identification via the relativistic rise in the ionization loss can be significantly improved by virtue of very small sampling intervals. A fast-sampling ADC and a longitudinal drift geometry were used to provide a large number of samples from a single drift chamber gap, achieving sampling intervals roughly 10 times smaller than any previous study. A single layer drift chamber was used, and tracks of 1 meter length were simulated by combining together samples from many identified particles in this detector. These data were used to study the resolving power for particle identification as a function of sample size, averaging technique, and the number of discrimination levels (ADC bits) used for pulse height measurements

A parallel optimization method for product configuration and supplier selection based on interval

Science.gov (United States)

Zheng, Jian; Zhang, Meng; Li, Guoxi

2017-06-01

In the process of design and manufacturing, product configuration is an important way of product development, and supplier selection is an essential component of supply chain management. To reduce the risk of procurement and maximize the profits of enterprises, this study proposes to combine the product configuration and supplier selection, and express the multiple uncertainties as interval numbers. An integrated optimization model of interval product configuration and supplier selection was established, and NSGA-II was put forward to locate the Pareto-optimal solutions to the interval multiobjective optimization model.

Optimal time interval for induction of immunologic adaptive response

International Nuclear Information System (INIS)

Ju Guizhi; Song Chunhua; Liu Shuzheng

1994-01-01

The optimal time interval between prior dose (D1) and challenge dose (D2) for the induction of immunologic adaptive response was investigated. Kunming mice were exposed to 75 mGy X-rays at a dose rate of 12.5 mGy/min. 3, 6, 12, 24 or 60 h after the prior irradiation the mice were challenged with a dose of 1.5 Gy at a dose rate of 0.33 Gy/min. 18h after D2, the mice were sacrificed for examination of immunological parameters. The results showed that with an interval of 6 h between D1 and D2, the adaptive response of the reaction of splenocytes to LPS was induced, and with an interval of 12 h the adaptive responses of spontaneous incorporation of 3 H-TdR into thymocytes and the reaction of splenocytes to Con A and LPS were induced with 75 mGy prior irradiation. The data suggested that the optimal time intervals between D1 and D2 for the induction of immunologic adaptive response were 6 h and 12 h with a D1 of 75 mGy and a D2 of 1.5 Gy. The mechanism of immunologic adaptation following low dose radiation is discussed

Global Optimization using Interval Analysis : Interval Optimization for Aerospace Applications

NARCIS (Netherlands)

Van Kampen, E.

2010-01-01

Optimization is an important element in aerospace related research. It is encountered for example in trajectory optimization problems, such as: satellite formation flying, spacecraft re-entry optimization and airport approach and departure optimization; in control optimization, for example in

A Hybrid Interval-Robust Optimization Model for Water Quality Management.

Science.gov (United States)

Xu, Jieyu; Li, Yongping; Huang, Guohe

2013-05-01

In water quality management problems, uncertainties may exist in many system components and pollution-related processes ( i.e. , random nature of hydrodynamic conditions, variability in physicochemical processes, dynamic interactions between pollutant loading and receiving water bodies, and indeterminacy of available water and treated wastewater). These complexities lead to difficulties in formulating and solving the resulting nonlinear optimization problems. In this study, a hybrid interval-robust optimization (HIRO) method was developed through coupling stochastic robust optimization and interval linear programming. HIRO can effectively reflect the complex system features under uncertainty, where implications of water quality/quantity restrictions for achieving regional economic development objectives are studied. By delimiting the uncertain decision space through dimensional enlargement of the original chemical oxygen demand (COD) discharge constraints, HIRO enhances the robustness of the optimization processes and resulting solutions. This method was applied to planning of industry development in association with river-water pollution concern in New Binhai District of Tianjin, China. Results demonstrated that the proposed optimization model can effectively communicate uncertainties into the optimization process and generate a spectrum of potential inexact solutions supporting local decision makers in managing benefit-effective water quality management schemes. HIRO is helpful for analysis of policy scenarios related to different levels of economic penalties, while also providing insight into the tradeoff between system benefits and environmental requirements.

Optimal parallel algorithms for problems modeled by a family of intervals

Science.gov (United States)

Olariu, Stephan; Schwing, James L.; Zhang, Jingyuan

1992-01-01

A family of intervals on the real line provides a natural model for a vast number of scheduling and VLSI problems. Recently, a number of parallel algorithms to solve a variety of practical problems on such a family of intervals have been proposed in the literature. Computational tools are developed, and it is shown how they can be used for the purpose of devising cost-optimal parallel algorithms for a number of interval-related problems including finding a largest subset of pairwise nonoverlapping intervals, a minimum dominating subset of intervals, along with algorithms to compute the shortest path between a pair of intervals and, based on the shortest path, a parallel algorithm to find the center of the family of intervals. More precisely, with an arbitrary family of n intervals as input, all algorithms run in O(log n) time using O(n) processors in the EREW-PRAM model of computation.

Optimal sampling in damage detection of flexural beams by continuous wavelet transform

International Nuclear Information System (INIS)

Basu, B; Broderick, B M; Montanari, L; Spagnoli, A

2015-01-01

Modern measurement techniques are improving in capability to capture spatial displacement fields occurring in deformed structures with high precision and in a quasi-continuous manner. This in turn has made the use of vibration-based damage identification methods more effective and reliable for real applications. However, practical measurement and data processing issues still present barriers to the application of these methods in identifying several types of structural damage. This paper deals with spatial Continuous Wavelet Transform (CWT) damage identification methods in beam structures with the aim of addressing the following key questions: (i) can the cost of damage detection be reduced by down-sampling? (ii) what is the minimum number of sampling intervals required for optimal damage detection ? The first three free vibration modes of a cantilever and a simple supported beam with an edge open crack are numerically simulated. A thorough parametric study is carried out by taking into account the key parameters governing the problem, including level of noise, crack depth and location, mechanical and geometrical parameters of the beam. The results are employed to assess the optimal number of sampling intervals for effective damage detection. (paper)

Estimating fluvial wood discharge from timelapse photography with varying sampling intervals

Science.gov (United States)

Anderson, N. K.

2013-12-01

There is recent focus on calculating wood budgets for streams and rivers to help inform management decisions, ecological studies and carbon/nutrient cycling models. Most work has measured in situ wood in temporary storage along stream banks or estimated wood inputs from banks. Little effort has been employed monitoring and quantifying wood in transport during high flows. This paper outlines a procedure for estimating total seasonal wood loads using non-continuous coarse interval sampling and examines differences in estimation between sampling at 1, 5, 10 and 15 minutes. Analysis is performed on wood transport for the Slave River in Northwest Territories, Canada. Relative to the 1 minute dataset, precision decreased by 23%, 46% and 60% for the 5, 10 and 15 minute datasets, respectively. Five and 10 minute sampling intervals provided unbiased equal variance estimates of 1 minute sampling, whereas 15 minute intervals were biased towards underestimation by 6%. Stratifying estimates by day and by discharge increased precision over non-stratification by 4% and 3%, respectively. Not including wood transported during ice break-up, the total minimum wood load estimated at this site is 3300 × 800$ m3 for the 2012 runoff season. The vast majority of the imprecision in total wood volumes came from variance in estimating average volume per log. Comparison of proportions and variance across sample intervals using bootstrap sampling to achieve equal n. Each trial was sampled for n=100, 10,000 times and averaged. All trials were then averaged to obtain an estimate for each sample interval. Dashed lines represent values from the one minute dataset.

Optimal test intervals of standby components based on actual plant-specific data

International Nuclear Information System (INIS)

Jones, R.B.; Bickel, J.H.

1987-01-01

Based on standard reliability analysis techniques, both under testing and over testing affect the availability of standby components. If tests are performed too often, unavailability is increased since the equipment is being used excessively. Conversely if testing is performed too infrequently, the likelihood of component unavailability is also increased due to the formation of rust, heat or radiation damage, dirt infiltration, etc. Thus from a physical perspective, an optimal test interval should exist which minimizes unavailability. This paper illustrates the application of an unavailability model that calculates optimal testing intervals for components with a failure database. (orig./HSCH)

A novel non-probabilistic approach using interval analysis for robust design optimization

International Nuclear Information System (INIS)

Sun, Wei; Dong, Rongmei; Xu, Huanwei

2009-01-01

A technique for formulation of the objective and constraint functions with uncertainty plays a crucial role in robust design optimization. This paper presents the first application of interval methods for reformulating the robust optimization problem. Based on interval mathematics, the original real-valued objective and constraint functions are replaced with the interval-valued functions, which directly represent the upper and lower bounds of the new functions under uncertainty. The single objective function is converted into two objective functions for minimizing the mean value and the variation, and the constraint functions are reformulated with the acceptable robustness level, resulting in a bi-level mathematical model. Compared with other methods, this method is efficient and does not require presumed probability distribution of uncertain factors or gradient or continuous information of constraints. Two numerical examples are used to illustrate the validity and feasibility of the presented method

An Improvement to Interval Estimation for Small Samples

Directory of Open Access Journals (Sweden)

SUN Hui-Ling

2017-02-01

Full Text Available Because it is difficult and complex to determine the probability distribution of small samples，it is improper to use traditional probability theory to process parameter estimation for small samples. Bayes Bootstrap method is always used in the project. Although，the Bayes Bootstrap method has its own limitation，In this article an improvement is given to the Bayes Bootstrap method，This method extended the amount of samples by numerical simulation without changing the circumstances in a small sample of the original sample. And the new method can give the accurate interval estimation for the small samples. Finally，by using the Monte Carlo simulation to model simulation to the specific small sample problems. The effectiveness and practicability of the Improved-Bootstrap method was proved.

Multi-Response Parameter Interval Sensitivity and Optimization for the Composite Tape Winding Process

Science.gov (United States)

Yu, Tao; Kang, Chao; Zhao, Pan

2018-01-01

The composite tape winding process, which utilizes a tape winding machine and prepreg tapes, provides a promising way to improve the quality of composite products. Nevertheless, the process parameters of composite tape winding have crucial effects on the tensile strength and void content, which are closely related to the performances of the winding products. In this article, two different object values of winding products, including mechanical performance (tensile strength) and a physical property (void content), were respectively calculated. Thereafter, the paper presents an integrated methodology by combining multi-parameter relative sensitivity analysis and single-parameter sensitivity analysis to obtain the optimal intervals of the composite tape winding process. First, the global multi-parameter sensitivity analysis method was applied to investigate the sensitivity of each parameter in the tape winding processing. Then, the local single-parameter sensitivity analysis method was employed to calculate the sensitivity of a single parameter within the corresponding range. Finally, the stability and instability ranges of each parameter were distinguished. Meanwhile, the authors optimized the process parameter ranges and provided comprehensive optimized intervals of the winding parameters. The verification test validated that the optimized intervals of the process parameters were reliable and stable for winding products manufacturing. PMID:29385048

Optimal Testing Intervals in the Squatting Test to Determine Baroreflex Sensitivity

OpenAIRE

Ishitsuka, S.; Kusuyama, N.; Tanaka, M.

2014-01-01

The recently introduced “squatting test” (ST) utilizes a simple postural change to perturb the blood pressure and to assess baroreflex sensitivity (BRS). In our study, we estimated the reproducibility of and the optimal testing interval between the STs in healthy volunteers. Thirty-four subjects free of cardiovascular disorders and taking no medication were instructed to perform the repeated ST at 30-sec, 1-min, and 3-min intervals in duplicate in a random sequence, while the systolic blood p...

Effects of Spatial Sampling Interval on Roughness Parameters and Microwave Backscatter over Agricultural Soil Surfaces

Directory of Open Access Journals (Sweden)

Matías Ernesto Barber

2016-06-01

Full Text Available The spatial sampling interval, as related to the ability to digitize a soil profile with a certain number of features per unit length, depends on the profiling technique itself. From a variety of profiling techniques, roughness parameters are estimated at different sampling intervals. Since soil profiles have continuous spectral components, it is clear that roughness parameters are influenced by the sampling interval of the measurement device employed. In this work, we contributed to answer which sampling interval the profiles needed to be measured at to accurately account for the microwave response of agricultural surfaces. For this purpose, a 2-D laser profiler was built and used to measure surface soil roughness at field scale over agricultural sites in Argentina. Sampling intervals ranged from large (50 mm to small ones (1 mm, with several intermediate values. Large- and intermediate-sampling-interval profiles were synthetically derived from nominal, 1 mm ones. With these data, the effect of sampling-interval-dependent roughness parameters on backscatter response was assessed using the theoretical backscatter model IEM2M. Simulations demonstrated that variations of roughness parameters depended on the working wavelength and was less important at L-band than at C- or X-band. In any case, an underestimation of the backscattering coefficient of about 1-4 dB was observed at larger sampling intervals. As a general rule a sampling interval of 15 mm can be recommended for L-band and 5 mm for C-band.

The Gas Sampling Interval Effect on V˙O2peak Is Independent of Exercise Protocol.

Science.gov (United States)

Scheadler, Cory M; Garver, Matthew J; Hanson, Nicholas J

2017-09-01

There is a plethora of gas sampling intervals available during cardiopulmonary exercise testing to measure peak oxygen consumption (V˙O2peak). Different intervals can lead to altered V˙O2peak. Whether differences are affected by the exercise protocol or subject sample is not clear. The purpose of this investigation was to determine whether V˙O2peak differed because of the manipulation of sampling intervals and whether differences were independent of the protocol and subject sample. The first subject sample (24 ± 3 yr; V˙O2peak via 15-breath moving averages: 56.2 ± 6.8 mL·kg·min) completed the Bruce and the self-paced V˙O2max protocols. The second subject sample (21.9 ± 2.7 yr; V˙O2peak via 15-breath moving averages: 54.2 ± 8.0 mL·kg·min) completed the Bruce and the modified Astrand protocols. V˙O2peak was identified using five sampling intervals: 15-s block averages, 30-s block averages, 15-breath block averages, 15-breath moving averages, and 30-s block averages aligned to the end of exercise. Differences in V˙O2peak between intervals were determined using repeated-measures ANOVAs. The influence of subject sample on the sampling effect was determined using independent t-tests. There was a significant main effect of sampling interval on V˙O2peak (first sample Bruce and self-paced V˙O2max P sample Bruce and modified Astrand P sampling intervals followed a similar pattern for each protocol and subject sample, with 15-breath moving average presenting the highest V˙O2peak. The effect of manipulating gas sampling intervals on V˙O2peak appears to be protocol and sample independent. These findings highlight our recommendation that the clinical and scientific community request and report the sampling interval whenever metabolic data are presented. The standardization of reporting would assist in the comparison of V˙O2peak.

The optimal sampling of outsourcing product

International Nuclear Information System (INIS)

Yang Chao; Pei Jiacheng

2014-01-01

In order to improve quality and cost, the sampling c = 0 has been introduced to the inspection of outsourcing product. According to the current quality level (p = 0.4%), we confirmed the optimal sampling that is: Ac = 0; if N ≤ 3000, n = 55; 3001 ≤ N ≤ 10000, n = 86; N ≥ 10001, n = 108. Through analyzing the OC curve, we came to the conclusion that when N ≤ 3000, the protective ability of optimal sampling for product quality is stronger than current sampling. Corresponding to the same 'consumer risk', the product quality of optimal sampling is superior to current sampling. (authors)

β-NMR sample optimization

CERN Document Server

Zakoucka, Eva

2013-01-01

During my summer student programme I was working on sample optimization for a new β-NMR project at the ISOLDE facility. The β-NMR technique is well-established in solid-state physics and just recently it is being introduced for applications in biochemistry and life sciences. The β-NMR collaboration will be applying for beam time to the INTC committee in September for three nuclei: Cu, Zn and Mg. Sample optimization for Mg was already performed last year during the summer student programme. Therefore sample optimization for Cu and Zn had to be completed as well for the project proposal. My part in the project was to perform thorough literature research on techniques studying Cu and Zn complexes in native conditions, search for relevant binding candidates for Cu and Zn applicable for ß-NMR and eventually evaluate selected binding candidates using UV-VIS spectrometry.

Optimizing structure of complex technical system by heterogeneous vector criterion in interval form

Science.gov (United States)

Lysenko, A. V.; Kochegarov, I. I.; Yurkov, N. K.; Grishko, A. K.

2018-05-01

The article examines the methods of development and multi-criteria choice of the preferred structural variant of the complex technical system at the early stages of its life cycle in the absence of sufficient knowledge of parameters and variables for optimizing this structure. The suggested methods takes into consideration the various fuzzy input data connected with the heterogeneous quality criteria of the designed system and the parameters set by their variation range. The suggested approach is based on the complex use of methods of interval analysis, fuzzy sets theory, and the decision-making theory. As a result, the method for normalizing heterogeneous quality criteria has been developed on the basis of establishing preference relations in the interval form. The method of building preferential relations in the interval form on the basis of the vector of heterogeneous quality criteria suggest the use of membership functions instead of the coefficients considering the criteria value. The former show the degree of proximity of the realization of the designed system to the efficient or Pareto optimal variants. The study analyzes the example of choosing the optimal variant for the complex system using heterogeneous quality criteria.

Human error considerations and annunciator effects in determining optimal test intervals for periodically inspected standby systems

International Nuclear Information System (INIS)

McWilliams, T.P.; Martz, H.F.

1981-01-01

This paper incorporates the effects of four types of human error in a model for determining the optimal time between periodic inspections which maximizes the steady state availability for standby safety systems. Such safety systems are characteristic of nuclear power plant operations. The system is modeled by means of an infinite state-space Markov chain. Purpose of the paper is to demonstrate techniques for computing steady-state availability A and the optimal periodic inspection interval tau* for the system. The model can be used to investigate the effects of human error probabilities on optimal availability, study the benefits of annunciating the standby-system, and to determine optimal inspection intervals. Several examples which are representative of nuclear power plant applications are presented

Computing interval-valued reliability measures: application of optimal control methods

DEFF Research Database (Denmark)

Kozin, Igor; Krymsky, Victor

2017-01-01

The paper describes an approach to deriving interval-valued reliability measures given partial statistical information on the occurrence of failures. We apply methods of optimal control theory, in particular, Pontryagin’s principle of maximum to solve the non-linear optimisation problem and derive...... the probabilistic interval-valued quantities of interest. It is proven that the optimisation problem can be translated into another problem statement that can be solved on the class of piecewise continuous probability density functions (pdfs). This class often consists of piecewise exponential pdfs which appear...... as soon as among the constraints there are bounds on a failure rate of a component under consideration. Finding the number of switching points of the piecewise continuous pdfs and their values becomes the focus of the approach described in the paper. Examples are provided....

Technical note: Instantaneous sampling intervals validated from continuous video observation for behavioral recording of feedlot lambs.

Science.gov (United States)

Pullin, A N; Pairis-Garcia, M D; Campbell, B J; Campler, M R; Proudfoot, K L

2017-11-01

When considering methodologies for collecting behavioral data, continuous sampling provides the most complete and accurate data set whereas instantaneous sampling can provide similar results and also increase the efficiency of data collection. However, instantaneous time intervals require validation to ensure accurate estimation of the data. Therefore, the objective of this study was to validate scan sampling intervals for lambs housed in a feedlot environment. Feeding, lying, standing, drinking, locomotion, and oral manipulation were measured on 18 crossbred lambs housed in an indoor feedlot facility for 14 h (0600-2000 h). Data from continuous sampling were compared with data from instantaneous scan sampling intervals of 5, 10, 15, and 20 min using a linear regression analysis. Three criteria determined if a time interval accurately estimated behaviors: 1) ≥ 0.90, 2) slope not statistically different from 1 ( > 0.05), and 3) intercept not statistically different from 0 ( > 0.05). Estimations for lying behavior were accurate up to 20-min intervals, whereas feeding and standing behaviors were accurate only at 5-min intervals (i.e., met all 3 regression criteria). Drinking, locomotion, and oral manipulation demonstrated poor associations () for all tested intervals. The results from this study suggest that a 5-min instantaneous sampling interval will accurately estimate lying, feeding, and standing behaviors for lambs housed in a feedlot, whereas continuous sampling is recommended for the remaining behaviors. This methodology will contribute toward the efficiency, accuracy, and transparency of future behavioral data collection in lamb behavior research.

The effects of varying sampling intervals on the growth and survival ...

African Journals Online (AJOL)

Four different sampling intervals were investigated during a six-week outdoor nursery management of Heterobranchus longifilis (Valenciennes, 1840) fry in outdoor concrete tanks in order to determine the most suitable sampling regime for maximum productivity in terms of optimum growth and survival of hatchlings and ...

Binomial Distribution Sample Confidence Intervals Estimation 1. Sampling and Medical Key Parameters Calculation

Directory of Open Access Journals (Sweden)

Tudor DRUGAN

2003-08-01

Full Text Available The aim of the paper was to present the usefulness of the binomial distribution in studying of the contingency tables and the problems of approximation to normality of binomial distribution (the limits, advantages, and disadvantages. The classification of the medical keys parameters reported in medical literature and expressing them using the contingency table units based on their mathematical expressions restrict the discussion of the confidence intervals from 34 parameters to 9 mathematical expressions. The problem of obtaining different information starting with the computed confidence interval for a specified method, information like confidence intervals boundaries, percentages of the experimental errors, the standard deviation of the experimental errors and the deviation relative to significance level was solves through implementation in PHP programming language of original algorithms. The cases of expression, which contain two binomial variables, were separately treated. An original method of computing the confidence interval for the case of two-variable expression was proposed and implemented. The graphical representation of the expression of two binomial variables for which the variation domain of one of the variable depend on the other variable was a real problem because the most of the software used interpolation in graphical representation and the surface maps were quadratic instead of triangular. Based on an original algorithm, a module was implements in PHP in order to represent graphically the triangular surface plots. All the implementation described above was uses in computing the confidence intervals and estimating their performance for binomial distributions sample sizes and variable.

Optimal interval for major maintenance actions in electricity distribution networks

Energy Technology Data Exchange (ETDEWEB)

Louit, Darko; Pascual, Rodrigo [Centro de Mineria, Pontificia Universidad Catolica de Chile, Av. Vicuna MacKenna, 4860 Santiago (Chile); Banjevic, Dragan [Centre for Maintenance Optimization and Reliability Engineering, University of Toronto, 5 King' s College Rd., Toronto, Ontario (Canada)

2009-09-15

Many systems require the periodic undertaking of major (preventive) maintenance actions (MMAs) such as overhauls in mechanical equipment, reconditioning of train lines, resurfacing of roads, etc. In the long term, these actions contribute to achieving a lower rate of occurrence of failures, though in many cases they increase the intensity of the failure process shortly after performed, resulting in a non-monotonic trend for failure intensity. Also, in the special case of distributed assets such as communications and energy networks, pipelines, etc., it is likely that the maintenance action takes place sequentially over an extended period of time, implying that different sections of the network underwent the MMAs at different periods. This forces the development of a model based on a relative time scale (i.e. time since last major maintenance event) and the combination of data from different sections of a grid, under a normalization scheme. Additionally, extended maintenance times and sequential execution of the MMAs make it difficult to identify failures occurring before and after the preventive maintenance action. This results in the loss of important information for the characterization of the failure process. A simple model is introduced to determine the optimal MMA interval considering such restrictions. Furthermore, a case study illustrates the optimal tree trimming interval around an electricity distribution network. (author)

Estimation of reference intervals from small samples: an example using canine plasma creatinine.

Science.gov (United States)

Geffré, A; Braun, J P; Trumel, C; Concordet, D

2009-12-01

According to international recommendations, reference intervals should be determined from at least 120 reference individuals, which often are impossible to achieve in veterinary clinical pathology, especially for wild animals. When only a small number of reference subjects is available, the possible bias cannot be known and the normality of the distribution cannot be evaluated. A comparison of reference intervals estimated by different methods could be helpful. The purpose of this study was to compare reference limits determined from a large set of canine plasma creatinine reference values, and large subsets of this data, with estimates obtained from small samples selected randomly. Twenty sets each of 120 and 27 samples were randomly selected from a set of 1439 plasma creatinine results obtained from healthy dogs in another study. Reference intervals for the whole sample and for the large samples were determined by a nonparametric method. The estimated reference limits for the small samples were minimum and maximum, mean +/- 2 SD of native and Box-Cox-transformed values, 2.5th and 97.5th percentiles by a robust method on native and Box-Cox-transformed values, and estimates from diagrams of cumulative distribution functions. The whole sample had a heavily skewed distribution, which approached Gaussian after Box-Cox transformation. The reference limits estimated from small samples were highly variable. The closest estimates to the 1439-result reference interval for 27-result subsamples were obtained by both parametric and robust methods after Box-Cox transformation but were grossly erroneous in some cases. For small samples, it is recommended that all values be reported graphically in a dot plot or histogram and that estimates of the reference limits be compared using different methods.

Sampling optimization for printer characterization by direct search.

Science.gov (United States)

Bianco, Simone; Schettini, Raimondo

2012-12-01

Printer characterization usually requires many printer inputs and corresponding color measurements of the printed outputs. In this brief, a sampling optimization for printer characterization on the basis of direct search is proposed to maintain high color accuracy with a reduction in the number of characterization samples required. The proposed method is able to match a given level of color accuracy requiring, on average, a characterization set cardinality which is almost one-fourth of that required by the uniform sampling, while the best method in the state of the art needs almost one-third. The number of characterization samples required can be further reduced if the proposed algorithm is coupled with a sequential optimization method that refines the sample values in the device-independent color space. The proposed sampling optimization method is extended to deal with multiple substrates simultaneously, giving statistically better colorimetric accuracy (at the α = 0.05 significance level) than sampling optimization techniques in the state of the art optimized for each individual substrate, thus allowing use of a single set of characterization samples for multiple substrates.

Optimization of the Reconstruction Interval in Neurovascular 4D-CTA Imaging

Science.gov (United States)

Hoogenboom, T.C.H.; van Beurden, R.M.J.; van Teylingen, B.; Schenk, B.; Willems, P.W.A.

2012-01-01

Summary Time resolved whole brain CT angiography (4D-CTA) is a novel imaging technology providing information regarding blood flow. One of the factors that influence the diagnostic value of this examination is the temporal resolution, which is affected by the gantry rotation speed during acquisition and the reconstruction interval during post-processing. Post-processing determines the time spacing between two reconstructed volumes and, unlike rotation speed, does not affect radiation burden. The data sets of six patients who underwent a cranial 4D-CTA were used for this study. Raw data was acquired using a 320-slice scanner with a rotation speed of 2 Hz. The arterial to venous passage of an intravenous contrast bolus was captured during a 15 s continuous scan. The raw data was reconstructed using four different reconstruction-intervals: 0.2, 0.3, 0.5 and 1.0 s. The results were rated by two observers using a standardized score sheet. The appearance of each lesion was rated correctly in all readings. Scoring for quality of temporal resolution revealed a stepwise improvement from the 1.0 s interval to the 0.3 s interval, while no discernable improvement was noted between the 0.3 s and 0.2 s interval. An increase in temporal resolution may improve the diagnostic quality of cranial 4D-CTA. Using a rotation speed of 0.5 s, the optimal reconstruction interval appears to be 0.3 s, beyond which, changes can no longer be discerned. PMID:23217631

Impact of sampling interval in training data acquisition on intrafractional predictive accuracy of indirect dynamic tumor-tracking radiotherapy.

Science.gov (United States)

Mukumoto, Nobutaka; Nakamura, Mitsuhiro; Akimoto, Mami; Miyabe, Yuki; Yokota, Kenji; Matsuo, Yukinori; Mizowaki, Takashi; Hiraoka, Masahiro

2017-08-01

To explore the effect of sampling interval of training data acquisition on the intrafractional prediction error of surrogate signal-based dynamic tumor-tracking using a gimbal-mounted linac. Twenty pairs of respiratory motions were acquired from 20 patients (ten lung, five liver, and five pancreatic cancer patients) who underwent dynamic tumor-tracking with the Vero4DRT. First, respiratory motions were acquired as training data for an initial construction of the prediction model before the irradiation. Next, additional respiratory motions were acquired for an update of the prediction model due to the change of the respiratory pattern during the irradiation. The time elapsed prior to the second acquisition of the respiratory motion was 12.6 ± 3.1 min. A four-axis moving phantom reproduced patients' three dimensional (3D) target motions and one dimensional surrogate motions. To predict the future internal target motion from the external surrogate motion, prediction models were constructed by minimizing residual prediction errors for training data acquired at 80 and 320 ms sampling intervals for 20 s, and at 500, 1,000, and 2,000 ms sampling intervals for 60 s using orthogonal kV x-ray imaging systems. The accuracies of prediction models trained with various sampling intervals were estimated based on training data with each sampling interval during the training process. The intrafractional prediction errors for various prediction models were then calculated on intrafractional monitoring images taken for 30 s at the constant sampling interval of a 500 ms fairly to evaluate the prediction accuracy for the same motion pattern. In addition, the first respiratory motion was used for the training and the second respiratory motion was used for the evaluation of the intrafractional prediction errors for the changed respiratory motion to evaluate the robustness of the prediction models. The training error of the prediction model was 1.7 ± 0.7 mm in 3D for all sampling

Low Carbon-Oriented Optimal Reliability Design with Interval Product Failure Analysis and Grey Correlation Analysis

Directory of Open Access Journals (Sweden)

Yixiong Feng

2017-03-01

Full Text Available The problem of large amounts of carbon emissions causes wide concern across the world, and it has become a serious threat to the sustainable development of the manufacturing industry. The intensive research into technologies and methodologies for green product design has significant theoretical meaning and practical value in reducing the emissions of the manufacturing industry. Therefore, a low carbon-oriented product reliability optimal design model is proposed in this paper: (1 The related expert evaluation information was prepared in interval numbers; (2 An improved product failure analysis considering the uncertain carbon emissions of the subsystem was performed to obtain the subsystem weight taking the carbon emissions into consideration. The interval grey correlation analysis was conducted to obtain the subsystem weight taking the uncertain correlations inside the product into consideration. Using the above two kinds of subsystem weights and different caution indicators of the decision maker, a series of product reliability design schemes is available; (3 The interval-valued intuitionistic fuzzy sets (IVIFSs were employed to select the optimal reliability and optimal design scheme based on three attributes, namely, low carbon, correlation and functions, and economic cost. The case study of a vertical CNC lathe proves the superiority and rationality of the proposed method.

Optimal sampling designs for large-scale fishery sample surveys in Greece

Directory of Open Access Journals (Sweden)

G. BAZIGOS

2007-12-01

The paper deals with the optimization of the following three large scale sample surveys: biological sample survey of commercial landings (BSCL, experimental fishing sample survey (EFSS, and commercial landings and effort sample survey (CLES.

Interval estimation methods of the mean in small sample situation and the results' comparison

International Nuclear Information System (INIS)

Wu Changli; Guo Chunying; Jiang Meng; Lin Yuangen

2009-01-01

The methods of the sample mean's interval estimation, namely the classical method, the Bootstrap method, the Bayesian Bootstrap method, the Jackknife method and the spread method of the Empirical Characteristic distribution function are described. Numerical calculation on the samples' mean intervals is carried out where the numbers of the samples are 4, 5, 6 respectively. The results indicate the Bootstrap method and the Bayesian Bootstrap method are much more appropriate than others in small sample situation. (authors)

Optimal Wind Power Uncertainty Intervals for Electricity Market Operation

Energy Technology Data Exchange (ETDEWEB)

Wang, Ying; Zhou, Zhi; Botterud, Audun; Zhang, Kaifeng

2018-01-01

It is important to select an appropriate uncertainty level of the wind power forecast for power system scheduling and electricity market operation. Traditional methods hedge against a predefined level of wind power uncertainty, such as a specific confidence interval or uncertainty set, which leaves the questions of how to best select the appropriate uncertainty levels. To bridge this gap, this paper proposes a model to optimize the forecast uncertainty intervals of wind power for power system scheduling problems, with the aim of achieving the best trade-off between economics and reliability. Then we reformulate and linearize the models into a mixed integer linear programming (MILP) without strong assumptions on the shape of the probability distribution. In order to invest the impacts on cost, reliability, and prices in a electricity market, we apply the proposed model on a twosettlement electricity market based on a six-bus test system and on a power system representing the U.S. state of Illinois. The results show that the proposed method can not only help to balance the economics and reliability of the power system scheduling, but also help to stabilize the energy prices in electricity market operation.

Sample size planning for composite reliability coefficients: accuracy in parameter estimation via narrow confidence intervals.

Science.gov (United States)

Terry, Leann; Kelley, Ken

2012-11-01

Composite measures play an important role in psychology and related disciplines. Composite measures almost always have error. Correspondingly, it is important to understand the reliability of the scores from any particular composite measure. However, the point estimates of the reliability of composite measures are fallible and thus all such point estimates should be accompanied by a confidence interval. When confidence intervals are wide, there is much uncertainty in the population value of the reliability coefficient. Given the importance of reporting confidence intervals for estimates of reliability, coupled with the undesirability of wide confidence intervals, we develop methods that allow researchers to plan sample size in order to obtain narrow confidence intervals for population reliability coefficients. We first discuss composite reliability coefficients and then provide a discussion on confidence interval formation for the corresponding population value. Using the accuracy in parameter estimation approach, we develop two methods to obtain accurate estimates of reliability by planning sample size. The first method provides a way to plan sample size so that the expected confidence interval width for the population reliability coefficient is sufficiently narrow. The second method ensures that the confidence interval width will be sufficiently narrow with some desired degree of assurance (e.g., 99% assurance that the 95% confidence interval for the population reliability coefficient will be less than W units wide). The effectiveness of our methods was verified with Monte Carlo simulation studies. We demonstrate how to easily implement the methods with easy-to-use and freely available software. ©2011 The British Psychological Society.

Constrained optimization of test intervals using a steady-state genetic algorithm

International Nuclear Information System (INIS)

Martorell, S.; Carlos, S.; Sanchez, A.; Serradell, V.

2000-01-01

There is a growing interest from both the regulatory authorities and the nuclear industry to stimulate the use of Probabilistic Risk Analysis (PRA) for risk-informed applications at Nuclear Power Plants (NPPs). Nowadays, special attention is being paid on analyzing plant-specific changes to Test Intervals (TIs) within the Technical Specifications (TSs) of NPPs and it seems to be a consensus on the need of making these requirements more risk-effective and less costly. Resource versus risk-control effectiveness principles formally enters in optimization problems. This paper presents an approach for using the PRA models in conducting the constrained optimization of TIs based on a steady-state genetic algorithm (SSGA) where the cost or the burden is to be minimized while the risk or performance is constrained to be at a given level, or vice versa. The paper encompasses first with the problem formulation, where the objective function and constraints that apply in the constrained optimization of TIs based on risk and cost models at system level are derived. Next, the foundation of the optimizer is given, which is derived by customizing a SSGA in order to allow optimizing TIs under constraints. Also, a case study is performed using this approach, which shows the benefits of adopting both PRA models and genetic algorithms, in particular for the constrained optimization of TIs, although it is also expected a great benefit of using this approach to solve other engineering optimization problems. However, care must be taken in using genetic algorithms in constrained optimization problems as it is concluded in this paper

V–V delay interval optimization in CRT using echocardiography compared to QRS width in surface ECG

Directory of Open Access Journals (Sweden)

Amr Nawar

2012-09-01

Conclusion: Significant correlation appeared to exist during optimization of CRT between VV programming based on the shortest QRS interval at 12-lead ECG pacing and that based on highest LVOT VTI by echocardiography. A combined ECG and echocardiographic approach could be a more convenient solution in performing V–V optimization.

Contrasting Perspectives of Anesthesiologists and Gastroenterologists on the Optimal Time Interval between Bowel Preparation and Endoscopic Sedation

Directory of Open Access Journals (Sweden)

Deepak Agrawal

2015-01-01

Full Text Available Background. The optimal time interval between the last ingestion of bowel prep and sedation for colonoscopy remains controversial, despite guidelines that sedation can be administered 2 hours after consumption of clear liquids. Objective. To determine current practice patterns among anesthesiologists and gastroenterologists regarding the optimal time interval for sedation after last ingestion of bowel prep and to understand the rationale underlying their beliefs. Design. Questionnaire survey of anesthesiologists and gastroenterologists in the USA. The questions were focused on the preferred time interval of endoscopy after a polyethylene glycol based preparation in routine cases and select conditions. Results. Responses were received from 109 anesthesiologists and 112 gastroenterologists. 96% of anesthesiologists recommended waiting longer than 2 hours until sedation, in contrast to only 26% of gastroenterologists. The main reason for waiting >2 hours was that PEG was not considered a clear liquid. Most anesthesiologists, but not gastroenterologists, waited longer in patients with history of diabetes or reflux. Conclusions. Anesthesiologists and gastroenterologists do not agree on the optimal interval for sedation after last drink of bowel prep. Most anesthesiologists prefer to wait longer than the recommended 2 hours for clear liquids. The data suggest a need for clearer guidelines on this issue.

Efficiency enhancement of optimized Latin hypercube sampling strategies: Application to Monte Carlo uncertainty analysis and meta-modeling

Science.gov (United States)

Rajabi, Mohammad Mahdi; Ataie-Ashtiani, Behzad; Janssen, Hans

2015-02-01

The majority of literature regarding optimized Latin hypercube sampling (OLHS) is devoted to increasing the efficiency of these sampling strategies through the development of new algorithms based on the combination of innovative space-filling criteria and specialized optimization schemes. However, little attention has been given to the impact of the initial design that is fed into the optimization algorithm, on the efficiency of OLHS strategies. Previous studies, as well as codes developed for OLHS, have relied on one of the following two approaches for the selection of the initial design in OLHS: (1) the use of random points in the hypercube intervals (random LHS), and (2) the use of midpoints in the hypercube intervals (midpoint LHS). Both approaches have been extensively used, but no attempt has been previously made to compare the efficiency and robustness of their resulting sample designs. In this study we compare the two approaches and show that the space-filling characteristics of OLHS designs are sensitive to the initial design that is fed into the optimization algorithm. It is also illustrated that the space-filling characteristics of OLHS designs based on midpoint LHS are significantly better those based on random LHS. The two approaches are compared by incorporating their resulting sample designs in Monte Carlo simulation (MCS) for uncertainty propagation analysis, and then, by employing the sample designs in the selection of the training set for constructing non-intrusive polynomial chaos expansion (NIPCE) meta-models which subsequently replace the original full model in MCSs. The analysis is based on two case studies involving numerical simulation of density dependent flow and solute transport in porous media within the context of seawater intrusion in coastal aquifers. We show that the use of midpoint LHS as the initial design increases the efficiency and robustness of the resulting MCSs and NIPCE meta-models. The study also illustrates that this

Optimal sampling schemes applied in geology

CSIR Research Space (South Africa)

Debba, Pravesh

2010-05-01

Full Text Available Methodology 6 Results 7 Background and Research Question for Study 2 8 Study Area and Data 9 Methodology 10 Results 11 Conclusions Debba (CSIR) Optimal Sampling Schemes applied in Geology UP 2010 2 / 47 Outline 1 Introduction to hyperspectral remote... sensing 2 Objective of Study 1 3 Study Area 4 Data used 5 Methodology 6 Results 7 Background and Research Question for Study 2 8 Study Area and Data 9 Methodology 10 Results 11 Conclusions Debba (CSIR) Optimal Sampling Schemes applied in Geology...

Enhanced nonlinearity interval mapping scheme for high-performance simulation-optimization of watershed-scale BMP placement

Science.gov (United States)

Zou, Rui; Riverson, John; Liu, Yong; Murphy, Ryan; Sim, Youn

2015-03-01

Integrated continuous simulation-optimization models can be effective predictors of a process-based responses for cost-benefit optimization of best management practices (BMPs) selection and placement. However, practical application of simulation-optimization model is computationally prohibitive for large-scale systems. This study proposes an enhanced Nonlinearity Interval Mapping Scheme (NIMS) to solve large-scale watershed simulation-optimization problems several orders of magnitude faster than other commonly used algorithms. An efficient interval response coefficient (IRC) derivation method was incorporated into the NIMS framework to overcome a computational bottleneck. The proposed algorithm was evaluated using a case study watershed in the Los Angeles County Flood Control District. Using a continuous simulation watershed/stream-transport model, Loading Simulation Program in C++ (LSPC), three nested in-stream compliance points (CP)—each with multiple Total Maximum Daily Loads (TMDL) targets—were selected to derive optimal treatment levels for each of the 28 subwatersheds, so that the TMDL targets at all the CP were met with the lowest possible BMP implementation cost. Genetic Algorithm (GA) and NIMS were both applied and compared. The results showed that the NIMS took 11 iterations (about 11 min) to complete with the resulting optimal solution having a total cost of 67.2 million, while each of the multiple GA executions took 21-38 days to reach near optimal solutions. The best solution obtained among all the GA executions compared had a minimized cost of 67.7 million—marginally higher, but approximately equal to that of the NIMS solution. The results highlight the utility for decision making in large-scale watershed simulation-optimization formulations.

A Two-Stage Method to Determine Optimal Product Sampling considering Dynamic Potential Market

Science.gov (United States)

Hu, Zhineng; Lu, Wei; Han, Bing

2015-01-01

This paper develops an optimization model for the diffusion effects of free samples under dynamic changes in potential market based on the characteristics of independent product and presents a two-stage method to figure out the sampling level. The impact analysis of the key factors on the sampling level shows that the increase of the external coefficient or internal coefficient has a negative influence on the sampling level. And the changing rate of the potential market has no significant influence on the sampling level whereas the repeat purchase has a positive one. Using logistic analysis and regression analysis, the global sensitivity analysis gives a whole analysis of the interaction of all parameters, which provides a two-stage method to estimate the impact of the relevant parameters in the case of inaccuracy of the parameters and to be able to construct a 95% confidence interval for the predicted sampling level. Finally, the paper provides the operational steps to improve the accuracy of the parameter estimation and an innovational way to estimate the sampling level. PMID:25821847

Test interval optimization of safety systems of nuclear power plant using fuzzy-genetic approach

International Nuclear Information System (INIS)

Durga Rao, K.; Gopika, V.; Kushwaha, H.S.; Verma, A.K.; Srividya, A.

2007-01-01

Probabilistic safety assessment (PSA) is the most effective and efficient tool for safety and risk management in nuclear power plants (NPP). PSA studies not only evaluate risk/safety of systems but also their results are very useful in safe, economical and effective design and operation of NPPs. The latter application is popularly known as 'Risk-Informed Decision Making'. Evaluation of technical specifications is one such important application of Risk-Informed decision making. Deciding test interval (TI), one of the important technical specifications, with the given resources and risk effectiveness is an optimization problem. Uncertainty is inherently present in the availability parameters such as failure rate and repair time due to the limitation in assessing these parameters precisely. This paper presents a solution to test interval optimization problem with uncertain parameters in the model with fuzzy-genetic approach along with a case of application from a safety system of Indian pressurized heavy water reactor (PHWR)

Allocation of ESS by interval optimization method considering impact of ship swinging on hybrid PV/diesel ship power system

International Nuclear Information System (INIS)

Wen, Shuli; Lan, Hai; Hong, Ying-Yi; Yu, David C.; Zhang, Lijun; Cheng, Peng

2016-01-01

Highlights: • An uncertainty model of PV generation on board is developed based on the experiments. • The moving and swinging of the ship are considered in the optimal ESS sizing problem. • Optimal sizing of ESS in a hybrid PV/diesel/ESS ship power system is gained by the interval optimization method. • Different cases were studied to show the significance of the proposed method considering the swinging effects on the cost. - Abstract: Owing to low efficiency of traditional ships and the serious environmental pollution that they cause, the use of solar energy and an energy storage system (ESS) in a ship’s power system is increasingly attracting attention. However, the swinging of a ship raises crucial challenges in designing an optimal system for a large oil tanker ship, which are associated with uncertainties in solar energy. In this study, a series of experiments are performed to investigate the characteristics of a photovoltaic (PV) system on a moving ship. Based on the experimental results, an interval uncertainty model of on-board PV generation is established, which considers the effect of the swinging of the ship. Due to the power balance equations, the outputs of the diesel generator and the ESS on a large oil tanker are also modeled using interval variables. An interval optimization method is developed to determine the optimal size of the ESS in this hybrid ship power system to reduce the fuel cost, capital cost of the ESS, and emissions of greenhouse gases. Variations of the ship load are analyzed using a new method, taking five operating conditions into account. Several cases are compared in detail to demonstrate the effectiveness of the proposed algorithm.

Life cycle cost optimization of biofuel supply chains under uncertainties based on interval linear programming.

Science.gov (United States)

Ren, Jingzheng; Dong, Liang; Sun, Lu; Goodsite, Michael Evan; Tan, Shiyu; Dong, Lichun

2015-01-01

The aim of this work was to develop a model for optimizing the life cycle cost of biofuel supply chain under uncertainties. Multiple agriculture zones, multiple transportation modes for the transport of grain and biofuel, multiple biofuel plants, and multiple market centers were considered in this model, and the price of the resources, the yield of grain and the market demands were regarded as interval numbers instead of constants. An interval linear programming was developed, and a method for solving interval linear programming was presented. An illustrative case was studied by the proposed model, and the results showed that the proposed model is feasible for designing biofuel supply chain under uncertainties. Copyright © 2015 Elsevier Ltd. All rights reserved.

Influence of sampling interval and number of projections on the quality of SR-XFMT reconstruction

International Nuclear Information System (INIS)

Deng Biao; Yu Xiaohan; Xu Hongjie

2007-01-01

Synchrotron Radiation based X-ray Fluorescent Microtomography (SR-XFMT) is a nondestructive technique for detecting elemental composition and distribution inside a specimen with high spatial resolution and sensitivity. In this paper, computer simulation of SR-XFMT experiment is performed. The influence of the sampling interval and the number of projections on the quality of SR-XFMT image reconstruction is analyzed. It is found that the sampling interval has greater effect on the quality of reconstruction than the number of projections. (authors)

Suboptimal and optimal order policies for fixed and varying replenishment interval with declining market

Science.gov (United States)

Yu, Jonas C. P.; Wee, H. M.; Yang, P. C.; Wu, Simon

2016-06-01

One of the supply chain risks for hi-tech products is the result of rapid technological innovation; it results in a significant decline in the selling price and demand after the initial launch period. Hi-tech products include computers and communication consumer's products. From a practical standpoint, a more realistic replenishment policy is needed to consider the impact of risks; especially when some portions of shortages are lost. In this paper, suboptimal and optimal order policies with partial backordering are developed for a buyer when the component cost, the selling price, and the demand rate decline at a continuous rate. Two mathematical models are derived and discussed: one model has the suboptimal solution with the fixed replenishment interval and a simpler computational process; the other one has the optimal solution with the varying replenishment interval and a more complicated computational process. The second model results in more profit. Numerical examples are provided to illustrate the two replenishment models. Sensitivity analysis is carried out to investigate the relationship between the parameters and the net profit.

Optimize the Coverage Probability of Prediction Interval for Anomaly Detection of Sensor-Based Monitoring Series

Directory of Open Access Journals (Sweden)

Jingyue Pang

2018-03-01

Full Text Available Effective anomaly detection of sensing data is essential for identifying potential system failures. Because they require no prior knowledge or accumulated labels, and provide uncertainty presentation, the probability prediction methods (e.g., Gaussian process regression (GPR and relevance vector machine (RVM are especially adaptable to perform anomaly detection for sensing series. Generally, one key parameter of prediction models is coverage probability (CP, which controls the judging threshold of the testing sample and is generally set to a default value (e.g., 90% or 95%. There are few criteria to determine the optimal CP for anomaly detection. Therefore, this paper designs a graphic indicator of the receiver operating characteristic curve of prediction interval (ROC-PI based on the definition of the ROC curve which can depict the trade-off between the PI width and PI coverage probability across a series of cut-off points. Furthermore, the Youden index is modified to assess the performance of different CPs, by the minimization of which the optimal CP is derived by the simulated annealing (SA algorithm. Experiments conducted on two simulation datasets demonstrate the validity of the proposed method. Especially, an actual case study on sensing series from an on-orbit satellite illustrates its significant performance in practical application.

Optimization of Allowed Outage Time and Surveillance Test Intervals

Energy Technology Data Exchange (ETDEWEB)

Al-Dheeb, Mujahed; Kang, Sunkoo; Kim, Jonghyun [KEPCO international nuclear graduate school, Ulsan (Korea, Republic of)

2015-10-15

The primary purpose of surveillance testing is to assure that the components of standby safety systems will be operable when they are needed in an accident. By testing these components, failures can be detected that may have occurred since the last test or the time when the equipment was last known to be operational. The probability a system or system component performs a specified function or mission under given conditions at a prescribed time is called availability (A). Unavailability (U) as a risk measure is just the complementary probability to A(t). The increase of U means the risk is increased as well. D and T have an important impact on components, or systems, unavailability. The extension of D impacts the maintenance duration distributions for at-power operations, making them longer. This, in turn, increases the unavailability due to maintenance in the systems analysis. As for T, overly-frequent surveillances can result in high system unavailability. This is because the system may be taken out of service often due to the surveillance itself and due to the repair of test-caused failures of the component. The test-caused failures include those incurred by wear and tear of the component due to the surveillances. On the other hand, as the surveillance interval increases, the component's unavailability will grow because of increased occurrences of time-dependent random failures. In that situation, the component cannot be relied upon, and accordingly the system unavailability will increase. Thus, there should be an optimal component surveillance interval in terms of the corresponding system availability. This paper aims at finding the optimal T and D which result in minimum unavailability which in turn reduces the risk. Applying the methodology in section 2 to find the values of optimal T and D for two components, i.e., safety injection pump (SIP) and turbine driven aux feedwater pump (TDAFP). Section 4 is addressing interaction between D and T. In general

Optimization of Allowed Outage Time and Surveillance Test Intervals

International Nuclear Information System (INIS)

Al-Dheeb, Mujahed; Kang, Sunkoo; Kim, Jonghyun

2015-01-01

The primary purpose of surveillance testing is to assure that the components of standby safety systems will be operable when they are needed in an accident. By testing these components, failures can be detected that may have occurred since the last test or the time when the equipment was last known to be operational. The probability a system or system component performs a specified function or mission under given conditions at a prescribed time is called availability (A). Unavailability (U) as a risk measure is just the complementary probability to A(t). The increase of U means the risk is increased as well. D and T have an important impact on components, or systems, unavailability. The extension of D impacts the maintenance duration distributions for at-power operations, making them longer. This, in turn, increases the unavailability due to maintenance in the systems analysis. As for T, overly-frequent surveillances can result in high system unavailability. This is because the system may be taken out of service often due to the surveillance itself and due to the repair of test-caused failures of the component. The test-caused failures include those incurred by wear and tear of the component due to the surveillances. On the other hand, as the surveillance interval increases, the component's unavailability will grow because of increased occurrences of time-dependent random failures. In that situation, the component cannot be relied upon, and accordingly the system unavailability will increase. Thus, there should be an optimal component surveillance interval in terms of the corresponding system availability. This paper aims at finding the optimal T and D which result in minimum unavailability which in turn reduces the risk. Applying the methodology in section 2 to find the values of optimal T and D for two components, i.e., safety injection pump (SIP) and turbine driven aux feedwater pump (TDAFP). Section 4 is addressing interaction between D and T. In general

Sampled-data and discrete-time H2 optimal control

NARCIS (Netherlands)

Trentelman, Harry L.; Stoorvogel, Anton A.

1993-01-01

This paper deals with the sampled-data H2 optimal control problem. Given a linear time-invariant continuous-time system, the problem of minimizing the H2 performance over all sampled-data controllers with a fixed sampling period can be reduced to a pure discrete-time H2 optimal control problem. This

Adaptive Kalman Filter Based on Adjustable Sampling Interval in Burst Detection for Water Distribution System

Directory of Open Access Journals (Sweden)

Doo Yong Choi

2016-04-01

Full Text Available Rapid detection of bursts and leaks in water distribution systems (WDSs can reduce the social and economic costs incurred through direct loss of water into the ground, additional energy demand for water supply, and service interruptions. Many real-time burst detection models have been developed in accordance with the use of supervisory control and data acquisition (SCADA systems and the establishment of district meter areas (DMAs. Nonetheless, no consideration has been given to how frequently a flow meter measures and transmits data for predicting breaks and leaks in pipes. This paper analyzes the effect of sampling interval when an adaptive Kalman filter is used for detecting bursts in a WDS. A new sampling algorithm is presented that adjusts the sampling interval depending on the normalized residuals of flow after filtering. The proposed algorithm is applied to a virtual sinusoidal flow curve and real DMA flow data obtained from Jeongeup city in South Korea. The simulation results prove that the self-adjusting algorithm for determining the sampling interval is efficient and maintains reasonable accuracy in burst detection. The proposed sampling method has a significant potential for water utilities to build and operate real-time DMA monitoring systems combined with smart customer metering systems.

Number of core samples: Mean concentrations and confidence intervals

International Nuclear Information System (INIS)

Jensen, L.; Cromar, R.D.; Wilmarth, S.R.; Heasler, P.G.

1995-01-01

This document provides estimates of how well the mean concentration of analytes are known as a function of the number of core samples, composite samples, and replicate analyses. The estimates are based upon core composite data from nine recently sampled single-shell tanks. The results can be used when determining the number of core samples needed to ''characterize'' the waste from similar single-shell tanks. A standard way of expressing uncertainty in the estimate of a mean is with a 95% confidence interval (CI). The authors investigate how the width of a 95% CI on the mean concentration decreases as the number of observations increase. Specifically, the tables and figures show how the relative half-width (RHW) of a 95% CI decreases as the number of core samples increases. The RHW of a CI is a unit-less measure of uncertainty. The general conclusions are as follows: (1) the RHW decreases dramatically as the number of core samples is increased, the decrease is much smaller when the number of composited samples or the number of replicate analyses are increase; (2) if the mean concentration of an analyte needs to be estimated with a small RHW, then a large number of core samples is required. The estimated number of core samples given in the tables and figures were determined by specifying different sizes of the RHW. Four nominal sizes were examined: 10%, 25%, 50%, and 100% of the observed mean concentration. For a majority of analytes the number of core samples required to achieve an accuracy within 10% of the mean concentration is extremely large. In many cases, however, two or three core samples is sufficient to achieve a RHW of approximately 50 to 100%. Because many of the analytes in the data have small concentrations, this level of accuracy may be satisfactory for some applications

Optimal sampling strategies for detecting zoonotic disease epidemics.

