Effect of microwave heating on quality and mycoflora of sorghum grain

Sorghum (cv. Maldandi M35-1) was modified to 12, 14 and 16% moisture content (m.c.) and heat-treated with microwave energy at 3 levels, for 30 sec (=4.5, 9 and 18 kJ), and 60 sec (=9, 18 and 36 kJ). The effect of microwave heating on rise and subsequent fall in grain temperature, reduction in m.c. and quality characteristics including germination, seedling dry matter, free fatty acids (FFA) and contaminant fungi was determined. The temperature attained and the moisture loss in the sorghum grain was affected by grain m.c. and the time of exposure. At the lowest and highest microwave treatment level grain temperatures reached 30–40°C and 90–101°C, respectively. However, a 60-sec treatment at the highest energy level was lethal for the grain, particularly at 14 and 16% m.c. The FFA values were unaffected by microwave treatment. Statistical analyses showed that the microwave power level and time individually, and power level × time interactions were significant for most quality characteristics. The fungi present most abundantly on the sorghum grain were Eurotium spp., Aspergillus candidus, A. niger and Penicillium spp. Increasing m.c. and microwave heating resulted in elimination of most fungi after a 30-sec exposure time. With a 60-sec exposure period, practically all fungi were eliminated from the grain.     

Hypersonic flight vehicle trajectory optimization using pattern search algorithm

In this work, trajectory optimization of an aerodynamically controlled hypersonic boost glide class of flight vehicle is presented. In order to meet the mission constraints such as controllability, skin temperature, and terminal conditions etc., the trajectory is optimized using a pattern search algorithm with the lift to drag (L/D) ratio as a control parameter. It is brought out that the approach offers a viable tool for optimizing trajectories for the considered class of vehicles. Further, the effects of the constraints on trajectory shape and performance are studied and the analysis is used to bring out an optimal vehicle configuration at the initial stage of the design process itself. The research also reveals that the pattern search algorithm offers superior performance in comparison with the genetic algorithm for this class of optimization problem.     

Vibration data-driven machine learning architecture for structural health monitoring of steel frame structures

A vibration data-based machine learning architecture is designed for structural health monitoring (SHM) of a steel plane frame structure. This architecture uses a Bag-of-Features algorithm that extracts the speeded-up robust features (SURF) from the time-frequency scalogram images of the registered vibration data. The discriminative image features are then quantised to a visual vocabulary using K-means clustering. Finally, a support vector machine (SVM) is trained to distinguish the undamaged and multiple damage cases of the frame structure based on the discriminative features. The potential of the machine learning architecture is tested for an unseen dataset that was not used in training as well as with some datasets from entirely new damages close to existing (i.e., trained) damage classes. The results are then compared with those obtained using three other combinations of features and learning algorithms—(i) histogram of oriented gradients (HOG) feature with SVM, (ii) SURF feature with k-nearest neighbours (KNN) and (iii) HOG feature with KNN. In order to examine the robustness of the approach, the study is further extended by considering environmental variabilities along with the localisation and quantification of damage. The experimental results show that the machine learning architecture can effectively classify the undamaged and different joint damage classes with high testing accuracy that indicates its SHM potential for such frame structures.     

Improving post irradiation stability of high density polyethylene by multi walled carbon nanotubes

Sterilization of implants and other clinical accessories is an integral part of any medical application. Although many materials are used as implants, polyethylene stands unique owing to its versatility. Carbon nanotubes are being used as a filler material to enhance the properties of polyethylene. However, the role of multi walled carbon nanotubes (MWCNTs) as an effective antioxidant and radical scavenger in resisting the deteriorating effects of sterilization is yet to be studied in detail. The present work is aimed to investigate the mechanical properties and oxidation stability of irradiated high density polyethylene (HDPE) reinforced by MWCNTs with various concentrations such as 0.25%, 0.50%, 0.75% and 1.00 wt.%. The composites were exposed to ⁶⁰Co source in air and irradiated at different dosage level starting from 25 to 100 kGy and then shelf aged for a period of 120 days prior to investigation. The loss in toughness, Young’s modulus and ultimate strength at 100 kGy for 1 wt.% MWCNTs composite were found to be 21.5%, 20.3% and 19.2%, respectively compared to that of unirradiated composite. FTIR and ESR studies confirmed the antioxidant and radical scavenging potentialities of MWCNTs with increased concentration and irradiation dosage. It was found that by the addition of 1 wt.% MWCNTs into virgin HDPE, the oxidation index of the composite at 100 kGy was decreased by 56.2%. It is concluded that the addition of MWCNTs into polyethylene not only limits the loss of mechanical properties but also improves its post irradiation oxidative stability.     

