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Using Eviews software, this paper makes an empirical analysis on the two kinds of factors that affect the housing price of 31 key cities in China: internal direct factors and external indirect factors, in order to reveal the reasons for the high housing price. A multivariate linear regression model was established in which the explanatory variables were land purchase price and residents' disposable income, and the explanatory variables were the average selling price of residential commercial housing. The research shows that the land purchase price has a greater positive impact on the average selling price of residential commercial housing, compared with residents' disposable income although also a positive impact but less impact. [ABSTRACT FROM AUTHOR]

Neonicotinoid insecticide (NEO) residues in agricultural soils have concerning and adverse effects on agroecosystems. Previous studies on the effects of farmland type on NEOs are limited to comparing greenhouses with open fields. On the other hand, both NEOs and microplastics (MPs) are commonly found in agricultural fields, but their co-occurrence characteristics under realistic fields have not been reported. This study grouped farmlands into three types according to the covering degree of the film, collected 391 soil samples in mainland China, and found significant differences in NEO residues in the soils of the three different farmlands, with greenhouse having the highest NEO residue, followed by farmland with film mulching and farmland without film mulching (both open fields). Furthermore, this study found that MPs were significantly and positively correlated with NEOs. As far as we know this is the first report to disclose the association of film mulching and MPs with NEOs under realistic fields. Moreover, multiple linear regression and random forest models were used to comprehensively evaluate the factors influencing NEOs (including climatic, soil, and agricultural indicators). The results indicated that the random forest model was more reliable, with MPs, farmland type, and total nitrogen having higher relative contributions. [Display omitted] • Soils with various mulching levels had significantly different NEO residue levels. • NEO residue levels positively correlated with MPs in agricultural soils. • Nine climatic, soil and agricultural factors significantly correlated with NEOs. • MPs and film mulching affected the NEO concentration and needed attention. [ABSTRACT FROM AUTHOR]

To study the difference between wave run-up and wave forces on a fixed column under the action of focused waves formed with different physical mechanisms, a 3D two-phase numerical wave tank is developed based on the Navier-Stokes equation and the Finite Volume Method. The influences of wave nonlinearity, column radius and water depth on the run-up and force discrepancies are systematically investigated in four sets of numerical experiments. For focused waves with identical crest height and trough-to-trough period, the run-up height and dynamic pressure around the circumference of a cylinder are mostly larger in linear focused waves than in nonlinear focused waves, due to their different hydrodynamic characteristics. With the increased wave nonlinearity either by changing the period or crest height of the focused waves, the discrepancies become greater for run-up height while smaller for dynamic pressure. Moreover, both of them, less affected by water depth, are more sensitive to the variation of column radius in linear focused waves, showing a steeper gradient in space. Therefore, in practical engineering, the physical generation mechanism must be considered in evaluating the safety of columns of wind turbines hit by freak waves. • Focused waves with identical profile can be formed by linear and nonlinear methods. • Hydrodynamic characteristic is different between linear and nonlinear focused waves. • Run-up height and dynamic pressure on cylinder are larger in linear focused waves. • Nonlinearities enlarge difference of run-up but suppress disparity of pressure. • Wave generation mechanism must be considered in evaluating the safety of column. [ABSTRACT FROM AUTHOR]

The cascaded packed bed latent thermal energy storage (PBLTES) system, an innovative and efficient technique, remains unexplored experimentally in terms of driving factors and cyclic stability. To address this gap, this study designed a cascaded PBLTES system, employing three phase-change-materials with varied phase transition temperatures. Parametric experiments were conducted to measure phase transition in capsules and temperature changes in heat transfer fluid. Pearson's correlation coefficients were used to establish relationships between driving factors and thermal performance metrics. This study developed multiple linear regression models based on experimental correlations to evaluate and predict thermal performance under various conditions. These results indicated that the employed multiple regression models are capable of making reliable quantitative predictions regarding the thermal behavior of cascaded PBLTES systems. The models showed a good fit to the experiment data (lowest R2 value at 0.776). The results also showed that the flow rate significantly affected total and phase transition times of the cascaded PBLTES for charging/discharging, with substantial Standardized Linear Regression Coefficients of −0.79/-0.8 and −0.74/-0.72, respectively. In contrast, inlet temperature, with coefficients of −0.18/0.15 and −0.34/0.21, has about a quarter of the flow rate's impact. These findings provide compelling experimental substantiation for the design of cascaded PBLTES. [ABSTRACT FROM AUTHOR]

Model selection criteria are rules used to select the best statistical model among a set of candidate models, striking a trade-off between goodness of fit and model complexity. Most popular model selection criteria measures the goodness of fit trough the model log-likelihood function, yielding to non-robust criteria. This paper presents a new family of robust model selection criteria for independent but not identically distributed observations (i.n.i.d.o.) based on the Rényi's pseudodistance (RP). The RP-based model selection criterion is indexed with a tuning parameter α controlling the trade-off between efficiency and robustness. Some theoretical results about the RP criterion are derived and the theory is applied to the multiple linear regression model, obtaining explicit expressions of the model selection criterion. Moreover, restricted models are considered and explicit expressions under the multiple linear regression model with nested models are accordingly derived. Finally, a simulation study empirically illustrates the robustness advantage of the method. • A procedure for model selection based on Rényi's pseudodistances is proposed. • It is proved that the procedure is unbiased. • A simulation study shows the robustness of the procedure in presence of outliers. • The distribution for the restricted case of i.n.i.d.o. is obtained. • Selecting the best model for the Multiple Linear Regression Problem is studied. [ABSTRACT FROM AUTHOR]

In this paper, we construct forecasting models (multivariate long short-term memory recurrent neural networks and multiple linear regression) for the resource usage prediction of four MapReduce applications and applications executed within the Spark framework. We have evaluated the impact of a sample size to prediction accuracy. Also, we propose a phase modelling approach for read/write-intensive applications. Our results show that models based on long short-term memory recurrent neural networks exhibit a higher accuracy than multiple linear regression models and the intensive characteristics of a resource are closely related to the prediction accuracy of forecasting models. We investigated the hyperparameter tuning of such models and showed that a randomly initialised, shallow, well-tuned network may outperform deeper models that use stacked autoencoder initialisation. Furthermore, multivariate long short-term memory recurrent neural network models are more sensitive to sample size than multiple linear regression models. We show that an LSTM model trained in a specific machine may be used to predict the resource usage in another machine. [ABSTRACT FROM AUTHOR]

