XV. yy. sonu ila XVI. yy. başında yaşayan Lâmi’î Çelebi, birçok telif eseri yanında Molla Câmî’den yaptığı çevirileri ile Câmî-i Rum olarak ünlenmiş usta Divan şairlerindendir. İyi bir eğitim gören Lâmi’î Çelebi’nin yaptığı çevirilerden dili bakımından Arapça ve Farsçaya hâkim olduğu, muhtevası bakımından ise tasavvufta engin bilgi ve tecrübeye sahip olduğu açıktır. Emir Buharî adlı tasavvuf bilginine intisabı neticesinde kendisine Bursa’da bir ilim dünyası oluşturan şair, devrin şartlarına göre çok kısa sayılabilecek sürede Herat, Semerkant, Buhara gibi uzak ilim beldelerinden eserler tercüme edebilmiştir. Salâmân ve Absâl adlı mesnevinin Yunan edebiyatı da dâhil çeşitli edebiyat sahalarında farklı isimlerle de olsa yer bulmuş olması, ilginç içeriği ve müelliflerin konuya müdahale edebileceği açık noktalar bırakan simgesel bir tarafının olmasından kaynaklanmıştır. Lâmi’î Çelebi gibi şairler de eserin bu özelliği sayesinde öğretilerini rahatça esere yerleştirebilmiştir. Bu yönü ile şairin ilmî bakışının eserde yoğun olacağını düşündüğümüzden çalışmamızda şairin telif-tercüme olarak kabul edilebilecek Salâmân ve Absâl adlı mesnevisi tasavvufi öğretileri bakımından ele alınarak şairin yaşadığı çağın yaptığı tercümelere etkisi de işlenmiştir.
Fish is a very diverse animal of all vertebrate animal groups, of which there are more than 33,000 species in the world. There are different types of fish in the four major geographical regions of Turkey. Classification of different fish species is very important for aquaculture, stock management of water bodies, monitoring of aquatic organisms and conservation of marine biology. In the classification of fish, both knowledge and great effort are required to determine the characteristics of fish. Traditionally, however, manual classification of extrinsic characteristics of different fish species has been a difficult and time-consuming process due to their close resemblance to each other. Recently, deep learning methods used in the light of developments in the field of computer vision have facilitated the training of fish image classification models and the recognition of various fish species. In this study, a new evolutionary neural network model classifying 8 different fish species using deep learning methods was proposed. The proposed model is compared with the ResNet-50, ResNet-101 and VGG16 models. The success accuracies obtained as a result of the comparison are respectively; 98.12% in the proposed model, 91.37% in the ResNet-50 model, 86.12% in the ResNet-101 model and 97.75% in the VGG16 model. It has been observed that the proposed model classifies sea fish, which is widely consumed in our country, with higher performance compared to other models.
Tasarım aşamasında betonarme döşemeleri kolon, kiriş gibi yük ve/veya moment taşıyan elemanlardan ayıran temel özellik kesme kuvvetinin beton tarafından karşılanarak enine donatıya ihtiyaç duyulmamasıdır. Buna ek olarak betonarme kiriş ve döşemelerinde eğilme davranışları kısmen birbirine benzerlik gösterse de korozyon oluşumu durumunda davranış farklıdır. Araştırma kapsamında monotonik eksenel eğilme yükü etkisi altında olacak şekilde laboratuvar şartlarında üretilecek bir (1) adedi referans numune olmak üzere toplam altı (6) adet iki doğrultulu ve korozyonlu betonarme taşıyıcı döşeme plaka numunelerinin deneysel çalışmalarının yürütülmesi planlanmıştır. Üretilecek betonarme taşıyıcı döşeme plaka numunelerin korozyona uğratılması amacıyla hızlandırılmış bir korozyon yöntemi kullanılacaktır. Araştırma kapsamında tasarlanan farklı düzeylerdeki korozyon sevilerinin gerçek değerleri, monotonik eğilme testlerinden sonra betonarme numuneler kırılarak ve tüm donatı çubukları çıkarılarak, yapılacak olan gravimetrik çalışmalar ile belirlenecektir. Betonarme taşıyıcı döşeme plaka numunelerinin her iki kısa ve uzun doğrultularındaki korozyon seviyeleri, Kırılma (Akma) Çizgileri Teorisine dayanan iki her iki doğrultuda belirlenecek olan moment kapasiteleri açısından dikkate alınacaktır. Böylece, her iki doğrultuda elde edilen gerçek korozyon seviyeleri ile moment taşıma kapasiteleri, test sonuçlarının 12 (on iki) adet korozyonlu betonarme döşeme plakaları için tartışılabilmesi sağlanacaktır. Moment taşıma kapasitesi için araştırma kapsamında geliştirilecek olan bir yeni model ile literatürde bulunan daha önceki çalışmalara ait verilerin doğrulanması yapılabilecektir.
