Enfermedad renal crónica : una epidemia

Fil: Alabart, E.. Universidad Nacional de TucumánFil: González, Roxana. Universidad Nacional de TucumánFil: Alós, B.. Universidad Nacional de TucumánFil: Romero, A.. Universidad Nacional de TucumánFil: Barada, C.. Universidad Nacional de TucumánFil: Zanetta, D.. Universidad Nacional de TucumánFil: Colli, G.. Universidad Nacional de Tucumá

Small Bowel Tumors: Clinical Presentation, Prognosis, and Outcome in 33 Patients in a Tertiary Care Center

Introduction. Small bowel cancers are rare. Accumulation of data regarding their clinical presentation, pathologic features, prognostic factors, treatment modalities, and outcome is difficult. Methods. This is a retrospective study of the medical records of 33 patients with small bowel cancers treated at the American University of Beirut-Medical Center over a 20-year period. Results. The study included 25 males (76%) and 8 females (24%). Median age at presentation was 56 years. Most common symptoms were abdominal pain (66.7%) and weight loss (57.6%). Thirteen patients presented with abdominal emergencies (39.3%). Lymphoma was the most common malignant tumor (36.4%), followed by adenocarcinoma (33.3%), leiomyosarcoma (15.2%), gastrointestinal stromal tumors (12.1%), and neuroendocrine tumors (3.0%). Tumors were located in the duodenum in 30% of patients, jejunum in 33%, and ileum in 36%. Resectability rate was 72.7% and curative R0 resection was achieved in 54.1% (13/24) of patients. 5-year survival of the 33 patients was 24.2%. Conclusion. Small bowel cancers are difficult to diagnose because of the nonspecific symptoms. Most patients present with advanced disease and have poor prognosis. Adenocarcinoma and duodenal location have the worst 5-year survival in contrast to stromal tumors and those with ileal location which have the best survival

Self-optimization of pilot power in enterprise femtocells using multi objective heuristic

Deployment of a large number of femtocells to jointly provide coverage in an enterprise environment raises critical challenges especially in future self-organizing networks which rely on plug-and-play techniques for configuration. This paper proposes a multi-objective heuristic based on a genetic algorithm for a centralized self-optimizing network containing a group of UMTS femtocells. In order to optimize the network coverage in terms of handled load, coverage gaps, and overlaps, the algorithm provides a dynamic update of the downlink pilot powers of the deployed femtocells. The results demonstrate that the algorithm can effectively optimize the coverage based on the current statistics of the global traffic distribution and the levels of interference between neighboring femtocells. The algorithm was also compared with the fixed pilot power scheme. The results show over fifty percent reduction in pilot power pollution and a significant enhancement in network performance. Finally, for a given traffic distribution, the solution quality and the efficiency of the described algorithm were evaluated by comparing the results generated by an exhaustive search with the same pilot power configuration

HPTS: heterogeneous parallel tabu search for VLSI placement

Parallelizing any algorithm on a cluster of heterogeneous workstations is not easy, as each workstation requires different wall clock time to execute the same instruction set. In this work, a parallel tabu search algorithm for heterogeneous workstations is presented using PVM. Two parallelization strategies, i.e., functional decomposition and multi-search thread strategies are integrated. The proposed algorithm is tested on the VLSI standard cell placement problem, however, the same algorithm can be used on any combinatorial optimization problem. The results are compared ignoring heterogeneity and are found to be superior in terms of execution tim

HPTS: heterogeneous parallel tabu search for VLSI placement

Parallelizing any algorithm on a cluster of heterogeneous workstations is not easy, as each workstation requires different wall clock time to execute the same instruction set. In this work, a parallel tabu search algorithm for heterogeneous workstations is presented using PVM. Two parallelization strategies, i.e., functional decomposition and multi-search thread strategies are integrated. The proposed algorithm is tested on the VLSI standard cell placement problem, however, the same algorithm can be used on any combinatorial optimization problem. The results are compared ignoring heterogeneity and are found to be superior in terms of execution tim

Parallel tabu search in a heterogeneous environment

We discuss a parallel tabu search algorithm with implementation in a heterogeneous environment. Two parallelization strategies are integrated: functional decomposition and multi-search threads. In addition, domain decomposition strategy is implemented probabilistically. The performance of each strategy is observed and analyzed in terms of speeding up the search and finding better quality solutions. Experiments were conducted for the VLSI cell placement. The objective was to achieve the best possible solution in terms of interconnection length, timing performance, circuit speed, and area. The multiobjective nature of this problem is addressed using a fuzzy goal-based cost computation