High Coefficient Of Determination Research Articles

Assessment of the Levels of Pesticide Residues and Trace Metal Elements (TME) in Soils in the Cotton-Growing Region of North-West Benin

ABSTRACT The objective of the study was to evaluate the levels of pesticide residues and trace metal elements (ETM) in the soils of the cotton-growing zone in north-west Benin. The soils under cotton, maize and soybean were retained in a Complete Random Block design with 12 repetitions. 36 sites were sampled, with 12 of them being allocated to each crop. The analysis involved determining permethrin, cypermethrin, and glyphosate from soil samples taken by HPLC. The determination of Cd and Pb was carried out by mass spectrometry with inductively coupled plasma after organic matter destruction. A analysis of variance was used to compare soil types with the data obtained. Linear regression equation models were created to estimate trace metal elements values based on pesticide residue values. The results showed that the average amounts of permethrin (0.61 ± 0.03 µg/mg), cypermethrin (0.69 ± 0.07 µg/mg), glyphosate (0.82 ± 0.01 µg/kg), cadmium (Cd) (1.78 ± 0.21 µg/kg), lead (Pb) (1.04 ± 0.14 µg/kg) were higher (p < 0.05) in soils under cotton crops. As for lead, it can only be estimated using permethrin (Pb = 0.18 + 0.37 Per) or cypermethrin (Pb = 0.24 + 0.34Cy) or both with high determination coefficient R2 (R2= 82.01%).

Evaluating the strength loss and the effectiveness of glass and eggshell powder for cement mortar under acidic conditions

Abstract The cementitious composite’s resistance to the introduction of harmful ions is the primary criterion that is used to evaluate its durability. The efficacy of glass and eggshell powder in cement mortar exposed to 5% sulfuric acid solutions was investigated in this study using artificial intelligence (AI)-aided approaches. Prediction models based on AI were built using experimental datasets with multi-expression programming (MEP) and gene expression programming (GEP) to forecast the percentage decrease in compressive strength (CS) after acid exposure. Furthermore, SHapley Additive exPlanations (SHAP) analysis was used to examine the significance of prospective constituents. The results of the experiments substantiated these models. High coefficient of determination (R 2) values (MEP: 0.950 and GEP: 0.913) indicated statistical significance, meaning that test results and anticipated outcomes were consistent with each other and with the MEP and GEP models, respectively. According to SHAP analysis, the amount of eggshell and glass powder (GP) had the most significant link with CS loss after acid deterioration, showing a positive and negative correlation, respectively. In order to optimize efficiency and cost-effectiveness, the created models possess the capability to theoretically assess the decline in CS of GP-modified mortar across various input parameter values.

THE ROLE OF LIMING IN COPPER ADSORPTION OF ACIDIC SOILS

Investigate the impact of varying rates of liming on the availability and sorption properties of copper in a range of acid soils with differing physico-chemical futures. Soil samples with varying acidity levels were collected from different locations in the acidic Black Sea Region of Türkiye. Out of the nine soil samples collected, four were obtained from regions where hazelnuts are grown, while the other five were obtained from areas dedicated to tea growth. The sorption properties of soils limed at 0%, 50%, and 100% of lime requirement and incubated for 12 months, then the Langmuir and Freundlich adsorption isotherms were determined by batch sorption technique. The results showed that increasing the soil pH resulted in an increase in Cu adsorption. Experimental results of Cu adsorption were explained with high coefficients of determination (93.44-98.98%) of the Langmuir model. In the three experimental groups with full, half, and no lime application, pH increased significantly to 3.95, 4.46, and 5.89, respectively, while the maximum adsorption obtained from Langmuir adsorption curves, Qmax1 1553, Qmax2 2205, and Qmax3 3902 mg kg-1, respectively. While the maximum adsorption in the tea plantation areas varied between 1491 and 1514 mg kg-1, the maximum adsorption in hazelnut cultivation areas varied between 777 and 3333 mg kg-1. These findings suggest that liming or other methods to increase soil pH could be effective soil management in reducing Cu mobility in acidic soils.

