Preparation and characterizations of activated carbon from kenaf fiber for equilibrium adsorption studies of copper from wastewater

The potential of activated carbon prepared from kenaf fiber (KF) to remove copper (II) from aqueous effluents was investigated. The fibers were first semi-carbonized, then impregnated with potassium hydroxide (KOH) and finally activated by using carbon dioxide (CO₂) gas to produce activated carbon. Pore structure and physical characteristics of the prepared kenaf fiber activated carbon (KFAC) were determined. Adsorption studies for divalent copper (Cu) ions were carried out to delineate the effect of contact time, temperature, pH and initial metal ion concentration on equilibrium adsorption capacity. The experimental data followed pseudo-second-order kinetics and Elovich Model than pseudo-first-order. Langmuir, Freundlich and Temkin models were implemented to analyze the parameters for adsorption at 30 °C, 50 °C and 70 °C. Thermodynamic parameters such as ??G ^o , ??H ^o and ??S ^o which represent Gibbs free energy, enthalpy and entropy, respectively, were evaluated. It was concluded that activated carbon from kenaf fiber (KFAC) can be used as an efficient adsorbent for removal of Cu (II) from synthetic wastewater.     

Effect of core–shell rubber toughening on mechanical,thermal, and morphological properties of poly(lactic acid)/multiwalled carbon nanotubes nanocomposites

The effect of core–shell rubber (CSR) toughening on mechanical and thermal properties of poly(lactic acid)/multiwalled carbon nanotubes (PLA/CNT) nanocomposites were investigated. The nanocomposites were prepared by direct melt blending method in a counter-rotating twin-screw extruder. The contents of CSR were varied between 5 and 20 wt % while the content of CNT was kept at 5 phr. The extruded samples were injection molded into the desired test specimens for mechanical and thermal properties analysis. The impact strength of PLA/CNT increased with increasing CSR content with concomitant decrease in tensile strength and modulus. Interestingly, the flexural strength increased at low CSR content before decreasing at 15 and 20% content. Differential scanning calorimetry analysis on the second heating cycle shows no crystallinity content for PLA/CNT and all CSR toughened PLA/CNT nanocomposites, while thermogravimetric analysis shows lower thermal degradation of all CSR toughened PLA/CNT as compared to PLA/CNT nanocomposite. This study reveals significant correlation between CSR loading with the mechanical and thermal properties of the nanocomposites. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47756.     

An application of paired quasilinearization on double‐diffusive convection flow over a cone embedded in a porous medium in the presence of nanoparticles

Mixed convection flow of a nanofluid near a vertical cone embedded in a a porous medium with Soret and Dufour effects is exercised. The bearing of a porous medium is recounted by the Darcy model. The partial differential equations, modeling the concerned problem, is nondimensionalised by implementing compatible transformations, which results in a similar form. A new paired spectral quasilinearization method is adopted to get the accurate numerical solution. Convergence and accuracy of the solution is elaborated by analyzing the norm of residual and solution errors. Alteration of velocity, temperature, nanoparticle and solute concentration profiles due to flow controlling parameters, namely, Brownian motion, thermophoresis, Soret, Dufour, Lewis number, and buoyancy ratio is outlined by reproducing the obtained numerical solution in graphs and tables. Analysis reveals that the flow profiles are greatly influenced by the physical parameters under investigation.     

An overview of spectrum sensing techniques for cognitive LTE and LTE-A radio systems

Advanced communication systems, such as long term evolution (LTE) and LTE-advanced (LTE-A) systems, promise to increase the number of users with high-speed data exchange. However, it leads to spectrum scarcity because of the huge size of data exchange with limited spectrum resources. Cognitive radio (CR) technique is considered the best solution for this spectrum scarcity problem. Spectrum sensing (SS), one of the CR techniques is used to detect the spectrum hole of primary user (PU) without interference with PU. In this paper, several SS approaches for LTE and LTE-A systems are investigated in the CR system. These SS approaches are based on two techniques, namely energy detection and cyclostationary feature detection techniques. The first technique includes four approaches of auto-correlation based advanced energy, time domain detection, Welch periodogram and two-stage model algorithms, while the second technique contains two approaches, namely pilot induced cyclostationary and second order cyclostationary algorithms. According to the analysis, the two-stage model and the second order cyclostationary algorithms are better than the other algorithms because they produce accurate results at the expense of system complexity. Hence, in general a good SS algorithms would require some trade-off between complexity and accuracy.     

A low complexity high efficiency hybrid multiplicative-additive crest factor reduction for OFDM systems

In this paper, we propose a novel technique to reduce the crest factor (CF) in orthogonal frequency division multiplexing systems. It consists of two inverse fast Fourier transform (IFFT) blocks, the input symbols of the first IFFT are the mapped symbols, whereas the input symbols of the second IFFT are the summations of the absolute value of the real part of the outer signal constellation points and zero symbols. First, the output of the two IFFT blocks is partitioned into four subblocks, which are subsequently used to rearrange the subblocks with padding zeros in a specific manner. Then, a new optimization scheme is introduced, in which only a single two-phase sequence and four iterations need to be applied. Numerical analysis shows that the proposed hybrid technique achieves better CF reduction performance with significantly lower complexity and better bit error rate performance than the existing scrambling (multiplicative) and additive CF techniques.     

Malware analysis performance enhancement using cloud computing

Nowadays, computer based technology has taken a central role in every person life. Hence, damage caused by malicious software (malware) can reach and effect many people globally as what could be in the early days of computer. A close look at the current approaches of malware analysis shows that the respond time of reported malware to public users is slow. Hence, the users are unable to get prompt feedback when reporting suspicious files. Therefore, this paper aims at introducing a new approach to enhance malware analyzer performance. This approach utilizes cloud computing features and integrates it with malware analyzer. To evaluate the proposed approach, two systems had been prepared carefully with the same malware analyzer, one of them utilizes cloud computing and the other left without change. The evaluation results showed that the proposed approach is faster by 23 % after processing 3,000 samples. Furthermore, utilizing cloud computing can open door to crowd-source this service hence encouraging malware reporting and accelerate malware detection by engaging the public users at large. Ultimately this proposed system hopefully can reduce the time taken to detect new malware in the wild.     

Additive noise reduction in natural images using second‐generation wavelet transform hidden Markov models

Noise reduction or denoising is required for visual improvement or as a preprocessing step for subsequent processing tasks, such as image compression and analysis. Therefore, denoising has become a highly desirable and essential process in multimedia applications. The aim of all denoising processes, especially in natural images, is to uncover the true image from the observed noisy image, ideally removing the additive white Gaussian noise (AWGN) while producing a sharp, useful image without losing finer details. Generally, most of the noise obtained during acquisition and transmission of the natural images is assumed to be AWGN. In this study, we propose a new adaptive denoising framework based on second‐generation wavelet domain using hidden Markov models (SGWD‐HMMs) with some new local structure, utilizing the fact that the images are nonstationary with singularities and some smooth areas that can be considered as stationary. The dependencies among wavelet coefficients can be efficiently captured by HMMs since the dependence between two wavelet coefficients dies down quickly as their distance becomes big. Quite remarkably, experimental results verify the effectiveness of SGWD‐HMMs in benchmark images when compared with other state‐of‐the‐art denoising algorithms. It gives competitive results in the subjective and objective assessments, but it is computationally more expensive. © 2016 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.