The relationship among teachers' general self-efficacy perceptions, job burnout and life satisfaction

The purpose of this study was to investigate the relationship among teachers' general self-efficacy perceptions, job burnout, and life satisfaction. The participants of the research consist of 412 teachers teaching at the elementary, secondary and high schools. Hypotheses have been developed related to the relationship among variables and a model has been proposed based on these hypotheses. In terms of analyzing the data, confirmatory factor analysis and structural equation modelling were used. As a result of the analysis, it was found that the general self-efficacy perceptions had the negative effects on the job burnout of teachers, and, it was determined that it had a positive effect on life satisfaction but teachers' vocational burnout had negative effect on life satisfaction. It was also found that teachers' vocational burnout played a mediating role between general self-efficacy perceptions and life satisfaction. © 2018 by authors, all rights reserved

Grain yield and agronomic characteristics of Romanian bread wheat varieties under the conditions of Northwestern Turkey

In this study, fourteen bread wheat varieties, twelve of which were introduced into Turkey from Romania, were evaluated for grain yield and seven agronomic properties in Biga, Çanakkale in northwest part of Turkey in 2005 - 2006 and 2006 - 2007 growing seasons. The objectives of the research, carried out in a completely randomized block design with 3 replicates, were to investigate Romanian wheat varieties, to study the associations between yield and yield components, and todetermine the most promising varieties suitable to Biga conditions. Based on a two-year data, all the characteristics examined showed significant difference (P < 0.05) and varied with a wide range in grainyield (344.0 - 475.5 kg da-1), plant height (78.1 - 103.3 cm), spike length (9.2 - 16.4 cm), number of spikelets (15.3 - 19.3 number), number of grains per spike (35.7 - 43.3 number), grain weight per spike(1.25 - 1.73 g), harvest index (34.2 - 43.8%) and 1000 grain weight (35.2 - 47.8 g). Except for harvest index, genotype x year interactions (GxY) was found to be significant for all the traits studied.Correlation coefficient analyses showed that the grain yield had positive and significant associations with plant height (r = 0.416***), grain weight per spike (r = 0.345**), number of grain per spike (r = 0.220*)and 1000 grain weight (r = 0.388***). Consequently, new bread wheat varieties, Joseph followed by Dumbrava and Trivale, from Romania gave rise to higher yield compared to the local varieties

Adnexal Torsion in the Third Trimester of Pregnancy: A Challenging Diagnosis

The incidence of adnexal torsion (AT) is reported 1 in 10000 births. AT is emergency condition in pregnancy, while the risk of late diagnosis is increased, in third trimester. Since it has been described as a severe complication after controlled ovarian hyper-stimulation for in vitro fertilization (IVF), it is more common in IVF pregnancies. This condition mainly occurs in the first trimester; it is rare during the late third trimester. Herein, we report a case of a 26-year-old woman, gravida 1, singleton pregnancy in the 30th week of gestation was presented to emergency department with 24-hour history of a stabbing pain because of AT. Removal of adnexa performed by laparotomy. The patient had labour pain and cervical dilatation at the 36th week of gestation and a healthy girl weighing 2,200 g was born by emergency caesarean section due to breech presentation

Revolutionizing Lithium Extraction: Analysis of Factors Affecting Commercialization Timelines with Insights from the Shale Gas Boom

This study draws parallels between Direct Lithium Extraction (DLE) and hydraulic fracturing to highlight the transformative potential of DLE technology in the lithium industry. The comparative analysis supports the hypothesis that DLE might expedite commercialization timelines, similar to the effect hydraulic fracturing has had on traditional shale gas extraction. The study includes a regression analysis to determine the factors affecting the commercialization of conventional lithium brine extraction methods, thereby better understanding the potential changes DLE will create. The regression model, based on data from 11 projects, examines the impact of variables such as temperature range, tax rates, royalty rates, regulatory quality, and logistics on project development timelines. This research contributes to understanding the factors influencing lithium project development timelines and offers valuable insights for stakeholders in optimizing new extraction technologies for faster commercialization. Although the results are encouraging, they also underscore the necessity for more extensive research to draw definitive conclusions

Deniz tarihimizin altın yaprakları:Barbaros Hayrettin Paşa'nın Tunus seferi

Taha Toros Arşivi, Dosya No: 44-Barbaro

k-Means

The k-means clustering algorithm (k-means for short) provides a method offinding structure in input examples. It is also called the Lloyd–Forgy algorithm as it was independently introduced by both Stuart Lloyd and Edward Forgy. k-means, like other algorithms you will study in this part of the book, is an unsupervised learning algorithm and, as such, does not require labels associated with input examples. Recall that unsupervised learning algorithms provide a way of discovering some inherent structure in the input examples. This is in contrast with supervised learning algorithms, which require input examples and associated labels so as to fit a hypothesis function that maps input examples to one or more output variables

On the integration of conceptual hierarchies with deep learning for explainable open-domain question answering

Question Answering, with its potential to make human-computer interactions more intuitive, has had a revival in recent years with the influx of deep learning methods into natural language processing and the simultaneous adoption of personal assistants such as Siri, Google Now, and Alexa. Unfortunately, Question Classification, an essential element of question answering, which classifies questions based on the class of the expected answer had been overlooked. Although the task of question classification was explicitly developed for use in question answering systems, the more advanced task of question classification, which classifies questions into between fifty and a hundred question classes, had developed into independent tasks with no application in question answering. The work presented in this thesis bridges this gap by making use of fine-grained question classification for answer selection, arguably the most challenging subtask of question answering, and hence the defacto standard of measure of its performance on question answering. The use of question classification in a downstream task required significant improvement to question classification, which was achieved in this work by integrating linguistic information and deep learning through what we call Types, a novel method of representing Concepts. Our work on a purely rule-based system for fine-grained Question Classification using Types achieved an accuracy of 97.2%, close to a 6 point improvement over the previous state of the art and has remained state of the art in question classification for over two years. The integration of these question classes and a deep learning model for Answer Selection resulted in MRR and MAP scores which outperform the current state of the art by between 3 and 5 points on both versions of a standard test set

k-Means

The k-means clustering algorithm (k-means for short) provides a method offinding structure in input examples. It is also called the Lloyd–Forgy algorithm as it was independently introduced by both Stuart Lloyd and Edward Forgy. k-means, like other algorithms you will study in this part of the book, is an unsupervised learning algorithm and, as such, does not require labels associated with input examples. Recall that unsupervised learning algorithms provide a way of discovering some inherent structure in the input examples. This is in contrast with supervised learning algorithms, which require input examples and associated labels so as to fit a hypothesis function that maps input examples to one or more output variables