Measurement of the inclusive and dijet cross-sections of b-jets in pp collisions at sqrt(s) = 7 TeV with the ATLAS detector

The inclusive and dijet production cross-sections have been measured for jets containing b-hadrons (b-jets) in proton-proton collisions at a centre-of-mass energy of sqrt(s) = 7 TeV, using the ATLAS detector at the LHC. The measurements use data corresponding to an integrated luminosity of 34 pb^-1. The b-jets are identified using either a lifetime-based method, where secondary decay vertices of b-hadrons in jets are reconstructed using information from the tracking detectors, or a muon-based method where the presence of a muon is used to identify semileptonic decays of b-hadrons inside jets. The inclusive b-jet cross-section is measured as a function of transverse momentum in the range 20 < pT < 400 GeV and rapidity in the range |y| < 2.1. The bbbar-dijet cross-section is measured as a function of the dijet invariant mass in the range 110 < m_jj < 760 GeV, the azimuthal angle difference between the two jets and the angular variable chi in two dijet mass regions. The results are compared with next-to-leading-order QCD predictions. Good agreement is observed between the measured cross-sections and the predictions obtained using POWHEG + Pythia. MC@NLO + Herwig shows good agreement with the measured bbbar-dijet cross-section. However, it does not reproduce the measured inclusive cross-section well, particularly for central b-jets with large transverse momenta.Comment: 10 pages plus author list (21 pages total), 8 figures, 1 table, final version published in European Physical Journal

