Second-order Polynomial Regression Research Articles

Predicting Soil Salinity Based on Soil/Water Extracts in a Semi-Arid Region of Morocco

Soil salinity is a major constraint to soil health and crop productivity, especially in arid and semi-arid regions. The most accurate measurement of soil salinity is considered to be the electrical conductivity of saturated soil extracts (ECe). Because this method is labor-intensive, it is unsuitable for routine analysis in large soil sampling campaigns. This study aimed to identify the best models to estimate soil salinity based on ECe in relation to a rapid electrical conductivity (EC) measurement in soil/water (referred to as S:W henceforward) extracts. We evaluated the relationship between ECe and the ECS:W extract ratios (1:1, 1:2, and 1:5) in salt-affected soils from the semi-arid Sehb El Masjoune region of Morocco. The soil salinity in this region is 0.5 to 235 dS/m, as determined by the ECe method. A total of 125 soil samples, from topsoil (0–15 cm) and subsoil (15–30 cm) with mainly fine to medium textures, were analyzed using linear, logarithmic, and second-order polynomial regression models. The models included all samples or grouped samples according to soil texture (fine, medium) or specific textural classes. The mean ECe values were 2.6, 3.1, and 7.9 times greater than the EC of 1:1, 1:2, and 1:5 S:W extracts, respectively. Polynomial regression models had the best predictive accuracy, R2 = 0.98, and the lowest root mean square error of 10.6 to 10.7 dS/m for the ECS:W extract ratios of 1:5 and 1:2. The polynomial models could represent the non-linear relationships between ECe and salinity indicators, especially in the 80–170 dS/m salinity range, where other models typically underestimate the salinity. These results confirm that advanced regression techniques are suitable for predicting soil salinity in a salt-affected semi-arid region. The site-specific models outperformed previously published models, because they consider the spatial variability and heterogeneity of the salinity in the study area explicitly. This confirms the importance of calibrating soil salinity models according to the local soil and environmental conditions. Consequently, we can undertake soil salinity assessments in hundreds of samples by using the simple, rapid ECS:W extraction method as a direct indicator of EC and extrapolate to ECe with a polynomial regression model. Our approach enables the widespread soil salinity assessments that are needed for land-use planning, irrigation management, and crop selection in salt-affected landscapes.

Modeling and Simulation of Vehicle Velocity-Density on Buah Batu Road Using Second-Order Polynomial Regression

The problem of traffic density is a complex problem in the world of land transportation, especially in urban areas, including Bandung City. Buah Batu Road, one of the main roads in Bandung City which is 13 meters wide and 1.70 kilometers long, connecting Bandung City and Bandung Regency. And this study examines the relationship between vehicle speed and traffic density on Buah Batu Road, Bandung. Using the macroscopic Lighthill-Whitham Richards (LWR) model, Second Order Polynomial Regression, and Lax-Wendroff scheme simulation. This study aims to obtain the speed-density function for traffic. The introduction emphasizes the importance of understanding traffic flow dynamics to reduce congestion, especially in areas with significant vehicle growth. The methodology used is direct observation of the Buah Batu Road section with an observed length of 18 meters, with data collected through cellphone camera recordings at various times. These observation data provide insight into vehicle density and speed under various conditions.

Optimization of Setpoint Conditions for Enhanced Biofabrication of Silver Nanoparticles Using Helichrysum crispum Extracts.

This study investigated the optimization of setpoint conditions used for the enhanced biofabrication of silver nanoparticles (H.C-AgNPs) using Helichrysum crispum extracts. A Box-Behnken Design (BBD) model was used to evaluate the effects of reaction time, temperature, an H. crispum extraction volume, and a 0.1 M AgNO3 solution volume. A second-order polynomial regression equation was developed with a high R² of 0.9629, indicating that the model explained 96.29% of the variability in the data. The statistical significance of the model was confirmed with an F-value of 25.92 and a p-value of less than 0.0001. The optimal biofabrication conditions were determined to be a reaction time of 60 min, a temperature of 50 °C, an H. crispum extract volume of 10 mL, and a silver nitrate volume of 90 mL, achieving a peak absorbance of 3.007 a.u. The optimized conditions were experimentally validated, resulting in an absorbance of 3.386 a.u., reflecting a 12.6% increase. UV-Vis spectroscopy showed a distinct surface plasmon resonance (SPR) peak at 433 nm. XRD analysis confirmed a crystalline face-centered cubic (FCC) structure with a primary diffraction peak at 2θ = 38.44° (111 plane). SEM and EDS results confirmed a uniform size and high purity, while FTIR spectra confirmed the involvement of phytochemicals in nanoparticle stabilization. TEM analysis revealed a uniform particle size distribution with a mean size of 19.46 nm and a dispersity of 0.16%, respectively. These results demonstrate the importance of statistical tools in optimizing the setpoint conditions used in the biofabrication of AgNPs, which have applications in various fields.