Directory of Open Access Journals (Sweden)

Jake M Ferguson

2014-06-01

Full Text Available The early detection of disease epidemics reduces the chance of successful introductions into new locales, minimizes the number of infections, and reduces the financial impact. We develop a framework to determine the optimal sampling strategy for disease detection in zoonotic host-vector epidemiological systems when a disease goes from below detectable levels to an epidemic. We find that if the time of disease introduction is known then the optimal sampling strategy can switch abruptly between sampling only from the vector population to sampling only from the host population. We also construct time-independent optimal sampling strategies when conducting periodic sampling that can involve sampling both the host and the vector populations simultaneously. Both time-dependent and -independent solutions can be useful for sampling design, depending on whether the time of introduction of the disease is known or not. We illustrate the approach with West Nile virus, a globally-spreading zoonotic arbovirus. Though our analytical results are based on a linearization of the dynamical systems, the sampling rules appear robust over a wide range of parameter space when compared to nonlinear simulation models. Our results suggest some simple rules that can be used by practitioners when developing surveillance programs. These rules require knowledge of transition rates between epidemiological compartments, which population was initially infected, and of the cost per sample for serological tests.

Optimal sampling strategies for detecting zoonotic disease epidemics.

Science.gov (United States)

Ferguson, Jake M; Langebrake, Jessica B; Cannataro, Vincent L; Garcia, Andres J; Hamman, Elizabeth A; Martcheva, Maia; Osenberg, Craig W

2014-06-01

The early detection of disease epidemics reduces the chance of successful introductions into new locales, minimizes the number of infections, and reduces the financial impact. We develop a framework to determine the optimal sampling strategy for disease detection in zoonotic host-vector epidemiological systems when a disease goes from below detectable levels to an epidemic. We find that if the time of disease introduction is known then the optimal sampling strategy can switch abruptly between sampling only from the vector population to sampling only from the host population. We also construct time-independent optimal sampling strategies when conducting periodic sampling that can involve sampling both the host and the vector populations simultaneously. Both time-dependent and -independent solutions can be useful for sampling design, depending on whether the time of introduction of the disease is known or not. We illustrate the approach with West Nile virus, a globally-spreading zoonotic arbovirus. Though our analytical results are based on a linearization of the dynamical systems, the sampling rules appear robust over a wide range of parameter space when compared to nonlinear simulation models. Our results suggest some simple rules that can be used by practitioners when developing surveillance programs. These rules require knowledge of transition rates between epidemiological compartments, which population was initially infected, and of the cost per sample for serological tests.

A multiobjective interval programming model for wind-hydrothermal power system dispatching using 2-step optimization algorithm.

Science.gov (United States)

Ren, Kun; Jihong, Qu

2014-01-01

Wind-hydrothermal power system dispatching has received intensive attention in recent years because it can help develop various reasonable plans to schedule the power generation efficiency. But future data such as wind power output and power load would not be accurately predicted and the nonlinear nature involved in the complex multiobjective scheduling model; therefore, to achieve accurate solution to such complex problem is a very difficult task. This paper presents an interval programming model with 2-step optimization algorithm to solve multiobjective dispatching. Initially, we represented the future data into interval numbers and simplified the object function to a linear programming problem to search the feasible and preliminary solutions to construct the Pareto set. Then the simulated annealing method was used to search the optimal solution of initial model. Thorough experimental results suggest that the proposed method performed reasonably well in terms of both operating efficiency and precision.

A Multiobjective Interval Programming Model for Wind-Hydrothermal Power System Dispatching Using 2-Step Optimization Algorithm

Science.gov (United States)

Jihong, Qu

2014-01-01

Wind-hydrothermal power system dispatching has received intensive attention in recent years because it can help develop various reasonable plans to schedule the power generation efficiency. But future data such as wind power output and power load would not be accurately predicted and the nonlinear nature involved in the complex multiobjective scheduling model; therefore, to achieve accurate solution to such complex problem is a very difficult task. This paper presents an interval programming model with 2-step optimization algorithm to solve multiobjective dispatching. Initially, we represented the future data into interval numbers and simplified the object function to a linear programming problem to search the feasible and preliminary solutions to construct the Pareto set. Then the simulated annealing method was used to search the optimal solution of initial model. Thorough experimental results suggest that the proposed method performed reasonably well in terms of both operating efficiency and precision. PMID:24895663

Direct Interval Forecasting of Wind Power

DEFF Research Database (Denmark)

Wan, Can; Xu, Zhao; Pinson, Pierre

2013-01-01

This letter proposes a novel approach to directly formulate the prediction intervals of wind power generation based on extreme learning machine and particle swarm optimization, where prediction intervals are generated through direct optimization of both the coverage probability and sharpness...

GENERALISED MODEL BASED CONFIDENCE INTERVALS IN TWO STAGE CLUSTER SAMPLING

Directory of Open Access Journals (Sweden)

Christopher Ouma Onyango

2010-09-01

Full Text Available Chambers and Dorfman (2002 constructed bootstrap confidence intervals in model based estimation for finite population totals assuming that auxiliary values are available throughout a target population and that the auxiliary values are independent. They also assumed that the cluster sizes are known throughout the target population. We now extend to two stage sampling in which the cluster sizes are known only for the sampled clusters, and we therefore predict the unobserved part of the population total. Jan and Elinor (2008 have done similar work, but unlike them, we use a general model, in which the auxiliary values are not necessarily independent. We demonstrate that the asymptotic properties of our proposed estimator and its coverage rates are better than those constructed under the model assisted local polynomial regression model.

Networked control systems with communication constraints :tradeoffs between sampling intervals, delays and performance

NARCIS (Netherlands)

Heemels, W.P.M.H.; Teel, A.R.; Wouw, van de N.; Nesic, D.

2010-01-01

There are many communication imperfections in networked control systems (NCS) such as varying transmission delays, varying sampling/transmission intervals, packet loss, communication constraints and quantization effects. Most of the available literature on NCS focuses on only some of these aspects,

A model for calculating the optimal replacement interval of computer systems

International Nuclear Information System (INIS)

Fujii, Minoru; Asai, Kiyoshi

1981-08-01

A mathematical model for calculating the optimal replacement interval of computer systems is described. This model is made to estimate the best economical interval of computer replacement when computing demand, cost and performance of computer, etc. are known. The computing demand is assumed to monotonously increase every year. Four kinds of models are described. In the model 1, a computer system is represented by only a central processing unit (CPU) and all the computing demand is to be processed on the present computer until the next replacement. On the other hand in the model 2, the excessive demand is admitted and may be transferred to other computing center and processed costly there. In the model 3, the computer system is represented by a CPU, memories (MEM) and input/output devices (I/O) and it must process all the demand. Model 4 is same as model 3, but the excessive demand is admitted to be processed in other center. (1) Computing demand at the JAERI, (2) conformity of Grosch's law for the recent computers, (3) replacement cost of computer systems, etc. are also described. (author)

An efficient sampling approach for variance-based sensitivity analysis based on the law of total variance in the successive intervals without overlapping

Science.gov (United States)

Yun, Wanying; Lu, Zhenzhou; Jiang, Xian

2018-06-01

To efficiently execute the variance-based global sensitivity analysis, the law of total variance in the successive intervals without overlapping is proved at first, on which an efficient space-partition sampling-based approach is subsequently proposed in this paper. Through partitioning the sample points of output into different subsets according to different inputs, the proposed approach can efficiently evaluate all the main effects concurrently by one group of sample points. In addition, there is no need for optimizing the partition scheme in the proposed approach. The maximum length of subintervals is decreased by increasing the number of sample points of model input variables in the proposed approach, which guarantees the convergence condition of the space-partition approach well. Furthermore, a new interpretation on the thought of partition is illuminated from the perspective of the variance ratio function. Finally, three test examples and one engineering application are employed to demonstrate the accuracy, efficiency and robustness of the proposed approach.

Determining optimal preventive maintenance interval for component of Well Barrier Element in an Oil & Gas Company

Science.gov (United States)

Siswanto, A.; Kurniati, N.

2018-04-01

An oil and gas company has 2,268 oil and gas wells. Well Barrier Element (WBE) is installed in a well to protect human, prevent asset damage and minimize harm to the environment. The primary WBE component is Surface Controlled Subsurface Safety Valve (SCSSV). The secondary WBE component is Christmas Tree Valves that consist of four valves i.e. Lower Master Valve (LMV), Upper Master Valve (UMV), Swab Valve (SV) and Wing Valve (WV). Current practice on WBE Preventive Maintenance (PM) program is conducted by considering the suggested schedule as stated on manual. Corrective Maintenance (CM) program is conducted when the component fails unexpectedly. Both PM and CM need cost and may cause production loss. This paper attempts to analyze the failure data and reliability based on historical data. Optimal PM interval is determined in order to minimize the total cost of maintenance per unit time. The optimal PM interval for SCSSV is 730 days, LMV is 985 days, UMV is 910 days, SV is 900 days and WV is 780 days. In average of all components, the cost reduction by implementing the suggested interval is 52%, while the reliability is improved by 4% and the availability is increased by 5%.

Dual-source CT coronary imaging in heart transplant recipients: image quality and optimal reconstruction interval

International Nuclear Information System (INIS)

Bastarrika, Gorka; Arraiza, Maria; Pueyo, Jesus C.; Cecco, Carlo N. de; Ubilla, Matias; Mastrobuoni, Stefano; Rabago, Gregorio

2008-01-01

The image quality and optimal reconstruction interval for coronary arteries in heart transplant recipients undergoing non-invasive dual-source computed tomography (DSCT) coronary angiography was evaluated. Twenty consecutive heart transplant recipients who underwent DSCT coronary angiography were included (19 male, one female; mean age 63.1±10.7 years). Data sets were reconstructed in 5% steps from 30% to 80% of the R-R interval. Two blinded independent observers assessed the image quality of each coronary segments using a five-point scale (from 0 = not evaluative to 4=excellent quality). A total of 289 coronary segments in 20 heart transplant recipients were evaluated. Mean heart rate during the scan was 89.1±10.4 bpm. At the best reconstruction interval, diagnostic image quality (score ≥2) was obtained in 93.4% of the coronary segments (270/289) with a mean image quality score of 3.04± 0.63. Systolic reconstruction intervals provided better image quality scores than diastolic reconstruction intervals (overall mean quality scores obtained with the systolic and diastolic reconstructions 3.03±1.06 and 2.73±1.11, respectively; P<0.001). Different systolic reconstruction intervals (35%, 40%, 45% of RR interval) did not yield to significant differences in image quality scores for the coronary segments (P=0.74). Reconstructions obtained at the systolic phase of the cardiac cycle allowed excellent diagnostic image quality coronary angiograms in heart transplant recipients undergoing DSCT coronary angiography. (orig.)

Binomial Distribution Sample Confidence Intervals Estimation 7. Absolute Risk Reduction and ARR-like Expressions

Directory of Open Access Journals (Sweden)

Andrei ACHIMAŞ CADARIU

2004-08-01

Full Text Available Assessments of a controlled clinical trial suppose to interpret some key parameters as the controlled event rate, experimental event date, relative risk, absolute risk reduction, relative risk reduction, number needed to treat when the effect of the treatment are dichotomous variables. Defined as the difference in the event rate between treatment and control groups, the absolute risk reduction is the parameter that allowed computing the number needed to treat. The absolute risk reduction is compute when the experimental treatment reduces the risk for an undesirable outcome/event. In medical literature when the absolute risk reduction is report with its confidence intervals, the method used is the asymptotic one, even if it is well know that may be inadequate. The aim of this paper is to introduce and assess nine methods of computing confidence intervals for absolute risk reduction and absolute risk reduction – like function.Computer implementations of the methods use the PHP language. Methods comparison uses the experimental errors, the standard deviations, and the deviation relative to the imposed significance level for specified sample sizes. Six methods of computing confidence intervals for absolute risk reduction and absolute risk reduction-like functions were assessed using random binomial variables and random sample sizes.The experiments shows that the ADAC, and ADAC1 methods obtains the best overall performance of computing confidence intervals for absolute risk reduction.

A proposal of optimal sampling design using a modularity strategy

Science.gov (United States)

Simone, A.; Giustolisi, O.; Laucelli, D. B.

2016-08-01

In real water distribution networks (WDNs) are present thousands nodes and optimal placement of pressure and flow observations is a relevant issue for different management tasks. The planning of pressure observations in terms of spatial distribution and number is named sampling design and it was faced considering model calibration. Nowadays, the design of system monitoring is a relevant issue for water utilities e.g., in order to manage background leakages, to detect anomalies and bursts, to guarantee service quality, etc. In recent years, the optimal location of flow observations related to design of optimal district metering areas (DMAs) and leakage management purposes has been faced considering optimal network segmentation and the modularity index using a multiobjective strategy. Optimal network segmentation is the basis to identify network modules by means of optimal conceptual cuts, which are the candidate locations of closed gates or flow meters creating the DMAs. Starting from the WDN-oriented modularity index, as a metric for WDN segmentation, this paper proposes a new way to perform the sampling design, i.e., the optimal location of pressure meters, using newly developed sampling-oriented modularity index. The strategy optimizes the pressure monitoring system mainly based on network topology and weights assigned to pipes according to the specific technical tasks. A multiobjective optimization minimizes the cost of pressure meters while maximizing the sampling-oriented modularity index. The methodology is presented and discussed using the Apulian and Exnet networks.

Optimal updating magnitude in adaptive flat-distribution sampling.

Science.gov (United States)

Zhang, Cheng; Drake, Justin A; Ma, Jianpeng; Pettitt, B Montgomery

2017-11-07

We present a study on the optimization of the updating magnitude for a class of free energy methods based on flat-distribution sampling, including the Wang-Landau (WL) algorithm and metadynamics. These methods rely on adaptive construction of a bias potential that offsets the potential of mean force by histogram-based updates. The convergence of the bias potential can be improved by decreasing the updating magnitude with an optimal schedule. We show that while the asymptotically optimal schedule for the single-bin updating scheme (commonly used in the WL algorithm) is given by the known inverse-time formula, that for the Gaussian updating scheme (commonly used in metadynamics) is often more complex. We further show that the single-bin updating scheme is optimal for very long simulations, and it can be generalized to a class of bandpass updating schemes that are similarly optimal. These bandpass updating schemes target only a few long-range distribution modes and their optimal schedule is also given by the inverse-time formula. Constructed from orthogonal polynomials, the bandpass updating schemes generalize the WL and Langfeld-Lucini-Rago algorithms as an automatic parameter tuning scheme for umbrella sampling.

Localized Multiple Kernel Learning Via Sample-Wise Alternating Optimization.

Science.gov (United States)

Han, Yina; Yang, Kunde; Ma, Yuanliang; Liu, Guizhong

2014-01-01

Our objective is to train support vector machines (SVM)-based localized multiple kernel learning (LMKL), using the alternating optimization between the standard SVM solvers with the local combination of base kernels and the sample-specific kernel weights. The advantage of alternating optimization developed from the state-of-the-art MKL is the SVM-tied overall complexity and the simultaneous optimization on both the kernel weights and the classifier. Unfortunately, in LMKL, the sample-specific character makes the updating of kernel weights a difficult quadratic nonconvex problem. In this paper, starting from a new primal-dual equivalence, the canonical objective on which state-of-the-art methods are based is first decomposed into an ensemble of objectives corresponding to each sample, namely, sample-wise objectives. Then, the associated sample-wise alternating optimization method is conducted, in which the localized kernel weights can be independently obtained by solving their exclusive sample-wise objectives, either linear programming (for l1-norm) or with closed-form solutions (for lp-norm). At test time, the learnt kernel weights for the training data are deployed based on the nearest-neighbor rule. Hence, to guarantee their generality among the test part, we introduce the neighborhood information and incorporate it into the empirical loss when deriving the sample-wise objectives. Extensive experiments on four benchmark machine learning datasets and two real-world computer vision datasets demonstrate the effectiveness and efficiency of the proposed algorithm.

Optimal Land Use Management for Soil Erosion Control by Using an Interval-Parameter Fuzzy Two-Stage Stochastic Programming Approach

Science.gov (United States)

Han, Jing-Cheng; Huang, Guo-He; Zhang, Hua; Li, Zhong

2013-09-01

Soil erosion is one of the most serious environmental and public health problems, and such land degradation can be effectively mitigated through performing land use transitions across a watershed. Optimal land use management can thus provide a way to reduce soil erosion while achieving the maximum net benefit. However, optimized land use allocation schemes are not always successful since uncertainties pertaining to soil erosion control are not well presented. This study applied an interval-parameter fuzzy two-stage stochastic programming approach to generate optimal land use planning strategies for soil erosion control based on an inexact optimization framework, in which various uncertainties were reflected. The modeling approach can incorporate predefined soil erosion control policies, and address inherent system uncertainties expressed as discrete intervals, fuzzy sets, and probability distributions. The developed model was demonstrated through a case study in the Xiangxi River watershed, China's Three Gorges Reservoir region. Land use transformations were employed as decision variables, and based on these, the land use change dynamics were yielded for a 15-year planning horizon. Finally, the maximum net economic benefit with an interval value of [1.197, 6.311] × 109 was obtained as well as corresponding land use allocations in the three planning periods. Also, the resulting soil erosion amount was found to be decreased and controlled at a tolerable level over the watershed. Thus, results confirm that the developed model is a useful tool for implementing land use management as not only does it allow local decision makers to optimize land use allocation, but can also help to answer how to accomplish land use changes.

Optimal land use management for soil erosion control by using an interval-parameter fuzzy two-stage stochastic programming approach.

Science.gov (United States)

Han, Jing-Cheng; Huang, Guo-He; Zhang, Hua; Li, Zhong

2013-09-01

Soil erosion is one of the most serious environmental and public health problems, and such land degradation can be effectively mitigated through performing land use transitions across a watershed. Optimal land use management can thus provide a way to reduce soil erosion while achieving the maximum net benefit. However, optimized land use allocation schemes are not always successful since uncertainties pertaining to soil erosion control are not well presented. This study applied an interval-parameter fuzzy two-stage stochastic programming approach to generate optimal land use planning strategies for soil erosion control based on an inexact optimization framework, in which various uncertainties were reflected. The modeling approach can incorporate predefined soil erosion control policies, and address inherent system uncertainties expressed as discrete intervals, fuzzy sets, and probability distributions. The developed model was demonstrated through a case study in the Xiangxi River watershed, China's Three Gorges Reservoir region. Land use transformations were employed as decision variables, and based on these, the land use change dynamics were yielded for a 15-year planning horizon. Finally, the maximum net economic benefit with an interval value of [1.197, 6.311] × 10(9) $ was obtained as well as corresponding land use allocations in the three planning periods. Also, the resulting soil erosion amount was found to be decreased and controlled at a tolerable level over the watershed. Thus, results confirm that the developed model is a useful tool for implementing land use management as not only does it allow local decision makers to optimize land use allocation, but can also help to answer how to accomplish land use changes.

Optimal relaxed causal sampler using sampled-date system theory

NARCIS (Netherlands)

Shekhawat, Hanumant; Meinsma, Gjerrit

This paper studies the design of an optimal relaxed causal sampler using sampled data system theory. A lifted frequency domain approach is used to obtain the existence conditions and the optimal sampler. A state space formulation of the results is also provided. The resulting optimal relaxed causal

A Note on Confidence Interval for the Power of the One Sample Test

OpenAIRE

A. Wong

2010-01-01

In introductory statistics texts, the power of the test of a one-sample mean when the variance is known is widely discussed. However, when the variance is unknown, the power of the Student's -test is seldom mentioned. In this note, a general methodology for obtaining inference concerning a scalar parameter of interest of any exponential family model is proposed. The method is then applied to the one-sample mean problem with unknown variance to obtain a ( 1 − ) 100% confidence interval for...

Sample Adaptive Offset Optimization in HEVC

Directory of Open Access Journals (Sweden)

Yang Zhang

2014-11-01

Full Text Available As the next generation of video coding standard, High Efficiency Video Coding (HEVC adopted many useful tools to improve coding efficiency. Sample Adaptive Offset (SAO, is a technique to reduce sample distortion by providing offsets to pixels in in-loop filter. In SAO, pixels in LCU are classified into several categories, then categories and offsets are given based on Rate-Distortion Optimization (RDO of reconstructed pixels in a Largest Coding Unit (LCU. Pixels in a LCU are operated by the same SAO process, however, transform and inverse transform makes the distortion of pixels in Transform Unit (TU edge larger than the distortion inside TU even after deblocking filtering (DF and SAO. And the categories of SAO can also be refined, since it is not proper for many cases. This paper proposed a TU edge offset mode and a category refinement for SAO in HEVC. Experimental results shows that those two kinds of optimization gets -0.13 and -0.2 gain respectively compared with the SAO in HEVC. The proposed algorithm which using the two kinds of optimization gets -0.23 gain on BD-rate compared with the SAO in HEVC which is a 47 % increase with nearly no increase on coding time.

spsann - optimization of sample patterns using spatial simulated annealing

Science.gov (United States)

Samuel-Rosa, Alessandro; Heuvelink, Gerard; Vasques, Gustavo; Anjos, Lúcia

2015-04-01

There are many algorithms and computer programs to optimize sample patterns, some private and others publicly available. A few have only been presented in scientific articles and text books. This dispersion and somewhat poor availability is holds back to their wider adoption and further development. We introduce spsann, a new R-package for the optimization of sample patterns using spatial simulated annealing. R is the most popular environment for data processing and analysis. Spatial simulated annealing is a well known method with widespread use to solve optimization problems in the soil and geo-sciences. This is mainly due to its robustness against local optima and easiness of implementation. spsann offers many optimizing criteria for sampling for variogram estimation (number of points or point-pairs per lag distance class - PPL), trend estimation (association/correlation and marginal distribution of the covariates - ACDC), and spatial interpolation (mean squared shortest distance - MSSD). spsann also includes the mean or maximum universal kriging variance (MUKV) as an optimizing criterion, which is used when the model of spatial variation is known. PPL, ACDC and MSSD were combined (PAN) for sampling when we are ignorant about the model of spatial variation. spsann solves this multi-objective optimization problem scaling the objective function values using their maximum absolute value or the mean value computed over 1000 random samples. Scaled values are aggregated using the weighted sum method. A graphical display allows to follow how the sample pattern is being perturbed during the optimization, as well as the evolution of its energy state. It is possible to start perturbing many points and exponentially reduce the number of perturbed points. The maximum perturbation distance reduces linearly with the number of iterations. The acceptance probability also reduces exponentially with the number of iterations. R is memory hungry and spatial simulated annealing is a

A risk explicit interval linear programming model for uncertainty-based environmental economic optimization in the Lake Fuxian watershed, China.

Science.gov (United States)

Zhang, Xiaoling; Huang, Kai; Zou, Rui; Liu, Yong; Yu, Yajuan

2013-01-01

The conflict of water environment protection and economic development has brought severe water pollution and restricted the sustainable development in the watershed. A risk explicit interval linear programming (REILP) method was used to solve integrated watershed environmental-economic optimization problem. Interval linear programming (ILP) and REILP models for uncertainty-based environmental economic optimization at the watershed scale were developed for the management of Lake Fuxian watershed, China. Scenario analysis was introduced into model solution process to ensure the practicality and operability of optimization schemes. Decision makers' preferences for risk levels can be expressed through inputting different discrete aspiration level values into the REILP model in three periods under two scenarios. Through balancing the optimal system returns and corresponding system risks, decision makers can develop an efficient industrial restructuring scheme based directly on the window of "low risk and high return efficiency" in the trade-off curve. The representative schemes at the turning points of two scenarios were interpreted and compared to identify a preferable planning alternative, which has the relatively low risks and nearly maximum benefits. This study provides new insights and proposes a tool, which was REILP, for decision makers to develop an effectively environmental economic optimization scheme in integrated watershed management.

A Risk Explicit Interval Linear Programming Model for Uncertainty-Based Environmental Economic Optimization in the Lake Fuxian Watershed, China

Directory of Open Access Journals (Sweden)

Xiaoling Zhang

2013-01-01

Full Text Available The conflict of water environment protection and economic development has brought severe water pollution and restricted the sustainable development in the watershed. A risk explicit interval linear programming (REILP method was used to solve integrated watershed environmental-economic optimization problem. Interval linear programming (ILP and REILP models for uncertainty-based environmental economic optimization at the watershed scale were developed for the management of Lake Fuxian watershed, China. Scenario analysis was introduced into model solution process to ensure the practicality and operability of optimization schemes. Decision makers’ preferences for risk levels can be expressed through inputting different discrete aspiration level values into the REILP model in three periods under two scenarios. Through balancing the optimal system returns and corresponding system risks, decision makers can develop an efficient industrial restructuring scheme based directly on the window of “low risk and high return efficiency” in the trade-off curve. The representative schemes at the turning points of two scenarios were interpreted and compared to identify a preferable planning alternative, which has the relatively low risks and nearly maximum benefits. This study provides new insights and proposes a tool, which was REILP, for decision makers to develop an effectively environmental economic optimization scheme in integrated watershed management.

Two sample Bayesian prediction intervals for order statistics based on the inverse exponential-type distributions using right censored sample

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M.M. Mohie El-Din

2011-10-01

Full Text Available In this paper, two sample Bayesian prediction intervals for order statistics (OS are obtained. This prediction is based on a certain class of the inverse exponential-type distributions using a right censored sample. A general class of prior density functions is used and the predictive cumulative function is obtained in the two samples case. The class of the inverse exponential-type distributions includes several important distributions such the inverse Weibull distribution, the inverse Burr distribution, the loglogistic distribution, the inverse Pareto distribution and the inverse paralogistic distribution. Special cases of the inverse Weibull model such as the inverse exponential model and the inverse Rayleigh model are considered.

On Optimal, Minimal BRDF Sampling for Reflectance Acquisition

DEFF Research Database (Denmark)

Nielsen, Jannik Boll; Jensen, Henrik Wann; Ramamoorthi, Ravi

2015-01-01

The bidirectional reflectance distribution function (BRDF) is critical for rendering, and accurate material representation requires data-driven reflectance models. However, isotropic BRDFs are 3D functions, and measuring the reflectance of a flat sample can require a million incident and outgoing...... direction pairs, making the use of measured BRDFs impractical. In this paper, we address the problem of reconstructing a measured BRDF from a limited number of samples. We present a novel mapping of the BRDF space, allowing for extraction of descriptive principal components from measured databases......, such as the MERL BRDF database. We optimize for the best sampling directions, and explicitly provide the optimal set of incident and outgoing directions in the Rusinkiewicz parameterization for n = {1, 2, 5, 10, 20} samples. Based on the principal components, we describe a method for accurately reconstructing BRDF...

Optimal Design and Tuning of PID-Type Interval Type-2 Fuzzy Logic Controllers for Delta Parallel Robots

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Xingguo Lu

2016-05-01

Full Text Available In this work, we propose a new method for the optimal design and tuning of a Proportional-Integral-Derivative type (PID-type interval type-2 fuzzy logic controller (IT2 FLC for Delta parallel robot trajectory tracking control. The presented methodology starts with an optimal design problem of IT2 FLC. A group of IT2 FLCs are obtained by blurring the membership functions using a variable called blurring degree. By comparing the performance of the controllers, the optimal structure of IT2 FLC is obtained. Then, a multi-objective optimization problem is formulated to tune the scaling factors of the PID-type IT2 FLC. The Non-dominated Sorting Genetic Algorithm (NSGA-II is adopted to solve the constrained nonlinear multi-objective optimization problem. Simulation results of the optimized controller are presented and discussed regarding application in the Delta parallel robot. The proposed method provides an effective way to design and tune the PID-type IT2 FLC with a desired control performance.

A Combined Weighting Method Based on Hybrid of Interval Evidence Fusion and Random Sampling

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Ying Yan

2017-01-01

Full Text Available Due to the complexity of system and lack of expertise, epistemic uncertainties may present in the experts’ judgment on the importance of certain indices during group decision-making. A novel combination weighting method is proposed to solve the index weighting problem when various uncertainties are present in expert comments. Based on the idea of evidence theory, various types of uncertain evaluation information are uniformly expressed through interval evidence structures. Similarity matrix between interval evidences is constructed, and expert’s information is fused. Comment grades are quantified using the interval number, and cumulative probability function for evaluating the importance of indices is constructed based on the fused information. Finally, index weights are obtained by Monte Carlo random sampling. The method can process expert’s information with varying degrees of uncertainties, which possesses good compatibility. Difficulty in effectively fusing high-conflict group decision-making information and large information loss after fusion is avertible. Original expert judgments are retained rather objectively throughout the processing procedure. Cumulative probability function constructing and random sampling processes do not require any human intervention or judgment. It can be implemented by computer programs easily, thus having an apparent advantage in evaluation practices of fairly huge index systems.

Rate-distortion optimization for compressive video sampling

Science.gov (United States)

Liu, Ying; Vijayanagar, Krishna R.; Kim, Joohee

2014-05-01

The recently introduced compressed sensing (CS) framework enables low complexity video acquisition via sub- Nyquist rate sampling. In practice, the resulting CS samples are quantized and indexed by finitely many bits (bit-depth) for transmission. In applications where the bit-budget for video transmission is constrained, rate- distortion optimization (RDO) is essential for quality video reconstruction. In this work, we develop a double-level RDO scheme for compressive video sampling, where frame-level RDO is performed by adaptively allocating the fixed bit-budget per frame to each video block based on block-sparsity, and block-level RDO is performed by modelling the block reconstruction peak-signal-to-noise ratio (PSNR) as a quadratic function of quantization bit-depth. The optimal bit-depth and the number of CS samples are then obtained by setting the first derivative of the function to zero. In the experimental studies the model parameters are initialized with a small set of training data, which are then updated with local information in the model testing stage. Simulation results presented herein show that the proposed double-level RDO significantly enhances the reconstruction quality for a bit-budget constrained CS video transmission system.

Confidence intervals for population allele frequencies: the general case of sampling from a finite diploid population of any size.

Science.gov (United States)

Fung, Tak; Keenan, Kevin

2014-01-01

The estimation of population allele frequencies using sample data forms a central component of studies in population genetics. These estimates can be used to test hypotheses on the evolutionary processes governing changes in genetic variation among populations. However, existing studies frequently do not account for sampling uncertainty in these estimates, thus compromising their utility. Incorporation of this uncertainty has been hindered by the lack of a method for constructing confidence intervals containing the population allele frequencies, for the general case of sampling from a finite diploid population of any size. In this study, we address this important knowledge gap by presenting a rigorous mathematical method to construct such confidence intervals. For a range of scenarios, the method is used to demonstrate that for a particular allele, in order to obtain accurate estimates within 0.05 of the population allele frequency with high probability (> or = 95%), a sample size of > 30 is often required. This analysis is augmented by an application of the method to empirical sample allele frequency data for two populations of the checkerspot butterfly (Melitaea cinxia L.), occupying meadows in Finland. For each population, the method is used to derive > or = 98.3% confidence intervals for the population frequencies of three alleles. These intervals are then used to construct two joint > or = 95% confidence regions, one for the set of three frequencies for each population. These regions are then used to derive a > or = 95%% confidence interval for Jost's D, a measure of genetic differentiation between the two populations. Overall, the results demonstrate the practical utility of the method with respect to informing sampling design and accounting for sampling uncertainty in studies of population genetics, important for scientific hypothesis-testing and also for risk-based natural resource management.

Confidence intervals for population allele frequencies: the general case of sampling from a finite diploid population of any size.

Directory of Open Access Journals (Sweden)

Tak Fung

Full Text Available The estimation of population allele frequencies using sample data forms a central component of studies in population genetics. These estimates can be used to test hypotheses on the evolutionary processes governing changes in genetic variation among populations. However, existing studies frequently do not account for sampling uncertainty in these estimates, thus compromising their utility. Incorporation of this uncertainty has been hindered by the lack of a method for constructing confidence intervals containing the population allele frequencies, for the general case of sampling from a finite diploid population of any size. In this study, we address this important knowledge gap by presenting a rigorous mathematical method to construct such confidence intervals. For a range of scenarios, the method is used to demonstrate that for a particular allele, in order to obtain accurate estimates within 0.05 of the population allele frequency with high probability (> or = 95%, a sample size of > 30 is often required. This analysis is augmented by an application of the method to empirical sample allele frequency data for two populations of the checkerspot butterfly (Melitaea cinxia L., occupying meadows in Finland. For each population, the method is used to derive > or = 98.3% confidence intervals for the population frequencies of three alleles. These intervals are then used to construct two joint > or = 95% confidence regions, one for the set of three frequencies for each population. These regions are then used to derive a > or = 95%% confidence interval for Jost's D, a measure of genetic differentiation between the two populations. Overall, the results demonstrate the practical utility of the method with respect to informing sampling design and accounting for sampling uncertainty in studies of population genetics, important for scientific hypothesis-testing and also for risk-based natural resource management.

An optimal design of cluster spacing intervals for staged fracturing in horizontal shale gas wells based on the optimal SRVs

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Lan Ren

2017-09-01

Full Text Available When horizontal well staged cluster fracturing is applied in shale gas reservoirs, the cluster spacing is essential to fracturing performance. If the cluster spacing is too small, the stimulated area between major fractures will be overlapped, and the efficiency of fracturing stimulation will be decreased. If the cluster spacing is too large, the area between major fractures cannot be stimulated completely and reservoir recovery extent will be adversely impacted. At present, cluster spacing design is mainly based on the static model with the potential reservoir stimulation area as the target, and there is no cluster spacing design method in accordance with the actual fracturing process and targets dynamic stimulated reservoir volume (SRV. In this paper, a dynamic SRV calculation model for cluster fracture propagation was established by analyzing the coupling mechanisms among fracture propagation, fracturing fluid loss and stress. Then, the cluster spacing was optimized to reach the target of the optimal SRVs. This model was applied for validation on site in the Jiaoshiba shale gasfield in the Fuling area of the Sichuan Basin. The key geological engineering parameters influencing the optimal cluster spacing intervals were analyzed. The reference charts for the optimal cluster spacing design were prepared based on the geological characteristics of south and north blocks in the Jiaoshiba shale gasfield. It is concluded that the cluster spacing optimal design method proposed in this paper is of great significance in overcoming the blindness in current cluster perforation design and guiding the optimal design of volume fracturing in shale gas reservoirs. Keywords: Shale gas, Horizontal well, Staged fracturing, Cluster spacing, Reservoir, Stimulated reservoir volume (SRV, Mathematical model, Optimal method, Sichuan basin, Jiaoshiba shale gasfield

Using remotely-sensed data for optimal field sampling

CSIR Research Space (South Africa)

Debba, Pravesh

2008-09-01

Full Text Available M B E R 2 0 0 8 15 USING REMOTELY- SENSED DATA FOR OPTIMAL FIELD SAMPLING BY DR PRAVESH DEBBA STATISTICS IS THE SCIENCE pertaining to the collection, summary, analysis, interpretation and presentation of data. It is often impractical... studies are: where to sample, what to sample and how many samples to obtain. Conventional sampling techniques are not always suitable in environmental studies and scientists have explored the use of remotely-sensed data as ancillary information to aid...

Interval Solution for Nonlinear Programming of Maximizing the Fatigue Life of V-Belt under Polymorphic Uncertain Environment

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Zhong Wan

2013-01-01

Full Text Available In accord with the practical engineering design conditions, a nonlinear programming model is constructed for maximizing the fatigue life of V-belt drive in which some polymorphic uncertainties are incorporated. For a given satisfaction level and a confidence level, an equivalent formulation of this uncertain optimization model is obtained where only interval parameters are involved. Based on the concepts of maximal and minimal range inequalities for describing interval inequality, the interval parameter model is decomposed into two standard nonlinear programming problems, and an algorithm, called two-step based sampling algorithm, is developed to find an interval optimal solution for the original problem. Case study is employed to demonstrate the validity and practicability of the constructed model and the algorithm.

Optimal debulking targets in women with advanced stage ovarian cancer: a retrospective study of immediate versus interval debulking surgery.

Science.gov (United States)

Altman, Alon D; Nelson, Gregg; Chu, Pamela; Nation, Jill; Ghatage, Prafull

2012-06-01

The objective of this study was to examine both overall and disease-free survival of patients with advanced stage ovarian cancer after immediate or interval debulking surgery based on residual disease. We performed a retrospective chart review at the Tom Baker Cancer Centre in Calgary, Alberta of patients with pathologically confirmed stage III or IV ovarian cancer, fallopian tube cancer, or primary peritoneal cancer between 2003 and 2007. We collected data on the dates of diagnosis, recurrence, and death; cancer stage and grade, patients' age, surgery performed, and residual disease. One hundred ninety-two patients were included in the final analysis. The optimal debulking rate with immediate surgery was 64.8%, and with interval surgery it was 85.9%. There were improved overall and disease-free survival rates for optimally debulked disease (advanced stage ovarian cancer, the goal of surgery should be resection of disease to microscopic residual at the initial procedure. This results in improved overall survival than lesser degrees of resection. Further studies are required to determine optimal surgical management.

Designing optimal sampling schemes for field visits

CSIR Research Space (South Africa)

Debba, Pravesh

2008-10-01

Full Text Available This is a presentation of a statistical method for deriving optimal spatial sampling schemes. The research focuses on ground verification of minerals derived from hyperspectral data. Spectral angle mapper (SAM) and spectral feature fitting (SFF...

Availability analysis of mechanical systems with condition-based maintenance using semi-Markov and evaluation of optimal condition monitoring interval

Science.gov (United States)

Kumar, Girish; Jain, Vipul; Gandhi, O. P.

2018-03-01

Maintenance helps to extend equipment life by improving its condition and avoiding catastrophic failures. Appropriate model or mechanism is, thus, needed to quantify system availability vis-a-vis a given maintenance strategy, which will assist in decision-making for optimal utilization of maintenance resources. This paper deals with semi-Markov process (SMP) modeling for steady state availability analysis of mechanical systems that follow condition-based maintenance (CBM) and evaluation of optimal condition monitoring interval. The developed SMP model is solved using two-stage analytical approach for steady-state availability analysis of the system. Also, CBM interval is decided for maximizing system availability using Genetic Algorithm approach. The main contribution of the paper is in the form of a predictive tool for system availability that will help in deciding the optimum CBM policy. The proposed methodology is demonstrated for a centrifugal pump.

An optimal dynamic interval preventive maintenance scheduling for series systems

International Nuclear Information System (INIS)

Gao, Yicong; Feng, Yixiong; Zhang, Zixian; Tan, Jianrong

2015-01-01

This paper studies preventive maintenance (PM) with dynamic interval for a multi-component system. Instead of equal interval, the time of PM period in the proposed dynamic interval model is not a fixed constant, which varies from interval-down to interval-up. It is helpful to reduce the outage loss on frequent repair parts and avoid lack of maintenance of the equipment by controlling the equipment maintenance frequency, when compared to a periodic PM scheme. According to the definition of dynamic interval, the reliability of system is analyzed from the failure mechanisms of its components and the different effects of non-periodic PM actions on the reliability of the components. Following the proposed model of reliability, a novel framework for solving the non-periodical PM schedule with dynamic interval based on the multi-objective genetic algorithm is proposed. The framework denotes the strategies include updating strategy, deleting strategy, inserting strategy and moving strategy, which is set to correct the invalid population individuals of the algorithm. The values of the dynamic interval and the selections of PM action for the components on every PM stage are determined by achieving a certain level of system availability with the minimum total PM-related cost. Finally, a typical rotary table system of NC machine tool is used as an example to describe the proposed method. - Highlights: • A non-periodic preventive maintenance scheduling model is proposed. • A framework for solving the non-periodical PM schedule problem is developed. • The interval of non-periodic PM is flexible and schedule can be better adjusted. • Dynamic interval leads to more efficient solutions than fixed interval does

Reliability-Based and Cost-Oriented Product Optimization Integrating Fuzzy Reasoning Petri Nets, Interval Expert Evaluation and Cultural-Based DMOPSO Using Crowding Distance Sorting

Directory of Open Access Journals (Sweden)

Zhaoxi Hong

2017-08-01

Full Text Available In reliability-based and cost-oriented product optimization, the target product reliability is apportioned to subsystems or components to achieve the maximum reliability and minimum cost. Main challenges to conducting such optimization design lie in how to simultaneously consider subsystem division, uncertain evaluation provided by experts for essential factors, and dynamic propagation of product failure. To overcome these problems, a reliability-based and cost-oriented product optimization method integrating fuzzy reasoning Petri net (FRPN, interval expert evaluation and cultural-based dynamic multi-objective particle swarm optimization (DMOPSO using crowding distance sorting is proposed in this paper. Subsystem division is performed based on failure decoupling, and then subsystem weights are calculated with FRPN reflecting dynamic and uncertain failure propagation, as well as interval expert evaluation considering six essential factors. A mathematical model of reliability-based and cost-oriented product optimization is established, and the cultural-based DMOPSO with crowding distance sorting is utilized to obtain the optimized design scheme. The efficiency and effectiveness of the proposed method are demonstrated by the numerical example of the optimization design for a computer numerically controlled (CNC machine tool.

Optimizing Soil Moisture Sampling Locations for Validation Networks for SMAP

Science.gov (United States)

Roshani, E.; Berg, A. A.; Lindsay, J.

2013-12-01

Soil Moisture Active Passive satellite (SMAP) is scheduled for launch on Oct 2014. Global efforts are underway for establishment of soil moisture monitoring networks for both the pre- and post-launch validation and calibration of the SMAP products. In 2012 the SMAP Validation Experiment, SMAPVEX12, took place near Carman Manitoba, Canada where nearly 60 fields were sampled continuously over a 6 week period for soil moisture and several other parameters simultaneous to remotely sensed images of the sampling region. The locations of these sampling sites were mainly selected on the basis of accessibility, soil texture, and vegetation cover. Although these criteria are necessary to consider during sampling site selection, they do not guarantee optimal site placement to provide the most efficient representation of the studied area. In this analysis a method for optimization of sampling locations is presented which combines the state-of-art multi-objective optimization engine (non-dominated sorting genetic algorithm, NSGA-II), with the kriging interpolation technique to minimize the number of sampling sites while simultaneously minimizing the differences between the soil moisture map resulted from the kriging interpolation and soil moisture map from radar imaging. The algorithm is implemented in Whitebox Geospatial Analysis Tools, which is a multi-platform open-source GIS. The optimization framework is subject to the following three constraints:. A) sampling sites should be accessible to the crew on the ground, B) the number of sites located in a specific soil texture should be greater than or equal to a minimum value, and finally C) the number of sampling sites with a specific vegetation cover should be greater than or equal to a minimum constraint. The first constraint is implemented into the proposed model to keep the practicality of the approach. The second and third constraints are considered to guarantee that the collected samples from each soil texture categories

An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting

Directory of Open Access Journals (Sweden)

Qiang Ni

2017-10-01

Full Text Available High quality photovoltaic (PV power prediction intervals (PIs are essential to power system operation and planning. To improve the reliability and sharpness of PIs, in this paper, a new method is proposed, which involves the model uncertainties and noise uncertainties, and PIs are constructed with a two-step formulation. In the first step, the variance of model uncertainties is obtained by using extreme learning machine to make deterministic forecasts of PV power. In the second stage, innovative PI-based cost function is developed to optimize the parameters of ELM and noise uncertainties are quantization in terms of variance. The performance of the proposed approach is examined by using the PV power and meteorological data measured from 1kW rooftop DC micro-grid system. The validity of the proposed method is verified by comparing the experimental analysis with other benchmarking methods, and the results exhibit a superior performance.

Determination of optimal samples for robot calibration based on error similarity

Directory of Open Access Journals (Sweden)

Tian Wei

2015-06-01

Full Text Available Industrial robots are used for automatic drilling and riveting. The absolute position accuracy of an industrial robot is one of the key performance indexes in aircraft assembly, and can be improved through error compensation to meet aircraft assembly requirements. The achievable accuracy and the difficulty of accuracy compensation implementation are closely related to the choice of sampling points. Therefore, based on the error similarity error compensation method, a method for choosing sampling points on a uniform grid is proposed. A simulation is conducted to analyze the influence of the sample point locations on error compensation. In addition, the grid steps of the sampling points are optimized using a statistical analysis method. The method is used to generate grids and optimize the grid steps of a Kuka KR-210 robot. The experimental results show that the method for planning sampling data can be used to effectively optimize the sampling grid. After error compensation, the position accuracy of the robot meets the position accuracy requirements.

Classifier-Guided Sampling for Complex Energy System Optimization

Energy Technology Data Exchange (ETDEWEB)

Backlund, Peter B. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States); Eddy, John P. [Sandia National Laboratories (SNL-NM), Albuquerque, NM (United States)

2015-09-01

This report documents the results of a Laboratory Directed Research and Development (LDRD) effort enti tled "Classifier - Guided Sampling for Complex Energy System Optimization" that was conducted during FY 2014 and FY 2015. The goal of this proj ect was to develop, implement, and test major improvements to the classifier - guided sampling (CGS) algorithm. CGS is type of evolutionary algorithm for perform ing search and optimization over a set of discrete design variables in the face of one or more objective functions. E xisting evolutionary algorithms, such as genetic algorithms , may require a large number of o bjecti ve function evaluations to identify optimal or near - optimal solutions . Reducing the number of evaluations can result in significant time savings, especially if the objective function is computationally expensive. CGS reduce s the evaluation count by us ing a Bayesian network classifier to filter out non - promising candidate designs , prior to evaluation, based on their posterior probabilit ies . In this project, b oth the single - objective and multi - objective version s of the CGS are developed and tested on a set of benchm ark problems. As a domain - specific case study, CGS is used to design a microgrid for use in islanded mode during an extended bulk power grid outage.

A Sequential Optimization Sampling Method for Metamodels with Radial Basis Functions

Science.gov (United States)

Pan, Guang; Ye, Pengcheng; Yang, Zhidong

2014-01-01

Metamodels have been widely used in engineering design to facilitate analysis and optimization of complex systems that involve computationally expensive simulation programs. The accuracy of metamodels is strongly affected by the sampling methods. In this paper, a new sequential optimization sampling method is proposed. Based on the new sampling method, metamodels can be constructed repeatedly through the addition of sampling points, namely, extrema points of metamodels and minimum points of density function. Afterwards, the more accurate metamodels would be constructed by the procedure above. The validity and effectiveness of proposed sampling method are examined by studying typical numerical examples. PMID:25133206

Optimism is universal: exploring the presence and benefits of optimism in a representative sample of the world.

Science.gov (United States)

Gallagher, Matthew W; Lopez, Shane J; Pressman, Sarah D

2013-10-01

Current theories of optimism suggest that the tendency to maintain positive expectations for the future is an adaptive psychological resource associated with improved well-being and physical health, but the majority of previous optimism research has been conducted in industrialized nations. The present study examined (a) whether optimism is universal, (b) what demographic factors predict optimism, and (c) whether optimism is consistently associated with improved subjective well-being and perceived health worldwide. The present study used representative samples of 142 countries that together represent 95% of the world's population. The total sample of 150,048 individuals had a mean age of 38.28 (SD = 16.85) and approximately equal sex distribution (51.2% female). The relationships between optimism, subjective well-being, and perceived health were examined using hierarchical linear modeling. Results indicated that most individuals and most countries worldwide are optimistic and that higher levels of optimism are associated with improved subjective well-being and perceived health worldwide. The present study provides compelling evidence that optimism is a universal phenomenon and that the associations between optimism and improved psychological functioning are not limited to industrialized nations. © 2012 Wiley Periodicals, Inc.

Enhancing sampling design in mist-net bat surveys by accounting for sample size optimization

OpenAIRE

Trevelin, Leonardo Carreira; Novaes, Roberto Leonan Morim; Colas-Rosas, Paul François; Benathar, Thayse Cristhina Melo; Peres, Carlos A.

2017-01-01

The advantages of mist-netting, the main technique used in Neotropical bat community studies to date, include logistical implementation, standardization and sampling representativeness. Nonetheless, study designs still have to deal with issues of detectability related to how different species behave and use the environment. Yet there is considerable sampling heterogeneity across available studies in the literature. Here, we approach the problem of sample size optimization. We evaluated the co...

A Note on Confidence Interval for the Power of the One Sample Test

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A. Wong

2010-01-01

Full Text Available In introductory statistics texts, the power of the test of a one-sample mean when the variance is known is widely discussed. However, when the variance is unknown, the power of the Student's -test is seldom mentioned. In this note, a general methodology for obtaining inference concerning a scalar parameter of interest of any exponential family model is proposed. The method is then applied to the one-sample mean problem with unknown variance to obtain a (1−100% confidence interval for the power of the Student's -test that detects the difference (−0. The calculations require only the density and the cumulative distribution functions of the standard normal distribution. In addition, the methodology presented can also be applied to determine the required sample size when the effect size and the power of a size test of mean are given.

Using remote sensing images to design optimal field sampling schemes

CSIR Research Space (South Africa)

Debba, Pravesh

2008-08-01

Full Text Available sampling schemes case studies Optimized field sampling representing the overall distribution of a particular mineral Deriving optimal exploration target zones CONTINUUM REMOVAL for vegetation [13, 27, 46]. The convex hull transform is a method... of normalizing spectra [16, 41]. The convex hull technique is anal- ogous to fitting a rubber band over a spectrum to form a continuum. Figure 5 shows the concept of the convex hull transform. The differ- ence between the hull and the orig- inal spectrum...

A hybrid reliability algorithm using PSO-optimized Kriging model and adaptive importance sampling

Science.gov (United States)

Tong, Cao; Gong, Haili

2018-03-01

This paper aims to reduce the computational cost of reliability analysis. A new hybrid algorithm is proposed based on PSO-optimized Kriging model and adaptive importance sampling method. Firstly, the particle swarm optimization algorithm (PSO) is used to optimize the parameters of Kriging model. A typical function is fitted to validate improvement by comparing results of PSO-optimized Kriging model with those of the original Kriging model. Secondly, a hybrid algorithm for reliability analysis combined optimized Kriging model and adaptive importance sampling is proposed. Two cases from literatures are given to validate the efficiency and correctness. The proposed method is proved to be more efficient due to its application of small number of sample points according to comparison results.

Statistical intervals a guide for practitioners

CERN Document Server

Hahn, Gerald J

2011-01-01

Presents a detailed exposition of statistical intervals and emphasizes applications in industry. The discussion differentiates at an elementary level among different kinds of statistical intervals and gives instruction with numerous examples and simple math on how to construct such intervals from sample data. This includes confidence intervals to contain a population percentile, confidence intervals on probability of meeting specified threshold value, and prediction intervals to include observation in a future sample. Also has an appendix containing computer subroutines for nonparametric stati

Optimized preparation of urine samples for two-dimensional electrophoresis and initial application to patient samples

DEFF Research Database (Denmark)

Lafitte, Daniel; Dussol, Bertrand; Andersen, Søren

2002-01-01

OBJECTIVE: We optimized of the preparation of urinary samples to obtain a comprehensive map of urinary proteins of healthy subjects and then compared this map with the ones obtained with patient samples to show that the pattern was specific of their kidney disease. DESIGN AND METHODS: The urinary...

Rescaled Range Analysis and Detrended Fluctuation Analysis: Finite Sample Properties and Confidence Intervals

Czech Academy of Sciences Publication Activity Database

Krištoufek, Ladislav

4/2010, č. 3 (2010), s. 236-250 ISSN 1802-4696 R&D Projects: GA ČR GD402/09/H045; GA ČR GA402/09/0965 Grant - others:GA UK(CZ) 118310 Institutional research plan: CEZ:AV0Z10750506 Keywords : rescaled range analysis * detrended fluctuation analysis * Hurst exponent * long-range dependence Subject RIV: AH - Economics http://library.utia.cas.cz/separaty/2010/E/kristoufek-rescaled range analysis and detrended fluctuation analysis finite sample properties and confidence intervals.pdf

Tracking a changing environment: optimal sampling, adaptive memory and overnight effects.

Science.gov (United States)

Dunlap, Aimee S; Stephens, David W

2012-02-01

Foraging in a variable environment presents a classic problem of decision making with incomplete information. Animals must track the changing environment, remember the best options and make choices accordingly. While several experimental studies have explored the idea that sampling behavior reflects the amount of environmental change, we take the next logical step in asking how change influences memory. We explore the hypothesis that memory length should be tied to the ecological relevance and the value of the information learned, and that environmental change is a key determinant of the value of memory. We use a dynamic programming model to confirm our predictions and then test memory length in a factorial experiment. In our experimental situation we manipulate rates of change in a simple foraging task for blue jays over a 36 h period. After jays experienced an experimentally determined change regime, we tested them at a range of retention intervals, from 1 to 72 h. Manipulated rates of change influenced learning and sampling rates: subjects sampled more and learned more quickly in the high change condition. Tests of retention revealed significant interactions between retention interval and the experienced rate of change. We observed a striking and surprising difference between the high and low change treatments at the 24h retention interval. In agreement with earlier work we find that a circadian retention interval is special, but we find that the extent of this 'specialness' depends on the subject's prior experience of environmental change. Specifically, experienced rates of change seem to influence how subjects balance recent information against past experience in a way that interacts with the passage of time. Copyright © 2011 Elsevier B.V. All rights reserved.

Optimal experiment design in a filtering context with application to sampled network data

OpenAIRE

Singhal, Harsh; Michailidis, George

2010-01-01

We examine the problem of optimal design in the context of filtering multiple random walks. Specifically, we define the steady state E-optimal design criterion and show that the underlying optimization problem leads to a second order cone program. The developed methodology is applied to tracking network flow volumes using sampled data, where the design variable corresponds to controlling the sampling rate. The optimal design is numerically compared to a myopic and a naive strategy. Finally, w...

Optimization of protein samples for NMR using thermal shift assays

International Nuclear Information System (INIS)

Kozak, Sandra; Lercher, Lukas; Karanth, Megha N.; Meijers, Rob; Carlomagno, Teresa; Boivin, Stephane

2016-01-01

Maintaining a stable fold for recombinant proteins is challenging, especially when working with highly purified and concentrated samples at temperatures >20 °C. Therefore, it is worthwhile to screen for different buffer components that can stabilize protein samples. Thermal shift assays or ThermoFluor"® provide a high-throughput screening method to assess the thermal stability of a sample under several conditions simultaneously. Here, we describe a thermal shift assay that is designed to optimize conditions for nuclear magnetic resonance studies, which typically require stable samples at high concentration and ambient (or higher) temperature. We demonstrate that for two challenging proteins, the multicomponent screen helped to identify ingredients that increased protein stability, leading to clear improvements in the quality of the spectra. Thermal shift assays provide an economic and time-efficient method to find optimal conditions for NMR structural studies.

