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 physiological adaptive regulation of healthy population with a high fitness level is associated with enhanced cognitive control in brain. This study further investigated the effects of different levels of sporting experience on intrinsic brain networks involved in central autonomic processing using resting-state functional magnetic resonance imaging. We explored functional connectivity of four core regions within central autonomic network (CAN), namely posterior midcingulate cortex (pMCC), left amygdala (AMYG), and right anterior (aINS) and left posterior insular cortices, in advanced and intermediate baseball players, and compared their strength of connectivity with individuals without baseball-playing experience. Functional connectivity maps across three groups confirmed a close relationship between CAN and large-scale brain networks in sensory, motor and cognitive domains. Crucially, both advanced and intermediate batters demonstrated enhanced connectivity between pMCC and sensorimotor network, between right aINS and dorsal anterior cingulate cortex, and between left AMYG and right putamen, than controls. These results reflected a stronger interregional coupling in sensorimotor and cognitive control, and in motor skill consolidation. In conclusion, we provided evidence that different levels of sporting experience could reorganize/enhance intrinsic functional connectivity for central autonomic processing.

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.

The diurnal temperature range (DTR) is a significant indicator of climate change, and a previous study has shown its impact on human health. However, research investigating the influence of DTR on road traffic accidents is scarce. Thus, this study aims to evaluate the impact of changes in DTR on road traffic accidents. The present study employs two methods to address the complexities of road accidents. Firstly, panel data from 20 cities and counties in Taiwan are utilized, and the autoregressive distributed lag (ARDL) model is employed for estimation. Secondly, distributed lag non-linear models (DLNMs) are used with quasi-Poisson regression analysis to assess the DTR’s lagged and non-linear relationships with road accidents using time series data from six Taiwanese metropolitan cities. The study results indicate that a decrease of 1 °C in DTR raises long-term road traffic accidents by 17.1%. In the short term, the impact of declining DTR on road accidents is around 4%. Moreover, the effect of low DTR values differs in each city in Taiwan. Three cities had high levels of road accidents, as evidenced by an increase in the relative risk value; two cities had moderate responses; and one city had a relatively lower response compared to high DTR values. Finally, based on the cumulative relative risk estimations, the study found that a low diurnal temperature range is linked to a high road traffic accident rate, especially during the lag-specific 0–5 months. The findings of this study offer fresh evidence of the negative impact of climate factor on road traffic accidents.