Coomans plot classification of lard in the ink blends and ink extracts from printed packaging films using lard Fourier transform infrared spectral profile and multivariate analysis

Lard in blends of commercial gravure ink (ranging from 0.5 to 20%) and ink extracts from eleven commercially printed packaging films for foodstuffs was characterized using Fourier transform infrared (FTIR) spectroscopy. The FTIR spectral bands at 4000 ‒ 650 cm-1 associated with the lard fingerprint were acquired and used to classify the lard and its blends using partial least squares (PLS) regression and discriminant analysis (DA). Commercial gravure inks (also used for preparing calibration curve samples), blends of lard ranging from 0.5 to 20% in gravure inks, and commercially printed food packaging films were tested. Linear correlation of predicted and actual values of lard were determined using PLS calibration and validation models. They produced a high coefficient of determination (R2 ) of 0.943, a low root mean square error of calibration (RMSEC) of 1.674, as well as a high R2 = 0.999 and a low root mean square error of prediction (RMSEP) of 1.233, respectively. The PLS calibration was verified employing a leave-oneout cross-validation, while DA was used to classify a series of lard standard, lard-added ink, and commercial food packaging films. The Coomans plot classification of the lardadded ink and commercial food packaging films illustrated that the food packaging samples were plotted in the right and left hemispheres in the lard-added ink class. This result also demonstrated that FTIR coupled with chemometric PLS predicted the lard content in the printing inks with high overall accuracy, as indicated by a low mean difference (MD) value of 0.577 and a low standard deviation of difference (SDD) value of 0.599. The DA allowed the ink packaging samples that potentially contained lard to be distinguished from those without lard. Sample 7 (commercially printed food packaging ink) exhibits the highest possibility of containing lard.

DAILY DANUBE RIVER WATER LEVEL PREDICTION USING EXTREME LEARNING MACHINE APPROACH

Anticipating water levels in vast riverbeds is crucial for preventing and mitigating floods or droughts, assessing power plant capacity, and facilitating navigation management. This study introduces an innovative water level prediction model utilizing an Extreme Learning Machine developed to solve the issues of low performance of existing forecasting methods. Development of such a system is of extreme importance when talking about the largest European river – the Danube River. Experimental findings reveal the model's satisfactory performance across various accuracy metrics, complexity considerations, and calculation speed. The prediction with the highest error rate based on MAPE criteria was for Prahovo water level prediction over a 365-day period at 2.02%, whilst the most accurate predictions were for Novi Sad and Banatska Palanka over 30 days and 180 days horizons, respectively, at 0.0550%. The highest coefficient of determination (R2) was achieved with the Novi Sad data at 0.9968, whilst the lowest was observed with the Prahovo data at 0.7353. The ELM model achieved high precision by adjusting the activation functions of the hidden layer neurons, which involved using different combinations of sigmoid and radial-basis activation functions.

Application of machine learning models to improve the prediction of pesticide photodegradation in water by ZnO-based photocatalysts

