An investigation on determining optimum wall ratio–cost relationship of shear walled reinforced concrete buildings

Reinforced concrete walls are very efficient structural elements in terms of carrying the lateral loads that are expected to affect the structures during the service of the buildings. These elements, which are not used for economic reasons in buildings designed in areas with low seismic hazard, can actually provide a significant increase in performance with a very small increase in construction cost. In this study, a total of 9 building models have been created and the relationship between optimum reinforced concrete wall ratio and cost on these buildings has been investigated. The design and analysis of the models were carried out according to the criteria specified in TSC 2018. Three different structural systems specified in TSC 2018 were used in the designed models. These structural systems used; RC frame structures, RC wall-frame structures and RC wall structures. These structures were analyzed by Response Spectrum Method which is linear analysis method and base shear forces were obtained. Then, push-over analysis, which is a nonlinear analysis method, was applied to obtain the base shear forces that the structure can actually carry. After the analysis, the quantities of materials to be used for the construction of the structural systems of the models were calculated and current manufacturing prices and rough costs were calculated. In order to compare the obtained costs with the structural performances, nonlinear shear forces and linear shear forces ratios were calculated and the over strength factors were calculated for each model. In the light of the data obtained from the studies in the literature, when the over strength factors and cost values are examined together, it is concluded that the optimum design for the conditions specified in TSC 2018 will be provided with the RC wall ratio between 0.001 - 0.0016. It is concluded that lateral load carrying capacity of construction increases up to 650% by increasing the construction cost by 17% for the designed models

SÜRÜ ZEKASI YÖNTEMLERİYLE AŞIRI ÖĞRENME MAKİNESİ’NİN ÖĞRENME PARAMETRELERİ OPTİMİZASYONU

SÜRÜ ZEKASI YÖNTEMLERİYLE AŞIRI ÖĞRENME MAKİNESİ’NİN ÖĞRENME PARAMETRELERİ OPTİMİZASYONUÖzetSinir ağları algoritmalarından olan Aşırı Öğrenme Makinesi (AÖM)’de giriş ağırlığı ve gizli eşik değeri parametrelerinin rastgele seçilmekte ve çıktı katman ağırlıkları analitik olarak hesaplanmaktadır. Bundan dolayı ağın öğrenme işlemi hızlı bir şekilde gerçekleşmektedir. Ayrıca AÖM’nin gradyan temelli algoritmalara göre gizli katmanda ihtiyaç duyduğu nöron sayısı daha fazla olmaktadır. Bu nedenle giriş ağırlıkları ve gizli nöron eşik değerlerinin optimum değerlerinin bulunması AÖM'nin performansına etki etmektedir. Bu çalışmada bu optimum değerlerin belirlenmesinde sürü zekası algoritmalarından Parçacık Sürü Optimizasyonu (PSO) ve Rekabetçi Sürü İyileştirici (RSİ) kullanılmıştır. Optimum giriş ağırlıkları ve gizli eşik değerlerinin belirlenerek çıkış ağırlıkları Moore-Penrose genelleştirilmiş tersiyle analitik olarak hesaplanmıştır. AÖM, RSİ-AÖM ve PSO-AÖM modellerinin çok sınıflı tiroit veri setine uyarlanarak öğrenme parametrelerinin optimizasyonu ile en iyi doğruluk oranları sırasıyla %94.74, %94.86, %95.42 olarak elde edilmiştir. Optimizasyon metotlarının AÖM modellerinin sınıflandırma performansını artırdığı görülmüştür.Anahtar Kelimeler: Aşırı Öğrenme Makinesi (AÖM), Metasezgisel, Parçacık Sürü Optimizasyonu (PSO), Rekabetçi Sürü İyileştirici (RSİ)OPTIMIZATION OF LEARNING PARAMETERS OF EXTREME LEARNING MACHINE WITH SWARM INTELLIGENCE METHODSAbstractIn the Extreme Learning Machine (ELM), which is one of the neural networks algorithms, the input weight and hidden bias value parameters are randomly selected and the output layer weights are calculated analytically. Therefore, the learning process of the network takes place quickly. In addition, the number of neurons needed by the hidden layer is higher than the gradient-based algorithms. Finding optimum values of entry weights and hidden neuron bias values affects the performance of the ELM. In this study, Particle Swarm Optimization (PSO) and Competitive Swarm Optimizer (CSO) were used to determine these optimum values. By determining the optimum input weights and hidden bias values, the output weights were analytically calculated by Moore-Penrose generalized inverse. By adapting the multi-class thyroid data set of ELM, CSO-ELM and PSO-ELM models, the best accuracy rates were obtained as 94.74%, 94.86%, 95.42% respectively. It has been seen that optimization methods increase the classification performance of the ELM models.Keywords: Extreme Learning Machine (ELM), Metaheuristic, Particle Swarm Optimization (PSO), Competitive Swarm Optimizer (CSO

