Impact of early or delayed elective resection in complicated diverticulitis

AIM： To investigate the outcomes of early and delayed elective resection after initial antibiotic treatment in patients with complicated diverticulitis. METHODS： The study, a non-randomized comparison of the two approaches, included 421 consecutive patients who underwent surgical resection for complicated sigmoid diverticulitis （Hinchey classification I - II ） at the Department of Surgery, University Medical Center Hamburg-Eppendorf between 2004 and 2009. The operating procedure, duration of hospital and intensive care unit stay, outcome, complications and socioeconomic costs were analyzed, with comparison made between the early and delayed elective resection strategies. RESULTS： The severity of the diverticulitis and American Society of Anesthesiologists score were comparable for the two groups. Patients who underwent delayed elective resection had a shorter hospital stay and operating time, and the rate of successfully completed laparoscopic resections was higher （80% vs 75%）. Eight patients who were scheduled for delayed elective resection required urgent surgery because of complications of the diverticulitis, which resulted in a high rate of morbidity. Analysis of the socioeconomic effects showed that hospitalization costs were significantly higher for delayed elective resection compared with early elec- tive resection （9296 ± 694 vs 8423 ± 968 ; P = 0.001）. Delayed elective resection showed a trend toward lower complications, and the operation appeared simpler to perform than early elective resection. Nevertheless, delayed elective resection carries a risk of complications occurring during the period of 6-8 wk that could necessitate an urgent resection with its consequent high morbidity, which counterbalanced many of the advantages.

Meeting deadlines for approximation processing in MapReduce environments

To provide timely results for big data analytics, it is crucial to satisfy deadline requirements for MapReduce jobs in today＇s production environments. Much effort has been devoted to the problem of meeting deadlines, and typically there exist two kinds of solutions. The first is to allocate appropriate resources to complete the entire job before the specified time limit, where missed deadlines result because of tight deadline constraints or lack of resources; the second is to run a pre-constructed sample based on deadline constraints, which can satisfy the time requirement but fail to maximize the volumes of processed data. In this paper, we propose a deadline-oriented task scheduling approach, named ＇Dart＇, to address the above problem. Given a specified deadline and restricted resources, Dart uses an iterative estimation method, which is based on both historical data and job running status to precisely estimate the real-time job completion time. Based on the estimated time, Dart uses an approach-revise algorithm to make dynamic scheduling decisions for meeting deadlines while maximizing the amount of processed data and mitigating stragglers. Dart also efficiently handles task failures and data skew, protecting its performance from being harmed. We have validated our approach using workloads from OpenCloud and Facebook on a cluster of 64 virtual machines. The results show that Dart can not only effectively meet the deadline but also process near-maximum volumes of data even with tight deadlines and limited resources.