Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
Permanent URI for this collectionhttps://hdl.handle.net/11147/7148
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Article Reduced Phase Space Quantization and Quantum Corrected Entropy of Schwarzschild-De Sitter Horizons(Elsevier B.V., 2026) Jalalzadeh, S.; Moradpour, H.This paper investigates the quantization of the Schwarzschild–de Sitter (SdS) black hole (BH) using the Misner–Sharp–Hernandez (MSH) mass as the internal energy in a reduced phase space framework. After introducing the canonical variables of the reduced phase space, we derive a discrete spectrum for the surface areas of the BH event horizon (EH) as well as MSH masses. We utilized the MSH mass spectrum to obtain the entropy of the BH. The entropy of the BH and cosmic EHs reveals a logarithmic correction to the Bekenstein–Hawking term. Our results support the robustness of the logarithmic form of quantum corrections in SdS thermodynamics. © 2026 The Authors.Article Reversibility and Entropy in Bubbling Fluidized Beds: A Recurrence-Based Analysis(Elsevier, 2026) Zarghami, Reza; Mohammadpourfard, Mousa; Akkurt, Gulden GokcenNonlinear time series analysis techniques were applied to characterize bubbling fluidization. The delay method was used to reconstruct the state space attractor and analyze the reconstructed state space. The experiments were carried out in a laboratory-scale fluidized bed, operated under ambient conditions and with various sizes of particles, settled bed heights, measurement heights, and superficial gas velocities. The reversibility of the gas-solid fluidized bed hydrodynamics was investigated using pressure fluctuations by recurrence plot analysis. The anti-diagonal lines of the recurrence plot (RP) were regarded as a measure of reversibility. It was shown that the reversibility versus gas velocity has a concave shape in the bubbling regime. The highest reversibility occurs at velocities remarkably lower than the turbulent transition velocity. In addition, reversibility increases as the size of the particles increases. The Kolmogorov entropy was also estimated to confirm the reversibility analysis in the state space domain. In addition, the average cycle frequency and wideband energy in the frequency domain were also used to clarify the results in the state domain. It was found that a minimum in average cycle frequency, wideband energy, and entropy with an increase in the velocity corresponds to the transition between macro-structures and finer structures of the fluidization system. This minimum was primarily found in the macro-structures of the bubbling fluidization system. These findings can provide a practical tool for the optimal design and operation of the fluidized bed.Article Task-Specific Dynamical Entropy Variations in EEG as a Biomarker for Parkinson's Disease Progression(Springer, 2025) Onay, Fatih; Karacali, BilgeUncovering the neuronal mechanisms un-derlying optimal behavioral performance is essential to understand how the brain dynamically adapts to changing conditions. In Parkinson's disease (PD), these neuronal mechanisms are disrupted and lead to impairments in motor coordination and higher-order cognitive functions. This study investigates neuronal dynamics during a lower-limb pedaling task by analyzing the dynamical entropy of EEG signals in healthy controls (HC), PD patients, and PD patients with freezing of gait (PDFOG). We examined both average entropy changes and entropy variability across trials to characterize task-specific neural adaptations across disease progression. Results showed that PD and PDFOG patients exhibited decreased levels of permutation entropy in frontal and parietal regions, which may be associated with loss of cognitive adapta-tion due to altered information processing. Additionally, Vasicek's entropy variability in both PD groups was significantly diminished in occipital and left frontal regions, suggesting reduced cognitive capacity to dy-namically allocate neuronal resources during task engagement. We extended this analysis to the classification of groups using LDA and SVM classifiers, where entropy-derived features achieved a classification accuracy of up to 96.15% when distinguishing HC from PDFOG patients. This dynamical entropic framework provides a novel approach for capturing neural complexity changes during task performance, revealing subtle cognitive-motor impairments in PD. Understanding the maintenance of cognitive information processing and flexibility in response to motor and cognitive task demands could be a useful tool to track PD diagnosis and progression in addition to resting-state analyses.Article Citation - WoS: 1Citation - Scopus: 1A Novel Ml-Dem Algorithm for Predicting Particle Motion in Rotary Drums(Elsevier Sci Ltd, 2025) Kazemi, Saman; Zarghami, Reza; Mostoufi, Navid; Sotudeh-Gharebagh, Rahmat; Al-Raoush, Riyadh I.The discrete element method (DEM) is a widely used approach for studying the behavior of particles in industrial equipment, including rotary drums. Although DEM is highly accurate and efficient, it suffers from the computational cost in simulations. The primary objective of this research is to reduce the computational costs of DEM by introducing a novel machine learning (ML) approach based on a deep neural network for predicting particle behavior in rotary drums. The proposed approach utilizes a continuous convolution operator in a neural network. To evaluate its effectiveness, the results of the proposed ML-DEM approach were compared quantitatively and qualitatively with the experimental data and the conventional DEM results. It was shown that in addition to its high accuracy, the proposed approach reduces the computational costs by approximately 35 % and 65 % compared to the conventional DEM simulations on GPU and CPU (with 8 processors), respectively. Furthermore, to ensure the comprehensive and independent validation of the proposed algorithm, the study investigated the effects of various parameters such as drum rotational speed and fill ratio on lateral entropy-based mixing, circulation time, and velocity profile in the active layer. The results were then compared with those obtained using the conventional DEM and found to be in good agreement. This new algorithm can serve as a starting point for reducing computational costs in simulating particle motion in granular systems.Article Citation - WoS: 6Citation - Scopus: 10Using Chemosensory-Induced Eeg Signals To Identify Patients With <i>de Novo</I> Parkinson's Disease(Elsevier Sci Ltd, 2024) Olcay, Orkan; Onay, Fatih; Ozturk, Guliz Akin; Oniz, Adile; Ozgoren, Murat; Hummel, Thomas; Guducu, CagdasObjective: Parkinson's disease (PD) patients generally exhibit an olfactory loss. Hence, psychophysical or electrophysiological tests are used for diagnosis. However, these tests are susceptible to the subjects' behavioral response bias and require advanced techniques for an accurate analysis. Proposed Approach: Using well-known feature extraction methods, we characterized chemosensory-induced EEG responses of the participants to classify whether they have PD. The classification was performed for different time intervals after chemosensory stimulation to see which temporal segment better separates healthy controls and subjects with de novo PD. Results: The performances show that entropy and connectivity features discriminate effectively PD and HC participants when olfactory-induced EEG signals were used. For these methods, discrimination is over 80% for segments 100-700 and 200-800 milliseconds after stimulus onset. Comparison with Existing Methods: We compared the performance of our framework with linear predictive coding, bispectrum, wavelet entropy-based methods, and TDI score-based classification. While the entropy- and connectivity-based methods elicited the highest classification performances for olfactory stimuli, the linear predictive coding-based method elicited slightly higher performance than our framework when the trigeminal stimuli were used. Conclusion: This is one of the first studies that use chemosensory-induced EEG signals along with different feature extraction methods to classify healthy subjects and subjects with de novo PD. Our results show that entropy and functional connectivity methods unravel the chemosensory-induced neural dynamics encapsulating critical information about the subjects' olfactory performance. Furthermore, time- and frequency-resolved feature analysis is beneficial for capturing disease-affected neural patterns.