Median costs of 150 Kansas rural high schools, 1929-1930

Thesis (M.A.Ed.)--University of Kansas, Education, 1931

Effect of nitrogen rate and forecrop on nitrogen use efficiency in winter wheat (Triticum aestivum)

ArticleApplication of plant nutrient is one of the most important measures increasing grain yield and yield quality. Excessive application of nitrogen fertilizers leads to nitrogen leaching and it affects the quality of groundwater and surface water. The objective of this research was to evaluate the effect of nitrogen fertilizer rate on nitrogen use efficiency in winter wheat after two forecrops. The experiment was conducted at the Research and Study farm ‘Pēterlauki’ of Latvia University of Life Sciences and Technologies (56° 30.658’ N and 23° 41.580’ E) in four growing seasons: 2014/2015, 2015/2016, 2016/2017 and 2017/2018. Researched factors were crop rotation (wheat/wheat and oilseed rape (Brassica napus ssp. oleifera/wheat) and five nitrogen fertilizer rates (kg ha-1 ): N0 or control, N60, N120(90+30), N180(90+60+30) and N240(120+60+60). Nitrogen fertilizer affected winter wheat grain yield significantly (P < 0.001) and average grain yield increased significantly (P < 0.049) until nitrogen rate N180. But analyzing it after each forecrop separately, yield increased significantly (P < 0.05) until N120 after both forecrops. Nitrogen fertilizer affected nitrogen use efficiency (NUE), nitrogen uptake efficiency (NUpE), nitrogen utilization efficiency (NUtE) and protein content significantly (P < 0.001). When increasing nitrogen fertilizer rate NUE, NUpE and NUtE decreased, and higher results were observed at the lowest nitrogen rates. Increased nitrogen fertilizer rate also increased crude protein content in grain, and for bread baking suitable grain was obtained only with the highest N rate: N 240. Forecrop did not affect winter wheat grain yield, however, it affected NUtE (P < 0.01), NUE (P < 0.001) and nitrogen harvesting index (P < 0.001) significantly; higher results were observed when wheat was grown after wheat

Winter wheat leaf blotches development depending on fungicide treatment and nitrogen level in two contrasting years

Received: January 31st, 2021 ; Accepted: December 1st, 2021 ; Published: December 4th, 2021 ; Correspondence: [email protected] spot (caused by Pyrenophora tritici-repentis) and Septoria tritici blotch (caused by Zymoseptoria tritici) are the most widespread winter wheat leaf diseases in Latvia. The aim of the present research was to clarify the development of leaf blotches on winter wheat depending on fungicide treatment schemes under four nitrogen rates. A two-factorial trial was conducted at the Research and Study farm “Pēterlauki” (Latvia) of Latvia University of Life Sciences and Technologies. For this study, data from the 2018/2019 and 2019/2020 growing seasons was used. Four schemes of fungicide application and an untreated variant, as well as four nitrogen rates (N120, N150, N180, and N210 kg ha-1 ) were used. The total disease impact during the vegetation period was estimated by calculating the area under the disease progress curve (AUDPC). The severity of leaf blotches in winter wheat leaves differed significantly during both vegetation seasons. Tan spot was the dominant disease in 2019 (18.7% in untreated variant). The development of tan spot was reduced by fungicide treatment; however, only in 2019, the influence of fungicide was significant. Septoria tritici blotch was the dominant disease in 2020 (11.4% in untreated variant), and its development was decreased by fungicides. Nitrogen fertilizer rate had no significant effect on the development of Septoria tritici blotches. Yield harvested in 2020 were significantly higher than those in 2019 (on average 5.23 t ha-1 in 2019, 8.40 t ha-1 in 2020). The using of fungicides provided significant increase of yield but there were no significant differences among fungicide treatment schemes

Farming and Folk Art in Prince George’s County

Final project for ENST 472: Capstone (Spring 2021). University of Maryland, College Park.The semester was divided into a group of students who studied to evaluate the linkages between folk art and farming and analyze the opportunities that will benefit both farmers and artists including by identifying common materials used by local artists, evaluating the production capacity of local farmers, and inquiring about possibilities to establish a network between farmers and artists. To achieve those outcomes, the students identified local artists and farmers and conducted interviews to assess their opinions and lastly the team summarizes their preliminary findings and provide recommendations for future iterations of this project.Prince George's County Planning Department (PGPD

Knowledge and attitude toward theranostics among Nuclear Medicine Technologists

This study was conducted to determine the knowledge and attitude of nuclear medicine technologists toward theranostics. This research utilized a quantitative correlational research design. Data were gathered from 69 practicing nuclear medicine technologists, specifically those that do not perform or have theranostics procedures. The data were gathered using a self-made questionnaire and statistically treated using frequency, percentage, range, mean, standard deviation, t-test, ANOVA, Kruskal-Wallis H test, Mann-Whitney U test, and Spearman Rank Order Correlation. Findings show that the majority of the respondents are male, and most are 20-30 years old. Most of the respondents have good knowledge and show a “positive attitude” toward theranostics. In general, there are no significant differences in the knowledge of the respondents about theranostics when they are grouped according to sex, years of experience and type of hospital they are currently employed in. However, there is a significant difference when grouped according to age, where the 31–40-year-old group showed a higher level of knowledge than the 20-30-year-old group possibly due to learning more about practices with theranostics. With the respondents’ attitude toward theranostics, there are no significant differences when they are grouped according to age, sex, years of experience, and type of hospital they are currently employed in. The findings also show that there is a weak positive relationship between the knowledge and attitude of the respondents toward theranostics. Generally, the results show that nuclear medicine technologists have very good attitude toward and good knowledge of theranostics

