Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2026Scopus SJRQ2/r/bayburt/11555
  Machine Learning-Based Material Thickness and Type Classification via Scattering Parameters

  Engin, Mustafa Alptekin; Cakir, Mehmet

  The precise determination of material type and thickness is of major significance in non-destructive testing, quality assurance, and materials science, as it influences the functionality, reliability, and performance of materials in engineering applications. This study proposes a methodology for the classification of material thickness and type through the analysis of scattering parameters within the 8–12 GHz frequency range. A database was created, encompassing real, imaginary, and dB values of reflection and transmission parameters for nine real-world materials with thicknesses ranging from 1 to 10 mm. This study addressed two main classification tasks, namely material thickness and material type. A variety of training–testing splits were employed in conjunction with 10-fold cross-validation to facilitate a comparison of classifier performance. In the material type classification, incorporating multiple thickness levels of each material enabled the model to distinguish materials with similar reflection characteristics more accurately, thereby enhancing discrimination performance. The results showed that Fine KNN consistently achieved 100% accuracy in thickness classification, while Quadratic SVM achieved 100% accuracy in material type classification, even when utilising only three thickness levels. Furthermore, the 90% training–10% testing split yielded the highest performance in thickness classification, whereas the optimal data split for material type classification differed across classifiers. Overall, this study demonstrates that the combination of scattering parameters and machine learning serves as a reliable and efficient approach for non-destructive material characterization.

  AtıfScopus1·WoS1·OpenAlex1

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• MakaleAçık Erişim2026Scopus SJRQ2/r/bayburt/11111
  Dual-Band Electromagnetic Shielding of Concrete Block Walls Using Concentric Double Square-Ring Frequency-Selective Surfaces near the n78 and n79 5G Frequency Regions

  Cakir, Mehmet

  Featured Application: This study demonstrates a concrete-integrated dual-band frequency-selective shielding approach for selectively attenuating the main sub-6 GHz 5G n78 and n79 frequency regions in building walls without increasing wall thickness or altering conventional concrete block geometry. This paper presents a dual-band shielding approach in which two concentric square rings, fabricated on a single 0.2 mm copper sheet, are embedded in the mortar joint of prefabricated concrete blocks. The outer ring targets n78 (design center 3.5 GHz) and the inner ring targets n79 (design center 4.7 GHz). Both rings are dimensioned from the effective wavelength inside the concrete medium (εr = 5.0): the outer ring has an outer side of 9.93 mm and the inner ring an outer side of 7.43 mm, with a 180° gap offset between the two elements to suppress inter-ring coupling. CST Microwave Studio simulations predicted two shielding peaks of 37.2 dB at 3.48 GHz and 35.8 dB at 4.72 GHz, with −10 dB bandwidths of 580 MHz and 490 MHz, respectively. Computer numerical control (CNC)-milled copper panels cast into ordinary Portland cement (OPC) concrete blocks achieved measured peak SE of 34.9 dB (n78) and 33.4 dB (n79), with bandwidths of 510 MHz and 420 MHz. The 2.3–2.4 dB shortfall relative to simulation is attributed to coupling-gap drift during milling and spacer-induced surface voids. A permittivity sweep from εr = 4.5 to 6.0 showed that peak SE remains above 30 dB throughout.

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• MakaleAçık Erişim2025Scopus SJRQ2/r/bayburt/12660
  Development of a Clinical Decision Support System Using Artificial Intelligence Methods for Liver Transplant Centers

  Yağanoğlu, Mete; Öztürk, Gürkan; Bozkurt, Ferhat; Bilen, Zeynep; Yetiş Demir, Zühal; Kul, Sinan; Şimşek, Emrah; Kara, Salih; Eygu, Hakan; Altundaş, Necip

  The objective of this study is to utilize artificial intelligence techniques for the diagnosis of complications and diseases that may arise after liver transplantation, as well as for the identification of patients in need of transplantation. To achieve this, an interface was developed to collect patient information from Atatürk University Research Hospital, specifically focusing on individuals who have undergone liver transplantation. The collected data were subsequently entered into a comprehensive database. Additionally, relevant patient information was obtained through the hospital’s information processing system, which was used to create a data pool. The classification of data was based on four dependent variables, namely, the presence or absence of death (“exitus”), recurrence location, tumor recurrence, and cause of death. Techniques such as Principal Component Analysis and Linear Discriminant Analysis (LDA) were employed to enhance the performance of the models. Among the various methods employed, the LDA method consistently yielded superior results in terms of accuracy during k-fold cross-validation. Following k-fold cross-validation, the model achieved the highest accuracy of 98% for the dependent variable “exitus”. For the dependent variable “recurrence location”, the highest accuracy obtained after k-fold cross-validation was 91%. Furthermore, the highest accuracy of 99% was achieved for both the dependent variables “tumor recurrence” and “cause of death” after k-fold cross-validation.

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  AtıfScopus2·WoS2·OpenAlex2

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• MakaleAçık Erişim2024Scopus SJRQ2/r/bayburt/12972
  Gelatine-Based Biopolymer Film Produced from Ozone-Treated Film-Forming Solutions Containing Whey Protein Concentrate: Effects on Physical, Mechanical, and Thermal Characteristics

  Mercan, Emin

  This study aimed to determine the effects of the ozone treatment of film-forming solutions (FFSs) containing whey protein concentrate (WPC) and gelatine on biopolymer films’ physical, mechanical, and thermal properties. Film samples were produced from a FFS that was ozone-treated at 0 (control), 5, 10, and 30 min. Ozone treatment caused an increase in the pH values of the FFS, whereas the zeta potential remained negative. The films became lighter in colour, slightly greenish, and more opaque with longer ozonation times. The control sample showed the highest thermogravimetric weight loss (92.15%). The weight loss of the samples decreased with increasing ozone treatment time. The application of ozone treatment on the FFS enhanced the films’ mechanical properties. Increased ozone treatment time improved the film samples’ tensile strength, elongation at break, and toughness values. In conclusion, the results of this study demonstrate that the ozone treatment of FFS containing whey protein concentrate and gelatine can significantly enhance the physical, mechanical, and thermal properties of biopolymer films. These results highlight the potential of ozone treatment as a viable method for improving the performance and quality of biopolymer films used in food packaging, offering promising advantages for sustainable and environmentally friendly packaging solutions.

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  AtıfScopus5·WoS5·OpenAlex6

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