Experimental Study on Optimizing Hydrogen Production from Sludge by Microwave Catalytic Pyrolysis Using Response Surface Methodology.

Catalytic pyrolysis technology is a harmless and useful solid waste treatment method. Studying the catalytic pyrolysis of sludge for hydrogen production is of practical significance. Therefore, this paper prepared a bifunctional catalyst with both microwave absorption and catalytic properties from electroplating sludge through carbonization and acid modification processes and then was characterized by XRD, BET, SEM, XPS, and FTIR. An experimental study employed a conventional-then-microwave pyrolysis method to investigate the catalytic pyrolysis process of municipal sludge. Combining central composite design (CCD) and response surface methodology (RSM) with three factors and five levels, this paper investigated the interactive effects of the conventional pyrolysis temperature, microwave irradiation time, and catalyst addition ratio on the unit hydrogen production (UHP) of sludge. A predictive model based on a second-order polynomial regression equation was developed. The results revealed that the catalyst possesses a specific surface area and pore structure and that the second-order polynomial model fits well. The conventional pyrolysis temperature, microwave irradiation time, catalyst addition ratio, and interaction between the latter two significantly affected the UHP of sludge. The optimal operation conditions of conventional pyrolysis temperature, microwave irradiation time, and catalyst addition ratio were 462.7 °C, 8.8 min, and 12.4%, respectively. Under these optimal conditions, the UHP was 13.22 mmol/g, with a relative error of only 1.12% compared to the predicted model value of 13.37 mmol/g.

Experimental Optimization of the Propeller Turbine Performance Using the Response Surface Methodology

The growing global energy demand necessitates a shift towards sustainable sources to mitigate environmental issues and ensure energy security. This work explores the design and optimization of propeller-type hydrokinetic turbines to efficiently harness renewable energy from water currents. Through experimental testing and regression modeling, the research aimed to maximize the power coefficient (Cp) by determining the optimal values of the number of blades (Z) and the turbine diameter to hub diameter ratio (d/D). By correcting for experimental biases, the study elucidates the importance of factors such as the blockage ratio and turbine configuration on its performance. A second-order polynomial regression model, which was validated through analysis of variance, determined that when Z and d/D were set at 4 and 0.15, respectively, the optimal value for Cp was 53.62%. These findings provide valuable insights for optimizing hydrokinetic turbine efficiency, contributing to the advancement of renewable energy technologies.

Statistical Analysis-Based Prediction Model for Fatigue Characteristics in Lap Joints Considering Weld Geometry, Including Gaps

Automotive chassis components, constructed as lap joints and produced by gas metal arc welding (GMAW), require fatigue durability. The fatigue properties of the weld in a lap joint are largely determined by weld geometry factors. When there is no gap or a consistent gap in the lap joint, improving the geometry of the weld toe can alleviate stress concentration and enhance fatigue properties. However, due to machining tolerances, it is difficult to completely eliminate or consistently manage the gap in the joint. In the case of a lap-welded joint with an inconsistent gap, it is necessary to identify the weld geometry factors related to fatigue properties. Evaluating the fatigue behavior of materials and welded joints requires significant time and cost, meaning that research that seeks to predict fatigue properties is essential. More research is needed on predicting fatigue properties related to automotive chassis components, particularly studies on predicting the fatigue properties of lap-welded joints with gaps. This study proposed a regression model for predicting fatigue properties based on crucial weld geometry factors in lap-welded joints with gaps using statistical analysis. Welding conditions were varied in order to build various weld geometries in joints configured in a lap with gaps of 0, 0.2, 0.5, and 1.0 mm, and 87 S–N curves for the lap-welded joints were derived. As input variables, 17 weld geometry factors (7 lengths, 7 angles, and 3 area factors) were selected. The slope of the S–N curve using the Basquin model from the S–N curve and the safe fatigue strength were selected as output variables for prediction in order to develop the regression model. Multiple linear regression models, multiple non-linear regression models, and second-order polynomial regression models were proposed to predict fatigue properties. Backward elimination was applied to simplify the models and reduce overfitting. Among the three proposed regression models, the multiple non-linear regression model had a coefficient of determination greater than 0.86. In lap-welded joints with gaps, the weld geometry factors representing fatigue properties were identified through standardized regression coefficients, and four weld geometry factors related to stress concentration were proposed.

