Gaziosmanpaşa University

Porous orthotropic laminated plates are increasingly used in lightweight structural applications due to their high stiffness-to-weight ratio and tunable mechanical behavior. However, their vibrational performance is strongly affected by the distribution of porosity, the lamination scheme, and interaction with the underlying support medium, particularly when resting on elastic foundations. This study aims to analyze the fundamental natural frequencies of porous orthotropic laminated plates considering various porosity distribution patterns (PDP, UDP, NUDP1-3), lamination sequences, and two types of elastic foundation models: Winkler and orthotropic Pasternak foundations. The equations of motion are derived using Hamilton’s principle and higher-order shear deformation theory (HSDT) and solved analytically via Galerkin’s method. A comprehensive parametric study is conducted to assess the impact of foundation stiffness, shear layer stiffness ratio, porosity coefficient, orthotropy ratio, aspect ratio, side-to-thickness ratio, and fiber orientation angle on the dynamic response. The results reveal that increasing foundation stiffness significantly enhances natural frequencies and reduces the adverse effects of porosity on structural stiffness. Orthotropic shear interactions further amplify frequency gains, particularly in soft porosity distributions. Lamination sequences with higher in-plane stiffness and lower fiber angles exhibit better vibrational capacity. Additionally, geometric and material orthotropy parameters significantly impact the frequency trends, with elastic foundations enhancing configuration-specific behaviors.

In this study, a dual-modification strategy was employed to transform chitosan into a more effective nanocarrier for cellular internalization by covalently conjugating α-linolenic acid (ALA) and Tricine (Tri). While native chitosan suffers from poor solubility at physiological pH and limited biological performance, the dual-modified polymer (ChiALA−Tri) was designed to overcome these drawbacks. ALA was introduced to impart hydrophobic and bioactive functionality, thereby facilitating cell uptake, whereas Tricine was incorporated to balance hydrophilicity and enhance aqueous solubility. Structural characterization by FTIR, ¹H NMR, and GPC confirmed successful conjugation, demonstrating increased molecular weight and improved solubility at pH 7.4 compared with native chitosan. Nanoparticles prepared from ChiALA−Tri yielded an optimal formulation at a 2:1 polymer: TPP ratio (n1ChiALA − Tri), with a particle size of 123 ± 11 nm and a PDI of 0.35 ± 0.02. Curcumin (CUR), selected as a model hydrophobic drug, was successfully encapsulated, achieving encapsulation efficiencies of up to 40.7 ± 0.6

To get more accurate/reliable results on the basic electronic-parameters and current transport/conduction mechanism (CCMs), the current–voltage (I–V) measurements were measured in a wide temperature and voltage regions in the Schottky diodes (SDs). Therefore, these measurements of Re/n-GaAs/Au SDs were performed over wide temperature (100–380 K) and voltage (− 1.0 V/1.4 V) ranges. Experimental-findings indicate that while the barrier height, BH at V = 0 (ΦB0) inclines with inclining temperature almost as exponentially, ideality factor (n) and series resistance (Rs) declines. These parameters were also obtained from the Cheung -functions a second way and observed an important discrepancy between them have been explained by the nature of calculated-model, voltage dependence of them, and barrier-inhomogeneities. CCMs were also investigated in detail, and it shows that field emission (FE) is more dominant than thermionic/emission (TE) and thermionic field/emission (TFE). For various constant current values, temperature coefficient of voltage was found higher than − 1 mV/K, so these samples may be used in thermal—sensor applications. To determine the change of BH with voltage, the BH—q/2kT graph was plotted in voltage range of 0.0–0.6 V and these two linear-components which are corresponding to low high temperatures and the value of BH decreases with rising temperature. In the calculation of BH the effect of voltage must be considered as well as temperature.

Tibial fractures are among the most common complex orthopedic injuries. The mechanical strength and biomaterial properties of implants used in the treatment of such fractures directly affect the healing process. In this study, the mechanical effects of different implant designs and biomaterials on obliquely fractured tibia were analyzed. In addition, it was aimed to evaluate the data obtained from finite element analysis (FEA) with machine learning (ML) algorithms. Seven implant models for tibial shaft fractures were analyzed using static structural simulations in Ansys Workbench. Implants and cortical screws were made of Ti–6Al–4 V alloy or 316 L stainless steel (SS), and axial loads of 600, 800, and 1000 N simulated single-leg stance. A dataset of 1008 points, including maximum stress and total displacement, was generated and used to train Multilayer Perceptron (MLP), Support Vector Machine (SVM) and Decision Tree (DT) models in WEKA. The mechanical behaviors of different implant and biomaterial combinations were compared, and the maximum stress value in implants with 316 L SS material properties was higher than the maximum stress value in Ti-6Al-4 V alloy implants. When the total displacement values ​​in the tibia fracture region were examined, 316 L SS implants gave better results. In the machine learning estimations, the SVM model outperformed the MLP and DT algorithms. For maximum stress prediction, SVM achieved an mean absolute error (MAE) of 0.24 and 0.41 for the training and test sets, respectively, while MLP and DT showed higher errors (3.27/4.02 and 10.99/14.90, respectively). Similarly, for total displacement prediction, SVM showed the lowest errors with MAE values of 0.0003 and 0.0015 for the training and test sets, whereas MLP and DT had higher MAE (0.0032/0.0040 and 0.0058/0.0072, respectively). This study evaluated the effects of different implant designs and biomaterials on oblique tibial fractures and demonstrated that finite element results can be accurately predicted using machine learning models. The SVM algorithm showed superior performance, with prediction errors of approximately 0.24–0.41