Background/Objectives: Traditional medical image analysis methods often suffer from locality bias, limiting their ability to model long-range contextual relationships between spatially distributed anatomical structures. To overcome this challenge, this study proposes SPX-GNN (Superpixel Explainable Graph Neural Network). This novel method reformulates image analysis as a structural graph learning problem, capturing both local anomalies and global topological patterns in a holistic manner. Methods: The proposed framework decomposes images into semantically coherent superpixel regions, converting them into graph nodes that preserve topological relationships. Each node is enriched with a comprehensive feature vector encoding complementary diagnostic clues, including colour (CIELAB), texture (LBP and Haralick), shape (Hu moments), and spatial location. A Graph Neural Network is then employed to learn the relational dependencies between these enriched nodes. The method was rigorously evaluated using 5-fold stratified cross-validation on a public dataset comprising 4200 chest X-ray images. Results: SPX-GNN demonstrated exceptional performance in tuberculosis classification, achieving a mean accuracy of 99.82%, an F1-score of 99.45%, and a ROC-AUC of 100.00%. Furthermore, an integrated Explainable Artificial Intelligence module addresses the black box problem by generating semantic importance maps, which illuminate the decision mechanism and enhance clinical reliability. Conclusions: SPX-GNN offers a novel approach that successfully combines high diagnostic accuracy with methodological transparency. By providing a robust and interpretable workflow, this study presents a promising solution for medical imaging tasks where structural information is critical, paving the way for more reliable clinical decision support systems.

Bu çalışma kapsamında, yerli karbon fiber takviyeli poliüretan plakalar ile donatılandırılmış beton traverslerin merkez negatif eğilme performansı, öngerilmeli B70 tipi traverslerle karşılaştırmalı yürütülen deneyler ile araştırılmıştır. Yapılan testler sonucunda, yeni tip traverslerin yorulma esaslı dizayn yükünün, B70 tipi öngerilmeli traverslerin 2 katına ulaşan oranda daha yüksek sonuçlandığı görülmüştür. Ayrıca yeni tip traverslerde herhangi bir öngerilme kuvveti veya etriye kullanılmamasına rağmen, B70 tipi öngerilmeli traverslerin kırılma yükünün üzerindeki yük değerlerinde dahi, meydana gelen çatlak genişliklerinin halen 0,05 mm’nin altında kaldığı görülmüştür. Bu avantajlar, raylı sistemlerde ekartman açıklıklarının korunarak deray ve kazaların önlenmesi ve travers servis ömrünün artırılması adına oldukça önemlidir.

This research develops and evaluates a recyclable corrugated cardboard vibration isolation box reinforced with balsa wood as an alternative to traditional open trench methods for mitigating ground-borne environmental body waves. This study includes designing and testing scaled prototypes, laboratory analyses, prototype fabrication, and full-scale field experiments. In soft ground conditions, ensuring slope stability during deep excavations is a key engineering challenge for open trenches. For this purpose, scaled prototypes were subjected to laboratory tests to assess the resistance of the wave barrier’s wall surface. Numerical analyses were also conducted to evaluate the strength of the internal lattice structure under various loads. A prototype was fabricated for on-site experiments simulating real-world conditions. Field experiments evaluated the vibration isolation performance of the proposed barrier. Accelerometer sensors were strategically placed to gather data, analyzing ground surface vibrations for free field motions to assess the vibration shielding efficiency of both the open trench method and the corrugated vibration isolation box, with and without Styrofoam infill. This study concludes that the recyclable corrugated vibration isolation box is a viable alternative, offering comparable or improved vibration isolation efficiency in soft soil conditions while promoting environmental sustainability using recyclable materials.