Model R2 Research Articles

Silver nanoparticles modified titanium carbide MXene composite for RSM-CCD optimised chloride removal from water

Unsafe levels of chloride in drinking water can make it unpalatable, susceptible to infrastructure corrosion and prone to heavy metals mobility. Conventional chloride mitigation strategies are subjected to inefficient performance and costly operation, necessitating innovations for more sustainable, affordable, and scalable technologies. In this study, silver nanoparticles-modified Ti3C2 MXene nanocomposite (AgMX) is synthesised via dry impregnation method for effective removal of chloride ion from water. The composite physicochemical properties were thoroughly characterised using various analytical techniques, including TEM, SEM, XRD, EDS, BET, zeta potential and pHpzc analysis. The experimental testing was optimised using CCD-RSM method in terms of adsorbent dosage (0.2–2 g/L), reaction time (1–17 min), and chloride concentration (10–90 mg/L). Under optimal conditions (adsorbent:1.55 g/L, time: 12.19 min, & concentration: 52.17 mg/L), a promising chloride removal of 91.8 % was achieved. Langmuir model showed the best fit to adsorption isotherm (R2: 0.9852) comparing to Freundlich and Dubinin-Kaganer-Radushkevich (DKR) isotherms, while pseudo-second-order kinetic model offered the closest data to the experimental results (R2: 9893) compared to the pseudo-first-order, Elovich and Intraparticle diffusion models R2: 0.2335,0.1212 and 0.2050, respectively. The composite reusability and regeneration potential after four repeated cycles were found practically efficient as ≥ 68 % and ≥ 84 %, respectively. The outcomes of this study can demonstrate the efficiency of the formulated composite as a promising material for the sustainable treatment of chloride-contaminated water.

Research On Laptop Price Predictive Model Based on Linear Regression, Random Forest and Xgboost

In today's rapidly evolving technological environment, the portability and versatility of laptops have led to a significant growth in their user base as well, making them essential tools for individuals and businesses alike. Particularly since the COVID-19 pandemic, the rate of Work From Home (WFH) partially or overall WFH has exceeded 30%. Global laptop shipments have also far surpassed desktop PCs so far in 2017. Accurately predicting laptop prices is beneficial for retailers to devise competitive pricing strategies and for consumers to effectively budget and select the most suitable laptops. In this study, we used a dataset of 1320 samples to investigate the significance of features in a laptop price prediction model using linear regression, random forest, and XGBoost methods. We incorporated 13 features in the modeling process, including laptop brand, type, screen size, RAM, GPU, operating system, and weight. Three mathematical models were established to predict the price of laptop. By comparing the regression model metrics RMSE and R2 under linear regression, random forest and XGBoost models, the RMSE under the XGBoost model is 294.11 with an R2 of 0.85. It is evident that the XGBoost model exhibits the smallest RMSE and the highest R2 value closest to 1. This suggests that the XGBoost model provides the highest accuracy and best fit for the predictive model.

A novel land surface temperature reconstruction method and its application for downscaling surface soil moisture with machine learning

