Fabrication of silicon microstructure for cell separation using ultrashort laser ablation

Many decades have seen the exploration of a novel methodology for developing microstructure in Microfluidic devices. Despite many methodologies, femtosecond laser technique has been a rising method in the conspicuous way of developing microstructures through direct write method which provides an ease of fabrication. For fabrication precise microstructure in semiconductor devices such as silicon, the femtosecond laser is preferred. An array of micro-holes has been created in monocrystalline silicon (100) using femtosecond laser ablation with multiple pulses. An investigation is carried out for obtaining the minimum diameter holes by varying the power for multiple pulses. The fabricated microhole array find its application in cancer cell separation from human blood.

     

Big data feature selection using fish and frog optimization

Big data denotes a large amount of data which includes a wide range of such methodologies like big data collection, storage, analysis, and managing the data. Every data collected in this process (homogeneous or heterogeneous considered as data), we called as big data. In this article, fish colony and their social behavior are used recently for developing an algorithm, we called as novel represented as fish swarm optimization algorithm (FSOA), which is based on the fish swarm and its behavior while search for food. The shuffled frog leaping algorithm (SFLA) is one which we introduced recently for finding near optimal solutions. The technique of Hybrid FSO-SFLA is used here for evaluating performance in big data queries.     

Tour Planning Design for Mobile Robots Using Pruned Adaptive Resonance Theory Networks

The development of intelligent algorithms for controlling autonom- ous mobile robots in real-time activities has increased dramatically in recent years. However, conventional intelligent algorithms currently fail to accurately predict unexpected obstacles involved in tour paths and thereby suffer from inefficient tour trajectories. The present study addresses these issues by proposing a potential field integrated pruned adaptive resonance theory (PPART) neural network for effectively managing the touring process of autonomous mobile robots in real-time. The proposed system is implemented using the AlphaBot platform, and the performance of the system is evaluated according to the obstacle prediction accuracy, path detection accuracy, time-lapse, tour length, and the overall accuracy of the system. The proposed system provide a very high obstacle prediction accuracy of 99.61%. Accordingly, the proposed tour planning design effectively predicts unexpected obstacles in the environment and thereby increases the overall efficiency of tour navigation.     

Influence of optimal alkali treated Areca catechu L. peduncle fiber for light weight polymer composites applications

The chemical, physico-mechanical, morphological, and thermal characteristics of alkali treated natural cellulosic sustainable eco-friendly fiber from peduncle of Areca Catechu tree were investigated. Areca Catechu fruit peduncle fiber (ACFPF) treated with 5% (w/v) NaOH solution for 60 min is found as optimally alkali treated ACFPF (OAACFPF) witnessed an increase in cellulose content by 17%. Single fiber tensile test perceived that OAACFPF enhanced tensile strength by 12.9% and x-ray diffraction analysis depicts crystallinity index of OAACFPF improved by 14.2% compared with ACFPF. Also, Fourier transform infrared spectroscopy analysis endorsed partial removal of amorphous contents from fibers due to alkali treatment. In addition, alkali treatment has enhanced thermal stability of OAACFPF from 226°C to 235°C verified through Thermogravimetric analysis. Likewise, Differential scanning calorimetry analysis confirmed improvement in thermal degradation temperature of OAACFPF after alkali treatment. Moreover, the rougher surface of OAACFPF confirmed through scanning electron microscope and atomic force microscopy is due to partial removal of amorphous contents thus ensuing in good interfacial bonding characteristics with the matrix during reinforcement for bio-composite fabrication. The above findings validated OAACFPF as a worthy substitute to harmful synthetic fibers for development of eco-friendly and sustainable bio-composites.     

Size-controlled synthesis and gas sensing application of tungsten oxide nanostructures produced by arc discharge

Several different synthetic methods have been developed to fabricate tungsten oxide (WO(3)) nanostructures, but most of them require exotic reagents or are unsuitable for mass production. In this paper, we present a systematic investigation demonstrating that arc discharge is a fast and inexpensive synthesis method which can be used to produce high quality tungsten oxide nanostructures for NO(2) gas sensing measurements. The as-synthesized WO(3) nanostructures are characterized by x-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), finger-print Raman spectroscopy and proton induced x-ray emission (PIXE). The analysis shows that spheroidal-shaped monoclinic WO(3) crystal nanostructures were produced with an average diameter of 30?nm (range 10-100?nm) at an arc discharge current of 110?A and 300?Torr oxygen partial pressure. It is found that the morphology is controlled by the arc discharge parameters of current and oxygen partial pressure, e.g.?a high arc discharge current combined with a low oxygen partial pressure results in small WO(3) nanostructures with improved conductivity. Sensors produced from the WO(3) nanostructures show a strong response to NO(2) gas at 325?°C. The ability to tune the morphology of the WO(3) nanostructures makes this method ideal for the fabrication of gas sensing materials.     

