Optimization of fermentation condition for co-production of ethanol and 2,3-butanediol (2,3-BD) from hemicellolosic hydrolysates by Klebsiella oxytoca XF7

Microbial production of ethanol and 2,3-butanediol (2,3-BD) from agro-residues has been attracting interest because of their applications in various industries, including generation of biofuel molecules. In the present investigation, the hemicellulosic fraction of corncob was hydrolyzed by indigenous holocellulase from novel psychrotolerant Aspergillus niger SH3 resulting in high xylose release (34.61?g?L^?1), followed by the bioconversion of xylose to ethanol and 2,3-BD. Taguchi design was adopted to optimize the process which resulted in 5.25- and 3.31-fold increase in 2,3-BD (12.18?±?0.53?g?L^?1) and ethanol (4.08?±?0.03?g?L^?1), as compared with un-optimized condition. For the first time, co-production of ethanol and 2,3-BD from the corncob hemicellulosic hydrolysate was performed using a newly isolated Klebsiella oxytoca XF7 strain, under the optimized fermentation conditions. These results suggest that K. oxytoca XF7 is a promising candidate for co-production of ethanol and 2,3-BD, with high xylose conversion efficiency (96.65%), facilitating the economical production of biofuel molecules.     

Inactivation efficacy of plasma-activated water: influence of plasma treatment time,exposure time and bacterial species

This study aimed to evaluate the influence of plasma treatment time, bacterial exposure time to PAW and bacterial species on the inactivation efficacy of plasma-activated water (PAW), with additional investigation of the inactivation mechanisms of PAW. Six bacterial species, including Listeria innocua, Staphyloccus aureus, Escherichia coli, Pseudomonas fluorescens, Shewanella putrefaciens and Aeromonas hydrophila were selected as the representative bacteria. The initial bacterial concentration was around 7 log CFU ml⁻¹ after mixing with PAW, and the inactivation efficacy was measured after different exposure times during the 4 °C storage. Scanning electron microscopy (SEM) images of the bacteria after PAW treatment were carried out to inspect the cell structure damage, and physicochemical properties of PAW, including pH, conductivity and long-living reactive species of H₂O₂, , and , were examined. The results showed that the inactivation efficacy of PAW was positively correlated with plasma treatment time and bacterial exposure time, and for the species examined in this study, the Gram-negative species were more sensitive to PAW than the Gram-positive species. Cell structure damage, including shrinkage, distortion, or holes, was observed after PAW treatment. The pH of PAW was acidified to 2.5–2.9, and conductivity was significantly increased to 518.0 μs cm⁻¹. and H₂O₂ were reduced during the 48 h storage, while an increased concentration was observed for . This study demonstrated that the processing parameters of plasma treatment time, exposure time and characteristics of bacteria can significantly affect the inactivation efficacy of PAW.     

A multilevel hybrid anomaly detection scheme for industrial wireless sensor networks

Real-time sensing plays an important role in ensuring the reliability of industrial wireless sensor networks (IWSNs). Sensor nodes in IWSNs have inherent limitations that give rise to different anomalies in the network. These anomalies can lead to disastrous and harmful situations or even serious system failures. This article presents a formulation to the design of an anomaly detection scheme for detecting the anomalous node along with the type of anomaly. The proposed scheme is divided into two major parts. First, spatiotemporal correlation within a cluster is obtained for the normal and anomalous behavior of sensor nodes. Second, the multilevel hybrid classifier is used by combining the sequential minimal optimization support vector machine (SMO-SVM) as a binary classifier with optimally pruned extreme learning machine (OP-ELM) as a multiclass classifier for detection of an anomalous node and type of anomalies, respectively. Mahalanobis distance-based lightweight K-Medoid clustering is used to build a new set of training datasets that represents the original training dataset, by significantly reducing the training time of a multilevel hybrid classifier. Results are analyzed using standard WSN datasets. The proposed model shows high accuracy, i.e., 94.79% and detection rate, i.e., 94.6% with a reduced false positive rate as compared to existing hybrid methods.     

An efficient image encryption scheme using elementary cellular automata with novel permutation box

Multimedia Tools and Applications - Digital image communication over public networks requires a high level of security to protect picture elements that represent information. Security is an...     

