Sürdürülebilir Kalkınma Amaçları

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Araştırmacılar(25)

• Akinoglu, Bulent G.
• Altas, Ismail H.
• Aydın, Kemal
• Balcı, Zekeriya
• Barut, İlhami
• Başoğlu, Mustafa Engin
• Benli, Emrah
• Bingöl, Özhan
• Çakmak, Reçep
• Çalik, İsmet
• Çetin, Gökhan
• Dogan, Yusuf
• Efeoglu, Hasan
• Eroğlu, H.
• Gunes, Nurhan
• Kayikcioglu, Temel
• Küçük, Mehmet Ali
• Melek, Negin
• Oğuz, Yasin
• Ozden, Talat
• Senturk, Bilge
• Simsek, Cemaleddin
• Sümbül, Harun
• Yıldırım, Ömer
• Yüksek, Halil İbrahim

• MakaleYalnız künye2027/r/gumushane/13789
  Analytical UWB waveform design based on linear combination of Gaussian-modulated sinusoids

  Oğuz, Yasin; Kaya, Haydar

  This study proposes an analytical ultra wide-band (UWB) waveform design based on linear combination of Gaussian-modulated sine waves to prevent interference with existing narrowband systems, in accordance with the Federal Communications Commission (FCC) UWB spectral mask. The proposed model determines the common pulse duration and the center frequencies of the components in closed-form using the relationships among the number of components, the overlap of components and the design band frequency limits. As a result, UWB waveforms for different design scenarios can be generated without computationally intensive algorithms. The method was evaluated in terms of spectrum utilization efficiency (SUE) within the 3.1-10.6 GHz indoor FCC UWB band. Results show that same SUE can be achieved with fewer components by appropriate overlap level. Comparisons with existing studies demonstrate that the proposed approach provides high performance for similar component numbers while avoiding complex optimization-based models with high computational cost. Moreover, the normalized spectral form and SUE are preserved for different sub-band widths when the analytical parameters are updated according to the new frequency limits. The results demonstrate that the proposed method is an effective waveform design approach due to its low computational complexity, high SUE, and scalability to multi-subband full-band UWB designs.

  İncele
• MakaleAçık Erişim2026/r/gumushane/19459
  Enhanced control of a two-wheeled gyroscopic combat robot with variable CMG disk speeds using LQR-Based neural network

  Oğuz, Yasin; Ünker, Faruk; Cetin, Gokhan

  This study proposes a hybrid control approach to improve the stability and adaptability of a two-wheeled gyroscopic combat robot by integrating deep learning with Linear Quadratic Regulator (LQR) control. Due to their inherently unstable nature, two-wheeled robots face challenges maintaining balance, especially when Control Moment Gyroscope (CMG) disk speeds vary, which significantly affect system dynamics. While LQR provides effective stabilization by minimizing a cost function, its performance degrades when disk speeds change. To overcome this limitation, a Neural Network (NN) was trained on 42001 precomputed LQR gain sets optimized for CMG disk speeds ranging from 800 to 5000 rad/s. The trained NN predicts appropriate coefficients in real time, enabling adaptive control under dynamic operating conditions. Simulation results demonstrate that the hybrid NN-LQR controller maintains stability under recoil forces up to 3000 N, where the conventional LQR loses balance about 4–6 seconds. Furthermore, the proposed controller reduces energy consumption by 21.6% during steady-state operation within the first 5 seconds and 10.6% under recoil conditions within the first 3 seconds compared to classical LQR. Although developed for a combat robot, the proposed framework offers a scalable solution for broader robotic applications such as search and rescue, surveillance, and autonomous navigation in complex environments, contributing to the advancement of intelligent control in robotics.

  İncele
• MakaleAçık Erişim2026/r/gumushane/16702
  The Anageometric Calculus: Theory, Fundamental Results, and Connections With Classical Analysis

  Yalcin, Numan; Dedeturk, Mutlu

  In this study, we develop a systematic framework for anageometric calculus as an extension of non-Newtonian analysis. Fundamental concepts, including limits, continuity, differentiation, and higher order derivatives, are established, and the connection to classical calculus is clarified through a logarithmic transformation. Within this framework, we derive the properties of the anageometric derivative, inverse function relations, partial derivatives, and mean value theorems. The theory is extended to integration by establishing the first and second fundamental theorems of anageometric calculus. Furthermore, the structural implications of the anageometric derivative are investigated, demonstrating that the equation D similar to f=lambda f naturally generates power-law solutions rather than classical exponential ones. This highlights the suitability of anageometric analysis for scale-invariant phenomena and proportional growth processes, supported by a practical heat-conduction example.

