嵩草属植物的系统学研究

嵩草属隶属于莎草科苔草亚科苔草族，主要分布于北半球温带地区，少数种类为环北极分布，有一种产于泰国清迈，一种产于苏门达腊岛北部高山。本‘文在形态学、微形态学、解剖学和胚胎学研究的基础上，对嵩草属植物进行了全面的分类学修订，根据属内各类群之间的系统演化关系建立了一个新的属下分类系统，确认了世界范围内53种3亚种嵩草属植物，并做出5个种上等级的新组合，描述了一个新亚组。 作者研究了国内外14家标本馆（室）的3000多份腊叶标本并进行多次野外的实地观察，对嵩草属植物的形态学性状进行了详细的比较和分析，评价了它们的系统学价值及其演化趋势。在嵩草属中，花序是由小穗排列成的圆锥花序或穗状花序，花序各部分的形态性状是种及种上等级分类的基础;花序的演化趋势是由圆锥花序到穗状花序，小穗是从由数朵雄花与1朵雌花组成简化到由1朵雌花组成。但是，花序和小穗由复杂到简单的进化在嵩草属中平行地发生于不同的类群中。先出叶的性状状态是分种的主要特征之一，通常认为边缘开裂的先出叶是原始的，边缘合生而为囊状的先出叶是进化的。同样，先出叶由开裂到合生的进化也是多次发生的。此外，根状茎、秆、叶鞘、叶片、柱头及小坚果等的性状状态对于种及种上等级的分类都具有重要的意义。 应用扫描电子显微镜对38种（或亚种）嵩草属植物的小坚果表面进行了观察，证明小坚果纹饰在种及种上等级的分类中具有重要的参考价值，并能揭示种及种下等级的亲缘关系。例如，分布于喜马拉雅东部至横断山地区的3种植物，K．clarkeana、K．curvata和K．fragilis外部形态非常相似，难于区分，而其小坚果的微形态特征却可以提供3种之间关系的证据。K．clarkeana与K．fragilis果实表面的特征完全一致，且与其它植物有显著区别，应为同种植物;K．curvata与它们明显不同，也与其它种有较大差异，应为独立的种。K．gramini folia，K．cercostach ys和K. nepalensis果实表面纹饰具有一些共同的特征，说明它们之间的亲缘关系较近。K．filicina和K．duthiei也存在同样的情形。 通过对秆和叶片的横切面和表皮的解剖学观察比较，发现嵩草属植物秆的横切面表现出由三角形到圆形的一系列变化。秆的横切面明显地分两个区域，中部的髓由较大而无色的细胞组成，其中心常碎裂形成大的气腔;外围的绿色部分，由绿色组织及分布其中的气腔和外韧维管束及与其相伴的厚壁组织组成。秆的表皮与叶片下表皮非常相象。叶片横切面的外形为V形、新月形或半圆形。V形的叶片具有明显发育的中脉并且在远轴面凸起，形成脊;新月形和半圆形的叶片中脉发育不明显，也无脊。叶片的表皮细胞均为长方形，垂周壁波纹状;平列型的气孔器纵向成行排列，多局限于下表皮;上表皮近边缘及脉附近的细胞常常在细胞的一端形成乳突。秆和叶片横切面的形态对于分种及种上等级的划分具有参考价值。 胚胎学研究表明小孢子、胚囊和胚的发育与莎草科其它类群一致。花粉为假单体花粉( pseudomonad)，成熟花粉三核。胚珠倒生，厚珠心，双层珠被，珠孑L由内珠被形成。胚囊的发育为蓼型，原胚的发育为柳叶菜型灯芯草变型。首次观察到，在大孢子四分体时期，合点端和珠孔端两个大孢子细胞开始时体积都增大，而中部两个很快退化，稍后珠孔端一个也退化，合点端一个为功能大孢子，发育成为胚囊。根据胚胎学证据，不支持将嵩草属与苔草族一起另立为嵩草科。 嵩草属中较原始的一个亚属subg. Compositae主要分布于西喜马拉雅至横断山地区，还有一种见于泰国，一种产于苏门达腊，而后2种植物还具有一些最原始的形态性状。结合地史的变化推测，嵩草属可能在第三纪早期起源于古地中海的东部和北部。 根据形态学和解剖学性状的分析表明，许多性状在嵩草属中是平行演化的，如花序和小穗由复杂到简单、秆由圆柱形到三棱形、叶片横切面由V形到半圆形等。