Responsive Production in Manufacturing: A Modular Architecture

[EN] This paper proposes an architecture aiming at promoting the convergence of the physical and digital worlds, through CPS and IoT technologies, to accommodate more customized and higher quality products following Industry 4.0 concepts. The architecture combines concepts such as cyber-physical systems, decentralization, modularity and scalability aiming at responsive production. Combining these aspects with virtualization, contextualization, modeling and simulation capabilities it will enable self-adaptation, situational awareness and decentralized decision-making to answer dynamic market demands and support the design and reconfiguration of the manufacturing enterprise.The research leading to these results has received funding from the European Union H2020 project C2 NET (FoF-01-2014) nr 636909.Marques, M.; Agostinho, C.; Zacharewicz, G.; Poler, R.; Jardim-Goncalves, R. (2018). Responsive Production in Manufacturing: A Modular Architecture. Studies in Systems, Decision and Control. 140:231-254. https://doi.org/10.1007/978-3-319-78437-3_10S231254140European Commission: For a European Industrial Renaissance, Communication from the Commission to the European Parliament, the Council, the European Economic and Social Committee and the Committee of the Regions (2014)Hartmann, B., King, W.P., Narayanan, S.: Digital manufacturing: the revolution will be virtualized. McKinsey & Company (2015)European Forum for Manufacture: Driving Innovation and Growth in European Manufacturing (2015)European Factories of the Future Research Association (EFFRA): Factories of the Future: Multi-annual Roadmap for the Contractual PPP under the Horizon 2020 (2013)European Commission: Horizon 2020—Work Programme 2016–2017: 17. Cross-cutting Activities (2016)Schlaepfer, R.C., Koch, M., Merkofer, P.: Industry 4.0 challenges and solutions for the digital transformation and use of exponential technologies. Deloitte AG (2015)7iD: Industry 4.0. https://www.7id.com/technology/industry-4-0/ (2016)European Commission: Horizon 2020—Work Program 2016-2017—Cross-cutting Activities, 25 July 2016EFFRA: Factories of the Future: Multi-annual Roadmap for the Contractual PPP under the Horizon 2020 (2013)FInES Research Roadmap Task Force (2012)Jacinto, J.: Smart manufacturing? Industry 4.0? What’s it all about? Siements Totally Integrated Automation, Automation World & Design World (2014)Monostori, L.: Cyber-physical production systems: roots, expectations and R&D challenges. Procedia CIRP 17, 9–13 (2014)Adolphs, P.: RAMI 4.0—An architectural Model for Industrie 4.0. Platform Industrie 4.0 (2015)Collins, M.: Why America has a shortage of skilled workers. Industry Week (2015)Forbes, J., Naujok, N., Geissbauer, R., Vedso, J., Schrauf, S.: Industry 4.0: building the digital enterprise. PWC (2016)World Economic Forum Industrial Internet Survey (2014)Chen, D., Vernadat, F.B.: Enterprise interoperability: a standardisation view. Enterprise Inter- and Intra-Organizational Integration, Volume 108 of the series IFIP—The International Federation for Information Processing, pp. 273–282 (2003)Yan, L., Li, Z., Yuan, X.: Study on method-of-robust-multidisciplinary-design-collaborative-decision for product design. Inf. Technol. J. 8(4), 441–452 (2009)Ruiz Dominguez, G. A.: Caractérisation de l’activité de conception collaborative à distance: étude des effets de synchronisation cognitive (2005)Jung, J.J.: Reusing ontology mappings for query routing in semantic peer-to-peer environment. Inf. Sci. (2010). https://doi.org/10.1016/j.ins.2010.04.018Ranjan, R., Zhao, L., Wu, X., Liu, A., Quiroz, A., Parashar, M.: Peer-to-Peer Cloud Provisioning: Service Discovery and Load-Balancing. https://doi.org/10.1007/978-1-84996-241-4_12Agostinho, C., Pinto, P., Jardim-goncalves, R.: Dynamic adaptors to support model-driven interoperability and enhance sensing enterprise networks. In: 19th World Congress of the International Federation of Automatic Control (IFAC’14), Cape Town, South Africa (2014)Chen, D., Doumeingts, G., Vernadat, F.: Architectures for enterprise integration and interoperability: past, present and future. Comput. Ind. 59, 647–659 (2008). https://doi.org/10.1016/j.compind.2007.12.016Ducq, Y., Chen, D., Alix, T.: Principles of servitization and definition of an architecture for model driven service system engineering. In: 4th International IFIP Working Conference on Enterprise Interoperability (IWEI 2012), Harbin, China, 2012. https://doi.org/10.1007/978-3-642-33068-17_12Elvesæter, B., Hahn, A., Berre, A., Neple, T.: Towards an interoperability framework for model-driven development of software systems. In: 1st International Conference on Interoperability Enterprise Software and