DSpace Arşivi :: Ana Sayfa

Muş Alparslan Üniversitesi Kurumsal Akademik Arşivi

DSpace@MAUN, Muş Alparslan 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 yayınların etkisini artırmak için telif haklarına uygun olarak Açık Erişime sunar.

WoS Yayınları

Scopus Yayınları

TR-Dizin Yayınları

PubMed Yayınları

Makaleler

Kitaplar

Bildiriler

Tezler

Güncel Gönderiler

2-(N-Hexylcarbazole-3′-yl)-4-pyridinealdehyde: Cyanide Detection via Benzoin Condensation
(Wiley, 2025) Battal, Ahmet; Tavasli, Mustafa
In this research, 2-(N-hexylcarbazole-3 '-yl)-4-pyridinealdehyde (probe A) with a donor-pi-acceptor (D-pi-A) structure was tested as a new fluorescence sensor. The fluorescence sensor properties of probe A were investigated by using UV-Vis and PL spectrophotometers. The absorption spectrum of probe A did not show a significant change against analytes added to the solution. However, when the emission spectrum of probe A was examined, it gave two very low-intensity emissions at 400 nm and 540 nm. Probe A showed superior selectivity only for cyanide ions (CN-) with the limit of detection (LOD) of 1.42 nM in the presence of various competing analytes. This LOD value is the lowest value reported so far. For the first time, CN- sensing proceeds via benzoin condensation reaction. The stoichiometric ratio and detection mechanism between probe A and CN- were confirmed by Job's plot, HRMS, 1H-NMR, and FT-IR analyses. Probe A also worked successfully in the detection of CN-, which is a highly hazardous and toxic substance, including practical applications (three different real-world water samples). Therefore, probe A was introduced to the field as an effective and striking potential sensor candidate in water quality testing and disease diagnosis, etc.
Copula-Based Data Augmentation and Machine Learning for Predicting Tensile Strength of 3D-Printed PLA Under Anisotropic Conditions
(Wiley, 2025) Saylik, Ahmet; Kosedag, Ertan; Etem, Taha
In this study, 48 polylactic acid (PLA) samples were produced via 3D printing, incorporating four infill geometries (gyroid, lattice, honeycomb, and linear), four infill rates (15%-60%), and three printing directions (x, y, z). Tensile testing revealed anisotropic mechanical behavior, with the x-direction consistently outperforming y- and z-directions due to layer adhesion dynamics. A machine learning framework leveraging copula-based data augmentation was developed to predict tensile strength at untested infill rates. The framework employed least squares regression, support vector machines (SVM), Gaussian process regression (GPR), and artificial neural networks (ANNs), augmented with 20,000 synthetic data points to enhance model robustness. Results demonstrated that gyroid geometry in the x-direction achieved the highest tensile strength (53.4 MPa at 60% infill), while Lattice patterns underperformed. Data augmentation improved prediction accuracy across all models, with SVM achieving the lowest RMSE (1.53 MPa) and R 2 values exceeding 0.87. This study highlights the critical interplay of infill parameters, directional anisotropy, and machine learning in optimizing 3D-printed PLA components for industrial applications, offering a data-driven pathway to reduce experimental costs and accelerate material design.
Copula-Based Data Augmentation and Machine Learning for Predicting Tensile Strength of 3D-Printed PLA Under Anisotropic Conditions [2]
(Wiley, 2025) Saylik, Ahmet; Kosedag, Ertan; Etem, Taha
In this study, 48 polylactic acid (PLA) samples were produced via 3D printing, incorporating four infill geometries (gyroid, lattice, honeycomb, and linear), four infill rates (15%-60%), and three printing directions (x, y, z). Tensile testing revealed anisotropic mechanical behavior, with the x-direction consistently outperforming y- and z-directions due to layer adhesion dynamics. A machine learning framework leveraging copula-based data augmentation was developed to predict tensile strength at untested infill rates. The framework employed least squares regression, support vector machines (SVM), Gaussian process regression (GPR), and artificial neural networks (ANNs), augmented with 20,000 synthetic data points to enhance model robustness. Results demonstrated that gyroid geometry in the x-direction achieved the highest tensile strength (53.4 MPa at 60% infill), while Lattice patterns underperformed. Data augmentation improved prediction accuracy across all models, with SVM achieving the lowest RMSE (1.53 MPa) and R2 values exceeding 0.87. This study highlights the critical interplay of infill parameters, directional anisotropy, and machine learning in optimizing 3D-printed PLA components for industrial applications, offering a data-driven pathway to reduce experimental costs and accelerate material design.