该属的属下分类应该追溯这些平行的演化线，而不能像以前的分类那样，将它们横向地划分为几个组或亚属。作者认为嵩草属有3个大的进化分支，据此将其划分为3个亚属。Subg. Compositae，12种，是较原始的一个分支。叶近基生，叶片扁平;花序多疏松圆锥状，少穗状，苞片多为叶状;小穗两性到单性，先出叶多为囊状，少边缘分离，退化小穗轴明显、扁平、较长。Subg. Blysmocarex，仅2种，是较早分化而相对隔离的一个分支。根状茎匍匐状;花序由圆锥状到穗状，小穗两性或单性;柱头2。Subg. Kobresia，种类最多。叶片扁平或内卷;秆三棱形到圆柱形;花序紧密，复杂到简单，苞片不为叶状;小穗两性或单性，先出叶由开裂到合生，退化小穗轴较小而不显著。根据该亚属呈现出的不同的性状演化系列，可以分为3个组。Sect. Kobresia花序圆锥状至穗状，小穗多为两性，少为单性，先出叶边缘分离。含3个亚组：subsect. Kobresia，8种2亚种．植株纤细，秆与叶均为丝状;subsect. Royleanae，8种l亚种，植株较粗壮，叶片扁平或对折;subsect. Sibiri-cae，4种，秆较粗，叶片内卷。Sect. Psmmostachys，仅2种，小穗两性，先出叶完全合生为囊状。Sect．Hemicarex花序一般为穗状，稀圆锥状，小穗多为单性，少两性。分为四个亚组：subsect. Forexeta，6种，叶片内卷或对折，先出叶线形，边缘分离或合生;subsect. Chlorostachys，3种，叶片扁平，小穗两性;subsect. Holmia，4种，叶片扁平，小穗单性;subsect．Utriculatae，5种，叶片丝状，先出叶完全合生为囊状，不为线形。嵩草属的属下分类纲要如下： Subgenus 1. Compositae (Clarke) Kukkonen Type: K. laxa Nees. Subgenus 2. Blysmocarex (Ivanova) S. R. Zhang Type: K. macrantha Boeck. Subgenus 3. Kobresia Section 1. Kobresia Subsection 1. Kobresia Type: K. simpliciuscula (Wahl. ) Mack. Subsection 2. Royleanae (Ivanova) S. R. Zhang Type: K. royleana (Nees) Boeck. Subsection 3. Sibiricae (Ivanova) Egorova Type: K. sibirica (Turcz. ex Ledeb. ) Boeck. Section 2. Psmmostachys Ivanova Type: K. robusta Maxim. Section 3. Hemicarex (Bentham) Clarke Subsection 4. Forexeta (Raffin. ) S. R. Zhang Type: K. cercostachys (Franch. ) C. B. Clarke. Subsection 5. Chlorostachys (Ivanova) S. R. Zhang Type: K. duthiei C B. Clarke. Subsection 6. Holmia (Boern. ) S. R. Zhang Type: K. esenbeckii (Kunth) Noltie. Subsection 7. Utriculatae S. R. Zhang Type: K. prainii Kukenthal.