Malaria is a disease that causes a parasite called plasmodium to be transmitted to humans as a result of the bite of female anopheles’ mosquitoes. Malaria is detected by examining the blood sample taken from the patient as a result of a microbiological examination under a microscope by specialist physicians. Although microscopy is widely used, its efficiency is low because it is time-consuming and depends on the interpretation of the specialist physician. In recent years, deep learning methods used in the field of computer vision increase the efficiency of specialist physicians by making a significant contribution to the decision-making process in solving real-life problems. In this study, ResNet architectures were preferred to quickly classify the malaria parasite using deep learning methods. For the training and testing of ResNet architectures, a dataset consisting of a total of 27558 red blood cell images containing 13779 parasitized and 13779 uninfected were used. Using this dataset, ResNet architectures were compared. As a result of the comparison, the best success accuracy (94.09%) was obtained with the ResNet-50 v2 model.
Reasons such as rapid population growth, urbanization, unconscious water use, environmental pollution, and changes in climate conditions increase water consumption, and water is consumed before completing its cycle in nature. This situation has directed the water producers to search for new resources in the face of increasing water demand and decreasing resources, but due to the high cost of the resource search, the water producers have turned to the understanding of reducing the high amounts of lost water and using water resources in a more planned and efficient manner. Minimizing water losses in drinking water distribution networks is among these objectives.
 In this study, drinking water data between January 2014 and January 2020 in Erzincan was examined, and the SCADA (Supervising Control and Data Acquisition) system placed in the drinking water distribution network in March 2018 was evaluated by considering the pre and post-drinking water data in the system. First of all, the terms between January 2014 and March 2018 which means before the installation of the SCADA system were examined, the data of the amount of water produced and the data of water consumed by the subscribers was collected from the Municipal Waterworks Unit, these data were transferred to the Water Balance Table, the results was analyzed and the actual water loss rates in the system were estimated. As a result of this estimation, before the SCADA system was established, the total physical and administrative water loss rate was seen as 64%, while the physical water loss rate was 28%. After the establishment of the SCADA automation system after March 2018, the date of the amount of water produced received from the SCADA system and the amount of water consumed was transferred to the Water Balance Table and the total physical and administrative loss was seen as 37% while the physical water loss rate was 14%. According to these results, it was observed that the water loss rate approached the minimum level within a short period with the SCADA automation system.
Structural blast design has become a necessary part of the designwith increasing terrorist attacks. Terrorist attacks are not the one to make the structures important against blast loading where other explosions such as high gas explosions also take an important place in structural safety.Themain objective of this studywas to verify the structural performance levels under the impact of different blast loading scenarios. The blast loads were represented by using triangular pulse for single degree of freedomsystem.The effect of blast load on both corroded and uncorroded reinforced concrete buildings was examined for different explosion distances. Modified plastic hinge properties were used to ensure the effects of corrosion. The results indicated that explosion distance and concrete strength were key parameters to define the performance of the structures against blast loading.
Recently, coronavirus disease (Covid-19) has become a serious public health threat, spreading worldwide in a very short time and threatening the lives of millions. Furthermore, many being infected with coronavirus have the potential to transmit the disease without showing any symptoms. Covid-19 causes upper respiratory and lung infections in many patients. With the increasing number of cases and mutations, medical resources are being drained day by day due to the rapid transmission of the disease, and the health systems of many countries are negatively affected. For this reason, it is very important to use available resources appropriately and timely for the detection and treatment of the disease. In this study, VGG16 and ResNet50 deep learning models were used to quickly evaluate x-ray images and to make the pre-diagnosis of Covid-19, and an alternative model was proposed. The proposed model was developed using the convolutional neural network deep learning architecture. VGG16, ResNet50 and the proposed model were trained and tested using a total of 12,739 x-ray images belonging to 6,157 patients (9,121 images with Covid-19 findings and 3,618 with normal findings reported by specialist physicians). As a result of the training of the models, success accuracy of 99.92% in the VGG16 model, 99.65% in the ResNet50 model and 99.76% in the proposed model were obtained.
