Arabian Journal for Science and Engineering最新文献

The growing demand for computation-intensive and delay-sensitive services in internet of things (IoT) networks is constrained by the limited computing capacity and battery life of device users, as well as bandwidth limitations in shared communication channels. Mobile-edge computing (MEC) emerges as a promising solution to address these resource limitations by offloading tasks. However, many existing offloading approaches may restrict performance gains due to the overloaded communication channels among multiple users. To tackle these issues, this research aims to develop an energy-efficient task offloading framework for multi-IoT, multi-server edge computing systems. This framework integrates a load balancing algorithm for optimal device distribution, a compression layer to reduce data transmission overhead, and a deep reinforcement learning technique to dynamically make offloading and compression decisions. Additionally, the proposed solution jointly formulates load balancing, task offloading, compression, and communication allocation, aiming to minimize the energy consumption of the entire system. Given the NP-hard nature of this problem, an efficient deep learning-based technique is developed to achieve a near-optimum solution. Finally, experimental results reveal that the model achieves significant energy savings, with reductions of up to 63.96% and 61.87% in local execution and offloading scenarios, respectively, in scenarios with low channel bandwidth availability. These findings confirm the effectiveness of the proposed solution in enhancing system efficiency and scalability in real-world MEC environments.

Concrete is the second most consumed material globally after water. In 2012, the concrete industry accounted for about 9% of global industrial water use, approximately 1.7% of total global water withdrawals. By 2050, around 75% of the water required for concrete production is expected to come from regions facing water stress. To reduce this pressure on freshwater resources, using wastewater (WW) in cementitious systems has been proposed as a sustainable alternative. However, a comprehensive understanding of how various WW types, such as domestic, industrial, and treated municipal sources affect concrete properties remain limited. This study addresses this gap through a combined bibliometric and systematic review. A five-stage methodology was adopted, beginning with formulating research objectives and data collection from the Scopus database using targeted keywords. A total of 91 relevant publications from 2000 to 2023 were analyzed using the Biblioshiny interface of the Bibliometrix R package to discover trends in research focus and geographic distribution. A subsequent systematic review examined the effects of WW on fresh, mechanical, microstructural, and durability properties of cement-based materials. Findings show a clear increase in publications over the last two decades, indicating rising interest in sustainable concrete. Approximately 45% of studies reported improvements in compressive strength with WW use, 35% found significant increases, and 20% observed no major change. These variations are largely attributed to the chemical composition of WW, including factors like total dissolved solids, suspended solids, biochemical oxygen demand, and chemical oxygen demand. An inverse relationship between workability and strength was often well-known. Higher WW replacement ratios typically led to increased porosity, chloride diffusion, and water absorption, posing durability concerns such as reinforcement corrosion. SEM images further showed reduced calcium-silicate-hydrate (C-S–H) gel development and increased cracks and voids. Despite growing interest, research gaps remain. Most studies focus on treated municipal WW, with limited attention to other sources. Durability aspects such as sulfate and acid resistance are underexplored, and the impact of WW in curing processes is rarely assessed. More in-depth studies on specific WW treatment methods and their influence on cementitious performance are needed.

The demand for nitrogen fertilizers, particularly urea, rises as food production rises to fulfill the world's food needs. The overuse of urea fertilizer to produce high food crop yields results in many environmental issues and low urea-N use efficiency due to nitrogen loss. This led to the invention of controlled-release urea (CRU). Most current CRU coatings are composed from expensive, hazardous, and nonbiodegradable synthetic polymers. This research reviews the problems with urea coating materials and discusses the possibility of using novel fly ash geopolymer as urea encapsulating materials and producing consistent CRU fertilizer. To verify the geopolymer performance as coating material, it is essential to perform the standard protocol of X-ray diffraction, infrared spectroscopy, thermogravimetric analysis, and shear rate. Discrete element method is employed to simulate the urea coating operation using the characterized geopolymer as a coating material to produce granules in a rotary coating pan equipment. From the statistical analysis, it was found that CoV_inter is significantly influenced by fill ratio followed by spray-gun angle, urea granular sizes, pan speed, and spray rate, respectively. The percentage contribution of fill ratio, spray-gun angle, urea size, pan speed and spray rate on CoV_inter was 39.16, 23.07, 6.54 3.70, and 0.40%, respectively. Analysis of variance revealed that the pan fill ratio, particle size, and the pan speed were the most influential parameters in this case. The linear regression model was used to fit the process parameters versus CoVinter with satisfactory R² value of 80.02%.

