Catalytic reduction of 2,4‐dinitrophenylhydrazine by cuttlebone supported Pd NPs prepared using Conium maculatum leaf extract

A facile and green process to synthesise cuttlebone supported palladium nanoparticles (Pd NPs/cuttlebone) is reported using Conium maculatum leaf extract and in the absence of chemical solvents and hazardous materials. The antioxidant content of the C. maculatum leaf extract played a significant role in converting Pd²⁺ ions to Pd NPs. Various techniques were used for the characterisation of the Pd NPs/cuttlebone such as field‐emission scanning electron microscopy, X‐ray diffraction, energy dispersive X‐ray spectroscopy, Fourier transform infrared and ultraviolet–visible spectroscopy. This Pd NPs/cuttlebone showed excellent catalytic activity in the reduction of 2,4‐dinitrophenylhydrazine to 2,4‐diaminophenylhydrazine by sodium borohydride as the source of hydrogen at ambient condition. The catalyst could be separated and recycled up to five cycles with no loss of its activity.Inspec keywords: catalysis, catalysts, chemical engineering, palladium, nanoparticles, field emission electron microscopy, scanning electron microscopy, X‐ray diffraction, X‐ray chemical analysis, sodium compounds, ultraviolet spectroscopy, visible spectroscopyOther keywords: catalytic reduction, 2,4‐dinitrophenylhydrazine, cuttlebone, Conium maculatum leaf extract, green process, palladium nanoparticles, antioxidant content, field‐emission scanning electron microscopy, X‐ray diffraction, energy dispersive X‐ray spectroscopy, Fourier transform infrared, ultraviolet–visible spectroscopy, 2,4‐diaminophenylhydrazine, sodium borohydride     

Tracking and Analysis of Pedestrian’s Behavior in Public Places

Crowd management becomes a global concern due to increased population in urban areas. Better management of pedestrians leads to improved use of public places. Behavior of pedestrian’s is a major factor of crowd management in public places. There are multiple applications available in this area but the challenge is open due to complexity of crowd and depends on the environment. In this paper, we have proposed a new method for pedestrian’s behavior detection. Kalman filter has been used to detect pedestrian’s using movement based approach. Next, we have performed occlusion detection and removal using region shrinking method to isolate occluded humans. Human verification is performed on each human silhouette and wavelet analysis and particle gradient motion are extracted for each silhouettes. Gray Wolf Optimizer (GWO) has been utilized to optimize feature set and then behavior classification has been performed using the Extreme Gradient (XG) Boost classifier. Performance has been evaluated using pedestrian’s data from avenue and UBI-Fight datasets, where both have different environment. The mean achieved accuracies are 91.3% and 85.14% over the Avenue and UBI-Fight datasets, respectively. These results are more accurate as compared to other existing methods.     

Royal Crown Shaped Polarization Insensitive Perfect Metamaterial Absorber for C-, X-, and Ku-Band Applications

This study proposed a new royal crown-shaped polarisation insensitive double negative triple band microwave range electromagnetic metamaterial absorber (MA). The primary purpose of this study is to utilise the exotic characteristics of this perfect metamaterial absorber (PMA) for microwave wireless communications. The fundamental unit cell of the proposed MA consists of two pentagonal-shaped resonators and two inverse C-shaped metallic components surrounded by a split ring resonator (SRR). The bottom thin copper deposit and upper metallic resonator surface are disjoined by an FR-4 dielectric substrate with 1.6 mm thickness. The CST MW studio, a high-frequency electromagnetic simulator has been deployed for numerical simulation of the unit cell in the frequency range of 4 to 14 GHz. In the TE mode, the offered MA structure demonstrated three different absorption peaks at 6.85 GHz (C-band), 8.87 GHz (X-band), and 12.03 GHz (Ku-band), with 96.82%, 99.24%, and 99.43% absorptivity, respectively. The electric field, magnetic field, and surface current distribution were analysed using Maxwell’s-Curl equations, whereas the angle sensitivity was investigated to comprehend the absorption mechanism of the proposed absorber. The numerical results were verified using the Ansys HFSS (high-frequency structure simulator) and ADS (advanced design system) for equivalent circuit models. Moreover, the proposed MA is polarisation and incident angle independent. Hence, the application of this MA can be extended to a great extent, including airborne radar applications, defence, and stealth-coating technology.     

Enhancing level set brain tumor segmentation using fuzzy shape prior information and deep learning

Magnetic resonance imaging (MRI) brain tumor segmentation is a crucial task for clinical treatment. However, it is challenging owing to variations in type, size, and location of tumors. In addition, anatomical variation in individuals, intensity non-uniformity, and noises adversely affect brain tumor segmentation. To address these challenges, an automatic region-based brain tumor segmentation approach is presented in this paper which combines fuzzy shape prior term and deep learning. We define a new energy function in which an Adaptively Regularized Kernel-Based Fuzzy C-Means (ARKFCM) Clustering algorithm is utilized for inferring the shape of the tumor to be embedded into the level set method. In this way, some shortcomings of traditional level set methods such as contour leakage and shrinkage have been eliminated. Moreover, a fully automated method is achieved by using U-Net to obtain the initial contour, reducing sensitivity to initial contour selection. The proposed method is validated on the BraTS 2017 benchmark dataset for brain tumor segmentation. Average values of Dice, Jaccard, Sensitivity and specificity are 0.93 ± 0.03, 0.86 ± 0.06, 0.95 ± 0.04, and 0.99 ± 0.003, respectively. Experimental results indicate that the proposed method outperforms the other state-of-the-art methods in brain tumor segmentation.     

