Response Surface Research Articles

Design and Optimization for Straw Treatment Device Using Discrete Element Method (DEM)

Due to the dense crop residue in the Huang-Huai-Hai region, challenges such as large resistance, increased power consumption, and straw backfilling arise in the process of no-till seeding under the high-speed operations. This paper presents the design of a straw treatment device to address these issues. The cutting edge of a straw-cutting disc is optimized using an involute curve, and the key structural parameters of the device are designed by analyzing the process of stubble cutting and clearing. In this study, the Discrete Element Method (DEM) was employed to construct models of compacted soil and hollow, flexible wheat straw, forming the foundation for a comprehensive interaction model between the tool, soil, and straw. Key experimental variables, including working speed, rotation speed, and installation centre distance, were selected. The power consumption of the straw-cutting disc (PCD) and the straw-clearing rate (SCR) were used as evaluation metrics. Response surface methodology was applied to develop regression models linking the experimental factors with the evaluation indexes using Design-Expert 12 software. Statistical significance was assessed through ANOVA (p < 0.05), and factor interactions were analyzed via response surface analysis. The optimal operational parameters were found to be a working speed of 14 km/h, a rotation speed of 339.2 rpm, and an installation centre distance of 100 cm. Simulation results closely matched the predicted values, with errors of 1.59% for SCR and 9.68% for PCD. Field validation showed an SCR of 86.12%, improved machine passability, and favourable seedling emergence. This research provides valuable insights for further parameter optimization and component development.

Macroscopic thermodynamic properties of ionic micellar solutions depending on physicochemical formulation for the sodium dodecyl sulfate/sodium sulfate/water/n‐heptane/1‐pentanol system

AbstractThis work characterized the macroscopic thermodynamic properties of ionic micellar solutions. Formulation‐composition studies were done along one dimension by varying the salinity (S) or oil–water ratio (WOR) in mixtures of sodium dodecyl sulfate, sodium sulfate, water, n‐heptane, and 1‐pentanol at 25°C and 1 atm. Material balance and multiple regression models were used to get the partial, mixed, and excess molar volumes. The UNIFAC local composition model, coupled with Debye–Hückel theory, was employed to calculate activity coefficients and dimensionless Gibbs energies (partial, mixed, and excess). The equilibrium SOW systems exhibited phase transitions (WI‐WIII‐WII), with micellar solubilization increasing as salinity increased at constant WOR. Solubilization peaked at the optimal formulation, and further increases in WOR enhanced solubilization up to the formation of a single‐phase system. Deviations from ideal behavior, in the thermodynamic properties between aqueous and oil micellar solutions, were mainly due to chemical interaction of solutes () respect to solvents (). Negative values of the Gibbs energy of mixing confirmed the stability of the liquid phases and their extension to the liquid–liquid equilibrium. The response surfaces V = f(WOR, S) and GE/RT = f(WOR, S) represent the macroscopic thermodynamic behavior of micellar solutions as a function of physicochemical formulation. These results can be extended to other colloidal systems for the design of formulations with surfactants and anionic salts, oriented to the diagnosis and resolution of problems in real systems, both at laboratory and industrial level.

A Study on the Effects of Welding Parameters on Weld Bead Geometry in TIG Welding Process of Zircaloy Fuel Pins Using Response Surface Methodology

This paper investigates influences of welding parameters on weld bead geometry in the tungsten inert gas welding process of Zircaloy-2. Initially, a “Definitive screening design” was employed for identifying the key factors affecting weld bead geometry. Subsequently, response surface methodology was utilized to generate an experimental matrix, and experiments were conducted to analyze the effects of each welding parameter on weld bead geometry. Mathematical models were developed using analysis of variance for two-weld bead geometrical parameters, and their validity was assessed by comparing experimentally obtained values with those predicted by the model. Finally, a dimensionless parameter, the aspect ratio, indicative of the weld quality, was optimized.

Structural Design Analysis of Substrate with Honeycomb Core Under Normal Pressure, Using RSM and ANN

This study presents a comprehensive structural performance analysis of a honeycomb-core substrate under normal pressure, highlighting the superior predictive accuracy of Artificial Neural Networks (ANNs) over Response Surface Methodology (RSM). The analysis focused on critical design parameters, such as material selection, coverage rate, and wall thickness, which significantly influence the substrate’s maximum deformation, elastic stress, and mass. The ANN model, trained on these parameters, optimized the design to achieve a cell size of 60 mm, a wall thickness of 12.5753 mm, a coverage rate of 64.38%, and selected aluminum as the material. This optimization resulted in a substrate with a maximum deformation of 7.21 × 10³ mm, an elastic stress of 1.9465 MPa, and a mass of 54.949 kg. The RSM-ANN method surpasses RSM in both optimization and accuracy, enhancing the understanding of how honeycomb design affects substrate properties.

