An introduction to new robust linear and monotonic correlation coefficients.

BACKGROUND: The most common measure of association between two continuous variables is the Pearson correlation (Maronna et al. in Safari an OMC. Robust statistics, 2019. https://login.proxy.bib.uottawa.ca/login?url=https://learning.oreilly.com/library/view/-/9781119214687/?ar&orpq&email=^u). When outliers are present, Pearson does not accurately measure association and robust measures are needed. This article introduces three new robust measures of correlation: Taba (T), TabWil (TW), and TabWil rank (TWR). The correlation estimators T and TW measure a linear association between two continuous or ordinal variables; whereas TWR measures a monotonic association. The robustness of these proposed measures in comparison with Pearson (P), Spearman (S), Quadrant (Q), Median (M), and Minimum Covariance Determinant (MCD) are examined through simulation. Taba distance is used to analyze genes, and statistical tests were used to identify those genes most significantly associated with Williams Syndrome (WS). RESULTS: Based on the root mean square error (RMSE) and bias, the three proposed correlation measures are highly competitive when compared to classical measures such as P and S as well as robust measures such as Q, M, and MCD. Our findings indicate TBL2 was the most significant gene among patients diagnosed with WS and had the most significant reduction in gene expression level when compared with control (P value = 6.37E-05). CONCLUSIONS: Overall, when the distribution is bivariate Log-Normal or bivariate Weibull, TWR performs best in terms of bias and T performs best with respect to RMSE. Under the Normal distribution, MCD performs well with respect to bias and RMSE; but TW, TWR, T, S, and P correlations were in close proximity. The identification of TBL2 may serve as a diagnostic tool for WS patients. A Taba R package has been developed and is available for use to perform all necessary computations for the proposed methods.

Residual Tensile Strength and Bond Properties of GFRP Bars after Exposure to Elevated Temperatures.

The use of fiber reinforced polymer (FRP) bars in reinforced concrete members enhances corrosion resistance when compared to traditional steel reinforcing bars. Although there is ample research available on the behavior of FRP bars and concrete members reinforced with FRP bars under elevated temperatures (due to fire), there is little published information available on their post-fire residual load capacity. This paper reports residual tensile strength, modulus of elasticity, and bond strength (to concrete) of glass fiber reinforced polymer (GFRP) bars after exposure to elevated temperatures of up to 400 °C and subsequent cooling to an ambient temperature. The results showed that the residual strength generally decreases with increasing temperature exposure. However, as much as 83% of the original tensile strength and 27% of the original bond strength was retained after the specimens were heated to 400 °C and then cooled to ambient temperature. The residual bond strength is a critical parameter in post-fire strength assessments of GFRP-reinforced concrete members.

A Novel Concrete-Based Sensor for Detection of Ice and Water on Roads and Bridges.

Hundreds of people are killed or injured annually in the United States in accidents related to ice formation on roadways and bridge decks. In this paper, a novel embedded sensor system is proposed for the detection of black ice as well as wet, dry, and frozen pavement conditions on roads, runways, and bridges. The proposed sensor works by detecting changes in electrical resistance between two sets of stainless steel poles embedded in the concrete sensor to assess surface and near-surface conditions. A preliminary decision algorithm is developed that utilizes sensor outputs indicating resistance changes and surface temperature. The sensor consists of a 102-mm-diameter, 38-mm-high, concrete cylinder. Laboratory results indicate that the proposed sensor can effectively detect surface ice and wet conditions even in the presence of deicing chlorides and rubber residue. This sensor can further distinguish black ice from ice that may exist within concrete pores.

Novel Applications of Laser Doppler Vibration Measurements to Medical Imaging.

Laser Doppler Vibrometry (LDV) has been widely used in engineering applications involving non-contact vibration and sound measurements. This technique has also been used in some biomedical applications including hearing research. The detectable frequencies are in the range of near-DC to 1 GHz or higher. This paper reviews applications of LDV in biomedical engineering and proposes new medical imaging applications based on measuring surface vibrations of tissues and organs. Tests were conducted on human skin using single point and scanning laser vibrometers. These tests suggest that skin vibrations due to the forcing excitation from the heart can be used in imaging of blood flow. The results of these tests illustrate the potential of such vibration measurements in a variety of diagnostic medical imaging applications including blood flow/restrictions, real-time monitoring of blood pressure variations, wound healing, muscle movements, etc. The fact that the measurements can be conducted remotely (non-contact) is an important benefit that adds to the promise of this approach.