The Anonymous 1821 Translation of Goetheas Faustus: A Cluster Analytic Approach

The scholars Frederick Burwick and James McKusick published at Oxford University Press Faustus from the German of Goethe translated by Samuel Taylor Coleridge in 2007 This edition articulated the result that Samuel Taylor Coleridge is the actual translator of the anonymously published translation Faustus from the German of Goethe London Boosey 1821 The present article tests that result The approach to test this result is stylometric Specifically function word usage is selected as the stylometric criterion and 80 function words are used to define a 73-dimensional function word frequency profile vector for each text in the corpus of Coleridge s literary works and for a selection of works by a range of contemporary English authors Each profile vector is a point in 80-dimensional vector space and 5 different cluster analytic methods are used to determine the distribution of profile vectors in the space If the result being tested is valid then the profile for the 1821 translation should be closer in the space to works known to be by Coleridge than to works by the other authors The cluster analytic results show however that this is not the case and the conclusion is that the Burwick and McKusick result is falsified relative to the stylometric criterion and analytic methodology used Where in Popperian terms falsification does not mean prove to be false It means that evidence which contradicts a hypothesis has been presented and it is up to the proposer of the hypothesis either to show that the evidence is inadmissible or irrelevant or else to emend the hypothesis accordingly The rest of the article is organized as follows In section 1 we give the motivation for doing this work In section 2 we provide a quick introduction to the 1821 Faustus translations that we hope will shed some light on the problem In section 3 we discuss the previous attempts to attribute the 1821 Faustus to Coleridge In section 4 we outline the methodology used to ad

Emotional intelligence and teachers’ performance in the classroom in Cairo, Egypt

Effective teachers continue to be in demand in the workforce. Schools and universities need professional teachers who show passion for their jobs and high work performance. This research will investigate the association among teachers’ age, gender, tenure, emotional intelligence (EI) scores, perception of emotional intelligence application in classroom, and perception of any potential impact on their prior year’s self-reported annual teaching evaluations in Madinaty National School in Egypt. For this study, the researcher will utilize mixed methods for data collection based on concurrent design (Creswell, 2014; Kummar, 2014). For additional data collection of teachers’ perspectives on emotional intelligence (EI), the researcher will embed a qualitative questionnaire and demographic questions into Wong and Law’s Emotional Intelligence Scale (WLEIS) survey (Wong & Law, 2002a). The theoretical foundation of this study will be based on the work of Daniel Goleman (2005), who tested the EI of leaders and its influence on leaders’ performance and followers’ behaviors at work. In particular, he explored leaders’ accomplishments, what it takes for a leader to be successful, and the importance of EI in comparison to intelligence quotient (IQ). In this study, teachers will be considered leaders in their jobs and students will be considered their followers. The researcher will utilize MT for data analysis and interpretation (Creswell, 2014). The study population will be obtained using a purposive sampling type that promotes researchers to use their judgment to survey the appropriate participants who may have the needed data for collection (Kummar, 2014). This study will offer insight to readers from the educational sector about EI role in the workforce, adding information to the literature about EI and its association, if any, with teachers’ age, gender, and performance

Agglomerative Hierarchical Clustering: An Introduction to Essentials (1) Proximity Coefficients and Creation of a Vector-Distance Matrix and (2) Construction of the Hierarchical Tree and a Selection of Methods

The article is on a particular type of cluster analysis agglomerative hierarchical analysis and is a series of four main parts The first part deals with proximity coefficients and the creation of a vector-distance matrix The second part deals with the construction of the hierarchical tree and introduces a selection of clustering methods The third deals with a variety of ways to transform data prior to agglomerative cluster analysis The fourth deals with deals with measures and methods of cluster validity The fifth and final part deals with hypothesis generation The present article covers the first and second partsonly It explains how agglomerative cluster analysis works by implementing it in a data matrix step by step Different types of agglomerative hierarchical clustering methods are applied on purposely-made data matrix so different types of cluster structures are made from that same dataset The last three parts will be covered in the next publication s There are many articles tutorials and books on this subject The article has two main objectives 1 to keep the discussion short and easy to understand by hopefully any reader and 2 to develop the motivation for using agglomerative hierarchical clustering to analyse any highdimensional data of interest with respect to some research questio

FDI In India: A Case Study Of Jammu And Kashmir

Foreign direct investment is considered to be the lifeblood of developing nations. Foreign direct investment is needed for achieving economic reforms and the growth and development of the economy. In India, FDI is considered a developmental tool, which helps in achieving self-reliance in various sectors and in the overall development of the economy. The study tries to find out the FDI inflow in India from 2000-01to 2014-15. The main purpose of the study is to find out the FDI inflow in Jammu and Kashmir State from 2012-13 to 2014-15. The study is based on secondary data