Microwave-assisted extraction of dandelion root polysaccharides: Extraction process optimization, purification, structural characterization, and analysis of antioxidant activity.

This study aimed to establish a microwave-assisted method (MAE) for the efficient extraction of polysaccharides from dandelion roots. This study investigated the molecular structure and bioactivity of the polysaccharides from dandelion roots. Extraction conditions were optimized using response surface methodology (RSM). The microwave extraction conditions were set to an extraction time of 42 min, an extraction temperature of 80 °C, and a solid-liquid ratio (g/mL) of 1:33. Under the optimized conditions, the highest dandelion root polysaccharides (DRP) yield was achieved (24.85 ± 0.457 %). Water-pure DRP (DRPw) and NaCl-pure DRP (DRPs) were purified by activated carbon decolorization and DEAE fiber column chromatography. The molecular weights of DRPw and DRPs were 8653 Da and 5930 Da, respectively. The Fourier transform infrared spectroscopy (FTIR) and nuclear magnetic resonance (NMR) analyses confirmed the existence of α- and β-pyranose in DRPw and DRPs. The results of X-ray diffraction (XRD) and scanning electron microscopy (SEM) showed that DRPw and DRPs were semi-crystalline substances with irregular shapes and rough surfaces. Bioactivity assays revealed the good antioxidant activities of DRPw and DRPs. The present study provides useful information about DRP as natural antioxidants for the benefit of food.

The effect of short-term exposure to air pollution on the admission of ischemic stroke and its interaction with meteorological factors.

The aim of this study was to investigate the associations, potential effects, and interactions between short-term exposure to air pollution and the risk of ischemic stroke (IS). An ecological study. Daily data on IS incidents, air pollution, and meteorological conditions were collected from 2017 to 2021 in Gannan. A time-stratified case-crossover design, combined with a distributional lag nonlinear model, was employed to analyze the relationship between air pollution exposure and the admission of IS. Additionally, the interaction between air pollutants and meteorological factors was examined using bivariate response surface modeling. The study also conducted stratified analyses based on gender, age, marital status, medical insurance purchase status, and season of admission. In the single lag day structure, extremely low levels of PM2.5 (RR=1.11, 95% CI: 1.03-1.20) and PM10 (RR=1.10, 95% CI: 1.02-1.20) peaked on lag 3. Conversely, extremely high levels of NO2 (RR=1.05, 95% CI: 1.01-1.10), CO (RR=1.19, 95% CI: 1.03-1.37), and extremely low levels of O3 (RR=1.09, 95% CI: 1.01-1.19) exhibited a greater relative risk on lag 4. In the cumulative lag-day structure, extremely high levels of NO2 exhibited the most significant hazard effect at lag 05 (RR=1.27, 95% CI: 1.01-1.52), while extremely low levels of CO at lag 02 (RR=1.15, 95% CI: 1.05-1.24) and extremely low levels of O3 at lag 01 (RR=1.20, 95% CI: 1.04-1.40) also demonstrated notable associations. In the subgroup stratum, the association between air pollution and IS was found to be more significant in patients who were male, aged <65 years, married, had medical insurance, and were admitted during the cold season. The lowest number of IS hospitalisations occurred under low relative humidity conditions alongside increasing concentrations of CO. Short-term exposure to air pollution was positively associated with an increased risk of IS. This association was influenced by factors such as being male, aged <65 years, married, having medical insurance, and admissions during the cold season. Additionally, an interaction was observed between air pollutants and meteorological factors. These findings carry significant public health implications for the prevention of IS.