This study shows a mathematical and statistical analysis to generate models based on multiple linear regression (MLR) and regression trees (RT) that allow a reliable prediction of the Mass Yield (MY) and the Higher Heating Value (HHV) of the final solid product obtained by Hydrothermal Carbonization, called hydrochar. MLR models were obtained for lignocellulosic and non-lignocellulosic biomass using a set of experimental data with more than 500 points collected from the literature. A new approach based on dimensionless groups of variables that describe the composition of biomass and operational conditions was used. The analysis for each equation indicated that the MY depends on the process conditions and the biomass composition, which is proportional to the Polarity Index (IP) and Reactive Index (IR) values. On the other hand, the severity factor (log Ro) and the initial calorific value (HHVo) were the main factors for the HHV, but also the raw biomass composition (IP and H/C ratio) had an opposite and equal significant effect. For these equations, the results indicated an adjusted R2 (R2 a) of about 0.90 and an average RMSE of 6% and 1.7 MJ/kg for MY and HHV, respectively. Besides, explanatory variables were analyzed by their Relative Importance for the RT models. The severity factor (65%) and the IR (18%) were the most decisive variable in the MY prediction. The R2 and RMSE were 0.73 and 2%, respectively. For HHV, the variables with the most significant impact were the HHVo (33%), the log Ro (24%), and the IP (22%). In this case, the R2 and RMSE were 0.87 and 0.68 MJ/kg, respectively. Therefore, the model equations obtained are a powerful tool to predict the mass yield and the energetic value of the hydrochar before developing an experimental study. • Hydrochar mass yield (MY) depends on HTC process conditions and biomass reactivity. • HHV depends on the initial energy content of raw biomass and operational conditions. • Linear regression had R2 above 0.90 with RMSE of 6%. • Regression trees had R2 about 0.80 with RMSE of 2%. • Equations allow screening of process conditions before the experimental study. [ABSTRACT FROM AUTHOR]

In this study, the impact of Earth's magnetic field on total electron content (TEC) was studied by using statistical multiple linear regression model and co-integration method. TEC values were measured over the Turkey-Istanbul (ista) station using date of global positioning system (GPS), and the magnetic field components of the Earth were measured from Boğaziçi University, Kandilli Observatory and Earthquake Research Institute, Geomagnetic observatory Istanbul (ISK) station. This examination has been carried out during the dates of March 14–19, 2015 covering the dates of March 17–18, 2015 known in the literature as St. Patrick's Day geomagnetic storm. The three days before the storm (March 14–16) were named as quiet days, whereas the other days (March 17–19) were named as disturbed days after which the two periods were examined separately. It was observed as a result of the examination that the x-component (south-north direction) of the magnetic field had a negative impact on TEC on quiet days, whereas the impact was positive on disturbed days. However, the y-component (east–west direction) of the magnetic field had an inverse relationship of the x-component on the quiet and disturbed days. In addition, it was deduced that the impact coefficient of the x and y-component of the magnetic field was higher on disturbed days in comparison with those on quiet days. The correlation coefficient between the TEC and the components of the Earth's magnetic field was 0.11 on quiet days and 0.95 on disturbed days. Therefore, it can be stated that the relationship of the TEC values with the geomagnetic field are higher on disturbed days. [ABSTRACT FROM AUTHOR]

• Proposed high-performance concrete and developed a regression model to predict HMRAC's compressive strength. Recycled Coarse Aggregate Concrete (RAC) is a type of concrete that uses recycled coarse aggregate (RCA) instead of natural coarse aggregate (NCA), which helps to reduce the negative environmental impact of construction waste. In order to improve the low compressive strength of RAC and solve the problem of shortage of natural aggregates, this paper develops a high-performance concrete called recycled coarse aggregate concrete with silica fume-slag-fly ash hybrid micro-powders (HMRAC) by replacing part of the cement with a mixture of micronized silica fume (SF), slag (SG) and fly ash (FA) and using RCA of 50% mass instead of NCA. The optimum combination parameters and prediction model for the compressive strength of HMRAC were also proposed by orthogonal experimental design. A constant water-cement ratio of 0.45 and a 50% mass replacement of natural coarse aggregate (NCA) by recycled coarse aggregate (RCA) were adopted in the experiment, and the mass replacement ratios of silica fume (SF), slag (SG), and fly ash (FA) for ordinary Portland cement (OPC) were used as the test variables. In total, 16 combinations were tested, including a control group where 50% of the NCA mass was replaced by RCA. First, we investigated the degree and significance of the effect of the mass substitution ratio of SF, SG, and FA on the compressive strength of HMRAC using analysis of variance (ANOVA) and extreme difference analysis. Then, we determined the optimal combination ratio of SF, SG, and FA. Secondly, multiple regression analysis was used to propose a multiple regression model for predicting the compressive strength of HMRAC. Finally, we computed and analyzed the carbon emissions of HMRAC. The results indicated that the mass substitution rates of FA and SG had a greater effect on the compressive strength, and the mass substitution rate of SF had a lesser effect on the compressive strength. The interaction of SF, SG, and FA can significantly enhance the compressive strength of RAC. The optimal compressive strength performance of HMRAC was observed when the proportions were as follows: SF constituted 10%, SG made up 15%, and FA accounted for 5%. The regression model has reasonable accuracy and a small standard deviation of residuals. It can effectively predict the compressive strength value of HMRAC, aligning well with the experimental results. It exhibits a superior carbon reduction rate of 21.90%, compared to conventional concrete. [ABSTRACT FROM AUTHOR]

