Task mapping on a dragonfly supercomputer

The dragonfly network topology has recently gained traction in the design of high performance computing (HPC) systems and has been implemented in large-scale supercomputers. The impact of task mapping, i.e., placement of MPI ranks onto compute cores, on the communication performance of applications on dragonfly networks has not been comprehensively investigated on real large-scale systems. This paper demonstrates that task mapping affects the communication overhead significantly in dragonflies and the magnitude of this effect is sensitive to the application, job size, and the OpenMP settings. Among the three task mapping algorithms we study (in-order, random, and recursive coordinate bisection), selecting a suitable task mapper reduces application communication time by up to 47%

Optimizing Energy Storage Participation in Emerging Power Markets

The growing amount of intermittent renewables in power generation creates challenges for real-time matching of supply and demand in the power grid. Emerging ancillary power markets provide new incentives to consumers (e.g., electrical vehicles, data centers, and others) to perform demand response to help stabilize the electricity grid. A promising class of potential demand response providers includes energy storage systems (ESSs). This paper evaluates the benefits of using various types of novel ESS technologies for a variety of emerging smart grid demand response programs, such as regulation services reserves (RSRs), contingency reserves, and peak shaving. We model, formulate and solve optimization problems to maximize the net profit of ESSs in providing each demand response. Our solution selects the optimal power and energy capacities of the ESS, determines the optimal reserve value to provide as well as the ESS real-time operational policy for program participation. Our results highlight that applying ultra-capacitors and flywheels in RSR has the potential to be up to 30 times more profitable than using common battery technologies such as LI and LA batteries for peak shaving.Comment: The full (longer and extended) version of the paper accepted in IGSC 201

Optimizing energy storage participation in emerging power markets

The growing amount of intermittent renewables in power generation creates challenges for real-time matching of supply and demand in the power grid. Emerging ancillary power markets provide new incentives to consumers (e.g., electrical vehicles, data centers, and others) to perform demand response to help stabilize the electricity grid. A promising class of potential demand response providers includes energy storage systems (ESSs). This paper evaluates the benefits of using various types of novel ESS technologies for a variety of emerging smart grid demand response programs, such as regulation services reserves (RSRs), contingency reserves, and peak shaving. We model, formulate and solve optimization problems to maximize the net profit of ESSs in providing each demand response. Our solution selects the optimal power and energy capacities of the ESS, determines the optimal reserve value to provide as well as the ESS real-time operational policy for program participation. Our results highlight that applying ultra-capacitors and flywheels in RSR has the potential to be up to 30 times more profitable than using common battery technologies such as LI and LA batteries for peak shaving

User-profile-based analytics for detecting cloud security breaches

While the growth of cloud-based technologies has benefited the society tremendously, it has also increased the surface area for cyber attacks. Given that cloud services are prevalent today, it is critical to devise systems that detect intrusions. One form of security breach in the cloud is when cyber-criminals compromise Virtual Machines (VMs) of unwitting users and, then, utilize user resources to run time-consuming, malicious, or illegal applications for their own benefit. This work proposes a method to detect unusual resource usage trends and alert the user and the administrator in real time. We experiment with three categories of methods: simple statistical techniques, unsupervised classification, and regression. So far, our approach successfully detects anomalous resource usage when experimenting with typical trends synthesized from published real-world web server logs and cluster traces. We observe the best results with unsupervised classification, which gives an average F1-score of 0.83 for web server logs and 0.95 for the cluster traces

The impact of the COVID-19 pandemic on primary school students’ mathematical reasoning skills: a mediation analysis