Optimization of protein samples for NMR using thermal shift assays

Energy Technology Data Exchange (ETDEWEB)

Kozak, Sandra [European Molecular Biology Laboratory (EMBL), Hamburg Outstation, SPC Facility (Germany); Lercher, Lukas; Karanth, Megha N. [European Molecular Biology Laboratory (EMBL), SCB Unit (Germany); Meijers, Rob [European Molecular Biology Laboratory (EMBL), Hamburg Outstation, SPC Facility (Germany); Carlomagno, Teresa, E-mail: teresa.carlomagno@oci.uni-hannover.de [European Molecular Biology Laboratory (EMBL), SCB Unit (Germany); Boivin, Stephane, E-mail: sboivin77@hotmail.com, E-mail: s.boivin@embl-hamburg.de [European Molecular Biology Laboratory (EMBL), Hamburg Outstation, SPC Facility (Germany)

2016-04-15

Maintaining a stable fold for recombinant proteins is challenging, especially when working with highly purified and concentrated samples at temperatures >20 °C. Therefore, it is worthwhile to screen for different buffer components that can stabilize protein samples. Thermal shift assays or ThermoFluor{sup ®} provide a high-throughput screening method to assess the thermal stability of a sample under several conditions simultaneously. Here, we describe a thermal shift assay that is designed to optimize conditions for nuclear magnetic resonance studies, which typically require stable samples at high concentration and ambient (or higher) temperature. We demonstrate that for two challenging proteins, the multicomponent screen helped to identify ingredients that increased protein stability, leading to clear improvements in the quality of the spectra. Thermal shift assays provide an economic and time-efficient method to find optimal conditions for NMR structural studies.

Monte Carlo importance sampling optimization for system reliability applications

International Nuclear Information System (INIS)

Campioni, Luca; Vestrucci, Paolo

2004-01-01

This paper focuses on the reliability analysis of multicomponent systems by the importance sampling technique, and, in particular, it tackles the optimization aspect. A methodology based on the minimization of the variance at the component level is proposed for the class of systems consisting of independent components. The claim is that, by means of such a methodology, the optimal biasing could be achieved without resorting to the typical approach by trials

Economic Statistical Design of Variable Sampling Interval X¯$\\overline X $ Control Chart Based on Surrogate Variable Using Genetic Algorithms

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Lee Tae-Hoon

2016-12-01

Full Text Available In many cases, a X¯$\\overline X $ control chart based on a performance variable is used in industrial fields. Typically, the control chart monitors the measurements of a performance variable itself. However, if the performance variable is too costly or impossible to measure, and a less expensive surrogate variable is available, the process may be more efficiently controlled using surrogate variables. In this paper, we present a model for the economic statistical design of a VSI (Variable Sampling Interval X¯$\\overline X $ control chart using a surrogate variable that is linearly correlated with the performance variable. We derive the total average profit model from an economic viewpoint and apply the model to a Very High Temperature Reactor (VHTR nuclear fuel measurement system and derive the optimal result using genetic algorithms. Compared with the control chart based on a performance variable, the proposed model gives a larger expected net income per unit of time in the long-run if the correlation between the performance variable and the surrogate variable is relatively high. The proposed model was confined to the sample mean control chart under the assumption that a single assignable cause occurs according to the Poisson process. However, the model may also be extended to other types of control charts using a single or multiple assignable cause assumptions such as VSS (Variable Sample Size X¯$\\overline X $ control chart, EWMA, CUSUM charts and so on.

An Optimization Model for Kardeh Reservoir Operation Using Interval-Parameter, Multi-stage, Stochastic Programming

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Fatemeh Rastegaripour

2010-09-01

Full Text Available The present study investigates water allocation of Kardeh Reservoir to domestic and agricultural users using an Interval Parameter, Multi-stage, Stochastic Programming (IMSLP under uncertainty. The advantages of the method include its dynamics nature, use of a pre-defined policy in its optimization process, and the use of interval parameter and probability under uncertainty conditions. Additionally, it offers different decision-making alternatives for different scenarios of water shortage. The required data were collected from Khorasan Razavi Regional Water Organization and from the Water and Wastewater Co. for the period 1988-2007. Results showed that, under the worst conditions, the water deficits expected to occur for each of the next 3 years will be 1.9, 2.55, and 3.11 million cubic meters for the domestic use and 0.22, 0.32, 0.75 million cubic meters for irrigation. Approximate reductions of 0.5, 0.7, and 1 million cubic meters in the monthly consumption of the urban community and enhanced irrigation efficiencies of about 6, 11, and 20% in the agricultural sector are recommended as approaches for combating the water shortage over the next 3 years.

Optimizing Sampling Efficiency for Biomass Estimation Across NEON Domains

Science.gov (United States)

Abercrombie, H. H.; Meier, C. L.; Spencer, J. J.

2013-12-01

Over the course of 30 years, the National Ecological Observatory Network (NEON) will measure plant biomass and productivity across the U.S. to enable an understanding of terrestrial carbon cycle responses to ecosystem change drivers. Over the next several years, prior to operational sampling at a site, NEON will complete construction and characterization phases during which a limited amount of sampling will be done at each site to inform sampling designs, and guide standardization of data collection across all sites. Sampling biomass in 60+ sites distributed among 20 different eco-climatic domains poses major logistical and budgetary challenges. Traditional biomass sampling methods such as clip harvesting and direct measurements of Leaf Area Index (LAI) involve collecting and processing plant samples, and are time and labor intensive. Possible alternatives include using indirect sampling methods for estimating LAI such as digital hemispherical photography (DHP) or using a LI-COR 2200 Plant Canopy Analyzer. These LAI estimations can then be used as a proxy for biomass. The biomass estimates calculated can then inform the clip harvest sampling design during NEON operations, optimizing both sample size and number so that standardized uncertainty limits can be achieved with a minimum amount of sampling effort. In 2011, LAI and clip harvest data were collected from co-located sampling points at the Central Plains Experimental Range located in northern Colorado, a short grass steppe ecosystem that is the NEON Domain 10 core site. LAI was measured with a LI-COR 2200 Plant Canopy Analyzer. The layout of the sampling design included four, 300 meter transects, with clip harvests plots spaced every 50m, and LAI sub-transects spaced every 10m. LAI was measured at four points along 6m sub-transects running perpendicular to the 300m transect. Clip harvest plots were co-located 4m from corresponding LAI transects, and had dimensions of 0.1m by 2m. We conducted regression analyses

A probabilistic approach for representation of interval uncertainty

International Nuclear Information System (INIS)

Zaman, Kais; Rangavajhala, Sirisha; McDonald, Mark P.; Mahadevan, Sankaran

2011-01-01

In this paper, we propose a probabilistic approach to represent interval data for input variables in reliability and uncertainty analysis problems, using flexible families of continuous Johnson distributions. Such a probabilistic representation of interval data facilitates a unified framework for handling aleatory and epistemic uncertainty. For fitting probability distributions, methods such as moment matching are commonly used in the literature. However, unlike point data where single estimates for the moments of data can be calculated, moments of interval data can only be computed in terms of upper and lower bounds. Finding bounds on the moments of interval data has been generally considered an NP-hard problem because it includes a search among the combinations of multiple values of the variables, including interval endpoints. In this paper, we present efficient algorithms based on continuous optimization to find the bounds on second and higher moments of interval data. With numerical examples, we show that the proposed bounding algorithms are scalable in polynomial time with respect to increasing number of intervals. Using the bounds on moments computed using the proposed approach, we fit a family of Johnson distributions to interval data. Furthermore, using an optimization approach based on percentiles, we find the bounding envelopes of the family of distributions, termed as a Johnson p-box. The idea of bounding envelopes for the family of Johnson distributions is analogous to the notion of empirical p-box in the literature. Several sets of interval data with different numbers of intervals and type of overlap are presented to demonstrate the proposed methods. As against the computationally expensive nested analysis that is typically required in the presence of interval variables, the proposed probabilistic representation enables inexpensive optimization-based strategies to estimate bounds on an output quantity of interest.

Optimal sampling plan for clean development mechanism energy efficiency lighting projects

International Nuclear Information System (INIS)

Ye, Xianming; Xia, Xiaohua; Zhang, Jiangfeng

2013-01-01

Highlights: • A metering cost minimisation model is built to assist the sampling plan for CDM projects. • The model minimises the total metering cost by the determination of optimal sample size. • The required 90/10 criterion sampling accuracy is maintained. • The proposed metering cost minimisation model is applicable to other CDM projects as well. - Abstract: Clean development mechanism (CDM) project developers are always interested in achieving required measurement accuracies with the least metering cost. In this paper, a metering cost minimisation model is proposed for the sampling plan of a specific CDM energy efficiency lighting project. The problem arises from the particular CDM sampling requirement of 90% confidence and 10% precision for the small-scale CDM energy efficiency projects, which is known as the 90/10 criterion. The 90/10 criterion can be met through solving the metering cost minimisation problem. All the lights in the project are classified into different groups according to uncertainties of the lighting energy consumption, which are characterised by their statistical coefficient of variance (CV). Samples from each group are randomly selected to install power meters. These meters include less expensive ones with less functionality and more expensive ones with greater functionality. The metering cost minimisation model will minimise the total metering cost through the determination of the optimal sample size at each group. The 90/10 criterion is formulated as constraints to the metering cost objective. The optimal solution to the minimisation problem will therefore minimise the metering cost whilst meeting the 90/10 criterion, and this is verified by a case study. Relationships between the optimal metering cost and the population sizes of the groups, CV values and the meter equipment cost are further explored in three simulations. The metering cost minimisation model proposed for lighting systems is applicable to other CDM projects as

A Comparative Theoretical and Computational Study on Robust Counterpart Optimization: I. Robust Linear Optimization and Robust Mixed Integer Linear Optimization

Science.gov (United States)

Li, Zukui; Ding, Ran; Floudas, Christodoulos A.

2011-01-01

Robust counterpart optimization techniques for linear optimization and mixed integer linear optimization problems are studied in this paper. Different uncertainty sets, including those studied in literature (i.e., interval set; combined interval and ellipsoidal set; combined interval and polyhedral set) and new ones (i.e., adjustable box; pure ellipsoidal; pure polyhedral; combined interval, ellipsoidal, and polyhedral set) are studied in this work and their geometric relationship is discussed. For uncertainty in the left hand side, right hand side, and objective function of the optimization problems, robust counterpart optimization formulations induced by those different uncertainty sets are derived. Numerical studies are performed to compare the solutions of the robust counterpart optimization models and applications in refinery production planning and batch process scheduling problem are presented. PMID:21935263

A new optimization tool path planning for 3-axis end milling of free-form surfaces based on efficient machining intervals

Science.gov (United States)

Vu, Duy-Duc; Monies, Frédéric; Rubio, Walter

2018-05-01

A large number of studies, based on 3-axis end milling of free-form surfaces, seek to optimize tool path planning. Approaches try to optimize the machining time by reducing the total tool path length while respecting the criterion of the maximum scallop height. Theoretically, the tool path trajectories that remove the most material follow the directions in which the machined width is the largest. The free-form surface is often considered as a single machining area. Therefore, the optimization on the entire surface is limited. Indeed, it is difficult to define tool trajectories with optimal feed directions which generate largest machined widths. Another limiting point of previous approaches for effectively reduce machining time is the inadequate choice of the tool. Researchers use generally a spherical tool on the entire surface. However, the gains proposed by these different methods developed with these tools lead to relatively small time savings. Therefore, this study proposes a new method, using toroidal milling tools, for generating toolpaths in different regions on the machining surface. The surface is divided into several regions based on machining intervals. These intervals ensure that the effective radius of the tool, at each cutter-contact points on the surface, is always greater than the radius of the tool in an optimized feed direction. A parallel plane strategy is then used on the sub-surfaces with an optimal specific feed direction for each sub-surface. This method allows one to mill the entire surface with efficiency greater than with the use of a spherical tool. The proposed method is calculated and modeled using Maple software to find optimal regions and feed directions in each region. This new method is tested on a free-form surface. A comparison is made with a spherical cutter to show the significant gains obtained with a toroidal milling cutter. Comparisons with CAM software and experimental validations are also done. The results show the

Multiobjective optimization of the inspection intervals of a nuclear safety system: A clustering-based framework for reducing the Pareto Front

International Nuclear Information System (INIS)

Zio, E.; Bazzo, R.

2010-01-01

In this paper, a framework is developed for identifying a limited number of representative solutions of a multiobjective optimization problem concerning the inspection intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are first clustered into 'families', which are then synthetically represented by a 'head of the family' solution. Three clustering methods are analyzed. Level Diagrams are then used to represent, analyse and interpret the Pareto Fronts reduced to their head-of-the-family solutions. Two decision situations are considered: without or with decision maker preferences, the latter implying the introduction of a scoring system to rank the solutions with respect to the different objectives: a fuzzy preference assignment is then employed to this purpose. The results of the application of the framework of analysis to the problem of optimizing the inspection intervals of a nuclear power plant safety system show that the clustering-based reduction maintains the Pareto Front shape and relevant characteristics, while making it easier for the decision maker to select the final solution.

OPTIMAL METHOD FOR PREPARATION OF SILICATE ROCK SAMPLES FOR ANALYTICAL PURPOSES

Directory of Open Access Journals (Sweden)

Maja Vrkljan

2004-12-01

Full Text Available The purpose of this study was to determine an optimal dissolution method for silicate rock samples for further analytical purposes. Analytical FAAS method of determining cobalt, chromium, copper, nickel, lead and zinc content in gabbro sample and geochemical standard AGV-1 has been applied for verification. Dissolution in mixtures of various inorganic acids has been tested, as well as Na2CO3 fusion technique. The results obtained by different methods have been compared and dissolution in the mixture of HNO3 + HF has been recommended as optimal.

Interpregnancy intervals: impact of postpartum contraceptive effectiveness and coverage.

Science.gov (United States)

Thiel de Bocanegra, Heike; Chang, Richard; Howell, Mike; Darney, Philip

2014-04-01

The purpose of this study was to determine the use of contraceptive methods, which was defined by effectiveness, length of coverage, and their association with short interpregnancy intervals, when controlling for provider type and client demographics. We identified a cohort of 117,644 women from the 2008 California Birth Statistical Master file with second or higher order birth and at least 1 Medicaid (Family Planning, Access, Care, and Treatment [Family PACT] program or Medi-Cal) claim within 18 months after index birth. We explored the effect of contraceptive method provision on the odds of having an optimal interpregnancy interval and controlled for covariates. The average length of contraceptive coverage was 3.81 months (SD = 4.84). Most women received user-dependent hormonal contraceptives as their most effective contraceptive method (55%; n = 65,103 women) and one-third (33%; n = 39,090 women) had no contraceptive claim. Women who used long-acting reversible contraceptive methods had 3.89 times the odds and women who used user-dependent hormonal methods had 1.89 times the odds of achieving an optimal birth interval compared with women who used barrier methods only; women with no method had 0.66 times the odds. When user-dependent methods are considered, the odds of having an optimal birth interval increased for each additional month of contraceptive coverage by 8% (odds ratio, 1.08; 95% confidence interval, 1.08-1.09). Women who were seen by Family PACT or by both Family PACT and Medi-Cal providers had significantly higher odds of optimal birth intervals compared with women who were served by Medi-Cal only. To achieve optimal birth spacing and ultimately to improve birth outcomes, attention should be given to contraceptive counseling and access to contraceptive methods in the postpartum period. Copyright © 2014 Mosby, Inc. All rights reserved.

Does a 4–6 Week Shoeing Interval Promote Optimal Foot Balance in the Working Equine?

Directory of Open Access Journals (Sweden)

Kirsty Leśniak

2017-03-01

Full Text Available Variation in equine hoof conformation between farriery interventions lacks research, despite associations with distal limb injuries. This study aimed to determine linear and angular hoof variations pre- and post-farriery within a four to six week shoeing/trimming interval. Seventeen hoof and distal limb measurements were drawn from lateral and anterior digital photographs from 26 horses pre- and post-farriery. Most lateral view variables changed significantly. Reductions of the dorsal wall, and weight bearing and coronary band lengths resulted in an increased vertical orientation of the hoof. The increased dorsal hoof wall angle, heel angle, and heel height illustrated this further, improving dorsopalmar alignment. Mediolateral measurements of coronary band and weight bearing lengths reduced, whilst medial and lateral wall lengths from the 2D images increased, indicating an increased vertical hoof alignment. Additionally, dorsopalmar balance improved. However, the results demonstrated that a four to six week interval is sufficient for a palmer shift in the centre of pressure, increasing the loading on acutely inclined heels, altering DIP angulation, and increasing the load on susceptible structures (e.g., DDFT. Mediolateral variable asymmetries suit the lateral hoof landing and unrollment pattern of the foot during landing. The results support regular (four to six week farriery intervals for the optimal prevention of excess loading of palmar limb structures, reducing long-term injury risks through cumulative, excessive loading.

Optimal sampling schemes for vegetation and geological field visits

CSIR Research Space (South Africa)

Debba, Pravesh

2012-07-01

Full Text Available The presentation made to Wits Statistics Department was on common classification methods used in the field of remote sensing, and the use of remote sensing to design optimal sampling schemes for field visits with applications in vegetation...

Robotic fish tracking method based on suboptimal interval Kalman filter

Science.gov (United States)

Tong, Xiaohong; Tang, Chao

2017-11-01

Autonomous Underwater Vehicle (AUV) research focused on tracking and positioning, precise guidance and return to dock and other fields. The robotic fish of AUV has become a hot application in intelligent education, civil and military etc. In nonlinear tracking analysis of robotic fish, which was found that the interval Kalman filter algorithm contains all possible filter results, but the range is wide, relatively conservative, and the interval data vector is uncertain before implementation. This paper proposes a ptimization algorithm of suboptimal interval Kalman filter. Suboptimal interval Kalman filter scheme used the interval inverse matrix with its worst inverse instead, is more approximate nonlinear state equation and measurement equation than the standard interval Kalman filter, increases the accuracy of the nominal dynamic system model, improves the speed and precision of tracking system. Monte-Carlo simulation results show that the optimal trajectory of sub optimal interval Kalman filter algorithm is better than that of the interval Kalman filter method and the standard method of the filter.

Optimizing incomplete sample designs for item response model parameters

NARCIS (Netherlands)

van der Linden, Willem J.

Several models for optimizing incomplete sample designs with respect to information on the item parameters are presented. The following cases are considered: (1) known ability parameters; (2) unknown ability parameters; (3) item sets with multiple ability scales; and (4) response models with

Integration of electromagnetic induction sensor data in soil sampling scheme optimization using simulated annealing.

Science.gov (United States)

Barca, E; Castrignanò, A; Buttafuoco, G; De Benedetto, D; Passarella, G

2015-07-01

Soil survey is generally time-consuming, labor-intensive, and costly. Optimization of sampling scheme allows one to reduce the number of sampling points without decreasing or even increasing the accuracy of investigated attribute. Maps of bulk soil electrical conductivity (EC a ) recorded with electromagnetic induction (EMI) sensors could be effectively used to direct soil sampling design for assessing spatial variability of soil moisture. A protocol, using a field-scale bulk EC a survey, has been applied in an agricultural field in Apulia region (southeastern Italy). Spatial simulated annealing was used as a method to optimize spatial soil sampling scheme taking into account sampling constraints, field boundaries, and preliminary observations. Three optimization criteria were used. the first criterion (minimization of mean of the shortest distances, MMSD) optimizes the spreading of the point observations over the entire field by minimizing the expectation of the distance between an arbitrarily chosen point and its nearest observation; the second criterion (minimization of weighted mean of the shortest distances, MWMSD) is a weighted version of the MMSD, which uses the digital gradient of the grid EC a data as weighting function; and the third criterion (mean of average ordinary kriging variance, MAOKV) minimizes mean kriging estimation variance of the target variable. The last criterion utilizes the variogram model of soil water content estimated in a previous trial. The procedures, or a combination of them, were tested and compared in a real case. Simulated annealing was implemented by the software MSANOS able to define or redesign any sampling scheme by increasing or decreasing the original sampling locations. The output consists of the computed sampling scheme, the convergence time, and the cooling law, which can be an invaluable support to the process of sampling design. The proposed approach has found the optimal solution in a reasonable computation time. The

Resolution optimization with irregularly sampled Fourier data

International Nuclear Information System (INIS)

Ferrara, Matthew; Parker, Jason T; Cheney, Margaret

2013-01-01

Image acquisition systems such as synthetic aperture radar (SAR) and magnetic resonance imaging often measure irregularly spaced Fourier samples of the desired image. In this paper we show the relationship between sample locations, their associated backprojection weights, and image resolution as characterized by the resulting point spread function (PSF). Two new methods for computing data weights, based on different optimization criteria, are proposed. The first method, which solves a maximal-eigenvector problem, optimizes a PSF-derived resolution metric which is shown to be equivalent to the volume of the Cramer–Rao (positional) error ellipsoid in the uniform-weight case. The second approach utilizes as its performance metric the Frobenius error between the PSF operator and the ideal delta function, and is an extension of a previously reported algorithm. Our proposed extension appropriately regularizes the weight estimates in the presence of noisy data and eliminates the superfluous issue of image discretization in the choice of data weights. The Frobenius-error approach results in a Tikhonov-regularized inverse problem whose Tikhonov weights are dependent on the locations of the Fourier data as well as the noise variance. The two new methods are compared against several state-of-the-art weighting strategies for synthetic multistatic point-scatterer data, as well as an ‘interrupted SAR’ dataset representative of in-band interference commonly encountered in very high frequency radar applications. (paper)

Ad-Hoc vs. Standardized and Optimized Arthropod Diversity Sampling

Directory of Open Access Journals (Sweden)

Pedro Cardoso

2009-09-01

Full Text Available The use of standardized and optimized protocols has been recently advocated for different arthropod taxa instead of ad-hoc sampling or sampling with protocols defined on a case-by-case basis. We present a comparison of both sampling approaches applied for spiders in a natural area of Portugal. Tests were made to their efficiency, over-collection of common species, singletons proportions, species abundance distributions, average specimen size, average taxonomic distinctness and behavior of richness estimators. The standardized protocol revealed three main advantages: (1 higher efficiency; (2 more reliable estimations of true richness; and (3 meaningful comparisons between undersampled areas.

Optimally decoding the input rate from an observation of the interspike intervals

Energy Technology Data Exchange (ETDEWEB)

Feng Jianfeng [COGS, University of Sussex at Brighton (United Kingdom) and Computational Neuroscience Laboratory, Babraham Institute, Cambridge (United Kingdom)]. E-mail: jf218@cam.ac.uk

2001-09-21

A neuron extensively receives both inhibitory and excitatory inputs. What is the ratio r between these two types of input so that the neuron can most accurately read out input information (rate)? We explore the issue in this paper provided that the neuron is an ideal observer - decoding the input information with the attainment of the Cramer-Rao inequality bound. It is found that, in general, adding certain amounts of inhibitory inputs to a neuron improves its capability of accurately decoding the input information. By calculating the Fisher information of an integrate-and-fire neuron, we determine the optimal ratio r for decoding the input information from an observation of the efferent interspike intervals. Surprisingly, the Fisher information can be zero for certain values of the ratio, seemingly implying that it is impossible to read out the encoded information at these values. By analysing the maximum likelihood estimate of the input information, it is concluded that the input information is in fact most easily estimated at the points where the Fisher information vanishes. (author)

Optimizing Water Allocation under Uncertain System Conditions for Water and Agriculture Future Scenarios in Alfeios River Basin (Greece—Part B: Fuzzy-Boundary Intervals Combined with Multi-Stage Stochastic Programming Model

Directory of Open Access Journals (Sweden)

Eleni Bekri

2015-11-01

Full Text Available Optimal water allocation within a river basin still remains a great modeling challenge for engineers due to various hydrosystem complexities, parameter uncertainties and their interactions. Conventional deterministic optimization approaches have given their place to stochastic, fuzzy and interval-parameter programming approaches and their hybrid combinations for overcoming these difficulties. In many countries, including Mediterranean countries, water resources management is characterized by uncertain, imprecise and limited data because of the absence of permanent measuring systems, inefficient river monitoring and fragmentation of authority responsibilities. A fuzzy-boundary-interval linear programming methodology developed by Li et al. (2010 is selected and applied in the Alfeios river basin (Greece for optimal water allocation under uncertain system conditions. This methodology combines an ordinary multi-stage stochastic programming with uncertainties expressed as fuzzy-boundary intervals. Upper- and lower-bound solution intervals for optimized water allocation targets and probabilistic water allocations and shortages are estimated under a baseline scenario and four water and agricultural policy future scenarios for an optimistic and a pessimistic attitude of the decision makers. In this work, the uncertainty of the random water inflows is incorporated through the simultaneous generation of stochastic equal-probability hydrologic scenarios at various inflow positions instead of using a scenario-tree approach in the original methodology.

SU-E-T-21: A Novel Sampling Algorithm to Reduce Intensity-Modulated Radiation Therapy (IMRT) Optimization Time

International Nuclear Information System (INIS)

Tiwari, P; Xie, Y; Chen, Y; Deasy, J

2014-01-01

Purpose: The IMRT optimization problem requires substantial computer time to find optimal dose distributions because of the large number of variables and constraints. Voxel sampling reduces the number of constraints and accelerates the optimization process, but usually deteriorates the quality of the dose distributions to the organs. We propose a novel sampling algorithm that accelerates the IMRT optimization process without significantly deteriorating the quality of the dose distribution. Methods: We included all boundary voxels, as well as a sampled fraction of interior voxels of organs in the optimization. We selected a fraction of interior voxels using a clustering algorithm, that creates clusters of voxels that have similar influence matrix signatures. A few voxels are selected from each cluster based on the pre-set sampling rate. Results: We ran sampling and no-sampling IMRT plans for de-identified head and neck treatment plans. Testing with the different sampling rates, we found that including 10% of inner voxels produced the good dose distributions. For this optimal sampling rate, the algorithm accelerated IMRT optimization by a factor of 2–3 times with a negligible loss of accuracy that was, on average, 0.3% for common dosimetric planning criteria. Conclusion: We demonstrated that a sampling could be developed that reduces optimization time by more than a factor of 2, without significantly degrading the dose quality

Optimization of the sampling scheme for maps of physical and chemical properties estimated by kriging

Directory of Open Access Journals (Sweden)

Gener Tadeu Pereira

2013-10-01

Full Text Available The sampling scheme is essential in the investigation of the spatial variability of soil properties in Soil Science studies. The high costs of sampling schemes optimized with additional sampling points for each physical and chemical soil property, prevent their use in precision agriculture. The purpose of this study was to obtain an optimal sampling scheme for physical and chemical property sets and investigate its effect on the quality of soil sampling. Soil was sampled on a 42-ha area, with 206 geo-referenced points arranged in a regular grid spaced 50 m from each other, in a depth range of 0.00-0.20 m. In order to obtain an optimal sampling scheme for every physical and chemical property, a sample grid, a medium-scale variogram and the extended Spatial Simulated Annealing (SSA method were used to minimize kriging variance. The optimization procedure was validated by constructing maps of relative improvement comparing the sample configuration before and after the process. A greater concentration of recommended points in specific areas (NW-SE direction was observed, which also reflects a greater estimate variance at these locations. The addition of optimal samples, for specific regions, increased the accuracy up to 2 % for chemical and 1 % for physical properties. The use of a sample grid and medium-scale variogram, as previous information for the conception of additional sampling schemes, was very promising to determine the locations of these additional points for all physical and chemical soil properties, enhancing the accuracy of kriging estimates of the physical-chemical properties.

A study on the optimization of test interval for check valves of Ulchin Unit 3 using the risk-informed in-service testing approach

International Nuclear Information System (INIS)

Kang, D. I.; Kim, K. Y.; Yang, Z. A.; Ha, J. J.

2002-01-01

We optimized the test interval for check valves of Ulchin Unit 3 using the risk-informed in-service testing (IST) approach. First, we categorized the IST check valves for Ulchin Unit 3 according to their contributions to the safety of Ulchin Unit 3. Next, we performed the risk analysis on the relaxation of test interval for check valves identified as relatively low important to the safety of Ulchin Unit 3 to identify the maximum increasable test interval of them. Finally, we estimated the number of tests of IST check valves to be performed due to the changes of test interval. These study results are as follows: The categorization of IST check valve importance; the number of the HSSCs is 24(11.48%), the ISSCs is 40 (19.14%), and the LSSCs is 462(69.38%). The maximum increasable test interval; 6 times of current test interval of ISSCs2 and 40 times of that of LSSCs. The number of tests of IST check valves to be performed during 6 refueling time can be reduced from 7692 to 1333 ( 82.7%)

Optimal preparation-to-colonoscopy interval in split-dose PEG bowel preparation determines satisfactory bowel preparation quality: an observational prospective study.

Science.gov (United States)

Seo, Eun Hee; Kim, Tae Oh; Park, Min Jae; Joo, Hee Rin; Heo, Nae Yun; Park, Jongha; Park, Seung Ha; Yang, Sung Yeon; Moon, Young Soo

2012-03-01

Several factors influence bowel preparation quality. Recent studies have indicated that the time interval between bowel preparation and the start of colonoscopy is also important in determining bowel preparation quality. To evaluate the influence of the preparation-to-colonoscopy (PC) interval (the interval of time between the last polyethylene glycol dose ingestion and the start of the colonoscopy) on bowel preparation quality in the split-dose method for colonoscopy. Prospective observational study. University medical center. A total of 366 consecutive outpatients undergoing colonoscopy. Split-dose bowel preparation and colonoscopy. The quality of bowel preparation was assessed by using the Ottawa Bowel Preparation Scale according to the PC interval, and other factors that might influence bowel preparation quality were analyzed. Colonoscopies with a PC interval of 3 to 5 hours had the best bowel preparation quality score in the whole, right, mid, and rectosigmoid colon according to the Ottawa Bowel Preparation Scale. In multivariate analysis, the PC interval (odds ratio [OR] 1.85; 95% CI, 1.18-2.86), the amount of PEG ingested (OR 4.34; 95% CI, 1.08-16.66), and compliance with diet instructions (OR 2.22l 95% CI, 1.33-3.70) were significant contributors to satisfactory bowel preparation. Nonrandomized controlled, single-center trial. The optimal time interval between the last dose of the agent and the start of colonoscopy is one of the important factors to determine satisfactory bowel preparation quality in split-dose polyethylene glycol bowel preparation. Copyright © 2012 American Society for Gastrointestinal Endoscopy. Published by Mosby, Inc. All rights reserved.

Optimal CCD readout by digital correlated double sampling

Science.gov (United States)

Alessandri, C.; Abusleme, A.; Guzman, D.; Passalacqua, I.; Alvarez-Fontecilla, E.; Guarini, M.

2016-01-01

Digital correlated double sampling (DCDS), a readout technique for charge-coupled devices (CCD), is gaining popularity in astronomical applications. By using an oversampling ADC and a digital filter, a DCDS system can achieve a better performance than traditional analogue readout techniques at the expense of a more complex system analysis. Several attempts to analyse and optimize a DCDS system have been reported, but most of the work presented in the literature has been experimental. Some approximate analytical tools have been presented for independent parameters of the system, but the overall performance and trade-offs have not been yet modelled. Furthermore, there is disagreement among experimental results that cannot be explained by the analytical tools available. In this work, a theoretical analysis of a generic DCDS readout system is presented, including key aspects such as the signal conditioning stage, the ADC resolution, the sampling frequency and the digital filter implementation. By using a time-domain noise model, the effect of the digital filter is properly modelled as a discrete-time process, thus avoiding the imprecision of continuous-time approximations that have been used so far. As a result, an accurate, closed-form expression for the signal-to-noise ratio at the output of the readout system is reached. This expression can be easily optimized in order to meet a set of specifications for a given CCD, thus providing a systematic design methodology for an optimal readout system. Simulated results are presented to validate the theory, obtained with both time- and frequency-domain noise generation models for completeness.

Improving the performance of the Egyptian second testing nuclear research reactor using interval type-2 fuzzy logic controller tuned by modified biogeography-based optimization

Energy Technology Data Exchange (ETDEWEB)

Sayed, M.M., E-mail: M.M.Sayed@ieee.org; Saad, M.S.; Emara, H.M.; Abou El-Zahab, E.E.

2013-09-15

Highlights: • A modified version of the BBO was proposed. • A novel method for interval type-2 FLC design tuned by MBBO was proposed. • The performance of the ETRR-2 was improved by using IT2FLC tuned by MBBO. -- Abstract: Power stabilization is a critical issue in nuclear reactors. The conventional proportional derivative (PD) controller is currently used in the Egyptian second testing research reactor (ETRR-2). In this paper, we propose a modified biogeography-based optimization (MBBO) algorithm to design the interval type-2 fuzzy logic controller (IT2FLC) to improve the performance of the Egyptian second testing research reactor (ETRR-2). Biogeography-based optimization (BBO) is a novel evolutionary algorithm that is based on the mathematical models of biogeography. Biogeography is the study of the geographical distribution of biological organisms. In the BBO model, problem solutions are represented as islands, and the sharing of features between solutions is represented as immigration and emigration between the islands. A modified version of the BBO is applied to design the IT2FLC to get the optimal parameters of the membership functions of the controller. We test the optimal IT2FLC obtained by modified biogeography-based optimization (MBBO) using the integral square error (ISE) and is compared with the currently used PD controller.

A normative inference approach for optimal sample sizes in decisions from experience

Science.gov (United States)

Ostwald, Dirk; Starke, Ludger; Hertwig, Ralph

2015-01-01

“Decisions from experience” (DFE) refers to a body of work that emerged in research on behavioral decision making over the last decade. One of the major experimental paradigms employed to study experience-based choice is the “sampling paradigm,” which serves as a model of decision making under limited knowledge about the statistical structure of the world. In this paradigm respondents are presented with two payoff distributions, which, in contrast to standard approaches in behavioral economics, are specified not in terms of explicit outcome-probability information, but by the opportunity to sample outcomes from each distribution without economic consequences. Participants are encouraged to explore the distributions until they feel confident enough to decide from which they would prefer to draw from in a final trial involving real monetary payoffs. One commonly employed measure to characterize the behavior of participants in the sampling paradigm is the sample size, that is, the number of outcome draws which participants choose to obtain from each distribution prior to terminating sampling. A natural question that arises in this context concerns the “optimal” sample size, which could be used as a normative benchmark to evaluate human sampling behavior in DFE. In this theoretical study, we relate the DFE sampling paradigm to the classical statistical decision theoretic literature and, under a probabilistic inference assumption, evaluate optimal sample sizes for DFE. In our treatment we go beyond analytically established results by showing how the classical statistical decision theoretic framework can be used to derive optimal sample sizes under arbitrary, but numerically evaluable, constraints. Finally, we critically evaluate the value of deriving optimal sample sizes under this framework as testable predictions for the experimental study of sampling behavior in DFE. PMID:26441720

Optimizing conditions for an accelerated leach test

International Nuclear Information System (INIS)

Pietrzak, R.F.; Fuhrmann, M.; Heiser, J.; Franz, E.M.; Colombo, P.

1988-01-01

An accelerated leach test for low-level radioactive waste forms is being developed to provide, in a short time, data that can be extrapolated to long time periods. The approach is to provide experimental conditions that will accelerate leaching without changing the dominant release mechanism. Experimental efforts have focused on combining individual factors that have been observed to accelerate leaching. These include elevated temperature, increased leachant volume, and reduced specimen size. The response of diffusion coefficients to various acceleration factors have been evaluated and provide information on experimental parameters that need to be optimized to increase leach rates. Preliminary modeling using a diffusion mechanism (allowing for depletion) of a finite cylinder geometry indicates that during early portions of experiments (daily sampling intervals), leaching is diffusion controlled and more rapid than later in the same experiments (weekly or greater sampling intervals). For cement waste forms, this reduction in rate may be partially controlled by changes in physical structure and chemistry (sometimes related to environmental influences such as CO 2 ), but it is more likely associated with the duration of the sampling interval. By using a combination of mathematical modeling and by experimentally investigating various leach rate controlling factors, a more complete understanding of leaching processes is being developed. This, in turn, is leading to optimized accelerating conditions for a leach test

Haemostatic reference intervals in pregnancy

DEFF Research Database (Denmark)

Szecsi, Pal Bela; Jørgensen, Maja; Klajnbard, Anna

2010-01-01

Haemostatic reference intervals are generally based on samples from non-pregnant women. Thus, they may not be relevant to pregnant women, a problem that may hinder accurate diagnosis and treatment of haemostatic disorders during pregnancy. In this study, we establish gestational age-specific refe......Haemostatic reference intervals are generally based on samples from non-pregnant women. Thus, they may not be relevant to pregnant women, a problem that may hinder accurate diagnosis and treatment of haemostatic disorders during pregnancy. In this study, we establish gestational age......-specific reference intervals for coagulation tests during normal pregnancy. Eight hundred one women with expected normal pregnancies were included in the study. Of these women, 391 had no complications during pregnancy, vaginal delivery, or postpartum period. Plasma samples were obtained at gestational weeks 13......-20, 21-28, 29-34, 35-42, at active labor, and on postpartum days 1 and 2. Reference intervals for each gestational period using only the uncomplicated pregnancies were calculated in all 391 women for activated partial thromboplastin time (aPTT), fibrinogen, fibrin D-dimer, antithrombin, free protein S...

Fuzzy solution of the linear programming problem with interval coefficients in the constraints

OpenAIRE

Dorota Kuchta

2005-01-01

A fuzzy concept of solving the linear programming problem with interval coefficients is proposed. For each optimism level of the decision maker (where the optimism concerns the certainty that no errors have been committed in the estimation of the interval coefficients and the belief that optimistic realisations of the interval coefficients will occur) another interval solution of the problem will be generated and the decision maker will be able to choose the final solution having a complete v...

Evaluation of optimized bronchoalveolar lavage sampling designs for characterization of pulmonary drug distribution.

Science.gov (United States)

Clewe, Oskar; Karlsson, Mats O; Simonsson, Ulrika S H

2015-12-01

Bronchoalveolar lavage (BAL) is a pulmonary sampling technique for characterization of drug concentrations in epithelial lining fluid and alveolar cells. Two hypothetical drugs with different pulmonary distribution rates (fast and slow) were considered. An optimized BAL sampling design was generated assuming no previous information regarding the pulmonary distribution (rate and extent) and with a maximum of two samples per subject. Simulations were performed to evaluate the impact of the number of samples per subject (1 or 2) and the sample size on the relative bias and relative root mean square error of the parameter estimates (rate and extent of pulmonary distribution). The optimized BAL sampling design depends on a characterized plasma concentration time profile, a population plasma pharmacokinetic model, the limit of quantification (LOQ) of the BAL method and involves only two BAL sample time points, one early and one late. The early sample should be taken as early as possible, where concentrations in the BAL fluid ≥ LOQ. The second sample should be taken at a time point in the declining part of the plasma curve, where the plasma concentration is equivalent to the plasma concentration in the early sample. Using a previously described general pulmonary distribution model linked to a plasma population pharmacokinetic model, simulated data using the final BAL sampling design enabled characterization of both the rate and extent of pulmonary distribution. The optimized BAL sampling design enables characterization of both the rate and extent of the pulmonary distribution for both fast and slowly equilibrating drugs.

CT coronary angiography: Influence of different cardiac reconstruction intervals on image quality and diagnostic accuracy

Energy Technology Data Exchange (ETDEWEB)

Dewey, Marc [Department of Radiology, Charite Medical School, Humboldt-Universitaet zu Berlin (Germany)], E-mail: marc.dewey@charite.de; Teige, Florian [Department of Radiology, Charite Medical School, Humboldt-Universitaet zu Berlin (Germany); Rutsch, Wolfgang [Department of Cardiology, Charite Medical School, Humboldt-Universitaet zu Berlin (Germany)], E-mail: wolfgang.rutsch@charite.de; Schink, Tania [Department of Medical Biometry, Charite Medical School, Humboldt-Universitaet zu Berlin (Germany)], E-mail: peter.martus@charite.de; Hamm, Bernd [Department of Radiology, Charite Medical School, Humboldt-Universitaet zu Berlin (Germany)

2008-07-15

Purpose: To prospectively analyze image quality and diagnostic accuracy of different reconstruction intervals of coronary angiography using multislice computed tomography (MSCT). Materials and methods: For each of 47 patients, 10 ECG-gated MSCT reconstructions were generated throughout the RR interval from 0 to 90%, resulting in altogether 470 datasets. These datasets were randomly analyzed for image quality and accuracy and compared with conventional angiography. Statistical comparison of intervals was performed using nonparametric analysis for repeated measurements to account for clustering of arteries within patients. Results: Image reconstruction intervals centered at 80, 70, and 40% of the RR interval resulted (in that order) in the best overall image quality for all four main coronary vessels. Eighty percent reconstructions also yielded the highest diagnostic accuracy of all intervals. The combination of the three best intervals (80, 70, and 40%) significantly reduced the nondiagnostic rate as compared with 80% alone (p = 0.005). However, the optimal reconstruction interval combination achieved significantly improved specificities and nondiagnostic rates (p < 0.05). The optimal combination consisted of 1.7 {+-} 0.9 reconstruction intervals on average. In approximately half of the patients (49%, 23/47) a single reconstruction was optimal. In 18 (38%), 3 (6%), and 3 (6%) patients one, two, and three additional reconstruction intervals were required, respectively, to achieve optimal quality. In 28% of the patients the optimal combination consisted of reconstructions other than the three best intervals (80, 70, and 40%). Conclusion: Multiple image reconstruction intervals are essential to ensure high image quality and accuracy of CT coronary angiography.

Correct Bayesian and frequentist intervals are similar

International Nuclear Information System (INIS)

Atwood, C.L.

1986-01-01

This paper argues that Bayesians and frequentists will normally reach numerically similar conclusions, when dealing with vague data or sparse data. It is shown that both statistical methodologies can deal reasonably with vague data. With sparse data, in many important practical cases Bayesian interval estimates and frequentist confidence intervals are approximately equal, although with discrete data the frequentist intervals are somewhat longer. This is not to say that the two methodologies are equally easy to use: The construction of a frequentist confidence interval may require new theoretical development. Bayesians methods typically require numerical integration, perhaps over many variables. Also, Bayesian can easily fall into the trap of over-optimism about their amount of prior knowledge. But in cases where both intervals are found correctly, the two intervals are usually not very different. (orig.)

Statistical surrogate model based sampling criterion for stochastic global optimization of problems with constraints

Energy Technology Data Exchange (ETDEWEB)

Cho, Su Gil; Jang, Jun Yong; Kim, Ji Hoon; Lee, Tae Hee [Hanyang University, Seoul (Korea, Republic of); Lee, Min Uk [Romax Technology Ltd., Seoul (Korea, Republic of); Choi, Jong Su; Hong, Sup [Korea Research Institute of Ships and Ocean Engineering, Daejeon (Korea, Republic of)

2015-04-15

Sequential surrogate model-based global optimization algorithms, such as super-EGO, have been developed to increase the efficiency of commonly used global optimization technique as well as to ensure the accuracy of optimization. However, earlier studies have drawbacks because there are three phases in the optimization loop and empirical parameters. We propose a united sampling criterion to simplify the algorithm and to achieve the global optimum of problems with constraints without any empirical parameters. It is able to select the points located in a feasible region with high model uncertainty as well as the points along the boundary of constraint at the lowest objective value. The mean squared error determines which criterion is more dominant among the infill sampling criterion and boundary sampling criterion. Also, the method guarantees the accuracy of the surrogate model because the sample points are not located within extremely small regions like super-EGO. The performance of the proposed method, such as the solvability of a problem, convergence properties, and efficiency, are validated through nonlinear numerical examples with disconnected feasible regions.

Determining the optimal screening interval for type 2 diabetes mellitus using a risk prediction model.

Directory of Open Access Journals (Sweden)

Andrei Brateanu

Full Text Available Progression to diabetes mellitus (DM is variable and the screening time interval not well defined. The American Diabetes Association and US Preventive Services Task Force suggest screening every 3 years, but evidence is limited. The objective of the study was to develop a model to predict the probability of developing DM and suggest a risk-based screening interval.We included non-diabetic adult patients screened for DM in the Cleveland Clinic Health System if they had at least two measurements of glycated hemoglobin (HbA1c, an initial one less than 6.5% (48 mmol/mol in 2008, and another between January, 2009 and December, 2013. Cox proportional hazards models were created. The primary outcome was DM defined as HbA1C greater than 6.4% (46 mmol/mol. The optimal rescreening interval was chosen based on the predicted probability of developing DM.Of 5084 participants, 100 (4.4% of the 2281 patients with normal HbA1c and 772 (27.5% of the 2803 patients with prediabetes developed DM within 5 years. Factors associated with developing DM included HbA1c (HR per 0.1 units increase 1.20; 95%CI, 1.13-1.27, family history (HR 1.31; 95%CI, 1.13-1.51, smoking (HR 1.18; 95%CI, 1.03-1.35, triglycerides (HR 1.01; 95%CI, 1.00-1.03, alanine aminotransferase (HR 1.07; 95%CI, 1.03-1.11, body mass index (HR 1.06; 95%CI, 1.01-1.11, age (HR 0.95; 95%CI, 0.91-0.99 and high-density lipoproteins (HR 0.93; 95% CI, 0.90-0.95. Five percent of patients in the highest risk tertile developed DM within 8 months, while it took 35 months for 5% of the middle tertile to develop DM. Only 2.4% percent of the patients in the lowest tertile developed DM within 5 years.A risk prediction model employing commonly available data can be used to guide screening intervals. Based on equal intervals for equal risk, patients in the highest risk category could be rescreened after 8 months, while those in the intermediate and lowest risk categories could be rescreened after 3 and 5 years

Optimization of the two-sample rank Neyman-Pearson detector

Science.gov (United States)

Akimov, P. S.; Barashkov, V. M.

1984-10-01

The development of optimal algorithms concerned with rank considerations in the case of finite sample sizes involves considerable mathematical difficulties. The present investigation provides results related to the design and the analysis of an optimal rank detector based on a utilization of the Neyman-Pearson criteria. The detection of a signal in the presence of background noise is considered, taking into account n observations (readings) x1, x2, ... xn in the experimental communications channel. The computation of the value of the rank of an observation is calculated on the basis of relations between x and the variable y, representing interference. Attention is given to conditions in the absence of a signal, the probability of the detection of an arriving signal, details regarding the utilization of the Neyman-Pearson criteria, the scheme of an optimal rank, multichannel, incoherent detector, and an analysis of the detector.

Optimal prediction intervals of wind power generation

DEFF Research Database (Denmark)

Wan, Can; Wu, Zhao; Pinson, Pierre

2014-01-01

direct optimization of both the coverage probability and sharpness to ensure the quality. The proposed method does not involve the statistical inference or distribution assumption of forecasting errors needed in most existing methods. Case studies using real wind farm data from Australia have been...

Preventive maintenance and the interval availability distribution of an unreliable production system

International Nuclear Information System (INIS)

Dijkhuizen, G. van; Heijden, M. van der

1999-01-01

Traditionally, the optimal preventive maintenance interval for an unreliable production system has been determined by maximizing its limiting availability. Nowadays, it is widely recognized that this performance measure does not always provide relevant information for practical purposes. This is particularly true for order-driven manufacturing systems, in which due date performance has become a more important, and even a competitive factor. Under these circumstances, the so-called interval availability distribution is often seen as a more appropriate performance measure. Surprisingly enough, the relation between preventive maintenance and interval availability has received little attention in the existing literature. In this article, a series of mathematical models and optimization techniques is presented, with which the optimal preventive maintenance interval can be determined from an interval availability point of view, rather than from a limiting availability perspective. Computational results for a class of representative test problems indicate that significant improvements of up to 30% in the guaranteed interval availability can be obtained, by increasing preventive maintenance frequencies somewhere between 10 and 70%

An approach to the selection of recommended cooling intervals for the activation analysis of unknown samples with Ge(Li) gamma-ray spectrometry

International Nuclear Information System (INIS)

Hirose, Akio; Ishii, Daido

1975-01-01

Estimation of the optimum cooling interval by the mathematic or graphic method for Ge(Li) γ-ray spectrometry performed in the presence of some Compton interferences, and the recommended cooling intervals available for activation analysis of unknown samples have been proposed, and applied to the non-destructive activation analysis of gold in pure copper. In the presence of Compton interferences, two kinds of optimum cooling intervals were discussed. One maximizes the S/N ratio of a desired photo-peak. This interval had been originated by Isenhour, et al. Using the computer technique, this work is abbreviated as tsub( s/ n). The other, which minimizes the relative standard deviation (delta s/S) of a net photo-peak counting rate of interest (S) was originated by Tomov, et al. and Quittner, et al., this work is abbreviated as tsub(opt) or t'sub(opt). All equations derived by the above authors, however, have the practical disadvantage of including a term relating to the intensity of the desired photo-peak, thus making it difficult to predict the optimum cooling interval before irradiation. Since in chemical analysis, the concentration of the desired element, or the intensity of the photo-peak of interest, should be considered as ''unknown''. In the present work, an approach to the selection of recommended cooling interval applicable to the unknown sample has been discussed, and the interval, tsub(opt), which minimizes the lower limit of detection of a desired element under given irradiation and counting conditions has been proposed. (Evans, J.)

Long-term lifestyle intervention with optimized high-intensity interval training improves body composition, cardiometabolic risk, and exercise parameters in patients with abdominal obesity.

Science.gov (United States)

Gremeaux, Vincent; Drigny, Joffrey; Nigam, Anil; Juneau, Martin; Guilbeault, Valérie; Latour, Elise; Gayda, Mathieu

2012-11-01

The aim of this study was to study the impact of a combined long-term lifestyle and high-intensity interval training intervention on body composition, cardiometabolic risk, and exercise tolerance in overweight and obese subjects. Sixty-two overweight and obese subjects (53.3 ± 9.7 yrs; mean body mass index, 35.8 ± 5 kg/m(2)) were retrospectively identified at their entry into a 9-mo program consisting of individualized nutritional counselling, optimized high-intensity interval exercise, and resistance training two to three times a week. Anthropometric measurements, cardiometabolic risk factors, and exercise tolerance were measured at baseline and program completion. Adherence rate was 97%, and no adverse events occurred with high-intensity interval exercise training. Exercise training was associated with a weekly energy expenditure of 1582 ± 284 kcal. Clinically and statistically significant improvements were observed for body mass (-5.3 ± 5.2 kg), body mass index (-1.9 ± 1.9 kg/m(2)), waist circumference (-5.8 ± 5.4 cm), and maximal exercise capacity (+1.26 ± 0.84 metabolic equivalents) (P high-density lipoprotein ratio were also significantly improved (P body mass and waist circumference loss were baseline body mass index and resting metabolic rate; those for body mass index decrease were baseline waist circumference and triglyceride/high-density lipoprotein cholesterol ratio. A long-term lifestyle intervention with optimized high-intensity interval exercise improves body composition, cardiometabolic risk, and exercise tolerance in obese subjects. This intervention seems safe, efficient, and well tolerated and could improve adherence to exercise training in this population.

Optimal spatio-temporal design of water quality monitoring networks for reservoirs: Application of the concept of value of information

Science.gov (United States)

Maymandi, Nahal; Kerachian, Reza; Nikoo, Mohammad Reza

2018-03-01

This paper presents a new methodology for optimizing Water Quality Monitoring (WQM) networks of reservoirs and lakes using the concept of the value of information (VOI) and utilizing results of a calibrated numerical water quality simulation model. With reference to the value of information theory, water quality of every checkpoint with a specific prior probability differs in time. After analyzing water quality samples taken from potential monitoring points, the posterior probabilities are updated using the Baye's theorem, and VOI of the samples is calculated. In the next step, the stations with maximum VOI is selected as optimal stations. This process is repeated for each sampling interval to obtain optimal monitoring network locations for each interval. The results of the proposed VOI-based methodology is compared with those obtained using an entropy theoretic approach. As the results of the two methodologies would be partially different, in the next step, the results are combined using a weighting method. Finally, the optimal sampling interval and location of WQM stations are chosen using the Evidential Reasoning (ER) decision making method. The efficiency and applicability of the methodology are evaluated using available water quantity and quality data of the Karkheh Reservoir in the southwestern part of Iran.

A pseudo-optimal inexact stochastic interval T2 fuzzy sets approach for energy and environmental systems planning under uncertainty: A case study for Xiamen City of China

International Nuclear Information System (INIS)

Jin, L.; Huang, G.H.; Fan, Y.R.; Wang, L.; Wu, T.

2015-01-01

Highlights: • Propose a new energy PIS-IT2FSLP model for Xiamen City under uncertainties. • Analyze the energy supply, demand, and its flow structure of this city. • Use real energy statistics to prove the superiority of PIS-IT2FSLP method. • Obtain optimal solutions that reflect environmental requirements. • Help local authorities devise an optimal energy strategy for this local area. - Abstract: In this study, a new Pseudo-optimal Inexact Stochastic Interval Type-2 Fuzzy Sets Linear Programming (PIS-IT2FSLP) energy model is developed to support energy system planning and environment requirements under uncertainties for Xiamen City. The PIS-IT2FSLP model is based on an integration of interval Type 2 (T2) Fuzzy Sets (FS) boundary programming and stochastic linear programming techniques, enables it to have robust abilities to the tackle uncertainties expressed as T2 FS intervals and probabilistic distributions within a general optimization framework. This new model can sophisticatedly facilitate system analysis of energy supply and energy conversion processes, and environmental requirements as well as provide capacity expansion options with multiple periods. The PIS-IT2FSLP model was applied to a real case study of Xiamen energy systems. Based on a robust two-step solution algorithm, reasonable solutions have been obtained, which reflect tradeoffs between economic and environmental requirements, and among seasonal volatility energy demands of the right hand side constraints of Xiamen energy system. Thus, the lower and upper solutions of PIS-IT2FSLP would then help local energy authorities adjust current energy patterns, and discover an optimal energy strategy for the development of Xiamen City

Tuning for temporal interval in human apparent motion detection.

Science.gov (United States)

Bours, Roger J E; Stuur, Sanne; Lankheet, Martin J M

2007-01-08

Detection of apparent motion in random dot patterns requires correlation across time and space. It has been difficult to study the temporal requirements for the correlation step because motion detection also depends on temporal filtering preceding correlation and on integration at the next levels. To specifically study tuning for temporal interval in the correlation step, we performed an experiment in which prefiltering and postintegration were held constant and in which we used a motion stimulus containing coherent motion for a single interval value only. The stimulus consisted of a sparse random dot pattern in which each dot was presented in two frames only, separated by a specified interval. On each frame, half of the dots were refreshed and the other half was a displaced reincarnation of the pattern generated one or several frames earlier. Motion energy statistics in such a stimulus do not vary from frame to frame, and the directional bias in spatiotemporal correlations is similar for different interval settings. We measured coherence thresholds for left-right direction discrimination by varying motion coherence levels in a Quest staircase procedure, as a function of both step size and interval. Results show that highest sensitivity was found for an interval of 17-42 ms, irrespective of viewing distance. The falloff at longer intervals was much sharper than previously described. Tuning for temporal interval was largely, but not completely, independent of step size. The optimal temporal interval slightly decreased with increasing step size. Similarly, the optimal step size decreased with increasing temporal interval.