Abstract The binding constant (K doc) of organic pollutants (OPs) with dissolved organic matter (DOM) is an important parameter in determining the partitioning of OPs in the aquatic environment. Most estimation models have focused on calculating the K doc of a specific group of OPs but failed to obtain K doc values of different OPs effectively over the last three decades. In this study, we attempted to build a general-applicable K doc model based on various organic compounds' K doc values from the literature since 1973. Two multiple linear regression models, a DOM nonspecific model and an Aldrich HA model, were developed based on two solid and easy to access parameters—molecular connectivity indices (MCI) and polarity correction factors (PCF). In addition, the models' corresponding K ow -K doc models, which were mostly used in previous model studies, were developed for comparison. The adjusted determining coefficient (adj-R2) and standard error of the estimate (SEE) of the DOM nonspecific MCI-PCF-K doc model were 0.815 and 0.579, respectively, whereas the adj-R2 and SEE for the MCI-PCF-K doc model of Aldrich HA reached 0.907 and 0.438, respectively. The Aldrich HA model showed higher pertinence to the nonspecific model. Furthermore, both models exhibited better fit than the K ow -K doc models. The dipole moment modification attempts did not significantly improve either MCI-PCF-K doc models; hence, the two models were not altered with the dipole moment. The robustness tests by a Jackknifed method showed that the two MCI-PCF-K doc models exhibited higher robustness than the K ow -K doc. Of all of the OPs, the phenols contributed the most to their robustness. Furthermore, a sensitivity analysis showed that the two MCI-PCF-K doc models were sensitive to the robust parameters. Graphical abstract Unlabelled Image Highlights • Two types of MCI-PCF-K doc models and the Kow-Kdoc models were developed. • Both MCI-PCF-K doc models exhibited better fit than the Kow-Kdoc models. • The Aldrich HA model showed higher pertinence to the DOM nonspecific MCI-PCF-K doc model. • Both MCI-PCF-K doc models were sensitive to the robust parameters. • Both MCI-PCF-K doc models were not altered with the dipole moment. [ABSTRACT FROM AUTHOR]

Highlights • Generate daily COMS Land Surface Temperature (LST) collected every 15 min. • Develop an multiple linear regression (MLR) model using daily COMS LST and MODIS NDVI data in addition to interpolated precipitation (PCP). • Estimate the Surface Soil Moisture (SSM) from January 2013 to May 2015 based on the MLR model. • Compare the quality of this product with in situ measurements and spatial distribution of the soil moisture index (SMI). Abstract This study attempts to estimate the spatial distribution of the soil moisture (SM) in South Korea (99,260 km2) from January 2013 to May 2015 using a multiple linear regression (MLR) model with data from the Korean multipurpose geostationary Communication, Ocean and Meteorological Satellite (COMS), and land surface temperature (LST) data. The normalized distribution vegetation index (NDVI) from the moderate-resolution imaging spectroradiometer (MODIS) on board the Terra satellite was used to reflect the vegetation variations. Observed precipitation data measured by automatic weather stations (AWSs) operated by the Korea Meteorological Administration (KMA) were collected, and SM data were collected from 38 stations operated by various institutions. The regression coefficients of the MLR model were estimated seasonally considering five days of preceding precipitation. The p-values of all regression coefficients were below 0.05, and all coefficients of determination (R2) ranged from 0.17 to 0.63. Specifically, the R2 of loam in the summer was the highest at 0.63, and most of the R2 values were 0.4 or higher. The results of the SM regression showed that the overall R2 was higher than 0.4 and that the root mean square error (RMSE) was less than 5% at all but a few stations. A time series analysis of the simulated SM revealed that the observed SM data ranged from 0 to 20% for sand, 10 to 30% for loam, 20 to 40% for clay and 30 to 50% for silt. The simulated SM followed the volatility of the observed data in most of the soils. The spatial distribution of the simulated SM showed the same trends with the observed data on the monthly spatial precipitation map. Consequently, the estimated map of the soil moisture index (SMI) can be used to better understand the severity of drought and the variability in the SM. [ABSTRACT FROM AUTHOR]

Refractory carbonaceous gold ore contains carbonaceous matters that seriously interfere with gold leaching. Therefore, this paper presented an in-depth experimental investigation on the inhibition gold-adsorbing behavior using Trichloroisocyanuric acid (TCCA). Various factors affecting the inhibition were analyzed. Moreover, the kinetics and isotherms associated with the adsorption were established to describe the gold adsorption of carbonaceous matters after TCCA treatment. A variety of analytical techniques were used to clarify the inhibition mechanism. The results showed the average gold adsorption percentage of elemental carbon decreased from 89.79 % to 9.33 % at pH 3.0, TCCA volume 15.17 mL, TCCA concentration 0.104 mol·L−1, 35 ℃ and 3 h. The average adsorption percentage of humic acid decreased from 56.19 % to 3.13 % at pH 5.0, TCCA volume 5.10 mL, TCCA concentration 0.012 mol·L−1, 35 ℃ and 4.5 h. The gold-adsorption behavior of the treated elemental carbon conformed to the pseudo-second-order and Langmuir model, accompanied by an activation energy of 9.42 kJ·mol−1. And the adsorption process of the treated humic acid could be explained by the pseudo-first-order and Langmuir model with an activation energy of 40.11 kJ·mol−1. TCCA molecules and their hydrolysis products covered the elemental carbon surface, resulting in a reduction in the size of the porous structure, which further affected the number and activity of surface-active sites. Treated with TCCA, the surface area of humic acid reduced from 42.84 m2·g−1 to 9.96 m2·g−1. Furthermore, spectroscopic analysis showed that the chemical groups of humic acid were destroyed, suggesting a sharp decrease in chemisorption capability. [Display omitted] • Adsorption percentage of the TCCA-treated elemental carbon decreased by 89.63 %. • Adsorption percentage of the TCCA-treated humic acid decreased by 94.47 %. • TCCA covered elemental carbon's surface to inhibit the surface activity. • TCCA destroyed humic acid's chemical groups of to decrease chemisorption ability. [ABSTRACT FROM AUTHOR]

