It became somehow personal

This thesis explores the inspiration and development of my two plays, Lost Girl and Gondal, and how those two processes mirror my own personal inspiration and development in graduate school. I will examine the relationship between my personal and artistic selves as the central pillar in my artmaking. As a framework for inquiry, I will also explore the work I’m writing in conversation with: the direct source material for my plays as well as the other work I read throughout my development process (Rebecca Solnit’s The Faraway Nearby, Maggie Nelson’s Bluets, and Anne Carson’s The Glass Essay) and how my relationship with outside material makes its way into my work.Theatre and Danc

Four Corners Exhibit

We chose the location of the “lynching tree” in downtown Columbus as our memorial location. Will Miles and Jesse Slayton were two African Americans who were lynched in Columbus, Georgia in 1896. Will Miles was lynched because of a previous rape of a white woman. Shortly after, Will Miles was lynched. We was brutally shot in the face with a shotgun. Then after he was shot, the mob of angry white southerners heard there was another individual who conducted a heinous act on a white woman and his name was Jesse SLayton. The mob rushed the Columbus jail without any resistance, grabbed Slayton and lynched him on the tree next to Will Miles on present day 11th st. The symbolism of that violence is present in our memorial.https://csuepress.columbusstate.edu/historyfrombelow/1001/thumbnail.jp

A case study of the carbon footprint of milk from high-performing confinement and grass-based dairy farms

Life cycle assessment (LCA) is the preferred methodology to assess carbon footprint per unit of milk. The objective of this case study was to apply a LCA method to compare carbon footprints of high performance confinement and grass-based dairy farms. Physical performance data from research herds were used to quantify carbon footprints of a high performance Irish grass-based dairy system and a top performing UK confinement dairy system. For the USA confinement dairy system, data from the top 5% of herds of a national database were used. Life cycle assessment was applied using the same dairy farm greenhouse gas (GHG) model for all dairy systems. The model estimated all on and off-farm GHG sources associated with dairy production until milk is sold from the farm in kg of carbon dioxide equivalents (CO2-eq) and allocated emissions between milk and meat. The carbon footprint of milk was calculated by expressing the GHG emissions attributed to milk per t of energy corrected milk (ECM). The comparison showed when GHG emissions were only attributed to milk, the carbon footprint of milk from the IRE grass-based system (837 kg of CO2-eq/t of ECM)¬ was 5% lower than the UK confinement system (877 kg of CO2-eq/t of ECM) and 7% lower than the USA confinement system (898 kg of CO2-eq/t of ECM). However, without grassland carbon sequestration, the grass-based and confinement dairy systems had similar carbon footprints per t of ECM. Emission algorithms and allocation of GHG emissions between milk and meat also affected the relative difference and order of dairy system carbon footprints. For instance, depending on the method chosen to allocate emissions between milk and meat, the relative difference between the carbon footprints of grass-based and confinement dairy systems varied by 2-22%. This indicates that further harmonization of several aspects of the LCA methodology is required to compare carbon footprints of contrasting dairy systems. In comparison to recent reports that assess the carbon footprint of milk from average Irish, UK and USA dairy systems, this case study indicates that top performing herds of the respective nations have carbon footprints 27-32% lower than average dairy systems. Although, differences between studies are partly explained by methodological inconsistency, the comparison suggests that there is potential to reduce the carbon footprint of milk in each of the nations by implementing practices that improve productivity

Disease surveillance using a hidden Markov model

<p>Abstract</p> <p>Background</p> <p>Routine surveillance of disease notification data can enable the early detection of localised disease outbreaks. Although hidden Markov models (HMMs) have been recognised as an appropriate method to model disease surveillance data, they have been rarely applied in public health practice. We aimed to develop and evaluate a simple flexible HMM for disease surveillance which is suitable for use with sparse small area count data and requires little baseline data.</p> <p>Methods</p> <p>A Bayesian HMM was designed to monitor routinely collected notifiable disease data that are aggregated by residential postcode. Semi-synthetic data were used to evaluate the algorithm and compare outbreak detection performance with the established Early Aberration Reporting System (EARS) algorithms and a negative binomial cusum.</p> <p>Results</p> <p>Algorithm performance varied according to the desired false alarm rate for surveillance. At false alarm rates around 0.05, the cusum-based algorithms provided the best overall outbreak detection performance, having similar sensitivity to the HMMs and a shorter average time to detection. At false alarm rates around 0.01, the HMM algorithms provided the best overall outbreak detection performance, having higher sensitivity than the cusum-based Methods and a generally shorter time to detection for larger outbreaks. Overall, the 14-day HMM had a significantly greater area under the receiver operator characteristic curve than the EARS C3 and 7-day negative binomial cusum algorithms.</p> <p>Conclusion</p> <p>Our findings suggest that the HMM provides an effective method for the surveillance of sparse small area notifiable disease data at low false alarm rates. Further investigations are required to evaluation algorithm performance across other diseases and surveillance contexts.</p

Design and Synthesis of Novel Chalcones as Anti-cancer Therapeutics

Design and synthesis of novel chalcones as anti-cancer therapeutics