Green Growth, Green Technology, and Environmental Health: Evidence From High-GDP Countries

Green growth is an exceptional strategy for sustainable development. It provides a pathway to combat environmental issues and the use of natural resources. This study investigates the effects of green technology and environmental factors on green growth in high-gross domestic product (GDP) countries from 2000 to 2020. In addition, it also probes the linear and nonlinear effects of GDP on green growth. To do so, we employ an advanced econometric approach, e.g., a cross-sectional autoregressive distributed lags estimator for long and short runs. The outcomes demonstrate that the linear effect of GDP is positive for green growth. On the contrary, the nonlinear effect of GDP has a negative magnitude for green growth. Besides, green technology substantially increases green growth. Energy consumption is found to be an important influencer, and it decreases green growth. Environmental factors such as emissions, according to the findings, also reduce green growth in the sample countries. It is worth noting that the joint effects of energy consumption and emissions deteriorate green growth in countries. Based on empirical findings, for policy makers, this study suggests that high-GDP countries should manage their economic and environmental activities in order to increase the amount of green growth that may protect the ecological environment.

Echo state network model for analyzing solar-wind effects on the AU and AL indices

The properties of the auroral electrojets are examined on the basis of a trained machine-learning model. The relationships between solar-wind parameters and the AU and AL indices are modeled with an echo state network (ESN), a kind of recurrent neural network. We can consider this trained ESN model to represent nonlinear effects of the solar-wind inputs on the auroral electrojets. To identify the properties of auroral electrojets, we obtain various synthetic AU and AL data by using various artificial inputs with the trained ESN. The analyses of various synthetic data show that the AU and AL indices are mainly controlled by the solar-wind speed in addition to Bz of the interplanetary magnetic field (IMF) as suggested by the literature. The results also indicate that the solar-wind density effect is emphasized when solar-wind speed is high and when IMF Bz is near zero. This suggests some nonlinear effects of the solar-wind density.

The effects of COVID-19 vaccines on economic activity

This paper empirically examines the economic effects of COVID-19 vaccine rollouts using a cross-country daily database of vaccinations and high-frequency indicators of economic activity—nitrogen dioxide (NO2) emissions, carbon monoxide (CO) emissions, and Google mobility indices—for a sample of 46 countries over the period December 16, 2020 to June 20, 2021. Using surprises in vaccines administered, we find that an unexpected increase in vaccination per capita is associated with a significant increase in economic activity. We also find evidence for nonlinear effects of vaccines, with the marginal economic benefits being larger when vaccination rates are higher. Country-specific conditions play an important role, with lower economic gains if strict containment measures are in place or if the country is experiencing a severe outbreak. Finally, the results provide evidence of spillovers across borders, highlighting the importance of equitable access to vaccines across nations.

Nonlinear Effects of Wind on Atlantic Ocean Circulation

Simulations reveal the influence of reduced and enhanced wind stress on the Atlantic Meridional Overturning Circulation.

A Simulation and Experimental Study on Identification of an Electromechanical System

The current applications in electromechanical energy conversion demand highly accurate speed and position control. For this purpose, a better understanding of the motion characteristics and dynamic behavior of electromechanical systems including nonlinear effects is needed. In this paper, a suitable model of Permanent Magnet Direct Current (PMDC) motor rotating in two directions is developed for identification purposes. Model is parameterized and identified via simulation and using real experimental data. Linear and nonlinear models for the system are built for identification, and the effective nonlinearities in the system, which are Coulomb friction and dead zone, are integrated into the nonlinear model. A Weiner- Hammerstein nonlinear system description is used for identification of the model. MATLAB is selected as the investigating tool, and a simulation model is used to observe the error between the simulated and estimated outputs. Identification of the linear and nonlinear system models using experimental data is performed using the least squares (LS) and recursive least squares (RLS) methods. Performance of the model and identification method with the real time experiments are presented numerically and graphically, revealing the advantages of the proposed nonlinear identification approach.

A weakly nonlinear framework to study shock–vorticity interaction

Linear interaction analysis (LIA) is routinely used to study the shock–turbulence interaction in supersonic and hypersonic flows. It is based on the inviscid interaction of elementary Kovásznay modes with a shock discontinuity. LIA neglects nonlinear effects, and hence it is limited to small-amplitude disturbances. In this work, we extend the LIA framework to study the fundamental interaction of a two-dimensional vorticity wave with a normal shock. The predictions from a weakly nonlinear framework are compared with high-order accurate numerical simulations over a range of wave amplitudes ( $\epsilon$ ), incidence angles ( $\alpha$ ) and shock-upstream Mach numbers ( $M_1$ ). It is found that the nonlinear generation of vorticity at the shock has a significant contribution from the intermodal interaction between vorticity and acoustic waves. Vorticity generation is also strongly influenced by the curvature of the normal shock wave, especially for high incidence angles. Further, the weakly nonlinear analysis is able to predict the correct scaling of the nonlinear effects observed in the numerical simulations. The analysis also predicts a Mach number dependent limit for the validity of LIA in terms of the maximum possible amplitude of the upstream vorticity wave.

Concurrent fMRI demonstrates propagation of TMS effects across task-related networks

Transcranial magnetic stimulation (TMS) has become an important technique in both scientific and clinical practices, and yet our understanding of how the brain responds to TMS is still limited. Concurrent neuroimaging during TMS may bridge this gap, and emerging evidence suggests widespread that modulatory effects of TMS may be best captured through changes in functional connectivity between distributed networks, rather than local changes in cortical activity. However, the relationship between TMS stimulation parameters and evoked changes in functional connectivity is unknown. In this study, 24 healthy volunteers received concurrent TMS-fMRI while performing a dot-motion direction discrimination task. An MR-compatible coil was used to apply trains of three pulses at 10 Hz rTMS over the primary visual cortex (V1) at the onset of the dot stimuli with four levels of stimulation intensity (20%, 40%, 80%, and 120% of resting motor threshold, RMT). Behavioral results demonstrated impairment of motion discrimination at 80% RMT. FMRI results yielded three findings. First, functional connectivity between visual and non-visual areas increased as a function of rTMS intensity. Second, connectivity within the visual network was positively associated with motion accuracy, while the connectivity between visual and non-visual regions was negatively associated with motion accuracy. Lastly, we found that reductions in the similarity between functional and structural connectivity associated with increasing TMS intensity were constrained to the visual network. These findings demonstrate spatially dependent nonlinear effects of TMS intensity on brain functional connectivity that proceed beyond the site of stimulation and influence associated behavior.

Analysis of the Performance of a Coherent SAC-OCDMA-OFDM-DWDM System Using a Flat Optical Frequency Comb Generator for Multiservice Networks.

A SAC-OCDMA-OFDM-DWDM system using a flat optical frequency comb generator and a coherent communication system is proposed in this research. The objective is to develop a novel OCDMA communication system that is appropriate and dedicated to multiservice networks. A comparison between the two codes, EDW and RD, has been presented. It has been concluded that the RD code system has better performance in terms of robustness and number of users than the EDW code. By analyzing the BER for several symbol rates and comparing it to the pre-FEC threshold, the proposed system demonstrates its effectiveness against linear and nonlinear effects. Performance evaluation of the proposed system is carried out via co-simulation of OptiSystem and MATLAB.