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Copyright of Journal of Management Theory & Practices Research (JMTPR) is the property of Journal of Management Theory & Practices (JMTP) 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 Turkiye Klinikleri Journal of Sports Sciences is the property of Turkiye Klinikleri 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.)

Permanent magnet synchronous motors (PMSMs) are widely used in industrial applications and electric vehicles which require high-performance variable torque and speed due to their high efficiency, simple structure, and wide speed range. In this study, an extended complex Kalman filter (ECKF) based observer is designed for simultaneous estimation of the load torque with the stator stationary axis components of stator currents and rotor angular velocity/position required for speed-sensorless model predictive current control of PMSMs. The designed ECKF observer and model predictive current control system has been tested and validated with challenging scenarios under different load torques in a wide speed range including zero speed and speed reversals. In addition, the performance of both the ECKF and the model predictive current control system is analyzed against parameter changes PMSM. The simulation results confirm that the ECKF observer and the speed-sensorless model predictive current control system using this observer have very high performance. In addition, the computational burden of the ECKF observer was compared with the conventional extended Kalman filter, which estimates the states and parameters estimated in this study, and it was shown that the processing computational burden decreased. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Management Theory & Practices Research (JMTPR) is the property of Journal of Management Theory & Practices (JMTP) 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.)

Introduction: In our study, we evaluated the demographic and clinical characteristics of patients with pediatric Behçet's disease (BD) and our treatment approaches in these patients. In addition, we aimed to determine the predictive factors in patients who need additional immunosuppressive therapy in addition to colchicine and short-term corticosteroid therapy in the treatment of BD. Materials and Methods: Pediatric patients followed up with BD between 2004- 2022 were included in the study. The patients were divided into two groups: those receiving colchicine and/or short-term corticosteroids (group A) and those needing additional immunosuppressive treatments (group B). Results: Of a total of 103 patients, 57 (55.3%) were being followed up with a diagnosis of BD, and 46 (44.7%) with a diagnosis of incomplete BD. The most preferred drug in the treatment was colchicine (92.2%). Corticosteroids (55.3%) were often preferred with additional immunosuppressive agents in patients with major organ involvement. Biologic agents were used in 15 (14.6%) patients who were resistant to other immunosuppressive treatments. There were 56 patients in group A and 47 patients in group B. Ocular involvement and venous thrombosis were more common in group B. In addition, acute phase reactants and BHAAF scores at the diagnosis were higher in group B than in group A patients. As a result of multivariate analysis, ocular involvement [odds ratio (OR) 4,045, 95% CI 6,205-525,470; p=0.001], venous thrombosis (OR 2,497, 95% CI 3.048-48.358; p=0.001) and elevated acute phase reactants (OR 1.312, 95% CI 0.086-0.842; p=0.024) were identified as independent factors predicting the need for additional immunosuppressive therapy. Conclusion: Correct management of pediatric BD cases is very important in order to prevent complications that may develop. Our results showed that patients with ocular or vascular involvement, high acute phase reactants, and BHAAF scores may need additional immunosuppressive therapy at admission. Determining the factors that predict the need for treatment is important in making the appropriate treatment and follow-up plan for patients. [ABSTRACT FROM AUTHOR]

Forecasting the direction of the gold index, which determines the dollar value of 1 ounce of gold, is an attractive research topic for investors, researchers and analysts. In this study, it is aimed to construct a model that predicts the direction of the gold index based on deep learning. The proposed model is obtained as a result of blending the numerical dataset of the gold index with the textual data. This is a hybrid prediction model both in this aspect and in terms of hybrid deep learning methods used in direction prediction. As far as we know, this is the first attempt in the literature that uses the social media platform as a source for financial sentiment analysis and constructs a deep learning-based direction prediction model for the gold index by blending it with numerical data. The contribution of the study to the literature is summarized in four stages: In the first stage, in order to carry out the sentiment analysis, the dataset is cleaned and ready for modeling by methods such as parsing the dataset collected from the Twitter environment, finding the correct forms of the words in the dictionary, finding the roots of the words, normalizing the words, and cleaning the unused characters. Afterwards, the dataset is classified using 14 different deep learning-based methods. Secondly, the results of the sentiment analysis are blended with the numerical data of XAU/USD. Third, the XAU/USD direction prediction model is constructed with deep learning models. Fourth, the performance of the results from five different forecasting models in predicting the direction of XAU/USD is presented. As a result, the performance of the proposed model is significantly superior with high accuracy when compared to the state-of-the-art studies. [ABSTRACT FROM AUTHOR]

