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    Exploration of the soliton solutions of the (n+1) dimensional generalized Kadomstev Petviashvili equation using an innovative approach
    (NATURE PORTFOLIO, HEIDELBERGER PLATZ 3, BERLIN 14197, GERMANY, 2025) Kopçasız, Bahadır; Sağlam, Fatma Nur Kaya; Bulut, Hasan; Radwan, Taha
    In this paper, we deal with the (n+1)-dimensional generalized Kadomtsev-Petviashvili equation (dgKPE). This is an important model in nonlinear science, with applications in various fields. Its integrability and rich soliton dynamics continue to attract researchers interested in the field of nonlinear partial differential equations (NLPDEs). We are interested in the new auxiliary equation method (NAEM). We reduce the equation to an ordinary differential equation (ODE) with the help of an appropriate wave transformation and search for different types of soliton solutions. Additionally, we demonstrated the efficacy of the NAEM as a straightforward yet powerful mathematical instrument for handling challenging issues, highlighting its potential to resolve the challenging problems related to the study of nonlinear equations. This technique yields several types of solutions for (n+1)-dgKPE, including trigonometric, hyperbolic, shock wave, singular soliton, exponential, and rational functions. A range of graphs showcasing the results are reviewed, as well as the wave behavior for the solutions in different circumstances. The obtained data provide important information for studying hydrodynamic waves, plasma fluctuations, and optical solitons. They also aid in understanding the behavior of the KPE in different physical situations. We clarify in this article how the (n+1)-dgKPE, when combined with NAEM, can result in better data transmission rates, optimized optical systems, and the advancement of nonlinear optics toward more dependable and efficient communication technologies. The obtained information clarifies the equation and opens up new avenues for investigation. To our knowledge, for this equation, these methods of investigation have not been utilized before. The accuracy of each solution has been verified using the Maple software program.
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    Radiomics of locally advanced rectal cancer: machine learning-based prediction of response to neoadjuvant chemoradiotherapy using pre-treatment sagittal T2-weighted MRI
    (Springer, 2023) Yardimci, Aytul Hande; Kocak, Burak; Sel, Ipek; Bulut, Hasan; Bektas, Ceyda Turan; Cin, Merve; Dursun, Nevra
    Purpose Variable response to neoadjuvant chemoradiotherapy (nCRT) is observed among individuals with locally advanced rectal cancer (LARC), having a significant impact on patient management. In this work, we aimed to investigate the potential value of machine learning (ML)-based magnetic resonance imaging (MRI) radiomics in predicting therapeutic response to nCRT in patients with LARC. Materials and methods Seventy-six patients with LARC were included in this retrospective study. Radiomic features were extracted from pre-treatment sagittal T2-weighted MRI images, with 3D segmentation. Dimension reduction was performed with a reliability analysis, pair-wise correlation analysis, analysis of variance, recursive feature elimination, Kruskal-Wallis, and Relief methods. Models were created using four different algorithms. In addition to radiomic models, clinical only and different combined models were developed and compared. The reference standard was tumor regression grade (TRG) based on the Modified Ryan Scheme (TRG 0 vs TRG 1-3). Models were compared based on net reclassification index (NRI). Clinical utility was assessed with decision curve analysis (DCA). Results Number of features with excellent reliability is 106. The best result was achieved with radiomic only model using eight features. The area under the curve (AUC), accuracy, sensitivity, and specificity for validation were 0.753 (standard deviation [SD], 0.082), 81.1%, 83.8%, and 75.0%; for testing, 0.705 (SD, 0.145), 73.9%, 81.2%, and 57.1%, respectively. Based on the clinical only model as reference, NRI for radiomic only model was the best. DCA also showed better clinical utility for radiomic only model. Conclusions ML-based T2-weighted MRI radiomics might have a potential in predicting response to nCRT in patients with LARC.