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There is widespread concern about pollution of freshwater ecosystems caused by heavy metals. The aim of this study was to determine how humic acid affected the bioavailability of Cu at a range of Cu concentrations in water. The concentrations of selected Cu species were characterized by spectroscopic methods and multiple regression models. The results showed that the dissolved Cu concentration decreased by an average of 13.4% for every 5-mg/l increase in the humic acid concentration in the Cu-treated water, indicating that humic acid could reduce the Cu bioavailability. Tests to show how DOM fluorescent components affected the Cu species concentrations showed that the Cu species were significantly correlated with five fluorescent components of DOM (P<0.01, linear goodness of fit: R2min=0.8374). We then examined how the pH and DOM fluorescent components together affected the concentrations of the Cu forms. When 3≤pH≤7, both tryptophan and fulvic acid promoted the transformation of dissolved Cu forms to suspended Cu forms. The Cu forms were significantly correlated with the pH and DOM fluorescence (P<0.01, linear regression goodness of fit: R2min=0.8898). However, tryptophan and fulvic acid had contrasting effects when the pH was less than, or greater than, 7. The influences on the migration ability and bioavailability of Cu therefore varied, depending on the water conditions. We conclude that it may be possible to identify the substances that have most effect on the bioavailability of Cu in water environments from the pH range of the water body, and that the bioavailability of heavy metals in water environments may be controlled by adding substances with specific properties.

There exists serious heavy metal contamination of agricultural soils in China. It is not only time- and labor-intensive to monitor soil contamination, but it also has limited scope when using conventional chemical methods. However, the method of the heavy metal monitoring of soil based on vegetation hyperspectral technology can break through the vegetation barrier and obtain the heavy metal content quickly over large areas. This paper discusses a highly accurate method for predicting the soil heavy metal content using hyperspectral techniques. We collected leaf hyperspectral data outdoors, and also collected soil samples to obtain heavy metal content data using chemical analysis. The prediction model for heavy metal content was developed using a difference spectral index, which was not highly satisfactory. Subsequently, the five factors that have a strong influence on the content of heavy metals were analyzed to determine multiple regression models for the elements As, Pb, and Cd. The results showed that the multiple regression model could better estimate the heavy metal content with stable fitting that has high prediction accuracy compared with the linear model. The results of this research provide a scientific basis and technical support for the hyperspectral inversion of the soil heavy metal content.

In this study, descriptive statistics, correlation matrix, multiple regression model, and geostatistical models were used to assess the contamination of groundwater with respect to trace elements in the Lower Tano river basin, Ghana, West Africa. A total number of 48 boreholes drilled across the basin with depths ranging from 18 to 60 m were used as data sources in this study. The results of the descriptive statistics showed that the average lead, iron, and aluminium concentrations exceeded the WHO permissible limits of 0.3 mg/L, 0.01 mg/L, and 0.2 mg/L respectively. Furthermore, copper, chromium, aluminium, zinc, manganese, nickel, iron, arsenic, electrical conductivity, and total dissolved solids were found to be extreme and highly positively skewed. Even though significant correlations exist among some variables, the statistical results showed that the quality of the boreholes drilled across the basin was mainly originating from geogenic and anthropogenic sources. In addition, each pair of correlated physical parameters and trace elements in the drilled boreholes were predicted using multiple regression models. Likewise, geostatistical modelling was used to assess the spatial analysis of each pair of correlated physical parameters and trace elements in the drilled boreholes. The cross-validation results revealed kriging model, as the most precise model for the spatial distribution maps for the correlated physical parameters, and correlated trace elements concentration in the boreholes drilled across the study region. The semivariogram models showed that most of the correlated physical parameters and correlated trace elements were weak moderately and strongly spatially dependent, suggesting fewer agronomic influences. The results of the spatial analysis were consistent with the multiple regression model and the Pearson correlation matrix.

To evaluate quality of cast aluminum alloys quantitatively and intuitively, some quality indices and quality maps have been introduced. Quality index is a measure to quantitatively evaluate the quality of cast aluminum alloys based on the tensile mechanical properties, and quality map is generated to intuitively support material selection on the basis of the quality index. Different quality indices such as Q, Q R , Q C and Q 0 have been used to evaluate the quality of the cast aluminum alloys. However, they lack in reflecting and evaluating overall performance throughout all the tensile mechanical properties such as YS, UTS, E f and W, comprehensively. The quality maps show the quality level according to only the pairs of tensile properties such as (UTS, E f ), (YS and E f ) or (YS, W). The quality indices and quality maps cannot directly show the quality levels according to artificial aging heat treatment condition. We developed overall quality indices according to tensile mechanical properties, and overall quality index and overall quality map according to artificial aging heat treatment condition for the cast aluminum alloys A357 by using multi-criteria decision-making (TOPSIS) and multiple regression model. The performances of the overall quality indices were evaluated using mean absolute errors, mean relative errors and determination coefficients. The overall quality indices and overall quality map help materials designers and engineers evaluate the overall performance of the cast aluminum alloy A357 and select the reasonable artificial aging heat treatment condition, quantitatively and intuitively.

