A Novel Beamforming Emulating Photonic Nanojets for Wireless Relay Networks

In this article,a low-cost electromagnetic structure emulating photonic nanojets is utilized to improve the efficiency of wireless relay networks.The spectral element method,due to its high accuracy,is used to verify the efficiency of the proposed structure by solving the associate field distribution.The application of optimal single-relay selection method shows that full diversity gain with low complexity can be achieved.In this paper,the proposed technique using smart relays combines the aforementioned two methods to attain the benefits of both methods by achieving the highest coding and diversity gain and enhances the overall network performance in terms of bit error rate(BER).Moreover,we analytically prove the advantage of using the proposed technique.In our simulations,it can be shown that the proposed technique outperforms the best known state-of-the-art single relay selection technique.Furthermore,the BER expressions obtained from the theoretical analysis are perfectly matched to those obtained from the conducted simulations.

Analysis of Twitter Data Using Evolutionary Clustering during the COVID-19 Pandemic

People started posting textual tweets on Twitter as soon as the novel coronavirus(COVID-19)emerged.Analyzing these tweets can assist institutions in better decision-making and prioritizing their tasks.Therefore,this study aimed to analyze 43 million tweets collected between March 22 and March 30,2020 and describe the trend of public attention given to the topics related to the COVID-19 epidemic using evolutionary clustering analysis.The results indicated that unigram terms were trended more frequently than bigram and trigram terms.A large number of tweets about the COVID-19 were disseminated and received widespread public attention during the epidemic.The high-frequency words such as“death”,“test”,“spread”,and“lockdown”suggest that people fear of being infected,and those who got infection are afraid of death.The results also showed that people agreed to stay at home due to the fear of the spread,and they were calling for social distancing since they become aware of the COVID-19.It can be suggested that social media posts may affect human psychology and behavior.These results may help governments and health organizations to better understand the psychology of the public,and thereby,better communicate with them to prevent and manage the panic.

Utilization of Machine Learning Methods in Modeling Specific Heat Capacity of Nanofluids

Nanofluids are extensively applied in various heat transfer mediums for improving their heat transfer characteristics and hence their performance.Specific heat capacity of nanofluids,as one of the thermophysical properties,performs principal role in heat transfer of thermal mediums utilizing nanofluids.In this regard,different studies have been carried out to investigate the influential factors on nanofluids specific heat.Moreover,several regression models based on correlations or artificial intelligence have been developed for forecasting this property of nanofluids.In the current review paper,influential parameters on the specific heat capacity of nanofluids are introduced.Afterwards,the proposed models for their forecasting and modeling are proposed.According to the reviewed works,concentration and properties of solid structures in addition to temperature affect specific heat capacity to large extent and must be considered as inputs for the models.Moreover,by using other effective factors,the accuracy and comprehensive of the models can be modified.Finally,some suggestions are offered for the upcoming works in the relevant topics.

Aeroelastic Optimization of the High Aspect Ratio Wing with Aileron

In aircraft wings,aileron mass parameter presents a tremendous effect on the velocity and frequency of the flutter problem.For that purpose,we present the optimization of a composite design wing with an aileron,using machine-learning approach.Mass properties and its distribution have a great influence on the multi-variate optimization procedure,based on speed and frequency of flutter.First,flutter speed was obtained to estimate aileron impact.Additionally mass-equilibrated and other features were investigated.It can deduced that changing the position and mass properties of the aileron are tangible following the speed and frequency of the wing flutter.Based on the proposed optimization method,the best position of the aileron is determined for the composite wing to postpone flutter instability and decrease the existed stress.The represented coupled aero-structural model is emerged from subsonic aerodynamics model,which has been developed using the panel method in multidimensional space.The structural modeling has been conducted by finite element method,using the p-k method.The fluid-structure equations are solved and the results are extracted.

Modeling of Heart Rate Variability Using Time-Frequency Representations

The heart rate variability signal is highly correlated with the respiration even at high workload exercise.It is also known that this phenomenon still exists during increasing exercise.In the current study,we managed to model this correlation during increasing exercise using the time varying integral pulse frequency modulation(TVIPFM)model that relates the mechanical modulation(MM)to the respiration and the cardiac rhythm.This modulation of the autonomic nervous system(ANS)is able to simultaneously decrease sympathetic and increase parasympathetic activity.The TVIPFM model takes into consideration the effect of the increasing exercise test,where the effect of a time-varying threshold on the heart period is studied.Our motivation is to analyze the heart rate variability(HRV)acquired by time varying integral pulse frequency modulation using time frequency representations.The estimated autonomic nervous system(ANS)modulating signal is filtered throughout the respiration using a time varying filtering,during exercise stress testing.And after summing power of the filtered signal,we compare the power of the filtered modulation of the ANS obtained with different time frequency representations:smoothed pseudo Wigner–Ville representation,spectrogram and their reassignments.After that,we used a student t-test p<0.01 to compare the power of heart rate variability in the frequency band of respiration and elsewhere.

Identification of Composite-Metal Bolted Structures with Nonlinear Contact Effect

The middle layer model has been used in recent years to better describe the connection behavior in composite structures.The influencing parameters including low pre-screw and high preload have the main effects on nonlinear behavior of the connection as well as the amplitude of the excitation force applied to the structure.Therefore,in this study,the effects of connection behavior on the general structure in two sections of increasing damping and reducing the stiffness of the structures that lead to non-linear phenomena have been investigated.Due to the fact that in composite structure we are faced to the limitation of increasing screw preload which tend to structural damage,so the investigation on the hybrid connection(metal-composite)behavior is conducted.In this research,using the two-dimensional middle layer theory,the stiffness properties of the connection are modeled by normal stiffness and the connection damping is modeled using the structural damping in the shear direction.Nonlinear frequency response diagrams have been extracted twice for two different excitation forces and then proposed by a high-order multitasking approximation according to the response range of the nonlinear finite element model for stiffness and damping of the connection.The effect of increasing the amplitude of the excitation force and decreasing the preload of the screw on the nonlinear behavior of the component has been extracted.The results show that the limited presented novel component model has been accurately verified on the model obtained from the vibration experimental test and the reduction of nonlinear model updating based on that is represented.The comparison results show good agreementwith a maximumof 1.33%error.

Modeling CO_(2) Emission of Middle Eastern Countries Using Intelligent Methods

CO_(2) emission is considerably dependent on energy consumption and on share of energy sources as well as on the extent of economic activities.Consequently,these factors must be considered for CO_(2) emission prediction for seven middle eastern countries including Iran,Kuwait,United Arab Emirates,Turkey,Saudi Arabia,Iraq and Qatar.In order to propose a predictive model,a Multilayer Perceptron Artificial Neural Network(MLP ANN)is applied.Three transfer functions including logsig,tansig and radial basis functions are utilized in the hidden layer of the network.Moreover,various numbers of neurons are applied in the structure of the models.It is revealed that using MLP ANN makes it possible to accurately predict CO_(2) emission of these countries.In addition,it is concluded that using logsig transfer function leads to the highest accuracy with minimum value of mean squared error(MSE)which is followed by the networks with radial basis and tansig transfer functions.The R-squared of the networks with logsig,radial basis and tansig transfer functions are 0.9998,0.9997 and 0.9996,respectively.Finally,comparison of the proposed model with a similar study,considered five countries in the same region,reveals higher accuracy in term of MSE.