Dietary condensed tannin supplementation improves growth performance and feed utilization of juvenile Largemouth bass (Micropterus salmoides) through positively regulating serum lipids and intestinal health

Considering the diverse biological activities of condensed tannin (CT), the study aimed to evaluate the effect of CT on growth performance and intestinal health in juvenile largemouth bass (Micropterus salmoides) through a 56-day feeding trial. Largemouth bass (5.99 ± 0.03 g) were fed a basic diet supplemented with different levels of CT: 0 g/kg (the control group, CG), 0.60 g/kg (the T0.6 group), 1.50 g/kg (the T1.5 group), 3.75 g/kg (the T3.75 group), and 9.38 g/kg (the T9.38 group). The results indicated that dietary supplementation of CT at 3.75 g/kg promoted growth performance, feed conversion ratio, the apparent digestibility coefficients (ADCs) of crude protein and crude lipid, the crude protein of whole-body, the intestinal antioxidant capacity, and intestinal immune response, as well as the development of intestinal villi (P < 0.05), while the fish fed with 9.38 g/kg CT exhibited the opposite trend. Further, dietary CT (3.75 g/kg) decreased crude lipid in the whole-body compared to the CG (P < 0.05). Dietary CT (3.75 g/kg) altered the microbial community, the relative abundance of Bacillus and Lysinibacillus were increased compared to the CG. Additionally, the serum metabolome results revealed a negative correlation between Lysinibacillus abundance and lipid metabolites in the serum. These findings suggested that supplementing the diet with 3.75 g/kg CT promoted the growth performance, feed utilization, and intestinal health of largemouth bass. The optimum CT level was 4.15 g/kg based on second-order polynomial regression analyses of weight gain rate.

Growth of peaches at three altitudes in the Santander mountains of Northeastern Colombia

A study was conducted in the high tropics in the province of Pamplona, Colombia, with the objective of evaluating the growth of peach, Jarillo variety, under three different altitudes (1670, 1870, and 2170 masl). A total of eight trees were sampled per altitude for the purposes of this study. Ten fruits were taken from the middle third, and their mass was measured as follows: fresh and dry from the fruit and fruit pulp, pulp-seed ratio, and the roundness index during the three stages of development after defoliation. A multivariate analysis of variance and a nested longitudinal mixed model were employed to analyze the total fresh mass from three altitudes. The simple effects of altitude and the phenological stages and their interaction were found to be significant when the nested structure of the random effects, fruits nested in trees, and trees in altitudes were incorporated. The effects of time and altitude demonstrated a significant interaction for all variables. In all cases, 1670 masl was the altitude which had the highest fresh mass and the pulp-seed relationship, which it allowed a higher percentage of pulp from harvested fruits for agro-industrial processes. The pattern of relationship between fruit-associated variables as a function of days after defoliation was explained using a second-order polynomial regression model.

A Comparative Study of Detrending Methods on Crop Yield Time Series for Drought Studies

Detrending is a statistical technique that removes systematic variations or trends from time series data, allowing analysts to focus on the underlying patterns or fluctuations. While multiple detrending approaches have been applied but rarely discussed their consistency of outcomes and effectiveness in accurately capturing better yield trends. The validation of drought occurrences has proven to be a challenging task due to the non-stationary characteristics of time series data related to crop yield. This research utilizes time series of cotton yield data from the Marathwada region covering the period from 1998 to 2021. Three traditional trend models, including simple linear regression, second-order polynomial regression and central moving average were applied. Additionally, two machine learning models (random forest and support vector regression) were tested with a novel approach. Moreover, two decomposition models (additive and multiplicative) were used to remove non-linear trends in crop yield time series data. The performance of the chosen models was evaluated based on metrics such as root mean square error, mean absolute error, Nash-Sutcliffe efficiency, and index of agreement. The results suggest that the most effective detrending approach involves combining a random forest machine learning model with an additive decomposition model.

