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.     

Efficacy of Ozone and Lactic Acid as Nonthermal Hurdles for Preservation of Sugarcane Juice

Thermal processing is the most widely adopted technology for preservation of juices; however, it is associated with significant changes in nutritional quality and flavor. Combination of nonthermal hurdles (ozone 1.2 g/h, 10 min, and lactic acid 0.5%) reduced total bacterial count of sugarcane juice by 4.3 log and controlled enzymatic activity to a moderate level (reduction of 60% and 72% activity of polyphenol oxidase and peroxidase, respectively). Combined treatment was comparable to thermal treatment in maintaining microbial and sensory quality of sugarcane juice during 1-month storage under refrigerated conditions; however, additional hurdles may be required for extended storage.     

Study of heat and mass transfer in indoor conditions for distillation

An indoor simulation study was carried out to evaluate heat and mass transfer relation for a semi-cylindrical metallic (opaque) condensing cover for higher yields at different operating temperatures under free and forced modes of operation. The objective is to design a distillation unit for higher yield for commercialization, particularly in India. Experiments have been conducted for the operating temperature range of 40°C to 80°C in a steady-state condition by using constant temperature bath. Data (temperature and yield) obtained from experimentation have been used to determine the values of coefficient C and n and, consequently, convective as well as evaporative heat transfer coefficients. It is inferred that a higher yield is obtained with an increase of temperature in the forced mode of operation as compared to that in the natural mode of operation due to a fast release of heat from the condensing cover.     

Experimental analysis and development of correlations for gas holdup in high pressure slurry co-current bubble columns

The effect of liquid and gas velocities, solid concentrations, and operating pressure has been studied experimentally in a 15 cm diameter air-water-glass beads bubble column. The superficial gas and liquid velocities varied from 1.0 to 40.00 cm/s and 0 to 16.04 cm/s, respectively, while the solid loading varied from 1 to 9%. The gas holdup in the column was reduced sharply as we switched from batch to co-current mode of operation. At low gas velocity, the effect of liquid velocity was insignificant; while at high gas velocity, increasing liquid velocity decreased the gas holdup. Drift flux approach was applied to quantify the combined effect of liquid and gas velocities over gas holdup. For co-current three phase flows, the gas holdup decreased with increase in solid loading for all pressures. But for batch operations, when solid loading was 5% or more, settling started leading to higher gas holdup. Increasing pressure from atmospheric conditions increased the gas holdup significantly, flattening asymptotically.     

Synthesis of highly pure and dense 0.9(KNbO3)-0.1(BaNi1/2Nb1/2O3-δ) ceramic with superior magnetic properties

Residual NiO phase is generally detected in 0.9(KNbO₃)–0.1(BaNi_1/2Nb_1/2O_3-δ) (KBNNO) synthesized using NiO as a nickel precursor by solid-state reaction. In this work, NiO phase is found to exist in the form of the residual NiO particles with a size of 100-200 nm using energy dispersive X-ray elemental mapping. These NiO residual particles are eliminated by using nickel acetate as a nickel precursor and a 100% perovskite phase KBNNO is successfully synthesized at as low as 600°C temperature. Furthermore, using the two-step sintering technique, 100% relative density is achieved in this material. The nickel acetate–based KBNNO shows a robust ferromagnetism with the saturation magnetization of 11.42 memu/g and the remanent magnetization of 3.89 memu/g which is 38 times higher than that of previously reported value in NiO-based KBNNO. Thus, a highly pure and fully dense KBNNO ceramic with superior magnetic properties is obtained using nickel acetate and by the two-step sintering method. This is a key step forward in the processing of KBNNO and is likely to have a significant impact on other physical properties of this newly invented and promising photovoltaic perovskite material.     

