Sürdürülebilir Kalkınma Amaçları

• 33
• 15
• 14
• 5
• 3
• 3
• 1
• 1
• 1
• 1

Araştırmacılar(21)

• Akmaz, Düzgün
• Aşkın, A.
• Aşkın, Ali
• Aşkın, Muzaffer
• Boydak, Merve
• Buldu, G.
• Caliskan, Abuzer
• Cevahir, Ceren
• Ertarğın, Merve
• Esmeray, Furkan
• Gülan, Harun
• Günay, Mihriban
• İmak, Andaç
• Korunur, Murat
• Omaç, Zeki
• Sezer, Elif
• Şeker, Burhan
• Uyanık, Hakan
• Üstündağ, Aybike
• Yildiz, Gul
• Zengin, Bilgin

• MakaleAçık Erişim2026/r/munzur/5948
  Energy density in Rainbow Gravity: Levi-Civita Model

  Korunur, Murat; Korunur, Sibel

  In this study, the energy density for the Levi-Civita space-time is analyzed within the framework of Rainbow Gravity Theory (RGT). RGT is a theoretical approach that assumes the geometry of space-time depends on the energy of the test particle, offering a promising perspective in the quest for quantum gravity. Energy densities are calculated using the Einstein, Bergmann-Thomson, and Landau-Lifshitz energy-momentum prescriptions and compared with the results obtained from General Relativity. The findings reveal that as the test particle’s energy increases, the energy density rises in the Einstein and Landau-Lifshitz prescriptions, while it decreases in the Bergmann-Thomson formulation. This highlights how the problem of energy localization varies across different energy-momentum definitions and demonstrates the impact of RGT on energy distribution. The study contributes to the literature exploring the influence of alternative gravitational theories on energy-momentum distributions.

  İncele
• MakaleYalnız künye2026Scopus SJRQ3/r/munzur/1506
  Impact of ZnF2 and PbF2 on the radiation attenuation performance of the fluoride-based bismuth borate glass system

  Aşkın, A.

  Among the various types of glasses, fluoride-based glasses stand out due to their chemical and mechanical stability, low refractive indices, low phonon energies, and low transition temperatures. In this study, key radiation shielding parameters, including the mass attenuation coefficient (µm), half-value thickness (HVL), mean free path (MFP), transmission fraction (T), effective atomic number (Zeff), exposure build-up factors (EBF) and fast neutron removal cross-section (∑R), of the newly developed fluoride-based glass system, xZnF2—(25-x)PbF2—25Bi2O3—49.8B2O3—0.2Cr2O3 (where x is between 0, and 25 mol%), were evaluated. The data was gathered through Monte Carlo simulations, utilizing the Geant4 framework for a germanium detector and the PHY-X/PSD software. The parameters were assessed for gamma-ray energies ranging from 0.284 to 1.33 MeV across all glass samples. The results clearly show that the presence of PbF2 significantly enhances radiation shielding, owing to the high atomic number (Z) of lead. The highest µm and Zeff values were found to be highest and MFP, HVL, and T values were found to be lowest for the glass sample with the highest PbF2 content. Furthermore, the ∑R values of the glasses were observed to decrease as the PbF2 content reduced, highlighting the importance of high-Z elements in neutron absorption. Therefore, gamma and neutron shielding abilities of the studied glasses improve with the addition of PbF2 into the glass structure and glass system containing 25% mole PbF2 exhibits superior gamma and neutron shielding performance compared to shielding concrete.