Carbohydrate-Restriction with High-Intensity Interval Training: An Optimal Combination for Treating Metabolic Diseases?

Directory of Open Access Journals (Sweden)

Monique E. Francois

2017-10-01

Full Text Available Lifestyle interventions incorporating both diet and exercise strategies remain cornerstone therapies for treating metabolic disease. Carbohydrate-restriction and high-intensity interval training (HIIT have independently been shown to improve cardiovascular and metabolic health. Carbohydrate-restriction reduces postprandial hyperglycemia, thereby limiting potential deleterious metabolic and cardiovascular consequences of excessive glucose excursions. Additionally, carbohydrate-restriction has been shown to improve body composition and blood lipids. The benefits of exercise for improving insulin sensitivity are well known. In this regard, HIIT has been shown to rapidly improve glucose control, endothelial function, and cardiorespiratory fitness. Here, we report the available evidence for each strategy and speculate that the combination of carbohydrate-restriction and HIIT will synergistically maximize the benefits of both approaches. We hypothesize that this lifestyle strategy represents an optimal intervention to treat metabolic disease; however, further research is warranted in order to harness the potential benefits of carbohydrate-restriction and HIIT for improving cardiometabolic health.

Carbohydrate-Restriction with High-Intensity Interval Training: An Optimal Combination for Treating Metabolic Diseases?

Science.gov (United States)

Francois, Monique E; Gillen, Jenna B; Little, Jonathan P

2017-01-01

Lifestyle interventions incorporating both diet and exercise strategies remain cornerstone therapies for treating metabolic disease. Carbohydrate-restriction and high-intensity interval training (HIIT) have independently been shown to improve cardiovascular and metabolic health. Carbohydrate-restriction reduces postprandial hyperglycemia, thereby limiting potential deleterious metabolic and cardiovascular consequences of excessive glucose excursions. Additionally, carbohydrate-restriction has been shown to improve body composition and blood lipids. The benefits of exercise for improving insulin sensitivity are well known. In this regard, HIIT has been shown to rapidly improve glucose control, endothelial function, and cardiorespiratory fitness. Here, we report the available evidence for each strategy and speculate that the combination of carbohydrate-restriction and HIIT will synergistically maximize the benefits of both approaches. We hypothesize that this lifestyle strategy represents an optimal intervention to treat metabolic disease; however, further research is warranted in order to harness the potential benefits of carbohydrate-restriction and HIIT for improving cardiometabolic health.

On the Influence of the Data Sampling Interval on Computer-Derived K-Indices

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A Bernard

2011-06-01

Full Text Available The K index was devised by Bartels et al. (1939 to provide an objective monitoring of irregular geomagnetic activity. The K index was then routinely used to monitor the magnetic activity at permanent magnetic observatories as well as at temporary stations. The increasing number of digital and sometimes unmanned observatories and the creation of INTERMAGNET put the question of computer production of K at the centre of the debate. Four algorithms were selected during the Vienna meeting (1991 and endorsed by IAGA for the computer production of K indices. We used one of them (FMI algorithm to investigate the impact of the geomagnetic data sampling interval on computer produced K values through the comparison of the computer derived K values for the period 2009, January 1st to 2010, May 31st at the Port-aux-Francais magnetic observatory using magnetic data series with different sampling rates (the smaller: 1 second; the larger: 1 minute. The impact is investigated on both 3-hour range values and K indices data series, as a function of the activity level for low and moderate geomagnetic activity.

Multiple sensitive estimation and optimal sample size allocation in the item sum technique.

Science.gov (United States)

Perri, Pier Francesco; Rueda García, María Del Mar; Cobo Rodríguez, Beatriz

2018-01-01

For surveys of sensitive issues in life sciences, statistical procedures can be used to reduce nonresponse and social desirability response bias. Both of these phenomena provoke nonsampling errors that are difficult to deal with and can seriously flaw the validity of the analyses. The item sum technique (IST) is a very recent indirect questioning method derived from the item count technique that seeks to procure more reliable responses on quantitative items than direct questioning while preserving respondents' anonymity. This article addresses two important questions concerning the IST: (i) its implementation when two or more sensitive variables are investigated and efficient estimates of their unknown population means are required; (ii) the determination of the optimal sample size to achieve minimum variance estimates. These aspects are of great relevance for survey practitioners engaged in sensitive research and, to the best of our knowledge, were not studied so far. In this article, theoretical results for multiple estimation and optimal allocation are obtained under a generic sampling design and then particularized to simple random sampling and stratified sampling designs. Theoretical considerations are integrated with a number of simulation studies based on data from two real surveys and conducted to ascertain the efficiency gain derived from optimal allocation in different situations. One of the surveys concerns cannabis consumption among university students. Our findings highlight some methodological advances that can be obtained in life sciences IST surveys when optimal allocation is achieved. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Optimization of Sample Preparation processes of Bone Material for Raman Spectroscopy.

Science.gov (United States)

Chikhani, Madelen; Wuhrer, Richard; Green, Hayley

2018-03-30

Raman spectroscopy has recently been investigated for use in the calculation of postmortem interval from skeletal material. The fluorescence generated by samples, which affects the interpretation of Raman data, is a major limitation. This study compares the effectiveness of two sample preparation techniques, chemical bleaching and scraping, in the reduction of fluorescence from bone samples during testing with Raman spectroscopy. Visual assessment of Raman spectra obtained at 1064 nm excitation following the preparation protocols indicates an overall reduction in fluorescence. Results demonstrate that scraping is more effective at resolving fluorescence than chemical bleaching. The scraping of skeletonized remains prior to Raman analysis is a less destructive method and allows for the preservation of a bone sample in a state closest to its original form, which is beneficial in forensic investigations. It is recommended that bone scraping supersedes chemical bleaching as the preferred method for sample preparation prior to Raman spectroscopy. © 2018 American Academy of Forensic Sciences.

An Indirect Simulation-Optimization Model for Determining Optimal TMDL Allocation under Uncertainty

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Feng Zhou

2015-11-01

Full Text Available An indirect simulation-optimization model framework with enhanced computational efficiency and risk-based decision-making capability was developed to determine optimal total maximum daily load (TMDL allocation under uncertainty. To convert the traditional direct simulation-optimization model into our indirect equivalent model framework, we proposed a two-step strategy: (1 application of interval regression equations derived by a Bayesian recursive regression tree (BRRT v2 algorithm, which approximates the original hydrodynamic and water-quality simulation models and accurately quantifies the inherent nonlinear relationship between nutrient load reductions and the credible interval of algal biomass with a given confidence interval; and (2 incorporation of the calibrated interval regression equations into an uncertain optimization framework, which is further converted to our indirect equivalent framework by the enhanced-interval linear programming (EILP method and provides approximate-optimal solutions at various risk levels. The proposed strategy was applied to the Swift Creek Reservoir’s nutrient TMDL allocation (Chesterfield County, VA to identify the minimum nutrient load allocations required from eight sub-watersheds to ensure compliance with user-specified chlorophyll criteria. Our results indicated that the BRRT-EILP model could identify critical sub-watersheds faster than the traditional one and requires lower reduction of nutrient loadings compared to traditional stochastic simulation and trial-and-error (TAE approaches. This suggests that our proposed framework performs better in optimal TMDL development compared to the traditional simulation-optimization models and provides extreme and non-extreme tradeoff analysis under uncertainty for risk-based decision making.

[Application of simulated annealing method and neural network on optimizing soil sampling schemes based on road distribution].

Science.gov (United States)

Han, Zong-wei; Huang, Wei; Luo, Yun; Zhang, Chun-di; Qi, Da-cheng

2015-03-01

Taking the soil organic matter in eastern Zhongxiang County, Hubei Province, as a research object, thirteen sample sets from different regions were arranged surrounding the road network, the spatial configuration of which was optimized by the simulated annealing approach. The topographic factors of these thirteen sample sets, including slope, plane curvature, profile curvature, topographic wetness index, stream power index and sediment transport index, were extracted by the terrain analysis. Based on the results of optimization, a multiple linear regression model with topographic factors as independent variables was built. At the same time, a multilayer perception model on the basis of neural network approach was implemented. The comparison between these two models was carried out then. The results revealed that the proposed approach was practicable in optimizing soil sampling scheme. The optimal configuration was capable of gaining soil-landscape knowledge exactly, and the accuracy of optimal configuration was better than that of original samples. This study designed a sampling configuration to study the soil attribute distribution by referring to the spatial layout of road network, historical samples, and digital elevation data, which provided an effective means as well as a theoretical basis for determining the sampling configuration and displaying spatial distribution of soil organic matter with low cost and high efficiency.

Generalized Likelihood Uncertainty Estimation (GLUE) Using Multi-Optimization Algorithm as Sampling Method

Science.gov (United States)

Wang, Z.

2015-12-01

For decades, distributed and lumped hydrological models have furthered our understanding of hydrological system. The development of hydrological simulation in large scale and high precision elaborated the spatial descriptions and hydrological behaviors. Meanwhile, the new trend is also followed by the increment of model complexity and number of parameters, which brings new challenges of uncertainty quantification. Generalized Likelihood Uncertainty Estimation (GLUE) has been widely used in uncertainty analysis for hydrological models referring to Monte Carlo method coupled with Bayesian estimation. However, the stochastic sampling method of prior parameters adopted by GLUE appears inefficient, especially in high dimensional parameter space. The heuristic optimization algorithms utilizing iterative evolution show better convergence speed and optimality-searching performance. In light of the features of heuristic optimization algorithms, this study adopted genetic algorithm, differential evolution, shuffled complex evolving algorithm to search the parameter space and obtain the parameter sets of large likelihoods. Based on the multi-algorithm sampling, hydrological model uncertainty analysis is conducted by the typical GLUE framework. To demonstrate the superiority of the new method, two hydrological models of different complexity are examined. The results shows the adaptive method tends to be efficient in sampling and effective in uncertainty analysis, providing an alternative path for uncertainty quantilization.

Magnetic Resonance Fingerprinting with short relaxation intervals.

Science.gov (United States)

Amthor, Thomas; Doneva, Mariya; Koken, Peter; Sommer, Karsten; Meineke, Jakob; Börnert, Peter

2017-09-01

The aim of this study was to investigate a technique for improving the performance of Magnetic Resonance Fingerprinting (MRF) in repetitive sampling schemes, in particular for 3D MRF acquisition, by shortening relaxation intervals between MRF pulse train repetitions. A calculation method for MRF dictionaries adapted to short relaxation intervals and non-relaxed initial spin states is presented, based on the concept of stationary fingerprints. The method is applicable to many different k-space sampling schemes in 2D and 3D. For accuracy analysis, T 1 and T 2 values of a phantom are determined by single-slice Cartesian MRF for different relaxation intervals and are compared with quantitative reference measurements. The relevance of slice profile effects is also investigated in this case. To further illustrate the capabilities of the method, an application to in-vivo spiral 3D MRF measurements is demonstrated. The proposed computation method enables accurate parameter estimation even for the shortest relaxation intervals, as investigated for different sampling patterns in 2D and 3D. In 2D Cartesian measurements, we achieved a scan acceleration of more than a factor of two, while maintaining acceptable accuracy: The largest T 1 values of a sample set deviated from their reference values by 0.3% (longest relaxation interval) and 2.4% (shortest relaxation interval). The largest T 2 values showed systematic deviations of up to 10% for all relaxation intervals, which is discussed. The influence of slice profile effects for multislice acquisition is shown to become increasingly relevant for short relaxation intervals. In 3D spiral measurements, a scan time reduction of 36% was achieved, maintaining the quality of in-vivo T1 and T2 maps. Reducing the relaxation interval between MRF sequence repetitions using stationary fingerprint dictionaries is a feasible method to improve the scan efficiency of MRF sequences. The method enables fast implementations of 3D spatially

Efficient Round-Trip Time Optimization for Replica-Exchange Enveloping Distribution Sampling (RE-EDS).

Science.gov (United States)

Sidler, Dominik; Cristòfol-Clough, Michael; Riniker, Sereina

2017-06-13

Replica-exchange enveloping distribution sampling (RE-EDS) allows the efficient estimation of free-energy differences between multiple end-states from a single molecular dynamics (MD) simulation. In EDS, a reference state is sampled, which can be tuned by two types of parameters, i.e., smoothness parameters(s) and energy offsets, such that all end-states are sufficiently sampled. However, the choice of these parameters is not trivial. Replica exchange (RE) or parallel tempering is a widely applied technique to enhance sampling. By combining EDS with the RE technique, the parameter choice problem could be simplified and the challenge shifted toward an optimal distribution of the replicas in the smoothness-parameter space. The choice of a certain replica distribution can alter the sampling efficiency significantly. In this work, global round-trip time optimization (GRTO) algorithms are tested for the use in RE-EDS simulations. In addition, a local round-trip time optimization (LRTO) algorithm is proposed for systems with slowly adapting environments, where a reliable estimate for the round-trip time is challenging to obtain. The optimization algorithms were applied to RE-EDS simulations of a system of nine small-molecule inhibitors of phenylethanolamine N-methyltransferase (PNMT). The energy offsets were determined using our recently proposed parallel energy-offset (PEOE) estimation scheme. While the multistate GRTO algorithm yielded the best replica distribution for the ligands in water, the multistate LRTO algorithm was found to be the method of choice for the ligands in complex with PNMT. With this, the 36 alchemical free-energy differences between the nine ligands were calculated successfully from a single RE-EDS simulation 10 ns in length. Thus, RE-EDS presents an efficient method for the estimation of relative binding free energies.

Simultaneous beam sampling and aperture shape optimization for SPORT.

Science.gov (United States)

Zarepisheh, Masoud; Li, Ruijiang; Ye, Yinyu; Xing, Lei

2015-02-01

Station parameter optimized radiation therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital linear accelerators, in which the station parameters of a delivery system, such as aperture shape and weight, couch position/angle, gantry/collimator angle, can be optimized simultaneously. SPORT promises to deliver remarkable radiation dose distributions in an efficient manner, yet there exists no optimization algorithm for its implementation. The purpose of this work is to develop an algorithm to simultaneously optimize the beam sampling and aperture shapes. The authors build a mathematical model with the fundamental station point parameters as the decision variables. To solve the resulting large-scale optimization problem, the authors devise an effective algorithm by integrating three advanced optimization techniques: column generation, subgradient method, and pattern search. Column generation adds the most beneficial stations sequentially until the plan quality improvement saturates and provides a good starting point for the subsequent optimization. It also adds the new stations during the algorithm if beneficial. For each update resulted from column generation, the subgradient method improves the selected stations locally by reshaping the apertures and updating the beam angles toward a descent subgradient direction. The algorithm continues to improve the selected stations locally and globally by a pattern search algorithm to explore the part of search space not reachable by the subgradient method. By combining these three techniques together, all plausible combinations of station parameters are searched efficiently to yield the optimal solution. A SPORT optimization framework with seamlessly integration of three complementary algorithms, column generation, subgradient method, and pattern search, was established. The proposed technique was applied to two previously treated clinical cases: a head and neck and a prostate case

Simultaneous beam sampling and aperture shape optimization for SPORT

Energy Technology Data Exchange (ETDEWEB)

Zarepisheh, Masoud; Li, Ruijiang; Xing, Lei, E-mail: Lei@stanford.edu [Department of Radiation Oncology, Stanford University, Stanford, California 94305 (United States); Ye, Yinyu [Department of Management Science and Engineering, Stanford University, Stanford, California 94305 (United States)

2015-02-15

Purpose: Station parameter optimized radiation therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital linear accelerators, in which the station parameters of a delivery system, such as aperture shape and weight, couch position/angle, gantry/collimator angle, can be optimized simultaneously. SPORT promises to deliver remarkable radiation dose distributions in an efficient manner, yet there exists no optimization algorithm for its implementation. The purpose of this work is to develop an algorithm to simultaneously optimize the beam sampling and aperture shapes. Methods: The authors build a mathematical model with the fundamental station point parameters as the decision variables. To solve the resulting large-scale optimization problem, the authors devise an effective algorithm by integrating three advanced optimization techniques: column generation, subgradient method, and pattern search. Column generation adds the most beneficial stations sequentially until the plan quality improvement saturates and provides a good starting point for the subsequent optimization. It also adds the new stations during the algorithm if beneficial. For each update resulted from column generation, the subgradient method improves the selected stations locally by reshaping the apertures and updating the beam angles toward a descent subgradient direction. The algorithm continues to improve the selected stations locally and globally by a pattern search algorithm to explore the part of search space not reachable by the subgradient method. By combining these three techniques together, all plausible combinations of station parameters are searched efficiently to yield the optimal solution. Results: A SPORT optimization framework with seamlessly integration of three complementary algorithms, column generation, subgradient method, and pattern search, was established. The proposed technique was applied to two previously treated clinical cases: a head and

Simultaneous beam sampling and aperture shape optimization for SPORT

International Nuclear Information System (INIS)

Zarepisheh, Masoud; Li, Ruijiang; Xing, Lei; Ye, Yinyu

2015-01-01

Purpose: Station parameter optimized radiation therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital linear accelerators, in which the station parameters of a delivery system, such as aperture shape and weight, couch position/angle, gantry/collimator angle, can be optimized simultaneously. SPORT promises to deliver remarkable radiation dose distributions in an efficient manner, yet there exists no optimization algorithm for its implementation. The purpose of this work is to develop an algorithm to simultaneously optimize the beam sampling and aperture shapes. Methods: The authors build a mathematical model with the fundamental station point parameters as the decision variables. To solve the resulting large-scale optimization problem, the authors devise an effective algorithm by integrating three advanced optimization techniques: column generation, subgradient method, and pattern search. Column generation adds the most beneficial stations sequentially until the plan quality improvement saturates and provides a good starting point for the subsequent optimization. It also adds the new stations during the algorithm if beneficial. For each update resulted from column generation, the subgradient method improves the selected stations locally by reshaping the apertures and updating the beam angles toward a descent subgradient direction. The algorithm continues to improve the selected stations locally and globally by a pattern search algorithm to explore the part of search space not reachable by the subgradient method. By combining these three techniques together, all plausible combinations of station parameters are searched efficiently to yield the optimal solution. Results: A SPORT optimization framework with seamlessly integration of three complementary algorithms, column generation, subgradient method, and pattern search, was established. The proposed technique was applied to two previously treated clinical cases: a head and

Numerical calculation of economic uncertainty by intervals and fuzzy numbers

DEFF Research Database (Denmark)

Schjær-Jacobsen, Hans

2010-01-01

This paper emphasizes that numerically correct calculation of economic uncertainty with intervals and fuzzy numbers requires implementation of global optimization techniques in contrast to straightforward application of interval arithmetic. This is demonstrated by both a simple case from managerial...... World Academic Press, UK. All rights reserved....

Automatic Motion Generation for Robotic Milling Optimizing Stiffness with Sample-Based Planning

Directory of Open Access Journals (Sweden)

Julian Ricardo Diaz Posada

2017-01-01

Full Text Available Optimal and intuitive robotic machining is still a challenge. One of the main reasons for this is the lack of robot stiffness, which is also dependent on the robot positioning in the Cartesian space. To make up for this deficiency and with the aim of increasing robot machining accuracy, this contribution describes a solution approach for optimizing the stiffness over a desired milling path using the free degree of freedom of the machining process. The optimal motion is computed based on the semantic and mathematical interpretation of the manufacturing process modeled on its components: product, process and resource; and by configuring automatically a sample-based motion problem and the transition-based rapid-random tree algorithm for computing an optimal motion. The approach is simulated on a CAM software for a machining path revealing its functionality and outlining future potentials for the optimal motion generation for robotic machining processes.

Confidence intervals for correlations when data are not normal.

Science.gov (United States)

Bishara, Anthony J; Hittner, James B

2017-02-01

With nonnormal data, the typical confidence interval of the correlation (Fisher z') may be inaccurate. The literature has been unclear as to which of several alternative methods should be used instead, and how extreme a violation of normality is needed to justify an alternative. Through Monte Carlo simulation, 11 confidence interval methods were compared, including Fisher z', two Spearman rank-order methods, the Box-Cox transformation, rank-based inverse normal (RIN) transformation, and various bootstrap methods. Nonnormality often distorted the Fisher z' confidence interval-for example, leading to a 95 % confidence interval that had actual coverage as low as 68 %. Increasing the sample size sometimes worsened this problem. Inaccurate Fisher z' intervals could be predicted by a sample kurtosis of at least 2, an absolute sample skewness of at least 1, or significant violations of normality hypothesis tests. Only the Spearman rank-order and RIN transformation methods were universally robust to nonnormality. Among the bootstrap methods, an observed imposed bootstrap came closest to accurate coverage, though it often resulted in an overly long interval. The results suggest that sample nonnormality can justify avoidance of the Fisher z' interval in favor of a more robust alternative. R code for the relevant methods is provided in supplementary materials.

Computing Statistics under Interval and Fuzzy Uncertainty Applications to Computer Science and Engineering

CERN Document Server

Nguyen, Hung T; Wu, Berlin; Xiang, Gang

2012-01-01

In many practical situations, we are interested in statistics characterizing a population of objects: e.g. in the mean height of people from a certain area.   Most algorithms for estimating such statistics assume that the sample values are exact. In practice, sample values come from measurements, and measurements are never absolutely accurate. Sometimes, we know the exact probability distribution of the measurement inaccuracy, but often, we only know the upper bound on this inaccuracy. In this case, we have interval uncertainty: e.g. if the measured value is 1.0, and inaccuracy is bounded by 0.1, then the actual (unknown) value of the quantity can be anywhere between 1.0 - 0.1 = 0.9 and 1.0 + 0.1 = 1.1. In other cases, the values are expert estimates, and we only have fuzzy information about the estimation inaccuracy.   This book shows how to compute statistics under such interval and fuzzy uncertainty. The resulting methods are applied to computer science (optimal scheduling of different processors), to in...

Optimization of Sample Preparation and Instrumental Parameters for the Rapid Analysis of Drugs of Abuse in Hair samples by MALDI-MS/MS Imaging

Science.gov (United States)

Flinders, Bryn; Beasley, Emma; Verlaan, Ricky M.; Cuypers, Eva; Francese, Simona; Bassindale, Tom; Clench, Malcolm R.; Heeren, Ron M. A.

2017-08-01

Matrix-assisted laser desorption/ionization-mass spectrometry imaging (MALDI-MSI) has been employed to rapidly screen longitudinally sectioned drug user hair samples for cocaine and its metabolites using continuous raster imaging. Optimization of the spatial resolution and raster speed were performed on intact cocaine contaminated hair samples. The optimized settings (100 × 150 μm at 0.24 mm/s) were subsequently used to examine longitudinally sectioned drug user hair samples. The MALDI-MS/MS images showed the distribution of the most abundant cocaine product ion at m/z 182. Using the optimized settings, multiple hair samples obtained from two users were analyzed in approximately 3 h: six times faster than the standard spot-to-spot acquisition method. Quantitation was achieved using longitudinally sectioned control hair samples sprayed with a cocaine dilution series. A multiple reaction monitoring (MRM) experiment was also performed using the `dynamic pixel' imaging method to screen for cocaine and a range of its metabolites, in order to differentiate between contaminated hairs and drug users. Cocaine, benzoylecgonine, and cocaethylene were detectable, in agreement with analyses carried out using the standard LC-MS/MS method. [Figure not available: see fulltext.

Interval Optimization Model Considering Terrestrial Ecological Impacts for Water Rights Transfer from Agriculture to Industry in Ningxia, China.

Science.gov (United States)

Sun, Lian; Li, Chunhui; Cai, Yanpeng; Wang, Xuan

2017-06-14

In this study, an interval optimization model is developed to maximize the benefits of a water rights transfer system that comprises industry and agriculture sectors in the Ningxia Hui Autonomous Region in China. The model is subjected to a number of constraints including water saving potential from agriculture and ecological groundwater levels. Ecological groundwater levels serve as performance indicators of terrestrial ecology. The interval method is applied to present the uncertainty of parameters in the model. Two scenarios regarding dual industrial development targets (planned and unplanned ones) are used to investigate the difference in potential benefits of water rights transfer. Runoff of the Yellow River as the source of water rights fluctuates significantly in different years. Thus, compensation fees for agriculture are calculated to reflect the influence of differences in the runoff. Results show that there are more available water rights to transfer for industrial development. The benefits are considerable but unbalanced between buyers and sellers. The government should establish a water market that is freer and promote the interest of agriculture and farmers. Though there has been some success of water rights transfer, the ecological impacts and the relationship between sellers and buyers require additional studies.

National Survey of Adult and Pediatric Reference Intervals in Clinical Laboratories across Canada: A Report of the CSCC Working Group on Reference Interval Harmonization.

Science.gov (United States)

Adeli, Khosrow; Higgins, Victoria; Seccombe, David; Collier, Christine P; Balion, Cynthia M; Cembrowski, George; Venner, Allison A; Shaw, Julie

2017-11-01

Reference intervals are widely used decision-making tools in laboratory medicine, serving as health-associated standards to interpret laboratory test results. Numerous studies have shown wide variation in reference intervals, even between laboratories using assays from the same manufacturer. Lack of consistency in either sample measurement or reference intervals across laboratories challenges the expectation of standardized patient care regardless of testing location. Here, we present data from a national survey conducted by the Canadian Society of Clinical Chemists (CSCC) Reference Interval Harmonization (hRI) Working Group that examines variation in laboratory reference sample measurements, as well as pediatric and adult reference intervals currently used in clinical practice across Canada. Data on reference intervals currently used by 37 laboratories were collected through a national survey to examine the variation in reference intervals for seven common laboratory tests. Additionally, 40 clinical laboratories participated in a baseline assessment by measuring six analytes in a reference sample. Of the seven analytes examined, alanine aminotransferase (ALT), alkaline phosphatase (ALP), and creatinine reference intervals were most variable. As expected, reference interval variation was more substantial in the pediatric population and varied between laboratories using the same instrumentation. Reference sample results differed between laboratories, particularly for ALT and free thyroxine (FT4). Reference interval variation was greater than test result variation for the majority of analytes. It is evident that there is a critical lack of harmonization in laboratory reference intervals, particularly for the pediatric population. Furthermore, the observed variation in reference intervals across instruments cannot be explained by the bias between the results obtained on instruments by different manufacturers. Copyright © 2017 The Canadian Society of Clinical Chemists

High-intensity interval training: Modulating interval duration in overweight/obese men.

Science.gov (United States)

Smith-Ryan, Abbie E; Melvin, Malia N; Wingfield, Hailee L

2015-05-01

High-intensity interval training (HIIT) is a time-efficient strategy shown to induce various cardiovascular and metabolic adaptations. Little is known about the optimal tolerable combination of intensity and volume necessary for adaptations, especially in clinical populations. In a randomized controlled pilot design, we evaluated the effects of two types of interval training protocols, varying in intensity and interval duration, on clinical outcomes in overweight/obese men. Twenty-five men [body mass index (BMI) > 25 kg · m(2)] completed baseline body composition measures: fat mass (FM), lean mass (LM) and percent body fat (%BF) and fasting blood glucose, lipids and insulin (IN). A graded exercise cycling test was completed for peak oxygen consumption (VO2peak) and power output (PO). Participants were randomly assigned to high-intensity short interval (1MIN-HIIT), high-intensity interval (2MIN-HIIT) or control groups. 1MIN-HIIT and 2MIN-HIIT completed 3 weeks of cycling interval training, 3 days/week, consisting of either 10 × 1 min bouts at 90% PO with 1 min rests (1MIN-HIIT) or 5 × 2 min bouts with 1 min rests at undulating intensities (80%-100%) (2MIN-HIIT). There were no significant training effects on FM (Δ1.06 ± 1.25 kg) or %BF (Δ1.13% ± 1.88%), compared to CON. Increases in LM were not significant but increased by 1.7 kg and 2.1 kg for 1MIN and 2MIN-HIIT groups, respectively. Increases in VO2peak were also not significant for 1MIN (3.4 ml·kg(-1) · min(-1)) or 2MIN groups (2.7 ml · kg(-1) · min(-1)). IN sensitivity (HOMA-IR) improved for both training groups (Δ-2.78 ± 3.48 units; p < 0.05) compared to CON. HIIT may be an effective short-term strategy to improve cardiorespiratory fitness and IN sensitivity in overweight males.

Optimized IMAC-IMAC protocol for phosphopeptide recovery from complex biological samples

DEFF Research Database (Denmark)

Ye, Juanying; Zhang, Xumin; Young, Clifford

2010-01-01

using Fe(III)-NTA IMAC resin and it proved to be highly selective in the phosphopeptide enrichment of a highly diluted standard sample (1:1000) prior to MALDI MS analysis. We also observed that a higher iron purity led to an increased IMAC enrichment efficiency. The optimized method was then adapted...... to phosphoproteome analyses of cell lysates of high protein complexity. From either 20 microg of mouse sample or 50 microg of Drosophila melanogaster sample, more than 1000 phosphorylation sites were identified in each study using IMAC-IMAC and LC-MS/MS. We demonstrate efficient separation of multiply phosphorylated...... characterization of phosphoproteins in functional phosphoproteomics research projects....

Optimizing the triple-axis spectrometer PANDA at the MLZ for small samples and complex sample environment conditions

Science.gov (United States)

Utschick, C.; Skoulatos, M.; Schneidewind, A.; Böni, P.

2016-11-01

The cold-neutron triple-axis spectrometer PANDA at the neutron source FRM II has been serving an international user community studying condensed matter physics problems. We report on a new setup, improving the signal-to-noise ratio for small samples and pressure cell setups. Analytical and numerical Monte Carlo methods are used for the optimization of elliptic and parabolic focusing guides. They are placed between the monochromator and sample positions, and the flux at the sample is compared to the one achieved by standard monochromator focusing techniques. A 25 times smaller spot size is achieved, associated with a factor of 2 increased intensity, within the same divergence limits, ± 2 ° . This optional neutron focusing guide shall establish a top-class spectrometer for studying novel exotic properties of matter in combination with more stringent sample environment conditions such as extreme pressures associated with small sample sizes.

Searching for the Optimal Sampling Solution: Variation in Invertebrate Communities, Sample Condition and DNA Quality.

Directory of Open Access Journals (Sweden)

Martin M Gossner

Full Text Available There is a great demand for standardising biodiversity assessments in order to allow optimal comparison across research groups. For invertebrates, pitfall or flight-interception traps are commonly used, but sampling solution differs widely between studies, which could influence the communities collected and affect sample processing (morphological or genetic. We assessed arthropod communities with flight-interception traps using three commonly used sampling solutions across two forest types and two vertical strata. We first considered the effect of sampling solution and its interaction with forest type, vertical stratum, and position of sampling jar at the trap on sample condition and community composition. We found that samples collected in copper sulphate were more mouldy and fragmented relative to other solutions which might impair morphological identification, but condition depended on forest type, trap type and the position of the jar. Community composition, based on order-level identification, did not differ across sampling solutions and only varied with forest type and vertical stratum. Species richness and species-level community composition, however, differed greatly among sampling solutions. Renner solution was highly attractant for beetles and repellent for true bugs. Secondly, we tested whether sampling solution affects subsequent molecular analyses and found that DNA barcoding success was species-specific. Samples from copper sulphate produced the fewest successful DNA sequences for genetic identification, and since DNA yield or quality was not particularly reduced in these samples additional interactions between the solution and DNA must also be occurring. Our results show that the choice of sampling solution should be an important consideration in biodiversity studies. Due to the potential bias towards or against certain species by Ethanol-containing sampling solution we suggest ethylene glycol as a suitable sampling solution when

An Optimized Method for Quantification of Pathogenic Leptospira in Environmental Water Samples.

Science.gov (United States)

Riediger, Irina N; Hoffmaster, Alex R; Casanovas-Massana, Arnau; Biondo, Alexander W; Ko, Albert I; Stoddard, Robyn A

2016-01-01

Leptospirosis is a zoonotic disease usually acquired by contact with water contaminated with urine of infected animals. However, few molecular methods have been used to monitor or quantify pathogenic Leptospira in environmental water samples. Here we optimized a DNA extraction method for the quantification of leptospires using a previously described Taqman-based qPCR method targeting lipL32, a gene unique to and highly conserved in pathogenic Leptospira. QIAamp DNA mini, MO BIO PowerWater DNA and PowerSoil DNA Isolation kits were evaluated to extract DNA from sewage, pond, river and ultrapure water samples spiked with leptospires. Performance of each kit varied with sample type. Sample processing methods were further evaluated and optimized using the PowerSoil DNA kit due to its performance on turbid water samples and reproducibility. Centrifugation speeds, water volumes and use of Escherichia coli as a carrier were compared to improve DNA recovery. All matrices showed a strong linearity in a range of concentrations from 106 to 10° leptospires/mL and lower limits of detection ranging from Leptospira in environmental waters (river, pond and sewage) which consists of the concentration of 40 mL samples by centrifugation at 15,000×g for 20 minutes at 4°C, followed by DNA extraction with the PowerSoil DNA Isolation kit. Although the method described herein needs to be validated in environmental studies, it potentially provides the opportunity for effective, timely and sensitive assessment of environmental leptospiral burden.

Generalized Confidence Intervals and Fiducial Intervals for Some Epidemiological Measures

Directory of Open Access Journals (Sweden)

Ionut Bebu

2016-06-01

Full Text Available For binary outcome data from epidemiological studies, this article investigates the interval estimation of several measures of interest in the absence or presence of categorical covariates. When covariates are present, the logistic regression model as well as the log-binomial model are investigated. The measures considered include the common odds ratio (OR from several studies, the number needed to treat (NNT, and the prevalence ratio. For each parameter, confidence intervals are constructed using the concepts of generalized pivotal quantities and fiducial quantities. Numerical results show that the confidence intervals so obtained exhibit satisfactory performance in terms of maintaining the coverage probabilities even when the sample sizes are not large. An appealing feature of the proposed solutions is that they are not based on maximization of the likelihood, and hence are free from convergence issues associated with the numerical calculation of the maximum likelihood estimators, especially in the context of the log-binomial model. The results are illustrated with a number of examples. The overall conclusion is that the proposed methodologies based on generalized pivotal quantities and fiducial quantities provide an accurate and unified approach for the interval estimation of the various epidemiological measures in the context of binary outcome data with or without covariates.

Biostratigraphic analysis of core samples from wells drilled in the Devonian shale interval of the Appalachian and Illinois Basins

Energy Technology Data Exchange (ETDEWEB)

Martin, S.J.; Zielinski, R.E.

1978-07-14

A palynological investigation was performed on 55 samples of core material from four wells drilled in the Devonian Shale interval of the Appalachian and Illinois Basins. Using a combination of spores and acritarchs, it was possible to divide the Middle Devonian from the Upper Devonian and to make subdivisions within the Middle and Upper Devonian. The age of the palynomorphs encountered in this study is Upper Devonian.

Time optimization of 90Sr measurements: Sequential measurement of multiple samples during ingrowth of 90Y

International Nuclear Information System (INIS)

Holmgren, Stina; Tovedal, Annika; Björnham, Oscar; Ramebäck, Henrik

2016-01-01

The aim of this paper is to contribute to a more rapid determination of a series of samples containing 90 Sr by making the Cherenkov measurement of the daughter nuclide 90 Y more time efficient. There are many instances when an optimization of the measurement method might be favorable, such as; situations requiring rapid results in order to make urgent decisions or, on the other hand, to maximize the throughput of samples in a limited available time span. In order to minimize the total analysis time, a mathematical model was developed which calculates the time of ingrowth as well as individual measurement times for n samples in a series. This work is focused on the measurement of 90 Y during ingrowth, after an initial chemical separation of strontium, in which it is assumed that no other radioactive strontium isotopes are present. By using a fixed minimum detectable activity (MDA) and iterating the measurement time for each consecutive sample the total analysis time will be less, compared to using the same measurement time for all samples. It was found that by optimization, the total analysis time for 10 samples can be decreased greatly, from 21 h to 6.5 h, when assuming a MDA of 1 Bq/L and at a background count rate of approximately 0.8 cpm. - Highlights: • An approach roughly a factor of three more efficient than an un-optimized method. • The optimization gives a more efficient use of instrument time. • The efficiency increase ranges from a factor of three to 10, for 10 to 40 samples.

Optimizing Time Intervals of Meteorological Data Used with Atmospheric Dose Modeling at SRS

International Nuclear Information System (INIS)

Simpkins, A.A.

1999-01-01

Measured tritium oxide concentrations in air have been compared with calculated values using routine release Gaussian plume models for different time intervals of meteorological data. These comparisons determined an optimum time interval of meteorological data used with atmospheric dose models at the Savannah River Site (SRS). Meteorological data of varying time intervals (1-yr to 10-yr) were used for the comparison. Insignificant differences are seen in using a one-year database as opposed to a five-year database. Use of a ten-year database results in slightly more conservative results. For meteorological databases of length one to five years the mean ratio of predicted to measured tritium oxide concentrations is approximately 1.25 whereas for the ten-year meteorological database the ration is closer to 1.35. Currently at the Savannah River Site a meteorological database of five years duration is used for all dose models. This study suggests no substantially improved accuracy using meteorological files of shorter or longer time intervals

Racing Sampling Based Microimmune Optimization Approach Solving Constrained Expected Value Programming

Directory of Open Access Journals (Sweden)

Kai Yang

2016-01-01

Full Text Available This work investigates a bioinspired microimmune optimization algorithm to solve a general kind of single-objective nonlinear constrained expected value programming without any prior distribution. In the study of algorithm, two lower bound sample estimates of random variables are theoretically developed to estimate the empirical values of individuals. Two adaptive racing sampling schemes are designed to identify those competitive individuals in a given population, by which high-quality individuals can obtain large sampling size. An immune evolutionary mechanism, along with a local search approach, is constructed to evolve the current population. The comparative experiments have showed that the proposed algorithm can effectively solve higher-dimensional benchmark problems and is of potential for further applications.

Design and Analysis of Schemes for Adapting Migration Intervals in Parallel Evolutionary Algorithms.

Science.gov (United States)

Mambrini, Andrea; Sudholt, Dirk

2015-01-01

The migration interval is one of the fundamental parameters governing the dynamic behaviour of island models. Yet, there is little understanding on how this parameter affects performance, and how to optimally set it given a problem in hand. We propose schemes for adapting the migration interval according to whether fitness improvements have been found. As long as no improvement is found, the migration interval is increased to minimise communication. Once the best fitness has improved, the migration interval is decreased to spread new best solutions more quickly. We provide a method for obtaining upper bounds on the expected running time and the communication effort, defined as the expected number of migrants sent. Example applications of this method to common example functions show that our adaptive schemes are able to compete with, or even outperform, the optimal fixed choice of the migration interval, with regard to running time and communication effort.

Optimization of multi-channel neutron focusing guides for extreme sample environments

International Nuclear Information System (INIS)

Di Julio, D D; Lelièvre-Berna, E; Andersen, K H; Bentley, P M; Courtois, P

2014-01-01

In this work, we present and discuss simulation results for the design of multichannel neutron focusing guides for extreme sample environments. A single focusing guide consists of any number of supermirror-coated curved outer channels surrounding a central channel. Furthermore, a guide is separated into two sections in order to allow for extension into a sample environment. The performance of a guide is evaluated through a Monte-Carlo ray tracing simulation which is further coupled to an optimization algorithm in order to find the best possible guide for a given situation. A number of population-based algorithms have been investigated for this purpose. These include particle-swarm optimization, artificial bee colony, and differential evolution. The performance of each algorithm and preliminary results of the design of a multi-channel neutron focusing guide using these methods are described. We found that a three-channel focusing guide offered the best performance, with a gain factor of 2.4 compared to no focusing guide, for the design scenario investigated in this work.

Interval mellem operation for ovariecancer og kemoterapi--sekundaerpublikation

DEFF Research Database (Denmark)

Larsen, Erling Peter; Blaakaer, Jan

2009-01-01

Worldwide, much effort goes into performing optimal surgery in treatment of epithelial ovarian cancer (EOC). However, the optimal timing (TI) of postoperative chemotherapy for ovarian cancer remains poorly defined. The relevant literature comprises seven studies with varying characteristics and i...... and includes different prognostic factors. The general supposition is that the time interval does not have a prognostic influence, but experimental studies have shown that it does affect cancer prognosis. Udgivelsesdato: 2009-Nov-2...

Relationship between heart rate and quiescent interval of the cardiac cycle in children using MRI

Energy Technology Data Exchange (ETDEWEB)

Zhang, Wei [Texas Children' s Hospital, E. B. Singleton Department of Pediatric Radiology, Houston, TX (United States); Bogale, Saivivek [Baylor University Medical Center, Department of Radiology, Dallas, TX (United States); Golriz, Farahnaz [Baylor College of Medicine, Department of Radiology, Houston, TX (United States); Krishnamurthy, Rajesh [Nationwide Children' s Hospital, Department of Diagnostic Radiology, Columbus, OH (United States)

2017-11-15

Imaging the heart in children comes with the challenge of constant cardiac motion. A prospective electrocardiography-triggered CT scan allows for scanning during a predetermined phase of the cardiac cycle with least motion. This technique requires knowing the optimal quiescent intervals of cardiac cycles in a pediatric population. To evaluate high-temporal-resolution cine MRI of the heart in children to determine the relationship of heart rate to the optimal quiescent interval within the cardiac cycle. We included a total of 225 consecutive patients ages 0-18 years who had high-temporal-resolution cine steady-state free-precession sequence performed as part of a magnetic resonance imaging (MRI) or magnetic resonance angiography study of the heart. We determined the location and duration of the quiescent interval in systole and diastole for heart rates ranging 40-178 beats per minute (bpm). We performed the Wilcoxon signed rank test to compare the duration of quiescent interval in systole and diastole for each heart rate group. The duration of the quiescent interval at heart rates <80 bpm and >90 bpm was significantly longer in diastole and systole, respectively (P<.0001 for all ranges, except for 90-99 bpm [P=.02]). For heart rates 80-89 bpm, diastolic interval was longer than systolic interval, but the difference was not statistically significant (P=.06). We created a chart depicting optimal quiescent intervals across a range of heart rates that could be applied for prospective electrocardiography-triggered CT imaging of the heart. The optimal quiescent interval at heart rates <80 bpm is in diastole and at heart rates ≥90 bpm is in systole. The period of quiescence at heart rates 80-89 bpm is uniformly short in systole and diastole. (orig.)

Robust Estimation of Diffusion-Optimized Ensembles for Enhanced Sampling

DEFF Research Database (Denmark)

Tian, Pengfei; Jónsson, Sigurdur Æ.; Ferkinghoff-Borg, Jesper

2014-01-01

The multicanonical, or flat-histogram, method is a common technique to improve the sampling efficiency of molecular simulations. The idea is that free-energy barriers in a simulation can be removed by simulating from a distribution where all values of a reaction coordinate are equally likely......, and subsequently reweight the obtained statistics to recover the Boltzmann distribution at the temperature of interest. While this method has been successful in practice, the choice of a flat distribution is not necessarily optimal. Recently, it was proposed that additional performance gains could be obtained...

Predicting fecal coliform using the interval-to-interval approach and SWAT in the Miyun watershed, China.

Science.gov (United States)

Bai, Jianwen; Shen, Zhenyao; Yan, Tiezhu; Qiu, Jiali; Li, Yangyang

2017-06-01

Pathogens in manure can cause waterborne-disease outbreaks, serious illness, and even death in humans. Therefore, information about the transformation and transport of bacteria is crucial for determining their source. In this study, the Soil and Water Assessment Tool (SWAT) was applied to simulate fecal coliform bacteria load in the Miyun Reservoir watershed, China. The data for the fecal coliform were obtained at three sampling sites, Chenying (CY), Gubeikou (GBK), and Xiahui (XH). The calibration processes of the fecal coliform were conducted using the CY and GBK sites, and validation was conducted at the XH site. An interval-to-interval approach was designed and incorporated into the processes of fecal coliform calibration and validation. The 95% confidence interval of the predicted values and the 95% confidence interval of measured values were considered during calibration and validation in the interval-to-interval approach. Compared with the traditional point-to-point comparison, this method can improve simulation accuracy. The results indicated that the simulation of fecal coliform using the interval-to-interval approach was reasonable for the watershed. This method could provide a new research direction for future model calibration and validation studies.

Calculation of solar irradiation prediction intervals combining volatility and kernel density estimates

International Nuclear Information System (INIS)

Trapero, Juan R.

2016-01-01

In order to integrate solar energy into the grid it is important to predict the solar radiation accurately, where forecast errors can lead to significant costs. Recently, the increasing statistical approaches that cope with this problem is yielding a prolific literature. In general terms, the main research discussion is centred on selecting the “best” forecasting technique in accuracy terms. However, the need of the users of such forecasts require, apart from point forecasts, information about the variability of such forecast to compute prediction intervals. In this work, we will analyze kernel density estimation approaches, volatility forecasting models and combination of both of them in order to improve the prediction intervals performance. The results show that an optimal combination in terms of prediction interval statistical tests can achieve the desired confidence level with a lower average interval width. Data from a facility located in Spain are used to illustrate our methodology. - Highlights: • This work explores uncertainty forecasting models to build prediction intervals. • Kernel density estimators, exponential smoothing and GARCH models are compared. • An optimal combination of methods provides the best results. • A good compromise between coverage and average interval width is shown.

SU-E-T-295: Simultaneous Beam Sampling and Aperture Shape Optimization for Station Parameter Optimized Radiation Therapy (SPORT)

Energy Technology Data Exchange (ETDEWEB)

Zarepisheh, M; Li, R; Xing, L [Stanford UniversitySchool of Medicine, Stanford, CA (United States); Ye, Y [Stanford Univ, Management Science and Engineering, Stanford, Ca (United States); Boyd, S [Stanford University, Electrical Engineering, Stanford, CA (United States)

2014-06-01

Purpose: Station Parameter Optimized Radiation Therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital LINACs, in which the station parameters of a delivery system, (such as aperture shape and weight, couch position/angle, gantry/collimator angle) are optimized altogether. SPORT promises to deliver unprecedented radiation dose distributions efficiently, yet there does not exist any optimization algorithm to implement it. The purpose of this work is to propose an optimization algorithm to simultaneously optimize the beam sampling and aperture shapes. Methods: We build a mathematical model whose variables are beam angles (including non-coplanar and/or even nonisocentric beams) and aperture shapes. To solve the resulting large scale optimization problem, we devise an exact, convergent and fast optimization algorithm by integrating three advanced optimization techniques named column generation, gradient method, and pattern search. Column generation is used to find a good set of aperture shapes as an initial solution by adding apertures sequentially. Then we apply the gradient method to iteratively improve the current solution by reshaping the aperture shapes and updating the beam angles toward the gradient. Algorithm continues by pattern search method to explore the part of the search space that cannot be reached by the gradient method. Results: The proposed technique is applied to a series of patient cases and significantly improves the plan quality. In a head-and-neck case, for example, the left parotid gland mean-dose, brainstem max-dose, spinal cord max-dose, and mandible mean-dose are reduced by 10%, 7%, 24% and 12% respectively, compared to the conventional VMAT plan while maintaining the same PTV coverage. Conclusion: Combined use of column generation, gradient search and pattern search algorithms provide an effective way to optimize simultaneously the large collection of station parameters and significantly improves

SU-E-T-295: Simultaneous Beam Sampling and Aperture Shape Optimization for Station Parameter Optimized Radiation Therapy (SPORT)

International Nuclear Information System (INIS)

Zarepisheh, M; Li, R; Xing, L; Ye, Y; Boyd, S

2014-01-01

Purpose: Station Parameter Optimized Radiation Therapy (SPORT) was recently proposed to fully utilize the technical capability of emerging digital LINACs, in which the station parameters of a delivery system, (such as aperture shape and weight, couch position/angle, gantry/collimator angle) are optimized altogether. SPORT promises to deliver unprecedented radiation dose distributions efficiently, yet there does not exist any optimization algorithm to implement it. The purpose of this work is to propose an optimization algorithm to simultaneously optimize the beam sampling and aperture shapes. Methods: We build a mathematical model whose variables are beam angles (including non-coplanar and/or even nonisocentric beams) and aperture shapes. To solve the resulting large scale optimization problem, we devise an exact, convergent and fast optimization algorithm by integrating three advanced optimization techniques named column generation, gradient method, and pattern search. Column generation is used to find a good set of aperture shapes as an initial solution by adding apertures sequentially. Then we apply the gradient method to iteratively improve the current solution by reshaping the aperture shapes and updating the beam angles toward the gradient. Algorithm continues by pattern search method to explore the part of the search space that cannot be reached by the gradient method. Results: The proposed technique is applied to a series of patient cases and significantly improves the plan quality. In a head-and-neck case, for example, the left parotid gland mean-dose, brainstem max-dose, spinal cord max-dose, and mandible mean-dose are reduced by 10%, 7%, 24% and 12% respectively, compared to the conventional VMAT plan while maintaining the same PTV coverage. Conclusion: Combined use of column generation, gradient search and pattern search algorithms provide an effective way to optimize simultaneously the large collection of station parameters and significantly improves

The Acute Effects of Interval-Type Exercise on Glycemic Control in Type 2 Diabetes Subjects: Importance of Interval Length. A Controlled, Counterbalanced, Crossover Study.

Directory of Open Access Journals (Sweden)

Ida Jakobsen

Full Text Available Interval-type exercise is effective for improving glycemic control, but the optimal approach is unknown. The purpose of this study was to determine the importance of the interval length on changes in postprandial glycemic control following a single exercise bout. Twelve subjects with type 2 diabetes completed a cross-over study with three 1-hour interventions performed in a non-randomized but counter-balanced order: 1 Interval walking consisting of repeated cycles of 3 min slow (aiming for 54% of Peak oxygen consumption rate [VO2peak] and 3 min fast (aiming for 89% of VO2peak walking (IW3; 2 Interval walking consisting of repeated cycles of 1 min slow and 1 min fast walking (IW1 and 3 No walking (CON. The exercise interventions were matched with regards to walking speed, and VO2 and heart rate was assessed throughout all interventions. A 4-hour liquid mixed meal tolerance test commenced 30 min after each intervention, with blood samples taken regularly. IW3 and IW1 resulted in comparable mean VO2 and heart rates. Overall mean postprandial blood glucose levels were lower after IW3 compared to CON (10.3±3.0 vs. 11.1±3.3 mmol/L; P 0.05 for both. Conversely blood glucose levels at specific time points during the MMTT differed significantly following both IW3 and IW1 as compared to CON. Our findings support the previously found blood glucose lowering effect of IW3 and suggest that reducing the interval length, while keeping the walking speed and time spend on fast and slow walking constant, does not result in additional improvements.ClinicalTrials.gov NCT02257190.

Adaptive adjustment of interval predictive control based on combined model and application in shell brand petroleum distillation tower

Science.gov (United States)

Sun, Chao; Zhang, Chunran; Gu, Xinfeng; Liu, Bin

2017-10-01

Constraints of the optimization objective are often unable to be met when predictive control is applied to industrial production process. Then, online predictive controller will not find a feasible solution or a global optimal solution. To solve this problem, based on Back Propagation-Auto Regressive with exogenous inputs (BP-ARX) combined control model, nonlinear programming method is used to discuss the feasibility of constrained predictive control, feasibility decision theorem of the optimization objective is proposed, and the solution method of soft constraint slack variables is given when the optimization objective is not feasible. Based on this, for the interval control requirements of the controlled variables, the slack variables that have been solved are introduced, the adaptive weighted interval predictive control algorithm is proposed, achieving adaptive regulation of the optimization objective and automatically adjust of the infeasible interval range, expanding the scope of the feasible region, and ensuring the feasibility of the interval optimization objective. Finally, feasibility and effectiveness of the algorithm is validated through the simulation comparative experiments.

Using the multi-objective optimization replica exchange Monte Carlo enhanced sampling method for protein-small molecule docking.

Science.gov (United States)

Wang, Hongrui; Liu, Hongwei; Cai, Leixin; Wang, Caixia; Lv, Qiang

2017-07-10

In this study, we extended the replica exchange Monte Carlo (REMC) sampling method to protein-small molecule docking conformational prediction using RosettaLigand. In contrast to the traditional Monte Carlo (MC) and REMC sampling methods, these methods use multi-objective optimization Pareto front information to facilitate the selection of replicas for exchange. The Pareto front information generated to select lower energy conformations as representative conformation structure replicas can facilitate the convergence of the available conformational space, including available near-native structures. Furthermore, our approach directly provides min-min scenario Pareto optimal solutions, as well as a hybrid of the min-min and max-min scenario Pareto optimal solutions with lower energy conformations for use as structure templates in the REMC sampling method. These methods were validated based on a thorough analysis of a benchmark data set containing 16 benchmark test cases. An in-depth comparison between MC, REMC, multi-objective optimization-REMC (MO-REMC), and hybrid MO-REMC (HMO-REMC) sampling methods was performed to illustrate the differences between the four conformational search strategies. Our findings demonstrate that the MO-REMC and HMO-REMC conformational sampling methods are powerful approaches for obtaining protein-small molecule docking conformational predictions based on the binding energy of complexes in RosettaLigand.

