Residual Sum Of Squares Values Research Articles

Optimization of landfill gas generation based on a modified first-order decay model: a case study in the province of Quebec, Canada

Landfills will likely remain an essential part of integrated solid waste management systems in many developed and developing countries for the foreseeable future. Further improvements are required to model the generated gas from landfills. The literature has not addressed detailed waste characterization in landfill gas (LFG) modeling by a first-order decay model such as LandGEM while using a genetic algorithm. Additionally, little has been done in the literature regarding H2S generation modeling. This paper uses a genetic algorithm to independently fit parameters to a CH4 and H2S generation model based on a modified first-order decay model. In the case of CH4 generation modeling, biodegradable organic waste (OW) was segregated into food waste, yard waste, paper, and wood. In addition to optimizing the OW fractions, key modeling parameters of OW, such as CH4 generation potential ({L}_{0}) and CH4 decay rate ({k}_{C{H}_{4}}), were determined independently for different periods in the landfill’s life. Similarly, in the case of H2S generation modeling, the construction and demolition waste (CD) was classified into fines (FCD) and bulky materials (BCD), and H2S generation potential ({S}_{0}) and H2S decay rate ({k}_{{H}_{2}S}) of FCD and BCD were determined. LFG collection data from a landfill site in the province of Quebec, Canada, was used to validate the LFG generation model. A range of scenarios was analyzed using the validated model, including fourteen scenarios (two benchmark and twelve optimizing) for CH4 and two for H2S modeling. The results showed that the differentiation of more waste types improves the modeling accuracy for CH4. Moreover, within the decade-long lifetime of a landfill, the waste management strategies change, requiring different assumptions for the modeling. Also, the work showed the importance of considering how different landfill sectors are filled over time. Finally, scenario twelve of optimizing scenarios, which assumed four waste types, constant three periodic waste fractions, and six sectors, had the lowest residual sum of squares (RSS) value. For H2S generation modeling, both scenarios, with or without separate fits of {S}_{0} and {k}_{{H}_{2}S} for FCD and BCD, predicted the generated H2S well and had a very similar RSS value. Further data could improve H2S generation modeling.

Kinetic parameter estimation of hepatocellular carcinoma on 18 F-FDG PET/CT based on Bayesian method.

Dynamic PET/CT combined with a dual-input three-compartment model can be applied to assess the kinetic parameters of hepatocellular carcinoma (HCC). The nonlinear least squares (NLLS) method is the most common method for fitting model parameters; however, some limitations remain. A novel Bayesian-based method was compared with the NLLS method to estimate the kinetic parameters for differentiating HCCs from background liver tissue. The proposed Bayesian method combined a priori knowledge of the physiological range and likelihood functions of HCC lesions to obtain HCC PET/CT measurements. Metropolis-Hastings sampling was used to numerically estimate the posterior distribution. This study used 5-minute dynamic PET imaging and 1-minute static PET imaging acquired 60 min post-injection from 19 HCC lesions and 17 background liver regions. The NLLS method indicated that k3 (p=0.001) and fa (p<0.001) were higher in HCCs than in background liver tissue, while K1 (p=0.603), k2 (p=0.405), k4 (p=0.492), Vb (p=0.112), and Ki (p=0.091) were not significantly different. The Bayesian method showed that k3 (p<0.001), fa (p<0.001), and Ki (p=0.002) were higher in HCCs than in background liver tissue, while K1 (p=0.195), k2 (p=0.028), k4 (p=0.723), and Vb (p=0.018) were not significantly different. For k3 and fa, the Bayesian method showed a higher AUC value for diagnostic performance in differentiating HCCs from background liver tissue than the NLLS method (0.853vs. 0.745 and 0.928vs. 0.886). Additionally, the Bayesian method had smaller Akaike information criteria and residual sum of squares values, as well as fewer parameter estimation outliers, than the NLLS method. The proposed Bayesian method can accurately and robustly estimate liver kinetic parameters, effectively distinguish between lesions and background liver tissue, and provide accurate information about the uncertainty in parameter estimation.

