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Solar forecasting plays a key part in the renewable energy transition. Major challenges, related to load balancing and grid stability, emerge when a high percentage of energy is provided by renewables. These can be tackled by new energy management strategies guided by power forecasts. This paper presents a data-driven and contextual optimisation forecasting (DCF) algorithm for solar irradiance that was comprehensively validated using short- and long-term predictions, in three US cities: Denver, Boston, and Seattle. Moreover, step-by-step implementation guidelines to follow and reproduce the results were proposed. Initially, a comparative study of two machine learning (ML) algorithms, the support vector machine (SVM) and Facebook Prophet (FBP) for solar prediction was conducted. The short-term SVM outperformed the FBP model for the 1- and 2- hour prediction, achieving a coefficient of determination (R2) of 91.2% in Boston. However, FBP displayed sustained performance for increasing the forecast horizon and yielded better results for 3-hour and long-term forecasts. The algorithms were optimised by further contextual model adjustments which resulted in substantially improved performance. Thus, DCF utilised SVM for short-term and FBP for long-term predictions and optimised their performance using contextual information. DCF achieved consistent performance for the three cities and for long- and short-term predictions, with an average R2 of 85%. [ABSTRACT FROM AUTHOR]

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]

Many of the larger US metropolitan regions promote polycentric development as a way of fostering livability, accessibility, and sustainability. Polycentric urban structures can increase transit ridership, promote active transportation, and decrease vehicle miles traveled (VMT) and CO2 emissions. Although many regions include ambitious polycentric aspirations in their plans, only a few follow up with rigorous implementation and see their efforts come to fruition. The topic of implementation is also widely omitted from scholarly inquiry. This research aims to explore three examples of successful implementation of urban polycentricity: Portland, Oregon; Seattle, Washington; and Denver, Colorado. Each region employs a very distinct polycentric development model, but each relies heavily on its regional governance organization for direction, guidance, and even command in the implementation process. To understand specific strategies and methods used by each region, the authors conducted interviews with metropolitan planning organizations, central cities, and transit agencies in the three regions and used qualitative techniques to analyze the interview transcripts and collected documents. As regional governance organizations play a crucial role in implementing regional plans, their policies and practices were also investigated by the authors. Based on collected data and insights, we conclude that the three regions are great examples of an advanced implementation of polycentric development. This research can be helpful to other US metropolitan regions that wish to promote polycentric development. The lessons learned from the three case studies can provide guidance and possible paths to successful implementation. [ABSTRACT FROM AUTHOR]

Simple Summary: Coyotes live in most major cities across North and Central America. As their habitat shrinks, human–coyote interactions increase, spurring debate about how to respond. Residents often fear coyotes and want extermination, but scientists argue they are a permanent fixture that play a vital ecosystem role and that eradicating them does not work and has negative impacts. Instead, ecologists argue that residents need to change their behavior to coexist with coyotes. Whether the public supports coexistence plans stems from public perceptions of coyotes, which are influenced by affective feelings, personal beliefs and experiences, and the media. Consequently, understanding how the media discusses these issues, whether they frame coyotes as friend or foe, and whether they cover coexistence is essential. To do this, we analyze coyote news coverage from 2000 to 2022 in three US cities: Los Angeles, CA; Seattle, WA; and Boston, MA. We find that the media uses friendly language to discuss coexistence and unfriendly language to justify eradication. Cities vary in the degree to which their coverage aligns with the scientific consensus; this likely reflects historical, cultural, and political views of coyotes. In Los Angeles, where coyote conflict is long-running and political, the coverage is the most unfriendly and is significantly more likely to include discussion of eradication; in Seattle and Boston, where coyotes have more recently become an issue, the coverage is far more likely to focus on coexistence. We argue that if governments want increased support for coexistence, there must be efforts to both account for media coverage in management plans and to educate the media on scientific consensus and the ecological implications of lethal management. Human–coyote sightings and interactions are becoming more frequent in urban areas across North and Central America. While many species have lost territory, the coyote range has expanded. Relatively recently, ecologists have coalesced around the idea that coexistence is the most promising avenue to reduce human–coyote conflict in urban areas. Despite this, calls for the eradication of coyotes continue. We apply and extend the theory of survival of the friendliest to evaluate how the media is framing coyotes and management strategies and what the implications of this framing might be. Through a content analysis of newspaper articles from three different urban areas in the US (Los Angeles, CA; Seattle, WA; and Boston, MA), from 2000 to 2022, we find that friendly language is used to promote coexistence, while unfriendly language (threat, hostile, unfriendly, and danger) is used to justify eradication. We also find considerable variation in the type of coverage and consistency with scientific consensus across cities, likely reflecting the cities' varied histories and cultural understandings of the species. Given the media's influence on the public's views of coyotes and their support for management strategies, these findings suggest that the media plays a central role in shaping coyote–human relationships and management strategies. [ABSTRACT FROM AUTHOR]

