African Journal of Advanced Pure and Applied Sciences

Evaluation of Shear Bond Strength and Biaxial Flexural Strength of Zirconia Cores Veneered Using CAD-on and Press-on Techniques

Shaza Mustafa — 2026-10-01

Objectives: This in vitro study compared the shear bond strength and biaxial flexural strength of CAD/CAM-fabricated zirconia cores veneered using CAD-on and Press-on techniques and evaluated their modes of failure. Materials and Methods: Twenty zirconia core discs were fabricated from IPS e.max Zir CAD using CAD/CAM technology. Ten specimens were prepared for shear bond strength testing and ten for biaxial flexural strength testing, with five specimens allocated to each veneering technique per test. The CAD-on group was veneered using IPS e.max CAD, whereas the Press-on group was veneered using IPS e.max ZirPress. All specimens underwent thermocycling before testing. Failure modes were evaluated using scanning electron microscopy, and data were analyzed using SPSS software with the level of statistical significance set at p≤0.05. Results: The t-test analysis of the shear strength test showed a statistically significant difference (p < 0.05) for the CAD-on group. There were no statistically significant differences (p > 0.05) for the biaxial flexural strength test between the two groups. The CAD-on group showed adhesive/cohesive failure, while the Press-on group showed cohesive failure within the veneering ceramic. Conclusion: Within the limitations of this in vitro study, the veneering technique affected shear bond strength. The CAD-on technique showed significantly higher shear bond strength than the Press-on technique. Biaxial flexural strength did not differ significantly between the techniques.

In Silico Docking and ADMET Profiling of Cyclopentane-Based Enaminones as Candidate PARP1 Inhibitors

Abdalhamid A. Alarafi — 2026-10-05

Poly(ADP-ribose) polymerase 1 (PARP1) represents a significant therapeutic target in cancer treatment focused on DNA repair; yet, cyclopentane-derived enaminones have not been extensively investigated as chemotypes that bind to PARP1. In this research, ten previously created cyclopentane-derived enaminone compounds (1A–5A and 1B–5B) were assessed through structure-based molecular docking against the catalytic domain of human PARP1 (PDB ID: 7KK4), followed by thorough in silico evaluations of pharmacokinetics, drug-likeness, and toxicity. The docking protocol was confirmed through re-docking the co-crystallized inhibitor olaparib, resulting in an RMSD of 1.183 Å. The studied derivatives showed docking S-scores between −6.234 and −7.972 kcal/mol, with compounds 1B (−7.972 kcal/mol) and 1A (−7.748 kcal/mol) presenting the most advantageous scores compared to olaparib (−8.714 kcal/mol). Binding-mode assessment identified significant interactions within the PARP1 catalytic pocket, highlighting connections with TYR889, TYR896, ARG878, ASP766, SER864, and ALA880. Significantly, 4B established a strong hydrogen-bond interaction with TYR896, while 2B paired ARG878 hydrogen bonding with a TYR896 π–π interaction. All derivatives adhered to Lipinski’s rule of five and exhibited a predicted bioavailability score of 0.55, although 1A and 1B showed increased lipophilicity, with 1B surpassing the Veber rotatable-bond criterion. ADMET predictions also suggested that blood–brain barrier penetration varied by compound and highlighted significant potential issues related to CYP2C19, CYP2C8, and certain CYP1A2 and CYP2C9 pathways. Together, these results pinpoint 1A and 1B as the top docking candidates, underscoring the importance of metabolic optimization and experimental confirmation before definitive PARP1 inhibitory effects can be confirmed.

Subject-Independent Automated Detection of Amyotrophic Lateral Sclerosis from Electromyography Signals Using ALSNet

Sabah Al-Bashir Salim Rashid — 2026-10-06

Amyotrophic Lateral Sclerosis (ALS) is a progressive disorder of the nervous system that affects motor neurons and causes progressive weakness and loss of function of motor neurons. Clinically relevant information on neuromuscular activity is provided by Electromyography (EMG) and can be used to assist the diagnosis of ALS. The use of EMG interpretation can be difficult due to their complex temporal and morphological characteristics, however. In this study, the novel approach of a deep learning method (ALSNet) to automatically classify ALS and Normal EMG signals is explored. The experiments were performed with processed EMG data extracted from publicly available N2001 EMGLAB database. All 302 EMGs (151 Normal, 151 ALS) were divided into 3,322 one-second EMG segments. A subject-independent partitioning strategy for the dataset was used. ALSNet is a deep network developed based on Keras and TensorFlow frameworks, and trained with Adam optimizer by using binary cross-entropy loss. The accuracy, sensitivity, specificity, precision, F1-score, and balanced accuracy were used for evaluation of the model. ALSNet obtained 97.74% accuracy, 96.77% sensitivity, 98.59% specificity, 98.36% precision, 97.56% F1-score and 97.68% balanced accuracy on the independent test set. The findings show the promise of deep learning analysis of EMG signals for automated ALS detection and offer a foundation for future analysis with more and varied clinical datasets.

Effect of Heat Treatment on the Hardness of Cast EN AC-43000-F (AlSi10Mg) Aluminum Alloy

AlMabrouk Salim AlMabrouk — 2026-10-08

An Heat treatment is an important method for modifying the mechanical properties of heat-treatable cast aluminum alloys. This study experimentally investigates the effects of annealing and T6 heat treatment on the Rockwell hardness of cast EN AC-43000-F (AlSi10Mg) aluminum alloy. The investigated conditions included the as-cast state, annealing at 350 °C for 1.5 h followed by furnace cooling, solution treatment at 540 °C for 2 h followed by water quenching, and artificial aging at 170 °C for 4, 8, and 12 h. The specimens measured 50 × 40 × 10 mm. Hardness was measured using the Rockwell HRF scale in accordance with ASTM E18. For each heat-treatment condition, three independent specimens were evaluated, and the reported hardness values represent their average. The mean hardness of the as-cast alloy was 64.9 HRF. Annealing reduced the hardness to 52.9 HRF, corresponding to an approximately 18.5% decrease relative to the as-cast condition. Following solution treatment and water quenching, the mean hardness increased to 74.4 HRF. Subsequent artificial aging increased the mean hardness to 91.5, 110.4, and 102.5 HRF after 4, 8, and 12 h, respectively. The highest mean hardness was therefore obtained after 8 h at 170 °C. The subsequent decrease after 12 h is consistent with a transition beyond the maximum age-hardening condition; however, without direct microstructural characterization, it cannot be considered conclusive evidence of over-aging. Overall, the results demonstrate that hardness is strongly dependent on thermal history, with T6 treatment producing a substantial increase compared with the as-cast and annealed states.

Design and Implementation of a Multi-Sensor IoT System for Smart Laboratory Monitoring and Access Control Using ESP32

Abdelsalam Melad Faitouri — 2026-10-10

This paper presents the design and practical implementation of an IoT-based smart laboratory monitoring and access-control system using an ESP32 microcontroller. The proposed system addresses limitations of conventional laboratory monitoring by integrating environmental monitoring, human presence detection, and access control within a unified platform. The implemented system consists of a DHT22 sensor for temperature and humidity measurement, an MQ-2 sensor for smoke and gas detection, a PIR motion sensor for human presence detection, and an RC522 RFID module for automated access control. The ESP32 serves as the central processing and communication unit, collecting data from the connected sensors, processing the measurements, and supporting wireless communication through its integrated Wi-Fi capability. The study adopts a design-based Cisco PPDIOO framework to structure the implementation process. Experimental results confirm the feasibility and reliability of the deployed system in detecting environmental hazards and managing laboratory safety in real time.