R2 Values Research Articles

Establishing Optimal Machine Learning Models for Monitoring Water Quality in Vietnam’s Upper Ma River

This study aims to establish the optimal regression model for predicting total suspended solids (TSS) and Turbidity based on in situ data and spectral regions of Sentinel-2 images. Various machine learning models were evaluated, including Multilayer Perceptron Regression (MLPR), Random Forest Regression (RFR), AdaBoost Regression (ABR), Multiple Linear Regression (MLR), and K-Nearest Neighbors Regression (KNNR). These models were applied to different band combinations of spectral regions: visible (VIS), near-infrared (NIR), shortwave-infrared (SWIR), VIS+NIR (VNIR), and VIS+NIR+SWIR (VNIR+SWIR). The study results revealed that the MLR model, while not the best performer during training (R2 = 0.89 for TSS and R2 = 0.66 for turbidity), did not exhibit overfitting, with corresponding R² values in testing being 0.80 and 0.42, respectively. Variable selection for MLR models identified optimal spectral bands: B3, B5, B6, B8, B11, and B12 for TSS, and B4, B8, B11, and B12 for Turbidity. The final no-intercept multiple linear regression models achieved R2 = 0.88 for TSS and R2 = 0.62 for turbidity. Performance metrics for TSS were superior, with lower MAE, MSE, and RMSE compared to Turbidity. This study underscores the efficacy of using MLR models with selected spectral bands for accurate and generalizable predictions of TSS and turbidity.

A hybrid approach to enhance streamflow simulation in data-constrained Himalayan basins: combining the Glacio-hydrological Degree-day Model and recurrent neural networks

Abstract. The Glacio-hydrological Degree-day Model (GDM) is a distributed model, but it is prone to uncertainties due to its conceptual nature, parameter estimation, and limited data in the Himalayan basins. To enhance accuracy without sacrificing interpretability, we propose a hybrid model approach that combines GDM with recurrent neural networks (RNNs), hereafter referred to as GDM–RNN. Three RNN types – a simple RNN model, a gated recurrent unit (GRU) model, and a long short-term memory (LSTM) model – are integrated with GDM. Rather than directly predicting streamflow, RNNs forecast GDM's residual errors. We assessed performance across different data availability scenarios, with promising results. Under limited-data conditions (1 year of data), GDM–RNN models (GDM–simple RNN, GDM–LSTM, and GDM–GRU) outperformed standalone GDM and machine learning models. Compared with GDM's respective Nash–Sutcliffe efficiency (NSE), R2, and percent bias (PBIAS) values of 0.80, 0.63, and −4.78, the corresponding values for the GDM–simple RNN were 0.85, 0.82, and −6.21; for GDM–LSTM, they were 0.86, 0.79, and −6.37; and for GDM–GRU, they were 0.85, 0.8, and −5.64. Machine learning models yielded similar results, with the simple RNN at 0.81, 0.7, and −16.6; LSTM at 0.79, 0.65, and −21.42; and GRU at 0.82, 0.75, and −12.29, respectively. Our study highlights the potential of machine learning with respect to enhancing streamflow predictions in data-scarce Himalayan basins while preserving physical streamflow mechanisms.

Linkage Disequilibrium Decay in Selected Cattle Breeds

Linkage disequilibrium (LD) maps are fundamental tools for exploring the genetic basis of traits of interest in any species. Quantifying LD patterns in cattle breeds has been made possible due to the availability of huge quantities of SNPs through modern sequencing technology. The present research aims to determine and compare linkage disequilibrium levels at different distances on the genome of Sistani domestic cattle and subspecies of Bos taurus and Bos indicus based on genome SNP data. A total of 60 Bos indicus Sahiwal (dairy) and Nellore (beef), Bos taurus Hereford (beef) and Holstein (dairy), and Sistani cattle were sampled and genotyped using Illumina Bovine HD 770 k chip. To ensure the caliber of the sequencing, 10 samples (genetically sequenced cattle) were randomly chosen among all breeds represented. LD was evaluated at distances of 1–50 Kb, 50–100 Kb, 100–500 Kb, and 0.5–1 Mb, and average r2 values for all autosomes were calculated within distance classes. For all breeds, the average r2 was over 0.2 at distances less than 100 Kb, while for Sistani, Nellore, and Sahiwal, the average r2 was above 0.2 between 100 and 500 Kb. Furthermore, for all breeds, the average r2 exceeding 0.3 was noted at distances smaller than 50 Kb, while this amount for Holstein and Hereford was observed at distances between 50 and 100 Kb. In various breeds, greater changes in LD levels were observed (at <10 Kb distance). In this study, the Sistani breed showed LD decay patterns similar to indicine cattle (Nellore and Sahiwal), which may be due to the geographic proximity of the Sistan and Baluchestan province to Pakistan, the origin of indicine breeds (they may have had genetic or kinship relationships over a long historical period), or due to ascertainment bias in the SNP chips used.

Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India

Large-scale mapping of soil resources can be crucial and indispensable for several of the managerial applications and policy implications. With machine learning models being the most utilized modeling technique for digital soil mapping (DSM), the implementation of model-based deep learning methods for spatial soil predictions is still under scrutiny. In this study, soil continuous (pH and OC) and categorical variables (order and suborder) were predicted using deep learning–multi layer perceptron (DL-MLP) and one-dimensional convolutional neural networks (1D-CNN) for the entire state of Tamil Nadu, India. For training the deep learning models, 27,098 profile observations (0–30 cm) were extracted from the generated soil database, considering soil series as the distinctive stratum. A total of 43 SCORPAN-based environmental covariates were considered, of which 37 covariates were retained after the recursive feature elimination (RFE) process. The validation and test results obtained for each of the soil attributes for both the algorithms were most comparable with the DL-MLP algorithm depicting the attributes’ most intricate spatial organization details, compared to the 1D-CNN model. Irrespective of the algorithms and datasets, the R2 and RMSE values of the pH attribute ranged from 0.15 to 0.30 and 0.97 to 1.15, respectively. Similarly, the R2 and RMSE of the OC attribute ranged from 0.20 to 0.39 and 0.38 to 0.42, respectively. Further, the overall accuracy (OA) of the order and suborder classification ranged from 39% to 67% and 35% to 64%, respectively. The explicit quantification of the covariate importance derived from the permutation feature importance implied that both the models tried to incorporate the covariate importance with respect to the genesis of the soil attribute under study. Such approaches of the deep learning models integrating soil–environmental relationships under limited parameterization and computing costs can serve as a baseline study, emphasizing opportunities in increasing the transferability and generalizability of the model while accounting for the associated environmental dependencies.

Limited Sampling Strategy for Predicting the Area under Plasma Concentration–Time Curve of Nadolol in Healthy Subjects

AbstractNadolol is a hydrophilic β‐adrenoceptor blocker with a relatively long half‐life and negligible metabolism. It is a substrate of P‐glycoprotein and organic anion transporting polypeptide 1A2, and may serve as an in vivo probe drug for the assessment of drug–drug and food–drug interactions mediated by these transporters. In the present study, we aimed to develop limited sampling strategy (LSS) models for predicting the area under the plasma concentration–time curve (AUC0‐∞) of nadolol. Plasma concentration data (Ct) in healthy volunteers reported in four previous studies were randomly divided into a training dataset for model development (n = 15) and a test dataset for model validation (n = 16). By multiple linear regression analysis, we confirmed that four out of the eight models using two time points and all models using three time points met the acceptable criteria. In particular, the three time point models using (C3, C6, and C24) and (C4, C8, and C24) showed better predictive performances with r2 values of 0.983 and 0.980, respectively. In drug interaction studies of nadolol with itraconazole, rifampicin, grapefruit juice, and green tea extract, both LSS models accurately predicted the AUC0‐∞ with percent mean absolute error ≤11% and percent root mean square error ≤12%. In addition, using digitized pharmacokinetic data of nadolol, both LSS models were further validated by predicting the AUC0‐∞ in different doses. The results suggest that the LSS models using three time points allow a reliable prediction of AUC0‐∞ of nadolol in healthy individuals.

