Scopus İndeksli Yayınlar Koleksiyonu

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Citation - Scopus: 18
Characterization of Local Sorghum (Sorghum Bicolor L.) Population Grains in Terms of Nutritional Properties and Evaluation by GT Biplot Approach
(Wiley-VCH Verlag info@wiley-vch.de, 2020) Kaplan, Mahmut; Kale, Hasan; Kardeş, Yusuf Murat; Karaman, Kevser; Kahraman, Kevser; Yilmaz, Mehmet Fatih; Akar, Taner
This study is conducted to characterize some nutritional attributes and starch properties of 156 Turkish sorghum populations and 4 standard cultivars (Sorghum bicolor L.). Crude protein contents of the populations vary between 6.67% and 14.33%, digestible protein ratios between 6.67% and 82.24%, crude oil contents between 2.15% and 6.40%, phytic acid contents between 0.37% and 4.09%, resistant starch between 1.10% and 34.23%, nonresistant starch between 10.79% and 79.61%, total starch between 15.42% and 85.54%, amylose between 5.67% and 43.48%, amylopectin between 9.45% and 65.67%, total phenolic between 0.19% and 5.06 mg GAE/g and antiradical activity between 3.72% and 91.48%. Significant differences are obtained from starch-based Rapid Visco Analyzer parameters of sorghum genotypes. As compared standard cultivars, several superior genotypes are identified in terms of nutritional characteristics. Genotype treatment (GT) biplot analysis revealed ideal genotypes for investigated parameters. Present findings confirmed that there are many genotypes with superior nutritional properties in local sorghum genotypes. © 2020 Elsevier B.V., All rights reserved.
Citation - WoS: 2
Citation - Scopus: 2
Crown Shaped Edge Multiband Antenna Design for 5G and X-Band Applications
(Springer, 2023) Hakanoglu, Baris Gurcan; Kilic, Veli Tayfun; Altindis, Fatih; Turkmen, Mustafa
Nowadays we are experiencing the fifth-generation (5G) technology with new frequency bands to achieve high broadband speed, minimum latency and more developed end user devices. Due to the different frequency ranges for different applications at 5G bands the antennas should support multiband operation in a compact structure. This paper proposes a new multiband microstrip patch antenna design operating at mid band 5G frequencies and in the X band. The structure of the antenna includes simply loading the top radiating edge with rhombic shaped stubs and slots. This configuration yields the antenna to have resonances at multiple frequencies based on the fact that the stubs and slots affect capacitive and inductive impedances on the lower and higher operating frequencies of the antenna. The unique design enables the antenna to have reasonably high gains at four different bands of 6.76 dBi, 6.47 dBi, 7.76 dBi and 5.51 dBi at 3.34 GHz, 4.61 GHz, 6.01 and 8.02 GHz, respectively. Also, the simulated antenna has been manufactured and measured. The measurement results are in good agreement with the simulation results. The proposed design can be used with many other frequency bands and dielectric materials as well to achieve multiband operation.
Citation - WoS: 21
Citation - Scopus: 24
Microstructure and Mechanical Properties of Dense Si3N4 Ceramics Prepared by Direct Coagulation Casting and Cold Isostatic Pressing
(Elsevier Science SA, 2022) Marulcuoglu, Hande; Kara, Ferhat
Complex shaped dense Si3N4 ceramics were produced by using direct coagulation casting technique via dispersant reaction method of Si3N4 suspension, followed by gas pressure sintering. The effects of solid content of the suspension, additional cold isostatic pressing of the cast parts, and sintering behaviour and on the mechanical reliability of silicon nitride ceramics were investigated. It was observed that all slurries exhibited rheological properties suitable for casting in the range of 44-50 vol.% solid concentrations. Nevertheless, higher solid concentration suspensions resulted in smaller floc size and thus better green microstructures. Parts shaped by direct coagulation casting at all the solid loadings had relatively low strength and reliability after sintering. However, application of additional cold isostatic pressing to the cast parts increased the strength and, particularly, reliability. Dense Si3N4 ceramics with relative density above 99.5%, average bending strength 760 +/- 39 MPa and Weibull module 23.5 had been obtained with 50 vol.% solids content after DCC + CIP process.