Comparison of additive and isometric log-ratio transformations combined with machine learning and regression kriging models for mapping soil particle size fractions

Digital soil mapping approaches relating to the soil particle size fractions (psf) face the challenge around how to establish the statistical or geostatistical models from large sets of environmental variables, especially in a situation with sparse soil profile data. Recently, many machine learning (ML) models have sprung up with advantages over statistical models. However, few studies focused on the comprehensive comparative analyses between ML and geostatistical models in the soil psf mapping. And the exploration of optimal combination of data transformation and model simulation was even less. Therefore, two transformed methods such as additive log-ratio (ALR) and isometric log-ratio (ILR) transformations combine with two ML models such as boosted regression tree (BRT), random forest (RF) and a classic geostatistical model of regression kriging (RK) were implemented to map soil psf in the Heihe River basin, China. A total of 640 samples and thirteen scorpan factors were collected and used for the comprehensive comparative analysis. Results showed that the scorpan factors such as temperature, precipitation, elevation, soil type, soil organic carbon, vegetation types and normalized difference vegetation index had important impacts on the soil psf mapping. ILR transformation was better than ALR transformation with advantage of improving stability of data distributions and ML models could also improve the mapping performance in comparison with RK models for better handling candidate factors. For these ML models, the RF models had better accuracy performance than the BRT models. In contrast, ILR transformation combined with RF model (ILR_RF) had the best performance, with the lowest root mean square error values (sand, 15.35%; silt, 14.20%; and clay, 6.66%), Aitchison distance value (0.86), standardized residual sum of squares value (0.60), and the highest concordance correlation coefficient value (0.73) and coefficient of determination value (56.69%) for clay content. In addition, ILR_RF had a relatively higher right ratio of soil texture type (68.44%) and better predict performance for most soil texture types. The predicted maps generated from ILR_RF presented more reasonable and smoother transitions. In the future, more ML models should be explored and more variables related to soil psf should be introduced into the models to improve the predictive performance.

A Calibrated, Watershed-Specific SCS-CN Method: Application to Wangjiaqiao Watershed in the Three Gorges Area, China

The Soil Conservation Service curve number ( S C S-C N) method is one of the most popular methods used to compute runoff amount due to its few input parameters. However, recent studies challenged the inconsistent runoff results obtained by the method which set the initial abstraction ratio λ as 0.20. This paper developed a watershed-specific S C S-C N calibration method using non-parametric inferential statistics with rainfall–runoff data pairs. The proposed method first analyzed the data and generated confidence intervals to determine the optimum values for S C S- C N model calibration. Subsequently, the runoff depth and curve number were calculated. The proposed method outperformed the runoff prediction accuracy of the asymptotic curve number fitting method, linear regression model and the conventional S C S-C N model with the highest Nash–Sutcliffe index value of 0.825, the lowest residual sum of squares value of 133.04 and the lowest prediction error. It reduced the residual sum of squares by 66% and the model prediction errors by 96% when compared to the conventional S C S-C N model. The estimated curve number was 72.28, with the confidence interval ranging from 62.06 to 78.00 at a 0.01 confidence interval level for the Wangjiaqiao watershed in China.

Temperature-dependent development of Oenopia conglobata (Col.: Coccinellidae) fed on Aphis gossypii (Hem.: Aphididae)

AbstractLadybeetles are known beneficial insects, with a long history in augmentative and classical biological control. The ladybeetle Oenopia conglobata (L.) is a natural enemy of many herbivores, particularly aphids. The temperature-dependent development of O. conglobata was studied at six constant temperatures (22.5, 25, 27.5, 30, 32.5 and 35 °C) to understand its development rate and environmental constraints better. Linear and nonlinear (Lactin) models were fitted to the data. In the thermal range from 22.5 to 32.5 °C, the rate of development increased for all stages; 35 °C was lethal for all stages and no eggs hatched. The Tb and K values for the biological cycle (egg–adult) were 8.84 °C and 263.15 DD, respectively. Depending on the model, tmin values for the total development time of the coccinellid ranged from 8.45 to 8.82 °C. The nonlinear model of Lactin estimated the optimum and upper temperature thresholds for the total development time of the ladybeetle to be 33.2 and 35.0 °C, respectively. High R2 values and low residual sum of squares values revealed a good fit to the experimental data for total development and different developmental stages of O. conglobata. The results may contribute to the improvement of practical methods for mass rearing of O. conglobata.

Geostatistical methods in evaluating spatial variability of groundwater quality in Al-Kharj Region, Saudi Arabia

The analyses of 180 groundwater samples of Al-Kharj, Saudi Arabia, recorded that most groundwaters are unsuitable for drinking uses due to high salinity; however, they can be used for irrigation with some restriction. The electric conductivity of studied groundwater ranged between 1.05 and 10.15 dS m−1 with an average of 3.0 dS m−1. Nitrate was also found in high concentration in some groundwater. Piper diagrams revealed that the majority of water samples are magnesium–calcium/sulfate–chloride water type. The Gibbs’s diagram revealed that the chemical weathering of rock-forming minerals and evaporation are influencing the groundwater chemistry. A kriging method was used for predicting spatial distribution of salinity (EC dS m−1) and NO3− (mg L−1) in Al-Kharj’s groundwater using data of 180 different locations. After normalization of data, variogram was drawn, for selecting suitable model for fitness on experimental variogram, less residual sum of squares value was used. Then cross-validation and root mean square error were used to select the best method for interpolation. The kriging method was found suitable methods for groundwater interpolation and management using either GS+ or ArcGIS.