One of the processes used in the production of fertilizers, which has become an important part of agriculture, is the drying process. Determination of proper drying parameters is important both in terms of product quality and production efficiency. Regression methods are used to determine the drying process parameters frequently. In this study, in addition to the regression method, machine learning techniques are also examined such as artificial neural network, long short-term memory method. The data obtained from the drying process of a commercial organomineral fertilizer consisting of a mixture of 5% nitrogen and 10% phosphorus at 70˚C, 75˚C, and 80˚C were used for modelling. The simulation results obtained from the models of the methods and the data obtained from the experiments were compared. The predictions and performances of each model were presented. Determination the appropriate drying parameters is It is important for the drying efficiency of the product. In addition, model selection plays an important role in obtaining successful results in drying simulations. As a result, it has been observed that the prediction performance of the model created with the artificial neural network is more successful than the others. While regressions are efficient in modelling existing data, they are not successful in predicting. Moreover, it is not enough to predict the peak and pits in the drying data. [ABSTRACT FROM AUTHOR]

Copyright of DSI Technical Bulletin / DSİ Teknik Bülteni is the property of DSI Technical Bulletin / DSI Teknik Bulteni 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 Financial Politic & Economic Reviews / Finans Politik & Ekonomik Yorumlar is the property of Journal of Financial Politic & Economic Reviews / Finans Politik & Ekomomik Yorumlar 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.)

Technology forecasting constitutes the basis of technology development and investment in technology. In a dynamic environment where technology is rapidly evolving and growing, the need to check the availability of a single prediction model has become an important research topic. The aim of this study is to follow the development of safety technologies in the field of occupational health and safety by using statistical quality control charts. Expanding the I-MR control chart rules used in monitoring the reliability of the technology forecasting model and considering the safety technologies in the field of occupational health and safety (OHS), which were previously discussed in the literature, are the original aspects of the study. At the beginning of the study, 91,580 patent data, was anlaysed using the United States Patent and Trademark Office (USPTO) database during including 1942-2020 (December) period. Time series modeling is performed using patent data on safety technologies, and I-MR graph is created using residual values of the model. The reliability of the model is monitored by controlling significant deviations with the obtained I-MR graphics. In addition, an S-curve is created for the patent numbers. The results of this study show that using a single technology forecasting model for a long period of time is misleading. Additionally, the forecast model for the period between 1942 and 2020 should be updated in various periods. Occupational health and safety technologies are an emerging technology field and appear to be incentives for policy makers and safety engineers to allocate resources. [ABSTRACT FROM AUTHOR]

Objective: Surgical site infection (SSI) surveillance is time-consuming and hard. Identifying high-risk patients and focusing on these patients will be cost and time effective. This study aims to develop a model to identify high-risk patients for the development of SSI after hip replacement surgery and to estimate the utility of the model. Methods: Logistic regression model was created to determine the risk of SSI development using the National Health Service Associated Surveillance Network (USHİİSA) data. The stability of the model was tested using the Bootstrap resampling method. The individual probability of developing SSI was determined for each patient by using the model. The threshold probability to be used in distinguishing high-risk patients was found 1.2% by ROC analysis. For hospitals with different SSI rates and surveillance sensitivity, the utility of the model has been estimated by various parameters. Results: Female gender (OR:1.52; 95% CI:1.22-1.88), being over 65 years of age (OR:2.06; 95% CI:1.63-2.62), procedure duration longer than 75th percentile (OR:1.32; 95% CI:1.07-1.63), ASA score over 3 (OR:2.10; 95% CI:1.48-2.99), and surgery performed in a hospital other than a private hospital (p<0.001) were found to be independent risk factors for the development of SSI. When focusing on high-risk patients, as the rate of SSI of a hospital increases, the number of patients that need to be focused on detecting one more SSI decreased, and the number of additional SSIs increased. As the surveillance sensitivity of the hospitals decreases, the new rate obtained differs more from the old rate. Conclusions: Focusing on high-risk patients identified using the model caused to eliminate approximately half of the patients, thus saving labor and time. Using this model can be particularly beneficial for hospitals with a high SSI burden and low surveillance capacity. The model can be integrated into the national surveillance system so that highrisk patients can be prioritized. Modeling may be considered for the other surgeries. [ABSTRACT FROM AUTHOR]