An LED-Based Structured Illumination Microscope Using A Digital Micromirror Device And GPU Accelerated Image Reconstruction

When combined with computational approaches, fluorescence imaging becomes one of the most powerful tools in biomedical research. It is possible to achieve resolution figures beyond the diffraction limit, and improve the performance and flexibility of high-resolution imaging systems with techniques such as structured illumination microscopy (SIM) reconstruction. In this study, the hardware and software implementation of an LED-based superresolution imaging system using SIM employing GPU accelerated parallel image reconstruction is presented. The sample is illuminated with two-dimensional sinusoidal patterns with various orientations and lateral phase shifts generated using a digital micromirror device (DMD). SIM reconstruction is carried out in frequency space using parallel CUDA kernel functions. Furthermore, a general purpose toolbox for the parallel image reconstruction algorithm and an infrastructure that allows all users to perform parallel operations on images without developing any CUDA kernel code is presented. The developed image reconstruction algorithm was run separately on a CPU and a GPU. Two different SIM reconstruction algorithms have been developed for the CPU as mono-thread CPU algorithm and multi-thread OpenMP CPU algorithm. SIM reconstruction of 1024 × 1024 px images was achieved in 1.49 s using GPU computation, indicating an enhancement by *28 and *20 in computation time when compared with mono-thread CPU computation and multi-thread OpenMP CPU computation, respectively

Fabrication and Characterization of Large Numerical Aperture, High-Resolution Optical Fiber Bundles Based on High-Contrast Pairs of Soft Glasses for Fluorescence Imaging

Fabrication and characterization of flexible optical fiber bundles (FBs) with inhouse synthesized high-index and low-index thermally matched glasses are presented. The FBs composed of around 15000 single-core fibers with pixel sizes between 1.1 and 10 μm are fabricated using the stack-and-draw technique from sets of thermally matched zirconiumsilicate ZR3, borosilicate SK222, sodium-silicate K209, and F2 glasses. With high refractive index contrast pair of glasses ZR3/SK222 and K209/F2, FBs with numerical apertures (NAs) of 0.53 and 0.59 are obtained, respectively. Among the studied glass materials, ZR3, SK222, and K209 are in-house synthesized, while F2 is commercially acquired. Seven different FBs with varying pixel sizes and bundle diameters are characterized. Brightfield imaging of a micro-ruler and a Convallaria majalis sample and fluorescence imaging of a dye-stained paper tissue and a cirrhotic mice liver tissue are demonstrated using these FBs, demonstrating their good potential for microendoscopic imaging. Brightfield and fluorescence imaging performance of the studied FBs are compared. For both sets of glass compositions, good imaging performance is observed for FBs, with core diameter and core-to-core distance values larger than 1.6 μm and 2.3 μm, respectively. FBs fabricated with K209/F2 glass pairs revealed better performance in fluorescence imaging due to their higher NA of 0.59

Monitoring and conservation of Loggerhead Turtle's nests on Fethiye Beaches, Turkey