The present research aimed to reveal how the COVID-19 pandemic influenced the mathematical reasoning of primary school students through mediation analysis. It was designed as ex post facto research. The research sample consisted of two cohorts. Cohort 1 included 415 primary school children who received face-to-face instruction by attending school for six months until COVID-19 emerged. Cohort 2 included 964 children who were taught curricular skills through distance education due to COVID-19 and school closures. In total, 1,379 primary school children were recruited into the research sample. Data were collected through a mathematical reasoning test by sending items from the instrument via Google Docs. The data were analysed with mediation analysis. Results demonstrated that the school closures due to the COVID-19 pandemic negatively influenced mathematical reasoning skills. Findings are discussed in the light of human interaction and Cattell’s intelligence theory

Effect of different levels of royal jelly on biochemical parameters of swimmers

This study aims to investigate the effects of different levels of royal jelly supplementation on biochemical parameters in swimmers. Randomly selected 40 male swimmers aged 18 to 25 years attending the same trainings were recruited. Swimmers were assigned to 4 groups each with 10 subjects. Varying amounts of royal jelly (2, 1 g and 500 mg) were given to the 1st, 2nd and 3rd groups and placebo (corn starch) to the 4th group. Participants were trained by swimming totally 20 km in 2 h on 5 days a week for 4 weeks. Resting blood samples were taken before royal jelly administration and after 30 days of application. Then biochemical analyses were performed. Different levels of royal jelly were found to be ineffective on glucose, total cholesterol, high density lipoprotein (HDL) cholesterol, low density lipoprotein (LDL) cholesterol, lactate dehydrogenase (LDH), creatine kinase (CK), aspartate aminotransferase (AST), alanine aminotransferase (ALT) levels of the swimmers. Blood urea nitrogen (BUN) and creatinine levels increased after the training program, and BUN level was higher in the group receiving 500 mg royal jelly than those in the other groups. The increment in creatinine levels was higher in those groups receiving higher amounts of royal jelly after the training. A supplementation of 500 mg, 1 and 2 g/day of royal jelly throughout the 30 day-exercise program was not significantly effective in the swimmers. Also, due to its high amino acid content, BUN and creatinine levels tended to increase.Key words: Royal jelly, swimming, exercise, biochemical parameters, ergogenic aids

Appearance of Renal Replacement Lipomatosis on Computed Tomography : Rare pseudotumour

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Quantifying population-specific growth in benthic bacterial communities under low oxygen using H218O

© The Author(s), 2019. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in ISME Journal (2019), doi:10.1038/s41396-019-0373-4.The benthos in estuarine environments often experiences periods of regularly occurring hypoxic and anoxic conditions, dramatically impacting biogeochemical cycles. How oxygen depletion affects the growth of specific uncultivated microbial populations within these diverse benthic communities, however, remains poorly understood. Here, we applied H218O quantitative stable isotope probing (qSIP) in order to quantify the growth of diverse, uncultured bacterial populations in response to low oxygen concentrations in estuarine sediments. Over the course of 7- and 28-day incubations with redox conditions spanning from hypoxia to euxinia (sulfidic), 18O labeling of bacterial populations exhibited different patterns consistent with micro-aerophilic, anaerobic, facultative anaerobic, and aerotolerant anaerobic growth. 18O-labeled populations displaying anaerobic growth had a significantly non-random phylogenetic distribution, exhibited by numerous clades currently lacking cultured representatives within the Planctomycetes, Actinobacteria, Latescibacteria, Verrucomicrobia, and Acidobacteria. Genes encoding the beta-subunit of the dissimilatory sulfate reductase (dsrB) became 18O labeled only during euxinic conditions. Sequencing of these 18O-labeled dsrB genes showed that Acidobacteria were the dominant group of growing sulfate-reducing bacteria, highlighting their importance for sulfur cycling in estuarine sediments. Our findings provide the first experimental constraints on the redox conditions underlying increased growth in several groups of “microbial dark matter”, validating hypotheses put forth by earlier metagenomic studies.This work was supported by a grant OR 417/1-1 from the Deutsche Forschungsgemeinschaft, and a Junior Researcher Fund grant from LMU Munich to WDO. This work was performed in part, through the Master’s Program in Geobiology and Paleontology (MGAP) at LMU Munich