Stochastic models for quality of service of component connectors

The intensifying need for scalable software has motivated modular development and using systems distributed over networks to implement large-scale applications. In Service-oriented Computing, distributed services are composed to provide large-scale services with a specific functionality. In this way, reusability of existing services can be increased. However, due to the heterogeneity of distributed software systems, software composition is not easy and requires additional mechanisms to impose some form of a coordination on a distributed software system. Besides functional correctness, a composed service must satisfy various quantitative requirements for its clients, which are generically called its quality of service (QoS). Particularly, it is tricky to obtain the overall QoS of a composed service even if the QoS information of its constituent distributed services is given. In this thesis, we propose Stochastic Reo to specify software composition with QoS aspects and its compositional semantic models. They are also used as intermediate models to generate their corresponding stochastic models for practical analysis. Based on this, we have implemented the tool Reo2MC. Using Reo2MC, we have modeled and analyzed an industrial software, the ASK system. Its analysis results provided the best cost-effective resource utilization and some suggestions to improve the performance of the system.UBL - phd migration 201

A concerted systems biology analysis of phenol metabolism in Rhodococcus opacus PD630

Rhodococcus opacus PD630 metabolizes aromatic substrates and naturally produces branched-chain lipids, which are advantageous traits for lignin valorization. To provide insights into its lignocellulose hydrolysate utilization, we performed 13C-pathway tracing, 13C-pulse-tracing, transcriptional profiling, biomass composition analysis, and metabolite profiling in conjunction with 13C-metabolic flux analysis (13C-MFA) of phenol metabolism. We found that 1) phenol is metabolized mainly through the ortho–cleavage pathway; 2) phenol utilization requires a highly active TCA cycle; 3) NADPH is generated mainly via NADPH-dependent isocitrate dehydrogenase; 4) active cataplerotic fluxes increase plasticity in the TCA cycle; and 5) gluconeogenesis occurs partially through the reversed Entner–Doudoroff pathway (EDP). We also found that phenol-fed R. opacus PD630 generally has lower sugar phosphate concentrations (e.g., fructose 1,6-bisphosphatase) compared to metabolite pools in 13C-glucose-fed Escherichia coli (set as internal standards), while its TCA metabolites (e.g., malate, succinate, and α-ketoglutarate) accumulate intracellularly with measurable succinate secretion. In addition, we found that phenol utilization was inhibited by benzoate, while catabolite repressions by other tested carbon substrates (e.g., glucose and acetate) were absent in R. opacus PD630. Three adaptively-evolved strains display very different growth rates when fed with phenol as a sole carbon source, but they maintain a conserved flux network. These findings improve our understanding of R. opacus’ metabolism for future lignin valorization

A High-Quality Genome-Scale Model for Rhodococcus opacus Metabolism

Rhodococcus opacus is a bacterium that has a high tolerance to aromatic compounds and can produce significant amounts of triacylglycerol (TAG). Here, we present iGR1773, the first genome-scale model (GSM) of R. opacus PD630 metabolism based on its genomic sequence and associated data. The model includes 1773 genes, 3025 reactions, and 1956 metabolites, was developed in a reproducible manner using CarveMe, and was evaluated through Metabolic Model tests (MEMOTE). We combine the model with two Constraint-Based Reconstruction and Analysis (COBRA) methods that use transcriptomics data to predict growth rates and fluxes: E-Flux2 and SPOT (Simplified Pearson Correlation with Transcriptomic data). Growth rates are best predicted by E-Flux2. Flux profiles are more accurately predicted by E-Flux2 than flux balance analysis (FBA) and parsimonious FBA (pFBA), when compared to 44 central carbon fluxes measured by 13C-Metabolic Flux Analysis (13C-MFA). Under glucose-fed conditions, E-Flux2 presents an R2 value of 0.54, while predictions based on pFBA had an inferior R2 of 0.28. We attribute this improved performance to the extra activity information provided by the transcriptomics data. For phenol-fed metabolism, in which the substrate first enters the TCA cycle, E-Flux2’s flux predictions display a high R2 of 0.96 while pFBA showed an R2 of 0.93. We also show that glucose metabolism and phenol metabolism function with similar relative ATP maintenance costs. These findings demonstrate that iGR1773 can help the metabolic engineering community predict aromatic substrate utilization patterns and perform computational strain design

Choice of implicit and explicit operators for the upwind differencing method

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/76274/1/AIAA-1988-624-513.pd

Solar Flares and Coronal Mass Ejections: A Statistically Determined Flare Flux-CME Mass Correlation

In an effort to examine the relationship between flare flux and corresponding CME mass, we temporally and spatially correlate all X-ray flares and CMEs in the LASCO and GOES archives from 1996 to 2006. We cross-reference 6,733 CMEs having well-measured masses against 12,050 X-ray flares having position information as determined from their optical counterparts. For a given flare, we search in time for CMEs which occur 10-80 minutes afterward, and we further require the flare and CME to occur within +/-45 degrees in position angle on the solar disk. There are 826 CME/flare pairs which fit these criteria. Comparing the flare fluxes with CME masses of these paired events, we find CME mass increases with flare flux, following an approximately log-linear, broken relationship: in the limit of lower flare fluxes, log(CME mass)~0.68*log(flare flux), and in the limit of higher flare fluxes, log(CME mass)~0.33*log(flare flux). We show that this broken power-law, and in particular the flatter slope at higher flare fluxes, may be due to an observational bias against CMEs associated with the most energetic flares: halo CMEs. Correcting for this bias yields a single power-law relationship of the form log(CME mass)~0.70*log(flare flux). This function describes the relationship between CME mass and flare flux over at least 3 dex in flare flux, from ~10^-7 to 10^-4 W m^-2.Comment: 28 pages, 16 figures, accepted to Solar Physic