Objective: This study was planned to evaluate effectiveness of cardiac surgery score (CASUS) system used for the prediction of mortality after the cardiac surgery and to compare it with EuroSCORE, a tested and approved method. Method: Fifty patients over the age of 18 who came to the intensive care unit (ICU) after open heart surgery and stayed in the ICU for more than 48 hours were prospectively included in the study. After the preoperative EuroSCORE evaluation of the patients, CASUS scoring systems were used for risk scoring from postoperative 0 day to the service or up to the 6th postoperative day (or possible mortality) in patients whose stay in ICU was prolonged. We evaluated the place of both models in the prediction of ICU mortality. Results: The study was conducted with 50 patients- 21 female, 29 male-, aged between 19 and 79 years (mean age: 60.38±12.50). Five (10%) cases exited.The areas under the ROC curve were estimated as follows: EuroSCORE, 45.3%; and for CASUS 0,1, and 2. day scores AUCs of ROC curve were 66,9%, 64,7%, and 79,8%, respectively. The CASUS 2nd day score was found to be valuable in predicting mortality (p<0.01). A statistically significant relationship was observed between the days of stay in the ICU and mortality. Conclusion: Our results show that the rate of the accurate prediction of mortality of CASUS is insufficient on postop 0th and 1st day; however CASUS score on postoperative 2nd day has sufficient.predictive accuracy EuroSCORE is not found effective in the prediction of mortality. It is observed that if the duration of stay in ICU is prolonged, it statistically significantly increases the risk of mortality. [ABSTRACT FROM AUTHOR]

In today's world, there is a steady increase in demand for healthcare. It is very important to realize these services that involve human health without interruption. For this reason, it is very important planning future steps for health services. Estimates made by scientific methods will greatly assist the planning of these units. This study; tries to determine the number of Inpatient Forecast with Grey Forecasting for Süleyman Demirel University Faculty of Dentistry. For the study, the data of 56 months is collected from the inpatient service between January 2015 and August 2019 is used during the process of estimation. In this study, estimations are made for 28 months covering the period of September 2019 to December 2021. Multiplication separation method is used in the process of separating the data with the time series feature from the seasonal effect. The accuracy (p) and error rate (C) criteria which are frequently used in MAPE and Gray Estimation are used to determine the predictive success of the model. According to the results of these evaluations made, P value is 0.82, C value is 0.46 and MAPE value is % 21. As a result of these values, a successful result was obtained from the study. [ABSTRACT FROM AUTHOR]

Energy demand forecasts are needed for developing and emerging countries to be able to determine sustainable energy policies. Grey prediction models can make successful predictions with limited data without the need for any prior information. In this study, Grey prediction models are considered for the total energy demand of Turkey that shows significant economic and social development in recent years. Grey prediction includes forecasting models based on time series and cause-effect relationships. Four different grey prediction models including GM (1,1) and Grey Verhulst from the time series models, and GM (0,N) and GM (1,N) based on the cause-effect relationships were discussed. The purpose of using two types of forecasting structures is to achieve reliable and strong forecasts by capturing the recent trend with the time series and obtaining the change in energy demand by cause-effect relationship. GM (1,1) and Grey Verhulst model has been established with past total energy consumption data. By using independent variables of GDP, population, import, export and building surface area for GM (0, N) and GM (1, N) models, GM (0,6) and GM (1,6)models were formed. All applied models have been compared according to performance criteria, GM (1,1) and GM (1,6) have been designated to be superior models that performed successful predictions. As a result; Turkey’s total energy demand has been forecasted up to 2025 with GM (1,1) based on time series and GM (1,6) including high and low scenarios. [ABSTRACT FROM AUTHOR]