Response surface methodology and repeated-batch fermentation strategies for enhancing lipid production from marine oleaginous Candida parapsilosis Y19 using orange peel waste

Oleaginous yeasts are considered promising sources for lipid production due to their ability to accumulate high levels of lipids under appropriate growth conditions. The current study aimed to isolate and identify oleaginous yeasts having superior ability to accumulate high quantities of lipids; and enhancing lipid production using response surface methodology and repeated-batch fermentation. Results revealed that, twenty marine oleaginous yeasts were isolated, and the most potent lipid producer isolate was Candida parapsilosis Y19 according to qualitative screening test using Nile-red dye. Orange peels was used as substrate where C. parapsilosis Y19 produced 1.14 g/l lipids at 23.0% in batch fermentation. To enhance the lipid production, statistical optimization using Taguchi design through Response surface methodology was carried out. Total lipids were increased to 2.46 g/l and lipid content increased to 30.7% under optimal conditions of: orange peel 75 g/l, peptone 7 g/l, yeast extract 5 g/l, inoculum size 2% (v/v), pH 5 and incubation period 6 d. Furthermore, repeated-batch fermentation of C. parapsilosis Y19 enhanced lipid production where total lipids increased at 4.19 folds (4.78 g/l) compared to batch culture (before optimization). Also, the lipid content was increased at 1.7 folds (39.1%) compared to batch culture (before optimization). Fatty acid profile of the produced lipid using repeated-batch fermentation includes unsaturated fatty acids (USFAs) at 74.8% and saturated fatty acids (SFAs) at 25.1%. Additionally, in repeated-batch fermentation, the major fatty acid was oleic acid at 45.0%; followed by linoleic acid at 26.0%. In conclusion, C. parapsilosis Y19 is considered a promising strain for lipid production. Also, both statistical optimizations using RSM and repeated-batch fermentation are efficient methods for lipid production from C. parapsilosis Y19.

Targeted Assay Engineering Enhances Bile Salt Hydrolase Activity in Heyndrickxia coagulans ATCC 7050 and Lactiplantibacillus plantarum ATCC 10012.

The recent emergence of bile salt hydrolase (BSH) enzyme as a therapeutic target reflects its unbound potential in mitigating hypercholesterolemia, obesity, and gastrointestinal issues. However, to bolster its industrial application, optimization of BSH assay lays the cornerstone for enhancing sensitivity, specificity, and reproducibility. The current study delved into optimizing the BSH assay parameters utilizing response surface methodology (RSM) and one-factor-at-a-time (OFAT) method for two novel, natural BSH producers, Heyndrickxia coagulans ATCC 7050 and Lactiplantibacillus plantarum ATCC 10012. Factors such as pH, temperature, cell concentration, and substrate concentration were optimized using RSM and numerical optimization. The analysis of responses unveiled significant insights into the biochemical characteristics of BSH from both organisms. The optimal pH for BSH activity from H. coagulans ATCC 7050 and L. plantarum ATCC 10012 was determined to be 6.1 and 6.0, with their corresponding optimal temperatures being 60°C and 40°C, respectively. Subsequent to RSM, optimization of the remaining parameters such as buffer type, buffer molarity, cells-to-substrate ratio, etc., performed using the classical OFAT approach further enhanced BSH activity, with H. coagulans ATCC 7050 and L. plantarum ATCC 10012 exhibiting a 1.45 and 0.87-fold increase, respectively. Conventionally, even though BSH has been optimized using the OFAT approach, this is the first instance in which a hybrid model using RSM has been applied to achieve a greater performance. These findings offer valuable insights in augmenting the specificity, efficiency, and stability of BSH and broaching new avenues for industrial and therapeutic applications.

RSM modelling and yield prediction of base-catalyzed transesterification of pumpkin-seed oil to biodiesel: artificial neural network, kinetics, and thermodynamics

The study examined the extraction of bio-oil from pumpkin seed and compared the optimization of the production of Fatty acid ethyl ester (FAEE) via the transesterification process using Response Surface Methodology (RSM), and Artificial Neural Network (ANN). This research uniquely highlights the utilization of pumpkin seed oil, a non-edible and sustainable feedstock, and combines RSM and ANN methodologies to enhance the precision of biodiesel optimization. The transesterification experiment was conducted at 60 min reaction time under varying temperature ranges, catalyst weight, stirrer speed, and ethanol-oil molar ratio. The RSM optimized conditions for maximum production were determined to be a 1.3% catalyst concentration, 6:1 ethanol-to-oil molar ratio, 50 °C temperature, and 550 rpm stirrer speed, resulting in a 90% biodiesel yield. The statistical evaluation metrics confirmed the neural network predictions were compromised to 80% yield due to limitations such as insufficient data size and the inherent complexity of the ANN model. The biofuel produced satisfied ASTM specifications peculiar to sustainable environmental applications. The mechanistic parameters revealed variations in thermodynamic stability and feasibility across reaction orders (zero, pseudo-first, and pseudo-second), emphasizing the temperature-dependent effects of the transesterification kinetic pathway on yield, design, and efficiency.

