Face masks are playing an essential role in preventing the spread of COVID-19. Face masks such as N95, and surgical masks, contain a considerable portion of non-recyclable plastic material. Marine ...plastic pollution is likely to increase due to the rapid use and improper dispensing of face masks, but until now, no extensive quantitative estimation exists for coastal regions. Linking behaviour dataset on face mask usage and solid waste management dataset, this study estimates annual face mask utilization and plastic pollution from mismanaged face masks in coastal regions of 46 countries. It is estimated that approximately 0.15 million tons to 0.39 million tons of plastic debris could end up in global oceans within a year. With lower waste management facilities, the number of plastic debris entering the ocean will rise. Significant investments are required from global communities in improving the waste management facilities for better disposal of masks and solid waste.
•Estimation of daily & annual face masks usage by coastal population of 46 countries•Behavioural data on face masks usage and solid waste management data are used.•Mismanaged plastic waste from face masks are also determined.•About 0.15 MT to 0.39 MT plastic debris may enter global oceans.•Significant efforts are required to protect our oceans.
Due to recent advances in digital technologies, and availability of credible data, an area of artificial intelligence, deep learning, has emerged and has demonstrated its ability and effectiveness in ...solving complex learning problems not possible before. In particular, convolutional neural networks (CNNs) have demonstrated their effectiveness in the image detection and recognition applications. However, they require intensive CPU operations and memory bandwidth that make general CPUs fail to achieve the desired performance levels. Consequently, hardware accelerators that use application-specific integrated circuits, field-programmable gate arrays (FPGAs), and graphic processing units have been employed to improve the throughput of CNNs. More precisely, FPGAs have been recently adopted for accelerating the implementation of deep learning networks due to their ability to maximize parallelism and their energy efficiency. In this paper, we review the recent existing techniques for accelerating deep learning networks on FPGAs. We highlight the key features employed by the various techniques for improving the acceleration performance. In addition, we provide recommendations for enhancing the utilization of FPGAs for CNNs acceleration. The techniques investigated in this paper represent the recent trends in the FPGA-based accelerators of deep learning networks. Thus, this paper is expected to direct the future advances on efficient hardware accelerators and to be useful for deep learning researchers.
The ambitious high data-rate applications in the envisioned future beyond fifth-generation (B5G) wireless networks require new solutions, including the advent of more advanced architectures than the ...ones already used in 5G networks, and the coalition of different communications schemes and technologies to enable these applications requirements. Among the candidate communications schemes for future wireless networks are non-orthogonal multiple access (NOMA) schemes that allow serving more than one user in the same resource block by multiplexing users in other domains than frequency or time. In this way, NOMA schemes tend to offer several advantages over orthogonal multiple access (OMA) schemes such as improved user fairness and spectral efficiency, higher cell-edge throughput, massive connectivity support, and low transmission latency. With these merits, NOMA-enabled transmission schemes are being increasingly looked at as promising multiple access schemes for future wireless networks. When the power domain is used to multiplex the users, it is referred to as the power domain NOMA (PD-NOMA). In this paper, we survey the integration of PD-NOMA with the enabling communications schemes and technologies that are expected to meet the various requirements of B5G networks. In particular, this paper surveys the different rate optimization scenarios studied in the literature when PD-NOMA is combined with one or more of the candidate schemes and technologies for B5G networks including multiple-input-single-output (MISO), multiple-input-multiple-output (MIMO), massive-MIMO (mMIMO), advanced antenna architectures, higher frequency millimeter-wave (mmWave) and terahertz (THz) communications, advanced coordinated multi-point (CoMP) transmission and reception schemes, cooperative communications, cognitive radio (CR), visible light communications (VLC), unmanned aerial vehicle (UAV) assisted communications and others. The considered system models, the optimization methods utilized to maximize the achievable rates, and the main lessons learnt on the optimization and the performance of these NOMA-enabled schemes and technologies are discussed in detail along with the future research directions for these combined schemes. Moreover, the role of machine learning in optimizing these NOMA-enabled technologies is addressed.
•Nine recent meta-heuristics are used to optimize eight mechanical design problems.•Theoretical and numerical comparisons are extensively investigated.•The results show the merits of the methods used ...in solving the case studies.
Determining the solution for real mechanical design problems is a challenging task when using the newly developed and efficient swarm intelligence algorithms. There are so many difficulties to be addressed, including but not limited to mixed decision variables, diverse constraints, inherent errors, conflicting objectives, and numerous locally optimal solutions. This work analyzes the behavior of nine metaheuristic algorithms, namely, salp swarm algorithm (SSA), multi-verse optimizer (MVO), moth-flame optimizer (MFO), atom search optimization (ASO), ecogeography-based optimization (EBO), queuing search algorithm (QSA), equilibrium optimizer (EO), evolutionary strategy (ES) and hybrid self-adaptive orthogonal genetic algorithm (HSOGA). The efficiency of these algorithms is evaluated on eight mechanical design problems using the solution quality and convergence analysis, which verifies the wide applicability of these algorithms to real-world application problems.