  İncele
• MakaleAçık Erişim2026/r/gumushane/14224
  When the Human Firewall Fails: Techno-Strain as the Hidden Link Between Technostress and Information Security Policy Violations

  Demirbağ, Orkun; Kaymak, Halil İbrahim; Alan, Hale; Akdeniz, Ferhan

  In today’s dynamic business environment, organizations are increasingly investing in strategies to protect themselves against information system violations. While these technologies offer remarkable benefits—boosting efficiency, productivity, and overall performance—they also bring significant risks, particularly regarding information security breaches. This study delves into the critical connections between technostress, techno-strain, and the violation of information security policies. Our research aims to shed light on how technostress, which is commonly experienced by engineers working in technology-intensive environments within the IT sector, drives information security violations. Importantly, we will also explore how techno-strain mediates this relationship. By focusing on engineers who are consistently engaged with advanced technology, we seek to answer essential questions about their experiences. It is worth noting that the requirements for information security technology can widely vary based on factors such as industry type, organizational structure, departmental roles, and cultural norms. Therefore, this study examines how technostress increases the likelihood of information security policy violations and how techno-strain mediates this relationship. Looking ahead, future research should consider both the broader institutional contexts and the individual characteristics that may shape the relationship between information security violations and technostress. Furthermore, understanding the repercussions of information security violations—stemming from technostress—on a company’s financial health is vital for organizations aiming to safeguard their assets and maintain a competitive edge. Emphasizing these insights can lead to more effective strategies for managing both technology and talent in the workplace.

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• MakaleAçık Erişim2026/r/gumushane/13892
  The Non-Newtonian Differential Operators Associated With Iterated Generators

  Dedeturk, Mutlu; Yalcin, Numan

  This paper investigates non-Newtonian differentiation associated with integer iterates of generator functions. For (Formula presented.), we consider the derivative operators (Formula presented.) and derive explicit first-order representation, transformation, and factorization formulas that relate operators corresponding to different iteration levels. The construction is then extended to higher-order derivatives, where the resulting operators are represented through conjugation with the classical derivative operator Dr. Faà di Bruno’s formula and exponential partial Bell polynomials are used to obtain explicit higher-order expressions. Several examples involving exponential, double-exponential, and quadratic generators illustrate how the general results specialize to familiar non-Newtonian calculi. The results provide a unified treatment of non-Newtonian differentiation based on iterated generators and clarify its relationship with classical differentiation.

  İncele
• MakaleYalnız künye2026/r/gumushane/13880
  A comprehensive optimization framework for indoor laser-based optical wireless communication using gimbal-steered transmitters

  Başer, Oğuzhan; Küçük, Mehmet Ali; Türk, Kadir

  The growing demand for high-speed wireless communication, driven by data-intensive applications such as high-definition video streaming and augmented reality, has highlighted the limitations of the Radio Frequency (RF) spectrum. Optical Wireless Communication (OWC) has emerged as a promising complementary technology, offering wide bandwidth, license-free operation, high security, and immunity to electromagnetic interference. Among OWC transmitters, Laser Diodes (LDs) provide high modulation bandwidth and narrow divergence angles, enabling data rates of several tens of gigabits per second. However, the narrow beam of LDs requires precise Line-of-Sight (LoS) alignment, making beam steering essential. This study focuses on optimizing the positioning and orientation of gimbal- or galvo-based LD transmitters in indoor OWC systems to minimize outage probability under LoS constraints and physical obstructions. An outage probability model considering room geometry and device field-of-view angles is developed and analyzed for three scenarios within a 7 × 5 m room: an empty environment, a room with a 1 × 1 m column, and a user-occupancy-aware environment. The complex optimization problem is solved using multiple meta-heuristic algorithms, including Particle Swarm Optimization (PSO), L-SHADE, and Stochastic Fractal Search with Fitness-Distance Balance (FDB-SFS). The obtained results are subsequently validated through grid search approach. The proposed approach achieves significant outage reduction, demonstrating that incorporating obstacle and user-distribution information enables efficient coverage with fewer terminals, reaching outage probabilities as low as 0.4%.

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• MakaleYalnız künye2026/r/gumushane/13856
  Identification of patulin-producing Penicillium isolates from decayed apples, their patulin production profiles, and the feasibility of high-purity patulin preparation

  Dağ, Murat; Bozbeyoğlu, Pınar; Gül, Abdulkadir; Karpuz, Ömer; Baltacı, Cemalettin

  This study presents a combined evaluation of patulin contamination by integrating fungal identification, assessment of toxin production, and the feasibility of obtaining high-purity patulin from biological sources. Fungal isolates recovered from naturally decayed apples collected from different agricultural environments in Türkiye were identified using MALDI-TOF MS and morphological characteristics, and were assigned to Penicillium expansum at a probable species level. The patulin production potential of the isolates was evaluated by inoculating them onto red, yellow, and green apple matrices under controlled laboratory conditions over an extended incubation period. Quantitative analysis using HPLC–UV, supported by LC–MS/MS confirmation, revealed substantial variability in patulin levels (7.58–49.80 mg/kg) depending on both isolate and apple matrix. Higher patulin levels were generally observed in yellow apple tissues, whereas lower levels were detected in red apples, suggesting a possible influence of matrix-related compositional factors. In addition, patulin was isolated and enriched using column chromatography with TLC-guided fractionation, yielding a high-purity compound (98.8%) with low moisture content (0.21%). Structural identity was confirmed by LC–MS/MS analysis. Overall, the findings indicate that patulin production is influenced by strain-level variability and matrix-related factors. Furthermore, the study demonstrates the feasibility of producing high-purity patulin suitable for in-house analytical applications. However, the produced material should not be considered a certified reference material, and further validation would be required for broader analytical use.

