The presented study investigates the effects of weight percentages of boron carbide reinforcement on the wear properties of aluminum alloy composites. Composites were fabricated via ball milling and the hot extrusion process. During the fabrication of composites, B4C content was varied (0, 5, and 10 wt.%), as well as milling time (0, 10, and 20 h). Microstructural observations with SEM microscopy showed that with an increase in milling time, the distribution of B4C particles is more homogeneous without agglomerates, and that an increase in wt.% of B4C results in a more uniform distribution with distinct grain boundaries. Taguchi and ANOVA analyses are applied in order to investigate how parameters like particle content of B4C, normal load, and milling time affect the wear properties of AA2024-based composites. The ANOVA results showed that the most influential parameters on wear loss and coefficient of friction were the content of B4C with 51.35% and the normal load with 45.54%, respectively. An artificial neural network was applied for the prediction of wear loss and the coefficient of friction. Two separate networks were developed, both having an architecture of 3-10-1 and a tansig activation function. By comparing the predicted values with the experimental data, it was demonstrated that the well-trained feed-forward-back propagation ANN model is a powerful tool for predicting the wear behavior of Al2024-B4C composites. The developed models can be used for predicting the properties of Al2024-B4C composite powders produced with different reinforcement ratios and milling times.

This study aims to quantify the effectiveness of lockdown as a non-pharmacological solution for managing the COVID-19 pandemic. Daily COVID-19 death counts were collected for four states: California, Georgia, New Jersey, and South Carolina. The effectiveness of the lockdown was studied and the number of people saved during 7 days was evaluated. Five neural network models (MLP, FFNN, CFNN, ENN, and NARX) were implemented, and the results indicate that FFNN is the best prediction model. Based on this model, the total number of survivors over a 7-day period is 211, 270, 989, and 60 in California, Georgia, New Jersey, and South Carolina, respectively. The coefficients and weights of the FFNN for each state differ due to various factors, including socio-demographic conditions and the behavior of citizens towards lockdown laws. New Jersey and South Carolina have the most lockdowns and the least.

Phillinus gilvus (Schwein.) Pat has pharmacological effects such as tonifying the spleen, dispelling dampness, and strengthening the stomach, in which sterol is one of the main compounds of P. gilvus, but there has not been thought you to its extraction and detailed identification of its composition, in the present study, we used artificial neural network (ANN) and response surface methodology (RSM) to optimize the conditions of ultrasonic-assisted extraction, and the parameters of the independent and interaction effects were evaluated. Ultra performance liquid chromatography-quadrupole-time of flight mass spectrometry (UPLC-Q-TOF-MS/MS) was used to identify the major components in the purified extract. The results showed that the optimal extraction process conditions were: ultrasonic time 96 min, ultrasonic power 140 W, liquid to material ratio 1:25 g/ml, and ultrasonic temperature 30.7 °C. The compliance rates of the predicted and experimental values for the artificial neural network model and the response surface model were 98.3% and 96.12%, respectively, indicating that both models have the potential to be used for optimizing the extraction process of P. gilvus in industry. A total of 120 compounds and 30 major steroids were identified by comparison with the reference compounds. Among the major steroidal components are these findings will contribute to the isolation and utilization of active ingredients in P. gilvus.

This work intended to improve the precision and machining efficiency of Magnesium alloy (Mg-Li-Sr) using Wire electrical discharge machining (WEDM). Mg-Li-Sr alloy is prepared through inert gas assisted stir casting route. Taguchi approach is used for experimental design for WEDM parameter such as pulse OFF time, pulse ON time, wire feed rate, servo voltage and current. L27 orthogonal array is considered to understand the influence of control parameter such as Kerf Width (KW), Roughness of the surface (Ra), Material Removal Rate (MRR). Integration of the CRITIC (Criteria Importance Through Intercriteria Correlation) -WASPAS (Weighted Aggregated Sum Product Assessment) multi-objective optimization method with Artificial Neural Network (ANN) modelling with different network structure for prediction and optimization is a novel approach that significantly improves prediction accuracy and machining outcomes. The developed ANN model with better R2 value of 99.9 % has better ability for prediction while correlated with formulated conventional regression equation. The error percentages identified through confirmation tests for regression and ANN models are Ra - 8.5 % and 3.4 %, MRR - 5.9 % and 2.8 %, KW - 6.7 % and 2.2 % respectively. Optimal output response attained by CRITIC-WASPAS approach yields surface roughness of 4.62 μm, material removal rate of 0.073 g/min and kerf width of 0.388 μm.

