TR-Dizin İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/396
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Publication Nikel Lateritlerin Hidrometalurjik İşlemi - Nikel ve Kobalt Ayırım ve Saflaştırması İçin Solvent Ekstraksiyon Kullanımı ve Nikel Kobalt Projelerine Kısa Bir Bakış(2019) Kaya, Muammer; Kurşunoglu, SaitBu çalışmada, lateritik nikel cevherlerinin hidrometalurjik işlemlerinde nikel ve kobalt ayırma ve saflaştırılmasında kullanılan solvent ekstraksiyon (SX) yöntemi için kısa bir değerlendirme yapılmıştır. Bu çalışma iki bölümden oluşmaktadır. Sülfat liç çözeltilerinden nikel ve kobalt solvent ekstraksiyonu ilk olarak tanımlanmıştır. Solvent ekstraksiyon tekniğinin bulunduğu lateritik nikel cevherlerinin işletimi için geliştirilen hidrometalurji tesisler ikinci olarak tartışılmıştır. En önemli ekstraktantlar ilk bölümde kısaca verilmiştir. Laterit liç çözeltisinde bulunan safsızlıklardan nikel ve kobalt ayırma ve saflaştırma işlemi ya tekli ekstraktant sistemi olarak adlandırılan direkt solvent ekstraksiyon (DSX) ya da iki veya daha fazla ekstraktant karışımından oluşan sinerjistik solvent ekstraksiyon (SSX) yöntemleriyle gerçekleştirilebileceği görülmüştür. Karışık sülfür çökeleği (MSP) ve karışık hidroksit çökeleği (MHP) işlemlerinden bahsedilmiştir. Bu makale aynı zamanda her bir ekstraksiyon sisteminin avantaj ve dezavantajlarını ele almaktadır. İlk yatırım maliyeti, işlem maliyeti ve minerolojik yapının lateritik nikel cevheri için uygun bir hidrometalurjik yöntem seçimini etkileyebilen en önemli faktörler olduğu görülmüştür.Article Solutions to Nonlinear Second-Order Three-Point Boundary Value Problems of Dynamic Equations on Time Scales(Tubitak Scientific & Technological Research Council Turkey, 2019-05-29) Dogan, AbdulkadirIn this paper, we consider existence criteria of three positive solutions of three-point boundary value problems for p-Laplacian dynamic equations on time scales. To show our main results, we apply the well-known Leggett-Williams fixed point theorem. Moreover, we present some results for the existence of single and multiple positive solutions for boundary value problems on time scales, by applying fixed point theorems in cones. The conditions we used in the paper are different from those in [Dogan A. On the existence of positive solutions for the one-dimensional p-Laplacian boundary value problems on time scales. Dynam Syst Appl 2015; 24: 295-304].Article Citation - WoS: 1Citation - Scopus: 1Prediction of Preference and Effect of Music on Preference: A Preliminary Study on Electroencephalography from Young Women(Tubitak Scientific & Technological Research Council Turkey, 2019-03-01) Yilmaz, Bulent; Gazeloglu, Cengiz; Altindis, FatihNeuromarketing is the application of the neuroscientific approaches to analyze and understand economically relevant behavior. In this study, the effect of loud and rhythmic music in a sample neuromarketing setup is investigated. The second aim was to develop an approach in the prediction of preference using only brain signals. In this work, 19-channel EEG signals were recorded and two experimental paradigms were implemented: no music/silence and rhythmic, loud music using a headphone, while viewing women shoes. For each 10-sec epoch, normalized power spectral density (PSD) of EEG data for six frequency bands was estimated using the Burg method. The effect of music was investigated by comparing the mean differences between music and no music groups using independent two-sample t-test. In the preference prediction part sequential forward selection, k-nearest neighbors (k-NN) and the support vector machines (SVM), and 5-fold cross-validation approaches were used. It is found that music did not affect like decision in any of the power bands, on the contrary, music affected dislike decisions for all bands with no exceptions. Furthermore, the accuracies obtained in preference prediction study were between 77.5 and 82.5% for k-NN and SVM techniques. The results of the study showed the feasibility of using EEG signals in the investigation of the music effect on purchasing behavior and the prediction of preference of an individual.Article Citation - WoS: 4Citation - Scopus: 5Noise-Assisted Multivariate Empirical Mode Decomposition Based Emotion Recognition(Istanbul Univ-Cerrahapasa, 