Downscaling of soil moisture data is important for high resolution hydrological modeling. Most downscaling studies in the literature have used spatially discontinuous land surface temperature (LST) maps as the main auxiliary parameter, which limits the creation of continuous soil moisture maps. The number of studies on soil moisture downscaling with machine learning that use gapless LST maps is limited. With this motivation, a hybrid reconstruction method has been proposed in this study to practically obtain continuous LST maps, which are then used to produce high resolution surface soil moisture (SSM) datasets. The proposed method is shown to have high mean performance with R2 and RMSE values of 0.94 and 1.84°K, respectively, for the period between 2019 and 2022. The developed reconstructed LST maps were then used to downscale original 9 km spatial resolution soil moisture datasets of SMAP L3 and SMAP L4 with Random Forest (RF) machine learning algorithm. The RF model were run with four different rainfall datasets, and the MSWEP rainfall dataset was found to produce the best results. The use of antecedent rainfall values as input variables in machine learning models has been shown to improve the performance of the models R2 0.76 to 0.93. The accuracy of the downscaled data was later evaluated for Western Anatolia Basins (WAB) in Türkiye with 31 in-situ stations. The downscaled SMAP L4 had good average statistical indicators R (0.815 ± 0.1), RMSE (0.09 ± 0.047 cm3/cm3), and ubRMSE (0.058 ± 0.025 cm3/cm3). Downscaled SMAP L3 was also validated with in-situ observations with satisfactory R (0.79 ± 0.074), RMSE (0.09 ± 0.043 cm3/cm3), and ubRMSE (0.06 ± 0.026 cm3/cm3) statistics. Furthermore, the performance of the downscaled SMAP L3 was also cross validated with SMAP + Sentinel 1 (L2) dataset between 2019 and 2022. The mean statistics of R (0.761 ± 0.11) and Root Mean Squared Difference (RMSD) (0.05 ± 0.014 cm3/cm3) between downscaled SMAP L3 and L2 data revealed that the new reconstruction method of LST used in the RF model for downscaling of soil moisture performed well to obtain high resolution soil moisture datasets. The proposed technique also overcame the difficulties associated with coastal regions where data was masked for quality considerations, by not only enhancing overall spatial resolution but also filling these data gaps and giving a complete SSM coverage.

Predicting Personal Exposure to PM2.5 Using Different Determinants and Machine Learning Algorithms in Two Megacities, China

The primary aim of this study is to explore the utility of machine learning algorithms for predicting personal PM2.5 exposures of elderly participants and to evaluate the effect of individual variables on model performance. Personal PM2.5 was measured on five consecutive days across seasons in 66 retired adults in Beijing (BJ) and Nanjing (NJ), China. The potential predictors were extracted from routine monitoring data (ambient PM2.5 concentrations and meteorological factors), basic questionnaires (personal and household characteristics), and time-activity diary (TAD). Prediction models were developed based on either traditional multiple linear regression (MLR) or five advanced machine learning methods. Our results revealed that personal PM2.5 exposures were well predicted by both MLR and machine learning models with predictors extracted from routine monitoring data, which was indicated by the high nested cross-validation (CV) R2 ranging from 0.76 to 0.88. The addition of predictors from either the questionnaire or TAD did not improve predictive accuracy for all algorithms. The ambient PM2.5 concentrations were the most important predictor. Overall, the random forest, support vector machine, and extreme gradient boosting algorithms outperformed the reference MLR method. Compared with the traditional MLR approach, the CV R2 of the RF model increased up to 7% (from 0.82±0.13 to 0.88±0.10), while the RMSE reduced up to 18% (from 19.8±5.4 to 16.3±4.5) in BJ.

Development of an Abbreviated Model for Predicting Functional Movement Screen Score Within Tactical Populations.

Thompson, MB, Johnson, QR, Lindsay, KG, and Dawes, JJ. Development of an abbreviated model for predicting functional movement screen score within tactical populations. J Strength Cond Res 38(3): 607-611, 2024-The Functional Movement Screen (FMS) is a tool commonly used to identify compensations when performing 7 specific movement patterns. Timely administration of the full FMS is largely dependent on the practitioner's familiarity and experience with the screening battery. When working in populations that are time-poor (i.e., tactical professionals), administration of the full movement pattern battery is not always feasible. The purpose of this study was to determine which, if any, combination of movement patterns that comprise the FMS could be used to predict total score on this screen among first responders. Functional Movement Screen scores for 99 male subjects (mean ± SD; age: 37.55 ± 9.83 years; height: 180.38 ± 6.59 cm; and body mass: 97.87 ± 15.32 kg) and 9 female subjects (age: 33.22 ± 3.99 years; height: 172.11 ± 8.19 cm; and body mass: 83.99 ± 14.09 kg) from a single law enforcement and fire agency were used for this analysis. Subjects performed the full FMS, which consisted of the following movement patterns: deep squat (DS), hurdle step (HS), in-line lunge (ILL), shoulder mobility (SM), active straight leg raise (ASLR), trunk stability push-up (PU), and rotary stability (RS). A stepwise regression was used to determine the best predictors of the FMS 7-pattern model based on the model's R2. Cronbach's alpha and Guttman's lambda-2 were used to determine the reliability of the proposed models. The regression indicated that a 4-pattern model consisting of DS, ILL, SM, and PU was sufficient to predict approximately 84% of the full model (adjusted R2 = 0.84, p ≤ 0.001). This 4-pattern model was shown to be reliable with the 7-pattern model (α = 0.93, λ = 0.93). Using this modified version of the FMS may allow practitioners working in tactical populations a time-efficient method of identifying dysfunctional movement and determine whether the full 7-pattern model of the FMS should be considered.

