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Öğe ANFIS-SA-based design of a hybrid reconfigurable antenna for L-Band, C-band, 5G and ISM band applications(Pergamon-Elsevier Science Ltd, 2025) Gencoglan, Duygu NazanThis study presents a novel hybrid reconfigurable antenna design optimized using an Adaptive Neuro-Fuzzy Inference System (ANFIS) enhanced with a Simulated Annealing (SA) algorithm for L-band, C-band, 5G, and ISM applications. The antenna is fabricated on an FR-4 substrate with dimensions of 17 x 28 x 1.6 mm3, and two PIN diodes are employed to achieve frequency and radiation pattern reconfigurability. In the ON-ON state, the antenna operates in dual bands, covering 1.33-1.38 GHz (L-band) and 3.57-3.95 GHz (C-band). For the OFF-ON state, it operates from 3.56 to 3.95 GHz (C-band, 5G). In the ON-OFF state, it covers 1.50-1.54 GHz (L-band) and 5.66-5.90 GHz (ISM band), while in the OFF-OFF state, it operates from 5.49 to 5.82 GHz (ISM band). The antenna exhibits common bands at 3.8 GHz (C-band) and 5.8 GHz (ISM) across different states, facilitating pattern reconfigurability. ANFIS-SA is applied to optimize the switch locations, significantly improving resonance frequency and S11 performance. The antenna supports beam steering between 0 degrees and 180 degrees, enhancing adaptive coverage for modern applications such as Wi-Fi, Vehicle-to-Vehicle (V2 V), and Vehicle-to-Infrastructure (V2I) communication. This study addresses a critical gap by combining hybrid optimization techniques to improve frequency agility and radiation pattern control for next-generation wireless systems.Öğe A comprehensive benchmark of machine learning-based algorithms for medium-term electric vehicle charging demand prediction(Springer, 2025) Tolun, Omer Can; Zor, Kasim; Tutsoy, OnderThe current difficulties faced by evolutionary smart grids, as well as the widespread electric vehicles (EVs) into the modernised electric power system, highlight the crucial balance between electricity generation and consumption. Focusing on renewable energy sources instead of fossil fuels can provide an enduring environment for future generations by mitigating the impacts of global warming. At this time, the popularity of EVs has been ascending day by day due to the fact that they have several advantages such as being environmentally friendly and having better mileage performance in city driving over conventional vehicles. Despite the merits of the EVs, there are also a few disadvantages consisting of the integration of the EVs into the existing infrastructure and their expensiveness by means of initial investment cost. In addition to those, machine learning (ML)-based techniques are usually employed in the EVs for battery management systems, drive performance, and passenger safety. This paper aims to implement an EV monthly charging demand prediction by using a novel technique based on an ensemble of Pearson correlation (PC) and analysis of variance (ANOVA) along with statistical and ML-based algorithms including seasonal auto-regressive integrated moving average with exogenous variables (SARIMAX), convolutional neural networks (CNNs), extreme gradient boosting (XGBoost) decision trees, gated recurrent unit (GRU) networks, long short-term memory (LSTM) networks, bidirectional LSTM (Bi-LSTM) and GRU (Bi-GRU) networks for the Eastern Mediterranean Region of T & uuml;rkiye. The performance and error metrics, including determination coefficient (R 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>2$$\end{document} ), mean absolute percentage error (MAPE), mean absolute error (MAE), and mean absolute scaled error (MASE), are evaluated in a benchmarking manner. According to the obtained results, in Scenario 1, a hybrid of PC and XGBoost decision trees model achieved an R 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>2$$\end{document} of 96.21%, MAPE of 5.52%, MAE of 6.5, and MASE of 0.195 with a training time of 2.08 s and a testing time of 0.016 s. In Scenario 2, a combination of ANOVA and XGBoost decision trees model demonstrated an R 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$<^>2$$\end{document} of 96.83%, a MAPE of 5.29%, a MAE of 6.0, and a MASE of 0.180 with a training time of 1.62 s and a testing time of 0.012 s. These findings highlight the superior accuracy and computational efficiency of the XGBoost models for both scenarios compared to others and reveal XGBoost's suitability for EV charging demand prediction.