High Coefficient Of Determination Research Articles

Optimization of bioethanol production from sugar beet processing by-product molasses using response surface methodology

AbstractBioethanol production from renewable biomass sources has garnered significant interest due to its potential as a sustainable alternative to fossil fuels. In this study, we investigated the optimization of bioethanol production from molasses, a by-product of the sugar production process using Saccharomyces cerevisiae through Response Surface Methodology (RSM). Initially, the fermentation process was optimized using RSM, considering four independent variables: substrate concentration, pH, temperature, and fermentation time. Subsequently, the effects of these variables on bioethanol yield were evaluated, and a quadratic model was developed to predict the optimum conditions. Analysis of variance (ANOVA) indicated a high coefficient of determination (R2) for the model, suggesting its adequacy for prediction. The optimized conditions for bioethanol production were determined as follows: substrate concentration of 200 g L−1, pH of 5.0, temperature of 30 °C and fermentation time of 72 h. Under these conditions, the predicted bioethanol yield was 84%. Overall, this study demonstrates the successful application of RSM for optimizing bioethanol production from molasses using S. cerevisiae, highlighting its potential as a promising feedstock for biofuel production.

Concordance between methods of estimating body fat in pediatric and adolescent age.

Background and objective: The percentage of fat (%BF), essential data in the health or nutrition field, can be estimated by different methods. This work compares the %BF values obtained by anthropometry, Near Infrared Interactance (NIR), and Bioimpedance Analysis (BIA), in childhood and adolescence. Material and Methods: The sample consisted of 1015 schoolchildren aged 9 to 16 years. Height, weight, and four adipose folds were measured: tricipital, bicipital, subscapular, and suprailiac. The %BF by anthropometry was calculated using a specific formula based on body density (BD). A tetrapolar analyzer was used for the BIA calculation. The optical density in the biceps was measured for the one observed by NIR. Results: The high correlation and determination coefficients show that the relationship between %BF by anthropometry, BIA, or NIR is very strong, mainly between the first two. The intraclass correlation coefficient (ICC) results between Anthropometry and BIA show an almost perfect agreement. Its graphical contrast attests that the correlation is higher in the male sex. In general, the agreement between Anthropometry and BIA is higher than between Anthropometry and NIR. Conclusions: The three techniques used to measure %BF show high concordance. All ICC values between pairs of methods are above 0.90. The comparison of %BF by anthropometry with BIA or NIR shows that the affinity is higher in intermediate adiposity percentages than in extreme ones. This trend occurs in both sexes and age groups.

Microbial detoxification of chlorpyrifos, profenofos, monocrotophos, and dimethoate by a multifaceted rhizospheric Bacillus cereus strain PM38 and its potential for the growth promotion in cotton.

Bacillus genera, especially among rhizobacteria, are known for their ability to promote plant growth and their effectiveness in alleviating several stress conditions. This study aimed to utilize indigenous Bacillus cereus PM38 to degrade four organophosphate pesticides (OPs) such as chlorpyrifos (CP), profenofos (PF), monocrotophos (MCP), and dimethoate (DMT) to mitigate the adverse effects of these pesticides on cotton crop growth. Strain PM38 exhibited distinct characteristics that set it apart from other Bacillus species. These include the production of extracellular enzymes, hydrogen cyanide, exopolysaccharides, Indol-3-acetic acid (166.8μg/mL), siderophores (47.3μg/mL), 1-aminocyclopropane-1-carboxylate deaminase activity (32.4μg/mL), and phosphorus solubilization (162.9μg/mL), all observed at higher concentrations. This strain has also shown tolerance to salinity (1200mM), drought (20% PEG-6000), and copper and cadmium (1200mg/L). The amplification of multi-stress-responsive genes, such as acdS, ituC, czcD, nifH, sfp, and pqqE, further confirmed the plant growth regulation and abiotic stress tolerance capability in strain PM38. Following the high-performance liquid chromatography (HPLC) analysis, the results showed striking compatibility with the first kinetic model. Strain PM38 efficiently degraded CP (98.4%), PF (99.7%), MCP (100%), and DMT (95.5%) at a concentration of 300ppm over 48h at 35°C under optimum pH conditions, showing high coefficients of determination (R2) of 0.974, 0.967, 0.992, and 0.972, respectively. The Fourier transform infrared spectroscopy (FTIR) analysis and the presence of opd, mpd, and opdA genes in the strain PM38 further supported the potential to degrade OPs. In addition, inoculating cotton seedlings with PM38 improved root length under stressful conditions. Inoculation of strain PM38 reduces stress by minimizing proline, thiobarbituric acid-reactive compounds, and electrolyte leakage. The strain PM38 has the potential to be a good multi-stress-tolerant option for a biological pest control agent capable of improving global food security and managing contaminated sites.

