Facilitating self-adaptable inter-cloud management

Cloud Computing infrastructures have been developed as individual islands, and mostly proprietary solutions so far. However, as more and more infrastructure providers apply the technology, users face the inevitable question of using multiple infrastructures in parallel. Federated cloud management systems offer a simplified use of these infrastructures by hiding their proprietary solutions. As the infrastructure becomes more complex underneath these systems, the situations (like system failures, handling of load peaks and slopes) that users cannot easily handle, occur more and more frequently. Therefore, federations need to manage these situations autonomously without user interactions. This paper introduces a methodology to autonomously operate cloud federations by controlling their behavior with the help of knowledge management systems. Such systems do not only suggest reactive actions to comply with established Service Level Agreements (SLA) between provider and consumer, but they also find a balance between the fulfillment of established SLAs and resource consumption. The paper adopts rule-based techniques as its knowledge management solution and provides an extensible rule set for federated clouds built on top of multiple infrastructures. © 2012 IEEE

Biomonitoring PM using transplanted lichens in an urban-industrial area of the Lisbon Metropolitan Area and integration with reference monitoring method

Trabalho apresentado em 11th International Aerosol Conference (IAC2022), 4-9 setembro 2022, Atenas, GréciaN/

A new method for the quantification of ambient particulate-matter emission fluxes

An inversion method has been developed in order to quantify the emission fluxes of certain aerosol pollution sources across a wide region in the Northern Hemisphere, mainly in Europe and western Asia. The data employed are the aerosol contribution factors deducted by positive matrix factorization (PMF) on a PM2.5 chemical composition dataset from 16 European and Asian cities for the period 2014 to 2016. The spatial resolution of the method corresponds to the geographic grid cell size of the Lagrangian particle dispersion model (Flexible Particle Dispersion Model, FLEXPART, 1∘ × 1∘) which was utilized for the air mass backward simulations. The area covered is also related to the location of the 16 cities under study. Species with an aerodynamic geometric mean diameter of 400 nm and 3.1 µm and a geometric standard deviation of 1.6 and 2.25, respectively, were used to model the secondary sulfate and dust aerosol transport. Potential source contribution function (PSCF) analysis and generalized Tikhonov regularization were applied so as to acquire potential source areas and quantify their emission fluxes. A significant source area for secondary sulfate on the east of the Caspian Sea is indicated, when data from all stations are used. The maximum emission flux in that area is as high as 10 × 10−12 kg m−2 s−1. When Vilnius, Dushanbe, and Kurchatov data were excluded, the areas with the highest emission fluxes were the western and central Balkans and southern Poland. The results display many similarities to the SO2 emission maps provided by the OMI-HTAP (Ozone Monitoring Instrument-Hemispheric Transport Air Pollution) and ECLIPSE (Evaluating the Climate and Air Quality Impacts of Short-Lived Pollutants) databases. For dust aerosol, measurements from Athens, Belgrade, Debrecen, Lisbon, Tirana, and Zagreb are utilized. The west Sahara region is indicated as the most important source area, and its contribution is quantified, with a maximum of 17.6 × 10−12 kg m−2 s−1. When we apply the emission fluxes from every geographic grid cell (1∘ × 1∘) for secondary sulfate aerosol deducted with the new method to air masses originating from Vilnius, a useful approximation to the measured values is achieved.</p

Detecting modules in dense weighted networks with the Potts method

We address the problem of multiresolution module detection in dense weighted networks, where the modular structure is encoded in the weights rather than topology. We discuss a weighted version of the q-state Potts method, which was originally introduced by Reichardt and Bornholdt. This weighted method can be directly applied to dense networks. We discuss the dependence of the resolution of the method on its tuning parameter and network properties, using sparse and dense weighted networks with built-in modules as example cases. Finally, we apply the method to data on stock price correlations, and show that the resulting modules correspond well to known structural properties of this correlation network.Comment: 14 pages, 6 figures. v2: 1 figure added, 1 reference added, minor changes. v3: 3 references added, minor change