Synthesis of metal intercalated clay catalysts for selective hydrogenation reactions

Interlamellar space of the smectite clay minerals have been found as an excellent nano phase reactor due to their easy swelling property and the adsorption capacity. In this work size quantized nano particles of catalytically active noble metals have been generated within the interlamellar space of the smectite type clay minerals. Platinum and ruthenium intercalated/impregnated montmorillonite and hectorite catalysts were synthesised and characterized by various instrumental techniques. Catalytic activity of the synthesised catalysts was evaluated towards hydrogenation of cinnamaldehyde in liquid phase. The effect of various reaction parameters such as temperature, hydrogen pressure, solvents, and amount of catalyst and time on stream on conversion and selectivity were studied and the results are discussed. The studies revealed that noble metal intercalated/impregnated clay catalysts were very effective in hydrogenation reactions as they showed significant conversion and selectivity. Comparison of catalytic activity made with the impregnated catalysts showed that the intercalated catalysts were better than impregnated catalysts in terms of conversion and selectivity.     

Prediction of biosorption efficiency for the removal of copper(II) using artificial neural networks

Various low-cost adsorbents have been used for removing Cu(II) ions from aqueous solutions for the treatment of copper containing wastewaters to remove organic compounds and color. Sawdust is an impressive adsorbent in terms of adsorption efficiency, cost and availability; hence the use of sawdust as biosorbent has been widely studied. Many earlier investigations tried to correlate the experimental data with available models or some modified empirical equations, but these results were unable to predict the values of parameters from a single equation. Artificial neural networks (ANN) are effective in modeling and simulation of highly non-liner multivariable relationships. A well-designed and very well trained network can converge even on multiple number of variables at a time without any complex modeling and empirical calculations. In this present work ANN is applied for the prediction of percentage adsorption efficiency for the removal of Cu(II) ions from aqueous solutions by sawdust. Artificial neural network model, based on multilayered partial recurrent back-propagation algorithm has been used. The performance of the network for predicting the sorption efficiency of sawdust for copper is found to be very impressive.     

Network Performance Analysis of TCP-Based 3GPP LTE Time Division Duplex Systems

This paper deals with the determination of downlink (DL) and uplink (UL) channel split ratio for time division duplex (TDD) based long term evolution (LTE) networks. In a TDD system, UL and DL transmissions are carried out at different time intervals, but share the same frequency band. The TDD framing in LTE is adaptive in the sense that the DL to UL bandwidth ratio may vary with time. This paper proposes an adaptive split ratio (ASR) scheme for LTE networks to automatically adjust the bandwidth ratio of DL to UL, according to the current traffic profile, wireless interference, and transport layer parameters. This provides the maximum aggregate throughput in LTE systems. The performance analysis shows that ASR scheme outperforms static allocation in terms of higher aggregate throughput and better adaptively to network dynamics. Further, it is also observed that the ASR scheme performs well for LTE, compared to worldwide interoperability for microwave access (WiMAX) system.     

Trust-based hexagonal clustering for efficient certificate management scheme in mobile ad hoc networks

The wireless and dynamic nature of mobile ad hoc networks (MANET) render them more vulnerable to security attacks. However, providing a security mechanism implicitly has been a major challenge in such an ad-hoc environment. Certificate management plays an important role in securing an ad-hoc network. Certificate assignment, verification, and revocation complexity associated with the Public Key Infrastructure (PKI) framework is significantly large. Smaller the size of the network lesser will be the certificate management complexity. However, smaller the size, large will be the overall infrastructural cost, and also larger will be the overall redundant certificates due to multiple certificate assignment at the boundary regions, that in turn affects the prompt and accurate certificate revocation. By taking these conflicting requirements into consideration, we propose the trust-based hexagonal clustering for an efficient certificate management (THCM) scheme, to bear an absolutely protected MANET Disparate to the existing clustering techniques, we present a hexagonal geographic clustering model with Voronoi technique where trust is accomplished. In particular, to compete against attackers, we initiate a certificate management strategy in which certificate assignment, verification, and revocation are carried out efficiently. The performance of THCM is evaluated by both simulation and empirical analysis in terms of effectiveness of revocation scheme (with respect to revocation rate and time), security, and communication cost. Besides, we conduct a mathematical analysis of measuring the parameters obtained from the two platforms in multiple times. Relevant results demonstrate that our design is efficient to guarantee a secured mobile ad hoc network.