Arab Academy for Science, Technology and Maritime Transport

The goal of hybrid fiber-wireless (Fi-Wi) access networks is to ubiquitously integrate the accessible vast amounts of optical network bandwidth with wireless network mobility at minimal cost and simple network installation. Fi-Wi networks can employ both Radio over Fiber (RoF) and Radio and Fiber (R&F) technologies. The difference between them is that R&F uses two MAC controls: one for optical fiber and one for wireless networks. One can reduce interference within the wireless subnetwork while avoiding the negative effects of fiber optic propagation delays on the network. RoF, on the other hand, uses MAC control for both optical and wireless networks, resulting in network latency and congestion. This paper demonstrates (i) the use of semi-dynamic bandwidth allocation and (ii) the use of hierarchical frame aggregation for access in next-level integrated Ethernet passive optical network EPON/WLAN-based generations and presents the ongoing research to improve the Quality of Service in wireless and fiber access networks. The study includes an offered load up to 10 Gbps, reaching the up-to-date levels. The obtained results reveal that the data, voice, and video latency are reduced, respectively, by 35%, 25%, and 30%. At the same time, the corresponding throughput is increased, respectively, for data, voice, and video by 35%, 25%, and 30%.

Alkali zinc lead fluorophosphate glasses doped with praseodymium oxide were fabricated via the standard melt-quenching technique. The glasses underwent comprehensive analysis through various spectroscopic techniques, including measurements of their absorption, reflectance, excitation, and emission spectra. Different physical and optical characteristics were assessed and deliberated upon, such as density, molar volume, polaron radius, optical bandgap, Urbach energy, extinction coefficient, refractive index, and Abbe number. Analysis of the optical absorption spectra showed nine bands attributable to the 4f-4f transitions of the Pr3+ ion. The Judd-Ofelt theory was used to assess the radiative properties of the Pr3+ ion. All glasses exhibited a uniform pattern for their Judd-Ofelt intensity parameters: Omega(4) > Omega(6) > Omega(2). The photoluminescence emission spectra exhibited eight distinct bands attributable to the P-3(0) -> F-3(4) (726 nm), P-3(1) -> F-3(4) (697 nm), P-3(0) -> F-3(2) (642 nm), P-3(0) -> H-3(6) (612 nm), P-3(0) -> H-3(5) (538 nm), P-3(1) -> H-3(5) (526 nm), P-3(2) -> H-3(5) (496 nm), and P-3(1) -> H-3(4) (476 nm) transitions. The chromaticity coordinates revealed a tunable luminescence, with the emission color progressively shifting from reddish orange to yellowish green. This was accompanied by an increase in correlated color temperature from 2032.73 to 6196.52 K.

This paper presents and establishes the Chebyshev collocation method, which generates numerical solutions for nonlinear fractional partial differential equations such as the fractional diffusion, wave, and Korteweg–De Vries equations. To obtain the novel fractional derivative operational matrix of the shifted Chebyshev polynomials, new theorems and lemmas have been proved. Caputo’s fractional-order derivative definition is used to represent the fractional-order terms. This approach transforms the problem under discussion into a nonlinear algebraic system of equations that Newton’s method is applied to solve numerically. The error analysis is determined to justify the proposed technique. Several numerical examples are provided to illustrate the accuracy and applicability of the suggested method. The estimated, residual, and absolute errors are computed for each numerical example, and comparisons with other approaches are shown to strengthen the reliability and effectiveness of the suggested method.

The rapid digitization of financial transactions has increased efficiency but also exposed systems to sophisticated fraud attempts, posing significant challenges to ensuring transaction security. Traditional fraud detection approaches, including rule-based systems and conventional machine learning models, struggle to adapt to evolving fraud patterns, resulting in high false-positive rates and limited scalability. State-of-the-art methods, while leveraging deep learning, face limitations such as computational overhead, lack of transparency, and vulnerability to adversarial attacks. This study explores the integration of lightweight blockchain technology and deep learning for robust fraud detection in financial transactions. Lightweight blockchain ensures transaction immutability, transparency, and tamper-proof data sharing across nodes, addressing trust and security challenges. Meanwhile, deep learning provides dynamic and adaptive detection capabilities, employing neural networks to identify anomalous patterns in complex datasets. By reducing the computational and storage demands of traditional blockchain systems, the lightweight approach facilitates real-time fraud detection in resource-constrained environments, such as mobile and IoT devices. Our model combines these components, ensuring data integrity through blockchain while enabling efficient pattern recognition via deep learning, creating a system capable of addressing scalability, energy efficiency, and adaptability. Preliminary experiments demonstrate the model's effectiveness in reducing false positives, enhancing detection rates, and achieving scalability without compromising security or performance, marking a significant step toward secure and efficient financial ecosystems.

Ni0.5Zn0.5-xCuxAlyFe2-yO4 nanoparticles x = y= (0.0, 0.1, 0.2 and 0.3) wt.% were formed by implementing selfpropagating auto-combustion technic. The structure, morphology, magnetic, optical, and photocatalytic properties of the developed nanocomposite were evaluated. XRD samples' examination confirmed cubic spinel phase presence. Also, the structure's crystallite sizes were diminished from 35.11 to 29.25 nm as Cu2+ and Al3+ ions concentrations were increased. FTIR spectra revealed characteristic metal-oxygen stretching bands near 565-568 cm-c1, while FE-SEM and HR-TEM analyses showed nanocrystalline, cubic morphologies with increasing surface irregularity at higher dopant levels. VSM results demonstrated composition-dependent magnetic behavior, with saturation magnetization (Ms) magnitudes ranging from 41.50 to 48.77 emu/g. UV-Vis optical analysis revealed an increasing direct band gap energy from 1.11 to 1.18 eV. The efficiency of methylene blue photocatalytic degradation process by visible light irradiation was improved as the dopants contents were increased. This degradation had reached a maximum of 35.1 % after 120 min of interaction with the Ni0.5Zn0.5xCuxAlyFe2-yO4 nanocomposite. Density Functional Theory (DFT) and Fukui function analysis identified key reactive oxygen sites and confirmed the favorable electronic structure for photocatalysis. These findings validate the engineered ferrites as effective, tunable photocatalyst for dye degradation and environmental remediation.