Öğe Improving multi-class classification: scaled extensions of harmonic mean-based adaptive k-nearest neighbors(Springer, 2025) Acikkar, Mustafa; Tokgoz, SelcukThis paper proposes a novel extension of the harmonic mean-based adaptive k-nearest neighbors (HMAKNN) algorithm, called scaled HMAKNN (SHMAKNN), which builds on HMAKNN's strengths to achieve improved multi-class classification accuracy. HMAKNN uses a modified voting mechanism based on the harmonic mean and adaptive k-value selection to address issues like the sensitivity to k-value selection and the limitations of majority voting. SHMAKNN further improves the decision process by adjusting the components of the harmonic mean, focusing on voting values and the average distances of each class label. Additionally, SHMAKNN applies a re-scaling process to adjust the distances of the nearest neighbors within a specific range, enhancing the consistency of distances at different scales. These improvements help align the elements of the harmonic mean more effectively, leading to a balanced and less biased classification process. The study utilized 26 benchmark datasets, carefully curated to ensure accuracy and consistency, selected from diverse domains to evaluate the proposed method on real-world problems. These datasets were chosen to represent challenges like noise, imbalance, and sparsity, ensuring robustness in handling common data complexities. Additionally, small to medium-sized datasets were used to reduce computational burden and allow for efficient evaluation. The evaluation results show that the proposed SHMAKNN models outperform existing methods in both accuracy and F1-score for datasets with four or more classes. Specifically, SHMAKNN achieved the highest average accuracy and F1-score (86.36% and 86.16%) compared to HMAKNN (86.10% and 85.74%) and traditional k-nearest neighbors (84.87% and 84.69%). The performance improvements were validated using Friedman's test at a significance level of 0.05, confirming their statistical significance of the results. Consequently, the findings indicate that the proposed algorithm exhibits remarkable performance, thereby confirming its reliability and validity in the context of real-world applications, particularly those involving multiple classes.Öğe A novel repair method for the lifespan and performance improvement of a shell-and-tube heat exchanger: A thermo-mechanical approach(Pergamon-Elsevier Science Ltd, 2025) Delibas, Hulusi; Yilmaz, Ibrahim HalilHeat exchangers play a critical role in the functioning of many engineering systems. Shell-and-tube heat exchangers (STHEs) are more traditional and widely used devices due to their efficiency, versatility, and ability to handle a range of flow conditions and fluid types. STHEs experience a number of problems over time, including corrosion, mechanical wear, or leaking, and thus need repairs to keep operating. This study has introduced a novel repair approach for extending the lifespan of damaged STHE tubes by fitting new tubes. An original thermo-mechanical model, including the analyses of the STHE, thermal contact resistance between the fitted tubes, and mechanical design of the built structures, is proposed for the problem solution, and all governing equations are simultaneously solved in Engineering Equation Solver (EES). All submodels are validated with analytical or experimental data, and good agreements are obtained. The most significant design parameters and their effects on the thermal and mechanical performances of an STHE are parametrically investigated. Results reveal that increasing the contact surface slope over 10 degrees but lowering the effective surface roughness below 3 mu m provides an advantage for keeping the heat load of the STHE high. Among the interference fits, the locational interference fit is the most advantageous in terms of thermal and mechanical performances relative to other fit conditions. Both increasing operating pressure and tube diameter are two key pillars that can allow for a safety factor > 1.5. Fitting tube materials are parametrically independent and applicable to any STHE tube diameter as the yield strength > 300 MPa. Even if all tubes are press-fitted, the maximum heat load drop in the current repair method corresponds to 4.23 % which is lower than the tolerable value i.e., <10 % of the initially planned heat load.