Beam-like members use corrugated webs to increase their shear strength, stability, and efficiency. The corrugation positively affects the members\' structural characteristics, especially those governed by the web parameters, such as the shear strength, while reducing the total weight. Existing code and analytical models for predicting the shear strength of trapezoidal corrugated steel webs (TCSWs) are summarized. This paper presents an optimized Artificial Neural Network (ANN)-based model to estimate the shear strength of steel girders with a TCSW subjected to a concentrated force. A database of 206 experimental results from the literature is used to feed the ANNs. Six geometrical and material parameters were identified as input variables, and the experimental shear strength at failure was considered the output variable. Four hyperparameter optimization techniques are applied to refine the ANN models: Bayesian Optimization (BO), Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS), Firefly Algorithm (FA), and African Buffalo Optimization (ABO). The performance metrics indicate that the ABO-ANN model is the most effective among these. The predictions of the developed ML model were also compared with those of existing code and analytical models. The comparisons illustrated that the ANN-based model outperforms the other existing models. The sensitivity analysis using the proposed ANN-based model captured the relationships and interactions among the geometric and material parameters and their impact on shear strength. One main finding is that the corrugation angle in the 35-45° range maximized the TCSW shear strength.

Construction and global infrastructure depend on cement production. It is one of the biggest carbon emitters, making it an aspect of environmental sustainability and climate change mitigation. Each stage of cement production releases CO2 and other greenhouse gasses. About 8% of worldwide CO2 emissions come from the cement sector, making it a major contributor. Different supplementary cementitious materials (SCMs) like fly ash (FA), silica fume (SF), and slag are used to partially replace traditional raw materials like limestone, reducing the environmental impact. This study investigated the use of supplementary cementitious materials, specifically FA and alccofine (AF), as partial replacements for cement in concrete to reduce environmental impact. The study first identified an optimal replacement percentage for FA (20%, 30%, and 40%) by weight of cement. Subsequently, using the optimal FA percentage, AF was added at varying percentages (5%, 10%, and 15%) by weight of cement. The study evaluated the mechanical properties of the concrete mixtures, including workability, compressive strength, split tensile strength, and flexural strength. Durability, measured by water sorptivity and rapid chloride penetrability tests, was also assessed. The microstructural properties of the concrete were analyzed to understand their influence on performance. As a result of the significant environmental implications of producing and using concrete for all activities, an in-depth life cycle assessment (LCA) was conducted. Additionally, artificial neural networks were employed to predict the compressive strength of the concrete. The study concluded that incorporating FA and AF in concrete mixtures is a promising approach to producing more environmentally friendly concrete.

The objective of this study was to investigate the change in mineral composition depending on tea variety, tea concentration, and steeping time. Four different tea varieties, black Ceylon (BC), black Turkish (BT), green Ceylon (GC), and green Turkish (GT), were used to produce teas at concentrations of 1, 2, and 3%, respectively. These teas were produced using 7 different steeping times: 2, 5, 10, 20, 30, 45, and 60 min. It was also aimed to optimize the regression equations utilizing these factors to identify parameters conducive to maximizing Zn, K, Cu, Mg, Ca, Na, and Fe levels; minimizing Al content, and maintaining Mn level at 5.3 mg/L. The optimal conditions for achieving a Mn content of 5.3 mg/L in black Turkish tea entailed steeping at a concentration of 1.94% for 11.4 min. Variations in K and Mg levels across teas were inconsistent with those observed for other minerals, whereas variations in Al, Cu, Fe, Mn, Na, and Zn levels exhibited a close relationship. Overall, mineral levels in tea can be predicted through regression analysis, and by mathematically optimizing the resultant equations, the requisite conditions for tea production can be determined to achieve maximum, minimum, or target mineral values.

