Second-order Polynomial Model Research Articles

Optimising nanoparticles: harnessing quality by design through 3-level factorial design and box-behnken methodology

Nanoparticles (NPs) have become essential elements in a number of scientific and industrial domains, such as materials research, drug delivery, and diagnostics, because of their special qualities and potential for focused uses. An ideal formulation procedure is essential to maximising the effectiveness and efficiency of nanoparticles. In order to optimise the formulation and production processes for nanoparticles, this research investigates the application of Quality by Design (QbD) principles in conjunction with sophisticated statistical approaches, namely the 3-level factorial design and the Box-Behnken methodology. The Quality by Design methodology is a methodical approach that prioritises predetermined goals, in-depth process comprehension, and careful control grounded in reliable science and quality risk management. When QbD is incorporated into nanoparticle optimisation, reliable and repeatable formulations that adhere to specifications with little variation are guaranteed. To look into how different factors interact and affect the properties of nanoparticles, the 3-level factorial design is used. With this design approach, a thorough understanding of the process variables can be obtained by exploring a broad variety of component values. Critical quality attributes (CQAs) include important parameters such drug encapsulation efficiency, zeta potential, polydispersity index (PDI), and particle size. These parameters are methodically examined to determine the ideal amounts of each. To further refine the 3-level factorial design, the Box-Behnken methodology is applied. Without the requirement for extensive combination testing, this response surface methodology (RSM) is especially useful for building second-order polynomial models and investigating quadratic response surfaces. By enabling a more accurate comprehension of the interactions between inputs and responses, the Box-Behnken design improves the optimisation process and makes it easier to identify the ideal circumstances for nanoparticle synthesis. By utilising both approaches in tandem, this work methodically determines ideal formulation parameters that minimise nanoparticle size and polydispersity while optimising stability and drug loading efficiency. The outcomes highlight how important a QbD framework is for directing the creation of reliable nanoparticle systems that function consistently and predictably.

Colour Analysis of Sausages Stuffed with Modified Casings Added with Citrus Peel Extracts Using Hyperspectral Imaging Combined with Multivariate Analysis

Recycling citrus peel waste offers several significant contributions to sustainability, transforming what would otherwise be discarded into valuable resources. In this study, the colour of sausages stored for 16 days, with varying amounts of orange extract added to the modified casing solution, was evaluated using response surface methodology (RSM) and a hyperspectral imaging system within the spectral range of 350–1100 nm for the first time. To enhance model performance, spectral pre-treatments such as normalisation, first derivative, standard normal variate (SNV), second derivative, and multiplicative scatter correction (MSC) were applied. Both raw and pre-treated spectral data, along with colour attributes, were fitted to a partial least squares regression model. The RSM results indicated that the highest R2 value, 80.61%, was achieved for the b* (yellowness) parameter using a second-order polynomial model. The interactive effects of soy oil and orange extracts on b* were found to be significant (p < 0.05), and the square effects of soy oil on b* were significant at the 1% level. The identified key wavelengths for colour parameters can simplify the model, making it more suitable for practical industrial applications.

Optimization of Combined Hydrothermal and Mechanical Refining Pretreatment of Forest Residue Biomass for Maximum Sugar Release during Enzymatic Hydrolysis

This study aimed to investigate the effect of chemical-free two-stage hydrothermal and mechanical refining pretreatment on improving the sugar yields during enzymatic hydrolysis of forest residue biomass (FRB) and optimize the pretreatment conditions. Hot-water pretreatment experiments were performed using a central composite design for three variables: temperature (160–200 °C), time (10–20 min), and solid loading (10–20%). Hydrothermally pretreated biomass was subsequently pretreated using three cycles of disk refining. The combined pretreatment was found to be highly effective in enhancing sugar yields during enzymatic hydrolysis, with almost 99% cellulose conversion for biomass pretreated at 213.64 °C, 15 min, and 15% solid loading. However, the xylose concentrations in the hydrolysate were found to be low under these conditions due to sugar degradation. Thus, less severe optimum pretreatment conditions (194.78 °C, 12.90 min, and 13.42% solid loading) were predicted using a second-order polynomial model. The response surface model optimized the hydrothermal pretreatment of FRB and predicted the glucan, xylan, and overall conversions of 94.57%, 79.78%, and 87.84%, respectively, after the enzymatic hydrolysis. The model-predicted biomass conversion values were validated by the experimental results.