Öğe K-Salp Swarm Anomaly Detection (K-SAD): A novel clustering and threshold-based approach for cybersecurity applications(Elsevier Advanced Technology, 2025) Kilic, Vahide Nida; Essiz, Esra SaracAnomaly detection is a critical task in various domains, particularly in cybersecurity, where ensuring data integrity and security is paramount. In this study, we propose a novel approach to anomaly detection utilizing both the K-medoid and Salp Swarm Algorithms. Our methodology involves clustering the data using K-medoid and determining thresholds with an improved Salp Swarm Algorithm, enabling the identification of outliers within datasets. We conducted experiments on real-world datasets to evaluate the effectiveness of our approach. Significantly, proposed method surpassed alternative methods in performance across 5 of the 10 datasets, thereby showcasing its superior efficacy. For example, It demonstrated superior performance compared to alternative methods, achieving an AUC value of 0.8651 on the Thyroid dataset. Additionally, our approach yielded outcomes falling within the average spectrum across 3 datasets. These observations underscore the effectiveness of our proposed method in factifying anomaly detection methods and factifying cybersecurity protocols.Öğe Deep learning-based landslide tsunami run-up prediction from synthetic gage data(Elsevier Sci Ltd, 2025) Acikkar, Mustafa; Aydin, BaranThe present study proposes a deep learning model based on Long-Short Term Memory (LSTM) that uses gage measurements for prediction of landslide-driven maximum tsunami run-up. In an attempt to overcome the limitation of insufficient real-world data in the field, our methodology refers to analytical models to create a comprehensive dataset employing a time series recorded from an offshore gage as input and its corresponding maximum run-up at the shoreline as output, for different landslide scenarios with pre-determined parameters. The LSTM-based model is then trained using this dataset in order to predict the maximum run-up. The results, with mean values of 0.211 m, 0.149 m, 1.745% and 0.9988 for RMSE, MAE, MAPE and R2, respectively, indicate that our model is both accurate and precise. As the data-driven models such as the one proposed here are often utilized to identify relationships that may not be immediately apparent from the physical models alone, our interdisciplinary approach has the potential to foster the development of innovative solutions and methodologies for addressing complex natural hazards by enhancing early warning systems, preparedness and response to tsunamis.Öğe The single-step operation for enzyme-modified cheese production: Influence and importance of process parameters(Elsevier Sci Ltd, 2025) Bolat, Enise Betul; Salum, Pelin; Erbay, ZaferEnzyme-modified cheeses (EMCs) are natural flavour ingredients with intense cheese flavour. This study utilized a single-step process to produce an EMC; a single step process offers benefits such as shorter processing time, reduced equipment needs, and lower costs. However, product development and optimization are challenging due to multiple influencing factors. The study investigated incubation parameters (temperature, time, agitation speed), enzyme concentrations (proteolytic and lipolytic ratios), and homogenisation pressures to determine the important factors. The proteolytic ripening parameters, free fatty acid profiles, and volatile compounds of EMCs were examined. Key findings indicate that incubation conditions and protease concentration significantly influence EMC properties. Proteolytic enzyme concentration and incubation time were the main contributors to proteolytic ripening, while lipolytic ripening was primarily influenced by incubation temperature and agitation speed. Notably, incubation temperature was the most critical factor affecting the variation of volatile compounds in EMC production.Öğe Development of molecularly imprinted nanoparticles for the detection of cardiovascular diseases biomarker Angiotensin II in human serum(Wiley, 2025) Arisoy, Piril; Pesint, Gozde BaydemirAngiotensin II (Ang II) is a peptide hormone that causes vasoconstriction and an increase in blood pressure. Due to its relationship with cardiovascular diseases, it is an important biomarker in blood serum. In this study, Ang II imprinted nanoparticles were synthesized by miniemulsion polymerization reaction for the determination of Ang II from human serum. Hydroxyethyl methacrylate (HEMA) based Ang II imprinted (Ang II-MIPnp) and non-imprinted nanoparticles (NIPnp) were synthesized, characterized by zeta size analysis, scanning electron microscopy (SEM), transmission electron microscopy (TEM) and Fourier transform infrared spectrophotometer (FTIR-ATR). The average particle size of the NPs was recorded as 50 nm. Ang II molecules were successfully removed from the Ang II-MIPnp with a 98% success rate using 0.5 M NaCl solution to obtain template-specific cavities. Then, the adsorption studies were achieved. The binding capacity was found as 4500 pg. g(-1) at 700 pg. mL(-1) Ang II concentration. The selectivity studies showed that Ang II-MIPnp can recognize Ang II molecules 2.76 times and 3.23 times selectivity than Ang I and Vsp respectively. Reusability studies shows that the synthesized nanomaterial is reusable.