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Article Citation - WoS: 5Citation - Scopus: 7China's Charm Defensive: Image Protection by Acquiring Mass Entertainment(Wiley, 2020) Yildirim, Nilgun Elikucuk; Aslan, MesutFocusing on discussion of China's soft power resources, this article argues that China performs two kinds of soft power strategies in developing and developed countries: offensive and defensive, respectively. While China's charm offensive aims to consolidate her comprehensive power through a development model, aid, investment, traditional culture, foreign policy, and international broadcasting in developing countries, the defensive aspect of China's soft power strategy aims to soften the rise of China with traditional culture by introducing appealing parts of Chinese culture through investments and international broadcasting in Western countries. China applies classical soft power tools in developing countries while she endeavors to protect her image in Western countries defensively. China's alternative defensive approach to soft power is mostly implemented through the acquisition of media outlets, and via the entertainment sector and gaming industry by Chinese-owned companies. However, even in the defensive and offensive bifurcation, if charm attacks result in failure, China could turn take a defensive stance in developing countries.Article Patient Safety in Healthcare: A Proposal for Ensuring the Use of Regulation-Compliant Safety Devices(Springer Heidelberg, 2025) Bayrak, TuncayMedical devices used in health care should fulfill the requirements of the technical regulations to protect patient health. Difficulties in enforcing stricter rules in the new medical device regulations may negatively affect the continuity of care. This study examines the status of manufacturers' compliance with medical device regulations, based on predefined criteria, and proposes a collaborative action plan and an approach to verify regulatory compliance. We conducted a nationwide survey comprising questions grouped by criteria to understand the status of the manufacturers in terms of compliance with the Medical Device Regulation. Four hundred sixty-seven manufacturers participated in the survey. We achieved a Cronbach's alpha of 0.77, which indicates that the survey is statistically reliable. We applied the independent samples t-test to the responses to determine significant features per question and employed factor analysis to investigate the relationships of the questions. The results of independent samples t-tests showed statistically significant differences across groups in replies to several survey items (p < 0.05), indicating that participants' opinions varied based on their demographic characteristics. We applied Exploratory Factor Analysis to introduce the relationships between the questions. The analysis revealed that manufacturers continue to face substantial challenges in acquiring sufficient knowledge and operational capability to meet MDR requirements. In light of these findings, we focused on the person responsible for regulatory compliance, who plays a central role in maintaining regulatory compliance within manufacturing organizations. We proposed an action plan at the macro level to introduce more effective action plans in cooperation with other stakeholders, including healthcare providers, and a verification approach for regulatory compliance to enhance the Person Responsible for Regulatory Compliance's competence. Manufacturers should implement effective postmarketing clinical follow-up plans involving device-oriented parameters for monitoring in the healthcare system, especially in collaboration with health professionals.Article Citation - WoS: 11Citation - Scopus: 20Reinforcement Learning Using Fully Connected, Attention, and Transformer Models in Knapsack Problem Solving(Wiley, 2022) Yildiz, Beytullah; Yıldız, Beytullah; Yıldız, BeytullahKnapsack is a combinatorial optimization problem that involves a variety of resource allocation challenges. It is defined as non-deterministic polynomial time (NP) hard and has a wide range of applications. Knapsack problem (KP) has been studied in applied mathematics and computer science for decades. Many algorithms that can be classified as exact or approximate solutions have been proposed. Under the category of exact solutions, algorithms such as branch-and-bound and dynamic programming and the approaches obtained by combining these algorithms can be classified. Due to the fact that exact solutions require a long processing time, many approximate methods have been introduced for knapsack solution. In this research, deep Q-learning using models containing fully connected layers, attention, and transformer as function estimators were used to provide the solution for KP. We observed that deep Q-networks, which continued their training by observing the reward signals provided by the knapsack environment we developed, optimized the total reward gained over time. The results showed that our approaches give near-optimum solutions and work about 40 times faster than an exact algorithm using dynamic programming.Article W-Band RCS Prediction of Small Objects: Comparing Two Widely Used Methods with Experimental Validation(Gazi University, 2025) Kara, Ali; Aydın, Elif; Yardım, Funda Ergün; Sezgin, DenizThis paper compares the accuracy of Shooting and Bouncing Rays and Electric Field Integral Equation methods for Radar Cross Section prediction of small objects at 77-81 GHz band. Existing studies on RCS prediction methods often lack comprehensive comparisons between computational and experimental results, particularly for small objects measured with a 77 GHz radar. This study addresses this gap by presenting an