Final Construction and Interim Performance Report: Experimental Use of Thermoplastic Pavement-Striping Materials

The purposes and objectives of this study are: 1) to evaluate the application and performance characteristics of hot-melt plastic, pavement-striping materials which are presently prominent and known commercially as Catatherm and Perma-Line ; 2) to compare the performance of these materials with the performance of painted stripes applied and re-newed according to the current practices of the Kentucky Department of Highways; and 3) to evaluate the economics of these striping materials in terms of cost-per-pile per-day-of-useful-life. The project is described more fully in the Proposal... (approved by Division Engineer, September 7, 1962) and in Report No. 1 (Pre-Construction Report) submitted September 19, 1962. Attachment No. 1 shows the location of the test sites

Holding On!: Supporting Successful Tenancies for the Hard to House

Report: 103 pp; ill., digital fileThe following report assembles a preliminary examination of eviction prevention approaches used across Canada (also known as housing retention practices). The particular focus was to better understanding how persons experiencing homelessness and have presence of mental illness transition into permanent housing.Mental Health Commission of Canad

At Home/Chez Soi Winnipeg Site: Later Implementation Evaluation Report

Research report. iv, 20 pp., digital file.This report is the second documenting the implementation of the Mental Health Commission of Canada’s At Home/Chez Soi project in Winnipeg, covering the late 2010 to late 2011 period. It reports on the changes in program fidelity over this time, and reflects on continued and emerging strengths and challenges in the implementation of the project. The results demonstrate that, while there are challenges, there have been many positive results for participants.Mental Health Commission of Canad

Development and Application of Tools for Avalanche Forecasting, Avalanche Detection, and Snowpack Characterization

Avalanche formation is a complex interaction between the snowpack, weather, and terrain. However, detailed observations typically can only be made at a single point and must be extrapolated over the slope or regional scale. This study aims to provide avalanche forecasters with tools to evaluate the snowpack, avalanche hazard, and avalanche occurrence when manual observations are not feasible. Avalanches that occur within the new storm snow are a prevalent problem for the avalanche forecasters with the Idaho Transportation Department (ITD) along Highway 21. We have implemented a real time SNOw Slope Stability (SNOSS) model that provides an index to the stability of that layer. SNOSS has been run real time starting during the winter of 2011/2012 with model results outputted to a webpage for easy viewing by avalanche forecasters. To further improve the accuracy of SNOSS, the model was evaluated with a large database of avalanches from the Utah Department of Transportation (UDOT). Using weather data and SNOSS results, the probability of an avalanche day producing a natural direct action avalanche was calculated using a Balanced Random Forest (BRF). In the future, we hope that the BRF can provide a probability of an avalanche occurrence given the current weather and snowpack conditions that can be utilized by avalanche forecasters in their normal operations. The concern for avalanche forecasters with highway operations is the threat of an avalanche releasing and hitting a highway. Infrasound generated by an avalanche moving downhill can be detected and tracked using array processing techniques. This will allow avalanche forecasters to evaluate the avalanche hazard more effectively by determining when and where avalanches have occurred. An avalanche detection system has been developed to detect avalanches in near real time using infrasound arrays. The system processes the infrasound data on-site, automatically detects events, and classifies the events using multiple neural networks. If an avalanche has been detected, the system will transmit the necessary information over satellite to be viewed by avalanche forecasters on a webpage

Enabling Explainable Fusion in Deep Learning with Fuzzy Integral Neural Networks

Information fusion is an essential part of numerous engineering systems and biological functions, e.g., human cognition. Fusion occurs at many levels, ranging from the low-level combination of signals to the high-level aggregation of heterogeneous decision-making processes. While the last decade has witnessed an explosion of research in deep learning, fusion in neural networks has not observed the same revolution. Specifically, most neural fusion approaches are ad hoc, are not understood, are distributed versus localized, and/or explainability is low (if present at all). Herein, we prove that the fuzzy Choquet integral (ChI), a powerful nonlinear aggregation function, can be represented as a multi-layer network, referred to hereafter as ChIMP. We also put forth an improved ChIMP (iChIMP) that leads to a stochastic gradient descent-based optimization in light of the exponential number of ChI inequality constraints. An additional benefit of ChIMP/iChIMP is that it enables eXplainable AI (XAI). Synthetic validation experiments are provided and iChIMP is applied to the fusion of a set of heterogeneous architecture deep models in remote sensing. We show an improvement in model accuracy and our previously established XAI indices shed light on the quality of our data, model, and its decisions.Comment: IEEE Transactions on Fuzzy System

Calculating the Velocity of a Fast-Moving Snow Avalanche Using an Infrasound Array