Öğe Analytical study on mild steel corrosion inhibition in acidic environment: DFT modeling and RSM optimization(Elsevier Sci Ltd, 2025) Mert, Mehmet Erman; Gungor, Ceyla; Mert, Basak DogruThis study investigates the corrosion inhibition potential of various heterocyclic compounds, including 1,3-Thiazole-4-carbothioamide, 4-aminopyrazolo[3,4-d]pyrimidine, pyrimidine-2-thiocarboxamide, 1,2,4-oxadiazole-3carbothioamide, 1H-imidazole-4-carbothioamide, 2-methyl-1,3-thiazole-4-carbothioamide, 4-aminothieno[2,3d]pyrimidine-2-thiol, and 2-isopropyl-4-methyl-1,3-thiazole-5-carboxylic acid, selected for their structural characteristics that make them effective in fuel applications. The presence of functional groups such as thiol, amide, carboxylic acid, imidazole, and thiazole in these compounds enhances their ability to adsorb onto metal surfaces, forming protective layers that significantly inhibit corrosion. These compounds were chosen not only for their strong interaction with metal substrates but also for their stability and durability under various environmental conditions, which are important for fuel systems. Density Functional Theory (DFT) calculations were performed to give structural insights, which are essential for understanding the corrosion inhibition mechanism of the examined compounds. The inhibition performance of these molecules were investigated in 0.5 M HCl via electrochemical impedance spectroscopy technique for mild steel (MS) containing various inhibitor concentrations (1;3 and 5 mM) and exposure times (1; 24 and 48 h). Particularly, the higher inhibition efficiency of compounds; 2-methyl-1,3-thiazole-4-carbothioamide and 4-aminothieno[2,3-d]pyrimidine-2-thiol from their structural and electronic properties. The variable inhibition efficiency observed among different compounds investigates the importance of methods Response Surface Methodology (RSM) for systematically analyzing concentration, time, and molecular structure interactions. The experimental results indicated that 2-methyl-1,3thiazole-4-carbothioamide and 4-aminothieno[2,3-d]pyrimidine-2-thiol exhibited significantly higher inhibition efficiency at a concentration of 5 mM and an exposure duration of 48 h, with inhibition efficiencies of 98.96 % and 98.66 % respectively.Öğe Acrylamide exposure of infants and toddlers through baby foods and current progress on regulations(Elsevier Sci Ltd, 2022) Boyaci-Gunduz, Cennet PelinAcrylamide is a thermal-process contaminant and found at different levels in various carbohydrate-rich foods processed at for babies and children since acrylamide-rich foods comprise an important amount of their diet. Consumption of acrylamiderich foods results in higher acrylamide-exposure levels of babies and children than adults due to their lower body weight. In recent years, with an increased number of exposureassessment studies and researches, acrylamide exposure of babies and children is very well defined. In that context, mitigation strategies have been conducted and also, some regulations have been implemented in some countries for baby foods. The aim of this study is to evaluate recent data about acrylamide levels and acrylamide exposure of infants (0-1 years old) and toddlers (1-3 years old) through commonly consumed baby foods in the last decade, and to discuss current progress on regulations.Öğe ELF-aware pre-service teacher education: practices and perspectives(Oxford Univ Press, 2020) Deniz, Esma Biricik; Kemaloglu-Er, Elif; Ozkan, YoncaEnglish as a lingua franca (ELF) is a recent paradigm in ELT which acknowledges non-native varieties in their own right and NNSs as having their own unique characteristics rather than assessing them against an NS benchmark. Despite the changing needs of today's English language learners, there is little research on how to integrate ELF into pre-service teacher education programmes and English language teaching practice since there is a theory-practice gap due to lack of clear pedagogical descriptions and concrete classroom-driven data. This study presents an intensive educational model for pre-service teachers aiming to raise their awareness of the pedagogy of ELF, synthesizing theory with practice. It investigates how prospective teachers exposed to the ELF-aware teacher education model integrated ELF into their teaching and their viewpoints about the process. The entire process has been reported to contribute significantly to the participants' professional development as well as presenting several challenges.