Uncertainty Assessment in Cognitive Load for Multiple Object Tracking Based on EEG

Cognitive load varies the attention level, which has serious consequences in complex dynamic situations. Assessment of uncertainty in cognitive load during multiple object tracking task is necessary, as it is used to improve the cognitive capabilities. The present research work investigates the uncertainty in cognitive load of multiple object tracking task using electroencephalograph (EEG) on 25 football players. A d2 test of neuropsychological measure of attention was employed before starting the experiment. Each player participated in four levels of the task with variation in the cognitive load, which varies in terms of the targets from 2 to 5. Percentage changes in the power spectral density were estimated for the cognitive levels. Results show that the percentage changes were much more in high cognitive load than in low cognitive load. Significant changes (p < 0.05) were observed in level 1 (− 11.07 to 1.91%), level 2 (− 3.13 to − 14.51%), level 3 (− 6.33 to − 19.46%) and level 4 (− 8.10 to − 20.88%). Variation in the EEG data in terms of the combined uncertainty corresponds very well with low to high cognitive loads. The fourth level of the task with high cognitive load has more uncertainty than the low cognitive load levels. The results are useful for assessing the cognitive state of the player, which is valuable for the design of the effective training model.     

Catalytic properties of dispersed iron oxides Fe2O3/MO2 (M = Zr,Ce, Ti and Si) for sulfuric acid decomposition reaction: Role of support

Supported iron oxides have been established as an important class of catalyst for high temperature sulfuric acid decomposition. With an objective to elucidate the role of support in modifying the overall catalytic properties of dispersed iron oxide catalysts, a series of supported iron oxide based catalysts, Fe₂O₃ (15 wt%)/MO₂ (M = Zr, Ce, Ti and Si), synthesized by adsorption-equilibrium method, is investigated for sulfuric acid decomposition reaction. The structure of dispersed iron oxide phases largely depended on the nature of the support oxide as revealed by the XRD and Mössbauer studies. α-Fe₂O₃ is found to be present as a major phase on ZrO₂ and CeO₂ support while ε-Fe₂O₃ was the major phase on silica supported iron oxide. On the other hand, presence of mixed oxide Fe₂TiO₅ was revealed over TiO₂ support. Strong dispersed metal oxide-support interactions inhibited the total reduction of the dispersed phase on SiO₂ and TiO₂ as compared to complete reduction of dispersed iron oxide on CeO₂ and ZrO₂ supports during temperature programmed reduction upto 1000 °C. The order of catalytic activity at a temperature of ～750 °C is observed as Fe₂O₃/SiO₂ > Fe₂TiO₅/TiO₂ > Fe₂O₃/ZrO₂ > Fe₂O₃/CeO₂, while at higher temperatures of ～900 °C the SO₂ yield is found to be comparable for all catalysts. A relationship between the rate of sulfate decomposition and catalytic activity is established through detailed TG-DTA investigations of sulfated catalyst and support. Considerable influence of the support oxide on the composition, structure, redox properties, morphology and catalytic activities of the active iron oxide dispersed phase has been observed. Thus, the support oxides operate as a critical component in the complex supported metal oxide catalysts and these findings might influence the design and development of future high temperature sulfuric acid decomposition catalysts.     

Dietary exposure assessment of β-glucan in a barley and oat based bread

The objective of this study was to develop a β-glucan human exposure assessment model for a barley and a oat based bread and to compare the resulting exposure to the current FDA recommendation for a health promoting effect (3 g β-glucan/day, 0.75 g/portion of β-glucan). Three formulated barley and oat based breads with 30% (S1), 50% (S2) and 70% (S3) substitution of wheat flour were used in the Monte Carlo simulation model to predict human consumption levels. The level of soluble β-glucan was found to reach 0.77 g/portion for some barley and oat based breads with a substitution level of S3. Under normal consumption patterns, consumption of barley based bread can meet up to 50% of the FDA recommended intake with S1, 70% with S2, and 100% with S3, whereas by consuming an oat based bread the FDA recommended intake is met 30% with S1, 50% with S2 and 70% with S3. The model predicted that total cholesterol (TC) lowered with an increase intake of β-glucan content from ?0.27 to ?0.30 mmol/l and ?0.18 to ?0.29 mmol/l from S1 to S3 for barley and oats based bread, respectively. No significant change was noted for the blood glucose level.