To facilitate the development of PEG-cleavable pH-sensitive liposomes (CL-pPSL), this study aimed to fully characterize a new pH-sensitive polymer, PEG B -Hz-CHEMS. Polyethylene glycol (PEG) functionalised with 4-carboxybenzaldehyde (PEG B ) was linked to cholesteryl hemisuccinate (CHEMS) via an acid labile hydrazide–hydrazone hybrid bond ( CO NH N CH ) to form PEG B -Hz-CHEMS. The polymer was post-inserted into DOPE/CHEMS liposomes to form CL-pPSL. A validated stability-indicating HPLC-UV method was developed with the aid of multiple linear regression for the mobile phase. The assay was used to evaluate the pH-sensitivity, pathways of cleavage of the polymer and the PEGylation degree of CL-pPSL. The pH-sensitivity of CL-pPSL was compared with conventional PEGylated pH-sensitive (pPSL) using a calcein leakage assay. At 37 °C, PEG B -Hz-CHEMS was relatively stable at pH 7.4 with a half-life of 24 h. In comparison, at pH 5.5 and pH 6.5 PEG detachment within 1 h was determined as 80%, and 50%, respectively. PEG detachment of the polymer was through simultaneous cleavage of the hydrazine (CO N) and hydrazone (N C) bonds, depending on pH, thus the polymer is more pH-sensitive than those with a hydrazine bond only. The grafting densities of PEG B -Hz-CHEMS on CL-pPSL were optimised to achieve a PEG density of 1.7% (mol). The unilamellar CL-pPSL (123 nm) were shown to be sable at least for 3 months at 4 °C and have enhanced pH-sensitivity compared with pPSL in the calcein leakage assay. Therefore, the smart cleavable PEG polymer is promising in liposome formulation to overcome the PEG dilemma. [ABSTRACT FROM AUTHOR]

Hyperspectral remote sensing generally provides more detailed spectral information and greater accuracy than multispectral remote sensing for identification of surface materials. However, there have been no hyperspectral imagers that cover the entire Earth surface. This lack points to a need for producing pseudo-hyperspectral imagery by hyperspectral transformation from multispectral images. We have recently developed such a method, a Pseudo-Hyperspectral Image Transformation Algorithm (PHITA), which transforms Landsat 7 ETM+ images into pseudo-EO-1 Hyperion images using multiple linear regression models of ETM+ and Hyperion band reflectance data. This study extends the PHITA to transform TM, OLI, and EO-1 ALI sensor images into pseudo-Hyperion images. By choosing a part of the Fish Lake Valley geothermal prospect area in the western United States for study, the pseudo-Hyperion images produced from the TM, ETM+, OLI, and ALI images by PHITA were confirmed to be applicable to mineral mapping. Using a reference map as the truth, three main minerals (muscovite and chlorite mixture, opal, and calcite) were identified with high overall accuracies from the pseudo-images ( > 95% and > 42% for excluding and including unclassified pixels, respectively). The highest accuracy was obtained from the ALI image, followed by ETM+, TM, and OLI images in descending order. The TM, OLI, and ALI images can be alternatives to ETM+ imagery for the hyperspectral transformation that aids the production of pseudo-Hyperion images for areas without high-quality ETM+ images because of scan line corrector failure, and for long-term global monitoring of land surfaces. [ABSTRACT FROM AUTHOR]

Carbonyl compounds, an important category of volatile organic compounds (VOCs), play important roles in ozone (O 3 ) formation and atmospheric chemistry. To better understand the characteristics and sources of carbonyl compounds and their effects on O 3 formation, C 1 -C 8 carbonyls were measured at Mount Tai, the highest mountain in the North China Plain (NCP), in summer 2014. Acetone (3.57 ± 0.55 ppbv), formaldehyde (3.48 ± 0.98 ppbv) and acetaldehyde (1.27 ± 0.78 ppbv) are the three most abundant species, comprising as high as 90% of the total observed compounds. Isovaleraldehyde (0.37 ± 0.17 ppbv) presents another important carbonyl compound despite its high reactivity. Comparison with the observations available in China highlights the serious situation of carbonyls pollution in the NCP region. The sources of carbonyls are dominated by photo-oxidation of VOCs during the daytime and regional transport at night. Secondary sources from oxidation of hydrocarbons contribute on average 44% of formaldehyde, 31% of acetone, 85% of acetaldehyde, 78% of benzaldehyde, and 84% of isovaleraldehyde, demonstrating the dominant role of secondary formation in the ambient carbonyl levels. Formaldehyde, acetaldehyde and isovaleraldehyde are the most important contributors to the OH reactivity and O 3 production among the measured carbonyls. This study shows that carbonyl compounds contribute significantly to the photochemical pollution in the NCP region and hence understanding their sources and characteristics is essential for developing the science-based O 3 pollution control strategies. [ABSTRACT FROM AUTHOR]

Accurate prediction of the quantity of household solid waste generation is very much essential for effective management of municipal solid waste (MSW). In actual practice, modelling methods are often found useful for precise prediction of MSW generation rate. In this study, two models have been proposed that established the relationships between the household solid waste generation rate and the socioeconomic parameters, such as household size, total family income, education, occupation and fuel used in the kitchen. Multiple linear regression technique was applied to develop the two models, one for the prediction of biodegradable MSW generation rate and the other for non-biodegradable MSW generation rate for individual households of the city Dhanbad, India. The results of the two models showed that the coefficient of determinations (R 2 ) were 0.782 for biodegradable waste generation rate and 0.676 for non-biodegradable waste generation rate using the selected independent variables. The accuracy tests of the developed models showed convincing results, as the predicted values were very close to the observed values. Validation of the developed models with a new set of data indicated a good fit for actual prediction purpose with predicted R 2 values of 0.76 and 0.64 for biodegradable and non-biodegradable MSW generation rate respectively. [ABSTRACT FROM AUTHOR]

