This study is focused on water quality of Melen River (Turkey) and evaluation of 26 physical and chemical pollution data obtained five monitoring stations during the period [1995][1996][1997][1998][1999][2000][2001][2002][2003][2004][2005][2006]. It presents the application of multivariate statistical methods to the data set, namely, principal component and factor analysis (PCA/FA), multiple regression analysis (MRA) and discriminant analysis (DA). The PCA/FA was employed to evaluate the high-low flow periods correlations of water quality parameters, while the principal factor analysis technique was used to extract the parameters that are most important in assessing high-low flow periods variations of river water quality. Latent factors were identified as responsible for data structure explaining 72-97% of the total variance of the each data sets. PCA/FA was supported with multiple regression analysis to determine the most important parameter in each factor. It examines the relation between a single dependent variable and a set of independent variables to best represent the relation in the each factor. Obtained important parameters provided us to determine the major pollution sources in Melen River Basin. So factors are conditionally named soil structure and erosion, domestic, municipal and industrial effluents, agricultural activities (fertilizer, irrigation water and livestock wastes), atmospheric deposition and seasonal effects factors. DA applied the data set to obtain the parameters responsible for temporal and spatial variations. Assessment of high-low flow period changes in surface water quality is an important aspect for evaluating temporal and spatial variations of river pollution. The aim of this study is illustration the usefulness of multivariate statistical analysis for evaluation of complex data sets, in Melen River water quality assessment identification of factors and pollution sources, for effective water quality management determination the spatial and temporal variations in water quality.

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In this study, the effect of domestic and industrial pollutants on the water quality of Mudurnu River was searched. Water and benthic macroinvertebrate samples were taken from five stations selected on Mudurnu River during 12 months (2006)(2007). COD (Chemical Oxygen Demand), BOD (Biochemical Oxygen Demand), TKN (Total Kjeldahl Nitrogen), NO − 3 -N (Nitrate-Nitrogen), PO −3 4 -P (Phosphate-Phosphorous), NH + 4 -N (Ammonium-Nitrogen), Phenol data and scores of BMWP (Biological Monitoring Working Party) score system, ASPT (Average Score per Taxon), TBI (Trent Biotic Index), BBI (Belgian Biotic Index), Margalef's index (R), Shannon-Wiener diversity index (H), Simpson's diversity index (D) were determined. The relationship between data of chemical parameters and scores of biotic indices were investigated by using statistical methods. With decision tree technique, artificial neural network (ANN) and logistic regression model, chemical water quality was predicted from scores of biotic indices. A success at 67% was provided in the prediction of chemical water quality class of Mudurnu River.

Supply chain management has become one of the focal points of competition nowadays. The importance of information sharing in supply chain management is also increasing in terms of business performance improvement. In this study, five different models were developed to investigate the effect of information sharing in the supply chain process of businesses on cost, flexibility, response, delivery and financial performance. The relationships among these models are examined by means of a structural equation model (SEM). This paper is focused on supply chain process, supply chain flexibility, environmental uncertainties and information sharing latent structures whose cumulative effects on the determined performance indicators have been examined. The survey data obtained from the companies within the scope of the ISO (Istanbul Chamber of Industry) 1000 were used in the analyses conducted, whose results proved that all the suggested models were adequate in terms of validity and reliability. Furthermore, it was determined in all models that environmental uncertainties affect the supply chain process, while supply chain flexibility affects the supply chain process and information sharing.

This study is aimed at obtaining a relationship between the values defining bead geometry and the welding parameters and also to select optimum welding parameters. For this reason, an experimental study has been realized. The welding parameters such as the arc current, arc voltage, and welding speed which have the most effect on bead geometry are considered, and the other parameters are held as constant. Four, three, and five different values for the arc current, the arc voltage, and welding speed are used, respectively. So, sixty samples made of St 52-3 material were prepared. The bead geometries of the samples are analyzed, and the thickness and penetration values of the weld bead are measured. Then, the relationship between the welding parameters is modeled by using artificial neural network (ANN) and neurofuzzy system approach. Each model is checked for its adequacy by using test data which are selected from experimental results. Then, the models developed are compared with regard to accuracy. Also, the appropriate welding parameters values can be easily selected when the models improve.