Optimization of dietary protein based on ideal protein concept for genetically improved farmed tilapia (GIFT) juveniles reared in inland saline water

A 60-day feeding trial was performed to optimize dietary protein based on the ideal protein concept for genetically improved farmed tilapia (GIFT) juveniles reared in inland saline water (ISW) of 10 ppt salinity. Seven heteronitrogenous (373.6–256.5 g crude protein/kg), isocaloric (∼ 16 MJ digestible energy/kg) and isolipidic (∼ 80 g lipid/kg) semi-purified feeds were formulated and prepared with decreasing levels of crude protein (CP), namely 373.6 (CP1), 352.2 (CP2), 334.5 (CP3), 314.9 (CP4), 293.7 (CP5), 275.2 (CP6) and 256.5 g/kg (CP7). The proportion of total essential and semi-essential amino acids was kept constant in all diets as ideal amino acid profiles and total non-essential amino acids were reduced linearly to reduce the dietary CP levels. Three hundred fifteen juveniles (average weight 4.76 ± 0.01 g) were randomly assigned into seven groups in triplicate. Fish of various groups were fed thrice daily at an apparent satiation level. The growth of juveniles increased linearly and quadratically (p<0.05) when the CP level of diets was decreased from 373.6 (CP1) to 334.5 g/kg (CP3), then declined linearly. The CP2 and CP3 groups had significantly higher feed intake and lower feed conversion ratio. In contrast, low CP-fed groups had superior protein utilization, viscerosomatic index and hepatosomatic index than other groups. The CP1 and CP2 groups had significantly higher whole-body protein and lower lipid content. The whole-body non-essential amino acid content diminished linearly (p<0.05) as dietary CP levels were decreased; the whole-body essential amino acid content remained unchanged (p>0.05). The high CP-fed juveniles (CP1 and CP2) showed a significantly (p<0.05) elevated protease activity, whereas the opposite trend was noticed for amylase activity. The aminotransferase activities of the liver and muscle were reduced linearly (p<0.05) with reduced dietary CP levels. Serum glucose and cholesterol contents elevated linearly (p<0.05) with decreased dietary CP levels. Whereas, the hemoglobin content and RBC count decreased (p<0.05) in the high CP-fed juveniles. The second-order polynomial regression analysis revealed that the optimal dietary CP levels could range from 311.1 to 318.0 g/kg for GIFT juveniles reared in ISW of 10 ppt salinity. Furthermore, by adopting the ideal protein concept, the CP levels in the diet of GIFT juveniles could be reduced from 373.6 to 311.1 g/kg without affecting growth performance. The outcome of this study will assist in developing a low-protein feed with balanced essential amino acids for GIFT culture in ISW.

Effects of Dietary Chitosan on Growth Performance, Serum Biochemical Indices, Antioxidant Capacity, and Immune Response of Juvenile Tilapia (Oreochromis niloticus) under Cadmium Stress.