Rank-based model selection for multiple ions quantum tomography

The statistical analysis of measurement data has become a key component of many quantum engineering experiments. As standard full state tomography becomes unfeasible for large dimensional quantum systems, one needs to exploit prior information and the "sparsity" properties of the experimental state in order to reduce the dimensionality of the estimation problem. In this paper we propose model selection as a general principle for finding the simplest, or most parsimonious explanation of the data, by fitting different models and choosing the estimator with the best trade-off between likelihood fit and model complexity. We apply two well established model selection methods -- the Akaike information criterion (AIC) and the Bayesian information criterion (BIC) -- to models consising of states of fixed rank and datasets such as are currently produced in multiple ions experiments. We test the performance of AIC and BIC on randomly chosen low rank states of 4 ions, and study the dependence of the selected rank with the number of measurement repetitions for one ion states. We then apply the methods to real data from a 4 ions experiment aimed at creating a Smolin state of rank 4. The two methods indicate that the optimal model for describing the data lies between ranks 6 and 9, and the Pearson $\chi^{2}$ test is applied to validate this conclusion. Additionally we find that the mean square error of the maximum likelihood estimator for pure states is close to that of the optimal over all possible measurements.Comment: 24 pages, 6 figures, 3 table

The recent intellectual structure of geography

An active learning project in an introductory graduate course used multidimensional scaling of the name index in Geography in America at the Dawn of the 21st Century, by Gary Gaile and Cort Willmott, to reveal some features of the discipline\u27s recent intellectual structure relevant to the relationship between human and physical geography. Previous analyses, dating to the 1980s, used citation indices or Association of American Geographers spe- cialty-group rosters to conclude that either the regional or the methods and environmental subdisciplines bridge human and physical geography. The name index has advantages over those databases, and its analysis reveals that the minimal connectivity that occurs between human and physical geography has recently operated more through environmental than through either methods or regional subdisciplines

Air transport liberalisation and airport dependency: developing a composite index

Air transport liberalisation in Europe has produced some major changes to the networks operated by airlines and the services available at airports. Within this context the degree of airport dependency in terms of market, spatial and temporal concentration is important to know from an economic geography and risk management perspective. A composite index called the Airport Dependency Index (ADI) is developed to measure airport dependency based on the concept of the relative Gini coefficient. Liberalisation has had varying impacts depending on the size and type of airport and so a comparison is made of the degree of dependency at a large sample of European airports using the ADI. The ADI has the potential to provide insight on the sustainability and worthiness of financing airport projects, and on whether airports should diversify further their activities by investing in the growth and expansion of their network

A comprehensive re-analysis of the Golden Spike data: Towards a benchmark for differential expression methods

<p>Abstract</p> <p>Background</p> <p>The Golden Spike data set has been used to validate a number of methods for summarizing Affymetrix data sets, sometimes with seemingly contradictory results. Much less use has been made of this data set to evaluate differential expression methods. It has been suggested that this data set should not be used for method comparison due to a number of inherent flaws.</p> <p>Results</p> <p>We have used this data set in a comparison of methods which is far more extensive than any previous study. We outline six stages in the analysis pipeline where decisions need to be made, and show how the results of these decisions can lead to the apparently contradictory results previously found. We also show that, while flawed, this data set is still a useful tool for method comparison, particularly for identifying combinations of summarization and differential expression methods that are unlikely to perform well on real data sets. We describe a new benchmark, AffyDEComp, that can be used for such a comparison.</p> <p>Conclusion</p> <p>We conclude with recommendations for preferred Affymetrix analysis tools, and for the development of future spike-in data sets.</p

Empirical Bayes models for multiple probe type microarrays at the probe level

<p>Abstract</p> <p>Background</p> <p>When analyzing microarray data a primary objective is often to find differentially expressed genes. With empirical Bayes and penalized t-tests the sample variances are adjusted towards a global estimate, producing more stable results compared to ordinary t-tests. However, for Affymetrix type data a clear dependency between variability and intensity-level generally exists, even for logged intensities, most clearly for data at the probe level but also for probe-set summarizes such as the MAS5 expression index. As a consequence, adjustment towards a global estimate results in an intensity-level dependent false positive rate.</p> <p>Results</p> <p>We propose two new methods for finding differentially expressed genes, Probe level Locally moderated Weighted median-t (PLW) and Locally Moderated Weighted-t (LMW). Both methods use an empirical Bayes model taking the dependency between variability and intensity-level into account. A global covariance matrix is also used allowing for differing variances between arrays as well as array-to-array correlations. PLW is specially designed for Affymetrix type arrays (or other multiple-probe arrays). Instead of making inference on probe-set summaries, comparisons are made separately for each perfect-match probe and are then summarized into one score for the probe-set.</p> <p>Conclusion</p> <p>The proposed methods are compared to 14 existing methods using five spike-in data sets. For RMA and GCRMA processed data, PLW has the most accurate ranking of regulated genes in four out of the five data sets, and LMW consistently performs better than all examined moderated t-tests when used on RMA, GCRMA, and MAS5 expression indexes.</p