Al-Salam Journal for Engineering and Technology

This study investigates the sol-gel synthesis of aluminum oxide (Al₂O₃) and silicon oxide (SiO₂) nanoparticles, with a particular emphasis on mullite (3Al₂O₃•2SiO₂). The precursors used were TEOS (99.95%), Al(NO₃)₃•9H₂O (98.99%), and anhydrous citric acid (C₆H₈O₇, ≥99.5%) to produce a gel without any further purification. The resulting amorphous gel underwent dehydration and was sintered for 2 hours at 80°C. then, the samples were calcined at 900 oC. The synthesis involved equal amounts of Si(OC₂H₅)₄ and Al(NO₃)₃•9H₂O. The produced nanoparticles\\\\' structural and morphological characteristics were characterized using FTIR, XRD, AFM, UV, and FE-SEM. XRD examination showed rhombohedral Al₂O₃, SiO₂, and mullite production, with an average crystallite size of 26 nm. Synthesized Al₂O₃ and SiO₂ nanoparticles were tested for antibacterial abilities against E. coli and S. aureus. The inhibition zones for S. aureus and E. coli were 26.03 and 20.83 mm, respectively, indicating antibacterial effectiveness. At 100 mg/mL nanoparticles, 70% inhibition was reported. This research found that sol–gel-produced mullite and oxide nanoparticles are efficient broad-spectrum antibacterial agents.

Network Intrusion Detection Systems (NIDS) have played an important role in protecting computer networks against illegal or unauthorized access and many cyberattacks. Given the advancement of machine learning (ML) approaches, NIDS have become more effective in detecting various complex anomalies in computer networks. However, the increasing complexity in adversarial attacks (AAs) poses a significant challenge to such systems. Cyberattacks are estimated to cost approximately $10.5 trillion annually by 2025, and this encourages researchers to improve and develop ML-based NIDS in order to address adversarial vulnerabilities in these systems to remain the systems resilient to modern attacks. In this review, we reveal the weaknesses of ML-based IDS to AAs in which many different attack techniques, such as evasion, poisoning, and generative adversarial networks (GAN) have been included. Also, this study presents and evaluates current possible defensive approaches against AAs, including but not limited to anomaly detection approaches, adversarial training, and feature selection. We also provide a comparative analysis of different ML models used in NIDS in order to further evaluate the system’s susceptibility to sophisticated AAs. A discussion on future work to improve ML-based NIDS resilience against sophisticated attacks is also given.

Combining economics emissions power dispatch (CEED) challenging optimization issue that involves is a lowering the entire cost of electricity production yet satisfying overall environment emissions constraints. The issue at hand is complex because of the nonlinear and not convex character of the function's goals and restrictions. Fuels are the primary form of electrical power production; coal, is the globe's principal fuel, accounting for 42% of the entire electrical power produced internationally. Energy from electricity is excessively pricey because of the substantial amounts invested by generation corporations as a consequence of the high reliability of fuel for generating electricity. A new two ways to solve CEED problem is to use a Algorithms for Red-Tailed Hawk Optimize (RTHOA) and Walrus Optimize (WOA). By the advantages of nature-inspired metaheuristic techniques, we can reduce generation costs and reduce emissions, enhancing power system efficiency and sustainability. As inspired by walruses' social behavior and movement patterns, the WOA demonstrates significant potential for exploring and exploiting the solution space. RTHOA, which mimics hawks' hunting strategy and sharp vision, is just as good. Three examples have been validated by a simulated research investigated. The IEEE 30-bus with six generators in Case 1 has a power consumption of 2.834 p.u., the 10-unit in Case 2 has a power requirement of 2000 MW, and the 40-unit in Case 3 has a demand of 10,500 MW. Comparison with alternative approaches documented in the published works, the simulation outcomes of the created techniques showed interest in the area of lowering emission and the expenses of electric generation. With a tiny standard deviations and a significant correlation between the optimum and poorest fitness figures, the WOA demonstrated strong performance and great consistency, especially in Case 1. As demonstrated in instance 1, the RTHOA also demonstrated strong features, particularly in preserving stability and attaining targeted fitness goals. But in Case 3, the RTHOA showed more fluctuations, suggesting a wider capacity for investigation. These results imply that the two algorithms provide useful methods for solving the CEED phenomenon.