Optimizing COD reduction in dairy wastewater treatment using magnetic coagulant derived from Moringa oleifera

In wastewater treatment scenarios, traditional coagulants are becoming increasingly complex and raising environmental concerns. This has led to the exploration of magnetized plant-derived coagulant as an alternative. In this paper, coagulation parameters such as coagulant dosage and pH were optimized through response surface methodology (RSM) based on a central composite design (CCD) employing a magnetic coagulant derived from Moringa oleifera seeds (M. oleifera–CoFe2O4). The optimized response variable during the treatment of dairy wastewater was the chemical oxygen demand (COD) reduction. The response surface methodology revealed a statistically significant second-order polynomial model (R2 99.24%) for maximizing COD reduction. The maximum COD reduction achieved was 76.13% under optimal conditions with coagulant dosage of 14.24 g/L and pH of 9.38. The results indicated that magnetic coagulant derived from M. oleifera seeds demonstrated significant potential and can be used in an efficient and eco-friendly process for dairy wastewater treatment. Keywords: coagulation, dairy wastewater, flocculation, magnetic coagulant, Moringa Oleifera.

Synergistic effects of fly ash and graphene oxide composites at high temperatures and prediction using ANN and RSM approach

Fly ash (FA) is a consequence of burning coal and is widely used in construction because of its pozzolanic qualities, which increase the strength and longevity of materials. Graphene oxide (GO) is a functionalized version of graphene with low electrical conductivity, high mechanical strength, and a large surface area. By examining the behavior of fly ash and GO composites at high temperatures, new materials with improved mechanical and functional qualities that are appropriate for a range of industrial uses can be created. By improving the material quality of cement and reducing material use while boosting durability, adding graphene oxide to cement offers an opportunity to drastically lower carbon emissions. However, the entire effect is dependent on the GO emissions, manufacturing procedures, and viability from an economic standpoint; to fully reap the benefits of this novel strategy for the environment, more research and development are necessary. this paper primarily examined the effects of high temperatures on the mechanical, microstructural, and thermal properties of concrete when fly ash was replaced with 20% by weight of the cement, graphene oxide were added by 0.08% by the weight of the cement, and their combinations i.e. (20% FA + 0.08% GO) by the weight of the cement were tested at various temperatures 200, 400, 600, and 800 °C for 4 h. The ideal temperature decided by the mechanical characteristics is 200 °C, and to understand the microstructure of graphene oxide (GO) material is essential for understanding its performance, stability, and the principles underlying its behavior, particularly at elevated temperatures. Machine learning tools such as Response Surface Methodology (RSM) and Artificial Neuron Network (ANN) have been used to forecast the mechanical properties of concrete.

Modeling Mechanical Properties of Concrete with Bida Natural Stones as Coarse Aggregate

This paper presents the results of statistical models for the mechanical properties of concrete made using Bida natural stones (pebbles) as coarse aggregate. Fresh and hardened concrete properties were analyzed using response surface methodology (RSM). The research employed central composite design using three (3) independent variables with equivalent ranges as Water to Cement ratio (W/C) = 0.4, 0.5, 0.6, Coarse Aggregate to Total Aggregate ratio (CA/TA) = 0.55, 0.615, 0.68 and Total Aggregate to Cement ratio (TA/C) = 3.0, 4.5 and 6.0. Minitab 14 (2004) selected 20 possible mix proportions for the experiment. The concrete specimens that were used for this research work include 150mm x 150mm x 150mm concrete cube, Ø150mm x 300mm concrete cylinder and 100mm x 100mm x 500mm concrete prism for compressive strength, elastic modulus/splitting tensile strength and flexural strength respectively. For each mix, five numbers of specimens were cast and cured for 7, 14, 21 and 28days. The twenty-eight days measured responses were used to develop models for the mechanical properties. Statistical and experimental model validations were employed. The p-value for all individual terms were less than 0.05, p-values for lack of fit were also greater than 0.05 in all cases tested, coefficient of determination (R2), adjusted coefficient of determination (adjR2) were in the range of 89.7% to 100% for compressive strength, splitting tensile strength, elastic modulus, flexural strength, and slump respectively which indicated adequate model validity. The predicted responses closely agree with experimental values, this further confirms the validity of the models developed. The study therefore suggested interaction, pure quadratic, reduced full quadratic, interaction models for the mechanical properties and slump respectively for the concrete made using Bida natural stones as coarse aggregate. The concrete with mix constituent mentioned above can be used for structural application such as reinforced concrete slabs, beams, columns and foundations.