In this study, the vibration ratios obtained from the sensor and smartphone placed in the steel model structure in the vibration table and the vibration ratios generated using the MATLAB mathematical prediction model were compared. The steel model structure used in the experimental study; made of single-span, four-ply steel. Column-beam connections in the steel model structure are rigid and the base connections on all four sides of the model building are mounted to the vibration table. The vibration values obtained from the experimental study were obtained from the sensor and the smartphone placed at the base of the vibration table and at the top of the steel model structure. The vibration table was forced with the vibration values of El-Centro, Loma-Prieta, Kobe and Sakarya earthquakes and the vibrations caused by the strain caused by the structure were recorded with the help of sensor and smart phone. Sensor and smartphone vibration values at the base of the shaking table are defined as input, sensor and smartphone vibration values at the top of the steel model structure are defined as output to the system. As a result of the studies, the compatibility ratios between the input-output data obtained experimentally, the autoregressive ARX in the Matlab program and the autoregressive moving averages were compared between the prediction-prediction found in the ARMAX model and the input-output produced. In addition, the difference between sensor and smartphone acceleration data was investigated and which of the methods used gave better results. [ABSTRACT FROM AUTHOR]

Since climate change will adversely affect the agricultural sector as well as many other sectors, the effects of climate change on agricultural production should be estimated and necessary measures should be taken. The aim of this study is to determine the effect of climate change on wheat yield in the short (2020-2030), medium (2046- 2055) and long (2076-2085) term periods in the Thrace Region using AquaCrop and WOFOST models. RegCM3 Regional Climate Model, reference and A2 scenario outputs were used to predict climate change. In the 1970- 1990 period, the measured temperature and precipitation data measured in the research area were compared with the model reference data and changes were estimated in the short, medium and long term periods. Wheat yields obtained from farmer fields in three different locations (Akıncılar, Sofular, Çövenli) in Çorlu Pınarbaşı sub-basin in 2016-2017 growing period were compared with the model prediction in order to do the calibration and yields were forecasted in the future periods. As a result, in the short term, an average 0.27 °C decrease in temperature were forecasted whereas 1,43 °C and 3,05 °C increase were estimated for medium and long terms, respectively when compared to model reference years. In total rainfall, 87 mm (13%) increase, 91 mm (14%) and 78 mm (12%) decreases were simulated for the short, medium and long-terms. AquaCrop Model predicted yield increases up to 50% in Akıncılar and Çövenli and yield decreases of 6% - 34% in Sofular whereas WOFOST Model predicted increases of 40% in Akıncılar and 12% in Sofular and decrease up to 2 - 7% in Çövenli. [ABSTRACT FROM AUTHOR]

Copyright of Karaelmas Science & Engineering Journal / Karaelmas Fen ve Mühendislik Dergisi is the property of Karaelmas Science & Engineering 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.)

The main reason of obesity occurring in the future years is strongly related with the lifestyle and eating habits in childhood. This study focuses on developing an urgent precaution system which calculates the obesity risks. An original clinical decision support system (CDSS) has been developed by using Ensemble Classification methods in Machine Learnings. A questionnaire has been prepared and applied to the patients in the hospitals and to the elementary school students with official permissions in order to construct an original and reliable dataset. Extended versions of k Nearest Neighbors methods are used in Voting, Bagging, Boosting and Random Subspaces algorithms as base learners. During the experimental studies in the applications of cross validation procedures, successful results have been computed and Random Subspaces has been chosen as the most successful algorithm with 0.839 MCC (Matthews Correlation Coefficient) scores. With the help of the suggested model to a worldly wide health problem, the future probability of obesity risk for a child might be easily determined. Additionally, it has been enabled that some precautions can be taken by responsible people if there is a computed high risk for this child. [ABSTRACT FROM AUTHOR]