Pesticide pollution has been posing a significant risk to human and ecosystems, and photocatalysis is widely applied for the degradation of pesticides. Machine learning (ML) emerges as a powerful method for modeling complex water treatment processes. For the first time, this study developed novel ML models that improved the estimation of the photocatalytic degradation of various pesticides using ZnO-based photocatalysts. The input parameters encompassed the source of light, mass proportion of dopants to Zn, initial pesticide concentration (C0), pH of the solution, catalyst dosage and irradiation time. Additionally, physicochemical properties such as the molecular weight of the dopants and pesticides, as well as the water solubility of both dopants and pesticides, were considered. Notably, the numerical data were extracted from the literature via relevant tables (directly) or graphs (indirectly) using the web-based tool WebPlotDigitizer. Four ML models including multi-layer perceptron artificial neural network (MLP-ANN), particle swarm optimization-adaptive neuro fuzzy inference system (PSO-ANFIS), radial basis function (RBF), and coupled simulated annealing-least squares support vector machine (CSA-LSSVM) were developed. In comparison, RBF showed the best accuracy of modeling among all models, with the highest determination coefficient (R2) of 0.978 and average absolute relative deviation (AARD) of 4.80%. RBF model was effective in estimating the photocatalytic degradation of pesticides except for 2-chlorophenol, triclopyr and lambda-cyhalothrin, where CSA-LSSVM model demonstrated superior performance. Dichlorvos was completely degraded by ZnO photocatalyst under visible light. The sensitivity analysis by relevancy factor exhibited that light irradiation time and initial pesticide concentration were the most important parameters influencing photocatalytic degradation of pesticides positively and negatively, respectively. The new ML models provide a powerful tool for predicting pesticide degradation in wastewater treatment, which will reduce photochemical experiments and promote sustainable development.

Performance prediction and regression analysis of scroll expander based on response surface methodology

The Organic Rankine Cycle (ORC) plays a pivotal role in the domain of renewable energy, adeptly harnessing thermal energy from sources such as solar and geothermal to generate electrical power. Central to this system is the scroll expander, whose operational efficacy directly influences the ORC's overall efficiency. This study initiates with a detailed numerical analysis of the impact of various operational parameters on both the steady and transient output performance of the scroll expander. Following this, a sophisticated performance prediction model is developed employing the response surface methodology. The model boasts high coefficients of determination, R2,values of 0.9967 and 0.9916, respectively, underscoring its superior predictive accuracy. Further, this model delves into the complex interactions among operational parameters and their consequent effects on the expander's performance. Validation of this predictive model is achieved through rigorous testing on an experimental platform designed for the ORC low-temperature waste heat power generation system. The discrepancies between predicted and actual performance measurements fall within a commendable margin of 15 %, affirming the model's robustness in forecasting scroll expander performance. The culmination of this research furnishes a novel and efficacious optimization strategy, establishing a robust theoretical foundation and offering fresh insights for the enhancement and prediction of scroll expander performance. This not only underscores the innovation of the approach but also its practical applicability in advancing the efficiency of renewable energy systems.

Estimating Hardening Soil-Brick model parameters for sands based on CPTU tests and laboratory experimental evidence

The Hardening Soil-Brick model for soils is designed to carry out complex numerical analyses of soil-structure interaction problems taking into account strong stiffness variation in the range of small strains. However, to calibrate its parameters advanced triaxial and oedometric tests are required. In case of uncemented sands laboratory testing is usually difficult. Therefore, to facilitate calibration procedures, a CPTU based method, enhanced by an experimental evidence derived from advanced triaxial drained and oedometric tests, has been proposed and verified. It is shown in the paper that using exclusively the CPTU test results one can calibrate most important model parameters for sands with accuracy that is sufficient for solving real life problems. The major goal of this paper is to identify correlations between all reference stiffness moduli, then verify them, and finally link with the CPTU based identification procedures. It is shown in the paper that such correlations exist and they exhibit very high coefficients of determination. Moreover, as the seismic version of the CPTU test is not often available in the practice, an enhanced procedure for identification of very small strain shear stiffness modulus has been proposed and then verified, using set of the SCPTU tests conducted in Gdańsk sands (Poland).

A new human metabolic rate sensing model optimization and wearable sensor realization

