26800 research outputs found
Sort by
ThermalTrack Dataset - Testing Labels
Get PDFWe present a wheel track detection system that leverages RGB-Thermal (RGB-T) imaging, where thermal channels reveal critical temperature differentials between compacted tracks and loose snow - tracks exhibit higher thermal inertia and lower reflectivity, emitting stronger radiation signatures even in visually homogeneous conditions. By fusing these distinctive thermal patterns with RGB spatial information, our method reliably identifies navigable tracks, enabling robust path-following in complete white-out conditions where snow textures and terrain features become indistinguishable
Economic impact of potential aquaculture production in Michigan, USA
No full textThe state of Michigan, USA, places significant value on its fisheries—including fish for fun and fish as food. Commercial fishing has historically contributed to seafood production in Michigan (MI) and its decline is an opportunity for aquaculture to provide supplemental harvest and restoration activities. While recent work has evaluated the economic impacts of aquaculture in different areas of the United States, much is still unknown about aquaculture in Michigan, therefore limiting state resources towards industry growth. As a first step towards understanding the impacts of potential aquaculture growth in Michigan, we perform an economic impact analysis of increased aquaculture within the state. We use the input–output model IMPLAN to evaluate 12 scenarios across three different regions: the State of Michigan, Alcona County, MI, and Delta County, MI. We find that the economic impacts of aquaculture expansion vary by region and that each additional aquaculture job leads to 60,546 and $64,197 in output, respectively. Additionally, our results suggest that beyond providing access to local food, aquaculture has the potential to create employment opportunities, improve community resilience, enhance fisheries management, and spur economic activity in Michigan
Reference Correlations for the Density and Viscosity of Molten Alkali and Alkaline Earth Fluoride Salts
Get PDFWhile there is a significant body of literature pertaining to thermophysical property measurements of molten salts, there is often a wide degree of variability among independent measurements of the same compounds. As such, the scientific community benefits greatly from an unbiased, independent assessment of duplicate datasets, so that reference correlations which describe these thermophysical properties as functions of temperature can be determined and then commonly used by researchers, scientists, and engineers. With regard to molten fluoride compounds, a significant time has elapsed since density and viscosity reference correlations have been determined; Janz conducted the most recent effort, in 1988, to provide reference correlations for the densities and viscosities of molten fluoride compounds via the National Standard Reference Data System coordinated by the National Bureau of Standards. Since then, new data have been published for molten fluoride compounds, and a new precedent has surfaced for putting forth reference correlations that involve fitting to multiple primary datasets. In this work, reference correlations are put forth for molten alkali and alkaline earth fluoride compounds in an effort to provide updated, improved correlations for general use. For molten alkali fluoride densities, estimated uncertainties with a 95% confidence interval are summarized as follows: LiF (0.63%), NaF (0.48%), KF (0.76%), RbF (0.93%), and CsF (0.75%). For molten alkaline earth fluoride densities, an estimated uncertainty was not able to be quantified for BeF2 because of limited data; however, estimated uncertainties with a 95% confidence interval are summarized as follows for the remaining alkaline earth fluorides: MgF2 (1.5%), CaF2 (0.92%), SrF2 (1.6%), and BaF2 (0.23%). For molten alkali fluoride viscosities, uncertainty was not able to be quantified for RbF and CsF because of limited data; however, estimated uncertainties with a 95% confidence interval are summarized as follows for the remaining alkali fluorides: LiF (4.4%), NaF (3.0%), and KF (4.0%). For molten alkaline earth fluoride viscosities, limited consistent data resulted in the recommendation of single datasets (from literature) that are deemed to be the most trustworthy based on the quality of the underlying experimental studies
Robust Statistical Techniques for Operational Maintenance of the 10.7 cm Solar Radio Flux
Get PDFThe F10.7 solar radio flux is a critical quantity for operational space weather nowcasting and forecasting, where it is routinely used as a driver for coupled atmospheric models to estimate a variety of important quantities such as the neutral atmospheric density. Although there have been several successful developments in the way of parametric modeling to ensure F10.7 coverage during outages (often using the sunspot number or radio flux observations at neighboring wavelengths), these developments have refrained from employing comprehensive cross-validation schemes to ensure model generalizability, and can benefit from recently-developed techniques for modeling nonlinear phenomena. We present an approach that uses Feature Ordering by Conditional Independence (FOCI) to identify favorable surrogates for the F10.7 index and combines this with modeling of F10.7 with linear models and Generalized Additive Models (GAMs). We find that this approach offers notable improvements in reconstructing F10.7 over gaps of various lengths, with GAMs yielding mean error of (Formula presented.) 2.8%, compared to polynomial methods that yield mean errors of (Formula presented.) %. We additionally demonstrate the effect of reconstruction error on neutral densities modeled by the NRLMSISE2.0 thermosphere model