Recently, considerable efforts have been devoted to the estimation of soil properties using optical payloads mounted on drones or satellites. Nevertheless, many studies focus on diverse pretreatments and modeling techniques, while there continues to be a conspicuous absence of research examining the impact of parameters related to optical remote sensing payloads on predictive performance. The main aim of this study is to evaluate how the spectral resolution and signal-to-noise ratio (SNR) of spectrometers affect the precision of predictions for soil organic matter (SOM) content. For this purpose, the initial soil spectral library was partitioned into to two simulated soil spectral libraries, each of which were individually adjusted with respect to the spectral resolutions and SNR levels. To verify the consistency and generality of our results, we employed four multiple regression models to develop multivariate calibration models. Subsequently, in order to determine the minimum spectral resolution and SNR level without significantly affecting the prediction accuracy, we conducted ANOVA tests on the RMSE and R2 obtained from the independent validation dataset. Our results revealed that (i) the factors significantly affecting SOM prediction performance, in descending order of magnitude, were the SNR levels > spectral resolutions > estimation models, (ii) no substantial difference existed in predictive performance when the spectral resolution fell within 100 nm, and (iii) when the SNR levels exceeded 15%, altering them did not notably affect the SOM predictive performance. This study is expected to provide valuable insights for the design of future optical remote sensing payloads aimed at monitoring large-scale SOM dynamics.

As a kind of novel porous ceramics, wood ceramics can be used for filtration, friction, energy storage and electrode materials, etc. In current work, the carbon based wood ceramics (C WCMs) was prepared using pine wood powder and phenolic resin as starting materials. The effects of filling rate of wood powder and resin concentration on pore characteristics and properties of C WCMs were characterized and analyzed with different techniques. Furthermore, the association among porosity of C WCMs, filling rate of wood powder and resin concentration was explored with multiple regression model. The results showed that: increasing the resin concentration and the filling rate of wood powder can improve the mechanical properties of C WCMs, but reduce the porosity and air permeability; when resin concentration is more than 50%, a large amount of caking will appear in the C WCMs, causing internal defects; changing the filling rate under a certain resin concentration can obtain the C WCMs with better pore structure; the porosity of C WCMs has a good linear relationship with resin concentration and filling rate, under the condition that sintering process and the size of wood powder are determined.

Purpose The housing market in Kenya continues to experience an excessive imbalance between supply and demand. This imbalance renders the housing market volatile, and stakeholders lose repeatedly. The purpose of the study was to forecast housing prices (HPs) in Kenya using simple and complex regression models to assess the best model for projecting the HPs in Kenya. Design/methodology/approach The study used time series data from 1975 to 2020 of the selected macroeconomic factors sourced from Kenya National Bureau of Statistics, Central Bank of Kenya and Hass Consult Limited. Linear regression, multiple regression, autoregressive integrated moving average (ARIMA) and autoregressive distributed lag (ARDL) models regression techniques were used to model HPs. Findings The study concludes that the performance of the housing market is very sensitive to changes in the economic indicators, and therefore, the key players in the housing market should consider the performance of the economy during the project feasibility studies and appraisals. From the results, it can be deduced that complex models outperform simple models in forecasting HPs in Kenya. The vector autoregressive (VAR) model performs the best in forecasting HPs considering its lowest root mean squared error (RMSE), mean absolute error (MAE), mean absolute percentage error (MAPE) and bias proportion coefficient. ARIMA models perform dismally in forecasting HPs, and therefore, we conclude that HP is not a self-projecting variable. Practical implications A model for projecting HPs could be a game changer if applied during the project appraisal stage by the developers and project managers. The study thoroughly compared the various regression models to ascertain the best model for forecasting the prices and revealed that complex models perform better than simple models in forecasting HPs. The study recommends a VAR model in forecasting HPs considering its lowest RMSE, MAE, MAPE and bias proportion coefficient compared to other models. The model, if used in collaboration with the already existing hedonic models, will ensure that the investments in the housing markets are well-informed, and hence, a reduction in economic losses arising from poor market forecasting techniques. However, these study findings are only applicable to the commercial housing market i.e. houses for sale and rent. Originality/value While more research has been done on HP projections, this study was based on a comparison of simple and complex regression models of projecting HPs. A total of five models were compared in the study: the simple regression model, multiple regression model, ARIMA model, ARDL model and VAR model. The findings reveal that complex models outperform simple models in projecting HPs. Nonetheless, the study also used nine macroeconomic indicators in the model-building process. Granger causality test reveals that only household income (HHI), gross domestic product, interest rate, exchange rates (EXCR) and private capital inflows have a significant effect on the changes in HPs. Nonetheless, the study adds two little-known indicators in the projection of HPs, which are the EXCR and HHI.