An experimental study was performed to investigate the effects of polypropylene fibers on uncorroded and corroded reinforced concrete beams. Three different volume fractions of polypropylene fibers having 0, 0.5, and 1.5%, were tested at four corrosion levels of 0% and approximately 5, 7, and 9%. A full scale of an accelerated corrosion pool was used for the accelerated corrosion process. Reinforced concrete beams were used for an under monotonic bending test. The contribution of the actual corrosion levels of transverse and longitudinal reinforcement bars to the total corrosion levels were obtained from reinforcement bars fully extracted from concrete. Flexural strength, bond-slip, and moment-curvature relationships were examined for uncorroded and corroded reinforced concrete beams. A new model was developed to predict the flexural strength of corroded reinforced concrete beams. The proposed model for predicting the residual flexural strength of corroded beams was compared with test data published in previous studies. Furthermore, a novel model is presented for improved predictions between the actual and theoretically estimated corrosion mass losses, based on Faraday’s law, with the aid of fully extracted reinforcement bars. The model used to predict the flexural strength of corroded reinforced concrete beams with large sizes demonstrated good agreement with current and previously published literature data. In the case of corroded beams comprising differing amounts of polypropylene fibers, the performance of the corroded beams was limited by a fiber volume fraction of 1.5% at low corrosion levels.
This study develops empirical models for the prediction of the bond strength of uncorroded and corroded reinforcement bars. The effects of hooked reinforcement on the bar’s development length when covered fully and partially are examined. An accelerated corrosion method is used to corrode the reinforcement bars embedded in concrete specimens. Pull-out tests are performed to investigate the ultimate bond strength of the concrete specimens. The effects of two different geometries of reinforcement bars are discussed by considering two different concrete strength levels and concrete cover depths. It is found that partly covered hooked reinforcement bars increase the radial stress on the concrete surface and reduce the bond strength. Increases in the bond strength due to the increased roughness of the steel bar caused by the confined corrosion products are less for hooked bars. The results reveal that the developed models show good relationships with the experimentally computed test results.
Bu calismada, bilisim teknolojileri (BT) calisanlarinin yenilik yapma davranislarinin bazi demografik ozelliklere gore farklilik gosterip gostermediginin tespit edilmesi amaclanmistir. Bu amacla Istanbul’daki BT calisanlarina bazi demografik sorularin yaninda Yenilikci Davranis Anketi uygulanmistir. Verilerin analizinde; guvenilirlik analizi, aciklayici faktor analizi (AFA), dogrulayici faktor analizi (DFA) ve tek yonlu varyans analizi teknikleri kullanilmistir. AFA ile incelenen olcegin gecerliligi DFA ile de dogrulanmistir. Olcegin BT calisanlarinin yenilikci davranisini olcmek icin kullanilabilecegine karar verilmistir. Ayrica olcegin oldukca guvenilir oldugu gorulmustur (Cronbach Alpha katsayisi = 0.932). Arastirma sonucunda, BT calisanlarinin yenilikci davranisinin cinsiyet (p = 0.030) ve uzmanlik alanina (p = 0.008) gore anlamli farklilik gosterdigi, yas (p = 0.102), medeni durum (p = 0.633), egitim durumu (p = 0.840) ve mesleki deneyime (p = 0.536) gore farklilik gostermedigi tespit edilmistir.
Güneş kollektörleri kullanım suyu ısıtma uygulamalarında sıklıkla kullanılmaktadır. Tasarım aşamasında güneş kollektörlerinin en uygun eğim açısında konumlandırılması güneş enerjisinden verimli faydalanılması için önemlidir. Bu çalışmada, Erzincan ilinde konumlandırılan bir güneş kollektörünün eğim açısının aylık/günlük ortalama ışınım değerlerine, su çıkış sıcaklığına, enerji ve ekserji verimlerine etkisi teorik olarak incelenmiştir. Optimum güneş kollektörü eğim açısının en yüksek ve en düşük değerlerinin Aralık ve Haziran aylarında sırasıyla 63° ve 0° olduğu görülmüştür. Erzincan için önerilen (27.14°) ve hesaplanan eğim açıları için günlük ortalama saatlik ışınım değerleri karşılaştırılmış olup en büyük değişimin Ocak ayında %26 olduğu görülmüştür. Buna ek olarak, güneş kollektörünün enerji ve ekserji verimleri hesaplanmış olup optimum eğim açıları için en yüksek verimleri sırasıyla %74.2 ve %9.7 olarak belirlenmiştir.
Water is an indispensable natural resource for living life. Therefore, protection and control of water resources are of great importance. Since river flow estimation and modeling are very important in cases such as the management of water resources, irrigation, it is included in the literature as an issue that needs constant research and development. A large number of techniques are being used for estimation and modeling; thus, the estimation results are gradually improving with the development of the studies carried out, the comparison of techniques, and the determination and removal of the shortcomings. In this study, Random Forest and K-Nearest Neighbors nonlinear regression models, which are two of the machine learning methods, were used to evaluating the estimation results, to find the better estimation method, and to determine the advantages and disadvantages of these methods. In addition, Random Search and Grid Search methods were used to make the hyperparameter selection and comparison for the Random Forest model. In this study, in which daily flow data of 1981-2011 of the two stations in the Euphrates were used, and, when compared to other models, it was observed that better results were obtained when Random Search was applied to determine the hyperparameters of the Random Forest model.
