Engineering and Technology Journal

In modern networking, ensuring both Quality of Experience (QoE) and Quality of Service (QoS) is paramount due to the increasing demand for efficient, reliable, and high-performance networks. With the surge in data traffic, traditional network management techniques often struggle to maintain optimal service levels, particularly in addressing challenges such as packet loss, excessive delay, and jitter. This paper presents an approach that integrates an Artificial Neural Network (ANN) into a multi-level queuing system, combined with Weighted Round Robin (WRR) scheduling, to address these persistent issues effectively. The key innovation lies in the dynamic and intelligent adaptation of network resource allocation based on real-time traffic conditions, which significantly outperforms static management methods. The proposed solution optimizes performance across various types of network traffic, enabling efficient handling of voice, video, and other data packets. This enhancement results in measurable improvements in both QoE and QoS, establishing the approach as an invaluable tool for network operators and service providers striving to meet the growing demands of today’s dynamic network environments. The system offers an adaptable and forward-looking solution for next-generation networks. Its effectiveness is demonstrated through a substantial reduction in average delay to 10.842 ms, jitter to 0.031 ms, and packet loss to 0–1%.

Magnetite (Fe3O4) nanoparticles (NPs) were synthesized using a straightforward co-precipitation method from Fe2+/Fe3+ salt solutions as starting materials. This method was optimized to control particle size and morphology, resulting in highly magnetic NPs with crystallite sizes ranging from 20 to 70 nm. The synthesis process involved magnetic stirring at 700 rpm at room temperature, yielding approximately 5 mg of Fe3O4 powder. Structural characterization via X-ray diffraction (XRD) confirmed the stoichiometric composition and particle dimensions. To enhance functionality, the Fe3O4 NPs were coated with chitosan (Cs), polyethylene glycol (PEG), and folic acid (FA), and loaded with the anticancer drug paclitaxel (PTX), forming nanoencapsulated Fe3O4@Cs-PEG-PTX-FA particles with sizes ranging from 100 to 130 nm. Magnetic properties were assessed using a vibrating sample magnetometer (VSM), revealing saturation magnetization (Ms) values of 59.6 emu/mg for Fe3O4 and 19.9 emu/mg for Fe3O4@Cs-PEG-PTX-FA. Encapsulation efficiency of 82.4% and drug-loading content of 14.4% were achieved. These results demonstrate the potential of Fe3O4 NPs as multifunctional agents in the biomedical field, particularly for targeted drug delivery systems, where magnetic properties can facilitate precise localization and release of therapeutic agents. In addition to size uniformity, biocompatibility, and responsiveness to external magnetic fields make, these properties make them promising candidates for cancer treatment, particularly in targeting fibrosarcoma cells.

Edge detection still represents a major challenge in image processing and computer vision because of the complexity and variability of real-world images. The canny algorithm is powerful in detecting edges. However, it is sensitive to noise, and as a result, may produce weak edges. The use of machine learning algorithms has significantly improved the performance of edge detection techniques. In this paper, an improved Canny edge detection algorithm is proposed by replacing the Gaussian filter with a bilateral filter. Also, a new approach for estimating Canny algorithm thresholds has been developed using the Flower Pollination algorithm. Subsequently, the improved Canny algorithm with a machine learning model was integrated to enhance edge detection accuracy. The performance of the improved algorithm was evaluated using 50 images from the Berkeley Computer Vision dataset. The experiment results show that the enhanced algorithm has an AUC of 0.81 for RF (Random Forest) and 0.75 for the LR (Logistic Regression) classifier, which can detect edges more accurately than the traditional Canny algorithm, which has an AUC of about 0.57. The proposed method sets a new standard for edge detection performance

The objective of the current work is to evaluate the antibacterial efficiency of cadmium oxide nanomaterials (CdO NPs) produced through pulsed laser ablation in liquid (PLAL) with laser energy ranging from 400 to 600 mJ. Several characterization techniques, including X-ray diffraction (XRD), UV-Vis spectroscopy, and atomic force microscopy (AFM), were harnessed to analyze synthesized CdO NPs. The nanoparticles had a spherical shape and a size distribution that reduced with higher laser energy. The XRD study confirmed a polycrystalline cubic structure, while AFM measurements disclosed a drop from 133 to 84 nm in the particle size as laser intensity rose. UV-Vis spectra revealed that intensified laser energy caused a narrowing of the bandgap, which was measured from 2.6 eV at 400 mJ to 2.38 eV at 600 mJ. CdO NPs' antibacterial activity has been determined against Staphylococcus aureus (SA) and Escherichia coli (EC) using the colony counting method. The results indicated a considerable reduction in bacterial colonies, with E. coli demonstrating more susceptibility than S. aureus. The findings indicate that PLAL-produced CdO NPs have high antibacterial characteristics, making them promising candidates for biomedical applications, particularly antibacterial coatings and therapies