Optimizing the Extraction of Polyphenols from the Bark of Terminalia arjuna and an In-silico Investigation on its Activity in Colorectal Cancer.

The interconnection between different fields of research has gained interest due to its cutting-edge perspectives in solving scientific problems. Terminalia arjuna is indigenously used in India for curing several diseases, and its pharmacological activities are being revisited in recent drug-repurposing research. Efficient ultrasound-assisted extraction of phytochemicals from the bark of Terminalia arjuna is highlighted in this study. Following the optimization of the extraction process, the crude hydroethanolic extract is subjected to phytochemical profiling and an in-silico investigation of its anti-cancer properties. A three-level four-factor Box-Behnken design is exploited to optimize four operational parameters, namely extraction time, ultrasonic power, ethanol concentration (as the extracting solvent) and solute (in g): solvent (in mL) ratio. At the optimum parametric condition, the crude extract is obtained, and its GC-MS analysis is carried out. An analysis of network pharmacology (by constructing and visualizing biological networks using Cytoscape) combined with molecular docking reveals the potential antineoplastic targets of the crude extract. The ANOVA table exhibits the significance, adequacy and reliability of the proposed second-order polynomial model with the R² value of 0.917 and adjusted R² of 0.865. Experimental results portray the significant antioxidant potential of the prepared extract in its crude form. The GC-MS analysis of the crude extract predicts the extracted phytochemicals, while the constructed biological networks highlight its multi-targeted activity in colorectal cancer. The study identifies three phytochemicals viz. luteolin, β-sitosterol and arjunic acid as potent anti-cancer agents and can be extended with in-vitro and in-vivo experiments to validate the in-silico results, thus establishing lead phytochemicals in multi-targeted colorectal cancer therapies.

Optimization of Ultrasound-Assisted Extraction of Phenolics from Satureja hortensis L. and Antioxidant Activity: Response Surface Methodology Approach

The extract of the plant species Satureja hortensis L. (often used as traditional ethno-therapy and in food processing) was prepared using the ultrasonic extraction technique, and contains a large quantity of secondary metabolites, with scientific evidence for antioxidant, antimicrobial, sedative, antispastic and antidiarrheal activities. Process optimization was carried out using a mathematical–statistical method (response surface methodology—RSM), which models and examines the effects of three levels and three variables on the observed response. The investigated responses were the content of total phenolic components (TPC) and total flavonoids (TFC), as well as tests of antioxidant activity at the level of radicals. The independent variables were ethanol concentration (40–80%), temperature (40–80 °C) and the liquid–solid ratio (10–30 mL/g). Results from this study were entered into a second-degree polynomial with multiple non-linear regression. Analysis of variance (ANOVA) was applied to find the most favorable environment for assessing the model’s performance and effectiveness with an ethanol concentration of 20%, temperature of 80 °C and LSR of 21.4 mL/g. ANOVA assessed the model’s significance, and a second-order polynomial model described the relationships between variables and responses. Diagnostic plots confirmed the model’s adequacy and reliability. The estimated values were 4.11 mg chlorogenic acid equivalents per gram of dry weight (CEA/g), 2.18 mg of rutin equivalents per gram of dry weight (RE/g), and 0.030 mg/mL and 0.030 mg/mL for TPC, TFC, IC50 and EC50, respectively. High-Performance Liquid Chromatography with diode array detection (HPLC-DAD) examination revealed that the prominent substance in the tested extract is rosmarinic acid (46,172 µg/mL), followed by chlorogenic acid (1519 µg/mL).

