Analysis of spatially distributed FU-FB S-ALOHA in fading channels using TUA

The tagged user analysis (TUA) is a generic approximate method developed to analyze random access protocols for finite-user finite-buffer systems that avoids complexities of Markovian analysis by using available queueing results. In this paper, TUA method is extended to the analysis of finite buffer S-ALOHA operating over flat fading radio channels when the user population is spatially distributed. The expressions for system performance indices are derived. It is shown that for moderate number of active users, the simulation and analytical results have a close fit.     

Advances in reduction of NOx and N2O1 emission formation in an oxy-fired fluidized bed boiler

Fossil fuel combustion is one of the major means to meet the mounting global energy demand. However, the increasing NO_x and N₂O emissions arising from fossil fuel combustion process have hazardous effects. Thus, mitigating these gases is vital to attain a sustainable environment. Interestingly, oxy-fuel combustion in fluidized bed for carbon capture and minimized NO_x emissions is strongly sustainable compare to the other approaches. It was assessed that NO_x formation and fuel-N conversion have significant limitation under oxy-fluidized bed compared to air mode and the mechanism of NO_x formation is still deficient and requires further development. In addition, this review paper discussed the potential of primary measure as low emission process with others supplementary techniques for feasible NO_x reduction. The influences of combustion mode, operating parameters, and reduction techniques such as flue gas recirculation, oxygen staging, biomass co-firing, catalyst, influence of fluidized bed design and structure, decoupling combustion and their merges are respectively evaluated. Findings show that significant minimization of NO_x emission can be achieved through combination of primary and secondary reduction techniques.     

Stripping and recycling of metal ions in aqueous nitric acid solutions: Experimental and molecular dynamics insights

Stripping of metal ions (i.e., Cs⁺ and Na⁺) in presence of ionophore such as dibenzo-18-crown-6, (DB18C6) from the ionic liquid phase to the aqueous nitric acid phase by molecular dynamics simulation is reported. The experimentally determined stripping percentages of Na⁺ (i.e., 43.4, 38.5, 34.4, and 31.9%) were found to be higher than the same for Cs⁺ (i.e., 32.6, 32.0, 31.3, and 30.2%). The nonbonded and the hydrogen bond energies between Na⁺ and water (i.e., −356.41 and −363.77 kcal/mol) were higher when compared with Cs⁺ (i.e., −212.43 and −221.04 kcal/mol). The spatial distribution functions further confirmed that the surfaces of Na⁺ were very closely distributed around the active sides of water whereas for Cs⁺, it was distributed very far from the water molecules. In the penultimate section, the effect of methanol to the aqueous phase was studied so as to enhance the extraction efficiency of the complex.     

Facile modification of polysulfone hollow-fiber membranes via the incorporation of well-dispersed iron oxide nanoparticles for protein purification

Protein existence in wastewater is an important issue in wastewater management because proteins are generally present as contaminants and foulants. Hence, in this study, we focused on designing a polysulfone (PSf) hollow-fiber membrane embedded with hydrophilic iron oxide nanoparticles (IONPs) for protein purification by means of ultrafiltration. Before membrane fabrication, the dispersion stability of the IONPs was enhanced by the addition of a stabilizer, namely, citric acid (CA). Next, PSf–IONP–CA nanocomposite hollow-fiber membranes were prepared via a dry–wet spinning process and then characterized in terms of their hydrophilicity and morphology. Ultrafiltration and adsorption experiments were then conducted with bovine serum albumin as a model protein. The results that an IONP/CA weight ratio of 1:20 contributed to the most stable IONP dispersion. It was also revealed that the membrane incorporated with IONP–CA at a weight ratio of 1:20 exhibited the highest pure water permeability (58.6 L m⁻² h⁻¹ bar⁻¹) and protein rejection (98.5%) while maintaining a low protein adsorption (3.3 μg/cm²). The addition of well-dispersed IONPs enhanced the separation features of the PSf hollow-fiber membrane for protein purification. © 2019 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2019 , 136, 47502.     

