Some isotopes such as ¹²³I and ¹²⁴I are useful in medical science, and thus, the production of these isotopes has great importance. Iodine-123 is the gamma-emitting radioisotope of radioiodine, and ¹²⁴I is the long-lived positron-emitting radioisotope of radioiodine, and they have applications in diagnosis via both Single Photon Emission Computed Tomography (SPECT)/Positron Emission Tomography (PET) and radiotherapy. Therefore, many theoretical and experimental studies are performed for these isotopes. In this study, the cross sections of the ¹²³Te(p,n), ¹²⁴Te(p,n), and ¹²⁴Te(p,2n) reactions up to 31 MeV, where ¹²³I and ¹²⁴I can be produced, are calculated by importing the Collective Semi-Classical Fermi Gas Model (CSCFGM) to the Talys 1.96 computer code. The predictions are compared with the default theoretical calculations of Talys 1.96 and existing experimental data taken from the EXFOR library. The results are in good agreement with the experimental data, and therefore, CSCFGM looks to be a useful tool for predicting the production reactions of some therapeutic isotopes.

The elastic and inelastic cross sections of ${}^6\mathrm{Li}$ on ${}^{28}\mathrm{Si}$ at 240 MeV, and quasielastic cross section of ${}^7\mathrm{Li}$ and ${}^{11}\mathrm{Li}$ on ${}^{28}\mathrm{Si}$ at $177.8$ and 319 MeV, respectively, are analyzed with the coupled-channels method. The collective nuclear level density is used to determine the deformation parameter regarding to the first-excited state of ${}^{28}\mathrm{Si}$. The results are in agreement with the experimental data and indicate the need of using a nuclear structure model such as nuclear level density to reduce the ambiguity between the optical model parameters and the deformation parameter. Additionally, the spin-orbit potential is found to have an important role in reproducing the data of the quasielastic scattering of ${}^7\mathrm{Li}$ and ${}^{11}\mathrm{Li}$ on a ${}^{28}\mathrm{Si}$ target.

55Mn izotopunun (γ,n), (γ,2n) ve (γ,3n) fotonükleer reaksiyonlarının tesir kesitleri eşik enerjisinden 37 MeV'e kadar TALYS 1.95 bilgisayar kodu kullanılarak hesaplandı. Elde edilen teorik sonuçlar reaksiyonların deneysel verileri ile karşılaştırıldı. Hesaplamalarda, kollektif nükleer seviye yoğunluğu modeli (CSCFGM-Collective semi-classical Fermi gas model) bilgisayar programında opsiyonel bir girdi olarak dahil edildi ve kollektif etkilerin (rotasyonel ve vibrasyonel) fotonükleer tesir kesitleri üzerindeki etkisi araştırıldı. Sonuçlar incelendiğinde, kollektif yarı-klasik nükleer seviye yoğunluğu modelinin bu reaksiyonların tesir kesiti hesaplamalarında güvenilir bir araç olarak kullanılabileceği gösterildi. Ayrıca, dev dipol rezonansın baskın olduğu 30 MeV gama enerjisi altında gerçekleşen reaksiyonlarda kollektif katkıları içeren nükleer seviye yoğunluğu fonksiyonunun etkisi analiz edildi.

Pile foundations are critical structural elements, and their integrity is essential for ensuring the stability and safety of construction projects. Low-strain pile integrity testing (LSPIT) is widely used for defect detection; however, conventional manual interpretation of reflectograms is both time-consuming and susceptible to human error. This study presents a deep learning-driven approach utilizing transfer learning with convolutional neural networks (CNNs) to automate pile defect detection. A dataset of 328 reflectograms collected from real construction sites, including 246 intact and 82 defective samples, was used to train and evaluate the model. To address class imbalance, oversampling techniques were applied. Several state-of-the-art pretrained CNN architectures were compared, with ConvNeXtLarge achieving the highest accuracy of 98.2%. The accuracy reported was achieved on a dedicated test set using real reflectogram data from actual construction sites, distinguishing this study from prior work relying primarily on synthetic data. The proposed novelty includes adapting pre-trained CNN architectures specifically for real-world pile integrity testing, addressing practical challenges such as data imbalance and limited dataset size through targeted oversampling techniques. The proposed approach demonstrates significant improvements in accuracy and efficiency compared to manual interpretation methods, making it a promising solution for practical applications in the construction industry. The proposed method demonstrates potential for generalization across varying pile lengths and geological conditions, though further validation with broader datasets is recommended.