A new classification scheme for the family Araliaceae

The present paper deals with the following three aspects: 1. It attempts to discuss the problems on primitive forms of the family Araliaceae. The genus Tupidanthus Hook. f. & Thoms. was considered by H. Harms (1894) and H. L. Li (1942) as primitive, whilst another genus Plerandra A. Gray was regarded as primitive by R. H. Eyde & C. C. Tseng in 1971. Having made a detailed comparison of the taxonomical characters of these two genera, the present authors believe that both genera are not the most primitive in the Araliaceae. Their affinit yis not close enough and they possibly evolved in parallel lines from a common ancestor which is so far unknown yet. 2. By studying the systems of the past, the present authors believe that none of them is entirely satisfactory. Bentham (1867) recognized five ‘series’ (in fact, equivalent to ‘tribe’ with the ending-eae of names) based on the petaline arrangement in the bud, the numbers of stamen and the types of endospem. This is a plausible fundamental treatment for the Araliaceae, but choosing the endosperm as a criteria in dividing tribe is artifical. As we know today, both ruminate and uniform endosperm are usually presente in the same genus. Seemann’s system (1868) divided the Hederaceae (excl. Trib. Aralieae) into five tribes, in addition to the locules of ovary. The criteria are essentially the same as Bentham’s. The system of Hams (1894) divided the family into three tribes. Two tribes, Aralieae and Mackinlayeae, of Bentham are retained, but other groups were combined in the Trib. Schefflereae. However, Harms did not retain one of those three oldest legitimate names which had named by Bentham, that is contrary to the law of priority in the International Code of Botanical Nomenelature. Hutchinson (1967) adopted seven tribes for the family. The criteria essentially follow those of Bentham, but the inflorescence is overstressed. The inflorescence is an artifical taxonomical character in dividing tribes, because of some dioecious plants, such as Meryta sinclairii (Hook. f.) Seem., have two types of inflorescence in male and female plants. According to Hutchinson’s arrangement, the male and female plants would be put in separate tribes. 3. The present authors are of the opinion that in the study of a natural classification of plant groups emphasis should be laid not only on the characters of the reproductive organs, but on those of vegetative organs as well. The present revised system is based principally upon the characters of both flowers and leaves of the five tribes as follows: Trib. 1. Plerandreae Benth. emend. Hoo & Tseng Trib. 2. Tetraplasandreae Hoo & Tseng Trib. 3. Mackinlayeae Benth. Trib. 4. Aralieae Benth. Trib. 5. Panaceae Benth. emend. Hoo & Tseng     

A fault tolerant routing scheme for advanced metering infrastructure: an approach towards smart grid

Cluster Computing - The wireless sensor network is gaining significant attention because of its ubiquitous deployability nature. In general, the role of the wireless sensor network is remarkable in...     

A classification scheme for mobilization regions of bacterial plasmids

Transmissible plasmids can be classified according to their mobilization ability, as being conjugative (self-transmissible) or mobilizable (transmissible only in the presence of additional conjugative functions). Naturally occurring mobilizable plasmids carry the genetic information necessary for relaxosome formation and processing, but lack the functions required for mating pair formation. Mobilizable plasmids have a tremendous impact in horizontal gene transfer in nature, including the spread of antibiotic resistance. However, analysis of their promiscuity and diversity has attracted less attention than that of conjugative plasmids. This review will focus on the analysis of the diversity of mobilizable plasmids. For this purpose, we primarily compared the amino acid sequences of their relaxases and, when pertinent, we compared these enzymes with conjugative plasmid relaxases. In this way, we established phylogenetic relationships among the members of each superfamily. We conducted a database and literature analysis that led us to propose a classification system for small mobilizable plasmids in families and superfamilies according to their mobilization regions. This review outlines the genetic organization of each family of mobilization regions, as well as the most relevant properties and relationships among their constituent encoded proteins. In this respect, the present review constitutes a first approach to the characterization of the global gene pool of mobilization regions of small mobilizable plasmids.     

A classification scheme for scoring subcutaneous fat depots of shorebirds

ABSTRACT.   Different scoring systems have been developed for assessing fat stores in birds. Most have been used with passerines, and there is no widely used standard for studies of shorebirds. I developed a new fat-scoring classification scheme for shorebirds that was tested in Poland with juvenile Dunlins ( Calidris alpina ) during autumn and Wood Sandpipers ( Tringa glareola ) during spring migration. My proposed fatness scale is based on a qualitative assessment of the amount of fat in the axillary region and, if necessary, the furcular region. The resulting fat scores do not predict the actual amount of fat carried by a bird, but can be used to assign birds to a particular state of fatness and allow comparison of birds with different scores. Analysis revealed that the relationship between fat scores and predicted fat mass was approximately linear for both Dunlins and Wood Sandpipers. Although the scale I propose was tested with 20 species of shorebirds, sample sizes for larger species were small and consisted exclusively of lean and moderately fat birds. As a result, my proposed fat-scoring system should be evaluated using larger and fatter shorebirds because it has only been validated for one small and one medium-sized species.     