Analysis of Silver Nanoparticles in Engine Oil: Atangana–Baleanu Fractional Model

The present article aims to examine the heat and mass distribution in a free convection flow of electrically conducted, generalized Jeffrey nanofluid in a heated rotatory system. The flow analysis is considered in the presence of thermal radiation and the transverse magnetic field of strength B₀. The medium is porous accepting generalized Darcy’s law. The motion of the fluid is due to the cosine oscillations of the plate. Nanofluid has been formed by the uniform dispersing of the Silver nanoparticles in regular engine oil. The problem has been modeled in the form of classical partial differential equations and then generalized by replacing time derivative with Atangana–Baleanu (AB) time-fractional derivative. Upon taking the Laplace transform technique (LTT) and using physical boundary conditions, exact expressions have been obtained for momentum, energy, and concentration distributions. The impact of a number of parameters on fluid flow is shown graphically. The numerical tables have been computed for variation in the rate of heat and mass transfer with respect to rooted parameters. Finally, the classical solution is recovered by taking the fractional parameter approaching unity. It is worth noting that by adding silver nanoparticles in regular engine oil, its heat transfer rate increased by 14.59%, which will improve the life and workability of the engine.     

Predicting Drying Performance of Osmotically Treated Heat Sensitive Products Using Artificial Intelligence

The main goal of this research is to develop and apply a robust Artificial Neural Networks (ANNs) model for predicting the characteristics of the osmotically drying treated potato and apple samples as a model heat-sensitive product in vacuum contact dryer. Concentrated salt and sugar solutions were used as the osmotic solutions at 27C. Series of experiments were performed at various temperatures of 35C, 40C, and 55C for conduction heat input under vacuum ( −760 mm Hg) condition. Some experiments were also performed in a pure vacuum without heat addition. Dimensionless moisture content (DMC), effective moisture diffusivity, and mass flux were considered as the performance parameters in this study. Results revealed that the osmotic dehydration using a concentrated sugar solution shows a higher reduction in the initial moisture loss of 19.87% compared to 5.3% in the salt solution. Furthermore, a significant enhancement of drying performance of about 27% in DMC was observed for both samples at vacuum and 40C compared to pure vacuum drying conditions. Using the experimental data, a robust artificial neural network (ANN) was proposed to describe the osmotic dehydration’s behavior on the drying process. The ANN model outputs are the dimensionless moisture contents (DMC), the diffusivity, and the mass flux. Whereas the ANN inputs were the drying time, the percent of sugar solution, and the percent of salt solution. For the ANN apple’s model, the minimum root mean square error (RMSE) values were 0.0261, 0.0349 and 0.0406, for DMC, diffusivity, and mass flux, respectively. Whereas the best correlation coefficients of the above three parameters’ determination values were 0.9909, 0.9867 and 0.9744, respectively. For the ANN potato’s model, the minimum RMSE values were 0.0124, 0.0140 and 0.0333, for DMC, diffusivity, and mass flux, respectively. And the best correlation coefficients of the parameters’ values were found 0.9969, 0.9968 and 0.9736, respectively. Accordingly, the ANN model’s prediction has a perfect agreement with the experimental dataset, which confirmed the ANN model’s accuracy.     

Emotion Recognition from Occluded Facial Images Using Deep EnsembleModel

Facial expression recognition has been a hot topic for decades, but high intraclass variation makes it challenging. To overcome intraclass variation for visual recognition, we introduce a novel fusion methodology, in which the proposed model first extract features followed by feature fusion. Specifically, RestNet-50, VGG-19, and Inception-V3 is used to ensure feature learning followed by feature fusion. Finally, the three feature extraction models are utilized using Ensemble Learning techniques for final expression classification. The representation learnt by the proposed methodology is robust to occlusions and pose variations and offers promising accuracy. To evaluate the efficiency of the proposed model, we use two wild benchmark datasets Real-world Affective Faces Database (RAF-DB) and AffectNet for facial expression recognition. The proposed model classifies the emotions into seven different categories namely: happiness, anger, fear, disgust, sadness, surprise, and neutral. Furthermore, the performance of the proposed model is also compared with other algorithms focusing on the analysis of computational cost, convergence and accuracy based on a standard problem specific to classification applications.     

Steps Towards Redesigning Cryptosystems by a Non-associative Algebra of IP-Loops

Wireless Personal Communications - In block ciphers, the substitution boxes (S-boxes) are used with the purpose to induce confusion in cryptosystems. For the last three decades most of algebraic...