Characterization of Nanocomposite Polymeric Membrane

Composite membrane has good permeability and selectivity for gases, which depends upon the operating conditions. The solubility and diffusivity of the penetrant in the polymer matrix control the transport through non-porous dense membrane. In the present work, the nano sized particles of Co_0.6Zn_0.4Fe₂O₄ were prepared by the co-precipitation method and characterized by the XRD technique with particle size of 10 nm. Nanocomposite polycarbonate membranes were studied before and after irradiation by the ³⁵Cl⁹⁺ ion of 120 MeV at the Nuclear Science Centre, New Delhi. The distribution of nanoparticles throughout the membrane was characterized by the optical microscope. Gas transport properties of H₂, CO₂ and air for these nanocomposite membranes were investigated. It was found that membrane containing nanoparticles shows low permeability with high permselectivity. After irradiation by swift heavy ions (SHI), high gas permeability and high permselectivity has been observed for these nanocomposite membranes.     

Optimization of cryo-treated EDM variables using TOPSIS-based TLBO algorithm

In order to machine hard and high-strength-to-weight ratio materials, electrical discharge machining (EDM) process is extensively used in aerospace, automobile and other industrial applications. However, high erosion of tool and improper selection of machining variables have emerged as a major obstruction to achieve productivity in this direction. High erosion of tool not only enhances the cost of machining but also increases the machining time by causing interruption during machining. Therefore, proper selection of machining variables and tool material life are the two vital aspects for the tool engineers working in EDM. In view of this, the present work proposes an extensive experimental investigation and optimization of machining variables of cryogenically treated brass tool materials on machining competences of Inconel 718 workpiece. The study primarily highlights the outcome of cryogenically treated soaking duration of tools along with other important process variables, viz. discharge current, open-circuit voltage, pulse-on time, duty factor and flushing pressure, on the performance measures such as electrode wear ratio (EWR), surface roughness and radial over-cut. The study revealed that soaking duration in deep cryo-treatment of the electrode is a significant variable to achieve improved machining characteristics. The performance measures are converted into equivalent single performance measure by calculating the relative closeness coefficient by the techniques for order preferences by similarity to ideal solution (TOPSIS) approach. Finally, a novel teaching–learning-based optimization (TLBO) algorithm has been proposed to find the optimal level of machining variables for the performance measures. The optimal levels of cutting variables obtained through the algorithm are validated through confirmation test, predicting an error of 2.171 percentages between the computational and experimental results. The predicted result suggests that the proposed model can be used to select the ideal process states to achieve productivity for the cryo-treated EDM.     

Exploring Plasmonic Resonances Toward “Large-Scale” Flexible Optical Sensors with Deformation Stability

The next generation of sensors requires a simple yet compact lab on chip-based precise optical detection mechanism where data interpretation can be achieved with minimum effort. Hereby, cost-efficient strategies of manufacturing both propagating surface plasmon polariton (SPP) and localized surface plasmon resonance (LSPR) sensors on flexible platforms are explored via mechanical instabilities and oblique-angled metal evaporation. Centimeter scaled dielectric grating structures produced by plasma oxidation of pre-stressed polydimethylsiloxane film have comprised the substrates, thus imparting inherent flexibility. Subsequently, both continuous and discontinuous 1D-metallic lattices are obtained via vapor deposition of gold at different angles. The optical isotropy (gold surface-grating) and anisotropy (gold edge-grating) are distinctly observed as a difference between forward and backward diffraction efficiencies, backed by analytical correlation to the observed orders. Supported with electromagnetic modeling, the SPP and LSPR excitations are experimentally characterized under reflectance and transmittance measurements, along with a demonstration of their sensing capabilities. The LSPR supported flexible sensor provides superiority in terms of sensitivity, which is investigated under mechanical deformations to exhibit consistency of the resonant wavelength. Such consistency is strategically unraveled via “finite element method” based approaches, thus providing a new paradigm of cost-efficient, large-scaled flexible sensors.     

Malicious Traffic Detection in IoT and Local Networks Using Stacked Ensemble Classifier

Malicious traffic detection over the internet is one of the challenging areas for researchers to protect network infrastructures from any malicious activity. Several shortcomings of a network system can be leveraged by an attacker to get unauthorized access through malicious traffic. Safeguard from such attacks requires an efficient automatic system that can detect malicious traffic timely and avoid system damage. Currently, many automated systems can detect malicious activity, however, the efficacy and accuracy need further improvement to detect malicious traffic from multi-domain systems. The present study focuses on the detection of malicious traffic with high accuracy using machine learning techniques. The proposed approach used two datasets UNSW-NB15 and IoTID20 which contain the data for IoT-based traffic and local network traffic, respectively. Both datasets were combined to increase the capability of the proposed approach in detecting malicious traffic from local and IoT networks, with high accuracy. Horizontally merging both datasets requires an equal number of features which was achieved by reducing feature count to 30 for each dataset by leveraging principal component analysis (PCA). The proposed model incorporates stacked ensemble model extra boosting forest (EBF) which is a combination of tree-based models such as extra tree classifier, gradient boosting classifier, and random forest using a stacked ensemble approach. Empirical results show that EBF performed significantly better and achieved the highest accuracy score of 0.985 and 0.984 on the multi-domain dataset for two and four classes, respectively.     