Statistical investigation of interdependence of trabecular microarchitectural parameters from micro-computed tomography

Standard microarchitectural analysis of bone using micro-computed tomography produces a large number of parameters that quantify the structure of the trabecular network. Analyses that perform statistical tests on many parameters are at elevated risk of making Type I errors. However, when multiple testing correction procedures are applied, the risk of Type II errors is elevated if the parameters being tested are strongly correlated. In this article, we argue that four commonly used trabecular microarchitectural parameters (thickness, separation, number, and bone volume fraction) are interdependent and describe only two independent properties of the trabecular network. We first derive theoretical relationships between the parameters based on their geometric definitions. Then, we analyze these relationships with an aggregated in vivo dataset with 2987 images from 1434 participants and a synthetically generated dataset with 144 images using principal component analysis (PCA) and linear regression analysis. With PCA, when trabecular thickness, separation, number, and bone volume fraction are combined, we find that 92 % to 97 % of the total variance in the data is explained by the first two principal components. With linear regressions, we find high coefficients of determination (0.827–0.994) and fitted coefficients within expected ranges. These findings suggest that to maximize statistical power in future studies, only two of trabecular thickness, separation, number and bone volume fraction should be used for statistical testing.

Magnetic dispersive solid-phase extraction of clarithromycin and azithromycin by Fe3O4@ionic salt g-C3N4 adsorbent and HPLC-UV

In this study, Fe3O4@ionic salt g-C3N4 was synthesized as a new adsorbent for magnetic dispersive solid phase extraction (MDSPE) of two antibiotics (clarithromycin and azithromycin) in biological samples followed by HPLC-UV. The new nanocomposite was characterized by Fourier-transform infrared (FT-IR), field emission-scanning electronic microscope (FE-SEM), transmission electron microscope (TEM), nitrogen adsorption–desorption analysis, X-ray powder diffraction (XRD), and vibrating sample magnetometer (VSM). Box-Behnken design (BBD) and response surface methodology (RSM) were used for the optimization and design of experiments. The method had a suitable linear range (1–300 µg/L), a high coefficient of determination (R2; 0.9988 and 0.9993), and a low detection limit (0.33 and 0.42 µg/L) for clarithromycin and azithromycin, respectively. The applicability of the method was evaluated by estimating the recovery of analytes from urine and blood serum samples. Recovery of more than 90.0 % and RSDs of less than 5.2 % show the acceptable accuracy and precision of the proposed method.

Degree of bending of FRP-reinforced tubular X-joints in offshore jacket-type structures under in-plane bending moment

In offshore jacket structures, the stress within the chord thickness of tubular joints arises from both membrane and bending stresses. The Degree of Bending (DoB)—the ratio of bending stress to the total stress—is key to evaluating these joints' fatigue resistance. This study delves into the DoB in tubular X-joints reinforced with fiber-reinforced polymer (FRP) subjected to in-plane bending moment. It begins by validating finite element (FE) analysis accuracy through comparison with existing theoretical and experimental evidence. Following this, the analysis of 166 joints was conducted to examine how variations in FRP characteristics (such as type, layer count, and layout) and the joints' dimensionless geometric factors influence the DoB and its ratio in reinforced joints compared to their unreinforced counterparts. Findings indicate that FRP layers enhance fatigue resistance by lowering hot spot stresses on the chord's inner and outer surfaces and elevating the DoB value by up to 26.16%. Consequently, the study introduces a parametric formula for calculating the DoB in FRP-reinforced tubular X-joints under an in-plane bending moment. This development is significant, as there was no existing parametric formula for determining DoB in FRP-reinforced joints. The proposed formula stands out for its high determination coefficient and minimal error, effectively addressing a notable gap in the field.