Planning paradigms, which aim to overcome urban transport problems, have to evaluate the mobility prediction of goods besides the mobility of individuals. This is a requirement and it comes up with different scales and features in the cities which contain high density of regional interactions. Urban roads respond from freight transport due to economy besides relation of productions and consumptions. This study is an instance to generate solutions for future which tries to evaluate respective interaction in the process of urban planning and transport planning. Study area has been stated as the city Izmir, TURKEY. The exact area is the legal responsibility border of the law 3030. The location and impact of freight transport generation stations cause total freight transport demands to rise up. Concurrently, there is a high ratio of freight transport in the transit traffic. Freight vehicles may be seen in every level of roads due to features of the road network. Therefore, it has been aimed to correlate freight transport moves with the location and impacts of the freight generation stations. This study also assumes that freight transport moves may have some measurable reasons and may contain usable parameters, for this purpose. The measurement of freight transport in Izmir, the density of the vehicles which interacts with the several aggregation areas and the location or impacts of the freight transport generation stations in those areas have been tried to evaluate simultaneously. The stages of the paradigm are determined as below: • To determine the categories of the freight transport generation stations and measurement values on urban roads, • To draw the borders of several aggregations areas and CBD area borders by determining enumeration points, • To measure density of freight vehicles move in terms of inner traffic. [ABSTRACT FROM AUTHOR]

Copyright of Sakarya Journal of Economics / Sakarya Iktisat Dergisi is the property of Sakarya Journal of Economics / Sakarya Iktisat Dergisi 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.)

Objective: EuroSCORE is the most widely used risk prediction system. Standard EuroSCORE which had been published in 1999, was revised as a Logistic EuroSCORE in 2003. Further it was reconsidered and published as EuroSCORE II in 2011. In this study we compared Standard, Logistic EuroSCORE and EuroSCORE II in prediction of early mortality following coronary artery bypass grafting. Methods: We retrospectively analyzed 406 patients who underwent coronary artery bypass grafting operation between 2011-1012. Standard, Logistic and new version were compared with ROC analysis. Results: In general population, mean standard EuroSCORE was 3.25±1.05, mean logistic EuroSCORE was found 2.48±0.58, mean EuroSCORE II was found 1.30 ± 0.09 and overall mortality was 10 (10/406 2.46%). Area under curve (AUC) was found 0.992 95% CI: 0.978-0.998 for standard EuroSCORE, 0.992 95% CI: 0.977-0.998 for logistic EuroSCORE and 0.990 95% CI: 0.975-0.997 for EuroSCORE II. In high risk patients (patients with standard EuroSCORE > 6) AUC was found 0.870 95% CI 0.707-0.961 for standard EuroSCORE, 0.857 95% CI 0.691-0.954 for logistic EuroSCORE, and 0.961 95% CI: 0.829-0.998 for EuroSCORE II. Conclusion: Standard, Logisti c EuroSCORE and EuroSCORE II are similarly successful in mortality prediction. EuroSCORE II may be better in high-risk patients which needs confirmation in large prospective studies. [ABSTRACT FROM AUTHOR]

Copyright of Educational Administration: Theory & Practice is the property of Educational Administration: Theory & Practice 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.)

Prediction and evaluation which are metacognitive skills called calibration. Test calibration is a measure of difference of current test score and predicted score. With this respect, the purpose of this study is to investigate primary school fifth grade students' problem solving achievement and their test calibration, and the relationship between problem solving skills and calibration skills including prediction and evaluation. One hundered twenty five students enrolling to fifth grade are participated in the study. Students' problem solving skills and their test calibration status were measured by administering the Problem Solving Achievement Test (PC AT) which is developed by the authors. The results of the study revealed that there is no significant releationship between problem solving achievement and test calibration (r = 0.091,p > .05); additionally results also showed that there is a significant negative relationship (r = - 0.816, p < .01) between problem solving achievement and confirmed test calibration. [ABSTRACT FROM AUTHOR]

Copyright of Electronic Turkish Studies is the property of Electronic Turkish Studies 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.)

