Energy aware approach for HPC systems

International audienceHigh‐performance computing (HPC) systems require energy during their full life cycle from design and production to transportation to usage and recycling/dismanteling. Because of increase of ecological and cost awareness, energy performance is now a primary focus. This chapter focuses on the usage aspect of HPC and how adapted and optimized software solutions could improve energy efficiency. It provides a detailed explanation of server power consumption, and discusses the application of HPC, phase detection, and phase identification. The chapter also suggests that having the load and memory access profiles is insufficient for an effective evaluation of the power consumed by an application. The available leverages in HPC systems are also shown in detail. The chapter proposes some solutions for modeling the power consumption of servers, which allows designing power prediction models for better decision making.These approaches allow the deployment and usage of a set of available green leverages, permitting energy reduction

Highly Sensitive OFET Based Room Temperature Operated Gas Sensors Using Thieno[3,2-b]thiophene Extended Phthalocyanine Semiconductor

Over the past decades, organic field-effect transistor (OFET) gas sensors have maintained a rapid development. However, the majority of OFET gas sensors show insufficient detection capability towards oxidizing and hazardous gases such as nitrogen dioxide (NO2) and sulfide dioxide (SO2). In this report, a sustainable approach toward fabrication of OFET gas sensor, consisting of thieno[3,2-b]thiophene (TT) and phthalocyanine (Pc) based electron rich structure (TT-Pc) for detection of both nitrogen dioxide (NO2) and sulfide dioxide (SO2) is disclosed for the first time. Khaya gum (KG), a natural, biodegradable biopolymer is used as the gate dielectric in these OFET-based sensors. Thin film properties and surface morphology of TT-Pc were investigated by UV-Vis, SEM, AFM and contact angle measurements, which indicated a uniform and smooth film formation. The UV-Vis properties were supported by computational chemistry, performed using density functional theory (DFT) for optimizing geometry and absorption of TT-Pc models. Sensitive and selective responses of 90% and 60% were obtained from TT-Pc OFET-based sensors upon exposure to 20 ppm of NO2 and SO2, respectively, under ambient conditions. One of the lowest limits of detection of ~165 ppb was achieved for both NO2 and SO2 using solution-processed TT-Pc sensor with natural, biodegradable dielectric biopolymer. The sensors showed excellent long-term environmental and operational stability with only a 7% reduction of the sensor’s initial response (%) upon exposure to NO2 and SO2 over nine months of operation in air

Switchgrass is a promising, high-yielding crop for California biofuel

Ethanol use in California is expected to rise to 1.62 billion gallons per year in 2012, more than 90% of which will be trucked or shipped into the state. Switchgrass, a nonnative grass common in other states, has been identified as a possible high-yielding biomass crop for the production of cellulosic ethanol. The productivity of the two main ecotypes of switchgrass, lowland and upland, was evaluated under irrigated conditions across four diverse California ecozones - from Tulelake in the cool north to warm Imperial Valley in the south. In the first full year of production, the lowland varieties yielded up to 17 tons per acre of biomass, roughly double the biomass yields of California rice or maize. The yield response to nitrogen fertilization was statistically insignificant in the first year of production, except for in the Central Valley plots that were harvested twice a year. The biomass yields in our study indicate that switchgrass is a promising biofuel crop for California

