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Copyright of Journal of Communication Theory & Research / Iletisim Kuram ve Arastirma Dergisi is the property of Gazi University, Faculty of Communication and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Management & Economics Research is the property of Journal of Management & Economics Research and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of SDU Journal of Engineering Sciences & Design / Mühendislik Bilimleri ve Tasarım Dergisi is the property of Journal of Engineering Sciences & Design and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

School burnout can be defined as a student's negative attitudes towards school, their decreased school attachment, and their sense of inadequacy. According to this definition, burnout is related to a decrease in school attachment. Burnout and attachment notions, which constitute a conceptual contrast, are mostly dealt with alone and rarely together in studies conducted with students. This study aims to determine whether secondary school students' burnout dimensions predict their level of school attachment. In addition, the study explores whether students' school burnout and school attachment differ according to some demographic variables. Participants in the study, which was conducted with a correlational survey design, consisted of 550 students (276 females and 274 males) receiving education in secondary schools in a city east of Turkey in the 2018-2019 academic year. The research data were collected using the School Attachment Scale and School Burnout Scale. Data were analyzed by multiple linear regression, an independent sample t-test, and a one-way ANOVA. The results revealed a negative relationship between school attachment and school burnout. The key finding was that feelings of low success and emotional exhaustion were significant predictors of school attachment. Moreover, some differences existed in terms of gender and class level. Although the research achieved significant results, it will be useful to conduct studies with larger sample groups, including private schools, in the future. [ABSTRACT FROM AUTHOR]

This study has two purposes. Firstly, it aims to identify models that best estimate tourism demand. Secondly, it also aims to estimate the demand of Spanish tourists who visit Cappadocia, by using the identified proper models. As international tourism demand continually grows, the importance and magnitude of the tourism sector for the economies of the countries increase. In order to take into account the tourism demand, countries need to be prepared and therefore they want to know about the future demand. However, it is not always possible to know the actual demand and one can only make forecasts in such cases. This paper deals with forecasting international tourism demand, specifically focusing on the Spanish tourist visits in Cappadocia region of Turkey. In accordance with this aim, eight forecasting models are used. The results of the analyses for each model are obtained and the forecasting accuracy examined. It is seen that the Artificial Neural Networks and the Multiple Regression Model outperforms the other models. Finally, limitations of the study and future resarch directions are discussed. [ABSTRACT FROM AUTHOR]

Abstract Natural gas is a foreign-dependent source of energy in many countries and a rapid increase of its consumption is mainly associated with the increase of living standards and needs. In this work, Turkey was taken as a case study with high degree of foreign dependence of energy, and the future natural gas consumption was predicted by several different multiple regression models using socio-economic indicators as the descriptor variables. Among these, gross domestic product and inflation rate were found to be the only significant ones for this prediction. Next, three different projections for the future values of the significant descriptor variables were tested, and the natural gas consumption was predicted to rise gradually in the range 1.3 ± 0.2 billion m3 per year reaching to a consumption of 64.0 ± 3.5 billion m3 in the year 2025. It was then discussed that this additional natural gas can be compensated by utilizing local lignite sources or by starting a nuclear energy program although these two methods to reduce the future natural gas consumption have some conflictions with the general European energy matrix and environmental politics. Thus, it was concluded that resuming the wind and solar-based electricity generation programs can be considered as a more reasonable option. Highlights • 99% of natural gas source is imported in Turkey. • Gross domestic product and inflation affect natural gas consumption in Turkey. • Future natural gas consumption was predicted by multiple nonlinear regression. • 12 to 19 billion m3 of additional natural gas will be required in 2025. • 6.57–10.4 GW of additional power will be needed in 2025. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Finance Letters / Maliye Finans Yazıları Dergisi is the property of Maliye Finans Yazilari Yayimcilik Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The objective of this study is to estimate the leaf area index (LAI) of a forest ecosystem using two different satellite images, WorldView-2 and Aster. For this purpose, 108 sample plots were taken from pure Crimean pine forest stands of Yenice Forest Management Planning Unit in Ilgaz Forest Management Enterprise, Turkey. Each sample plot was imaged with hemispherical photographs with a fish-eye camera to determine the LAI. These photographs were analyzed with the help of Hemisfer Hemiview software program, and thus, the LAI of each sample plot was estimated. Furthermore, multiple regression analysis method was used to model the statistical relationships between the LAI values and band spectral reflection values and some vegetation indices (Vis) obtained from satellite images. The results show that the high-resolution WorldView-2 satellite image is better than the medium-resolution Aster satellite image in predicting the LAI. It was also seen that the results obtained by using the VIs are better than the bands when the LAI value is predicted with satellite images. [ABSTRACT FROM AUTHOR]