The Riva River is a water basin located within the borders of Istanbul in the Marmara Region (Turkey) in the south-north direction. Water samples were taken for the 35 km drainage area of the Riva River Basin before the river flows into the Black Sea at 4 stations on the Riva River every month and analyses were carried out. Changes were observed in the quality of water from upstream to downstream. For this purpose, the spatial and temporal variations of water quality were investigated using 13 water quality variables with the ANOVA test. It was observed that COD, DO, S and BOD were important in determining the spatial variation. On the other hand, it was found out that all the variables were effective in determining the temporal variation. Moreover, the correlation analysis which was carried out in order to assess the relations between water quality variables showed that the variables of BOD-COD, BOD-EC, COD-EC, BOD-T and COD-T were correlated and the regression analysis showed that COD, TKN and NH4-N explained BOD and BOD, NH4-N, T and TSS explained COD by approximately 80 %. Consequently, the Artificial Neural Network (ANN), Decision Tree and Logistic Regression models were developed using the data of training set in order to predict the water quality classes of the variables of COD, BOD and NH4-N. Quality classes were predicted for the variables by inputting the data of testing set into the developed models. According to these results, it was seen that the ANN was the best prediction model for COD, the Decision Tree for BOD and the ANN and Decision Tree for NH4-N.

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Öz İşletme olarak hastaneler, her türlü sağlık hizmetinin üretildiği, karmaşık yapılı ve yüksek düzeyde uzmanlaşmayı gerektiren sağlık kurumlarıdır. Hastaneler hem emek hem de sermaye yoğun işletme özelliğine sahip sağlık kurumlardır. Sağlık sektöründe giderek artan bir rekabet ortamı oluşmaktadır. Böyle bir ortamda rakiplerden önde olabilmek için örgütsel iklim ve örgütsel bağlılık kadar bu iki kavramı etkileyen faktörler de son derece önemlidir. Bir örgütte çalışanların örgüt amaçlarını benimsemeleri, değer yargılarını kabul etmeleri, inanç ve kurallara uygun davranışlarda bulunmaları ve kendilerinden beklenen davranışları göstermelerinin anahtarı olarak "örgütsel iklim"; işletmelerin rekabet avantajı sağlayabilmeleri ve vasıflı iş görenleri bünyelerinde tutabilmeleri için ise "örgütsel bağlılık" vazgeçilmez olarak görülebilir. Bu çalışma sağlık çalışanlarının algıladıkları hizmetkâr liderlik anlayışı çerçevesinde örgütsel iklim ve örgütsel bağlılık arasında anlamlı bir ilişkinin var olup olmadığını belirlemek amacıyla yapılmıştır. Çalışma 30.10.2017 ile 30.12.2017 tarihleri arasında İstanbul İli Kamu Hastaneler Birliğine bağlı bir devlet hastanesinde aktif olarak çalışan 495 sağlık personelinden 254'üne uygulanmıştır. Örneklem, rastgele örneklem seçim tekniği olup katılım %52,93'dür. Çalışmada veri toplama aracı olarak; Robert Stringer tarafından geliştirilmiş, örgüt iklimi ölçeği; Meyer ve Allen tarafından geliştirilmiş örgütsel bağlılık ölçeği ve çalışanların sosyal-demografik niteliklerinden oluşan bir anket formu kullanılmıştır. Ölçek ve alt boyut puanları normal dağılım gösterdiğinden cinsiyet, medeni durum; yaş, öğrenim düzeyi, kadro durumu, kıdem, kamuda çalışma süresi, toplam aylık gelir değişkenlerine göre karşılaştırılmıştır. Gruplar arasındaki anlamlı farklılık için de ikili karşılaştırmalar yapılmıştır. Örgütsel iklim ölçeği ve alt boyut puanlarının hem cinsiyete hem de medeni duruma göre, aynı zamanda yaş gruplarına, öğrenim düzeyine, mesleki kıdemine, kamuda çalışma süresine göre anlamlı farklılık göstermediği belirlenmiştir.