Effectiveness of 2% peracetic acid for the disinfection of gutta-percha cones

The aim of this study was to evaluate the effectiveness of 2% peracetic acid for the disinfection of gutta-percha cones contaminated in vitro with Escherichia coli, Staphylococcus aureus, Streptococcus mutans, Candida albicans and Bacillus subtilus (in spore form). Two hundred and twenty-five gutta-percha cones were contaminated with standardized suspensions of each microorganism and incubated at 37°C for 24 h. The cones were divided into 10 experimental groups (n = 15), according to the microorganism tested and disinfection testing times. The disinfection procedure consisted of immersing each cone in a plastic tube containing the substance. The specimens remained in contact with the substance for 1 or 2.5 minutes. Afterwards, each cone was transferred to a 10% sodium thiosulphate solution (Na2S2O3) to neutralize the disinfectant. Microbial biofilms adhering to the cones were dispersed by agitation. Aliquots of 0.1 ml of the suspensions obtained were plated on Sabouraud dextrose agar, or brain and heart infusion agar, and incubated at 37°C for 24 h. The results were expressed in colony forming units (CFU/ml) and the data were submitted to the Wilcoxon Signed Rank Test (level of significance at 0.05). A significant reduction was observed, after 1 minute of exposure, in the test solution for C. albicans (p = 0.0190), S. aureus (p = 0.0001), S. mutans (p = 0.0001), B. subtilis (p = 0.0001), and E. coli (p = 0.0001). After 2.5 minutes of exposure, 100% of the microbial inocula were eliminated. It was concluded that the 2% peracetic acid solution was effective against the biofilms of the tested microorganisms on gutta-percha cones at 1 minute of exposure

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020

Chromo- and Fluorogenic Organometallic Sensors

Compounds that change their absorption and/or emission properties in the presence of a target ion or molecule have been studied for many years as the basis for optical sensing. Within this group of compounds, a variety of organometallic complexes have been proposed for the detection of a wide range of analytes such as cations (including H+), anions, gases (e.g. O 2, SO2, organic vapours), small organic molecules, and large biomolecules (e.g. proteins, DNA). This chapter focuses on work reported within the last few years in the area of organometallic sensors. Some of the most extensively studied systems incorporate metal moieties with intense long-lived metal-to-ligand charge transfer (MLCT) excited states as the reporter or indicator unit, such as fac-tricarbonyl Re(I) complexes, cyclometallated Ir(III) species, and diimine Ru(II) or Os(II) derivatives. Other commonly used organometallic sensors are based on Pt-alkynyls and ferrocene fragments. To these reporters, an appropriate recognition or analyte-binding unit is usually attached so that a detectable modification on the colour and/or the emission of the complex occurs upon binding of the analyte. Examples of recognition sites include macrocycles for the binding of cations, H-bonding units selective to specific anions, and DNA intercalating fragments. A different approach is used for the detection of some gases or vapours, where the sensor's response is associated with changes in the crystal packing of the complex on absorption of the gas, or to direct coordination of the analyte to the metal centre

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions

Differential predictors for alcohol use in adolescents as a function of familial risk

Abstract: Traditional models of future alcohol use in adolescents have used variable-centered approaches, predicting alcohol use from a set of variables across entire samples or populations. Following the proposition that predictive factors may vary in adolescents as a function of family history, we used a two-pronged approach by first defining clusters of familial risk, followed by prediction analyses within each cluster. Thus, for the first time in adolescents, we tested whether adolescents with a family history of drug abuse exhibit a set of predictors different from adolescents without a family history. We apply this approach to a genetic risk score and individual differences in personality, cognition, behavior (risk-taking and discounting) substance use behavior at age 14, life events, and functional brain imaging, to predict scores on the alcohol use disorders identification test (AUDIT) at age 14 and 16 in a sample of adolescents (N = 1659 at baseline, N = 1327 at follow-up) from the IMAGEN cohort, a longitudinal community-based cohort of adolescents. In the absence of familial risk (n = 616), individual differences in baseline drinking, personality measures (extraversion, negative thinking), discounting behaviors, life events, and ventral striatal activation during reward anticipation were significantly associated with future AUDIT scores, while the overall model explained 22% of the variance in future AUDIT. In the presence of familial risk (n = 711), drinking behavior at age 14, personality measures (extraversion, impulsivity), behavioral risk-taking, and life events were significantly associated with future AUDIT scores, explaining 20.1% of the overall variance. Results suggest that individual differences in personality, cognition, life events, brain function, and drinking behavior contribute differentially to the prediction of future alcohol misuse. This approach may inform more individualized preventive interventions