The objective of this study was to examine the effects of varying levels of dietary chitosan supplementation on mitigating cadmium stress and its influence on growth performance, serum biochemical indices, antioxidant capacity, immune response, inflammatory response, and the expression of related genes in juvenile Genetically Improved Farmed Tilapia (GIFT, Oreochromis niloticus). Five groups of juvenile tilapias (initial body weight 21.21 ± 0.24 g) were fed five diets with different levels (0%, 0.5%, 1.0%, 1.5%, and 2.0%) of chitosan supplementation for 60 days under cadmium stress (0.2 mg/L Cd2+). The findings indicated that, compared with the 0% chitosan group, dietary chitosan could significantly increase (p < 0.05) the final weight (Wf), weight gain rate (WGR), specific growth rate (SGR), daily growth index (DGI), and condition factor (CF), while the feed conversion ratio (FCR) expressed the opposite trend in juvenile GIFT. Dietary chitosan could significantly increase (p < 0.05) the activities (contents) of cholinesterase (CHE), albumin (ALB), lactate dehydrogenase (LDH), alkaline phosphatase (ALP), acid phosphatase (ACP), and lysozyme (LZM), while glutamic pyruvic transaminase (GPT), glutamic oxaloacetic transaminase (GOT), and complement 3 (C3) in the serum of juvenile GIFT expressed the opposite trend. Dietary chitosan could significantly increase (p < 0.05) the activities of superoxide dismutase (SOD) and catalase (CAT) and significantly decrease (p < 0.05) the activities (contents) of glutathione S-transferase (GST), glutathione peroxidase (GSH-Px), and malondialdehyde (MDA) in the serum of juvenile GIFT. Dietary chitosan could significantly increase (p < 0.05) the activities (contents) of CAT, GST, GSH-Px, and total antioxidant capacity (T-AOC) and significantly decrease (p < 0.05) the contents of MDA in the liver of juvenile GIFT. Dietary chitosan could significantly increase (p < 0.05) the activities (contents) of SOD, GSH-Px, T-AOC, Na+-K+-ATPase, and Ca2+-ATPase and significantly decrease (p < 0.05) the activities (contents) of CAT, GST, and MDA in the gills of juvenile GIFT. Dietary chitosan could significantly up-regulate (p < 0.05) the gene expression of cat, sod, gst, and gsh-px in the liver of juvenile GIFT. Dietary chitosan could significantly up-regulate (p < 0.05) the gene expression of interferon-γ (inf-γ) in the gills and spleen and significantly down-regulate (p < 0.05) the gene expression of inf-γ in the liver and head kidney of juvenile GIFT. Dietary chitosan could significantly down-regulate (p < 0.05) the gene expression of interleukin-6 (il-6), il-8, and tumor necrosis factor-α (tnf-α) in the liver, gills, head kidney, and spleen of juvenile GIFT. Dietary chitosan could significantly up-regulate (p < 0.05) the gene expression of il-10 in the liver, gills, head kidney, and spleen of juvenile GIFT. Dietary chitosan could significantly up-regulate (p < 0.05) the gene expression of transforming growth factor-β (tgf-β) in the liver and significantly down-regulate (p < 0.05) the gene expression of tgf-β in the head kidney and spleen of juvenile GIFT. In conclusion, dietary chitosan could mitigate the impact of cadmium stress on growth performance, serum biochemical indices, antioxidant capacity, immune response, inflammatory response, and related gene expression in juvenile GIFT. According to the analysis of second-order polynomial regression, it was found that the optimal dietary chitosan levels in juvenile GIFT was approximately 1.42% to 1.45%, based on its impact on Wf, WGR, SGR, and DGI.

Optimization and Modelling of the Physical and Mechanical Properties of Grass Fiber Reinforced with Slag-Based Composites Using Response Surface Methodology.

The construction industry's high energy consumption and carbon emissions negatively impact the ecological environment; large-scale construction projects consume much energy and emit a significant amount of CO2 into the atmosphere. Statistics show that 30% of energy loss and 40% of solid waste in the construction industry are generated during construction. Therefore, reducing emissions during construction has significant research potential and value. Many scholars have recently studied eco-friendly building materials to facilitate the use of high-carbon emission materials like cement. Adding fibers to composite materials has become a research hotspot among these studies. Although adding fibers to composite materials has many advantages, it mainly reduces the compressive strength of the composite material. This research used the response surface methodology (RSM) to optimize the raw material ratios and thus improve the performance of plant fiber composite materials. Single-factor experiments were conducted to analyze the effects of grass size, grass content, and quicklime content on the composite materials' compressive strength, flexural strength, and water absorption. The influencing factors and levels for the response surface experiment were determined based on the results of the single-factor analysis. Using the response surface methodology (RSM), a second-order polynomial regression model was established to analyze the interaction effects of the three factors on the composite materials' compressive strength, flexural strength, and water absorption rate. The optimal ratio was determined: the optimized options for grass size, grass content, and quicklime content are 2.0 mm, 8.2 g, and 38 g, respectively. The actual values of compressive strength, flexural strength, and water absorption rate of the composite materials made according to the predicted ratio are 11.425 MPa, 2.145 MPa, and 21.89%, respectively, with a relative error of 8% between the actual and predicted values. X-ray diffraction and scanning electron microscopy were also used to reveal the factors contributing to the relatively high strength of the optimized samples.

Optimization of iron electrocoagulation parameters for enhanced turbidity and chemical oxygen demand removal from laundry greywater

This study explores the optimization of iron electrocoagulation for treating laundry greywater, which accounts for up to 38% of domestic greywater. Characterized by high concentrations of surfactants, detergents, and suspended solids, laundry greywater presents complex challenges for treatment processes, posing significant environmental and health risks. Utilizing response surface methodology (RSM), this research developed a second-order polynomial regression model focused on key operational parameters such as the area-to-volume ratio (A/V), current density, electrolysis time, and settling time. Optimal treatment conditions were identified: an A/V ratio of 30 m2/m3, a current density of 10 mA/cm2, an electrolysis duration of 50 min, and a settlement period of 12 h. Under these conditions, exceptional treatment outcomes were achieved, with turbidity removal reaching 94.26% and COD removal at 99.64%. The model exhibited high effectiveness for turbidity removal, with an R2 value of 94.16%, and moderate effectiveness for COD removal, with an R2 value of 75.90%. The interaction between the A/V ratio and electrolysis time particularly underscored their critical role in electrocoagulation system design. Moreover, these results highlight the potential for optimizing electrocoagulation parameters to adapt to daily fluctuations in greywater production and meet specific household reuse needs, such as toilet flushing. This tailored approach aims to maximize contaminant separation and coagulant efficiency, balance energy use and operational costs, and contribute to sustainable water management.

