Response Surface Grey Relational Analysis On The Manufacturing of High Grade Biomedical Ti-13Zr-13Nb

Optimization of the manufacturing conditions with more than one performance characteristics have been a thing of concern, especially for Response Surface Method (RSM) optimization. Hence, this study addressed this challenge by reanalyzing a data presented in a previous study using grey relational analysis (GRA) and regression analysis. Central Composite Design (CCD) of RSM with high and low values of manufacturing conditions; voltage (50, 70) V, current (8, 16) A, pulse ON time (6, 10) μs, and pulse OFF time (7, 11) μs. The manufacturing conditions for optimal biomedical Ti-13Zr-13Nb alloy were obtained to be 50V voltage, 8A current, 6 μs pulse ON time, and 11 μs pulse OFF time. It was also revealed that the mathematical model was very efficient because the modeled GRG was in consonant with the experimental one. In addition, it was also established that current was the most significant manufacturing condition with a contribution of 47.27%. Voltage, factors interactions and residual error were insignificant on the GRG value of the titanium alloy. In conclusion, it can be deduced that the a small value of voltage within the considered settings could be used to manufacture better grade Ti-13Zr-13Nb alloy and also the small value of residual error showed the high manufacturability of the material.

Enhancement of safety communication model

A fundamental measure of safety communication is the residual error probability, i. e., the probability of undetected errors. For the detection of data errors, typically a Cyclic Redundancy Check (CRC) is applied, and the resulting residual error probability is determined based on the Binary Symmetric Channel (BSC) model. The use of this model had been questioned since several error types cannot be sufficiently described. Especially the increasing introduction of security algorithms into underlying communication layers requires a more adequate channel model. This paper introduces an enhanced model that extends the list of considered data error types by combining the BSC model with a Uniformly Distributed Segments (UDS) model. Although models beyond BSC are applied, the hitherto method of the calculation of the residual error probability can be maintained.

Adaptive space-time finite element methods for parabolic optimal control problems

We present, analyze, and test locally stabilized space-time finite element methods on fully unstructured simplicial space-time meshes for the numerical solution of space-time tracking parabolic optimal control problems with the standard L 2-regularization. We derive a priori discretization error estimates in terms of the local mesh-sizes for shape-regular meshes. The adaptive version is driven by local residual error indicators, or, alternatively, by local error indicators derived from a new functional a posteriori error estimator. The latter provides a guaranteed upper bound of the error, but is more costly than the residual error indicators. We perform numerical tests for benchmark examples having different features. In particular, we consider a discontinuous target in form of a first expanding and then contracting ball in 3d that is fixed in the 4d space-time cylinder.

Measurement Invariance of Perfectionism Measures in Students with and without a History of Non-Suicidal Self-Injury

Perfectionism is a transdiagnostic process which may be implicated in the onset and maintenance of non-suicidal self-injury. No study has evaluated whether reported differences in perfectionism between individuals with and without a history of self-injury represent genuine group differences or measurement artefacts. The present study reports an investigation of the measurement invariance of two common scales of perfectionism, the Frost Multidimensional Perfectionism Scale-Brief (FMPS-Brief) and the Clinical Perfectionism Questionnaire (CPQ), among university students (Mage = 20.48, SDage = 2.22, 75.3% female, 22.8% male) with and without a history of self-injury (total n = 711). Results revealed full residual error invariance for the two-factor model of FMPS-Brief, while the bifactor model of the FMPS-Brief and the two-factor model of the CPQ demonstrated partial metric invariance. Accounting for partial metric invariance, the bifactor model of the FMPS-Brief also demonstrated partial residual error invariance. The current findings suggest that observed differences using the FMPS-Brief reflect genuine differences in perfectionism between individuals with and without a history of self-injury. Further, while researchers using the bi-factor model can have confidence that the general factor can adequately assess group differences, differential item functioning should be considered if using the strivings and concerns factors. Finally, in the current data, the CPQ did not perform as expected in baseline model fit and future research should replicate assessments of measurement invariance in this measure.

Risk of aortic aneurysm and dissection following exposure to fluoroquinolones, common antibiotics, and febrile illness using a self-controlled case series study design: Retrospective analyses of three large healthcare databases in the US

Objective Recent observational studies suggest increased aortic aneurysm or dissection (AAD) risk following fluoroquinolone (FQ) exposure but acknowledge potential for residual bias from unreported patient characteristics. The objective of our study is to evaluate the potential association between FQ, other common antibiotics and febrile illness with risk of AAD using a self-controlled case series (SCCS) study design. Design Retrospective database analysis–SCCS. Setting Primary and Secondary Care. Study population 51,898 patients across 3 US claims databases (IBM® MarketScan® commercial and Medicare databases, Optum Clinformatics). Exposure FQ or other common antibiotics or febrile illness. Outcome AAD. Methods We studied patients with exposures and AAD between 2012 and 2017 in 3 databases. Risk windows were defined as exposure period plus 30 days. Diagnostic analyses included p-value calibration to account for residual error using negative control exposures (NCE), and pre-exposure outcome analyses to evaluate exposure-outcome timing. The measure of association was the incidence rate ratio (IRR) comparing exposed and unexposed time. Results Most NCEs produced effect estimates greater than the hypothetical null, indicating positive residual error; calibrated p (Cp) values were therefore used. The IRR following FQ exposure ranged from 1.13 (95% CI: 1.04–1.22 –Cp: 0.503) to 1.63 (95% CI: 1.45–1.84 –Cp: 0.329). An AAD event peak was identified 60 days before first FQ exposure, with IRR increasing between the 60- to 30- and 29- to 1-day pre-exposure periods. It is uncertain how much this pre-exposure AAD event peak reflects confounding versus increased antibiotic use after a surgical correction of AADs. Conclusion This study does not confirm prior studies. Using Cp values to account for residual error, the observed FQ-AAD association cannot be interpreted as significant. Additionally, an AAD event surge in the 60 days before FQ exposure is consistent with confounding by indication, or increased use of antibiotics post-surgery. Registration NCT03479736.

Calculation and realization of new method grey residual error correction model

Aiming at the problem of prediction accuracy of stochastic volatility series, this paper proposes a method to optimize the grey model(GM(1,1)) from the perspective of residual error. In this study, a new fitting method is firstly used, which combines the wavelet function basis and the least square method to fit the residual data of the true value and the predicted value of the grey model(GM(1,1)). The residual prediction function is constructed by using the fitting method. Then, the prediction function of the grey model(GM(1,1)) is modified by the residual prediction function. Finally, an example of the wavelet residual-corrected grey prediction model (WGM) is obtained. The test results show that the fitting accuracy of the wavelet residual-corrected grey prediction model has irreplaceable advantages.