Fethiye beaches are the one of the most important sea turtle nesting beaches from Turkey. The reproductive biology of nesting loggerhead turtles was investigated there during three consecutive nesting seasons (2011-2013). A total of 253 nests were recorded in three seasons, and these nests included an average 80.4 eggs per nests. The incubation periods have decreased considerably over the last decade, potentially pointing to a climate change effect. The nest density was 7.2 nests/km in 2011, 10.7 nests/km in 2012 and 12.6 nests/km in 2013. Despite this 3-year increase, the overall number of nests over the last two decades shows a gradual decline, although the pattern differs from beach to beach. It was determined that the habitat loss and tourism activities are the main problems and are effect breeding activities of the species. In this respect, site-specific conservation actions were started at the Fethiye beaches. ©Biharean Biologist, 2016

An expanded molecular phylogeny of Plumbaginaceae, with emphasis on Limonium (sea lavenders): taxonomic implications and biogeographic considerations

Plumbaginaceae is characterized by a history of multiple taxonomic rearrangements and lacks a broad molecular phylogenetic framework. Limonium is the most speciesrich genus of the family with ca. 600 species and cosmopolitan distribution. Its center of diversity is the Mediterranean region, where ca. 70% of all Limonium species are endemic. In this study, we sample 201 Limonium species covering all described infrageneric entities and spanning its wide geographic range, along with 64 species of other Plumbaginaceae genera, representing 23 out of 29 genera of the family. Additionally, 20 species of the sister family Polygonaceae were used as outgroup. Sequences of three chloroplast (trnL‐F, matK, and rbcL) and one nuclear (ITS) loci were used to infer the molecular phylogeny employing maximum likelihood and Bayesian analyses. According to our results, within Plumbaginoideae, Plumbago forms a nonmonophyletic assemblage, with Plumbago europaea sister to Plumbagella, while the other Plumbago species form a clade sister to Dyerophytum. Within Limonioideae, Ikonnikovia is nested in Goniolimon, rejecting its former segregation as genus distinct from Goniolimon. Limonium is divided into two major clades: Limonium subg. Pteroclados s.l., including L. sect. Pteroclados and L. anthericoides, and L. subg. Limonium. The latter is divided into three well‐supported subclades: the monospecific L. sect. Limoniodendron sister to a clade comprising a mostly non‐Mediterranean subclade and a Mediterranean subclade. Our results set the foundation for taxonomic proposals on sections and subsections of Limonium, namely: (a) the newly described L. sect. Tenuiramosum, created to assign L. anthericoides at the sectional rank; (b) the more restricted circumscriptions of L. sect. Limonium (= L. sect. Limonium subsect. Genuinae) and L. sect. Sarcophyllum (for the Sudano‐Zambezian/Saharo‐Arabian clade); (c) the more expanded circumscription of L. sect. Nephrophyllum (including species of the L. bellidifolium complex); and (d) the new combinations for L. sect. Pruinosum and L. sect. Pteroclados subsect. Odontolepideae and subsect. Nobiles.European Union’s Seventh Framework Programme (FP7/2007–2013) under grant agreement no. 226,506, Grant/Award Number: SYNTHESYS project GB-TAF-5704; Georges‐und‐Antoine‐Claraz‐Schenkung; University of Zurich (Department of Systematic and Evolutionary Botany); Seventh Framework Programme, Grant/ Award Number: FP7, 2007 and 2013; University of Zuric

A Scanning Electron Microscope Survey Of Floral Micromorphology İn The Genus Alopecurus L. (tribe Phleeae Dum. Gramineae)

Bu projenin kapsamı gen merkezi Türkiye olan Alopecurus L(Gramineae) cinsinde bulunan taksonlar tabanında çiçek micromorfolojik verilerinin olası sistematik rolünün saptanmasıdır. Bu bağlamda elde edilecek verilere dayalı olarak bu cinsin doğal bir sınıflandırması ortaya konulacaktır. Bu araştırmada güncel taramalı Elektron Microskop(SEM) teknikleri kullanılacaktır