Phenolic resin-based composite sheets filled with mixtures of reduced graphene oxide, γ-Fe2O3 and carbon fibers for excellent electromagnetic interference shielding in the X-band

Composite sheets consisting of phenolic resin filled with a mixture of reduced graphene oxide (RGO), γ-Fe₂O₃ and carbon fibers have been produced by compression molding. Its electrical conductivity lies in the range 0.48–171.21 S/cm. Transmission and scanning electron microscopy observations confirm the presence of nano particles of γ-Fe₂O₃ (～9.8 nm) and carbon fiber (～1 mm) which gives flexural strength to composite sheets. Thermogravimetric analysis show that the thermal stability of the sheets depend upon the amount of RGO and phenol resin in the composite. Complex parameters, i.e., permittivity (ε* = ε′ ? iε″) and permeability (μ* = μ′ ? iμ″) of RGO/γ-Fe₂O₃/carbon fiber have been calculated from experimental scattering parameters (S₁₁ and S₂₁) using theoretical calculations given in Nicholson?Ross and Weir algorithms. The microwave absorption properties of the sheets have been studied in the 8.2–12.4 GHz (X-Band) frequency range. The maximum shielding effectiveness observed is 45.26 dB, which strongly depends on dielectric loss and volume fraction of γ-Fe₂O₃ in RGO matrix.     

Review of research work in sinking EDM and WEDM on metal matrix composite materials

Metal matrix composites (MMCs) are newly advanced materials having the properties of light weight, high specific strength, good wear resistance and a low thermal expansion coefficient. These materials are extensively used in industry. Greater hardness and reinforcement makes it difficult to machine using traditional techniques, which has impeded the development of MMCs. The use of traditional machinery to machine hard composite materials causes serious tool wear due to the abrasive nature of reinforcement. These materials can be machined by many non-traditional methods like water jet and laser cutting but these processes are limited to linear cutting only. Electrical discharge machining (EDM) shows higher capability for cutting complex shapes with high precision for these materials. The paper presents a review of EDM process and year wise research work done in EDM on MMCs. The paper also discusses the future trend of research work in the same area.     

Optimum design of cyclone separator

Cyclone separators are one of the most widely used gas-solid separators. Although the optimal design of cyclone separators has been suggested earlier, the earlier works do not include all the critical parameters responsible for minimizing the pressure drop which is quite decisive to obtain a correct optimal design. In this article, the optimal design of the cyclone separator has been formulated as a geometric programming with a single degree of difficulty. The solution of the problem yields the optimum values of the number of cyclones to be used in parallel, and the inside diameter of cyclone shell and exit pipe, when a specified flow rate of gas is to be separated from solid particles, when the cut diameter is already specified. © 2009 American Institute of Chemical Engineers AIChE J, 2009     

On the ideal convergence of sequences of fuzzy numbers

In this paper we introduce and study the concepts of I-convergence, I^∗-convergence and I-Cauchy sequence for sequences of fuzzy numbers where I denotes the ideal of subsets of N, the set of positive integers.     

Multi-objective semi-supervised clustering of tissue samples for cancer diagnosis

In the domain of bioinformatics, the clustering of gene expression profiles of different tissue samples over different experimental conditions has gained importance with the invention of micro-array based technology. This study also has some impact on cancer diagnosis. The proper classification of cancer tissue samples generated using the micro-array technology helps in detecting cancers in an automated way. In the current paper we have developed a semi-supervised clustering technique for proper partitioning of these gene expression data sets. Semi-supervised clustering is a combination of unsupervised and supervised classification techniques. It uses some amount of supervised information and a large collection of unsupervised data. Here a multi-objective based semi-supervised clustering technique is developed for solving the cancer tissue classification problem. Different combinations of objective functions are used. As the supervised information we assume that class labels of 10 % data are available. The proposed technique is evaluated for three open source benchmark cancer data sets (brain tumor data set, adult malignancy and small round blood cell tumors). Two classification quality measures, viz., Adjusted Rand Index and Classification Accuracy are used to measure the goodness of the obtained partitionings. Obtained results are compared with several state-of-the-art clustering techniques. Moreover, significant gene markers have been identified and demonstrated visually from the clustering solutions obtained.     

