Comparison of Teacher and School Managers` Assignment Policies Between South Korea, Singapore, Japan, Finland and Turkey

In this study, South Korea, Singapore, Japan, Finland and Turkey Countries teachers and school administrators were compared in terms of assignment policies. In this research, a holistic multiple state pattern, which is one of the qualitative research types, was used. The reason for using this method has been examined in accordance with the problem situation of these countries and then compared with each other. The appointment policies of teachers and school administrators should be evaluated along with other processes. South Korea, Singapore, Japan, Finland countries excelled in PISA 2003, 2006, 2009 and 2012 exams. The selection of qualified teachers and school administrators plays an important role in this success of these countries.While choosing candidates for education faculties in these countries, exams measure their teaching skills via central exams. In the process of assigning teachers, central exams are conducted, but institutions again conduct exams that measure their teaching skills. Teaching appeal is a profession in these countries. Because in these countries, teaching has all the features such as respectability, high status, job guarantee and high salary. Teacher salaries in these countries are above the average of OECD countries. In addition, great importance is attached to the in-service training of school administrators and teachers in these countries. The salaries of teachers in Turkey is below the average of OECD countries

Organik Koşullarda Uzun Süreli Sanayi Domatesi (Lycopersicon lycopersicum L. cv. Rio Grande) Yetiştiriciliğinin Meyve ve Salça Verimine Etkileri

Çalışma, 2001-2009 yılları arasında E.Ü. Ziraat Fakültesi Menemen Araştırma Uygulama ve Üretim Çiftliğinde organiğe geçiş ve organik süreçteki alanda Yalova Rio Grande domates çeşidinin meyve ve salça verimi ile bazı kalite özelliklerindeki değişimin belirlenmesi amacıyla yürütülmüştür. 2001-2003 yılları arasında 3 yıl geçiş, 2004-2009 yılları arasında da organik sertifikalı süreçteki parseller ile konvansiyonel alanda paralel yürütülen çalışmada bakım işlemleri yönetmeliklere göre uygulanmıştır. Meyve ve salça verimi ile briks gibi kalite özellikleri açısından organik ve konvansiyonel parseller arasında istatistiki anlamda önemli fark bulunmamıştır. Her iki uygulamada da ortalama 145 kg/parsel ve 8400 kg/da meyve verimi elde edilmiştir. Organik ve konvansiyonel parsellerden elde edilen % 5.0 briks değeri ile 1500 kg/da salça verimine ulaşıldığı belirlenmiştir. Buna karşılık verim değerleri bakımından deneme yılları arasında ise istatistiki olarak önemli farklılık tespit edilmiş, organik parselde geçiş yıllarında 9.0-9.8 ton/da olan meyve verim değeri organik sertifika sürecinin ilk yıllarında 7.6-8.0 ton/da, son organik yılında da 9.00 ton/da bulunmuştur

Organik Çiftlik Yönetim Modeli

Ege Üniversitesi Ziraat Fakültesi Menemen Araştırma Uygulama ve Üretim Çiftliğinde 2003 yılında başlatılan “Organik Üretim Projesi” ile organik tarımın temel ilkelerinden olan ve bir işletmenin kendi kaynaklarının kullanıldığı, işletmenin kendine yeterli olabildiği “kapalı sistem” tarım şekli hedeflenmiştir. Bu amaçla, 20 da sebze, 55 da bağ, 35 da zeytin ve 406 da fıstık çamı alanında “Organik Üretim Projesi” araştırma, üretim ve uygulama çalışmaları başlatılmıştır. Proje çalışmaları, 2006- 2007 yıllarında projeye dahil edilen 61 da yonca ve 248 da buğday ve mısır üretim alanı ile birlikte yem bitkilerinin de programa alınmasıyla halen toplam 1064 da alanda sürdürülmektedir. Bugün büyük parsellerde üretilen meyve, sebze, kuru üzüm, çam fıstığı ve yonca ile 50 da alandaki mısır ve buğday ürünleri için “Organik Ürün Sertifikası” alınmıştır. Önümüzdeki yıllarda geçiş sürecinin tamamlanması ile 194 da alanda üretim yapılan buğday, fiğ+arpa ve mısır için “organik ürün sertifikası” alınabilecektir. Proje faaliyetlerinin başlaması ile birlikte günümüze kadar bitkisel üretim faaliyetleri açısından proje hedefine ulaşılmıştır. Projenin bundan sonraki ilk hedefi ise organik hayvancılık faaliyetlerine başlanmasıdır. Organik hayvan üretimi ile birlikte “organik süt ve süt ürünleri” ile “organik tarhana” üretimi de gelecekteki hedefler arasında yer almaktadır

Multi-Label Noise Robust Collaborative Learning Model for Remote Sensing Image Classification