In vitro biocompatibility and antimicrobial activity of chitin monomer obtain from hollow fiber membrane

This study for the first time shows the effective utilization and production of chitin monomers at laboratory level, with immense potential for its biomedical application. Low molecular weight (LMW) N-acetylglucosamine (GlcNAc) is prepared by depolymerization of chitin using chemical method coupled with a physical separation method. A novel filtration strategy exploiting polysulfone hollow fiber membrane is used for the preparation of GlcNAc particles with 94% yield within 8.5 ± 0.5 h. This high efficiency is analyzed using high-pressure liquid chromatography. The GlcNAc obtained was further analyzed using dynamic light scattering, first derivative Fourier transform infrared spectroscopy, and X-ray diffraction techniques. The antimicrobial properties of GlcNAc, chitin, and GlcNAc/chitin mixture were investigated using minimal inhibitory concentration against S. aureus and E. coli. Bacteriostatic property was exhibited by high molecular weight chitin, while GlcNAc and GlcNAc/chitin mixture (LMW) demonstrated bactericidal activity. Blood biocompatibility below 0.25 g/ml and cytocompatibility with NIH3T3 fibroblast cells and the proliferative efficacy suggested its utilization and suitability of these particles in biological applications.     

Deep transfer modeling for classification of Maize Plant Leaf Disease

Multimedia Tools and Applications - Currently, Deep Learning is playing an influential role for Image analysis and object classification. Maize’s diseases reduce production that subsequently...     

Fusion of probabilistic A* algorithm and fuzzy inference system for robotic path planning

Robotic Path planning is one of the most studied problems in the field of robotics. The problem has been solved using numerous statistical, soft computing and other approaches. In this paper we solve the problem of robotic path planning using a combination of A* algorithm and Fuzzy Inference. The A* algorithm does the higher level planning by working on a lower detail map. The algorithm finds the shortest path at the same time generating the result in a finite time. The A* algorithm is used on a probability based map. The lower level planning is done by the Fuzzy Inference System (FIS). The FIS works on the detailed graph where the occurrence of obstacles is precisely known. The FIS generates smoother paths catering to the non-holonomic constraints. The results of A* algorithm serve as a guide for FIS planner. The FIS system was initially generated using heuristic rules. Once this model was ready, the fuzzy parameters were optimized using a Genetic Algorithm. Three sample problems were created and the quality of solutions generated by FIS was used as the fitness function of the GA. The GA tried to optimize the distance from the closest obstacle, total path length and the sharpest turn at any time in the journey of the robot. The resulting FIS was easily able to plan the path of the robot. We tested the algorithm on various complex and simple paths. All paths generated were optimal in terms of path length and smoothness. The robot was easily able to escape a variety of obstacles and reach the goal in an optimal manner.     

Prediction of Alzheimer’s Using Random Forest with Radiomic Features

Alzheimer’s disease is a non-reversible, non-curable, and progressive neurological disorder that induces the shrinkage and death of a specific neuronal population associated with memory formation and retention. It is a frequently occurring mental illness that occurs in about 60%–80% of cases of dementia. It is usually observed between people in the age group of 60 years and above. Depending upon the severity of symptoms the patients can be categorized in Cognitive Normal (CN), Mild Cognitive Impairment (MCI) and Alzheimer’s Disease (AD). Alzheimer’s disease is the last phase of the disease where the brain is severely damaged, and the patients are not able to live on their own. Radiomics is an approach to extracting a huge number of features from medical images with the help of data characterization algorithms. Here, 105 number of radiomic features are extracted and used to predict the alzhimer’s. This paper uses Support Vector Machine, K-Nearest Neighbour, Gaussian Naïve Bayes, eXtreme Gradient Boosting (XGBoost) and Random Forest to predict Alzheimer’s disease. The proposed random forest-based approach with the Radiomic features achieved an accuracy of 85%. This proposed approach also achieved 88% accuracy, 88% recall, 88% precision and 87% F1-score for AD vs. CN, it achieved 72% accuracy, 73% recall, 72% precisionand 71% F1-score for AD vs. MCI and it achieved 69% accuracy, 69% recall, 68% precision and 69% F1-score for MCI vs. CN. The comparative analysis shows that the proposed approach performs better than others approaches.     

Ergonomic Chair Design by Fusing Qualitative and Quantitative Criteria Using Interactive Genetic Algorithms

This paper emphasizes the necessity of formally bringing qualitative and quantitative criteria of ergonomic design together, and provides a novel complementary design framework with this aim. Within this framework, different design criteria are viewed as optimization objectives, and design solutions are iteratively improved through the cooperative efforts of computer and user. The framework is rooted in multiobjective optimization, genetic algorithms, and interactive user evaluation. Three different algorithms based on the framework are developed, and tested with an ergonomic chair design problem. The parallel and multiobjective approaches show promising results in fitness convergence, design diversity, and user satisfaction metrics.