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Bu çalışmada çok değişkenli uyarlanabilir regresyon eğrileri (MARS) ve TreeNet gradyan arttırma makinesi (TreeNet) isimli regresyon tabanlı yöntemler kullanılarak çözünmüş oksijen (ÇO) konsantrasyonu modellemesi amaçlanmıştır. Modelleme çalışmasında kentsel atıksuları bünyesine alarak yer yer kirlenen Harşit Çayı (Gümüşhane) üzerinde belirlenmiş altı su kalitesi gözlem istasyonunda, 15 gün aralıklarla ve 24 kez yerinde gerçekleştirilen ÇO konsantrasyonu (mg/L), sıcaklık (°C), pH ve elektriksel iletkenlik (mS/cm) ölçümleri yanı sıra akarsudan alınan su örneklerinde laboratuvarda gerçekleştirilen sertlik (°dH) tayinleri neticesinde elde edilen veriler kullanılmıştır. Elde edilen veri setinin % 80’i kurulan modellerin eğitilmesinde geriye kalan % 20’si ise söz konusu modellerin test edilmesinde kullanılmıştır. Kurulan modellerin eğitim ve test veri seti performanslarını değerlendirmek amacıyla ortalama karesel hatanın karekökü (OKHK), ortalama mutlak hata (OMH), ortalama rölatif hata (ORH) ve determinasyon katsayısı (R2) performans istatistikleri kullanılmıştır. En düşük OKHK, OMH ve ORH ile en yüksek R2 değerleri eğitim veri seti için sırasıyla 0,2247 mg/L, 0,0666 mg/L, % 0,66 ve 0,9995 olarak TreeNet yönteminden, test veri seti için ise 0,2911 mg/L, 0,2336 mg/L, % 2,27 ve 0,9992 olarak MARS yönteminden elde edilmiştir. Her iki veri seti için ortalamalar dikkate alındığında ise, MARS yönteminden elde edilen performans değerlerinin TreeNet yönteminden elde edilenlere kıyasla daha iyi olduğu sonucuna ulaşılmıştır.

International migration significantly affects economic, social, cultural, and political factors of the country. Owing to this situation, it can be said that the reasons of international migration should be analyzed in order to control this problem. The purpose of this study is to determine the influencing factors of international migration in Azerbaijan. In this scope, annual data of 11 explanatory variables for the period of 1995-2015 was analyzed via Multivariate Adaptive Regression Splines (MARS) method. According to the results of this analysis, it was identified that people prefer to move other countries in case of high unemployment rates. In addition, the results of the study show that population growth and high mortality rate increases the migration level. While considering these results, it was recommended that Azerbaijan should focus on these aspects to control international migration problem. [ABSTRACT FROM AUTHOR]

In this study, a platform‐independent energy estimation methodology is proposed to estimate the energy consumption of reconfigurable video coding (RVC)‐CAL video codec specifications. This methodology is based on the performance monitoring counters (PMCs) of embedded platforms and demonstrates its portability, simplicity and accuracy for on‐line estimation. It has two off‐line procedure stages: the former, which automatically identifies the most appropriate PMCs with no specific detailed knowledge of the employed platform, and the latter, which trains the model using either a linear regression or a multivariable adaptive regression splines (MARS) method. Experimenting on an RVC‐CAL decoder, the proposed PMC‐driven model can achieve an average estimation error <10%. In addition, the maximal model computation overhead is 4.04%. The results show that the training video sequence has significant influence on the model accuracy. An experimental metric is introduced to achieve more stable accurate models based on a combination of training sequences. Furthermore, a comparison demonstrates better predictive ability of MARS techniques in scenarios with multi‐core platforms. Finally, the experimental results show a good potential of energy efficiency improvement when the estimation model is combined into the RVC framework. In two different scenarios, the battery lifetime is increased 5.16% and 20.9%, respectively. [ABSTRACT FROM AUTHOR]

The pre-treatment stage of wastewater treatment plants (WWTP), where most of the larger waste, including sand and fat, is removed, is of great importance for the performance and durability of these plants. This work develops a model that predicts the sand content that reaches the plant. For this purpose, data were collected from one operation year of the Villapérez Wastewater Treatment Plant located in the northeast of the city of Oviedo (Asturias, Spain) and the MARS (Multivariate Adaptive Regression Splines) method was used for modelling. The accuracy of the MARS model developed using the determination coefficient is R2 = 0.74 for training data and R2 = 0.70 in validation data. These results indicate that it is possible to predict trend changes in sand production as a function of input variables changes such as flow rate, pH, ammonia, etc. This will prevent the plant from possible operational problems, as actions could be taken, such as starting up more pre-treatment lines or emptying the containers, so that the arrival of the sand can be assumed without any problem. In this way, the possibility of letting sand contents over the established limits pass that could affect the following processes of the treatment plant is avoided. [ABSTRACT FROM AUTHOR]