This paper proposes multiple performances optimization methodology for computer numerical controlled (CNC) machining based on Taguchi method, multi-criteria decision-making (MCDM) and multiple regression (MR) model. It consists of the following steps: (1) setting levels of process parameters and selecting suitable Taguchi orthogonal array (OA), (2) arranging the process parameters on the OA and measuring machining performance values at every trials, (3) calculating comprehensive performance (CP) by integrating the multiple performances using a reasonable MCDM, (4) developing MR model between the CP and the process parameters, (5) analyzing influence of the process parameters based on correlation analysis, and (6) determining the optimal process parameters using grid search method. The methodology was applied to analyze and determine the influence and optimal turning process parameters such as cutting speed (CS), feed rate (FR), cutting depth (CD), cutting environment (CE) and tool nose radius (NR) for optimizing four machining performances such as surface roughness (SR), cutting force (CF), tool life (TL) and power consumption (PC) in the high speed CNC turning of AISI P20 tool steel. As the result, the optimal values of the turning process parameters were determined as CS of 160 m/min, FR of 0.1 mm/r, CD of 0.2 mm, CE of cryogenic environment, and NR of 1.1 mm. The influence analysis and optimization results of the process parameters were compared with the results obtained from the Taguchi method. The proposed methodology could be widely applied to many practical machining process optimization problems in small medium enterprise (SME) or fabrication laboratory (FabLab).

The mechanical properties of resin samples in low-force stereolithography additive manufacturing were affected by the printing orientation, and were investigated and optimized to achieve excellent single or comprehensive tensile strength, compressive strength, and flexural modulus. The resin samples were fabricated using a Form3 3D printer based on light curing technology according to the corresponding national standards, and they were detected using a universal testing machine to test their mechanical properties. The influence of the printing orientation was represented by the rotation angle of the resin samples relative to the x–axis, y–axis and z–axis, and the parameters was selected in the range 0°–90° with an interval of 30°. The multiple regression models for the mechanical properties of the prepared resin samples were obtained based on least square estimation, which offered a foundation from which to optimize the parameters of the printing orientation by cuckoo search algorithm. The optimal parameters for the tensile strength, compressive strength and flexural modulus were ‘α = 45°, β = 25°, γ = 90°’, ‘β = 0°, β = 51°, γ = 85°’ and ‘α = 26°, β = 0°, γ = 90°’, respectively, which obtained the improvements of 80.52%, 15.94%, and 48.85%, respectively, relative to the worst conditions. The mechanism was qualitatively discussed based on the force analysis. The achievements obtained in this study proved that optimization of the printing orientation could improve the mechanical properties of the fabricated sample, which provided a reference for all additive manufacturing methods.

After a consistent drop in daily new coronavirus cases during the second wave of COVID-19 in India, there is speculation about the possibility of a future third wave of the virus. The pandemic is returning in different waves; therefore, it is necessary to determine the factors or conditions at the initial stage under which a severe third wave could occur. Therefore, first, we examine the effect of related multi-source data, including social mobility patterns, meteorological indicators, and air pollutants, on the COVID-19 cases during the initial phase of the second wave so as to predict the plausibility of the third wave. Next, based on the multi-source data, we proposed a simple short-term fixed-effect multiple regression model to predict daily confirmed cases. The study area findings suggest that the coronavirus dissemination can be well explained by social mobility. Furthermore, compared with benchmark models, the proposed model improves prediction R 2 by 33.6%, 10.8%, 27.4%, and 19.8% for Maharashtra, Kerala, Karnataka, and Tamil Nadu, respectively. Thus, the simplicity and interpretability of the model are a meaningful contribution to determining the possibility of upcoming waves and direct pandemic prevention and control decisions at a local level in India.