  AtıfScopus1·WoS1

  İncele
• MakaleAçık Erişim2026Scopus SJRQ1/r/munzur/1408
  Multi-Task NILM with Anomaly Detection Using a Hybrid CNN–BilSTM–Transformer Model

  Gunay, Mihriban; Demir, Yakup; Zhilevski, Marin

  Non-Intrusive Load Monitoring (NILM) enables estimation of the energy use of individual appliances in smart buildings from a single aggregate meter. In practice, however, this task is not straightforward. Signals from different appliances can overlap, and the measured data may also include distortions such as spikes, drops, and noise. To address these issues, this study presents a multi-task triple-hybrid deep learning framework that handles appliance classification and anomaly detection together. The model brings together 1D-CNN, BiLSTM, and Transformer Attention so that local patterns, temporal dependencies, and wider contextual information can be learned within the same structure. It also uses a dual-output design to classify appliance categories and detect anomaly types simultaneously. Experiments were carried out on Building 1 of the UK-DALE dataset with four appliances: kettle, microwave, washer dryer, and fridge freezer. For the anomaly task, synthetic disturbances were added to segmented signal windows and grouped as normal, spike, drop, and noise. To check how well the proposed framework handled different scenarios, it was tested on both the UK-DALE and REDD datasets. Looking at the main UK-DALE results, the model correctly identified appliances 99.48% of the time and spotted anomalies with 98.80% accuracy. A secondary test on the REDD dataset yielded an 86.44% classification score. This proves the architecture can adjust to completely new power grid environments without losing its edge. On top of that, when pitted against standard benchmark models like Seq2Point, this triple-hybrid design clearly does a better job of mapping out complex signal changes. As a result, it yields much stronger anomaly detection metrics.

  • 
  • 
  AtıfOpenAlex1

  İncele
• MakaleAçık Erişim2026Scopus SJRQ1/r/munzur/1397
  A Metric-Driven Evaluation Framework for Remaining Useful Life Prognosis with Quantified Uncertainty

  Vashishtha, Govind; Chauhan, Sumika; Ertarğın, Merve

  This paper introduces a novel metric-driven evaluation framework for Remaining Useful Life (RUL) prognosis in rotating machinery, featuring robust uncertainty quantification. Accurate RUL prediction is vital for optimizing maintenance and preventing failures, but existing methods often struggle with complex nonlinear degradation or lack reliable uncertainty estimates. Our proposed framework integrates a probabilistic Deep State Space Model (DSSM) with a variational inference approach to model complex, non-linear degradation trends and inherent aleatoric uncertainty. A key innovation is the use of the Slime Mold Algorithm (SMA) for efficient hyperparameter optimization, ensuring maximum accuracy. Furthermore, an online adaptation mechanism, governed by a heuristic reinforcement learning agent, allows the model to continuously update its knowledge and adapt to concept drift in real-time. Experimental validation on the IMS bearing dataset demonstrates superior RUL prediction accuracy, evidenced by the lowest Root Mean Square Error (RMSE) of 8.1829 cycles, and a PICP of 0.59416. This dual capability makes the framework highly suitable for real-world predictive maintenance, enhancing safety and reliability.

  • 
  İncele
• MakaleAçık Erişim2026Scopus SJRQ1/r/munzur/1382
  A smartphone-based vibration dataset for induction motor fault diagnosis under different speed and load conditions

  Ertarğın, Merve; Orhan, Ahmet; Yıldırım, Özal; Chauhan, Sumika; Vashishtha, Govind

  Induction motors are foundational components across industrial applications, valued for their inherent robustness, operational simplicity, and cost-efficiency. Maintaining their reliable function is paramount for a wide range of mechanical and electrical systems. However, they are prone to various mechanical and electrical faults, such as bearing defects, rotor issues, and voltage imbalances, which can significantly impair their performance and reliability. This study presents a novel vibration dataset for induction motor fault diagnosis, uniquely acquired using a smartphone-based inertial sensor rather than conventional industrial accelerometers. Vibration signals were recorded along three orthogonal axes (gx, gy, gz), alongside gravity-compensated acceleration components (guserx, gusery, guserz), enabling detailed analysis of both raw and gravity-free vibration characteristics. Data were collected under diverse conditions, including healthy operation and several fault types, across varying rotational speeds and load states. The dataset features long-duration vibration recordings sampled at 100 Hz, suitable for both time-domain analysis and window-based feature extraction. Its inclusion of multiple operating speeds and load conditions is ideal for studying the impact of operational variability on fault signatures. By leveraging low-cost and readily accessible smartphone sensors, this dataset supports practical and accessible vibration data acquisition for supporting the development, benchmarking, and validation of data-driven fault diagnosis methods. This resource is expected to significantly advance research in condition monitoring of induction motor, particularly for machine learning and signal processing applications using vibration data.