Öğe Special Issue: The International Conference on Raw Materials to Processed Foods(Wiley, 2019) Selli, Serkan; Kelebek, HaşimThis special issue of Journal of Food Processing and Preservation contains a collection of extended and revised versions of 12 selected papers from The International Conference on Raw Materials to Processed Foods (www.rpfoods2018.org) held in Antalya, Turkey in 2018. RP-Foods Conference was inspired from the main concern of humans of all time which is all kinds of foods. Food is essential for us to live, grow, and function and in addition to these essential needs, it has an underestimated effect on the economy with daily produced crops. The consumption of raw materials has been carried out with a broad range of results comprising hunger satisfaction to nutrient supplement. This wide usage of raw materials naturally necessitates food processing technologies to be developed. Food processing is one of the main events that paved the way into the sedentary lifestyle and civilization. Food processing is already a crucial field and topics like public health, sustainability challenges, and preference of consumers require this field to change and improve continuously. With this reason in mind, the food industry is decisively looking for different procedures to produce and store crops where the researchers put forward in the development of new innovative and novel technologies.Öğe Polar Codes with Higher-Order Memory(Maik Nauka/Interperiodica/Springer, 2018) Afser, H.; Delic, H.We introduce a construction of a set of code sequences {C-n((m)) : n 1, m 1} with memory order m and code length N(n). {C-n((m))} is a generalization of polar codes presented by Arkan in [1], where the encoder mapping with length N(n) is obtained recursively from the encoder mappings with lengths N(n - 1) and N(n - m), and {C-n((m))} coincides with the original polar codes when m = 1. We show that {C-n((m))} achieves the symmetric capacity I(W) of an arbitrary binary-input, discrete-output memoryless channel W for any fixed m. We also obtain an upper bound on the probability of block-decoding error P-e of {C-n((m))} and show that Pe=O(2-N) is achievable for < 1/[1+m(phi - 1)], where phi (1, 2] is the largest real root of the polynomial F(m, ) = (m) - (m - 1) - 1. The encoding and decoding complexities of {C-n((m))} decrease with increasing m, which proves the existence of new polar coding schemes that have lower complexity than Arkan's construction.Öğe Shielding effectiveness performance of polyaniline-NiFe2O4:Cu composites for sub-8 GHz applications (vol 55, 500, 2023)(Springer, 2023) Sahin, Ethem Ilhan; Emek, Mehriban; Ibrahim, Jamal Eldin F. M.; Yumusak, Goerkem; Kartal, Mesut[Abstract Not Available]Öğe Current-driven magnetization switching under zero field in Pt/Ta(wedge)/CoFeB/MgO multilayers(Aip Publishing, 2022) Akyol, Mustafa; Yu, Guoqiang; Wong, Kin; Wang, Kang L.The switching of perpendicularly magnetized ferromagnets via current-induced spin-orbit torques is of great interest because of its potential applications in memory and logic devices. However, the in-plane electric current itself is not enough to switch the magnetization. In addition to the electric current, an in-plane external magnetic field is required for magnetization switching. This limits the usage of such devices in spintronic applications. Here, we work on the current-driven perpendicular magnetization switching in the Pt/Ta(wedge)/CoFeB/MgO multilayer. The structural symmetry is broken in both z-axis and in-plane due to the wedge Ta layer, which results in a field-like spin-orbit torque. The beta( z )value extracted from the slope of the offset field vs current density increases with Ta layer thickness (< 1.0 nm) and then decreases up to z-axis asymmetries that enable the current-driven magnetization switching without the need for a magnetic field. We showed switching of the magnetization with a perpendicular magnetic anisotropy, switching in a wide range of Ta layer in Pt/Ta(wedge)/CoFeB/MgO multilayer. Published under an exclusive license by AIP Publishing.Öğe The international conference on raw materials to processed foods editorial(Wiley, 2022) Bordiga, Matteo; Selli, Serkan; Kelebek, Haşim[Abstract Not Available]Öğe Inhibitory spillover: Implicitly induced urinary urgency facilitates inhibition of unwanted thoughts(John Wiley & Sons Ltd, 2024) Cetinkaya, Hakan; Gunduz, Turan; Gunduz, Hasan[Abstract Not Available]Öğe Synthesis and fabrication of Mg-doped ZnO-based dye-sensitized solar cells (vol 25, pg 3173, 2014)(Springer, 2014) Polat, I.; Yilmaz, S.; Bacaksiz, E.; Atasoy, Y.; Tomakin, M.[Abstract Not Available]Öğe The Contradictory Syrian Presence in Turkey's Southern Borderlands(Univ California Press, 2022) Dagtas, Secil; Can, Sule[Abstract Not Available]Öğe The cyber security strategy of Israel(Voprosy Istorii, 2021) Burak, Daricili Ali; Emin, Erendor Mehmet[Abstract Not Available]