High Pressure Hydrothermal Purification of Natural Flake Graphite Based on Response Surface Methodology

High Pressure Hydrothermal Purification of Natural Flake Graphite Based on Response Surface Methodology

Experimental-design-based optimization of dispersive liquid-liquid microextraction coupled with gas chromatography–negative-ion chemical ionization–mass spectrometry for the determination of pyrethroids in agricultural products and drinks

Pyrethroids are synthetic chemicals that account for 16% of the international insecticide market and have been shown to be of varying toxicity to different species. There are various methods available for detecting pyrethroids in agricultural products, but these products must be pre-treated to remove interference from the food matrix, such as through dispersion liquid-liquid microextraction (DLLME). This study employed two experimental design methods to optimize the continuous and discontinuous experimental parameters of DLLME and investigated whether DLLME combined with GC-NICI-MS is effective for detecting pyrethroids in agricultural products. The Taguchi design with an L9(34) orthogonal array and response surface methodology were employed to optimize the discontinuous and continuous parameters of the DLLME process, respectively. To validate the performance of GC-NICI-MS after optimized DLLME, pyrethroids in mixed standard solutions at levels ranging from 0.02 to 50.00 µg/L were measured, and the resultant calibration curves were fitted. Adequate linearity was found for the six investigated pyrethroids (r = 0.9908–0.9960). The limits of detection and quantification ranged from 0.005 to 0.035 µg/L and 0.02 to 0.1 µg/L, respectively. The proposed approach simplifies the optimization of parameters compared to reported methods and achieves considerably lower limits of detection. The concept of mixed application based on the dual experimental design method can be applied to other regulated compounds to enhance the safety of agricultural products. The feasibility of the method was confirmed by successfully detecting pyrethroids in 13 types of teas, fruit, and vegetables.

Optimization of Key Parameters for Coal Seam L-CO2 Phase Transition Blasting Based on Response Surface Methodology

Optimization of Key Parameters for Coal Seam L-CO2 Phase Transition Blasting Based on Response Surface Methodology

Synergistic Effect of Chlorination and Ozonation on Residual Organophosphate and Pyrethroid in Cowpea: Quality Evaluation and Process Optimization

ABSTRACT Cowpea (Vigna unguiculata) is widely recognized in food and feed systems but is susceptible to pest infestations, necessitating pesticide use that often leaves harmful residues. This study aims to optimize the combined chlorination and ozonation treatment parameters for effective pesticide removal from cowpeas using Response Surface Methodology (RSM). The targeted pesticides in this study include Malathion, Chlorpyrifos, and Deltamethrin. The study investigated the impact of varying ozone dosage rates (0.1085–0.1899 g kg-1 min-1), chlorination levels (0–200 ppm), and ozonation times (5–30 min) on pesticide degradation. The results indicate that the optimal conditions achieved degradation rates of 99.5% for Malathion, 64.87% for Chlorpyrifos, and 67.79% for Deltamethrin. Additionally, the quality parameters of cowpeas, including moisture content, water activity, color difference, and texture, were evaluated to ensure minimal adverse effects. This study outlines a practical and feasible option for removing chemically stable residual pesticides in the cowpea supply chain.

Optimal nitrite degradation by isolated Bacillus subtilis sp. N4 and applied for intensive aquaculture water quality management with immobilized strains.

The toxicity of nitrite is an issue that cannot be overlooked in nitrogen pollution within aquaculture. A highly efficient bacterium capable of simultaneous nitrification and denitrification was screened from natto, and its 16S rRNA gene sequence was compared to existing records, confirming its identification as Bacillus subtilis sp. N4. The optimal conditions for nitrite degradation by B. subtilis sp. N4 were identified using response surface methodology as 167rpm, pH 6.4, 1g/8mL feed, 0.6 OD600, and 30°C, with a predicted 99 % nitrite removal efficiency. The B. subtilis sp. N4 demonstrated a maximum nitrite concentration tolerance of 60mg/L, with μmax and Ks values calculated using a Monod model analysis of 1.67mg/L/h and 0.29mg/L, respectively. Immobilized B. subtilis sp. N4 could be reused for ten cycles while maintaining a nitrite degradation efficiency of >99 %, and retained a high nitrite-degrading ability after being refrigerated at 4°C for three months. Immobilized B. subtilis sp. N4 effectively reduced ammonia nitrogen, nitrite, and nitrate concentrations in Nile tilapia aquaculture, maintaining them at consistently low levels. Therefore, free or immobilized B. subtilis sp. N4, with both nitrification and denitrification capabilities, has considerable potential for application in the aquaculture industry in the future.