Effects of dietary marine red yeast supplementation on growth performance, antioxidant, immunity, lipid metabolism and mTOR signaling pathway in Juvenile Tilapia (Oreochromis niloticus)

A 60-day feeding trial was conducted to investigate the effects of dietary marine red yeast supplementation on growth performance, body composition, antioxidant, immunity, lipid metabolism, and mTOR signaling pathway in juvenile GIFT tilapia (Initial weight 21.12 ± 0.86 g). Five isonitrogenous and isolipidic experimental diets were formulated with graded levels of marine red yeast: 0 % (control group), 0.25 %, 0.50 %, 0.75 %, and 1.00 %. The results showed that different marine red yeast levels significantly increased (p < 0.05) the final weight, weight gain rate, specific growth rate, daily growth index, protein efficiency ratio, and condition factor, while significantly decreased (p < 0.05) the feed conversion ratio, viscera index, and hepatosomatic index. Different marine red yeast levels significantly increased (p < 0.05) the activities of foregut lipase, hindgut lipase, foregut α-amylase, midgut α-amylase, hindgut α-amylase, superoxide dismutase, catalase, glutathione peroxidase, and total antioxidant capacity, while the malondialdehyde content expressed an opposite trend. Different marine red yeast levels significantly increased (p < 0.05) the activities (contents) of lysozyme, alkaline phosphatase, immunoglobulin M, and complement protein 3 in serum. Different marine red yeast levels significantly increased (p < 0.05) the relative expression of phosphoryl inositol-3-kinase, protein kinase B, mammalian target of rapamycin, ribosome S6 protein kinase 1, cholesterol response element binding protein 1, peroxisome proliferator-activated receptor γ, leptin, acetyl-CoA carboxylase α, fatty acid synthase, and glucose-6-phosphate dehydrogenase in liver, while the relative expression of PPARα, carnitine palmitoyl transferase 1, lipoprotein lipase, and hormone sensitive lipase expressed an opposite trend. In conclusion, dietary supplementation with marine red yeast significantly increased the growth performance, antioxidant capacity, initial immunity, lipid synthesis, and mTOR signaling pathway in juvenile GIFT tilapia. Based on the second-order polynomial regression analysis, the optimal dietary marine red yeast level was between 0.53 % and 0.60 %.

Machine learning prediction and characterization of sigma-free high-entropy alloys

Precipitation hardening provides a great potential to achieve superior strength-ductility trade-off in high-entropy alloys (HEAs). However, the formation of some precipitates such as brittle sigma phases may significantly deteriorate the ductility. Empirical criteria such as PSFE, VEC, and Md were proposed to predict the formation of undesirable phases during aging, nevertheless, they are insufficient to describe their stability and are not dependable for the noted purpose. Accordingly, for the first time, machine-learning models were conducted for this prediction. A feature selection based on Pearson correlation and mutual information has been used to improve the performance of models. Linear regression, second-order polynomial regression, decision tree, and neural network are conducted by machine learning methods in this paper. Among the implemented methods, the neural network had the best performance which improved the accuracy of the prediction by ∼20% compared to the conventional methods. Additionally, a new parameter based on linear regression, which is more reliable and user-friendly than thermodynamic parameters was developed in this investigation. The results showed that some elements such as Cr, Mo, and V may promote sigma precipitation. A HEA with a sigma-free microstructure was developed for validation of the model. We strongly believe that this HEA has a great potential for overcoming the strength-ductility trade-off because of not only the lack of sigma precipitate but also the presence of NiAl precipitate. The results are a step forward in designing alloys with the potential of microstructure engineering to achieve superior properties.

