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Objective To investigate cortical thickness changes in patients with vestibular migraine (VM), and to provide a basis for studying the pathogenesis of VM. Methods From June 2021 to December 2022, we enrolled 27 patients with VM visiting the department of neurology of our hospital and 28 healthy volunteers as control group. All the subjects underwent whole-brain magnetic resonance (MR) scanning. Using surface-based morphometry, the thickness of the cortex was analyzed on MR images and compared between the two groups. In the VM group, Spearman correlation analysis was performed to detect the correlation between cortical thickness changes and VM course, headache attack frequency, dizziness attack frequency, and headache intensity on the Visual Analogue Scale (VAS). Results Compared with the control group, patients with VM had a significant decrease in the cortical thickness of the left inferior parietal lobule (Pvertex0.05). Conclusion The cortical thickness of the left inferior parietal lobule is altered in patients with VM. The left inferior parietal lobule may be involved in the pathophysiological process of VM.

Freeze–thaw cycles are frequently overlooked as a pivotal factor contributing to leakage and structural failures in clayey soil-impermeable barriers used in landfills or tailings repositories in regions subject to seasonal freezing. This investigation explores the recovery and residual strength properties of Jilin ball clay undergoing six freeze–thaw cycles, and assesses the pore structure characteristics through a series of nuclear magnetic resonance (NMR) tests. The results indicate that normal stress has a greater impact on peak recovery strength than dry density and rest periods. Cohesion increases earlier and more significantly during rest periods compared to internal friction angle. Although the pore diameter remains consistent within the micropores during the freeze–thaw cycles, the soil's structural integrity undergoes notable changes. The concluding analysis provides valuable insights for the construction and management of impermeable barriers in landfills or tailings repositories within seasonally frozen areas. [ABSTRACT FROM AUTHOR]

Currently, the treatment of miscellaneous fill foundations, composed of a mixture of domestic garbage, construction solid waste, and natural soil, presents a significant challenge in urban peripheral engineering construction. This paper discusses the application of vibrating rod compaction technology for foundation treatment in Xinjiang. It evaluates the effectiveness of cross-section vibrating rod compaction equipment in reinforcing fine-grained miscellaneous fill foundations. The study analyzes the impact of construction disturbances caused by the insertion of the vibrating rod, monitoring horizontal stresses at various depths. Both laboratory and field tests show significant improvements: soil dry density increased by 8% to 18%, porosity decreased by 10% to 23%, compression modulus increased by 22% to 246%, and compression coefficient decreased by 8% to 70%. Additionally, cohesion (C) and angle of friction (ɸ) saw increases ranging from 7 to 38% and 3% to 25%, respectively. Below a depth of 3 m, cone tip resistance exceeded 10 MPa, and sidewall friction resistance increased to over 100 kPa, surpassing pre-treatment values. The standard penetration test results doubled stroke length compared to pre-treatment, indicating a substantial improvement in foundation bearing capacity. Surface wave tests before and after treatment showed a 15% increase in wave velocity, reflecting a more compact soil structure. The vibrating rod compaction method is innovative, energy-efficient, environmentally friendly, and economically beneficial, holding great potential for future miscellaneous fill treatments. [ABSTRACT FROM AUTHOR]

The effectiveness of national policies for air pollution control has been demonstrated, but the relative effectiveness of short-term emission reduction measures in comparison with national policies has not. Here we show that short-term abatement measures during important international events substantially reduced PM2.5 concentrations, but air quality rebounded to pre-event levels after the measures ceased. Long-term adherence to strict emission reduction policies led to successful decreases of 54% in PM2.5 concentrations in Beijing, and 23% in atmospheric nitrogen deposition in China from 2012 to 2020. Incentivized by "blue skies" type campaigns, economic development and reactive nitrogen pollution are quickly decoupled, showing that a combination of inspiring but aggressive short-term measures and effective but durable long-term policies delivers sustainable air quality improvement. However, increased ammonia concentrations, transboundary pollutant flows, and the complexity to achieving reduction targets under climate change scenarios, underscore the need for the synergistic control of multiple pollutants and inter-regional action. In response to the rebound of air pollution during large events in China, the authors propose that a combination of ambitious short-term measures and enduring long-term pollution control policies is a sustainable solution for improving air quality. [ABSTRACT FROM AUTHOR]

The construction of new towns is one of the main measures to evacuate urban populations and promote regional coordination and urban–rural integration in China. Mining the spatio-temporal pattern of new town hot spots based on multivariate data and analyzing the influencing factors of new town construction hot spots can provide a strategic basis for new town construction, but few researchers have extracted and analyzed the influencing factors of new town internal hot spots and their classification. In order to define the key points of Wuhan's new town construction and promote the construction of new cities in an orderly and efficient manner, this paper first constructs a space-time cube based on the luminous remote sensing data from 2010 to 2019, extracts hot spots and emerging hot spots in Wuhan New City, selects 14 influencing factor indicators such as population density, and uses bivariate Moran's index to analyze the influencing factors of hot spots, indicating that the number of bus stops and vegetation coverage rate are the most significant. Secondly, the disorderly multivariate logistic regression model is used to analyze the influencing factors of emerging hot spots. The results show that population density, vegetation coverage, road density, distance to water bodies, and distance to train stations are the most significant factors. Finally, based on the analysis results, some relevant suggestions for the construction of Wuhan New City are proposed, providing theoretical support for the planning and policy guidance of new cities, and offering reference for the construction of new towns in other cities, promoting the construction of high-quality cities. [ABSTRACT FROM AUTHOR]

Rainfall data with high spatial and temporal resolutions are essential for urban hydrological modeling. Ubiquitous surveillance cameras can continuously record rainfall events through video and audio, so they have been recognized as potential rain gauges to supplement professional rainfall observation networks. Since video-based rainfall estimation methods can be affected by variable backgrounds and lighting conditions, audio-based approaches could be a supplement without suffering from these conditions. However, most audio-based approaches focus on rainfall-level classification rather than rainfall intensity estimation. Here, we introduce a dataset named Surveillance Audio Rainfall Intensity Dataset (SARID) and a deep learning model for estimating rainfall intensity. First, we created the dataset through audio of six real-world rainfall events. This dataset's audio recordings are segmented into 12,066 pieces and annotated with rainfall intensity and environmental information, such as underlying surfaces, temperature, humidity, and wind. Then, we developed a deep learning-based baseline using Mel-Frequency Cepstral Coefficients (MFCC) and Transformer architecture to estimate rainfall intensity from surveillance audio. Validated from ground truth data, our baseline achieves a root mean absolute error of 0.88 mm h -1 and a coefficient of correlation of 0.765. Our findings demonstrate the potential of surveillance audio-based models as practical and effective tools for rainfall observation systems, initiating a new chapter in rainfall intensity estimation. It offers a novel data source for high-resolution hydrological sensing and contributes to the broader landscape of urban sensing, emergency response, and resilience., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 Published by Elsevier B.V. on behalf of Chinese Society for Environmental Sciences, Harbin Institute of Technology, Chinese Research Academy of Environmental Sciences.)