Applications. Springer. http://www.springerlink.com/index/L10NU4306N054T6G.pdf (2005)OMG: MDA Guide Version 1.0.1 (omg/2003-06-01), Object Management Group. http://www.omg.org/cgibin/doc?omg/03-06-01.pdf (2003)Agostinho, C., Ducq, Y., Zacharewicz, G., Sarraipa, J., Lampathaki, F., Poler, R., Jardim-Goncalves, R.: Towards a sustainable interoperability in networked enterprise information systems: trends of knowledge and model-driven technology. Comput. Ind. (2015). https://doi.org/10.1016/j.compind.2015.07.001Santucci, G., Martinez, C., Vlad-câlcic, D.: The sensing enterprise. In: FInES Work. FIA 2012, Aalborg, Denmark. http://www.theinternetofthings.eu/sites/default/files/%5Buser-name%5D/Sensing-enterprise.pdf (2012)Sriram, R.: Smart networked systems and societies: what will the future look like? In: IEEE IT Professional Conference (IT Pro). IEEE Computer Society (2014)Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., et al.: Big data: the next frontier for innovation, competition, and productivity. http://www.mckinsey.com/insights/business_technology/big_data_the_next_frontier_for_innovation (2011)Zacharewicz, G., Diallo, S., Ducq, Y., Agostinho, C., Jardim-Goncalves, R., Bazoun, H., Wang, Z., Doumeingts, G.: Model-based approaches for interoperability of next generation enterprise information systems: state of the art and future challenges. Inf. Syst. e-Bus. Manag. (2016). https://doi.org/10.1007/s10257-016-0317-8Jardim-Goncalves, R., Agostinho, C., Steiger-Garcao, A.: A reference model for sustainable interoperability in networked enterprises: towards the foundation of EI science base. Int. J. Comput. Integr. Manuf. 25(10) (2012). (Special Issue on Collaborative Manufacturing and Supply Chains). https://doi.org/10.1080/0951192x.2011.653831Schatsky, D., Muraskin, C.: Blockchain is coming to disrupt your industry. Deloitte (2015)Shi, J., Wan, J., Yan, H., Suo, H.: A survey of cyber-physical systems. In: International Conference on Wireless Communications and Signal Processing, pp. 1–6 (2011)Rajkumar, R.: Workshop report on foundations for innovation in cyber-physical systems. NIST. http://www.nist.gov/el/upload/CPS-WorkshopReport-1-30-13-Final.pdf/ (2013)Lee, J., Lapira, E., Yang, S. Kao, H.-A.: Predictive manufacturing system trends of next generation production systems. In: 11th IFAC Workshop on Intelligent Manufacturing Systems, vol. 11, issue 1, pp. 150–156 (2013)IDC: The digital universe of opportunities: rich data and increasing value of the internet of things. EMC Digital Universe. emc.com/collateral/analyst-reports/idc-digital-universe-2014.pdf . (2014)Baheti, R., Gill, H.: Cyber-physical systems. Impact Control Technol. 1–6 (2011)Lee, J., Bagheri, B., Kao, H.-A.: A cyber physical systems architecture for Industry 4.0-based manufacturing system. Manuf. Lett. 2015, 3, 18–23 (2014). https://doi.org/10.1016/j.mfglet.2014.12.001Bagheri, B., Lee, J.: Big future for cyber-physical manufacturing systems. Design World. http://www.designworldonline.com/big-future-for-cyber-physical-manufacturing-systems/ (2015)Lucke, D., Constantinescu, C., Westkämper, E.: Smart factory-a step towards the next generation of manufacturing. Manufacturing Systems and Technologies for the New Frontier, pp. 115–118. Springer, London (2008)Weiser, M.: The Computer for the 21st Century. Scientific American, Special Issue on Communications. Comput. Netw. (1991)Westkämper, E., Jendoubi, L., Eissele, M., Ertl, T.: Smart factory—bridging the gap between digital planning and reality. Manuf. Syst. 35(4), 307–314 (2006)Goryachev, A., Kozhevnikov, S., Kolbova, E., Kuznetsov, O., Simonova, E., Skobelev, P., Tsarev, A., Shepilov, Y.: Smart factory: intelligent system for workshop resource allocation, scheduling, optimization and controlling in real time. Adv. Mater. Res. 630, 508–513 (2012)Agostinho, C., Marques-Lucena, C., Sesana, M., Felic, A., Fischer, K., Rubattino, C., Sarraipa, J.: Osmosis process development for innovative product design and validation. 2015 ASME IMECE, Houston, USA (2015)Ko, J., Lee, B., Lee, K., Hong, S.G., Kim, N., Paek, J.: Sensor virtualization module: virtualizing IoT devices on mobile smartphones for effective sensor data management. Int. J. Distrib. Sens. Netw. (2015). https://doi.org/10.1155/2015/730762Guo, T., Papaioannou, T.G., Aberer, K.: Efficient indexing and query processing of model-view sensor data in the cloud. J. Big Data Res. 1, 52–65 (2014)Kumra, S., Sharma, L., Khanna, Y., Chattri, A.: Analysing an industrial automation pyramid and providing service oriented architecture. Int. J. Eng. Trends Technol. 