Citation - WoS: 14
Citation - Scopus: 15
Networking and Communications for Smart Cities Special Issue Editorial
(Elsevier Science Bv, 2015) Theoleyre, Fabrice; Watteyne, Thomas; Bianchi, Giuseppe; Tuna, Gurkan; Gungor, V. Cagri; Pang, Ai-Chun
Citation - WoS: 5
Citation - Scopus: 9
A Battery-Friendly Data Acquisition Model for Vehicular Speed Estimation
(Pergamon-Elsevier Science Ltd, 2016) Kaya, Sevgi; Kilic, Necati; Kocak, Taskin; Gungor, Cagri
Modeling traffic flow and gathering accurate traffic congestion information are two challenging problems in smart transportation systems. Most of the traffic flow models and velocity estimation methodologies that have been proposed so far gather the data from GPS-equipped smart phones and extract the flow model based on GPS sampling. However, these approaches tend to fail in real life scenarios due to the insufficient vehicle data and unpredictable dynamics of the flow. Furthermore, utilization of GPS sensor leads to a battery drainage and hence reduces the overall system performance. In this paper, we propose a new battery-friendly data acquisition model to obtain the raw data. We then evaluate our model under various traffic conditions to determine its feasibility in vehicle speed estimation. The proposed model results in 88% location accuracy whereas it reduces the battery consumption by half. (C) 2016 Elsevier Ltd. All rights reserved.
Citation - Scopus: 5
Development of Knowledge Based Response Correction for a Reconfigurable N-Shaped Microstrip Antenna Design
(Institute of Electrical and Electronics Engineers Inc., 2015) Aoad, Ashrf; Simsek, Murat; Aydin, Zafer
This study presents the use of prior knowledge of inverse artificial neural network (ANN) to model and optimize a reconfigurable N-shaped microstrip antenna. Three accurate prior knowledge inverse ANNs with large amount training data are proposed where the frequency information is incorporated into the structure of ANN. The complexity of the input/output relationship is reduced by using prior knowledge. Three separate methods of incorporating knowledge in the second step of the training process with a multilayer perceptron (MLP) in the first step are demonstrated and their results are compared to EM simulation. © 2023 Elsevier B.V., All rights reserved.
Citation - WoS: 2
Citation - Scopus: 1
Computational Prediction of MicroRNAs in Histoplasma Capsulatum
(Academic Press Ltd- Elsevier Science Ltd, 2020) Demirci, Mueserref Duygu Sagar
MicroRNAs (miRNAs) are small and non-coding RNAs that regulate gene expression through post-transcriptional regulation. Although, the standard miRNA repository, MiRBase, lists more than 200 organisms having miRNA mediated regulation mechanism and thousands of miRNAs, there is not enough information about miRNAs of fungal species. Considering that there are various fungal pathogens causing disease phenotypes, it is important to search for miRNAs of those organisms. The leading cause of endemic mycosis in the USA is a fungal disease known as histoplasmosis, which is resulted by infection with a fungal intracellular parasite, Histoplasma capsulatum (H. capsulatum). In this work, genomes of H. capsulatum strains NAm1 and G217B were explored for potential miRNA like sequences and structures. Through a complex workflow involving miRNA detection and target prediction, several miRNA candidates of H. capsulatum and their possible targets in human were identified. The results presented here indicate that H. capsulatum might be one of the fungal pathogens having a miRNA based post-transcriptional gene regulation mechanism and it might have a miRNA mediated host - parasite interaction with human.
Range-Based Wireless Sensor Network Localization by a Circumnavigating Mobile Anchor Without Position Information
(IEEE, 2024) Guler, Samet
Typical range-based wireless sensor network (WSN) localization approaches aim at estimating the sensor node positions by using a set of anchors with known positions. In some applications, assuming the knowledge of the anchors' positions may be impractical, and estimation of the sensors' positions in an arbitrary fixed frame may be sufficient. Considering such scenarios, we propose a WSN localization algorithm by single mobile anchor without self location information. The mobile anchor obtains distance measurements from the sensors while tracking a custom trajectory which is shown to improve the localization performance over time for high signal-to-noise ratio cases. By utilizing two stationary reference nodes within the WSN, the proposed framework generates sensor node position estimation up to translation and rotation with sufficient precision in the absence of global positioning aids. We foresee that the proposed framework can demonstrate benefits in several WSN applications ranging from internet-of-things to service robotics.