Previous studies have indicated a tight linkage between resting-state functional connectivity of the human brain and creative ability. This study aimed to further investigate the association between the topological organization of resting-state brain networks and creativity. Therefore, we acquired resting-state fMRI data from 22 high-creativity participants and 22 low-creativity participants (as determined by their Torrance Tests of Creative Thinking scores). We then constructed functional brain networks for each participant and assessed group differences in network topological properties before exploring the relationships between respective network topological properties and creative ability. We identified an optimized organization of intrinsic brain networks in both groups. However, compared with low-creativity participants, high-creativity participants exhibited increased global efficiency and substantially decreased path length, suggesting increased efficiency of information transmission across brain networks in creative individuals. Using a multiple linear regression model, we further demonstrated that regional functional integration properties (i.e., the betweenness centrality and global efficiency) of brain networks, particularly the default mode network (DMN) and sensorimotor network (SMN), significantly predicted the individual differences in creative ability. Furthermore, the associations between network regional properties and creative performance were creativity-level dependent, where the difference in the resource control component may be important in explaining individual difference in creative performance. These findings provide novel insights into the neural substrate of creativity and may facilitate objective identification of creative ability. [ABSTRACT FROM AUTHOR]

This study shows the results of field tests on heat accumulation in asphalt surfaces and concrete curbs on two road underpasses in Osijek, Republic of Croatia. The tests were conducted in 9 daytime periods over several months in 2014. The MLR model (multiple linear regression model) was made using measurements from 36 hourly periods and the model was tested in 9 hourly periods chosen at random from the overall data. The conducted research showed that by using the MLR models it was possible to successfully predict heat accumulation in the asphalt surfaces and concrete curbs of the underpasses using varying air temperature and humidity. Finally, a schematic view is shown of critical points in road underpasses in the event of the possible occurrences of slippery road surfaces, freezing road surfaces, plastic deformation of asphalt surfaces and any possible health hazards for pedestrians in the event of great temperature differences between road surfaces. [ABSTRACT FROM AUTHOR]

This paper proposed a nonlinear chemical fingerprint method for simultaneous determination of urea and melamine in milk powder using “ H + + Ce 4 + + BrO 3 - + malonic acid ” as reaction system. A multiple linear relationship was obtained between the adulterants content in milk powder and inductive time of corresponding mixed milk powder. System analysis model established with classical least squares (CLS) method was then used to calculate the content of urea and melamine in milk powder. The method was successfully applied to milk powder samples and had good recoveries in the range of 99.17–100.25%, with the relative standard deviation (RSD) in the range of 0.60–4.12%. The limits of detection for urea and melamine were 0.33 μg·g −1 and 0.05 μg·g −1 , respectively. The limits of quantification were 1.11 μg·g −1 and 0.18 μg·g −1 , respectively. The results indicated that the new method was feasible and had the advantages of low cost, simple operation and without pretreatment of samples. [ABSTRACT FROM AUTHOR]

Huge amounts of fruit residues are produced and abandoned annually. The high moisture and organic contents of these residues makes them a big problem to the environment. Conversely, they are a potential resource to the world. Anaerobic digestion is a good way to utilize these organic wastes. In this study, the biomethane conversion performances of a large number of fruit residues were determined and compared using batch anaerobic digestion, a reliable and easily accessible method. The results showed that some fruit residues containing high contents of lipids and carbohydrates, such as loquat peels and rambutan seeds, were well fit for anaerobic digestion. Contrarily, residues with high lignin content were strongly recommended not to be used as a single substrate for methane production. Multiple linear regression model was adopted to simulate the correlation between the organic component of these fruit residues and their experimental methane yield, through which the experimental methane yield could probably be predicted for any other fruit residues. Four kinetic models were used to predict the batch anaerobic digestion process of different fruit residues. It was shown that the modified Gompertz and Cone models were better fit for the fruit residues compared to the first-order and Fitzhugh models. The first findings of this study could provide useful reference and guidance for future studies regarding the applications and potential utilization of fruit residues. [ABSTRACT FROM AUTHOR]

Viral waterborne diseases are widespread in cities due largely to the occurrence of enteric viruses in urban rivers, which pose a significant concern to human health. Yet, the application of rapid detection technology for enteric viruses in environmental water remains undeveloped globally. Here, multiple linear regression (MLR) modeling and artificial neural network (ANN) modeling, which used frequently measured physicochemical parameters in river water, were constructed to predict the concentration of enteric viruses including human enteroviruses (EnVs), rotaviruses (HRVs), astroviruses (AstVs), noroviruses GⅡ (HuNoVs GⅡ), and adenoviruses (HAdVs) in rivers. After training, testing, and validating, ANN models showed better performance than any MLR model for predicting the viral concentration in Jinhe River. All determined R -values for ANN models exceeded 0.89, suggesting a strong correlation between the predicted and measured outputs for target enteric viruses. Furthermore, ANN models provided a better congruence between the observed and predicted concentrations of each virus than MLR models did. Together, these findings strongly suggest that ANN modeling can provide more accurate and timely predictions of viral concentrations based on frequent (or routine) measurements of physicochemical parameters in river water, which would improve assessments of waterborne disease prevalence in cities. [Display omitted] Our study is the first to analyze correlations between enteric viruses' concentration and water quality over a 4-year period in Jinhe River (Tianjin, China), MLR and ANN models were built and validated to predict enteric viruses including EnVs, HRVs, AstVs, HuNoVs GII, and HAdVs in urban rivers. This study provides a beneficial tool to predict the real-time occurrence of enteric viruses in urban surface water, which should help to better assess the prevalence of waterborne disease in cities. • ANN models accurately predicted enteric viruses' concentration in Tianjin's rivers. • ANN outperformed MLR models in predicting enteric viruses' concentration in Tianjin's rivers. • ANN models hold promise to predict virus concentrations in urban rivers rapidly. [ABSTRACT FROM AUTHOR]