Coverage hole is a crucial and fundamental problem in mobile wireless sensor networks. Maintaining coverage in the targeted area is one of the fundamental challenges that researchers are facing. In the recent era, many researchers have been working to increase the lifetime of the mobile wireless sensor network and the coverage lifetime of that network. For many applications, to provide high-quality services in the area of interest, complete coverage must be ensured. A coverage hole may be a cause of network failure or low-quality services. In this paper, a new Delaunay Triangulation-Driven Coverage Hole Management algorithm has been proposed. The proposed algorithm detects coverage holes based on Delaunay triangulation locally in its detection phase and restores the coverage efficiently. Thus, it tries to maximize the coverage lifetime by delaying the network outage and also indicates the restoration failure due to a large coverage hole or due to more and more sensor nodes dying. Simulations are conducted, and the results show that the proposed algorithm detects coverage holes quickly and efficiently and restores coverage more effectively than other related state-of-the-art research work.

The exponential increase in internet usage and data traffic has significantly increased network complexity. Although fiber optic networks are widely deployed and recognized as the backbone of communication infrastructure due to their reliability, security, and high data throughput, they remain susceptible to failures. Traditionally, faults have been detected manually using optical time-domain reflectometry (OTDR) and visual fault locators (VFL). However, these techniques have become impractical due to the rapid expansion of fiber optic networks. In contrast, this work proposes an advanced multitasking learning framework for efficient fault detection, localization, and faulty link identification in passive optical networks (PONs) with reduced prediction delay. The proposed model leveraged a hybrid long and short-term time-series network (LSTNet) model that integrates an autoencoder, a convolutional layer, and a gated recurrent unit to process sequential data. Our model improves fault management in fiber optic communication networks by forecasting fault severity. Additionally, we apply LIME (Local Interpretable Model-agnostic Explanations) and SHAP (SHapley Additive ExPlanations) explainable artificial intelligence (XAI) techniques to ensure transparency. Finally, the performance of our model is compared with that of convolutional neural networks (CNN), gated recurrent unit (GRU) models, and other existing approaches. The proposed model achieved the highest fault detection accuracy of 99.8%, a mean square error (MSE) of 0.00016, and the shortest prediction delay of 3.2s. For fault localization and link identifications, it achieved an accuracy of 94.2% and 99.04%, respectively, with corresponding MSE values of 0.00071 and 0.00019.

Human error is one of the leading causes of data and security breaches, as cybersecurity attackers prey on psychological manipulations to push users into performing unwanted actions or providing information. Humans act as a weak link in cyberattacks, and as a result, organizations are prone to phishing, business email compromise, and malware types of cybersecurity attacks. In this study, we identify the human-centric barriers and success factors that influence an organization's readiness to handle cybersecurity threats. Moreover, we develop a readiness model to help organizations assess and implement security practices for cybersecurity from the human factor perspective. We conducted a multivocal literature review on 120 primary studies to identify human barriers, success factors, and best practices that positively influence cybersecurity. The results show that researchers consider trust, ignorance, and a lack of technological knowledge the significant obstacles, while industry practitioners point to a lack of technological knowledge, negligence, and impulsive or reckless behavior as the primary barriers. On the other hand, knowledge, proactive awareness, and cognitive ability are the most significant success factors from both researchers’ and industry practitioners’ perspectives. We mapped the identified barriers to the CyBOK cybersecurity knowledge areas. Next, we used the identified success factors to develop a cybersecurity readiness model. The readiness model was validated by applying it to a real-world scenario using the case studies approach. This paper provides a knowledge base to develop threat prevention strategies for human factors in cybersecurity and assist organizations in devising approaches to tackle pressing security issues.

Rapid advancements in artificial intelligence have driven the integration of learning algorithms-machine learning (ML) and deep learning (DL) models-across various industries, posing new challenges for testing these complex systems. Rigorous testing of ML/DL-based systems (MLSs) is especially critical in high-stakes domains like autonomous driving, healthcare diagnostics, and financial forecasting, where system reliability is paramount. Unlike traditional software, MLS quality relies not only on model architecture and development processes but also significantly on the quality of the training data. This study offers a comprehensive review of MLS testing methodologies, with a focus on the emerging role of Data-Box testing, alongside established Black-Box and White-Box techniques. Data-Box testing assesses training data quality to ensure it meets criteria such as sufficiency and adequacy, bridging Black-Box and White-Box methods to enhance system reliability. The study further addresses the increasing use of mutation testing (MT) in DL, exploring MT techniques and mutation operators to ensure adequate coverage. By synthesizing recent advances, we propose an integrated MLS testing framework that encapsulates these critical aspects, offering insights and highlighting areas for future research to refine MLS testing practices.