Waste-derived activated carbons for effective adsorptive removal of strontium, barium, and binary pollutants: A response surface methodology study

A double-pronged approach to pollution management is proposed by sustainably managing solid wastes and converting them to activated carbons that are then utilized for water treatment. In this study, gas-to-liquids (GTL) derived biosolids, carboard and their mixed samples are used to produce the activated carbons. In a laboratory batch study, a Box-Behnken experimental design was used with four factors and three levels to optimize the removal of single component strontium and barium, and the binary system of pollutants from an aqueous solution. The design incorporated response surface modeling (RSM) techniques with a total of 29 different experimental data points collected and analyzed in this study. The study was conducted considering four parameters: the initial pH of the solution (ranging from 4.0 to 8.0), temperature (ranging from 20 to 40 ℃), the percentage of cardboard (fixed at 0 %, 50 %, and 100 %), and the amount of adsorbent (between 0.05 and 0.5 g). These factors were assigned three levels, represented as −1, 0, and 1. A second-order polynomial regression equation was then developed to estimate the responses. The statistical significance of the independent variables and their interactions was assessed using analysis of variance (ANOVA) with a 95 % confidence level (α = 0.05). The results revealed that only temperature and dosage show significant effects on the responses and that optimum values of the selected parameters were obtained by solving the prediction equations, which were validated with less than 4 % error %. In an attempt to optimize the factors, a pH of 5.5, temperature of 40 ℃ and dose of 0.3 g is found for all three samples. Validation results for optimization also proved that varying the percentage cardboard showed little difference in the percentage removal of all the pollutants. The results from this study can be directly applied for any such systems trying to optimize these parameters and the prediction equations can be utilized effectively.

Investigating the adsorption potential of char derived from waste latex for methylene blue removal

This study presents the adsorption of methylene blue (MB) dye using latex char derived from pyrolysis of latex gloves. The adsorption process was investigated systematically using Response Surface Methodology (RSM) with a Central Composite Design (CCD). The effects of four key variables, namely pH, time, temperature, and adsorbent dosage, were studied using a factorial design enriched with center points and axial points. Experimental data were analyzed using a second-order polynomial regression model to construct a response surface model, which elucidated the relationship between the variables and MB removal efficiency. The study found that the char obtained at 800 °C exhibited the highest adsorption capacity due to its increased carbonization, expanded surface area, and diverse pore structure. Analysis of Variance (ANOVA) confirmed the significance of the quadratic model, with remarkable agreement between predicted and experimental outcomes. Diagnostic plots validated the model's reliability, while 3D contour graphs illustrated the combined effects of variables on MB removal efficiency. Optimization using DoE software identified optimal conditions resulting in a 99% removal efficiency, which closely matched experimental results. Additionally, adsorption isotherms revealed that the Freundlich model best described the adsorption behavior, indicating heterogeneous surface adsorption with multilayer adsorption. This comprehensive study provides valuable insights into the adsorption process of MB dye using latex char, with implications for wastewater treatment and environmental remediation.

Effects of electrolyte balance on intestinal barrier, amino acid metabolism, and mTORC1 signaling pathway in piglets fed low-protein diets

A proper dietary electrolyte balance (dEB) is essential to ensure optimal growth performance of piglets. In the low-protein diet, this balance may be affected by the reduction of soybean meal and the inclusion of high levels of synthetic amino acids. The objective of this experiment was to evaluate the optimal dEB of low-protein diets and its impact on the growth performance of piglets. A total of 108 piglets (initial age of 35 d) were randomly divided into 3 groups with 6 replicates of 6 pigs each as follows: low electrolyte diet (LE group; dEB = 150 milliequivalents [mEq]/kg); medium electrolyte diet (ME group; dEB = 250 mEq/kg); high electrolyte diet (HE group; dEB = 350 mEq/kg). Results indicated that the LE and HE diet significantly decreased the average daily gain, average daily feed intake, and crude protein digestibility (P < 0.05) in piglets. Meanwhile, LE diets disrupted the structural integrity of the piglets' intestines and decreased jejunal tight junction protein (occludin and claudin-1) expression (P < 0.05). Additionally, the pH and HCO3− in the arterial blood of piglets in the LE group were lower than those in the ME and HE groups (P < 0.05). Interestingly, the LE diet significantly increased lysine content in piglet serum (P < 0.05), decreased the levels of arginine, leucine, glutamic acid, and alanine (P < 0.05), and inhibited the mammalian target of rapamycin complex 1 (mTORC1) pathway by decreasing the phosphorylation abundance of key proteins. In summary, the dietary electrolyte imbalance could inhibit the activation of the mTORC1 signaling pathway, which might be a key factor in the influence of the dEB on piglet growth performance and intestinal health. Moreover, second-order polynomial (quadratic) regression analysis showed that the optimal dEB of piglets in the low-protein diet was 250 to 265 mEq/kg.