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  İncele
• MakaleAçık Erişim2026/r/gumushane/13855
  Regression-Based Controller Design Using LQR Data for a Two-Wheeled Gyroscopic Combat Robot

  Oğuz, Yasin; Çetin, Gökhan; Ünker, Faruk

  Two-wheeled gyroscopic robots are challenging to balance due to their intrinsic instability and sensitivity to external disturbances. This study proposes a hybrid control approach that combines a linear quadratic regulator (LQR) and a regression-based machine learning model to increase stability and adaptability. The regression model is trained with data collected from an LQR-controlled system to predict the optimal control responses under different conditions. The proposed approach utilizes state-space representation and the Jacobian linearization technique to design and simulate the robot’s performance under both controlled and random disturbance profiles. Simulation results indicate that the proposed method ensures steady motion even in dynamic, uncertain environments and increases resistance to external disturbances. Compared to the conventional LQR control method, the regression-enhanced model dynamically adjusts the control parameters, effectively reducing oscillations throughout the robot’s motion and achieving an average energy saving of approximately 80 %. These results demonstrate that robust, adaptive control in autonomous two-wheeled robotic systems can be achieved by fusing data-driven models with classical control techniques.

  İncele
• MakaleAçık Erişim2026/r/gumushane/13769
  Enhanced Sensorless Current Control in Boost Converters for EV Fast Charging: Equilibrium Optimization-Tuned Cascaded ANN-Fuzzy Logic Control

  Altan, Aytac; Yanarates, Cagfer; Alzaidi, Mohammed S.

  Efficient current control in boost converters is critical for electric vehicle (EV) battery charging applications, where dynamic operating conditions challenge conventional control strategies. This paper presents a novel cascaded artificial neural network (ANN) and fuzzy logic (ANFIS) controller designed to enhance current regulation in boost converters. The performance of the proposed approach is validated through comparison with single PI, cascaded PI, and single ANFIS control schemes. To improve system performance, a sensorless current estimation technique is implemented, reducing hardware dependency and enhancing robustness, while lowering system cost. Experimental evaluations demonstrate that the proposed cascaded ANFIS controller maintains consistent current levels under rapid load changes, providing improved transient response and steady-state accuracy. Specifically, the proposed method achieves a settling time of 23 ms, compared to 185 ms for single PI and 66 ms for single ANFIS control, corresponding to an 87.6% improvement over PI. In addition, overshoot is reduced from 37.25% (PI) and approximately 20% (ANFIS) to negligible levels, while a fast rise time of 3.62 ms is obtained. These improvements reduce current deviation and transient stress, contributing to more stable and reliable charging operation. Although charging time, efficiency, and battery lifetime are not directly measured, the results indicate strong potential for improved practical charging performance. The proposed approach provides valuable insights into the integration of intelligent control methods in power electronics and supports the development of high-performance EV charging systems.

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• MakaleAçık Erişim2026/r/gumushane/13725
  A leakage-free analytics framework for subject-wise electrocardiogram-based sleep apnea detection using machine learning

  Melek, Negin

  Sleep apnea is associated with impaired sleep quality and elevated cardiovascular risk, creating a need for reliable automated screening approaches. Although electrocardiogram (ECG)-based detection methods have shown promising results, reported performance values can be affected by evaluation strategies that allow information leakage between training and testing data. This study investigates ECG-based sleep apnea detection under strict subject-wise conditions using the PhysioNet Apnea-ECG database. Thirty-five annotated single-lead ECG recordings sampled at 100 Hz were divided into non-overlapping 60-s segments according to minute-wise apnea labels. Statistical ECG descriptors and time-domain heart rate variability features were extracted and evaluated using k-nearest neighbors (KNN), support vector machine (SVM), and Random Forest (RF) classifiers. In addition, a lightweight one-dimensional convolutional neural network (1D-CNN) was trained directly on filtered ECG segments. Repeated holdout validation with subject-level train-test separation was adopted, and normalization parameters were estimated exclusively from training data in each repetition to prevent information leakage. Model performance was assessed using accuracy, balanced accuracy, precision, recall, specificity, F1-score, ROC-AUC, Cohen's kappa, and Golden Distance (GD). In the representative subject-wise evaluation, Random Forest achieved the most balanced performance, yielding the highest balanced accuracy (0.70) together with the lowest GD (0.56). Across the repeated subject-wise holdout experiments, SVM achieved the strongest average overall performance, producing the highest mean accuracy (0.72), balanced accuracy (0.69), ROC-AUC (0.77), and Cohen's kappa (0.39), and the lowest mean GD value (0.58). The lightweight 1D-CNN converged stably during training, but its performance on unseen subjects remained lower than that of the handcrafted feature-based models.

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