Clar's Theory, STM Images, and Geometry of Graphene Nanoribbons

We show that Clar's theory of the aromatic sextet is a simple and powerful tool to predict the stability, the \pi-electron distribution, the geometry, the electronic/magnetic structure of graphene nanoribbons with different hydrogen edge terminations. We use density functional theory to obtain the equilibrium atomic positions, simulated scanning tunneling microscopy (STM) images, edge energies, band gaps, and edge-induced strains of graphene ribbons that we analyze in terms of Clar formulas. Based on their Clar representation, we propose a classification scheme for graphene ribbons that groups configurations with similar bond length alternations, STM patterns, and Raman spectra. Our simulations show how STM images and Raman spectra can be used to identify the type of edge termination

Rapid detection of peptide markers for authentication purposes in raw and cooked meat using ambient liquid extraction surface analysis mass spectrometry

In this paper, our previously developed ambient LESA-MS methodology is implemented to analyze five types of thermally treated meat species, namely beef, pork, horse, chicken, and turkey meat, in order to select and identify heat-stable and species-specific peptide markers. In-solution tryptic digests of cooked meats were deposited onto a polymer surface, followed by LESA-MS analysis and evaluation using multivariate data analysis and tandem electrospray MS. The five types of cooked meat were clearly discriminated using principal component analysis and orthogonal partial least squares discriminant analysis. A number of 23 heat stable peptide markers unique to species and muscle protein were identified following data-dependent tandem LESA-MS analysis. Surface extraction and direct ambient MS analysis of mixtures of cooked meat species was performed for the first time and enabled detection of 10% (w/w) of pork, horse, and turkey meat, and 5% (w/w) of chicken meat in beef, using the developed LESA-MS/MS analysis. The study shows, for the first time, that ambient LESA-MS methodology displays specificity sufficient to be implemented effectively for the analysis of processed and complex peptide digests. The proposed approach is much faster and simpler than other measurement tools for meat speciation; it has potential for application in other areas of meat science or food production

Photoinduced Structural Phase Transitions in Polyacene

There exist two types of structural instability in polyacene: double bonds in a cis pattern and those in a trans pattern. They are isoenergetic but spectroscopically distinct. We demonstrate optical characterization and manipulation of Peierls-distorted polyacene employing both correlated and uncorrelated Hamiltonians. We clarify the phase boundaries of the cis- and trans-distorted isomers, elucidate their optical-conductivity spectra, and then explore their photoresponses. There occurs a photoinduced transformation in the polyacene structure, but it is one-way switching: The trans configuration is well convertible into the cis one, whereas the reverse conversion is much less feasible. Even the weakest light irradiation can cause a transition of uncorrelated electrons, while correlated electrons have a transition threshold against light irradiation.Comment: 14 pages with 15 figures embedde

Preferential regulation of miRNA targets by environmental chemicals in the human genome

<p>Abstract</p> <p>Background</p> <p>microRNAs (miRNAs) represent a class of small (typically 22 nucleotides in length) non-coding RNAs that can degrade their target mRNAs or block their translation. Recent disease research showed the exposure to some environmental chemicals (ECs) can regulate the expression patterns of miRNAs, which raises the intriguing question of how miRNAs and their targets cope with the exposure to ECs throughout the genome.</p> <p>Results</p> <p>In this study, we comprehensively analyzed the properties of genes regulated by ECs (EC-genes) and found miRNA targets were significantly enriched among the EC-genes. Compared with the non-miRNA-targets, miRNA targets were roughly twice as likely to be EC-genes. By investigating the collection methods and other properties of the EC-genes, we demonstrated that the enrichment of miRNA targets was not attributed to either the potential collection bias of EC-genes, the presence of paralogs, longer 3'UTRs or more conserved 3'UTRs. Finally, we identified 1,842 significant concurrent interactions between 407 miRNAs and 497 ECs. This association network of miRNAs-ECs was highly modular and could be separated into 14 interconnected modules. In each module, miRNAs and ECs were closely connected, providing a good method to design accurate miRNA markers for ECs in toxicology research.</p> <p>Conclusions</p> <p>Our analyses indicated that miRNAs and their targets played important roles in cellular responses to ECs. Association analyses of miRNAs and ECs will help to broaden the understanding of the pathogenesis of such chemical components.</p