Evolving support vector regression based on improved grey wolf optimization for predicting settlement during construction of high-filled roadbed

The reconstruction and expansion of highways have become the dominant focus of current infrastructure development. However, this process often results in significant differential settlement between the new and old roadbed, leading to various pavement issues. Machine learning methods undoubtedly provide a new approach for predicting roadbed settlement. However, most of the current research on predicting roadbed settlement focuses on post-construction settlement, and there is relatively little involvement in predicting settlement during roadbed filling. Therefore, it is necessary to accurately predict the settlement during the roadbed construction period. This study establishes an improved settlement prediction model for roadbed construction using a combination of differential evolution (DE), grey wolf optimization (GWO), and support vector regression (SVR). The model incorporates cumulative filling time, cumulative filling height, and daily rainfall as input parameters, while the corresponding cumulative settlement serves as the output. To evaluate the effectiveness of the proposed DE-GWO-SVR model, the training set consists of two weeks' data of sample data, while the latter week's data is used as the test set to predict settlement. Meanwhile, the predictive performance is assessed using various evaluation metrics. The coefficients of determination (R2) for the settlement prediction results were all higher than 0.95. The findings indicate that the settlement predictions made by the model align well with the measured values. Moreover, the conventional SVR and artificial neural network (ANN) models for predicting roadbed settlement have been established. The R2 for the conventional SVR model is 0.7793, while that for the DE-GWO-SVR model is 0.9977. The results demonstrate that the optimized DE-GWO-SVR model outperforms the conventional approaches across all evaluation criteria. This affirms the high prediction accuracy and strong generalization ability of the established DE-GWO-SVR settlement prediction model. Therefore, the DE-GWO-SVR settlement prediction model can realize high-precision prediction of settlement during roadbed construction. This is conducive to the timely adjustment of the filling scheme during the roadbed construction to ensure the safety and stability of the high-filled roadbed.

Extracting shallow water depth from the fusion of multi-temporal ICESat-2 data and multi-spectral imageries

With the development of satellite remote sensing and laser altimetry data, the fusion of laser altimetry data and multi-spectral images for shallow water depth inversion has become an economically convenient way. However, there are few methods that take into account the temporal dimension of data to integrate the results of water depth inversion in multiple temporal phases. In response to this issue, this article proposed a shallow water depth inversion method that integrates multi-source and multi-temporal remote sensing data. This method utilized the random forest (RF) algorithm to estimate the water depth values at different time, taking into account the contextual information of Sentinel-2 imagery and using the overall least squares as the theoretical model to fuse the multi-temporal water depth inversion results. This article took the Yongle Islands in the South China Sea as the research area and conducted the shallow water inversion experiments using ICESat-2 (Ice, Cloud and Land Elevation Satellite 2) data from 5 temporal phases and one Sentinel-2 imagery. The results show that the mean value of RMSE (root mean square error) and R2 (determination coefficient) of the proposed method using single temporal imagery was 1.53m and 0.7610, which outperformed the inversion accuracy of traditional methods that ignore image context information. The RMSE and R2 of the multi-temporal fusion model was 1.15m and 0.8622, which was 0.08m and 0.0199m higher than existing median filtering fusion method.