Modelling and optimization of shea butter biodiesel engine performance evaluation using response surface methodology

In this study, the performance and emission parameters of compression ignition (CI) diesel engine powered by shea butter biodiesel blends with diesel via clay doped ionic liquid catalyst (CD-IL) were modelled, optimized and predicted using Response surface methodology (RSM) technique. Using the (American Society for Testing Materials) ASTM D 6751 criteria, the produced biodiesel's quality was successfully evaluated. The biodiesel blends (B0, B50, B100), load (100, 200, 300)kg and speeds (1400, 1800, 2200) rpm were considered as the factors while Brake thermal efficiency, Brake specific consumption, Brake power, Nitrogen oxide and carbon monoxide emission were considered as the responses of the shea butter biodiesel engine. Fourier Transform Infrared Spectroscopy (FTIR), Scanning Electron Microscopy (SEM), X-ray diffraction (XRD), X-ray fluorescence (XRF), and Brunaeuer Emmet and Teller (BET) were used to analyze the catalyst before and after treatment to ascertain their suitability for the process. The capability of the models was evaluated using the correlation coefficient (R2) and mean square error (MSE). The second-order polynomial model is shown in the Analysis of Variance (ANOVA) with an (R2 -0.9948, Adjusted R2–0.9880, Predicted R2–0.9163) for brake thermal efficiency, (R2 -0.9908, Adjusted R2–0.9790, Predicted R2–0.8529) for brake specific consumption,(R2 -0.9988, Adjusted R2–0.9972, Predicted R2–0.9807) for brake power, (R2 -0.9995, Adjusted R2–0.9988, Predicted R2–0.9917) for Carbon monoxide emission and (R2 -0.9979, Adjusted R2–0.9951, Predicted R2–0.9659) Nitrogen dioxide emission demonstrating the model's acceptance. The optimal condition for shea butter biodiesel engine performance of brake thermal efficiency (16.3%), brake specific consumption(0.83),brake power (1.5), Carbon monoxide emission (175 ppm) and Nitrogen oxide (150 ppm) was obtained at a biodiesel blend (B50), load of 300 kg and speed of 2200 rpm. The 3D model graphs showed the process parameters impacted on the response significantly with better combustion, cleaner air.fuel ration blend, lesser friction loss and air consumption. Hence the proposed RSM modelling tool for shea butter biodiesel engine performance prediction had the best performance at (B50) (50% biodiesel and 50% diesel).

Kinetics and soft computing evaluation of Linseed oil transesterification via CD-BaCl-IL catalyst

A novel clay-doped ionic liquid and BaCl (CD-BaCl-IL) heterogeneous catalyst for biodiesel synthesis from linseed oil (LSO) was generated after 4 h of calcination at 600°C using Scanning Electron Micrograph (SEM), X-ray Diffraction (XRD), Brunauer-Emmett-Teller (BET), Fourier Transform Infrared Spectroscopy (FT-IR) and X-ray fluorescence (XRF) was used to evaluate the catalyst's processability. After optimization using response surface methodology (RSM), the second-order polynomial model was shown in the Analysis of variance (ANOVA) with R2 values of 0.9947, Adj R2 (0.9850), and Pred R2 (0.8594), demonstrating model acceptability. The maximum biodiesel yield (97.097 %) was obtained with 2.6 wt% catalyst, 6 mol/mol methanol/molar ratio, 1.5 h, 50 °C, and 400 rpm agitation. ANFIS predicted biodiesel yield more accurately than ANN (R2 = 0.999, MSE = 0.27594), with the lowest MSE (R2 = 0.99, MSE = 0.00038). Under optimal conditions, this study employed a kinetic model based on two elementary chemical processes: Eley-Rideal (ER) and Langmuir-Hinshelwood-Hougen-Watson (LHHW). The LHHW model accurately described CD-BaCl-IL catalyst experimental data at 50 °C, with favourable parameters, an R2 value of 0.9348, and a variance of 2.61E-8. The surface reaction between adsorbed triglyceride and alcohol dictated the rate-determining step. Temperature increased the rate, indicating an endothermic process. The reaction's activation energy and frequency factor were 10.22 kJ/mol and 6.41 h-1, respectively. Linseed biodiesel met the D6751 criterion.