The development of accurate methods for multi-label classification (MLC) of remote sensing (RS) images is one of the most important research topics in RS. Methods based on Deep Convolutional Neural Networks (CNNs) have shown strong performance gains in RS MLC problems. However, CNN-based methods usually require a high number of reliable training images annotated by multiple land-cover class labels. Collecting such data is time-consuming and costly. To address this problem, the publicly available thematic products, which can include noisy labels, can be used to annotate RS images with zero-labeling cost. However, multi-label noise (which can be associated with wrong and missing label annotations) can distort the learning process of the MLC algorithm. The detection and correction of label noise are challenging tasks, especially in a multi-label scenario, where each image can be associated with more than one label. To address this problem, we propose a novel noise robust collaborative multi-label learning (RCML) method to alleviate the adverse effects of multi-label noise during the training phase of the CNN model. RCML identifies, ranks and excludes noisy multi-labels in RS images based on three main modules: 1) discrepancy module; 2) group lasso module; and 3) swap module. The discrepancy module ensures that the two networks learn diverse features, while producing the same predictions. The task of the group lasso module is to detect the potentially noisy labels assigned to the multi-labeled training images, while the swap module task is devoted to exchanging the ranking information between two networks. Unlike existing methods that make assumptions about the noise distribution, our proposed RCML does not make any prior assumption about the type of noise in the training set. Our code is publicly available online: http://www.noisy-labels-in-rs.orgComment: Our code is publicly available online: http://www.noisy-labels-in-rs.or

Determination of Direct and Indirect Effects on Milk Yield of Anatolian Buffaloes Using Path Analysis

This study aimed to determine the direct and indirect effects of the independent variables presented by the lactation length (LL), age of calving (CAGE), and daily milk yield (DMY) on the dependent variable of lactation milk yield (LMY) in Anatolian buffaloes. In this study, 3761 LMY records of the 834 Anatolian buffaloes calving between 2012 and 2017 in Tokat province and around were used as the research material. In the study, the simple correlation coefficients between the dependent variable of LMY and independent variables were determined to be positive and significant (P[removed

Tourism students’ entrepreneurial personality traits: An emprical research on undergraduate associate and bachelor’s students

Bu araştırmanın amacı, turizm eğitimi alan lisans ve ön lisans öğrencilerinin girişimci kişilik özelliklerinin tespit edilmesidir. Bu amaçla, Sakarya Üniversitesi ve Abant İzzet Baysal Üniversitesi’nde turizm eğitimi alan 308 lisans ve ön lisans öğrencisi üzerinde bir alan araştırması gerçekleştirilmiştir. Araştırmadan elde edilen veriler istatistiki metotlarla analiz edilmiştir. Araştırma sonucu elde edilen bulgular, turizm öğrencilerinin girişimci kişilik özelliklerine önemli ölçüde sahip olduklarını göstermiştir. Ayrıca, girişimci kişilik özellikleri bakımından lisans ve ön lisans öğrencileri arasında anlamlı bir farklılık olduğu tespit edilmiştir.Aim of this study is to determine entrepreneurial personality traits of associate and bachelor’s degree tourism students. For this purpose, an empirical research was carried out on undergraduate 308 tourism students that studying at Sakarya University and Abant Dzzet Baysal University. Obtained data from the research was analyzed by statistical methods. Results of this research indicated that undergraduate tourism students dramatically have entrepreneurial personality traits. However, at the conclusion of the research it was determined that there was significant difference between associate and bachelor’s degree tourism students’ entrepreneurial personality traits

A Lagrange Polynomial Chebyshev Pseudo Spectral Time Domain Method in One Dimensional Large Scale Applications

Abstract Pseudo Spectral Time Domain method based on Discrete Fourier series has been widely used in computational electromagnetics. However, this method has some disadvantages such as, the Gibbs phenomena, source conditioning and errors due to interpolation and staircase modeling of complex objects. To overcome these limitations, a Lagrange Polynomial Chebyshev Pseudo Spectral Time Domain method has been proposed. In this work, the efficiency of this method for large scale problems is examined in the sense of numerical dispersion errors (accuracy) by solving one dimensional wave equation in a simple medium. The numerical results are compared for validation with the analytical solution and standard Finite Difference Time Domain method solution. Introduction Spectral Time Domain methods are a generalization of separation of variable techniques in the time domain. They are favorable when direct analytical time domain solution is desired Two fundamental steps in the PSTD method are to choose type of interpolation function and collocation points. Although different versions of the PSTD methods are present in the literature of different scientific disciplines, Fourier Pseudo Spectral Time Domain (F-PSTD) method is widely used in numerical electromagnetic problems In order to overcome these difficulties, a Lagrange Polynomial Chebyshev Pseudo Spectral Time Domain method is considered in this wor

A Consensual Collaborative Learning Method for Remote Sensing Image Classification Under Noisy Multi-Labels

Collecting a large number of reliable training images annotated by multiple land-cover class labels in the framework of multi-label classification is time-consuming and costly in remote sensing (RS). To address this problem, publicly available thematic products are often used for annotating RS images with zero-labeling-cost. However, such an approach may result in constructing a training set with noisy multi-labels, distorting the learning process. To address this problem, we propose a Consensual Collaborative Multi-Label Learning (CCML) method. The proposed CCML identifies, ranks and corrects training images with noisy multi-labels through four main modules: 1) discrepancy module; 2) group lasso module; 3) flipping module; and 4) swap module. The discrepancy module ensures that the two networks learn diverse features, while obtaining the same predictions. The group lasso module detects the potentially noisy labels by estimating the label uncertainty based on the aggregation of two collaborative networks. The flipping module corrects the identified noisy labels, whereas the swap module exchanges the ranking information between the two networks. The experimental results confirm the success of the proposed CCML under high (synthetically added) multi-label noise rates. The code of the proposed method is publicly available at https://noisy-labels-in-rs.orgComment: Accepted in ICIP 2021. Our code is available at https://noisy-labels-in-rs.or