  • 
  İncele
• MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/1337
  Single-Ended Fault Detection and Fault Location in Transmission Lines Using Approximate Derivative

  Akdağ, Mustafa; Mamiş, Mehmet Salih; Akmaz, Düzgün

  Fault location in power transmission lines (PTLs) relies on impedance or traveling wave (TW) principles. TW approaches offer superior accuracy and high robustness against fault resistance. While multi-ended methods require precise terminal synchronization, single-ended TW (SETW) methods utilize measurements from one terminal, requiring accurate distinction of reflected waves. This study employs the computationally efficient approximate derivative (AD)—the difference between consecutive samples—for SETW fault detection and location. Normally near zero, the AD of modal signals produces sharp transitions during faults. Comparing AD output to a threshold achieves fault detection. The AD then identifies arrival times of the incident and reflected TWs. When using TW theory to distinguish reflections from the fault point and remote end, the fault distance is calculated from their arrival time difference. Validated through 293 diverse ATP simulated fault scenarios, the approach delivered highly accurate results despite using a lower sampling rate than established methods, utilizing an exceptionally short data window—only 2.03 ms for a 300 km line. Finally, operational boundaries for the signal-to-noise ratio (SNR) in noisy conditions are established.

  • 
  İncele
• MakaleAçık Erişim2026Scopus SJRQ2/r/munzur/1262
  A Genetic Algorithm-Optimized ConvNeXtV2-YOLOv8 Framework for Dental Caries Detection in Panoramic Radiographs

  Sobahi, Nebras; Küçük, Deniz Bora; Kılıç, Kazım; İmak, Andaç; Çelebi, Adalet; Alghamedi, Yazyd; Yazicioglu, Cafer; Türkoğlu, Muammer; Şengür, Abdulkadir

  Background/Objectives: Dental caries is one of the most common oral diseases worldwide, and early diagnosis is essential for effective treatment. However, detecting carious lesions in panoramic radiographs is challenging because of low image contrast, anatomical complexity, and overlapping structures. This study aimed to develop an improved object detection framework for dental caries localization in panoramic radiographs. Methods: A modified YOLOv8 architecture was developed by integrating ConvNeXtV2 blocks into the Cross-Stage Partial (C2f) modules to enhance feature extraction and gradient propagation. To improve detection performance and reduce overfitting, key training hyperparameters were optimized using a genetic algorithm. The proposed model was evaluated on a dataset of 474 panoramic dental radiographs annotated by experts using bounding boxes. Results: The optimized model achieved a box precision of 78.4%, a recall of 53.6%, and a mean average precision at 50% intersection-over-union threshold (AP50) of 62.6%. Compared with the baseline YOLOv8 model, the proposed approach improved precision and AP50. Visual and quantitative analyses demonstrated that the ConvNeXtV2-enhanced architecture enabled more accurate localization of carious regions. Conclusions: The results indicate that combining ConvNeXtV2-based architectural enhancement with genetic algorithm-based hyperparameter optimization is an effective strategy for dental caries localization in panoramic radiographs. Although recall remains a limitation, the proposed framework shows potential as a computer-aided diagnostic tool for supporting clinical caries assessment.

