Field collections in the Southern Gulf of Oman and the Northwestern Arabian Sea, and examination of museum collections from this study area, yielded 7 genera and 33 species of apogonid fishes. Twenty one species of Apogon, one Archamia, four Cheilodipterus, three Fowleria, one Rhabdamia, two Siphamia, and one Holapogon are reviewed and illustrated. The Dhofar Cardinalfish, Apogon dhofar, nov. sp. is described from 21 specimens collected in the Arabian Sea, off southern Oman. It differs from the very similar A. pseudotaeniatus Gon, 1986 in its higher gill-raker count (12-17 developed rakers vs. 9-11) and coloration. Apogon dhofar has narrower dark vertical bars (one scale row or less wide vs. two or more for A. pseudotaeniatus) which are often indistinct or absent in life and tend to fade with size; and a caudal spot which is much smaller (2-3% SL vs. 4-6% SL for pseudotaeniatus) and often absent in life. Both A. dhofar and A. pseudotaeniatus have small dark chromatophores covering their bodies, but A. dhofar differs in having these chromatophores concentrated under the posterior edge of each scale producing a reticulate pattern on the body. The Cryptic Cardinalfish, Apogon species C., is described from 19 specimens collected in the Gulf of Oman and Arabian Gulf It differs from the similar A. taeniatus Ehrenberg, 1828 in its lower gill-raker count (8 developed rakers vs. 10-15) and horizontal stripes (7-8 dark stripes vs. 5-6 indistinct stripes). Apogon species C. also has 3-4 short brown stripes radiating away from the eye whereas A. taeniatus occasionally has one narrow dark stripe. Apogon species C. lacks any caudal spot which is usually present in A. taeniatus. Apogon thurstoni Day, 1888 is shown to be a junior synonym of Apogon nigripinnis Cuvier, 1828, and Apogon smithvanizi Allen and Randall, 1994 is shown to be a junior synonym of Apogon gularis Fraser and Lachner, 1986. Apogon pharaonis Bellotti, 1874, formerly considered a junior synonym of Apogon nigripinnis Cuvier, 1828, is shown to be a valid species occurring in the Red Sea and western Indian Ocean, and the range of A. nigripinnis is redefined as eastern Indian to western Pacific. Apogon suezi Sauvage, 1883 is shown to be a junior synonym of A. pharaonis. A review is presented of the systematic literature of the apogonid fishes from the study area, and a key to genera and species is provided. Included in the key are 33 apogonid species known from the area and an additional 7 species (and one genus) not yet recorded but likely to occur. / Graduation date: 1997

Thesis (Ph.D.)--University of Delaware, 2006. / Principal faculty advisor: Vijay K. Shanker, Dept. of Computer & Information Sciences. Includes bibliographical references.

Thesis (Th. M.)--Dallas Theological Seminary, 2004. / Includes bibliographical references (leaves 416-417).

In this work we investigate the structure of this type of Lie algebras over arbitrary fields F by constructing them from their Abelian ideal. To accomplish this, an algorithm is developed and as application a classification up to 7-dimensional Lie Algebras is given. We discuss a recent example of financial mathematics as well.

As people across the globe are becoming more interested in watching their weight, eating more healthily, and avoiding obesity, a system that can measure calories and nutrition in everyday meals can be very useful. Recently, there has been an increase in the usage of personal mobile technology such as smartphones or tablets, which users carry with them practically all the time. In this paper, we proposed a food calorie and nutrition measurement system that can help patients and dieticians to measure and manage daily food intake. Our system is built on food image processing and uses nutritional fact tables. Via a special calibration technique, our system uses the built-in camera of such mobile devices and records a photo of the food before and after eating it in order to measure the consumption of calorie and nutrient components. The proposed algorithm used color, texture and contour segmentation and extracted important features such as shape, color, size and texture. Using various combinations of these features and applying a support vector machine as a classifier, a good classification was achieved and simulation results show that the algorithm recognizes food categories with an accuracy rate of 92.2%, on average.

