消落带是位于水陆交错带的一种特殊生态系统.消落带土壤作为生态系统中氮、磷元素重要的“源”与“汇”,在生物地球化学循环研究中具有重要意义.该研究基于中国知网CNKI以及Web of Science数据库检索近10年三峡消落带土壤氮、磷研究文献...消落带是位于水陆交错带的一种特殊生态系统.消落带土壤作为生态系统中氮、磷元素重要的“源”与“汇”,在生物地球化学循环研究中具有重要意义.该研究基于中国知网CNKI以及Web of Science数据库检索近10年三峡消落带土壤氮、磷研究文献,提取w(TN)、w(TP)数据并进行统计,从宏观尺度上分析w(TN)、w(TP)的时空分布特征.结果表明:①高程分布上,当高程高于155 m时,土壤w(TN)(0.75~1.17 g kg)随着高程增加呈下降趋势,土壤w(TP)未发生显著变化(0.53~0.60 g kg);当高程低于155 m时,土壤w(TN)处于较低水平(0.66~0.86 g kg),但w(TP)维持较高水平(0.60~0.76 g kg).②时间分布上,消落带土壤w(TN)整体呈现逐年递减的趋势,Pearson相关系数为-0.64,但是w(TP)没有显著变化.③地区分布上,三峡上游库区消落带土壤w(TP)出现显著高值,下游库区w(TN)出现显著高值.研究显示:不同高程土壤受植物残体分解等因素影响,在落干期w(TN)上升,在浸没期向上覆水体释放TN;水位调节导致的淹没强度变化对消落带土壤中TN产生淋溶作用,而对TP的影响较小;对于消落带上游地区应更关注土壤中高w(TP)带来的环境风险,而对于消落带下游地区应更关注因水土流失和非点源输出导致的高w(TN)所致环境风险.展开更多
For accuracy and rapidity of audio event detection in the mass-data audio pro- cessing tasks, a generic method of rapidly recognizing audio event based on 2D-Haar acoustic super feature vector and AdaBoost is proposed...For accuracy and rapidity of audio event detection in the mass-data audio pro- cessing tasks, a generic method of rapidly recognizing audio event based on 2D-Haar acoustic super feature vector and AdaBoost is proposed. Firstly, it combines certain number of con- tinuous audio frames to be an ＂acoustic feature image＂, secondly, uses AdaBoost.MH or fast Random AdaBoost feature selection algorithm to select high representative 2D-Haar pattern combinations to construct super feature vectors; thirdly, analyzes the commonality and differ- ences between subcategories, then extracts common features and reduces different features to obtain a generic audio event template, which can support the accurate identification of multi- ple sub-classes and detect and locate the specific audio event from the audio stream accurately. Experimental results show that the use of 2D-Haar acoustic feature super vector can make recog- nition accuracy 5% higher than ones that MFCC, PLP, LPCC and other traditional acoustic features yielded, and can make tile training processing 7 20 times faster and the recognition processing 5-10 times faster, it can even achieve an average precision of 93.38%, an average recall of 95.03% under the optimal parameter configuration found by grid method. Above all, it can provide an accurate and fast mass-data processing method for audio event detection.展开更多
文摘消落带是位于水陆交错带的一种特殊生态系统.消落带土壤作为生态系统中氮、磷元素重要的“源”与“汇”,在生物地球化学循环研究中具有重要意义.该研究基于中国知网CNKI以及Web of Science数据库检索近10年三峡消落带土壤氮、磷研究文献,提取w(TN)、w(TP)数据并进行统计,从宏观尺度上分析w(TN)、w(TP)的时空分布特征.结果表明:①高程分布上,当高程高于155 m时,土壤w(TN)(0.75~1.17 g kg)随着高程增加呈下降趋势,土壤w(TP)未发生显著变化(0.53~0.60 g kg);当高程低于155 m时,土壤w(TN)处于较低水平(0.66~0.86 g kg),但w(TP)维持较高水平(0.60~0.76 g kg).②时间分布上,消落带土壤w(TN)整体呈现逐年递减的趋势,Pearson相关系数为-0.64,但是w(TP)没有显著变化.③地区分布上,三峡上游库区消落带土壤w(TP)出现显著高值,下游库区w(TN)出现显著高值.研究显示:不同高程土壤受植物残体分解等因素影响,在落干期w(TN)上升,在浸没期向上覆水体释放TN;水位调节导致的淹没强度变化对消落带土壤中TN产生淋溶作用,而对TP的影响较小;对于消落带上游地区应更关注土壤中高w(TP)带来的环境风险,而对于消落带下游地区应更关注因水土流失和非点源输出导致的高w(TN)所致环境风险.
文摘为考察干制“储良”龙眼含水率与水分活度、贮藏温度之间的关系,以及探讨净等量吸附热、焓变、熵变和自由能等热力学特性,采用静态称重法,测定干制“储良”龙眼在20、30、40 ℃和水分活度为0.113~0.946条件下的吸附平衡含水率,并绘制其吸附等温线。采用6种常用的农产品吸附模型,对实验结果进行拟合分析。结果表明,干制“储良”龙眼的水分吸附呈Ⅲ型等温线,Halsey模型是描述吸附等温线的最适模型。热力学特性结果表明,净等量吸附热随含水率的升高而降低,当平衡含水率 M e大于30%d.b.(干基)时趋近于0;净等量吸附热与焓变相等,其范围为0.2~467.69 kJ/mol;熵变随含水率的增加而降低,但温度对其影响不显著;干制“储良”龙眼的水分吸附过程可用焓-熵互补理论解释,此过程是焓驱动过程。研究结果可为“储良”龙眼的加工、包装和安全贮藏提供参考。
文摘For accuracy and rapidity of audio event detection in the mass-data audio pro- cessing tasks, a generic method of rapidly recognizing audio event based on 2D-Haar acoustic super feature vector and AdaBoost is proposed. Firstly, it combines certain number of con- tinuous audio frames to be an ＂acoustic feature image＂, secondly, uses AdaBoost.MH or fast Random AdaBoost feature selection algorithm to select high representative 2D-Haar pattern combinations to construct super feature vectors; thirdly, analyzes the commonality and differ- ences between subcategories, then extracts common features and reduces different features to obtain a generic audio event template, which can support the accurate identification of multi- ple sub-classes and detect and locate the specific audio event from the audio stream accurately. Experimental results show that the use of 2D-Haar acoustic feature super vector can make recog- nition accuracy 5% higher than ones that MFCC, PLP, LPCC and other traditional acoustic features yielded, and can make tile training processing 7 20 times faster and the recognition processing 5-10 times faster, it can even achieve an average precision of 93.38%, an average recall of 95.03% under the optimal parameter configuration found by grid method. Above all, it can provide an accurate and fast mass-data processing method for audio event detection.