Ezetimibe Loaded Nanostructured Lipid Carriers Tablets: Response Surface Methodology, In-vitro Characterization, and Pharmacokinetics Study in Rats

PurposeThe present study aims to overcome the poor oral bioavailability of ezetimibe (EZ), a selective Biopharmaceutics Classification System (BCS) Class II cholesterol absorption inhibitor drug. EZ-loaded nanostructured lipid carriers (EZ-NLCs) were dried by lyophilization and incorporated in a convenient oral solid dosage form to enhance its dissolution, and absorption and increase patient compliance. Response surface methodology (RSM) was employed to systematically optimize formulation variables, improving the efficiency of disintegration and drug release characteristics.MethodsRSM was adopted to study the effects of (A) increasing the amount of the super-disintegrant, crosscarmelose sodium, (CCS), and (B) varying the ratio between the used drying excipients Avicel and mannitol (A: M) on the disintegration time (R1), and the percentage drug released after 24 h (R2). Thirteen EZ-NLCs tablets were prepared and subjected to pre-compression and post-compression evaluation. Furthermore, a bioequivalence study was conducted by administering EZ-NLCs and ezetrol® tablets to Sprague Dawley male rats.ResultsThe optimized EZ-NLCs tablet (prepared with the ratio of Avicel: mannitol (7.5:0) using 30 mg CCS), revealed a disintegration time of 3.85 ± 0.03 min, and 98 ± 3.09% of the drug were released at the end of the 24 h. EZ-NLCs tablet displayed a maximum concentration (Cmax) of 3.57 ± 0.27 ng/mL and an area under the curve (AUC0− 24) of 22.44 ± 2.68 ng.hr/mL, while those of ezetrol® were 2.79 ± 0.15 ng/mL and 15.36 ± 0.86 ng.hr/mL, respectively.ConclusionThe assessed relative bioavailability demonstrated the superiority of EZ-NLCs tablet over ezetrol® with 1.5 fold improvement which proves that EZ-NLCs tablet could be a good candidate to enhance the oral bioavailability of EZ.Graphical

Modeling suction of unsaturated granular soil treated with biochar in plant microbial fuel cell bioelectricity system

There is an initiative driven by the carbon-neutrality nature of biochar in recent times, where various countries across Europe and North America have introduced perks to encourage the production of biochar for construction purposes. This objective aligns with the zero greenhouse emission targets set by COP27 for 2050. This research work seeks to assess the effectiveness of biochar in soils with varying grain size distributions in enhancing the soil–water characteristic curve (SWCC). This work further explores the effect of different combinations of biochar content (0 to 15 mass %) on the bioelectricity generation from biochar-improved plant microbial fuel cells (BPMFC). Additionally, different machine learning models such as the “Gradient Boosting (GB)”, “CN2 Rule Induction (CN2)”, “Naive Bayes (NB)”, “Support vector machine (SVM), “Stochastic Gradient Descent (SGD)”, “K-Nearest Neighbors (KNN)”, “Tree Decision (Tree)”, “Random Forest (RF)”, and “Response Surface Methodology” (RSM), have been developed to predict SWCC based on soil suction, electric current, electrical potential, volumetric water content, temperature, and bulk density. The newly established model demonstrates a reasonable ability to predict SWCC and a cheaper technology in predicting the suction of unsaturated soils in relation to the studied bioelectric factors of the BPMFC. Overall, in this research paper, the GB, SVM and CN2 outclassed the other regression techniques in this order thereby proposing the cheapest technology with the highest performance index to predict the SWCC behavior of unsaturated soils in a BPMFC system.

Optimization of ultrasonic-assisted extraction of total carotenoids from larvae of Antheraea pernyi (Lepidoptera: Saturniidae) using response surface methodology

Optimization of ultrasonic-assisted extraction of total carotenoids from larvae of Antheraea pernyi (Lepidoptera: Saturniidae) using response surface methodology