Adaptation of Chinese perch (Siniperca chuatsi) to different levels of dietary carbohydrates

The objective of the study was to investigate the effects of dietary carbohydrate on growth performance, feed utilization, proximate compositions, and hepatic glucolipid metabolism in Chinese perch. Triplicate groups of Chinese perch (29.51 ± 0.24 g) were fed five isonitrogenous and isolipidic diets containing graded levels of carbohydrate (2.4%, 9.7%, 16.5%, 23.9%, and 30.1%) for 8 weeks. The results based on second-order polynomial regression analysis of specific growth rate (SGR) proved that Chinese perch obtained the best growth performance at the 10.93% dietary carbohydrate level. Compared to 2.4%-carbohydrate group, 9.7%-carbohydrate group exhibited the increase in daily weight gain (DWG) and protein retention ratio (PER) and the decrease in feed conversion efficiency (FCR). Moreover, 9.7%-carbohydrate group showed the increase in the mRNA levels of pfk and cs involved in aerobic oxidation pathway, the content of liver glycogen increased significantly, and no change in glucose level. Above results suggested that a part of carbohydrates may be converted into energy for growth by aerobic oxidation pathway, or converted into glycogen for storage to maintain glucose homeostasis in Chinese perch fed with moderate-carbohydrate diet. Differently, 23.9%-carbohydrate and 30.1%-carbohydrate groups showed the decrease in DWG and PER, and the increase in FCR, compared to 2.4%-carbohydrate group. Additionally, 23.9%-carbohydrate and 30.1%-carbohydrate group presented the increase in the mRNA levels of g6pca and pc involved in gluconeogenesis, which caused the increase in plasma glucose level. However, other plasma indices related to TG-metabolite and its transport (total cholesterol, and low-density lipoprotein) did not show difference, which combined with elevated fatty acid synthesis-related gene accα mRNA level resulted in excessive liver lipid deposition. Above results indicated that unregulated gluconeogenesis and invalid outward transportation of liver TG-rich metabolites may account for the glucose intolerance of Chinese perch fed with carbohydrate-rich diet. Furthermore, compared to the results from the 4th week, the expression of gk, pfk, pc, cs, and accα genes significantly increased in the 30.1%-carbohydrate group at the 8th week. Both liver and muscle exhibited a significant elevation in crude fat content, while Daily DWG experienced a noticeable decrease. These results indicated that the glucose metabolism and growth of Chinese perch in the high-carbohydrate group are influenced by the interaction of feeding duration and carbohydrate levels. And within a certain time frame, Chinese perch can tolerate a diet rich in carbohydrates.

Corner cutting accuracy for thin-walled CFRPC parts using HS-WEDM

Carbon Fiber-Reinforced polymer (CFRP) composite parts with thin-walled corners are in great demand in aircraft, cars, and precision instruments. Nonetheless, the fabrication of these parts is difficult due to their low stiffness. High-speed WEDM is an advanced technique for cutting thin CFRP components as it is a non-contact method for removing materials. Nonetheless, testing results demonstrate an unavoidable deformation in the thin-walled corners of the CFRP composite. The objective of this study is to improve the accuracy of corners in thin-walled CFRP composite parts. The research utilized a Taguchi L16 orthogonal array to investigate the influence of various process parameters, including pulse-on duration (Pon), pulse-off duration (Poff), and input current (I), as well as the parameter CFRP plate thickness (T), on corner inaccuracy. The CFRP thickness varied between 0.5, 1.0, 1.5, and 2.0 mm, and the corner angles examined were 30°, 60°, 90°, and 120°. Additionally, a second-order polynomial regression model was used to determine the correlation between the process parameters and corner inaccuracy at various corner angles. Also, a multi-response optimization technique using a composite desirability coupled with a generalized reduced gradient were used to find the optimal process combination across various CFRP thicknesses. According to the research findings, the most relevant process parameters impacting corner accuracy at different angles were the pulse-on duration and input current. To achieve accurate corners with different angles, the optimal process parameters were identified: Pon (40µs), Poff (15µs), and I (4A) for CFRP thicknesses 0.5 and 1.0mm, and Pon (45μs), Poff (30μs), and I (2A) for thicknesses 1.5 and 2.0mm.