Arabian Journal for Science and Engineering最新文献

Water pollution is the emerging issue in modern world that may cause water scarcity for our future generations. Therefore, it is ultimate need to develop highly efficient and cost-effective methods to solve this issue. Due to this intense demand, a new hyper-cross-linked polymer (HCP) of resorcinol (1, 3-dihydroxybenzol) “R-HCP” is synthesized using Friedel–Craft reaction for the removal of cadmium metal ions from industrial wastewater. Real industrial wastewater samples are used to evaluate the adsorption capability of R-HCP. Fabricated R-HCP is characterized through Fourier transform infrared spectroscopy (FTIR), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction analysis (XRD), EDX (energy-dispersive X-ray spectroscopy) and BET (Brunauer–Emmett–Teller). The BET surface area of R-HCP is 221.5848 m²g⁻¹. Through salt addition method, the point of zero charge (PZC) was also determined, and its value is 2.0 pH. The maximum adsorption capacity of R-HCP is 10 mg/g for cadmium metal ions. The greatest adsorption value of 93% was obtained at pH 10 with 0.5 g of adsorbent and 9-min contact time and favors exothermic reaction. Langmuir, Freundlich and Temkin isotherms were studied, and results shows that Freundlich model is the best fit with R² value of 0.9917. Adsorption kinetic investigation shows that it follows pseudo-first-order kinetic model R² value 0.9874. The study of the effect of interfering ions including calcium, magnesium, sodium and potassium demonstrates the decrease in the adsorption capacity to a little extent. R-HCP can be recycled and have regeneration capacity, which is novel and distinguished feature of this adsorbent.

This study aims to comprehensively investigate pollutant dispersion within a scaled urban model and assess associated risks from emissions. Specifically, we focus on a ground-level point-source in the first row of buildings, continuously releasing a tracer gas for passive scalar transport analysis. The research seeks to understand flow patterns and pollutant dispersion considering the diverse heights and rooftop configurations typical of urban environments. Turbulence significantly influences pollutant dispersion and airflow around structures, prompting large-Eddy simulation (LES) to quantify these effects within the urban model’s regularly spaced buildings. We utilize the dynamic Smagorinsky subgrid-scale (SGS) model to resolve the instantaneous flow field and passive scalar transport. Artificial turbulent structures are generated at the inlet using the synthetic inflow generator method. The validation shows that, the average deviations from the wind tunnel measurements for Wall A at positions (x_2/H=0) and (x_2/H=3.79) are approximately 12.09% and 16.52%, respectively. We found that, as free-stream flow encounters the first high-rise buildings in the urban canyon, high streamwise velocity is experienced, followed by the formation of a wake region around obstacles, causing flow separation due to boundary layer detachment from building rooftops. Pollutants released from the ground-level point-source are transported from primary recirculations to secondary ones through turbulent diffusion and advection until evacuated from the urban area. Velocity and concentration contours reveal that in-canyon vortex dynamics and pollutant distribution are highly sensitive to rooftop configurations. The height and shape of buildings not only influence in-canyon vortex structure, but also determine vortex strength. Furthermore, pollutant dispersion characteristics and pollution levels vary across buildings, with distinct regions near high- and low-rise structures showing differing patterns.

The smart buildings’ load forecasting is necessary for efficient energy management, and it is easily possible because of the data availability based on widespread use of Internet of Things (IoT) devices and automation systems. The information of buildings’ occupancy is directly associated with energy consumption. Therefore, we present a hybrid model consisting of a Long Short-Term Memory (LSTM) network, Extreme Gradient Boosting (XgBoost), Random Forest (RF) and Linear Regression (LR) for commercial and academic buildings’ load forecasting. The correlation between occupants’ count and total load of the building is calculated using Pearson Correlation Coefficient (PCC). The comparative analysis of the proposed approach with LSTM, XgBoost, RF and Gated Recurrent Unit (GRU) is also performed. Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Square Error (MSE) and Normalized Root Mean Square Error (NRMSE) are used as performance indicators for evaluating performance. Findings indicate that the proposed hybrid approach outperforms other models. The RMSE and MAE of 2.99 and 2.18, respectively, are recorded by the proposed model for commercial building dataset while for academic building the RMSE and MAE are 4.48 and 2.85, respectively. Occupancy and load consumption have a positive correlation as evident from PCC analysis. Therefore, we have scheduled the forecasted load based on occupancy patterns for two different cases. Cost is reduced by 17.42% and 33.40% in case 1 and case 2, respectively. Moreover, the performance of the proposed hybrid approach is compared with different techniques presented in literature for buildings load forecasting.

