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This paper addresses an asset management problem in the context of the wind energy industry. Asset management decisions (including operation and maintenance, retrofitting and purchasing) for assets reached their end-of-life are explicitly examined in a linear programming model over a planning horizon. Unfortunately, almost all important generic classes of integer programming problems are NP-hard and many of these problems are large-size. Therefore, in order to solve practical integer programming problems we may need to use problem specific algorithms which can exploit some special structures of the problem at hand. We propose a solution approach based on a lagrangian relaxation and the subgradient method for a large size parallel asset management problem, which originally solved by using mixed integer linear programming (MILP). The decomposition approach considers the relaxation of different sets of constraints, including the budget and energy constraints. The computational results show that the incorporation of langrangian relaxation significantly improves the duality gap and solution time of a case study from wind turbine (WT) sector

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Bu çalışmada gerçek zamanlı DNA dizilemesi sağlayan, düşük maliyetli vetaşınabilir bir yeni nesil dizileme teknolojisi olan nanogözenek dizilemeteknolojisini kullanarak gerçek zamanlı ve düşük maliyetli patojen/etken tespitalgoritmaları sunulmaktadır. Çalışma kapsamında Oxford Nanopore MinION DNAdizileyicisi ile sekanslanan bakterileri gerçek zamanlı tanıyabilecek bilgi kuramıtemelli biyoinformatik teşhis algoritmaları geliştirilmiş ve performansları gerçekveri üzerinde test edilmiştir. Taksona özgü oligomer karakterizasyonunu sağlayanBağıl Bolluk Endeksleri (ing: Relative Abundance Index-RAI) ve nükleotid dizileriiçindeki korelasyonları ortaya seren Ortalama Karşılıklı Bilgi (OKB) yöntemi ile DNAkarakterizasyonu yapılarak patojen tanıma algoritmaları geliştirilmiştir. Tasarlanansimülasyonlar ile ortalama teşhis koyma süreleri ve doğrulukları hakkındakiistatistikler elde edilerek bu yönde oluşturulacak sistemlerin rutin kullanım içinfizibilitesi ortaya konmuştur. Önerilen OKB profili ve RAI tabanlı algoritmaların hızlıpatojen tanıma konusunda yeterli doğruluk seviyesinde ve kısa sürede tanımayapabilecek hızda olduğu ve mevcut programlarla rekabet edebilen performanstaolduğu nanogözenek dizilemesi yapılan patojen paneli üzerinde gösterilmiştir.Geliştirilen yöntem kısıtlı bir patojen paneli üzerinde etkinlik göstermektedir; busebeple geniş çaplı kullanım için daha ileri çalışmaların yürütülmesi gerekmektedir.

• ÖZ

In this study, we aimed to determine the toxic effect of increasing doses ofBendiocarb, an insecticide from the carbamate group, on human blood cells in vitro viaby Comet test, determination of total antioxidant capacity, antioxidant enzyme activityand malondialdehyde (MDA) level. Leukocyte isolation was performed from bloodsamples taken from six healthy male individuals who did not smoke and use alcohol andwere not exposed to any chemicals in the working environment, and then theirleukocytes exposed to increasing doses of Bendiocarb (20, 40, 80, 160 µg / mL). Comettest, iron reduction antioxidant capacity (FRAP), 2-2'azinobis 3-ethylbenzothiazoline-6-sulfonic acid (ABTS/TEAC) analysis with superoxide dismutase (SOD), catalase (CAT),glutathione peroxidase (GPx) and MDA were evaluated. At the end of the study, it wasdetermined that antioxidant enzyme levels decreased significantly due to dose increase,whereas MDA levels increased significantly in all doses (p<0.05). According to FRAP andTEAC methods, the antioxidant capacity of blood cells decreased significantly inincreasing doses of Bendiocarb compared to the control group (p<0.05). In the Comettest, DNA tail percentage, length and moment were increased significantly in increasingdoses of Bendiocarb and DNA damage was detected (p<0.05). This study is a preliminarystudy on this subject, and it has been shown in our study that the uncontrolled use ofBendiocarb pesticide in nature will have harmful effects on the environment and livingthings. We believe that the research will contribute to the literature and shed light onfuture comprehensive studies.

• ÖZ

In this study, stone samples taken from volcanic tuffs at seven different locations in the Cappadocia region were examined. In order to obtain the powder diffraction pattern of the samples, they were pulverized by grinding in agate mortar. Diffraction patterns were taken on a Bruker AXS D8 Advance Model diffractometer. Qualitative analysis of the pattern data was performed using the XRD Evolution program. The most common compounds as a result of the analysis: it has been identified as Silicon Oxide [SiO2], Magnesium Silicate Hydroxide [Mg3(Si2O5)(OH)4], Sodium Calcium Aluminum Silicate [(Na0.45Ca0.55)(Al1.55Si2.45O8)], Sodium Calcium Magnesium Aluminum Iron Titanium Aluminum Silicon Oxide Hydroxide Hydrate [(Mg2.46Al0.3Fe0.22Ti0.021)2(Mg0.38Ca0.03Na0.02)2((Si2.83 Al1.17)O10)2 (OH)4(H2O)3.4], Potassium Aluminum Silicate Hydroxide [(K, Ca, Na)(Al, Mg, Fe)2(Si, Al)4O10(OH)2], Sodium Magnesium Aluminum Iron Silicon Oxide Hydroxide Hydrate [Na0.930Mg2.810Fe0.065Al1.185Si2.895O10(OH)2 (H2O)3], Calcium Magnesium Aluminum Silicate Hydroxide Hydrate [Ca0.2 (Al, Mg)2Si4O10(OH)2.xH2O].

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Gradient Boosting Decision Trees (GBDT) algorithms have been proven to be among the best algorithms in machine learning. XGBoost, the most popular GBDT algorithm, has won many competitions on websites like Kaggle. However, XGBoost is not the only GBDT algorithm with state-of-the-art performance. There are other GBDT algorithms that have more advantages than XGBoost and sometimes even more potent like LightGBM and CatBoost. This paper aims to compare the performance of CPU implementation of the top three gradient boosting algorithms. We start by explaining how the three algorithms work and the hyperparameters similarities between them. Then we use a variety of performance criteria to evaluate their performance. We divide the performance criteria into four: accuracy, speed, reliability, and ease of use. The performance of the three algorithms has been tested with five classification and regression problems. Our findings show that the LightGBM algorithm has the best performance of the three with a balanced combination of accuracy, speed, reliability, and ease of use, followed by XGBoost with the histogram method, and CatBoost came last with slow and inconsistent performance.