Sorptive Removal of 133Ba from Aqueous Solution Using a Novel Cellulose Hydroxyapatite Composite Derived from Cigarette Waste

133Ba is a hazardous radionuclide generated during the operation of nuclear power plants. 133Ba needs to be removed from waste solutions because its half-life (10.55 years) and gamma energy pose a significant threat to human health. Cigarette butt (CB) is a waste that causes serious environmental problems. Various types of adsorbent materials are prepared based on the cellulose in its structure. The focus of the present study is to synthesize a novel composite material derived from CBs and to investigate its 133Ba removal capability. Microfibrillated cellulose (MFC) obtained from CBs was modified with hydroxyapatite (HAp) via the co‐precipitation method and converted into a composite adsorbent for the removal of 133Ba ions. Response surface methodology (RSM) based on Box‐Behnken Design (BBD) was employed for the examination of process variables such as initial pH, metal concentration, and adsorbent amount on 133Ba sorption. XRD and FTIR data confirmed the successful isolation of cellulose and the modification of the cellulose surface with HAp. The model F-value (100.04) and R2 (0.99) suggested that the proposed model was significant. Optimum conditions were determined as initial pH of 8, contact time of 134 min, and concentration of 0.01 mol/L, and the barium sorption capacity of MFC‐HAp was found to be 0.75 mmol/g under these conditions. The maximum monolayer barium sorption capacity was determined to be 2.92 mmol/g. Combining cellulose and HAp to be a novel composite adsorbent is useful for reusing CB waste and promising for removing 133Ba ions from aqueous solutions.

Application of near-infrared spectroscopy for hay evaluation at different degrees of sample preparation.

A study was conducted to quantify the performance differences of the nearinfrared spectroscopy (NIRS) calibration models developed with different degrees of hay sample preparations. A total of 227 imported alfalfa (Medicago sativa L.) and another 360 imported timothy (Phleum pratense L.) hay samples were used to develop calibration models for nutrient value parameters such as moisture, neutral detergent fiber, acid detergent fiber, crude protein, and in vitro dry matter digestibility. Spectral data of hay samples prepared by milling into 1-mm particle size or unground were separately regressed against the wet chemistry results of the abovementioned parameters. The performance of the developed NIRS calibration models was evaluated based on R2, standard error, and ratio percentage deviation (RPD). The models developed with ground hay were more robust and accurate than those with unground hay based on calibration model performance indexes such as R2 (coefficient of determination), standard error, and RPD. Although the R2 of calibration models was mainly greater than 0.90 across the feed value indexes, the R2 of cross-validations was much lower. The R2 of cross-validation varies depending on feed value indexes, which ranged from 0.61 to 0.81 in alfalfa, and from 0.62 to 0.95 in timothy. Estimation of feed values in imported hay can be achievable by the calibrated NIRS. However, the NIRS calibration models must be improved by including a broader range of imported hay samples in the modeling. Although the analysis accuracy of NIRS was substantially higher when calibration models were developed with ground samples, less sample preparation will be more advantageous for achieving rapid delivery of hay sample analysis results. Therefore, further research warrants investigating the level of sample preparations compromising analysis accuracy by NIRS.

Characterizing the 3-D structure of each building in the conterminous United States

The three-dimensional building structure is crucial for understanding human activities and environmental interactions. Previous studies have estimated building height on a grid-scale, lacking height estimation results concurrently over large areas and at fine resolution. Here we mapped the height of each building in the conterminous US in 2020, using integrated multi-source datasets (i.e., satellite observations and geometry features). Our proposed model for height estimation is proven reliable (i.e., The model's R2 is 0.82, and RMSE is 3.35 m in the conterminous US) and superior in cross-validation with other existing datasets at different scales. We found an explicit pattern of buildings decaying from the urban core to rural areas, which is more noticeable in big cities (e.g., New York). Furthermore, we proposed the seamless and explicit Urban Canopy Parameters (UCPs) datasets, including six UCPs (e.g., building area fraction and height -to-width ratio) based on our height dataset. As key parameters used in climate models, our UCPs improved the fine-grained description of 3-D urban structures compared with those traditionally used. Overall, our building-by-building height dataset and UCP datasets reveal the significant heterogeneity of urban underlying surface, which can help socioeconomic and climatological urban studies.