Many machine learning-based classification models for autism spectrum disorder (ASD) using neuroimaging data have been proposed. In recent developments, research has transitioned its focus to using extensive multi-site brain imaging datasets to increase the clinical applicability and statistical robustness of findings. However, the classification performance is hampered by the inherent heterogeneity of these combined datasets. This paper introduces a novel correlation-based functional connectivity method designed to extract improved Region of Interest (ROI) coupling features from the Autism Brain Imaging Data Exchange (ABIDE) dataset. We assess graph embedding domain adaptation (GEDA) to mitigate dataset heterogeneity, mapping data points from source and target domains into a common low-dimensional space while preserving their similarity relationships. We employ a novel dataset-splitting approach called the ’rectified environment’ to enhance classification accuracy. To validate our proposed model, we compared it with related works. Our result shows that the proposed model with support vector machine (SVM) has an accuracy of 78.1% and AUROC 83.9% in identifying ASD patients. Our model demonstrates a substantial improvement, increasing accuracy by 6.1% and AUROC by 5.3% compared to the maximum independence domain adaptation (MIDA) model. These findings reveal an anticorrelation in brain function and disruptions in brain connectivity between anterior and posterior brain regions in ASD.

In this study, highly hydrophilic TiO₂/Fe₃O₄ nanoparticles with a high level of photocatalytic activity were used to improve the performance of thin-film nanocomposite (TFN) membranes in forward osmosis (FO) process. The influence of TiO₂/Fe₃O₄ nanoparticles binary metal oxides incorporation on the properties of the TFC FO membrane in terms of hydrophilicity, porosity, pore size, and cross-sectional morphology was thoroughly studied. Results demonstrate that with the addition of TiO₂/Fe₃O₄ nanoparticles, the structure of the membrane top layer has changed due to nanoparticles’ reaction with the amino and organic monomers in the surface polymerization process. Furthermore, the thickness of the membrane cross section has changed with the addition of TiO₂/Fe₃O₄ nanoparticles due to changes in the rate of the amine monomer penetration into the sublayer. The TiO₂/Fe₃O₄ loading caused changes in the overall porosity and improved membrane hydrophilicity. The effect of UV light on the synthesized membranes was also tested. It was found that in the presence of UV light, the high photocatalytic activity of TiO₂/Fe₃O₄ nanoparticles is the primary cause of their excellent performance in the membrane structure. As the membrane was exposed to UV light, the increase in hydrophilicity increases the membrane flux and decreases its structural parameter. These changes resulted in a 43% improvement in membrane water permeability and reduced the structural parameter up to 410 μm. Water flux of improved membrane also increased by 74% in the forward osmosis process, which was achieved without significantly decreasing membrane selectivity.

The health status of bearings seriously affects the operational efficiency of equipment, and it is important to carry out bearing health status detection. A bearing fault diagnosis method based on stepwise variational modal decomposition (SVMD) with adaptive initialization center frequency and Gramian angular difference field is proposed. Firstly, a method of center frequency initialization base on frequency energy distribution characteristics is proposed to improve the decomposition speed and stability. Secondly, SVMD with single component decomposition and local decomposition is proposed to improve decomposition efficiency. It can effectively avoid inconsistency in different signal parameter settings and ensures consistency in the number of signal components, which is very suitable for batch processing of signals. Finally, Gramian angular field (GAF) and convolutional neural networks (CNNs) are combined to extract features of the reconstructed signal spectrum and enhance the differential characteristics between different signal spectrum. The experiment shows that the center frequency initialization method can shorten the single decomposition time from 11.13 to 6.71 s. The overall recognition rate can reach 95.2%, which is at least 1.9% higher than other decomposition methods.

In this study, it has been focused on examining the effects of production parameters on quality parameters such as surface roughness and geometric tolerances in the production of AlSi10Mg samples by the additive manufacturing method. The experimental design has been prepared according to the Taguchi L₂₇ orthogonal array. As a result, in the production of samples, increasing laser power (P) contributed positively to surface roughness and diameter change, and increasing scanning distance (SD) negatively contributed to circularity change and concentricity. Further, it has been determined that increasing the scanning speed (SS) negatively affects the concentricity change of the produced samples. The optimum production parameters for surface roughness and diameter variation has been determined as A₁B₁C₃. The optimum production parameters for circularity variation and concentricity have been determined as A₃B₃C₁ and A₃B₁C₁, respectively. According to the ANOVA analysis results, the most effective parameters for surface roughness, diameter change, circularity change and concentricity have been 53.22% P, 62.45% SD, 37.23% SS and 40.41% SD, respectively. Furthermore, as a result of the gray relationship analysis (GRA) performed for the output parameters, the optimum production parameter has been determined as A₂B₁C₃.

