Arabian Journal for Science and Engineering最新文献

Rapid adoption of industrial robots in manufacturing highlights their critical role in achieving high productivity and precision while minimizing errors and physical strain on workers. Among these, industrial serial robots, such as SCARA manipulators, are widely implied for tasks requiring speed and accuracy, including assembly and material handling. This study presents a robust two stage trajectory tracking control scheme designed for a SCARA type robot manipulator. The approach addresses uncertainties in payload mass, external disturbances, and system dynamics. In first stage, optimal control is applied to quasilinear nominal model drive through Taylor series approximation, following Lin’s robust control frame work. The second stage incorporated an integral sliding mode controller ISMC to enhance the robustness against bounded disturbances and the system nonlinearities. The stability of the proposed method is rigorously validated using the Lyapunov function, while a boundary layer approach mitigates chattering effects and control inputs. Numerical simulation conducted on a 2-DoF SCARA manipulator demonstrate effective trajectory tracking under varying payload masses and external disturbances. The results affirm the proposed method capability to deliver robust and efficient trajectory tracking control for industrial robotic applications.

Sentiment Analysis (SA) is pivotal for extracting people’s opinions and feedback from texts on specific topics or products. Arabic texts often present unique challenges for SA due to their intricate combination of multiple dialects alongside Modern Standard Arabic (MSA), leading to complex regional variations. This paper addresses these complexities by introducing a sentiment analysis system tailored for the travel domain. Our system is equipped to analyze sentences encompassing a blend of Arabic dialects from regions such as the Maghreb, Nile Basin, Levant, Gulf, and Yemen, spanning 25 cities. Initially, sentences undergo preprocessing and tokenization. To account for potential misspellings, we developed a probability-driven approach to generate word variations. These variations are then processed by a trained Long Short-Term Memory Recurrent Neural Network (LSTM RNN) to predict their MSA synonyms. Concurrently, a comprehensive ontology is employed, which not only extracts the relevant dialectal variation mapped to the predicted MSA using similarity measurements but also assigns the appropriate sentiment class. The ontology further enhances accuracy by addressing excessive words and negations, given their significant role in sentiment determination. Our approach showcases its robustness with promising experimental results, achieving an overall accuracy of 85%. This work signifies a notable advancement in sentiment analysis for multi-dialectal Arabic texts, bridging the gap between diverse regional nuances and effective opinion mining.

Remote sensing (RS) has emerged as a transformative tool in geotechnical earthquake engineering, enabling large-scale, high-resolution assessment of ground displacements and failure mechanisms. This review critically examines recent advancements in RS technologies including Interferometric Synthetic Aperture Radar (InSAR), Light Detection and Ranging (LiDAR), Unmanned Aerial Vehicles (UAVs), and Structure-from-Motion (SfM) photogrammetry. Each method is reviewed in terms of its application to seismic hazard assessment and ground displacement monitoring. The paper highlights the integration of RS techniques in generating 3D digital elevation models, measuring multi-axis displacements, and supporting post-disaster reconnaissance. A case study on the 2023 Kahramanmaraş earthquake in Türkiye showcases the power of multi-platform remote sensing in mapping over 3600 coseismic landslides and analyzing their spatial relationship with geological and seismic parameters. Despite substantial progress, critical gaps remain in sensor integration, real-time processing, and predictive modeling. This review emphasizes the need for AI-driven, multi-sensor frameworks and standardized workflows to move from observational insights to operational impact. By bridging geotechnical science with RS innovation, this interdisciplinary approach offers a robust pathway toward resilient infrastructure and disaster-ready communities in seismic regions.

This paper presents the realization of a current-mode and wide-range logarithmic circuit. The circuit is based on the Taylor series expansion approximation of the logarithmic function and employs MOSFET operating in saturation. The proposed circuit is insensitive to process, voltage and temperature (PVT) variations. The post-layout simulation using CADENCE Virtuoso tools validates the proposed design functionality in 0.18 ({upmu })m TSMC CMOS technology. The circuit is powered by a ±1.25 volt DC source with a normalized input range that can go from (-1) to 2 and can be used in up to 2.8 MHz application.

