The university course timetable problem (UCTP) is typically a combinatorial optimization problem. Manually achieving a useful timetable requires many days of effort, and the results are still unsatisfactory. unsatisfactory. Various states of art methods (heuristic, meta-heuristic) are used to satisfactorily solve UCTP. However, these approaches typically represent the instance-specific solutions. The hyper-heuristic framework adequately addresses this complex problem. This research proposed Particle Swarm Optimizer-based Hyper Heuristic (HH PSO) to solve UCTP efficiently. PSO is used as a higher-level method that selects low-level heuristics (LLH) sequence which further generates an optimal solution. The proposed a

Precise and interpretable classification of autism-related behaviors is important for initial diagnosis, personalized intervention, and support arrangements. This study proposes an interpretable machine learning (ML) model using Light Gradient Boosting Machine (LightGBM) and Categorical Boosting (CatBoost) to classify behavioral patterns into four categories (normal, mild, moderate, and severe) associated with Autism Spectrum Disorder (ASD) based on a custom 377-instance survey dataset from Iraqi parents and teachers of children aged 6-12. The model observes 16 key features across communication and social interaction, repetitive behaviors, language, and adaptive skills, preprocessed via interquartile range (IQR) outlier removal, me

Abstract: Many treatment modalities have been introduced for correction of deficient alveolar ridge width. Bone expansion through the ridge split procedure is one of them. This technique was initiated in order to overcome many problems that usually associated with grafting procedures. The present study evaluated the effectiveness of piezoelectric device in performing ridge split using single-stage approach and assessed the outcomes during a period of about 4 months after surgery. A total of 23 patients aged in range between 18 and 60 years underwent ridge split procedure with immediate insertion of dental implants. A panoramic radiograph was obtained before surgery. The gained bone was measured and surgical complications were recorded. Impl

The deposition method of perovskite solar cell layers significantly impacts device functionality and the achievement of industrial goals. Aluminum (Al) nanoparticles with rutile titanium oxide (TiO2) nanoparticle thin films are fabricated on Fluorine Tin Oxide (FTO) glass substrates by nanosecond pulsed fiber laser deposition (PLD) to be used as a plasmonic electron transport layer (ETL) in perovskite solar cell (PSC). The effect of various pulsed fiber laser parameters on the structural, optical, and surface morphology on Al/TiO2 films is extensively examined utilizing a variety of measurement techniques; X-ray diffraction (XRD), Ultraviolet–visible (UV–Vis) spectroscopy, Field emission scanning electron microscopy (FE-SEM) and Atomic

We demonstrate that the selective hydrogenation of acetylene depends on energy profile of the partial and full hydrogenation routes and the thermodynamic stability of adsorbed C₂H₂ in comparison to C₂H₄.

This study investigates the influence of zinc oxide (ZnO) nanoparticles on the mechanical, thermal, and surface properties of low-density polyethylene (LDPE) nanocomposites under accelerated weatheringconditions, including UV exposure, elevated temperature, and humidity. LDPE samples were reinforced with varying ZnO concentrations (0.5%, 1%, and 2%) and compared to neat LDPE (D0). Shore D hardness and Charpy impact tests showed enhanced mechanical stability in nanocomposites, with D1 (0.5% ZnO) exhibiting improved hardness and impact resistance before and after exposure. Contact angle measurements indicated increased hydrophobicity in D1 due to better dispersion of ZnO. At the same time, higher loading in D2  led to a decrease in c

Metasurface polarizers are essential optical components in modern integrated optics and play a vital role in many optical applications including Quantum Key Distribution systems in quantum cryptography. However, inverse design of metasurface polarizers with high efficiency depends on the proper prediction of structural dimensions based on required optical response. Deep learning neural networks can efficiently help in the inverse design process, minimizing both time and simulation resources requirements, while better results can be achieved compared to traditional optimization methods. Hereby, utilizing the COMSOL Multiphysics Surrogate model and deep neural networks to design a metasurface grating structure with high extinction rat