HPLC/DAD determination of rosmarinic acid in Salvia officinalis: sample preparation optimization by factorial design

Energy Technology Data Exchange (ETDEWEB)

Oliveira, Karina B. de [Universidade Federal do Parana (UFPR), Curitiba, PR (Brazil). Dept. de Farmacia; Oliveira, Bras H. de, E-mail: bho@ufpr.br [Universidade Federal do Parana (UFPR), Curitiba, PR (Brazil). Dept. de Quimica

2013-01-15

Sage (Salvia officinalis) contains high amounts of the biologically active rosmarinic acid (RA) and other polyphenolic compounds. RA is easily oxidized, and may undergo degradation during sample preparation for analysis. The objective of this work was to develop and validate an analytical procedure for determination of RA in sage, using factorial design of experiments for optimizing sample preparation. The statistically significant variables for improving RA extraction yield were determined initially and then used in the optimization step, using central composite design (CCD). The analytical method was then fully validated, and used for the analysis of commercial samples of sage. The optimized procedure involved extraction with aqueous methanol (40%) containing an antioxidant mixture (ascorbic acid and ethylenediaminetetraacetic acid (EDTA)), with sonication at 45 deg C for 20 min. The samples were then injected in a system containing a C{sub 18} column, using methanol (A) and 0.1% phosphoric acid in water (B) in step gradient mode (45A:55B, 0-5 min; 80A:20B, 5-10 min) with flow rate of 1.0 mL min-1 and detection at 330 nm. Using this conditions, RA concentrations were 50% higher when compared to extractions without antioxidants (98.94 {+-} 1.07% recovery). Auto-oxidation of RA during sample extraction was prevented by the use of antioxidants resulting in more reliable analytical results. The method was then used for the analysis of commercial samples of sage. (author)

HPLC/DAD determination of rosmarinic acid in Salvia officinalis: sample preparation optimization by factorial design

International Nuclear Information System (INIS)

Oliveira, Karina B. de; Oliveira, Bras H. de

2013-01-01

Sage (Salvia officinalis) contains high amounts of the biologically active rosmarinic acid (RA) and other polyphenolic compounds. RA is easily oxidized, and may undergo degradation during sample preparation for analysis. The objective of this work was to develop and validate an analytical procedure for determination of RA in sage, using factorial design of experiments for optimizing sample preparation. The statistically significant variables for improving RA extraction yield were determined initially and then used in the optimization step, using central composite design (CCD). The analytical method was then fully validated, and used for the analysis of commercial samples of sage. The optimized procedure involved extraction with aqueous methanol (40%) containing an antioxidant mixture (ascorbic acid and ethylenediaminetetraacetic acid (EDTA)), with sonication at 45 deg C for 20 min. The samples were then injected in a system containing a C 18 column, using methanol (A) and 0.1% phosphoric acid in water (B) in step gradient mode (45A:55B, 0-5 min; 80A:20B, 5-10 min) with flow rate of 1.0 mL min−1 and detection at 330 nm. Using this conditions, RA concentrations were 50% higher when compared to extractions without antioxidants (98.94 ± 1.07% recovery). Auto-oxidation of RA during sample extraction was prevented by the use of antioxidants resulting in more reliable analytical results. The method was then used for the analysis of commercial samples of sage. (author)

Determination of the optimal sample size for a clinical trial accounting for the population size.

Science.gov (United States)

Stallard, Nigel; Miller, Frank; Day, Simon; Hee, Siew Wan; Madan, Jason; Zohar, Sarah; Posch, Martin

2017-07-01

The problem of choosing a sample size for a clinical trial is a very common one. In some settings, such as rare diseases or other small populations, the large sample sizes usually associated with the standard frequentist approach may be infeasible, suggesting that the sample size chosen should reflect the size of the population under consideration. Incorporation of the population size is possible in a decision-theoretic approach either explicitly by assuming that the population size is fixed and known, or implicitly through geometric discounting of the gain from future patients reflecting the expected population size. This paper develops such approaches. Building on previous work, an asymptotic expression is derived for the sample size for single and two-arm clinical trials in the general case of a clinical trial with a primary endpoint with a distribution of one parameter exponential family form that optimizes a utility function that quantifies the cost and gain per patient as a continuous function of this parameter. It is shown that as the size of the population, N, or expected size, N∗ in the case of geometric discounting, becomes large, the optimal trial size is O(N1/2) or O(N∗1/2). The sample size obtained from the asymptotic expression is also compared with the exact optimal sample size in examples with responses with Bernoulli and Poisson distributions, showing that the asymptotic approximations can also be reasonable in relatively small sample sizes. © 2016 The Author. Biometrical Journal published by WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Brachytherapy dose-volume histogram computations using optimized stratified sampling methods

International Nuclear Information System (INIS)

Karouzakis, K.; Lahanas, M.; Milickovic, N.; Giannouli, S.; Baltas, D.; Zamboglou, N.

2002-01-01

A stratified sampling method for the efficient repeated computation of dose-volume histograms (DVHs) in brachytherapy is presented as used for anatomy based brachytherapy optimization methods. The aim of the method is to reduce the number of sampling points required for the calculation of DVHs for the body and the PTV. From the DVHs are derived the quantities such as Conformity Index COIN and COIN integrals. This is achieved by using partial uniform distributed sampling points with a density in each region obtained from a survey of the gradients or the variance of the dose distribution in these regions. The shape of the sampling regions is adapted to the patient anatomy and the shape and size of the implant. For the application of this method a single preprocessing step is necessary which requires only a few seconds. Ten clinical implants were used to study the appropriate number of sampling points, given a required accuracy for quantities such as cumulative DVHs, COIN indices and COIN integrals. We found that DVHs of very large tissue volumes surrounding the PTV, and also COIN distributions, can be obtained using a factor of 5-10 times smaller the number of sampling points in comparison with uniform distributed points

Variance of discharge estimates sampled using acoustic Doppler current profilers from moving boats

Science.gov (United States)

Garcia, Carlos M.; Tarrab, Leticia; Oberg, Kevin; Szupiany, Ricardo; Cantero, Mariano I.

2012-01-01

This paper presents a model for quantifying the random errors (i.e., variance) of acoustic Doppler current profiler (ADCP) discharge measurements from moving boats for different sampling times. The model focuses on the random processes in the sampled flow field and has been developed using statistical methods currently available for uncertainty analysis of velocity time series. Analysis of field data collected using ADCP from moving boats from three natural rivers of varying sizes and flow conditions shows that, even though the estimate of the integral time scale of the actual turbulent flow field is larger than the sampling interval, the integral time scale of the sampled flow field is on the order of the sampling interval. Thus, an equation for computing the variance error in discharge measurements associated with different sampling times, assuming uncorrelated flow fields is appropriate. The approach is used to help define optimal sampling strategies by choosing the exposure time required for ADCPs to accurately measure flow discharge.

Life cycle cost optimization of biofuel supply chains under uncertainties based on interval linear programming

DEFF Research Database (Denmark)

Ren, Jingzheng; Dong, Liang; Sun, Lu

2015-01-01

in this model, and the price of the resources, the yield of grain and the market demands were regarded as interval numbers instead of constants. An interval linear programming was developed, and a method for solving interval linear programming was presented. An illustrative case was studied by the proposed...

Interval selection with machine-dependent intervals

OpenAIRE

Bohmova K.; Disser Y.; Mihalak M.; Widmayer P.

2013-01-01

We study an offline interval scheduling problem where every job has exactly one associated interval on every machine. To schedule a set of jobs, exactly one of the intervals associated with each job must be selected, and the intervals selected on the same machine must not intersect.We show that deciding whether all jobs can be scheduled is NP-complete already in various simple cases. In particular, by showing the NP-completeness for the case when all the intervals associated with the same job...

A general method to determine sampling windows for nonlinear mixed effects models with an application to population pharmacokinetic studies.

Science.gov (United States)

Foo, Lee Kien; McGree, James; Duffull, Stephen

2012-01-01

Optimal design methods have been proposed to determine the best sampling times when sparse blood sampling is required in clinical pharmacokinetic studies. However, the optimal blood sampling time points may not be feasible in clinical practice. Sampling windows, a time interval for blood sample collection, have been proposed to provide flexibility in blood sampling times while preserving efficient parameter estimation. Because of the complexity of the population pharmacokinetic models, which are generally nonlinear mixed effects models, there is no analytical solution available to determine sampling windows. We propose a method for determination of sampling windows based on MCMC sampling techniques. The proposed method attains a stationary distribution rapidly and provides time-sensitive windows around the optimal design points. The proposed method is applicable to determine sampling windows for any nonlinear mixed effects model although our work focuses on an application to population pharmacokinetic models. Copyright © 2012 John Wiley & Sons, Ltd.

Complete Blood Count Reference Intervals for Healthy Han Chinese Adults

Science.gov (United States)

Mu, Runqing; Guo, Wei; Qiao, Rui; Chen, Wenxiang; Jiang, Hong; Ma, Yueyun; Shang, Hong

2015-01-01

Background Complete blood count (CBC) reference intervals are important to diagnose diseases, screen blood donors, and assess overall health. However, current reference intervals established by older instruments and technologies and those from American and European populations are not suitable for Chinese samples due to ethnic, dietary, and lifestyle differences. The aim of this multicenter collaborative study was to establish CBC reference intervals for healthy Han Chinese adults. Methods A total of 4,642 healthy individuals (2,136 males and 2,506 females) were recruited from six clinical centers in China (Shenyang, Beijing, Shanghai, Guangzhou, Chengdu, and Xi’an). Blood samples collected in K2EDTA anticoagulant tubes were analyzed. Analysis of variance was performed to determine differences in consensus intervals according to the use of data from the combined sample and selected samples. Results Median and mean platelet counts from the Chengdu center were significantly lower than those from other centers. Red blood cell count (RBC), hemoglobin (HGB), and hematocrit (HCT) values were higher in males than in females at all ages. Other CBC parameters showed no significant instrument-, region-, age-, or sex-dependent difference. Thalassemia carriers were found to affect the lower or upper limit of different RBC profiles. Conclusion We were able to establish consensus intervals for CBC parameters in healthy Han Chinese adults. RBC, HGB, and HCT intervals were established for each sex. The reference interval for platelets for the Chengdu center should be established independently. PMID:25769040

Unit Stratified Sampling as a Tool for Approximation of Stochastic Optimization Problems

Czech Academy of Sciences Publication Activity Database

Šmíd, Martin

2012-01-01

Roč. 19, č. 30 (2012), s. 153-169 ISSN 1212-074X R&D Projects: GA ČR GAP402/11/0150; GA ČR GAP402/10/0956; GA ČR GA402/09/0965 Institutional research plan: CEZ:AV0Z10750506 Institutional support: RVO:67985556 Keywords : Stochastic programming * approximation * stratified sampling Subject RIV: BB - Applied Statistics, Operational Research http://library.utia.cas.cz/separaty/2013/E/smid-unit stratified sampling as a tool for approximation of stochastic optimization problems.pdf

Behavioural sampling techniques and activity pattern of Indian Pangolin Manis crassicaudata (Mammalia: Manidae in captivity

Directory of Open Access Journals (Sweden)

R.K. Mohapatra

2013-12-01

Full Text Available The study presents data on six Indian Pangolins Manis crassicaudata observed in captivity at the Pangolin Conservation Breeding Centre, Nandankanan, Odisha, India over 1377 hours of video recordings for each pangolin between 1500hr and 0800hr on 81 consecutive observational days. Video recordings were made through digital systems assisted by infrared enabled CCTV cameras. The data highlights patterns relate to 12 different behaviour and enclosure utilization. Different interval periods for sampling of instantaneous behaviour from video recordings have been evaluated to develop optimal study methods for the future. The activity budgets of pangolins displayed natural patterns of nocturnal activity with a peak between 20:00-21:00 hr. When out of their burrow, they spent about 59% of the time walking in the enclosure, and 14% of the time feeding. The repeatability of the behaviours has a significant negative correlation with the mean time spent in that behaviour. Focal behavioural samples significantly correlated with instantaneous samples up to 15 minutes interval. The correlation values gradually decreased with the increase in sampling interval. The results indicate that results obtained from focal sampling and instantaneous sampling with relatively shorter intervals (=5 minutes are about equally reliable. The study suggests use of focal sampling, instead of instantaneous sampling to record behaviour relating to social interactions.

A genetic algorithm-based framework for wavelength selection on sample categorization.

Science.gov (United States)

Anzanello, Michel J; Yamashita, Gabrielli; Marcelo, Marcelo; Fogliatto, Flávio S; Ortiz, Rafael S; Mariotti, Kristiane; Ferrão, Marco F

2017-08-01

In forensic and pharmaceutical scenarios, the application of chemometrics and optimization techniques has unveiled common and peculiar features of seized medicine and drug samples, helping investigative forces to track illegal operations. This paper proposes a novel framework aimed at identifying relevant subsets of attenuated total reflectance Fourier transform infrared (ATR-FTIR) wavelengths for classifying samples into two classes, for example authentic or forged categories in case of medicines, or salt or base form in cocaine analysis. In the first step of the framework, the ATR-FTIR spectra were partitioned into equidistant intervals and the k-nearest neighbour (KNN) classification technique was applied to each interval to insert samples into proper classes. In the next step, selected intervals were refined through the genetic algorithm (GA) by identifying a limited number of wavelengths from the intervals previously selected aimed at maximizing classification accuracy. When applied to Cialis®, Viagra®, and cocaine ATR-FTIR datasets, the proposed method substantially decreased the number of wavelengths needed to categorize, and increased the classification accuracy. From a practical perspective, the proposed method provides investigative forces with valuable information towards monitoring illegal production of drugs and medicines. In addition, focusing on a reduced subset of wavelengths allows the development of portable devices capable of testing the authenticity of samples during police checking events, avoiding the need for later laboratorial analyses and reducing equipment expenses. Theoretically, the proposed GA-based approach yields more refined solutions than the current methods relying on interval approaches, which tend to insert irrelevant wavelengths in the retained intervals. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Evaluation of sample preparation methods and optimization of nickel determination in vegetable tissues

Directory of Open Access Journals (Sweden)

Rodrigo Fernando dos Santos Salazar

2011-02-01

Full Text Available Nickel, although essential to plants, may be toxic to plants and animals. It is mainly assimilated by food ingestion. However, information about the average levels of elements (including Ni in edible vegetables from different regions is still scarce in Brazil. The objectives of this study were to: (a evaluate and optimize a method for preparation of vegetable tissue samples for Ni determination; (b optimize the analytical procedures for determination by Flame Atomic Absorption Spectrometry (FAAS and by Electrothermal Atomic Absorption (ETAAS in vegetable samples and (c determine the Ni concentration in vegetables consumed in the cities of Lorena and Taubaté in the Vale do Paraíba, State of São Paulo, Brazil. By means of the analytical technique for determination by ETAAS or FAAS, the results were validated by the test of analyte addition and recovery. The most viable method tested for quantification of this element was HClO4-HNO3 wet digestion. All samples but carrot tissue collected in Lorena contained Ni levels above the permitted by the Brazilian Ministry of Health. The most disturbing results, requiring more detailed studies, were the Ni concentrations measured in carrot samples from Taubaté, where levels were five times higher than permitted by Brazilian regulations.

Optimal time interval between capecitabine intake and radiotherapy in preoperative chemoradiation for locally advanced rectal cancer

International Nuclear Information System (INIS)

Yu, Chang Sik; Kim, Tae Won; Kim, Jong Hoon; Choi, Won Sik; Kim, Hee Cheol; Chang, Heung Moon; Ryu, Min Hee; Jang, Se Jin; Ahn, Seung Do; Lee, Sang-wook; Shin, Seong Soo; Choi, Eun Kyung; Kim, Jin Cheon

2007-01-01

Purpose: Capecitabine and its metabolites reach peak plasma concentrations 1 to 2 hours after a single oral administration, and concentrations rapidly decrease thereafter. We performed a retrospective analysis to find the optimal time interval between capecitabine administration and radiotherapy for rectal cancer. Methods and Materials: The time interval between capecitabine intake and radiotherapy was measured in patients who were treated with preoperative radiotherapy and concurrent capecitabine for rectal cancer. Patients were classified into the following groups. Group A1 included patients who took capecitabine 1 hour before radiotherapy, and Group B1 included all other patients. Group B1 was then subdivided into Group A2 (patients who took capecitabine 2 hours before radiotherapy) and Group B2. Group B2 was further divided into Group A3 and Group B3 with the same method. Total mesorectal excision was performed 6 weeks after completion of chemoradiation and the pathologic response was evaluated. Results: A total of 200 patients were enrolled in this study. Pathologic examination showed that Group A1 had higher rates of complete regression of primary tumors in the rectum (23.5% vs. 9.6%, p = 0.01), good response (44.7% vs. 25.2%, p = 0.006), and lower T stages (p = 0.021) compared with Group B1; however, Groups A2 and A3 did not show any improvement compared with Groups B2 and B3. Multivariate analysis showed that increases in primary tumors in the rectum and good response were only significant when capecitabine was administered 1 hour before radiotherapy. Conclusion: In preoperative chemoradiotherapy for rectal cancer, the pathologic response could be improved by administering capecitabine 1 hour before radiotherapy

Optimization of the test intervals of a nuclear safety system by genetic algorithms, solution clustering and fuzzy preference assignment

International Nuclear Information System (INIS)

Zio, E.; Bazzo, R.

2010-01-01

In this paper, a procedure is developed for identifying a number of representative solutions manageable for decision-making in a multiobjective optimization problem concerning the test intervals of the components of a safety system of a nuclear power plant. Pareto Front solutions are identified by a genetic algorithm and then clustered by subtractive clustering into 'families'. On the basis of the decision maker's preferences, each family is then synthetically represented by a 'head of the family' solution. This is done by introducing a scoring system that ranks the solutions with respect to the different objectives: a fuzzy preference assignment is employed to this purpose. Level Diagrams are then used to represent, analyze and interpret the Pareto Fronts reduced to the head-of-the-family solutions

Neuro-genetic system for optimization of GMI samples sensitivity.

Science.gov (United States)

Pitta Botelho, A C O; Vellasco, M M B R; Hall Barbosa, C R; Costa Silva, E

2016-03-01

Magnetic sensors are largely used in several engineering areas. Among them, magnetic sensors based on the Giant Magnetoimpedance (GMI) effect are a new family of magnetic sensing devices that have a huge potential for applications involving measurements of ultra-weak magnetic fields. The sensitivity of magnetometers is directly associated with the sensitivity of their sensing elements. The GMI effect is characterized by a large variation of the impedance (magnitude and phase) of a ferromagnetic sample, when subjected to a magnetic field. Recent studies have shown that phase-based GMI magnetometers have the potential to increase the sensitivity by about 100 times. The sensitivity of GMI samples depends on several parameters, such as sample length, external magnetic field, DC level and frequency of the excitation current. However, this dependency is yet to be sufficiently well-modeled in quantitative terms. So, the search for the set of parameters that optimizes the samples sensitivity is usually empirical and very time consuming. This paper deals with this problem by proposing a new neuro-genetic system aimed at maximizing the impedance phase sensitivity of GMI samples. A Multi-Layer Perceptron (MLP) Neural Network is used to model the impedance phase and a Genetic Algorithm uses the information provided by the neural network to determine which set of parameters maximizes the impedance phase sensitivity. The results obtained with a data set composed of four different GMI sample lengths demonstrate that the neuro-genetic system is able to correctly and automatically determine the set of conditioning parameters responsible for maximizing their phase sensitivities. Copyright © 2015 Elsevier Ltd. All rights reserved.

The Proteome of Ulcerative Colitis in Colon Biopsies from Adults - Optimized Sample Preparation and Comparison with Healthy Controls.

Science.gov (United States)

Schniers, Armin; Anderssen, Endre; Fenton, Christopher Graham; Goll, Rasmus; Pasing, Yvonne; Paulssen, Ruth Hracky; Florholmen, Jon; Hansen, Terkel

2017-12-01

The purpose of the study was to optimize the sample preparation and to further use an improved sample preparation to identify proteome differences between inflamed ulcerative colitis tissue from untreated adults and healthy controls. To optimize the sample preparation, we studied the effect of adding different detergents to a urea containing lysis buffer for a Lys-C/trypsin tandem digestion. With the optimized method, we prepared clinical samples from six ulcerative colitis patients and six healthy controls and analysed them by LC-MS/MS. We examined the acquired data to identify differences between the states. We improved the protein extraction and protein identification number by utilizing a urea and sodium deoxycholate containing buffer. Comparing ulcerative colitis and healthy tissue, we found 168 of 2366 identified proteins differently abundant. Inflammatory proteins are higher abundant in ulcerative colitis, proteins related to anion-transport and mucus production are lower abundant. A high proportion of S100 proteins is differently abundant, notably with both up-regulated and down-regulated proteins. The optimized sample preparation method will improve future proteomic studies on colon mucosa. The observed protein abundance changes and their enrichment in various groups improve our understanding of ulcerative colitis on protein level. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Sensitivity and specificity of normality tests and consequences on reference interval accuracy at small sample size: a computer-simulation study.

Science.gov (United States)

Le Boedec, Kevin

2016-12-01

According to international guidelines, parametric methods must be chosen for RI construction when the sample size is small and the distribution is Gaussian. However, normality tests may not be accurate at small sample size. The purpose of the study was to evaluate normality test performance to properly identify samples extracted from a Gaussian population at small sample sizes, and assess the consequences on RI accuracy of applying parametric methods to samples that falsely identified the parent population as Gaussian. Samples of n = 60 and n = 30 values were randomly selected 100 times from simulated Gaussian, lognormal, and asymmetric populations of 10,000 values. The sensitivity and specificity of 4 normality tests were compared. Reference intervals were calculated using 6 different statistical methods from samples that falsely identified the parent population as Gaussian, and their accuracy was compared. Shapiro-Wilk and D'Agostino-Pearson tests were the best performing normality tests. However, their specificity was poor at sample size n = 30 (specificity for P Box-Cox transformation) on all samples regardless of their distribution or adjusting, the significance level of normality tests depending on sample size would limit the risk of constructing inaccurate RI. © 2016 American Society for Veterinary Clinical Pathology.

Haemostatic reference intervals in pregnancy

DEFF Research Database (Denmark)

Szecsi, Pal Bela; Jørgensen, Maja; Klajnbard, Anna

2010-01-01

largely unchanged during pregnancy, delivery, and postpartum and were within non-pregnant reference intervals. However, levels of fibrinogen, D-dimer, and coagulation factors VII, VIII, and IX increased markedly. Protein S activity decreased substantially, while free protein S decreased slightly and total......Haemostatic reference intervals are generally based on samples from non-pregnant women. Thus, they may not be relevant to pregnant women, a problem that may hinder accurate diagnosis and treatment of haemostatic disorders during pregnancy. In this study, we establish gestational age......-20, 21-28, 29-34, 35-42, at active labor, and on postpartum days 1 and 2. Reference intervals for each gestational period using only the uncomplicated pregnancies were calculated in all 391 women for activated partial thromboplastin time (aPTT), fibrinogen, fibrin D-dimer, antithrombin, free protein S...

On the optimal sampling of bandpass measurement signals through data acquisition systems

International Nuclear Information System (INIS)

Angrisani, L; Vadursi, M

2008-01-01

Data acquisition systems (DAS) play a fundamental role in a lot of modern measurement solutions. One of the parameters characterizing a DAS is its maximum sample rate, which imposes constraints on the signals that can be alias-free digitized. Bandpass sampling theory singles out separated ranges of admissible sample rates, which can be significantly lower than carrier frequency. But, how to choose the most convenient sample rate according to the purpose at hand? The paper proposes a method for the automatic selection of the optimal sample rate in measurement applications involving bandpass signals; the effects of sample clock instability and limited resolution are also taken into account. The method allows the user to choose the location of spectral replicas of the sampled signal in terms of normalized frequency, and the minimum guard band between replicas, thus introducing a feature that no DAS currently available on the market seems to offer. A number of experimental tests on bandpass digitally modulated signals are carried out to assess the concurrence of the obtained central frequency with the expected one

A novel variable selection approach that iteratively optimizes variable space using weighted binary matrix sampling.

Science.gov (United States)

Deng, Bai-chuan; Yun, Yong-huan; Liang, Yi-zeng; Yi, Lun-zhao

2014-10-07

In this study, a new optimization algorithm called the Variable Iterative Space Shrinkage Approach (VISSA) that is based on the idea of model population analysis (MPA) is proposed for variable selection. Unlike most of the existing optimization methods for variable selection, VISSA statistically evaluates the performance of variable space in each step of optimization. Weighted binary matrix sampling (WBMS) is proposed to generate sub-models that span the variable subspace. Two rules are highlighted during the optimization procedure. First, the variable space shrinks in each step. Second, the new variable space outperforms the previous one. The second rule, which is rarely satisfied in most of the existing methods, is the core of the VISSA strategy. Compared with some promising variable selection methods such as competitive adaptive reweighted sampling (CARS), Monte Carlo uninformative variable elimination (MCUVE) and iteratively retaining informative variables (IRIV), VISSA showed better prediction ability for the calibration of NIR data. In addition, VISSA is user-friendly; only a few insensitive parameters are needed, and the program terminates automatically without any additional conditions. The Matlab codes for implementing VISSA are freely available on the website: https://sourceforge.net/projects/multivariateanalysis/files/VISSA/.

An optimal cut-off point for the calving interval may be used as an indicator of bovine abortions.

Science.gov (United States)

Bronner, Anne; Morignat, Eric; Gay, Emilie; Calavas, Didier

2015-10-01

The bovine abortion surveillance system in France aims to detect as early as possible any resurgence of bovine brucellosis, a disease of which the country has been declared free since 2005. It relies on the mandatory notification and testing of each aborting cow, but under-reporting is high. This research uses a new and simple approach which considers the calving interval (CI) as a "diagnostic test" to determine optimal cut-off point c and estimate diagnostic performance of the CI to identify aborting cows, and herds with multiple abortions (i.e. three or more aborting cows per calving season). The period between two artificial inseminations (AI) was considered as a "gold standard". During the 2006-2010 calving seasons, the mean optimal CI cut-off point for identifying aborting cows was 691 days for dairy cows and 703 days for beef cows. Depending on the calving season, production type and scale at which c was computed (individual or herd), the average sensitivity of the CI varied from 42.6% to 64.4%; its average specificity from 96.7% to 99.7%; its average positive predictive value from 27.6% to 65.4%; and its average negative predictive value from 98.7% to 99.8%. When applied to the French bovine population as a whole, this indicator identified 2-3% of cows suspected to have aborted, and 10-15% of herds suspected of multiple abortions. The optimal cut-off point and CI performance were consistent over calving seasons. By applying an optimal CI cut-off point to the cattle demographics database, it becomes possible to identify herds with multiple abortions, carry out retrospective investigations to find the cause of these abortions and monitor a posteriori compliance of farmers with their obligation to report abortions for brucellosis surveillance needs. Therefore, the CI could be used as an indicator of abortions to help improve the current mandatory notification surveillance system. Copyright © 2015 Elsevier B.V. All rights reserved.

Optimization of Decision-Making for Spatial Sampling in the North China Plain, Based on Remote-Sensing a Priori Knowledge

Science.gov (United States)

Feng, J.; Bai, L.; Liu, S.; Su, X.; Hu, H.

2012-07-01

In this paper, the MODIS remote sensing data, featured with low-cost, high-timely and moderate/low spatial resolutions, in the North China Plain (NCP) as a study region were firstly used to carry out mixed-pixel spectral decomposition to extract an useful regionalized indicator parameter (RIP) (i.e., an available ratio, that is, fraction/percentage, of winter wheat planting area in each pixel as a regionalized indicator variable (RIV) of spatial sampling) from the initial selected indicators. Then, the RIV values were spatially analyzed, and the spatial structure characteristics (i.e., spatial correlation and variation) of the NCP were achieved, which were further processed to obtain the scalefitting, valid a priori knowledge or information of spatial sampling. Subsequently, founded upon an idea of rationally integrating probability-based and model-based sampling techniques and effectively utilizing the obtained a priori knowledge or information, the spatial sampling models and design schemes and their optimization and optimal selection were developed, as is a scientific basis of improving and optimizing the existing spatial sampling schemes of large-scale cropland remote sensing monitoring. Additionally, by the adaptive analysis and decision strategy the optimal local spatial prediction and gridded system of extrapolation results were able to excellently implement an adaptive report pattern of spatial sampling in accordance with report-covering units in order to satisfy the actual needs of sampling surveys.

Memory-Optimized Software Synthesis from Dataflow Program Graphs with Large Size Data Samples

Directory of Open Access Journals (Sweden)

Hyunok Oh

2003-05-01

Full Text Available In multimedia and graphics applications, data samples of nonprimitive type require significant amount of buffer memory. This paper addresses the problem of minimizing the buffer memory requirement for such applications in embedded software synthesis from graphical dataflow programs based on the synchronous dataflow (SDF model with the given execution order of nodes. We propose a memory minimization technique that separates global memory buffers from local pointer buffers: the global buffers store live data samples and the local buffers store the pointers to the global buffer entries. The proposed algorithm reduces 67% memory for a JPEG encoder, 40% for an H.263 encoder compared with unshared versions, and 22% compared with the previous sharing algorithm for the H.263 encoder. Through extensive buffer sharing optimization, we believe that automatic software synthesis from dataflow program graphs achieves the comparable code quality with the manually optimized code in terms of memory requirement.

Determination of total concentration of chemically labeled metabolites as a means of metabolome sample normalization and sample loading optimization in mass spectrometry-based metabolomics.

Science.gov (United States)

Wu, Yiman; Li, Liang

2012-12-18

For mass spectrometry (MS)-based metabolomics, it is important to use the same amount of starting materials from each sample to compare the metabolome changes in two or more comparative samples. Unfortunately, for biological samples, the total amount or concentration of metabolites is difficult to determine. In this work, we report a general approach of determining the total concentration of metabolites based on the use of chemical labeling to attach a UV absorbent to the metabolites to be analyzed, followed by rapid step-gradient liquid chromatography (LC) UV detection of the labeled metabolites. It is shown that quantification of the total labeled analytes in a biological sample facilitates the preparation of an appropriate amount of starting materials for MS analysis as well as the optimization of the sample loading amount to a mass spectrometer for achieving optimal detectability. As an example, dansylation chemistry was used to label the amine- and phenol-containing metabolites in human urine samples. LC-UV quantification of the labeled metabolites could be optimally performed at the detection wavelength of 338 nm. A calibration curve established from the analysis of a mixture of 17 labeled amino acid standards was found to have the same slope as that from the analysis of the labeled urinary metabolites, suggesting that the labeled amino acid standard calibration curve could be used to determine the total concentration of the labeled urinary metabolites. A workflow incorporating this LC-UV metabolite quantification strategy was then developed in which all individual urine samples were first labeled with (12)C-dansylation and the concentration of each sample was determined by LC-UV. The volumes of urine samples taken for producing the pooled urine standard were adjusted to ensure an equal amount of labeled urine metabolites from each sample was used for the pooling. The pooled urine standard was then labeled with (13)C-dansylation. Equal amounts of the (12)C

Effect of aeration interval on oxygen consumption and GHG emission during pig manure composting.

Science.gov (United States)

Zeng, Jianfei; Yin, Hongjie; Shen, Xiuli; Liu, Ning; Ge, Jinyi; Han, Lujia; Huang, Guangqun

2018-02-01

To verify the optimal aeration interval for oxygen supply and consumption and investigate the effect of aeration interval on GHG emission, reactor-scale composting was conducted with different aeration intervals (0, 10, 30 and 50 min). Although O 2 was sufficiently supplied during aeration period, it could be consumed to  0.902), suggesting that lengthening the duration of aeration interval to some extent could effectively reduce GHG emission. Copyright © 2017 Elsevier Ltd. All rights reserved.

Reference Intervals of Common Clinical Chemistry Analytes for Adults in Hong Kong.

Science.gov (United States)

Lo, Y C; Armbruster, David A

2012-04-01

Defining reference intervals is a major challenge because of the difficulty in recruiting volunteers to participate and testing samples from a significant number of healthy reference individuals. Historical literature citation intervals are often suboptimal because they're be based on obsolete methods and/or only a small number of poorly defined reference samples. Blood donors in Hong Kong gave permission for additional blood to be collected for reference interval testing. The samples were tested for twenty-five routine analytes on the Abbott ARCHITECT clinical chemistry system. Results were analyzed using the Rhoads EP evaluator software program, which is based on the CLSI/IFCC C28-A guideline, and defines the reference interval as the 95% central range. Method specific reference intervals were established for twenty-five common clinical chemistry analytes for a Chinese ethnic population. The intervals were defined for each gender separately and for genders combined. Gender specific or combined gender intervals were adapted as appropriate for each analyte. A large number of healthy, apparently normal blood donors from a local ethnic population were tested to provide current reference intervals for a new clinical chemistry system. Intervals were determined following an accepted international guideline. Laboratories using the same or similar methodologies may adapt these intervals if deemed validated and deemed suitable for their patient population. Laboratories using different methodologies may be able to successfully adapt the intervals for their facilities using the reference interval transference technique based on a method comparison study.

A novel interval type-2 fractional order fuzzy PID controller: Design, performance evaluation, and its optimal time domain tuning.

Science.gov (United States)

Kumar, Anupam; Kumar, Vijay

2017-05-01

In this paper, a novel concept of an interval type-2 fractional order fuzzy PID (IT2FO-FPID) controller, which requires fractional order integrator and fractional order differentiator, is proposed. The incorporation of Takagi-Sugeno-Kang (TSK) type interval type-2 fuzzy logic controller (IT2FLC) with fractional controller of PID-type is investigated for time response measure due to both unit step response and unit load disturbance. The resulting IT2FO-FPID controller is examined on different delayed linear and nonlinear benchmark plants followed by robustness analysis. In order to design this controller, fractional order integrator-differentiator operators are considered as design variables including input-output scaling factors. A new hybridized algorithm named as artificial bee colony-genetic algorithm (ABC-GA) is used to optimize the parameters of the controller while minimizing weighted sum of integral of time absolute error (ITAE) and integral of square of control output (ISCO). To assess the comparative performance of the IT2FO-FPID, authors compared it against existing controllers, i.e., interval type-2 fuzzy PID (IT2-FPID), type-1 fractional order fuzzy PID (T1FO-FPID), type-1 fuzzy PID (T1-FPID), and conventional PID controllers. Furthermore, to show the effectiveness of the proposed controller, the perturbed processes along with the larger dead time are tested. Moreover, the proposed controllers are also implemented on multi input multi output (MIMO), coupled, and highly complex nonlinear two-link robot manipulator system in presence of un-modeled dynamics. Finally, the simulation results explicitly indicate that the performance of the proposed IT2FO-FPID controller is superior to its conventional counterparts in most of the cases. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

INTERVAL STATE ESTIMATION FOR SINGULAR DIFFERENTIAL EQUATION SYSTEMS WITH DELAYS

Directory of Open Access Journals (Sweden)

T. A. Kharkovskaia

2016-07-01

Full Text Available The paper deals with linear differential equation systems with algebraic restrictions (singular systems and a method of interval observer design for this kind of systems. The systems contain constant time delay, measurement noise and disturbances. Interval observer synthesis is based on monotone and cooperative systems technique, linear matrix inequations, Lyapunov function theory and interval arithmetic. The set of conditions that gives the possibility for interval observer synthesis is proposed. Results of synthesized observer operation are shown on the example of dynamical interindustry balance model. The advantages of proposed method are that it is adapted to observer design for uncertain systems, if the intervals of admissible values for uncertain parameters are given. The designed observer is capable to provide asymptotically definite limits on the estimation accuracy, since the interval of admissible values for the object state is defined at every instant. The obtained result provides an opportunity to develop the interval estimation theory for complex systems that contain parametric uncertainty, varying delay and nonlinear elements. Interval observers increasingly find applications in economics, electrical engineering, mechanical systems with constraints and optimal flow control.

An interval-valued reliability model with bounded failure rates

DEFF Research Database (Denmark)

Kozine, Igor; Krymsky, Victor

2012-01-01

The approach to deriving interval-valued reliability measures described in this paper is distinctive from other imprecise reliability models in that it overcomes the issue of having to impose an upper bound on time to failure. It rests on the presupposition that a constant interval-valued failure...... rate is known possibly along with other reliability measures, precise or imprecise. The Lagrange method is used to solve the constrained optimization problem to derive new reliability measures of interest. The obtained results call for an exponential-wise approximation of failure probability density...

A Sensitivity Study of Human Errors in Optimizing Surveillance Test Interval (STI) and Allowed Outage Time (AOT) of Standby Safety System

International Nuclear Information System (INIS)

Chung, Dae Wook; Shin, Won Ky; You, Young Woo; Yang, Hui Chang

1998-01-01

In most cases, the surveillance test intervals (STIs), allowed outage times (AOTS) and testing strategies of safety components in nuclear power plant are prescribed in plant technical specifications. And, in general, it is required that standby safety system shall be redundant (i.e., composed of multiple components) and these components are tested by either staggered test strategy or sequential test strategy. In this study, a linear model is presented to incorporate the effects of human errors associated with test into the evaluation of unavailability. The average unavailabilities of 1/4, 2/4 redundant systems are computed considering human error and testing strategy. The adverse effects of test on system unavailability, such as component wear and test-induced transient have been modelled. The final outcome of this study would be the optimized human error domain from 3-D human error sensitivity analysis by selecting finely classified segment. The results of sensitivity analysis show that the STI and AOT can be optimized provided human error probability is maintained within allowable range. (authors)

Rationalizing method of replacement intervals by using Bayesian statistics

International Nuclear Information System (INIS)

Kasai, Masao; Notoya, Junichi; Kusakari, Yoshiyuki

2007-01-01

This study represents the formulations for rationalizing the replacement intervals of equipments and/or parts taking into account the probability density functions (PDF) of the parameters of failure distribution functions (FDF) and compares the optimized intervals by our formulations with those by conventional formulations which uses only representative values of the parameters of FDF instead of using these PDFs. The failure data are generated by Monte Carlo simulations since the real failure data can not be available for us. The PDF of PDF parameters are obtained by Bayesian method and the representative values are obtained by likelihood estimation and Bayesian method. We found that the method using PDF by Bayesian method brings longer replacement intervals than one using the representative of the parameters. (author)

Confidence interval procedures for Monte Carlo transport simulations

International Nuclear Information System (INIS)

Pederson, S.P.

1997-01-01

The problem of obtaining valid confidence intervals based on estimates from sampled distributions using Monte Carlo particle transport simulation codes such as MCNP is examined. Such intervals can cover the true parameter of interest at a lower than nominal rate if the sampled distribution is extremely right-skewed by large tallies. Modifications to the standard theory of confidence intervals are discussed and compared with some existing heuristics, including batched means normality tests. Two new types of diagnostics are introduced to assess whether the conditions of central limit theorem-type results are satisfied: the relative variance of the variance determines whether the sample size is sufficiently large, and estimators of the slope of the right tail of the distribution are used to indicate the number of moments that exist. A simulation study is conducted to quantify the relationship between various diagnostics and coverage rates and to find sample-based quantities useful in indicating when intervals are expected to be valid. Simulated tally distributions are chosen to emulate behavior seen in difficult particle transport problems. Measures of variation in the sample variance s 2 are found to be much more effective than existing methods in predicting when coverage will be near nominal rates. Batched means tests are found to be overly conservative in this regard. A simple but pathological MCNP problem is presented as an example of false convergence using existing heuristics. The new methods readily detect the false convergence and show that the results of the problem, which are a factor of 4 too small, should not be used. Recommendations are made for applying these techniques in practice, using the statistical output currently produced by MCNP

Near-optimal alternative generation using modified hit-and-run sampling for non-linear, non-convex problems

Science.gov (United States)

Rosenberg, D. E.; Alafifi, A.

2016-12-01

Water resources systems analysis often focuses on finding optimal solutions. Yet an optimal solution is optimal only for the modelled issues and managers often seek near-optimal alternatives that address un-modelled objectives, preferences, limits, uncertainties, and other issues. Early on, Modelling to Generate Alternatives (MGA) formalized near-optimal as the region comprising the original problem constraints plus a new constraint that allowed performance within a specified tolerance of the optimal objective function value. MGA identified a few maximally-different alternatives from the near-optimal region. Subsequent work applied Markov Chain Monte Carlo (MCMC) sampling to generate a larger number of alternatives that span the near-optimal region of linear problems or select portions for non-linear problems. We extend the MCMC Hit-And-Run method to generate alternatives that span the full extent of the near-optimal region for non-linear, non-convex problems. First, start at a feasible hit point within the near-optimal region, then run a random distance in a random direction to a new hit point. Next, repeat until generating the desired number of alternatives. The key step at each iterate is to run a random distance along the line in the specified direction to a new hit point. If linear equity constraints exist, we construct an orthogonal basis and use a null space transformation to confine hits and runs to a lower-dimensional space. Linear inequity constraints define the convex bounds on the line that runs through the current hit point in the specified direction. We then use slice sampling to identify a new hit point along the line within bounds defined by the non-linear inequity constraints. This technique is computationally efficient compared to prior near-optimal alternative generation techniques such MGA, MCMC Metropolis-Hastings, evolutionary, or firefly algorithms because search at each iteration is confined to the hit line, the algorithm can move in one

Daily Average Wind Power Interval Forecasts Based on an Optimal Adaptive-Network-Based Fuzzy Inference System and Singular Spectrum Analysis

Directory of Open Access Journals (Sweden)

Zhongrong Zhang

2016-01-01

Full Text Available Wind energy has increasingly played a vital role in mitigating conventional resource shortages. Nevertheless, the stochastic nature of wind poses a great challenge when attempting to find an accurate forecasting model for wind power. Therefore, precise wind power forecasts are of primary importance to solve operational, planning and economic problems in the growing wind power scenario. Previous research has focused efforts on the deterministic forecast of wind power values, but less attention has been paid to providing information about wind energy. Based on an optimal Adaptive-Network-Based Fuzzy Inference System (ANFIS and Singular Spectrum Analysis (SSA, this paper develops a hybrid uncertainty forecasting model, IFASF (Interval Forecast-ANFIS-SSA-Firefly Alogorithm, to obtain the upper and lower bounds of daily average wind power, which is beneficial for the practical operation of both the grid company and independent power producers. To strengthen the practical ability of this developed model, this paper presents a comparison between IFASF and other benchmarks, which provides a general reference for this aspect for statistical or artificially intelligent interval forecast methods. The comparison results show that the developed model outperforms eight benchmarks and has a satisfactory forecasting effectiveness in three different wind farms with two time horizons.

Development and optimization of the determination of pharmaceuticals in water samples by SPE and HPLC with diode-array detection.

Science.gov (United States)

Pavlović, Dragana Mutavdžić; Ašperger, Danijela; Tolić, Dijana; Babić, Sandra

2013-09-01

This paper describes the development, optimization, and validation of a method for the determination of five pharmaceuticals from different therapeutic classes (antibiotics, anthelmintics, glucocorticoides) in water samples. Water samples were prepared using SPE and extracts were analyzed by HPLC with diode-array detection. The efficiency of 11 different SPE cartridges to extract the investigated compounds from water was tested in preliminary experiments. Then, the pH of the water sample, elution solvent, and sorbent mass were optimized. Except for optimization of the SPE procedure, selection of the optimal HPLC column with different stationary phases from different manufacturers has been performed. The developed method was validated using spring water samples spiked with appropriate concentrations of pharmaceuticals. Good linearity was obtained in the range of 2.4-200 μg/L, depending on the pharmaceutical with the correlation coefficients >0.9930 in all cases, except for ciprofloxacin (0.9866). Also, the method has revealed that low LODs (0.7-3.9 μg/L), good precision (intra- and interday) with RSD below 17% and recoveries above 98% for all pharmaceuticals. The method has been successfully applied to the analysis of production wastewater samples from the pharmaceutical industry. © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Population Pharmacokinetics and Optimal Sampling Strategy for Model-Based Precision Dosing of Melphalan in Patients Undergoing Hematopoietic Stem Cell Transplantation.

Science.gov (United States)

Mizuno, Kana; Dong, Min; Fukuda, Tsuyoshi; Chandra, Sharat; Mehta, Parinda A; McConnell, Scott; Anaissie, Elias J; Vinks, Alexander A

2018-05-01

High-dose melphalan is an important component of conditioning regimens for patients undergoing hematopoietic stem cell transplantation. The current dosing strategy based on body surface area results in a high incidence of oral mucositis and gastrointestinal and liver toxicity. Pharmacokinetically guided dosing will individualize exposure and help minimize overexposure-related toxicity. The purpose of this study was to develop a population pharmacokinetic model and optimal sampling strategy. A population pharmacokinetic model was developed with NONMEM using 98 observations collected from 15 adult patients given the standard dose of 140 or 200 mg/m 2 by intravenous infusion. The determinant-optimal sampling strategy was explored with PopED software. Individual area under the curve estimates were generated by Bayesian estimation using full and the proposed sparse sampling data. The predictive performance of the optimal sampling strategy was evaluated based on bias and precision estimates. The feasibility of the optimal sampling strategy was tested using pharmacokinetic data from five pediatric patients. A two-compartment model best described the data. The final model included body weight and creatinine clearance as predictors of clearance. The determinant-optimal sampling strategies (and windows) were identified at 0.08 (0.08-0.19), 0.61 (0.33-0.90), 2.0 (1.3-2.7), and 4.0 (3.6-4.0) h post-infusion. An excellent correlation was observed between area under the curve estimates obtained with the full and the proposed four-sample strategy (R 2  = 0.98; p strategy promises to achieve the target area under the curve as part of precision dosing.

Retrieval interval mapping, a tool to optimize the spectral retrieval range in differential optical absorption spectroscopy

Science.gov (United States)

Vogel, L.; Sihler, H.; Lampel, J.; Wagner, T.; Platt, U.

2012-06-01

Remote sensing via differential optical absorption spectroscopy (DOAS) has become a standard technique to identify and quantify trace gases in the atmosphere. The technique is applied in a variety of configurations, commonly classified into active and passive instruments using artificial and natural light sources, respectively. Platforms range from ground based to satellite instruments and trace-gases are studied in all kinds of different environments. Due to the wide range of measurement conditions, atmospheric compositions and instruments used, a specific challenge of a DOAS retrieval is to optimize the parameters for each specific case and particular trace gas of interest. This becomes especially important when measuring close to the detection limit. A well chosen evaluation wavelength range is crucial to the DOAS technique. It should encompass strong absorption bands of the trace gas of interest in order to maximize the sensitivity of the retrieval, while at the same time minimizing absorption structures of other trace gases and thus potential interferences. Also, instrumental limitations and wavelength depending sources of errors (e.g. insufficient corrections for the Ring effect and cross correlations between trace gas cross sections) need to be taken into account. Most often, not all of these requirements can be fulfilled simultaneously and a compromise needs to be found depending on the conditions at hand. Although for many trace gases the overall dependence of common DOAS retrieval on the evaluation wavelength interval is known, a systematic approach to find the optimal retrieval wavelength range and qualitative assessment is missing. Here we present a novel tool to determine the optimal evaluation wavelength range. It is based on mapping retrieved values in the retrieval wavelength space and thus visualize the consequence of different choices of retrieval spectral ranges, e.g. caused by slightly erroneous absorption cross sections, cross correlations and

Optimizing conditions for an accelerated leach test

International Nuclear Information System (INIS)

Pietrzak, R.F.; Fuhrmann, M.; Heiser, J.; Franz, E.M.; Colombo, P.

1988-01-01

An accelerated leach test for low-level radioactive waste forms is being developed to provide, in a short time, data that can be extrapolated to long time periods. The approach is to provide experimental conditions that will accelerate leaching without changing the dominant release mechanism. Experimental efforts have focused on combining individual factors that have been observed to accelerate leaching. These include elevated temperature, increased leachant volume, and reduced specimen size. The response of diffusion coefficients to various acceleration factors have been evaluated and provide information on experimental parameters that need to be optimized to increase leach rates. For example, these data show that large volumes of leachant are required when leaching portland cement waste forms at elevated temperatures because of high concentrations of dissolved species. Sr-85 leaching is particularly susceptible to suppression due to concentration effects while Cs-137 leaching is less so. Preliminary modeling using a diffusion mechanism (allowing for depletion) of a finite cylinder geometry indicates that during early portions of experiments (daily sampling intervals), leaching is diffusion controlled and more rapid than later in the same experiments (weekly or greater sampling intervals). For cement waste forms, this reduction in rate may be partially controlled by changes in physical structure and chemistry (sometimes related to environmental influences such as CO 2 ), but it is more likely associated with the duration of the sampling interval. 9 refs., 6 figs

Water pollution control in river basin by interactive fuzzy interval multiobjective programming

Energy Technology Data Exchange (ETDEWEB)

Chang, N.B.; Chen, H.W. [National Cheng-Kung Univ., Tainan (Taiwan, Province of China). Dept. of Environmental Engineering; Shaw, D.G.; Yang, C.H. [Academia Sinica, Taipei (Taiwan, Province of China). Inst. of Economics

1997-12-01

The potential conflict between protection of water quality and economic development by different uses of land within river basins is a common problem in regional planning. Many studies have applied multiobjective decision analysis under uncertainty to problems of this kind. This paper presents the interactive fuzzy interval multiobjective mixed integer programming (IFIMOMIP) model to evaluate optimal strategies of wastewater treatment levels within a river system by considering the uncertainties in decision analysis. The interactive fuzzy interval multiobjective mixed integer programming approach is illustrated in a case study for the evaluation of optimal wastewater treatment strategies for water pollution control in a river basin. In particular, it demonstrates how different types of uncertainty in a water pollution control system can be quantified and combined through the use of interval numbers and membership functions. The results indicate that such an approach is useful for handling system complexity and generating more flexible policies for water quality management in river basins.

The SDSS-IV MaNGA Sample: Design, Optimization, and Usage Considerations

Science.gov (United States)

Wake, David A.; Bundy, Kevin; Diamond-Stanic, Aleksandar M.; Yan, Renbin; Blanton, Michael R.; Bershady, Matthew A.; Sánchez-Gallego, José R.; Drory, Niv; Jones, Amy; Kauffmann, Guinevere; Law, David R.; Li, Cheng; MacDonald, Nicholas; Masters, Karen; Thomas, Daniel; Tinker, Jeremy; Weijmans, Anne-Marie; Brownstein, Joel R.

2017-09-01

We describe the sample design for the SDSS-IV MaNGA survey and present the final properties of the main samples along with important considerations for using these samples for science. Our target selection criteria were developed while simultaneously optimizing the size distribution of the MaNGA integral field units (IFUs), the IFU allocation strategy, and the target density to produce a survey defined in terms of maximizing signal-to-noise ratio, spatial resolution, and sample size. Our selection strategy makes use of redshift limits that only depend on I-band absolute magnitude (M I ), or, for a small subset of our sample, M I and color (NUV - I). Such a strategy ensures that all galaxies span the same range in angular size irrespective of luminosity and are therefore covered evenly by the adopted range of IFU sizes. We define three samples: the Primary and Secondary samples are selected to have a flat number density with respect to M I and are targeted to have spectroscopic coverage to 1.5 and 2.5 effective radii (R e ), respectively. The Color-Enhanced supplement increases the number of galaxies in the low-density regions of color-magnitude space by extending the redshift limits of the Primary sample in the appropriate color bins. The samples cover the stellar mass range 5× {10}8≤slant {M}* ≤slant 3× {10}11 {M}⊙ {h}-2 and are sampled at median physical resolutions of 1.37 and 2.5 kpc for the Primary and Secondary samples, respectively. We provide weights that will statistically correct for our luminosity and color-dependent selection function and IFU allocation strategy, thus correcting the observed sample to a volume-limited sample.

Optimal Sample Size for Probability of Detection Curves

International Nuclear Information System (INIS)

Annis, Charles; Gandossi, Luca; Martin, Oliver

2012-01-01

The use of Probability of Detection (POD) curves to quantify NDT reliability is common in the aeronautical industry, but relatively less so in the nuclear industry. The European Network for Inspection Qualification's (ENIQ) Inspection Qualification Methodology is based on the concept of Technical Justification, a document assembling all the evidence to assure that the NDT system in focus is indeed capable of finding the flaws for which it was designed. This methodology has become widely used in many countries, but the assurance it provides is usually of qualitative nature. The need to quantify the output of inspection qualification has become more important, especially as structural reliability modelling and quantitative risk-informed in-service inspection methodologies become more widely used. To credit the inspections in structural reliability evaluations, a measure of the NDT reliability is necessary. A POD curve provides such metric. In 2010 ENIQ developed a technical report on POD curves, reviewing the statistical models used to quantify inspection reliability. Further work was subsequently carried out to investigate the issue of optimal sample size for deriving a POD curve, so that adequate guidance could be given to the practitioners of inspection reliability. Manufacturing of test pieces with cracks that are representative of real defects found in nuclear power plants (NPP) can be very expensive. Thus there is a tendency to reduce sample sizes and in turn reduce the conservatism associated with the POD curve derived. Not much guidance on the correct sample size can be found in the published literature, where often qualitative statements are given with no further justification. The aim of this paper is to summarise the findings of such work. (author)

Gamut Volume Index: a color preference metric based on meta-analysis and optimized colour samples.

Science.gov (United States)

Liu, Qiang; Huang, Zheng; Xiao, Kaida; Pointer, Michael R; Westland, Stephen; Luo, M Ronnier

2017-07-10

A novel metric named Gamut Volume Index (GVI) is proposed for evaluating the colour preference of lighting. This metric is based on the absolute gamut volume of optimized colour samples. The optimal colour set of the proposed metric was obtained by optimizing the weighted average correlation between the metric predictions and the subjective ratings for 8 psychophysical studies. The performance of 20 typical colour metrics was also investigated, which included colour difference based metrics, gamut based metrics, memory based metrics as well as combined metrics. It was found that the proposed GVI outperformed the existing counterparts, especially for the conditions where correlated colour temperatures differed.

Optimal methods for fitting probability distributions to propagule retention time in studies of zoochorous dispersal.

Science.gov (United States)

Viana, Duarte S; Santamaría, Luis; Figuerola, Jordi

2016-02-01

recommend the use of cumulative probability to fit parametric probability distributions to propagule retention time, specifically using maximum likelihood for parameter estimation. Furthermore, the experimental design for an optimal characterization of unimodal propagule retention time should contemplate at least 500 recovered propagules and sampling time-intervals not larger than the time peak of propagule retrieval, except in the tail of the distribution where broader sampling time-intervals may also produce accurate fits.

Constraining neutron guide optimizations with phase-space considerations

Energy Technology Data Exchange (ETDEWEB)

Bertelsen, Mads, E-mail: mads.bertelsen@gmail.com; Lefmann, Kim

2016-09-11

We introduce a method named the Minimalist Principle that serves to reduce the parameter space for neutron guide optimization when the required beam divergence is limited. The reduced parameter space will restrict the optimization to guides with a minimal neutron intake that are still theoretically able to deliver the maximal possible performance. The geometrical constraints are derived using phase-space propagation from moderator to guide and from guide to sample, while assuming that the optimized guides will achieve perfect transport of the limited neutron intake. Guide systems optimized using these constraints are shown to provide performance close to guides optimized without any constraints, however the divergence received at the sample is limited to the desired interval, even when the neutron transport is not limited by the supermirrors used in the guide. As the constraints strongly limit the parameter space for the optimizer, two control parameters are introduced that can be used to adjust the selected subspace, effectively balancing between maximizing neutron transport and avoiding background from unnecessary neutrons. One parameter is needed to describe the expected focusing abilities of the guide to be optimized, going from perfectly focusing to no correlation between position and velocity. The second parameter controls neutron intake into the guide, so that one can select exactly how aggressively the background should be limited. We show examples of guides optimized using these constraints which demonstrates the higher signal to noise than conventional optimizations. Furthermore the parameter controlling neutron intake is explored which shows that the simulated optimal neutron intake is close to the analytically predicted, when assuming that the guide is dominated by multiple scattering events.

