University of Maryland, Baltimore County

About: University of Maryland, Baltimore County is a education organization based out in Baltimore, Maryland, United States. It is known for research contribution in the topics: Population & Aerosol. The organization has 8749 authors who have published 20843 publications receiving 795706 citations. The organization is also known as: UMBC.

Interactive relations of central versus total obesity and blood pressure to cognitive function.

Abstract: To examine the potential interactive relations of central versus total obesity and blood pressure (BP) to cognitive function. In all, 90 healthy, stroke, and dementia-free middle-aged and older adults (ages 54–81 years; 63% male; 93% White) underwent biomedical and neuropsychological assessment. Relations of central obesity (assessed by waist circumference (WC)) and systolic or diastolic BP to cognitive function were examined in multiple regression models. Next, body mass index (BMI) was substituted for WC in the models. After statistical adjustment for age, education, gender, and other potential confounders including components of the metabolic syndrome (depending on the model), significant interactions of WC and systolic (or diastolic) BP were noted for the Grooved Pegboard – Dominant Hand and Stroop Interference scores, with marginally significant results for Grooved Pegboard – Nondominant Hand. In general, individuals with greater WC and higher BP performed most poorly on these measures. Similar results were obtained for BMI. Independent of other confounders including facets of the metabolic syndrome, the combination of greater WC (or BMI) and higher (systolic or diastolic) BP was associated with diminished performance on tests of motor speed and manual dexterity, and executive function (i.e. response inhibition) accounting for 3–13% of the variance in these measures. In healthy older adults, there are similar, negative relations of central and total obesity to cognitive function that are potentiated by higher BP levels.

Can Two-photon Interference be Considered the Interference of Two Photons?

Abstract: We report on a ``postponed compensation'' experiment in which the observed two-photon entangled state interference cannot be pictured in terms of the overlap of the two individual photon wave packets of a parametric down-conversion pair on a beam splitter. In the sense of a quantum eraser, the distinguishability of the different two-photon Feynman amplitudes leading to a coincidence detection is removed by delaying the compensation until after the output of an unbalanced two-photon interferometer.

Scientific Impact of MODIS C5 Calibration Degradation and C6+ Improvements

Abstract: . The Collection 6 (C6) MODIS (Moderate Resolution Imaging Spectroradiometer) land and atmosphere data sets are scheduled for release in 2014. C6 contains significant revisions of the calibration approach to account for sensor aging. This analysis documents the presence of systematic temporal trends in the visible and near-infrared (500 m) bands of the Collection 5 (C5) MODIS Terra and, to lesser extent, in MODIS Aqua geophysical data sets. Sensor degradation is largest in the blue band (B3) of the MODIS sensor on Terra and decreases with wavelength. Calibration degradation causes negative global trends in multiple MODIS C5 products including the dark target algorithm's aerosol optical depth over land and Angstrom exponent over the ocean, global liquid water and ice cloud optical thickness, as well as surface reflectance and vegetation indices, including the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI). As the C5 production will be maintained for another year in parallel with C6, one objective of this paper is to raise awareness of the calibration-related trends for the broad MODIS user community. The new C6 calibration approach removes major calibrations trends in the Level 1B (L1B) data. This paper also introduces an enhanced C6+ calibration of the MODIS data set which includes an additional polarization correction (PC) to compensate for the increased polarization sensitivity of MODIS Terra since about 2007, as well as detrending and Terra–Aqua cross-calibration over quasi-stable desert calibration sites. The PC algorithm, developed by the MODIS ocean biology processing group (OBPG), removes residual scan angle, mirror side and seasonal biases from aerosol and surface reflectance (SR) records along with spectral distortions of SR. Using the multiangle implementation of atmospheric correction (MAIAC) algorithm over deserts, we have also developed a detrending and cross-calibration method which removes residual decadal trends on the order of several tenths of 1% of the top-of-atmosphere (TOA) reflectance in the visible and near-infrared MODIS bands B1–B4, and provides a good consistency between the two MODIS sensors. MAIAC analysis over the southern USA shows that the C6+ approach removed an additional negative decadal trend of Terra ΔNDVI ~ 0.01 as compared to Aqua data. This change is particularly important for analysis of vegetation dynamics and trends in the tropics, e.g., Amazon rainforest, where the morning orbit of Terra provides considerably more cloud-free observations compared to the afternoon Aqua measurements.

Retrieval of large volcanic SO2 columns from the Aura Ozone Monitoring Instrument: Comparison and limitations

Abstract: [1] To improve global measurements of atmospheric sulfur dioxide (SO2), we have developed a new technique, called the linear fit (LF) algorithm, which uses the radiance measurements from the Ozone Monitoring Instrument (OMI) at a few discrete ultraviolet wavelengths to derive SO2, ozone, and effective reflectivity simultaneously. We have also developed a sliding median residual correction method for removing both the along- and cross-track biases from the retrieval results. The achieved internal consistencies among the LF-retrieved geophysical parameters clearly demonstrate the success of this technique. Comparison with the results from the Band Residual Difference technique has also illustrated the drastic improvements of this new technique at high SO2 loading conditions. We have constructed an error equation and derived the averaging kernel to characterize the LF retrieval and understand its limitations. Detailed error analysis has focused on the impacts of the SO2 column amounts and their vertical distributions on the retrieval results. The LF algorithm is robust and fast; therefore it is suitable for near real-time application in aviation hazards and volcanic eruption warnings. Very large SO2 loadings (>100 DU) require an off-line iterative solution of the LF equations to reduce the retrieval errors. Both the LF and sliding median techniques are very general so that they can be applied to measurements from other backscattered ultraviolet instruments, including the series of Total Ozone Mapping Spectrometer (TOMS) missions, thereby offering the capability to update the TOMS long-term record to maintain consistency with its OMI extension.

Predicting and explaining patronage behavior toward web and traditional stores using neural networks: a comparative analysis with logistic regression

Abstract: Web stores, where buyers place orders over the Internet, have emerged to become a prevalent sales channel. In this research, we developed neural network models, which are known for their capability of modeling noncompensatory decision processes, to predict and explain consumer choice between web and traditional stores. We conducted an empirical survey for the study. Specifically, in the survey, the purchases of six distinct products from web stores were contrasted with the corresponding purchases from traditional stores. The respondents' perceived attribute performance was then used to predict the customers' channel choice between web and traditional stores. We have provided statistical evidence that neural networks significantly outperform logistic regression models for most of the surveyed products in terms of the predicting power. To gain more insights from the models, we have identified the factors that have significant impact on customers' channel attitude through sensitivity analyses on the neural networks. The results indicate that the influential factors are different across product categories. The findings of the study offer a number of implications for channel management.