Citation - Scopus: 4
CCPred: Global and Population-Specific Colorectal Cancer Prediction and Metagenomic Biomarker Identification at Different Molecular Levels Using Machine Learning Techniques
(Elsevier Ltd, 2024) Bakir-Güngör, Burcu; Temiz, Mustafa; Inal, Yasin; Cicekyurt, Emre; Yousef, Malik
Colorectal cancer (CRC) ranks as the third most common cancer globally and the second leading cause of cancer-related deaths. Recent research highlights the pivotal role of the gut microbiota in CRC development and progression. Understanding the complex interplay between disease development and metagenomic data is essential for CRC diagnosis and treatment. Current computational models employ machine learning to identify metagenomic biomarkers associated with CRC, yet there is a need to improve their accuracy through a holistic biological knowledge perspective. This study aims to evaluate CRC-associated metagenomic data at species, enzymes, and pathway levels via conducting global and population-specific analyses. These analyses utilize relative abundance values from human gut microbiome sequencing data and robust classification models are built for disease prediction and biomarker identification. For global CRC prediction and biomarker identification, the features that are identified by SelectKBest (SKB), Information Gain (IG), and Extreme Gradient Boosting (XGBoost) methods are combined. Population-based analysis includes within-population, leave-one-dataset-out (LODO) and cross-population approaches. Four classification algorithms are employed for CRC classification. Random Forest achieved an AUC of 0.83 for species data, 0.78 for enzyme data and 0.76 for pathway data globally. On the global scale, potential taxonomic biomarkers include ruthenibacterium lactatiformanas; enzyme biomarkers include RNA 2′ 3′ cyclic 3′ phosphodiesterase; and pathway biomarkers include pyruvate fermentation to acetone pathway. This study underscores the potential of machine learning models trained on metagenomic data for improved disease prediction and biomarker discovery. The proposed model and associated files are available at https://github.com/TemizMus/CCPRED. © 2024 Elsevier B.V., All rights reserved.
Citation - WoS: 3
Citation - Scopus: 3
Robust Reactivity, Neutron Source, and Precursor Estimators for Nuclear Reactors
(Elsevier Science SA, 2013) Ablay, Gunyaz
Reactivity, precursor concentration, and external neutron source strength determine control, operation and performance of nuclear reactors. These main reactor quantities are not directly measurable and must be calculated or estimated using reactor kinetics. This study presents efficient and robust nonlinear estimation algorithms for predicting these fundamental reactor quantities. The effectiveness of the proposed estimators is assessed through chirp and step test signals in the presence of parameter uncertainties and measurement noise. (C) 2013 Elsevier B.V. All rights reserved.
Citation - Scopus: 13
Staging of the Liver Fibrosis From CT Images Using Texture Features
(2012) Kayaaltı, Ömer; Aksebzeci, Bekir Hakan; Karahan, Ökkeş Ibrahim; Deniz, Kemal; Öztürk, Menmet; Yilmaz, Bulent; Asyali, Musa Hakan
Even though liver biopsy is critical for evaluating chronic hepatitis and fibrosis, it is an invasive, costly, and difficult to standardize approach. The developments in medical image processing and artificial intelligence methods have advanced the potential of using computer-aided diagnosis techniques in the classification of liver tissues. The aim of this study was to develop a non-invasive, cost-effective, and fast approach to specify fibrosis stage using the texture properties of computed tomography images of liver. Gray level co-occurrence matrix, discrete wavelet transform, and discrete Fourier transform were the image analysis tools in the feature extraction phase. Following dimension reduction of the texture features support vector machines and k-nearest neighbor methods were used in the classification phase of this study. Our results showed that our approach is feasible in fibrosis staging especially in pairwise stage comparisons with success rate of approximately 90%. © 2012 IEEE. © 2012 Elsevier B.V., All rights reserved.