Efficient approach for reliability-based optimization based on weighted importance sampling approach

International Nuclear Information System (INIS)

Yuan, Xiukai; Lu, Zhenzhou

2014-01-01

An efficient methodology is presented to perform the reliability-based optimization (RBO). It is based on an efficient weighted approach for constructing an approximation of the failure probability as an explicit function of the design variables which is referred to as the ‘failure probability function (FPF)’. It expresses the FPF as a weighted sum of sample values obtained in the simulation-based reliability analysis. The required computational effort for decoupling in each iteration is just single reliability analysis. After the approximation of the FPF is established, the target RBO problem can be decoupled into a deterministic one. Meanwhile, the proposed weighted approach is combined with a decoupling approach and a sequential approximate optimization framework. Engineering examples are given to demonstrate the efficiency and accuracy of the presented methodology

A predictive score for optimal cytoreduction at interval debulking surgery in epithelial ovarian cancer: a two- centers experience.

Science.gov (United States)

Ghisoni, Eleonora; Katsaros, Dionyssios; Maggiorotto, Furio; Aglietta, Massimo; Vaira, Marco; De Simone, Michele; Mittica, Gloria; Giannone, Gaia; Robella, Manuela; Genta, Sofia; Lucchino, Fabiola; Marocco, Francesco; Borella, Fulvio; Valabrega, Giorgio; Ponzone, Riccardo

2018-05-30

Optimal cytoreduction (macroscopic Residual Tumor, RT = 0) is the best survival predictor factor in epithelial ovarian cancer (EOC). It doesn't exist a consolidated criteria to predict optimal surgical resection at interval debulking surgery (IDS). The aim of this study is to develop a predictive model of complete cytoreduction at IDS. We, retrospectively, analyzed 93 out of 432 patients, with advanced EOC, underwent neoadjuvant chemotherapy (NACT) and IDS from January 2010 to December 2016 in two referral cancer centers. The correlation between clinical-pathological variables and residual disease at IDS has been investigated with univariate and multivariate analysis. A predictive score of cytoreduction (PSC) has been created by combining all significant variables. The performance of each single variable and PSC has been reported and the correlation of all significant variables with progression free survival (PFS) has been assessed. At IDS, 65 patients (69,8%) had complete cytoreduction with no residual disease (R = 0). Three criteria independently predicted R > 0: age ≥ 60 years (p = 0.014), CA-125 before NACT > 550 UI/dl (p = 0.044), and Peritoneal Cancer Index (PCI) > 16 (p  16, a PSC ≥ 3 and the presence of R > 0 after IDS were all significantly associated with shorter PFS (p  0). The PSC should be prospectively validated in a larger series of EOC patients undergoing NACT-IDS.

Analytical method for optimization of maintenance policy based on available system failure data

International Nuclear Information System (INIS)

Coria, V.H.; Maximov, S.; Rivas-Dávalos, F.; Melchor, C.L.; Guardado, J.L.

2015-01-01

An analytical optimization method for preventive maintenance (PM) policy with minimal repair at failure, periodic maintenance, and replacement is proposed for systems with historical failure time data influenced by a current PM policy. The method includes a new imperfect PM model based on Weibull distribution and incorporates the current maintenance interval T 0 and the optimal maintenance interval T to be found. The Weibull parameters are analytically estimated using maximum likelihood estimation. Based on this model, the optimal number of PM and the optimal maintenance interval for minimizing the expected cost over an infinite time horizon are also analytically determined. A number of examples are presented involving different failure time data and current maintenance intervals to analyze how the proposed analytical optimization method for periodic PM policy performances in response to changes in the distribution of the failure data and the current maintenance interval. - Highlights: • An analytical optimization method for preventive maintenance (PM) policy is proposed. • A new imperfect PM model is developed. • The Weibull parameters are analytically estimated using maximum likelihood. • The optimal maintenance interval and number of PM are also analytically determined. • The model is validated by several numerical examples

Optimization of liquid scintillation measurements applied to smears and aqueous samples collected in industrial environments

Directory of Open Access Journals (Sweden)

Arnaud Chapon

Full Text Available Search for low-energy β contaminations in industrial environments requires using Liquid Scintillation Counting. This indirect measurement method supposes a fine control from sampling to measurement itself. Thus, in this paper, we focus on the definition of a measurement method, as generic as possible, for both smears and aqueous samples’ characterization. That includes choice of consumables, sampling methods, optimization of counting parameters and definition of energy windows, using the maximization of a Figure of Merit. Detection limits are then calculated considering these optimized parameters. For this purpose, we used PerkinElmer Tri-Carb counters. Nevertheless, except those relative to some parameters specific to PerkinElmer, most of the results presented here can be extended to other counters. Keywords: Liquid Scintillation Counting (LSC, PerkinElmer, Tri-Carb, Smear, Swipe

RF power consumption emulation optimized with interval valued homotopies

DEFF Research Database (Denmark)

Musiige, Deogratius; Anton, François; Yatskevich, Vital

2011-01-01

This paper presents a methodology towards the emulation of the electrical power consumption of the RF device during the cellular phone/handset transmission mode using the LTE technology. The emulation methodology takes the physical environmental variables and the logical interface between...... the baseband and the RF system as inputs to compute the emulated power dissipation of the RF device. The emulated power, in between the measured points corresponding to the discrete values of the logical interface parameters is computed as a polynomial interpolation using polynomial basis functions....... The evaluation of polynomial and spline curve fitting models showed a respective divergence (test error) of 8% and 0.02% from the physically measured power consumption. The precisions of the instruments used for the physical measurements have been modeled as intervals. We have been able to model the power...

Hybrid Multi-objective Forecasting of Solar Photovoltaic Output Using Kalman Filter based Interval Type-2 Fuzzy Logic System

DEFF Research Database (Denmark)

Hassan, Saima; Ahmadieh Khanesar, Mojtaba; Hajizadeh, Amin

2017-01-01

Learning of fuzzy parameters for system modeling using evolutionary algorithms is an interesting topic. In this paper, two optimal design and tuning of Interval type-2 fuzzy logic system are proposed using hybrid learning algorithms. The consequent parameters of the interval type-2 fuzzy logic...... system in both the hybrid algorithms are tuned using Kalman filter. Whereas the antecedent parameters of the system in the first hybrid algorithm is optimized using the multi-objective particle swarm optimization (MOPSO) and using the multi-objective evolutionary algorithm Based on Decomposition (MOEA...

Adjusted Wald Confidence Interval for a Difference of Binomial Proportions Based on Paired Data

Science.gov (United States)

Bonett, Douglas G.; Price, Robert M.

2012-01-01

Adjusted Wald intervals for binomial proportions in one-sample and two-sample designs have been shown to perform about as well as the best available methods. The adjusted Wald intervals are easy to compute and have been incorporated into introductory statistics courses. An adjusted Wald interval for paired binomial proportions is proposed here and…

Simultaneous parameter and tolerance optimization of structures via probability-interval mixed reliability model

DEFF Research Database (Denmark)

Luo, Yangjun; Wu, Xiaoxiang; Zhou, Mingdong

2015-01-01

Both structural sizes and dimensional tolerances strongly influence the manufacturing cost and the functional performance of a practical product. This paper presents an optimization method to simultaneously find the optimal combination of structural sizes and dimensional tolerances. Based...... transformed into their equivalent formulations by using the performance measure approach. The optimization problem is then solved with the sequential approximate programming. Meanwhile, a numerically stable algorithm based on the trust region method is proposed to efficiently update the target performance...

Optimizing 4-Dimensional Magnetic Resonance Imaging Data Sampling for Respiratory Motion Analysis of Pancreatic Tumors

Energy Technology Data Exchange (ETDEWEB)

Stemkens, Bjorn, E-mail: b.stemkens@umcutrecht.nl [Department of Radiotherapy, University Medical Center Utrecht, Utrecht (Netherlands); Tijssen, Rob H.N. [Department of Radiotherapy, University Medical Center Utrecht, Utrecht (Netherlands); Senneville, Baudouin D. de [Imaging Division, University Medical Center Utrecht, Utrecht (Netherlands); L' Institut de Mathématiques de Bordeaux, Unité Mixte de Recherche 5251, Centre National de la Recherche Scientifique/University of Bordeaux, Bordeaux (France); Heerkens, Hanne D.; Vulpen, Marco van; Lagendijk, Jan J.W.; Berg, Cornelis A.T. van den [Department of Radiotherapy, University Medical Center Utrecht, Utrecht (Netherlands)

2015-03-01

Purpose: To determine the optimum sampling strategy for retrospective reconstruction of 4-dimensional (4D) MR data for nonrigid motion characterization of tumor and organs at risk for radiation therapy purposes. Methods and Materials: For optimization, we compared 2 surrogate signals (external respiratory bellows and internal MRI navigators) and 2 MR sampling strategies (Cartesian and radial) in terms of image quality and robustness. Using the optimized protocol, 6 pancreatic cancer patients were scanned to calculate the 4D motion. Region of interest analysis was performed to characterize the respiratory-induced motion of the tumor and organs at risk simultaneously. Results: The MRI navigator was found to be a more reliable surrogate for pancreatic motion than the respiratory bellows signal. Radial sampling is most benign for undersampling artifacts and intraview motion. Motion characterization revealed interorgan and interpatient variation, as well as heterogeneity within the tumor. Conclusions: A robust 4D-MRI method, based on clinically available protocols, is presented and successfully applied to characterize the abdominal motion in a small number of pancreatic cancer patients.

What is the optimal interval between successive home blood pressure readings using an automated oscillometric device?

Science.gov (United States)

Eguchi, Kazuo; Kuruvilla, Sujith; Ogedegbe, Gbenga; Gerin, William; Schwartz, Joseph E; Pickering, Thomas G

2009-06-01

To clarify whether a shorter interval between three successive home blood pressure (HBP) readings (10 s vs. 1 min) taken twice a day gives a better prediction of the average 24-h BP and better patient compliance. We enrolled 56 patients from a hypertension clinic (mean age: 60 +/- 14 years; 54% female patients). The study consisted of three clinic visits, with two 4-week periods of self-monitoring of HBP between them, and a 24-h ambulatory BP monitoring at the second visit. Using a crossover design, with order randomized, the oscillometric HBP device (HEM-5001) could be programmed to take three consecutive readings at either 10-s or 1-min intervals, each of which was done for 4 weeks. Patients were asked to measure three HBP readings in the morning and evening. All the readings were stored in the memory of the monitors. The analyses were performed using the second-third HBP readings. The average systolic BP/diastolic BP for the 10-s and 1-min intervals at home were 136.1 +/- 15.8/77.5 +/- 9.5 and 133.2 +/- 15.5/76.9 +/- 9.3 mmHg (P = 0.001/0.19 for the differences in systolic BP and diastolic BP), respectively. The 1-min BP readings were significantly closer to the average of awake ambulatory BP (131 +/- 14/79 +/- 10 mmHg) than the 10-s interval readings. There was no significant difference in patients' compliance in taking adequate numbers of readings at the different time intervals. The 1-min interval between HBP readings gave a closer agreement with the daytime average BP than the 10-s interval.

Modeling of frequency-domain scalar wave equation with the average-derivative optimal scheme based on a multigrid-preconditioned iterative solver

Science.gov (United States)

Cao, Jian; Chen, Jing-Bo; Dai, Meng-Xue

2018-01-01

An efficient finite-difference frequency-domain modeling of seismic wave propagation relies on the discrete schemes and appropriate solving methods. The average-derivative optimal scheme for the scalar wave modeling is advantageous in terms of the storage saving for the system of linear equations and the flexibility for arbitrary directional sampling intervals. However, using a LU-decomposition-based direct solver to solve its resulting system of linear equations is very costly for both memory and computational requirements. To address this issue, we consider establishing a multigrid-preconditioned BI-CGSTAB iterative solver fit for the average-derivative optimal scheme. The choice of preconditioning matrix and its corresponding multigrid components is made with the help of Fourier spectral analysis and local mode analysis, respectively, which is important for the convergence. Furthermore, we find that for the computation with unequal directional sampling interval, the anisotropic smoothing in the multigrid precondition may affect the convergence rate of this iterative solver. Successful numerical applications of this iterative solver for the homogenous and heterogeneous models in 2D and 3D are presented where the significant reduction of computer memory and the improvement of computational efficiency are demonstrated by comparison with the direct solver. In the numerical experiments, we also show that the unequal directional sampling interval will weaken the advantage of this multigrid-preconditioned iterative solver in the computing speed or, even worse, could reduce its accuracy in some cases, which implies the need for a reasonable control of directional sampling interval in the discretization.

An approach to solve group-decision-making problems with ordinal interval numbers.

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Fan, Zhi-Ping; Liu, Yang

2010-10-01

The ordinal interval number is a form of uncertain preference information in group decision making (GDM), while it is seldom discussed in the existing research. This paper investigates how the ranking order of alternatives is determined based on preference information of ordinal interval numbers in GDM problems. When ranking a large quantity of ordinal interval numbers, the efficiency and accuracy of the ranking process are critical. A new approach is proposed to rank alternatives using ordinal interval numbers when every ranking ordinal in an ordinal interval number is thought to be uniformly and independently distributed in its interval. First, we give the definition of possibility degree on comparing two ordinal interval numbers and the related theory analysis. Then, to rank alternatives, by comparing multiple ordinal interval numbers, a collective expectation possibility degree matrix on pairwise comparisons of alternatives is built, and an optimization model based on this matrix is constructed. Furthermore, an algorithm is also presented to rank alternatives by solving the model. Finally, two examples are used to illustrate the use of the proposed approach.

Method optimization for non-equilibrium solid phase microextraction sampling of HAPs for GC/MS analysis

Science.gov (United States)

Zawadowicz, M. A.; Del Negro, L. A.

2010-12-01

Hazardous air pollutants (HAPs) are usually present in the atmosphere at pptv-level, requiring measurements with high sensitivity and minimal contamination. Commonly used evacuated canister methods require an overhead in space, money and time that often is prohibitive to primarily-undergraduate institutions. This study optimized an analytical method based on solid-phase microextraction (SPME) of ambient gaseous matrix, which is a cost-effective technique of selective VOC extraction, accessible to an unskilled undergraduate. Several approaches to SPME extraction and sample analysis were characterized and several extraction parameters optimized. Extraction time, temperature and laminar air flow velocity around the fiber were optimized to give highest signal and efficiency. Direct, dynamic extraction of benzene from a moving air stream produced better precision (±10%) than sampling of stagnant air collected in a polymeric bag (±24%). Using a low-polarity chromatographic column in place of a standard (5%-Phenyl)-methylpolysiloxane phase decreased the benzene detection limit from 2 ppbv to 100 pptv. The developed method is simple and fast, requiring 15-20 minutes per extraction and analysis. It will be field-validated and used as a field laboratory component of various undergraduate Chemistry and Environmental Studies courses.

Prevalence of E/A wave fusion and A wave truncation in DDD pacemaker patients with complete AV block under nominal AV intervals.

Directory of Open Access Journals (Sweden)

Wolfram C Poller

Full Text Available Optimization of the AV-interval (AVI in DDD pacemakers improves cardiac hemodynamics and reduces pacemaker syndromes. Manual optimization is typically not performed in clinical routine. In the present study we analyze the prevalence of E/A wave fusion and A wave truncation under resting conditions in 160 patients with complete AV block (AVB under the pre-programmed AVI. We manually optimized sub-optimal AVI.We analyzed 160 pacemaker patients with complete AVB, both in sinus rhythm (AV-sense; n = 129 and under atrial pacing (AV-pace; n = 31. Using Doppler analyses of the transmitral inflow we classified the nominal AVI as: a normal, b too long (E/A wave fusion or c too short (A wave truncation. In patients with a sub-optimal AVI, we performed manual optimization according to the recommendations of the American Society of Echocardiography.All AVB patients with atrial pacing exhibited a normal transmitral inflow under the nominal AV-pace intervals (100%. In contrast, 25 AVB patients in sinus rhythm showed E/A wave fusion under the pre-programmed AV-sense intervals (19.4%; 95% confidence interval (CI: 12.6-26.2%. A wave truncations were not observed in any patient. All patients with a complete E/A wave fusion achieved a normal transmitral inflow after AV-sense interval reduction (mean optimized AVI: 79.4 ± 13.6 ms.Given the rate of 19.4% (CI 12.6-26.2% of patients with a too long nominal AV-sense interval, automatic algorithms may prove useful in improving cardiac hemodynamics, especially in the subgroup of atrially triggered pacemaker patients with AV node diseases.

Optimization of sampling pattern and the design of Fourier ptychographic illuminator.

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Guo, Kaikai; Dong, Siyuan; Nanda, Pariksheet; Zheng, Guoan

2015-03-09

Fourier ptychography (FP) is a recently developed imaging approach that facilitates high-resolution imaging beyond the cutoff frequency of the employed optics. In the original FP approach, a periodic LED array is used for sample illumination, and therefore, the scanning pattern is a uniform grid in the Fourier space. Such a uniform sampling scheme leads to 3 major problems for FP, namely: 1) it requires a large number of raw images, 2) it introduces the raster grid artefacts in the reconstruction process, and 3) it requires a high-dynamic-range detector. Here, we investigate scanning sequences and sampling patterns to optimize the FP approach. For most biological samples, signal energy is concentrated at low-frequency region, and as such, we can perform non-uniform Fourier sampling in FP by considering the signal structure. In contrast, conventional ptychography perform uniform sampling over the entire real space. To implement the non-uniform Fourier sampling scheme in FP, we have designed and built an illuminator using LEDs mounted on a 3D-printed plastic case. The advantages of this illuminator are threefold in that: 1) it reduces the number of image acquisitions by at least 50% (68 raw images versus 137 in the original FP setup), 2) it departs from the translational symmetry of sampling to solve the raster grid artifact problem, and 3) it reduces the dynamic range of the captured images 6 fold. The results reported in this paper significantly shortened acquisition time and improved quality of FP reconstructions. It may provide new insights for developing Fourier ptychographic imaging platforms and find important applications in digital pathology.

Improved detection of multiple environmental antibiotics through an optimized sample extraction strategy in liquid chromatography-mass spectrometry analysis.

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Yi, Xinzhu; Bayen, Stéphane; Kelly, Barry C; Li, Xu; Zhou, Zhi

2015-12-01

A solid-phase extraction/liquid chromatography/electrospray ionization/multi-stage mass spectrometry (SPE-LC-ESI-MS/MS) method was optimized in this study for sensitive and simultaneous detection of multiple antibiotics in urban surface waters and soils. Among the seven classes of tested antibiotics, extraction efficiencies of macrolides, lincosamide, chloramphenicol, and polyether antibiotics were significantly improved under optimized sample extraction pH. Instead of only using acidic extraction in many existing studies, the results indicated that antibiotics with low pK a values (antibiotics with high pK a values (>7) were extracted more efficiently under neutral conditions. The effects of pH were more obvious on polar compounds than those on non-polar compounds. Optimization of extraction pH resulted in significantly improved sample recovery and better detection limits. Compared with reported values in the literature, the average reduction of minimal detection limits obtained in this study was 87.6% in surface waters (0.06-2.28 ng/L) and 67.1% in soils (0.01-18.16 ng/g dry wt). This method was subsequently applied to detect antibiotics in environmental samples in a heavily populated urban city, and macrolides, sulfonamides, and lincomycin were frequently detected. Antibiotics with highest detected concentrations were sulfamethazine (82.5 ng/L) in surface waters and erythromycin (6.6 ng/g dry wt) in soils. The optimized sample extraction strategy can be used to improve the detection of a variety of antibiotics in environmental surface waters and soils.

Efficient Estimation for Diffusions Sampled at High Frequency Over a Fixed Time Interval

DEFF Research Database (Denmark)

Jakobsen, Nina Munkholt; Sørensen, Michael

Parametric estimation for diffusion processes is considered for high frequency observations over a fixed time interval. The processes solve stochastic differential equations with an unknown parameter in the diffusion coefficient. We find easily verified conditions on approximate martingale...

High-low search for a point in an interval

NARCIS (Netherlands)

Shah, M.

2011-01-01

Analysis of the high-low search game for a point on an interval. By limiting the number of guesses for the searcher, it is possible to study the game numerically. The principal motivation for this analysis is an old and open question due to Baston, Bostock and Alpern: is the optimal strategy for the

A modified Wald interval for the area under the ROC curve (AUC) in diagnostic case-control studies.

Science.gov (United States)

Kottas, Martina; Kuss, Oliver; Zapf, Antonia

2014-02-19

The area under the receiver operating characteristic (ROC) curve, referred to as the AUC, is an appropriate measure for describing the overall accuracy of a diagnostic test or a biomarker in early phase trials without having to choose a threshold. There are many approaches for estimating the confidence interval for the AUC. However, all are relatively complicated to implement. Furthermore, many approaches perform poorly for large AUC values or small sample sizes. The AUC is actually a probability. So we propose a modified Wald interval for a single proportion, which can be calculated on a pocket calculator. We performed a simulation study to compare this modified Wald interval (without and with continuity correction) with other intervals regarding coverage probability and statistical power. The main result is that the proposed modified Wald intervals maintain and exploit the type I error much better than the intervals of Agresti-Coull, Wilson, and Clopper-Pearson. The interval suggested by Bamber, the Mann-Whitney interval without transformation and also the interval of the binormal AUC are very liberal. For small sample sizes the Wald interval with continuity has a comparable coverage probability as the LT interval and higher power. For large sample sizes the results of the LT interval and of the Wald interval without continuity correction are comparable. If individual patient data is not available, but only the estimated AUC and the total sample size, the modified Wald intervals can be recommended as confidence intervals for the AUC. For small sample sizes the continuity correction should be used.

Optimization of sampling parameters for standardized exhaled breath sampling.

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Doran, Sophie; Romano, Andrea; Hanna, George B

2017-09-05

The lack of standardization of breath sampling is a major contributing factor to the poor repeatability of results and hence represents a barrier to the adoption of breath tests in clinical practice. On-line and bag breath sampling have advantages but do not suit multicentre clinical studies whereas storage and robust transport are essential for the conduct of wide-scale studies. Several devices have been developed to control sampling parameters and to concentrate volatile organic compounds (VOCs) onto thermal desorption (TD) tubes and subsequently transport those tubes for laboratory analysis. We conducted three experiments to investigate (i) the fraction of breath sampled (whole vs. lower expiratory exhaled breath); (ii) breath sample volume (125, 250, 500 and 1000ml) and (iii) breath sample flow rate (400, 200, 100 and 50 ml/min). The target VOCs were acetone and potential volatile biomarkers for oesophago-gastric cancer belonging to the aldehyde, fatty acids and phenol chemical classes. We also examined the collection execution time and the impact of environmental contamination. The experiments showed that the use of exhaled breath-sampling devices requires the selection of optimum sampling parameters. The increase in sample volume has improved the levels of VOCs detected. However, the influence of the fraction of exhaled breath and the flow rate depends on the target VOCs measured. The concentration of potential volatile biomarkers for oesophago-gastric cancer was not significantly different between the whole and lower airway exhaled breath. While the recovery of phenols and acetone from TD tubes was lower when breath sampling was performed at a higher flow rate, other VOCs were not affected. A dedicated 'clean air supply' overcomes the contamination from ambient air, but the breath collection device itself can be a source of contaminants. In clinical studies using VOCs to diagnose gastro-oesophageal cancer, the optimum parameters are 500mls sample volume

Confidence intervals for distinguishing ordinal and disordinal interactions in multiple regression.

Science.gov (United States)

Lee, Sunbok; Lei, Man-Kit; Brody, Gene H

2015-06-01

Distinguishing between ordinal and disordinal interaction in multiple regression is useful in testing many interesting theoretical hypotheses. Because the distinction is made based on the location of a crossover point of 2 simple regression lines, confidence intervals of the crossover point can be used to distinguish ordinal and disordinal interactions. This study examined 2 factors that need to be considered in constructing confidence intervals of the crossover point: (a) the assumption about the sampling distribution of the crossover point, and (b) the possibility of abnormally wide confidence intervals for the crossover point. A Monte Carlo simulation study was conducted to compare 6 different methods for constructing confidence intervals of the crossover point in terms of the coverage rate, the proportion of true values that fall to the left or right of the confidence intervals, and the average width of the confidence intervals. The methods include the reparameterization, delta, Fieller, basic bootstrap, percentile bootstrap, and bias-corrected accelerated bootstrap methods. The results of our Monte Carlo simulation study suggest that statistical inference using confidence intervals to distinguish ordinal and disordinal interaction requires sample sizes more than 500 to be able to provide sufficiently narrow confidence intervals to identify the location of the crossover point. (c) 2015 APA, all rights reserved).

Optimism and self-esteem are related to sleep. Results from a large community-based sample.

Science.gov (United States)

Lemola, Sakari; Räikkönen, Katri; Gomez, Veronica; Allemand, Mathias

2013-12-01

There is evidence that positive personality characteristics, such as optimism and self-esteem, are important for health. Less is known about possible determinants of positive personality characteristics. To test the relationship of optimism and self-esteem with insomnia symptoms and sleep duration. Sleep parameters, optimism, and self-esteem were assessed by self-report in a community-based sample of 1,805 adults aged between 30 and 84 years in the USA. Moderation of the relation between sleep and positive characteristics by gender and age as well as potential confounding of the association by depressive disorder was tested. Individuals with insomnia symptoms scored lower on optimism and self-esteem largely independent of age and sex, controlling for symptoms of depression and sleep duration. Short sleep duration (self-esteem when compared to individuals sleeping 7-8 h, controlling depressive symptoms. Long sleep duration (>9 h) was also related to low optimism and self-esteem independent of age and sex. Good and sufficient sleep is associated with positive personality characteristics. This relationship is independent of the association between poor sleep and depression.

Continuous time modelling with individually varying time intervals for oscillating and non-oscillating processes.

Science.gov (United States)

Voelkle, Manuel C; Oud, Johan H L

2013-02-01

When designing longitudinal studies, researchers often aim at equal intervals. In practice, however, this goal is hardly ever met, with different time intervals between assessment waves and different time intervals between individuals being more the rule than the exception. One of the reasons for the introduction of continuous time models by means of structural equation modelling has been to deal with irregularly spaced assessment waves (e.g., Oud & Delsing, 2010). In the present paper we extend the approach to individually varying time intervals for oscillating and non-oscillating processes. In addition, we show not only that equal intervals are unnecessary but also that it can be advantageous to use unequal sampling intervals, in particular when the sampling rate is low. Two examples are provided to support our arguments. In the first example we compare a continuous time model of a bivariate coupled process with varying time intervals to a standard discrete time model to illustrate the importance of accounting for the exact time intervals. In the second example the effect of different sampling intervals on estimating a damped linear oscillator is investigated by means of a Monte Carlo simulation. We conclude that it is important to account for individually varying time intervals, and encourage researchers to conceive of longitudinal studies with different time intervals within and between individuals as an opportunity rather than a problem. © 2012 The British Psychological Society.

Time-variant random interval natural frequency analysis of structures

Science.gov (United States)

Wu, Binhua; Wu, Di; Gao, Wei; Song, Chongmin

2018-02-01

This paper presents a new robust method namely, unified interval Chebyshev-based random perturbation method, to tackle hybrid random interval structural natural frequency problem. In the proposed approach, random perturbation method is implemented to furnish the statistical features (i.e., mean and standard deviation) and Chebyshev surrogate model strategy is incorporated to formulate the statistical information of natural frequency with regards to the interval inputs. The comprehensive analysis framework combines the superiority of both methods in a way that computational cost is dramatically reduced. This presented method is thus capable of investigating the day-to-day based time-variant natural frequency of structures accurately and efficiently under concrete intrinsic creep effect with probabilistic and interval uncertain variables. The extreme bounds of the mean and standard deviation of natural frequency are captured through the embedded optimization strategy within the analysis procedure. Three particularly motivated numerical examples with progressive relationship in perspective of both structure type and uncertainty variables are demonstrated to justify the computational applicability, accuracy and efficiency of the proposed method.

Optimal sample to tracer ratio for isotope dilution mass spectrometry: the polyisotopic case

International Nuclear Information System (INIS)

Laszlo, G.; Ridder, P. de; Goldman, A.; Cappis, J.; Bievre, P. de

1991-01-01

The Isotope Dilution Mass Spectrometry (IDMS) measurement technique provides a means for determining the unknown amount of various isotopes of an element in a sample solution of known mass. The sample solution is mixed with an auxiliary solution, or tracer, containing a known amount of the same element having the same isotopes but of different relative abundances or isotopic composition and the induced change in the isotopic composition measured by isotope mass spectrometry. The technique involves the measurement of the abundance ratio of each isotope to a (same) reference isotope in the sample solution, in the tracer solution and in the blend of the sample and tracer solution. These isotope ratio measurements, the known element amount in the tracer and the known mass of sample solution are used to calculate the unknown amount of one isotope in the sample solution. Subsequently the unknown amount of element is determined. The purpose of this paper is to examine the optimization of the ratio of the estimated unknown amount of element in the sample solution to the known amount of element in the tracer solution in order to minimize the relative uncertainty in the determination of the unknown amount of element

Optimization of thermal processing of canned mussels.

Science.gov (United States)

Ansorena, M R; Salvadori, V O

2011-10-01

The design and optimization of thermal processing of solid-liquid food mixtures, such as canned mussels, requires the knowledge of the thermal history at the slowest heating point. In general, this point does not coincide with the geometrical center of the can, and the results show that it is located along the axial axis at a height that depends on the brine content. In this study, a mathematical model for the prediction of the temperature at this point was developed using the discrete transfer function approach. Transfer function coefficients were experimentally obtained, and prediction equations fitted to consider other can dimensions and sampling interval. This model was coupled with an optimization routine in order to search for different retort temperature profiles to maximize a quality index. Both constant retort temperature (CRT) and variable retort temperature (VRT; discrete step-wise and exponential) were considered. In the CRT process, the optimal retort temperature was always between 134 °C and 137 °C, and high values of thiamine retention were achieved. A significant improvement in surface quality index was obtained for optimal VRT profiles compared to optimal CRT. The optimization procedure shown in this study produces results that justify its utilization in the industry.

Discussion of “Prediction intervals for short-term wind farm generation forecasts” and “Combined nonparametric prediction intervals for wind power generation”

DEFF Research Database (Denmark)

Pinson, Pierre; Tastu, Julija

2014-01-01

A new score for the evaluation of interval forecasts, the so-called coverage width-based criterion (CWC), was proposed and utilized.. This score has been used for the tuning (in-sample) and genuine evaluation (out-ofsample) of prediction intervals for various applications, e.g., electric load [1......], electricity prices [2], general purpose prediction [3], and wind power generation [4], [5]. Indeed, two papers by the same authors appearing in the IEEE Transactions On Sustainable Energy employ that score and use it to conclude on the comparative quality of alternative approaches to interval forecasting...

Concurrent variable-interval variable-ratio schedules in a dynamic choice environment.

Science.gov (United States)

Bell, Matthew C; Baum, William M

2017-11-01

Most studies of operant choice have focused on presenting subjects with a fixed pair of schedules across many experimental sessions. Using these methods, studies of concurrent variable- interval variable-ratio schedules helped to evaluate theories of choice. More recently, a growing literature has focused on dynamic choice behavior. Those dynamic choice studies have analyzed behavior on a number of different time scales using concurrent variable-interval schedules. Following the dynamic choice approach, the present experiment examined performance on concurrent variable-interval variable-ratio schedules in a rapidly changing environment. Our objectives were to compare performance on concurrent variable-interval variable-ratio schedules with extant data on concurrent variable-interval variable-interval schedules using a dynamic choice procedure and to extend earlier work on concurrent variable-interval variable-ratio schedules. We analyzed performances at different time scales, finding strong similarities between concurrent variable-interval variable-interval and concurrent variable-interval variable- ratio performance within dynamic choice procedures. Time-based measures revealed almost identical performance in the two procedures compared with response-based measures, supporting the view that choice is best understood as time allocation. Performance at the smaller time scale of visits accorded with the tendency seen in earlier research toward developing a pattern of strong preference for and long visits to the richer alternative paired with brief "samples" at the leaner alternative ("fix and sample"). © 2017 Society for the Experimental Analysis of Behavior.

Optimized measurement of radium-226 concentration in liquid samples with radon-222 emanation

International Nuclear Information System (INIS)

Perrier, Frédéric; Aupiais, Jean; Girault, Frédéric; Przylibski, Tadeusz A.; Bouquerel, Hélène

2016-01-01

Measuring radium-226 concentration in liquid samples using radon-222 emanation remains competitive with techniques such as liquid scintillation, alpha or mass spectrometry. Indeed, we show that high-precision can be obtained without air circulation, using an optimal air to liquid volume ratio and moderate heating. Cost-effective and efficient measurement of radon concentration is achieved by scintillation flasks and sufficiently long counting times for signal and background. More than 400 such measurements were performed, including 39 dilution experiments, a successful blind measurement of six reference test solutions, and more than 110 repeated measurements. Under optimal conditions, uncertainties reach 5% for an activity concentration of 100 mBq L"−"1 and 10% for 10 mBq L"−"1. While the theoretical detection limit predicted by Monte Carlo simulation is around 3 mBq L"−"1, a conservative experimental estimate is rather 5 mBq L"−"1, corresponding to 0.14 fg g"−"1. The method was applied to 47 natural waters, 51 commercial waters, and 17 wine samples, illustrating that it could be an option for liquids that cannot be easily measured by other methods. Counting of scintillation flasks can be done in remote locations in absence of electricity supply, using a solar panel. Thus, this portable method, which has demonstrated sufficient accuracy for numerous natural liquids, could be useful in geological and environmental problems, with the additional benefit that it can be applied in isolated locations and in circumstances when samples cannot be transported. - Highlights: • Radium-226 concentration measured with optimized accumulation in a container. • Radon-222 in air measured precisely with scintillation flasks and long countings. • Method tested by repetition tests, dilution experiments, and successful blind tests. • Estimated conservative detection limit without pre-concentration is 5 mBq L"−"1. • Method is portable, cost

A Novel Optimal Control Method for Impulsive-Correction Projectile Based on Particle Swarm Optimization

Directory of Open Access Journals (Sweden)

Ruisheng Sun

2016-01-01

Full Text Available This paper presents a new parametric optimization approach based on a modified particle swarm optimization (PSO to design a class of impulsive-correction projectiles with discrete, flexible-time interval, and finite-energy control. In terms of optimal control theory, the task is described as the formulation of minimum working number of impulses and minimum control error, which involves reference model linearization, boundary conditions, and discontinuous objective function. These result in difficulties in finding the global optimum solution by directly utilizing any other optimization approaches, for example, Hp-adaptive pseudospectral method. Consequently, PSO mechanism is employed for optimal setting of impulsive control by considering the time intervals between two neighboring lateral impulses as design variables, which makes the briefness of the optimization process. A modification on basic PSO algorithm is developed to improve the convergence speed of this optimization through linearly decreasing the inertial weight. In addition, a suboptimal control and guidance law based on PSO technique are put forward for the real-time consideration of the online design in practice. Finally, a simulation case coupled with a nonlinear flight dynamic model is applied to validate the modified PSO control algorithm. The results of comparative study illustrate that the proposed optimal control algorithm has a good performance in obtaining the optimal control efficiently and accurately and provides a reference approach to handling such impulsive-correction problem.

Isolation and identification of phytase-producing strains from soil samples and optimization of production parameters

Directory of Open Access Journals (Sweden)

Masoud Mohammadi

2017-09-01

Discussion and conclusion: Penicillium sp. isolated from a soil sample near Qazvin, was able to produce highly active phytase in optimized environmental conditions, which could be a suitable candidate for commercial production of phytase to be used as complement in poultry feeding industries.

Heuristic algorithms for the minmax regret flow-shop problem with interval processing times.

Science.gov (United States)

Ćwik, Michał; Józefczyk, Jerzy

2018-01-01

An uncertain version of the permutation flow-shop with unlimited buffers and the makespan as a criterion is considered. The investigated parametric uncertainty is represented by given interval-valued processing times. The maximum regret is used for the evaluation of uncertainty. Consequently, the minmax regret discrete optimization problem is solved. Due to its high complexity, two relaxations are applied to simplify the optimization procedure. First of all, a greedy procedure is used for calculating the criterion's value, as such calculation is NP-hard problem itself. Moreover, the lower bound is used instead of solving the internal deterministic flow-shop. The constructive heuristic algorithm is applied for the relaxed optimization problem. The algorithm is compared with previously elaborated other heuristic algorithms basing on the evolutionary and the middle interval approaches. The conducted computational experiments showed the advantage of the constructive heuristic algorithm with regards to both the criterion and the time of computations. The Wilcoxon paired-rank statistical test confirmed this conclusion.

Experimental uncertainty estimation and statistics for data having interval uncertainty.

Energy Technology Data Exchange (ETDEWEB)

Kreinovich, Vladik (Applied Biomathematics, Setauket, New York); Oberkampf, William Louis (Applied Biomathematics, Setauket, New York); Ginzburg, Lev (Applied Biomathematics, Setauket, New York); Ferson, Scott (Applied Biomathematics, Setauket, New York); Hajagos, Janos (Applied Biomathematics, Setauket, New York)

2007-05-01

This report addresses the characterization of measurements that include epistemic uncertainties in the form of intervals. It reviews the application of basic descriptive statistics to data sets which contain intervals rather than exclusively point estimates. It describes algorithms to compute various means, the median and other percentiles, variance, interquartile range, moments, confidence limits, and other important statistics and summarizes the computability of these statistics as a function of sample size and characteristics of the intervals in the data (degree of overlap, size and regularity of widths, etc.). It also reviews the prospects for analyzing such data sets with the methods of inferential statistics such as outlier detection and regressions. The report explores the tradeoff between measurement precision and sample size in statistical results that are sensitive to both. It also argues that an approach based on interval statistics could be a reasonable alternative to current standard methods for evaluating, expressing and propagating measurement uncertainties.

Application of Interval Predictor Models to Space Radiation Shielding

Science.gov (United States)

Crespo, Luis G.; Kenny, Sean P.; Giesy,Daniel P.; Norman, Ryan B.; Blattnig, Steve R.

2016-01-01

This paper develops techniques for predicting the uncertainty range of an output variable given input-output data. These models are called Interval Predictor Models (IPM) because they yield an interval valued function of the input. This paper develops IPMs having a radial basis structure. This structure enables the formal description of (i) the uncertainty in the models parameters, (ii) the predicted output interval, and (iii) the probability that a future observation would fall in such an interval. In contrast to other metamodeling techniques, this probabilistic certi cate of correctness does not require making any assumptions on the structure of the mechanism from which data are drawn. Optimization-based strategies for calculating IPMs having minimal spread while containing all the data are developed. Constraints for bounding the minimum interval spread over the continuum of inputs, regulating the IPMs variation/oscillation, and centering its spread about a target point, are used to prevent data over tting. Furthermore, we develop an approach for using expert opinion during extrapolation. This metamodeling technique is illustrated using a radiation shielding application for space exploration. In this application, we use IPMs to describe the error incurred in predicting the ux of particles resulting from the interaction between a high-energy incident beam and a target.

Interval Size and Affect: An Ethnomusicological Perspective

Directory of Open Access Journals (Sweden)

Sarha Moore

2013-08-01

Full Text Available This commentary addresses Huron and Davis's question of whether "The Harmonic Minor Provides an Optimum Way of Reducing Average Melodic Interval Size, Consistent with Sad Affect Cues" within any non-Western musical cultures. The harmonic minor scale and other semitone-heavy scales, such as Bhairav raga and Hicaz makam, are featured widely in the musical cultures of North India and the Middle East. Do melodies from these genres also have a preponderance of semitone intervals and low incidence of the augmented second interval, as in Huron and Davis's sample? Does the presence of more semitone intervals in a melody affect its emotional connotations in different cultural settings? Are all semitone intervals equal in their effect? My own ethnographic research within these cultures reveals comparable connotations in melodies that linger on semitone intervals, centered on concepts of tension and metaphors of falling. However, across different musical cultures there may also be neutral or lively interpretations of these same pitch sets, dependent on context, manner of performance, and tradition. Small pitch movement may also be associated with social functions such as prayer or lullabies, and may not be described as "sad." "Sad," moreover may not connote the same affect cross-culturally.

Differentially Private Confidence Intervals for Empirical Risk Minimization

OpenAIRE

Wang, Yue; Kifer, Daniel; Lee, Jaewoo

2018-01-01

The process of data mining with differential privacy produces results that are affected by two types of noise: sampling noise due to data collection and privacy noise that is designed to prevent the reconstruction of sensitive information. In this paper, we consider the problem of designing confidence intervals for the parameters of a variety of differentially private machine learning models. The algorithms can provide confidence intervals that satisfy differential privacy (as well as the mor...

Dual-mode nested search method for categorical uncertain multi-objective optimization

Science.gov (United States)

Tang, Long; Wang, Hu

2016-10-01

Categorical multi-objective optimization is an important issue involved in many matching design problems. Non-numerical variables and their uncertainty are the major challenges of such optimizations. Therefore, this article proposes a dual-mode nested search (DMNS) method. In the outer layer, kriging metamodels are established using standard regular simplex mapping (SRSM) from categorical candidates to numerical values. Assisted by the metamodels, a k-cluster-based intelligent sampling strategy is developed to search Pareto frontier points. The inner layer uses an interval number method to model the uncertainty of categorical candidates. To improve the efficiency, a multi-feature convergent optimization via most-promising-area stochastic search (MFCOMPASS) is proposed to determine the bounds of objectives. Finally, typical numerical examples are employed to demonstrate the effectiveness of the proposed DMNS method.

Compensation Methods for Non-uniform and Incomplete Data Sampling in High Resolution PET with Multiple Scintillation Crystal Layers

International Nuclear Information System (INIS)

Lee, Jae Sung; Kim, Soo Mee; Lee, Dong Soo; Hong, Jong Hong; Sim, Kwang Souk; Rhee, June Tak

2008-01-01

To establish the methods for sinogram formation and correction in order to appropriately apply the filtered backprojection (FBP) reconstruction algorithm to the data acquired using PET scanner with multiple scintillation crystal layers. Formation for raw PET data storage and conversion methods from listmode data to histogram and sinogram were optimized. To solve the various problems occurred while the raw histogram was converted into sinogram, optimal sampling strategy and sampling efficiency correction method were investigated. Gap compensation methods that is unique in this system were also investigated. All the sinogram data were reconstructed using 2D filtered backprojection algorithm and compared to estimate the improvements by the correction algorithms. Optimal radial sampling interval and number of angular samples in terms of the sampling theorem and sampling efficiency correction algorithm were pitch/2 and 120, respectively. By applying the sampling efficiency correction and gap compensation, artifacts and background noise on the reconstructed image could be reduced. Conversion method from the histogram to sinogram was investigated for the FBP reconstruction of data acquired using multiple scintillation crystal layers. This method will be useful for the fast 2D reconstruction of multiple crystal layer PET data

Optimization of a sample processing protocol for recovery of Bacillus anthracis spores from soil

Science.gov (United States)

Silvestri, Erin E.; Feldhake, David; Griffin, Dale; Lisle, John T.; Nichols, Tonya L.; Shah, Sanjiv; Pemberton, A; Schaefer III, Frank W

2016-01-01

Following a release of Bacillus anthracis spores into the environment, there is a potential for lasting environmental contamination in soils. There is a need for detection protocols for B. anthracis in environmental matrices. However, identification of B. anthracis within a soil is a difficult task. Processing soil samples helps to remove debris, chemical components, and biological impurities that can interfere with microbiological detection. This study aimed to optimize a previously used indirect processing protocol, which included a series of washing and centrifugation steps. Optimization of the protocol included: identifying an ideal extraction diluent, variation in the number of wash steps, variation in the initial centrifugation speed, sonication and shaking mechanisms. The optimized protocol was demonstrated at two laboratories in order to evaluate the recovery of spores from loamy and sandy soils. The new protocol demonstrated an improved limit of detection for loamy and sandy soils over the non-optimized protocol with an approximate matrix limit of detection at 14 spores/g of soil. There were no significant differences overall between the two laboratories for either soil type, suggesting that the processing protocol will be robust enough to use at multiple laboratories while achieving comparable recoveries.

Profile-likelihood Confidence Intervals in Item Response Theory Models.

Science.gov (United States)

Chalmers, R Philip; Pek, Jolynn; Liu, Yang

2017-01-01

Confidence intervals (CIs) are fundamental inferential devices which quantify the sampling variability of parameter estimates. In item response theory, CIs have been primarily obtained from large-sample Wald-type approaches based on standard error estimates, derived from the observed or expected information matrix, after parameters have been estimated via maximum likelihood. An alternative approach to constructing CIs is to quantify sampling variability directly from the likelihood function with a technique known as profile-likelihood confidence intervals (PL CIs). In this article, we introduce PL CIs for item response theory models, compare PL CIs to classical large-sample Wald-type CIs, and demonstrate important distinctions among these CIs. CIs are then constructed for parameters directly estimated in the specified model and for transformed parameters which are often obtained post-estimation. Monte Carlo simulation results suggest that PL CIs perform consistently better than Wald-type CIs for both non-transformed and transformed parameters.

Optimized sample preparation for two-dimensional gel electrophoresis of soluble proteins from chicken bursa of Fabricius

Directory of Open Access Journals (Sweden)

Zheng Xiaojuan

2009-10-01

Full Text Available Abstract Background Two-dimensional gel electrophoresis (2-DE is a powerful method to study protein expression and function in living organisms and diseases. This technique, however, has not been applied to avian bursa of Fabricius (BF, a central immune organ. Here, optimized 2-DE sample preparation methodologies were constructed for the chicken BF tissue. Using the optimized protocol, we performed further 2-DE analysis on a soluble protein extract from the BF of chickens infected with virulent avibirnavirus. To demonstrate the quality of the extracted proteins, several differentially expressed protein spots selected were cut from 2-DE gels and identified by matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS. Results An extraction buffer containing 7 M urea, 2 M thiourea, 2% (w/v 3-[(3-cholamidopropyl-dimethylammonio]-1-propanesulfonate (CHAPS, 50 mM dithiothreitol (DTT, 0.2% Bio-Lyte 3/10, 1 mM phenylmethylsulfonyl fluoride (PMSF, 20 U/ml Deoxyribonuclease I (DNase I, and 0.25 mg/ml Ribonuclease A (RNase A, combined with sonication and vortex, yielded the best 2-DE data. Relative to non-frozen immobilized pH gradient (IPG strips, frozen IPG strips did not result in significant changes in the 2-DE patterns after isoelectric focusing (IEF. When the optimized protocol was used to analyze the spleen and thymus, as well as avibirnavirus-infected bursa, high quality 2-DE protein expression profiles were obtained. 2-DE maps of BF of chickens infected with virulent avibirnavirus were visibly different and many differentially expressed proteins were found. Conclusion These results showed that method C, in concert extraction buffer IV, was the most favorable for preparing samples for IEF and subsequent protein separation and yielded the best quality 2-DE patterns. The optimized protocol is a useful sample preparation method for comparative proteomics analysis of chicken BF tissues.

Prediction Interval: What to Expect When You're Expecting … A Replication.

Directory of Open Access Journals (Sweden)

Jeffrey R Spence

Full Text Available A challenge when interpreting replications is determining whether the results of a replication "successfully" replicate the original study. Looking for consistency between two studies is challenging because individual studies are susceptible to many sources of error that can cause study results to deviate from each other and the population effect in unpredictable directions and magnitudes. In the current paper, we derive methods to compute a prediction interval, a range of results that can be expected in a replication due to chance (i.e., sampling error, for means and commonly used indexes of effect size: correlations and d-values. The prediction interval is calculable based on objective study characteristics (i.e., effect size of the original study and sample sizes of the original study and planned replication even when sample sizes across studies are unequal. The prediction interval provides an a priori method for assessing if the difference between an original and replication result is consistent with what can be expected due to sample error alone. We provide open-source software tools that allow researchers, reviewers, replicators, and editors to easily calculate prediction intervals.

Bootstrap confidence intervals for three-way methods

NARCIS (Netherlands)

Kiers, Henk A.L.

Results from exploratory three-way analysis techniques such as CANDECOMP/PARAFAC and Tucker3 analysis are usually presented without giving insight into uncertainties due to sampling. Here a bootstrap procedure is proposed that produces percentile intervals for all output parameters. Special

Optimized Analytical Method to Determine Gallic and Picric Acids in Pyrotechnic Samples by Using HPLC/UV (Reverse Phase)

International Nuclear Information System (INIS)

Garcia Alonso, S.; Perez Pastor, R. M.

2013-01-01

A study on the optimization and development of a chromatographic method for the determination of gallic and picric acids in pyrotechnic samples is presented. In order to achieve this, both analytical conditions by HPLC with diode detection and extraction step of a selected sample were studied. (Author)

An Integrated Method for Interval Multi-Objective Planning of a Water Resource System in the Eastern Part of Handan

Directory of Open Access Journals (Sweden)

Meiqin Suo

2017-07-01

Full Text Available In this study, an integrated solving method is proposed for interval multi-objective planning. The proposed method is based on fuzzy linear programming and an interactive two-step method. It cannot only provide objectively optimal values for multiple objectives at the same time, but also effectively offer a globally optimal interval solution. Meanwhile, the degree of satisfaction related to different objective functions would be obtained. Then, the integrated solving method for interval multi-objective planning is applied to a case study of planning multi-water resources joint scheduling under uncertainty in the eastern part of Handan, China. The solutions obtained are useful for decision makers in easing the contradiction between supply of multi-water resources and demand from different water users. Moreover, it can provide the optimal comprehensive benefits of economy, society, and the environment.

Interpregnancy interval and risk of autistic disorder.

Science.gov (United States)

Gunnes, Nina; Surén, Pål; Bresnahan, Michaeline; Hornig, Mady; Lie, Kari Kveim; Lipkin, W Ian; Magnus, Per; Nilsen, Roy Miodini; Reichborn-Kjennerud, Ted; Schjølberg, Synnve; Susser, Ezra Saul; Øyen, Anne-Siri; Stoltenberg, Camilla

2013-11-01

A recent California study reported increased risk of autistic disorder in children conceived within a year after the birth of a sibling. We assessed the association between interpregnancy interval and risk of autistic disorder using nationwide registry data on pairs of singleton full siblings born in Norway. We defined interpregnancy interval as the time from birth of the first-born child to conception of the second-born child in a sibship. The outcome of interest was autistic disorder in the second-born child. Analyses were restricted to sibships in which the second-born child was born in 1990-2004. Odds ratios (ORs) were estimated by fitting ordinary logistic models and logistic generalized additive models. The study sample included 223,476 singleton full-sibling pairs. In sibships with interpregnancy intervals autistic disorder, compared with 0.13% in the reference category (≥ 36 months). For interpregnancy intervals shorter than 9 months, the adjusted OR of autistic disorder in the second-born child was 2.18 (95% confidence interval 1.42-3.26). The risk of autistic disorder in the second-born child was also increased for interpregnancy intervals of 9-11 months in the adjusted analysis (OR = 1.71 [95% CI = 1.07-2.64]). Consistent with a previous report from California, interpregnancy intervals shorter than 1 year were associated with increased risk of autistic disorder in the second-born child. A possible explanation is depletion of micronutrients in mothers with closely spaced pregnancies.

Optimizing headspace sampling temperature and time for analysis of volatile oxidation products in fish oil

DEFF Research Database (Denmark)

Rørbæk, Karen; Jensen, Benny

1997-01-01

Headspace-gas chromatography (HS-GC), based on adsorption to Tenax GR(R), thermal desorption and GC, has been used for analysis of volatiles in fish oil. To optimize sam sampling conditions, the effect of heating the fish oil at various temperatures and times was evaluated from anisidine values (AV...

Persistent fluctuations in stride intervals under fractal auditory stimulation.

Science.gov (United States)

Marmelat, Vivien; Torre, Kjerstin; Beek, Peter J; Daffertshofer, Andreas

2014-01-01

Stride sequences of healthy gait are characterized by persistent long-range correlations, which become anti-persistent in the presence of an isochronous metronome. The latter phenomenon is of particular interest because auditory cueing is generally considered to reduce stride variability and may hence be beneficial for stabilizing gait. Complex systems tend to match their correlation structure when synchronizing. In gait training, can one capitalize on this tendency by using a fractal metronome rather than an isochronous one? We examined whether auditory cues with fractal variations in inter-beat intervals yield similar fractal inter-stride interval variability as isochronous auditory cueing in two complementary experiments. In Experiment 1, participants walked on a treadmill while being paced by either an isochronous or a fractal metronome with different variation strengths between beats in order to test whether participants managed to synchronize with a fractal metronome and to determine the necessary amount of variability for participants to switch from anti-persistent to persistent inter-stride intervals. Participants did synchronize with the metronome despite its fractal randomness. The corresponding coefficient of variation of inter-beat intervals was fixed in Experiment 2, in which participants walked on a treadmill while being paced by non-isochronous metronomes with different scaling exponents. As expected, inter-stride intervals showed persistent correlations similar to self-paced walking only when cueing contained persistent correlations. Our results open up a new window to optimize rhythmic auditory cueing for gait stabilization by integrating fractal fluctuations in the inter-beat intervals.

Interval methods: An introduction

DEFF Research Database (Denmark)

Achenie, L.E.K.; Kreinovich, V.; Madsen, Kaj

2006-01-01

This chapter contains selected papers presented at the Minisymposium on Interval Methods of the PARA'04 Workshop '' State-of-the-Art in Scientific Computing ''. The emphasis of the workshop was on high-performance computing (HPC). The ongoing development of ever more advanced computers provides...... the potential for solving increasingly difficult computational problems. However, given the complexity of modern computer architectures, the task of realizing this potential needs careful attention. A main concern of HPC is the development of software that optimizes the performance of a given computer....... An important characteristic of the computer performance in scientific computing is the accuracy of the Computation results. Often, we can estimate this accuracy by using traditional statistical techniques. However, in many practical situations, we do not know the probability distributions of different...

Optimization of sampling for the determination of the mean Radium-226 concentration in surface soil

International Nuclear Information System (INIS)

Williams, L.R.; Leggett, R.W.; Espegren, M.L.; Little, C.A.