Integrating toxin gene expression,growth and fumonisin B1 and B2 production by a strain of Fusarium verticillioides under different environmental factors

The objective of this study was to integrate data on the effect of water activity (a_w; 0.995–0.93) and temperature (20–35°C) on activation of the biosynthetic FUM genes, growth and the mycotoxins fumonisin (FB₁, FB₂) by Fusarium verticillioides in vitro. The relative expression of nine biosynthetic cluster genes (FUM1, FUM7, FUM10, FUM11, FUM12, FUM13, FUM14, FUM16 and FUM19) in relation to the environmental factors was determined using a microarray analysis. The expression was related to growth and phenotypic FB₁ and FB₂ production. These data were used to develop a mixed-growth-associated product formation model and link this to a linear combination of the expression data for the nine genes. The model was then validated by examining datasets outside the model fitting conditions used (35°C). The relationship between the key gene (FUM1) and other genes in the cluster (FUM11, FUM13, FUM9, FUM14) were examined in relation to a_w, temperature, FB₁ and FB₂ production by developing ternary diagrams of relative expression. This model is important in developing an integrated systems approach to develop prevention strategies to control fumonisin biosynthesis in staple food commodities and could also be used to predict the potential impact that climate change factors may have on toxin production.     

Provably secure blockchain privacy-preserving smart contract centric dynamic group key agreement for large WSN

The Journal of Supercomputing - The contemporary Group Key Agreement (GKA) entails lightweight computing, reduced communication, decentralized certification, personal privacy protection,...     

On Effects of Frequency Offset on Simulink Model of OFDM

Wireless Personal Communications - OFDM is a multicarrier transmission technique, widely preferred due to advantages like high data rate, less bandwidth consumption and channel robustness. IEEE...     

Insulating and Other Physical Properties of CoO‐Doped Zinc Oxyfluoride‐Borate Glass‐Ceramics

Zinc oxy fluoro borate glasses mixed with different concentrations of CoO (ranging from 0 to 2.0 mol%) are synthesized and subsequently crystallized. The scanning electron microscopy pictures have exhibited crystallinity. Differential scanning calorimetric studies have indicated that the prepared samples consist of multiple crystal phases. The X‐ray diffraction patterns have indicated that the glass‐ceramic samples are composed of αZn(BO₂)_2, (Zn)₃(BO₃)_2, CoF₂, CoF₃, Co₃FB₇O₁₃, ZnCo₂O₄, Co₃O₄ crystalline phases. The optical absorption and photoluminescence studies have indicated that there is a gradual increase of tetrahedral cobalt ion concentration with increase of CoO concentration in the glass network. IR spectroscopic studies have pointed out increased degree of polymerization of the zinc oxy fluoro borate glass network with increase of CoO content. The analysis of results of dielectric properties indicated increase of insulating strength of the glass‐ceramics with increase of CoO content. Finally, the dielectric breakdown strength of the samples is measured at room temperature in air medium and it is found to increase from 12.9 to 19.2 kV/cm with increase of CoO from 0.2 to 2.0 mol%. The reasons for such increase of breakdown strength are discussed quantitatively in terms of dielectric parameters with aid of data on spectroscopic properties.     

A hybrid optimization model for proficient bidding strategies with no Karush–Kuhn–Tucker optimality conditions

The computational complexity behind the bi‐level optimization problem has led the researchers to adopt Karush–Kuhn–Tucker (KKT) optimality conditions. However, the problem function has more number of complex constraints to be satisfied. Classical optimization algorithms are impotent to handle the function. This paper presents a simplified minimization function, in which both the profit maximization problem and the ISO market clearance problem are considered, but with no KKT optimality conditions. Subsequently, this paper solves the minimization function using a hybrid optimization algorithm. The hybrid optimization algorithm is developed by combining the operations of group search optimizer (GSO) and genetic algorithm (GA). The hybridization enables the dispersion process of GSO to be a new mutated dispersion process for improving the convergence rate. We evaluate the methodology by experimenting on IEEE 14 and IEEE 30 bus systems. The obtained results are compared with the outcomes of bidding strategies that are based on GSO, PSO, and GA. The results demonstrate that the hybrid optimization algorithm solves the minimization function better than PSO, GA, and GSO. Hence, the profit maximization in the proposed methodology is relatively better than that of the conventional algorithms. Copyright © 2015 John Wiley & Sons, Ltd.