Sum Of Squared Values Research Articles

Comparison of the Effectiveness Lateral Position 30 Degrees and 90 Degrees on Decubitus Events in Stroke Patients

Aims: Stroke is the second leading cause of death and disability globally, characterized by sudden neurological deficits. Pressure ulcers, caused by prolonged bed rest, can be managed non-pharmacologically through immobilization and routine tilting to prevent decubitus. To compare the effectiveness of the lateral position with an inclination of 30 and 90 degrees on the risk of decubitus in stroke patients, in this study the control group was tilted 90 degrees and the intervention group was tilted 30 degrees. Method: The sampling method is non-probability sampling with purposive sampling, a sample of 22 patients. Results: Data analysis using Reapeted Measurement Anova resulted in a value in the experimental group resulting in a sum of square value of 24.9, f value 14.3, P < 0.001, while in the control group a sum of square value of 76.5, f value 26.8, P < 0.001 was obtained, so it can be concluded that the lateral position with an inclination of 90 degrees and 30 degrees has the same effect, namely providing a significant influence on reducing the risk of decubitus. Conclusions: The study reveals that stroke patients aged 56-65 have a higher risk of pressure ulcers, with low-weight patients at higher risk. Early lateral position intervention and nutrition can significantly reduce the risk of decubitus, highlighting the importance of early wound healing.

Dual-responsive drug carrier based on zeolitic imidazolate framework-8 incorporated into gold nanoparticles/chitosan/copper sulfide for controlled drug delivery

Currently, two major obstacles in effective treatment of breast cancer are development of resistance to chemotherapeutic drugs by cancer cells and the off-target cytotoxicity of these drugs. The main aim of this study was to synthesize a dual responsive nanocarrier by incorporating the zeolitic-imidazolate framework-8 into the gold nanoparticles/chitosan/copper sulfide for controlled release of doxorubicin. The prepared nanocarrier was characterized using various techniques, including Fourier transform infrared spectroscopy, scanning electron microscopy, thermogravimetric analysis, X-ray diffraction, and zeta potentials. The optimum experimental conditions, including adsorbent dosage (0.01–0.03 g), pH of solutions (5–9), contact time (5–15 min), and temperature (25–45 °C) at three levels were studied using the central composite design based on the response surface methodology. Our results indicated highest response (99 %) with 0.03 g of nanocarrier, at pH of 5, time of 10 min, and temperature of 35 °C. The obtained data were further studied with seven isotherm models. Based on the lower error sum of square values (0.059–0.514) and higher correlation coefficient values (0.9857–0.9972), Langmuir and Sips isotherm models were observed to be the best fitted. The in-vitro drug release study indicated 37.97 % of the drug being released in the simulated environment with physiological conditions, while 71.29 % of the drug was released in the simulated cancer cells condition at higher temperature. This data confirms the pH and temperature sensitivity of the nanocarrier. Enhanced drug release from the nanocarrier indicated low cell viability in the breast cancer cell line (MCF-7) under near-infrared laser irradiation. The IC50 values of nanocarrier and pure drug in MCF-7 cells were 243.95 and 4.442 μg L−1, respectively. Based on these data, we are concluding that our dual responsive nanocarrier has the potential to overcome the problems development of resistance of cancer cells and toxicity associated with current treatment of breast cancer.

Role of Two-Way Asymmetrical Communication in Sustaining Public Relations

Internet technology's worldwide success and adoption have provided organizations with direct access to their constituents and customers. Especially, organizations relying on online platforms provide comparatively better services and have strong relations with their clients. This research also focused on relevant phenomena in the United Arab Emirates banking sector organizations. The researchers employed a cross-sectional design and randomly selected a sample of n=400 individuals. Results revealed a significant impact of customer support services on providing product information (p>0.008) and service quality (p>0.000). Further, the effect of service quality on Artificial Intelligence also remained significant (p>0.000). Besides, Artificial Intelligence is also found significantly impact the Public Relations of Emirati banks (p>0.006). Finally, the mediating impact of communication skills on Artificial Intelligence and Public Relations remained significant (p>0.088). Moreover, the Artificial Neural Network (ANN) revealed the Sum of Square Values at 568.19, the Overall Relative Error value at 0.813, and the accuracy level at 18.7% (training). While, regarding the testing, the Sum of Square Values remained at 256.80 and the Average Overall Relative Error value remained at 0.861, indicating an overall accuracy of 13.9%. Thus, it is concluded that the importance of two-way communication can be determined because it helps determine and understand the customers' needs and demands. The more an organization understands its customers, the more it fulfills their expectations, indicating the importance of two-way communication. Finally, this research recommends more studies regarding AI-enabled Emotional Intelligence in other sectors to dig out in-depth results. Doi: 10.28991/ESJ-2024-08-03-020 Full Text: PDF

Associations of Grazing and Rumination Behaviours with Performance Parameters in Spring-Calving Dairy Cows in a Pasture-Based Grazing System.