Optimisation of quality features of new wheat beers fermented through sequential inoculation of non-Saccharomyces and Saccharomyces yeasts

The choice of the starchy ingredients as well as that of the yeasts strongly can represent a useful tool to differentiate the final beers. Our research investigated twelve white beers obtained applying a 2-factor mixed 3-level/4-level experimental design. The first factor was the cereal mixture, with 3 combinations of barley malt (65 %) and unmalted wheat (35 % of common, durum, or emmer). The second factor was the yeast used to carry out the fermentation trials, i.e.: a S. cerevisiae starter strain (WB06); an oenological S. cerevisiae strain (9502); two mixed starters made of an oenological Schizosaccharomyces pombe strain (6956) and, alternatively, one of the two S. cerevisiae strains. Most beer attributes were significantly (p < 0.05) influenced by the two considered factors with the following exceptions: the wheat species did not affect maltotriose, maltose, pH, total and volatile acidity, floral flavour, and sweetness; the yeast did not exert significant effects on foam colour, turbidity, overall olfactory intensity, yeast flavour, and body. The flavour of fruits and aromatic herbs were not influenced by the factors studied. Alcohol content was maximised using the unmalted durum wheat (∼7 %) and S. cerevisiae WB06 (∼6.8 %). The beer antioxidant content was increased by the use of emmer (566 mg/L) and by the application of the mixed inoculum (478–487 mg/L). The beers made with unmalted common wheat and fermented by the S. cerevisiae strains alone obtained the best overall sensory score (3.7). As shown by the Principal Component Analysis, the beers were better classified by the type of unmalted wheat than by the fermenting yeast. A multiple regression analysis was performed by fitting the analytical parameters that highlighted significant differences among the beers to a second-order polynomial model. Data concerning colour, glycerol concentration, FC-TPC, and antioxidant activity were satisfactorily predicted (R2 > 0.8) by the fitted models.

Validation of Serbian version of the LittlEARS® Early Speech Production Questionnaire for the assessment of early language development in typically developing children.

The LittlEARS® Early Speech Production Questionnaire (LEESPQ) was developed to provide professionals with valuable information about children's earliest language development and has been successfully validated in several languages. This study aimed to validate the Serbian version of the LEESPQ in typically developing children and compare the results with validation studies in other languages. The English version of the LEESPQ was back-translated into Serbian. Parents completed the questionnaire in paper or electronic form either during the visit to the paediatric clinic or through personal contact. A total of 206 completed questionnaires were collected. Standardized expected values were calculated using a second-order polynomial model for children up to 18 months of age to create a norm curve for the Serbian language. The results were then used to determine confidence intervals, with the lower limit being the critical limit for typical speech-language development. Finally, the results were compared with German and Canadian English developmental norms. The Serbian LEESPQ version showed high homogeneity (r = .622) and internal consistency (α = .882), indicating that it almost exclusively measures speech production ability. No significant difference in total score was found between male and female infants (U = 4429.500, p = .090), so it can be considered a gender-independent questionnaire. The results of the comparison between Serbian and German (U = 645.500, p = .673) and Serbian and English norm curves (U = 652.000, p = .725) show that the LEESPQ can be applied to different population groups, regardless of linguistic, cultural or sociological differences. The LEESPQ is a valid, age-dependent and gender-independent questionnaire suitable for assessing early speech development in children aged from birth to 18 months.