in-depth analysis of both simulation and measurement data. In this work, three targets with varying geometries and materials were measured with a frequency modulated continuous wave radar and simulated using Ansys HFSS and CST Studio Suite. The measurements were performed with a commercial off-the-shelf (COTS) frequency modulated continuous wave radar operating at 77–81 GHz. This study aims to emphasize the importance of considering both efficiency and accuracy when opting for an RCS prediction method. Overall, the outcomes of both methods have largely demonstrated good alignment. It has been noted that, while Shooting and Bouncing Rays method offers promising time-saving advantages, Electric Field Integral Equation method remains a valuable tool for complex geometries where precise results are crucial.Article Citation - WoS: 7Citation - Scopus: 10Comparison of Three Different Learning Methods of Multilayer Perceptron Neural Network for Wind Speed Forecasting(Gazi Univ, 2021) Bulut, Mehmet; Tora, Hakan; Buaisha, Dr.magdiIn the world, electric power is the highest need for high prosperity and comfortable living standards. The security of energy supply is an essential concept in national energy management. Therefore, ensuring the security of electricity supply requires accurate estimates of electricity demand. The share of electricity generation from renewables is significantly growing in the world. This kind of energy types are dependent on weather conditions as the wind and solar energies. There are two vital requirements to locate and measure specific systems to utilize wind power: modelling and forecasting of the wind velocity. To this end, using only 4 years of measured meteorological data, the present research attempts to estimate the related speed of wind within the Libyan Mediterranean coast with the help of ANN (artificial neural networking) with three different learning algorithms, which are Levenberg-Marquardt, Bayesian Regularization and Scaled Conjugate Gradient. Conclusions reached in this study show that wind speed can be estimated within acceptable limits using a limited set of meteorological data. In the results obtained, it was seen that the SCG algorithm gave better results in tests in this study with less data.Article Citation - WoS: 29Citation - Scopus: 36Massive Mimo Systems for 5g: a Systematic Mapping Study on Antenna Design Challenges and Channel Estimation Open Issues(inst Engineering Technology-iet, 2021) Benzaghta, Mohamed; Rabie, Khaled M.The next generation of mobile networks (5G) is expected to achieve high data rates, reduce latency, as well as improve the spectral and energy efficiency of wireless communication systems. Several technologies are being explored to be used in 5G systems. One of the main promising technologies that is seen to be the enabler of 5G is massive multiple-input multiple-output (mMIMO) systems. Numerous studies have indicated the utility of mMIMO in upcoming wireless networks. However, there are several challenges that needs to be unravelled. In this paper, the latest progress of research on challenges in mMIMO systems is tracked, in the context of mutual coupling, antenna selection, pilot contamination and feedback overhead. The results of a systematic mapping study performed on 63 selected primary studies, published between the year 2017 till the second quarter of 2020, are presented. The main objective of this secondary study is to identify the challenges regarding antenna design and channel estimation, give an overview on the state-of-the-art solutions proposed in the literature, and finally, discuss emerging open research issues that need to be considered before the implementation of mMIMO systems in 5G networks.Article Citation - WoS: 1Citation - Scopus: 1Machine Vs. Deep Learning Comparision for Developing an International Sign Language Translator(Taylor & Francis Ltd, 2022) Eryilmaz, Meltem; Balkaya, Ecem; Ucan, Eylul; Turan, Gizem; Oral, Seden GulayThis study aims to enable deaf and hard-of-hearing people to communicate with other individuals who know and do not know sign language. The mobile application was developed for video classification by using MediaPipe Library in the study. While doing this, considering the problems that deaf and hearing loss individuals face in Turkey and abroad modelling and training stages were carried out with the English language option. With the real-time translation feature added to the study individuals were provided with instant communication. In this way, communication problems experienced by hearing-impaired individuals will be greatly reduced. Machine learning and Deep learning concepts were investigated in the study. Model creation and training stages were carried out using VGG16, OpenCV, Pandas, Keras, and Os libraries. Due to the low success rate in the model created using VGG16, the MediaPipe library was used in the formation and training stages of the model. The reason for this is that, thanks to the solutions available in the MediaPipe library, it can normalise the coordinates in 3D by marking the regions to be detected in the human body. Being able to extract the coordinates independently of the background and body type in the videos in the dataset increases the success rate of the model in the formation and training stages. As a result of an experiment, the accuracy rate of the deep learning model is 85% and the application can be easily integrated with different languages. It is concluded that deep learning model is more accure than machine learning one and the communication problem faced by hearing-impaired individuals in many countries can be reduced easily.Article Examining Shared Leadership Dimensions Through a Social Network Approach: a Case From