Revitalizing sumatriptan: transforming expired medication into a potent solution for corrosion control in acidic environments

The concerns regarding pharmaceutical waste, particularly expired drugs have prompted the exploration of their alternative applications. This research highlights the potential of repurposing expired pharmaceuticals as eco-friendly corrosion inhibitors for their industrial applications. An expired drug sumatriptan was investigated for its anticorrosive properties on MS in 1 M HCl medium. Electrochemical techniques, including potentiodynamic polarization and electrochemical impedance spectroscopy, were employed to evaluate the inhibition efficiency and complemented by surface characterization studies (SEM, EDAX, CA and AFM). The results demonstrated that sumatriptan acts as an effective mixed-type inhibitor, achieving maximum inhibition efficiencies of 94.51% and 93.85% from PDP and EIS studies respectively for 500 ppm at 318 K. The adsorption of sumatriptan on the metal surface followed the Langmuir isotherm having an R2 value of ∼1, suggesting a physical adsorption behaviour with Δ G° values ranging from −16.286 to −21.518 kJ mol−1. The results demonstrate the effectiveness of sumatriptan as a corrosion inhibitor and the potential of repurposing expired pharmaceuticals as eco-friendly and sustainable alternatives.

Logistics Management and Service Delivery in Humanitarian Organisations in Kenya: A Case Study of Kenya Red Cross Society

The goal was to assess the effects of supplier management and information management on the KRCS’s service delivery. Goal-Setting Theory is the main theory, supported by resource-based view and institutional theory. The study targeted a population of 300 individuals using a descriptive research approach. A total of 171 individuals were chosen for the sample size using stratified random sampling. Data was acquired by means of surveys. Basic statistics were used to assess the quantitative data, and SPSS version 27 was used for data analysis. Tables were used to display the data. Inferential statistics were used to illustrate the link between the variables. Throughout the course of the study, the research investigation was guided by a few ethical criteria. The R2 values for the relationship between supplier management and service delivery were 0.642, indicating a positive association, and 0.499, suggesting that changes in supplier management may account for 49.9% of the variation in service delivery. fundamental correlation between service delivery and information management (R = 0.595), with an R2 value of 0.431, while the DV was service delivery. To ensure successful logistics management and service delivery, KRCS should improve supplier management and service delivery, which is necessary, and that they can balance organizational goals and priorities with the supplier's perspective on challenges. This will improve the efficiency and effectiveness of the processes that the KRCS and communities in need of support use. KRCS service delivery can be increased by using auto- technologies. Independent variable technologies are welcomed since they will make product marking more accurate and efficient, which will improve operations.

Full-coverage estimation of CO2 concentrations in China via multisource satellite data and Deep Forest model.

Monitoring China's carbon dioxide (CO2) concentration is essential for formulating effective carbon cycle policies to achieve carbon peaking and neutrality. Despite insufficient satellite observation coverage, this study utilizes high-resolution spatiotemporal data from the Orbiting Carbon Observatory 2 (OCO-2), supplemented with various auxiliary datasets, to estimate full-coverage, monthly, column-averaged carbon dioxide (XCO2) values across China from 2015 to 2022 at a spatial resolution of 0.05° via the deep forest model. The 10-fold cross-validation results indicate a correlation coefficient (R) of 0.95 and a determination coefficient (R²) of 0.90. Validation against ground-based station data yielded R values of 0.93, and R² values reached 0.81. Further validation from the Greenhouse Gases Observing Satellite (GOSAT) and the Copernicus Atmosphere Monitoring Service Reanalysis dataset (CAMS) produced R² values of 0.87 and 0.80, respectively. During the study period, CO2 concentrations in China were higher in spring and winter than in summer and autumn, indicating a clear annual increase. The estimates generated by this study could potentially support CO2 monitoring in China.

