从有文献收录(1983年)开始至2024年中国知网和Web of Sciences核心合集数据库中筛选出1063篇有关毛乌素沙地生态水文研究的文献,采用文献计量和可视化分析的方法,分析了上述文献发表数量随时间变化、国家及地区研究情况、发文机构、关...从有文献收录(1983年)开始至2024年中国知网和Web of Sciences核心合集数据库中筛选出1063篇有关毛乌素沙地生态水文研究的文献,采用文献计量和可视化分析的方法,分析了上述文献发表数量随时间变化、国家及地区研究情况、发文机构、关键词及其聚类等,系统归纳并评述了毛乌素沙地生态水文研究的关键进展。结果表明,毛乌素沙地生态水文研究方向主要集中在沙地生态水文要素的演变规律与互馈机理、沙地生态水文演变的驱动机制以及沙地荒漠化的影响效应及应对等方面。基于前述研究基础,本文就持续性巩固毛乌素沙地荒漠化防治成果提出了相关建议,未来研究仍需重点关注该区域的生态水文、气候变化适应以及生态保护等核心议题。展开更多
【目的】明确库布齐沙漠湿地表层土壤含盐量(Soil Salt Content,SSC)的时空演变规律及其气象驱动因素。【方法】基于ReliefF特征选择和机器学习算法构建表层SSC反演模型,绘制2000—2024年库布齐沙漠北缘表层SSC反演图,采用趋势分析、相...【目的】明确库布齐沙漠湿地表层土壤含盐量(Soil Salt Content,SSC)的时空演变规律及其气象驱动因素。【方法】基于ReliefF特征选择和机器学习算法构建表层SSC反演模型,绘制2000—2024年库布齐沙漠北缘表层SSC反演图,采用趋势分析、相关性分析及地理探测器模型,分析库布齐沙漠北缘表层SSC的时空变化特征及其与气象因素之间的关系。【结果】PSO-SVM算法的性能优于PLSR和RF算法(R^(2)=0.63,RMSE=0.009,MAE=0.007);基于最佳模型绘制的表层SSC反演图表明,2000—2024年库布齐沙漠北缘的年平均SSC呈波动上升趋势;研究区表层SSC的空间分布异质性显著,较高值主要集中于研究区西南部并延伸至北部,其在像元尺度上与气温和降水量的相关性最强,与真实水汽压和混合比的相关性最弱;气温、降水量、水汽压亏缺和风速是影响库布齐沙漠北缘表层SSC时空分布格局的重要驱动因素。【结论】库布齐沙漠表层SSC存在明显的时空分异特征,气温和降水量对表层SSC的时空分布格局具有重要影响。展开更多
目的 探究电针结合康复训练对缺血性脑卒中患者疗效、神经功能、生活质量影响。方法 研究共计纳入120例缺血性脑卒中患者(2017年6月—2021年6月),将120例患者以随机数字表法分成参照组与观察组,各60例患者,其中参照组脱落1例,剩余59例,...目的 探究电针结合康复训练对缺血性脑卒中患者疗效、神经功能、生活质量影响。方法 研究共计纳入120例缺血性脑卒中患者(2017年6月—2021年6月),将120例患者以随机数字表法分成参照组与观察组,各60例患者,其中参照组脱落1例,剩余59例,观察组无脱落,参照组患者采取常规治疗及康复训练,观察组患者在参照组治疗基础上结合电针治疗,患者数据对比:患者治疗前后中医证候(神识昏蒙、偏瘫、口舌歪斜、目偏不瞬、共济失调等)总积分变化及神经功能缺损量表(national institute of health stroke scale, NIHSS)评分变化、日常生活能力评分(activities of daily living scale, ADL)变化、患者治疗效果、治疗前后患者C反应蛋白(C-reactive protein, CRP)水平变化及CRP水平异常率情况、治疗前后患者简易运动功能评价量表(Fugl-Meyer assessment upper extremity scale, FMA)及中国卒中量表(chinese stroke scale, CCS)评分变化、治疗前后血液流变学指标变化、生活质量综合量表问卷(generic quality of life inventory-74,GQOL-74)评分变化。结果 治疗前,两组患者中医证候总积分、NIHSS评分与ADL评分、CRP水平及CRP水平异常率、FMA及CCS评分、血液流变学指标、GQOL-74评分等观察指标对比,P>0.05,治疗后各组患者中医证候总积分、NIHSS评分与ADL评分、CRP水平及CRP水平异常率、FMA及CCS评分、血液流变学指标、GQOL-74评分等观察指标均较治疗前改善,治疗后观察组患者中医证候总积分、NIHSS评分与ADL评分、CRP水平及CRP水平异常率、FMA及CCS评分、血液流变学指标、GQOL-74评分等观察指标优于参照组(P<0.05);与参照组患者(81.36%,48/59)相比,观察组患者治疗总有效率(95.00%,57/60)更高(P<0.05)。结论 电针结合康复训练治疗缺血性脑卒中效果良好,患者恢复较好,生活质量提升,值得应用。展开更多
This study employs the Long Short-Term Memory(LSTM)rainfall-runoff model to simulate and predict runoff in typical basins of the Jiziwan Region of the Yellow River,aiming to overcome the shortcomings of traditional hy...This study employs the Long Short-Term Memory(LSTM)rainfall-runoff model to simulate and predict runoff in typical basins of the Jiziwan Region of the Yellow River,aiming to overcome the shortcomings of traditional hydrological models in complex nonlinear environments.The Jiziwan Region of the Yellow River is affected by human activities such as urbanization,agricultural development,and water resource management,leading to increasingly complex hydrological processes.Traditional hydrological models struggle to effectively capture the relationship between rainfall and runoff.The LSTM rainfall-runoff model,using deep learning