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Examining several aspects of reinforced coir, sugarcane, and glass fiber hybrid composites with CoFe
No full textThe mechanical and dielectric characteristics of coir and sugarcane fibers are investigated in this work using epoxy composites augmented with NiFe and CoFe metallic fillers. The strength, stiffness, and deformation behavior of four distinct compositions—C1-Coir with NiFe, C2-Coir with CoFe, S1-Sugarcane with NiFe, and S2-Sugarcane with CoFe—were assessed using tensile and flexural testing. With C1 showing the maximum strength of 160.215 N/mm² and S1 showing the highest tensile modulus of 2859.09 N/mm², indicating an improvement in stiffness, the results show that coir-based composites have higher tensile strength. These results demonstrate the promise of composites made of sugarcane and coir as sustainable substitutes for structural applications that need to balance stiffness and strength. More charge storage capacity is shown by a higher dielectric constant in CoFe GFRP (~2.03 pF). The Q factor of NiFe GFRP is much greater, at about 13.4. Their performance for industrial applications in lightweight structures and electromagnetic shielding could be improved by further optimizing fiber-metal interactions and resin composition
Finite element optimization of flywheel employed in internal combustion engines
No full textThe paper researched a flywheel design based on the replacement of typical circular cutouts with longitudinal radial slots which optimizes weight with simultaneous enhancement of the dynamic stress capabilities. A grey cast iron flywheel (FG 200), under conditions representative of its operating conditions, has been modelled using finite element analysis (FEA) in ANSYS and analysed using harmonic, stress analysis and static- equivalent stress analyses. Comparisons of traditional circular cutouts with longitudinal radial slits at the same mass reductions achieved had been done. Slot-based design had less maximum von Mises stress and more evenly spread stress along with smaller resonant amplitudes on the operating band. They will give detailed material properties, boundary condition, meshing and convergence and run sensitivity analysis of slot length, width and end radius to quantify the trade-off between weight reduction and stress. The findings encourage longitudinal radial slot as a viable alternative of reducing inertia and enhancing robustness, so far as slot geometry is optimized and operating speeds would not be resonating
Predictive modelling of compressive strength in silicon nitride-reinforced aluminium composites using supervised machine learning: A comparative study of random forest and artificial neural networks
No full textThe integration of machine learning and material science is now changing the way property predictions are made, accelerating the process of discovery and optimization of advanced materials. For this research, we employed a supervised ML to estimate the compressive strength of AMCs reinforced by silicon nitride. To sharpen predictive accuracy, hyperparameter tuning using GridSearch CV was performed. We employed two different algorithms-RF and ANN-to unravel the complex links between the inputs, such as compaction pressure, reinforcement content, sintering temperature, and sintering time with the target strength. Regularization was used to guard against overfitting, and training versus testing performance was compared rigorously. The results indicated that the RF outperformed the ANN model, giving an R² of 0.88, while the ANN reached an R² of only 0.80. These results suggested that the RF model had tighter residuals and higher accuracy since it emphasizes the parameters that drive most of the variance in the dataset. This work illustrates the potential of ML, especially RF, in reliably predicting properties of materials and informing the design of high-performance composites
Modelling esterification of palm kernel, neem, and jatropha seed oils using a simplex lattice approach
No full textHigh concentrations of free fatty acids (FFAs) in many non- edible oils interfere with biodiesel conversion, causing soap formation throughout the transesterification reaction. Although esterification in single and binary oil systems has been well-researched, there is a lack of knowledge regarding ternary blends. The knowledge gap on interactions of multi-oils limits optimization of blended feedstocks for resource-efficient and scaled biodiesel generation. The investigation optimizes the pre- treatment of PKO, NSO, and JSO using SLMD. Esterification was carried out using methanol at 25 wt.% and sulfuric acid at 1 wt% for 1.5 h at 60 °C. In this study, SLMD was used to optimize blend composition to minimize FFA and improve the viability of hybrid biodiesel feedstocks. Fuel properties of ternary biodiesel (PNJO methyl ester) were determined using standard transesterification conditions, 6:1 methanol-to-oil ratio, 1 wt.% catalyst, 60 °C, according to density and viscosity requirements in diesel blends. ANOVA analysis confirmed the significance of the model by a p- value of less than 0.001, with very high values of R²: 0.9983, adjusted R²: 0.9957, and predicted R²: 0.9295. The model validation revealed an RMSE value of 0.106 and SEP of 2.37%, MAE and AAD of 0.076 and 1.72%, respectively. The best formulation-0.0112 % JSO, 0.9653 % NSO, and 0.0235 % PKO gave 1.93 % FFA and produced biodiesel that met requirements according to both ASTM D6751 and EN 14214. This novel ternary optimization demonstrates that SLMD is a reliable predictive framework for sustainable biodiesel production
Identification of bridge section flutter derivatives and numerical calculation of critical flutter wind speed based on deep learning