Nonlinear biological batch process monitoring and fault identification based on kernel fisher discriminant analysis

A novel nonlinear biological batch process monitoring and fault identification approach based on kernel Fisher discriminant analysis (kernel FDA) is proposed. This method has a powerful ability to deal with nonlinear data and does not need to predict the future observations of variables. So it is more sensitive to fault detection. In order to improve the monitoring performance, variable trajectories of the batch processes are separated into several blocks. Then data in the original space is mapped into high-dimensional feature space via nonlinear kernel function and the optimal kernel Fisher feature vector and discriminant vector are extracted to perform process monitoring and fault identification. The key to the proposed approach is to calculate the distance of block data which are projected to the optimal kernel Fisher discriminant vector between new batch and reference batch. Through comparing distance with the predefined threshold, it can be considered whether the batch is normal or abnormal. Similar degree between the present discriminant vector and the optimal discriminant vector of fault in historical data set is used to perform fault diagnosis. The proposed method is applied to the process of fed-batch penicillin fermentation simulator benchmark and shows that it can effectively capture nonlinear relationships among process variables and is more efficient than MPCA approach.     

优势种植被分类系统的逻辑分析与示例方案化

逻辑学原理是各种分类系统科学性及规范性的必要检验工具。本文采用逻辑学原理检验基于优势种的《中国植被》的植被分类系统, 结果发现目前常用的植被分类系统存在较多逻辑错误, 需要予以纠正。于是, 在强调植物生活型分类系统和植被分类系统一致性的基础上, 依据逻辑学原理给出建立植被分类系统的步骤和方法, 提出规范的植物生活型分类系统和植被分类系统示例方案。鉴于多建群种植被的客观存在及其存在形式多样, 在分类系统中给出相应的位置——多建群种植被纲。同时, 针对国内植被分类学界从未形成统一的植被命名规则, 且又有多种命名方式并存的现状, 提出了函数命名法。     

A framework for protein structure classification and identification of novel protein structures

Background  

Protein structure classification plays a central role in understanding the function of a protein molecule with respect to all known proteins in a structure database. With the rapid increase in the number of new protein structures, the need for automated and accurate methods for protein classification is increasingly important.     

Monitoring and classification of dimensional faults for automotive body assembly

Sudden process changes occurring during automobile body assembly processes will influence the downstream assembly process and the functionality and final appearance of the vehicle. Furthermore, these faults could result in a decreased production rate and an increase in the cost if sudden process changes are so serious that the production line has to be stopped for investigation. Thus, sudden process changes should be detected and eliminated as soon as possible to prevent defective products from being produced and to reduce the cost of repairs/reworks. A monitoring algorithm is developed to detect, classify, and group process changes by analyzing the dimensional data of car bodies. The results of this monitoring algorithm can help diagnose the root causes of variation according to the locations of measurement points, body structure, assembly sequence, and tooling layout. Measurement data obtained from an optical coordinate measuring machine (OCMM) are used to demonstrate the monitoring technique.     

中国植被分类系统修订方案

为了推动《中国植被志》研编工作, 该文回顾了中国植被分类系统的发展过程和主要阶段性成果, 提出了作为《中国植被志》研编技术框架组成部分的中国植被分类系统修订方案, 对各植被型组及各植被型进行了简单定义和描述, 并针对中国植被分类系统若干问题, 特别就中国植被分类系统总体框架、混交林的界定以及土壤在植被分类中的重要性等问题进行了讨论。1960年侯学煜在《中国的植被》中首次提出了中国植被分类的原则和系统, 1980年出版的《中国植被》制定了分类等级和划分依据等更加完善的系统, 之后《中国植被及其地理格局——中华人民共和国1:1 000 000植被图说明书》和《中国植物区系与植被地理》以及很多省区的植被专著对该系统进行过修订。2017年宋永昌在《植被生态学》中提出了一个分类等级单位调整的方案。本次提出的中国植被分类系统修订方案基本沿用《中国植被》的植被分类原则、分类单位及系统, 采用“植物群落学-生态学”分类原则, 主要以植物群落特征及其与环境的关系作为分类依据, 包含三级主要分类单位, 即植被型(高级单位)、群系(中级单位)和群丛(低级单位); 在三个主要分类单位之上分别增加辅助单位植被型组、群系组和群丛组, 在植被型和群系之下主要根据群落的生态差异和实际需要可再增加植被亚型或亚群系。修订方案包含了森林、灌丛、草本植被(草地)、荒漠、高山冻原与稀疏植被、沼泽与水生植被(湿地)、农业植被、城市植被和无植被地段9个植被型组, 划分为48个植被型(含30个自然植被型、12个农业植被型、5个城市植被型和无植被地段)。自然植被中有23个植被型进一步划分出了81个植被亚型。     

Semiconductor fault detection and classification for yield enhancement and manufacturing intelligence

Process monitoring and profile analysis are crucial in detecting various abnormal events in semiconductor manufacturing, which consists of highly complex, interrelated, and lengthy wafer fabrication processes for yield enhancement and quality control. To address real requirements, this study aims to develop a framework for semiconductor fault detection and classification (FDC) to monitor and analyze wafer fabrication profile data from a large number of correlated process variables to eliminate the cause of the faults and thus reduce abnormal yield loss. Multi-way principal component analysis and data mining are used to construct the model to detect faults and to derive the rules for fault classification. An empirical study was conducted in a leading semiconductor company in Taiwan to validate the model. Use of the proposed framework can effectively detect abnormal wafers based on a controlled limit and the derived simple rules. The extracted information can be used to aid fault diagnosis and process recovery. The proposed solution has been implemented in the semiconductor company. This has simplified the monitoring process in the FDC system through the fewer key variables. The results demonstrate the practical viability of the proposed approach.     

