Altınbaş Üniversitesi Kurumsal Akademik Arşivi
DSpace@Altınbaş, Altınbaş Üniversitesi tarafından doğrudan ve dolaylı olarak yayınlanan; kitap, makale, tez, bildiri, rapor, araştırma verisi gibi tüm akademik kaynakları uluslararası standartlarda dijital ortamda depolar, Üniversitenin akademik performansını izlemeye aracılık eder, kaynakları uzun süreli saklar ve telif haklarına uygun olarak Açık Erişime sunar.
Güncel Gönderiler
Music Therapy may Decrease Radial Artery Spasm Rates and Increase Satisfaction during Coronary Angiography
(2025) Aslan, Muzaffer; Okşen, Doğaç; Yavuz, Yunus Emre; Kaynak, Çağdaş
Introduction: With the widespread use of the radial artery in catheterization procedures, radial artery spasm (RAS) is frequently considered an undesirable event. It is known that anxiety increases RAS, and listening to music helps individuals control anxiety during the procedure. This study aimed to investigate the effects of music concerts on RAS.
Methods: In this prospective study, imaging and interventional coronary catheterization procedures using the radial artery were included. One group listened to a musical recital during the procedure, while the other group was treated in a quiet environment. The demographics, procedural parameters, and complications of both groups were compared.
Results: The study included a total of 147 patients, with an average age of 51.6 ± 11.1 years. Of these, 78 patients (53%) listened to music, while 69 patients (46.9%) underwent catheterization in a quiet environment. The impact of music therapy on the RAS was found to be significant (11.5% vs. 20.3%; p=0.035). While music therapy showed a potential to reduce RAS rates, its effect was not statistically significant in multivariate analysis (p=0.055).
Conclusion: Music is a feasible, simple, and inexpensive method for reducing anxiety levels in patients. Listening to music during catheterization can reduce procedural discomfort and the frequency of undesirable events by helping people control their anxiety.
Cholinergic system in patients with chronic kidney disease: cognitive and renal implications
(2025) Xu, Hong; Eriksdotter, Maria; Hafez, Gaye; Sumonto, Mitra; Bruchfeld, Annette; Pesic, Vesna; Unwin, Robert; Wagner, Carden A.; Massy, Carsten; Massy, Ziad A.; Zoccali, Carmine; Pepin, Marion; Capasso, Giovambattista; Liabeuf, Sophie; CONNECT Action (Cognitive Decline in Nephro-Neurology European Cooperative Target)
Cholinergic synapses are widespread throughout the human central nervous system. Their high density in the thalamus, neocortex, limbic system, and striatum suggests that cholinergic transmission plays a vital role in memory, attention, learning and other higher cognitive functions. As a result, the brain's cholinergic system occupies a central position in research on normal cognition and age-related cognitive decline, including dementias such as Alzheimer's disease. In addition to its role in the brain, neuronal cholinergic pathways are essential for the physiological regulation of bodily organs, including the kidneys, through the parasympathetic branch of the peripheral nervous system. Chronic kidney disease (CKD) is a non-communicable disease with a global prevalence of approximately 10%. Cognitive impairment is common among patients with CKD, with reported prevalence rates ranging from 30% to 60%, depending on definitions and assessment methods used. Given the importance of the cholinergic system in cognitive processes, it may be a key area of focus for evaluating cognitive function in this population. In this current narrative review, we will first examine evidence linking the cholinergic system to cognitive functions; with a specific focus on drugs that affect this system. we will then discuss the potential implications of cholinergic function in patients with CKD.
