Arabian Journal for Science and Engineering最新文献

Efficient removal of industrial effluents from wastewater is critical for a clean and sustainable water supply. In this study, novel nanosized SnO₂/MnO₂ photocatalysts with crystallite size between 34–40 nm were synthesized and evaluated for methylene blue (MB) degradation under visible light. The optimal percentage of MnO₂ nanowires was explored for superior photocatalytic efficiency by varying its amount in the composites. The findings suggested that the SnO₂/MnO₂ composites exhibited enhanced photocatalytic performance compared to their individual components, which was attributed to the synergistic interaction between SnO₂ and MnO₂. Preliminary analysis by X-ray diffraction, Raman spectra, and EDX confirmed the crystalline structure and chemical composition of SnO₂, MnO₂ and their composites. Additionally, the morphology of MnO₂ was observed to be of nanowires; while SnO₂ was found to be comprised of agglomerated particles. Notably, the photocatalysts demonstrated a systematic reduction in the bandgap of the composites with increasing MnO₂ content, leading to improved visible light utilization. Among all the prepared photocatalysts, the optimized SnO₂/MnO₂ composite with 75 wt. % MnO₂ (denote as SM-3) revealed exceptional photocatalytic activity by degrading 93% of MB in 150 min of light exposure. Moreover, the catalytic process followed pseudo-first-order kinetics, highlighting the efficiency of the composites. The scavenger studies suggested that holes, hydroxyl and superoxide radicals are primarily responsible for the MB degradation. The composite SM-3 also exhibited impressive stability and reusability. This study demonstrates the potential of SnO₂/MnO₂ composites as effective photocatalysts for wastewater treatment under visible light.

Acquiring precise geologic parameters for obstructed or complex geologic regions poses a difficult task in practical engineering. Current predictions depend on the expertise of engineers, leading to inadequate levels of precision. Therefore, in this study, geotechnical stratigraphic data were transformed into visualization images containing only red information corresponding to R values in RGB images. The generated visualization images were analyzed using a super-resolution convolutional neural network (SRCNN) for prediction and compared with linear interpolation-based prediction methods. Subsequently, a dataset containing 430,000 patches was generated using real geologic data from a specific project, and this dataset was used for SRCNN training to validate its prediction. The results showed that SRCNN yields a peak signal-to-noise ratio (PSNR) of 40.22 dB, exceeding the linear interpolation on the geologic map (39.93 dB). The SRCNN training was successful and outperformed the linear interpolation. The PSNR values of the SRCNN were higher (34.69 dB, 37.68 dB, 38.79 dB, 37.56 dB, and 44.99 dB) compared to linear interpolation (34.53 dB, 37.43 dB, 38.38 dB, 37.29 dB, and 44.31 dB). These findings confirmed the significant potential of the application of super-resolution reconstruction for predicting soil distribution, and this method is expected to yield more precise soil prediction results as the dataset grows.

The objective of this study was to synthesize and evaluate aluminum-doped zinc-manganese ferrite (Zn_0.5Mn_0.5AlxFe_2−xO₄ (X = 0, 0.2)) nanoparticles as efficient visible-light-driven photocatalysts for atrazine degradation in water. The nanoparticles were synthesized via the sol–gel method and characterized using XRD, FTIR, SEM–EDX, BET, UV–Vis DRS, and electrical resistivity measurements. Aluminum doping decreased the bandgap from 2.4 to 2.0 eV and improved the adsorption properties by increasing the surface area and pore volume compared to undoped Zn_0.5Mn_0.5Fe₂O₄. Photodegradation experiments revealed that Zn_0.5Mn_0.5Al_0.2Fe_1.8O₄ achieved 95% atrazine removal in 150 min under visible-light irradiation, outperforming the 75.45% removal achieved by undoped Zn_0.5Mn_0.5Fe₂O₄. This enhanced performance was attributed to aluminum-induced structural modifications that facilitated charge-separation and radical generation. The degradation followed first-order kinetics and hydroxyl radicals were identified as the primary reactive species. The effects of operational parameters, including the solution pH, atrazine concentration, catalyst dosage, temperature, light intensity, and H₂O₂ addition, were systematically investigated. Zn_0.5Mn_0.5Al_0.2Fe_1.8O₄ demonstrated reusability over five consecutive cycles with a slight decrease in efficiency. These findings highlight the potential of aluminum-doped zinc-manganese ferrites as efficient visible-light photocatalysts for environmental remediation.

