Prenatal breastfeeding self efficacy scale: validity and reliability study

Aim: To determine the validity and the reliability of the Prenatal Breastfeeding Self-Efficacy Scale. Material and Methods: This was a methodologic study. The sample of the research comprised 200 pregnant women who presented to the outpatient clinic of Gynecology between April and June 2015. An introductory information form and the Prenatal Breast Self-Efficacy Scale were used to collect the data. In the analysis of the data, descriptive statistics, content validity index for coverage validity, exploratory factor analysis, and confirmatory factor analysis for construct validity, and Cronbach-alfaα for reliability were used. Results: In the explanatory factor analysis of the scale, the Kaiser-Meyer- Olkin floor number was 0.84 and the Barlett’s sphericity test results were χ2=1812.608; df=171; p<0.001. The contribution of the factors to total variance was 59.06%. According to confirmatory factor analysis of the scale, the Chi-square test result was as follows: χ2=254.23 (p<0.001, SD=146). The model fit indices were as follows: χ2/SD=1.74, Root Mean Square Error of Approximation=0.06, Comparative Fit Index=0.96, Normed Fit Index=0.92, Non-Normed Fit Index=0.96, Goodness of Fit Index=0.88 and Adjusted Goodness of Fit Index=0.85. The internal consistency reliability coefficient of Prenatal Breastfeeding Self-Efficacy Scale was 0.86. Conclusion: The Prental Breastfeeding Self-Efficacy Scale is a valid and reliable scale which is applicable to Turkish culture and an appropriate tool which can be used by all healthcare workers who wish to design and evaluate interventions to support breastfeeding in the prenatal period. © 2018 by Turkish Pediatric Association

Soil micromorphology for construction science: the mortar archaeometry

Micromorphology revealed in depth evaluation of materials particularly soil micromorphology yielded numerous data on processes such as formation, neoformation and transformation of minerals and microstructure in soils, pottery and construction materials. Mortars, one of the first human made materials for construction of Byzantine and the Ottoman worlds were compared in terms of micromorphology and mineralogy

TRANSLATE CHALLENGE AS AN OPPORTUNITY: VISUALIZING OPTIONS OF SUSTAINABLE AGRICULTURE IN SEMI-ARID AREA OF TURKEY

地域環境知プロジェクト第1回国際シンポジウム，総合地球環境学研究所 講演室，2014-09-13，総合地球環境学研究所 地域環境知プロジェク

Feature engineering in biomedical data processing- a case study

Günümüzde sağlık alanında yapılan yapay zekâ çalışmalarının en önemli girdisi sağlık verisidir. Sağlık verisinin alan bilgisi uzmanları ve hekimler tarafından toplanması ve makine öğrenme algoritmalarında eğitilmesi oldukça zahmetli bir iş olup bu verilerin doğru algoritma ve parametreler ile işlenmesi, çalışmaların başarısını ortaya koymaktadır. Bu nedenlerden ötürü sağlık verisini işlemek isteyen akademisyenlere yol gösterici olması arzusu ile bir biyomedikal veri seti üzerinde özellik mühendisliği pilot çalışması amaçlandı. Bu amaç doğrultusunda uluslararası bir veri tabanından kalp yetmezliği ile ilgili örnek bir veri seti kullanıldı. Bu tezin amacına uygun olarak belirlenen veriler üzerinde yapay zekâ yöntemleri ve parametre optimizasyonu için farklı modeller kurularak deneysel çalışmalar yapıldı. Yapılan bu çalışmada veri seti üzerinde tahmine dayalı öğrenme modelleri kullanılarak hangi yapay zekâ algoritmalarının hangi parametre setleri ile en doğru sonuca ulaşıldığı raporlandı. Sonuçlar incelendiğinde özellik mühendisliğinin veri seti üzerindeki olumlu-olumsuz performans değişimlerini kıyaslayarak karar destek sistemi oluşturmak isteyen akademisyenlere önerilerde bulunuldu. Gelecek çalışmalara zemin olacağı düşünülen bu çalışmanın farklı alanlardaki sağlık verileri için de örnek alınabileceği öngörülmektedir.Today, the most important input of artificial intelligence studies in the field of health is medical data. The collection of medical data by field specialists and physicians and training the machine learning algorithms is a very laborious task and processing these data with the right algorithms and parameters determines the success of the study. For these reasons, a dataset on heart failure from an international database was used as a model study by feature engineering on a biomedical dataset, with the desire to guide academics who want to process health data. For this thesis, experimental studies were carried out for parameter optimization with artificial intelligence methods. In this study, which artificial intelligence algorithm performs best is specified, by using predictive learning models on the data set. When the results were examined, suggestions were made to the academicians who wanted to create a decision support system by comparing the positive-negative performance changes on the feature engineering dataset. This study is believed to form a basis for future studies, which also may set an example for health data in different fields

