Design of reaction conditions for the enhancement of microbial degradation of dyes in sequential cycles

The present study evaluated the potential of white-rot fungal strain Coriolus versicolor to decolorize five structurally different dyes in sequential batch reactors under optimized conditions. The experiments were run continuously for seven cycles of 8 d each. High decolorizing activity was observed even during the repeated reuse of the fungus, especially when the old medium was replaced with fresh medium after every cycle. Biodegradation was the dominating factor as the fungus was able to produce the enzyme laccase mainly, to mineralize synthetic dyes. The nutrients and composition of the medium played important roles in sustaining the decolorisation potential of the fungus. Corncob was found be an easy and cheap substitute for carbon source for the fungus. Glucose consumption by the fungus was in accordance to its decolorisation activity and chemical oxygen demand （COD） reduction.

Experimental investigation and optimization of weld bead characteristics during submerged arc welding of AISI 1023 steel

Submerged arc welding(SAW), owing to its high deposition rate and high welding quality, is widely used in the fabrication of pressure vessel, marine vessel, pipelines and offshore structures. However, selection of an optimum combination of welding parameters is critical in achieving high weld quality and productivity. In this work, initially, the SAW experiments were performed using fractional factorial design to analyze the effect of direct and indirect input parameters, namely, welding voltage, wire feed rate,welding speed, nozzle to plate distance, flux condition, and plate thickness on weld bead geometrical responses viz. bead width, reinforcement, and penetration. The bead on plate technique was used to deposit weld metal on AISI 1023 steel plates. The effect of SAW input parameters on response variables were analyzed using main and interaction effects. The linear regression was used to develop the mathematical models for the response variable. Then, the multi-objective optimization of input parameters was carried out using desirability approach, genetic algorithm and Jaya algorithm. The Jaya algorithm offered better optimization results as compared to desirability approach, genetic algorithm.

Rheological fluid motion in tube by metachronal waves of cilia

This paper presents a theoretical study of a non-linear rheological fluid transport in an axisymmetric tube by cilia. An attempt has been made to explain the role of cilia motion in the transport of fluid through the ductus efferent of the male reproductive tract. The Ostwald-de Waele power-law viscous fluid is considered to represent the rheological fluid. We analyze pumping by means of a sequence of cilia beats from rowto-row of cilia in a given row of cells and from one row of cells to the next(metachronal wave movement). For this purpose, we consider the conditions that the corresponding Reynolds number is small enough for inertial effects to be negligible, and the wavelengthto-diameter ratio is large enough so that the pressure can be considered uniform over the cross section. Analyses and computations of the fluid motion reveal that the time-average flow rate depends on ε, a non-dimensional measure involving the mean radius a of the tube and the cilia length. Thus, the flow rate significantly varies with the cilia length.Moreover, the flow rate has been reported to be close to the estimated value 6 × 10ml/h for human efferent ducts if ε is near 0.4. The estimated value was suggested by Lardner and Shack(Lardner, T. J. and Shack, W. J. Cilia transport. Bulletin of Mathematical Biology, 34, 325–335(1972)) for human based on the experimental observations of flow rates in efferent ducts of other animals, e.g., rat, ram, and bull. In addition, the nature of the rheological fluid, i.e., the value of the fluid index n strongly influences various flow-governed characteristics. An interesting feature of this paper is that the pumping improves the thickening behavior for small values of ε or in free pumping(?P = 0) and pumping(?P > 0) regions.

Organic bulk heterojunction enabled with nanocapsules of hydrate vanadium pentaoxide layer for high responsivity self-powered photodetector

This article demonstrates the fabrication of organic-based devices using a low-cost solution-processable technique.A blended heterojunction of chlorine substituted 2D-conjugated polymer PBDB-T-2Cl,and PC71BM supported nanocapsules hy-drate vanadium penta oxides(HVO)as hole transport layer(HTL)based photodetector fabricated on an ITO coated glass sub-strate under ambient condition.The device forms an excellent organic junction diode with a good rectification ratio of~200.The device has also shown excellent photodetection properties under photoconductive mode(at reverse bias)and zero bias for green light wavelength.A very high responsivity of~6500 mA/W and high external quantum efficiency(EQE)of 1400%have been reported in the article.The proposed organic photodetector exhibits an excellent response and recovery time of~30 and~40 ms,respectively.

Simulation Analysis on Applicability of Meta Material and PBG Based mm-Wave Planar Antenna for Advanced Cellular Technologies

Metamaterials have got a unique identification in the field of electromagnetic. Left handed metamaterials have been recognized through its working at high frequencies with larger bandwidth in antenna design. The author has proposed a multi substrates LH metamaterials with an array of split triangular antennas for wideband as well as for larger bandwidth. FR4 is being used at frequency 42 - 26 GHz with thickness of 3 mm for substrate and matching circuit is inserted to get high gain with minimum reflections. The number of rods in PBG is 100, which is found to get high return losses and ideal behavior of VSWR. Obtained results have been in good agreement such as S11 is approximately ﹣30 dB, VSWR 1.8 dB and the bandwidth enhancement is 2 GHz at frequency 5 GHz. All results are plotted using commercial software CST microwave studio (Version 2012) and MATLAB.

Stock values predictions using deep learning based hybrid models

Predicting the correct values of stock prices in fast fluctuating high-frequency financial data is always a challenging task.A deep learning-based model for live predictions of stock values is aimed to be developed here.The authors'have proposed two models for different applications.The first one is based on Fast Recurrent Neural Networks(Fast RNNs).This model is used for stock price predictions for the first time in this work.The second model is a hybrid deep learning model developed by utilising the best features of FastRNNs,Convolutional Neural Networks,and Bi-Directional Long Short Term Memory models to predict abrupt changes in the stock prices of a company.The 1-min time interval stock data of four companies for a period of one and three days is considered.Along with the lower Root Mean Squared Error(RMSE),the proposed models have low computational complexity as well,so that they can also be used for live predictions.The models'performance is measured by the RMSE along with computation time.The model outperforms Auto Regressive Integrated Moving Average,FBProphet,LSTM,and other proposed hybrid models on both RMSE and computation time for live predictions of stock values.

Spectrum Occupancy Prediction for Realistic Traffic Scenarios: Time Series versus Learning-Based Models

Spectrum occupancy information is neces-sary in a cognitive radio network(CRN)as it helps in modeling and predicting the spectrum availability for efficient dynamic spectrum access(DSA).However,in a CRN,it is difficult to ascertain a priori the pattern of the spectrum usage of the primary user due to its stochastic behavior.In this context,the spectrum occupancy predic-tion proves to be very useful in enhancing the quality of experience of the secondary user.This paper investigates the practical prowess of various time-series modeling approaches and the machine learning(ML)techniques for predicting spectrum occupancy,based on a spectrum measurement campaign conducted in Jaipur,Rajasthan,India.Moreover,the comparison analysis conducted between the above two approaches highlights the trade-off in terms of the respective performance depending upon the nature of the spectrum occupancy data.Nevertheless,prediction through ML-based recurrent neural network proves to perform reasonably well,thereby providing an accurate future spectrum occupancy information for DSA.