1987-08-01

This report describes a field experiment that identifies an optimal method for determination of compliance with the US Environmental Protection Agency's Ra-226 guidelines for soil. The primary goals were to establish practical levels of accuracy and precision in estimating the mean Ra-226 concentration of surface soil in a small contaminated region; to obtain empirical information on composite vs. individual soil sampling and on random vs. uniformly spaced sampling; and to examine the practicality of using gamma measurements in predicting the average surface radium concentration and in estimating the number of soil samples required to obtain a given level of accuracy and precision. Numerous soil samples were collected on each six sites known to be contaminated with uranium mill tailings. Three types of samples were collected on each site: 10-composite samples, 20-composite samples, and individual or post hole samples; 10-composite sampling is the method of choice because it yields a given level of accuracy and precision for the least cost. Gamma measurements can be used to reduce surface soil sampling on some sites. 2 refs., 5 figs., 7 tabs

Design and Simulation of MEMS Devices using Interval Analysis

International Nuclear Information System (INIS)

Shanmugavalli, M; Uma, G; Vasuki, B; Umapathy, M

2006-01-01

Modeling and simulation of MEMS devices are used to optimize the design, to improve the performance of the device, to reduce time to market, to minimize development time and cost by avoiding unnecessary design cycles and foundry runs. The major design objectives in any device design, is to meet the required functional parameters and the reliability of the device. The functional parameters depend on the geometry of the structure, material properties and process parameters. All model parameters act as input to optimize the functional parameters. The major difficulty the designer faces is the dimensions and properties used in the simulation of the MEMS devices can not be exactly followed during fabrication. In order to overcome this problem, the designer must test the device in simulation for bound of parameters involved in it. The paper demonstrates the use of interval methods to assess the electromechanical behaviour of micro electromechanical systems (MEMS) under the presence of manufacturing and process uncertainties. Interval method guides the design of pullin voltage analysis of fixed-fixed beam to achieve a robust and reliable design in a most efficient way. The methods are implemented numerically using Coventorware and analytically using Intlab

Transthoracic Doppler echocardiography to predict optimal tube pulsing window for coronary artery CT angiography

Energy Technology Data Exchange (ETDEWEB)

Sun, Gang, E-mail: cjr.sungang@vip.163.com [Department of Medical Imaging, Jinan Military General Hospital, No. 25, Shifan Road, Jinan, Shandong Province 250031 (China); Li, Min, E-mail: limin22000@yahoo.com.cn [Department of Medical Imaging, Jinan Military General Hospital, No. 25, Shifan Road, Jinan, Shandong Province 250031 (China); Jiang, Xiang-sen, E-mail: jiangxiangsen123@126.com [Department of Medical Imaging, Jinan Military General Hospital, No. 25, Shifan Road, Jinan, Shandong Province 250031 (China); Li, Li, E-mail: leely1976@yahoo.com.cn [Department of Medical Imaging, Jinan Military General Hospital, No. 25, Shifan Road, Jinan, Shandong Province 250031 (China); Peng, Zhao-hui, E-mail: zhaohuipeng_R@163.com [Department of Medical Imaging, Jinan Military General Hospital, No. 25, Shifan Road, Jinan, Shandong Province 250031 (China); Mu, Nan-nan, E-mail: munannan22000@sohu.com [Department of Medical Imaging, Jinan Military General Hospital, No. 25, Shifan Road, Jinan, Shandong Province 250031 (China)

2012-09-15

Rationale and objective: To evaluate the feasibility of transthoracic Doppler echocardiography to determine the optimal pulsing windows for CT coronary angiography to narrow the pulsing windows further, especially in higher heart rate. Materials and methods: Doppler was performed on 135 patients before CT scanning. For Doppler, the intervals with minimal motion were evaluated during both systole and diastole integrating electrocardiogram (ECG) intervals. For CT scanning, the retrospective ECG-gating was applied and the optimal reconstruction intervals were determined. The accuracy of Doppler analysis to predict the optimal reconstruction intervals was tested. The predicted length of pulsing windows was compared between Doppler analysis and traditional prospective ECG-gating protocol (heart rate ≦ 65 bpm, 60–76%; 66–79 bpm, 30–77%; ≧80 bpm, 31–47%). Results: According to Doppler analysis, the mean length of intervals with minimal motion in systole was 106.4 ± 39.2 ms and 125.2 ± 92.0 ms in diastole. When the intervals with minimal motion during diastole > 90 ms, the optimal reconstruction intervals were located at diastole; otherwise, at systole (P < 0.001). The optimal reconstruction intervals in 93.8% (132/135) patients could be predicted accurately by Doppler analysis. If the optimal reconstruction intervals predicted by Doppler were applied as the exposure windows, the mean length of pulsing windows should has been 105.2 ± 69.4 ms (range: 26.9–510.3 ms), which was significantly shorter than that of traditional prospective ECG-gating protocol (232.0 ± 120.2 ms, range: 93.2–427.3 ms, P < 0.001). Conclusion: Doppler can help detecting the optimal pulsing windows accurately. Prospective ECG-gating incorporating Doppler analysis may narrow pulsing windows significantly while maintaining image quality.

Transthoracic Doppler echocardiography to predict optimal tube pulsing window for coronary artery CT angiography

International Nuclear Information System (INIS)

Sun, Gang; Li, Min; Jiang, Xiang-sen; Li, Li; Peng, Zhao-hui; Mu, Nan-nan

2012-01-01

Rationale and objective: To evaluate the feasibility of transthoracic Doppler echocardiography to determine the optimal pulsing windows for CT coronary angiography to narrow the pulsing windows further, especially in higher heart rate. Materials and methods: Doppler was performed on 135 patients before CT scanning. For Doppler, the intervals with minimal motion were evaluated during both systole and diastole integrating electrocardiogram (ECG) intervals. For CT scanning, the retrospective ECG-gating was applied and the optimal reconstruction intervals were determined. The accuracy of Doppler analysis to predict the optimal reconstruction intervals was tested. The predicted length of pulsing windows was compared between Doppler analysis and traditional prospective ECG-gating protocol (heart rate ≦ 65 bpm, 60–76%; 66–79 bpm, 30–77%; ≧80 bpm, 31–47%). Results: According to Doppler analysis, the mean length of intervals with minimal motion in systole was 106.4 ± 39.2 ms and 125.2 ± 92.0 ms in diastole. When the intervals with minimal motion during diastole > 90 ms, the optimal reconstruction intervals were located at diastole; otherwise, at systole (P < 0.001). The optimal reconstruction intervals in 93.8% (132/135) patients could be predicted accurately by Doppler analysis. If the optimal reconstruction intervals predicted by Doppler were applied as the exposure windows, the mean length of pulsing windows should has been 105.2 ± 69.4 ms (range: 26.9–510.3 ms), which was significantly shorter than that of traditional prospective ECG-gating protocol (232.0 ± 120.2 ms, range: 93.2–427.3 ms, P < 0.001). Conclusion: Doppler can help detecting the optimal pulsing windows accurately. Prospective ECG-gating incorporating Doppler analysis may narrow pulsing windows significantly while maintaining image quality

Visual Sample Plan (VSP) - FIELDS Integration

Energy Technology Data Exchange (ETDEWEB)

Pulsipher, Brent A.; Wilson, John E.; Gilbert, Richard O.; Hassig, Nancy L.; Carlson, Deborah K.; Bing-Canar, John; Cooper, Brian; Roth, Chuck

2003-04-19

Two software packages, VSP 2.1 and FIELDS 3.5, are being used by environmental scientists to plan the number and type of samples required to meet project objectives, display those samples on maps, query a database of past sample results, produce spatial models of the data, and analyze the data in order to arrive at defensible decisions. VSP 2.0 is an interactive tool to calculate optimal sample size and optimal sample location based on user goals, risk tolerance, and variability in the environment and in lab methods. FIELDS 3.0 is a set of tools to explore the sample results in a variety of ways to make defensible decisions with quantified levels of risk and uncertainty. However, FIELDS 3.0 has a small sample design module. VSP 2.0, on the other hand, has over 20 sampling goals, allowing the user to input site-specific assumptions such as non-normality of sample results, separate variability between field and laboratory measurements, make two-sample comparisons, perform confidence interval estimation, use sequential search sampling methods, and much more. Over 1,000 copies of VSP are in use today. FIELDS is used in nine of the ten U.S. EPA regions, by state regulatory agencies, and most recently by several international countries. Both software packages have been peer-reviewed, enjoy broad usage, and have been accepted by regulatory agencies as well as site project managers as key tools to help collect data and make environmental cleanup decisions. Recently, the two software packages were integrated, allowing the user to take advantage of the many design options of VSP, and the analysis and modeling options of FIELDS. The transition between the two is simple for the user – VSP can be called from within FIELDS, automatically passing a map to VSP and automatically retrieving sample locations and design information when the user returns to FIELDS. This paper will describe the integration, give a demonstration of the integrated package, and give users download

Mist Interval and Hormone Concentration Influence Rooting of Florida and Piedmont Azalea

Science.gov (United States)

Native azalea (Rhododendron spp.) vegetative propagation information is limited. The objective of this experiment is to determine optimal levels of K-IBA and mist intervals for propagation of Florida azalea (Rhododendron austrinum) and Piedmont azalea (Rhododendron canescens). Florida azalea roote...

Fuzzy Investment Portfolio Selection Models Based on Interval Analysis Approach

Directory of Open Access Journals (Sweden)

Haifeng Guo

2012-01-01

Full Text Available This paper employs fuzzy set theory to solve the unintuitive problem of the Markowitz mean-variance (MV portfolio model and extend it to a fuzzy investment portfolio selection model. Our model establishes intervals for expected returns and risk preference, which can take into account investors' different investment appetite and thus can find the optimal resolution for each interval. In the empirical part, we test this model in Chinese stocks investment and find that this model can fulfill different kinds of investors’ objectives. Finally, investment risk can be decreased when we add investment limit to each stock in the portfolio, which indicates our model is useful in practice.

Suitable or optimal noise benefits in signal detection

International Nuclear Information System (INIS)

Liu, Shujun; Yang, Ting; Tang, Mingchun; Wang, Pin; Zhang, Xinzheng

2016-01-01

Highlights: • Six intervals of additive noises divided according to the two constraints. • Derivation of the suitable additive noise to meet the two constraints. • Formulation of the suitable noise for improvability or nonimprovability. • Optimal noises to minimize P FA , maximize P D and maximize the overall improvement. - Abstract: We present an effective way to generate the suitable or the optimal additive noises which can achieve the three goals of the noise enhanced detectability, i.e., the maximum detection probability (P D ), the minimum false alarm probability (P FA ) and the maximum overall improvement of P D and P FA , without increasing P FA and decreasing P D in a binary hypothesis testing problem. The mechanism of our method is that we divide the discrete vectors into six intervals and choose the useful or partial useful vectors from these intervals to form the additive noise according to different requirements. The form of the optimal noise is derived and proven as a randomization of no more than two discrete vectors in our way. Moreover, how to choose suitable and optimal noises from the six intervals are given. Finally, numerous examples are presented to illustrate the theoretical analysis, where the background noises are Gaussian, symmetric and asymmetric Gaussian mixture noise, respectively.

A spreadsheet template compatible with Microsoft Excel and iWork Numbers that returns the simultaneous confidence intervals for all pairwise differences between multiple sample means.

Science.gov (United States)

Brown, Angus M

2010-04-01

The objective of the method described in this paper is to develop a spreadsheet template for the purpose of comparing multiple sample means. An initial analysis of variance (ANOVA) test on the data returns F--the test statistic. If F is larger than the critical F value drawn from the F distribution at the appropriate degrees of freedom, convention dictates rejection of the null hypothesis and allows subsequent multiple comparison testing to determine where the inequalities between the sample means lie. A variety of multiple comparison methods are described that return the 95% confidence intervals for differences between means using an inclusive pairwise comparison of the sample means. 2009 Elsevier Ireland Ltd. All rights reserved.

Optimal cross-sectional sampling for river modelling with bridges: An information theory-based method

Energy Technology Data Exchange (ETDEWEB)

Ridolfi, E.; Napolitano, F., E-mail: francesco.napolitano@uniroma1.it [Sapienza Università di Roma, Dipartimento di Ingegneria Civile, Edile e Ambientale (Italy); Alfonso, L. [Hydroinformatics Chair Group, UNESCO-IHE, Delft (Netherlands); Di Baldassarre, G. [Department of Earth Sciences, Program for Air, Water and Landscape Sciences, Uppsala University (Sweden)

2016-06-08

The description of river topography has a crucial role in accurate one-dimensional (1D) hydraulic modelling. Specifically, cross-sectional data define the riverbed elevation, the flood-prone area, and thus, the hydraulic behavior of the river. Here, the problem of the optimal cross-sectional spacing is solved through an information theory-based concept. The optimal subset of locations is the one with the maximum information content and the minimum amount of redundancy. The original contribution is the introduction of a methodology to sample river cross sections in the presence of bridges. The approach is tested on the Grosseto River (IT) and is compared to existing guidelines. The results show that the information theory-based approach can support traditional methods to estimate rivers’ cross-sectional spacing.

Optimal cross-sectional sampling for river modelling with bridges: An information theory-based method

International Nuclear Information System (INIS)

Ridolfi, E.; Napolitano, F.; Alfonso, L.; Di Baldassarre, G.

2016-01-01

The description of river topography has a crucial role in accurate one-dimensional (1D) hydraulic modelling. Specifically, cross-sectional data define the riverbed elevation, the flood-prone area, and thus, the hydraulic behavior of the river. Here, the problem of the optimal cross-sectional spacing is solved through an information theory-based concept. The optimal subset of locations is the one with the maximum information content and the minimum amount of redundancy. The original contribution is the introduction of a methodology to sample river cross sections in the presence of bridges. The approach is tested on the Grosseto River (IT) and is compared to existing guidelines. The results show that the information theory-based approach can support traditional methods to estimate rivers’ cross-sectional spacing.

Resampling methods in Microsoft Excel® for estimating reference intervals.

Science.gov (United States)

Theodorsson, Elvar

2015-01-01

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.

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States.

Directory of Open Access Journals (Sweden)

Andreas Steimer

Full Text Available Oscillations between high and low values of the membrane potential (UP and DOWN states respectively are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon's implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs of the exponential integrate and fire (EIF model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike's preceding ISI. As we show, the EIF's exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron's ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing

Random Sampling with Interspike-Intervals of the Exponential Integrate and Fire Neuron: A Computational Interpretation of UP-States.

Science.gov (United States)

Steimer, Andreas; Schindler, Kaspar

2015-01-01

Oscillations between high and low values of the membrane potential (UP and DOWN states respectively) are an ubiquitous feature of cortical neurons during slow wave sleep and anesthesia. Nevertheless, a surprisingly small number of quantitative studies have been conducted only that deal with this phenomenon's implications for computation. Here we present a novel theory that explains on a detailed mathematical level the computational benefits of UP states. The theory is based on random sampling by means of interspike intervals (ISIs) of the exponential integrate and fire (EIF) model neuron, such that each spike is considered a sample, whose analog value corresponds to the spike's preceding ISI. As we show, the EIF's exponential sodium current, that kicks in when balancing a noisy membrane potential around values close to the firing threshold, leads to a particularly simple, approximative relationship between the neuron's ISI distribution and input current. Approximation quality depends on the frequency spectrum of the current and is improved upon increasing the voltage baseline towards threshold. Thus, the conceptually simpler leaky integrate and fire neuron that is missing such an additional current boost performs consistently worse than the EIF and does not improve when voltage baseline is increased. For the EIF in contrast, the presented mechanism is particularly effective in the high-conductance regime, which is a hallmark feature of UP-states. Our theoretical results are confirmed by accompanying simulations, which were conducted for input currents of varying spectral composition. Moreover, we provide analytical estimations of the range of ISI distributions the EIF neuron can sample from at a given approximation level. Such samples may be considered by any algorithmic procedure that is based on random sampling, such as Markov Chain Monte Carlo or message-passing methods. Finally, we explain how spike-based random sampling relates to existing computational

The Applicability of Confidence Intervals of Quantiles for the Generalized Logistic Distribution

Science.gov (United States)

Shin, H.; Heo, J.; Kim, T.; Jung, Y.

2007-12-01

The generalized logistic (GL) distribution has been widely used for frequency analysis. However, there is a little study related to the confidence intervals that indicate the prediction accuracy of distribution for the GL distribution. In this paper, the estimation of the confidence intervals of quantiles for the GL distribution is presented based on the method of moments (MOM), maximum likelihood (ML), and probability weighted moments (PWM) and the asymptotic variances of each quantile estimator are derived as functions of the sample sizes, return periods, and parameters. Monte Carlo simulation experiments are also performed to verify the applicability of the derived confidence intervals of quantile. As the results, the relative bias (RBIAS) and relative root mean square error (RRMSE) of the confidence intervals generally increase as return period increases and reverse as sample size increases. And PWM for estimating the confidence intervals performs better than the other methods in terms of RRMSE when the data is almost symmetric while ML shows the smallest RBIAS and RRMSE when the data is more skewed and sample size is moderately large. The GL model was applied to fit the distribution of annual maximum rainfall data. The results show that there are little differences in the estimated quantiles between ML and PWM while distinct differences in MOM.

Reference Intervals for Urinary Cotinine Levels and the Influence of Sampling Time and Other Predictors on Its Excretion Among Italian Schoolchildren

Directory of Open Access Journals (Sweden)

Carmela Protano

2018-04-01

Full Text Available (1 Background: Environmental Tobacco Smoke (ETS exposure remains a public health problem worldwide. The aims are to establish urinary (u- cotinine reference values for healthy Italian children, to evaluate the role of the sampling time and of other factors on children’s u-cotinine excretion. (2 Methods: A cross-sectional study was performed on 330 children. Information on participants was gathered by a questionnaire and u-cotinine was determined in two samples for each child, collected during the evening and the next morning. (3 Results: Reference intervals (as the 2.5th and 97.5th percentiles of the distribution in evening and morning samples were respectively equal to 0.98–4.29 and 0.91–4.50 µg L−1 (ETS unexposed and 1.39–16.34 and 1.49–20.95 µg L−1 (ETS exposed. No statistical differences were recovered between median values found in evening and morning samples, both in ETS unexposed and exposed. Significant predictors of u-cotinine excretions were ponderal status according to body mass index of children (β = 0.202; p-value = 0.041 for evening samples; β = 0.169; p-value = 0.039 for morning samples and paternal educational level (β = −0.258; p-value = 0.010; for evening samples; β = −0.013; p-value = 0.003 for morning samples. (4 Conclusions: The results evidenced the need of further studies for assessing the role of confounding factors on ETS exposure, and the necessity of educational interventions on smokers for rising their awareness about ETS.

Sample-Path Optimal Stationary Policies in Stable Markov Decision Chains with Average Reward Criterion

Czech Academy of Sciences Publication Activity Database

Cavazos-Cadena, R.; Montes-de-Oca, R.; Sladký, Karel

2015-01-01

Roč. 52, č. 2 (2015), s. 419-440 ISSN 0021-9002 Grant - others:GA AV ČR(CZ) 171396 Institutional support: RVO:67985556 Keywords : Dominated Convergence theorem for the expected average criterion * Discrepancy function * Kolmogorov inequality * Innovations * Strong sample-path optimality Subject RIV: BC - Control Systems Theory Impact factor: 0.665, year: 2015 http://library.utia.cas.cz/separaty/2015/E/sladky-0449029.pdf

Optimization of a radiochemistry method for plutonium determination in biological samples

International Nuclear Information System (INIS)

Cerchetti, Maria L.; Arguelles, Maria G.

2005-01-01

Plutonium has been widely used for civilian an military activities. Nevertheless, the methods to control work exposition have not evolved in the same way, remaining as one of the major challengers for the radiological protection practice. Due to the low acceptable incorporation limit, the usual determination is based on indirect methods in urine samples. Our main objective was to optimize a technique used to monitor internal contamination of workers exposed to Plutonium isotopes. Different parameters were modified and their influence on the three steps of the method was evaluated. Those which gave the highest yield and feasibility were selected. The method involves: 1-) Sample concentration (coprecipitation); 2-) Plutonium purification; and 3-) Source preparation by electrodeposition. On the coprecipitation phase, changes on temperature and concentration of the carrier were evaluated. On the ion-exchange separation, changes on the type of the resin, elution solution for hydroxylamine (concentration and volume), length and column recycle were evaluated. Finally, on the electrodeposition phase, we modified the following: electrolytic solution, pH and time. Measures were made by liquid scintillation counting and alpha spectrometry (PIPS). We obtained the following yields: 88% for coprecipitation (at 60 C degree with 2 ml of CaHPO 4 ), 71% for ion-exchange (resins AG 1x8 Cl - 100-200 mesh, hydroxylamine 0.1N in HCl 0.2N as eluent, column between 4.5 and 8 cm), and 93% for electrodeposition (H 2 SO 4 -NH 4 OH, 100 minutes and pH from 2 to 2.8). The expand uncertainty was 30% (NC 95%), the decision threshold (Lc) was 0.102 Bq/L and the minimum detectable activity was 0.218 Bq/L of urine. We obtained an optimized method to screen workers exposed to Plutonium. (author)

Optimizing Groundwater Monitoring Networks Using Integrated Statistical and Geostatistical Approaches

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Jay Krishna Thakur

2015-08-01

Full Text Available The aim of this work is to investigate new approaches using methods based on statistics and geo-statistics for spatio-temporal optimization of groundwater monitoring networks. The formulated and integrated methods were tested with the groundwater quality data set of Bitterfeld/Wolfen, Germany. Spatially, the monitoring network was optimized using geo-statistical methods. Temporal optimization of the monitoring network was carried out using Sen’s method (1968. For geostatistical network optimization, a geostatistical spatio-temporal algorithm was used to identify redundant wells in 2- and 2.5-D Quaternary and Tertiary aquifers. Influences of interpolation block width, dimension, contaminant association, groundwater flow direction and aquifer homogeneity on statistical and geostatistical methods for monitoring network optimization were analysed. The integrated approach shows 37% and 28% redundancies in the monitoring network in Quaternary aquifer and Tertiary aquifer respectively. The geostatistical method also recommends 41 and 22 new monitoring wells in the Quaternary and Tertiary aquifers respectively. In temporal optimization, an overall optimized sampling interval was recommended in terms of lower quartile (238 days, median quartile (317 days and upper quartile (401 days in the research area of Bitterfeld/Wolfen. Demonstrated methods for improving groundwater monitoring network can be used in real monitoring network optimization with due consideration given to influencing factors.

Does the time interval between antimüllerian hormone serum sampling and initiation of ovarian stimulation affect its predictive ability in in vitro fertilization-intracytoplasmic sperm injection cycles with a gonadotropin-releasing hormone antagonist?

DEFF Research Database (Denmark)

Polyzos, Nikolaos P; Nelson, Scott M; Stoop, Dominic

2013-01-01

To investigate whether the time interval between serum antimüllerian hormone (AMH) sampling and initiation of ovarian stimulation for in vitro fertilization-intracytoplasmic sperm injection (IVF-ICSI) may affect the predictive ability of the marker for low and excessive ovarian response.......To investigate whether the time interval between serum antimüllerian hormone (AMH) sampling and initiation of ovarian stimulation for in vitro fertilization-intracytoplasmic sperm injection (IVF-ICSI) may affect the predictive ability of the marker for low and excessive ovarian response....

Convex Interval Games

NARCIS (Netherlands)

Alparslan-Gok, S.Z.; Brânzei, R.; Tijs, S.H.

2008-01-01

In this paper, convex interval games are introduced and some characterizations are given. Some economic situations leading to convex interval games are discussed. The Weber set and the Shapley value are defined for a suitable class of interval games and their relations with the interval core for

Relation of increased short-term variability of QT interval to congenital long-QT syndrome

DEFF Research Database (Denmark)

Hinterseer, Martin; Beckmann, Britt-Maria; Thomsen, Morten B

2009-01-01

Apart from clinical symptoms the diagnosis and risk stratification in long-QT syndrome (LQTS) is usually based on the surface electrocardiogram. Studies have indicated that not only prolongation of the QT interval but also an increased short-term variability of QT interval (STV(QT)) is a marker...... that an STV(QT) of 4.9 ms was the optimal cut-off value to predict mutation carriers. When incorporating an STV(QT) >4.9 ms for those whose QTc interval was within the normal limits, sensitivity to distinguish mutation carriers increased to 83% with a specificity of 68%, so that another 15 mutation carriers...

PETA KENDALI X DENGAN UKURAN SAMPEL DAN INTERVAL PENGAMBILAN SAMPEL YANG BERVARIASI

Directory of Open Access Journals (Sweden)

Tanti Octavia

2000-01-01

Full Text Available Shewhart X chart is widely used in statistical process control for monitoring variable data and has shown good performance in detecting large mean shift but less sensitive in detecting moderate to small process shift. X chart with variable sample size and sampling interval (VSSI X chart is proposed to enhance the ability of detecting moderate to small process shift. The performance of VSSI X chart is compared with those of Shewhart X chart, VSS X chart (Variable Sample Size X chart and VSI X chart (Variable Sampling Interval X chart. Performance of these control charts is presented in the form of ATS (Average Time to Signal which is obtained from computer simulation and markov chain approach. The VSSI X chart shows better performance in detecting moderate mean shift. The simulation is then continued for VSSI X chart and VSS X chart with minimum sample size n 1=1 and n 1=2. Abstract in Bahasa Indonesia : Peta kendali X Shewhart telah umum digunakan dalam pengendalian proses statistis untuk data variabel dan terbukti berfungsi dengan baik untuk mendeteksi pergeseran rerata yang besar, namun kurang cepat dalam mendeteksi pergeseran rerata yang sedang hingga kecil. Untuk mengatasi kelemahan ini, diusulkan penggunaan peta kendali X dengan ukuran sampel dan interval pengambilan sampel yang bervariasi (peta kendali VSSI. Kinerja peta kendali X VSSI dibandingkan dengan kinerja peta kendali Shewhart, peta kendali X VSS (peta kendali X dengan ukuran sampel yang bervariasi, dan peta kendali X VSI (peta kendali X dengan interval waktu pengambilan sampel yang bervariasi. Kinerja peta kendali dinyatakan dalam nilai ATS (Average Time to Signal yang didapatkan dari hasil simulasi program komputer maupun perhitungan Rantai Markov. Peta kendali X VSSI terbukti mempunyai kinerja yang lebih baik dalam mendeteksi pergeseran rerata yang sedang. Selain itu juga disimulasikan penggunaan peta kendali X VSSI dan peta kendali X VSS dengan ukuran sampel minimum n1=1 dan n1

Optimization of Sample Preparation for the Identification and Quantification of Saxitoxin in Proficiency Test Mussel Sample using Liquid Chromatography-Tandem Mass Spectrometry

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Kirsi Harju

2015-11-01

Full Text Available Saxitoxin (STX and some selected paralytic shellfish poisoning (PSP analogues in mussel samples were identified and quantified with liquid chromatography-tandem mass spectrometry (LC-MS/MS. Sample extraction and purification methods of mussel sample were optimized for LC-MS/MS analysis. The developed method was applied to the analysis of the homogenized mussel samples in the proficiency test (PT within the EQuATox project (Establishment of Quality Assurance for the Detection of Biological Toxins of Potential Bioterrorism Risk. Ten laboratories from eight countries participated in the STX PT. Identification of PSP toxins in naturally contaminated mussel samples was performed by comparison of product ion spectra and retention times with those of reference standards. The quantitative results were obtained with LC-MS/MS by spiking reference standards in toxic mussel extracts. The results were within the z-score of ±1 when compared to the results measured with the official AOAC (Association of Official Analytical Chemists method 2005.06, pre-column oxidation high-performance liquid chromatography with fluorescence detection (HPLC-FLD.

Persistent fluctuations in stride intervals under fractal auditory stimulation.

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Vivien Marmelat

Full Text Available Stride sequences of healthy gait are characterized by persistent long-range correlations, which become anti-persistent in the presence of an isochronous metronome. The latter phenomenon is of particular interest because auditory cueing is generally considered to reduce stride variability and may hence be beneficial for stabilizing gait. Complex systems tend to match their correlation structure when synchronizing. In gait training, can one capitalize on this tendency by using a fractal metronome rather than an isochronous one? We examined whether auditory cues with fractal variations in inter-beat intervals yield similar fractal inter-stride interval variability as isochronous auditory cueing in two complementary experiments. In Experiment 1, participants walked on a treadmill while being paced by either an isochronous or a fractal metronome with different variation strengths between beats in order to test whether participants managed to synchronize with a fractal metronome and to determine the necessary amount of variability for participants to switch from anti-persistent to persistent inter-stride intervals. Participants did synchronize with the metronome despite its fractal randomness. The corresponding coefficient of variation of inter-beat intervals was fixed in Experiment 2, in which participants walked on a treadmill while being paced by non-isochronous metronomes with different scaling exponents. As expected, inter-stride intervals showed persistent correlations similar to self-paced walking only when cueing contained persistent correlations. Our results open up a new window to optimize rhythmic auditory cueing for gait stabilization by integrating fractal fluctuations in the inter-beat intervals.

Uncertainty analysis of neural network based flood forecasting models: An ensemble based approach for constructing prediction interval

Science.gov (United States)

Kasiviswanathan, K.; Sudheer, K.

2013-05-01

Artificial neural network (ANN) based hydrologic models have gained lot of attention among water resources engineers and scientists, owing to their potential for accurate prediction of flood flows as compared to conceptual or physics based hydrologic models. The ANN approximates the non-linear functional relationship between the complex hydrologic variables in arriving at the river flow forecast values. Despite a large number of applications, there is still some criticism that ANN's point prediction lacks in reliability since the uncertainty of predictions are not quantified, and it limits its use in practical applications. A major concern in application of traditional uncertainty analysis techniques on neural network framework is its parallel computing architecture with large degrees of freedom, which makes the uncertainty assessment a challenging task. Very limited studies have considered assessment of predictive uncertainty of ANN based hydrologic models. In this study, a novel method is proposed that help construct the prediction interval of ANN flood forecasting model during calibration itself. The method is designed to have two stages of optimization during calibration: at stage 1, the ANN model is trained with genetic algorithm (GA) to obtain optimal set of weights and biases vector, and during stage 2, the optimal variability of ANN parameters (obtained in stage 1) is identified so as to create an ensemble of predictions. During the 2nd stage, the optimization is performed with multiple objectives, (i) minimum residual variance for the ensemble mean, (ii) maximum measured data points to fall within the estimated prediction interval and (iii) minimum width of prediction interval. The method is illustrated using a real world case study of an Indian basin. The method was able to produce an ensemble that has an average prediction interval width of 23.03 m3/s, with 97.17% of the total validation data points (measured) lying within the interval. The derived

Optimal sampling strategy for data mining

International Nuclear Information System (INIS)

Ghaffar, A.; Shahbaz, M.; Mahmood, W.

2013-01-01

Latest technology like Internet, corporate intranets, data warehouses, ERP's, satellites, digital sensors, embedded systems, mobiles networks all are generating such a massive amount of data that it is getting very difficult to analyze and understand all these data, even using data mining tools. Huge datasets are becoming a difficult challenge for classification algorithms. With increasing amounts of data, data mining algorithms are getting slower and analysis is getting less interactive. Sampling can be a solution. Using a fraction of computing resources, Sampling can often provide same level of accuracy. The process of sampling requires much care because there are many factors involved in the determination of correct sample size. The approach proposed in this paper tries to find a solution to this problem. Based on a statistical formula, after setting some parameters, it returns a sample size called s ufficient sample size , which is then selected through probability sampling. Results indicate the usefulness of this technique in coping with the problem of huge datasets. (author)

Bootstrap Prediction Intervals in Non-Parametric Regression with Applications to Anomaly Detection

Science.gov (United States)

Kumar, Sricharan; Srivistava, Ashok N.

2012-01-01

Prediction intervals provide a measure of the probable interval in which the outputs of a regression model can be expected to occur. Subsequently, these prediction intervals can be used to determine if the observed output is anomalous or not, conditioned on the input. In this paper, a procedure for determining prediction intervals for outputs of nonparametric regression models using bootstrap methods is proposed. Bootstrap methods allow for a non-parametric approach to computing prediction intervals with no specific assumptions about the sampling distribution of the noise or the data. The asymptotic fidelity of the proposed prediction intervals is theoretically proved. Subsequently, the validity of the bootstrap based prediction intervals is illustrated via simulations. Finally, the bootstrap prediction intervals are applied to the problem of anomaly detection on aviation data.

Multifactor analysis of multiscaling in volatility return intervals.

Science.gov (United States)

Wang, Fengzhong; Yamasaki, Kazuko; Havlin, Shlomo; Stanley, H Eugene

2009-01-01

We study the volatility time series of 1137 most traded stocks in the U.S. stock markets for the two-year period 2001-2002 and analyze their return intervals tau , which are time intervals between volatilities above a given threshold q . We explore the probability density function of tau , P_(q)(tau) , assuming a stretched exponential function, P_(q)(tau) approximately e;(-tau;(gamma)) . We find that the exponent gamma depends on the threshold in the range between q=1 and 6 standard deviations of the volatility. This finding supports the multiscaling nature of the return interval distribution. To better understand the multiscaling origin, we study how gamma depends on four essential factors, capitalization, risk, number of trades, and return. We show that gamma depends on the capitalization, risk, and return but almost does not depend on the number of trades. This suggests that gamma relates to the portfolio selection but not on the market activity. To further characterize the multiscaling of individual stocks, we fit the moments of tau , mu_(m) identical with(tautau);(m);(1m) , in the range of 10portfolio optimization.

Multifactor analysis of multiscaling in volatility return intervals

Science.gov (United States)

Wang, Fengzhong; Yamasaki, Kazuko; Havlin, Shlomo; Stanley, H. Eugene

2009-01-01

We study the volatility time series of 1137 most traded stocks in the U.S. stock markets for the two-year period 2001-2002 and analyze their return intervals τ , which are time intervals between volatilities above a given threshold q . We explore the probability density function of τ , Pq(τ) , assuming a stretched exponential function, Pq(τ)˜e-τγ . We find that the exponent γ depends on the threshold in the range between q=1 and 6 standard deviations of the volatility. This finding supports the multiscaling nature of the return interval distribution. To better understand the multiscaling origin, we study how γ depends on four essential factors, capitalization, risk, number of trades, and return. We show that γ depends on the capitalization, risk, and return but almost does not depend on the number of trades. This suggests that γ relates to the portfolio selection but not on the market activity. To further characterize the multiscaling of individual stocks, we fit the moments of τ , μm≡⟨(τ/⟨τ⟩)m⟩1/m , in the range of 10portfolio optimization.

Evaluation of optimization strategies and the effect of initial conditions on IMAT optimization using a leaf position optimization algorithm

International Nuclear Information System (INIS)

Oliver, Mike; Jensen, Michael; Chen, Jeff; Wong, Eugene

2009-01-01

Intensity-modulated arc therapy (IMAT) is a rotational variant of intensity-modulated radiation therapy (IMRT) that can be implemented with or without angular dose rate variation. The purpose of this study is to assess optimization strategies and initial conditions using a leaf position optimization (LPO) algorithm altered for variable dose rate IMAT. A concave planning target volume (PTV) with a central cylindrical organ at risk (OAR) was used in this study. The initial IMAT arcs were approximated by multiple static beams at 5 deg. angular increments where multi-leaf collimator (MLC) leaf positions were determined from the beam's eye view to irradiate the PTV but avoid the OAR. For the optimization strategy, two arcs with arc ranges of 280 deg. and 150 deg. were employed and plans were created using LPO alone, variable dose rate optimization (VDRO) alone, simultaneous LPO and VDRO and sequential combinations of these strategies. To assess the MLC initialization effect, three single 360 deg. arc plans with different initial MLC configurations were generated using the simultaneous LPO and VDRO. The effect of changing optimization degrees of freedom was investigated by employing 3 deg., 5 deg. and 10 deg. angular sampling intervals for the two 280 deg., two 150 deg. and single arc plans using LPO and VDRO. The objective function value, a conformity index, a dose homogeneity index, mean dose to OAR and normal tissues were computed and used to evaluate the treatment plans. This study shows that the best optimization strategy for a concave target is to use simultaneous MLC LPO and VDRO. We found that the optimization result is sensitive to the choice of initial MLC aperture shapes suggesting that an LPO-based IMAT plan may not be able to overcome local minima for this geometry. In conclusion, simultaneous MLC leaf position and VDRO are needed with the most appropriate initial conditions (MLC positions, arc ranges and number of arcs) for IMAT.

Relationships between depressive symptoms and perceived social support, self-esteem, & optimism in a sample of rural adolescents.

Science.gov (United States)

Weber, Scott; Puskar, Kathryn Rose; Ren, Dianxu

2010-09-01

Stress, developmental changes and social adjustment problems can be significant in rural teens. Screening for psychosocial problems by teachers and other school personnel is infrequent but can be a useful health promotion strategy. We used a cross-sectional survey descriptive design to examine the inter-relationships between depressive symptoms and perceived social support, self-esteem, and optimism in a sample of rural school-based adolescents. Depressive symptoms were negatively correlated with peer social support, family social support, self-esteem, and optimism. Findings underscore the importance for teachers and other school staff to provide health education. Results can be used as the basis for education to improve optimism, self-esteem, social supports and, thus, depression symptoms of teens.

Optimization of a method based on micro-matrix solid-phase dispersion (micro-MSPD for the determination of PCBs in mussel samples

Directory of Open Access Journals (Sweden)

Nieves Carro

2017-03-01

Full Text Available This paper reports the development and optimization of micro-matrix solid-phase dispersion (micro-MSPD of nine polychlorinated biphenyls (PCBs in mussel samples (Mytilus galloprovincialis by using a two-level factorial design. Four variables (amount of sample, anhydrous sodium sulphate, Florisil and solvent volume were considered as factors in the optimization process. The results suggested that only the interaction between the amount of anhydrous sodium sulphate and the solvent volume was statistically significant for the overall recovery of a trichlorinated compound, CB 28. Generally most of the considered species exhibited a similar behaviour, the sample and Florisil amounts had a positive effect on PCBs extractions and solvent volume and sulphate amount had a negative effect. The analytical determination and confirmation of PCBs were carried out by using GC-ECD and GC-MS/MS, respectively. The method was validated having satisfactory precision and accuracy with RSD values below 6% and recoveries between 81 and 116% for all congeners. The optimized method was applied to the extraction of real mussel samples from two Galician Rías.

Acute beneficial hemodynamic effects of a novel 3D-echocardiographic optimization protocol in cardiac resynchronization therapy.

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Carolin Sonne

Full Text Available BACKGROUND: Post-implantation therapies to optimize cardiac resynchronization therapy (CRT focus on adjustments of the atrio-ventricular (AV delay and ventricular-to-ventricular (VV interval. However, there is little consensus on how to achieve best resynchronization with these parameters. The aim of this study was to examine a novel combination of doppler echocardiography (DE and three-dimensional echocardiography (3DE for individualized optimization of device based AV delays and VV intervals compared to empiric programming. METHODS: 25 recipients of CRT (male: 56%, mean age: 67 years were included in this study. Ejection fraction (EF, the primary outcome parameter, and left ventricular (LV dimensions were evaluated by 3DE before CRT (baseline, after AV delay optimization while pacing the ventricles simultaneously (empiric VV interval programming and after individualized VV interval optimization. For AV delay optimization aortic velocity time integral (AoVTI was examined in eight different AV delays, and the AV delay with the highest AoVTI was programmed. For individualized VV interval optimization 3DE full-volume datasets of the left ventricle were obtained and analyzed to derive a systolic dyssynchrony index (SDI, calculated from the dispersion of time to minimal regional volume for all 16 LV segments. Consecutively, SDI was evaluated in six different VV intervals (including LV or right ventricular preactivation, and the VV interval with the lowest SDI was programmed (individualized optimization. RESULTS: EF increased from baseline 23±7% to 30±8 (p<0.001 after AV delay optimization and to 32±8% (p<0.05 after individualized optimization with an associated decrease of end-systolic volume from a baseline of 138±60 ml to 115±42 ml (p<0.001. Moreover, individualized optimization significantly reduced SDI from a baseline of 14.3±5.5% to 6.1±2.6% (p<0.001. CONCLUSIONS: Compared with empiric programming of biventricular pacemakers

Penentuan Interval Waktu Penggantian Optimal Komponen Berdasarkan Model Opportunity Based-Age Replacement

OpenAIRE

Giatman, Muhammad

2008-01-01

Maintenance system, especially replacement that is not good, can cause much lose out for the company. The lose out is caused production process disturbing bay unexpectadly or unscheduled replacement. This will lose out for factory that have continue flow shop type, because replacement of the component that is need shut down machine will cause all machine in the process production stop. To anticipate of lose out that cause by replacement activity, so in this research will search interval of op...

Foam generation and sample composition optimization for the FOAM-C experiment of the ISS

International Nuclear Information System (INIS)

Carpy, R; Picker, G; Amann, B; Ranebo, H; Vincent-Bonnieu, S; Minster, O; Winter, J; Dettmann, J; Castiglione, L; Höhler, R; Langevin, D

2011-01-01

End of 2009 and early 2010 a sealed cell, for foam generation and observation, has been designed and manufactured at Astrium Friedrichshafen facilities. With the use of this cell, different sample compositions of 'wet foams' have been optimized for mixtures of chemicals such as water, dodecanol, pluronic, aethoxisclerol, glycerol, CTAB, SDS, as well as glass beads. This development is performed in the frame of the breadboarding development activities of the Experiment Container FOAM-C for operation in the ISS Fluid Science Laboratory (ISS). The sample cell supports multiple observation methods such as: Diffusing-Wave and Diffuse Transmission Spectrometry, Time Resolved Correlation Spectroscopy and microscope observation, all of these methods are applied in the cell with a relatively small experiment volume 3 . These units, will be on orbit replaceable sets, that will allow multiple sample compositions processing (in the range of >40).

Global robust exponential stability analysis for interval recurrent neural networks

International Nuclear Information System (INIS)

Xu Shengyuan; Lam, James; Ho, Daniel W.C.; Zou Yun

2004-01-01

This Letter investigates the problem of robust global exponential stability analysis for interval recurrent neural networks (RNNs) via the linear matrix inequality (LMI) approach. The values of the time-invariant uncertain parameters are assumed to be bounded within given compact sets. An improved condition for the existence of a unique equilibrium point and its global exponential stability of RNNs with known parameters is proposed. Based on this, a sufficient condition for the global robust exponential stability for interval RNNs is obtained. Both of the conditions are expressed in terms of LMIs, which can be checked easily by various recently developed convex optimization algorithms. Examples are provided to demonstrate the reduced conservatism of the proposed exponential stability condition

The prognostic value of the QT interval and QT interval dispersion in all-cause and cardiac mortality and morbidity in a population of Danish citizens.

Science.gov (United States)

Elming, H; Holm, E; Jun, L; Torp-Pedersen, C; Køber, L; Kircshoff, M; Malik, M; Camm, J

1998-09-01

To evaluate the prognostic value of the QT interval and QT interval dispersion in total and in cardiovascular mortality, as well as in cardiac morbidity, in a general population. The QT interval was measured in all leads from a standard 12-lead ECG in a random sample of 1658 women and 1797 men aged 30-60 years. QT interval dispersion was calculated from the maximal difference between QT intervals in any two leads. All cause mortality over 13 years, and cardiovascular mortality as well as cardiac morbidity over 11 years, were the main outcome parameters. Subjects with a prolonged QT interval (430 ms or more) or prolonged QT interval dispersion (80 ms or more) were at higher risk of cardiovascular death and cardiac morbidity than subjects whose QT interval was less than 360 ms, or whose QT interval dispersion was less than 30 ms. Cardiovascular death relative risk ratios, adjusted for age, gender, myocardial infarct, angina pectoris, diabetes mellitus, arterial hypertension, smoking habits, serum cholesterol level, and heart rate were 2.9 for the QT interval (95% confidence interval 1.1-7.8) and 4.4 for QT interval dispersion (95% confidence interval 1.0-19-1). Fatal and non-fatal cardiac morbidity relative risk ratios were similar, at 2.7 (95% confidence interval 1.4-5.5) for the QT interval and 2.2 (95% confidence interval 1.1-4.0) for QT interval dispersion. Prolongation of the QT interval and QT interval dispersion independently affected the prognosis of cardiovascular mortality and cardiac fatal and non-fatal morbidity in a general population over 11 years.

Restricted Interval Valued Neutrosophic Sets and Restricted Interval Valued Neutrosophic Topological Spaces

Directory of Open Access Journals (Sweden)

Anjan Mukherjee

2016-08-01

Full Text Available In this paper we introduce the concept of restricted interval valued neutrosophic sets (RIVNS in short. Some basic operations and properties of RIVNS are discussed. The concept of restricted interval valued neutrosophic topology is also introduced together with restricted interval valued neutrosophic finer and restricted interval valued neutrosophic coarser topology. We also define restricted interval valued neutrosophic interior and closer of a restricted interval valued neutrosophic set. Some theorems and examples are cites. Restricted interval valued neutrosophic subspace topology is also studied.

The optimally sampled galaxy-wide stellar initial mass function. Observational tests and the publicly available GalIMF code

Science.gov (United States)

Yan, Zhiqiang; Jerabkova, Tereza; Kroupa, Pavel

2017-11-01

Here we present a full description of the integrated galaxy-wide initial mass function (IGIMF) theory in terms of the optimal sampling and compare it with available observations. Optimal sampling is the method we use to discretize the IMF deterministically into stellar masses. Evidence indicates that nature may be closer to deterministic sampling as observations suggest a smaller scatter of various relevant observables than random sampling would give, which may result from a high level of self-regulation during the star formation process. We document the variation of IGIMFs under various assumptions. The results of the IGIMF theory are consistent with the empirical relation between the total mass of a star cluster and the mass of its most massive star, and the empirical relation between the star formation rate (SFR) of a galaxy and the mass of its most massive cluster. Particularly, we note a natural agreement with the empirical relation between the IMF power-law index and the SFR of a galaxy. The IGIMF also results in a relation between the SFR of a galaxy and the mass of its most massive star such that, if there were no binaries, galaxies with SFR first time, we show optimally sampled galaxy-wide IMFs (OSGIMF) that mimic the IGIMF with an additional serrated feature. Finally, a Python module, GalIMF, is provided allowing the calculation of the IGIMF and OSGIMF dependent on the galaxy-wide SFR and metallicity. A copy of the python code model is available at the CDS via anonymous ftp to http://cdsarc.u-strasbg.fr (http://130.79.128.5) or via http://cdsarc.u-strasbg.fr/viz-bin/qcat?J/A+A/607/A126

State transformations and Hamiltonian structures for optimal control in discrete systems

Science.gov (United States)

Sieniutycz, S.

2006-04-01

Preserving usual definition of Hamiltonian H as the scalar product of rates and generalized momenta we investigate two basic classes of discrete optimal control processes governed by the difference rather than differential equations for the state transformation. The first class, linear in the time interval θ, secures the constancy of optimal H and satisfies a discrete Hamilton-Jacobi equation. The second class, nonlinear in θ, does not assure the constancy of optimal H and satisfies only a relationship that may be regarded as an equation of Hamilton-Jacobi type. The basic question asked is if and when Hamilton's canonical structures emerge in optimal discrete systems. For a constrained discrete control, general optimization algorithms are derived that constitute powerful theoretical and computational tools when evaluating extremum properties of constrained physical systems. The mathematical basis is Bellman's method of dynamic programming (DP) and its extension in the form of the so-called Carathéodory-Boltyanski (CB) stage optimality criterion which allows a variation of the terminal state that is otherwise fixed in Bellman's method. For systems with unconstrained intervals of the holdup time θ two powerful optimization algorithms are obtained: an unconventional discrete algorithm with a constant H and its counterpart for models nonlinear in θ. We also present the time-interval-constrained extension of the second algorithm. The results are general; namely, one arrives at: discrete canonical equations of Hamilton, maximum principles, and (at the continuous limit of processes with free intervals of time) the classical Hamilton-Jacobi theory, along with basic results of variational calculus. A vast spectrum of applications and an example are briefly discussed with particular attention paid to models nonlinear in the time interval θ.

Triangular Geometrized Sampling Heuristics for Fast Optimal Motion Planning

Directory of Open Access Journals (Sweden)

Ahmed Hussain Qureshi

2015-02-01

Full Text Available Rapidly-exploring Random Tree (RRT-based algorithms have become increasingly popular due to their lower computational complexity as compared with other path planning algorithms. The recently presented RRT* motion planning algorithm improves upon the original RRT algorithm by providing optimal path solutions. While RRT determines an initial collision-free path fairly quickly, RRT* guarantees almost certain convergence to an optimal, obstacle-free path from the start to the goal points for any given geometrical environment. However, the main limitations of RRT* include its slow processing rate and high memory consumption, due to the large number of iterations required for calculating the optimal path. In order to overcome these limitations, we present another improvement, i.e, the Triangular Geometerized-RRT* (TG-RRT* algorithm, which utilizes triangular geometrical methods to improve the performance of the RRT* algorithm in terms of the processing time and a decreased number of iterations required for an optimal path solution. Simulations comparing the performance results of the improved TG-RRT* with RRT* are presented to demonstrate the overall improvement in performance and optimal path detection.

A rapid sample screening method for authenticity control of whiskey using capillary electrophoresis with online preconcentration.

Science.gov (United States)

Heller, Melina; Vitali, Luciano; Oliveira, Marcone Augusto Leal; Costa, Ana Carolina O; Micke, Gustavo Amadeu

2011-07-13

The present study aimed to develop a methodology using capillary electrophoresis for the determination of sinapaldehyde, syringaldehyde, coniferaldehyde, and vanillin in whiskey samples. The main objective was to obtain a screening method to differentiate authentic samples from seized samples suspected of being false using the phenolic aldehydes as chemical markers. The optimized background electrolyte was composed of 20 mmol L(-1) sodium tetraborate with 10% MeOH at pH 9.3. The study examined two kinds of sample stacking, using a long-end injection mode: normal sample stacking (NSM) and sample stacking with matrix removal (SWMR). In SWMR, the optimized injection time of the samples was 42 s (SWMR42); at this time, no matrix effects were observed. Values of r were >0.99 for the both methods. The LOD and LOQ were better than 100 and 330 mg mL(-1) for NSM and better than 22 and 73 mg L(-1) for SWMR. The CE-UV reliability in the aldehyde analysis in the real sample was compared statistically with LC-MS/MS methodology, and no significant differences were found, with a 95% confidence interval between the methodologies.

AMORE-HX: a multidimensional optimization of radial enhanced NMR-sampled hydrogen exchange

International Nuclear Information System (INIS)

Gledhill, John M.; Walters, Benjamin T.; Wand, A. Joshua

2009-01-01

The Cartesian sampled three-dimensional HNCO experiment is inherently limited in time resolution and sensitivity for the real time measurement of protein hydrogen exchange. This is largely overcome by use of the radial HNCO experiment that employs the use of optimized sampling angles. The significant practical limitation presented by use of three-dimensional data is the large data storage and processing requirements necessary and is largely overcome by taking advantage of the inherent capabilities of the 2D-FT to process selective frequency space without artifact or limitation. Decomposition of angle spectra into positive and negative ridge components provides increased resolution and allows statistical averaging of intensity and therefore increased precision. Strategies for averaging ridge cross sections within and between angle spectra are developed to allow further statistical approaches for increasing the precision of measured hydrogen occupancy. Intensity artifacts potentially introduced by over-pulsing are effectively eliminated by use of the BEST approach

Event- and interval-based measurement of stuttering: a review.

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Valente, Ana Rita S; Jesus, Luis M T; Hall, Andreia; Leahy, Margaret

2015-01-01

Event- and interval-based measurements are two different ways of computing frequency of stuttering. Interval-based methodology emerged as an alternative measure to overcome problems associated with reproducibility in the event-based methodology. No review has been made to study the effect of methodological factors in interval-based absolute reliability data or to compute the agreement between the two methodologies in terms of inter-judge, intra-judge and accuracy (i.e., correspondence between raters' scores and an established criterion). To provide a review related to reproducibility of event-based and time-interval measurement, and to verify the effect of methodological factors (training, experience, interval duration, sample presentation order and judgment conditions) on agreement of time-interval measurement; in addition, to determine if it is possible to quantify the agreement between the two methodologies The first two authors searched for articles on ERIC, MEDLINE, PubMed, B-on, CENTRAL and Dissertation Abstracts during January-February 2013 and retrieved 495 articles. Forty-eight articles were selected for review. Content tables were constructed with the main findings. Articles related to event-based measurements revealed values of inter- and intra-judge greater than 0.70 and agreement percentages beyond 80%. The articles related to time-interval measures revealed that, in general, judges with more experience with stuttering presented significantly higher levels of intra- and inter-judge agreement. Inter- and intra-judge values were beyond the references for high reproducibility values for both methodologies. Accuracy (regarding the closeness of raters' judgements with an established criterion), intra- and inter-judge agreement were higher for trained groups when compared with non-trained groups. Sample presentation order and audio/video conditions did not result in differences in inter- or intra-judge results. A duration of 5 s for an interval appears to be

A novel approach based on preference-based index for interval bilevel linear programming problem.

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Ren, Aihong; Wang, Yuping; Xue, Xingsi

2017-01-01

This paper proposes a new methodology for solving the interval bilevel linear programming problem in which all coefficients of both objective functions and constraints are considered as interval numbers. In order to keep as much uncertainty of the original constraint region as possible, the original problem is first converted into an interval bilevel programming problem with interval coefficients in both objective functions only through normal variation of interval number and chance-constrained programming. With the consideration of different preferences of different decision makers, the concept of the preference level that the interval objective function is preferred to a target interval is defined based on the preference-based index. Then a preference-based deterministic bilevel programming problem is constructed in terms of the preference level and the order relation [Formula: see text]. Furthermore, the concept of a preference δ -optimal solution is given. Subsequently, the constructed deterministic nonlinear bilevel problem is solved with the help of estimation of distribution algorithm. Finally, several numerical examples are provided to demonstrate the effectiveness of the proposed approach.

A novel approach based on preference-based index for interval bilevel linear programming problem

Directory of Open Access Journals (Sweden)

Aihong Ren

2017-05-01

Full Text Available Abstract This paper proposes a new methodology for solving the interval bilevel linear programming problem in which all coefficients of both objective functions and constraints are considered as interval numbers. In order to keep as much uncertainty of the original constraint region as possible, the original problem is first converted into an interval bilevel programming problem with interval coefficients in both objective functions only through normal variation of interval number and chance-constrained programming. With the consideration of different preferences of different decision makers, the concept of the preference level that the interval objective function is preferred to a target interval is defined based on the preference-based index. Then a preference-based deterministic bilevel programming problem is constructed in terms of the preference level and the order relation ⪯ m w $\\preceq_{mw}$ . Furthermore, the concept of a preference δ-optimal solution is given. Subsequently, the constructed deterministic nonlinear bilevel problem is solved with the help of estimation of distribution algorithm. Finally, several numerical examples are provided to demonstrate the effectiveness of the proposed approach.