On 19 January 2012, a large D3 avalanche (approximately 103 t) was recorded with an infrasound array ideally situated for observing the avalanche velocity. The avalanche crossed Highway 21 in Central Idaho during the largest avalanche cycle in the 15 years of recorded history and deposited approximately 8 m of snow on the roadway. Possible source locations along the avalanche path were estimated at 0.5 s intervals and were used to calculate the avalanche velocity during the 64 s event. Approximately 10 s prior to the main avalanche signal, a small infrasound signal originated from the direction of the start zone. We infer this to be the initial snow pack failure, a precursory signal to the impending avalanche. The avalanche accelerated to a maximum velocity of 35.9 ± 7.6 m s−1 within 30 s before impacting the highway. We present a new technique to obtain high spatial and temporal resolution velocity estimates not previously demonstrated with infrasound for avalanches and other mass wasting events

Approximating Input Data to a Snowmelt Model Using Weather Research and Forecasting Model Outputs in Lieu of Meteorological Measurements

Forecasting the timing and magnitude of snowmelt and runoff is critical to managing mountain water resources. Warming temperatures are increasing the rain–snow transition elevation and are limiting the forecasting skill of statistical models relating historical snow water equivalent to streamflow. While physically based methods are available, they require accurate estimations of the spatial and temporal distribution of meteorological variables in complex terrain. Across many mountainous areas, measurements of precipitation and other meteorological variables are limited to a few reference stations and are not adequate to resolve the complex interactions between topography and atmospheric flow. In this paper, we evaluate the ability of the Weather Research and Forecasting (WRF) Model to approximate the inputs required for a physics-based snow model, iSnobal, instead of using meteorological measurements, for the Boise River Basin (BRB) in Idaho, United States. An iSnobal simulation using station data from 40 locations in and around the BRB resulted in an average root-mean-square error (RMSE) of 4.5 mm compared with 12 SNOTEL measurements. Applying WRF forcings alone was associated with an RMSE of 10.5 mm, while including a simple bias correction to the WRF outputs of temperature and precipitation reduced the RMSE to 6.5 mm. The results highlight the utility of using WRF outputs as input to snowmelt models, as all required input variables are spatiotemporally complete. This will have important benefits in areas with sparse measurement networks and will aid snowmelt and runoff forecasting in mountainous basins

Mitigating cyanobacterial harmful algal blooms in aquatic ecosystems impacted by climate change and anthropogenic nutrients

Mitigating the global expansion of cyanobacterial harmful blooms (CyanoHABs) is a major challenge facing researchers and resource managers. A variety of traditional (e.g., nutrient load reduction) and experimental (e.g., artificial mixing and flushing, omnivorous fish removal) approaches have been used to reduce bloom occurrences. Managers now face the additional effects of climate change on watershed hydrologic and nutrient loading dynamics, lake and estuary temperature, mixing regime, internal nutrient dynamics, and other factors. Those changes favor CyanoHABs over other phytoplankton and could influence the efficacy of control measures. Virtually all mitigation strategies are influenced by climate changes, which may require setting new nutrient input reduction targets and establishing nutrient-bloom thresholds for impacted waters. Physical-forcing mitigation techniques, such as flushing and artificial mixing, will need adjustments to deal with the ramifications of climate change. Here, we examine the suite of current mitigation strategies and the potential options for adapting and optimizing them in a world facing increasing human population pressure and climate change

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It Takes Two to Tango: When and Where Dual Nutrient (N & P) Reductions Are Needed to Protect Lakes and Downstream Ecosystems

Preventing harmful algal blooms (HABs) is needed to protect lakes and downstream ecosystems. Traditionally, reducing phosphorus (P) inputs was the prescribed solution for lakes, based on the assumption that P universally limits HAB formation. Reduction of P inputs has decreased HABs in many lakes, but was not successful in others. Thus, the "P-only" paradigm is overgeneralized. Whole-lake experiments indicate that HABs are often stimulated more by combined P and nitrogen (N) enrichment rather than N or P alone, indicating that the dynamics of both nutrients are important for HAB control. The changing paradigm from P-only to consideration of dual nutrient control is supported by studies indicating that (1) biological N fixation cannot always meet lake ecosystem N needs, and (2) that anthropogenic N and P loading has increased dramatically in recent decades. Sediment P accumulation supports long-term internal loading, while N may escape via denitrification, leading to perpetual N deficits. Hence, controlling both N and P inputs will help control HABs in some lakes and also reduce N export to downstream N-sensitive ecosystems. Managers should consider whether balanced control of N and P will most effectively reduce HABs along the freshwater-marine continuum