Heavy snow and freezing rain hit essential parts of highway networks in winter time, bringing mobility to a halt, and causing chaos and major disruptions to the road transport. Understanding the role that deicing practices and deicers play in winter road maintenance operations underscores the concordant importance of protecting transportation infrastructures and the environment. To this end, this paper aims to provide a thorough review of the existing literature and to gather prominent findings elucidating the impact of each technique and agent commonly employed in snow-fighting. Findings from previous studies revealed that, in most cases, the chloride salts-driven deteriorations provided a particularly stark example of the difficult-to reverse and unpredictable hazards to highway structures, the ecosystem and, in turn, human life. It is therefore vital to select and apply the right type and amount of deicing materials in the right place at the right time. More specifically, past scholarly work suggested that the use of chlorides should be strictly regulated, optimized and diminished by at least 50% to recover the current conjuncture. On the other hand, the adverse effects of other approaches, particularly the contribution of winter service vehicles employed in mechanical snow and ice removal to greenhouse gas emission, air pollution and climate change should not be underestimated. As this critical environmental issue has not been systematically investigated in any other reviews to date, future endeavors should address this research gap regarding the cold-climate regions overburdened by fossil-fuel reliant vehicles in snow and ice clearing. Solving this problem could promote mechanical snow clearing and lead to more extensive applications. Building upon previous literature, the purpose of this paper is to identify further research gaps in snow-fighting and to lay the groundwork for future works. Hence, this study is an attempt to incentivize policymakers and key stakeholders in transportation engineering to discover chloride salt-free deicing sources, eco-friendlier techniques, and sustainable maintenance strategies for winter roads.

With the rapid advancement of technology, wireless sensor networks (WSNs) have become a prominent focus of research and development, playing a vital role in a wide range of modern sensing applications. However, their performance is often constrained by the limited energy resources of sensor nodes (SNs), significantly affecting network lifetime, particularly when the base station (BS) is located far from the sensing area. This excessive energy demand significantly reduces the overall network lifetime. Unmanned aerial vehicles (UAVs) have emerged as a promising solution to address the limitations of fixed base stations in WSNs by serving as aerial base stations. To tackle the aforementioned challenges, this paper formulates the problem as two sub-problems: clustering optimization and the strategic deployment of UAVs as aerial base stations. These problems are known to be NP-hard and cannot be solved utilizing a deterministic approach. Thus, we propose applying improved particle swarm optimization (IPSO) to tackle clustering and deployment issues, minimizing energy consumption and prolonging the sensor network’s lifespan. The simulation results of optimal clustering show significant improvement in network life time and energy consumption. Results of comparison demonstrate that the proposed algorithm outperforms the PSO, LEACH_GA algorithms in terms of the lifespan of the network and the remaining energy consumption, reaching 33.5% and 17.4% for the first node to die. Moreover, the results of UAV-assisted demonstrate a significant improvement in remaining energy consumption and network lifetime reaching 54.3% and 87.3453%, respectively. A one-way ANOVA test and 95% confidence intervals validate the statistical significance of the proposed approach performance.

Wet storage of natural gas on porous materials in form of hydrate is an emerging and promising technology for natural gas storage and transportation. This work aims to systematically investigate methane storage on porous media in form of hydrate. A custom-designed high-pressure autoclave was constructed to evaluate methane adsorption under controlled conditions. Five commercially available porous media were used. In adsorption experiments, powdered activated carbon (PAC) shows the best methane uptake capability of 114.0 V/V among the five porous media. For most porous materials, wet storage dramatically improves methane uptake compared with dry adsorption. Notably, sodium dodecyl sulphate (SDS) aqueous solution proves to be more effective than pure water in accelerating hydrate formation. Methane uptake of SDS-added PAC reaches up to 139.5 V/V at 8 MPa, achieving a 22.4% improvement when compared with pure PAC. It is found that 200 mL of SDS solution and 2 °C of ambient temperature in the given experimental condition present the best methane uptake. Moreover, morphology of methane hydrate was directly observed and mechanism of wet storage of methane on porous media was analysed from the perspective of pore structure. Sensitivity analysis revealed that optimal methane storage on porous material requires specific water content and temperature conditions. Grain diameter of porous media presents a complex impact on the methane storage capacity and methane hydrate formation process. These findings confirm the potential of wet storage of methane in form of hydrate on porous materials and are conducive for this technology towards industrial application.