Agglomerative Hierarchical Clustering: An Introduction to Essentials. (3) Standardization, Normalization and Dimensionality Reduction of a Data Matrix

In a previous tutorial article I looked at a proximity coefficient and in the light of that proximity created a vectordistance matrix and used it to construct a hierarchical tree using different hierarchical clustering methods which will be the basis for exploratory multivariate analysis The present article deals with three topics i standardization for variable scales variation ii normalization for sample length variation and iii dimensionality reduction or minimization of data space These techniques reflect the author s academic background and particular area of interest and are by necessity not a particular purpose and are straightforwardly applicable to other kinds of data and thus to a wide range of analysis in Linguistics My treatment of these techniques is necessarily introductory and brief I hope that this article will provide practitioners with an introductory overview of these techniques used for cluster analysis of electronic corpora of linguistic data The assumption is that the data is in the form of an m x n matrix D in which may require to transform it in various ways prior to cluster analyzing it Standardized data matrix enables practitioners to measure the variation between n-variables and to cluster the cases they describe in common scales and values regardless of their original scales and values Normalized data matrix enables practitioners to eliminate the effect of variation in length among n-samples and to cluster them as if they were all about the same length regardless of their original length Dimensionality-reduced space data matrix enables practitioners to select and or extract n-most interesting variables relevant to the research question and to visualize an existing pattern regardless of the original space A worked example is given to illustrate the effect each transformation technique has on a given data matrix These transformation techniques have their own strengths and weakness but are beyond the scope o

Exploring The Impact of Stemming on Text Topic-Based Classification Accuracy

Text classification attempts to assign written texts to specific group types that share the same linguistic features. One class of features that have been widely employed for a wide range of classification tasks is lexical features. This study explores the impact of stemming on text classification using lexical features. To explore, this study is based on a corpus of thirty texts written by six authors with topics that focus on politics, history, science, prose, sport, and food. These texts are stemmed using a light stemming algorithm. In order to classify these texts according to the topic by means of lexical features, linear hierarchical clustering and non-linear clustering (SOM) is carried out on the stemmed and unstemmed texts. Although both clustering methods are able to classify texts by topic with two models produce accurate and stable results, the results suggest that the impact of a light stemming on the accuracy of text classification by topic is ineffectual. The accuracy is neither increased nor decreased on the stemmed texts, whereby the stemming algorithm helped reducing the dimensionality of feature vector space model

Quantitative Criminology: An Evaluation of Sources of Crime Data

Crime data is at the heart of quantitative criminology research in particular and social science research in general In the past years many sources of crime data have been proposed to understand describe and explain crime and criminality but never before have the majority of these sources been tested using a huge number of crimes and applying different multivariate methods A large-scale analysis and comparison of various sources of crime data is crucial if current analytical methods are to be used effectively and if new and more powerful methods are to be developed This article presents the results of a comparison of the four main sources of crime data commonly used in quantitative criminology in order to determine the best data source that can tell the whole truth about the extent or the true level of crime occurring in a society Based on the results of these tests a more comprehensive approach to measure crime is proposed which represents all categories of crime and covers the offences committed The result of the analysis is empirically-based objective and replicable evidence which can be used in conjunction with existing literature on the quantitative methods in criminolog

Hierarchical and Non-Hierarchical Linear and Non-Linear Clustering Methods to aoShakespeare-De Vere Authorship Question

In my previous article entitled Hierarchical and Non- Hierarchical Linear and Non-Linear Clustering Methods to Shakespeare Authorship Question I used Mean Proximity as a linear hierarchical clustering method and Principal Components Analysis as a non-hierarchical linear clustering method Self-Organizing Map U-matrix and Voronoi Map as non-linear clustering methods to examine various works and plays assumed to have been written by Shakespeare and Sir Francis Bacon Christopher Marlowe John Fletcher and Thomas Kyd to determine which of them wrote some of Shakespeare s disputed plays based on similarities in the use of function words word-bi grams and character-tri grams The article showed that Shakespeare is not the author of all the disputed plays traditionally attributed to him according to the validated cluster analytic results and the stylistic criteria used The article also indicated that the author did not consider it fair to include Edward de Vere the strongest candidate in the Shakespeare authorship debate and compare his poemsto Shakespeare s disputed plays because poetry tends to have a particular style and a different structure than plays and additional test was promised The present article provides that test In this article I examined the 154 sonnets traditionally attributed to Shakespeare and 38 surviving poems attributed to Edward de Vere The purpose is to give a hypothesis whether de Vere has an identifiable self-similarity and a measure of how far from similar to Shakespeare based on the use of function words word bi-grams character bi-grams and character tri-grams applying four different clustering methods four hierarchical linear methods using Euclidean distance Single Average Complete and Ward non-hierarchical linear multidimensional Scaling MDS and Kernel K-means clustering and Voronoi mapas non-linear methods The cophenetic correlation coefficient is used to select the best result obtained from a set o

