989 resultados para Sanfelice, Giuseppe, 1665-1737.


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In the recent years, the unpredictable growth of the Internet has moreover pointed out the congestion problem, one of the problems that historicallyha ve affected the network. This paper deals with the design and the evaluation of a congestion control algorithm which adopts a FuzzyCon troller. The analogyb etween Proportional Integral (PI) regulators and Fuzzycon trollers is discussed and a method to determine the scaling factors of the Fuzzycon troller is presented. It is shown that the Fuzzycon troller outperforms the PI under traffic conditions which are different from those related to the operating point considered in the design.

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This paper presents the results of the application of a parallel Genetic Algorithm (GA) in order to design a Fuzzy Proportional Integral (FPI) controller for active queue management on Internet routers. The Active Queue Management (AQM) policies are those policies of router queue management that allow the detection of network congestion, the notification of such occurrences to the hosts on the network borders, and the adoption of a suitable control policy. Two different parallel implementations of the genetic algorithm are adopted to determine an optimal configuration of the FPI controller parameters. Finally, the results of several experiments carried out on a forty nodes cluster of workstations are presented.

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This paper focuses on improving computer network management by the adoption of artificial intelligence techniques. A logical inference system has being devised to enable automated isolation, diagnosis, and even repair of network problems, thus enhancing the reliability, performance, and security of networks. We propose a distributed multi-agent architecture for network management, where a logical reasoner acts as an external managing entity capable of directing, coordinating, and stimulating actions in an active management architecture. The active networks technology represents the lower level layer which makes possible the deployment of code which implement teleo-reactive agents, distributed across the whole network. We adopt the Situation Calculus to define a network model and the Reactive Golog language to implement the logical reasoner. An active network management architecture is used by the reasoner to inject and execute operational tasks in the network. The integrated system collects the advantages coming from logical reasoning and network programmability, and provides a powerful system capable of performing high-level management tasks in order to deal with network fault.

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This paper presents a parallel genetic algorithm to the Steiner Problem in Networks. Several previous papers have proposed the adoption of GAs and others metaheuristics to solve the SPN demonstrating the validity of their approaches. This work differs from them for two main reasons: the dimension and the characteristics of the networks adopted in the experiments and the aim from which it has been originated. The reason that aimed this work was namely to build a comparison term for validating deterministic and computationally inexpensive algorithms which can be used in practical engineering applications, such as the multicast transmission in the Internet. On the other hand, the large dimensions of our sample networks require the adoption of a parallel implementation of the Steiner GA, which is able to deal with such large problem instances.

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In real world applications sequential algorithms of data mining and data exploration are often unsuitable for datasets with enormous size, high-dimensionality and complex data structure. Grid computing promises unprecedented opportunities for unlimited computing and storage resources. In this context there is the necessity to develop high performance distributed data mining algorithms. However, the computational complexity of the problem and the large amount of data to be explored often make the design of large scale applications particularly challenging. In this paper we present the first distributed formulation of a frequent subgraph mining algorithm for discriminative fragments of molecular compounds. Two distributed approaches have been developed and compared on the well known National Cancer Institute’s HIV-screening dataset. We present experimental results on a small-scale computing environment.

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Structured data represented in the form of graphs arises in several fields of the science and the growing amount of available data makes distributed graph mining techniques particularly relevant. In this paper, we present a distributed approach to the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The problem is characterized by a highly irregular search tree, whereby no reliable workload prediction is available. We describe the three main aspects of the proposed distributed algorithm, namely a dynamic partitioning of the search space, a distribution process based on a peer-to-peer communication framework, and a novel receiver-initiated, load balancing algorithm. The effectiveness of the distributed method has been evaluated on the well-known National Cancer Institute’s HIV-screening dataset, where the approach attains close-to linear speedup in a network of workstations.

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Frequent pattern discovery in structured data is receiving an increasing attention in many application areas of sciences. However, the computational complexity and the large amount of data to be explored often make the sequential algorithms unsuitable. In this context high performance distributed computing becomes a very interesting and promising approach. In this paper we present a parallel formulation of the frequent subgraph mining problem to discover interesting patterns in molecular compounds. The application is characterized by a highly irregular tree-structured computation. No estimation is available for task workloads, which show a power-law distribution in a wide range. The proposed approach allows dynamic resource aggregation and provides fault and latency tolerance. These features make the distributed application suitable for multi-domain heterogeneous environments, such as computational Grids. The distributed application has been evaluated on the well known National Cancer Institute’s HIV-screening dataset.