Improvement of Winter Wheat Aboveground Biomass Estimation Using Digital Surface Model Information Extracted from Unmanned-Aerial-Vehicle-Based Multispectral Images

Aboveground biomass (AGB) is an important indicator for characterizing crop growth conditions. A rapid and accurate estimation of AGB is critical for guiding the management of farmland and achieving production potential, and it can also provide vital data for ensuring food security. In this study, by applying different water and nitrogen treatments, an unmanned aerial vehicle (UAV) equipped with a multispectral imaging spectrometer was used to acquire images of winter wheat during critical growth stages. Then, the plant height (Hdsm) extracted from the digital surface model (DSM) information was used to establish and improve the estimation model of AGB, using the backpropagation (BP) neural network, a machine learning method. The results show that (1) the R2, root-mean-square error (RMSE), and relative predictive deviation (RPD) of the AGB estimation model, constructed directly using the Hdsm, are 0.58, 4528.23 kg/hm2, and 1.25, respectively. The estimated mean AGB (16,198.27 kg/hm2) is slightly smaller than the measured mean AGB (16,960.23 kg/hm2). (2) The R2, RMSE, and RPD of the improved AGB estimation model, based on AGB/Hdsm, are 0.88, 2291.90 kg/hm2, and 2.75, respectively, and the estimated mean AGB (17,478.21 kg/hm2) is more similar to the measured mean AGB (17,222.59 kg/hm2). The improved AGB estimation model boosts the accuracy by 51.72% compared with the AGB directly estimated using the Hdsm. Moreover, the improved AGB estimation model shows strong transferability in regard to different water treatments and different year scenarios, but there are differences in the transferability for different N-level scenarios. (3) Differences in the characteristics of the data are the key factors that lead to the different transferability of the AGB estimation model. This study provides an antecedent in regard to model construction and transferability estimation of AGB for winter wheat. We confirm that, when different datasets have similar histogram characteristics, the model is applicable to new scenarios.

Quantitative structure-electrochemistry relationship modeling of a series of anticancer agents using MLR and ANN approaches

Abstract QSPR is a powerful tool for elucidating the correlation between chemical structure and property for both natural and synthesized compounds. In the present work, the half-wave reduction potential for a set of aziridinylquinones (Anticancer Agents [AA]) is modelled using a quantitative structure-electrochemistry relationship (QSER) based on multilinear regression (MLR) and artificial neural network (ANN). Molecular descriptors introduced in this work were computed using the Dragon software (V5). Before the model’s generation, using the Kennard and Stone algorithm, the data set of 84 aziridinylquinones was divided into training and prediction sets consisting of 70 % and 30 % of data points. Quantitative Structure Electrochemistry Relationship (QSER) models were developed using the Genetic Algorithm Multiple Linear Regressions (GA-MLR) and an Artificial Neural Network (ANN). The coefficient of determination (R 2) and Root Mean Squared Error of prediction (RMSE) were mentioned to demonstrate the QSER model’s prediction abilities. Calculated R 2 and RMSEval values for the MLR model were 0.858 and 0.054, respectively. The R 2 and RMSEval values for the ANN training set were calculated to be 0.914 and 0.050, respectively. Findings show that GA is a powerful tool for selecting variables in QSER analysis. Comparing the two employed regression methods showed that ANN is superior to MLR in predictive ability.

X-ray luminescent nanocomposite LaF3:Tb/Citr@[Ru(bpy)2(nic)2]2+ as a model targeted delivery system for photopharmacology and photodynamic therapy

