Analysis of other transaction agreements to acquire innovative renewable energy solutions for the Department of the Navy

MBA Professional ReportThe purpose of this project is to use a case-study approach to analyze the effectiveness and efficiency of other transaction (OT) agreements and the OT Consortium Model to acquire innovative renewable energy solutions. OTs are typically used for prototypes; however, the fiscal year (FY) 2016 National Defense Authorization Act (NDAA) expands the use of OT authority per statute 10 U.S.C. § 2371. Our research includes interviews with Defense Innovative Unit–Experimental personnel to highlight their experience with innovative businesses previously reluctant to pursue federal contracts. Additionally, our research leverages best practices from the Army Contracting Command–New Jersey, as well as industry partners, such as the Consortium for Energy, Environment, and Demilitarization and the National Security Technology Accelerator consortium, to compile recommendations for the Department of the Navy's acquisition strategy for renewable energy. The results of this case study include recommendations on the best use of OT agreements to drive innovation into the procurement of renewable energy solutions in accordance with Better Buying Power 3.0 initiatives.http://archive.org/details/analysisofothert1094551627Lieutenant Commander, United States NavyLieutenant Commander, United States NavyLieutenant, United States NavyApproved for public release; distribution is unlimited

Dating violence: college students\u27 experiences and intervention suggestions

The dating violence relationship experiences of students were investigated at a southeast regional university. A third of the 509 participants indicated they were victims of dating violence (n = 173), and almost 25% (n = 124) indicated they had victimized someone they had dated. Weapons included guns, knives, golf clubs, machetes, and tasers. Student participants offered three categories of interventions: Counseling, Improved Campus Security, and Educational Programs. Their experiences and suggestions are discussed

Plague Dot Text:Text mining and annotation of outbreak reports of the Third Plague Pandemic (1894-1952)

The design of models that govern diseases in population is commonly built on information and data gathered from past outbreaks. However, epidemic outbreaks are never captured in statistical data alone but are communicated by narratives, supported by empirical observations. Outbreak reports discuss correlations between populations, locations and the disease to infer insights into causes, vectors and potential interventions. The problem with these narratives is usually the lack of consistent structure or strong conventions, which prohibit their formal analysis in larger corpora. Our interdisciplinary research investigates more than 100 reports from the third plague pandemic (1894-1952) evaluating ways of building a corpus to extract and structure this narrative information through text mining and manual annotation. In this paper we discuss the progress of our ongoing exploratory project, how we enhance optical character recognition (OCR) methods to improve text capture, our approach to structure the narratives and identify relevant entities in the reports. The structured corpus is made available via Solr enabling search and analysis across the whole collection for future research dedicated, for example, to the identification of concepts. We show preliminary visualisations of the characteristics of causation and differences with respect to gender as a result of syntactic-category-dependent corpus statistics. Our goal is to develop structured accounts of some of the most significant concepts that were used to understand the epidemiology of the third plague pandemic around the globe. The corpus enables researchers to analyse the reports collectively allowing for deep insights into the global epidemiological consideration of plague in the early twentieth century.Comment: Journal of Data Mining & Digital Humanities 202

Bovine Colostrum Supplementation Optimises Earnings, Performance and Recovery in Racing Thoroughbreds

Bovine colostrum (BC) is the first milk produced by cows after calving and contains numerous beneficial substances for the immunity and development of the newborn calf. Because of the growth and immune factors in BC, it has become an attractive supplement for use by athletes to support immunity and health during athletic performance. In order to evaluate the effects of oral BC supplementation on equine athletes, this study evaluated the earnings, performance, recovery and incidence of upper respiratory infections (URTI) in racing horses. The study design was a randomized cross-over racing performance study. 21 horses in race training were randomly assigned to train and compete with or without BC supplementation. After each horse competed in three races, it was crossed over to the other group, allowed a three week washout period, and then competed in three additional races. Horses in public training stables of 3 participating trainers were used. Race performance as determined by earnings, Bloodstock Research Information System (BRIS) speed figures, recovery as determined by number of days between races and incidence of upper respiratory tract disease was recorded. 11 horses completed the study. There was no effect of the order of BC supplementation on the measured variables. Horses on BC supplementation earned $ 2,088 more purse money per race, than when unsupplemented (P = 0.016), and ran an average of 5 BRIS speed points higher (P = 0.03). Horses returned to racing on average 7.5 days faster (16.9 days vs 24.4 days, P = 0.048). There were no URTI among the horses on BC supplementation and two infections while not on BC supplementation (z-test, P = 0.11). Statistical analysis showed that horses recovered more quickly, earned three times more money and raced better as judged by BRIS scores while competing with BC supplementation. BC supplemented horses also experienced fewer URTI, although this effect was not significant

Enhanced Bovine Colostrum Supplementation Shortens the Duration of Respiratory Disease in Thoroughbred Yearlings