Departure Interval Optimization of Electric Bus Rapid Transit Considering Level of Service%考虑服务水平的纯电动快速公交发车间隔优化研究

Institute of Scientific and Technical Information of China (English)

王雪然; 刘文峰; 李斌; 张沫

2017-01-01

With the electric vehicles applied in the field of bus rapid transit,due to the special operational needs of charging or battery swap for electric vehicles,the traditional methods of departure interval optimization are no longer applicable.Considering the level of service and charging time constraints,this paper studies the departure interval optimization of electric bus rapid transit.Firstly,the service level evaluation system associated with energy consumption is established by analyzing the correlation between service level indexes and energy consumption;secondly,considering the level of service and charging time constraints,the departure interval optimization model is established by setting energy consumption as the objective function;finally,taking line 1 of Jinhua E-BRT as an example to examine the effect of the optimization model,the optimization results demonstrate that compared with the current operation plans the energy consumption is saved by 6.21％ under the premise of the same level of service.Therefore,the departure interval optimization method of electric bus rapid transit considering level of service established in this paper has certain practical significance and provides an important guidance for reducing the energy consumption under a certain service level.%随着电动汽车被引入城市快速公交领域,由于其充换电等特殊的运营调度需求,在发车间隔计划等制定过程中传统方法不再适用.本文在综合考虑服务水平和车辆充电时间约束条件下,对纯电动快速公交(E-BRT)发车间隔优化进行了研究.根据分析服务水平指标项与车辆能耗的相关性,建立了面向能耗评估的服务水平评价体系;综合考虑服务水平和充电时间约束条件,以车辆运营能耗为目标函数,建立了发车间隔优化模型;基于金华市E-BRT1号线运营数据进行实例验证,结果表明,在满足相同服务水平情况下,本文提出的优化模型可以节约能耗6.21�

Estimating reliable paediatric reference intervals in clinical chemistry and haematology.

Science.gov (United States)

Ridefelt, Peter; Hellberg, Dan; Aldrimer, Mattias; Gustafsson, Jan

2014-01-01

Very few high-quality studies on paediatric reference intervals for general clinical chemistry and haematology analytes have been performed. Three recent prospective community-based projects utilising blood samples from healthy children in Sweden, Denmark and Canada have substantially improved the situation. The present review summarises current reference interval studies for common clinical chemistry and haematology analyses. ©2013 Foundation Acta Paediatrica. Published by John Wiley & Sons Ltd.

Efficient Approximation of Optimal Control for Markov Games

DEFF Research Database (Denmark)

Fearnley, John; Rabe, Markus; Schewe, Sven

2011-01-01

We study the time-bounded reachability problem for continuous-time Markov decision processes (CTMDPs) and games (CTMGs). Existing techniques for this problem use discretisation techniques to break time into discrete intervals, and optimal control is approximated for each interval separately...

Indirect methods for reference interval determination - review and recommendations.

Science.gov (United States)

Jones, Graham R D; Haeckel, Rainer; Loh, Tze Ping; Sikaris, Ken; Streichert, Thomas; Katayev, Alex; Barth, Julian H; Ozarda, Yesim

2018-04-19

Reference intervals are a vital part of the information supplied by clinical laboratories to support interpretation of numerical pathology results such as are produced in clinical chemistry and hematology laboratories. The traditional method for establishing reference intervals, known as the direct approach, is based on collecting samples from members of a preselected reference population, making the measurements and then determining the intervals. An alternative approach is to perform analysis of results generated as part of routine pathology testing and using appropriate statistical techniques to determine reference intervals. This is known as the indirect approach. This paper from a working group of the International Federation of Clinical Chemistry (IFCC) Committee on Reference Intervals and Decision Limits (C-RIDL) aims to summarize current thinking on indirect approaches to reference intervals. The indirect approach has some major potential advantages compared with direct methods. The processes are faster, cheaper and do not involve patient inconvenience, discomfort or the risks associated with generating new patient health information. Indirect methods also use the same preanalytical and analytical techniques used for patient management and can provide very large numbers for assessment. Limitations to the indirect methods include possible effects of diseased subpopulations on the derived interval. The IFCC C-RIDL aims to encourage the use of indirect methods to establish and verify reference intervals, to promote publication of such intervals with clear explanation of the process used and also to support the development of improved statistical techniques for these studies.

Sampling Development

Science.gov (United States)

Adolph, Karen E.; Robinson, Scott R.

2011-01-01

Research in developmental psychology requires sampling at different time points. Accurate depictions of developmental change provide a foundation for further empirical studies and theories about developmental mechanisms. However, overreliance on widely spaced sampling intervals in cross-sectional and longitudinal designs threatens the validity of…

Optimal sampling plan for clean development mechanism lighting projects with lamp population decay

International Nuclear Information System (INIS)

Ye, Xianming; Xia, Xiaohua; Zhang, Jiangfeng

2014-01-01

Highlights: • A metering cost minimisation model is built with the lamp population decay to optimise CDM lighting projects sampling plan. • The model minimises the total metering cost and optimise the annual sample size during the crediting period. • The required 90/10 criterion sampling accuracy is satisfied for each CDM monitoring report. - Abstract: This paper proposes a metering cost minimisation model that minimises metering cost under the constraints of sampling accuracy requirement for clean development mechanism (CDM) energy efficiency (EE) lighting project. Usually small scale (SSC) CDM EE lighting projects expect a crediting period of 10 years given that the lighting population will decay as time goes by. The SSC CDM sampling guideline requires that the monitored key parameters for the carbon emission reduction quantification must satisfy the sampling accuracy of 90% confidence and 10% precision, known as the 90/10 criterion. For the existing registered CDM lighting projects, sample sizes are either decided by professional judgment or by rule-of-thumb without considering any optimisation. Lighting samples are randomly selected and their energy consumptions are monitored continuously by power meters. In this study, the sampling size determination problem is formulated as a metering cost minimisation model by incorporating a linear lighting decay model as given by the CDM guideline AMS-II.J. The 90/10 criterion is formulated as constraints to the metering cost minimisation problem. Optimal solutions to the problem minimise the metering cost whilst satisfying the 90/10 criterion for each reporting period. The proposed metering cost minimisation model is applicable to other CDM lighting projects with different population decay characteristics as well

Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing.

Directory of Open Access Journals (Sweden)

Luigi Acerbi

Full Text Available Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests that they build an internal representation of both the experimentally imposed distribution of time intervals (the prior and of the error (the loss function. The responses of a Bayesian ideal observer depend crucially on these internal representations, which have only been previously studied for simple distributions. To study the nature of these representations we asked subjects to reproduce time intervals drawn from underlying temporal distributions of varying complexity, from uniform to highly skewed or bimodal while also varying the error mapping that determined the performance feedback. Interval reproduction times were affected by both the distribution and feedback, in good agreement with a performance-optimizing Bayesian observer and actor model. Bayesian model comparison highlighted that subjects were integrating the provided feedback and represented the experimental distribution with a smoothed approximation. A nonparametric reconstruction of the subjective priors from the data shows that they are generally in agreement with the true distributions up to third-order moments, but with systematically heavier tails. In particular, higher-order statistical features (kurtosis, multimodality seem much harder to acquire. Our findings suggest that humans have only minor constraints on learning lower-order statistical properties of unimodal (including peaked and skewed distributions of time intervals under the guidance of corrective feedback, and that their behavior is well explained by Bayesian decision theory.

Optimal Wavelength Selection in Ultraviolet Spectroscopy for the Estimation of Toxin Reduction Ratio during Hemodialysis

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Amir Ghanifar

2016-06-01

Full Text Available Introduction The concentration of substances, including urea, creatinine, and uric acid, can be used as an index to measure toxic uremic solutes in the blood during dialysis and interdialytic intervals. The on-line monitoring of toxin concentration allows for the clearance measurement of some low-molecular-weight solutes at any time during hemodialysis.The aim of this study was to determine the optimal wavelength for estimating the changes in urea, creatinine, and uric acid in dialysate, using ultraviolet (UV spectroscopy. Materials and Methods In this study, nine uremic patients were investigated, using on-line spectrophotometry. The on-line absorption measurements (UV radiation were performed with a spectrophotometer module, connected to the fluid outlet of the dialysis machine. Dialysate samples were obtained and analyzed, using standard biochemical methods. Optimal wavelengths for both creatinine and uric acid were selected by using a combination of genetic algorithms (GAs, i.e., GA-partial least squares (GA-PLS and interval partial least squares (iPLS. Results The Artifitial Neural Network (ANN sensitivity analysis determined the wavelengths of the UV band most suitable for estimating the concentration of creatinine and uric acid. The two optimal wavelengths were 242 and 252 nm for creatinine and 295 and 298 nm for uric acid. Conclusion It can be concluded that the reduction ratio of creatinine and uric acid (dialysis efficiency could be continuously monitored during hemodialysis by UV spectroscopy.Compared to the conventional method, which is particularly sensitive to the sampling technique and involves post-dialysis blood sampling, iterative measurements throughout the dialysis session can yield more reliable data.

Final screening round of the NELSON lung cancer screening trial: the effect of a 2.5-year screening interval

NARCIS (Netherlands)

Yousaf-Khan, U.; Aalst, C. van der; Jong, P.A. de; Heuvelmans, M.; Scholten, E.T.; Lammers, J.-W.J.; Ooijen, P. van; Nackaerts, K.; Weenink, C.; Groen, H.; Vliegenthart, R.; Haaf, K. Ten; Oudkerk, M.; Koning, H. de

2016-01-01

In the USA annual lung cancer screening is recommended. However, the optimal screening strategy (eg, screening interval, screening rounds) is unknown. This study provides results of the fourth screening round after a 2.5-year interval in the Dutch-Belgian Lung Cancer Screening trial

Final screening round of the NELSON lung cancer screening trial : the effect of a 2.5-year screening interval

NARCIS (Netherlands)

Yousaf-Khan, Uraujh; van der Aalst, Carlijn; de Jong, Pim A; Heuvelmans, Marjolein; Scholten, Ernst; Lammers, Jan-Willem; van Ooijen, Peter; Nackaerts, Kristiaan; Weenink, Carla; Groen, Harry; Vliegenthart, Rozemarijn; Ten Haaf, Kevin; Oudkerk, Matthijs; de Koning, Harry

BACKGROUND: In the USA annual lung cancer screening is recommended. However, the optimal screening strategy (eg, screening interval, screening rounds) is unknown. This study provides results of the fourth screening round after a 2.5-year interval in the Dutch-Belgian Lung Cancer Screening trial

Final screening round of the NELSON lung cancer screening trial : the effect of a 2.5-year screening interval

NARCIS (Netherlands)

Yousaf-Khan, Uraujh; van der Aalst, Carlijn; de Jong, Pim A.; Heuvelmans, Marjolein; Scholten, Ernst; Lammers, Jan-Willem; van Ooijen, Peter; Nackaerts, Kristiaan; Weenink, Carla; Groen, Harry; Vliegenthart, Rozemarijn; Ten Haaf, Kevin; Oudkerk, Matthijs; de Koning, Harry

Background In the USA annual lung cancer screening is recommended. However, the optimal screening strategy (eg, screening interval, screening rounds) is unknown. This study provides results of the fourth screening round after a 2.5-year interval in the Dutch-Belgian Lung Cancer Screening trial

A Class of Prediction-Correction Methods for Time-Varying Convex Optimization

Science.gov (United States)

Simonetto, Andrea; Mokhtari, Aryan; Koppel, Alec; Leus, Geert; Ribeiro, Alejandro

2016-09-01

This paper considers unconstrained convex optimization problems with time-varying objective functions. We propose algorithms with a discrete time-sampling scheme to find and track the solution trajectory based on prediction and correction steps, while sampling the problem data at a constant rate of $1/h$, where $h$ is the length of the sampling interval. The prediction step is derived by analyzing the iso-residual dynamics of the optimality conditions. The correction step adjusts for the distance between the current prediction and the optimizer at each time step, and consists either of one or multiple gradient steps or Newton steps, which respectively correspond to the gradient trajectory tracking (GTT) or Newton trajectory tracking (NTT) algorithms. Under suitable conditions, we establish that the asymptotic error incurred by both proposed methods behaves as $O(h^2)$, and in some cases as $O(h^4)$, which outperforms the state-of-the-art error bound of $O(h)$ for correction-only methods in the gradient-correction step. Moreover, when the characteristics of the objective function variation are not available, we propose approximate gradient and Newton tracking algorithms (AGT and ANT, respectively) that still attain these asymptotical error bounds. Numerical simulations demonstrate the practical utility of the proposed methods and that they improve upon existing techniques by several orders of magnitude.

Using an R Shiny to Enhance the Learning Experience of Confidence Intervals

Science.gov (United States)

Williams, Immanuel James; Williams, Kelley Kim

2018-01-01

Many students find understanding confidence intervals difficult, especially because of the amalgamation of concepts such as confidence levels, standard error, point estimates and sample sizes. An R Shiny application was created to assist the learning process of confidence intervals using graphics and data from the US National Basketball…

Optimization of sample preparation variables for wedelolactone from Eclipta alba using Box-Behnken experimental design followed by HPLC identification.

Science.gov (United States)

Patil, A A; Sachin, B S; Shinde, D B; Wakte, P S

2013-07-01

Coumestan wedelolactone is an important phytocomponent from Eclipta alba (L.) Hassk. It possesses diverse pharmacological activities, which have prompted the development of various extraction techniques and strategies for its better utilization. The aim of the present study is to develop and optimize supercritical carbon dioxide assisted sample preparation and HPLC identification of wedelolactone from E. alba (L.) Hassk. The response surface methodology was employed to study the optimization of sample preparation using supercritical carbon dioxide for wedelolactone from E. alba (L.) Hassk. The optimized sample preparation involves the investigation of quantitative effects of sample preparation parameters viz. operating pressure, temperature, modifier concentration and time on yield of wedelolactone using Box-Behnken design. The wedelolactone content was determined using validated HPLC methodology. The experimental data were fitted to second-order polynomial equation using multiple regression analysis and analyzed using the appropriate statistical method. By solving the regression equation and analyzing 3D plots, the optimum extraction conditions were found to be: extraction pressure, 25 MPa; temperature, 56 °C; modifier concentration, 9.44% and extraction time, 60 min. Optimum extraction conditions demonstrated wedelolactone yield of 15.37 ± 0.63 mg/100 g E. alba (L.) Hassk, which was in good agreement with the predicted values. Temperature and modifier concentration showed significant effect on the wedelolactone yield. The supercritical carbon dioxide extraction showed higher selectivity than the conventional Soxhlet assisted extraction method. Copyright © 2013 Elsevier Masson SAS. All rights reserved.

Foam generation and sample composition optimization for the FOAM-C experiment of the ISS

Science.gov (United States)

Carpy, R.; Picker, G.; Amann, B.; Ranebo, H.; Vincent-Bonnieu, S.; Minster, O.; Winter, J.; Dettmann, J.; Castiglione, L.; Höhler, R.; Langevin, D.

2011-12-01

End of 2009 and early 2010 a sealed cell, for foam generation and observation, has been designed and manufactured at Astrium Friedrichshafen facilities. With the use of this cell, different sample compositions of "wet foams" have been optimized for mixtures of chemicals such as water, dodecanol, pluronic, aethoxisclerol, glycerol, CTAB, SDS, as well as glass beads. This development is performed in the frame of the breadboarding development activities of the Experiment Container FOAM-C for operation in the ISS Fluid Science Laboratory (ISS). The sample cell supports multiple observation methods such as: Diffusing-Wave and Diffuse Transmission Spectrometry, Time Resolved Correlation Spectroscopy [1] and microscope observation, all of these methods are applied in the cell with a relatively small experiment volume 40).

Influence of length of interval between pulses in PDR brachytherapy (PDRBT on value of Biologically Equivalent Dose (BED in healthy tissues

Directory of Open Access Journals (Sweden)

Tomasz Piotrowski

2010-07-01

Full Text Available Purpose: Different PDR treatment schemas are used in clinical practice, however optimal length of interval between pulses still remains unclear. The aim of this work was to compare value of BED doses measured in surrounded healthy tissues according to different intervals between pulses in PDRBT. Influence of doses optimization on BED values was analyzed.Material and methods: Fifty-one patients treated in Greater Poland Cancer Centre were qualified for calculations.Calculations of doses were made in 51 patients with head and neck cancer, brain tumor, breast cancer, sarcoma, penis cancer and rectal cancer. Doses were calculated with the use of PLATO planning system in chosen critical points in surrounded healthy tissues. For all treatment plans the doses were compared using Biologically Equivalent Dose formula.Three interval lengths (1, 2 and 4 hours between pulses were chosen for calculations. For statistical analysis Friedman ANOVA test and Kendall ratio were used.Results: The median value of BED in chosen critical points in healthy tissues was statistically related to the length of interval between PDR pulses and decreased exponentially with 1 hour interval to 4 hours (Kendall = from 0.48 to 1.0; p = from 0.002 to 0.00001.Conclusions: Prolongation of intervals between pulses in PDR brachytherapy was connected with lower values of BED doses in healthy tissues. It seems that longer intervals between pulses reduced the risk of late complications, but also decreased the tumour control. Furthermore, optimization influenced the increase of doses in healthy tissues.

Matrix-assisted laser desorption/ionization sample preparation optimization for structural characterization of poly(styrene-co-pentafluorostyrene) copolymers

International Nuclear Information System (INIS)

Tisdale, Evgenia; Kennedy, Devin; Wilkins, Charles

2014-01-01

Graphical abstract: -- Highlights: •We optimized sample preparation for MALDI TOF poly(styrene-copentafluorostyrene) co-polymers. •Influence of matrix choice was investigated. •Influence of matrix/analyte ratio was examined. •Influence of analyte/salt ratio (for Ag+ salt) was studied. -- Abstract: The influence of the sample preparation parameters (the choice of the matrix, matrix:analyte ratio, salt:analyte ratio) was investigated and optimal conditions were established for the MALDI time-of-flight mass spectrometry analysis of the poly(styrene-co-pentafluorostyrene) copolymers. These were synthesized by atom transfer radical polymerization. Use of 2,5-dihydroxybenzoic acid as matrix resulted in spectra with consistently high ion yields for all matrix:analyte:salt ratios tested. The optimized MALDI procedure was successfully applied to the characterization of three copolymers obtained by varying the conditions of polymerization reaction. It was possible to establish the nature of the end groups, calculate molecular weight distributions, and determine the individual length distributions for styrene and pentafluorostyrene monomers, contained in the resulting copolymers. Based on the data obtained, it was concluded that individual styrene chain length distributions are more sensitive to the change in the composition of the catalyst (the addition of small amount of CuBr 2 ) than is the pentafluorostyrene component distribution

Matrix-assisted laser desorption/ionization sample preparation optimization for structural characterization of poly(styrene-co-pentafluorostyrene) copolymers

Energy Technology Data Exchange (ETDEWEB)

Tisdale, Evgenia; Kennedy, Devin; Wilkins, Charles, E-mail: cwilkins@uark.edu

2014-01-15

Graphical abstract: -- Highlights: •We optimized sample preparation for MALDI TOF poly(styrene-copentafluorostyrene) co-polymers. •Influence of matrix choice was investigated. •Influence of matrix/analyte ratio was examined. •Influence of analyte/salt ratio (for Ag+ salt) was studied. -- Abstract: The influence of the sample preparation parameters (the choice of the matrix, matrix:analyte ratio, salt:analyte ratio) was investigated and optimal conditions were established for the MALDI time-of-flight mass spectrometry analysis of the poly(styrene-co-pentafluorostyrene) copolymers. These were synthesized by atom transfer radical polymerization. Use of 2,5-dihydroxybenzoic acid as matrix resulted in spectra with consistently high ion yields for all matrix:analyte:salt ratios tested. The optimized MALDI procedure was successfully applied to the characterization of three copolymers obtained by varying the conditions of polymerization reaction. It was possible to establish the nature of the end groups, calculate molecular weight distributions, and determine the individual length distributions for styrene and pentafluorostyrene monomers, contained in the resulting copolymers. Based on the data obtained, it was concluded that individual styrene chain length distributions are more sensitive to the change in the composition of the catalyst (the addition of small amount of CuBr{sub 2}) than is the pentafluorostyrene component distribution.

Identification of optimal inspection interval via delay-time concept

Directory of Open Access Journals (Sweden)

Glauco Ricardo Simões Gomes

2016-06-01

Full Text Available This paper presents an application of mathematical modeling aimed at managing maintenance based on the delay-time concept. The study scenario was the manufacturing sector of an industrial unit, which operates 24 hours a day in a continuous flow of production. The main idea was to use the concepts of this approach to determine the optimal time of preventive action by the maintenance department in order to ensure the greatest availability of equipment and facilities at appropriate maintenance costs. After a brief introduction of the subject, the article presents topics that illustrate the importance of mathematical modeling in maintenance management and the delay-time concept. It also describes the characteristics of the company where the study was conducted, as well as the data related to the production process and maintenance actions. Finally, the results obtained after applying the delay-time concept are presented and discussed, as well as the limitations of the article and the proposals for future research.

Integral equations with difference kernels on finite intervals

CERN Document Server

Sakhnovich, Lev A

2015-01-01

This book focuses on solving integral equations with difference kernels on finite intervals. The corresponding problem on the semiaxis was previously solved by N. Wiener–E. Hopf and by M.G. Krein. The problem on finite intervals, though significantly more difficult, may be solved using our method of operator identities. This method is also actively employed in inverse spectral problems, operator factorization and nonlinear integral equations. Applications of the obtained results to optimal synthesis, light scattering, diffraction, and hydrodynamics problems are discussed in this book, which also describes how the theory of operators with difference kernels is applied to stable processes and used to solve the famous M. Kac problems on stable processes. In this second edition these results are extensively generalized and include the case of all Levy processes. We present the convolution expression for the well-known Ito formula of the generator operator, a convolution expression that has proven to be fruitful...

Dynamic detection-rate-based bit allocation with genuine interval concealment for binary biometric representation.

Science.gov (United States)

Lim, Meng-Hui; Teoh, Andrew Beng Jin; Toh, Kar-Ann

2013-06-01

Biometric discretization is a key component in biometric cryptographic key generation. It converts an extracted biometric feature vector into a binary string via typical steps such as segmentation of each feature element into a number of labeled intervals, mapping of each interval-captured feature element onto a binary space, and concatenation of the resulted binary output of all feature elements into a binary string. Currently, the detection rate optimized bit allocation (DROBA) scheme is one of the most effective biometric discretization schemes in terms of its capability to assign binary bits dynamically to user-specific features with respect to their discriminability. However, we learn that DROBA suffers from potential discriminative feature misdetection and underdiscretization in its bit allocation process. This paper highlights such drawbacks and improves upon DROBA based on a novel two-stage algorithm: 1) a dynamic search method to efficiently recapture such misdetected features and to optimize the bit allocation of underdiscretized features and 2) a genuine interval concealment technique to alleviate crucial information leakage resulted from the dynamic search. Improvements in classification accuracy on two popular face data sets vindicate the feasibility of our approach compared with DROBA.

Rats track odour trails accurately using a multi-layered strategy with near-optimal sampling.

Science.gov (United States)

Khan, Adil Ghani; Sarangi, Manaswini; Bhalla, Upinder Singh

2012-02-28

Tracking odour trails is a crucial behaviour for many animals, often leading to food, mates or away from danger. It is an excellent example of active sampling, where the animal itself controls how to sense the environment. Here we show that rats can track odour trails accurately with near-optimal sampling. We trained rats to follow odour trails drawn on paper spooled through a treadmill. By recording local field potentials (LFPs) from the olfactory bulb, and sniffing rates, we find that sniffing but not LFPs differ between tracking and non-tracking conditions. Rats can track odours within ~1 cm, and this accuracy is degraded when one nostril is closed. Moreover, they show path prediction on encountering a fork, wide 'casting' sweeps on encountering a gap and detection of reappearance of the trail in 1-2 sniffs. We suggest that rats use a multi-layered strategy, and achieve efficient sampling and high accuracy in this complex task.

The Interval-Valued Triangular Fuzzy Soft Set and Its Method of Dynamic Decision Making

Directory of Open Access Journals (Sweden)

Xiaoguo Chen

2014-01-01

Full Text Available A concept of interval-valued triangular fuzzy soft set is presented, and some operations of “AND,” “OR,” intersection, union and complement, and so forth are defined. Then some relative properties are discussed and several conclusions are drawn. A dynamic decision making model is built based on the definition of interval-valued triangular fuzzy soft set, in which period weight is determined by the exponential decay method. The arithmetic weighted average operator of interval-valued triangular fuzzy soft set is given by the aggregating thought, thereby aggregating interval-valued triangular fuzzy soft sets of different time-series into a collective interval-valued triangular fuzzy soft set. The formulas of selection and decision values of different objects are given; therefore the optimal decision making is achieved according to the decision values. Finally, the steps of this method are concluded, and one example is given to explain the application of the method.

Programming with Intervals

Science.gov (United States)

Matsakis, Nicholas D.; Gross, Thomas R.

Intervals are a new, higher-level primitive for parallel programming with which programmers directly construct the program schedule. Programs using intervals can be statically analyzed to ensure that they do not deadlock or contain data races. In this paper, we demonstrate the flexibility of intervals by showing how to use them to emulate common parallel control-flow constructs like barriers and signals, as well as higher-level patterns such as bounded-buffer producer-consumer. We have implemented intervals as a publicly available library for Java and Scala.

Optimal sampling theory and population modelling - Application to determination of the influence of the microgravity environment on drug distribution and elimination

Science.gov (United States)

Drusano, George L.

1991-01-01

The optimal sampling theory is evaluated in applications to studies related to the distribution and elimination of several drugs (including ceftazidime, piperacillin, and ciprofloxacin), using the SAMPLE module of the ADAPT II package of programs developed by D'Argenio and Schumitzky (1979, 1988) and comparing the pharmacokinetic parameter values with results obtained by traditional ten-sample design. The impact of the use of optimal sampling was demonstrated in conjunction with NONMEM (Sheiner et al., 1977) approach, in which the population is taken as the unit of analysis, allowing even fragmentary patient data sets to contribute to population parameter estimates. It is shown that this technique is applicable in both the single-dose and the multiple-dose environments. The ability to study real patients made it possible to show that there was a bimodal distribution in ciprofloxacin nonrenal clearance.

Optimal and Most Exact Confidence Intervals for Person Parameters in Item Response Theory Models

Science.gov (United States)

Doebler, Anna; Doebler, Philipp; Holling, Heinz

2013-01-01

The common way to calculate confidence intervals for item response theory models is to assume that the standardized maximum likelihood estimator for the person parameter [theta] is normally distributed. However, this approximation is often inadequate for short and medium test lengths. As a result, the coverage probabilities fall below the given…

Optimization of inspection and replacement period by using Bayesian statistics

International Nuclear Information System (INIS)

Kasai, Masao; Watanabe, Yasushi; Kusakari, Yoshiyuki; Notoya, Junichi

2006-01-01

This study describes the formulations to optimize the time interval of inspections and/or replacements of equipment/parts taking into account the probability density functions (PDF) for failure rates and parameters of failure distribution functions (FDF) and evaluates the optimized results of these time intervals using our formulations by comparing with those using only representative values of failure rates and the parameters of FDF instead of using these PDFs. The PDFs are obtained with Bayesian method and the representative values are obtained with likelihood estimation method. However, any significant difference is not observed between both optimized results within our preliminary calculations. (author)

Optimal sampling period of the digital control system for the nuclear power plant steam generator water level control

International Nuclear Information System (INIS)

Hur, Woo Sung; Seong, Poong Hyun

1995-01-01

A great effort has been made to improve the nuclear plant control system by use of digital technologies and a long term schedule for the control system upgrade has been prepared with an aim to implementation in the next generation nuclear plants. In case of digital control system, it is important to decide the sampling period for analysis and design of the system, because the performance and the stability of a digital control system depend on the value of the sampling period of the digital control system. There is, however, currently no systematic method used universally for determining the sampling period of the digital control system. Generally, a traditional way to select the sampling frequency is to use 20 to 30 times the bandwidth of the analog control system which has the same system configuration and parameters as the digital one. In this paper, a new method to select the sampling period is suggested which takes into account of the performance as well as the stability of the digital control system. By use of the Irving's model steam generator, the optimal sampling period of an assumptive digital control system for steam generator level control is estimated and is actually verified in the digital control simulation system for Kori-2 nuclear power plant steam generator level control. Consequently, we conclude the optimal sampling period of the digital control system for Kori-2 nuclear power plant steam generator level control is 1 second for all power ranges. 7 figs., 3 tabs., 8 refs. (Author)

Sleep and optimism: A longitudinal study of bidirectional causal relationship and its mediating and moderating variables in a Chinese student sample.

Science.gov (United States)

Lau, Esther Yuet Ying; Hui, C Harry; Lam, Jasmine; Cheung, Shu-Fai

2017-01-01

While both sleep and optimism have been found to be predictive of well-being, few studies have examined their relationship with each other. Neither do we know much about the mediators and moderators of the relationship. This study investigated (1) the causal relationship between sleep quality and optimism in a college student sample, (2) the role of symptoms of depression, anxiety, and stress as mediators, and (3) how circadian preference might moderate the relationship. Internet survey data were collected from 1,684 full-time university students (67.6% female, mean age = 20.9 years, SD = 2.66) at three time-points, spanning about 19 months. Measures included the Attributional Style Questionnaire, the Pittsburgh Sleep Quality Index, the Composite Scale of Morningness, and the Depression Anxiety Stress Scale-21. Moderate correlations were found among sleep quality, depressive mood, stress symptoms, anxiety symptoms, and optimism. Cross-lagged analyses showed a bidirectional effect between optimism and sleep quality. Moreover, path analyses demonstrated that anxiety and stress symptoms partially mediated the influence of optimism on sleep quality, while depressive mood partially mediated the influence of sleep quality on optimism. In support of our hypothesis, sleep quality affects mood symptoms and optimism differently for different circadian preferences. Poor sleep results in depressive mood and thus pessimism in non-morning persons only. In contrast, the aggregated (direct and indirect) effects of optimism on sleep quality were invariant of circadian preference. Taken together, people who are pessimistic generally have more anxious mood and stress symptoms, which adversely affect sleep while morningness seems to have a specific protective effect countering the potential damage poor sleep has on optimism. In conclusion, optimism and sleep quality were both cause and effect of each other. Depressive mood partially explained the effect of sleep quality on optimism

Optimizing the data acquisition rate for a remotely controllable structural monitoring system with parallel operation and self-adaptive sampling

International Nuclear Information System (INIS)

Sheng, Wenjuan; Guo, Aihuang; Liu, Yang; Azmi, Asrul Izam; Peng, Gang-Ding

2011-01-01

We present a novel technique that optimizes the real-time remote monitoring and control of dispersed civil infrastructures. The monitoring system is based on fiber Bragg gating (FBG) sensors, and transfers data via Ethernet. This technique combines parallel operation and self-adaptive sampling to increase the data acquisition rate in remote controllable structural monitoring systems. The compact parallel operation mode is highly efficient at achieving the highest possible data acquisition rate for the FBG sensor based local data acquisition system. Self-adaptive sampling is introduced to continuously coordinate local acquisition and remote control for data acquisition rate optimization. Key issues which impact the operation of the whole system, such as the real-time data acquisition rate, data processing capability, and buffer usage, are investigated. The results show that, by introducing parallel operation and self-adaptive sampling, the data acquisition rate can be increased by several times without affecting the system operating performance on both local data acquisition and remote process control

Optimal diving under the risk of predation.

Science.gov (United States)

Heithaus, Michael R; Frid, Alejandro

2003-07-07

Many air-breathing aquatic foragers may be killed by aerial or subsurface predators while recovering oxygen at the surface; yet the influence of predation risk on time allocation during dive cycles is little known in spite of numerous studies on optimal diving. We modeled diving behavior under the risk of predation at the surface. The relationship between time spent at the surface and the risk of death is predicted to influence the optimal surface interval, regardless of whether foragers accumulate energy at a constant rate while at the food patch, deplete food resources over the course of the dive, or must search for food during the dive. When instantaneous predation risk during a single surface interval decreases with time spent at the surface, a diver should increase its surface interval relative to that which maximizes energy intake, thereby increasing dive durations and reducing the number of surfacings per foraging bout. When instantaneous risk over a single surface interval does not change or increases with increasing time at the surface, divers should decrease their surface interval (and consequently their dive duration) relative to that which maximizes energy intake resulting in more dives per foraging bout. The fitness consequences of selecting a suboptimal surface interval vary with the risk function and the way divers harvest energy when at depth. Finally, predation risk during surface intervals should have important consequences for habitat selection and other aspects of the behavioral ecology of air-breathing aquatic organisms.

Conducting an acute intense interval exercise session during the Ramadan fasting month: what is the optimal time of the day?

Science.gov (United States)

Aziz, Abdul Rashid; Chia, Michael Yong Hwa; Low, Chee Yong; Slater, Gary John; Png, Weileen; Teh, Kong Chuan

2012-10-01

This study examines the effects of Ramadan fasting on performance during an intense exercise session performed at three different times of the day, i.e., 08:00, 18:00, and 21:00 h. The purpose was to determine the optimal time of the day to perform an acute high-intensity interval exercise during the Ramadan fasting month. After familiarization, nine trained athletes performed six 30-s Wingate anaerobic test (WAnT) cycle bouts followed by a time-to-exhaustion (T(exh)) cycle on six separate randomized and counterbalanced occasions. The three time-of-day nonfasting (control, CON) exercise sessions were performed before the Ramadan month, and the three corresponding time-of-day Ramadan fasting (RAM) exercise sessions were performed during the Ramadan month. Note that the 21:00 h session during Ramadan month was conducted in the nonfasted state after the breaking of the day's fast. Total work (TW) completed during the six WAnT bouts was significantly lower during RAM compared to CON for the 08:00 and 18:00 h (p effect size [d] = .55 [small] and .39 [small], respectively) sessions, but not for the 21:00 h (p = .03, d = .18 [trivial]) session. The T(exh) cycle duration was significantly shorter during RAM than CON in the 18:00 (p Ramadan fasting had a small to moderate, negative impact on quality of performance during an acute high-intensity exercise session, particularly during the period of the daytime fast. The optimal time to conduct an acute high-intensity exercise session during the Ramadan fasting month is in the evening, after the breaking of the day's fast.

Distributed fiber sparse-wideband vibration sensing by sub-Nyquist additive random sampling

Science.gov (United States)

Zhang, Jingdong; Zheng, Hua; Zhu, Tao; Yin, Guolu; Liu, Min; Bai, Yongzhong; Qu, Dingrong; Qiu, Feng; Huang, Xianbing

2018-05-01

The round trip time of the light pulse limits the maximum detectable vibration frequency response range of phase-sensitive optical time domain reflectometry ({\\phi}-OTDR). Unlike the uniform laser pulse interval in conventional {\\phi}-OTDR, we randomly modulate the pulse interval, so that an equivalent sub-Nyquist additive random sampling (sNARS) is realized for every sensing point of the long interrogation fiber. For an {\\phi}-OTDR system with 10 km sensing length, the sNARS method is optimized by theoretical analysis and Monte Carlo simulation, and the experimental results verify that a wide-band spars signal can be identified and reconstructed. Such a method can broaden the vibration frequency response range of {\\phi}-OTDR, which is of great significance in sparse-wideband-frequency vibration signal detection, such as rail track monitoring and metal defect detection.

Comparison of Bootstrap Confidence Intervals Using Monte Carlo Simulations

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Roberto S. Flowers-Cano

2018-02-01

Full Text Available Design of hydraulic works requires the estimation of design hydrological events by statistical inference from a probability distribution. Using Monte Carlo simulations, we compared coverage of confidence intervals constructed with four bootstrap techniques: percentile bootstrap (BP, bias-corrected bootstrap (BC, accelerated bias-corrected bootstrap (BCA and a modified version of the standard bootstrap (MSB. Different simulation scenarios were analyzed. In some cases, the mother distribution function was fit to the random samples that were generated. In other cases, a distribution function different to the mother distribution was fit to the samples. When the fitted distribution had three parameters, and was the same as the mother distribution, the intervals constructed with the four techniques had acceptable coverage. However, the bootstrap techniques failed in several of the cases in which the fitted distribution had two parameters.

An Interval-Parameter Fuzzy Linear Programming with Stochastic Vertices Model for Water Resources Management under Uncertainty

Directory of Open Access Journals (Sweden)

Yan Han

2013-01-01

Full Text Available An interval-parameter fuzzy linear programming with stochastic vertices (IFLPSV method is developed for water resources management under uncertainty by coupling interval-parameter fuzzy linear programming (IFLP with stochastic programming (SP. As an extension of existing interval parameter fuzzy linear programming, the developed IFLPSV approach has advantages in dealing with dual uncertainty optimization problems, which uncertainty presents as interval parameter with stochastic vertices in both of the objective functions and constraints. The developed IFLPSV method improves upon the IFLP method by allowing dual uncertainty parameters to be incorporated into the optimization processes. A hybrid intelligent algorithm based on genetic algorithm and artificial neural network is used to solve the developed model. The developed method is then applied to water resources allocation in Beijing city of China in 2020, where water resources shortage is a challenging issue. The results indicate that reasonable solutions have been obtained, which are helpful and useful for decision makers. Although the amount of water supply from Guanting and Miyun reservoirs is declining with rainfall reduction, water supply from the South-to-North Water Transfer project will have important impact on water supply structure of Beijing city, particularly in dry year and extraordinary dry year.

Neutron activation analysis for the optimal sampling and extraction of extractable organohalogens in human hari

International Nuclear Information System (INIS)

Zhang, H.; Chai, Z.F.; Sun, H.B.; Xu, H.F.

2005-01-01

Many persistent organohalogen compounds such as DDTs and polychlorinated biphenyls have caused seriously environmental pollution problem that now involves all life. It is know that neutron activation analysis (NAA) is a very convenient method for halogen analysis and is also the only method currently available for simultaneously determining organic chlorine, bromine and iodine in one extract. Human hair is a convenient material to evaluate the burden of such compounds in human body and dan be easily collected from people over wide ranges of age, sex, residential areas, eating habits and working environments. To effectively extract organohalogen compounds from human hair, in present work the optimal Soxhelt-extraction time of extractable organohalogen (EOX) and extractable persistent organohalogen (EPOX) from hair of different lengths were studied by NAA. The results indicated that the optimal Soxhelt-extraction time of EOX and EPOX from human hair was 8-11 h, and the highest EOX and EPOX contents were observed in hair powder extract. The concentrations of both EOX and EPOX in different hair sections were in the order of hair powder ≥ 2 mm > 5 mm, which stated that hair samples milled into hair powder or cut into very short sections were not only for homogeneous. hair sample but for the best hair extraction efficiency.

Quasicanonical structure of optimal control in constrained discrete systems

Science.gov (United States)

Sieniutycz, S.

2003-06-01

This paper considers discrete processes governed by difference rather than differential equations for the state transformation. The basic question asked is if and when Hamiltonian canonical structures are possible in optimal discrete systems. Considering constrained discrete control, general optimization algorithms are derived that constitute suitable theoretical and computational tools when evaluating extremum properties of constrained physical models. The mathematical basis of the general theory is the Bellman method of dynamic programming (DP) and its extension in the form of the so-called Carathéodory-Boltyanski (CB) stage criterion which allows a variation of the terminal state that is otherwise fixed in the Bellman's method. Two relatively unknown, powerful optimization algorithms are obtained: an unconventional discrete formalism of optimization based on a Hamiltonian for multistage systems with unconstrained intervals of holdup time, and the time interval constrained extension of the formalism. These results are general; namely, one arrives at: the discrete canonical Hamilton equations, maximum principles, and (at the continuous limit of processes with free intervals of time) the classical Hamilton-Jacobi theory along with all basic results of variational calculus. Vast spectrum of applications of the theory is briefly discussed.

NONLINEAR ASSIGNMENT-BASED METHODS FOR INTERVAL-VALUED INTUITIONISTIC FUZZY MULTI-CRITERIA DECISION ANALYSIS WITH INCOMPLETE PREFERENCE INFORMATION

OpenAIRE

TING-YU CHEN

2012-01-01

In the context of interval-valued intuitionistic fuzzy sets, this paper develops nonlinear assignment-based methods to manage imprecise and uncertain subjective ratings under incomplete preference structures and thereby determines the optimal ranking order of the alternatives for multiple criteria decision analysis. By comparing each interval-valued intuitionistic fuzzy number's score function, accuracy function, membership uncertainty index, and hesitation uncertainty index, a ranking proced...

Population-Based Pediatric Reference Intervals in General Clinical Chemistry: A Swedish Survey.

Science.gov (United States)

Ridefelt, Peter

2015-01-01

Very few high quality studies on pediatric reference intervals for general clinical chemistry and hematology analytes have been performed. Three recent prospective community-based projects utilising blood samples from healthy children in Sweden, Denmark and Canada have substantially improved the situation. The Swedish survey included 701 healthy children. Reference intervals for general clinical chemistry and hematology were defined.

Optimal iodine staining of cardiac tissue for X-ray computed tomography.

Science.gov (United States)

Butters, Timothy D; Castro, Simon J; Lowe, Tristan; Zhang, Yanmin; Lei, Ming; Withers, Philip J; Zhang, Henggui

2014-01-01

X-ray computed tomography (XCT) has been shown to be an effective imaging technique for a variety of materials. Due to the relatively low differential attenuation of X-rays in biological tissue, a high density contrast agent is often required to obtain optimal contrast. The contrast agent, iodine potassium iodide ([Formula: see text]), has been used in several biological studies to augment the use of XCT scanning. Recently I2KI was used in XCT scans of animal hearts to study cardiac structure and to generate 3D anatomical computer models. However, to date there has been no thorough study into the optimal use of I2KI as a contrast agent in cardiac muscle with respect to the staining times required, which has been shown to impact significantly upon the quality of results. In this study we address this issue by systematically scanning samples at various stages of the staining process. To achieve this, mouse hearts were stained for up to 58 hours and scanned at regular intervals of 6-7 hours throughout this process. Optimal staining was found to depend upon the thickness of the tissue; a simple empirical exponential relationship was derived to allow calculation of the required staining time for cardiac samples of an arbitrary size.

Quality assurance for high dose rate brachytherapy treatment planning optimization: using a simple optimization to verify a complex optimization

International Nuclear Information System (INIS)

Deufel, Christopher L; Furutani, Keith M

2014-01-01

As dose optimization for high dose rate brachytherapy becomes more complex, it becomes increasingly important to have a means of verifying that optimization results are reasonable. A method is presented for using a simple optimization as quality assurance for the more complex optimization algorithms typically found in commercial brachytherapy treatment planning systems. Quality assurance tests may be performed during commissioning, at regular intervals, and/or on a patient specific basis. A simple optimization method is provided that optimizes conformal target coverage using an exact, variance-based, algebraic approach. Metrics such as dose volume histogram, conformality index, and total reference air kerma agree closely between simple and complex optimizations for breast, cervix, prostate, and planar applicators. The simple optimization is shown to be a sensitive measure for identifying failures in a commercial treatment planning system that are possibly due to operator error or weaknesses in planning system optimization algorithms. Results from the simple optimization are surprisingly similar to the results from a more complex, commercial optimization for several clinical applications. This suggests that there are only modest gains to be made from making brachytherapy optimization more complex. The improvements expected from sophisticated linear optimizations, such as PARETO methods, will largely be in making systems more user friendly and efficient, rather than in finding dramatically better source strength distributions. (paper)

Multiple response optimization for Cu, Fe and Pb determination in naphtha by graphite furnace atomic absorption spectrometry with sample injection as detergent emulsion

International Nuclear Information System (INIS)

Brum, Daniel M.; Lima, Claudio F.; Robaina, Nicolle F.; Fonseca, Teresa Cristina O.; Cassella, Ricardo J.

2011-01-01

The present paper reports the optimization for Cu, Fe and Pb determination in naphtha by graphite furnace atomic absorption spectrometry (GF AAS) employing a strategy based on the injection of the samples as detergent emulsions. The method was optimized in relation to the experimental conditions for the emulsion formation and taking into account that the three analytes (Cu, Fe and Pb) should be measured in the same emulsion. The optimization was performed in a multivariate way by employing a three-variable Doehlert design and a multiple response strategy. For this purpose, the individual responses of the three analytes were combined, yielding a global response that was employed as a dependent variable. The three factors related to the optimization process were: the concentration of HNO 3 , the concentration of the emulsifier agent (Triton X-100 or Triton X-114) in aqueous solution used to emulsify the sample and the volume of solution. At optimum conditions, it was possible to obtain satisfactory results with an emulsion formed by mixing 4 mL of the samples with 1 mL of a 4.7% w/v Triton X-100 solution prepared in 10% v/v HNO 3 medium. The resulting emulsion was stable for 250 min, at least, and provided enough sensitivity to determine the three analytes in the five samples tested. A recovery test was performed to evaluate the accuracy of the optimized procedure and recovery rates, in the range of 88-105%; 94-118% and 95-120%, were verified for Cu, Fe and Pb, respectively.

Multiple response optimization for Cu, Fe and Pb determination in naphtha by graphite furnace atomic absorption spectrometry with sample injection as detergent emulsion

Energy Technology Data Exchange (ETDEWEB)

Brum, Daniel M.; Lima, Claudio F. [Departamento de Quimica, Universidade Federal de Vicosa, A. Peter Henry Rolfs s/n, Vicosa/MG, 36570-000 (Brazil); Robaina, Nicolle F. [Departamento de Quimica Analitica, Universidade Federal Fluminense, Outeiro de S.J. Batista s/n, Centro, Niteroi/RJ, 24020-141 (Brazil); Fonseca, Teresa Cristina O. [Petrobras, Cenpes/PDEDS/QM, Av. Horacio Macedo 950, Ilha do Fundao, Rio de Janeiro/RJ, 21941-915 (Brazil); Cassella, Ricardo J., E-mail: cassella@vm.uff.br [Departamento de Quimica Analitica, Universidade Federal Fluminense, Outeiro de S.J. Batista s/n, Centro, Niteroi/RJ, 24020-141 (Brazil)

2011-05-15

The present paper reports the optimization for Cu, Fe and Pb determination in naphtha by graphite furnace atomic absorption spectrometry (GF AAS) employing a strategy based on the injection of the samples as detergent emulsions. The method was optimized in relation to the experimental conditions for the emulsion formation and taking into account that the three analytes (Cu, Fe and Pb) should be measured in the same emulsion. The optimization was performed in a multivariate way by employing a three-variable Doehlert design and a multiple response strategy. For this purpose, the individual responses of the three analytes were combined, yielding a global response that was employed as a dependent variable. The three factors related to the optimization process were: the concentration of HNO{sub 3}, the concentration of the emulsifier agent (Triton X-100 or Triton X-114) in aqueous solution used to emulsify the sample and the volume of solution. At optimum conditions, it was possible to obtain satisfactory results with an emulsion formed by mixing 4 mL of the samples with 1 mL of a 4.7% w/v Triton X-100 solution prepared in 10% v/v HNO{sub 3} medium. The resulting emulsion was stable for 250 min, at least, and provided enough sensitivity to determine the three analytes in the five samples tested. A recovery test was performed to evaluate the accuracy of the optimized procedure and recovery rates, in the range of 88-105%; 94-118% and 95-120%, were verified for Cu, Fe and Pb, respectively.

Focusing light through dynamical samples using fast continuous wavefront optimization.

Science.gov (United States)

Blochet, B; Bourdieu, L; Gigan, S

2017-12-01

We describe a fast continuous optimization wavefront shaping system able to focus light through dynamic scattering media. A micro-electro-mechanical system-based spatial light modulator, a fast photodetector, and field programmable gate array electronics are combined to implement a continuous optimization of a wavefront with a single-mode optimization rate of 4.1 kHz. The system performances are demonstrated by focusing light through colloidal solutions of TiO 2 particles in glycerol with tunable temporal stability.

On sampling social networking services

OpenAIRE

Wang, Baiyang

2012-01-01

This article aims at summarizing the existing methods for sampling social networking services and proposing a faster confidence interval for related sampling methods. It also includes comparisons of common network sampling techniques.

Two Topics in Data Analysis: Sample-based Optimal Transport and Analysis of Turbulent Spectra from Ship Track Data

Science.gov (United States)

Kuang, Simeng Max

This thesis contains two topics in data analysis. The first topic consists of the introduction of algorithms for sample-based optimal transport and barycenter problems. In chapter 1, a family of algorithms is introduced to solve both the L2 optimal transport problem and the Wasserstein barycenter problem. Starting from a theoretical perspective, the new algorithms are motivated from a key characterization of the barycenter measure, which suggests an update that reduces the total transportation cost and stops only when the barycenter is reached. A series of general theorems is given to prove the convergence of all the algorithms. We then extend the algorithms to solve sample-based optimal transport and barycenter problems, in which only finite sample sets are available instead of underlying probability distributions. A unique feature of the new approach is that it compares sample sets in terms of the expected values of a set of feature functions, which at the same time induce the function space of optimal maps and can be chosen by users to incorporate their prior knowledge of the data. All the algorithms are implemented and applied to various synthetic example and practical applications. On synthetic examples it is found that both the SOT algorithm and the SCB algorithm are able to find the true solution and often converge in a handful of iterations. On more challenging applications including Gaussian mixture models, color transfer and shape transform problems, the algorithms give very good results throughout despite the very different nature of the corresponding datasets. In chapter 2, a preconditioning procedure is developed for the L2 and more general optimal transport problems. The procedure is based on a family of affine map pairs, which transforms the original measures into two new measures that are closer to each other, while preserving the optimality of solutions. It is proved that the preconditioning procedure minimizes the remaining transportation cost

CLSI-based transference of CALIPER pediatric reference intervals to Beckman Coulter AU biochemical assays.

Science.gov (United States)

Abou El Hassan, Mohamed; Stoianov, Alexandra; Araújo, Petra A T; Sadeghieh, Tara; Chan, Man Khun; Chen, Yunqi; Randell, Edward; Nieuwesteeg, Michelle; Adeli, Khosrow

2015-11-01

The CALIPER program has established a comprehensive database of pediatric reference intervals using largely the Abbott ARCHITECT biochemical assays. To expand clinical application of CALIPER reference standards, the present study is aimed at transferring CALIPER reference intervals from the Abbott ARCHITECT to Beckman Coulter AU assays. Transference of CALIPER reference intervals was performed based on the CLSI guidelines C28-A3 and EP9-A2. The new reference intervals were directly verified using up to 100 reference samples from the healthy CALIPER cohort. We found a strong correlation between Abbott ARCHITECT and Beckman Coulter AU biochemical assays, allowing the transference of the vast majority (94%; 30 out of 32 assays) of CALIPER reference intervals previously established using Abbott assays. Transferred reference intervals were, in general, similar to previously published CALIPER reference intervals, with some exceptions. Most of the transferred reference intervals were sex-specific and were verified using healthy reference samples from the CALIPER biobank based on CLSI criteria. It is important to note that the comparisons performed between the Abbott and Beckman Coulter assays make no assumptions as to assay accuracy or which system is more correct/accurate. The majority of CALIPER reference intervals were transferrable to Beckman Coulter AU assays, allowing the establishment of a new database of pediatric reference intervals. This further expands the utility of the CALIPER database to clinical laboratories using the AU assays; however, each laboratory should validate these intervals for their analytical platform and local population as recommended by the CLSI. Copyright © 2015 The Canadian Society of Clinical Chemists. Published by Elsevier Inc. All rights reserved.

A swarm-trained k-nearest prototypes adaptive classifier with automatic feature selection for interval data.

Science.gov (United States)

Silva Filho, Telmo M; Souza, Renata M C R; Prudêncio, Ricardo B C

2016-08-01

Some complex data types are capable of modeling data variability and imprecision. These data types are studied in the symbolic data analysis field. One such data type is interval data, which represents ranges of values and is more versatile than classic point data for many domains. This paper proposes a new prototype-based classifier for interval data, trained by a swarm optimization method. Our work has two main contributions: a swarm method which is capable of performing both automatic selection of features and pruning of unused prototypes and a generalized weighted squared Euclidean distance for interval data. By discarding unnecessary features and prototypes, the proposed algorithm deals with typical limitations of prototype-based methods, such as the problem of prototype initialization. The proposed distance is useful for learning classes in interval datasets with different shapes, sizes and structures. When compared to other prototype-based methods, the proposed method achieves lower error rates in both synthetic and real interval datasets. Copyright © 2016 Elsevier Ltd. All rights reserved.

Optimal Computing Budget Allocation for Particle Swarm Optimization in Stochastic Optimization.

Science.gov (United States)

Zhang, Si; Xu, Jie; Lee, Loo Hay; Chew, Ek Peng; Wong, Wai Peng; Chen, Chun-Hung

2017-04-01

Particle Swarm Optimization (PSO) is a popular metaheuristic for deterministic optimization. Originated in the interpretations of the movement of individuals in a bird flock or fish school, PSO introduces the concept of personal best and global best to simulate the pattern of searching for food by flocking and successfully translate the natural phenomena to the optimization of complex functions. Many real-life applications of PSO cope with stochastic problems. To solve a stochastic problem using PSO, a straightforward approach is to equally allocate computational effort among all particles and obtain the same number of samples of fitness values. This is not an efficient use of computational budget and leaves considerable room for improvement. This paper proposes a seamless integration of the concept of optimal computing budget allocation (OCBA) into PSO to improve the computational efficiency of PSO for stochastic optimization problems. We derive an asymptotically optimal allocation rule to intelligently determine the number of samples for all particles such that the PSO algorithm can efficiently select the personal best and global best when there is stochastic estimation noise in fitness values. We also propose an easy-to-implement sequential procedure. Numerical tests show that our new approach can obtain much better results using the same amount of computational effort.

Multi-cluster processor operating only select number of clusters during each phase based on program statistic monitored at predetermined intervals

Science.gov (United States)

Balasubramonian, Rajeev [Sandy, UT; Dwarkadas, Sandhya [Rochester, NY; Albonesi, David [Ithaca, NY

2009-02-10

In a processor having multiple clusters which operate in parallel, the number of clusters in use can be varied dynamically. At the start of each program phase, the configuration option for an interval is run to determine the optimal configuration, which is used until the next phase change is detected. The optimum instruction interval is determined by starting with a minimum interval and doubling it until a low stability factor is reached.

A Combined Weighting Method Based on Hybrid of Interval Evidence Fusion and Random Sampling