This study investigates the influence of five nanomaterials nano-alumina (NA), nano-silica (NS), nano-titanium (NT), nano-zinc oxide (NZ), and carbon nanotubes (CNT)on enhancing the fatigue resistance of asphalt binders. NA, NS, and NT were incorporated at dosages of 2%, 4%, 6%, 8%, and 10%, while NZ and CNT were added at 1%, 2%, 3%, 4%, and 5%. A series of physical, rheological, and performance-based tests were conducted, including penetration, softening point, ductility, and rotational viscosity. Based on the outcomes of the overall desirability evaluation, the first three dosages of each nanomaterial were selected for further testing due to their superior workability and binder flexibility. Subsequent investigations included the high-temperature performance grade, fatigue parameter (G*.sin δ), Linear Amplitude Sweep (LAS), and IDEAL-CT test integrated with Digital Image Correlation (DIC). The results confirmed that nanomaterial modification significantly enhanced asphalt binder performance, though the effectiveness varied with type and dosage. Physical tests demonstrated improved stiffness, softening point, and reduced temperature susceptibility, with slight ductility losses at higher dosages. Rotational viscosity analysis indicated that low-to-moderate contents ensured workability excluding high CNT dosages which exceeded Superpave limits. High-temperature PG improved notably with NS, NZ, and CNT, while NA and NT showed limited gains. Fatigue parameter results (G*.sin δ) identified NA and NT as the most consistent in reducing cracking susceptibility. LAS testing confirmed superior fatigue lives at optimal dosages of 6% NA, 6% NT, 2% NS, 2% CNT, and 1% NZ, while higher concentrations often caused agglomeration and performance decline. IDEAL-CT and DIC analyses validated these findings by demonstrating increased fracture energy, CT index, and more uniform strain distributions in nano-modified mixtures compared to neat asphalt. FTIR spectra confirmed reduced oxidative aging most prominently with NT and NA while SEM revealed enhanced microstructural cohesion and reduced surface defects. The integration of the Overall Desirability (OD) framework confirmed NT-6 as the most effective dosage, followed by NZ-1 and NS-2, while higher dosages often led to poor compatibility and performance decline. Complementary cost–effectiveness analysis further demonstrated that lower dosages of NZ, NT, and NS achieved the best balance between technical performance and economic viability, whereas excessive CNT and NT contents were not recommended due to unfavorable cost-to-performance ratios. These findings highlight that dosage optimization is critical for translating nanomaterial benefits into practical pavement engineering applications, ensuring enhanced durability with rational investment of resources.
A numerical method for the calculation of the three-dimensional wake rollup behind symmetric wings with ground effect and its aerodynamic characteristics for steady low subsonic flow have been developed. A non-planar quadrilateral vortex-ring method with vortex wake relaxation iterative scheme for lifting surfaces is obtained. A computer program was build to treat wings with breaks, span wise trailing edge flaps, local dihedral angle, camber, twist and ground effect. Forces and moments are obtained from vector product of local velocity and vortex strength multiplied by density. The program has been validated for a number of configurations for which experimental data is available. Good agreement has been obtained for these configurations. Al
... Show MoreThe current research was aimed at the following:
1. Measurement of Personality Type Observer of the University students.
2. Identify the differences in Personality Type Observer among the University students according to variable of Sex (male / female). And according to variable of Specialization (scientific / literary)
3. Measurement of Withdrawal of the University students.
4. Identify the differences in Withdrawal among the University students according to variable of Sex (male / female). And according to variable of Specialization (scientific / literary).
5. Identify the relationship between Personality Type Observer and Withdrawal.
To achieve this aims of the research, the researchers set up the instrument is scale
Document clustering is the process of organizing a particular electronic corpus of documents into subgroups of similar text features. Formerly, a number of conventional algorithms had been applied to perform document clustering. There are current endeavors to enhance clustering performance by employing evolutionary algorithms. Thus, such endeavors became an emerging topic gaining more attention in recent years. The aim of this paper is to present an up-to-date and self-contained review fully devoted to document clustering via evolutionary algorithms. It firstly provides a comprehensive inspection to the document clustering model revealing its various components with its related concepts. Then it shows and analyzes the principle research wor
... Show MoreBACKGROUND: Preeclampsia (PE) is a possible etiology of obstetrical and neonatal complications which are increased in resource-limited settings and developing countries. AIM: We aimed to find out the prevalence of PE in Iraqi ladies and specific outcomes, including gestational weight gain (GWG), cesarean section (CS), preterm delivery (PD), and low birth weight (LBW). METHODS: All singleton pregnant women visiting our tertiary center for delivery were involved over 3 years. PE women were compared with non-PE ladies. Complete history and examination were done during pregnancy and after delivery by the attending obstetrician and neonatologist with full documentation in medical records. RESULTS: PE prevalence was 4.79
... Show MoreThe proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of input data frames using 16 Gabor filters indifferent directions and then feed them to a binary CNN classifier instead of using the red-green-blue
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