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Pinus koraiensis is one of the important tree species in Northeast China. Due to its high pine-needle-oil content and the density of human activities in its habitat, the forest-fire prevention situation is severe in the context of climate change. The rate of surface-fire spread is one of the key indicators for scientifically advancing early fire prevention and is crucial for guiding forest firefighting operations. In this study, we investigated how moisture content, load, and slope impact the surface-fire spread rate using indoor-simulated fire-spread experiments. Furthermore, we analyzed the limitations in the Rothermel model for predicting the surface-fire spread rate in P. koraiensis plantations and proposed modifications to the model by the modification method of priority to no-wind or slope conditions and slope conditions. Additionally, we evaluated the prediction accuracy of the original Rothermel model and two other modified models on the surface-fire spread rate. A high moisture content and low slope demonstrated an absolute inhibitory effect on the rate of surface-fire spread, whereas the promotional effect of a low moisture content and high slope was easily disturbed by the other factors. Under high-slope conditions, an overestimation situation was observed in the Rothermel model. Both of the modification methods involving priority to no-wind or slope conditions and slope conditions could improve this situation. Furthermore, the modification method demonstrated a better improvement effect on the prediction accuracy. Our findings provide valuable insights for refining the Rothermel model and offer guidance for improving the accuracy of predicting fire spread rates and behavior for Pinus koraiensis. This bears immense significance for advancing the understanding and calculation of the ROS of forest fires in the region. [ABSTRACT FROM AUTHOR]

To highlight the characteristics of PM2.5–O3 pollution in the Central Plains Urban Agglomeration, spatial and temporal characteristics, key meteorological factors, and source pollution data for the area were analyzed. These data from the period 2014–2020 were obtained from state-controlled environmental monitoring stations in seven major cities of the agglomeration. The results revealed the following: (1) Spatially, the PM2.5–O3 pollution days were aggregated in the central area of Xinxiang and decreased toward the north and south. Temporally, during the 2014–2020 period, 50 days of PM2.5–O3 pollution were observed in the major cities of the Central Plains Urban Agglomeration, with an overall decreasing trend. (2) A low-temperature, high-pressure environment appeared unfavorable for the occurrence of PM2.5–O3 pollution days. Wind speeds of 2.14–2.19 m/s and a southerly direction increased the incidence of PM2.5–O3 pollution days. (3) The external transport range in summer was smaller and mainly originated from within Henan Province. These results can provide important reference information for achieving a synergistic control of PM2.5–O3 pollution, determining the meteorological causes, as well as the potential sources, of PM2.5–O3 pollution in polluted areas and promoting air pollution control. [ABSTRACT FROM AUTHOR]

This article discusses the neuropathological mechanisms underlying childhood ADHD, focusing on the connection between macroscopic brain connectome aberrations and microscopic biological architectures. The study used neuroimaging data to examine functional connectivity in ADHD children and found perturbations in cortical gradients compared to typically developing peers. These gradient perturbations were associated with neurotransmitter receptors and genetic transcriptional expressions linked to episodic memory and emotional regulation. The article suggests further research into the specific mechanisms underlying brain connectome aberrations in ADHD and the developmental trajectories of these abnormalities. Early detection and interventions are important for managing ADHD and reducing its impact. [Extracted from the article]

The zero‐velocity update (ZUPT) method is an effective way to reduce accumulated velocity errors of pedestrian navigation systems (PNSs). For a typical scheme, a stance phase detection module based on a fixed threshold is used to trigger the ZUPT algorithm. However, the detector is not robust enough for dynamic gait speeds. The false detection will degrade the navigation performance. In this letter, to improve the stance phase detector, the adaptive zero‐velocity detection problem is cast under dynamic gait speeds as a sequential threshold of a traditional detector inferring problem and a zero‐velocity detection framework proposed by combining a deep neural network with a traditional binary gait phase detector. Sufficient experimental results show that the proposed method outperforms other discussed learning‐based methods taking into account the trade‐off among model performance, structure, and size. Compared with the traditional method with a fixed threshold, the real‐world high‐dynamic positioning experiments show that this proposed method reduces the root mean squared error (RMSE) of absolute distance error by 48.7%, RMSE of start‐end error by 12.5%, and average RMSE of position error by 19.2%. [ABSTRACT FROM AUTHOR]