The capacity to innovate is commonly regarded as a key response mechanism, a critical organisational competence for success, even survival, for organisations operating in turbulent conditions. Understanding how innovation works, therefore, continues to be a significant agenda item for many researchers. Innovation, however, is generally recognised to be a complex and multi-dimensional phenomenon. Classificatory approaches have been used to provide conceptual frameworks for descriptive purposes and to help better understand innovation. Further, by the facility of pattern recognition, classificatory approaches also attempt to elevate theorising from the specific and contextual to something more abstract and generalisable. Over the last 50 years researchers have sought to explain variance in innovation activities and processes, adoption and diffusion patterns and, performance outcomes in terms of these different ‘types’ of innovation. Three generic approaches to the classification of innovations can be found in the literature (innovation newness, area of focus and attributes). In this research, several limitations of these approaches are identified: narrow specification, inconsistent application across studies and, indistinct and permeable boundaries between categories. One consequence is that opportunities for cumulative and comparative research are hampered. The assumption underpinning this research is that, given artefact multidimensionality, it is not unreasonable to assume that we might expect to see the diversity of attributes being patterned into distinct configurations. In a mixed-method study, comprising of three empirical phases, the innovation classification problem is addressed through the design, testing and application of a multi-dimensional framework of innovation, predicated on perceived attributes. Phase I is characterised by an iterative process, in which data from four case studies of successful innovation in the UK National Health Service are synthesised with those drawn from an extensive thematic interrogation of the literature, in order to develop the framework. The second phase is concerned with identifying whether or not innovations configure into discrete, identifiable types based on the multidimensional conceptualisation of innovation artefact, construed in terms of innovation attributes. The framework is operationalised in the form of a 56-item survey instrument, administered to a sample consisting of 310 different innovations. 196 returns were analysed using methods developed in biological systematics. From this analysis, a taxonomy consisting of three discrete types (type 1, type 2 and type 3 innovations) emerges. The taxonomy provides the basis for additional theoretical development. In phase III of the research, the utility of the taxonomy is explored in a qualitative investigation of the processes underpinning the development of exemplar cases of each of the three innovation types. This research presents an integrative approach to the study of innovation based on the attributes of the innovation itself, rather than its effects. Where the challenge is to manage multiple discrete data combinations along a number of dimensions, the configurational approach is especially relevant and can provide a richer understanding and description of the phenomenon of interest. Whilst none of the dimensions that comprise the proposed framework are new in themselves, what is original is the attempt to deal with them simultaneously in order that innovations may be classified according to differences in the way in which their attributes configure. This more sensitive classification of the artefact permits a clearer exploration of relationship issues between the innovation, its processes and outcomes.

Support Vector Machines have been used to do classification and regression analysis. One important part of SVMs are the kernels. Although there are several widely used kernel functions, a carefully designed kernel will help to improve the accuracy of SVMs. We present two methods in terms of customizing kernels: one is combining existed kernels as new kernels, the other one is to do feature selection. We did theoretical analysis in the interpretation of feature spaces of combined kernels. Further an experiment on a chemical data set showed improvements of a linear-Gaussian combined kernel over single kernels. Though the improvements are not universal, we present a new idea of creating kernels in SVMs.

This degree project is mainly discussing about a web service classification approach based on suffix tree algorithm. Nowadays, Web Services are made up of WSDL web Service, RESTful web Service and many traditional component Services on Internet. The cost of manual classification cannot satisfy the increasing web services, so this paper proposes an approach to automatically classify web service because of this approach only relies on the textual description of service. Though semantic similarity calculation, we achieve web service classification automatically. Experimental evaluation results show that this approach has an acceptable and stable efficiency on precision and recall.

Support Vector Machines have been used to do classification and regression analysis. One important part of SVMs are the kernels. Although there are several widely used kernel functions, a carefully designed kernel will help to improve the accuracy of SVMs. We present two methods in terms of customizing kernels: one is combining existed kernels as new kernels, the other one is to do feature selection. We did theoretical analysis in the interpretation of feature spaces of combined kernels. Further an experiment on a chemical data set showed improvements of a linear-Gaussian combined kernel over single kernels. Though the improvements are not universal, we present a new idea of creating kernels in SVMs.

Bogs and fens, which comprise > 90% of the landscape near the De Beers Victor diamond mine, 90 km west of Attawapiskat, ON, provide different hydrological functions in connecting water flow pathways to the regional drainage network. It is essential to define their distribution, area and arrangement to understand the impact of mine dewatering, which is expected to increase groundwater recharge. Classification was achieved by developing a technique that uses IKONOS satellite imagery coupled with LiDAR-derived DEM derivatives to identify peatland classes. A supervised maximum likelihood classification was performed on the 1 m resolution IKONOS Red/Green/Blue without the infrared (RGB) and with the infrared (IR_RGB) band to determine the overall accuracy prior to inclusion of the DEM derivatives. Confusion matrices indicated 62.9% and 65.8% overall accuracy for the RGB and IR_RGB, respectively. Terrain derivatives were computed from the DEM including slope, vertical distance to channel network (VDCN), deviation from mean elevation (DME), percentile (PER) and difference from mean elevation (DiME). These derivatives were computed at a local (15-cell grid size) and meso (250-cell grid size) scale to capture terrain morphology. The mesoscale 250-cell grid analysis produced the most accurate classifications for all derivatives. However, spectral confusion still occurred (regardless of scale) most frequently in the Fen Dense Conifer vs. Bog Dense Conifer classes and also in the Bog Lichen vs. Bog Lichen Conifer. Despite this confusion, by combining the larger scale LiDAR DEM derivatives and the IKONOS imagery it was found that the overall classification accuracy could be improved by 13%. Specifically, the DiME derivative combined with the multispectral IKONOS (IR_RGB) produced an overall accuracy of 76.5%, and increased to 83.7% when Bog Lichen and Bog Lichen Conifer were combined during a post hoc analysis. This classification revealed the landscape composition of the North Granny Creek subwatershed, which is divided into north and south. The north portion comprises 67.4% bog, 13.6% fen and 18.9% water class, while the south is 63.7% bog, 15.2% fen and 21.1% water class. These proportions provide insight into the hydrology of the landscape and are indicative of the storage and conveyance properties of the subwatershed based on the percentage of bog, fen, or open water.