techniques,automatically extracts features from data,identifies complex patterns and long-term dependency in time series,and provides more accurate and reliable runoff predictions.The results demonstrate that the LSTM rainfall-runoff model adapts well to the complex hydrological characteristics of the Jiziwan Region,showing superior performance over traditional hydrological models,especially in addressing the changing trends under the influence of climate change and human activities.By analyzing the interannual and within-year variations of runoff under different climate change scenarios,the model can predict the evolution trends of runoff under future climate conditions,providing a scientific basis for water resource management and decision-making.The results indicate that under different climate change scenarios,the runoff in several typical basins of the Jiziwan Region exhibits different variation trends.Under SSP1-2.6 and SSP2-4.5,some basins,such as the Wuding River Basin,Tuwei River Basin,and Gushanchuan Basin,show a decreasing trend in annual runoff.For example,in the Wuding River Basin,the average runoff from 2025 to 2040 is 12.48 m^(3)/s(SSP1-2.6),with an annual decrease of 0.10 m^(3)/s;in the Tuwei River Basin,the runoff from 2025 to 2040 is 12.96 m^(3)/s(SSP1-2.6),with an annual decrease of 0.10 m^(3)/s.In contrast,under SSP3-7.0 and SSP5-8.5,with climate warming and changes in precipitation patterns,runoff in some basins shows an increasing trend,particularly during the snowmelt period and with increased summer precipitation,leading to a significant rise in runoff.展开更多
文摘从有文献收录(1983年)开始至2024年中国知网和Web of Sciences核心合集数据库中筛选出1063篇有关毛乌素沙地生态水文研究的文献,采用文献计量和可视化分析的方法,分析了上述文献发表数量随时间变化、国家及地区研究情况、发文机构、关键词及其聚类等,系统归纳并评述了毛乌素沙地生态水文研究的关键进展。结果表明,毛乌素沙地生态水文研究方向主要集中在沙地生态水文要素的演变规律与互馈机理、沙地生态水文演变的驱动机制以及沙地荒漠化的影响效应及应对等方面。基于前述研究基础,本文就持续性巩固毛乌素沙地荒漠化防治成果提出了相关建议,未来研究仍需重点关注该区域的生态水文、气候变化适应以及生态保护等核心议题。
文摘【目的】明确库布齐沙漠湿地表层土壤含盐量(Soil Salt Content,SSC)的时空演变规律及其气象驱动因素。【方法】基于ReliefF特征选择和机器学习算法构建表层SSC反演模型,绘制2000—2024年库布齐沙漠北缘表层SSC反演图,采用趋势分析、相关性分析及地理探测器模型,分析库布齐沙漠北缘表层SSC的时空变化特征及其与气象因素之间的关系。【结果】PSO-SVM算法的性能优于PLSR和RF算法(R^(2)=0.63,RMSE=0.009,MAE=0.007);基于最佳模型绘制的表层SSC反演图表明,2000—2024年库布齐沙漠北缘的年平均SSC呈波动上升趋势;研究区表层SSC的空间分布异质性显著,较高值主要集中于研究区西南部并延伸至北部,其在像元尺度上与气温和降水量的相关性最强,与真实水汽压和混合比的相关性最弱;气温、降水量、水汽压亏缺和风速是影响库布齐沙漠北缘表层SSC时空分布格局的重要驱动因素。【结论】库布齐沙漠表层SSC存在明显的时空分异特征,气温和降水量对表层SSC的时空分布格局具有重要影响。
文摘目的 探究电针结合康复训练对缺血性脑卒中患者疗效、神经功能、生活质量影响。方法 研究共计纳入120例缺血性脑卒中患者(2017年6月—2021年6月),将120例患者以随机数字表法分成参照组与观察组,各60例患者,其中参照组脱落1例,剩余59例,观察组无脱落,参照组患者采取常规治疗及康复训练,观察组患者在参照组治疗基础上结合电针治疗,患者数据对比:患者治疗前后中医证候(神识昏蒙、偏瘫、口舌歪斜、目偏不瞬、共济失调等)总积分变化及神经功能缺损量表(national institute of health stroke scale, NIHSS)评分变化、日常生活能力评分(activities of daily living scale, ADL)变化、患者治疗效果、治疗前后患者C反应蛋白(C-reactive protein, CRP)水平变化及CRP水平异常率情况、治疗前后患者简易运动功能评价量表(Fugl-Meyer assessment upper extremity scale, FMA)及中国卒中量表(chinese stroke scale, CCS)评分变化、治疗前后血液流变学指标变化、生活质量综合量表问卷(generic quality of life inventory-74,GQOL-74)评分变化。结果 治疗前,两组患者中医证候总积分、NIHSS评分与ADL评分、CRP水平及CRP水平异常率、FMA及CCS评分、血液流变学指标、GQOL-74评分等观察指标对比,P>0.05,治疗后各组患者中医证候总积分、NIHSS评分与ADL评分、CRP水平及CRP水平异常率、FMA及CCS评分、血液流变学指标、GQOL-74评分等观察指标均较治疗前改善,治疗后观察组患者中医证候总积分、NIHSS评分与ADL评分、CRP水平及CRP水平异常率、FMA及CCS评分、血液流变学指标、GQOL-74评分等观察指标优于参照组(P<0.05);与参照组患者(81.36%,48/59)相比,观察组患者治疗总有效率(95.00%,57/60)更高(P<0.05)。结论 电针结合康复训练治疗缺血性脑卒中效果良好,患者恢复较好,生活质量提升,值得应用。