This study investigated the relationship of the length of time spent grazing and ruminating with the performance parameters of spring-calved grazing dairy cows (n = 162) over the lactation period for three lactation seasons (n = 54 per season). The cows were Holstein Friesian (HFR), Jersey (JE), and a crossbreed of Holstein Friesian/Jersey (KiwiCross), with 18 cows from each breed. The cows were either in their 1st, 2nd, 3rd, or 4th lactation year, and had different breeding worth (BW) index values (103 < BW > 151). The cows were managed through a rotational grazing scheme with once-a-day milking in the morning at 05:00 h. The cows were mainly fed on grazed pastures consisting of perennial ryegrass (Lolium perenne), red clover (Trifolium pretense), and white clover (Trifolium repens), and received additional feeds on various days in the summer and autumn seasons. This study used an automated AfiCollar device to continuously record the grazing time and rumination time (min/h) of the individual cows throughout the lactation period (~270 days) for three consecutive years (Year-1, Year-2, and Year-3). The milk yield, milk fat, milk protein, milk solids, liveweight, and body condition score data of the individual animals for the study years were provided by the farm. PROC CORR was used in SAS to determine the correlation coefficients (r) between the behaviour and production parameters. A general linear model fitted with breed × lactation year, individual cows, seasons, feed within the season, grazing time, rumination time, as well as their interactions, was assessed to test the differences in milk yield, milk fat, milk protein, milk solids, liveweight, and body condition score. The type I sum of squares values were used to quantify the magnitude of variance explained by each of the study factors and their interactions in the study variables. Grazing time exhibited positive associations with MY (r = 0.34), MF (r = 0.43), MP (r = 0.22), MS (r = 0.39), LW (r = -0.47), and BCS (r = -0.24) throughout the study years. Rumination time was associated with MY (r = 0.64), MF (r = 0.57), MP (r = 0.52), and MS (r = 0.57) in all study years, while there were no effects of rumination time on LW (r = 0.26) and BCS (r = -0.26). Grazing time explained up to 0.32%, 0.49%, 0.17%, 0.31%, 0.2%, and 0.02%, and rumination time explained up to 0.39%, 6.73%, 4.63%, 6.53%, 0.44%, and 0.17% of the variance in MY, MF, MP, MS, LW, and BCS, respectively.

Parameter identification of proton exchange membrane fuel cell based on swarm intelligence algorithm

Accurate estimation of the model parameters of a fuel cell is extremely important to study its system behavior. This paper presents a parameter identification method based on improved artificial bee colony algorithm (IABC). Inspired by the cross operation of genetic algorithm and the cross and mutation operation of differential evolution algorithm, this study improves the position update formula of ABC in the parameter optimization process, thus the convergence accuracy and speed of IABC algorithm are improved. The algorithm was used to identify 3 typical standard test cases, namely 250W FC, BCS 500W and NedStack PS6 stack, and the minimum sum of squares (SSE) error between PEMFC measured voltage data and estimated voltage data was defined as the objective function. The SSE values of the three PEMFCs reached 0.29686, 0.011718 and 2.9848, respectively. The model I/V polarization curve estimated by IABC algorithm has a high fitting degree with the experimental measured curve, which indicates the accuracy of IABC algorithm. To verify the superiority of the proposed IABC algorithm, the identification results obtained by some optimization algorithms are compared and ANOVA (analysis of variance) is performed. Results show that the IABC algorithm has faster convergence speed and better robustness.