Effect on Calf Sex of Milk Yield in Holstein Friesian Cattle Breed
(Sivar-Soc Italiana Veterinari Animali Reddito, 2025) Kaygisiz, Ali; Yilmaz, Isa; Sahin, Onur
This study compared the effects of the sex of Holstein Friesian (HF) calves registered in the herdbook system on milk yield. The study material comprised 802042 milk and calving records between 2000 and 2014. When analyzing the data, the GLM ANO-VA method was used to investigate the effects of the sex of the calf and other environmental factors on milk yield. Of the calves born, 54.3% were female and 45.7% were male calves. The results show that the average lactation milk yield (LMY) of (HF) cows is 8604 +/- 3.6 kg, 305-day milk yield (305 MY) is 7028 +/- 2.4 kg and lactation length (LL) is 372 +/- 0.1 days. When cows give birth to female and male calves, LMY, 305 MY and LL are 8720 +/- 15.3 kg, 7148 +/- 10.0 kg and 370 +/- 0.5 days, respectively; 8482 +/- 15.3 kg, 6901 +/- 10.0 kg and 373 +/- 0.5 days were determined. Based on these results, it was found that cows that gave birth to female calves had a higher milk yield, and the effect of sex on milk yield was significant. The results obtained demonstrate distinct differences between cows giving birth to female versus male calves. Specifically, it was found that (HF) cows that give birth to female calves have a higher milk yield during lactation, and these differences were statistically significant. When evaluated by lactation number, the milk yield superiority of cows giving birth to female calves over those giving birth to male calves ranged from 3.89% to 1.13% for total lactation milk yield and from 4.98% to 1.80% for 305-day milk yield. Additionally, it was observed that the lactation length of cows giving birth to male calves was 1.06% to 0.55% longer than that of cows giving birth to female calves. Overall, across all lactations, HF cows that gave birth to female calves had a higher milk yield, indicating that the sex of the calf significantly influenced the milk yield of the mother.
Luminescent Sensing of 1,3,5-Trinitrotoluene Using a Schiff Base Ligand and Cd(II) Complex: Structural and Photophysical Investigations
(Wiley, 2025) Turan, Nevin; Altun, Ayhan; Buldurun, Kenan; Aydin, Olcay
The Schiff base (H2L) was synthesized by condensing 2-amino-6-methyl-4,5,6,7-tetrahydrothieno[2,3-c]pyridine-3-carboxamide with 5-bromo-2-hydroxybenzaldehyde in a 1:2 M ratio, yielding the product with high efficiency. This Schiff base then formed a coordination complex with Cd(II) in a 1:1 M ratio, resulting in the complex [H2LCd(H2O)2]1.5H2O. The synthesis and structural characterization of H2L and its Cd(II) complex were carried out using various techniques, including microanalysis (CHNS), FTIR, 1H and 13C-NMR, mass, UV-visible, magnetic susceptibility, thermogravimetric analysis (TGA), and fluorescence spectroscopy. The spectral data suggested that the Cd(II) complex adopted an octahedral geometry. In addition to the structural studies, the fluorescence properties of H2L and its Cd(II) complex were investigated. Sensor properties of the Cd(II) complex for detecting nitroaromatic compounds were assessed using fluorescence spectroscopy. Both compounds exhibited high sensitivity for the detection of 1,3,5-trinitrotoluene (TNT). Specifically, H2L demonstrated a low limit of detection (LOD) of 0.094 mu M in the linear range of 1-10 mu M. The fluorescence studies revealed that H2L and its Cd(II) complex displayed strong fluorescence emissions. Furthermore, the Schiff base was employed for detecting TNT through fluorescence quenching, showing excellent selectivity and sensitivity.