Winter oilseed rape (WOSR) is an important oil and protein crop in Europe, used in biofuel production and as protein source in livestock farming. In contrast to cereals, WOSR seed yields are still increasing in most countries but yield stability was not improved during the last decades. In our study, we analyzed the effects of weather conditions during different growth stages on maximum seed yield, maximum oil yield, number of seeds per m 2 and 1000-seed weight to get further information on yield formation processes. Field trials performed at 34 environments (site × year combinations) representing different soil characteristics and climate regions in Germany were used for the analysis. About 40% of seed yield variability could be explained by weather conditions during specific growth phases. The most important phenological phases thereby were: onset of pods and seeds (BBCH 50–65) and seed development (BBCH 71–79). During onset of pods and seeds, yield was significantly influenced by temperature, radiation and drought stress. Assimilate availability during this phase determines the number of seeds per m 2 (sink size). After flowering, only temperature significantly affected WOSR yield. Temperature is the major parameter determining the duration of growth stages. Lower temperature elongates the time of assimilate production and translocation to the seeds. During this growth stage, seed weight is determined. In our data sets, low sink size was not yield limiting due to compensatory effects between the yield components number of seeds per m 2 and 1000-seed weight. Yield response pattern suggests that WOSR yield is predominantly source-limited, especially during the late reproductive phase. [ABSTRACT FROM AUTHOR]

Evidence suggests that biochar influences soil physical properties, especially soil hydrology, yet relatively little data exists on this topic, especially in relation to soil type or characteristics. This paper presents a novel attempt at analysing the influence of biochar (applied at 0.1, 0.5 and 2.5%) on the physical properties of soil with respect to quantified soil variables. Pot experiments were used to establish the effect of biochar on: bulk density, soil moisture content at field capacity and available water capacity. The aggregate effect of biochar across all soils was significant (P < 0.01) for all of the properties. With increasing amount of biochar, changes to bulk density, field capacity and available water were more pronounced. In the 2.5% treatments these changes ranged from − 4.2% to − 19.2%, 1.3% to 42.2% and 0.3% to 48.4%, respectively. Regression revealed that soil silt content negatively moderated the influence of biochar on field capacity and available water capacity. The results suggested that medium (20 t ha − 1 ) and high (100 t ha − 1 ) biochar applications could improve water-holding capacity (by up to 22%) and ameliorate compaction (by up to 15%) and that soils low in silt are likely to be more hydrologically responsive to biochar application. [ABSTRACT FROM AUTHOR]

This work presents an experimental study focusing on the preparation and characterization of modified green clay with quaternary ammonium salts alkyl dimethyl benzyl ammonium chloride (ADMBAC) and distearyl dimethyl ammonium chloride (DSDMAC) intended to be used as adsorbent in the process of removing oil emulsion in an oil–water system using finite bath. X-ray diffraction (XRD), infrared spectroscopy (IR) and expansion test (adsorption capacity and Foster swelling) measurements were performed in order to evaluate the performance of the ion exchange reactions and the degree of affinity with oil products. There was an increase in the XRD basal spacing of the modified clays (1.96 nm and 2.25 nm for DSDMAC and ADMBAC salts, respectively) in comparison to the observed value (1.56 nm) for the unmodified clay. The IR results revealed that salts were successfully incorporated to clay structure. Based on the expansion tests the organoclays presented the best efficiency of separation, independent on the kind of solvent used in comparison with the unmodified clay performance. The modified clays exhibit a very high capacity of adsorption. The predictions of multiple linear regression models determined based on the factorial design of experiments are excellent. [ABSTRACT FROM AUTHOR]

Abstract: Methicillin resistant Staphylococcus aureus (MRSA) is among the major drug resistant bacteria that persist in both the community and clinical settings due to resistance to commonly used antimicrobials. This continues to fuel the need for novel compounds that are active against this organism. For this purpose we have targeted the type IIA bacterial topoisomerase, DNA gyrase, an essential enzyme involved in bacterial replication, through the ATP-dependent supercoiling of DNA. The virtual screening tool Shape Signatures was applied to screen a large database for agents with shape similar to Novobiocin, a known gyrase B inhibitor. The binding energetics of the top hits from this initial screen were further validated by molecular docking. Compounds with the highest score on available crystal structure of homologous DNA gyrase from Thermus thermophilus were selected. From this initial set of compounds, several rhodanine-substituted derivatives had the highest antimicrobial activity against S. aureus, as determined by minimal inhibitory concentration assays, with Novobiocin as the positive control. Further activity validation of the rhodanine compounds through biochemical assays confirmed their inhibition of both the supercoiling and the ATPase activity of DNA gyrase. Subsequent docking and molecular dynamics on the crystal structure of DNA gyrase from S. aureus when it became available, provides further rationalization of the observed biochemical activity and understanding of the receptor–ligand interactions. A regression model for MIC prediction against S. aureus is generated based on the current molecules studied as well as other rhodanines derivatives found in the literature. [Copyright &y& Elsevier]

Abstract: The export of fresh blueberries is an important productive activity in Chile, in terms of the labour employed, the number of hectares cultivated and the resulting trade flow with the northern hemisphere. The export of fresh blueberries requires planning based on early estimates of the yield of the orchard. The growers keep plots with plants of more or less the same age and variety; thus, it is possible to estimate the yield of the whole orchard, based on the yield per plant. Two factors must be considered in estimating the yield per plant: the number of fruits and their fresh weight. An early estimate of the number of fruits can be based on the number of flower buds and their viability during flowering and fruit development. The aim of the research was to find a way of estimating plant yields in commercial orchards by proposing models which relate the number of fruits available for harvest to the number of flower buds and to climatic variables. The estimated value incorporates the fruit weight appropriate to the variety cultivated. When the potential yield estimated is compared to the yield reported by the growers, the estimated errors are less than 12% (overestimation) and the performance achieved by yield models is as high as 0.57 and 0.96 for the correlation coefficients. The obtained models can be used by producers to plan their harvests several months in advance, and can be adjusted to the current season. [Copyright &y& Elsevier]

