No abstract

Previous functional imaging studies have identified the role of central autonomic network (CAN) in autonomic regulation during various tasks. However, its variability with respect to gender and age, particularly in the resting state, remains poorly understood. Therefore, in this study we systematically investigated gender- and age-related differences in the resting-state functional connectivity (rsFC) seeded from core regions of this network, namely posterior mid-cingulate gyrus (pMCC), left amygdala, right anterior and left posterior insula, and ventromedial prefrontal cortex (vmPFC), using a large cross-sectional adulthood sample. Results revealed that each of the seeded connectivity maps engaged in at least one of the large-scale brain networks including sensorimotor, attentional, basal ganglia, limbic, and default mode networks (DMN). In the early-adulthood stage, females had stronger negative rsFC in pMCC and right anterior INS (aINS) with the medial DMN than males, possibly reflecting their greater suppression of the sympathoexcitation associated with sex hormonal estrogen. Whereas in the late-adulthood stage, they showed stronger positive rsFC in pMCC with postcentral gyrus and weaker negative rsFC with the most DMN, possibly relating to their higher risk of depression, anxiety, and dementia than males after menopause. Moreover, females demonstrated reduced negative rsFC in pMCC with dorsal PCUN/PCC and left AG with advancing age, whereas males showed the opposite pattern, namely increased positive rsFC, in pMCC with right SMG, and in vmPFC with ventral PCUN. We interpret these results as their differences of altered autonomic regulation associated with pain experience and reflective movement, respectively, due to aging. In sum, our findings add in literature that autonomic responses can be also represented intrinsically in the resting brain, and gender- and age-related differences might be associated with sex hormones and sensorimotor abilities, respectively.

The study aims to adopt an artificial neural network (ANN) for modeling industrial energy demand in Taiwan related to the subsector manufacturing output and climate change. This is the first study to use the ANN technique to measure the industrial energy demand–manufacturing output–climate change nexus. The ANN model adopted in this study is a multilayer perceptron (MLP) with a feedforward backpropagation neural network. This study compares the outcomes of three ANN activation functions with multiple linear regression (MLR). According to the estimation results, ANN with a hidden layer and hyperbolic tangent activation function outperforms other techniques and has statistical solid performance values. The estimation results indicate that industrial electricity demand in Taiwan is price inelastic or has a negative value of −0.17 to −0.23, with climate change positively influencing energy demand. The relationship between manufacturing output and energy consumption is relatively diverse at the disaggregated level.

We present a theoretical analysis of stochastic resonance phenomena in n-GaAs microwave diodes. When stimulated by external laser beams under a nonlinear feedback control, two stable periodic domain trains can coexist at the same values of the control parameters. If this bistable state is subjected to both modulation and noise, we show that the signal-to-noise ratio will exhibit a maximum at finite noise amplitudes. This analysis may be the first to consider stochastic resonance phenomena in microwave systems.

We use numerical methods to study two-dimensional flow passing a square cylinder at low Reynolds numbers, and observe period-1 and -3 vortices behind the cylinder at the same Reynolds number Re. When Re increases from a small number to a critical value Re(c) approximately equal to 320, the system could change from bistability, which maintains the spatial symmetry, to tristability, which breaks the spatial symmetry. Our results suggest many interesting problems for further studies.