Beni-Suef University

Abstract Built in 1356, the school, mosque, and mausoleum complex of Prince Sorghatmesh is a major Bahri Mamluk monument that has been affected by long-term environmental deterioration and undocumented interventions. This study presents an integrated diagnostic framework for structural assessment that combines three-dimensional (3D) laser scanning, nondestructive testing, laboratory testing, field measurements, and experimentally validated finite element modeling. High-resolution geometric data and measured mechanical properties were used to construct a numerical model calibrated against local in situ load testing, which showed good agreement between the numerical and measured responses, thereby supporting the reliability of the simulation framework. Under vertical loading, the courtyard arches were identified as the most critical structural components, exhibiting peak stresses of 4.17 MPa. However, under lateral loading, the minaret’s lateral displacement reached about 35 mm, and there was a clear concentration of stresses at the marble columns supporting the top of the minaret. Groundwater intrusion was found to reduce material mechanical properties by approximately 42%, accelerating localized deterioration in lower wall zones. Nevertheless, the global structural response remained within ranges typically observed for comparable historic masonry systems, indicating that only localized replacement of deteriorated elements is required. The proposed workflow provides a transferable and minimally invasive approach for evidence-based preservation and first-level structural assessment of historic structures.

The local tube cladding utilizes mechanics of deformation at the interface of the punch and the tube. More specifically, the punch shape influences the material flow and the pressure at the interface. This study investigates the cladding of AA6060/SS304 bimetallic tubes using a spherical tipped punch of varying diameter at a constant rate of 5 mm/min. Different from previous studies, where axial feed rate seemed to take precedence, in this study the punch diameter was isolated as the primary variable, and the effects on deformation stability, bonding mechanics, and surface finish, were documented.Two sets of experiments were performed using a spherical tipped punch of 65 mm and 69 mm diameter. Under the studied conditions, cladding was successfully completed using the 65 mm diameter punch with a maximum forming load of 44 kN. The 69 mm diameter punch, however, achieved a maximum forming load of 88 kN, showing unstable deformation with flange rupture, material back flow, and thinning. Although forming was not successful, the larger diameter punch increased the maximum interfacial shear load from 5.35 kN to 21.46 kN and decreased the surface roughness from 0.7133 µm to 0.6325 µm. The 65 mm punch produced cladded tubes with a greater average hardness value of 52.06 HV compared to 51.89 HV of the 69 mm punch. Overall, under the investigated forming conditions, changing the punch diameter from 65 mm to 69 mm resulted in clear differences in forming load, deformation stability, maximum interfacial shear load, and surface roughness.

The paper introduces a powerful, data-driven framework for predicting the electrochemical behavior of Polyindole (PIn)-based supercapacitor electrodes through the integration of traditional machine learning (ML) techniques with modern deep learning (DL) architectures. Motivation behind this study comes from the necessity for reliable and accurate models that would enable quick and efficient prediction of the behavior of the conducting polymer electrodes and thus minimize expenses in terms of time, effort, and resources spent on experimental tests needed for characterization of their energy storage capabilities. PIn was prepared through potentiostatic electrochemical polymerization of indole on a graphite sheet. The deposition time was carefully varied to control film growth and active mass loading, and the fabricated electrodes were characterized using cyclic voltammetry (CV) within a potential window from -0.2 to 1.2 V at scan rates ranging from 5 to 75 mV/s. These measurements were then used to assemble a large dataset of over 30,000 experimental points, and provide this dataset to all five ML models Random Forest (RF), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Extreme Gradient Boosting (XGBoost), and Ridge Regression (RR) and four DL networks Artificial Neural Network (ANN), Recurrent Neural Network (RNN), Long Short-Term Memory (LSTM), and a hybrid network using RNN-LSTM. The performance of the model was determined by the coefficient of determination (R-2), mean absolute error (MAE), and root mean square error (RMSE). The findings indicate that KNN was best in a high-rate situation (R-2 = 0.9387, MAE = 0.000289, RMSE = 0.000644 when 5 mV/s), whereas ANN was more robust because it sustained a high predictive accuracy (R-2 > 0.9) at all scan rates. Comprehensively, the proposed framework reflects the complementary efficiency of both classical and DL algorithms and enables the application of hybrid deep architectures for reliable predictive modeling of conducting-polymer Supercapacitor (SC) electrodes.

Thrombosis is built, not merely catalyzed. Across arterial and venous disease, thrombus formation depends on a succession of protein–protein interactions (PPIs) that coordinate platelet capture and activation, thromboinflammatory amplification, and assembly of membrane-bound coagulation complexes. Although active-site inhibitors and receptor antagonists have transformed antithrombotic therapy, clinical benefit is often constrained by bleeding because many targets are indispensable for everyday hemostasis when inhibited systemically. This review advances a “thrombus assembly” framework that reframes drug discovery around disrupting interfaces that organize pathologic clot growth in a context-dependent manner shaped by shear, surfaces, local cofactors, and transient complex formation. We highlight translational proof that interface blockade works in humans, focusing on VWF A1–GPIbα inhibition and GPVI-directed strategies as exemplars of lesion- and shear-dependent antiplatelet therapy. We then survey platelet adhesion and activation PPIs (VWF–GPIb, collagen–GPVI, and αIIbβ3–ligand interactions), thromboinflammatory interfaces (including P-selectin–PSGL-1 and CLEC-2–podoplanin), and opportunities to target coagulation complex assembly and thrombin exosites without directly inhibiting catalytic active sites. A modality-focused section links interface class to therapeutic format—antibodies/nanobodies, aptamers with antidotes, peptides and macrocycles, and small-molecule PPI inhibitors—and summarizes trade-offs in reversibility, half-life, manufacturability, and immunogenicity. Finally, we discuss assays that preserve interface biology (whole-blood flow systems, microfluidics, thrombin generation, and clot mechanics) and propose clinical positioning and trial endpoints for context-gated mechanisms. By targeting the contacts that assemble and stabilize thrombi, PPI disruption offers a pragmatic route toward more selective, potentially bleeding-sparing antithrombotic therapy and a roadmap for next-generation pipeline development.