Removal of the bemacid blue E-TL using granular activated carbon from abundant biomass: adsorption isotherm, thermodynamic and kinetic studies

In this work, we were interested in the adsorption of an acid dye, Bemacid Blue E-TL by granular activated carbon based on olive stones. To shed light on the adsorption process, batch experiments were performed to study the effect of operating parameters on the adsorption process such as equilibrium time, GAC dose, pH, initial dye concentration, and temperature. The experimental results showed that the adsorption of the dye Bemacid Blue E-TL by GAC based on olive stones depends on the equilibrium time at 8 hours, the pH is 2 of the solution and the dose is 5 g.L-1. To explain the adsorption equilibrium, the experimental values were examined by Langmuir, Freundlich, and Temkin models. The equilibrium is perfectly described by the Langmuir model whose correlation coefficient is higher than 0.99. The maximum retained quantity of the Bemacid Blue E-TL is 55.6 mg.g-1. Different kinetic models such as pseudo-first order, pseudo-second order, and the intraparticle diffusion equation were used to evaluate the adsorption kinetics. The results of the kinetic modeling showed that the pseudo-second order fitted the data well, with a high coefficient of determination (R2 > 0.99) and intra-particle diffusion is not the only rate-limiting step. The thermodynamic parameter values enthalpy (ΔH°), entropy (ΔS°), and energy change (ΔG°) show that the adsorption processes are endothermic and spontaneous. The various results obtained are promising and encouraging to consider a more comprehensive study with the objective of showing that the adsorbent chosen for this study is effective and could be used as a low-cost adsorbent for acid dye removal.

Heavy Metal Concentration Estimation for Different Farmland Soils Based on Projection Pursuit and LightGBM with Hyperspectral Images.

Heavy metal pollution in farmland soil threatens soil environmental quality. It is an important task to quickly grasp the status of heavy metal pollution in farmland soil in a region. Hyperspectral remote sensing technology has been widely used in soil heavy metal concentration monitoring. How to improve the accuracy and reliability of its estimation model is a hot topic. This study analyzed 440 soil samples from Sihe Town and the surrounding agricultural areas in Yushu City, Jilin Province. Considering the differences between different types of soils, a local regression model of heavy metal concentrations (As and Cu) was established based on projection pursuit (PP) and light gradient boosting machine (LightGBM) algorithms. Based on the estimations, a spatial distribution map of soil heavy metals in the region was drawn. The findings of this study showed that considering the differences between different soils to construct a local regression estimation model of soil heavy metal concentration improved the estimation accuracy. Specifically, the relative percent difference (RPD) of As and Cu element estimations in black soil increased the most, by 0.30 and 0.26, respectively. The regional spatial distribution map of heavy metal concentration derived from local regression showed high spatial variability. The number of characteristic bands screened by the PP method accounted for 10-13% of the total spectral bands, effectively reducing the model complexity. Compared with the traditional machine model, the LightGBM model showed better estimation ability, and the highest determination coefficients (R2) of different soil validation sets reached 0.73 (As) and 0.75 (Cu), respectively. In this study, the constructed PP-LightGBM estimation model takes into account the differences in soil types, which effectively improves the accuracy and reliability of hyperspectral image estimation of soil heavy metal concentration and provides a reference for drawing large-scale spatial distributions of heavy metals from hyperspectral images and mastering soil environmental quality.

Improved forecasting of the compressive strength of ultra‐high‐performance concrete (UHPC) via the CatBoost model optimized with different algorithms