An advanced mixed finite element formulation for flexural analysis of laminated composite plates incorporating HSDT and transverse stretching effect
(Springer, 2025) Kanığ, Doğan; Kutlu, Akif
The modeling and analysis of laminated composite plates are performed using a unified Higher Order Shear Deformation Theory (HSDT) that accounts for transverse stretching effect. The adopted unified HSDT formulation allows the implementation of various shear functions. To derive a weak form from the generalized displacement fields of HSDTs, a variational principle is applied within a two-field mixed approach. The stationarity of the functional for laminated plate structures is obtained through the application of the Hellinger-Reissner variational principle. Hence, displacements and stress resultants, namely two independent fields, are included in finite element equations. Four-noded, quadrilateral elements are employed for the discretization of the plate's domain. While the generated functional initially had C1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C<^>{1}$$\end{document} continuity, benefiting from the two-fields property of the mixed finite element formulation, integration by parts is performed that results with a functional requiring only C0\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C<^>{0}$$\end{document} continuity. To effectively capture the nonlinear and parabolic variation of transverse shear stress, it is determined that even with varying functions, the results are theoretically consistent with the elasticity method and the employed HSDT model. Also, when compared to the theories that are already accessible in the literature, for the bending behavior of composite plates, incorporating the stretching effect converges the exact results for laminated composite plates more than the studies where that effect is neglected.
A Novel Flip-Filtered Orthagonal Frequency Division Multiplexing-Based Visible Light Communication System: Peak-to-Average-Power Ratio Assessment and System Performance Improvement
(Multidisciplinary Digital Publishing Institute (MDPI), 2025) Hujijo, Hayder S. R.; Ilyas, Muhammad
Filtered orthogonal frequency division multiplexing (F-OFDM), employed in visible light communication (VLC) systems, has been considered a promising technique for overcoming OFDM’s large out-of-band emissions and thus reducing bandwidth efficiency. However, due to Hermitian symmetry (HS) imposition, a challenge in VLC involves increasing power consumption and doubling inverse fast Fourier transform IFFT/FFT length. This paper introduces the non-Hermitian symmetry (NHS) Flip-F-OFDM technique to enhance bandwidth efficiency, reduce the peak–average-power ratio (PAPR), and lower system complexity. Compared to the traditional HS-based Flip-F-OFDM method, the proposed method achieves around 50% reduced system complexity and prevents the PAPR from increasing. Therefore, the proposed method offers more resource-saving and power efficiency than traditional Flip-F-OFDM. Then, the proposed scheme is assessed with HS-free Flip-OFDM, asymmetrically clipped optical (ACO)-OFDM, and direct-current bias optical (DCO)-OFDM. Concerning bandwidth efficiency, the proposed method shows better spectral efficiency than HS-free Flip-OFDM, ACO-OFDM, and DCO-OFDM.
A fast and efficient machine learning assisted prediction of urea and its derivatives to screen crystal propensity with experimental validation
(Elsevier Ltd, 2025) Güleryüz, Cihat; Sumrra, Sajjad H.; Hassan, Abrar U.; Mohyuddin, Ayesha; Noreen, Sadaf; Elnaggar, Ashraf Y.
Predicting crystal propensity is crucial yet challenging in various industries where it significantly influences product stability, performance, and efficacy. Predicting a crystal propensity identifies their optimal chemical structures for desired properties including solubility, bioavailability, shelf-life stability etc. Herein, A machine learning (ML) assisted analysis is performed to predict their crystal propensity by collecting a dataset of 6000 non-crystalline and over 200 crystalline urea and its derivatives. The data is trained by employing a Support Vector Machine (SVM) with its Radial Basis Function (RBF) and linear kernels along with Random Forest regression analysis. The trained data is compared with four other ML models, including Linear Regression, Gradient Boosting, Random Forest and Decision Tree Regressions to predict their crystal propensity. It yields an accuracy of 79 % for identifying their non-crystalline compounds and 59 % in predicting crystallization failure. Their dimensionality reduction via t-SNE reveals their distinct clustering patterns to underscore their complex interplay between molecular structure and crystal propensity. Their experimental validation also corroborates the current findings to demonstrate their efficacy to streamline their crystal engineering for pharmaceutical formulation-based workflows. Notably, the number of rotatable bonds and molecular connectivity index (χov) emerges as pivotal descriptors for enabling their accurate classification with minimal input features. This study elucidates its quantitative structure-crystallinity relationship to provide a valuable tool for crystal design and optimization.