Abstract: Detailed estimates of carbon dioxide emissions at fine spatial scales are critical to both modelers and decision makers dealing with global warming and climate change. Globally, traffic-related emissions of carbon dioxide are growing rapidly. This paper presents a new method based on a multiple linear regression model to disaggregate traffic-related CO2 emission estimates from the parish-level scale to a 1×1km grid scale. Considering the allocation factors (population density, urban area, income, road density) together, we used a correlation and regression analysis to determine the relationship between these factors and traffic-related CO2 emissions, and developed the best-fit model. The method was applied to downscale the traffic-related CO2 emission values by parish (i.e. county) for the State of Louisiana into 1-km2 grid cells. In the four highest parishes in traffic-related CO2 emissions, the biggest area that has above average CO2 emissions is found in East Baton Rouge, and the smallest area with no CO2 emissions is also in East Baton Rouge, but Orleans has the most CO2 emissions per unit area. The result reveals that high CO2 emissions are concentrated in dense road network of urban areas with high population density and low CO2 emissions are distributed in rural areas with low population density, sparse road network. The proposed method can be used to identify the emission “hot spots” at fine scale and is considered more accurate and less time-consuming than the previous methods. [ABSTRACT FROM AUTHOR]

Abstract: Adaptive neuro-fuzzy inference system (ANFIS) models are proposed as an alternative approach of evaporation estimation for Yuvacik Dam. This study has three objectives: (1) to develop ANFIS models to estimate daily pan evaporation from measured meteorological data; (2) to compare the ANFIS model to the multiple linear regression (MLR) model; and (3) to evaluate the potential of ANFIS model. Various combinations of daily meteorological data, namely air temperature, relative humidity, solar radiation and wind speed, are used as inputs to the ANFIS so as to evaluate the degree of effect of each of these variables on daily pan evaporation. The results of the ANFIS model are compared with MLR model. Mean square error, average absolute relative error and coefficient of determination statistics are used as comparison criteria for the evaluation of the model performances. The ANFIS technique whose inputs are solar radiation, air temperature, relative humidity and wind speed, gives mean square errors of 0.181mm, average absolute relative errors of 9.590%mm, and determination coefficient of 0.958 for Yuvacik Dam station, respectively. Based on the comparisons, it was found that the ANFIS technique could be employed successfully in modelling evaporation process from the available climatic data. [Copyright &y& Elsevier]

There has been a considerable and continuous interest to develop equations for rapid and accurate prediction of the ME of meat and bone meal. In this study, an artificial neural network (ANN), a partial least squares (PLS), and a multiple linear regression (MLR) statistical method were used to predict the TME11 of meat and bone meal based on its CP, ether extract, and ash content. The accuracy of the models was calculated by R2 value, MS error, mean absolute percentage error, mean absolute deviation, bias, and Theil's U. The predictive ability of an ANN was compared with a PLS and a MLR model using the same training data sets. The squared regression coefficients of prediction for the MLR, PLS, and ANN models were 0.38, 0.36, and 0.94, respectively. The results revealed that ANN produced more accurate predictions of TME as compared with PLS and MLR methods. Based on the results of this study, ANN could be used as a promising approach for rapid prediction of nutritive value of meat and bone meal. [ABSTRACT FROM AUTHOR]

Abstract: In the present study we have tried to trace the growth of malaria research at Global Level and the distribution of articles in various journals for the period 1955–2005. The data have been extracted from a database, which has been developed in-house from MEDLINE, SCI, TDB, Ovid Heath Information and Indian Science Abstracts. Study indicates that the exponential model fits the data on journals, articles and authors. The R 2 value for the trend for journals, articles, and authors are 0.9502, 0.9475, and 0.9651, respectively. The growth rates for journals, articles and authors are 5.31%, 7.38%, and 10.06%, respectively. The linear multiple regression equation that Articles=−39.2771+3.61719*journals+0.085882*Authors (R 2 =99.16%) is most meaningful and it may be used to estimate the articles for given numbers of journals and authors. [Copyright &y& Elsevier]

Human and animal wastes are major sources of environmental pollution. Reliable methods of identifying waste sources are necessary to specify the types and locations of measures that best prevent and mitigate pollution. This investigation demonstrates the use of chemical markers (fecal sterols and bile acids) to identify selected sources of fecal pollution in the environment. Fecal sterols and bile acids were determined for pig, horse, cow, and chicken feces (10–26 feces samples for each animal). Concentrations of major fecal sterols (coprostanol, epicoprostanol, cholesterol, cholestanol, stigmastanol, and stigmasterol) and bile acids (lithocholic acid, deoxycholic acid, cholic acid, chenodeoxycholic acid, ursodeoxycholic acid, and hyodeoxycholic acid) were determined using a gas chromatography and mass spectrometer (GC–MS) technique. The fecal sterol and bile acid concentration data were used to estimate parameters of a multiple linear regression model for fecal source identification. The regression model was calibrated using 75% of the available data validated against the remaining 25% of the data points in a jackknife process that was repeated 15 times. The regression results were very favorable in the validation data set, with an overall coefficient of determination between predicted and actual fecal source of 0.971. To check the potential of the proposed model, it was applied on a set of simulated runoff data in predicting the specific animal sources. Almost 100% accuracy was obtained between the actual and predicted fecal sources. While additional work using polluted water (as opposed to fresh fecal samples) as well as multiple pollution sources are needed, results of this study clearly indicate the potential of this model to be useful in identifying the individual sources of fecal pollution. [Copyright &y& Elsevier]

The sound quality of a range hood is one of the most common complaints from consumers. As a supplement to sound power target, sound quality evaluation and prediction describes subjective feeling more suitably. It is proved that conventional sound quality model is able to predict sound quality accurately. However, the conventional model does not offer practical strategies for the design of a range hood. In this paper, specific psychoacoustic metrics including Area of Derivative of Specific Loudness (ADSL) and Gravity of Specific Loudness (GSL) are proposed. The multiple linear regression model based on ADSL and GSL has an adjustable R square of 0.92. The target determined by the model is proven more preferred than original by subjective assessment. An innovative method of sound reinforcement is proposed in the present work, and is applied on a range hood based on the strategies. The sound pressure levels in the target frequency region are reinforced, which makes the sound loudness curve more close to the target curve. The work in this paper offers an example of application of sound quality model on the design of a range hood. [ABSTRACT FROM AUTHOR]