Certain materials (ceramics and polymers) are capable of converting mechanical energy into electrical energy via the piezoelectric effect. The piezoelectric effect is fundamentally associated with momentary electric dipoles that occur in solids. The external surface may be borne directly by molecular groups or excited in the crystal lattice by an asymmetric peripheral charge. Using molecular dynamics simulation, the current study examined the effect of atomic vacancies on the piezoelectric properties of barium titanate crystals. For this reason, the diffusion coefficient, ferroelectric hysteresis loop, piezoelectric hysteresis loop, and strain–polarization curve were all examined. Increasing atomic vacancy to 20% increased the maximum (Max) value of residual strain and polarization in the simulated structure, according to the results. Optimal orientation, appropriate displacement of charged atoms, and the formation of effective dipoles all contributed to this. Consequently, the ferroelectric and piezoelectric properties of the structure were enhanced. In the sample containing 20% atomic vacancies, atomic movement was also extremely high. As opposed to the sample containing 30% atomic vacancy, however, its structure was less porous. Hence, when 20% atomic vacancy was present, the structure exhibited its most optimal polarization.

Water-in-diesel (W/D) emulsion is a promising alternative fuel candidate, as it can simultaneously reduce nitrogen oxides (NOx) and particulate matter (PM) while improving engine performance. Wide scale adoption of this fuel is difficult due to high production and storage costs. Hence, Real-Time Non-Surfactant Emulsion Fuel Supply System (RTES) is a proposed technology to solve these issues by mixing diesel and water in-line directly to the engine. This study presents an updated RTES prototype which incorporated a modular design, with a feedback system to control water injection rate. In this paper, RTES was installed to a common rail injection diesel-powered vehicle and the biodiesel-diesel W/D produced by RTES was analyzed to determine the effect of common rail pressure toward water droplet size and distribution. The vehicle was then tested under the New European Driving Cycle (NEDC) to evaluate vehicle emissions, which will serve as the basis for evaluating the emissions profile of W/D produced by RTES under urban and extra-urban driving conditions. It was found that when subjected to high common rail pressures, W/D droplets produced by RTES reduced by 21.1% compared to freshly produced W/D. NEDC emissions data revealed that NOx emission was reduced to a maximum of 25.3% in urban driving conditions. Hydrocarbons and carbon monoxide increased marginally throughout urban driving phase; while, carbon dioxide emissions were comparable between biodiesel-diesel and W/D. However, extra-urban driving conditions were unfavorable for RTES activation, as substantial emission increases were observed during high-speed accelerations. Nonetheless, W/D reduced PM emissions by 51% throughout NEDC.

Recycled concrete is a green material composed of natural aggregate and recycled aggregate mixed with cementing material and water in a certain ratio. The random distribution of aggregate in concrete has a great impact on material properties due to its supporting effect. In order to explore the characteristics of aggregate and the influence of its random distribution on the compressive strength performance of recycled aggregate concrete, numerical simulation is adopted in this paper. By using RAND function in ANSYS software APDL, a five-phase random aggregate model of recycled aggregate concrete was generated based on Monte Carlo method, and numerical simulation of uniaxial compression mechanical properties of concrete specimens under different recycled aggregate substitution rates (0%, 35% and 100%) was carried out. On the basis of reliability of the numerical simulation method verified by laboratory tests, multiple groups of stress–strain characteristic curves were obtained, the failure characteristics and stress characteristics of recycled concrete were analyzed, and Weibull probability statistical distribution was introduced to characterize the randomness and heterogeneity of mechanical characteristic parameters of recycled aggregate concrete. The results show that the compressive strength of recycled aggregate concrete is subject to Weibull distribution under random distribution of aggregate, and the shape coefficient m in Weibull distribution parameter can characterize the randomness and complexity of the failure of recycled concrete. The dispersion of the compressive strength of recycled aggregate concrete shows a decreasing trend with the increase in the substitution rate of recycled aggregate.