Prediction of crippling load of I-shaped steel columns by using soft computing techniques

AbstractThis study is primarily aimed at creating three machine learning models: artificial neural network (ANN), random forest (RF), and k-nearest neighbour (KNN), so as to predict the crippling load (CL) of I-shaped steel columns. Five input parameters, namely length of column (L), width of flange (bf), flange thickness (tf), web thickness (tw) and height of column (H), are used to compute the crippling load (CL). A range of performance indicators, including the coefficient of determination (R2), variance account factor (VAF), a-10 index, root mean square error (RMSE), mean absolute error (MAE) and mean absolute deviation (MAD), are used to assess the effectiveness of the established machine learning models. The results show that all of the three ML (machine learning) models can accurately predict the crippling load, but the performance of ANN is superior: it delivers the highest value of R2 = 0.998 and the lowest value of RMSE = 0.008 in the training phase, as well as the highest value of R2 = 0.996 and the smaller value of RMSE = 0.012 in the testing phase. Additional methods, including rank analysis, reliability analysis, regression plot, Taylor diagram and error matrix plot, are employed to assess the models’ performance. The reliability index (β) of the models is calculated by using the first-order second moment (FOSM) technique, and the result is compared with the actual value. Additionally, sensitivity analysis is performed to check the impact of the input variables on the output (CL), finding that bf has the greatest impact on the crippling load, followed by tf, tw, H and L, in that order. This study demonstrates that ML techniques are useful for developing a reliable numerical tool for measuring the crippling load of I-shaped steel columns. It is found that the proposed techniques can also be used to predict other kinds of failures as well as different kinds of perforated columns.

Quantitative analysis of excipients to the permeability of BCS class III drugs.

BCS III drugs exhibit high solubility and low permeability, and some excipients were reported to increase their permeability. Although some permeability-enhancing excipients were investigated, permeability-enhancing strategy still need to be improved. Firstly, we established a database and analyzed the possible effects of excipients. Sodium lauryl sulfate (SLS) was found to be the most-used permeability-enhancing excipients. Moreover, the quantitative models for predicting Papp and Peff of BCS III drugs with SLS were developed, and statistically meaningful descriptors include molecular weight (MW), pKa, logP, solubility, hydrogen bond (HB) count, rotatable bond count (RBC), and topological polar surface area. The models demonstrated a good fit and effective predictive capability with all the correlation R2 values over 0.7. Hydrogen bonding remains the most significant factor in enhancing drug permeability with SLS, while hydrophilicity is also vital in this process. It was also found that MW, logP, pKa, and RBC play significant roles in paracellular transport. In summary, current research did the systematic and quantitative analysis of BCS III drugs and their excipients, which may accelerate formulation research on BCS III products.

Forecasting Blue and Green Water Footprint of Wheat Based on Single, Hybrid, and Stacking Ensemble Machine Learning Algorithms Under Diverse Agro-Climatic Conditions in Nile Delta, Egypt

The aim of this research is to develop and compare single, hybrid, and stacking ensemble machine learning models under spatial and temporal climate variations in the Nile Delta regarding the estimation of the blue and green water footprint (BWFP and GWFP) for wheat. Thus, four single machine learning models (XGB, RF, LASSO, and CatBoost) and eight hybrid machine learning models (XGB-RF, XGB-LASSO, XGB-CatBoost, RF-LASSO, CatBoost-LASSO, CatBoost-RF, XGB-RF-LASSO, and XGB-CatBoost-LASSO) were used, along with stacking ensembles, with five scenarios including climate and crop parameters and remote sensing-based indices. The highest R2 value for predicting wheat BWFP was achieved with XGB-LASSO under scenario 4 at 100%, while the minimum was 0.16 with LASSO under scenario 3 (remote sensing indices). To predict wheat GWFP, the highest R2 value of 100% was achieved with RF-LASSO across scenario 1 (all parameters), scenario 2 (climate parameters), scenario 4 (Peeff, Tmax, Tmin, and SA), and scenario 5 (Peeff and Tmax). The lowest value was recorded with LASSO and scenario 3. The use of individual and hybrid machine learning models showed high efficiency in predicting the blue and green water footprint of wheat, with high ratings according to statistical performance standards. However, the hybrid programs, whether binary or triple, outperformed both the single models and stacking ensemble.

Comprehensive Symptom Prediction in Inpatients With Acute Psychiatric Disorders Using Wearable-Based Deep Learning Models: Development and Validation Study.