Response surface methodology based investigations of total organic carbon removal using cerium oxide nanoparticle

This study aimed to optimize the removal of total organic carbon (TOC) from drinking water using cerium oxide nanoparticle (CONP) through response surface methodology (RSM) based approach. The CONP was synthesized using an efficient microwave combustion (EMC) technique. It was further characterised by High-resolution transmission electron microscopy (HR-TEM), X-Ray diffraction (XRD), energy dispersive X-ray (EDX), Brunauer-Emmett-Teller (BET) surface area analysis, and zeta potential. CONP was found highly efficient in removing up to 94 % TOC at the optimum condition i.e., pH = 7, dosage = 0.5 g/L, and contact time = 30 min. Pareto analysis revealed that amongst all the selected variables, the CONP dose had the greatest cumulative impact on TOC removal. The second-order polynomial model developed using the RSM technique was found highly accurate in predicting the removal of TOC from drinking water (R2 = 0.98). The study offers a unique approach for reducing the number of experimental trials and arriving at an optimal experimental conditions to achieve higher TOC adsorption efficiency.

Fire Segmentation with an Optimized Weighted Image Fusion Method

In recent decades, earlier fire detection has become a research priority. Since visible and infrared images cannot produce clear and complete information, we propose in this work to combine two images with an appropriate fusion technique to improve the quality of fire detection, segmentation, and localization. The visible image is at first weighted before being used in the fusion process. The value of the optimal weight is estimated from the mean of the visible image with a second-order polynomial model. The parameters of this model are optimized with the least squares method from the curve of optimal weights according to the mean of visible images. Finally, a major voting method based on deep learning models is used. Experiments include an assessment of the framework’s performance not only with respect to its visual appearance but also across a spectrum of predefined evaluation criteria. The experiments show that the proposed model, which includes an optimized weighted image fusion stage before segmentation, has a high Intersection over Union (IoU) score of more than 94%.

Enhanced removal of tetrabromobisphenolA (TBBPA) from e-waste by Fe–S nanoparticles and Fe–S/CuS nanocomposite with response surface methodology (RSM)

TetrabromobisphenolA is a well-known member of the brominated flame retardant group and is widely used as a highly effective fire-retardant additive in electronic and electrical equipment. TBBPA is commonly found in various environmental sources and can be harmful to human health. This study presents a simple approach to preparing a magnetic nanocomposite, offering a straightforward method that results in consistent quality and low resource consumption. The nanocomposite has a high surface-to-volume ratio for the removal of tetrabromobisphenolA. Various characterization techniques, including XRD, FTIR, EDX, FESEM, VSM, TEM, and BET analyses were used to characterize the Fe–S nanoparticles and Fe–S/CuS. The results showed that Fe–S/CuS nanocomposite successfully removed over 97% of the initial TBBPA (15 mg L−1) under optimized conditions determined by RSM, such as a contact time of 15 min, pH of 7, Fe–S/CuS nanocomposite dosage of 0.69 g L−1, and salt concentration of 0.10%. The RSM analysis provided a second-order polynomial model with a confidence level of 93% (F = 29.58; p < 0.0001) to predict the TBBPA removal efficiency at various concentrations. In the adsorption kinetic studies, the second-order kinetic model provided the best fit for the experimental data. Additionally, Fe–S/CuS nanocomposite shows great potential for practical applications and environmental remediation efforts, making it a valuable asset for real-sample use in environmental settings.

Optimising ultrasound-assisted extraction conditions for maximising phenolic, flavonoid content and antioxidant activity in hog plum peel and seed: A response surface methodology approach

