E. AGHEMENLOH

This study develops a machine learning model to predict abnormalities in commercial airplanes using real-world Automatic Dependent Surveillance-Broadcast (ADS-B) data, focusing on altitude changes exceeding 100 feet in 10 seconds. Following the methodology established by Passarella et al. (2024), this research implements and compares 25 different machine learning algorithms, ultimately selecting Quadratic Discriminant Analysis (QDA) as the optimal approach. The dataset comprises 167,844 records, including 84,074 normal and 83,770 abnormal instances, with features such as altitude, velocity, heading, latitude, and longitude. The theoretical foundation covers the comprehensive taxonomy of machine learning methods, from supervised learning algorithms like Support Vector Machines and Decision Trees to unsupervised approaches such as K-Means clustering. The QDA model achieves superior performance with 93-97% accuracy, 0.96-0.97 ROC-AUC, validated through stratified 5-fold cross-validation. Visualizations, including altitude plots and ROC curves, enhance interpretability for aviation professionals. This research demonstrates that QDA's ability to model non-linear decision boundaries with class-specific covariance matrices makes it particularly suitable for complex aviation data patterns, supporting enhanced flight safety and operational efficiency.

This study has used the recently established combination between EAM and
the TB-SMA scheme to determine the n, p, q parameters values needed for the
calculation of total energy of the three FCC metals which include Ag, Pd and Pt. The EAM and TB-SMA was established to replace the old approach of determining parameters for calculating total energy because of its improved computational efficiency and accurate results. The Microsoft excel programming language has been employed in this study to reproduce results with good accuracy as compared with previous studies using other programming software.

The face-centered cubic (FCC) crystal structure is one of the most significant arrangements in materials science, particularly in metals such as aluminum, copper, and gold. This research explores the crystallographic arrangement of FCC atoms into distinct planes, emphasizing their geometric configuration, atomic packing, and the significance of close-packed structures. The study provides an in-depth analysis of Miller indices to describe the most prominent planes in FCC lattices, including the {111}, {110}, and {100} planes.

The surface free energy of nanoparticles is important as it gives us vital information about the reactivity and stability of nanoparticles. Starting from a previously reported equation, a theoretical model that involves a specific term for calculating the cohesive energy of nanoparticles, is established in a view to describe the surface free energy of metallic nanoparticles ( using different shapes of particle; sphere, cube and disk). The results show that the behaviour of surface free energy for spherical nanoparticles is the most realistic shape compared to disk and cubic shaped nanoparticles. As the surface free energy differs from shape to shape we also see that its value falls as the number of atoms (nanoparticle size) decreases. The results are in close agreement with the results of Fathi and Ayyad (2014).