  • 
  İncele
• MakaleYalnız künye2026Scopus SJRQ1/r/munzur/1174
  Modeling stack voltage and remaining useful life of proton exchange membrane fuel cells using deep neural networks

  Dogan, Gurkan; Imak, Andaç

  Currently, various energy conversion systems are being developed to reduce environmental impacts. Proton exchange membrane fuel cells (PEMFCs), which stand out with their high efficiency characteristics, have emerged as an important alternative. However, these advantages, along with high costs and uncertainties regarding remaining useful life (RUL) estimation, restrict their further commercialization, especially in the automotive industry. This study presents a forecasting framework using a long short-term memory (LSTM) architecture enhanced with the Bahdanau attention mechanism for predicting RUL and long-term degradation trends. The model thus exhibits a performance that can more effectively capture dependencies in time series and adaptively weight significant time steps. Furthermore, the tree-structured parzen estimator (TPE) was chosen for the appropriate selection of hyperparameters in the developed forecasting framework. The validity, stability, and performance of the proposed model under different operating conditions were examined on the FC1 and FC2 datasets. Experimental studies have shown that the proposed method is the most successful among the best methods, achieving RE metric values of 1.628 × 10−3, 0.003 × 10−1, 99.16 × 10−2, and 0.044 × 10−1 respectively in estimating the best RMSE, MAPE, R2, and RUL.

  • 
  • 
  İncele
• MakaleYayıncıda açık erişim2026Scopus SJRQ1/r/munzur/1123
  Explainable AI-based deep learning architectures for hydrogen prediction in biomass syngas and photocatalysis processes: Towards a sustainable and efficient energy economy

  Imak, Andaç

  This study, focusing on sustainable energy production and particularly the hydrogen (H2) economy, presents an advanced artificial intelligence (AI) framework for accurately predicting H2 production from sucrose photocatalysis catalyzed by LaFeO3 (GLFO) and the H2 concentration in syngas derived from biomass conversion. The research was conducted along two main directions: (i) deep learning (DL) architectures comprising nine specially designed recurrent neural network-based models and (ii) six conventional machine learning (ML) algorithms. For both H2 production systems, the best-performing models were identified and further interpreted using explainable AI (XAI) based on SHapley Additive exPlanations (SHAP). Among all investigated algorithms, a hybrid One-dimensional Convolutional Neural Network–Bidirectional Long Short-Term Memory–Bidirectional Gated Recurrent Unit (1D-CNN–BiLSTM–BiGRU) model demonstrated the highest predictive performance. Using 10-fold cross-validation, the proposed model achieved outstanding accuracy, with an R2 value of 0.9987 ± 0.0011 for photocatalytic H2 production prediction and 0.9980 ± 0.0006 for syngas H2 concentration prediction. These results highlight the potential of the proposed framework as a precise, reliable, and practical methodological approach for modeling and optimizing complex thermochemical energy systems.

  • 
  • 
  • 
  İncele
• MakaleAçık Erişim2026Scopus SJRQ1/r/munzur/973
  Forked hybrid architecture combining 1D-CNN and quantum convolutional networks for EEG signal classification

  Esmeray, Furkan; Gülten, Arif

  Detecting epileptic seizures from electroencephalogram (EEG) signals presents a significant challenge due to the nonlinear and non-stationary nature of brain activity. In this study, we propose a hybrid classification framework that integrates quantum-assisted and classical deep learning techniques to enhance detection accuracy and model interpretability. First, discrete wavelet transform (DWT) is applied to extract multiscale time–frequency features from raw EEG signals. Subsequently, three comparative models are developed: a conventional 1D-CNN model, an IQCNN model incorporating a parametric quantum circuit, and a novel Forked Enhanced Model that processes EEG features simultaneously through both 1D-CNN and IQCNN branches and combines them in a fully connected layer. The proposed hybrid model leverages the expressive power of quantum entanglement while preserving the robustness of classical deep learning. Evaluations using 5-fold stratified cross-validation on a real EEG dataset show that the forked architecture outperforms both classical CNNs and quantum-only based models in terms of accuracy, F1 score, precision, recall, number of incorrectly classified subjects and AUC. In this study, the best result was obtained with the Denoise IQCNNNet hybrid model. The accuracy of this model is 97.25%, precision 92.74%, recall 93.66%, F-1 score 93.15%, AUC 99.54%, number of incorrectly classified subjects are 57. This study demonstrates the feasibility and potential of hybrid quantum-classical networks in biomedical signal processing and provides a foundation for real-time clinical seizure monitoring systems.

  İncele