Morpho-Molecular Genetic Diversity Analysis of Little Millet (Panicum sumatrense) using Yield Attributing Traits and ISSR Markers to Evaluate its Performance as a Summer Crop

The knowledge of genetic diversity among the genotypes and its relationship plays a vital role for genetic improvement in crop breeding programs. The present study was conducted to assess the performance of 18 little millet genotypes in local conditions of Bihar and analyze the genetic diversity by using morphological traits and 16 ISSR markers. The field experiment was conducted in RBD design in the month of April 2020, to evaluate the performance of the plant as a summer crop. ANOVA analysis revealed the presence of substantial amount of variability among 18 genotypes. The maximum mean sum of square values was recorded for plant height followed by flag leaf length within the genotypes. The descriptive statistical analysis showed that plant height followed by length of flag leaf contributed the most towards the divergence. A total of 117 alleles including 43 unique alleles and 74 shared alleles were obtained by amplification with ISSR primers. All the primers were highly polymorphic and produced amplified product with a size ranged between 354-2315 bp. The primers UBC 807 followed by HB 10 and UBC 814 were found to be the most effective in detecting genetic diversity. The dendrogram based on Dice’s similarities coefficient, grouped the genotypes into five clusters. The genotypes WV-168 and WV-115 followed by WV-122 and WV-124 were found to have greatest variability. The present study identified genotypes which can be grown as summer crops. The diversified genotypes among the population were also identified, which could be utilized as parents for breeding program.

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.

Forward Problems Solving of Groundwater Flow using Stochastic Groundwater Vistas Method

In the forward problems, the hydraulic head value can be found by knowing the value of the groundwater parameter. Parameters of groundwater such as hydraulic conductivity, vary over space due to the variation of aquifer properties. Consequently, it is difficult or almost impossible to treat these kinds of variability by a deterministic approach because there is no exact value to be used as input for a parameter. The objective of this research was to obtain a mathematical model of groundwater flow made with the Groundwater Vistas Program that is in accordance with the physical model. Mathematical modeling of groundwater flow using the Groundwater Vistas Program with a stochastic approach and Monte Carlo simulation method where the input data (hydraulic conductivity, hydraulic head) is obtained from the physical model. Results showed that the sum of squares value from the scatter plot diagram of all realization points had a very small value (close to or even zero). The residual mean diagram showed the error value of all realizations had a very low value close to zero. The calculated head value (computed) compared with the results of the observation had a fairly small difference value (ranging from 0.0006−0.009 m). These results were considered quite good because in modeling it is impossible to get modeling results that are exactly the same as those being modeled. The results show that Groundwater Vistas can be used for modeling with very small errors and it can estimate values of hydraulic heads quite well.

Improving drilling performance through optimizing controllable drilling parameters

The prediction of the drilling rate of penetration (ROP) is one of the key aspects of drilling optimization due to its significant role in reducing expensive drilling costs. Many variables could affect ROP, which can be classified into two general categories; controllable operational variables and uncontrollable or environmental variables. Minimizing the drilling cost can be achieved through optimizing the controllable drilling parameters. As a direct result, the drilling speed will be increased while maintaining safe practices. The primary purpose of this study is to address the simultaneous impact of controllable parameters such as weight on bit (WOB), revolutions per minute, and flow rate (FR) on the rate of penetration (ROP). Response surface methodology was applied to develop a mathematical relation between operational controllable drilling parameters and ROP. To accomplish this, actual field datasets from several wells drilled in Southern Iraq in different fields were used. The second purpose of this study was to identify all prospective optimal ranges of these controllable parameters to obtain superior drilling performance with an optimum ROP. The obtained results showed that the developed model offers a cost-effective tool for determining the maximum ROP as a function of controllable parameters with reasonable accuracy. In addition, the proposed model was used to estimate optimal combinations of controllable drilling parameters for various depths. The results have shown that FR has the most significant effect on ROP variation with a sum of squares values of 23.47. Applying high WOB does not permanently improve ROP but could result in reducing ROP for some cases. The developed mechanical specific energy model for polycrystalline diamond compact (PDC) bit with vertical and deviated wells can estimate combinations of controllable drilling parameters. The developed model can be successfully applied to predict and optimize the drilling rate when using PDC bits, hence reducing the drilling time and the associated drilling cost for future wells.

Relative contribution of expected sum of squares values for soybean genotypes × growing environments interaction

The objective of this work was to assess the effects and tendencies weighted by genotypes x environments interaction for soybean, as well as to employ a biometric approach through the relative contribution of the sum of squares expected values (RCSS) and to define which levels of the variation sources determine the differential effects of the interaction. The experimental design was randomized blocks arranged in a factorial scheme (four growing environments x 20 soybean genotypes). The relative contribution of expected sums of squares values to soybean genotypes x growing environments interaction defined that the environment Tenente Portela - RS significantly influence plant height, number of pods per plant, number of reproductive nodes in the main stem, number of reproductive nodes in the ramifications, number of grains per plant and grain yield. The variation factor soybean genotypes define that number of pods per plant, number of reproductive nodes in the ramifications, number of grains per plant and grain yield are potentiated by genotype TMG 7161 RR. The biometric approach is efficient to understand the treatment levels and the slicing of simple effects of a factorial experiment, being possible to apply this methodology extensively in soybean.