Advanced diffusion models in head and neck squamous cell carcinoma patients: Goodness of fit, relationships among diffusion parameters and comparison with dynamic contrast-enhanced perfusion

PurposeWe assessed advanced fitting models of diffusion weighted imaging (DWI) in head/neck squamous cell carcinoma (HNSCC) patients to determine the best goodness of fit and correlations among diffusion parameters. We compared these results with those of dynamic contrast-enhanced (DCE) perfusion parameters. Materials and methodsWe retrospectively evaluated 32 HNSCC patients (12 sinonasal, 20 pharynx/oral cavity). The DWI acquisition used single-shot spin-echo echo-planar imaging (EPI) with 12 b-values (0−2000). We calculated 14 DWI parameters using mono-exponential, bi-exponential, and tri-exponential models, stretched exponential model (SEM) and diffusion kurtosis imaging (DKI) models. We compared each model's goodness of fit using the residual sum of squares (RSS), Akaike Information Criterion (AIC) and Bayesian information criterion (BIC) value. We determined the correlation between each pair of DWI parameters and between each DWI parameter and DCE perfusion parameter. ResultsThe tri-exponential fit's RSS, AIC and BIC values were significantly smaller than those for bi-exponential fit. The RSS, AIC and BIC values of the SEM fit and DKI fit were significantly smaller than mono-exponential model. Significant correlations were observed in 30 pairs (sinonasal cavity) and 31 (sinonasal cavity group) among 91 DWI parameter combinations. Significant correlations were also observed in nine pairs (both sinonasal cavity and pharynx/oral cavity group) among 64 DWI/DCE perfusion parameter pairs, in particular, high positive correlations between the tri-exponential model's intermediate diffusion fraction (f2) and the volume of the extracellular extravascular space per unit volume of tissue (ve) were observed in both patient groups. ConclusionWe identified several correlations between DWI parameters by advanced fitting models and correlations between DWI and DCE parameters. These will help determine HNSCC patients' detailed tissue structures.

Parallelizing Nonlinear Least-Squares Regression with Application to Analyses of Microalgae

ABSTRACTNonlinear least-squares regression is a valuable tool for gaining chemical insights into complex systems. Yet, the success of nonlinear regression as measured by residual sum of squares (RSS), correlation, and reproducibility of fit parameters strongly depends on the availability of a good initial solution. Without such, iterative algorithms quickly become trapped in an unfavorable local RSS-minimum. For determining an initial solution, a high-dimensional parameter space needs to be screened, a process that is very time-consuming but can be parallelized. Another advantage of parallelization is equally important: After determining initial solutions, the used processors can be tasked to each optimize an initial guess. Even if several of these optimizations become stuck in a shallow local RSS-minimum, other processors continue and improve the regression outcome. A software package for parallel processing-based constrained nonlinear regression (RegressionLab) has been developed, implemented, and tested on a variety of hardware configurations. As proof-of-principle, microalgae to environment interactions have been studied by infrared attenuated total reflection spectroscopy. Additionally, light microscopy has been used to monitor cell production. It is shown that spectroscopic data sets with 10,000 s of data points and >1000 nonlinear model parameters as well as imaging data with 100,000s of data points and >2000 nonlinear model parameters may now be investigated by constrained nonlinear regression. Acceleration factors of up to 8.1 have been obtained which is of high practical relevance when computations take weeks on single-processor machines. Solely using parallel processing, the RSS values may be improved up to a factor of 5.5.