Rhamnolipids are green surfactants and suitable alternatives to their chemical counterparts because of their biodegradability, nontoxicity, and environmental compatibility. This study investigated the modeling and global optimization of biosurfactant production from newly isolated Pseudomonas aeruginosa MYSAG using a sequential statistical and crow search algorithm (CSA). The effects of six variables: frying oil waste (FOW), glucose, NH₄Cl, urea, salt, and media pH, were evaluated first using the Plackett–Burman Design (PBD). It was found that FOW and urea had a higher impact than others. Hybridizing Central Composite Design (CCD) with CSA was employed for multi-objective global optimization. The optimal set of 17% FOW and 1% urea gave the maximum biosurfactant and biomass yields of 5.66 g/L and 2.81 g/L, respectively. The models were assessed via several performance indicators: R², Relative Error (RE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). The R² values were > 82%, and all error values appeared small. The produced biosurfactant was characterized using oil displacement (OD), surface tension (ST), thin-layer chromatography (TLC), and Fourier transform infrared (FTIR) assays. The OD indicated that the biosurfactant was a growth-associated product. The measured ST value was less than 30 mN/m. The retention factors (R_f) in TLC for the mono- and di-rhamnolipids were calculated to be 0.88 and 0.17, respectively. The FTIR spectrum showed major peaks almost identical to those of pure rhamnolipid. The results were consistent with those of the literature. This article demonstrated the utilization of FOW for biosurfactant production with less cost and environmental hazards.

Fractal structures have brought forth the seamless emergence of wireless devices wherein every constituting component of the integrated radio frequency front-end circuitry is getting miniaturized. Fractal antennas offer solutions of not just the size reduction but also provide multiple resonances with wide or broad or ultra-wide impedance bandwidth, and high gain response. These antennas do not require additional loading components which lead to ease in the fabrication process. Despite numerous benefits, fractals find limited application because of the conventionally slow process that is involved in their development. Artificial intelligence (AI) algorithms aid in skipping one or more stages and boosting the overall design procedure. Not much is available in the literature on implementing these methods to design fractal antennas for multiple requirements. The purpose of the present work is, therefore, to provide a comprehensive review of the various categories of fractal structures in electromagnetics that have been exploited by the modern-day antenna technology to serve the numerous applications. The theory and analysis techniques and the existing state-of-the-art works of the commonly used fractals, viz., the Koch, Minkowski, Hilbert, Trees, and the Sierpinski class have been reviewed in this manuscript. The present study shall leverage meaningful data insights into the fractal philosophy which would help the antenna engineers to rapidly facilitate custom antenna prototypes for their global clients by using AI techniques.

Environmentally compatible water-based drilling fluids are key enablers for sustainable drilling in shale formations. Water-based drilling fluids generally promote clay hydration and swelling during shale drilling. Therefore, a compatible drilling fluid additive is required to inhibit clay swelling. In this study, an eco-friendly hydroxyethyl acrylate chitosan (HEAC) shale inhibitor was synthesized, and its performance as an inhibitor was evaluated. The evaluation experiments include Fourier transform infrared spectroscopy, X-ray powder diffraction, thermogravimetric analysis, static filtration tests, thermal stability tests, shale dispersion tests, slake durability tests, and zeta potential analysis. The inhibitive performance of HEAC was compared with that of polyethylenimine (PEI) as additives in a base drilling fluid. The results revealed a reduction in filtration loss from 36.19 to 55.71% after the addition of 0.3 to 1.5 w/v% HEAC in the base fluids, whereas the addition of PEI resulted in a reduction from 10 to 24.76% for the same concentration in base fluids. After hot rolling, the HEAC had exhibited properties (rheology and filtration) similar to those of PEI. Moreover, as the concentration of HEAC in the base fluid increased from 0.3 to 1.5 w/v%, the shale recovery percentages increased from 66.4 to 88%. The slake durability index of the base fluid was 40.7%, whereas that of the developed drilling formulation ranged from 65.3 to 72.8%, which shows that the resistance of the shale samples to deterioration by a standard cycle of wetting and drying. Overall, the study suggests that the drilling mud exhibits excellent inhibitory properties after the addition of HEAC.