This paper focuses on the evaluation of water quality variations in Hirfanlı Water Reservoir, which is one of the most important water resources in Turkey, through EO-1 (Earth Observing-1) Advanced Land Imager (ALI) multispectral data and real-time field sampling. The study was materialized in 20 different sampling points during the overpass of the EO-1 ALI sensor over the study area. A multi-linear regression technique was used to explore the relationships between radiometrically corrected EO-1 ALI image data and water quality parameters: chlorophyll a, turbidity, and suspended solids. The retrieved and verified results show that the measured and estimated values of water quality parameters are in good agreement ( R > 0.93). The resulting thematic maps derived from EO-1 multispectral data for chlorophyll a, turbidity, and suspended solids show the spatial distribution of the water quality parameters. The results indicate that the reservoir has average nutrient values. Furthermore, chlorophyll a, turbidity, and suspended solids values increased at the upstream reservoir and shallow coast of the Hirfanlı Water Reservoir. [ABSTRACT FROM AUTHOR]

In this research, several models were developed to forecast the daily mean indoor temperature (IT) and relative humidity values in an education building in Izmir, Turkey. The city is located at a hot–humid climatic region. In order to forecast the IT and internal relative humidity (IRH) parameters in the building, a number of artificial neural networks (ANN) models were trained and tested with a dataset including outdoor climatic conditions, day of year and indoor thermal comfort parameters. The indoor thermal comfort parameters, namely, IT and IRH values between 6 June and 21 September 2009 were collected via HOBO data logger. Fraction of variance (R2) and root-mean squared error values calculated by the use of the outputs of different ANN architectures were compared. Moreover, several multiple regression models were developed to question their performance in comparison with those of ANNs. The results showed that an ANN model trained with inconsiderable amount of data was successful in the prediction of IT and IRH parameters in education buildings. It should be emphasized that this model can be benefited in the prediction of indoor thermal comfort conditions, energy requirements, and heating, ventilating and air conditioning system size. [ABSTRACT FROM AUTHOR]

Pavements deteriorate under traffic loads and climatic effects. Flexible pavements are formed by multiple layers. Each layer distributes and transfers the effecting loads to its subordinate. When road construction projects are prepared, the single axle load value is considered. Even though projects for State Highways in Turkey are prepared for 8.2 tons single axle load, the maximum axle load was initially increased to 10 tons and subsequently to 13 tons later on. Even though maximum value per single axel load was decreased to 11.5 tons in 1996, it is still over the initial project preparation value for the roads built before the adjustment. In addition, drivers load their vehicles above the allowed limits which results in faster deterioration of the pavement. Even if the pavement is not to be adversely affected by heavy traffic volume, it will still continue to deteriorate naturally due to climatic variables. In Turkey road condition assessments and traffic volume measurements are carried out by the General Directorate of Highways, which is also referred to as KGM. In Turkey, KGM is responsible for the condition evaluation, maintenance, rehabilitation and reconstruction of the road network totaling more than 64000 kilometers. This time consuming and expensive process is conducted by using expensive evaluation equipment known as road profilometre. However KGM only owns a few of these to make necessary measurements and evaluations on its road network. The deterioration measurements are calculated as "Ride Number" (RN) which varies from zero to five, with five representing the best condition of a pavement. KGM starts a rehabilitation process when the measured RN value for a road section drops below the critical limit of 2.5. Every vehicle type has a different effect on the road depending on its number of axles and its weight. Traffic volumes for all road sections in Turkey are measured for four different vehicle categories. These categories are car, bus, truck and trailers. To be able to evaluate the total effect, different vehicle categories on a road must be reduced into one. Each category's volume must be multiplied with its respective coefficient determined by the General Directorate of Highways. The resulting number is called "Equivalent Single Axle Load" (ESAL) value. In this study, a multiple regression model was introduced that can predict the RN value by using cumulative equivalent single axle load for the period of analysis and climate variables of the area where the pavement resides. Modeling is a process that requires extensive amount of data and complex calculations. This data evaluation process has the need for information technology support. In pavement engineering, the use of information technology has an immense power when data storage, process, and evaluation are concerned. The role of suitable, userfriendly software is essential for a fast, accurate, and applicable decision making process. Programming knowledge will not be sufficient by itself to develop such a useful software application. An effective result can be achieved by using the right hardware with an interface, programmed by an expert in the field of study. In this study a decision support system was used to process the high data volume. This decision support system has a large relational database management system (RDBMS), managed by MySQL. This database consists of traffic volume data for all State Highways along with climatic variables for each city in Turkey. This data was gathered by using the measurements conducted by KGM for the period beginning in 1993 through 2007 and detailed climatic data measured by Turkish State Meteorological Service. Forty road sections were determined for the multiple regression model to represent the State Highways. The dataset for these road sections includes cumulative traffic loads for the analysis period, and climatic data for the area upon which the road resides. Using SPSS statistical data analysis software, numerous combinations of multiple regression analysis were applied to different variations of the dataset. In addition, the stepwise method was also applied to determine the independent variables that predict the dependent variable RN with highest accuracy. The multiple regression analysis results indicate that cumulative ESAL, minimum temperature, solar exposure and precipitation levels were significant independent variables for predicting the dependent variable RN at a 95% confidence level. Detailed statistical test results verified the model validity, and revealed that the model was sufficient enough to predict the value RN. [ABSTRACT FROM AUTHOR]