In this paper, Istanbul Stock Exchange (ISE) National 100 Index retrospective predictability was tested by using the Adaptive Neuro Fuzzy Inference System (ANFIS). In addition, contribution of the inputs to the model for index forecasting was evaluated on the basis of prediction performance. The most important factor to successful stock market prediction is achieving best results using minimum required input data and the least complex stock market model. In this context, we aim to prove consistent prediction of ISE 100 index without having to use a lot of input variables with ANFIS. For this purpose, about four and a half year period was chosen as the analysis period; two input variables (the exchange rate and repurchasing interest rate) and three input variables (the exchange rate, repurchasing interest rate and trading volume) were established in two different models. Consistent prediction results have been obtained with high predictive factor with both of the models. As a result, that is concluded that the ISE 100 Index has short-term predictability using only two input variables, without the need for a complex model with ANFIS. [ABSTRACT FROM AUTHOR]

This study was conducted to determine the socio-demographic factors affecting 12th grade students' depression levels. The sample of the research consists of 146 (46%female, 54% male), 12thgrade students from ismetpas.a high school in şehit Kamil, Gaziantep. The data were obtained from 146 students and were collected through "Beck Depression Inventory" and a questionnaire. The data were analyzed by stepwise linear regression analysis. The results of the study showed that the significant predictors of students' depression levels were the socio-economic level according to their perception, sleep problems, perception of their health status, number of brothers/sisters and students' gender. There is need for more research to determine psychological and psychiatric problems among high school students. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Physical Medicine & Rehabilitation Sciences is the property of Turkiye Klinikleri 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.)

Objectives: Violence is an important social problem, which frequently coexist with offense. In this study, sociodemografic, historical and clinical features of the male offenders, and the relationship of these features with violent behaviour and feelings of compunction has been investigated. Method: One hundred and sixteen male offenders were enrolled in the study. Subjects were separated into two groups as violente offenders (42) non-violente offenders (74). Participants' history, sociodemographic data and clinical futures are assessed with a semi-structured interview. Previous violence, age of first violent behavior, relationship instability, employment problems, substance use, self destruction and compunction were asked. Temperemant and Character Inventory, Self Directedness Subscale, general symptom level, Plutchik and van Praag's impulsivity test were applied. Results: Employment problems and young age at first violent incident were more common in violent offenders. In addition, although major mental illnesses in this group were higher, it didn't show statistical significance. Major mental illness in the history and employment problems were predicted violent behavior. None of the variables was predicted compunction. Conclusion: The history of the employment problems, the young age at first violent and the psychiatric disorders are seen to be the risk factor for the violent behavior in the offenders. To create data about the understanding of our population provides a right basic which is necessary for community and psychiatric rehabilitation interventions dealing with prevention of violent and offense. [ABSTRACT FROM AUTHOR]