Accurate assessment of metabolic rate is crucial for predicting human thermal comfort. However, many existing instruments are bulky, invasive, and inconvenient. Additionally, wearable metabolic rate measurements have faced long-standing challenges due to a lack of a comprehensive theoretical model with easy-to-measure parameters. This paper optimizes our previously proposed theoretical approach for wearable metabolic rate measurement that incorporates heart rate, whole-body unit heat loss, skin resistance, and body muscle percentage. We have improved the evaluation approach for whole-body unit heat loss by introducing a newly designed convective heat loss coefficient from various body segments. Regarding the wearable sensor design, a heat loss measuring system with serpentine channel was verified as the most suitable design following calibration experiments. We also integrated a modified square-wave-based skin resistance design, resulting in an upgraded integrated metabolic sensor. To validate our sensor and model performance, we conducted experiments with ten subjects of both genders at three temperature conditions (22, 25 and 28°C) and four levels of activities (rest, tiptoe heel, slight walk, and moderate walk). First, our results demonstrate a strong positive relationship between the metabolic rate obtained by our optimized sensor and Quark CPET instrument, with high coefficients of determination (highest:0.97 from the male group, 0.98 from the female group). Our approach achieved at least 96% accuracy and less than 2.5% uncertainty for both groups. Second and increasingly, we found that only one metabolic rate model was required across combined temperature conditions when the person was fixed. Third, the positive relationship was further improved by incorporating whole-body unit heat loss. Finally, we obtained information from different segments on skin temperatures. In summary, our study provides a powerful methodology for predicting and evaluating human metabolic rate through an innovative wearable approach. Our approach and sensor have the potential to substantially contribute to energy-saving efforts in smart buildings.

Microstructure Analysis, Piezoelectrical Resistivity, and Compressive Strength Concrete Incorporated with Waste Steel Slag as a Fine Aggregate Replacement

Abstract This study aims to investigate the effect of waste steel slag (SS) as partially replaced with cement and fine aggregate on conventional concrete for different mixes named M25, M35, and M47 in terms of compressive strength (CS), electrical resistivity (ER), and piezoresistivity behavior. SS is a molten mixture of silicates and oxides that solidifies upon cooling, a byproduct of the steel-making process. Before doing the design experiments, the optimum value of SS as powder and fine aggregate was determined using seven different mixes to investigate the effect of different SS sizes on the CS and piezoresistivity of normal concrete. Based on the results achieved, the optimum value and size of SS were selected to modify and investigate the effect of SS on three different mixes of conventional concrete named M25, M35, and M47 in terms of CS, ER, and piezoresistivity behavior. The resistivity of all concrete mixes was measured using four-probe from early curing to 28 days of curing time. The results demonstrated that M47 mix modified with SS has lower resistivity than the rest of the concrete mixes. The results of piezoresistivity behavior indicated that M47 mix modified with SS has a higher resistivity change while applying stress at 3 days of curing compared to the M25 and M35 concrete mix modified with SS by 44.1 % and 37.6 %, respectively. The Vipulandanan p-q model was applied to predict both ER versus time and change of resistivity versus stress for all mixes. The results demonstrated that the model predicted the change of resistivity versus applied stress with a high coefficient of determination that varied between 0.82 and 0.989, and a low root mean square error changed between 0.81 Ω.m and 7.94 Ω.m.

Raising double-muscled breed cattle and their crossbreds in the tropics: insight from growth models

Background and Aim: In tropical conditions, modeling the predictive parameters of live weight, including those at birth, pre-weaning, post-weaning, finishing, and maturing, and the average daily gain, is challenging. The heat load significantly influences the growth rate and final mature weights in the tropics. The study compared the growth rates of Kedah-Kelantan (KK), Brahman (BRAH), and Belgian Blue (BB) crossbred calves. Materials and Methods: The study conducted growth analysis using the non-linear regression growth models as it approximates the sex, breed, and growth physiology changes in beef cattle. It is supported by the utility of the most common growth functions (Brody, Logistic, von Bertalanffy, and Richard’s model) in normal-muscled tropical breeds and double-muscled crossbred beef cattle in the tropics. Results: The BB crossbreds outperformed the KK and BRAH breeds by 50%–100% in live weight gains under tropical conditions. The crossbreds display the double-muscled effect and highlight the advantages of heterosis, making them suitable for upgrading local herds. The study’s findings on the growth characteristics of BB crossbred cattle were best described by the von Bertalanffy growth model, which had a high coefficient of determination (R2 &gt; 0.8) and yielded estimated mature weights of 527.5 kg for males and 518.5 kg for females. Conclusion: According to results, raising BB crossbreds in the tropics as a solution to ensure a sustainable beef supply could yield significant growth and economic benefits. Keywords: beef cattle, Belgian blue crossbreds, Brahman, double-muscled, growth performance, Kedah-Kelantan, non-linear regression growth functions.

