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Although the effect of campus landscape space on stress relief among college students has been confirmed, few existing studies have considered the impact on stress recovery from the perspective of factor combination, and the key visual elements and the most effective combination of visual elements to relieve stress are still unclear. This study attempts to conduct a natural experiment within Chinese campuses, measuring physiological indicators of stress such as heart rate (HR), frequency domain index of heart rate variability (LF/HF), skin conductance level (SCL), skin temperature (SKT), and respiratory rate (RESP) using physiological instruments. It explored the effects of visual elements and their combinations in campus landscape spaces on short-term stress relief among college students through semantic segmentation, multifactorial analysis of variance, and post hoc multiple comparison methods. Research results demonstrate that the presence of water elements in the field of vision can effectively improve the stress relief effects of landscape spaces. Reasonable combinations of natural landscape elements and artificial landscape elements in the design can also effectively promote stress relief among students. Building facade area and sky area, water area and sky area, and plant species and pavement area are three combinations of factors with the strongest interactive effects. "Natural water scenery" and "exquisite artificial" are two campus landscape design patterns most conducive to short-term stress relief. [ABSTRACT FROM AUTHOR]

The impact of COVID-19 has boosted growth in the takeaway and medical industries but has also generated a large amount of plastic waste. Peanut shells (PS) are produced in large quantities and are challenging to recycle in China. Co-pyrolysis of peanut shells (PS) and polypropylene (PP) is an effective method for processing plastic waste and energy mitigation. Thermogravimetric analysis was conducted on PS, PP, and their blends (PS-PP) at different heating rates (10, 20, 30 °C·min−1). The results illustrated that the co-pyrolysis process of PS-PP was divided into two distinct decomposition stages. The first stage (170–400 °C) was predominantly linked to PS decomposition. The second stage (400–520 °C) resulted from the combinations of PS and PP's thermal degradations, with the most contribution from PP degradation. With the increase in heating rate, thermogravimetric hysteresis appeared. Kinetic analysis indicated that the co-pyrolysis process reduced the individual pyrolysis activation energy, especially in the second stage, with a correlation coefficient (R2) generally maintained above 0.95. The multi-level reaction mechanism function model can effectively reveal the co-pyrolysis process mechanism. PS proved to be high-quality biomass for co-pyrolysis with PP, and all mixtures exhibited synergistic effects at a mixing ratio of 1:1 (PS1-PP1). This study accomplished effective waste utilization and optimized energy consumption. It holds significance in determining the interaction mechanism of mixed samples in the co-pyrolysis process. [ABSTRACT FROM AUTHOR]

Wind profiling within operating wind farms is important for both wind resource assessment and wind power prediction. With increasing wind turbine size, it is getting difficult to obtain wind profiles covering the turbine-affecting area due to the limited height of wind towers. In this study, a stepwise quality control and optimizing process for deriving high-quality near-surface wind profiles within wind farms is proposed. The method is based on the radial wind speed obtained by the Doppler Wind Lidar velocity-azimuth display (VAD) technique. The method is used to obtain the whole wind profile from ground level to the height affected by wind turbines within a utility-scale onshore wind farm, in northern China. Compared with the traditional carrier-to-noise ratio (CNR) filter-based quality control method, the proposed data processing method can significantly improve the accuracy of the derived wind. For a 10 m wind speed, an increase in coefficient of determination (R2) from 0.826 to 0.932, and a decrease in mean absolute error (MAE) from 1.231% to 0.927% are obtained; while for 70 m wind speed, R2 increased from 0.926 to 0.958, and MAE decreased from 1.023% to 0.771%. For wind direction, R2 increased from 0.978 to 0.992 at 10 m, and increased from 0.983 to 0.995 at 70 m. The optimized method also presents advantages in improving the accuracy of derived wind under complex wind environments, e.g., inside a wind farm, and increasing the data availability during clear nights. The proposed method could be used to derive wind profiles from below the minimum range of a vertically operating scanning Doppler Lidar to a height affected by wind turbines. Combined with Doppler beam-swinging (DBS) scanning data, the method could be used to obtain the complete wind profile in the boundary layer. These wind profiles could be further used to predict wind power and evaluate the climate and environmental effects of wind farms. [ABSTRACT FROM AUTHOR]

The escalating frequency and severity of extreme climate greatly impact the carbon dynamics of terrestrial ecosystems worldwide. To understand the multi-temporal response of net ecosystem productivity (NEP) to extreme climate, we investigated 11 temperature and precipitation extreme indices across different vegetation types in China. From 1981 to 2019, the results showed that NEP in China increased at a rate of 0.64 g·m−2·a−2. Extreme climate demonstrated a significant warming trend and a non-significant moistening trend; specifically, maximum daily minimum temperature (TNx) exhibited a significant increase at a rate of 0.34 °C/10 a, while maximum 5-day precipitation (Rx5day) showed an insignificant increase at a rate of 1.78 mm/10 a. NEP was significantly impacted by extreme temperature at the annual, seasonal, and monthly scales, but moderately impacted by extreme precipitation. Specifically, extreme temperature had the most significant effect on grassland, with minimal influence on cropland. In contrast, extreme precipitation had the most significant effect on forest, with minimal impact on cropland. Moreover, the lagged time for extreme precipitation was longer than that for extreme temperature. Extreme precipitation exhibited a corresponding lagged time of at least 2 months (p < 0.01), while extreme temperature exhibited a lagged time of at least 1 month (p < 0.01). The maximum lag time observed was 4 months (p < 0.01). Our findings provide valuable insights into the multi-temporal response of NEP to extreme climate in China and inform sustainable development practices in the region. [ABSTRACT FROM AUTHOR]