WebGraphEd, an open source graph drawing editor for the web

WebGraphEd is an open source software for graph visualization and manipulation. It is especially designed to work for the web platform through a web browser. The web application has been written in JavaScript and compacted later, which makes it a very lightweight software. There is no need of additional software, and the only requirement is to have an HTML5 compliant browser. WebGraphEd works with scalable vector graphics (SVG), which it makes possible to create lossless graph drawings.

A note on the periodic orbits and topological entropy of graph maps

This paper deals with the relationship between the periodic orbits of continuous maps on graphs and the topological entropy of the map. We show that the topological entropy of a graph map can be approximated by the entropy of its periodic orbits.

Stable isotopes unveil habitat partitioning among the marine mammals off NW Africa and reveal unique trophic niches for two globally threatened species.

Stable isotope abundances of carbon (δ13C) and nitrogen (δ15N) in the bone of 13 species of marine mammals from the northwest coast of Africa were investigated to assess their positions in the local trophic web and their preferred habitats. Also, samples of primary producers and potential prey species from the study area were collected to characterise the local isotopic landscape. This characterisation indicated that δ13C values increased from offshore to nearshore and that δ15N was a good proxy for trophic level. Therefore, the most coastal species were Monachus monachus and Sousa teuszii, whereas the most pelagic were Physeter macrocephalus and Balaenoptera acutorostrata. δ15N values indicated that marine mammals located at the lowest trophic level were B. acutorostrata, Stenella coeruleoalba and Delphinus sp., and those occupying the highest trophic level were M. monachus and P. macrocephalus. The trophic level of Orcinus orca was similar to that of M. monachus, suggesting that O. orca preys on fish. Conservation of coastal and threatened species (M. monachus and S. teuszii) off NW Africa should be a priority because these species, as the main apex predators, cannot be replaced by other marine mammals.

A Note on the periodic orbits and topological entropy of graph maps

This paper deals with the relationship between the periodic orbits of continuous maps on graphs and the topological entropy of the map. We show that the topological entropy of a graph map can be approximated by the entropy of its periodic orbits

Can affinity interactions influence the partitioning of glucose-6-phosphate dehydrogenase in two-phase aqueous micellar systems?

In this work, we provide an investigation of the role and strength of affinity interactions on the partitioning of the glucose-6-phosphate dehydrogenase in aqueous two-phase micellar systems. These systems are constituted of micellar surfactant solutions and offer both hydrophobic and hydrophilic environments, providing selectivity to biomolecules. We studied G6PD partitioning in systems composed of the nonionic surfactants, separately, in the presence and absence of affinity ligands. We observed that G6PD partitions to the micelle-poor phase, owing to the strength of excluded-volume interactions in these systems that drive the protein to the micelle-poor phase, where there is more free volume available.

Applications of graph transformation in tools for domain-specific modeling languages

The use of domain-specific languages (DSLs) has been proposed as an approach to cost-e ectively develop families of software systems in a restricted application domain. Domain-specific languages in combination with the accumulated knowledge and experience of previous implementations, can in turn be used to generate new applications with unique sets of requirements. For this reason, DSLs are considered to be an important approach for software reuse. However, the toolset supporting a particular domain-specific language is also domain-specific and is per definition not reusable. Therefore, creating and maintaining a DSL requires additional resources that could be even larger than the savings associated with using them. As a solution, di erent tool frameworks have been proposed to simplify and reduce the cost of developments of DSLs. Developers of tool support for DSLs need to instantiate, customize or configure the framework for a particular DSL. There are di erent approaches for this. An approach is to use an application programming interface (API) and to extend the basic framework using an imperative programming language. An example of a tools which is based on this approach is Eclipse GEF. Another approach is to configure the framework using declarative languages that are independent of the underlying framework implementation. We believe this second approach can bring important benefits as this brings focus to specifying what should the tool be like instead of writing a program specifying how the tool achieves this functionality. In this thesis we explore this second approach. We use graph transformation as the basic approach to customize a domain-specific modeling (DSM) tool framework. The contributions of this thesis includes a comparison of di erent approaches for defining, representing and interchanging software modeling languages and models and a tool architecture for an open domain-specific modeling framework that e ciently integrates several model transformation components and visual editors. We also present several specific algorithms and tool components for DSM framework. These include an approach for graph query based on region operators and the star operator and an approach for reconciling models and diagrams after executing model transformation programs. We exemplify our approach with two case studies MICAS and EFCO. In these studies we show how our experimental modeling tool framework has been used to define tool environments for domain-specific languages.