Tourism Industry(Univ Novi Sad, Fac Economics Subotica, 2024) Asbas, Caner; Tuzlukaya, Şule; Tuzlukaya, Sule Erdem; Maaroof, Aymen; Tuzlukaya, Şule; Business; BusinessBackground : Shared leadership is regarded as a fundamental approach to complexity leadership theory in terms of adaptability and flexibility. It emerges from communication among team members in a complex environment and consists of three dimensions: task coordination, personal support, and information sharing. Purpose: This study investigates shared leadership and its dimensions which are task coordination, personal support, and information sharing using social network analysis. By incorporating social network theory, the social and relational aspects of shared leadership can be revealed and emphasized. Study design/methodology/approach : Social network analysis was used to test the hypotheses on the data collected from the employees of a tourism organization. Findings/conclusions: The findings indicate that the individuals in task coordination, personal support and information sharing networks have a medium or low percentage of degree centrality in the social networks of their units or departments. The social networks of task coordination, personal support and information sharing have a high percentage of degree density when all individuals are treated as a total network and individuals in different departments and units as separate networks. This situation is led by the more balanced distribution of the power among the actors, dense communication between the members and intense network relations in task coordination, personal support and information sharing networks. Limitations/future research: The present study focuses only on internal network relations. As a future body of work, the study could be expanded to include both external and internal network relations to provide a wider understanding of the shared leadership concept. As another future body of work, to reach more generalizable results, this study can be expanded with a meta-analysis that will be performed on the results obtained by applying the survey on other organizations and processing the data collected with social network analysis methods again.Article A Modeling Approach for Designing New Acoustic Materials(Gazi Univ, 2024) Koçyiğit, Filiz Bal; Köse, Ercan; Buluklu, Hatice MehtapIn this study, mathematical modeling design based on Sound Transmission Loss measurement results of new acoustic material samples with natural content was carried out. Using the test samples in question, transfer function of acoustic materials based on electronic filter circuit design and a transition design method for the production of new acoustic materials by utilizing the transfer function is presented. Based on the experimental results of the test samples, it is the most suitable low-pass filter structure for the proposed design. In this study, active Sallen-Key low-pass filter structure is preferred and used. Sound Transmission Losses in dB (decibels) of acoustic samples were obtained experimentally for 500, 1000, 2000 and 4000 Hz. fundamental frequencies in the literature. Based on these data, transfer function simulation suppression gain results were obtained in TINA-TI program, active filter circuit designed, and MATLAB program. When the other results were compared in the experimental results, it was seen that very close values were obtained. It has been demonstrated that the proposed method can be used effectively in the design and examination of new acoustic materials.Article Selection of DNA Aptamers Against Parathyroid Hormone for Electrochemical Impedimetric Biosensor System Development(John Wiley and Sons Inc, 2025) Didarian, Reza; Bargh, Saharnaz; Gulerman, Almina; Ozalp, Veli Cengiz; Erel, Ozcan; Yildirim-Tirgil, NimetThis work presents the pioneering development of an aptamer-based electrochemical biosensor for real-time monitoring of parathyroid hormone (PTH) levels, with a focus on intraoperative assessment during parathyroid surgery. It introduces, for the first time, the selection and characterization of aptamers targeting distinct segments of the PTH peptide. The study demonstrates the feasibility and efficacy of the biosensing platform through a precisely designed experimental framework, including SELEX-based aptamer selection, aptamer-peptide interaction analysis, and biosensor fabrication. The SELEX process yields aptamers with notable binding affinities to different fragments of PTH, with the PTH (53-84) aptamer showing particularly sensitive binding to the hormone's C terminus, allowing for precise PTH analysis. Electrochemical characterization reveals significant changes in electrochemical impedance spectroscopy (EIS) signals upon exposure to varying PTH concentrations, highlighting the sensor's sensitivity and selectivity. The increase in charge transfer resistance (Rct) values with rising PTH concentrations underscores the biosensor's capability to detect PTH-induced structural changes, validating its potential for accurate measurement. The biosensor shows remarkable selectivity in the presence of common interferents in serum samples, ensuring precise PTH detection. Stability assessments over a 45-day storage period demonstrate the biosensor's robustness and long-term reliability, affirming its practical suitability. In summary, the developed aptamer-based biosensor represents a promising tool for sensitive and selective PTH detection, with potential applications in biomedical research and clinical diagnostics, particularly for intraoperative PTH analysis during parathyroidectomy. Continued research and optimization efforts hold promise for enhancing its performance and expanding its utility in diverse healthcare settings.
