University of Siena

About: University of Siena is a education organization based out in Siena, Italy. It is known for research contribution in the topics: Population & Cancer. The organization has 12179 authors who have published 33334 publications receiving 1008287 citations. The organization is also known as: Università degli studi di Siena & Universita degli studi di Siena.

Patterns and Meanings: Using corpora for English language research and teaching

Abstract: Patterns and Meanings consists of case studies which make use of corpora and concordance technology. Each case study elaborates a problem area, makes reference to both the descriptive and applied literature thus far, and then suggests ways of exploiting corpus data to shed light on the problem. Language phenomena investigated include word sense, phraseology and syntax, metaphor and creative use, text reference, idiom, and translation. Emphasis is given to information that usually cannot be found in dictionaries, grammars, language textbooks or other resources, but which the study of corpus data makes available. This work is particularly important not only for its language description insights, but also for pedagogical application. Further useful suggestions are included on setting up a medium-sized corpus on a personal computer.

Cytotoxin production by Campylobacter pylori strains isolated from patients with peptic ulcers and from patients with chronic gastritis only.

Abstract: A total of 66.6% of Campylobacter pylori strains isolated from patients with peptic ulcers produced a cytotoxin active against mammalian cells in vitro, versus 30.1% of strains isolated from patients with chronic gastritis of various degrees of severity only. This difference was statistically significant and suggests that the toxic substance could be involved in the development of peptic ulcers.

Scheduling Problems with Two Competing Agents

Abstract: We consider the scheduling problems arising when two agents, each with a set of nonpreemptive jobs, compete to perform their respective jobs on a common processing resource. Each agent wants to minimize a certain objective function, which depends on the completion times of its jobs only. The objective functions we consider in this paper are maximum of regular functions (associated with each job), number of late jobs, and total weighted completion times. We obtain different scenarios, depending on the objective function of each agent, and on the structure of the processing system (single machine or shop). For each scenario, we address the complexity of various problems, namely, finding the optimal solution for one agent with a constraint on the other agent's cost function, finding single nondominated schedules (i.e., such that a better schedule for one of the two agents necessarily results in a worse schedule for the other agent), and generating all nondominated schedules.