The study is devoted to the development of the concept of targeted delivery of pharmacological agents. A nanocomposite (NC) – (LaF3:Tb/Citr@RuBiNic), which is formed by luminescent nanoparticles (ScNPs) – LaF3: Tb/Citr and a photosensitive substance – [Ru(bpy)2(nic)2]2+ (where bpy = 2,2′ bipyridine and nic = nicotine (3-[(2S)-1-methylpyrrolidin-2-yl] pyridine) – (RuBiNic), in response to X-ray irradiation, emits quanta of visible light, as a result is released of a pharmacological agent – nicotine from the NC surface. In principle, when implementing the methods of photopharmacology, or photodynamic therapy, it allows controlling the desired physiological or therapeutic effect in a specific place and at the right time with high specificity of influence.To conduct research, LaF3:Tb/Citr nanoparticles were synthesized, and RuBiNic was used as a model photosensitive substance. The size and morphology (TEM), surface (FTIR-spectroscopy), thermogravimetric (TG/DTG/DTA) and X-ray structural analysis were carried out, SSA value and acid-base characteristics of the surface were determined. In the static mode at 25°С, the processes of adsorption immobilization of the RuBiNic complex on the surface of nanoparticles in the medium of an aqueous solution were investigated. Diffuse kinetics models, pseudo-first- and pseudo-second-order kinetics models, Langmuir and Freundlich models were used to quantitatively describe the equilibrium adsorption processes. The kinetics of RuBiNic adsorption is characterized by a mixed-diffusion mechanism. The pseudo-second-order model was also found to have a higher correlation coefficient than the pseudo-first-order model, confirming the chemisorption process. The data of fitting the adsorption isotherm according to the Langmuir adsorption model (r2 = 0.99) indicate chemisorption of RuBiNic on the surface of ScNPs LaF3:Tb/Citr with a maximum adsorption capacity of 7.47 mg⋅g−1.The obtained data are useful for optimizing the conditions of adsorption immobilization of molecules of photosensitive substances on the surface of phosphors based on lanthanum fluoride. The LaF3:Tb/Citr@RuBiNic system shows potential for future applications in photopharmacology and X-ray photodynamic therapy.

Research on plasma modified fly ash denitration

The effects of reactor parameters and process parameters on the denitration rate of modified fly ash in different gas atmospheres were studied by using a dielectric barrier plasma reactor and using orthogonal experiments. The characteristics of modified fly ash were analyzed using scanning electron microscope, specific surface area analyzer, X-ray diffraction, Boehm titration and Fourier transform infrared spectroscopy. The experimental data were processed by variance analysis and linear regression to induce the denitration mechanism. R2 of the linear regression analysis model is 0.789, which means that the adsorption pore size, acid groups and basic group can explain 78.9% of the change in denitration rate. The basic group will have a significant positive impact on the denitration rate, and the adsorption pore size and acidic group will have a significant negative impact on the denitration rate. Through variance analysis of the experimental data, it was found that the input power and discharge gap have a significant effect on the denitration rate, but the ionization time and discharge length have no significant effect. The input power affects the denitration rate by impacting the basic group, and the discharge gap affects the denitration rate by influencing the adsorption pore size. There are three denitration mechanisms on the surface of fly ash: physical adsorption, chemical adsorption and absorption process. Among them, chemical adsorption is the main mechanism of action, accounting for approximately 60.86%.

LSTM-based deformation forecasting for additional stress estimation of existing tunnel structure induced by adjacent shield tunneling

A redistribution of stress is caused by the excavation of a new tunnel within the soil surrounding the existing shield tunnel structure. This stress redistribution results in alterations to the soil and water loads around the existing tunnel structure, ultimately impacting its stress and deformation. The deformation response of existing tunnels is primarily focused on in most existing studies, with limited research conducted on the coupling system of new tunnel excavation, soil stress state, and deformation and stress of existing tunnels. This paper proposed an innovative methodology to predict deformation in existing tunnel structures and estimate additional stress during the excavation of a new shield tunnel. The reliability of the suggested calculation method was verified using measured internal force and convergence data of the structures. The results demonstrated that the Long Short-Term Memory (LSTM) model could more effectively establish the temporal correlation between the excavation parameters of new tunnels and the deformation of existing tunnels. Compared to the Recurrent Neural Network (RNN) model, the R2 of the LSTM model increased by 11.81% in the prediction of the tunnel waist and 9.38% in the prediction of the tunnel crown. The Light gradient boosting machine (LightGBM) algorithm was used to analyze the crucial factors of deformation of the existing tunnel structures during shield tunneling, and the results showed that the relative position and the average earth pressure were the essential factors. Furthermore, a calculation method for the additional stress of existing tunnel structures was proposed based on the load structure model. The method assumed that the additional load was distributed rectangularly at the top and bottom of the existing tunnel, and triangularly at the waist of the tunnel. The comparison between the calculated results and the measured results indicated that the phased characteristics of the internal force changes in the existing tunnel structure during the construction of a new tunnel could be accurately reflected by this method.

