Parameters' Confidence Intervals Evaluation for Heterogeneous Strain Field Specimen Designs by Using Digital Image Correlation

This paper aims to compare different heterogeneous test designs from the perspective of the confidence interval quantification of inversely identified parameters, where the influence of a DIC optical system systematic and random error are taken into account. Because the errors in optical measurement can arise from many reasons and sources, our methodology relies on the system's errors determined from initial sets of pictures acquired at the load-free state for hundreds of specimens (over 850 tests over the past three years). In this way, a prior probability distribution of systematic and random error, arisen from the system initial settings and testing procedures are determined. Further, by conducting an inverse identification procedure of linear orthotropic elastic material parameters, the influence of the error distributions is studied for different types of heterogeneous specimens. The presented methodology determines the DIC bias and random error propagation through the inverse identification procedure to individual parameters. For each specimen design, confidence intervals of identified material parameters were determined. The results show the appropriateness of a specimen design for the identification of particular material parameters.

On the Propagation of Satellite Precipitation Estimation Errors: From Passive Microwave to Infrared Estimates

ABSTRACTThe launch of NOAA’s latest generation of geostationary satellites known as the Geostationary Operational Environmental Satellite (GOES)-R Series has opened new opportunities in quantifying precipitation rates. Recent efforts have strived to utilize these data to improve space-based precipitation retrievals. The overall objective of the present work is to carry out a detailed error budget analysis of the improved Self-Calibrating Multivariate Precipitation Retrieval (SCaMPR) algorithm for GOES-R and the passive microwave (MW) combined (MWCOMB) precipitation dataset used to calibrate it with an aim to provide insights regarding strengths and weaknesses of these products. This study systematically analyzes the errors across different climate regions and also as a function of different precipitation types over the conterminous United States. The reference precipitation dataset is Ground-Validation Multi-Radar Multi-Sensor (GV-MRMS). Overall, MWCOMB reveals smaller errors as compared to SCaMPR. However, the analysis indicated that that the major portion of error in SCaMPR is propagated from the MWCOMB calibration data. The major challenge starts with poor detection from MWCOMB, which propagates in SCaMPR. In particular, MWCOMB misses 90% of cool stratiform precipitation and the overall detection score is around 40%. The ability of the algorithms to quantify precipitation amounts for the Warm Stratiform, Cool Stratiform, and Tropical/Stratiform Mix categories is poor compared to the Convective and Tropical/Convective Mix categories with additional challenges in complex terrain regions. Further analysis showed strong similarities in systematic and random error models with both products. This suggests that the potential of high-resolution GOES-R observations remains underutilized in SCaMPR due to the errors from the calibrator MWCOMB.

VACUUM AND THERMOPLASTIC MOULD-BASED IMMOBILIZATION SYSTEMS USED IN PATIENT UNDERGOING PELVIC RADIATION THERAPY: A COMPARATIVE STUDY

Introduction: The aim of the present study was to compare two immobilization systems for comparison of setup errors in targeted radiotherapy. Methods: Retrospective analysis was done for the patients undergoing radiotherapy from May 2012 to December 2018 at our institution. Immobilization was performed on 30 patients sessions (Vacuum cushion i.e., Vac-Lok™ = 15; Thermoplastic mould i.e., Pelvicast pelvic masks = 15). A total of 763 cone-beams were analysed. The target lesion location was verified by cone-beam computed tomography (CBCT) prior to each session, with displacements assessed by CBCT simulation prior to each treatment session. Systematic setup errors, random setup errors, isocenter deviations in the Medio-lateral (ML), Supero-inferior (SI), Antero-posterior (AP), Rotation (yaw) directions of the patient position was calculated. Results: On comparing the Vac-Lok™ and Pelvicast pelvic masks group with respect to Systematic and random error in the lateral, longitudinal, vertical and YAW direction, no statistically significant difference was seen except the random error in YAW direction (P=0.037, Unpaired t-test). There was no difference observed in comparing the isocentric deviation. Conclusion: It was inferred and concluded that using a vacuum cushion for pelvic radiotherapy provides no added benefit compared to using a thermoplastic mould. Thermoplastic mould is recommended for patients receiving pelvic radiotherapy to improve overall reproducibility.Keywords: Rotational therapy; Radiotherapy; Systematic, random error; Thermoplastic mould; Vacuum cushion.

Bias And Random Measurement Error In Accounting-Based Surrogates For Internal Rate Of Return

<span>Limitations inherent in alternative profitability measures as estimates of internal rate of return (IRR) often require that managers and researchers employ accounting-based profitability measures. Using published accounting and stock market data, this study models accounting rate of return (ARR) and conditional estimate of internal rate of return (CIRR) as functions of product market risk; growth (g) inventory cost flow assumption (INV), and depreciation method (DE). The models support inferences about the bias and efficiency (i.e. systematic and random error) in the relationships between the two accounting-based profitability measure and IRR, as estimated by the bias and efficiency in their relationships with a factor that is suggested in the finance literature as a determinant of systematic risk (product market risk). The results indicate that ARR estimates IRR with bias attributable to g; however, ARR is unaffected by INV and DEP. Whether g, INV, or DEP affect CIRRs ability to estimate IRR depends on the interval over which CIRR is estimated and the assumed cash flow pattern. On the other hand, CIRR generally estimates IRR with significantly greater efficiency. These results have research design implications, as well as implications for both accounting policy formulation and anti-trust policies.</span>

An Information System for a Bauxite Mine

Bauxite is mined and transported by conveyor to a processing plant, screened and washed, then placed into blended stockpiles to feed the alumina refinery. While being stacked to the stockpile, the ore is sampled. Completed stockpiles must be acceptably close to target grade (composition), not only in alumina, but also in residual silica, carbon and sodium carbonate. The mine is an open-cut pit. Each day the choice of ore to mine, from multiple locations in the pit, is based upon estimates of grade. Estimated grade, from exploration drilling of the area before mining, has both systematic and random error. This paper describes an information system to guide the daily choice of ore to mine. Continually updating the comparison between forecasts and sampled product, the system provides adjusted forecasts. Ore is selected to bring the exponentially smoothed grade to target, in each of the control minerals.

Evaluation of Heavy Precipitation Forecasts Using Composite-Based Methods: A Distributions-Oriented Approach

Numerical forecasts of heavy warm-season precipitation events are verified using simple composite collection techniques. Various sampling methods and statistical measures are employed to evaluate the general characteristics of the precipitation forecasts. High natural variability is investigated in terms of its effects on the relevance of the resultant statistics. Natural variability decreases the ability of a verification scheme to discriminate between systematic and random error. The effects of natural variability can be mitigated by compositing multiple events with similar properties. However, considerable sample variance is inevitable because of the extreme diversity of mesoscale precipitation structures. The results indicate that forecasts of heavy precipitation were often correct in that heavy precipitation was observed relatively close to the predicted area. However, many heavy events were missed due in part to the poor prediction of convection. Targeted composites of the missed events indicate that a large percentage of the poor forecasts were dominated by convectively parameterized precipitation. Further results indicate that a systematic northward bias in the predicted precipitation maxima is related to the deficits in the prediction of subsynoptically forced convection.