This study optimised ultrasound-assisted extraction (UAE) temperature, time, and ethanol concentration to investigate total phenolic content (TPC), total flavonoid content (TFC), and antioxidant activity (DPPH free radical scavenging activity assay) from hog plum peel and seed. Optimisation used Response Surface Methodology (RSM) with a Box-Behnken design (BBD) of three factors at three levels. The variables for both peel and seed were temperature (40–60 °C), time (30–60 min), and ethanol concentrations (40–80 %). Fifteen experimental runs were conducted, using a second-order polynomial model (quadratic) and analysis of variance (ANOVA) to predict the responses and validate the model. The results revealed that the moisture, protein, fat, crude fibre, ash, and carbohydrate content of hog plum seed and peel differences in their nutrient content. Additionally, peel contains a TPC of 95–104 mg GAE/100 g DM, DPPH activity of 68–92 %, and TFC of 1.622–2.312 mg QE/g DM, while TPC varies between 97 and 110 mg GAE/100 g DM, DPPH activity ranges from 71 to 95 %, and TFC of 1–22 mg QE/g DM for seed. The R2 value of both peel and seed for TPC, TFC, and DPPH activity was 0.99, indicating that the quadratic model is satisfactory for analysing interactions between response and independent variables. Furthermore, RSM analysis identified the optimal extraction conditions of both peel and seed for maximising TPC, TFC, and DPPH activity at a temperature of 59.82 °C, a time of 41.08 min, and 76.64 % ethanol concentration. Under the optimal settings, the observed values were 108.34 ± 7.23 (peel) and 101.45 ± 3.65 mg GAE/100 g DM (seed) for TPC, 2.98 ± 2.10 (peel), and 3.98 ± 1.34 mg QE/g DM (seed) for TFC, and 85.13 ± 5.43 (peel), and 84.35 ± 4.10 % (seed) for DPPH activity. This study establishes a framework for further investigation into UAE parameters and extraction solvents to enhance the extraction efficiency of hog plum seed and peel.

Preparation of preservation paper containing essential oil microemulsion and its application in maintain the quality of post-harvest peaches

Post-harvest peaches are highly susceptible to fungal infections leading to spoilage. This study investigates the utilization of three edible plant essential oils (EOs) with antifungal properties for the preservation of peaches, along with an evaluation of their preservative efficacy. Initially, GC-MS was employed to identify the primary constituents of the EOs, revealing a predominance of phenols, alcohols, hydrocarbons and aldehydes. Principal component analysis was conducted to compare the chemical compositions of the EOs from the plants used in this study with those obtained from other regions. This analysis facilitates the quality assessment of the plant materials in the later stages of the research. In vitro antimicrobial assays indicated that the three EOs independently exhibited their respective antifungal activities against 7 fungi responsible for peach spoilage. The Response Surface Methodology was then applied to optimize the blend ratio of the composite EOs (CEOs) for maximum antifungal efficacy. Concurrently, this study validated the rationality of a second-order polynomial model that correlates the peach decay index with the concentration of the CEOs. To enhance the bioavailability of the CEOs, a pseudo-ternary phase diagram was employed to formulate the CEOs into a microemulsion. Based on the RSM optimization results for the CEOs, the optimal mass ratio of the components within the microemulsion was determined. Using this microemulsion as a base, preservative films were developed that demonstrated a significant preservation effect on peaches, highlighting the immense potential of this research in the food industry.

Response Surface Methodology: An Optimal Design for Maximising the Efficiency of Microwave-Assisted Extraction of Total Phenolic Compounds from Coriandrum sativum Leaves

The optimization of total phenolic compounds (TPC) extraction yield and maximization of total antioxidant capacity (TAC) from coriander leaves were investigated using response surface methodology. The extraction of TPC was carried out using microwave-assisted extraction. A Box-Behnken design was used to study the effects of the three independent variables, solvent concentration (ethanol/water 20–80%), microwave power (100–500 watt) and irradiation time (30–150 s) on the response. A second-order polynomial model was used to predict the reaction. The regression analysis showed that about 99% of the variations could be explained by the models. The predicted values were 50.97 GAE/g dw and 5.75 mg GAE/g dw for TPC and TAC. The reaction surface analysis showed that the optimum extraction parameters that maximized the extraction of antioxidants yield were 52.62% ethanol, 452.12 watt and 150 s. Under optimal conditions, the experimental values for TPC and TAC were 49.63 ± 0.93 mg GAE/g dw and 5.55 ± 0.07 mg GAE/g dw, respectively. The experimental values are in agreement with the predicted values, indicating the suitability of the model used and the success of the response surface methodology in optimizing the extraction conditions.