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The emergence behaviour of weed species in relation to cultural and meteorological events was studied. Dissimilarities between populations in dormancy and germination ecology, between-year maturation conditions and seed quality and burial site climate all contribute to potentially unpredictable variability. Therefore, a weed emergence data set was produced for weed seeds of Stellaria media and Chenopodium album matured and collected from three populations (Italy, Sweden and UK). The seeds were collected in two consecutive seasons (1999 and 2000) and subsequently buried in the autumn of the same year of maturation in eight contrasting climatic locations throughout Europe and the USA. The experiment sought to explore and explain differences between the three populations in their emergence behaviour. Evidence was demonstrated of synchrony in the timing of the emergence of different populations of a species at a given burial site. The relative magnitudes of emergence from the three populations at a given burial site in a given year were generally similar across all the burial sites in the study. The resulting data set was also used to construct a simple weed emergence model, which was tested for its application to the range of different burial environments and populations. The study demonstrated the possibility of using a simple thermal time-based model to describe part of the emergence behaviour across different burial sites, seed populations and seasons, and a simple winter chilling relationship to adjust for the magnitude of the flush of emergence at a given burial site. This study demonstrates the possibility of developing robust generic models for simple predictions of emergence timing across populations.

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Weeds are major constraints on crop production, yet as part of the primary producers within farming systems, they may be important components of the agroecosystem. Using published literature, the role of weeds in arable systems for other above-ground trophic levels are examined. In the UK, there is evidence that weed flora have changed over the past century, with some species declining in abundance, whereas others have increased. There is also some evidence for a decline in the size of arable weed seedbanks. Some of these changes reflect improved agricultural efficiency, changes to more winter-sown crops in arable rotations and the use of more broad-spectrum herbicide combinations. Interrogation of a database of records of phytophagous insects associated with plant species in the UK reveals that many arable weed species support a high diversity of insect species. Reductions in abundances of host plants may affect associated insects and other taxa. A number of insect groups and farmland birds have shown marked population declines over the past 30 years. Correlational studies indicate that many of these declines are associated with changes in agricultural practices. Certainly reductions in food availability in winter and for nestling birds in spring are implicated in the declines of several bird species, notably the grey partridge, Perdix perdix . Thus weeds have a role within agroecosystems in supporting biodiversity more generally. An understanding of weed competitivity and the importance of weeds for insects and birds may allow the identification of the most important weed species. This may form the first step in balancing the needs for weed control with the requirements for biodiversity and more sustainable production methods.

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Field experiments were conducted to quantify the natural levels of post-dispersal seed predation of arable weed species in spring barley and to identify the main groups of seed predators. Four arable weed species were investigated that were of high biodiversity value, yet of low to moderate competitive ability with the crop. These were Chenopodium album, Sinapis arvensis, Stellaria media and Polygonum aviculare. Exclusion treatments were used to allow selective access to dishes of seeds by different predator groups. Seed predation was highest early in the season, followed by a gradual decline in predation over the summer for all species. All species were taken by invertebrates. The activity of two phytophagous carabid genera showed significant correlations with seed predation levels. However, in general carabid activity was not related to seed predation and this is discussed in terms of the mainly polyphagous nature of many Carabid species that utilized the seed resource early in the season, but then switched to carnivory as prey populations increased. The potential relevance of post-dispersal seed predation to the development of weed management systems that maximize biological control through conservation and optimize herbicide use, is discussed.

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Buffer strips are refuges for a variety of plants providing resources, such as pollen, nectar and seeds, for higher trophic levels, including invertebrates, mammals and birds. Margins can also harbour plant species that are potentially injurious to the adjacent arable crop (undesirable species). Sowing perennial species in non-cropped buffer strips can reduce weed incidence, but limits the abundance of annuals with the potential to support wider biodiversity (desirable species). We investigated the responses of unsown plant species present in buffer strips established with three different seed mixes managed annually with three contrasting management regimes (cutting, sward scarification and selective graminicide). Sward scarification had the strongest influence on the unsown desirable (e.g. Sonchus spp.) and unsown pernicious (e.g. Elytrigia repens) species, and was generally associated with higher cover values of these species. However, abundances of several desirable weed species, in particular Poa annua, were not promoted by scarification. The treatments of cutting and graminicide tended to have negative impacts on the unsown species, except for Cirsium vulgare, which increased with graminicide application. Differences in unsown species cover between seed mixes were minimal, although the grass-only mix was more susceptible to establishment by C. vulgare and Galium aparine than the two grass and forb mixes. Annual scarification can enable desirable annuals and sown perennials to co-exist, however, this practice can also promote pernicious species, and so is unlikely to be widely adopted as a management tool in its current form.