North Dakota Economic-Demographic Assessment Model (NEDAM): Technical Description

This report describes the logic, structure, data bases, and operational procedures of the North Dakota model.Research Methods/ Statistical Methods,

A Binary Millisecond Pulsar in Globular Cluster NGC6544

We report the detection of a new 3.06 ms binary pulsar in the globular cluster NGC6544 using a Fourier-domain ``acceleration'' search. With an implied companion mass of ~0.01 solar masses and an orbital period of only P_b~1.7 hours, it displays very similar orbital properties to many pulsars which are eclipsed by their companion winds. The orbital period is the second shortest of known binary pulsars after 47 Tuc R. The measured flux density of 1.3 +/- 0.4 mJy at 1332 MHz indicates that the pulsar is almost certainly the known steep-spectrum point source near the core of NGC6544.Comment: Accepted by ApJ Letters on 11 October 2000, 5 page

Statistical and machine learning methods evaluated for incorporating soil and weather into corn nitrogen recommendations

Nitrogen (N) fertilizer recommendation tools could be improved for estimating corn (Zea mays L.) N needs by incorporating site-specific soil and weather information. However, an evaluation of analytical methods is needed to determine the success of incorporating this information. The objectives of this research were to evaluate statistical and machine learning (ML) algorithms for utilizing soil and weather information for improving corn N recommendation tools. Eight algorithms [stepwise, ridge regression, least absolute shrinkage and selection operator (Lasso), elastic net regression, principal component regression (PCR), partial least squares regression (PLSR), decision tree, and random forest] were evaluated using a dataset containing measured soil and weather variables from a regional database. The performance was evaluated based on how well these algorithms predicted corn economically optimal N rates (EONR) from 49 sites in the U.S. Midwest. Multiple algorithm modeling scenarios were examined with and without adjustment for multicollinearity and inclusion of two-way interaction terms to identify the soil and weather variables that could improve three dissimilar N recommendation tools. Results showed the out-of-sample root-mean-square error (RMSE) for the decision tree and some random forest modeling scenarios were better than the stepwise or ridge regression, but not significantly different than any other algorithm. The best ML algorithm for adjusting N recommendation tools was the random forest approach (r2 increased between 0.72 and 0.84 and the RMSE decreased between 41 and 94 kg N ha−1). However, the ML algorithm that best adjusted tools while using a minimal amount of variables was the decision tree. This method was simple, needing only one or two variables (regardless of modeling scenario) and provided moderate improvement as r2 values increased between 0.15 and 0.51 and RMSE decreased between 16 and 66 kg N ha−1. Using ML algorithms to adjust N recommendation tools with soil and weather information shows promising results for better N management in the U.S. Midwest

Soil hydrologic grouping guide which soil and weather properties best estimate corn nitrogen need

Nitrogen fertilizer recommendations in corn (Zea mays L.) that match the economically optimal nitrogen fertilizer rate (EONR) are imperative for profitability and minimizing environmental degradation. However, the amount of soil N available for the crop depends on soil and weather factors, making it difficult to know the EONR from year-to-year and from field-to-field. Our objective was to explore, within the framework of hydrologic soil groups and drainage classifications (HGDC), which site-specific soil and weather properties best estimated corn N needs (i.e., EONR) for two application timings (at-planting and side-dress). Included in this investigation was a validation step using an independent dataset. Forty-nine N response trials conducted across the U.S. Midwest Corn Belt over three growing seasons (2014–2016) were used for recommendation model development, and 181 independent site-years were used for validation. For HGDC models, soil organic matter (SOM), clay content, and evenness of rainfall distribution before side-dress N application were the properties generally most helpful in predicting EONR. Using the validation data, model recommendations were within 34 kg N ha–1 of EONR for 37 and 42% of the sites with a root mean square error (RMSE) of 70 and 68 kg N ha–1 for at-planting and side-dress applications, respectively. Compared to state-specific recommendations, sites needing ha–1 or no N were better estimated with HGDC models. In contrast, for sites where EONR was \u3e150 kg N ha–1, HGDC models underestimated N needs compared to state specific. These results show HGDC groupings could aid in developing tools for N fertilizer recommendations

IL-7 immunotherapy in a nonimmunocompromised patient with intractable fungal wound sepsis