Evaluating hydrothermal liquefaction hydrochar from sewage sludge as a phosphorus resource through struvite production

Hydrothermal liquefaction (HTL) has attracted considerable attention for valorizing sewage sludge to biofuels while the solid residue, hydrochar, has received little attention for its final application. In the current work hydrochar recovered from hot separation in a pilot-scale HTL plant is evaluated for its nutrient value, in particular as a sustainable Phosphorus (P) resource. Following thorough characterization of the hydrochar, P recovery as struvite (NH4MgPO4·6H2O) is investigated to produce a fertilizer with a lower heavy metal content that could be used in current agricultural practices. P extraction using inorganic (hydrochloric and sulphuric) and organic (citric) acids is evaluated by applying 0.1–1 mol/L of acids for 1–12 h with a liquid/solid ratio of 10 mL/g. A second-order polynomial model was established by response surface methodology (RSM) to predict the P extraction with different acids. The optimum operating conditions for maximum P extraction (94 %) are suggested as 0.7 M of acid strength (H2SO4) and 6.5 h of extraction time. Addition of citric acid (CA) during the struvite precipitation from acid-leached P solution was found to improve the struvite purity from 36.6 % to 54 % with a reduction in heavy metal content. Furthermore, morphological analysis validated the formation of struvite. Upon comparison with the regulations, hydrochar was found to exceed some of the heavy metal limits but was low in polyaromatic hydrocarbons (PAHs), per- and polyfluorinated substances (PFAS) and showed low toxicity to Aliivibrio fischeri. P extraction and struvite precipitation are shown to reduce heavy metal contents below regulatory limits with P recoveries from 40 % to 67 %. Thus, as an alternative to direct land application of hydrochar, harvested struvite from hydrochar could be applied as a fertilizer with no apparent environmental risk.

On the optimized fused filament fabrication of polylactic acid using multiresponse central composite design and desirability function algorithm

Efficient optimization of polymeric materials in fused filament fabrication 3D printing (FFF 3DP) is crucial for productivity, cost reduction, resource conservation, consistency, and enhanced part performance. This study employed a multiresponse central composite design of experiments (CCD-DOE) with the desirability function algorithm (DFA) to optimize printing settings on polylactic acid (PLA) using a commercial FFF 3D printer. The goal was to identify optimal parameters for faster build time and reduced material usage in PLA part fabrication. The fabrication process involved computer-aided design and modeling of standard PLA dogbone specimens, meeting ASTM-D638 Type 1 tensile test standards. These specimens were then 3D printed using Ultimaker Green RAL 6018 PLA filament and a 2+ model printer set at varying print parameters. Reduced second-order polynomial models for printing time and PLA weight were generated using stepwise regression, eliminating noninfluential parameters. The models revealed that higher layer thickness, increased print speed, and lower infill density resulted in faster printing times, while lower infill density and higher layer thickness led to lighter PLA prints. DFA analysis determined the optimal settings as a layer thickness of 0.26–0.30 mm and an infill density of 35% for minimizing printing time and PLA weight. The stress–strain curves displayed characteristic high-strength, brittle behavior under tension, while tensile testing of optimized PLA parts revealed increased strength with low strain at the break when layers were aligned parallel to the applied force. These findings advance additive manufacturing and provide practical guidelines for high-quality 3D-printed PLA components. Optimizing FFF 3DP parameters enables efficient production with reduced time and material usage, enhancing cost-effectiveness and the fabrication of high-performance 3D printed products.