The main goal of this study was to investigate spatial patterns in water quality in Lake Beysehir, which is the largest freshwater reservoir in Turkey, by using Landsat-5TM (Thematic Mapper) data and ground surveys. Suspended sediment (SS), turbidity, Secchi disk depth (SDD), and chlorophyll-a (chl-a) data were collected from 40 sampling stations in August, 2006. Spatial patterns in these parameters were estimated using bivariate and multiple regression (MR) techniques based on Landsat-5TM multispectral data and water quality sampling data. Single TM bands, band ratios, and combinations of TM bands were estimated and correlated with the measured water quality parameters. The best regression models showed that the measured and estimated values of water quality parameters were in good agreement (0.60 < R< 0.71). TM3 provided a significant relationship ( R = 0.67, p < 0.0001) with SS concentration. MR between chl-a and various combinations of TM bands showed that TM1, TM2, and TM4 are strongly correlated with measured chl-a concentrations ( R = 0.60, p < 0.0001). MR of turbidity showed that TM1, TM2, and TM3 explain 60% ( p < 0.0001) of the variance in turbidity. MR of SDD showed a strong relationship with measured SDD, with R = 0.71 ( p < 0.0001) for the ratio TM1/TM3 and TM1 band combinations. The spatial distribution maps present apparent spatial variations of selected parameters for the study area covering the largest freshwater lake and drinking water reservoir in Turkey. Interpretation of thematic water quality maps indicated similar spatial distributions for SS, turbidity, and SDD. A large area in the middle portion of the lake showed very low chl-a concentrations as it is far from point and nonpoint sources of incoming nutrients. The trophic state index values were calculated from chl-a and SDD measurements. Lake Beysehir was classified as a mesotrophic or eutrophic lake according to chl-a or SDD parameters, respectively. [ABSTRACT FROM AUTHOR]

Abstract: This paper reviews and discusses Internet issues and reports the findings of a survey concerning the impact of gender, age and income on employees’ Internet usage in Turkey. Internet usage was categorized in two empirical factors, namely usage profile (reason for using the Internet, average daily use of the Internet) and usage patterns (average daily use of the Internet for communication/e-mailing/chat, information access/downloading/entertainment and electronic services). The survey was conducted among 200 employees from public and private sector organizations. The results indicated that gender has a positive impact on average daily time spent on the use of the Internet for communication/e-mailing/chat and information access/downloading/entertainment. Age has a positive impact on average daily use of the Internet in general and a negative impact on the use of the Internet for information access/downloading/entertainment. Income was not found to have an impact on empirical factors. Finally, gender, age and income do not have any significant impact on average daily use of Internet for electronic services such as e-commerce/e-shopping/e-banking/e-government. [Copyright &y& Elsevier]

Copyright of Selcuk University Social Sciences Institute Journal is the property of Selcuk University Social Sciences Institute Journal and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents an application of high resolution satellite remote sensing data for mapping water quality in the Goldon Horn, Istanbul. It is an applied research emphasizing the present water quality conditions in this region for water quality parameters; secchi disc depth (SDD), chlorophyl-a (chl-a) and total suspended sediment (TSS) concentration. The study also examines the retrievals of these parameters through high resolution IKONOS multispectral data supported by in situ measurements. Image processing procedure involving radiometric correction is carried out for conversion from digital numbers (DNs) to spectral radiance to correlate water quality parameters and satellite data by using multiple regression technique. The retrieved and verified results show that the measured and estimated values of water quality parameters in good agreement ( R 2 > 0.97). The spatial distribution maps are developed by using multiple regression algorithm belonging to water quality parameters. These maps present apparent spatial variations of selected parameters and inform the decision makers of water quality variations in a large water region in the Istanbul metropolitan area. [ABSTRACT FROM AUTHOR]