Discovery of a Ni-based superalloy with low thermal expansion via machine learning

Traditional Ni-based superalloys are widely used due to their excellent high-temperature performance; however, their high thermal expansion at elevated temperatures limits their further application in the modern aerospace industry. To discover Ni-based superalloys with low thermal expansion for high-temperature environments, such as 900 °C. In this study, we developed a Support Vector Regression (SVR) model with a high coefficient of determination (R2 = 0.84) to predict the coefficient of thermal expansion (CTE) in Ni-based superalloys. Ten samples were selected and produced from a pool of candidates generated via genetic algorithm and the SVR model, revealing that seven of them exhibited superior CTE performance. Notably, the discovered alloy with the lowest CTE (11.0 e-6 °C-1 from RT to 900 °C) presented a remarkable 16.7 % reduction compared to the best performance in existing database. The finally selected alloy demonstrated both low thermal expansion and superior mechanical properties compared to traditional superalloys with low thermal expansion.

High-throughput near-infrared spectroscopy analysis of nutritional composition in sweet potato stem tips

Leaf-vegetable sweet potato is a nutritious food source, with stem tips as the harvest organ. However, the lack of rapid quantitative approaches for quality evaluation of stem tips largely limits the genetic improvement of leaf-vegetable sweet potato. This study aimed to establish a near-infrared spectroscopy (NIRS) methodology for the rapid analysis of both proximate (cellulose, crude protein, and soluble sugar) and functional (chlorogenic acid, total flavonoid content, and total phenolic content) components in the stem tips. Leveraging a dataset of 140 representative germplasm samples, we developed six robust NIRS models through the optimization of calibration sets and variable selection. These models exhibited exceptional accuracy, with high determination coefficients on calibration (R2C = 0.95–0.98), cross-validation (R2CV = 0.93–0.96), external validation (R2V = 0.91–0.95), and the ratio of prediction to deviation (RPD = 6.78–9.72). Overall, the NIRS models developed through this research facilitate high-throughput profiling of nutritional composition in stem tips, thereby enabling the swift identification of superior germplasm suited for leaf-vegetable sweet potato production.

Estimation of the coefficient of permeability as an example of the application of the Random Forest algorithm in Civil Engineering

A new world record for crude steel production was recorded in 2021, which increased by 3.8% over 2020. This also affected the amount of slag produced with this production. Total waste from industrial and construction production throughout the European Union accounts for as much as 48%. Therefore, waste management should provide for the recovery of as many resources as possible. European Union strategies in line with the circular economy objectives focus on ensuring policy coherence in the areas of climate, energy efficiency, construction and demolition waste management and resource efficiency. Slags are a material of interest to researchers in terms of their use in construction. Slags, on the one hand, are materials that are becoming better understood on the other hand, we are making sure of the heterogeneity of these materials. The characteristics of physical properties of slags are influenced by many factors, including the furnace split in which they are produced. This prompts the search for tools to help determine the parameters of slags based on already available data. The study aimed to verify the hypothesis that it is possible to determine the parameter of the filtration coefficient, relevant to applications in earth structures using the machine learning algorithm – Random Forest. In the study, two types of material were analysed: blast furnace slag and furnace slag. The results of the analysis yielded a high coefficient of determination (R2) – 0.84–0.92. This leads us to believe that the algorithm may prove useful in determining filtration parameters in slags.