LOW FLOW FREQUENCY ANALYSIS BY L-MOMENTS METHOD (CASE STUDY: IRANIAN CENTRAL PLATEAU RIVER BASIN)

Knowledge about low flow statistics is essential for effective water resource planning and management in ungauged orpoorly gauged catchment areas, especially in arid and semi-arid regions such as Iran. We employed a data set of 20 riverflow time-series from the Iranian Central Plateau River Basin, Iran to evaluate the low-flow series using several frequencyanalysis methods and compared the result of these methods in their ability to set low flows for the conservation ofinstream water uses. Theoretical frequency distributions including the log-normal, three-parameter lognormal, Gumbel,Pearson type III, and log Pearson type III were applied with the low-flow series. Goodness-of-fit tests including Lmomentand conventional moment methods for the observed data were applied to identify the best distributions. For eachdistribution, the calculated values of the residual sum of squares (RSS) was applied to compare between the conventionalmoment and L-moment methods, and the best method was selected to determine the most appropriate probabilitydistribution. The lowest RSS values were used to select the best distribution for each station. Then, T-year low-flow serieswas estimated using the best probability distribution. Our results suggested that, for annual low flows, based on thecomputed RSS, Pearson type III, log Pearson type III, Gumbel distributions, and L-moment method were suitablydistinguished for 85%, 10%, and 5% of the stations, respectively. Finally, Compared to the conventional moment method,L-moments method was found to be more adequate to identify low-flow series probability distributions in the IranianCentral Plateau River Basin, while Pearson type III was found to be the best probability distribution for modelingminimum flow series in the study area.

Black sesame (Sesamum indicum L.) seeds extracts by CO2 supercritical fluid extraction: Isotherms of global yield, kinetics data, total fatty acids, phytosterols and neuroprotective effects

In Brazilian folk medicine, black sesame (Sesamum indicum L.) seeds are one of the most important ingredients present in the tea used to treat stroke victims. Nevertheless, extracts by supercritical extraction of black sesame seeds have not been applied in neuroprotection studies. The objective of this work was to investigate some process variables of supercritical fluid extraction in black sesame to generate extracts applicable in stroke research. Two isothermal (40 and 60°C) were explored, combined with pressures that ranged from 200 to 400bar, at a constant mass flow rate of 5.9×10−5kg/s. The global yields ranged from 37 to 53% in dry basis. The highest mass yield was obtained at 60°C and 400bar. The fatty acid composition comprised a high unsaturated/saturated ratio. Chromatographic analysis of phytosterol content in the high global yield extract revealed higher amounts of β-sitosterol+sitostanol, cholesterol, campesterol+campestanol+24-methylene cholesterol, Δ-5 avenasterol and stigmasterol, while lower levels of Δ-5,24 stigmastadienol, brassicasterol, clerosterol+Δ-5-23 stigmastadienol, Δ-7 avenasterol, eritrodiol and Δ-7 stigmasterol were present in the black sesame extract. Overall extraction curves from the supercritical fluid extraction (SFE) in lower and higher global yields (200 and 400bar at 60°C) fitted the Tan and Liou, Goto et al. and Sovová models. These models presented the best residual sum of squares values. Pilot experiments suggest that black sesame extract is neuroprotective following endothelin-1-induced focal ischemia into the motor cortex of adult male rat, observing a decrease in leukocyte infiltration in the group treated with SFE of black sesame seeds when compared with control group.

Kinetic modeling of batch photofermentation hydrogen gas production byRhodopseudomonas palustrisPBUM001

An indigenous purple non-sulfur bacteria Rhodopseudomonas palustris PBUM001 was used to produce hydrogen gas via batch photofermentation of palm oil mill effluent (POME). The photofermentation hydrogen production was carried out in a 5-l reactor (B. Braun Biostat® B) with a working volume of 3.5 l (height: 39 cm and diameter: 16 cm) under anaerobic condition. The stirred tank reactor (STR) was conducted at temperature, 30 ± 2 °C; POME concentration, 100% (v/v); light intensity, 4.0 klux; pH 6, inoculum size, 10% (v/v); agitation rate, 250 rpm, and operated for 66 h. Two sets of experiments were run in STR (R1 and R2) and the data obtained were used for kinetic study of photofermentation hydrogen production. Unstructured models were used to describe the bacterial growth, substrate consumption, and hydrogen gas production by R. palustris PBUM001. The discrepancy between the proposed model and the experimental data in simulating hydrogen production from POME by R. palustris PBUM001 was measured by using residual sum of squares (RSS). Logistic model could be adopted to describe the kinetics of bacterial growth (RSS: 0.3039–0.2313) and the proposed model for substrate consumption agreed well with the experimental data obtained in this study as shown by its RSS value of 19.1319 and 26.8259 for R1 and R2, respectively. A modified Leudeking-Piret model was applied for the data fitting to determine the relationship between the cell growth and photofermentation hydrogen production (RSS: 1.3267–26.3741).