Glass-fiber reinforced polymer (GFRP) composites pipes are increasingly opted as an alternative to metallic pipes for transportation of crude oil due to their high specific strength, superior corrosion resistance and tailor-made properties. However, during transportation of crude oil, paraffin deposits over the inside pipe surface, reducing the cross-sectional area and affecting the performance. Therefore, an effective methodology for real-time measurement of thickness of wax deposition on the inside of a smart GFRP circular pipe is explored. The proposed approach employs sensors i.e. Fiber Bragg grating (FBG) sensors, strain gauges and thermocouples which are embedded during manufacturing to monitor the real-time temperature and strain under operating condition. Water at varying temperatures and flow rate is circulated through the pipe and the resulting temperature gradient along the thickness are measured. As wax builds up, it acts as an insulating layer, reduces heat transfer and results in a measurable temperature drop. This temperature drop is correlated with the thickness of wax deposition in the pipe using heat transfer equations, provided the film heat- transfer co-efficient is calculated accurately. Additionally, the strain is recorded on real-time basis which increases linearly with the thickness of deposit. While the temperature-based estimation may under-predict thin wax layer due to little change in outlet temperature, strain measurements offer great sensitivity even to a minor deposition. Strain data also helps to locate the place of deposition along the pipe axial length. This dual parameter monitoring strategy enhances the reliability and accuracy in estimating wax thickness without disruption.

With issues such as the modern fossil energy crisis and energy shortage, phase change energy storage plays an important role in the conversion of renewable energy sources such as solar energy. However, its application in thermal energy storage has been limited by problems such as easy leakage, low thermal conductivity, and low energy conversion efficiency. In this work, carbon-based carriers were obtained by acid–base activation pretreatment and hybrid ball-milling post-treatment with nanocellulose. Composite phase change materials (CPCMs) with high thermal conductivity, stable morphology, and good energy storage properties were prepared by loading paraffin wax (PW). Scanning electron microscopy and Fourier transform infrared spectroscopy showed that PW was encapsulated in the supporting skeleton. BET nitrogen adsorption–desorption demonstrated that the carbon material produced by activation pyrolysis had rich pore size structures. X-ray diffractometer, laser Raman spectrometer, and visible/near-infrared spectroscopy indicated that the carbon-based carrier had good crystallinity, highly graphitized, and good light absorption properties in the full band. Differential scanning calorimetry measured the latent heat of CPCMs reached around 100 J/g, which maintained cycle stability after 50 heating/cooling cycles. With a photothermal conversion efficiency of 85.71%, this biomass-based CPCM has promising applications in solar thermal storage applications and contributes to the sustainable utilization of biomass waste.

In this study, hydrogel microparticles of sodium alginate and natural zeolite – clinoptilolite enriched with silver ions were synthesized, and a zeolitic tuff from the Zlatokop mine (Vranjska Banja) containing clinoptilolite (73 wt%), quartz (10 wt%) and feldspar (7 wt%) was used. Zeolite phase from the tuff was enriched with silver ions using ion exchange method. For the synthesis, optimal parameters were determined as follows: suspension of zeolite enriched with silver ions (Ag-Z; 2.0 wt%) in water solution of sodium alginate (1.5 wt%) and 0.25 mol dm⁻³ solution of zinc chloride as a crosslinker. The prepared composite microparticles were characterized using Fourier-transform infrared spectroscopy (FT-IR analysis), thermal analysis (TG/DTG), swelling studies at pH = 7.4 and room temperature, and optical microscopy. Additionally, the desorption behavior of silver ions from the composites was investigated. Antibacterial activity was tested against Gram-negative bacterium Escherichia coli DSM 498 and Gram-positive bacterium Staphylococcus aureus ATCC 25923 by a disc diffusion method. The hydrogel microparticles with 2.0 wt% Ag-Z showed superior antibacterial properties compared to other synthesized samples.