The commercial fifth-generation (5G) wireless communications networks have already been deployed with the aim of providing high data rates. However, the rapid growth in the number of smart devices ...and the emergence of the Internet of Everything (IoE) applications, which require an ultra-reliable and low-latency communication, will result in a substantial burden on the 5G wireless networks. As such, the data rate that could be supplied by 5G networks will unlikely sustain the enormous ongoing data traffic explosion. This has motivated research into continuing to advance the existing wireless networks toward the future generation of cellular systems, known as sixth generation (6G). Therefore, it is essential to provide a prospective vision of the 6G and the key enabling technologies for realizing future networks. To this end, this paper presents a comprehensive review/survey of the future evolution of 6G networks. Specifically, the objective of the paper is to provide a comprehensive review/survey about the key enabling technologies for 6G networks, which include a discussion about the main operation principles of each technology, envisioned potential applications, current state-of-the-art research, and the related technical challenges. Overall, this paper provides useful information for industries and academic researchers and discusses the potentials for opening up new research directions.
Nanoparticles play an essential role in biomedical applications. A most promising area in nanomedicine is drug targeting which is done with the aid of magnetized nanoparticles. In this study, the ...hemodynamics of hybrid nanofluid flow with gold and copper nanoparticles suspended in it is investigated. This research primarily focuses on magnetic drug delivery which is propagated through a tapered stenosed artery under three situations, including converging, diverging, and non-tapering arteries. To explore the rheological characteristics of blood, a Sutterby fluid, which is a non-Newtonian fluid, is postulated. The energy equation also incorporates the effects of the magnetic field and joule heating, as well as the viscous dissipation function. Lubrication theory provides a mathematical framework for model formulation. The hypothesized modeling is simplified to a set of nonlinear differential equations that are then solved using a perturbation method up to the second order of approximation. Graphs are used to describe the outcomes of different evolving parameters. The Sutterby fluid parameter opposes the flow negligibly, whereas the Hartmann number and thermal Grashof number strengthen the flow field. Copper nanoparticles (in the absence of gold nanoparticles) are observed to deplete the thermal profile substantially more than gold nanoparticles. Nevertheless, the thermal profile is enhanced by the presence of both nanoparticles (hybrid nanofluids). For greater values of the Sutterby fluid parameter, the wall shear stress has been observed to rise considerably, whereas the inverse is true for the Hartmann number and the thermal Grashof number. The present results have been improved to give significant information for biomedical scientists who are striving to study blood flow in stenosis situations, as well as for those who will find the knowledge valuable in the treatment of different diseases.
Optimizing real-life engineering design problems are challenging and somewhat difficult if optimum solutions are expected. The development of new efficient optimization algorithms is crucial for this ...task. In this paper, a recently invented grasshopper optimization algorithm is upgraded from its original version. The method is improved by adding an elite opposition-based learning methodology to an elite opposition-based learning grasshopper optimization algorithm. The new optimizer, which is elite opposition-based learning grasshopper optimization method (EOBL-GOA), is validated with several engineering design probles such as a welded beam design problem, car side crash problem, multiple clutch disc problem, hydrostatic thrust bearing problem, three-bar truss, and cantilever beam problem, and finally used for the optimization of a suspension arm of the vehicles. The optimum results reveal that the EOBL-GOA is among the best algorithms reported in the literature.
Visible light communication (VLC) has been proposed as an alternative or complementary technology to radio frequency vehicular communications. Front and back vehicle lights can serve as wireless ...transmitters making VLC a natural vehicular connectivity solution. In this paper, we evaluate the performance limits of vehicular VLC systems. First, we use non-sequential ray tracing to obtain the channel impulse responses (CIRs) for vehicle-to-vehicle (V2V) link in various weather conditions. Based on these CIRs, we present a closed-form path loss expression which builds upon the summation of geometrical loss and attenuation loss and takes into account asymmetrical patterns of vehicle light sources and geometry of V2V transmission. The proposed expression is an explicit function of link distance, lateral shift between two vehicles, weather type (quantified by the extinction coefficient), transmitter beam divergence angle and receiver aperture diameter. Then, we utilize this expression to determine the maximum achievable link distance of V2V systems for clear, rainy and foggy weather conditions while ensuring a targeted bit error rate.
The basic motivation of this investigation is to develop an innovative mathematical model for electro-osmotic flow of Couette–Poiseuille nanofluids. The power-law model is treated as the base fluid ...suspended with nano-sized particles of aluminum oxide (Al2O3). The uniform speed of the upper wall in the axial path generates flow, whereas the lower wall is kept fixed. An analytic solution for nonlinear flow dynamics is obtained. The ramifications of entropy generation, magnetic field, and a constant pressure gradient are appraised. Moreover, the physical features of most noteworthy substantial factors such as the electro-osmotic parameter, magnetic parameter, power law fluid parameter, skin friction, Nusselt number, Brinkman number, volume fraction, and concentration are adequately delineated through various graphs and tables. The convergence analysis of the obtained solutions has been discussed explicitly. Recurrence formulae in each case are also presented.
In this paper, a mathematical analysis for three-dimensional Eyring–Powell nanofluid nonlinear thermal radiation with modified heat plus mass fluxes is investigated. To enhance the dynamical and ...physical study of structure, the slip condition is introduced. A Riga plate is employed for avoiding boundary-layer separation to diminish the friction and pressure drag of submarines. To evaluate the heat transfer, the Cattaneo–Christov heat flux model is implemented via appropriate transformation. A comparison between bvp4c results and shooting technique is made. Graphical and numerical illustrations are presented for prominent parameters.
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