Practical demonstration of the CIEDE2000 corrections to CIELAB using a small set of sample pairs

A set of 10 color pairs was proposed and produced in 2002 to show the advantages of the CIEDE2000 color‐difference formula with respect to CIELAB. These 10 color pairs illustrated each of the five corrections to CIELAB proposed by CIEDE2000. The 10 color pairs were visually assessed, under reference conditions close to those proposed by CIEDE2000, by two groups of 31 and 21 inexperienced observers, using two different gray scales. Average visual results in these experiments fitted CIEDE2000 predictions much better than CIELAB, as shown by a decrease of Standardized Residual Sum of Squares values of about 20 units. Current visual results showed only the improvement of CIEDE2000 upon CIELAB in predictions of perceived color differences, but they are not recommended for testing new advanced color‐difference formulas. © 2012 Wiley Periodicals, Inc. Col Res Appl, 38, 429–436, 2013.

DRYING OF EGGPLANT SLICES IN THIN LAYERS AT DIFFERENT AIR TEMPERATURES

ABSTRACT The effect of water-blanching treatment and air temperature on drying kinetics of eggplant slices were investigated at air temperatures (50, 60, 70 and 80C). Both the blanching and air temperature influenced the drying time of eggplant slices. Moisture ratios were fitted to 11 different mathematical models using nonlinear regression analysis. The regression results showed that the logarithmic, Wang and Singh, and Midilli et al. models satisfactorily described the drying behavior of eggplant slices with highest R2 value and lowest χ2, root mean square error and residual sum of squares values. The effective moisture diffusivity increases as temperature increases and range between 5.575 and 9.745 × 10-10 m2/s for unblanched samples, and between 5.851 and 11.077 × 10-10 m2/s for blanched samples. The activation energy values of unblanched and blanched samples were estimated to be 18.27 and 20.76 kJ/mol, respectively. PRACTICAL APPLICATIONS In this study, a laboratory convective hot-air dryer was used for the thin-layer drying of blanched/unblanched eggplant slices and experimental moisture ratio was compared with 11 thin-layer drying models. The blanching treatment followed by hot-air drying may provide a practical method for the preservation of eggplant and the production of dried eggplant slices. The dried product is a valuable ingredient in the preparation of soups, premixed foods, etc.

The chemical rank estimation for excitation-emission matrix fluorescence data by region-based moving window subspace projection technique and Monte Carlo simulation

A novel method, region-based on moving window subspace projection technique (RMWSPT) coupled with Monte Carlo simulation, was developed for the chemical rank estimation of excitation-emission matrix (EEM) fluorescence data arrays. RMWSPT determines the chemical rank by performing singular value decomposition (SVD) on the unfolded matrices of original data array and the subarrays yielded by a moving window, and through employing the subspace projection technique on the difference between the corresponding sub-bands of the significant eigenvectors and those of subarrays. Compared with the traditional methods, it utilizes the information from eigenvectors combined with the projection residual sum of squares values to estimate the rank of the EEM data arrays instead of using the eigenvalues. Two simulated and two real EEM data arrays were analyzed to demonstrate the excellent performance of the RMWSPT. Moreover, its performance was compared with that of other three factor-determining methods, i.e., factor indicator function (IND), the core consistency diagnostic (CORCONDIA) test and two-mode subspace comparison (TMSC) approaches. The results showed that the newly proposed method can accurately and quickly determine the chemical rank to fit the trilinear model, and it can deal with more complex situations in the presence of severe collinearity and trace concentration. The RMWSPT method thus lights a new avenue to determine the chemical rank of EEM data arrays and may hold great potential to be extended as a promising alternative for the chemical rank estimation.

Predictive modeling of biomass production by Spirulina platensis as function of nitrate and NaCl concentrations

Effects of nitrate (2.0, 2.5, and 3.0 g L −1) and salt (0.5, 1.0, 1.5, 2.0 g L −1) concentrations on biomass production by Spirulina platensis was examined in the Schlösser medium. The highest ( p < 0.001) biomass yields and chlorophyll a content was observed at 2.5 g L −1 nitrate and 1.5 g L −1 NaCl as 3.495 g L −1 and 29.92 mg L −1, respectively. Increment rate of biomass production was especially found between 72 and 216 h. Modified Richards, Schnute, Logistic and Gompertz models was successfully predicted ( r 2 > 0.96 and RSS ⩾ 0.003) biomass production by S. platensis as function of nitrate and salt concentrations. Low residual sum of squares (RSS) and high regression coefficients ( r 2) indicated that used models were well fitted to the experiment data and it could be regarded as sufficient to describe biomass production of Spirulina sp. Biological variables i.e. production rate ( μ) and lag time ( λ) for S. platensis ranged 0.012–0.034 h −1 and 2.43–5.85 h, respectively from biomass production were successfully predicted by modified Logistic model according to low RSS and F-testing value.