Rapid growth of network traffic causes the need for the development of new network technologies. Artificial intelligence provides suitable tools to improve currently used network optimization methods. In this paper, we propose a procedure for network traffic prediction. Based on optical networks' (and other network technologies) characteristics, we focus on the prediction of fixed bitrate levels called traffic levels. We develop and evaluate two approaches based on different supervised machine learning (ML) methods—classification and regression. We examine four different ML models with various selected features. The tested datasets are based on real traffic patterns provided by the Seattle Internet Exchange Point (SIX). Obtained results are analyzed using a new quality metric, which allows researchers to find the best forecasting algorithm in terms of network resources usage and operational costs. Our research shows that regression provides better results than classification in case of all analyzed datasets. Additionally, the final choice of the most appropriate ML algorithm and model should depend on the network operator expectations. [ABSTRACT FROM AUTHOR]

The Healthy Air, Healthy Schools Study was established to better understand the impact of ultrafine particles (UFPs) on indoor air quality in communities surrounding Seattle-Tacoma (Sea-Tac) International Airport. The study team took multipollutant measurements of indoor and outdoor air pollution at five participating school locations to estimate infiltration indoors. The schools participating in this project were located within a 7-mile radius of Sea-Tac International Airport and within 0.5 mile of an active flight path. Based on experimental measures in an unoccupied classroom, infiltration rates of (a) UFPs of aircraft origin, (b) UFPs of traffic origin, and (c) wildfire smoke or other outdoor pollutants were characterized before and after the introduction of a portable high-efficiency particulate air (HEPA) filter intervention. The portable HEPA cleaners were an effective short-term intervention to improve the air quality in classroom environments, reducing the UFP count concentration from one-half to approximately one-tenth of that measured outside. This study is unique in focusing on UFPs in schools and demonstrating that UFPs measured in classroom spaces are primarily of outdoor origin. Although existing research suggests that reducing particulate matter in homes can significantly improve asthma outcomes, further investigation is necessary to establish the benefits to student health and academic performance of reducing UFP exposures in schools. [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]

Gentrification and subsequent displacement are common problems in cities, and result in the removal of poor communities and communities of color from urban areas as they move to cheaper locations in the metropolitan region. Here we describe a community-based approach to redevelopment by historic Black churches that seeks to counter such displacement and cultural removal. We explain the history of a historically Black neighborhood in Seattle and the founding and rationale for a church-led project called the Nehemiah Initiative. Our perspective is that of participants in the work of the Nehemiah Initiative and as faculty and students from a local university partner supporting it. We conclude with policy strategies that can be used to support such redevelopment in Seattle, with understanding that some may be broadly applicable to other cities. [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]

The monitoring of tree species diversity is important for forest or wetland ecosystem service maintenance or resource management. Remote sensing is an efficient alternative to traditional field work to map tree species diversity over large areas. Previous studies have used light detection and ranging (LiDAR) and imaging spectroscopy (hyperspectral or multispectral remote sensing) for species richness prediction. The recent development of very high spatial resolution (VHR) RGB images has enabled detailed characterization of canopies and forest structures. In this study, we developed a three-step workflow for mapping tree species diversity, the aim of which was to increase knowledge of tree species diversity assessment using deep learning in a tropical wetland (Haizhu Wetland) in South China based on VHR-RGB images and LiDAR points. Firstly, individual trees were detected based on a canopy height model (CHM, derived from LiDAR points) by the local-maxima-based method in the FUSION software (Version 3.70, Seattle, USA). Then, tree species at the individual tree level were identified via a patch-based image input method, which cropped the RGB images into small patches (the individually detected trees) based on the tree apexes detected. Three different deep learning methods (i.e., AlexNet, VGG16, and ResNet50) were modified to classify the tree species, as they can make good use of the spatial context information. Finally, four diversity indices, namely, the Margalef richness index, the Shannon–Wiener diversity index, the Simpson diversity index, and the Pielou evenness index, were calculated from the fixed subset with a size of 30 × 30 m for assessment. In the classification phase, VGG16 had the best performance, with an overall accuracy of 73.25% for 18 tree species. Based on the classification results, mapping of tree species diversity showed reasonable agreement with field survey data (R2Margalef = 0.4562, root-mean-square error RMSEMargalef = 0.5629; R2Shannon–Wiener = 0.7948, RMSEShannon–Wiener = 0.7202; R2Simpson = 0.7907, RMSESimpson = 0.1038; and R2Pielou = 0.5875, RMSEPielou = 0.3053). While challenges remain for individual tree detection and species classification, the deep-learning-based solution shows potential for mapping tree species diversity. [ABSTRACT FROM AUTHOR]