Assessing the complex and multifaceted symptoms of patients with acute psychiatric disorders proves to be significantly challenging for clinicians. Moreover, the staff in acute psychiatric wards face high work intensity and risk of burnout, yet research on the introduction of digital technologies in this field remains limited. The combination of continuous and objective wearable sensor data acquired from patients with deep learning techniques holds the potential to overcome the limitations of traditional psychiatric assessments and support clinical decision-making. This study aimed to develop and validate wearable-based deep learning models to comprehensively predict patient symptoms across various acute psychiatric wards in South Korea. Participants diagnosed with schizophrenia and mood disorders were recruited from 4 wards across 3 hospitals and prospectively observed using wrist-worn wearable devices during their admission period. Trained raters conducted periodic clinical assessments using the Brief Psychiatric Rating Scale, Hamilton Anxiety Rating Scale, Montgomery-Asberg Depression Rating Scale, and Young Mania Rating Scale. Wearable devices collected patients' heart rate, accelerometer, and location data. Deep learning models were developed to predict psychiatric symptoms using 2 distinct approaches: single symptoms individually (Single) and multiple symptoms simultaneously via multitask learning (Multi). These models further addressed 2 problems: within-subject relative changes (Deterioration) and between-subject absolute severity (Score). Four configurations were consequently developed for each scale: Single-Deterioration, Single-Score, Multi-Deterioration, and Multi-Score. Data of participants recruited before May 1, 2024, underwent cross-validation, and the resulting fine-tuned models were then externally validated using data from the remaining participants. Of the 244 enrolled participants, 191 (78.3%; 3954 person-days) were included in the final analysis after applying the exclusion criteria. The demographic and clinical characteristics of participants, as well as the distribution of sensor data, showed considerable variations across wards and hospitals. Data of 139 participants were used for cross-validation, while data of 52 participants were used for external validation. The Single-Deterioration and Multi-Deterioration models achieved similar overall accuracy values of 0.75 in cross-validation and 0.73 in external validation. The Single-Score and Multi-Score models attained overall R² values of 0.78 and 0.83 in cross-validation and 0.66 and 0.74 in external validation, respectively, with the Multi-Score model demonstrating superior performance. Deep learning models based on wearable sensor data effectively classified symptom deterioration and predicted symptom severity in participants in acute psychiatric wards. Despite lower computational costs, Multi models demonstrated equivalent or superior performance than Single models, suggesting that multitask learning is a promising approach for comprehensive symptom prediction. However, significant variations were observed across wards, which presents a key challenge for developing clinical decision support systems in acute psychiatric wards. Future studies may benefit from recurring local validation or federated learning to address generalizability issues.

Relationship between the sense of nursing professional pride and adversity quotient, grit levels among nurses in blood purification centers: a multicenter cross-sectional study

ObjectiveThis study aims to examine nursing professional pride levels in blood purification center nurses, explore its relationship with adversity quotient and grit, and identify influencing factors.MethodsUsing convenience sampling, this study selected 231 nurses from blood purification centers at 11 tertiary hospitals in China during July to August 2023 as research subjects. A general data survey questionnaire, nurse professional pride scale, nurse adversity quotient self-assessment scale, and the Oviedo resilience scale were employed for the investigation. The results were statistically analyzed using descriptive analysis, t-tests, one-way ANOVA, Pearson correlation analysis, and multiple stepwise linear regression analysis.ResultsIn the blood purification center, the average scores were as follows: nursing professional pride (69.53 ± 15.21), adversity quotient (132.90 ± 41.10), and grit (30.95 ± 10.54). There was a significant positive correlation between nursing professional pride, adversity quotient, and grit (P < 0.001). Multiple linear regression analysis (n = 231) revealed that education background, professional title, average monthly income, adversity quotient, and grit significantly influenced nursing professional pride (P < 0.001). The R2 value indicates that this study accounted for 76.2% of the total variance in nursing professional pride.ConclusionThe findings of this study indicate that blood purification center nurses have a moderate level of nursing professional pride. The level of nursing professional pride can be enhanced by focusing on factors such as adversity quotient and grit.RegistrationThis study was approved by the Ethics Committee of Pidu District People's Hospital, Chengdu, China (2021 No. 231). All participants were informed of the relevant information and research purposes before their participation. Participants were also required to independently and anonymously complete the questionnaires.

