1. Introduction: Orifices are historically used in different hydraulic structures for flow regulation and removal of sediments fromreservoirs. The performance of orifices in drawdown flushing to increase the lifetime of dams is vital. Anayiotos etal. [1] performed extensive measurements of the velocity contours upstream of an orifice….

Purpose: In this paper k-level CONSTANT stress accelerated life TEST under Type-I progressive censoring for Lomax distribution with non-CONSTANT shape and scale parameters is investigated. The purpose of this paper is to estimate the model parameters using the EM algorithm and optimize the TEST design. Methodology: Life TESTing often consumes a very long time for TESTing and this is a fundamental problem in TEST design. This problem is solved by accelerated life TESTs. There is a recommended method for reducing the time of failure, such that the stress level of the TEST units will increase and then they will fail earlier than normal operating conditions. Therefore, these approaches reduced the running time. In this paper, the k-level CONSTANT stress accelerated life TEST under progressive Type-I censoring used. The Expectation-Maximization (EM) algorithm is used to determine the maximum likelihood estimates of the unknown parameters. This algorithm is a very powerful tool in handling the incomplete data problem. Two different criteria used to optimize the TEST plan. And the effect of the sample size, number of stress levels and inspection and the intermediate censoring proportion are assessed on the design efficiency. Findings: based on the simulation study and a real data set, it is demonstrated that the EM estimator is good. Also, under the optimization criterion II, a more efficient TEST was obtained than the optimization criterion I. In addition, the small sample size, the small number of stress levels, the small number of inspections and the large intermediate censoring proportion lead to a more efficient TEST. Originality/Value: In this paper, the periodic inspection is used to collect lifetime data. Although continuous is an ideal mode. But sometimes due to technical limitations and/or budgetary constraints, the continuous inspection is not possible in practice and the experimenter has to use the periodic inspection. In this case, the exact times of TEST units may not be available and only the failure counts are collected at certain time points during the TEST. Also, in this paper, we assumed that both scale and shape parameters to be log linear model by operating stress.

Background: Studies on the use of fecal immunochemical TEST (FIT) in colorectal screening have long assumed perfect accuracy for colonoscopy. No study to date has directly compared the diagnostic accuracy of colonoscopy and FIT to detect advanced neoplasia (AN) in a HEAD-to-HEAD diagnostic accuracy meta-analysis. Methods: A comprehensive electronic search was performed for a HEAD-to-HEAD comparison of FIT and colonoscopy using a third acceptable reference standard in asymptomatic adults. Cochrane methodology was used to perform a HEAD-to-HEAD diagnostic TEST accuracy meta-analysis. Quality assessment tool for diagnostic accuracy studies-2 (QUADAS-2) was used to assess the risk of bias in included studies. Results: Two studies met the eligibility criteria. Overall sensitivity and specificity were 98. 5 (95% CI 96. 3-100%) and 100% (99. 9-100%) for colonoscopy and 16. 4% (10. 3-22. 6%) and 95. 4% (94. 3-96. 4%) for FIT. Colonoscopy was significantly better than FIT (P<0. 0001). The positive and negative likelihood ratios were 1. 75 (1. 57-1. 96) and 0. 03 (0. 01-0. 08) for colonoscopy and 3. 02 (2. 01-4. 55) and 0. 88 (0. 82-0. 95) for FIT, respectively. Conclusion: Colonoscopy provides significantly better diagnostic accuracy to detect AN compared with FIT (GRADE: ⨁⨁◯◯). Our study provided precise sensitivity and specificity of both colonoscopy and FIT and a revision in screening policies based on an updated cost-effectiveness analysis considering the results of the HEAD-to-HEAD analysis.

In order to design or evaluate an irrigation system, the infiltration phenomenon and its variations should be determined accurately. In saline and sodic soil and water conditions, the importance of this issue will become greater. The main objective of this study was to estimate the coefficients of different infiltration equations (Kostiakov, Kostiakov-Lewis, Horton, Philip, and U. S. Soil Conservation) and to evaluate the performance of these equations under different qualities of irrigation water, initial soil moistures, and CONSTANT water HEAD. Using a laboratory method, infiltrations were measured in soil columns for CONSTANT water HEAD. Then, by applying the cumulative infiltration and drainage outflow data to HYDRUS-1D model, soil hydraulic parameters were determined by the inverse solution. To determine the coefficients of infiltration equations, the outputs of the HYDRUS-1D model including cumulative infiltration verses time were corrolated. The values of root mean square error (RMSE), standard deviation root mean square error (SDRMSE), normalized root mean square error (NRMSE), percent relative absolute error (AE) and percent relative error (RE), were used to evaluate the performance of each infiltration equation and to rank the equations. The equation that had the highest rank was considered as the best and more stable equation. The Horton equation with RMSE, SDRMSE, NRMSE and AE of 0.043, 0.018, 0.006 and 1 and the Kostiakov equation with the values of 0.234, 0.175, 0.025 and 4, were the most and the least suitable eqations, respectively. The evaluation of the performance of infiltration equations using statistical indicators showed that the Kostiakov-Lewis and the Kostiakov infiltration equations were the best and the worst equations, respectively. Comparison of NRMSE values showed that in most cases, under deficit irrigation, infiltration equations estimate infiltration more accurately. For a given treatment, the errors of Kostiakov-Lewis and Philip infiltration equations increased as the amount of irrigation water increased, and as the end of the season approached. The rest of the equations did not show any especial trends. To measure infiltration, it is necessary to consider the effects of irrigation water quality, initial soil moisture, and water HEADs, because these parameters influence the coefficients of infiltration equations and, consequently, the irrigation efficiency.