Trinity Church, Huntington, West Virginia: Something of Its Story

This is Chapters XI-XIV of Haworth\u27s history of Trinity Episcopal Church from its beginnings in 1869 through the early 1960s. The book focuses primarily on the rectors who have served the church over the years; there are also chapters with such titles as “The Building of the Church,” “The Vestrymen,” “The Women,” and “The Sunday School.”https://mds.marshall.edu/tyler_sroger/1003/thumbnail.jp

Trinity Church, Huntington, West Virginia: Something of Its Story

This is Chapter XVI of Haworth\u27s history of Trinity Episcopal Church from its beginnings in 1869 through the early 1960s. The book focuses primarily on the rectors who have served the church over the years; there are also chapters with such titles as “The Building of the Church,” “The Vestrymen,” “The Women,” and “The Sunday School.”https://mds.marshall.edu/atkinson_robertpoland/1003/thumbnail.jp

WeatherNet: Recognising weather and visual conditions from street-level images using deep residual learning

Extracting information related to weather and visual conditions at a given time and space is indispensable for scene awareness, which strongly impacts our behaviours, from simply walking in a city to riding a bike, driving a car, or autonomous drive-assistance. Despite the significance of this subject, it is still not been fully addressed by the machine intelligence relying on deep learning and computer vision to detect the multi-labels of weather and visual conditions with a unified method that can be easily used for practice. What has been achieved to-date is rather sectorial models that address limited number of labels that do not cover the wide spectrum of weather and visual conditions. Nonetheless, weather and visual conditions are often addressed individually. In this paper, we introduce a novel framework to automatically extract this information from street-level images relying on deep learning and computer vision using a unified method without any pre-defined constraints in the processed images. A pipeline of four deep Convolutional Neural Network (CNN) models, so-called the WeatherNet, is trained, relying on residual learning using ResNet50 architecture, to extract various weather and visual conditions such as Dawn/dusk, day and night for time detection, and glare for lighting conditions, and clear, rainy, snowy, and foggy for weather conditions. The WeatherNet shows strong performance in extracting this information from user-defined images or video streams that can be used not limited to: autonomous vehicles and drive-assistance systems, tracking behaviours, safety-related research, or even for better understanding cities through images for policy-makers.Comment: 11 pages, 8 figure

Rapid radiative clearing of protoplanetary discs

The lack of observed transition discs with inner gas holes of radii greater than ~50AU implies that protoplanetary discs dispersed from the inside out must remove gas from the outer regions rapidly. We investigate the role of photoevaporation in the final clearing of gas from low mass discs with inner holes. In particular, we study the so-called "thermal sweeping" mechanism which results in rapid clearing of the disc. Thermal sweeping was originally thought to arise when the radial and vertical pressure scale lengths at the X-ray heated inner edge of the disc match. We demonstrate that this criterion is not fundamental. Rather, thermal sweeping occurs when the pressure maximum at the inner edge of the dust heated disc falls below the maximum possible pressure of X-ray heated gas (which depends on the local X-ray flux). We derive new critical peak volume and surface density estimates for rapid radiative clearing which, in general, result in rapid dispersal happening less readily than in previous estimates. This less efficient clearing of discs by X-ray driven thermal sweeping leaves open the issue of what mechanism can clear gas from the outer disc sufficiently quickly to explain the non-detection of cold gas around weak line T Tauri stars.Comment: 13 pages, Accepted for publication in MNRA

predictSLUMS: A new model for identifying and predicting informal settlements and slums in cities from street intersections using machine learning

Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite image classification, relying on both supervised or unsupervised machine learning approaches, these models either require multiple input data to function or need further development with regards to precision. In this paper, we introduce a novel method for identifying and predicting informal settlements using only street intersections data, regardless of the variation of urban form, number of floors, materials used for construction or street width. With such minimal input data, we attempt to provide planners and policy-makers with a pragmatic tool that can aid in identifying informal zones in cities. The algorithm of the model is based on spatial statistics and a machine learning approach, using Multinomial Logistic Regression (MNL) and Artificial Neural Networks (ANN). The proposed model relies on defining informal settlements based on two ubiquitous characteristics that these regions tend to be filled in with smaller subdivided lots of housing relative to the formal areas within the local context, and the paucity of services and infrastructure within the boundary of these settlements that require relatively bigger lots. We applied the model in five major cities in Egypt and India that have spatial structures in which informality is present. These cities are Greater Cairo, Alexandria, Hurghada and Minya in Egypt, and Mumbai in India. The predictSLUMS model shows high validity and accuracy for identifying and predicting informality within the same city the model was trained on or in different ones of a similar context.Comment: 26 page

Uncertainty Quantification in the Road-Level Traffic Risk Prediction by Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network(STZINB-GNN) (Short Paper)

Urban road-based risk prediction is a crucial yet challenging aspect of research in transportation safety. While most existing studies emphasize accurate prediction, they often overlook the importance of model uncertainty. In this paper, we introduce a novel Spatial-Temporal Zero-Inflated Negative Binomial Graph Neural Network (STZINB-GNN) for road-level traffic risk prediction, with a focus on uncertainty quantification. Our case study, conducted in the Lambeth borough of London, UK, demonstrates the superior performance of our approach in comparison to existing methods. Although the negative binomial distribution may not be the most suitable choice for handling real, non-binary risk levels, our work lays a solid foundation for future research exploring alternative distribution models or techniques. Ultimately, the STZINB-GNN contributes to enhanced transportation safety and data-driven decision-making in urban planning by providing a more accurate and reliable framework for road-level traffic risk prediction and uncertainty quantification

Spatiotemporal Graph Neural Networks with Uncertainty Quantification for Traffic Incident Risk Prediction

Predicting traffic incident risks at granular spatiotemporal levels is challenging. The datasets predominantly feature zero values, indicating no incidents, with sporadic high-risk values for severe incidents. Notably, a majority of current models, especially deep learning methods, focus solely on estimating risk values, overlooking the uncertainties arising from the inherently unpredictable nature of incidents. To tackle this challenge, we introduce the Spatiotemporal Zero-Inflated Tweedie Graph Neural Networks (STZITD-GNNs). Our model merges the reliability of traditional statistical models with the flexibility of graph neural networks, aiming to precisely quantify uncertainties associated with road-level traffic incident risks. This model strategically employs a compound model from the Tweedie family, as a Poisson distribution to model risk frequency and a Gamma distribution to account for incident severity. Furthermore, a zero-inflated component helps to identify the non-incident risk scenarios. As a result, the STZITD-GNNs effectively capture the dataset's skewed distribution, placing emphasis on infrequent but impactful severe incidents. Empirical tests using real-world traffic data from London, UK, demonstrate that our model excels beyond current benchmarks. The forte of STZITD-GNN resides not only in its accuracy but also in its adeptness at curtailing uncertainties, delivering robust predictions over short (7 days) and extended (14 days) timeframes

Integrated digital avionics to improve aircraft environmental control systems

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/77327/1/AIAA-46929-196.pd