This study investigated the application of artificial intelligence algorithms (AIA) in the coagulation treatment of paint wastewater anchored by novel Phaseolus vulgaris seed extract (PVSE). Untreated wastewater discharge harms the ecosystem, and therefore harmful industrial effluent, such as paint wastewater, must be brought to safe discharge levels before being released into the environment. In addition to AIA, comprehensive characterization tests, coagulation kinetics, and process optimization were also executed. Characterization results revealed that total solid in the PWW was above allowable standard, justifying the need for effective particle decontamination. The XRD and FTIR characterization indicated that PVSE structure is amorphous with abundant amine groups. Results of analysis of variance (ANOVA) obtained from process modeling indicated that the coagulation-flocculation process was a nonlinear quadratic system (F-value = 45.51) which was mostly influenced by PVSE coagulant dosage (F-value = 222.48; standardized effect = 14.85). Artificial intelligence indicated that neural network training effectively captured the nonlinear nature of the system in ANN (RMSE = 0.00040194; R = 0.98497), and ANFIS (RMSE = 0.003961) algorithms. Regression coefficient obtained from process modeling highlighted the suitability of RSM (0.9662), ANN (0.9739), and ANFIS (0.9718) in forecasting the coagulation-flocculation process, while comparative statistical appraisal authenticated the superiority of ANN model over RSM and ANFIS models. The coagulation kinetics experiment, which used a coagulation kinetic model, revealed a constant flocculation constant (Kf-value) for all jar test batches and a strong association between the Menkonu coagulation-flocculation constant (Km) and Kf values. Best removal efficiency of 97.01 % was obtained using ANN coupled genetic algorithm optimization (ANN-GA) at PVSE dosage of 4 g/L, coagulation time of 29 min and temperature of 25.1oC.

Massive consumption of fossil fuels and alarming environmental degradation are motivating researchers to learn about alternative fuels. Straight vegetable oils are an alternative to fossil fuels to meet the standards. Microalgae is also a viable carbon-neutral alternative to depleting conventional fuel sources, a solution to the industrial requirement of organic consumables and an option for a green and sustainable economy for biofuels. In the present study, lipid was extracted from Karanja seeds and Dunaliella salina biomass. These were used to prepare different binary and ternary fuel blends with conventional reference diesel fuel with different proportions along with used cooking oil with their concentrations ranging from 10 to 20% (v/v). The influence of these blends on performance and emissions characteristics in CI engines has delved at varying engine loads from 0 to 100%. The binary blend with Dunaliella salina oil has increased the performance characteristics while decreasing all the major emission parameters compared to reference diesel fuel and shows a significant improvement among binary blends. Ternary blends with Dunaliella salina oil, on the other hand, have improved performance while lowering emission parameters when compared to reference diesel fuel and demonstrate a substantial improvement across ternary blends. For predicting the performance and emission characteristics of binary and ternary blends, an artificial neural network-based model was developed. The optimum blends, OB6 (90% RDF, 10% DO) and OB8 (80% RDF, 10% DO, 10% UCO), improved BSFC by 10.71%, BTE by 14.23%, and reduced BSEC by 12.45% at full load. Emissions were generally reduced, with CO2 decreasing by up to 39.39%. The simulation results demonstrated that the created 4-7-7 model was capable of accurately predicting the performance and emission characteristics of various alternative fuel blends and indicating a stronger correlation between the predicted and observed values, having a high correlation coefficient of 0.9974. Binary and ternary blends with straight vegetable oils improved CI engine performance and pollutants compared to reference diesel fuel, indicating they have the potential to replace conventional fuels for sustainable development.

Existing artificial neural networks (ANNs) have attempted to efficiently identify underlying patterns in environmental series, but their structure optimization needs a trial-and-error process or an external optimization effort. This makes ANNs time consuming and more complex to be applied in practice. To alleviate these issues, we propose a stabilized ANNs, called SANN. The SANN efficiently optimizes ANN structure via incorporation of an additional numeric parameter into every layer of the ANN. To exemplify the efficacy and efficiency of the proposed approach, we provided two practical case studies involving meteorological drought forecasting at cities of Burdur and Isparta, Türkiye. To enhance SANN forecasting accuracy, we further suggested the hybrid VMD-SANN that integrated variation mode decomposition (VMD) with SANN. To validate the new hybrid model, we compared its results with those obtained from hybrid VMD-ANN and VMD-Radial Base Function (VMD-RBF) models. The results showed superiority of the VMD-SANN to its counterparts. Regarding Nash Sutcliffe Efficiency measure, the VMD-SANN achieves accurate forecasts as high as 0.945 and 0.980 in Burdur and Isparta cities, respectively.