• Streamflow in the middle and lower basin of Hulu River increased lightly after 1999. • Quantitative analysis the impacts of SWC measures and precipitation on streamflow. • The relationship of SWC, precipitation and streamflow were estimated by MLR model. • Grain-for-Green program measure had a greater effect on reducing streamflow. • Increased precipitation had a significant effect on streamflow increased after 1999. Precipitation variation and soil and water conservation (SWC) measures mostly determine the dynamics of streamflow in the Hulu River Basin. In particular, SWC measures play an essential role in controlling streamflow reduction and soil loss in the Hulu River Basin. The objective of this study is to quantitatively explore the effect of SWC measures and precipitation on streamflow changes in the middle and lower reaches of the Hulu River Basin. In this study, a long-term measured annual streamflow data from two major hydrological stations was used to analyze variations in streamflow in this basin. The Mann-Kendall trend test and the change-point analysis method were applied to analyze the trend of streamflow datasets. The results indicated that the annual streamflow in the middle and lower basin of the Hulu River (1975–2016) had a similar decreasing trend to streamflow change of the entire basin (1960–2016), with the abrupt change point of annual streamflow occurred in the year 1986. In addition, the streamflow exhibited a slightly increasing trend due to the more precipitation increase in the basin throughout 1999–2016. From 1999 to 2016, the impact of precipitation and SWC measures on streamflow is quantitatively assessed by using a multiple linear regression model. The result showed that SWC measures could effectively delay and reduce streamflow. The analysis of the regression coefficients suggested that SWC measures had a positive ecological effect on streamflow decrease, whereas precipitation had a significantly positive effect on streamflow increase. The precipitation had a higher contribution to streamflow changes than SWC measures. For the change of annual streamflow, precipitation contributed 79.6%, the Grain-for-Green program and check dams contributed 13.4% and 7%, respectively. A comparison of the impact of SWC measures on streamflow showed that the Grain-for-Green program measure had a more significant impact on streamflow reduction in the middle and lower reaches of the Hulu River Basin. In conclusion, these results can guide future water resource planning and management, and the allocation of SWC measures in the entire Hulu River Basin. [ABSTRACT FROM AUTHOR]

This study uses multiple linear regression to identify factors contributing to perceived risk among residents near Taoyuan and Kaohsiung International Airports, the effect of perceived risk on their willingness to reduce risk, and consumption preferences that can reduce risk. Results indicated that residents' risk perception near Taoyuan Airport is lower than that near Kaohsiung Airport. Noise pollution experience, perceived probability of environmental contamination and negative effects, and perceived severity of catastrophic consequences significantly increase residents' perceived risks. Residents are willing to recognize and participate in mitigating the risks of aircraft noise pollution. The more risk residents perceive, the more willing they are to participate in disaster reduction and investigate means of improving the risk environment. [ABSTRACT FROM AUTHOR]

• Combined with concentric zone theory, buffer zones were built up in this study. • Landscape pattern indexes (LPIs) which are not influenced by area were selected. • The gradient changes of LPIs and ecosystem service values (ESVs) were analyzed. • Nine ESV types and ten LPIs were adopted to construct models. The spatial structure and function of Xi'an city is significantly affected by urbanization, a factor which can be considered as the main driver of landscape pattern and ecosystem service change. Due to these changes are a response to urban land use and land cover (LULC), remote sensing images are interpreted by the method of supervised classification and visual interpretation to obtain the LULC data for research on landscape pattern index (LPI) and ecosystem service value (ESV) of Xi'an city, China. Combined with urban planning theory, concentric buffer zones were used to explore the characteristics and relationships between LPIs and ESVs along an urban-rural gradient. Ten landscape indices and nine ecosystem service types were selected to analyze the correlation and construct multiple linear regression models, and principal component analysis (PCA) was used to eliminate the problem of multi-collinearity in the process of model construction. Results indicate that the highest landscape fragmentation was mainly distributed in the urban-rural fringe, 20–35 km from the urban center, and patch density (PD), edge density (ED), Shannon's diversity index (SHDI), and landscape shape index (LSI) recorded the highest values. Total ESV of Xi'an city was 20493.65 × 106 CNY in 2016, and the mean value of ESV increased from 0.06 × 106 CNY to 2.60 × 106 CNY along an urban-rural gradient. This finding indicates that a higher ESV was recorded for the natural landscape. Results also indicated that SHAPE_MN, FRAC_MN, PLADJ, and AI recorded a positive effect on total ESV whilst ED and PD have a negative effect on total ESV. Results from the regression models showed quantitative relationships between ESVs and LPIs which revealed how ecosystem service values were affected by the landscape pattern. This study will improve the quantitative assessment method on landscape ecology and provide a basis for further research on city's landscape pattern and ecosystem balance, especially for detecting and evaluating urban-rural development. [ABSTRACT FROM AUTHOR]

A standardized website evaluation model is needed in the tourism sector. This research article aims at revising previous models and updating them to contribute with a unified evaluation model for the analysis of web quality that incorporates a three-dimensional approach on usability, since usability is closely related to graphic design and navigability. This perspective has not been stated before. To test this correlation, a model to evaluate User Usable Experience (UUX) which integrates this three-dimensional approach on usability is proposed and a set of indicators that have been devised from a close bibliographic revision of previous web analysis models is shown. Its application to a purposive sampling verifies the positive correlation among the three above mentioned parameters by means of a multiple linear regression model. The results confirm the need to analyse UUX from a three-dimensional perspective on usability. Unlabelled Image • A multidisciplinary model for evaluating User Usable Experience (UUX) in tourism websites is proposed. • The proposed model combines previous contributions and updates them in the context of the web 2.0. • The positive correlation among usability, graphic design and navigability is verified (multiple linear regression model). • Graphic design and navigability affect UUX in websites when they both work together, not independently. • Hints to check and improve UUX in tourism websites. [ABSTRACT FROM AUTHOR]