AbstractThis paper focuses on the applicability of CatBoost models constructed using various optimization techniques for improved forecasting the compressive strength of ultra‐high‐performance concrete (UHPC). Phasor particle swarm optimization (PPSO), dwarf mongoose optimization (DMO), and atom search optimization (ASO), which have been very popular recently, are preferred as optimization algorithms. A comprehensive and reliable data set is used to develop the CatBoost models, which include 785 test results with 15 input features. The performance of the CatBoost models (PPSO‐CatBoost, DMO‐CatBoost, and ASO‐CatBoost) optimized with different algorithms is thoroughly assessed by means of various statistical metrics and error analysis to determine the model with the best forecasting capability, and this model is compared with the models obtained from previous studies. In addition, Shapley additive exPlanations (SHAP) analysis is used to ensure the interpretability of the forecasting models and to overcome the “black box” problem of machine learning (ML) models. The obtained results demonstrate that all CatBoost models outstandingly forecast the compressive strength of UHPC. Among these models, the DMO‐CatBoost model stands out compared to the other models in various performance metrics, such as high coefficient of determination (R2) values, low root mean squared error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE) values, along with a smaller error ratio. In other words, the RMSE, R2, MAPE, and MAE values of the DMO‐CatBoost model for the training set are 3.67, 0.993, 0.019, and 2.35, respectively, whereas those for the test set are 6.15, 0.978, 0.038, and 4.51. Additionally, the performance ranking of the algorithms used to optimize the hyperparameters of the CatBoost model is as follows: DMO > PPSO > ASO. On the other hand, SHAP analysis showed that age, fiber dosage, and cement dosage significantly influence the compressive strength of UHPC. These findings can guide structural engineers in the design and optimization of UHPC, thus assisting them in developing strategies to improve the strength properties of the material. Finally, based on the best forecasting model developed in this work, a graphical user interface has been developed to easily forecast the compressive strength of UHPC in practical applications without additional tools or software.

Adsorbent Biomaterials Based on Natural Clays and Orange Peel Waste for the Removal of Anionic Dyes from Water

This study demonstrates the efficient removal of Alizarin Yellow R anionic dye (AY) from aqueous solutions using green adsorbents. Natural kaolin clay (A1), acid-modified natural clay (A2), chemically treated orange peel (C1) and biochar produced by the thermal treatment of orange peel (C2) were tested for the adsorption of AY. The characteristics of the sorbents were determined by instrumental methods: SEM, EDS, FTIR, BET and TGA. The adsorption experiments were performed under different conditions, including the initial AY dye concentration, adsorbent weight, pH, temperature and contact time. The maximum adsorption capacities had values between 15.72 and 74.62 mg/g at 298 K and the optimal pH of 6.5 at initial concentrations ranging from 30 to 70 mg/L for all adsorbents. The equilibrium data were used for the adsorption isotherm models: Freundlich, Langmuir and Temkin. The Freundlich model fit best for the adsorbents A2, C1 and C2, and the Langmuir isotherm had the highest regression value for the adsorbent A1 (R2 = 0.9935). Thermodynamic parameters indicated the spontaneous and favorable adsorption process of AY. A study of the adsorption kinetics proved that they best fit the pseudo-second-order model, with the highest coefficients of determination (R2), outperforming the pseudo-first-order model. The results of this study indicate the potential for the valorization of locally available clays and orange peel waste in the purification processes of water.

A Transformer and LSTM-Based Approach for Blind Well Lithology Prediction

Petrographic prediction is crucial in identifying target areas and understanding reservoir lithology in oil and gas exploration. Traditional logging methods often rely on manual interpretation and experiential judgment, which can introduce subjectivity and constraints due to data quality and geological variability. To enhance the precision and efficacy of lithology prediction, this study employed a Savitzky–Golay filter with a symmetric window for anomaly data processing, coupled with a residual temporal convolutional network (ResTCN) model tasked with completing missing logging data segments. A comparative analysis against the support vector regression and random forest regression model revealed that the ResTCN achieves the smallest MAE, at 0.030, and the highest coefficient of determination, at 0.716, which are indicative of its proximity to the ground truth. These methodologies significantly enhance the quality of the training data. Subsequently, a Transformer–long short-term memory (T-LS) model was applied to identify and classify the lithology of unexplored wells. The input layer of the Transformer model follows an embedding-like principle for data preprocessing, while the encoding block encompasses multi-head attention, Add & Norm, and feedforward components, integrating the multi-head attention mechanism. The output layer interfaces with the LSTM layer through dropout. A performance evaluation of the T-LS model against established rocky prediction techniques such as logistic regression, k-nearest neighbor, and random forest demonstrated its superior identification and classification capabilities. Specifically, the T-LS model achieved a precision of 0.88 and a recall of 0.89 across nine distinct lithology features. A Shapley analysis of the T-LS model underscored the utility of amalgamating multiple logging data sources for lithology classification predictions. This advancement partially addresses the challenges associated with imprecise predictions and limited generalization abilities inherent in traditional machine learning and deep learning models applied to lithology identification, and it also helps to optimize oil and gas exploration and development strategies and improve the efficiency of resource extraction.