Assimilate partitioning and phytotoxicity in soybean plants treated with herbicides

To investigate the effects of trifluralin, chlorimuron and clomazone on morphology and assimilate partitioning during soybean development, plants were grown in a greenhouse and sampled at 14-day intervals. Clomazone reduced stem and leaf dry matter accumulation at 14 days after emergence (DAE), while trifluralin and chlorimuron reduced plant part dry matter accumulation up to 28 DAE. The number of leaves, plant height, mass and number of pods and seeds, and the shoot/root ratio were not influenced by the herbicides. Roots, stems and leaves were the preferred sinks up to the R2 growth stage, while pods and developing seeds became the preferred sinks later. This order was not altered by the herbicides.

Assimilate partitioning in leaves of the raffinose-storing herb Lamium album L.: photosynthesis and carbon partitioning throughout the photoperiod

Lamium album accumulates starch, sucrose and raffinose-family oligosaccharides (RFO) as the major products of photosynthesis. These products were measured in leaves throughout a sixteen-hour photoperiod and under various irradiance conditions. There was continuous accumulation of sucrose and starch. The rate of gas exchange was higher at 500 µEm² s-1 and 900 µEm²s-1 than at 300 µEm² s-1. The rate of photosynthesis did not decline over the sixteen-hour photoperiod, which suggested that there was no short-term feed back inhibition due to sucrose accumulation in this plant. When the products of photosynthesis were compared at the end of the photoperiod, only sucrose increased in abundance at high irradiance. The RFO pool in leaves was shown to contain raffinose, stachyose and verbascose; galactinol was also present. 14CO2 feeding demonstrated that roots and flowers were the major sinks. The middle leaves were major source leaves whilst young leaves acted as both sources and sinks.

Assimilate partitioning in leaves of the raffinose-storing herb Lamium album L.: the effects of altering source-sink balance

In Lamium album, sucrose and raffinose-family oligosaccharides are the major products of photosynthesis that are stored in leaves. Using gas analysis and 14CO2 feeding, we compared photosynthesis and the partitioning of recently-fixed carbon in plants where sink activity was lowered by excision of flowers and chilling of roots with those where sink activity was not modified. Reduction in sink activity led to a reduction in the maximum rate of photosynthesis, to retention of fixed carbon in source leaves and to the progressive accumulation of raffinose-family oligosaccharides. This ultimately affected the extractable activities of invertase and sucrose phosphate synthase. At the end of the light period, invertase activity was significantly higher in treated plants. By contrast sucrose phosphate synthase activity was significantly lower in treated plants. We propose that reducing sink activity in L. album is associated with a shift in metabolism away from starch and sucrose synthesis and towards sucrose catabolism, galactinol utilisation and the synthesis of raffinose-family oligosaccharides.

Properties and algorithms of the hyper-star graph and its related graphs

The hyper-star interconnection network was proposed in 2002 to overcome the drawbacks of the hypercube and its variations concerning the network cost, which is defined by the product of the degree and the diameter. Some properties of the graph such as connectivity, symmetry properties, embedding properties have been studied by other researchers, routing and broadcasting algorithms have also been designed. This thesis studies the hyper-star graph from both the topological and algorithmic point of view. For the topological properties, we try to establish relationships between hyper-star graphs with other known graphs. We also give a formal equation for the surface area of the graph. Another topological property we are interested in is the Hamiltonicity problem of this graph. For the algorithms, we design an all-port broadcasting algorithm and a single-port neighbourhood broadcasting algorithm for the regular form of the hyper-star graphs. These algorithms are both optimal time-wise. Furthermore, we prove that the folded hyper-star, a variation of the hyper-star, to be maixmally fault-tolerant.