The Vehicular Safety Consortium (VSC), the Crash-Avoidance Metrics Partnership (CAMP), and the Vehicle Infrastructure Initiative (VII) are working beside leading light-duty vehicle manufacturers to develop safety technology and applications for "Vehicular ad-hoc Networks". "VANETs" are poised to alter the future of transportation by enabling the real-time communication between vehicles (V2V) and vehicles to infrastructure (V2I). These networks are critical for increasing road safety, regulating traffic flow, and enabling the development of self-driving technologies. This paper provides a thorough examination of VANETs' fundamental architecture, outlining multi-layered communication protocols, incorporation of cellular networks such as 4G/5G, and their ways of use to improve these services. The paper also investigates the architecture of VANETs built to solve the unique challenges of automobile environments, such as high mobility, intermittent connectivity, and rapid topological change. It examines the scalability of VANETs in densely populated metropolitan regions, emphasizing the inadequacies of present protocols and proposing improvements through advances in wireless technologies and complex routing-algorithms. The paper predicts future developments of VANETs, particularly the combination of emerging technologies such as edge and cloud-computing, Internet of Things (IoT), and AI-enhanced traffic management systems. Furthermore, the growing significance of autonomous vehicles within VANET frameworks emphasizes the importance of seamless vehicle coordination and infrastructure improvement to enable self-driving cars. This paper contributes the discussion about the evolution of VANETs to meet the needs of more innovative, safer, and more efficient transportation systems, highlighting potential impact on global mobility trends.

Automatic summarization of long scientific texts has been established as an essential task within the domain of Natural Language Processing(NLP), aiming to reduce information overload and facilitate knowledge acquisition from complex academic documents. Despite its importance, fact-based conventional systems of Automatic Text Summarization have mostly failed in ensuring coherence on the document level and keeping factual correctness. To address these limitations, further recent progress in deep learning has turned more intelligent models toward the equilibrium structural fidelity with fluency. This paper presents a comparative study of two state-of-the-art summarization techniques: (1) hybrid deep learning, which combines Bidirectional LSTM-based sentence classification with a hierarchical attention-driven encoder-decoder for abstractive summarization, and (2) fine-tuned Transformer-based architectures, specifically T5-base and T5-large models. The first method emphasizes structural awareness and hierarchical processing to preserve document-level semantics and mitigate issues such as repetition and out-of-vocabulary (OOV) tokens. In contrast, the Transformer-based models leverage large-scale pretraining with self-attention mechanisms for producing fluent summaries that are richly filled with context. Both methods were evaluated on two benchmark datasets: arXiv and PubMed. The hybrid model achieved ROUGE-F1 scores of (46.7, 19.4, 35.4) and (47.0, 21.3, 39.7), respectively, while the T5-large model outperformed it with scores of (55.8, 33.8, 47.9) and (54.9, 32.0, 48.7). These results show that while Transformer models perform better in abstraction and fluency, the hybrid model has fact control ability that is much interpretable and aligns with document structure. This comparison gives important insight into the trade-off between structure-aware hybrid frameworks and large-scale generative models in academic summarization.

Cloud computing is a new development in the world of Information Technology (IT) infrastructure and it has brought could of challenges. Task scheduling is one of the main features that allows to be efficient in cloud-computing to guarantee the effectiveness of work with the resources and make the completion time as short as possible. It should, however, be pointed out that the task scheduling in cloud computing belongs to the NP-complete optimization problems. In order to eliminate the difficulties related to task scheduling in cloud computing, a number of algorithms have been presented. One of them is also an original version of the list scheduling scheduling technique, but its implementation is specifically aimed at efficient schedules of tasks execution and load balancing in a cloud environment. This method is based on the Heterogeneous Earliest Finish Time (HEFT) strategy but with some modifications that enhance its efficiency as compared to keeping a similar level of algorithm complexity. I carried out experiments on randomly created Directed Acyclic Graphs (DAGs) with a view to testing the usefulness of the algorithm. The test done on the WorkFlowSim simulator involves testing the real and synthetic workflows. The experiments point out the difference in the effectiveness of the offered mechanism compared to the existing algorithms. As demonstrated by the experiments, the suggested would outperform the current scheduling algorithms by efficiency and use of resources. This algorithm has potential of providing improved results than any prior solutions to the task scheduling problem in computer computing although the task problem is a complex one.