The structure and function of neurons with variable nonlinear transfer function

One novel neuron with variable nonlinear transfer function is firstly proposed, It could also be called as subsection transfer function neuron. With different transfer function components, by virtue of multi-thresholded, the variable transfer function neuron switch on among different nonlinear excitated state. And the comparison of output's transfer characteristics between it and single-thresholded neuron will be illustrated, with some practical application experiments on Bi-level logic operation, at last the simple comparison with conventional BP, RBF, and even DBF NN is taken to expect the development foreground on the variable neuron.. The novel nonlinear transfer function neuron could implement the random nonlinear mapping relationship between input layer and output layer, which could make variable transfer function neuron have one much wider applications on lots of reseach realm such as function approximation pattern recognition data compress and so on.

东濮凹陷文－桥－白地区异常高压储层沉积成岩特征及成因初探

In Dongpu depression, there are obviously overpressure phenomena below 2000-3200m. Research to the relationship between sedimentation-diagenesis and overpressure of reservoirs is in great need. In this paper, after analyzing and simulating the overpressure in Wendong, Qiaokou and Baimiao regions, we draw a conclusion that the fast sedimentation since Low Tertiary is one of the most important mechanisms for the formation of overpressure in Dongpu Depression. The gypsum in northern part of Dongpu Depression is the good seal for the development of overpressure. On the base of detailed work to the distribution and magnitude of overpressure in Wen-qiao-Bai regions, we selected several wells that have different overpressure to find the sedimentary and diagenetic differences of these wells. We find that compaction is obviously inhibited in overpressured reservoirs, which results in the linear relation between physical properties of reservoirs and sedimentary parameters, such as sorting coefficient, the content of matrix, etc. Reservoirs with great magnitude of overpressure have undergone more extensive erosion than the ones with low magnitude of overpressure, which probably is the result of the great solubility of CO_2 under high pressure. The great burial depth, the high content of matrix and the extensively developed cement of carbonate are the most important factors that influence the physical properties of reservoirs in Dongpu depression. Overpressure plays a constructive role in the physical properties of reservoirs. the overpressured reservoirs of Es_3~3 subsection in Wendong region are probably the ones that have good physical properties. From homogenetic temperatures that obtained form the fluid inclusions in quartz overgrowth, we find that there were 4 episodes of fluid flows in Dongpu depression. In conjunction with the analysis of the burial history of overpressured reservoirs, we draw conclusions that the first, second and third episodes of fluid flows took place in the extensive rifting stage of Dongpu Depression, the burial depth when the first episode of fluid flow took place was about 1500m, the age was about 36 my; the burial depth of the second and third episodes of fluid flow was between 1800-3000m at that time, the age was between 35-28my. The fluid flows of the second, third, and fourth episodes were in close relation to the overpressure and maybe were the results of the episodic hydrofracturing of overpressured mudstones and shales. The episodic fluid flow of overpressured mudstones and shales probably facilitates the cementation of carbonate, which decreases the physical properties of overpressured reservoirs. The dolomites and ferrodolomites maybe the products of the episodic hydrofracturing of overpressured mudstones and shales.

保重心脉冲压缩滤波及其在速度自动拾取中的应用

To pick velocity automatically is not only helpful to improve the efficiency of seismic data process, but also to provide quickly the initial velocity for prestack depth migration. In this thesis, we use the Viterbi algorithm to do automatic picking, but the velocity picked usually is immoderate. By thorough study and analysis, we think that the Viterbi algorithm has the function to do quickly and effectually automatic picking, but the data provided for picking maybe not continuous on derivative of its curved surface, viz., the curved face on velocity spectrum is not slick. Therefore, the velocity picked may include irrational velocity information. To solve the problem above, we develop a new method to filter signal by performing nonlinear transformation of coordinate and filter of function. Here, we call it as Gravity Center Preserved Pulse Compressed Filter (GCPPCF). The main idea to perform the GCPPCF as follows: separating a curve, such as a pulse, to several subsection, calculating the gravity center (coordinate displacement), and then assign the value (density) on the subsection to gravity center. When gravity center departure away from center of its subsection, the value assigned to gravity center is smaller than the actual one, but non other than gravity center anastomoses fully with its subsection center, the assigned value equal to the actual one. By doing so, the curve shape under new coordinate breadthwise narrows down compare to its original one. It is a process of nonlinear transformation of coordinate, due to gravity center changing with the shape of subsection. Furthermore, the gravity function is filter one, because it is a cause of filtering that the value assigned from subsection center to gravity center is obtained by calculating its weight mean of subsetion function. In addition, the filter has the properties of the adaptive time delay changed filter, owing to the weight coefficient used for weight mean also changes with the shape of subsection. In this thesis, the Viterbi algorithm inducted, being applied to auto pick the stack velocity, makes the rule to integral the max velocity spectrum ("energy group") forward and to get the optimal solution in recursion backward. It is a convenient tool to pick automatically velocity. The GCPPCF above not only can be used to preserve the position of peak value and compress the velocity spectrum, but also can be used as adaptive time delay changed filter to smooth object curved line or curved face. We apply it to smooth variable of sequence observed to get a favourable source data ta provide for achieving the final exact resolution. If there is no the adaptive time delay-changed filter to perform optimization, we can't get a finer source data and also can't valid velocity information, moreover, if there is no the Viterbi algorithm to do shortcut searching, we can't pick velocity automatically. Accordingly, combination of both of algorithm is to make an effective method to do automatic picking. We apply the method of automatic picking velocity to do velocity analysis of the wavefield extrapolated. The results calculated show that the imaging effect of deep layer with the wavefield extrapolated was improved dominantly. The GCPPCF above has achieved a good effect in application. It not only can be used to optimize and smooth velocity spectrum, but also can be used to perform a correlated process for other type of signal. The method of automatic picking velocity developed in this thesis has obtained favorable result by applying it to calculate single model, complicated model (Marmousi model) and also the practical data. The results show that it not only has feasibility, but also practicability.