The detection of skin cancer holds paramount importance worldwide due to its impact on global health. While deep convolutional neural networks (DCNNs) have shown potential in this domain, current approaches often stru...
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Water quality monitoring in aquaculture involves classifying and analyzing the collected data to assess the water quality that is appropriate for breeding, rearing and harvesting aquatic organisms. Systematic data cla...
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The rapid advancement of artificial intelligence (AI) in generating human-like text poses significant challenges in distinguishing between human-written and AI-generated content. Recent advancements in natural languag...
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This study employed a wet impregnation method to synthesize five types of Cu/HZSM-5 adsorbents with Si/Al ratios of 25,50,85,200,and 300,used for the removal of H_(2)S in lowtemperature,low-oxygen *** impact of differ...
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This study employed a wet impregnation method to synthesize five types of Cu/HZSM-5 adsorbents with Si/Al ratios of 25,50,85,200,and 300,used for the removal of H_(2)S in lowtemperature,low-oxygen *** impact of different Si/Al ratios on the adsorption oxidative performance of Cu_(30)/HZSM-5–85 adsorbents was *** to the performance test results,Cu_(30)/HZSM-5–85 exhibited the highest breakthrough capacity,reaching 231.75 mg H_(2)S/g_(sorbent).Cu/HZSM-5 sorbent maintains a strong ability to remove H_(2)S even under humid conditions and shows excellent water ***,BET,and XPS results revealed that CuO is the primary active species,with Cu_(30)/HZSM-5–85 having the largest surface area and highest CuO content,providing more active sites for H_(2)S adsorption.H_(2)-TPR and O_(2)-TPD results confirmed that Cu_(30)/HZSM-5–85 sorbent exhibits outstanding redox properties and oxygen storage capacity,contributing to excellent oxygen transferability in the molecular sieve adsorption-oxidation *** notable characteristics such as a large surface area,high desulfurization efficiency,and water resistance,Cu_(30)/HZSM-5–85 sorbents hold significant importance for industrial applications.
The objective of this study was to obtain and test NiO/Al2O3 catalysts in the advanced removal of Reactive Black 5 from aqueous solutions. Ozone was used as oxidant agent in tandem with prepared catalysts. It was foun...
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This research introduces a groundbreaking electronic nose (E-Nose) that integrates advanced sensing materials with machine learning. The sensing materials include molecularly imprinted polymers and multi-walled carbon...
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This research addresses the environmental challenges associated with traditional masonry blocks, which rely heavily on cement and river sand, leading to resource depletion and ecological degradation. Alkali-activated ...
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This research addresses the environmental challenges associated with traditional masonry blocks, which rely heavily on cement and river sand, leading to resource depletion and ecological degradation. Alkali-activated masonry blocks represent a promising alternative, utilizing sustainable materials to reduce carbon emissions and promote resource efficiency. This study investigates the potential of predicting the compressive strength of geopolymer mortar made from supplementary cementitious materials, such as eggshell ash and rice husk ash, alongside quarry waste, as viable replacements for conventional raw materials. The eggshell ash and rice husk ash are rich in calcium and silica, and both react with alkaline activators and are essential in producing a gel-like binder. These materials enhance the blocks’ mechanical properties and contribute to waste reduction and resource efficiency. Through experimental testing, 27 distinct mix designs were developed and evaluated, resulting in the testing of 189 mortar cubes for compressive strength. Six machine learning techniques, namely artificial neural networks, boosted decision trees, K-Nearest Neighbors, random forest regression, support vector regression, and XGBoost, were employed to predict compressive strength. The results revealed that XGBoost outperformed all other methods, achieving a training dataset accuracy of 97.6% and a testing dataset accuracy of 95.2% for dry conditions while also attaining a predictive accuracy of 91.8% and 85.7% for wet conditions. Notably, XGBoost demonstrated a coefficient of determination (R²) of 0.958 for dry conditions and 0.938 for wet conditions, indicating its precision in predicting compressive strength. Furthermore, the analysis of feature contributions highlighted that NaOH content played a critical role in strength predictions, underscoring the potential of machine learning, particularly XGBoost, as a transformative tool for optimizing geopolymer mortar formulations sustainably
Concrete, a fundamental construction material, is known for its durability and strength. However, over time, it is prone to developing cracks due to environmental stress, traffic loads, and material defects. This susc...
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This paper integrates the multi-region input-output model (MRIO) and data envelopment analysis (DEA) methods to analyze the freight transport efficiency in Europe. Social, economic, and environmental influences were c...
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In contemporary geotechnical projects,various approaches are employed for forecasting the settlement of shallow foundations(S_(m)).However,achieving precise modeling of foundation behavior using certain techniques(suc...
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In contemporary geotechnical projects,various approaches are employed for forecasting the settlement of shallow foundations(S_(m)).However,achieving precise modeling of foundation behavior using certain techniques(such as analytical,numerical,and regression)is challenging and sometimes *** is primarily due to the inherent nonlinearity of the model,the intricate nature of geotechnical materials,the complex interaction between soil and foundation,and the inherent uncertainty in soil ***,thesemethods often introduce assumptions and simplifications,resulting in relationships that deviate from the actual problem’s *** addition,many of these methods demand significant investments of time and resources but neglect to account for the uncertainty inherent in soil/rock *** study explores the application of innovative intelligent techniques to predict S_(m) to address these ***,two optimization algorithms,namely teaching-learning-based optimization(TLBO)and harmony search(HS),are harnessed for this *** modeling process involves utilizing input parameters,such as thewidth of the footing(B),the pressure exerted on the footing(q),the count of SPT(Standard Penetration Test)blows(N),the ratio of footing embedment(Df/B),and the footing’s geometry(L/B),during the training phase with a dataset comprising 151 data ***,the models’accuracy is assessed during the testing phase using statistical metrics,including the coefficient of determination(R^(2)),mean square error(MSE),and rootmean square error(RMSE),based on a dataset of 38 data *** findings of this investigation underscore the substantial efficacy of intelligent optimization algorithms as valuable tools for geotechnical engineers when estimating S_(m).In addition,a sensitivity analysis of the input parameters in S_(m) estimation is conducted using@RISK software,revealing that among the various input parameters,the N exerts the most pronou
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