AbstractBovine colostrum (BC) is used in humans as a nutritional supplement for immune support and has been shown to reduce Respiratory disease (RD). Other nutritional supplements, minerals and vitamins including mannan oligosaccharides (MOS), zinc and vitamins A, C and E have also been used for immune support. The aim of this prospective blinded randomized clinical trial was to evaluate the effects of a BC, MOS, zinc and vitamin based enhanced bovine colostrum supplement (BCS) on incidence and duration of RD occurring in yearling horses. 109 yearlings on two Thoroughbred farms in Central Kentucky were randomly assigned to treatment or placebo groups. Yearlings were supplemented once daily for 17 to 25 weeks with 100 g of a high quality commercial BCS (containing 50 g BC) or a full fat soy flour placebo, which were applied as a “top-dress” to feed. Yearlings were observed daily and evaluated weekly for signs of RD. All yearlings completed the study. The proportion of the study period during which each yearling exhibited illness was considerably shorter for BCS yearlings (least squares mean = 23% of the study period) than placebo yearlings (least squares mean = 34% of the study period, P = .002). The average duration of illness was shorter for BCS yearlings (1.96 weeks) than placebo yearlings (4.39 weeks, P < .0001). There was no statistical difference in the incidence of RD in these study yearlings

A systematic review of natural language processing applied to radiology reports

NLP has a significant role in advancing healthcare and has been found to be key in extracting structured information from radiology reports. Understanding recent developments in NLP application to radiology is of significance but recent reviews on this are limited. This study systematically assesses recent literature in NLP applied to radiology reports. Our automated literature search yields 4,799 results using automated filtering, metadata enriching steps and citation search combined with manual review. Our analysis is based on 21 variables including radiology characteristics, NLP methodology, performance, study, and clinical application characteristics. We present a comprehensive analysis of the 164 publications retrieved with each categorised into one of 6 clinical application categories. Deep learning use increases but conventional machine learning approaches are still prevalent. Deep learning remains challenged when data is scarce and there is little evidence of adoption into clinical practice. Despite 17% of studies reporting greater than 0.85 F1 scores, it is hard to comparatively evaluate these approaches given that most of them use different datasets. Only 14 studies made their data and 15 their code available with 10 externally validating results. Automated understanding of clinical narratives of the radiology reports has the potential to enhance the healthcare process but reproducibility and explainability of models are important if the domain is to move applications into clinical use. More could be done to share code enabling validation of methods on different institutional data and to reduce heterogeneity in reporting of study properties allowing inter-study comparisons. Our results have significance for researchers providing a systematic synthesis of existing work to build on, identify gaps, opportunities for collaboration and avoid duplication

The reporting quality of natural language processing studies: systematic review of studies of radiology reports.

BACKGROUND: Automated language analysis of radiology reports using natural language processing (NLP) can provide valuable information on patients' health and disease. With its rapid development, NLP studies should have transparent methodology to allow comparison of approaches and reproducibility. This systematic review aims to summarise the characteristics and reporting quality of studies applying NLP to radiology reports. METHODS: We searched Google Scholar for studies published in English that applied NLP to radiology reports of any imaging modality between January 2015 and October 2019. At least two reviewers independently performed screening and completed data extraction. We specified 15 criteria relating to data source, datasets, ground truth, outcomes, and reproducibility for quality assessment. The primary NLP performance measures were precision, recall and F1 score. RESULTS: Of the 4,836 records retrieved, we included 164 studies that used NLP on radiology reports. The commonest clinical applications of NLP were disease information or classification (28%) and diagnostic surveillance (27.4%). Most studies used English radiology reports (86%). Reports from mixed imaging modalities were used in 28% of the studies. Oncology (24%) was the most frequent disease area. Most studies had dataset size > 200 (85.4%) but the proportion of studies that described their annotated, training, validation, and test set were 67.1%, 63.4%, 45.7%, and 67.7% respectively. About half of the studies reported precision (48.8%) and recall (53.7%). Few studies reported external validation performed (10.8%), data availability (8.5%) and code availability (9.1%). There was no pattern of performance associated with the overall reporting quality. CONCLUSIONS: There is a range of potential clinical applications for NLP of radiology reports in health services and research. However, we found suboptimal reporting quality that precludes comparison, reproducibility, and replication. Our results support the need for development of reporting standards specific to clinical NLP studies

The reporting quality of natural language processing studies - systematic review of studies of radiology reports

Abstract Background Automated language analysis of radiology reports using natural language processing (NLP) can provide valuable information on patients’ health and disease. With its rapid development, NLP studies should have transparent methodology to allow comparison of approaches and reproducibility. This systematic review aims to summarise the characteristics and reporting quality of studies applying NLP to radiology reports. Methods We searched Google Scholar for studies published in English that applied NLP to radiology reports of any imaging modality between January 2015 and October 2019. At least two reviewers independently performed screening and completed data extraction. We specified 15 criteria relating to data source, datasets, ground truth, outcomes, and reproducibility for quality assessment. The primary NLP performance measures were precision, recall and F1 score. Results Of the 4,836 records retrieved, we included 164 studies that used NLP on radiology reports. The commonest clinical applications of NLP were disease information or classification (28%) and diagnostic surveillance (27.4%). Most studies used English radiology reports (86%). Reports from mixed imaging modalities were used in 28% of the studies. Oncology (24%) was the most frequent disease area. Most studies had dataset size > 200 (85.4%) but the proportion of studies that described their annotated, training, validation, and test set were 67.1%, 63.4%, 45.7%, and 67.7% respectively. About half of the studies reported precision (48.8%) and recall (53.7%). Few studies reported external validation performed (10.8%), data availability (8.5%) and code availability (9.1%). There was no pattern of performance associated with the overall reporting quality. Conclusions There is a range of potential clinical applications for NLP of radiology reports in health services and research. However, we found suboptimal reporting quality that precludes comparison, reproducibility, and replication. Our results support the need for development of reporting standards specific to clinical NLP studies