基金the National Key R&D Program of China(No.2023YFC3206504)National Natural Science Foundation of China(Nos.52121006,41961124006,51911540477)+1 种基金Young Top-Notch Talent Support Program of National High-level Talents Special Support PlanResearch Project of Ministry of Natural Resources(No.20210103)for providing financial support for this research。
文摘This study employs the Long Short-Term Memory(LSTM)rainfall-runoff model to simulate and predict runoff in typical basins of the Jiziwan Region of the Yellow River,aiming to overcome the shortcomings of traditional hydrological models in complex nonlinear environments.The Jiziwan Region of the Yellow River is affected by human activities such as urbanization,agricultural development,and water resource management,leading to increasingly complex hydrological processes.Traditional hydrological models struggle to effectively capture the relationship between rainfall and runoff.The LSTM rainfall-runoff model,using deep learning techniques,automatically extracts features from data,identifies complex patterns and long-term dependency in time series,and provides more accurate and reliable runoff predictions.The results demonstrate that the LSTM rainfall-runoff model adapts well to the complex hydrological characteristics of the Jiziwan Region,showing superior performance over traditional hydrological models,especially in addressing the changing trends under the influence of climate change and human activities.By analyzing the interannual and within-year variations of runoff under different climate change scenarios,the model can predict the evolution trends of runoff under future climate conditions,providing a scientific basis for water resource management and decision-making.The results indicate that under different climate change scenarios,the runoff in several typical basins of the Jiziwan Region exhibits different variation trends.Under SSP1-2.6 and SSP2-4.5,some basins,such as the Wuding River Basin,Tuwei River Basin,and Gushanchuan Basin,show a decreasing trend in annual runoff.For example,in the Wuding River Basin,the average runoff from 2025 to 2040 is 12.48 m^(3)/s(SSP1-2.6),with an annual decrease of 0.10 m^(3)/s;in the Tuwei River Basin,the runoff from 2025 to 2040 is 12.96 m^(3)/s(SSP1-2.6),with an annual decrease of 0.10 m^(3)/s.In contrast,under SSP3-7.0 and SSP5-8.5,with climate warming and changes in precipitation patterns,runoff in some basins shows an increasing trend,particularly during the snowmelt period and with increased summer precipitation,leading to a significant rise in runoff.
