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Anomaly detection in supercomputers is a very difficult problem due to the big scale of the systems and the high number of components. The current state of the art for automated anomaly detection employs Machine Learning methods or statistical regression models in a supervised fashion, meaning that the detection tool is trained to distinguish among a fixed set of behaviour classes (healthy and unhealthy states).

BackgroundThe aim of this study was to characterize the spread of carbapenemase-producing Klebsiella pneumoniae (CPKP) in a tertiary level hospital using ongoing active surveillance with rectal swab cultures. Furthermore, this study analyzed the presence of CPKP in the clinical samples (CS) of a single patient as well as the evolution of Colistin-sensitive strains (CoS) to Colistin-resistant strains (CoR).MethodsThis study was performed from January 1, 2012 to December 31, 2014. In 2012, a survey was conducted in the Intensive Care Department. In autumn 2013, active monitoring was extended to the Surgery Department, and since mid-2014, the surveillance has included the Medical Department as well. Only the first isolated strain from each patient was included. Antimicrobial susceptibility testing was performed on CPKP isolates: Klebsiella pneumoniae carbapenemase, oxacillinase-48, Verona integron-encoded metallo-β-lactamase and New Delhi metallo-β-lactamase were detected using a validated in-house PCR method, and multilocus sequence typing (MLST) was used to investigate the clonal transmission of strains.ResultsA total of 15,104 patients were included in the study, and 496 consecutive non-replicated strains of CPKP were collected: 149 strains were collected in 2012 (39 [26.2 %] from surveillance rectal swabs [SRS]), 133 strains were collected in 2013 (70 [52.6 %] from SRS) and 214 strains were collected in 2014 (164 [76.6 %] from SRS). We observed a significant increase in the percentage of positive SRS cases in 2014 relative to 2013 and 2012 (p = 0.0001 and p = 0.0172, respectively) and in the proportion of CPKP first isolated by SRS relative to those identified by CS (p < 0.0001). Among all available samples, the number of CoR isolated from SRS was higher in 2013 and 2014 compared with 2012 (p = 0.0019 and p = 0.008, respectively). ST-258 and ST-512 were more prevalent in the tested specimens, and a new single locus variant (SLV) of ST-512 (ST-745) was isolated.ConclusionsThe results of this 3-year study of 15,104 patients highlight the clinical relevance of antimicrobial resistance as well as the drug-selection pressure of colistin therapy. The active surveillance in the three different departments increased the level of CPKP cases isolated by SRS.

High-end multicore processors are characterized by high power density with significant spatial and temporal variability. This leads to power and temperature hot-spots, which may cause non-uniform ageing and accelerated chip failure. These critical issues can be tackled on-line by closed-loop thermal and reliability management policies. Model predictive controllers (MPC) outperform classic feedback controllers since they are capable of minimizing a cost function while enforcing safe working temperature. Unfortunately basic MPC controllers rely on a-priori knowledge of multicore thermal model and their complexity exponentially grows with the number of controlled cores.In this paper we present a scalable, fully-distributed, energy-aware thermal management solution. The modelpredictive controller complexity is drastically reduced by splitting it in a set of simpler interacting controllers, each allocated to a core in the system. Locally, each node selects the optimal frequency to meet temperature constraints while minimizing the performance penalty and system energy. Global optimality is achieved by letting controllers exchange a limited amount of information at run-time on a neighbourhood basis. We address model uncertainty by supporting learning of the thermal model with a novel distributed self-calibration approach that matches well the controller architecture.

BackgroundMultidrug resistance and, in particular, carbapenem resistance is spreading worldwide at an alarming rate, comprehending a variety of bacterial species and causing both nosocomial and community acquired outbursts. Early and efficient detection of infected patients or colonized carriers are mandatory steps in infection control and prevention of multidrug resistance diffusion. The latest EUCAST guidelines for detection of carbapenemase-producing Enterobacteriaceae have set low clinical breakpoints to ensure the maximum detection sensitivity of positive samples. Current workflows involve an initial screening step for species and resistance pattern detection, followed by phenotypic and/or genotypic confirmation. The aim of the present study was to assess the efficiency of six widely used and validated phenotypic assays for the detection of carbapenemases/AmpC in Enterobacteriaceae, to estimate the best workflow in the routine characterization of Enterobacteriaceae isolates.MethodsA panel of 108 non-repetitive Enterobacteriaceae isolates with reduced susceptibility to carbapenems was analyzed by means of 1) Modified Hodge Test, 2) Metallo Beta Lactamase Etest, 3) Double disk test with EDTA, 4) Rosco Diagnostica KPC and MBL confirm kit (RDCK™), 5) AmpC Etest and 6) Cloxacillin inhibition test. Confirmation and validation of results was achieved by genotypic analysis.ResultsThe most accurate identification of resistance determinants was obtained with the combined disc test (Rosco Diagnostica KPC and MBL confirm kit) which had to be coupled with the cloxacillin inhibition test for correct detection of AmpC enzymes. However, in general, phenotypic tests failed to characterize isolates harboring multiple carbapenem resistance determinants, which were successfully assessed only by PCR-based analysis.ConclusionsTo detect and control the spread of pathogens with complicated resistance patterns, both optimized phenotypic analysis (i.e. Rosco Diagnostica KPC and MBL confirm kit coupled with the cloxacillin inhibition test) and genotypic assays are recommended in the routine diagnostic of clinical laboratories.