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.

Machine Learning Algorithms Can Use Wearable Sensor Data to Accurately Predict Six-Week Patient-Reported Outcome Scores Following Joint Replacement in a Prospective Trial

BackgroundTracking patient-generated health data (PGHD) following total joint arthroplasty (TJA) may enable data-driven early intervention to improve clinical results. We aim to demonstrate the feasibility of combining machine learning (ML) with PGHD in TJA to predict patient-reported outcome measures (PROMs). MethodsTwenty-two TJA patients were recruited for this pilot study. Three activity trackers collected 35 features from 4 weeks before to 6 weeks following surgery. PROMs were collected at both endpoints (Hip and Knee Disability and Osteoarthritis Outcome Score, Knee Osteoarthritis Outcome Score, and Veterans RAND 12-Item Health Survey Physical Component Score). We used ML to identify features with the highest correlation with PROMs. The algorithm trained on a subset of patients and used 3 feature sets (A, B, and C) to group the rest into one of the 3 PROM clusters. ResultsFifteen patients completed the study and collected 3 million data points. Three sets of features with the highest R2 values relative to PROMs were selected (A, B and C). Data collected through the 11th day had the highest predictive value. The ML algorithm grouped patients into 3 clusters predictive of 6-week PROM results, yielding total sum of squares values ranging from 3.86 (A) to 1.86 (C). ConclusionThis small but critical proof-of-concept study demonstrates that ML can be used in combination with PGHD to predict 6-week PROM data as early as 11 days following TJA surgery. Further study is needed to confirm these findings and their clinical value.

PERFORMANCE EVALUATION OF MULTIBED ADSORBENT ON REMOVAL OF HEXAVALENT CHROMIUM THROUGH VARIOUS KINETIC MODELS

In this study, an idea of using multiple layers of adsorbents in adsorption column is proposed for treatment of synthetic wastewater for removal of Cr(VI) ions. In the present study, an effort has been made to study and compare the performance of fixed bed column with single bed adsorbent (RH only) and multi bed adsorbent of Rice Husk (RH), Saw Dust (SD) and Coir Dust (CD) in removal of Cr(VI) from synthetic wastewater and arrive at the parameters of the adsorption column that are useful for process design. From the characterization studies, it has been observed that carbon, aluminium and silica were the major components of the three natural adsorbents studied. It was found that, for better heavy metal removal, natural adsorbents with a percentage of fineness of around 54% could be used in adsorption studies. From the breakthrough curves of single bed and multi bed adsorption columns, it was evident that the multi bed adsorption column performed better. The time taken to achieve the breakthrough point and the exhaustion point in multi bed column is 2.5 times and 1.9 times greater than the time taken by single bed adsorption studies respectively. It was observed that in multi bed adsorption column, at greater bed depths, the significant increase in metal uptake capacity was due to the increase in contact time. Kinetic models viz. Thomas model, Yoon-Nelson model and Bed Depth Service Time model (BDST) were used to predict the performance of the column and sum of squares (SS) error analysis was carried out to test the accuracy of model equations. Higher R2 value and smaller SS value obtained from Thomas model proves that it is suitable to explain the adsorption of Cr(VI) in single and multi bed adsorption column of natural adsorbents. Experimental data fitted well with BDST model where R2 = 0.997. From the cost analysis, multi bed adsorption column was proven to be economical and confirmed that the locally and abundantly available agricultural wastes viz. RH, SD and CD could be used as an alternate to commercially available activated carbon for the removal of Cr(VI) from synthetic wastewater.

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.

Advantages of Binomial Likelihood Maximization for Analyzing and Modeling Cell Survival Curves.

