A new aluminum-based metal matrix composite reinforced with cobalt ferrite magnetic nanoparticle

A new composite with cobalt ferrite magnetic nanoparticle dispersed in an aluminum matrix has been prepared using the ball-milling technique followed by compaction and sintering. Our efforts were largely focused on investigating the contribution of cobalt ferrite to the enhancement of structural, mechanical and magnetic properties of aluminum. Incorporation of 1–10 weight (wt)% of nanosized cobalt ferrite into the aluminum matrix could affect remarkable change in mechanical properties. Enhancement of hardness value, elastic modulus, and compressive strength was observed in the case of cobalt ferrite-incorporated aluminum matrix as compared to the pure aluminum sample. Incorporation of cobalt ferrite could impart considerable improvement of magnetization value of the aluminum matrix with a saturation magnetization of 17.07 emu/g for the aluminum sample reinforced with 10 wt% of cobalt ferrite. A decrease in coercive force in the sample arising from the increase in surface effects and inter-particle interaction between the ferromagnetic cobalt ferrite and soft phases in the matrix was also observed.     

Preparation and characterization of CdS–Bi2S3 nanocomposite thin film by successive ionic layer adsorption and reaction (SILAR) method

CdS, Bi₂S₃ and CdS–Bi₂S₃ nanocomposite thin films were grown by successive ionic layer adsorption and reaction method (SILAR) onto the glass substrates at room temperature. These films were characterized by X-ray diffraction (XRD), scanning electron microscope (SEM) and electrical measurement systems. A comparative study was made between CdS, Bi₂S₃ and CdS–Bi₂S₃ nanocomposite thin films. The XRD patterns reveal that CdS, Bi₂S₃ and CdS–Bi₂S₃ nanocomposite thin film have hexagonal, orthorhombic and mixed phase of hexagonal CdS and orthorhombic Bi₂S₃ crystal structure, respectively. SEM images showed uniform deposition of the material over the entire glass substrate. The energy band gap for CdS, Bi₂S₃ and CdS–Bi₂S₃ thin films were revealed from the optical studies and were found to be 2.4, 1.6 and 1.69 eV, respectively. The thermoemf measurements of CdS–Bi₂S₃ nanocomposite thin film revealed n-type electrical conductivity, while the I–V measurement of CdS, Bi₂S₃ and CdS–Bi₂S₃ nanocomposite thin film under dark and illumination condition (100 mW/cm²) exhibited photoconductivity phenomena suggesting its applicability in photosensors devices.     

Faster Synchronization of Triple Layer Neural Network Using Nature Inspired Whale Optimization: A Key Exchange Protocol

Wireless Personal Communications - The secret key swap over protocol is developed using a Whale Optimization-guided Three Layer Neural Networks coordination mechanism in this article. An adversary...     

Interaction of the properties of individual cotton fibres in a blend

Yarn engineering plays an important role in producing fabric for particular use. It is a common practice that yarn properties are predicted from constituent fibre properties. Cotton properties being highly variable in nature attracted various scientists from all over the world to derive prediction equations. More than 200 articles have been published in this area. Most of these predictions are based on single cotton, whereas in practice, more than one cotton is mixed together to optimise cost and quality. This study reports the accuracy of using weighted average method to determine the properties of blended cottons. Also, a set of prediction equations, derived for single cotton, tried to predict the properties of blended cotton yarns. To increase the accuracy of predictions, a new set of equations have been derived for binary cotton-blended yarns.     