Citation - WoS: 13
Citation - Scopus: 13
Piezoresistivity and Piezopermittivity of Cement-Based Sensors Under Quasi-Static Stress and Changing Moisture
(Elsevier Sci Ltd, 2024) Zhang, Jiacheng; Heath, Andrew; Ball, Richard J.; Chen, Binling; Tan, Linzhen; Li, Guisheng; Paine, Kevin
Integrated cement-based sensors offer an economic alternative to extrinsic sensors for health monitoring applications in concrete structures due to their high strength to cost ratio, geometrical versatility, low shrinkage, and natural compatibility. Nonetheless, their performance under in-service conditions were in lack of investigations. While the piezoresistivity (change in resistance with stress) has been commonly used for mechanical sensing, the piezopermittivity (change in capacitive reactance with stress) is rarely characterized. Exploiting the high relative permittivity and electrical conductivity of carbon fibre reinforced cement-based sensors, this study investigates the piezoresistivity and piezopermittivity under changing stress and moisture using electrochemical impedance spectroscopy (EIS). Two types of sensors were evaluated: one containing 0.5 vol% of carbon fibres whose electrical conductivity was ionically dominant, and another with electronically dominant (1.2 vol% of carbon fibres) conductivity. Results highlighted that the piezopermittivity is "moisture content-dominant" whilst the piezoresistivity is "fibre content-dominant". As the moisture content decreased, the sensitivity of piezopermittivity for both sensor types decreased, while the sensitivity of piezoresistivity decreased for the ionically dominant sensor but increased for the electronically dominant sensor. The piezoresistivity of the electronically dominant sensor was less sensitive than piezopermittivity at a water saturation of 80%. Conversely, the piezoresistivity of the ionically dominant sensor was more sensitive than piezopermittivity at the tested water saturations <= 80%. For the first time, this study presents the combined effects of moisture and fibre content on the pressure sensitive response of cement-based sensors through a dual-phase (i.e., piezoresistivity and piezopermittivity) EIS interpretation technique, providing valuable information to benefit further behaviour prediction and single-effect recognition in the field scenario where the sensors are subject to simultaneous
Citation - WoS: 15
Citation - Scopus: 18
A Mathematical Model With Piecewise Constant Arguments of Colorectal Cancer With Chemo-Immunotherapy
(Pergamon-Elsevier Science Ltd, 2023) Bozkurt, Fatma; Yousef, Ali; Bilgil, Halis; Baleanu, Dumitru
We propose a new mathematical model with piecewise constant arguments of a system of ODEs to investigate the growth of colorectal cancer and its response to chemo-immunotherapy. Our main target in this paper is to analyze and represent the I.S.'s (immune system) efficiency during the chemotherapeutic process. Therefore, we proved and illustrated the necessity of IL-2 that supports the immune system, especially in early-detected cases of tumor density. Thus, the constructed model has been divided into sub-systems: the cell populations, the effects of the medications doxorubicin, and IL-2 concentration.Firstly, we analyze the stability of the equilibrium points (disease-free and co-existing) using the RouthHurwitz criteria. In addition, our study has shown that the system undergoes period-doubling, stationary and Neimark-Sacker bifurcations based on specific conditions. In the end, we illustrate some simulations to assist the theory of the manuscript.
Citation - WoS: 28
Citation - Scopus: 31
Liver Fibrosis Staging Using CT Image Texture Analysis and Soft Computing
(Elsevier, 2014) Kayaalti, Omer; Aksebzeci, Bekir Hakan; Karahan, Ibrahim Okkes; Deniz, Kemal; Ozturk, Mehmet; Yilmaz, Bulent; Asyali, Musa Hakan
Liver biopsy is considered to be the gold standard for analyzing chronic hepatitis and fibrosis; however, it is an invasive and expensive approach, which is also difficult to standardize. Medical imaging techniques such as ultrasonography, computed tomography (CT), and magnetic resonance imaging are non-invasive and helpful methods to interpret liver texture, and may be good alternatives to needle biopsy. Recently, instead of visual inspection of these images, computer-aided image analysis based approaches have become more popular. In this study, a non-invasive, low-cost and relatively accurate method was developed to determine liver fibrosis stage by analyzing some texture features of liver CT images. In this approach, some suitable regions of interests were selected on CT images and a comprehensive set of texture features were obtained from these regions using different methods, such as Gray Level Co-occurrence matrix (GLCM), Laws' method, Discrete Wavelet Transform (DWT), and Gabor filters. Afterwards, sequential floating forward selection and exhaustive search methods were used in various combinations for the selection of most discriminating features. Finally, those selected texture features were classified using two methods, namely, Support Vector Machines (SVM) and k-nearest neighbors (k-NN). The mean classification accuracy in pairwise group comparisons was approximately 95% for both classification methods using only 5 features. Also, performance of our approach in classifying liver fibrosis stage of subjects in the test set into 7 possible stages was investigated. In this case, both SVM and k-NN methods have returned relatively low classification accuracies. Our pairwise group classification results showed that DWT, Gabor, GLCM, and Laws' texture features were more successful than the others; as such features extracted from these methods were used in the feature fusion process. Fusing features from these better performing families further improved the classification performance. The results show that our approach can be used as a decision support system in especially pairwise fibrosis stage comparisons. (C) 2014 Elsevier B.V. All rights reserved.