Truth table classification and identification

A logical basis for classification is that elements grouped together and higher categories of elements should have a high degree of similarity with the provision that all groups and categories be disjoint to some degree. A methodology has been developed for constructingclassifications automatically that gives nearly instantaneous correlations of character patterns of orgnisms with time and clusters with apparent similarity. This means that automatic numericalidentification will always construct schemes from which disjoint answers can be obtained if test sensitivities for characters are correct. Unidentified organisms are recycled through continuous classification with reconstruction of identification schemes. This process is cyclic and self-correcting. The method also accumulates and analyzes data which updates and presents a more accurate biological picture.This work was supported by Grant AI 16385-02 and 16385-03 National Institutes of General Medical Sciences     

Dynamical model development and parameter identification for an anaerobic wastewater treatment process.

This paper deals with the development and the parameter identification of an anaerobic digestion process model. A two-step (acidogenesis-methanization) mass-balance model has been considered. The model incorporates electrochemical equilibria in order to include the alkalinity, which has to play a central role in the related monitoring and control strategy of a treatment plant. The identification is based on a set of dynamical experiments designed to cover a wide spectrum of operating conditions that are likely to take place in the practical operation of the plant. A step by step identification procedure to estimate the model parameters is presented. The results of 70 days of experiments in a 1-m(3) fermenter are then used to validate the model.     

A riboprinting scheme for identification of unknown Acanthamoeba isolates at species level

We describe a riboprinting scheme for identification of unknown Acanthamoeba isolates at the species level. It involved the use of PCR-RFLP of small subunit ribosomal RNA gene (riboprint) of 24 reference strains by 4 kinds of restriction enzymes. Seven strains in morphological group I and III were identified at species level with their unique sizes of PCR product and riboprint type by Rsa I. Unique RFCP of 17 strains in group II by Dde I, Taq I and Hae III were classified into: (1) four taxa that were identifiable at the species level, (2) a subgroup of 4 taxa and a pair of 2 taxa that were identical with each other, and (3) a species complex of 7 taxa assigned to A. castellanii complex that were closely related. These results were consistent with those obtained by 18s rDNA sequence analysis. This approach provides an alternative to the rDNA sequencing for rapid identification of a new clinical isolate or a large number of environmental isolates of Acanthamoeba.     

A field based statistical approach for validating a remotely sensed mangrove forest classification scheme

Amongst the most threatened ecosystems on Earth, mangrove forests are also one of the more difficult to work in due to their growth in mud and open water coastal zones and their dense tangled stems, branches and prop roots. Consequently, there has been an impetus to employ remotely sensed imagery as a means for rapid inventory of these coastal wetlands. To date, the majority of mangrove maps derived from satellite imagery utilize a simple mangrove classification scheme which does not distinguish mangrove species and may not be useful for conservation and management purposes. Although more elaborate satellite based mangrove classification schemes are being developed, given their enhanced complexity they deserve additional justification for end users. The purpose of this study was to statistically examine the appropriateness of one such classification scheme based on an inventory of field data. In January of 2007 and May of 2008, 61 field sample plots were selected in a stratified random fashion based on a previous classification of a degraded mangrove forest of the Isla La Palma (Sinaloa, Mexico) using Landsat TM5 data. Unlike other previous Landsat TM based classifications of this region, which simply identified the mangrove forests as one class, the mangroves were classified (i.e. mapped) according to four conditions; healthy tall, healthy dwarf, poor condition, and dead mangroves. Within each sample plot, all mangroves of diameter of breast height (dbh) greater than 2.5 cm were identified and their height, condition and dbh recorded. An estimated Leaf Area Index (LAI) value also was obtained for each sample and the shortest distance from the center of each sample plot to open flowing water was determined using a geographic information system (GIS) overlay procedure. These data were then used to calculate mean values for the four classes as well as to determine stem densities, basal areas, and the Shannon–Wiener diversity index. In order to assess the appropriateness of this mangrove classification scheme a discriminant analysis approach was then applied to these field data. The results indicate this forest has undergone severe degradation, with decreasing mean tree heights, mean dbh and species diversity. In regards to the discriminant analysis procedure, further classification of these field plots and cross-validation based on these significant variables provided high classification accuracy thus validating the appropriateness of the satellite based image classification scheme. Moreover, the discriminant analysis indicated that the estimated LAI, mean height, and mean dbh are significant in the separation of the classification of mangrove forest condition along these field sample plots.