2018-08-03) Ozel, Pinar; Akan, Aydin; Yilmaz, BulentEmotion state detection or emotion recognition cuts across different disciplines because of the many parameters that embrace the brain's complex neural structure, signal processing methods, and pattern recognition algorithms. Currently, in addition to classical time-frequency methods, emotional state data have been processed via data-driven methods such as empirical mode decomposition (EMD). Despite its various benefits, EMD has several drawbacks: it is intended for univariate data; it is prone to mode mixing; and the number of local extrema must be enough before the EMD process can begin. To overcome these problems, this study employs a multivariate EMD and its noise-assisted version in the emotional state classification of electroencephalogram signals. Emotion state detection or emotion recognition cuts across different disciplines because of the many parameters that embrace the brain's complex neural structure, signal processing methods, and pattern recognition algorithms. Currently, in addition to classical time-frequency methods, emotional state data have been processed via data-driven methods such as empirical mode decomposition (EMD). Despite its various benefits, EMD has several drawbacks: it is intended for univariate data; it is prone to mode mixing; and the number of local extrema must be enough before the EMD process can begin. To overcome these problems, this study employs a multivariate EMD and its noise-assisted version in the emotional state classification of electroencephalogram signals.Article Modified Self-Adaptive Local Search Algorithm for a Biobjective Permutation Flow Shop Scheduling Problem(Tubitak Scientific & Technological Research Council Turkey, 2019-07-26) Alabas Uslu, Cigdem; Dengiz, Berna; Aglan, Canan; Sabuncuoglu, Ihsan; Uslu, Çiğdem AlabaşInterest in multiobjective permutation flow shop scheduling (PFSS) has increased in the last decade to ensure effective resource utilization. This study presents a modified self-adaptive local search (MSALS) algorithm for the biobjective permutation flow shop scheduling problem where both makespan and total flow time objectives are minimized. Compared to existing sophisticated heuristic algorithms, MSALS is quite simple to apply to different biobjective PFSS instances without requiring effort or time for parameter tuning. Computational experiments showed that MSALS is either superior to current heuristics for Pareto sets or is incomparable due to other performance indicators of multiobjective problems.Article Citation - WoS: 3Citation - Scopus: 3MicroRNA Prediction Based on 3D Graphical Representation of RNA Secondary Structures(Tubitak Scientific & Technological Research Council Turkey, 2019-08-05) Sacar Demirci, Muserref Duygu; Demirci, Müşerref Duygu SaçarMicroRNAs (miRNAs) are posttranscriptional regulators of gene expression. While a miRNA can target hundreds of messenger RNA (mRNAs), an mRNA can be targeted by different miRNAs, not to mention that a single miRNA might have various binding sites in an mRNA sequence. Therefore, it is quite involved to investigate miRNAs experimentally. Thus, machine learning (ML) is frequently used to overcome such challenges. The key parts of a ML analysis largely depend on the quality of input data and the capacity of the features describing the data. Previously, more than 1000 features were suggested for miRNAs. Here, it is shown that using 36 features representing the RNA secondary structure and its dynamic 3D graphical representation provides up to 98% accuracy values. In this study, a new approach for ML-based miRNA prediction is proposed. Thousands of models are generated through classification of known human miRNAs and pseudohairpins with 3 classifiers: decision tree, naive Bayes, and random forest. Although the method is based on human data, the best model was able to correctly assign 96% of nonhuman hairpins from MirGeneDB, suggesting that this approach might be useful for the analysis of miRNAs from other species.Article Effective Processing of Specularite Ore by Wet Magnetic Separation and Reverse Flotation Techniques(2019-09-30) Top, Soner; Dhubaıbı, Ammar Mahdi Al; Vapur, HuseyinThe aim of this study was to obtain a high grade and yield percentage of iron concentratefrom a specularite ore by using wet magnetic separation