DIAGNOSTIC LABOR OPERATION REIMAGINED THROUGH MACHINE LEARNING AND STATISTICAL PATTERN RECOGNITION
No full textAutomotive warranty problems can be expensive ($20M per manufacturer or more annually) and can cost customer loyalty if not corrected quickly. Quickly determining a root cause and corrective action for automotive warranty concerns is imperative if warranty costs and failures are to be minimized. This becomes exceedingly difficult if the problem is intermittent or has not been experienced before. Dealerships diagnose problems every day but are often unable to find a root cause. When they are unable to find a root cause this claim is referred to as Trouble Not Found (TNF) and the dealer bills the automotive manufacturer for the time they spent trying to find the cause. This project reviewed automotive warranty analysis, specifically trouble not found (TNF), and found a way to predict root cause using statistical pattern recognition and numerous years of warranty data sets. Research into other industries was done to determine how they corrected their own TNF warranty reports, as well as other industries already using some statistical pattern recognition. Utilizing multiple methods (Doc2Vec, K-Means clustering and anomaly detection) research was done to determine the best method for solving TNF warranty. Finally, an algorithm was determined to be used for TNF warranty to assist in root cause determination and corrective actions. Testing with this algorithm was done utilizing a known issue and an unknown issue for proof of concept. The algorithm was determined after iterative testing and adjustments where each test run provided insight that guided the next steps. The process of refining with each test run led to substantial improvements. The fine-tuning process ensures that the algorithm evolves to handle challenges more effectively, improving its performance with each cycle. Promising success, showing expectations for 90% successful prediction rate, was accomplished by a combination of these techniques. These predictions will direct the dealership maintenance team to the part and/or software that holds the root cause
Aridity modulates grassland biomass responses to combined drought and nutrient addition
No full textPlant biomass tends to increase under nutrient addition and decrease under drought. Biotic and abiotic factors influence responses to both, making the combined impact of nutrient addition and drought difficult to predict. Using a globally distributed network of manipulative field experiments, we assessed grassland aboveground biomass response to both drought and increased nutrient availability at 26 sites across nine countries. Overall, drought reduced biomass by 19% and nutrient addition increased it by 24%, resulting in no net impact under combined drought and nutrient addition. Among the plant functional groups, only graminoids responded positively to nutrients during drought. However, these general responses depended on local conditions, especially aridity. Nutrient effects were stronger in arid grasslands and weaker in humid regions and nitrogen-rich soils, although nutrient addition alleviated drought effects the most in subhumid sites. Biomass responses were weaker with higher precipitation variability. Biomass increased more with increased nutrient availability and declined more with drought at high-diversity sites than at low-diversity sites. Our findings highlight the importance of local abiotic and biotic conditions in predicting grassland responses to anthropogenic nutrient and climate changes
Accessing Transient Isomers in the Photoreaction of Metastable-State Photoacid
No full textThe photoreaction of a metastable-state photoacid (mPAH) generally involves multiple isomers with various connected pathways of photoinduced structural changes during a single reaction cycle. However, only a limited number of isomers have been identified experimentally so far owing to the inherent complexity in combination with the presence of various competing electronic and vibrational processes, as well as the constantly varying interactions between mPAH isomers and solvent molecules. Here, we report an optical spectroscopic study on a benzimidazole-based mPAH, a novel photoacid using benzimidazole as the structural moiety with the active proton. Through measurements of linear absorption and steady-state fluorescence in neat solvents and binary mixtures, we discovered a pronounced effect of neat water and its binary mixture with glycerol on the photoreaction of this benzimidazole-mPAH, manifested by the remarkably distinct spectral responses to irradiation from that observed for an organic solution under an identical condition. Measurements of time- and frequency-resolved fluorescence emission further enable us to access transient isomers and the associated spectral characteristics process from other competing electronic excited-state relaxation processes. Spectral deconvolution analysis and time-dependent density functional theory (TDDFT) calculations were applied to separate distinct spectral components and access their potential origin
