Jejunogastric intussusception presented with hematemesis: a case presentation and review of the literature

BACKGROUND: Jejunogastric intussusception (JGI) is a rare but potentially very serious complication of gastrectomy or gastrojejunostomy. To avoid mortality early diagnosis and prompt surgical intervention is mandatory. CASE PRESENTATION: A young man presented with epigastric pain and bilous vomiting followed by hematemesis,10 years after vagotomy and gastrojejunostomy for a bleeding duodenal ulcer. Emergency endoscopy showed JGI and the CT scan of the abdomen was compatible with this diagnosis. At laparotomy a retrograde type II, JGI was confirmed and managed by reduction of JGI without intestinal resection. Postoperative recovery was uneventful. CONCLUSIONS: JGI is a rare condition and less than 200 cases have been published since its first description in 1914. The clinical picture is almost diagnostic. Endoscopy performed by someone familiar with this rare entity is certainly diagnostic and CT-Scan of the abdomen could also help. There is no medical treatment for acute JGI and the correct treatment is surgical intervention as soon as possible

Using parallelism and pipeline for the optimisation of join queries

Abstract: In this study we present a technique for the parallel optimisation of join queries, that uses the offered coarse-grain parallelism of the underlying architecture in order to reduce the CPU-bound optimisation overhead. The optimisation technique performs an almost exhaustive search of the solution space for small join queries and gradually, as the number of joins increases, it diverges towards iterative improvement. This technique has been developed on a low-parallelism transputer-based architecture, where its behaviour is studied for the optimisation of queries with many tenths of joins.

PARALLEL OPTIMISATION OF JOIN QUERIES USING A TECHNIQUE OF EXHAUSTIVE NATURE 1

Abstract. In this study we present a technique for the parallel optimisation of join queries that uses the offered coarse-grain parallelism of the underlying architecture in order to reduce the CPU-bound optimisation overhead. The optimisation technique performs an almost exhaustive search of the solution space for small join queries and gradually, as the number of joins increases, it diverges towards iterative improvement. This technique has been developed on a low-parallelism transputer-based architecture, where its behaviour is studied for the optimisation of queries with many tens of joins

A Cost Model for the Estimation of Query Execution Time in a Parallel Environment Supporting Pipeline

We propose a model for the estimation of query execution time in an environment supporting bushy and pipelined parallelism. We consider a parallel architecture of processors having private main memories, accessing a shared secondary storage and communicating to each other via a network. For this environment, we compute the cost of query operators when processed in isolation and when in pipeline mode. We use those formulae to incrementally compute the cost of a query execution plan from its components. Our cost model can be incorporated to any optimizer for parallel query processing, that considers parallel and pipelined execution of the query operators. Keywords: query cost estimation, query execution plan, query tree, pipeline, bushy parallelism, query optimisation, databases 1 Introduction The development of coarse-grain parallel systems allows the implementation of parallel databases, but extends the requirements posed on the query optimiser. For example, communication overhead an..

Combining Information Extraction Systems Using Voting and Stacked Generalization

This article investigates the effectiveness of voting and stacked generalization-also known as stacking- in the context of information extraction (IE). A new stacking framework is proposed that accommodates well-known approaches for IE. The key idea is to perform cross-validation on the base-level data set, which consists of text documents annotated with relevant information, in order to create a meta-level data set that consists of feature vectors. A classifier is then trained using the new vectors. Therefore, base-level IE systems are combined with a common classifier at the metalevel. Various voting schemes are presented for comparing against stacking in various IE domains. Well known IE systems are employed at the base-level, together with a variety of classifiers at the meta-level. Results show that both voting and stacking work better when relying on probabilistic estimates by the base-level systems. Voting proved to be effective in most domains in the experiments. Stacking, on the other hand, proved to be consistently effective over all domains, doing comparably or better than voting and always better than the best base-level systems. Particular emphasis is also given to explaining the results obtained by voting and stacking at the meta-level, with respect to the varying degree of similarity in the output of the base-level systems

Agents for Querying Distributed Statistical Databases Over the Internet

Distributed database techniques and the Internet provide producers of statistics with a means to publish their data and metadata widely and make them available to a variety of users. Data matching to a user query and data access as well as data harmonization are some of the problems that should be solved. Intelligence is required in various stages of query answering and data matching. Moreover, the breadth and distributed nature of the Internet urge for a distributed approach. Agents seem to be the means by which both intelligence and distributed processing can be achieved. This paper presents a distributed approach for answering queries on statistical data that exist over the Internet using a multi-agent framework. </jats:p