Solving Point-Reactor Kinetics Equations Using Exponential Moment Methods

A robust method of solving the reactor point kinetic equations was designed using exponential moment methods. Although the method requires a relatively large number of calculations to complete, the accuracy ensured by each individual step calculation allows larger time steps to be used. The algorithm designed was verified to converge to the correct value as step size was reduced. Additionally, the algorithm can take steps much larger than the average neutron lifetime while maintaining some precision. An error control scheme was designed based on changes observed in the results as a function of time step size. The error control adaptively approaches optimal step sizes within a factor of two for given tolerances. When used in conjunction with our algorithm, most cases show large mitigation of computational cost

ENHANCING THE FINANCIAL AND MARKETING PERFORMANCE OF FIRMS IN THE SMOKED AND PROCESSED MEAT INDUSTRY

Small smoked and processed meat manufacturers constitute a unique cottage industry in Texas. This paper assesses ways for such firms to improve their financial performance through better marketing strategies. The results indicate that, on average, small firms tend to be the most profitable. This is true whether size is measured in terms of dollars, pounds, or number of employees. The more profitable firms tend to be those that are located in rural areas or in shopping centers; those that make the majority of their sales at their own stores; those that do less of their own distribution; and those that emphasize fresh meat sales and de-emphasize jerky sales.Agribusiness,

Rule-based question answering system for reading comprehension tests

Journal ArticleWe have developed a rule-based system, Quarc, that can read a short story and find the sentence in the story that best answers a given question. Quarc uses heuristic rules that look for lexical and semantic clues in the question and the story. We have tested Quarc on reading comprehension tests typically given to children in grades 3-6. Overall, Quarc found the correct sentence 40% of the time, which is encouraging given the simplicity of its rules

ENHANCING THE FINANCIAL PERFORMANCE OF SMALL MEAT PROCESSORS

The small firms examined produce meats in the State of Texas and emphasize such products as sausage, jerky, brisket, and fresh meats. The authors test hypotheses with the intent to identify operational factors associated with firm financial success. A quartile model and an econometric model are both used for this purpose. Results generally suggest important factors for firms to be profitable include product selection, pricing strategies, special equipment, and location.Agribusiness, Agricultural Finance,

How Political Ideology Impacts Political Brand Image: Analysis of the 2016, 2018, and 2020 Elections

The U.S. presidential elections of 2016 and 2020 have both been characterized as “An Election like No Other” (Goodman 2020; Smith 2016) unparalleled to previous ones. The 2016 election saw an outsider, not an established politician, win the support of a major political party and eventually the presidency. The 2020 election was influenced by a once in a century pandemic that diminished all traditional election issues and greatly affected the nature of campaigning. This research examines whether political ideology played an invariant and stable role in shaping the brand image of the presidential election candidates. Analysis of 2016, 2018, and 2020 data sets identifies ten aspects of political ideology, finds that they are a significant determinant of the brand image of candidates in both elections, and concludes which aspects are stable versus unstable. The findings will help political marketers and researchers to create the strong brand image of political candidates, providing insights into the future U.S. presidential as well as overall election strategies

Abundance Measurements of Titan's Stratospheric HCN, HC3_3N, C3_3H4_4, and CH3_3CN from ALMA Observations

Previous investigations have employed more than 100 close observations of Titan by the Cassini orbiter to elucidate connections between the production and distribution of Titan's vast, organic-rich chemical inventory and its atmospheric dynamics. However, as Titan transitions into northern summer, the lack of incoming data from the Cassini orbiter presents a potential barrier to the continued study of seasonal changes in Titan's atmosphere. In our previous work (Thelen et al., 2018), we demonstrated that the Atacama Large Millimeter/submillimeter Array (ALMA) is well suited for measurements of Titan's atmosphere in the stratosphere and lower mesosphere (~100-500 km) through the use of spatially resolved (beam sizes <1'') flux calibration observations of Titan. Here, we derive vertical abundance profiles of four of Titan's trace atmospheric species from the same 3 independent spatial regions across Titan's disk during the same epoch (2012 to 2015): HCN, HC$_3$N, C$_3$H$_4$, and CH$_3$CN. We find that Titan's minor constituents exhibit large latitudinal variations, with enhanced abundances at high latitudes compared to equatorial measurements; this includes CH$_3$CN, which eluded previous detection by Cassini in the stratosphere, and thus spatially resolved abundance measurements were unattainable. Even over the short 3-year period, vertical profiles and integrated emission maps of these molecules allow us to observe temporal changes in Titan's atmospheric circulation during northern spring. Our derived abundance profiles are comparable to contemporary measurements from Cassini infrared observations, and we find additional evidence for subsidence of enriched air onto Titan's south pole during this time period. Continued observations of Titan with ALMA beyond the summer solstice will enable further study of how Titan's atmospheric composition and dynamics respond to seasonal changes.Comment: 15 pages, 16 figures, 2 tables. Accepted for publication in Icarus, September 201

Deep Depth From Focus

Depth from focus (DFF) is one of the classical ill-posed inverse problems in computer vision. Most approaches recover the depth at each pixel based on the focal setting which exhibits maximal sharpness. Yet, it is not obvious how to reliably estimate the sharpness level, particularly in low-textured areas. In this paper, we propose `Deep Depth From Focus (DDFF)' as the first end-to-end learning approach to this problem. One of the main challenges we face is the hunger for data of deep neural networks. In order to obtain a significant amount of focal stacks with corresponding groundtruth depth, we propose to leverage a light-field camera with a co-calibrated RGB-D sensor. This allows us to digitally create focal stacks of varying sizes. Compared to existing benchmarks our dataset is 25 times larger, enabling the use of machine learning for this inverse problem. We compare our results with state-of-the-art DFF methods and we also analyze the effect of several key deep architectural components. These experiments show that our proposed method `DDFFNet' achieves state-of-the-art performance in all scenes, reducing depth error by more than 75% compared to the classical DFF methods.Comment: accepted to Asian Conference on Computer Vision (ACCV) 201