Peak policy lab or chasing windmills? The overlooked issue of misaligned policy design
No full textPolicy innovation labs (PILs) are relatively new policy actors and are part of a larger global “labification” movement. They are touted as spaces for the novel development and testing of policy solutions. PILs have evolved into various forms–including those at different levels of government (central, sub-national, and local), sectoral (such as food, transportation, and environment), and cross-sectoral labs (social innovation and data labs). After a decade, some practitioners lament the effectiveness of their efforts and question if policy labs are indeed engines of innovation and change. We argue that the approach to policy design by PILs, in part, is an explanation for their perceived ineffectiveness. It is unclear what their role is in the policy design process. From a sample of 149 PILs worldwide, we employ Cashore and Howlett’s (2007) 3 × 3 nine-dimensional hierarchical policy classification framework characterized by policy focus (abstract goals, program objectives, and micro policy goal targets) and policy means (instrumental logic, program mechanism, and tool calibration). Our website content analysis found that key PIL characteristics, namely their broad focus and oversight, had little to no influence on their policy design activity. We develop five policy design typologies from the above policy mix framework, namely “Classic Policy Designers,” “Advisors,” “Dreamers,” “Planners,” and “Technicians.” The remaining labs\u27 policy design foci were too broad or misaligned
Systems Analysis and Optimization of Circular PET Packaging Supply Chains in the United States: Environmental and Socioeconomic Impacts
No full textMany actions are underway at global, national, and local levels to address the plastic waste problem and transition toward a circular economy of plastics. Studies evaluating environmental and socioeconomic impacts of such a transition are lacking. The purpose of this study is to conduct a national systems analysis of polyethylene terephthalate (PET) packaging supply chains in the United States. Material flow data was combined with environmental and socioeconomic indicators to evaluate and compare the sustainability of the linear PET packaging supply chain, current (2019) supply chain, and possible future circular supply chain options in the United States. Environmentally optimal circular US PET packaging material flows showed 31% and 38% savings of GHG emissions and energy demand, respectively, with a circularity of 77% when compared with a linear supply chain. Additionally, the environmentally optimal system showed higher employment (29%) and wages (31%) than a linear system, but with a 5% decrease in revenue generation. A socioeconomically optimal circular PET supply chain showed increased employment (by 52%), wages (by 67%), and revenues (by 1%), with a circularity of 59% when compared with the linear system. However, it showed 14% higher GHG emissions than a linear system, indicating a trade-off between environmentally and socioeconomically optimal circular PET packaging systems. Overall, linear-to-circular material flow transition may not necessarily lead to increased revenues and decreased environmental impacts of the entire system, but it does benefit society due to increased employment and wages. Future systems analysis work should focus on improving data quality for environmental and socioeconomic dimensions
Enforcing cryptographic distributed-VCS access control with no trust on servers
No full textVersion control systems (VCS), including central VCS (CVCS) and distributed VCS (DVCS), are widely adopted to manage changes to software code and various types of documents. Unlike CVCS, where entities obtain data from a central server, each entity in DVCS stores the entire repository and shares it independently. In VCS, existing access control schemes require the participation of a central server and cannot be deployed in a completely distributed scenario. Additionally, these schemes often fail to enforce fine-grained access control for write permissions, which is crucial for collaborative work in a distributed environment. In this paper, we propose a distributed version control system access control scheme (named DVAC), which enforces cryptographic access control on distributed user nodes based on attribute-based encryption (ABE) and attribute-based signature (ABS). DVAC is designed to enforce a cryptographic access control protocol for DVCS, which enables file granularity read and write separation access control without the support of a central server. To ensure the integrity of the core version control functions in DVCS while protecting data security, DVAC incorporates a version control adaptation protocol. Additionally, DVAC leverages Ethereum smart contracts to maintain access control policies, ensuring distributed storage and trusted management of access policies. The architecture of DVAC is designed to seamlessly integrate with existing mature DVCS, such as Git, with minimal modifications. We have implemented a prototype of DVAC and integrated it with Git. A comprehensive performance evaluation was conducted to assess the overhead introduced by DVAC, and it was demonstrated that the overhead is modest