Relationships between leadership style, career development, work stress, and turnover intention in a high-tech industry: Moderating role of psychological contract

Employee turnover is an unavoidable part of business development, costing important human resources and impeding long-term growth and strategy implementation. To maintain a steady workforce, businesses must prioritize understanding and addressing this issue. The objective of this study is to examine the impact of leadership style, career development, and work stress on turnover intention in the high-tech industry and to investigate the moderating role of psychological contracts in these relationships. The sample consists of 380 employees of the R&D departments in high-tech companies in Shenzhen, Guangdong province, China, mainly engaged in the electronic information, advanced manufacturing, and automation sectors. The data were gathered via an online questionnaire, and the Smart-PLS approach was used to analyze the data. The empirical findings indicate that transactional leadership, transformational leadership, and career development are significantly and negatively related to turnover intention, whereas work stress has a significant positive effect. The psychological contract moderates the relationship between career development and turnover intention; the higher the psychological contract, the stronger the relationship. In addition, the psychological contract moderates the relationship between work stress and turnover intention, whereby the higher the psychological contract, the weaker the relationship. However, no empirical evidence suggests that the psychological contract moderates the relationship between leadership styles and turnover intention. The R² value is 0.485, indicating moderate explanatory power, and with a GOF index of 0.492, the model also demonstrates a good fit with the data. This study offers considerable implications for both theoretical and practical applications.

Using data-driven models to simulate the performance of surfactants in reducing heavy oil viscosity.

There is a substantial body of literature exploring the challenges associated with exploring and exploiting these underground resources. Unconventional resources, particularly heavy oil reservoirs, are critical for meeting ever-increasing global energy demand. By injecting surfactants into heavy oil, chemically enhanced oil recovery (EOR) may enable emulsification, which may reduce the viscosity of heavy oil and facilitate extraction and transportation. In this work, a large experimental dataset, containing 2020 data points, was extracted from the literature for modeling oil-in-water (O/W) emulsion viscosity using machine learning (ML) methods. The algorithms used pressure, temperature, salinity, surfactant concentration, type of surfactant, shear rate, and crude oil density as inputs. For this purpose, five ML algorithms were selected and optimized, including adaptive boosting (AB), convolutional neural network (CNN), ensemble learning (EL), artificial neural network (ANN), and decision tree (DT). A combined simulated annealing (CSA) method was utilized to optimize all algorithms. With AARE, R2, MAE, MSE, and RMSE values of 8.982, 0.996, 0.004, 0.0002, and 0.0132, respectively, the ANN predictor exhibited higher accuracy in predicting O/W emulsion viscosity for total data (train and test subsets combined). A Monte-Carlo sensitivity analysis was also performed to determine the impact of input features on the model output. By using the proposed ML predictor, expensive and time-consuming experiments can be eliminated and emulsion viscosity predictions can be expedited without the need for costly experiment.

Delamination assessment and prediction in ultrasonic-assisted drilling of laminated hybrid composites

This study presents a novel application of the Random Forest (RF) algorithm to predict delamination in ultrasonic-assisted drilling (UAD) of carbon fiber reinforced polymers (CFRPs). It performs a multi-dimensional analysis of factors including graphene nanoplatelet (GNP) addition, ultrasonic vibration, cutting tool type, and feed rate on delamination damage. The RF algorithm was chosen for its ability to handle both regression and categorical tasks. The model demonstrated strong predictive performance, achieving an R² value of 0.9445 on test data, with a root mean squared error (RMSE) of 0.32% and a mean absolute error (MAE) of 0.29% relative to the average values. Analysis of variance (ANOVA), Sobol sensitivity, and Shapley additive explanations (SHAP) analysis were used to assess the impact of input parameters. Sobol identified the cutting tool type and feed rate as the most influential factors, contributing 37.7% and 34.3% to delamination variance, aligning with ANOVA findings. SHAP further confirmed the tooling type and feed rate as key factors, with contributions of 48.75% and 32.61%. The analyses revealed that GNPs increased delamination due to higher thrust forces, while ultrasonic vibration and high-cobalt tools reduced delamination. Optimal conditions were a feed rate of 0.08 mm/rev with an 8% cobalt tool and ultrasonic vibration, excluding GNPs.