Photogrammetric monitoring of an artificially generated shallow landslide

An artificial rainfall event was applied to a forested slope in Ruedlingen, northern Switzerland. The experiment triggered a landslide which resulted in mobilising about 130m3 of debris. The event was monitored by a photogrammetric network of four cameras, operating at 5 to 8 frames per second, in order to quantify spatial and temporal changes by tracking tennis balls pegged into the ground. Image measurements were performed using automated image matching methods, implemented through a software package developed in-house. Three-dimensional coordinates of the target points were estimated by running a customised type of bundle adjustment, achieving a positioning precision of +/- 1 center dot 8cm.This research was funded by the Competence Centre for Environment and Sustainability (CCES) within the framework of the TRAMM project. Amin Askarinejad, Professor Dr Sarah M. Springman, Marco Sperl, Stefan Moser, Ernst Bleiker, Felix Wietlisbach and Peter Kienzler kindly contributed to the work. The author is grateful to the Gemeinde of Ruedlingen and their President, Mrs Katy Leutenegger, for giving permission to carry out this experiment on their land. The author gratefully thanks Professor Dr Armin Gruen for his help and valuable comments. The author also thanks the anonymous reviewers for their valuable criticism and suggestions that improved the quality of the paperPublisher's VersionAuthor Post Prin

3D modeling of cultural heritage objects with a structured light system

3D modeling of cultural heritage objects is an expanding application area. The selection of the right technology is very important and strictly related to the project requirements, budget and user's experience. The triangulation based active sensors, e.g. structured light systems are used for many kids of 3D object reconstruction tasks and in particular for 3D recording of cultural heritage objects. This study presents the experiences in the results of two such projects in which a close-range structured light system is used for the 3D digitization. The paper includes the essential steps of the 3D object modeling pipeline, i.e. digitization, registration, surface triangulation, editing, texture mapping and visualization. The capabilities of the used hardware and software are addressed. Particular emphasis is given to a coded structured light system as an option for data acquisition.Publisher's Versio

Co-registration of surfaces by 3D least squares matching

A method for the automatic co-registration of 3D surfaces is presented. Die method utilizes the mathematical model of Least Squares 2D image matching and extends it for solving the 3D surface matching problem The transformation parameters of the search surfaces are estimated with respect to a template surface. The solution is achieved when the sum of the squares of the 3D Spatial (Euclidean) distances between the surfaces are minimized. The parameter estimation is achieved using the Generalized Gauss-Markov model. Execution level implementation details are given. Apart from the co-registration of the point clouds generated from spacaborne airborne and terrestinal sensors and techniques. the proposed method is also useful for change detection, 3D comparison, and quality assessment tasks Experiments, terrain data examples show file capabilities of the method.Publisher's Versio

Central Anatolian terrestrial sand dunes: enhancing carbon sequestration by indigenous vegetation

Assessment of the changes in natural resource quality requires long term monitoring. This study outlines the changes achieved in soils and vegetation quality in a sand dune area of Central Turkey maintained since 1960s

Polygenetic evolution and bioturbation: micromorphological study of a Terra Rossa soil in a traditional olive crop (Sardinia, Italy)

The origin of Mediterranean red soils has been the subject of numerous studies. Complex genetic processes, and massive inputs of allochtonous materials such as wind-blown Saharan dust and volcanic ashes, have been advocated to interpret their genesis. The present study was carried out in a traditional olive grove nearby Sassari (Sardinia, Italy), where the land use remained unchanged for the last 150 years, on Terra Rossa developed on Miocene marine limestone