Citation - WoS: 54
Citation - Scopus: 82
Edge AI: A Taxonomy, Systematic Review and Future Directions
(Springer, 2025) Gill, Sukhpal Singh; Golec, Muhammed; Hu, Jianmin; Xu, Minxian; Du, Junhui; Wu, Huaming; Uhlig, Steve
Edge Artificial Intelligence (AI) incorporates a network of interconnected systems and devices that receive, cache, process, and analyse data in close communication with the location where the data is captured with AI technology. Recent advancements in AI efficiency, the widespread use of Internet of Things (IoT) devices, and the emergence of edge computing have unlocked the enormous scope of Edge AI. The goal of Edge AI is to optimize data processing efficiency and velocity while ensuring data confidentiality and integrity. Despite being a relatively new field of research, spanning from 2014 to the present, it has shown significant and rapid development over the last five years. In this article, we present a systematic literature review for Edge AI to discuss the existing research, recent advancements, and future research directions. We created a collaborative edge AI learning system for cloud and edge computing analysis, including an in-depth study of the architectures that facilitate this mechanism. The taxonomy for Edge AI facilitates the classification and configuration of Edge AI systems while also examining its potential influence across many fields through compassing infrastructure, cloud computing, fog computing, services, use cases, ML and deep learning, and resource management. This study highlights the significance of Edge AI in processing real-time data at the edge of the network. Additionally, it emphasizes the research challenges encountered by Edge AI systems, including constraints on resources, vulnerabilities to security threats, and problems with scalability. Finally, this study highlights the potential future research directions that aim to address the current limitations of Edge AI by providing innovative solutions.
Citation - WoS: 1
Citation - Scopus: 3
Endüstriyel Kablosuz Algılayıcı Ağlarda Hata Kontrol Sistemlerinin Ağ Yaşam Süresine Etkileri
(IEEE, 2019) Tekin, Nazli; Gungor, V. Cagri
Due to the harsh channel conditions of the industrial environment, the data transmission over wireless channel suffers from erroneous packets. The energy consumption of error control schemes is of vital importance for battery-powered Wireless Sensor Networks (WSNs). In this paper, the performance evaluation of error control schemes namely, Automatic Repeat Request (ARQ), Forward Error Correction (FEC) and Hybrid ARQ (HARQ) in industrial environment in terms of energy efficiency is presented. The impact of the existing error control schemes on the industrial wireless sensor network lifetime is analyzed. A novel Mixed Integer Programming (MIP) framework is developed to maximize network lifetime. Performance results show that utilizing BCH (31,21,5) for Telos at the link layer maximizes the network lifetime while attaining the desired application reliability rate.
