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The present paper evaluated the accuracy of two different methods for transferring working length (WL) between manual endodontic instruments and nickel–titanium (NiTi) shaping files. Thirty root canals of extracted permanent teeth were used. Root canals were divided according to canal length (CL) and canal curvature (CC). The reference cusp and the root end were flattened to provide reproducible and accurate measurements. During shaping, the WL measurements were obtained with manual k-files (KF) and transferred to WaveOne (W1) NiTi reciprocating files using the traditional method with the endodontic ruler (method I) and an alternative clinical procedure based on the comparison of the instruments side by side from tip to shank (method II). For each file and each tested method, two measures were taken by two examiners using Rhino (ver. 4.0, McNeel, Seattle, WA, USA) software for a total of 360 (30 × 3 × 2 × 2) measures. Analysis of variance was performed by taking the difference in length (Delta WL, DWL) between files used for the same canal. The difference between methods I and II for WL transfer was found to be statistically significant (df = 1; F = 71.52; p < 0.001). The DWL absolute values obtained with method II were found to be closer to 0 mm (i.e., same length as corresponding KF) than those obtained with method I. Both CL (df = 2; F = 1.27; p = 0.300) and CC (df = 1; F = 2.22; p = 0.149) did not significantly influence WL measurements. With respect to WL transfer, method II seemed to better preserve the correct WL transfer between instruments during the clinical endodontic procedures. [ABSTRACT FROM AUTHOR]

The COVID-19 pandemic has impacted everyone in urban areas. Some of these impacts in the United States have negatively affected People of Color more than their White counterparts. Using Seattle, Washington as a case study, we investigated whether inequitable effects appear in residents' interactions with urban nature (such as urban green space). Using a 48-question instrument, 300 residents were surveyed, equally divided across four racial/ethnic groups: Asian, Black and African American, Latino/a/x, and White. Results showed that during the span of about 6 months after the onset of the pandemic, Black and Latino residents experienced a significant loss of time in urban nature, while Asian and White residents did not. The implications of these findings, including inequities in the potential buffering effects of urban nature against COVID-19 and the future of urban nature conservation, are discussed. Multiple variables were tested for association with the changes to time spent in urban nature, including themes of exclusion from urban nature spaces found throughout the existing literature. Findings show that decreases in time spent in urban nature among Black and Latino residents may be associated with their feeling as though they did not belong in urban nature. We provide recommendations based on these findings for how government agencies can promote more equitable access to urban nature during the pandemic and beyond. The results of this study have implications that extend beyond the US and are relevant to the international scholarly literature of inequities and urban nature interaction during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

The tidal flat is long and narrow area along rivers and coasts with high sediment content, so there is little feature difference between the waterbody and the background, and the boundary of the waterbody is blurry. The existing waterbody extraction methods are mostly used for the extraction of large water bodies like rivers and lakes, whereas less attention has been paid to tidal flat waterbody extraction. Extracting tidal flat waterbody accurately from high-resolution remote sensing imagery is a great challenge. In order to solve the low accuracy problem of tidal flat waterbody extraction, we propose a fine-grained tidal flat waterbody extraction method, named FYOLOv3, which can extract tidal flat water with high accuracy. The FYOLOv3 mainly includes three parts: an improved object detection network based on YOLOv3 (Seattle, WA, USA), a fully convolutional network (FCN) without pooling layers, and a similarity algorithm for water extraction. The improved object detection network uses 13 convolutional layers instead of Darknet-53 as the model backbone network, which guarantees the water detection accuracy while reducing the time cost and alleviating the overfitting phenomenon; secondly, the FCN without pooling layers is proposed to obtain the accurate pixel value of the tidal flat waterbody by learning the semantic information; finally, a similarity algorithm for water extraction is proposed to distinguish the waterbody from non-water pixel by pixel to improve the extraction accuracy of tidal flat water bodies. Compared to the other convolutional neural network (CNN) models, the experiments show that our method has higher accuracy on the waterbody extraction of tidal flats from remote sensing images, and the I o U of our method is 2.43% higher than YOLOv3 and 3.7% higher than U-Net (Freiburg, Germany). [ABSTRACT FROM AUTHOR]

