Hamatophyton from the Late Devonian of Anhui Province,South China and Evolution of Sphenophyllales

Well-preserved specimens of Hamatophyton verticillatum collected from the Upper Devonian （Famennian） Wutong Formation of Chaohu district, Anhui Province, South China, display more complete fertile axes in three orders and multiple divisions. Comparisons indicate that Hamatophyton possibly does not have palmate planate sterile leaves but hook-like linear ones with rare divisions. We propose seven definitive characters of Sphenophyllales： （1） completely whorled lateral organs; （2） sterile leaves; （3） strobili; （4） ＂sporangiophores＂ or stalks with reflexed tips bearing sporangia; （5） three- or four-ribbed primary xylem; （6） exarch maturation of primary xylem; and （7） secondary xylem. The Sphenophyllales probably originated from the Iridopteridales based on similarities in whorled lateral organs, ribbed primary xylem and peripheral protoxylem strands. In transition from Iridopteridales to Sphenophyllales, morphological changes involve partially whorled to completely whorled lateral organs, sterile ultimate appendages to leaves, and fertile ultimate appendages to ＂sporangiophores＂/stalks with bracts; anatomical modifications include configuration and maturation of primary xylem, and presence of secondary xylem.

基于BP神经网络的鄱阳湖水位模拟

考虑到鄱阳湖水位受流域五河与长江来水等多因素的共同作用而表现出高度非线性响应，采用典型的三层BPNN神经网络模型来模拟鄱阳湖水位与其主控因子之间的响应关系。分别将湖口、星子、都昌、棠荫和康山水位作为目标变量进行BPNN模型构建和适用性评估。结果显示：综合考虑流域五河及长江来水（汉口或九江）的BPNN水位模型，空间站点水位模拟精度（R2和Ens）可达0．90以上，各站点的均方根误差（R惝E）变化范围约0．50～1．0m，若忽略长江来水的影响作用，仅将流域五河来水作为湖泊水位的主控影响因子，模型训练期与测试期的纳希效率系数（Ens）和确定性系数（R2）显著降低，且低于0．50，均方根误差（RMSE）也明显增大（1．24～2．88m），意味着综合考虑流域五河与长江来水是获取结构合理、精度保证的鄱阳湖水位模型的重要前提。同时建议针对鄱阳湖湖盆变化对水位的影响，尽可能选择一致性较好的长序列数据集来训练和测试BPNN模型。所构建的BPNN神经网络模型可进一步结合流域水文模型，用来预测气候变化与人类活动下流域径流变化对湖泊水位的潜在影响，也可作为一种有效的模型工具来回答当前鄱阳湖一些备受关注的热点问题，如定量区分流域五河与长江来水对湖泊洪枯水位的贡献分量，为湖泊洪涝灾害的防治和对策制定提供科学依据。