Automatic Inference of Graph Models for Complex Networks with Genetic Programming

Complex networks can arise naturally and spontaneously from all things that act as a part of a larger system. From the patterns of socialization between people to the way biological systems organize themselves, complex networks are ubiquitous, but are currently poorly understood. A number of algorithms, designed by humans, have been proposed to describe the organizational behaviour of real-world networks. Consequently, breakthroughs in genetics, medicine, epidemiology, neuroscience, telecommunications and the social sciences have recently resulted. The algorithms, called graph models, represent significant human effort. Deriving accurate graph models is non-trivial, time-intensive, challenging and may only yield useful results for very specific phenomena. An automated approach can greatly reduce the human effort required and if effective, provide a valuable tool for understanding the large decentralized systems of interrelated things around us. To the best of the author's knowledge this thesis proposes the first method for the automatic inference of graph models for complex networks with varied properties, with and without community structure. Furthermore, to the best of the author's knowledge it is the first application of genetic programming for the automatic inference of graph models. The system and methodology was tested against benchmark data, and was shown to be capable of reproducing close approximations to well-known algorithms designed by humans. Furthermore, when used to infer a model for real biological data the resulting model was more representative than models currently used in the literature.

Network Similarity Measures and Automatic Construction of Graph Models using Genetic Programming

A complex network is an abstract representation of an intricate system of interrelated elements where the patterns of connection hold significant meaning. One particular complex network is a social network whereby the vertices represent people and edges denote their daily interactions. Understanding social network dynamics can be vital to the mitigation of disease spread as these networks model the interactions, and thus avenues of spread, between individuals. To better understand complex networks, algorithms which generate graphs exhibiting observed properties of real-world networks, known as graph models, are often constructed. While various efforts to aid with the construction of graph models have been proposed using statistical and probabilistic methods, genetic programming (GP) has only recently been considered. However, determining that a graph model of a complex network accurately describes the target network(s) is not a trivial task as the graph models are often stochastic in nature and the notion of similarity is dependent upon the expected behavior of the network. This thesis examines a number of well-known network properties to determine which measures best allowed networks generated by different graph models, and thus the models themselves, to be distinguished. A proposed meta-analysis procedure was used to demonstrate how these network measures interact when used together as classifiers to determine network, and thus model, (dis)similarity. The analytical results form the basis of the fitness evaluation for a GP system used to automatically construct graph models for complex networks. The GP-based automatic inference system was used to reproduce existing, well-known graph models as well as a real-world network. Results indicated that the automatically inferred models exemplified functional similarity when compared to their respective target networks. This approach also showed promise when used to infer a model for a mammalian brain network.

Object-Oriented Genetic Programming for the Automatic Inference of Graph Models for Complex Networks

Complex networks are systems of entities that are interconnected through meaningful relationships. The result of the relations between entities forms a structure that has a statistical complexity that is not formed by random chance. In the study of complex networks, many graph models have been proposed to model the behaviours observed. However, constructing graph models manually is tedious and problematic. Many of the models proposed in the literature have been cited as having inaccuracies with respect to the complex networks they represent. However, recently, an approach that automates the inference of graph models was proposed by Bailey [10] The proposed methodology employs genetic programming (GP) to produce graph models that approximate various properties of an exemplary graph of a targeted complex network. However, there is a great deal already known about complex networks, in general, and often specific knowledge is held about the network being modelled. The knowledge, albeit incomplete, is important in constructing a graph model. However it is difficult to incorporate such knowledge using existing GP techniques. Thus, this thesis proposes a novel GP system which can incorporate incomplete expert knowledge that assists in the evolution of a graph model. Inspired by existing graph models, an abstract graph model was developed to serve as an embryo for inferring graph models of some complex networks. The GP system and abstract model were used to reproduce well-known graph models. The results indicated that the system was able to evolve models that produced networks that had structural similarities to the networks generated by the respective target models.

A Note On Some Domination Parameters in Graph Products

In this paper, we study the domination number, the global dom ination number, the cographic domination number, the global co graphic domination number and the independent domination number of all the graph products which are non-complete extended p-sums (NEPS) of two graphs.