Fabrication of zinc‐loaded hollow glass microspheres (HGMs) for hydrogen storage

The greatest challenge for a feasible hydrogen economy lies on the production of pure hydrogen and the materials for its storage with controlled release at ambient conditions. Hydrogen with its great abundance, high energy density and clean exhaust is a promising candidate to meet the current global challenges of fossil fuel depletion and green house gases emissions. Extensive research on hollow glass microspheres (HGMs) for hydrogen storage is being carried out world‐wide, but the right material for hydrogen storage is yet underway. But many other characteristics, such as the poor thermal conductivity etc. of the HGMs, restrict the hydrogen storage capacity. In this work, we have attempted to increase the thermal conductivity of HGMs by ZnO doping. The HGMs with Zn weight percentage from 0 to 10 were prepared by flame spheroidization of amber‐colored glass powder impregnated with the required amount of zinc acetate. The prepared HGMs samples were characterized using field emission‐scanning electron microscope (FE‐SEM), environmental SEM (ESEM), high‐resolution transmission electron microscopy (HRTEM), Fourier transform infrared spectroscopy and X‐ray diffraction (XRD) techniques. The deposition of ZnO on the microsphere walls was observed using FE‐SEM, ESEM and HRTEM which was further confirmed using the XRD and ultraviolet–visible absorption data. The hydrogen storage studies done on these samples at 200 °C and 10‐bar pressure for 5 h showed that the hydrogen storage increased when the Zn percentage in the sample increased from 0 to 2%. The percentage of zinc beyond 2, in the microspheres, showed a decline in the hydrogen storage capacity. The closure of the nanopores due to the ZnO nanocrystal deposition on the microsphere surface reduced the hydrogen storage capacity. The hydrogen storage capacity of HAZn2 was found 3.26 wt% for 10‐bar pressure at 200 °C. Copyright © 2015 John Wiley & Sons, Ltd.     

Traffic video‐based intelligent traffic control system for smart cities using modified ant colony optimizer

Road traffic congestion is a serious problem in today's world and it happens because of urbanization and population growth. The traffic reduces the transport efficiency in the city, increases the waiting time and travel time, and also increases the usage of fuel and air pollution. To overcome these issues this papers propose an intelligent traffic control system using the Internet of Vehicles (IoV). The vehicles or nodes present in the IoV can communicate between themselves. This technique helps in determining the traffic intensity and the best route to reach the destination. The area of study used in this paper is Vellore city in Tamilnadu, India. The city map is separated into many segments of equal size and Ant Colony Algorithm (AOC) is applied to the separated maps to find the optimal route to reach the destination. Further, Support Vector Machine (SVM) is used to calculate the traffic density and to model the heavy traffic. The proposed algorithm performs better in finding the optimal route when compared to that of the existing path selection algorithms. From the results, it is evident that the proposed IoV‐based route selection method provides better performance.     

Extrinsic parameter's adjustment and potential implications in Plasmodium falciparum malaria diagnosis

Malaria is a major public health concern, affecting over 3.2 billion people in 91 countries. The advent of digital microscopy and Machine learning with the aim of automating Plasmodium falciparum diagnosis extensively depends on the extracted image features. The color of the cells, plasma, and stained artifacts influence the topological, geometrical, and statistical parameters being used to extract image features. During microscopic image acquisition, custom adjustments to the condenser and color temperature controls often have an influence on the extracted statistical features. But, our human visual system sub-consciously adjusts the color and retains the originality in a different lighting environment. Despite the use of appropriate image preprocessing, findings from the literature indicate that statistical feature variations exist, allowing the risk of P. falciparum misinterpretation. In order to eliminate this pervasive variation, the current work focuses on preprocessing the extracted statistical features rather than the prepossessing of the source image. It begins with the augmentation of series images for a microscopic field by inducing illumination variations during the microscopic image acquisition stage. A set of such image series is analyzed using a Nonlinear Regression Model to generalize the relationship between microscopic images acquired with variable ambient brightness and a specific feature. The projection point of the centroid feature onto the brightness parameter is identified in the model and it is denoted as the optimum brightness factor (OBF). Using the model, the feature correction factor (CF) is calculated from the rate of change of feature values over the interval OBF, and the brightness of the test image is processed. The present work has investigated OBF for selected image textural features, namely Contrast, Homogeneity, Entropy, Energy, and Correlation individually from its co-occurrence matrices. For performance analysis, the best state-of-the-art method uses selected texture as a subset feature to evaluate the effectiveness of P. falciparum malaria classification. Then, the impact of proposed feature processing is evaluated on 274 blood smear images with and without Feature Correction (FC). As a result, the “p” value is less than .05, which leads to the result that it is highly significant and the classification accuracy and F-score of P. falciparum malaria are increased.     

Selection of machining parameters for constrained machining problem using evolutionary computation

In this paper, the optimization of cutting parameters for constrained machining operations is reported. Modified genetic algorithm (MGA) is an evolutionary computation technique that has been proposed in this paper to solve the machining problem. Additional constraints have been incorporated to the multipass turning model. The optimization of drilling and facing parameters have also been carried out. To demonstrate the procedure and performance of the approach, an illustrative example is discussed. The results of the proposed algorithm are compared with other traditional and non-traditional techniques such as Newton’s method, hill climbing and ants colony technique.     

Self-imaging chirped holographic optical waveguides

To manipulate light propagation in optical waveguides, we have studied holographic, chirped structures within the waveguide's core. The holographic structures were embedded along the wave propagation direction and extended throughout the entire guide. Various self-imaging guides have been analyzed and realized to demonstrate the effect of different structures.     

Predicting throughput in IEEE 802.11 based wireless networks using directional antenna

Wireless Networks - In IEEE 802.11 based wireless networks interference increases as more access points are added. A metric helping to quantize this interference seems to be of high interest. In...     

Optimization of Liquid Scintillation Counter for Tritium Estimation in Water Samples

Radiochemistry - The liquid scintillation counting procedure for tritium quantification in environmental samples was optimized with respect to sample to cocktail volume ratio, fixing of region of...     

Impact of nickel doping on structural,optical, electrical,and third-order NLO behavior of piperazinium L-tartrate single crystals

Slow evaporation technique was used to grow single crystals of pure and nickel (Ni)-doped piperazinium L-tartrate (PPLT). Powder crystal X-ray diffraction was used to characterize the structural properties of the grown crystals. Fourier transform infrared spectroscopy was used to determine the functional groups. In the visible band, both pure and Ni-doped PPLT crystals have low absorbance, indicating their utility in nonlinear optical (NLO) applications. PPLT crystals, both pure and Ni doped, have bandgap energies that indicate their insulating nature, indicating their utility in electronic applications. The growth pattern and dislocation density of the crystal are revealed by etching analysis. The electric field response of generated single crystals was investigated in terms of dielectric constant and dielectric loss as a function of the frequency and temperature, with the findings described. The efficiency of nickel-doped PPLT crystal is 2.86 times larger than potassium dihydrogen phosphate (KDP), whereas the efficiency of pure PPLT is 1.38 times greater than KDP, according to NLO testing measured by Kurtz powder method. Because metal ions were incorporated into the crystal lattice, the SHG efficiency of nickel-doped PPLT was somewhat improved.