From Predicting Solar Activity to Forecasting Space Weather: Practical Examples of Research-to-Operations and Operations-to-Research

The successful transition of research to operations (R2O) and operations to research (O2R) requires, above all, interaction between the two communities. We explore the role that close interaction and ongoing communication played in the successful fielding of three separate developments: an observation platform, a numerical model, and a visualization and specification tool. Additionally, we will examine how these three pieces came together to revolutionize interplanetary coronal mass ejection (ICME) arrival forecasts. A discussion of the importance of education and training in ensuring a positive outcome from R2O activity follows. We describe efforts by the meteorological community to make research results more accessible to forecasters and the applicability of these efforts to the transfer of space-weather research.We end with a forecaster "wish list" for R2O transitions. Ongoing, two-way communication between the research and operations communities is the thread connecting it all.Comment: 18 pages, 3 figures, Solar Physics in pres

Magnetic Order in YBa2_2Cu3_3O6+x_{6+x} Superconductors

Polarized and unpolarized neutron diffraction has been used to search for magnetic order in YBa$_2$Cu$_3$O$_{6+x}$ superconductors. Most of the measurements were made on a high quality crystal of YBa$_2$Cu$_3$O$_{6.6}$. It is shown that this crystal has highly ordered ortho-II chain order, and a sharp superconducting transition. Inelastic scattering measurements display a very clean spin-gap and pseudogap with any intensity at 10 meV being 50 times smaller than the resonance intensity. The crystal shows a complicated magnetic order that appears to have three components. A magnetic phase is found at high temperatures that seems to stem from an impurity with a moment that is in the $a$-$b$ plane, but disordered on the crystal lattice. A second ordering occurs near the pseudogap temperature that has a shorter correlation length than the high temperature phase and a moment direction that is at least partly along the c-axis of the crystal. Its moment direction, temperature dependence, and Bragg intensities suggest that it may stem from orbital ordering of the $d$-density wave (DDW) type. An additional intensity increase occurs below the superconducting transition. The magnetic intensity in these phases does not change noticeably in a 7 Tesla magnetic field aligned approximately along the c-axis. Searches for magnetic order in YBa$_2$Cu$_3$O$_{7}$ show no signal while a small magnetic intensity is found in YBa$_2$Cu$_3$O$_{6.45}$ that is consistent with c-axis directed magnetic order. The results are contrasted with other recent neutron measurements.Comment: 11 pages with 10 figure

Observation of In-Plane Magnetic Field Induced Phase Transitions in FeSe

We investigate thermodynamic properties of FeSe under in-plane magnetic fields using torque magnetometry, specific heat, and magnetocaloric measurements. Below the upper critical field Hc2, we observed the field induced anomalies at H1 ∼ 15 T and H2 ∼ 22 T near H ∥ ab and below a characteristic temperature T* ∼ 2 K. The transition magnetic fields H1 and H2 exhibit negligible dependence on both temperature and field orientation. This contrasts to the strong temperature and angle dependence of Hc2, suggesting that these anomalies are attributed to the field induced phase transitions, originating from the inherent spin-density-wave instability of quasipaticles near the superconducting gap minima or possible Flude-Ferrell-Larkin-Ovchinnikov state in the highly spin-polarized Fermi surfaces. Our observations imply that FeSe, an atypical multiband superconductor with extremely small Fermi energies, represents a unique model system for stabilizing unusual superconducting orders beyond the Pauli limit

Modeling the Subsurface Structure of Sunspots

While sunspots are easily observed at the solar surface, determining their subsurface structure is not trivial. There are two main hypotheses for the subsurface structure of sunspots: the monolithic model and the cluster model. Local helioseismology is the only means by which we can investigate subphotospheric structure. However, as current linear inversion techniques do not yet allow helioseismology to probe the internal structure with sufficient confidence to distinguish between the monolith and cluster models, the development of physically realistic sunspot models are a priority for helioseismologists. This is because they are not only important indicators of the variety of physical effects that may influence helioseismic inferences in active regions, but they also enable detailed assessments of the validity of helioseismic interpretations through numerical forward modeling. In this paper, we provide a critical review of the existing sunspot models and an overview of numerical methods employed to model wave propagation through model sunspots. We then carry out an helioseismic analysis of the sunspot in Active Region 9787 and address the serious inconsistencies uncovered by \citeauthor{gizonetal2009}~(\citeyear{gizonetal2009,gizonetal2009a}). We find that this sunspot is most probably associated with a shallow, positive wave-speed perturbation (unlike the traditional two-layer model) and that travel-time measurements are consistent with a horizontal outflow in the surrounding moat.Comment: 73 pages, 19 figures, accepted by Solar Physic

Shrinking a large dataset to identify variables associated with increased risk of Plasmodium falciparum infection in Western Kenya

Large datasets are often not amenable to analysis using traditional single-step approaches. Here, our general objective was to apply imputation techniques, principal component analysis (PCA), elastic net and generalized linear models to a large dataset in a systematic approach to extract the most meaningful predictors for a health outcome. We extracted predictors for Plasmodium falciparum infection, from a large covariate dataset while facing limited numbers of observations, using data from the People, Animals, and their Zoonoses (PAZ) project to demonstrate these techniques: data collected from 415 homesteads in western Kenya, contained over 1500 variables that describe the health, environment, and social factors of the humans, livestock, and the homesteads in which they reside. The wide, sparse dataset was simplified to 42 predictors of P. falciparum malaria infection and wealth rankings were produced for all homesteads. The 42 predictors make biological sense and are supported by previous studies. This systematic data-mining approach we used would make many large datasets more manageable and informative for decision-making processes and health policy prioritization