Citation - WoS: 26
Citation - Scopus: 25
Trail Promotes the Polarization of Human Macrophages Toward a Proinflammatory M1 Phenotype and Is Associated With Increased Survival in Cancer Patients With High Tumor Macrophage Content
(Frontiers Media S.A., 2023) Gunalp, Sinem; Helvaci, Derya Goksu; Oner, Aysenur; Bursali, Ahmet; Conforte, Alessandra; Guener, Hueseyin; Sag, Duygu
BackgroundTNF-related apoptosis-inducing ligand (TRAIL) is a member of the TNF superfamily that can either induce cell death or activate survival pathways after binding to death receptors (DRs) DR4 or DR5. TRAIL is investigated as a therapeutic agent in clinical trials due to its selective toxicity to transformed cells. Macrophages can be polarized into pro-inflammatory/tumor-fighting M1 macrophages or anti-inflammatory/tumor-supportive M2 macrophages and an imbalance between M1 and M2 macrophages can promote diseases. Therefore, identifying modulators that regulate macrophage polarization is important to design effective macrophage-targeted immunotherapies. The impact of TRAIL on macrophage polarization is not known.MethodsPrimary human monocyte-derived macrophages were pre-treated with either TRAIL or with DR4 or DR5-specific ligands and then polarized into M1, M2a, or M2c phenotypes in vitro. The expression of M1 and M2 markers in macrophage subtypes was analyzed by RNA sequencing, qPCR, ELISA, and flow cytometry. Furthermore, the cytotoxicity of the macrophages against U937 AML tumor targets was assessed by flow cytometry. TCGA datasets were also analyzed to correlate TRAIL with M1/M2 markers, and the overall survival of cancer patients.ResultsTRAIL increased the expression of M1 markers at both mRNA and protein levels while decreasing the expression of M2 markers at the mRNA level in human macrophages. TRAIL also shifted M2 macrophages towards an M1 phenotype. Our data showed that both DR4 and DR5 death receptors play a role in macrophage polarization. Furthermore, TRAIL enhanced the cytotoxicity of macrophages against the AML cancer cells in vitro. Finally, TRAIL expression was positively correlated with increased expression of M1 markers in the tumors from ovarian and sarcoma cancer patients and longer overall survival in cases with high, but not low, tumor macrophage content.ConclusionsTRAIL promotes the polarization of human macrophages toward a proinflammatory M1 phenotype via both DR4 and DR5. Our study defines TRAIL as a new regulator of macrophage polarization and suggests that targeting DRs can enhance the anti-tumorigenic response of macrophages in the tumor microenvironment by increasing M1 polarization.
Citation - WoS: 15
Citation - Scopus: 18
Dissolution of Lateritic Nickel Ore Using Ascorbic Acid as Synergistic Reagent in Sulphuric Acid Solution
(Elsevier Science Bv, 2018) Kursunoglu, Sait; Ichlas, Zela Tanlega; Kaya, Muammer
The dissolution of nickel and cobalt from Caldag lateritic nickel ore using the combination of sulphuric and ascorbic acids was investigated. The use of other organic acids, namely citric, maleic and stearic acids, as synergistic reagents was studied for comparison. The results revealed that the use of ascorbic and citric acids markedly improved the dissolution of cobalt compared to the other two organic acids that only showed slight synergistic effect on the leaching rate. In terms of nickel dissolution, ascorbic acid is the most effective synergist, followed by citric, maleic and stearic acids in descending order. Under the most optimized conditions found in this study, i.e., using 1 mol/L of sulphuric acid with the presence of 4 g/L of ascorbic acid at 80 degrees C and solid-to-liquid ratio of 1/10, more than 99% and 98% leaching rates of cobalt and nickel, respectively, can be achieved within 4 h of leaching. In addition, the leaching performance is relatively insensitive to the change of ascorbic acid concentration from 2 to 4 g/L which is highly desirable from operational perspective.
Citation - WoS: 3
Citation - Scopus: 3
Performance Improvements of Photonic Lantern Based Coherent Receivers
(Institute of Electrical and Electronics Engineers Inc., 2014) Ozdur, Ibrahim T.; Toliver, Paul; Woodward, Ted K.
In this work, the signal-to-noise ratio improvement of photonic lantern-based coherent receivers over single-mode coherent receivers is demonstrated. The signal-to-noise ratio is improved by a factor of 2.8 when other parameters kept constant. © 2021 Elsevier B.V., All rights reserved.
Citation - Scopus: 2
PI-V plus Sliding Mode Based Cascade Control of Magnetic Levitation
(Institute of Electrical and Electronics Engineers Inc., 2016) Eroǧlu, Yakup; Ablay, Günyaz
Magnetic levitation systems are able to provide frictionless, reliable, fast and economical operations in wide-range applications. The effectiveness and applicability of these systems require precise feedback control design. The position control problem of the magnetic levitation can be solved with robust current control approaches. A cascade control approach consisting of PI-velocity plus sliding mode control (PI-V plus SMC) is designed to render high control performance and robustness to the magnetic levitation. It will be shown that the SMC designed for electrical part of the plant (current controller) is able to eliminate the effects of the inductance related uncertainties of the electromagnetic coil of the plant. Experimental results are provided to validate the efficacy of the approach. © 2016 Elsevier B.V., All rights reserved.

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