High Coefficient Of Determination Research Articles

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, and irradiation time. Notably, the numerical data were extracted from the literature via relevant tables (directly) or graphs (indirectly) using the web-based tool WebPlotDigitizer. 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. 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).

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.

Assessment of pyrazole Schiff base's corrosion inhibition effectiveness incorporating oxadiazole moiety on mild steel in 1 M HCl: A holistic theoretical and experimental analysis

Modern industries place a high value on reasonably priced, eco-friendly products that effectively stop metal breakdown. Hence, our goal was to create an ecologically acceptable pyrazole Schiff base (PSB) that would effectively preserve mild steel (MS) during pickling and descaling procedures. We achieved this by integrating oxadiazole, which demonstrated good anti-corrosion capabilities. A complete investigation was conducted using spectroscopic analysis, electrochemical techniques, and gravimetric measurement in 1 M HCl solution. The PSB inhibitor displayed considerable corrosion inhibition on MS. PSB exhibited both cathodic and anodic properties in its dual adsorption pattern, as shown by the electrochemical investigation. Polarization resistance (Rp) values rose with the PSB concentration from 50 to 300 ppm, peaking at 300 ppm with values ranging from 31.01 to 141.21 Ω.cm². At 300 ppm and 298 K, the inhibitor effectiveness reached 90.12 %. Electrochemical testing validated the protection against mixed-type corrosion, while surface examinations (SEM, EDS, CA, AFM) showed that PSB developed a protective antioxidant layer on the MS surface. According to theoretical DFT calculations, PSB adsorbs onto the steel surface in a flat alignment. The remarkable precision of the adsorption process was validated by the high coefficient of determination (R2 = 1) for both Langmuir and other adsorption isotherms. Computational, chemical, and electrochemical analyses all support PSB's superiority as a corrosion inhibitor. The incorporation of aryl and heterocyclic rings improves the PSB structure's molecular stiffness, which facilitates effective adsorption onto the MS surface. This feature creates novel prospects for material protection and corrosion prevention, presenting PSB as a viable and eco-friendly substitute for conventional corrosion inhibitors in industrial applications.

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 &gt; 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 &gt; PPSO &gt; 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 &amp; 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.