24-h energy expenditure in people with type 1 diabetes: impact on equations for clinical estimation of energy expenditure.

Type 1 diabetes (T1D) is associated with an increase in resting metabolic rate (RMR), but the impact of T1D on other components of 24-h energy expenditure (24-h EE) is not known. Also, there is a lack of equations to estimate 24-h EE in patients with T1D. The aims of this analysis were to compare 24-h EE and its components in young adults with T1D and healthy controls across the spectrum of body mass index (BMI) and derive T1D-specific equations from clinical variables. Thirty-three young adults with T1D diagnosed ≥1 year prior and 33 healthy controls matched for sex, age and BMI were included in this analysis. We measured 24-h EE inside a whole room indirect calorimeter (WRIC) and body composition with dual x-ray absorptiometry. Participants with T1D had significantly higher 24-h EE than healthy controls (T1D = 2047 ± 23 kcal/day vs control= 1908 ± 23 kcal/day; P < 0.01). We derived equations to estimate 24-h EE with both body composition (fat free mass + fat mass) and anthropometric (weight + height) models, which provided high coefficients of determination (R2 = 0.912 for both). A clinical model that did not incorporate spontaneous physical activity yielded high coefficients of determination as well (R2 = 0.897 and R2 = 0.880 for body composition and anthropometric models, respectively). These results confirm that young adults with established T1D have increased 24-h EE relative to controls without T1D. The derived equations from clinically available variables can assist clinicians with energy prescriptions for weight management in patients with T1D.

Synergistic enhancement mediated sensitive SERS-based immunoassay of PSA using versatile PDMS@AgNPs@ZIF-67 biomimetic substrates

Among various biomimetic polymer materials, polydimethylsiloxane (PDMS) stands out as an ideal matrix for surface-enhanced Raman scattering (SERS) due to its unique intrinsic Raman signal and tenacity. In order to realize the precise detection of prostate-specific antigen (PSA), we proposed a sandwich-type SERS-active immunostructure composed of PDMS@silver nanoparticles (Ag NPs)@ZIF-67 biomimetic film as the immunosubstrate and gold nanorods (Au NRs) as immunoprobes. Due to the synergistic effect of electromagnetic enhancement facilitated by biomimetic surfaces and chemical enhancement achieved by ZIF-67, this structure enabled an ultrasensitive and selective detection of PSA across a broad range from 10−3 to 10−9 mg/mL. The achieved limit of detection was as low as 3.0 × 10−10 mg/mL. Particularly, the intrinsic Raman signal of PDMS matrix at 2905 cm−1 was employed as a potential internal standard (IS) in the detection, achieving a high coefficient of determination (R2) value of 0.996. This multifunctional SERS substrate-mediated immunoassay holds vast potential for early diagnosis of prostate cancer, offering promising prospects for clinical applications.

Optimizing properties of clayey soil using lime and waste marble powder: a sustainable approach for engineering applications

Several studies have explored the potential of waste marble powder (WMP) and lime (LM) as solutions for issues associated with clayey soils. While WMP enhances mechanical properties and addresses environmental concerns, LM effectively improves soil characteristics. This research investigates the efficacy of LM and WMP, both individually and in combination, in addressing challenges specific to clayey soils in Bouzaroura El Bouni, Algeria. These soils typically exhibit low load-bearing capacity, poor permeability, and erosion susceptibility. LM demonstrates promise in enhancing soil properties, while WMP not only addresses environmental concerns but also enhances mechanical characteristics, providing a dual benefit. The study utilizes a three-variable experiment employing Response Surface Methodology (RSM) Box-Behnken Design, with variations in clay content (88%–100%), LM treatment (1.5%–9%), and WMP inclusion (1.5%–9%). Statistical analysis, including ANOVA, reveals significant patterns with p-values &amp;lt;5%. Functional relationships between input variables (clay, LM, and WMP) and output variables (cohesion, friction angle, and unconfined compressive strength) are expressed through high determination coefficients (R2 = 99.84%, 77.83%, and 96.78%, respectively). Numerical optimization identifies optimal mixtures with desirability close to one (0.899–0.908), indicating successful achievement of the objective with 88% clay content, 3% LM, and 6% WMP. This study provides valuable insights into optimizing clay soil behavior for environmental sustainability and engineering applications, emphasizing the potential of LM and WMP as strategic additives.