Optimization of Filling Material Ratio in Yellow Phosphorus Slag Mine

Yellow phosphorus slag has been considered as a potential cement substitute for mine filling material due to its cementing activity; however, its slow setting and low early strength have limited broader use. This study investigates the grading, compactness, and strength of yellow phosphorus slag combined with tailing sand. Using yellow phosphorus slag as an aggregate, cement as a binder, and mixing tailing sand in different ratios, this study evaluates its feasibility as a coarse aggregate in mine backfill. The key findings are as follows. (1) The grading index of tailing sand was 0.5, aligning with Fuller grading, but it required mixing with coarse aggregates to enhance strength and reduce cement consumption. Yellow phosphorus slag, with a grading index of 0.97, does not match Fuller’s curve and thus benefits from mixing with tailing sand. (2) For mixtures of waste rock and tailings, the 5:5 ratio aligned closely with Fuller’s theory, showing optimal packing density and strength. Mixtures of yellow phosphorus slag and tailings at ratios of 3:7, 4:6, and 5:5 had R2 values of 0.73, 0.80, and 0.85, respectively, confirming reliable fit. The 5:5 mixture provided the best packing density and strength. (3) A new strength prediction model, accounting for aggregate, cement, and water effects, suggests that a 5:5 ratio with a 71% mass concentration and 1/7 ash–sand ratio meets industrial strength requirements. FLAC3D simulations indicated that cemented backfill reduces stress concentrations caused by excavation and supports stability during mining while also absorbing energy through compaction, creating favorable conditions for safe mining operations.

Removal of As(V) and Cr(VI) using quinoxaline chitosan schiff base: synthesis, characterization and adsorption mechanism.

Elevated Arsenic and Chromium levels in surface and ground waters are a significant health concern in several parts of the world. Chitosan quinoxaline Schiff base (CsQ) and cross-linked chitosan quinoxaline Schiff base (CsQG) were prepared to adsorb both Arsenate [As(V)] and Chromium [Cr(VI)] ions. The thermo-gravimetric analysis (TGA), X-ray diffraction analysis (XRD), and Fourier-transform infrared spectroscopy (FTIR) were used to investigate the prepared Schiff bases (CsQ) and (CsQG). The UV-VIS spectra showed a shift in the wavelength area of the modified polymer, indicating the reaction occurrence, besides the variation of the shape and intensity of the peaks. The XRD patterns showed the incensement of the amorphous characteristic. On the other hand, the thermal stability of the modified polymers is better according to TGA studies; also, the morphology of the modified chitosan was investigated before and after crosslinking (CsQ and CsQG) using a scanning electron microscope (SEM) where the surface was fall of wrinkles and pores, which then were decreased after cross-linking. Contact time, temperature, pH, and initial metal ion concentration were all studied as factors influencing metal ion uptake behavior. The Langmuir, Temkin, Dubinin-Radushkevich, and Freundlich isotherm models were used to describe the equilibrium data using metal concentrations of 10-1000 mg/L at pH = 7 and 1 g of adsorbent. The pseudo-first-order and pseudo-second-order kinetic parameters were evaluated. The experimental data revealed that the adsorption kinetics follow the mechanism of the pseudo-second-order equation with R2 values (0.9969, 0.9061) in case of using CsQ and R2 values (0.9989, 0.9999) in case of using CsQG, demonstrating chemical sorption is the rate-limiting step of the adsorption mechanism. Comparing the adsorption efficiency of the synthesized Schiff base and the cross-linked one, it was found that CsQ is a better adsorbent than CsQG in both cases of As(V) and Cr(VI) removal. This means that cross-linking doesn't enhance the efficiency as expected, but on the contrary, in some cases, it decreases the removal. In addition, the newly modified chitosan polymers work better in As(V) removal than Cr(VI); the removal is 22.33% for Cr(VI) and 98.36% for As(V) using CsQ polymer, whereas using CsQG, the values are 6.20% and 91.75% respectively. On the other hand, the maximum adsorption capacity (Qm) for As(V) and Cr(VI) are 8.811 and 3.003 mg/g, respectively, using CsQ, while in the case of using CsQG, the Qm value reaches 31.95 mg/g for As(V), and 103.09 mg/g for Cr(VI).