Mathematical analysis of cell survival allows parameter estimation for radiobiological models and selection of an appropriate model. To our knowledge, no rigorous comparisons on the accuracy of various methods used for such analysis have been performed. In this study we compared three methods: 1. maximization of binomial log-likelihood (BLL); 2. minimization of sum of squares (SS); and 3. method 2 using log-transformed data (SSlog). Analysis of Monte Carlo simulated data (A) generated from the linear-quadratic (LQ) model showed that model parameter estimates from the BLL method were more accurate and less affected by "noise" than those from other methods. Analysis of actual breast cancer cell data showed substantial differences among LQ parameters estimated by the three methods (B). To select among radiobiological models, we used: 1. Sample size-corrected Akaike information criterion (AICc), calculated from BLL method-generated log-likelihood values; and 2. Adjusted coefficient of determination (R(2)), calculated from SS/SSlog method-generated SS values. Analysis of data simulated from the repair-misrepair (RMR) formalism (C) showed that the first approach outperformed the second approach at identifying the true data-generating model. Examples of how the first approach discriminates between several models were explored using actual mouse (H2AX-proficient and -deficient) and human [DNA-dependent protein kinase (DNA-PK)-proficient and -deficient] cell data (D). Based on this work, we concluded that BLL maximization combined with AICc-based model selection constitutes an effective method for analyzing cell survival data.

Simple and accurate allometric model for leaf area estimation in Vitis vinifera L. genotypes

The aim of the present experiment was to evaluate the currently used allometric models for Vitis vinifera L., as well as to develop a simple and accurate model using linear measurements [leaf length (L) and leaf width (W)], for estimating the individual leaf area (LA) of nine grapevine genotypes. For model construction, a total of 1,630 leaves coming from eight genotypes in 2010 was sampled during different leaf developmental stages and encompassed the full spectrum of leaf sizes. The model with single measurement of L could be considered an interesting option because it requires measurement of only one variable, but at the expense of accuracy. To find a model to estimate individual LA accurately for grapevine plants of all genotypes, both measurements of L and W should be involved. The proposed linear model [LA = −0.465 + 0.914 (L × W)] was adopted for its accuracy: the highest coefficient of determination (> 0.98), the smallest mean square error, the smallest prediction sum of squares, and the reasonably close prediction sum of squares value to error sum of squares. To validate the LW model, an independent data set of 200 leaves coming from another genotype in 2011 was used. Correlation coefficients showed that there was a highly reliable relationships between predicted leaf area and the observed leaf area, giving an overestimation of 0.8% in the prediction.

Study of the suitability of the central composite design to predict the inactivation kinetics by pulsed electric fields (PEF) in Escherichia coli, Staphylococcus aureus and Pseudomonas fluorescens in milk

The kinetics of inactivation of Escherichia coli K12, Staphylococcus aureus and Pseudomonas fluorescens due to pulsed electric fields (PEF) was investigated in the present study. The effects of treatment inlet temperature (20–45°C), electric field strength (EFS; 20–42.5kV/cm) and treatment time (68–170μs, as a function of the flow rate) on microbial inactivation were studied with a central composite design using a response surface model (ccRSM). Reductions of 5 and 5.2log10 cycles were achieved for E. coli and S. aureus, respectively, at 32.5°C, 40kV/cm and 89μs. Viability of P. fluorescens was reduced by 5.3log10 cycles at 32.5°C, but at slightly higher EFS (42.5kV/cm) and a longer treatment time (106μs). Statistical models describing microbial inactivation by PEF (R2≥0.91) were derived from the design. The study of the sum of square values after regression analysis showed that EFS was the factor with greatest effect on microbial inactivation. The present study confirms the suitability of using ccRSM as a tool for investigating and predicting microbial inactivation.

Selective removal of dibenzothiophene from commercial diesel using manganese dioxide-modified activated carbon: a kinetic study

With a total concentration of 7055 mgS/kgfuel, the content of organosulphur compounds (OSCs) in local diesel is 20 times higher than the regulated value. Analysis revealed that 30% of OSC is originated from dibenzothiophene (DBT). It is known that DBT is a hardly removable compound and selective adsorbents are often needed for its removal with low affinity for other diesel components. In this work, a selective adsorbent based on surface modification of activated carbon (AC) by MnO2 is prepared for DBT removal from diesel. The porous nature of AC enabled carrying large amounts of MnO2 particles to end up with a selective adsorber for DBT. The best performance was observed at a surface loading of 26.8% of Mn and DBT is favourably removed over mono- and diaromatics hydrocarbons in diesel. Adsorption kinetics of DBT is studied under a high initial concentration of 835–11,890 mg/kg and at a ratio of 11 cm3/g (diesel:carbon). The results indicated a fast removal process after surface modification where 96% of the surface is occupied within 30 min of interaction. Kinetic data were best presented by reaction-based models with low prediction error sum of squares values 0.5–47.0, while, diffusion-based models showed limited application for modelling DBT adsorption. Accordingly, adsorption process is controlled by surface reactions and pore diffusion has a minor role in the overall process. The modified adsorbent is satisfactorily regenerated using n-hexane at 65°C.