Subjects: Geosciences >> Geography submitted time 2024-04-01 Cooperative journals: 《干旱区地理》
Abstract：夜间文旅消费集聚区是推进文旅融合高质量发展的典型示范，对引领文旅产业发展、释放夜间消费潜力具有重要意义。采用GIS空间分析、地理探测器等方法探究中国夜间文旅消费集聚区空间格局及影响机理。结果表明：（1）夜间文旅消费集聚区呈现“东密西疏、南多北少”的集聚型分布态势，“环城、亲水、傍景”布局特征明显，“一主、两副、多微核”的空间形态显著。（2）夜间文旅消费集聚区分布的空间正相关性明显，呈现“东热西冷”圈层式递减的空间分异格局。（3）类型结构上，遗址遗迹类分布在历史文化悠久地区，风土民俗类集聚在少数民族地区，产业主题类空间分布广泛，风景名胜类分布较为均衡，文旅商综合类主要分布在经济发达地区。（4）地形、河流和气候是影响夜间文旅消费集聚区分布的基础要素，人口素质、客运能力、产业发展和政策支持是影响其分布的关键因素。
Subjects: Medicine, Pharmacy >> Clinical Medicine submitted time 2022-07-09 Cooperative journals: 《中国全科医学》
Abstract： Cushing's disease, the most common cause of endogenous Cushing's syndrome, is hypercortisolemia caused by adrenocorticotropic hormone-secreting pituitary adenoma. Due to the persistence of hypercortisolemia, clinical syndromes such as full moon face, buffalo back, central obesity and metabolic disorders may occur. For Cushing's disease, accurate diagnosis, appropriate treatment and follow-up are very important. Based on recent research evidence, the International Pituitary Association published the Consensus on diagnosis and management of Cushing’s disease: a guideline update in December 2021, which updated screening and diagnosis procedures, postoperative monitoring, medication and radiotherapy, and complication management for Cushing's disease. This article interprets the pharmaceutical section of the guidelines. It could be helpful for general practitioners and specialists to standardize the diagnosis and treatment of Cushing's disease.
Subjects: Geosciences >> Geography submitted time 2024-03-01 Cooperative journals: 《干旱区研究》
Abstract： As the largest carbon reservoir on land, forests play a crucial role in human life and development.Understanding the dynamic changes in forest resources and modernizing their sustainable development iscurrently a significant research focus. This study focuses on natural Picea forests in the Tianshan Mountains anduses ground measurement data, helicopter airborne LiDAR point cloud data, and Global Ecosystem DynamicsInvestigation (GEDI) data to construct a multisource fusion data framework. By utilizing deep learningalgorithms within the AutoKeras framework, the study aims to predict the regression model of multiple relativeheight quantiles of GEDI data and their aboveground biomass in the study area, thereby validating the feasibilityof GEDI data for large-scale aboveground biomass retrieval. The main conclusions are as follows: (1) GEDI dataare highly feasible for estimating forest aboveground biomass. Through automated deep learning algorithms andtraining and verification sets, the overall data achieve a coefficient of determination (R2) of 0.69, 0.63, and 0.67,respectively, along with a mean absolute error of 3.73 mg·hm−2, 4.22 mg·hm−2, and 3.89 mg·hm−2, demonstratinghigh prediction accuracy. (2) Helicopter LiDAR, an intermediate technology for estimating aboveground biomassusing GEDI data, exhibits a single tree recognition accuracy of over 0.75 across the study area. The studysuccessfully utilizes multimodal data fusion to quantitatively describe the structural parameters of the single treefoundation in the study area while verifying the potential of GEDI data for obtaining forest aboveground biomass.Moreover, the study provides a theoretical basis for estimating carbon sources and sinks, biomass, stock, forestmanagement, biodiversity protection, and other projects in similar areas, offering essential guidance, andfundamental data support.
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