Modelo predictivo en comprensión de lectura impresa y digital en estudiantes universitarios

Introduction: Reading comprehension (CL) is complex and in its process, faculties related to perception, motivation, attention, and interpretation are involved. Our goal was to design a predictive model in understanding printed and digital reading, based on the Theory of the six readings, in students from the universities of Manizales, Colombia and Kharkiv, Ukraine. Methods: During a period of 6 weeks, 70 students participated: Colombians (n=40) and Ukrainians (n=30), in which intervention was generated with the "six reading theory" course together with cognitive evaluation with the MoCA Test (Montreal Cognitive Assessment) instrument. Data were analyzed by descriptive statistics for categorical and numerical variables, multivariate analysis and prediction by multinomial regression techniques. Results: The intervention group that received training in the Theory of Six Readings generated better overall results in reading comprehension, a situation that correlated with the predictive model (r2=0.67; global classification index = 0.76). Conclusion: Implementing a print and digital reading program based on the theory of the six readings can allow the optimization of reading proficiency in public and private universities.

Response surface methodology for low-energy consumption electro-Fenton process for xanthene dye electrochemical degradation

Erythrosine B (EB) is a dye widely used in the food and textile industries. Despite many studies that have been proposed in the literature about the electrochemical oxidation of dyes, few studies considered such recalcitrant xanthene compound, although it has been recognized as a threat to health and the environment. Then, this study investigates the oxidation of EB by a homogeneous electro-Fenton process using iron (II) sulfate heptahydrate as a catalyst, carbon felt cathode, and Ti/RuO2 anode. The treated synthetic wastewater contains 100 mg L−1 of EB and has a pH = 3. The effects of three independent variables have been considered for process optimization, such as applied current intensity (0.1–0.5 A), iron concentration (1–10 mM), and stirring rate (100–1000 rpm). Their interactions were investigated considering response surface methodology (RSM) based on Doehlert design as optimization method. EB removal efficiency and energy consumption were considered as model responses after 30 min of electrolysis. Analysis of variance (ANOVA) revealed that the quadratic model was adequately fitted to the experimental data with R2 (0.9819), adj-R2 (0.9276), and low Fisher probability (< 0.0181) for the EB removal model, and R2 (0.9968), adj-R2 (0.9872) and low Fisher probability (< 0.0014) relative to the energy consumption model, suggesting a robust statistical significance. The energy consumption model significantly depends on current density, as expected. The foregoing results obtained by RSM led to the following optimal conditions for EB degradation: current intensity of 0.2 A, iron concentration of 9.397 mM, and stirring rate of 500 rpm, which gave a maximum decolorization rate of 98.15% with a minimum energy consumption of 0.74 kWh m−3 after 30 min of electrolysis. The competitiveness of the electro-Fenton process has been confirmed by the literature analysis proposed as well as by the preliminary economic analysis proposed in the second section of the study.Graphical abstract

Modeling human transmissibility via nighttime light remote sensing for Hyphantria cunea propagation pattern prediction

Human mobility and goods transportation are essential processes that lead to pest propagation, and accurately characterizing spatial transmissibility has always been a popular research topic. Therefore, we propose a spatial modeling and quantification method based on nighttime light remote sensing to reflect human transmissibility and integrate this method with other variables to map the pest spreading risk. First, residential areas in China are extracted from annual NPP/VIIRS datasets. Then, the human transmissibility index is established based on GIS cost-distance analysis with impedance parameters related to geographical and land use features. Consequently, the propagation risk of the human-induced pest Hyphantria cuneas in China is assessed with a binary logistic model. The results show that the human transmissibility index obtained from nighttime light remote sensing images can be used to assess human accessibility in a continuous way in the field. When the Hyphantria cunea risk prediction index was adopted, the accuracy of pest range prediction improved by nearly 5% for occurrence and 3.3% overall, with a significant difference observed in a Wilcoxon signed-rank test compared with the effect of anthropogenic factors quantified considering basic geographic information and infrastructure features. In addition, all the model R2 values increased at different levels. The human transmissibility index can be used to effectively characterize the spatial pattern of human impacts and displays high accuracy in pest risk prediction. Understanding the human transmissibility mechanism of pests is important and provides a reference for achieving early warnings regarding pest spreading, performing potential host analyses for human infection caused by unknown pathogens, and modeling human ecological interference.