A synergistic approach to optimizing the performance of a concentrating solar segmented variable area leg thermoelectric generator using numerical methods and neural networks

AbstractThis study presents an optimized design for segmented variable area leg thermoelectric modules using finite element methods and Bayesian regularized neural networks. We explored the impact of geometry and thermal parameters on module performance using ANSYS software, identifying optimal parameters for power output and efficiency. Key findings revealed the higher influence of geometric parameters and confirmed the advantages of segmented thermoelectric generators for high-temperature applications like concentrated solar systems. With this optimization, power output and efficiency of the module increased by 875% and 165%, respectively, under 25 Suns. To refine the optimization process, a Bayesian regularized neural network was utilized, proving effective in predicting module performance with a low mean squared error and high coefficient of determination. This research provides important insights into high-performance thermoelectric modules for sustainable energy applications, demonstrating the significant role of advanced computational methods in energy solutions.

Relationship between Ambient Temperature and Reasonable Heat Dissipation Coefficient of Mass Concrete Pouring Blocks.

In engineering practice, similar surface insulation measures are typically applied to different parts of mass concrete surfaces. However, this can lead to cracking at the edges of the concrete surface or the wastage of insulation materials. In comparison to flat surfaces, the edges of mass concrete structures dissipate heat more rapidly, leading to more pronounced stress concentration phenomena. Therefore, reinforced insulation measures are necessary. To reduce energy consumption and enhance overall insulation effectiveness, it is essential to study the specific insulation requirements of both the flat surfaces and edges of concrete separately and implement targeted surface insulation measures. Taking the bridge abutment planned for pouring in Nanjing City as the research object, this study established a finite element model to explore the effects of different ambient temperatures and different surface heat dissipation coefficients on the early-age temperature and stress fields of different parts of the abutment's surface. Based on simulation results, reasonable heat dissipation coefficients that meet the requirements for crack prevention on both the structure's plane and edges under different ambient temperatures were obtained. The results indicate that under the same conditions, the reasonable heat dissipation coefficient at the edges was smaller than that on the flat surfaces, indicating the need for stronger insulation measures at the edges. Finally, mathematical models correlating ambient temperature with reasonable heat dissipation coefficients for the structure's plane and edges at these temperatures were established, with high data correlation and determination coefficients (R2) of 0.95 and 0.92. The mathematical models were validated, and the results from finite element calculations were found to be consistent with those from the mathematical models, validating the accuracy of the mathematical models. The conclusions drawn can provide references for the insulation of similar engineering concrete planes and edges.

Chemical insight into thermal degradation, chemical kinetics, and thermodynamics behavior of sewage sludge through thermogravimetry and Fourier transform infrared analysis

Pyrolysis emerges as a promising path for sewage sludge (SS) management and value creation by producing compounds with enhanced utility. This study focuses on a comprehensive analysis of the thermal decomposition of SS using thermogravimetric analysis (TGA) and Fourier transform infrared spectrometry (FTIR). The TGA and FTIR were employed to evaluate the pyrolysis behavior, kinetics, and characteristics during pyrolysis of SS. The process revealed three distinct stages: 1st stage ranged between 30–185 °C with the removal of moisture, 2nd stage saw devolatilization of SS in the range of 185–595 °C, and the 3rd stage was mainly the formation of char in the temperature range of 595–900 °C. Pyrolysis kinetics, reaction mechanisms, and thermodynamic properties were extensively investigated at three different heating rates of 10, 20, and 30 °C /min. It was observed that with the temperature rise, the decomposition rate also increased rapidly, notably for the heating rate of 30 °C/min. Using Flynn-Wall-Ozawa, Kissinger-Akahira-Sunose, Starink, and Tang methods, activation energies for SS pyrolysis were estimated for α in the range of 0.2–0.85, and the average value for that range was found to be 84.72, 92.29, 91.99, and 91.76 kJ/mol, respectively for different iso-conversional methods. The chemical kinetics analysis through Coats-Redfern methods identified that 3-D diffusion-reaction mechanisms have the highest coefficient of determination among the other models. Criado z-master plot emphasizes the prevalence of Avrami-Erofeev (A4) as the bestsuited reaction model in combination with α, providing a detailed examination that illuminates the complex processes and mechanisms inherent in the pyrolysis of sewage sludge.