In this work, cellulose was isolated from the waste leaves of the Butia monosperma plant was tailored into cellulose/GO/TiO₂–Bi material via green and one-step method for photocatalytic degradation of methylene blue (MB) under the natural sunlight and UV light. The encroachment of TiO₂–Bi within the cellulose matrix with someway decrement of agglomeration was examined by SEM analysis. The XRD and FTIR analysis suggested the interaction of cellulose, graphene oxide (GO), and TiO₂–Bi, resulting in the formation of secondary bonding such as hydrogen bonds, and van der Waal bonds. The results suggested that the cellulose backbone has compatibility to anchor the GO and TiO₂–Bi onto the surface; hence, they perform with synergetic approaches for degradation of MB. DRS and PL analysis infers to the synergetic interaction among GO and TiO₂–Bi and the role of graphene oxide in photocatalytic performance of fabricated cellulose-based semiconductor. The photocatalytic activity of cellulose/GO/TiO₂–Bi composite is discussed in form of charge transfer and electron/hole recombination stoppage by the synergetic effect of GO and TiO₂–Bi anchored within cellulose backbone. The cellulose/GO/TiO₂–Bi composite displayed noteworthy photocatalytic performance for the degradation of MB under solar light and UV light. This work will underpin the extension of research areas where the biotemplate-based promising materials are synthesized for photocatalytic applications.

The effectiveness of protective armor supports the projectile ricochet phenomenon as it clearly restrains projectile from penetration and can potentially form basis for design optimization of protective systems. The present numerical study has been carried out to find the most appropriate obliquity/incident angle which can effectively be used for design of protective armor. Although it is not possible to practically control the incident projectile angles, but numerical investigation can potentially provide solution for design and performance optimization of overall structure and surface geometry of target plate, or adaptive adjustment in terms of target obliquity angle. The projectile has been impacted with velocity of 700 ± 20 m/s at incident angles ranging from (15^circ le theta le 75^circ). The experimental results at 0° has been taken as a reference to validate material model and simulation results. The verification parameters such as eroded mass and steady residual velocity have also been investigated. After all validations and calculations, the window of obliquities/incident angle, safe thickness limit as well as steady residual velocity was obtained. Based on the attained optimum angle 45°, the minimum target plate thickness calculated is 6.4 mm against the thickness of reference plate, 4 mm (UHSLA-XF1700) armor steel. The result was partial penetration at 0° incident angle. The numerical simulation for 6.4-mm-thick target plate under similar circumstances revealed that it has ability to defeat the incoming threat more effectively. For the respective cases of different incident angles, a modified analytical model has also been developed and results coincided with the findings of numerical simulations.

Metal foams are solids where the gas is filled inside uniformly in the metal matrix. Blowing agent supplies air inside the parent metal, and metal foam has emerged to be a promising material because of its low density, high absorption capacity, low thermal conductivity and high strength which finds its huge applications in automobile components. The present work deals with the application of the aluminium metal foam with different densities 200 and 400 kg/m³ in automobiles. Various tests such as toughness, hardness, bending and compression are carried out for four chosen densities, and the values are compared with the aluminium base metal. The result showed that the hardness value increased significantly by 24.48% with the rise in the density from 200 to 400 kg/m³. Maximum modulus of resilience for the low-density specimen is found to be 2.21 MJ/m³. Surface topography showed irregular pore shapes with discontinuity, resulting in a loss of cell integrity with the neighbouring cell walls. This affected the performance of the foam significantly. Thermal experiments were carried out to determine the thermal conductivity where thermal conductivity increased by 122% with the rise in the density from 200 to 400 kg/m³. Based on the results, it is concluded that aluminium foam with density 400 kg/m³ can be recommended for use in automobile applications due to its lightweight properties, which contribute to improving fuel efficiency, impact absorption capacity and the vehicle’s speed. Additionally, the air trapped within the foam cells serves as a sound barrier and insulator in cars.