Variation of permittivity in radial direction in concentric half‐split cylindrical dielectric resonator antenna for wideband application

Design and development of wideband concentric half‐split cylindrical dielectric resonator antenna (CDRA) using permittivity variation in radial direction has been proposed in this article. The homogeneous, two‐layer and three‐layer concentric half‐split CDRAs have been discussed separately. The effect of antenna parameters such as layer arrangement, geometrical parameters, and probe length are investigated. To validate the results, two‐layer and three‐layer concentric half‐split CDRA have been fabricated using commercially available microwave laminates. In each case, the input reflection coefficient, radiation pattern, and antenna gain are simulated and measured. Good agreements between simulated and measured results have been obtained. © 2014 Wiley Periodicals, Inc. Int J RF and Microwave CAE 25:321–329, 2015.     

Epoxidation of karanja (Pongamia glabra) oil catalysed by acidic ion exchange resin

Karanja oil with an iodine value of 89 g/100 g was epoxidised in situ with aqueous hydrogen peroxide and acetic acid in the presence of Amberlite IR‐120 acidic ion exchange resin as catalyst. The effect of the operating variables on the oxirane oxygen content, as well as on the oxirane ring stability and the iodine value of the epoxidised karanja oil, were determined. The variables studied were stirring speed, hydrogen peroxide‐to‐ethylenic unsaturation molar ratio, acetic acid‐to‐ethylenic unsaturation molar ratio, temperature, and catalyst loading. The effects of these parameters on the conversion to the epoxidised oil were studied and the optimum conditions for the maximum oxirane content were established. The proposed kinetic model takes into consideration the two side reactions, namely, epoxy ring opening involving the formation of hydroxy acetate and hydroxyl groups, and the reaction between the peroxyacid and the epoxy group. The kinetic and adsorption constants of the rate equations were estimated by the best fit using Marquardt's algorithm. Good agreement between experimental and predicted data validates the proposed kinetic model. From the estimated kinetic constants, the apparent activation energy for the epoxidation reaction was found to be 11 kcal/mol.     

Automatic runtime frequency-scaling system for energy savings in parallel applications

Although high-performance computing has always been about efficient application execution, both energy and power consumption have become critical concerns owing to their effect on operating costs and failure rates of large-scale computing platforms. Modern processors provide techniques, such as dynamic voltage and frequency scaling (DVFS) and CPU clock modulation (called throttling), to improve energy efficiency on-the-fly. Without careful application, however, DVFS and throttling may cause a significant performance loss due to system overhead. This paper proposes a novel runtime system that maximizes energy saving by selecting appropriate values for DVFS and throttling in parallel applications. Specifically, the system automatically predicts communication phases in parallel applications and applies frequency scaling considering both the CPU offload, provided by the network-interface card, and the architectural stalls during computation. Experiments, performed on NAS parallel benchmarks as well as on real-world applications in molecular dynamics and linear system solution, demonstrate that the proposed runtime system obtaining energy savings of as much as 14 % with a low performance loss of about 2 %.     

Application of source biasing technique for energy efficient DECODER circuit design: memory array application

Researchers have proposed many circuit techniques to reduce leakage power dissipation in memory cells.If we want to reduce the overall power in the memory system,we have to work on the input circuitry of memory architecture i.e.row and column decoder.In this research work,low leakage power with a high speed row and column decoder for memory array application is designed and four new techniques are proposed.In this work,the comparison of cluster DECODER,body bias DECODER,source bias DECODER,and source coupling DECODER are designed and analyzed for memory array application.Simulation is performed for the comparative analysis of different DECODER design parameters at 180 nm GPDK technology file using the CADENCE tool.Simulation results show that the proposed source bias DECODER circuit technique decreases the leakage current by 99.92％ and static energy by 99.92％ at a supply voltage of 1.2 V.The proposed circuit also improves dynamic power dissipation by 5.69％,dynamic PDP/EDP 65.03％ and delay 57.25％ at 1.2 V supply voltage.     

Airflow Management in a Contained Cold Aisle Using Active Fan Tiles for Energy Efficient Data-Center Operation

Generally, passive perforated tiles are used in a data center and the supplied airflow rate is underprovisioned; thus, the balance of the server air requirement is met by the hot air in the room, resulting in higher server inlet temperatures. Full provisioning of the supplied airflow rate and containing the cold aisle is expected to minimize the hot air leakage in the cold aisle, resulting in uniform and lower server inlet temperatures. Thus, the supply air temperature can be raised, resulting in energy savings at the chiller plant. Supplying extra air can be achieved using active perforated tiles, having multiple fans installed on them. In this paper, the underprovisioned case using passive tiles and the fully provisioned case using active tiles are investigated for both open and contained aisle conditions. Thermal field measurements suggest lower and uniform server inlet temperatures for the case with contained aisle as compared to open aisle and for the fully provisioned case using active tiles as compared to the underprovisioned case using passive tiles. System-level energy calculations suggests that containing the cold aisle results in lower (improved) power usage effectiveness (PUE); however, use of active tiles does not seem to improve the PUE.