Volatile and non-volatile compound profiles of commercial sweet pickled mango and its correlation with consumer preference

Sweet pickled mango named Ma-Muang Bao Chae-Im is a traditional preserved mango from Hat Yai, Thailand. This study investigated (I) volatile and non-volatile compound profiles of commercial Ma-Muang Bao Chae-Im and (II) their relationship to consumer preference. Untargeted metabolomics profiling was performed by gas chromatography-mass quadrupole-time of flight analysis. There were 117 volatile and 44 non-volatile compounds annotated in six commercial brands of Ma-Muang Bao Chae-Im. Furthermore, 46 volatile and 19 non-volatile compounds’ discriminant markers were found by Partial least square discriminant analysis. Among those markers, sorbic and benzoic acid were observed in several brands; moreover, the combination of both compounds altered the volatile profile, especially the ester group. Partial least square regression revealed that overall consumer liking is correlated to 1-heptanol; 1-octanol; acetoin; acetic acid, 2-phenylethyl ester; D-manitol; terpenes and terpenoids, while firmness to sucrose and L-(-)-sorbofuranose. On the other hand, most ester compounds were not related to consumer preference.     

The optimization of the photo-oxidation parameters to remediate wastewater from the textile dyeing industry in a continuous stirred tank reactor

The remediation of textile dying wastewater was carried out at ambient temperatures in a pilot-scale continuous stirred tank reactor by using the photo-Fenton oxidation process. The preliminary results suggest that the treatment system reached a steady state condition within 5–10 min after it was started up. By using a 2 ^k factorial design, the effects of various parameters on the removal efficiency of color, BOD and COD were identified under steady state conditions. The removal efficiencies of color and BOD were affected by the feed rate of H₂O₂ and Fe²⁺, whereas none of the parameters in the investigated ranges affected the removal efficiency of COD. Consequently, using univariate analysis to investigate higher parameter range values, the optimum conditions for treating textile wastewater were found to be 25 ml H₂O₂/min, 5 ml Fe²⁺/min and 90 W UV-A power for 20 min. In addition, the removal of all pollutants was enhanced within the acidic pH range. Approximately 69.2, 99.4 and 48.5% of color, BOD and COD were removed, respectively. However, the concentration of TDS increased slightly during the treatment period due to the formation of new species or intermediate oxidation products. Nevertheless, all values of pollutants in the treated wastewater except COD were in the range of the standard values permitted for discharge into the environment.     

Neural Agent Car-Following Models

This paper presents a car-following model that was developed using a neural network approach for mapping perceptions to actions. The model has a similar formulation to the desired spacing models that do not consider reaction time or attempt to explain the behavioral aspects of car following. The model's performance was evaluated based on field data and compared to a number of existing car-following models. The results showed that neural network models outperformed the Gipps and psychophysical family of car-following models. A qualitative drift behavior analysis also confirmed the findings. The model was validated at the microscopic and macroscopic levels, and the results showed very close agreement between field data and model outputs. Local and asymptotic stability analysis results also demonstrated the robustness of the model under mild and severe traffic disturbances     

Swarming Computational Techniques for the Influenza Disease System

The current study relates to designing a swarming computational paradigm to solve the influenza disease system (IDS). The nonlinear system’s mathematical form depends upon four classes: susceptible individuals, infected people, recovered individuals and cross-immune people. The solutions of the IDS are provided by using the artificial neural networks (ANNs) together with the swarming computational paradigm-based particle swarm optimization (PSO) and interior-point scheme (IPA) that are the global and local search approaches. The ANNs-PSO-IPA has never been applied to solve the IDS. Instead a merit function in the sense of mean square error is constructed using the differential form of each class of the IDS and then optimized by the PSOIPA. The correctness and accuracy of the scheme are observed to perform the comparative analysis of the obtained IDS results with the Adams solutions (reference solutions). An absolute error in suitable measures shows the precision of the proposed ANNs procedures and the optimization efficiency of the PSOIPA. Furthermore, the reliability and competence of the proposed computing method are enhanced through the statistical performances.     

Numerical Procedure for Fractional HBV Infection with Impact of Antibody Immune

The current investigations are presented to solve the fractional order HBV differential infection system (FO-HBV-DIS) with the response of antibody immune using the optimization based stochastic schemes of the Levenberg-Marquardt backpropagation (LMB) neural networks (NNs), i.e., LMBNNs. The FO-HBV-DIS with the response of antibody immune is categorized into five dynamics, healthy hepatocytes (H), capsids (D), infected hepatocytes (I), free virus (V) and antibodies (W). The investigations for three different FO variants have been tested numerically to solve the nonlinear FO-HBV-DIS. The data magnitudes are implemented 75% for training, 10% for certification and 15% for testing to solve the FO-HBV-DIS with the response of antibody immune. The numerical observations are achieved using the stochastic LMBNNs procedures for soling the FO-HBV-DIS with the response of antibody immune and comparison of the results is presented through the database Adams-Bashforth-Moulton approach. To authenticate the validity, competence, consistency, capability and exactness of the LMBNNs, the numerical presentations using the mean square error (MSE), error histograms (EHs), state transitions (STs), correlation and regression are accomplished.