All-atom modeling of methacrylate-based multi-modal chromatography resins for Langmuir constant prediction of peptides

In downstream processing, the intricate nature of the interactions between biomolecules and adsorbent materials presents a significant challenge in the prediction of their binding and elution behaviors. This complexity is further heightened in multi-modal chromatography (MMC), which employs two distinct binding mechanisms. To gain a deeper understanding of the involved interactions, simulating the adsorption of biomolecules on resin surfaces is a focal point of ongoing research. However, previous studies often simplified the adsorbent surface, modeling it as a flat or slightly curved plane without including a realistic backbone structure. Here, we introduce and validate two novel workflows aimed at predicting peptide binding behaviors in MMC, specifically targeting methacrylate-based resins. Our first achievement was the development of an all-atom model of a commercial MMC resin surface, incorporating its polymethacrylic backbone. Furthermore, we established and tested a workflow for rapid calculations of binding free energies (ΔG) with 10 linear peptides as target molecules. These ΔG calculations were effectively used to predict Langmuir constants, achieving a high coefficient of determination (R²) of 0.96. In subsequent benchmarking tests, our model outperformed established, simpler resin surface models in terms of predictive capabilities.

Development and Validation of an Enzyme-Linked Immunosorbent Assay-Based Protocol for Evaluation of Respiratory Syncytial Virus Vaccines.

Recently, respiratory syncytial virus (RSV) vaccines based on the prefusion F (pre-F) antigen were approved in the United States. We aimed to develop an enzyme-linked immunosorbent assay (ELISA)-based protocol for the practical and large-scale evaluation of RSV vaccines. Two modified pre-F proteins (DS-Cav1 and SC-TM) were produced by genetic recombination and replication using an adenoviral vector. The protocol was established by optimizing the concentrations of the coating antigen (pre-F proteins), secondary antibodies, and blocking buffer. To validate the protocol, we examined its accuracy, precision, and specificity using serum samples from 150 participants across various age groups and the standard serum provided by the National Institute of Health. In the linear correlation analysis, coating concentrations of 5 and 2.5 μg/mL of DS-Cav1 and SC-TM showed high coefficients of determination (r > 0.90), respectively. Concentrations of secondary antibodies (alkaline phosphatase-conjugated anti-human immunoglobulin G, diluted 1:2000) and blocking reagents (5% skim milk/PBS-T) were optimized to minimize non-specific reactions. High accuracy was observed for DS-Cav1 (r = 0.90) and SC-TM (r = 0.86). Further, both antigens showed high precision (coefficient of variation < 15%). Inhibition ELISA revealed cross-reactivity of antibodies against DS-Cav1 and SC-TM, but not with the attachment (G) protein.

Factors determining tropical upper-level cloud radiative effect in the radiative-convective equilibrium framework

Investigation of the major factors determining tropical upper-level cloud radiative effect (TUCRE) is crucial for understanding cloud feedback mechanisms. We examined the TUCRE inferred from the outputs of historical runs and AMIP runs from CMIP6 models employing a radiative-convective equilibrium (RCE). In this study, we incorporated the RCE model configurations of atmospheric dynamics and thermodynamics from the climate models, while simplifying the intricate systems. Using the RCE model, we adjusted the global mean surface temperature to achieve energy balance, considering variations in tropical cloud fraction, regional reflectivity, and emission temperature corresponding to each climate model. Subsequently, TUCRE was calculated as a unit of K/%, representing the change in global mean surface temperature (K) in response to an increment in the tropical upper-level clouds (%). Our RCE model simulation indicates that the major factors determining the TUCRE are the emission temperatures of tropical moist-cloudy and moist-clear regions, as well as the fraction of tropical upper-level clouds. The higher determination coefficients between TUCRE and both the emission temperature of tropical moist regions and the upper-level cloud fraction are attributable to their contribution to the trapping effect on the outgoing longwave radiations, which predominantly determines TUCRE. Consequently, the results of this study underscore the importance of accurately representing the upper-level cloud fraction and emission temperature in tropical moist regions to enhance the representation of TUCRE in climate models.

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.