• LST image for 2021 was projected using NDBI and NDVI. • Indices were simulated for 2021 using Markov Chain method. • Regression model was applied to obtain LST image 2021. • The impervious area was the main driving force for the distribution of SUHI effect. • If the urbanization rate continues, SUHI distribution would increase dramatically. This study aims to simulate the future form of Land Surface Temperature (LST) distribution and Surface Urban Heat Island (SUHI) evaluation based on the impervious surface area. For this purpose, Landsat-5 and Landsat-8 satellite data acquired in 2006, 2011 and 2016, respectively, were utilized, and Zonguldak, a province of Turkey, was selected as a test site. In the study, LST image for 2021 was projected using spectral indices, namely Normalized Difference Built-up Index (NDBI) and Normalized Difference Vegetation Index (NDVI). Actually, the correlation analysis between four spectral indices (NDVI, SAVI, MNDWI and NDBI) and LST was conducted. However, NDBI and NDVI were determined as highly correlated indices with LST having correlation coefficient 0.89 and −0.90, respectively, thus MNDWI and SAVI were not considered for the further steps. A Multiple Linear Regression (MLR) analysis was carried out between these two indices (NDBI and NDVI) and LST to extract a mathematical model for LST retrieval. In order to obtain the future form of LST image (2021), these indices were simulated for 2021 using Markov Chain method and then regression model was applied. Before generating LST image for 2021, LST for 2016 image was simulated and compared with the existing 2016 LST image using Kappa index of agreement technique to evaluate the performance. The Kappa value was obtained as 0.73 which is reasonable for future simulation. SUHI evaluation was conducted based on the observed (2016) and simulated (2021) impervious area and normalized LST images. The obtained results showed that the distribution of SUHI effect was highly related to impervious area and if the urbanization rate in the study area continues as it is, this expansion will cause to a dramatic increase in SUHI distribution unless any proper urban planning model is considered by decision makers. [ABSTRACT FROM AUTHOR]

Electricity consumption is on the rise in developing countries. Most of the research studies in energy demand forecasting aim to provide that sufficient electricity is produced to meet future needs. A reliable forecasting model is necessary for accurate investment planning of electricity generation and distribution. The main goal of this study is to develop effective and realistic solutions for electricity consumption forecasting in Turkey. This paper proposes a hybrid model based on least-square support vector machine and an autoregressive integrated moving average. This hybrid approach's forecast results are compared with multiple linear regression approach, a single autoregressive integrated moving average model, official forecasts and similar studies in literature. Also, it is applied to forecast the future net electricity consumption for Turkey until 2022. The study results indicate that the proposed model can generate more realistic and reliable forecasts. It can also be stated that it responds better to some unexpected reactions in the time series. • Modeling of Turkey's long term net electricity consumption. • Use of different forecasting approaches. • A autoregressive integrated model with a support vector approach is proposed. • The proposed model outperforms all other forecasting approaches. • Future electricity consumption of Turkey is predicted by the proposed model. [ABSTRACT FROM AUTHOR]

Available energy resources should be efficiently used to meet the global energy demand. The storage of energy resources, especially as natural gas, is important to balance extreme fluctuations in demand and supply of energy. Underground caverns in deep salt domes are used as storage for natural gas due to the low permeability and appropriate creep conditions of salt. Several projects are being carried out in Turkey to store imported natural gas. The design and construction parameters of these deep salt caverns should be determined to provide reliable and durable storage. Dissolution rate, which is an important parameter affecting the design and construction of underground salt caverns, is generally measured from core samples in laboratory settings. However, results do not match actual underground dissolution rates. The aim of this study was to predict actual dissolution rates from some rock properties. The physical and mechanical salt properties were determined from core samples obtained from different depths of drilled wells opened for salt caverns. The relationships between salt properties and actual dissolution rates were statistically analyzed. A statistical model was developed to predict actual dissolution rates using uniaxial compressive and tensile strengths, Poisson's ratio, and cohesion and experimental vertical dissolution rates. The model is reliable for predicting actual dissolution rates in construction of underground gas storage caverns in salt domes. Predicted dissolution rates can be used in the design of underground gas storage caverns in salt domes. [ABSTRACT FROM AUTHOR]