Synthesis of ribbon type carbon nanostructure using LiFePO4 catalyst and their electrochemical performance

Ribbon type of carbon nanostructure has been synthesized by chemical vapor deposition using a new catalyst (LiFePO₄) introduced for the first time and its electrochemical behavior has been determined from charge/discharge characteristics. The synthesized material characterized by X-ray diffraction, scanning electron microscopy, transmission electron microscopy and by Raman spectroscopy confirms the graphitic structure and ribbon type morphology of material. The performance of the single cell using purified carbon nanoribbon as the anode has been studied and the reversible lithium intercalation capacity has been found about 345 mAh/g, of which 335 mAh/g remain after 14th cycle. The columbic efficiency has been stabilized at approximately 98% from the 5th cycle.     

Synthesis of conducting ferromagnetic nanocomposite with improved microwave absorption properties

The present paper reports the synthesis of polyaromatic amine–ferromagnetic composite with nanosize TiO₂ (～70–90 nm) and γ-Fe₂O₃ (～10–15 nm) particles via in situ emulsion polymerization. Magnetic and conductivity studies demonstrate that the conducting ferromagnetic composite possesses saturation magnetization (M_S) value of 26.9 emu g^?1 and conductivity of the order of 0.46 S cm^?1, which are measured by vibrating sample magnetometer and four-probe technique, respectively. It is observed that the presence of the nanosized γ-Fe₂O₃ in the polyaniline–TiO₂ matrix affects the electromagnetic shielding property of the composite. Polyaniline–TiO₂–γ-Fe₂O₃ nanocomposite has shown better shielding effectiveness due to absorption (SE_A ～ 45 dB) than the polyaniline-γ-Fe₂O₃ (SE_A ～ 8.8 dB) and polyaniline–TiO₂ (SE_A ～ 22.4 dB) nanocomposite. The polymer composites were further characterized by high resolution transmission electron microscopy (HRTEM) and X-ray diffraction (XRD) technique.     

Deep learning approach for investigation of temporal radio frequency signatures of drones

The omnipresence of drones in the civilian air space has led to their malicious usage raising high alert security issues. In this paper, a deep learning approach to detect and identify drones and to determine their flight modes from the remotely sensed radio frequency (RF) signatures is presented. This work intends to detect the presence of drones using two-class classification, the presence along with identification of their make using four-class classification. And this is further extended to the determination of their flight modes using ten-class classification. It employs the proposed architectures of prominent deep learning classifiers, namely, autoencoder (AE), long short-term memory (LSTM), convolutional neural network (CNN), and CNN-LSTM hybrid model. To procure the relevant information from 227 RF signatures having 100 fragments each, the seven significant temporal statistical features, namely, maxima, minima, mean, variance, skewness, kurtosis, and root mean square, are extracted. In a two-class classification scenario, all considered classifiers perform near to idle, whereas in a four-class classification scenario, CNN performs best, followed by AE, CNN-LSTM, and LSTM, respectively. Moreover, in a ten-class classification scenario, AE far outperforms CNN, followed by LSTM and CNN-LSTM, respectively. The best performance in terms of accuracy and classification time confirms the feasibility of the proposed AE classifier for the three considered drone operations.     

Experimental investigations on silicon carbide mixed electric discharge machining

Silicon - In this study, silicon carbide mixed electrical discharge machining (SCMEDM) process has been developed and later on modelled also using an artificial neural network (ANN) based technique...     

Development of highly soluble and functional buffalo milk protein concentrate 60 by modifying ionic environment and characterisation thereof

The ionic and protein environment of buffalo skim milk was modified by instant pH drop and its restoration for the development of highly soluble milk protein concentrate-60 (BuMRate–60^@), which resulted in depletion of 88.64% calcium, 89.18% magnesium, 91.69% potassium and 93.96% phosphorous. BuMRate–60^@ displayed excellent wetting, rehydration and solubility (97.76%). The Fourier transform infrared spectrometer spectra revealed significant stretching (C-H and O-H), N-H bending and a large extent of H-bonding. Scanning electron microscopy micrographs presented particles with porous nature and dimples, while transmission electron microscopy confirmed a greater release of minerals with altered casein micelles.