Enhancing inter-subject reproducibility in insulin bioavailability measurements through real-time calibration

A real-time calibration to enhance the inter-subject reproducibility of insulin bioavailability measurements was proposed and validated on a diabetic patient with more than 49000 impedance values. The measurement method monitors drug absorption through a transducer consisting of a sensitive material (human abdominal tissue) and an impedance spectrometer. The in-vivo experiments revealed a statistically significant negative second-order polynomial trend in impedance magnitude corresponding to the amount of rapid-acting insulin injected. To manage the uncertainty inherent in the sensing block, the method integrates real-time calibration. Implementing a real-time calibration method based on a customized model identification at each insulin administration resulted in a reduction in deterministic error by a factor of 18. A further reduction in deterministic error for clinical application (46%) was achieved with a customized parameter second-order polynomial model with respect to the customized parameter linear model. Lastly, when less conductive long-acting insulin was administered, an increasing trend in impedance magnitude was observed, aligned with the theoretical framework of the proposed method.

Data-driven Discovery of Diffuse Interstellar Bands with APOGEE Spectra

Data-driven models of stellar spectra are useful tools to study nonstellar information, such as the diffuse interstellar bands (DIBs) caused by intervening interstellar material. Using ∼55,000 spectra of ∼17,000 red clump stars from the APOGEE DR16 data set, we create second-order polynomial models of the continuum-normalized flux as a function of stellar parameters (T eff, logg , [Fe/H], [α/Fe], and age). The model and data show good agreement within uncertainties across the APOGEE wavelength range, although many regions reveal residuals that are not in the stellar rest-frame. We show that many of these residual features—having average extrema at the level of ∼3% in stellar flux on average—can be attributed to incompletely removed spectral lines from the Earth’s atmosphere and DIBs from the interstellar medium (ISM). After removing most of the remaining contamination from Earth’s sky, we identify 84 absorption features not seen in unreddened sightlights that have <50% probability of being noise artifacts—with 25 of these features having <5% probability of being noise artifacts—including all 10 previously known DIBs in the APOGEE wavelength range. Because many of these features occur in the wavelength windows that APOGEE uses to measure chemical abundances, note that characterization and removal of this nonstellar contamination establish an important step in reaching the precision required for chemical tagging experiments. Proper characterization of these features will benefit Galactic ISM science and the currently ongoing Milky Way Mapper program of Sloan Digital Sky Survey V, which relies on the APOGEE spectrograph.

Modelling and optimizing the transesterification process of shea butter via CD-BaCl-IL catalyst using soft computing algorithms

Shortage and environmental threats posed by fossil fuel have become a critical issue requiring searching for alternative energy sources. This study utilized clay-doped barium chloride and ionic liquid (CD-BaCl-IL) as a catalyst for optimizing biodiesel production from shea butter. 10% barium chloride and ionic liquid were blended with clay, dried, and then calcined for 4 h at 600 °C to develop the catalyst. The synthesized catalyst and the biodiesel were suitably characterized. Response surface methodology (RSM) implementing Central Composite Design (CCD) and Genetic Algorithm (GA) were employed to model and optimize the effect of the process parameters on the response. The model's capabilities were evaluated using coefficient of determination (R2) and mean square error (MSE). The second-order polynomial model is shown in the Analysis of Variance (ANOVA) with an (R2 -0.9945, Adjusted R2-0.9846, Predicted R2-0.8694) demonstrating the model's acceptance. Artificial Neural Network (ANN) and Adaptive Neurofuzzy Inference System (ANFIS) were used to assess the model's capability. The obtained statistical results - (R2 = 0.8694, MSE = 0.7035), ANN (R2 = 0.999, MSE = 0.0001026), ANFIS (R2 = 0.99, MSE = 0.000041), showed that ANFIS had the best prediction with the lowest MSE. ANFIS integrated with GA (GA-ANFIS) gave the best optimization with a biodiesel yield (96.72%) at a catalyst concentration of 4 wt%, a methanol/mol ratio of 10:1, a time of 2.5 h, a temperature of 70 °C, and an agitation speed of 400 rpm. The optimal developed biodiesel properties were successfully evaluated within the ASTM D 6751 standards.