Study of pyro-carbon coated alumina kernel using mixed contrast transfer based X-ray phase retrieval technique

We have used the contrast transfer function based X-ray phase retrieval technique for phase retrieval studies on a two-component system. Pyro-carbon coated alumina matrix was chosen as a two-component system for these studies. Simulations as well as experimental results are presented. This paper shows that X-ray phase contrast along with phase retrieval can become an alternative tool for non-destructive characterization of these materials. We have also attempted to retrieve the spatial distribution of the projected thickness map of the two different elements.     

A Compact Frequency Diversity Antenna with DGS and SRR Mode Switching for LTE and WLAN/WiMAX Wireless Systems

Wireless Personal Communications - In this article, a novel low profile multi wideband planar monopole antenna is proposed to cover GSM-1800 MHz, WiMAX (3.5/5.5 GHz), WLAN...     

Development of FEA-ANN hybrid model for Equivalent Stress prediction of automobile structural member

Chassis serves as a backbone by supporting the body and diverse parts of the automobile. It ought to be sufficiently rigid to endure the shock, twist, vibration and extra stresses. Then, a vital consideration in chassis design is the strength (Equivalent Stress) for sufficient bending stiffness (Deflection). The primary goal of the research is to build up an Artificial Neural Network (ANN) model for identical stress prediction. Two side members joined to a series of cross members to make the chassis frame. The number of cross members and their locations, cross-section and the sizes of the side and the cross members turn into the design variables. The chassis frame model is created in Creo 3.0 and dissected using Ansys. Since, the number of parameters and levels are more, so the probable models are too much. By changing the Parameters, using the orthogonal array the weight of the sidebar is decreased. Then, FEA is performed on those models. ANN model is prepared by using the results of FEA. For training the ANN model, the standard back-propagation algorithm is observed to be the best. A multi-layer perception network is used for non-linear mapping between the input and the output parameters. FEA-ANN hybrid model can save material used, production cost and time.     

A serial sample loading system: interfacing multiwell plates with microfluidic devices

There is an increasing demand for novel high-throughput screening (HTS) technologies in the pharmaceutical and biotechnological industries. The robotic sample-handling techniques currently used in these industries, although fast, are still limited to operating in multiwell plates with the sample volumes per reaction in the microliter regime. Digital microfluidics offers an alternative for reduction in sample volume consumption for HTS but lacks a reliable technique for transporting a large number of samples to the microfluidic device. In this report, we develop a technique for serial delivery of sample arrays to a microfluidic device from multiwell plates, through a single sample inlet. Under this approach, a serial array of sample plugs, separated by an immiscible carrier fluid, is loaded into a capillary and delivered to a microfluidic device. Similar approaches have been attempted in the past, however, either with a slower sample loading device such as a syringe pump or vacuum-based sample loading with limited driving pressure. We demonstrated the application of our positive-pressure-based serial sample loading (SSL) system to load a series of sample plugs into a capillary. The adaptability of the SSL system to generate sample plugs with a variety of volumes in a predictable manner was also demonstrated.     

Modeling the recrystallization process using inverse cellular automata and genetic algorithms: Studies using differential evolution

An inverse modeling approach was taken up in this work to model the process of recrystallization using cellular automata (CA). Using this method after formulating a CA model of recrystallization, differential evaluation (DE), a real-coded variant of genetic algorithms, was used to search for the value of nucleation rate, providing an acceptable matching between the theoretical and experimentally observed values of fraction-recrystallized (X). Initially, the inverse modeling was attempted with a simple CA strategy, in which each of the CA cells had an equal probability of becoming nucleated. DE searched for the value of the nucleation rate yielding the best results for single-crystal iron at 550 °C. A good match could not be simultaneously achieved this way for the early stages of recrystallization as well as for the later stages. To overcome this difficulty, the CA grid was divided into two zones, having lower and higher probabilities of nucleation. This resulted in good correspondence between the predicted and experimental values of X for the entire duration of recrystallization. The introduction of a distribution in the probability of nucleation made the model even closer to the actual process, in which the probability of nucleation is often nonuniform due to nonuniformity in dislocation density as well as the presence of grain/interface boundaries.     

A two stroke free piston engine’s performance and exhaust emission using artificial neural networks

Journal of Mechanical Science and Technology - The performance and exhaust emissions of a Free piston linear engine (FPLE) were ascertained for various equivalence ratios (0.7, 0.8, 0.9, 1.0, 1.1...     

Unique hue data for colour appearance models. Part III: Comparison with NCS unique hues

In this study, Swedish Natural Color System (NCS) unique hue data were used to evaluate the performance of unique hue predictions by the CIECAM02 colour appearance model. The colour appearance of 108 NCS unique hue stimuli was predicted using CIECAM02, and their distributions were represented in a CIECAM02 a_c–b_c chromatic diagram. The best‐fitting line for each of the four unique hues was found using orthogonal distance regression in the a_c–b_c chromatic diagram. Comparison of these predicted unique hue lines (based on the NCS data) with the default unique hue loci in CIECAM02 showed that there were significant differences in both unique yellow (UY) and unique blue (UB). The same tendency was found for hue uniformity: hue uniformity is worse for UY and UB stimuli in comparison with unique red (UR) and unique green (UG). A comparison between NCS unique hue stimuli and another set of unique hue stimuli (obtained on a calibrated cathode ray tube) was conducted in CIECAM02 to investigate possible media differences that might affect unique hue predictions. Data for UY and UB are in very good agreement; largest deviations were found for UR. © 2014 Wiley Periodicals, Inc. Col Res Appl, 40, 256–263, 2015     

Controlling interferometric properties of nanoporous anodic aluminium oxide

A study of reflective interference spectroscopy [RIfS] properties of nanoporous anodic aluminium oxide [AAO] with the aim to develop a reliable substrate for label-free optical biosensing is presented. The influence of structural parameters of AAO including pore diameters, inter-pore distance, pore length, and surface modification by deposition of Au, Ag, Cr, Pt, Ni, and TiO₂ on the RIfS signal (Fabry-Perot fringe) was explored. AAO with controlled pore dimensions was prepared by electrochemical anodization of aluminium using 0.3 M oxalic acid at different voltages (30 to 70 V) and anodization times (10 to 60 min). Results show the strong influence of pore structures and surface modifications on the interference signal and indicate the importance of optimisation of AAO pore structures for RIfS sensing. The pore length/pore diameter aspect ratio of AAO was identified as a suitable parameter to tune interferometric properties of AAO. Finally, the application of AAO with optimised pore structures for sensing of a surface binding reaction of alkanethiols (mercaptoundecanoic acid) on gold surface is demonstrated.