3(5), 586–594 (2012)Endsley, M.: Design and evaluation for situational awareness enhancement. In: Proceedings of the Human Factors Society 32nd Annual Meeting. HFES, Santa Monica, pp. 97–10 (1988)Stanton, N.A., Chambers, P.R., Piggott, J.: Situational awareness and safety. Saf. Sci. 39(3), 189–204 (2001)Endsley, M.: Toward a theory of situation awareness in dynamic systems. Hum. Factors (The Journal of the Human Factors and Ergonomics Society) 37, 32–64 (1995)Bedny, G., Meister, D.: Theory of activity and situation awareness. Int. J. Cogn. Ergon. 3(1), 63–72 (1999)Smith, K., Hancock, P.A.: Situation awareness is adaptive, externally directed consciousness. Hum. Factors (The Journal of the Human Factors and Ergonomics Society) 37(1), 137–148 (1995)Ranganathan, A., Campbell, R.H.: An infrastructure for context-awareness based on first order logic. Pers. Ubiquit. Comput. 7(6), 353–364 (2003)Ning, K., Scholze, S., Marques, M., Campos, A, Neves-Silva, R. O’Sullivan, D.: A service oriented platform for context aware knowledge enhancing. In: 5th IFAC Conference on Management and Control of Production and Logistics (2010)Marques, M., Sucic, B., Vuk, T.: Context-based decision support for sustainable optimization of energy consumption. KES Trans. Sustain. Des. Manuf. 1(1), 899–910 (2014)Schneeweiss, C.: Distributed decision making in supply chain management. Int. J. Product. Econ. 84, 71–83 (2003)Alemany, M.M.E., Alarcón, F., Lario, F.C., Boj, J.J.: An application to support the temporal and spatial distributed decision-making process in supply chain collaborative planning. Comput. Ind. 62(5), 519–540 (2011). https://doi.org/10.1016/j.compind.2011.02.002Hong, I.H., Ammons, J.C., Realff, M.J.: Centralized versus decentralized decision-making for recycled material flows. Environ. Sci. Technol. 42(4), 1172–1177 (2008)Pibernik, R., Sucky, E.: An approach to inter-domain master planning in supply chains. Int. J. Product. Econ. 108, 200–212 (2007). https://doi.org/10.1016/j.ijpe.2006.12.010Lee, H., Whang, S.: Decentralized multi-echelon supply chains: incentives and information. Manag. Sci. 45(5), 633–640 (1999)Jung, H., Chen, F., Jeong, B.: Decentralized supply chain planning framework for third party logistics partnership. Comput. Ind. Eng. 55(2), 348–364 (2008). https://doi.org/10.1016/j.cie.2007.12.017Wang, K.-J., Chen, M.-J.: Cooperative capacity planning and resource allocation by mutual outsourcing using ant algorithm in a decentralized supply chain. Expert Syst. Appl. 36(2), 2831–2842 (2009)Simon, H.A.: The Science of the Artificial, 1st edn. MIT Press, Cambridge, Mass, (1969). (3rd ed. in 1996, MIT Press)Mesarovic, M.D., Masko, D., Takahara, Y.: Theory of Hierarchical Multilevel Systems. Academic Press, New York and London (1970)Camarinha-Matos, L.M., Afsarmanesh, H.J.: Collaborative networks: a new scientific discipline. J. Intell. Manuf. 16(4), 439–452 (2005)Popplewell, K., Stojanovic, N., Abecker, A., Apostolou, D., Mentzas, G., Harding, J.: Supporting adaptive enterprise collaboration through semantic knowledge services. In: Enterprise Interoperability Iii: New Challenges and Industrial Approaches, pp. 381–393 (2008). http://doi.org/10.1007/978-1-84800-221-0_30Agostinho, C., Ducq, Y., Zacharewicz, G., Sarraipa, J., Lampathaki, F., Jardim-Goncalves, R., Poler, R.: Towards a sustainable interoperability in networked enterprise information systems: trends of knowledge and model-driven technology. Accepted for Publication at Computers in Industry. http://doi.org/10.1016/j.compind.2015.07.001Agostinho, C., Jardim-Gonçalves, R.: Sustaining interoperability of networked liquid-sensing enterprises: a complex systems perspective. Annu. Rev. Control 39, 128–143 (2015). https://doi.org/10.1016/j.arcontrol.2015.03.012Weichhart, G., Molina, A., Chen, D., Whitman, L. E., Vernadat, F.: Challenges and current developments for sensing, smart and sustainable enterprise systems. Computers in Industry (2015). http://doi.org/10.1016/j.compind.2015.07.002Weichhart, G.: Supporting Interoperability for Chaotic and Complex Adaptive Enterprise Systems. On the Move to Meaningful Internet Systems: OTM 2013 Workshops. Confederated International Workshops: OTM Academy, OTM Industry Case Studies Program, ACM, EI2N, ISDE, META4eS, ORM, SeDeS, SINCOM, SMS, and SOMOCO 2013. Proceedings: LNCS 8186, 86–92. (2013). http://doi.org/10.1007/978-3-642-41033-8_14Truex, D.P., Baskerville, R., Klein, H.: Growing systems in emergent organizations. Mag. Commun. ACM CACM Homepage Arch. 42(8), 117–123 (1999)Weiberg, S.: Facilitating collaborative decision-making in six steps. International Association of Facilitators Annual Meeting, pp. 14–15 (1999)Delbecq, A.L., VandeVen, A.H.: A group process model for problem identification and program planning. J. Appl. Behav. Sci. 7, 466–492 (1971). https://doi.org/10.1177/002188637100700404Saaty, T.L.: The Analytic Hierarchy Process. McGraw-Hill, New York, USA (1980

