Prince George's Community College

Bolt loosening is a significant concern in structural health monitoring (SHM), and the nonlinear wave modulation (NWM) technique shows promise for its detection. However, temperature variations affect piezoelectric sensors, complicating detection. This study examines thermal stress effects on NWM-based damage detection by simulating a beam with boundary loosening under thermal stress. A temperature compensation method was developed to mitigate these effects. A multibolt structure was also analyzed using a hybrid deep learning model combining convolutional neural networks (CNNs) and Dezert-Smarandache Theory (DSmT) for multisensor data fusion. Results show that thermal stress significantly reduces classification accuracy. At 60 degrees C, the Damage Index dropped to zero, and CNN classification accuracy fell from 87.5% (at 25 degrees C) to 27.08%. Applying the compensation algorithm restored CNN accuracy to 87.27%. Statistical tests confirmed the compensated results at 60 degrees C were comparable to those at 25 degrees C. The DSmT-based fusion model achieved 98.92% accuracy on average, and even at 60 degrees C, maintained 98.80% accuracy with compensation for a multibolted plate. It combines the probabilistic outputs of multiple CNNs using the PCR6 rule, which redistributes conflicting evidence to improve decision reliability. This fusion strategy effectively handles uncertainty across sensors and significantly enhances classification robustness under temperature variations. In conclusion, thermal stress adversely impacts SHM damage detection, but the proposed compensation method effectively restores performance. Moreover, integrating multisensor fusion with DSmT greatly enhances accuracy, offering a robust solution for SHM in varying thermal environments.

Urban development, industrialization, and vehicular emissions lead to the accumulation of heavy metals in urban soils. Background soils, those without direct pollution sources, reflect regional contaminant transport and industrial history. In soils, metals occur in distinct geochemical fractions that control their mobility and bioavailability. While metal speciation and bioavailability are well studied, cross-city variability in geochemical phase distributions is poorly understood. Pittsburgh (PGH) is a compelling case study for urban soil contamination given its legacy of steel production. New York City (NYC) and Los Angeles (LA), the only US cities with comparable datasets, provide a broader perspective on regional factors and metal distributions. Regional factors (local geology, soil properties, environmental conditions, and industrial history) shape metal enrichment and bioavailability. This study addresses this gap by evaluating total metal concentrations and geochemical phase distributions of Pb, Cd, Zn, Ni, and Mn in PGH, NYC, and LA. Sequential extractions of PGH soil samples were compared with similar analyses in NYC and LA, using the modified BCR method, to assess variations in metal partitioning across geochemical phases. We used the National Uranium Resource Evaluation (NURE) stream sediment dataset to assess regional Mn enrichment and geologic context. PGH exhibited higher Mn concentrations than NYC and LA. Industrial history contributes to Mn enrichment in PGH, but the patterns also suggest a possible role for vegetation in Mn retention and phase partitioning. In contrast, LA soils had the highest exchangeable metal fractions, suggesting greater metal bioavailability. Our findings highlight the complexity of urban soil metal contamination and the importance of considering both industrial legacies and environmental factors in determining metal bioavailability.

Using the 2023 wave of the Panel Study of Income Dynamics (PSID), we examine whether there are significant differences in the tendency to donate (and/or) make contributions towards religious and secular charitable organizations based on religious affiliation. The secular charities of focus include organizations related to poverty, health, international peace, education, youth, cultural, environment, and other. We focus on two nonreligious groups – atheists/agnostics and nones – in comparison to other commonly recognized religious groups in the United States. Both groups of nonreligious individuals, net of controls, are significantly less likely to give their money to religious charitable causes. However, both groups are not meaningfully more or less likely to give to secular charitable organizations than those who are affiliated with a religion. Importantly, religious affiliation is not a strong predictor of likelihood to donate or how much an individual decides to give.

RFB cell/stack power characterization with polarization curves has been inconsistent, therefore a systematic the polarization step duration is proposed.