Using hyperspectral vegetation indices to estimate the fraction of photosynthetically active radiation absorbed by corn canopies

The fraction of photosynthetically active radiation (FPAR) absorbed by vegetation – a key parameter in crop biomass and yields as well as net primary productivity models – is critical to guiding crop management activities. However, accurate and reliable estimation of FPAR is often hindered by a paucity of good field-based spectral data, especially for corn crops. Here, we investigate the relationships between the FPAR of corn (Zea mays L.) canopies and vegetation indices (VIs) derived from concurrent in situ hyperspectral measurements in order to develop accurate FPAR estimates. FPAR is most strongly (positively) correlated to the green normalized difference vegetation index (GNDVI) and the scaled normalized difference vegetation index (NDVI*). Both GNDVI and NDVI* increase with FPAR, but GNDVI values stagnate as FPAR values increase beyond 0.75, as previously reported according to the saturation of VIs – such as NDVI – in high biomass areas, which is a major limitation of FPAR-VI models. However, NDVI* shows a declining trend when FPAR values are greater than 0.75. This peculiar VI–FPAR relationship is used to create a piecewise FPAR regression model – the regressor variable is GNDVI for FPAR values less than 0.75, and NDVI* for FPAR values greater than 0.75. Our analysis of model performance shows that the estimation accuracy is higher, by as much as 14%, compared with FPAR prediction models using a single VI. In conclusion, this study highlights the feasibility of utilizing VIs (GNDVI and NDVI*) derived from ground-based spectral data to estimate corn canopy FPAR, using an FPAR estimation model that overcomes limitations imposed by VI saturation at high FPAR values (i.e. in dense vegetation).     

Machine Learning and Deep Learning powered satellite communications: Enabling technologies,applications, open challenges,and future research directions

The recent wave of creating an interconnected world through satellites has renewed interest in satellite communications. Private and government-funded space agencies are making advancements in the creation of satellite constellations, and the introduction of 5G has brought a new focus to a fully connected world. Satellites are the proposed solutions for establishing high throughput and low latency links to remote, hard-to-reach areas. This has caused the injection of many satellites in Earth's orbit, which has caused many discrepancies. There is a need to establish highly adaptive and flexible satellite systems to overcome this. Machine Learning (ML) and Deep Learning (DL) have gained much popularity when it comes to communication systems. This review extensively provides insight into ML and DL's utilization in satellite communications. This review covers how satellite communication subsystems and other satellite system applications can be implemented through Artificial Intelligence (AI) and the ongoing open challenges and future directions.     

Study of interaction curves for composite laminate subjected to in-plane uniaxial and shear loadings

In this study an attempt has been made to incorporate the effect of transverse shear on the stability of moderately thick/very thick composite laminated plates under in-plane compressive and shear loading using a Simple Higher Order Shear Deformation Theory based on four unknown displacement functions (u₀,v₀,w_b,w_s) instead of five which is commonly used in most of the higher order theories. The finite element method is employed to study the initial buckling load of laminated plates. The change in initial buckling response of thick rectangular antisymmetric laminates with respect to the fibre orientation angle has been studied. The interaction curves (between N_x and N_xy for different parameters of the laminates) are studied in detail.     

Fast computation of smallest enclosing circle with center on a query line segment

Here we propose an efficient algorithm for computing the smallest enclosing circle whose center is constrained to lie on a query line segment. Our algorithm preprocesses a given set of n points P={p₁,p₂,…,pn} such that for any query line or line segment L, it efficiently locates a point c on L that minimizes the maximum distance among the points in P from c. Roy et al. [S. Roy, A. Karmakar, S. Das, S.C. Nandy, Constrained minimum enclosing circle with center on a query line segment, in: Proc. of the 31st Mathematical Foundation of Computer Science, 2006, pp. 765-776] have proposed an algorithm that solves the query problem in O(log²n) time using O(nlogn) preprocessing time and O(n) space. Our algorithm improves the query time to O(logn); but the preprocessing time and space complexities are both O(n²).     

Electron-positronium scattering in Debye plasma environment

Electron-positronium scattering has been investigated in the Debye plasma environment employing the close-coupling approximation. Three models, viz. 3-state CCA, 6-state CCA and 9-state CCA, have been employed. The 2s²¹S^e autodetaching resonant state of the positronium negative ion has been successfully predicted for various plasma environments. The position of the resonance for different Debye lengths are in close agreement with those of Kar and Ho [S. Kar, Y.K. Ho, Phys. Rev. A 71 (2005) 052503].