Agglomerative Hierarchical Clustering: An Introduction to Essentials (1) Proximity Coefficients and Creation of a Vector-Distance Matrix and (2) Construction of the Hierarchical Tree and a Selection of Methods

The article is on a particular type of cluster analysis agglomerative hierarchical analysis and is a series of four main parts The first part deals with proximity coefficients and the creation of a vector-distance matrix The second part deals with the construction of the hierarchical tree and introduces a selection of clustering methods The third deals with a variety of ways to transform data prior to agglomerative cluster analysis The fourth deals with deals with measures and methods of cluster validity The fifth and final part deals with hypothesis generation The present article covers the first and second partsonly It explains how agglomerative cluster analysis works by implementing it in a data matrix step by step Different types of agglomerative hierarchical clustering methods are applied on purposely-made data matrix so different types of cluster structures are made from that same dataset The last three parts will be covered in the next publication s There are many articles tutorials and books on this subject The article has two main objectives 1 to keep the discussion short and easy to understand by hopefully any reader and 2 to develop the motivation for using agglomerative hierarchical clustering to analyse any highdimensional data of interest with respect to some research questio

MOLECULAR STRUCTURE FEATURES AND NUTRIENT AVAILABILITY AND UTILIZATION OF BARLEY SILAGE VARIETIES WITH VARYING DIGESTIBLE STRUCTURAL CARBOHYDRATE IN COMPARISON WITH A NEW SHORT-SEASON CORN SILAGE IN HIGH-PRODUCING DAIRY CATTLE