Microfluidic inverse phase ELISA via manipulation of magnetic beads

We report a new technique for conducting immuno-diagnostics on a microfluidic platform. Rather than handling fluid reagents against a stationary solid phase, the platform manipulates analyte-coated magnetic beads through stationary plugs of fluid reagents to detect an antigenic analyte. These isolated but accessible plugs are pre-encapsulated in a microchannel by capillary force. We call this platform microfluidic inverse phase enzyme-linked immunosorbent assay (μIPELISA). μIPELISA has distinctive advantages in the family of microfluidic immunoassay. In particular, it avoids pumping and valving fluid reagents during assaying, thus leading to a lab-on-a-chip format that is free of instrumentation for fluid actuation and control. We use μIPELISA to detect digoxigenin-labeled DNA segments amplified from E. coli O157:H7 by polymerase chain reaction (PCR), and compare its detection capability with that of microplate ELISA. For 0.259 ng μl⁻¹ of digoxigenin-labeled amplicon, μIPELISA is as responsive as the microplate ELISA. Also, we simultaneously conduct μIPELISA in two parallel microchannels.     

A new set of sufficient conditions based on coupling parameters for synchronization of Hopfield like Chaotic Neural Networks

In this paper, we consider a Hopfield like Chaotic Neural Networks which have both self-coupling and non-invertible activation functions. We show that the interactions between neurons can be used as a means of chaos generation or suppression to neuron’s outputs when more adaptability or stability is required. Furthermore, a new set of sufficient conditions based on coupling weights is proposed so that the synchronization of all neuron’s outputs with each other is guaranteed, when all neuron’s have identical activation functions. Finally, the effectiveness of the proposed approach is evaluated by performing simulations on three illustrative examples.     

Controlled gates for multi-level quantum computation

Multi-level (ML) quantum logic can potentially reduce the number of inputs/outputs or quantum cells in a quantum circuit which is a limitation in current quantum technology. In this paper we propose theorems about ML-quantum and reversible logic circuits. New efficient implementations for some basic controlled ML-quantum logic gates, such as three-qudit controlled NOT, Cycle, and Self Shift gates are proposed. We also propose lemmas about r-level quantum arrays and the number of required gates for an arbitrary n-qudit ML gate. An equivalent definition of quantum cost (QC) of binary quantum gates for ML-quantum gates is introduced and QC of controlled quantum gates is calculated.     

Analysis of frequency‐selective impedance loading of transmission lines for dual‐band couplers

This article proposes an analytical design methodology for dual‐band hybrid couplers and baluns structures for any arbitrary frequency ratio using a stub‐loaded transmission line. An analysis of changing the impedance behavior of the stub, is carried out for the two bands of operation, which along with a dispersive analysis, emphasizes certain conditions where the existing methodology is not applicable. In addition, an extra degree of freedom has been included to increase the solutions for a given frequency ratio, thus providing greater flexibility and feasibility of the proposed structure. The design methodology is experimentally validated with the design and fabrication of dual‐band branch‐line and rat‐race couplers for various commercial frequency bands. © 2011 Wiley Periodicals, Inc. Int J RF and Microwave CAE , 2011.     

Adaptive simulation of two dimensional hyperbolic problems by Collocated Discrete Least Squares Meshless method

An adaptive refinement technique is presented in this paper and used in conjunction with the Collocated Discrete Least Squares Meshless (CDLSM) method for the effective simulation of two-dimensional shocked hyperbolic problems. The CDLSM method is based on minimizing the least squares functional calculated at collocation points chosen on the problem domain and its boundaries. The functional is defined as the weighted sum of the squared residuals of the differential equation and its boundary conditions. A Moving Least Squares (MLS) method is used here to construct the meshless shape functions. An error estimator based on the value of functional at nodal points used to discretize the problem domain and its boundaries is developed and used to predict the areas of poor solutions. A node moving strategy is then used to refine the predicted zones of poor solutions before the problem is resolved on the refined distribution of nodes. The proposed methodology is applied to some two dimensional hyperbolic benchmark problems and the results are presented and compared to the exact solutions. The results clearly show the capabilities of the proposed method for the effective and efficient solution of hyperbolic problems of shocked and high gradient solutions.     

Multi expression programming: a new approach to formulation of soil classification

This paper presents an alternative approach to formulation of soil classification by means of a promising variant of genetic programming (GP), namely multi expression programming (MEP). Properties of soil, namely plastic limit, liquid limit, color of soil, percentages of gravel, sand, and fine-grained particles are used as input variables to predict the classification of soils. The models are developed using a reliable database obtained from the previously published literature. The results demonstrate that the MEP-based formulas are able to predict the target values to high degree of accuracy. The MEP-based formulation results are found to be more accurate compared with numerical and analytical results obtained by other researchers.