A nonimmunocompromised patient developed life-threatening soft tissue infection wit

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 \u3c 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions

Corn Nitrogen Nutrition Index Prediction Improved by Integrating Genetic, Environmental, and Management Factors with Active Canopy Sensing Using Machine Learning

Accurate nitrogen (N) diagnosis early in the growing season across diverse soil, weather, and management conditions is challenging. Strategies using multi-source data are hypothesized to perform significantly better than approaches using crop sensing information alone. The objective of this study was to evaluate, across diverse environments, the potential for integrating genetic (e.g., comparative relative maturity and growing degree units to key developmental growth stages), environmental (e.g., soil and weather), and management (e.g., seeding rate, irrigation, previous crop, and preplant N rate) information with active canopy sensor data for improved corn N nutrition index (NNI) prediction using machine learning methods. Thirteen site-year corn (Zea mays L.) N rate experiments involving eight N treatments conducted in four US Midwest states in 2015 and 2016 were used for this study. A proximal RapidSCAN CS-45 active canopy sensor was used to collect corn canopy reflectance data around the V9 developmental growth stage. The utility of vegetation indices and ancillary data for predicting corn aboveground biomass, plant N concentration, plant N uptake, and NNI was evaluated using singular variable regression and machine learning methods. The results indicated that when the genetic, environmental, and management data were used together with the active canopy sensor data, corn N status indicators could be more reliably predicted either using support vector regression (R2 = 0.74–0.90 for prediction) or random forest regression models (R2 = 0.84–0.93 for prediction), as compared with using the best-performing single vegetation index or using a normalized difference vegetation index (NDVI) and normalized difference red edge (NDRE) together (R2 \u3c 0.30). The N diagnostic accuracy based on the NNI was 87% using the data fusion approach with random forest regression (kappa statistic = 0.75), which was better than the result of a support vector regression model using the same inputs. The NDRE index was consistently ranked as the most important variable for predicting all the four corn N status indicators, followed by the preplant N rate. It is concluded that incorporating genetic, environmental, and management information with canopy sensing data can significantly improve in-season corn N status prediction and diagnosis across diverse soil and weather conditions

The Treasured Hunt: Collecting Medieval and Renaissance Manuscripts, Past, Present, and Future

Welcome and Opening Remarks: E. Ann Matter, University of Pennsylvania, and Lynn Ransom, Free Library of Philadelphia Session 1. Beginnings: Collecting in the Middle Ages and Renaissance Session Chair: Emily Steiner, Department of English, University of Pennsylvania Claire Richter Sherman, Center for Advanced Study in the Visual Arts at the National Gallery of Art, The Manuscript Collection of King Charles V of France: The Personal and the Political David Rundle, History Faculty and Corpus Christi College, Oxford University, The Butcher of England and the Renaissance Arts of Book-Collecting Session 2: Civic Service: The Legacies of Philadelphia-Area Collectors Chair: Peter Stallybrass, Department of English, University of Pennsylvania James Tanis, Director of Libraries and Professor of History Emeritus, Bryn Mawr College, Migrating Manuscripts Derick Dreher, Director, The Rosenbach Museum & Library, Of Private Collectors and Public Libraries: Dr. A. S. W. Rosenbach and John Frederick Lewis Session 3: Keynote address Welcome: H. Carton Rogers, Vice Provost & Director of Libraries, University of Pennsylvania Chair: Robert Maxwell, Department of the History of Art, University of Pennsylvania Christopher de Hamel, Gaylord Donnelley Fellow Librarian, Corpus Christi College, Cambridge University, The Manuscript Collection of C. L. Ricketts (1859-1941) Session 4: The Hunters and the Hunted: A Roundtable Discussion with Private and Institutional Collectors Chair: David Wallace, Department of English, University of Pennsylvania Moderator: Richard Linenthal, Bernard Quaritch Ltd. Panelists: Lawrence J. Schoenberg, Private Collector Gifford Combs, Private Collector Toshiyuki Takamiya, Private Collector, Keio University Consuelo Dutschke, Curator of Medieval and Renaissance Manuscripts, Columbia University William Noel, Curator of Manuscripts and Rare Books, The Walters Art Museu