In the last quarter of previous century, economic, social and technological development caused a change in employment structure and an increase in the share of service sector. Demand for modern office space was driven by faster growth in finance, insurance, real estate (FIRE) subsectors. Existing office inventory could not meet modern space requirement, hence new metropolitan areas has started to develop. Office demand has an important role in metropolitan urban development and emergence of new subcentres. Therefore, office rent prediction is a crucial issue in decision making of new office investments and planning strategies. Office rent prediction models have been the major concern of academic research since 1980s. Hedonic office rent prediction models which are the most common method based on multiple regression are well established in the literature. A wide range of variables, categorised as econometric, architectural, spatial and tenure rights, are used in these models for various cities. It is difficult to incorporate large number of variables into a simple mathematical model. As a result, the need has arisen to reduce or group the excessive number of variables to achieve simplier mathematical expressions with greater explanatory power. In the light of previous studies, some difficulties can arise in gathering data and applying the hedonic theory. The major difficulty lies within the hedonic regression models is the multicollinearity problem that may exist between a large number of independent variables. The common solution is exclusion of some variables depending on significance level or using "stepwise" or "backward" procedure in regression models. Another problem with development of rent prediction model is selection of dependent variable. In the literature, asking rent is preferred in some models while contract rent or effective rent are used in others, as the dependent variable. It is reported that the use of contract or effective rent instead of asking rent, can provide more accurate predictions. However, it is difficult to obtain sufficient contract data from real estate firms, due to confidentiality and competition. The aim of this study is to examine the problem with construction of an office rent prediction model and development of a viable prediction model for Istanbul. For this purpose, a proposed regression model is constructed with using asking rent, gross and net contract rents, as dependent variable for 1996 2006 period. First, full model is developed with thirtyfour variables, then a reduced model is constructed by eliminating some variables using "backward" procedure in standard regression model. Finally, the performances of prediction models are compared according to Rsquared and tstatistics. In addition, Akaike Information Criteria and Schwarz Bayesian Criteria are also employed to test the accuracy of proposed models. Based on the findings of most accurate model, the significant variables are defined for Istanbul office market. The similarities and differences from literature findings are discussed. The results confirmed that use of contract rents instead of asking rents can provide robust predictions with higher explanatory power. Besides, the reduce models offer better solution for multicollinearity problem. Building and locational variables are found the most significant office rent determinants for Istanbul metropolitan areas. The findings point out importance of accessibility and locational prestige in site selection for new office investments. Especially, distance from the CBD and important transportation nodes (Bosphorus Bridge, highway connection) have an important role to explain in rental change, in line with global literature. Results reveal that secondary centres gain importance. This strengthens the assertion that the tendency for office investment in Istanbul is away from the traditional centre (CBD) and closer to secondary centres. However, rental values are still higher in the CBD. For further studies, it is aimed to increase number of data to obtain more accurate prediction model for Istanbul. It is expected that office rent prediction models will be helpful to determine new office areas. [ABSTRACT FROM AUTHOR]

Objective: In patients with tetralogy of Fallot, infants less than 12 months old and children between one and four years old were compared after total repair surgery for determination of outcome of surgery, risk factors influencing mortality and morbidity. Methods: Fifty- two patients with tetralogy of Fallot between five months and four years of ages were included into the study. The patients were divided into two groups depending on their ages; Group 1; one year old and younger (n=21) whereas, group 2; one and four years old (n=31). Statistical analysis was performed using Mann-Whitney U, Chi-square or where appropriate Fisher's exact tests and logistic regression analysis was applied for determination of predictors of mortality. Results: The parameters that were different between two groups include; age (Group 1; 10.00±1.67 months and Group 2; 2.39±0.77 years, p<0.001), weight (Group 1; 9.74±2.23 kg, Group 2; 11.97±1.78 kg, p<0.001), McGoon ratio (Group 1; 1.94±0.29, Group 2; 2.19±0.27, p=0.001). Mortality is found in 3 patients in group 1 (14.2%) whereas, in five patients in group 2 (16%) and the difference was not statistically significant. In group 2 in only one patient (0.03%) had complete atrioventricular block and required permanent pacemaker implantation. When patients were compared according to groups with and without mortality, the significant differences were found in following variables: peritoneal dialysis (p=0.001), pleural effusion (p=0.02), right ventricular pressure (p=0.001) and right ventricle/aorta pressures ratio (p=0.001). However, none of these risk factors had significant value in prediction of mortality. Conclusion: Depending on these results, in patients under one year of age with symptomatic tetralogy of Fallot, if there are no other pathologies that have potential to increase risk of mortality, the complete repair surgery can be performed with same amount of risks and similar morbidity and mortality ratios. [ABSTRACT FROM AUTHOR]

First and the most important phase of designing high cost coastal structures is to forecast the significant wave height (Hs). In this study, an alternative approach to the deterministic and stochastic methods found in literature is proposed. Neuro-fuzzy is a method in which advantages of Artificial Neural Networks (ANN) and Fuzzy Logic (FL) are combined. In the Neuro-Fuzzy models developed in this study, daily significant wave height can be estimated using daily average Hs and significant wave period (Ts) data. Hs and Ts data recorded at different time intervals were used in the proposed models. In this study, Hs and Ts measured in Filyos region of the Black Sea was estimated by the Adaptive Network-based Fuzzy Inference System (ANFIS). Predicted results from proposed models were compared with the measured wave data and it is found that high correlation values are obtained. It is thus concluded that the proposed models can efficiently be used to estimate missing wave data. [ABSTRACT FROM AUTHOR]