and reverse flotation techniques.The processing a specularite sample using wet magnetic separation and reverseflotation method was studied. During the magnetic separation process, particle size was-2000 μm and magnetic field applied at 0.25 T and 0.85 T which were performed at asolid-liquid ratio of 10% and 20% by weight, respectively. In the reverse flotation tests,experimental design (DOE) was applied, statistically. Depressant dosage, collector dosageand flotation time were selected as main parameters. PH value, frother dosage (MIBC) andparticle size were constant parameters. The results showed that particle size and magnetic fieldintensity had a significant effect on the iron concentrate grade and yield for wet magneticseparation. The optimum value of iron concentrate grade was 98.75% at 0.25 T andparticle size of -150 μm while the highest value of iron concentrate yield was 67% at 0.75T and particle size of -74 μm. In the flotation tests, depressant dosage had the greatestinfluence on the iron concentrate grade while the effect of the collector dosage and frothcollection time were less. Froth collection time had the greatest effect on iron concentrateyield.The maximum iron concentrate grade was 90.13% for the following conditions:5250 g/ton depressant, 1000 g/ton collector and 2-minute froth collection time. Themaximum iron concentrate yield was 98.96% for the following conditions: 5250 g/tondepressant, 1500 g/ton collector and 1 min froth collection time under fixed conditions.Article Control of Collective Bursting in Small Hodgkinhuxley Neuron Clusters(2018) Borisenok, Sergey; Catmabacak, Onder; Şenel, ZeynepThe speed gradient-based control algorithm for tracking the membranepotential of Hodgkin-Huxley neurons is applied to their small clusters modeling thebasic features of an epileptiform dynamics. One of the neurons plays a role of controlelement detecting the temporal hyper-synchronization among its network companionsand switching their bursting behavior to resting. The ‘toy’ model proposed in thepaper can serve as an algorithmic basement for developing special control elements atthe scale of one or few cells that may work autonomously and are able to detect andsuppress epileptic behavior in the networks of real biological neurons.Article Computational Identification of MicroRNAs From Ssdna Viruses(2018-09-30) Demirci, Müşerref Duygu SaçarMicroRNAs (miRNAs) are post-transcriptional regulators of gene expression and the fact that they are associated with variousdisease phenotypes is one of the main reasons for their importance. The complexity of experimental detection of miRNAs dueto their characteristics led to the development of computational methods. In this work, a machine learning based approach wasapplied to identify and analyze potential miRNAs that might be originated from 60 single strand DNA (ssDNA) viruses’genomes. The results suggest that 53 of these viruses may possibly produce proper miRNA precursors. Moreover, thepossibility of these candidate miRNA precursors’ ability to generate mature miRNAs that could target human genes and viralgenomes has been tested. Overall, the outcomes of this research indicate that there might be another level of host-virusinteraction through miRNAs which requires further experimental confirmation.Article Citation - WoS: 2Citation - Scopus: 3An Asymptotic-Numerical Hybrid Method for Singularly Perturbed System of Two-Point Reaction-Diffusion Boundary-Value Problems(Tubitak Scientific & Technological Research Council Turkey, 2019-01-18) Cengizci, Suleyman; Natesan, Srinivasan; Atay, Mehmet TankThis article focuses on the numerical approximate solution of singularly perturbed systems of second-order reaction-diffusion two-point boundary-value problems for ordinary differential equations. To handle these types of problems, a numerical-asymptotic hybrid method has been used. In this hybrid approach, an efficient asymptotic method, the so-called successive complementary expansion method (SCEM) is employed first, and then a numerical method based on finite differences is applied to approximate the solution of corresponding singularly perturbed reaction-diffusion systems. Two illustrative examples are provided to demonstrate the efficiency, robustness, and easy applicability of the present method with convergence properties.