Predicting Green Water Footprint of Sugarcane Crop Using Multi-Source Data-Based and Hybrid Machine Learning Algorithms in White Nile State, Sudan

Water scarcity and climate change present substantial obstacles for Sudan, resulting in extensive migration. This study seeks to evaluate the effectiveness of machine learning models in forecasting the green water footprint (GWFP) of sugarcane in the context of climate change. By analyzing various input variables such as climatic conditions, agricultural data, and remote sensing metrics, the research investigates their effects on the sugarcane cultivation period from 2001 to 2020. A total of seven models, including random forest (RF), extreme gradient boosting (XGBoost), and support vector regressor (SVR), in addition to hybrid combinations like RF-XGB, RF-SVR, XGB-SVR, and RF-XGB-SVR, were applied across five scenarios (Sc) which includes different combinations of variables used in the study. The most significant mean bias error (MBE) was recorded in RF with Sc3 (remote sensing parameters), at 5.14 m3 ton−1, followed closely by RF-SVR at 5.05 m3 ton−1, while the minimum MBE was 0.03 m3 ton−1 in RF-SVR with Sc1 (all parameters). SVR exhibited the highest R2 values throughout all scenarios. Notably, the R2 values for dual hybrid models surpassed those of triple hybrid models. The highest Nash–Sutcliffe efficiency (NSE) value of 0.98 was noted in Sc2 (climatic parameters) and XGB-SVR, whereas the lowest NSE of 0.09 was linked to SVR in Sc3. The root mean square error (RMSE) varied across different ML models and scenarios, with Sc3 displaying the weakest performance regarding remote sensing parameters (EVI, NDVI, SAVI, and NDWI). Effective precipitation exerted the most considerable influence on GWFP, contributing 81.67%, followed by relative humidity (RH) at 7.5% and Tmax at 5.24%. The study concludes that individual models were as proficient as, or occasionally surpassed, double and triple hybrid models in predicting GWFP for sugarcane. Moreover, remote sensing indices demonstrated minimal positive influence on GWFP prediction, with Sc3 producing the lowest statistical outcomes across all models. Consequently, the study advocates for the use of hybrid models to mitigate the error term in the prediction of sugarcane GWFP.

Machine-learning-aided biochar production from aquatic biomass

AbstractModeling hydrothermal carbonization (HTC) and pyrolysis carbonization (PLC) for the conversion of biomass into high-quality biochar for various applications shows promise. Unlike the extensive modeling studies on lignocellulosic biomass, research on aquatic biomass (AB) had not been reported until now. In this study, we compiled 586 data points from existing literature and trained five tree-based models to predict the yields of hydrochar and pyrochar and their properties, including nitrogen recovery degree, energy density, energy recovery degree, and residual sulfur degree, based on 10 feedstock and process parameters. The random forest regression (RFR) model demonstrated the highest predictive accuracy among these models. It achieved R2 values ranging from 0.89 to 0.98 for hydrochar yield, nitrogen recovery degree of hydrochar, energy recovery degree of hydrochar, and residual sulfur degree of hydrochar. The extreme gradient boosting (XGB) model also showed exemplary performance, with R2 values between 0.84 and 0.94 for energy density of hydrochar, pyrochar yield, and nitrogen recovery degree of pyrochar. Results on feature importance highlighted that, beyond the well-documented impact of process parameters, the properties of biochar were significantly influenced by the elemental compositions, such as nitrogen and sulfur contents of the feedstock. The relationship between these factors was further elucidated using partial dependence plots. Finally, we used RFR model for hydrochar yield and XGB model for pyrochar yield as examples, to test generalization ability of developed models with new data, further explaining their application methods. Overall, this study provided valuable insights into predicting and understanding the HTC and PLC processes of AB to produce high-quality biochar for various applications using low resources and time costs. Besides, we presented an iterative learning application method where the developed models demonstrated exceptionally high performance with new data. This method is highly versatile and can be adopted across various directions in the field of machine learning. Graphical Abstract