Modeling of Cotton Yield Estimation Based on Canopy Sun-Induced Chlorophyll Fluorescence

Cotton yield estimation is of great practical significance to producers, allowing them to make rational management decisions. At present, crop yield estimation methods mainly comprise traditional agricultural yield estimation methods, which have many shortcomings. As an ideal “probe” for detecting crop photosynthesis, sun-induced chlorophyll fluorescence (SIF) can directly reflect the dynamics of actual crop photosynthesis and has the potential to predict crop yield, in order to realize cotton yield estimation based on canopy SIF. In this study, we set up field trials with different nitrogen fertilizer gradients. The changes of canopy SIF and the physiological parameters of cotton in different growth periods were analyzed. To investigate the effects of LAI and AGB on canopy SIF estimation of cotton yield, four algorithms, Ada Boost (Adaptive Boosting), Bagging (Bootstrap Aggregating), RF (Random Forest), and BPNN (Backpropagation Neural Network), were used to construct cotton yield estimation models based on the SIF and SIFy (the normalization of SIF by incident photosynthetically active radiation) for different time and growth periods. The results include the following: (1) The effects of the leaf area index (LAI) and aboveground biomass (AGB) on cotton canopy SIF and cotton yield were similar. The correlation coefficients of LAI and AGB with cotton yield and SIF were significantly positively correlated with each other starting from the budding period, reaching the maximum at the flowering and boll period, and decreasing at the boll period; (2) In different monitoring time periods, the R2 of the cotton yield estimation model established based on SIF and SIFy showed a gradual increase from 10:00 to 14:00 and a gradual decrease from 15:00 to 19:00, while the optimal observation time was from 14:00 to 15:00. The R2 increased with the progression of growth from the budding period to the flowering and boll period and decreased at the boll period, while the optimum growth period was the flowering and boll period; (3) Compared to SIF, SIFy has a superior estimation of yield. The best yield estimation model based on the RF algorithm (R2 = 0.9612, RMSE = 66.27 kg·ha−1, RPD = 4.264) was found in the canopy SIFy of the flowering and boll period at 14:00–15:00, followed by the model utilizing the Bagging algorithm (R2 = 0.8898) and Ada Boost algorithm (R2 = 0.8796). In summary, SIFy eliminates the effect of PAR (photosynthetically active radiation) on SIF and can further improve the estimation of SIF production. This study provides empirical support for SIF estimation of cotton yield and methodological and modeling support for the accurate estimation of cotton yield.

Dynamic Foot Pressure During Walking: A Potential Indicator of Bone Mineral Density.

Physical skeletal loading can affect the bone mineral density (BMD). This study investigated the association between BMD and dynamic foot pressure during gait. A total of 104 patients (mean age, 62.6 ± 12.4 years; 23 male and 81 female) who underwent dual x-ray absorptiometry and pedobarography were included. BMD values of the lumbar spine, femoral neck, and total femur were assessed. The mean and maximum pressures were measured at the hallux, lesser toes, 1st metatarsal head, 2nd and 3rd metatarsal heads, 4th and 5th metatarsal heads, midfoot, medial heel, and lateral heel. Multivariable regression analysis was performed to identify factors significantly associated with BMD. The lumbar spine BMD was significantly associated with the mean pressure at the 4th and 5th metatarsal heads (p = 0.041, adjusted R 2 of model = 0.081). The femoral neck BMD was significantly associated with the maximum pressure at the 2nd and 3rd metatarsal heads (p = 0.002, adjusted R 2 = 0.213). The total femoral BMD also showed a significant association with the maximum pressure at the 2nd and 3rd metatarsal heads (p = 0.003, adjusted R 2 = 0.360). Foot plantar pressure during gait was significantly associated with BMD, and could potentially be used to predict the presence of osteoporosis. Prognostic Level III . See Instructions for Authors for a complete description of levels of evidence.