The spread of fake news poses significant challenges across various sectors, including health, the economy, politics, and national stability. Social media and modern technology have facilitated the rapid dissemination of fake news, predominantly in multimedia formats. Despite advancements, multimodal fake news detection models struggle to achieve optimal accuracy, primarily due to the quality of feature representation. This study aims to enhance feature representation to improve fake news identification. Pre-trained models for feature extraction, typically designed for general public domains, may not suit the specific characteristics of our task using the Fakeddit dataset. We propose a localized fine-tuning strategy, refining pre-trained BERT and VGG-19 models for accurate multimodal feature representation in fake news detection. BERT was fine-tuned by retraining all layers, while only the last block of VGG-19 was fine-tuned. To further enhance the representations, we made structural modifications to VGG-19, including the use of a global average pooling layer and a redesigned classifier. This approach significantly improved our multimodal fake news detection model’s performance, achieving a high accuracy of 92%. Compared to state-of-the-art studies that use generic pre-trained models, our model demonstrates superior performance. Our research underscores the importance of feature representation in multimodal contexts and opens avenues for exploring the synergy between textual and visual modalities in fake news detection.

Bentonite soil is frequently utilized as a compacted clay liner, which is a critical component of municipal waste landfill systems. This study aims to investigate the feasibility of treating sodium bentonite (NAB) with natural biopolymers to obtain an effective clay liner. The NAB was treated with three biopolymers: sodium alginate (SA), agar gum (A), and xanthan gum (X), at different replacement percentages (2%, 4%, 6%, and 8%). Additionally, an investigation was conducted to determine the extent to which replacing 50% of these additives with gypsum (G) would improve the biopolymer treatments. Fourier-transform infrared spectroscopy (FTIR), pH, one-dimensional swelling, and unconfined compressive strength (UCS) were carried out in this study. The FTIR results indicated the presence of intermolecular hydrogen bonding when NAB was treated with biopolymers and gypsum, which is crucial for enhancing the UCS. Furthermore, the thermal treatment of biopolymers significantly contributes to improving the UCS. Among the various biopolymers tested, agar gum demonstrated the most significant improvement, specifically, replacing 8% of the NAB with agar gum resulted in a 55% increase in UCS. Volume change behavior was most influenced by replacement of NAB with gypsum by 8%, which reduced the vertical swelling to 21% as opposed to 79% for the untreated NAB. The use of SA conversely resulted in an increased vertical swelling of 91%.

Strip surface defect detection is pivotal in the steel industry for improving strip production quality. However, there is still a big gap between the existing working and the detection of small defects in strip steel in practical applications. In this paper, we propose the SSD-YOLO model, which is designed specifically for detecting small defects on strip steel surfaces. Given the challenge of feature extraction due to the small defect size, it utilizes a dual-branch feature extraction and channel-level feature fusion to enhance the expression capability of small defects. Moreover, it integrates a multiscale high-resolution detection module to achieve precise segmentation, thereby improving the overall detection accuracy of the model. The experimental results illustrate that the SSD-YOLO model, as proposed, attains a 98.0% mean average precision (mAP) and operates at 66 frames per second (FPS) when evaluated on the SSDD (Steel Small Defect Dataset). In comparison with YoloV8s, the SSD-YOLO achieves a significant improvement in accuracy, with an increase of 19.9%. The inference time and performance of our SSD-YOLO is well balanced, making it suitable for real-world deployment.

This paper deals with the discrete-time antiwindup compensator (AWC) synthesis for nonlinear discrete-time systems under input saturation. The proposed method considers the objective of an optimal AWC design for fast convergence and for improved performance against the saturation nonlinearity. A discrete-time full-order AWC architecture is presented for nonlinear discrete-time systems to achieve an improved performance against the saturation nonlinearity. Additionally, an equivalent decoupled AWC architecture for nonlinear discrete-time system is derived through algebraic analysis and transformation of saturation to dead-zone function. To achieve fast convergence, a more generic Lyapunov function has been applied for the AWC design by incorporating an exponential term in the Lyapunov function. Then, new conditions for the AWC synthesis are revealed by application of the resultant decoupled discrete-time architecture, nonlinearity condition, a modified quadratic-exponential Lyapunov function, optimally exponential (L_{2}) approach, and input saturation properties. The design conditions are provided for both global and local design scenarios, which can be applied to both stable and unstable plants. Compared with the conventional methods, the proposed approach deals with nonlinear systems, can be more practical due to discrete-time scenario, provides an optimal design for both fast convergence and performance, and applicable to both stable and unstable plants. A simulation example has been provided to demonstrate the efficacy of the proposed nonlinear AWC design.