The purpose of this study is to develop two different prediction models based on regression analysis and fuzzy logic for prediction of compressive strength of concrete samples which were subjected to vibration for different amount of time. For this study, a total of 50 cubic samples in C 16 class were prepared. The sample sizes were 15x15x15 cm. After applying 10 second initial vibration to all samples, they were grouped in to tens. Later, 30, 60, 90 and 120 second vibrations were applied to four different groups of samples. The samples in the last group were left un-vibrated. On the 28th days, unit weights and compressive strengths of the samples were measured. Using these measurements, two prediction models were developed. With these newly developed regression analysis and fuzzy logic models, compressive strength values were predicted in relation to unit weight and vibration time. The results obtained from the prediction models were compared to the results obtained from the tests and prediction performances of the new models were evaluated comparatively. The results showed that compressive strength of concrete can be predicted using fuzzy logic and multiple linear regression models. Moreover, it has been observed that fuzz logic model can predict concrete compressive strength with less amount of error compared to the multiple-linear regression model. [ABSTRACT FROM AUTHOR]

Theory and models used for effective measures against traffic accidents, are main sources. Data must be in high quality and sufficient quantity in intersection safety reseaches and it is possible with making depth research. Until now, in no uncontrolled intersection study has been examined together with all effecting factors. Therefore, when creating accident prediction model sor performing intersection safety analysis, there is always a restriction for the effecting parameters. In this study, accident affecting factors except the human factor, road and environmental factors, especially the geometric features of intersections have been examined for urban uncontrolled intersections. The study aims to remendy deficiencies related to urban uncontrolled intersection safety analysis and it is thought the results could shed light on the values of intersection safety researches. [ABSTRACT FROM AUTHOR]

In this study, accident prediction models for Turkey were developed by using the historical data, between 1986 and 2005, including population (P), the number of vehicles (N), accidents (C), injuries (I), and fatalities (D). In the models development, the structural form of Smeed and Andreassen Models were employed. However, Smeed Model was developed in different point of view so that this model was called as Smeed Similarity Model. The number of accident, injury and fatalities in Turkey which is on the way of a full member of European Union, were tried to estimate under different three scenarios until the year of 2010. Both models were compared in terms of percent difference (PD), mean absolute percent errors (MAPE), and root mean square errors (RMSE). Despite that Andreassen model for the years between 1986 and 2005 had errors lower than Smeed Similarity model, for future estimates latter gave more plausible results with scenarios. [ABSTRACT FROM AUTHOR]

In this study, an adaptive Neuro-Fuzzy inference system (ANFIS) is used to forecast monthly water use from several socio-economic and climatic factors, which affect water use. Totally 108 data sets are collected and data sets are divided into two subsets, training and testing. The models consisting of the combination of the independent variables are constructed and the best fit input structure is investigated. The performance of ANFIS models in training and testing sets are compared with the observations and the best fit model forecasting model is identified. For this purpose, some criteria of performance evaluation such as, Root Mean Square Error (RMSE), efficiency (E) and correlation coefficient (CORR) are calculated for all models. Then, the best fit models are also trained and tested by Multiple Regression (MR). The results of models are compared to get more reliable comparison. The results indicated that ANFIS can be applied successfully for monthly water demand forecasting [ABSTRACT FROM AUTHOR]

Inflation is one of the most important indicators that economic agents follow closely in arranging and planning their activities. In most cases, the expected inflation rates, in other words, the inflation forecasts are as important for the economic agents as the realized inflation rates. In this study, the inflation series starting from 1998-January to 2007-March are fitted using ARIMA models. In addition to the ARIMA models, in the study, the alternate univariate time series models are applied to the inflation series. However, on the basis of performance criteria these models fall below the base model. According to these performance criteria, the SARIMA(0,1,1)(0,0,1) model is found to be as the best model. Finally this model is used to get inflation forecasts in sample and out of sample. The main forecasting implication of the model is although there will be a short term rise in inflation rates, this increase will not change the inflation's long term decrease tendency. [ABSTRACT FROM AUTHOR]