In this paper, we studied the process of dissociation unimolecular of the evaporation of H+2n+1 hydrogen clusters according to size, using the Rice-Ramsperger-Kassel-Marcus (RRKM) theory. The rate constants k(E) were ...In this paper, we studied the process of dissociation unimolecular of the evaporation of H+2n+1 hydrogen clusters according to size, using the Rice-Ramsperger-Kassel-Marcus (RRKM) theory. The rate constants k(E) were determined with the use of statistical theory of unimolecular reactions using various approximations. In our work, we used the products frequencies instead of transitions frequencies in the calculation of unimolecular dissociation rates obtained by three models RRKM. The agreement between the experimental cross section ratio and calculated rate ratio with direct count approximation seems to be reasonable.展开更多
We report on molecular dynamics simulations performed using microcanonical ensemble to predict the melting of argon particles in nanometer size range 10 nm and to investigate the effect of the time step integration. W...We report on molecular dynamics simulations performed using microcanonical ensemble to predict the melting of argon particles in nanometer size range 10 nm and to investigate the effect of the time step integration. We use the Lennard- Jones potential functions to describe the interatomic interactions, and the results are evaluated by using caloric curves of the melting phenomenon. Thermodynamic properties, including the total energy, Lindemann parameter, kinetic and potential distribution’s functions, are used to characterize the melting process. The data shows bimodal behavior only in a certain interval of integration time step Δt, while the internal energy increases monotonically with the temperature. For the other time step values, the back bending disappears. We claim that negative specific heat is related to a possible decrease of entropy in an isolated system;this can be interpreted as a result of the internal interactions, especially attractive process and specific relaxation time.展开更多
The present research employs artificial intelligence to come up with an automatic solution for the modulation's classification of various radio signal varieties.As a result,the work we performed involved selecting...The present research employs artificial intelligence to come up with an automatic solution for the modulation's classification of various radio signal varieties.As a result,the work we performed involved selecting the database required for supervised deep learning,evaluating the performance of current techniques on unprocessed communication signals,and suggesting a deep learning networkbased method that would enable the classification of modulation types with the best possible ratio between computation time and accuracy.We started by examining the automatic classification models that are currently in usage.In light of the difficulty of forecasting in low Signal Noise Ratio(SNR)situations,we suggested an ensemble learning strategy based on adjusted Res Net and Transformer Neural Network,which is effective at extracting multi-scale features from the raw I/Q sequence data.Finally,we produced an architecture that is simple to use and apply to communication signals.The architecture of this solution is strong and optimal,enabling it to determine the type of modulation with up to 95%accuracy automatically.展开更多
文摘In this paper, we studied the process of dissociation unimolecular of the evaporation of H+2n+1 hydrogen clusters according to size, using the Rice-Ramsperger-Kassel-Marcus (RRKM) theory. The rate constants k(E) were determined with the use of statistical theory of unimolecular reactions using various approximations. In our work, we used the products frequencies instead of transitions frequencies in the calculation of unimolecular dissociation rates obtained by three models RRKM. The agreement between the experimental cross section ratio and calculated rate ratio with direct count approximation seems to be reasonable.
文摘We report on molecular dynamics simulations performed using microcanonical ensemble to predict the melting of argon particles in nanometer size range 10 nm and to investigate the effect of the time step integration. We use the Lennard- Jones potential functions to describe the interatomic interactions, and the results are evaluated by using caloric curves of the melting phenomenon. Thermodynamic properties, including the total energy, Lindemann parameter, kinetic and potential distribution’s functions, are used to characterize the melting process. The data shows bimodal behavior only in a certain interval of integration time step Δt, while the internal energy increases monotonically with the temperature. For the other time step values, the back bending disappears. We claim that negative specific heat is related to a possible decrease of entropy in an isolated system;this can be interpreted as a result of the internal interactions, especially attractive process and specific relaxation time.
文摘The present research employs artificial intelligence to come up with an automatic solution for the modulation's classification of various radio signal varieties.As a result,the work we performed involved selecting the database required for supervised deep learning,evaluating the performance of current techniques on unprocessed communication signals,and suggesting a deep learning networkbased method that would enable the classification of modulation types with the best possible ratio between computation time and accuracy.We started by examining the automatic classification models that are currently in usage.In light of the difficulty of forecasting in low Signal Noise Ratio(SNR)situations,we suggested an ensemble learning strategy based on adjusted Res Net and Transformer Neural Network,which is effective at extracting multi-scale features from the raw I/Q sequence data.Finally,we produced an architecture that is simple to use and apply to communication signals.The architecture of this solution is strong and optimal,enabling it to determine the type of modulation with up to 95%accuracy automatically.