Barley silage is a main crop forage source that used by the dairy producer in Western Canada. There are many barley forage varieties that used for silage production. However, there is limited information in their nutritional characteristics and utilization in dairy cows, meanwhile new corn forages that developed to Western Canada that required less crop heat units to reach the maturity stage for silage production. The objectives of the Experiment 1 and 2 were: (1) to assess the magnitude of difference among barley silage varieties in comparison with short-season corn silage in terms of their chemical composition, energy values, protein and carbohydrates fractions, rumen degradation kinetics, and intestinal absorbed true protein supply to dairy cattle and (2) to define the interactive association between molecular structure of silages and carbohydrates or protein utilization in dairy cows. The two experiments were complete randomized design with four treatments: corn silage (P7213R), CDC Cowboy barley silage, CDC Copeland barley silage, and Xena barley silage. The barley silage varieties were selected based on varying rate of in vitro neutral detergent fiber digestibility (ivNDFD). Five cannulated lactating dairy cows were used for measuring in situ rumen degradation kinetics. Intestinal digestibility of rumen undegraded feed protein was estimated using three-step in vitro procedure. The protein and carbohydrates related-molecular structure spectral data was collected using attenuated total reflectance Fourier transform infrared (ATR-FT/IR) molecular vibrational spectroscopy. Corn silage showed the highest total digestible nutrient and energy content. Cowboy showed lower energy content and lower dry matter (DM) degradation in the rumen relative to other barley varieties. All studied silages exhibited the same level of metabolizable protein supply to dairy cows. Molecular structural analysis showed significant modifications in protein or carbohydrates related molecular spectral intensity. The protein structure α-helix to β-sheet ratio are correlated to total intestinally absorbed protein supply. The spectral intensities of carbohydrates were highly correlated with the digestible carbohydrate content of silages. The objective of the Experiment 3 was to evaluate the effect of barely silage varieties selected for varying rates of ivNDFD on DM intake (DMI), milk production, and total chewing activity of high-yield dairy cows in compared with short-season corn silage. Four mid-lactating multiparous Holstein cows (DIM = 101 ± 25; parities = 2.75 ± 0.83) were used in a 4 × 4 Latin square design. The CDC Cowboy with higher ivNDFD did not result in improvements in milk yield, feed efficiency, or total chewing activity compared with other barley silage varieties. Cows fed P7213R corn silage-based diet tended to have higher DMI (28.1 vs. 25.7; P = 0.10) and produce more milk (40.1 vs. 35.3 kg/d; P = 0.01) than those fed barley silage-based diets. This implies that the cows fed corn silage-based diet improved feed efficiency compared with those fed barley silage-based diets. The objective of the Experiment 4 was to investigate the effects of barely silage with varying ivNDFD in comparison to short-season corn silage on rumen fermentation characteristics and microbial protein synthesis using a rumen simulation technique. The experiment was a randomized complete block design with four treatments that previously used in the dairy trial. The experiment consisted of 10 d of adaptation and 6 d of data collection. The main results of this study are: Cowboy barley silage did not affect rumen fermentation characteristics when compared with other barley silage varieties. On the other hand, the short-season corn silage had lower ruminal pH, a greater molar proportion of propionate, and lower acetate to propionate ratio relative to the average of all barley silage varieties. Nutrients digestibility of total mixed ration were not affected by the treatments. The corn silage had higher DM digestibility (DMD) compared with the average all barley silage varieties. There was no significant effect of barely silage variety on the bacterial protein production, whereas the diet containing corn silage had exhibited higher bacterial protein production compared to barley silage. To sum up, the results indicate that the new short-season corn silage had a higher energy content than barley silage. Feeding the new short-season corn silage would increase the milk yield, microbial protein synthesis, and feed efficiency in dairy cows relative to barley silage. The FT/IR could be used as a rapid potential tool to predict the ruminal degradation of fiber and the rumen degradation kinetics of CP by using molecular spectral bands intensities in structural carbohydrates and protein regions, respectively. The short-season corn silage could be used as an alternative to other conventional forages in Western Canada. Selecting barley silage varieties based on ivNDFD level is not a satisfactory approach to improve the milk production and DMI in dairy cows. Thus, for the next two experiments (5 and 6) it was intended to improve ivNDFD of barley silage or barley silage-based diet using an exogenous fibrolytic enzymes derived from Trichoderma reesei to enhance ivNDFD, and to correlate this increase with dairy cows’ performance during mid-lactation or early-lactation, ultimately to see whether or not the effects on lactation performance in high producing dairy cows. In the experiment 5, effects of fibrolytic enzymes on lactation performance, digestibility, and feeding behavior of dairy cows during mid-lactation were assessed. Dairy cows were fed barley silage-based diet pre-treated with a new fibrolytic enzymes derived from Trichoderma reesei (FETR, mixture of xylanase and cellulase; AB Vista, UK). Two studies were conducted to evaluate the effect of this product on barley silage and barley silage-based diet. Before starting the dairy trial, in vitro incubations were conducted to predict whether this product would have a positive effect before proceeding to animal experiments. The dairy trial was performed using eight Holstein dairy cows. The cows were blocked by their parity and assigned randomly to one of 4 treatments: 0, 0.5, 0.75, and 1 mL of FETR / kg DM of diet in a replicated Latin square design. The application of FETR linearly (P = 0.02) increased in vitro DM digestibility and tended to improve (P = 0.08) ivNDFD in barley silage. The diet supplemented with an intermediate dosage level of FETR (0.75 ml FETR/ kg of TMR) had exhibited a higher milk fat (1.2 vs. 1.4 kg/cow/day) and fat-corrected milk (38.9 vs. 36.4 kg/d) compared to control. Increasing FETR levels resulted in a quadratic effect (P 0.10) of FETR level on feeding behavior. Based on the findings in this study, the optimal dosage of FETR was the 0.75 mL / kg DM of TMR. Adding this level of FETR to TMR, increased the digestibility of NDF, milk yield and milk fat yield in dairy cows. In experiment 6, effects of pre-treating barley silage-based diet with a fibrolytic enzyme derived from Trichoderma reesei on lactation performance, omasal nutrient flow and digestibility, rumen fermentation characteristics, and rumen pH profile in Holstein dairy cows during early lactation were estimated. The application of FETR tended to decrease the DM intake compared to control (32.8 vs. 33.7; P = 0.08). There was no effect of FETR (P > 0.10) on rumen fermentation characteristics, ruminal pH profile, omasal nutrient flow. There was a significant decrease (P = 0.05) in milk urea nitrogen as a consequence of adding FETR to the diet. In conclusion, dairy cows fed barley silage-based diet pre-treated with FETR had maintained milk yield with less amount of feed during early lactation. The positive effect of FETR may depend on diet composition, lactation stage and milk yield level