This review systematically examines recent technological advancements in medical robotics, with a particular focus on the design, functionality, and clinical integration of non-surgical robotic platforms used across various healthcare applications. Using scientific and medical databases, a literature search was carried out (PubMed and Web of Science) to analyze studies from 2015 to the present. From the chosen literature, details on the kinematic and mechanical characteristics were taken. Previous studies focused on whole-body recovery, limb rehabilitation, and the long-term effects of medical robots and wearable exoskeletons. Parallel rehabilitation robots have been considered and classified according to architectural and design elements. Most rehabilitation robot designs support flexion-extension, adduction-abduction (radial-ulnar deviation), pronation-supination, and internal-external rotation movements. However, with a few notable exceptions, most wearable robots do not support abduction (radial-ulnar deviation). The conclusions of this research are as follows: the use of parallel robots enables complicated and specific spatial movements. Due to its parallel construction, the robot has higher stiffness, a more even dispersion of forces, and a better capacity to adapt to the mechanical characteristics of human joints. Most rehabilitation robot designs support supination-pronation, adduction-abduction (radial-ulnar deviation), flexion-extension, and internal-external rotation motions. However, with a few notable exceptions, most wearable robots do not support the adduction-abduction action (radial-ulnar deviation). Robotic rehabilitation devices have great potential, but very few of them have been made available for purchase. Many ideas are not created to become commercialized or even used for personal purposes.

This study emphasises the advantages of simplicity, cost-effectiveness, and significant enhancement of material properties. It investigates the influence of heat treatment on the structural behaviour of an aluminium alloy through numerical simulation, focusing on its impact on performance assessment in future operational conditions. A comparative analysis was conducted between thermally treated and untreated aluminium alloy samples, evaluating key mechanical properties including stress resistance, strain, elongation, and fatigue life. The finite element model was developed, meshed, and analysed using Ansys Workbench 2020 R1. Compared to the overall experimental results reported in previous studies, the simulation outcomes demonstrated that heat treatment led to an improvement of roughly 21% in stress capacity and at least 75% in fatigue performance.

Video summarization has become a vital solution for handling the explosive growth of video data across domains such as surveillance, education, entertainment, and healthcare. As visual media increasingly dominate digital communication, users and systems alike require fast, semantically rich access to content without viewing entire videos. Deep learning has fundamentally transformed this task, enabling models to detect, rank, and condense relevant segments into concise summaries that retain meaning, context, and narrative coherence.
However, this change is still hindered by some problems that keep coming back: the large variety of video formats and domains, the non-uniform temporal structures of the content, and the restricted scalability of annotated datasets that are used for supervised learning. However, the diversity of video sources, inconsistency in temporal structure, and limited access to labeled training data pose persistent challenges. Traditional frame-based models often suffer from redundancy and fragmented outputs, while supervised methods are constrained by annotation cost and domain generalization. Many summarization systems still under-address multimodal fusion, temporal alignment, and long-range semantic reasoning, and benchmark evaluations rarely account for cross-modal contributions or human subjectivity in summary preferences. This review offers a comprehensive and technically grounded survey of 46 deep learning-based approaches, organized around five foundational techniques: multimodal representation and fusion, segment/shot-level summarization, graph-based modeling, transformer architectures, and learning paradigms including supervised, unsupervised, and self-supervised frameworks. By structuring the discussion through architectural innovations rather than individual models or datasets, we identify core methodological patterns, highlight the evolution of learning strategies, and analyze the impact of unit granularity and modality integration on summarization quality. We conclude with an original synthesis of trends, research gaps, and future opportunities in real-time, hybrid, and label-free summarization design. Key results from our comparative analysis show that segment- or shot-based methods comprise over 70% of modern models, reflecting a broad shift away from frame-based summarization. Additionally, transformer-based architectures, often combined with GNNs or hierarchical encoders, have overtaken RNNs as the dominant sequence modeling strategy. Examples from our analysis include the observation that over 70% of recent models now incorporate segment- or shot-level units rather than isolated frames, while transformer-based architectures—often fused with GNNs or hierarchical encoders—have replaced RNNs as the dominant modeling paradigm. Similarly, our comparative tables reveal that intermediate fusion techniques consistently outperform early and late strategies, especially when paired with attention-based alignment.These results show what kind of architectural and learning design decisions implementation are features, which mean better coverage of semantics, higher scalability and performance—thus giving practical insights to the researchers and developers who work on the next generation of summarization systems.