Street lighting is a fundamental aspect of security systems in homes, industrial facilities, and public places. To detect parking lot occupancy in outdoor environments, street light control plays a crucial role in smart surveillance applications that can perform robustly in extreme surveillance environments. However, traditional parking occupancy systems are mostly implemented for outdoor environments using costly sensor-based techniques. This study uses the Jetson TX2 to develop a method that can accurately identify street parking occupancy and control streetlights to assist occupancy detection, thereby reducing costs, and can adapt to various weather conditions. The proposed method adopts You Only Look Once version 3 (YOLO v3, Seattle, WA, USA) based on MobileNet version 2 (MobileNet v2, Salt Lake City, UT, USA), which is area-based and uses voting to stably recognize occupancy status. This solution was verified using the CNRPark + EXT dataset, a simulated model, and real scenes photographed with a camera. Our experiments revealed that the proposed framework can achieve stable parking occupancy detection in streets. [ABSTRACT FROM AUTHOR]

The National Land Cover Database (NLCD) provides time-series data characterizing the land surface for the United States, including land cover and tree canopy cover (NLCD-TC). NLCD-TC was first published for 2001, followed by versions for 2011 (released in 2016) and 2011 and 2016 (released in 2019). As the only nationwide tree canopy layer, there is value in assessing NLCD-TC accuracy, given the need for cross-city comparisons of urban forest characteristics. Accuracy assessments have only been conducted for the 2001 data and suggest substantial inaccuracies for that dataset in cities. For the most recent NLCD-TC version, we used various datasets that characterize the built environment, weather, and climate to assess their accuracy in different contexts within 27 cities. Overall, NLCD underestimates tree canopy in urban areas by 9.9% when compared to estimates derived from those high-resolution datasets. Underestimation is greater in higher-density urban areas (13.9%) than in suburban areas (11.0%) and undeveloped areas (6.4%). To evaluate how NLCD-TC error in cities could be reduced, we developed a decision tree model that uses various remotely sensed and built-environment datasets such as building footprints, urban morphology types, NDVI (Normalized Difference Vegetation Index), and surface temperature as explanatory variables. This predictive model removes bias and improves the accuracy of NLCD-TC by about 3%. Finally, we show the potential applications of improved urban tree cover data through the examples of ecosystem accounting in Seattle, WA, and Denver, CO. The outputs of rainfall interception and urban heat mitigation models were highly sensitive to the choice of tree cover input data. Corrected data brought results closer to those from high-resolution model runs in all cases, with some variation by city, model, and ecosystem type. This suggests paths forward for improving the quality of urban environmental models that require tree canopy data as a key model input. [ABSTRACT FROM AUTHOR]

An obvious decline in runoff of the Weihe River has been detected during the last half-century. Climate change and human activity acted as two major factors inducing the reduction. However, little knowledge is acquired on how and to what extent the decadal climate change and human activity induced runoff variations, which is essential for regional water resources planning and management. In this study, the observed data of 3 hydrological stations and 31 meteorological stations were used to analyze the runoff variability, and Variable Infiltration Capacity (VIC) model (Xu Liang, Seattle, WA, United States of America) coupled with scenario simulation was employed to attribute runoff variation of each period. The results showed that runoff decreased significantly at a rate of −1.01 × 108 m3·year−1 with obvious stage characteristic during 1961–2016. The water yield was highest in the 1960s and varying degrees of decline were detected in the following periods, resulting in a decrease of available freshwater by 20.54%–58.24%. Human activity had a dominant contribution to induce an increasing runoff decline from 2.068 to 5.776 km3, while the effect of climate was relatively small and lead to runoff reduction, except in the 1970s. This study gave a comprehensive understanding of time-varying runoff variability and highlighted the importance of appropriate human intervention with respect to climate change to ensure water resources security. [ABSTRACT FROM AUTHOR]