Introducing high-order response surface method for improving scour depth prediction downstream of weirs

Scour depth downstream of weirs is considered one of the most important hydraulic problems, which greatly influences the stability of weirs. Recently, artificial intelligence (AI) methods have become increasingly popular in modeling hydraulic variables, especially scour depth, because they can capture nonlinear relationships between input variables and their associated objectives. Despite their importance, these models have problems with hyperparameter tuning in scour depth modeling due to their structures, so algorithms must be used to tune the hyperparameters. Moreover, these algorithms are usually tuned by using the trial-and-error method to select the hyperparameters such as the number of hidden nodes, transfer function, and learning rate, and in this case, the main problem is overfitting during the training phase. To solve these problems, the high-order response surface method (HORSM), an improved version of the response surface method (RSM), is used as an alternative approach for the first time in this study to predict the scour depth. The HORSM model is based on high-order polynomial functions (from two to six) compared with the artificial neural network model (ANN). The findings indicate that the fifth order of the HORSM polynomial function yields the most precise predictions, with a higher coefficient of determination (R2) of 0.912 and Willmott Index (WI) of 0.972 compared to the values obtained using ANN (R2 = 0.886 and WI = 0.927). Moreover, the accuracy of the predictions is represented by a reduction of the mean square error by up to 44.17 and 29.01% compared to the classical RSM and ANN, respectively. The suggested model established an excellent correlation and accuracy with experimental values.

A practical equation for predicting saturated hydraulic conductivity of fine-grained soils

The saturated hydraulic conductivity (ks) of soils plays a crucial role in various agricultural and environmental engineering applications, particularly in relation to pollution prevention and water migration. Firstly, the traditional Kozeny-Carman (KC) equation, which are commonly used to estimate ks, has limitations in predicting ks for different types of fine-grained soils based on the analyses of permeability test data in the literature. Secondly, to tackle this issue, a modified KC equation was proposed by introducing an expression for the shape parameter and establishing a relationship between the tortuosity equation and the plastic limit. Additionally, the validation of the proposed equation was conducted using ks values of 25 fine-grained soils obtained from other sources. Finally, a comparison was made with the four commonly used ks models to evaluate its performance. The results indicate that the proposed equation demonstrates a relatively high coefficient of determination and a low root mean square error. This suggests that the proposed equation has the potential to accurately assess ks for fine-grained soils. The predictions for ks using the modified equation showed good agreement with the experimental data mentioned in the literature.

High-performing structural optimization of graphene quantum dots as glyphosate herbicide photoluminescent probes: real case studies and mechanism insights

The widespread usage of the herbicide glyphosate calls for urgent action, aiming at the development of new, simple, low-cost, and eco-friendly detection approaches. In the last decade, investigation of graphene quantum dots (GQDs) as potential optical probes for various pollutants rapidly grew, thanks to their easy-manipulative structure, remarkable photoluminescence (PL) in the visible part of the spectrum, good dispersibility, biocompatibility, and non-toxicity, as well. Herein, a fast, simple, and environmentally friendly method for GQDs structural modification is presented. GQDs raw powder was exposed to γ- rays at three different doses (100, 200, and 300 kGy) in air, without any solvent or reagents. Irradiation of dots under such affordable conditions led to the additional incorporation of oxygen-containing moieties in the GQD structure. For the first time, oxygen-rich GQDs irradiated at a 300 kGy dose were successfully applied as direct turn-off PL probe for glyphosate detection. The high coefficient of determination (R-squared (R2) = 0.99) and very low limit of detection (3.02 μmol L-1) reveal good linearity between the potential sensor and analyte, as well as sensitivity. Glyphosate was successfully detected in celery samples, with a recovery value of 107 ± 0.85%. To evaluate the biological safety of the proposed sensing probe, [3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide] (MTT) and the hemolysis assays were performed. Obtained results show that irradiated and non-irradiated GQDs did not cause the death of MRC-5 cells, and hemolysis of erythrocytes. The obtained results demonstrate that GQDs irradiated in an air medium can be potentially applied for glyphosate detection.