HU - The Big Pop Cosmogenesis Equation of State

HU is the Hypergeometrical Universe Theory (HU)[1-8], proposed in 2006, where the Universe is a Lightspeed Expanding Hyperspherical Hypersurface and Gravitation is an absolute-velocity-dependent, epoch-dependent force. Here we introduce the Big Pop Cosmogenesis and show our calculations associated with the Equation of State of the Universe. This article is the first in a series of articles[9-22] supporting the paradigm shift.

New conditional, scenario-based, and non-conditional cumulative absolute velocity models for subduction tectonic settings

The PEER NGA-Sub ground-motion intensity measure database is used to develop new conditional ground-motion models (CGMMs), a set of scenario-based models, and non-conditional models to estimate the cumulative absolute velocity ([Formula: see text]) of ground motions from subduction zone earthquakes. In the CGMMs, the median estimate of [Formula: see text] is conditioned on the estimated peak ground acceleration ([Formula: see text]), the time-averaged shear-wave velocity in the top 30 m of the soil ([Formula: see text]), the earthquake magnitude ([Formula: see text]), and the spectral acceleration at the period of 1 s ([Formula: see text]). Multiple scenario-based [Formula: see text] models are developed by combining the CGMMs with pseudo-spectral acceleration ([Formula: see text]) ground-motion models (GMMs) for [Formula: see text] and [Formula: see text] to directly estimate [Formula: see text] given an earthquake scenario and site conditions. Scenario-based [Formula: see text] models are capable of capturing the complex ground-motion effects (e.g. soil non-linearity and regionalization effects) included in their underlying [Formula: see text]/[Formula: see text] GMMs. This approach also ensures the consistency of the [Formula: see text] estimates with a [Formula: see text] design spectrum. In addition, two non-conditional [Formula: see text] GMMs are developed using Bayesian hierarchical regressions. Finally, we present comparisons between the developed models. The comparisons show that if non-conditional GMMs are properly constrained, they are consistent with scenario-based GMMs. The [Formula: see text] GMMs developed in this study advance the performance-based earthquake engineering practice in areas affected by subduction zone earthquakes.

Effect of Splitter Blades on Performances of a Very Low Specific Speed Pump

The usage of splitter blades to enhance the performances of low specific speed pumps is common practice. Based on experimental and numerical studies, the influence of the addition of one and two splitter blades is investigated on a very low specific speed pump to assess their impact not only on the performance characteristics but also on the losses in all pump domains. First, the main characteristic curves are discussed and it is shown that the usage of splitter blades enhances the head of the pump while not impairing its efficiency. Secondly, a detailed analysis of the losses in the pump reveals that splitter blades improve the flow in all parts of the pumps, but the volute. The flow at the impeller outlet shows that splitter blades largely benefit the slip factor and discharges a more blade-congruent flow in the volute. However, higher absolute velocity at the outlet of the impeller with splitter blades increases friction at the volute wall, as confirmed by the average wall shear stress in the different tested cases.

Structural Health Monitoring Using Machine Learning and Cumulative Absolute Velocity Features

Machine learning (ML)-aided structural health monitoring (SHM) can rapidly evaluate the safety and integrity of the aging infrastructure following an earthquake. The conventional damage features used in ML-based SHM methodologies face the curse of dimensionality. This paper introduces low dimensional, namely, cumulative absolute velocity (CAV)-based features, to enable the use of ML for rapid damage assessment. A computer experiment is performed to identify the appropriate features and the ML algorithm using data from a simulated single-degree-of-freedom system. A comparative analysis of five ML models (logistic regression (LR), ordinal logistic regression (OLR), artificial neural networks with 10 and 100 neurons (ANN10 and ANN100), and support vector machines (SVM)) is performed. Two test sets were used where Set-1 originated from the same distribution as the training set and Set-2 came from a different distribution. The results showed that the combination of the CAV and the relative CAV with respect to the linear response, i.e., RCAV, performed the best among the different feature combinations. Among the ML models, OLR showed good generalization capabilities when compared to SVM and ANN models. Subsequently, OLR is successfully applied to assess the damage of two numerical multi-degree of freedom (MDOF) models and an instrumented building with CAV and RCAV as features. For the MDOF models, the damage state was identified with accuracy ranging from 84% to 97% and the damage location was identified with accuracy ranging from 93% to 97.5%. The features and the OLR models successfully captured the damage information for the instrumented structure as well. The proposed methodology is capable of ensuring rapid decision-making and improving community resiliency.

Magnitude Estimation and Onsite Earthquake Early Warning using Cumulative Absolute Velocity in Taiwan

<p>Real-time magnitude determination is one of the critical issues for earthquake early warning (EEW). Magnitude determination may have saturation situation using initial seismic signals after an earthquake occurrence. Previous studies utilized eventual cumulative absolute velocity (eCAV) to determine magnitude up to 9.0 without any saturation. However, to determine eCAV will be too late for EEW application. In order to shorten time to obtain eCAV, 4,754 strong motion records from 64 events with M<sub>L </sub>large than 5.5 in Taiwan are used to establish the relationship between eCAV and initial shaking parameters (initial CAV, initial cumulative absolute displacement, initial cumulative absolute integral displacement,  P<sub>d</sub> and  τ<sub>c</sub>) from 1 s to 20 s after P arrival. Our preliminary results show that eCAV can be estimated using initial shaking parameters. Logarithm linear correlation coefficients vary from 0.78 to 0.97 with standard deviations from 0.27 to 0.10 for time windows from 1 s to 20 s after P arrival. Eventually, we can timely estimate eCAV for magnitude determination as well as or on-site EEW purpose.</p>

Abstract P350: Early Transcranial Doppler Velocity Changes as a Predictor for Delayed Cerebral Ischemia in Aneurysmal Subarachnoid Hemorrhage

Introduction: Although transcranial Doppler (TCD) evaluation for vasospasm remains an important study in aneurysmal subarachnoid hemorrhage (aSAH) management, its precise role in predicting delayed cerebral ischemia (DCI) remains unclear. Hypothesis: We evaluated optimal measures for evaluating TCD velocities and hypothesized that TCD velocity change would be the best predictor for DCI in patients with aSAH. Methods: Patients with aSAH over a two-year period were retrospectively analyzed. Baseline characteristics, outcomes, and TCD velocities in bilateral middle cerebral arteries (MCA) for hospital days 2 to14 were recorded. TCD variables, including absolute velocity and change in velocity, were obtained by creating a smoothing curve. A variable representing change in TCD velocity was then created through a linear regression model that confirmed greatest change in velocity associated with DCI occurred at days 2-7. Multivariate logistic regression analysis using DCI as outcome was then completed. Results: 95 patients with aSAH were evaluated. Increased TCD velocity at days 2-7 proved to be a better predictor for DCI than absolute velocity with an optimal cutoff of 8.9 cm/sec/day ( p = 0.019) and AUC 0.651. Multivariate logistic analysis using DCI as the outcome showed that poor admission Hunt-Hess scores (OR 5.02, 95%CI 1.22-22.67, p = 0.028) and increase in TCD velocity during days 2-7 (OR 5.32, 95%CI 1.41-23.33, p = 0.018) were independently associated with DCI. Conclusions: We found that relative increases in TCD velocities in the MCAs during the first 7 days (threshold increase of 8.9 cm/sec/day or 53.4 cm/sec from days 2-7) after aSAH were independently associated with DCI. Our findings suggest that vasospasm should be confirmed and treated aggressively when detected via increased TCD velocities during the first seven days in order to minimize DCI. This association requires independent confirmation.