No full textThe current bridge flutter derivative identification method has difficulty in data acquisition, and its precision is affected by parameter settings, making it challenging to obtain flutter derivatives efficiently and accurately, which affects the calculation precision of the critical flutter wind speed. To address the problems, this paper explores the application of deep learning methods in bridge flutter derivative identification to reduce dependence on experiments and simulation calculations and improve identification precision and calculation efficiency. First, the computational fluid dynamics (CFD) method is used to generate aerodynamic data of different bridge sections, and flutter derivatives are extracted as training labels. Then, a model combining a one-dimensional convolutional neural network (1D-CNN) and a bidirectional long short-term memory network (Bi-LSTM) is constructed to extract the aerodynamic time series' local features and temporal dependencies and realize flutter derivative identification. 1D-CNN automatically captures the instantaneous fluctuation features in the aerodynamic time series through local convolution kernels. Bi-LSTM mines the long-term dependency of aerodynamic forces through bidirectional time series modeling. The identified flutter derivatives are interpolated by the parabola fitting method to construct the aerodynamic parameter variation curve under continuous wind speed. The critical flutter wind speed is calculated based on the improved Scanlan-Tomko flutter criterion combined with the numerical iteration method. The results show that the mean absolute error (MAE) of the flutter derivative of this method is ≤3.21% under various bridge sections and wind speed conditions. In the circular streamlined box girder, the relative error of the critical wind speed at a wind speed of 25 m/s is as low as 2.02%. The calculation efficiency is improved by 24.54% compared with the traditional method, and the error is reduced by 26% compared with the control group, which verifies its high efficiency and accuracy in the wind-resistant design of bridges
Retraction Notice: Automated lung disease detection, classification and prediction using RNN framework
No full textWe take a zero tolerance to any situation where fraudulent research is published in our journals. As a result, this article has been retracted by the Publisher because it is suspected to be a nonsensical computer-generated publication with a number of tortured phrases and irrelevant references.
Additional measures have been implemented to prevent these issues from reoccurring.
EDP Sciences is extremely grateful to anonymous whistleblowers and the Problematic Paper Screene
Retraction Notice: Sound Classification Using Python
No full textWe take a zero tolerance to any situation where fraudulent research is published in our journals. As a result, this article has been retracted by the Publisher because it is suspected to be a nonsensical computer-generated publication with a number of tortured phrases and irrelevant references.
Additional measures have been implemented to prevent these issues from reoccurring.
EDP Sciences is extremely grateful to anonymous whistleblowers and the Problematic Paper Screene
In Vitro and In Silico Evaluation of
No full textCladophora sp. is known to contain chemical constituents with pharmacological properties. The secondary metabolites present in Cladophora sp. include alkaloids, phenolic compounds, saponins, and terpenoids, and it has been reported to exhibit anti-inflammatory activity. To evaluate its potential as an anti-inflammatory agent, a study was conducted using in silico and in vitro approaches. The in silico analysis involved screening the physicochemical characteristics and pharmacokinetic profiles of the compounds. Meanwhile, the in vitro analysis was performed using a bovine serum albumin (BSA) protein denaturation assay. Docking studies with the receptor protein showed that compounds from the ethanol extract of Cladophora with lower binding affinity to COX-II (PDB ID: 5kir) compared to the control drug rofecoxib were (2R)-5-hydroxy-7-methoxy-2-phenyl-3,4-dihydro-2H-1-benzopyran-4-one (-9 kcal/mol) and pinocembrin (-8.9 kcal/mol). The compound (2R)-5-hydroxy-7-methoxy-2-phenyl-3,4-dihydro-2H-1-benzopyran-4-one showed 100% similarity in amino acid residues with the control, forming hydrogen bonds at His90 and Arg513. The in vitro anti-inflammatory assay produced a linear regression equation of y = 352.52x - 1506.3 with an r2 value of 0.9179, and an IC50 value of 82.664 ppm, indicating strong anti-inflammatory activity. Further studies are recommended to isolate (2R)-5-hydroxy-7-methoxy-2-phenyl-3,4-dihydro-2H-1-benzopyran-4-one for subsequent in vitro and in vivo antiinflammatory evaluations
Determination of Total Phenol Compound Content in Mango Mistletoe Leaves (
No full textMango mistletoe leaves (Dendrophthoe pentandra (L.) Miq) are parasitic plants traditionally used as medicine due to their secondary metabolites and strong antioxidant activity. This study aimed to determine the total phenolic content and assess the effect of concentration on absorbance values using UV-Vis spectrophotometry, as a specific parameter for the development of herbal products. The research included sample preparation, extraction, preparation of gallic acid standards (10, 15, 20, and 25 ppm), and measurement of absorbance at 764 nm. Ethanol extracts of mango mistletoe leaves were evaluated at concentrations of 100, 200, 300, and 1000 ppm. The gallic acid standard curve showed a regression coefficient of R2 = 0.994, confirming its reliability. The total phenolic content obtained was 77.19 ± 0.44 mg/mL (100 ppm), 125.05 ± 0.45 mg/mL (200 ppm), 159.58 ± 2.00 mg/mL (300 ppm), and 228.73 ± 33.06 mg/mL (1000 ppm). The results demonstrated that phenolic content increased proportionally with concentration. In conclusion, mango mistletoe leaves contain significant levels of phenolic compounds, supporting their potential as a raw material for herbal products with vigorous antioxidant activity and fulfilling the criteria as a parameter for herbal product development