Noise Reduction by Diffusional Dissipation in a Minimal Quorum Sensing Motif

Cellular interactions are subject to random fluctuations (noise) in quantities of interacting molecules. Noise presents a major challenge for the robust function of natural and engineered cellular networks. Past studies have analyzed how noise is regulated at the intracellular level. Cell–cell communication, however, may provide a complementary strategy to achieve robust gene expression by enabling the coupling of a cell with its environment and other cells. To gain insight into this issue, we have examined noise regulation by quorum sensing (QS), a mechanism by which many bacteria communicate through production and sensing of small diffusible signals. Using a stochastic model, we analyze a minimal QS motif in Gram-negative bacteria. Our analysis shows that diffusion of the QS signal, together with fast turnover of its transcriptional regulator, attenuates low-frequency components of extrinsic noise. We term this unique mechanism “diffusional dissipation” to emphasize the importance of fast signal turnover (or dissipation) by diffusion. We further show that this noise attenuation is a property of a more generic regulatory motif, of which QS is an implementation. Our results suggest that, in a QS system, an unstable transcriptional regulator may be favored for regulating expression of costly proteins that generate public goods

Measurement of the azimuthal anisotropy for charged particle production in SNN=2.76\sqrt{^{S}NN}=2.76 TeV lead-lead collisions with the ATLAS detector

Contains fulltext : 103506.pdf (preprint version ) (Open Access

Search for the standard model Higgs boson in the diphoton decay channel with 4.9fb -1 of pp collision data at √s=7TeV with atlas

A search for the standard model Higgs boson is performed in the diphoton decay channel. The data used correspond to an integrated luminosity of 4.9  fb-1 collected with the ATLAS detector at the Large Hadron Collider in proton-proton collisions at a center-of-mass energy of √s=7  TeV. In the diphoton mass range 110–150 GeV, the largest excess with respect to the background-only hypothesis is observed at 126.5 GeV, with a local significance of 2.8 standard deviations. Taking the look-elsewhere effect into account in the range 110–150 GeV, this significance becomes 1.5 standard deviations. The standard model Higgs boson is excluded at 95% confidence level in the mass ranges of 113–115 GeV and 134.5–136 GeV

Search for the standard model Higgs boson in the diphoton decay channel with 4.9fb -1 of pp collision data at √s=7TeV with atlas

A search for the standard model Higgs boson is performed in the diphoton decay channel. The data used correspond to an integrated luminosity of 4.9  fb-1 collected with the ATLAS detector at the Large Hadron Collider in proton-proton collisions at a center-of-mass energy of √s=7  TeV. In the diphoton mass range 110–150 GeV, the largest excess with respect to the background-only hypothesis is observed at 126.5 GeV, with a local significance of 2.8 standard deviations. Taking the look-elsewhere effect into account in the range 110–150 GeV, this significance becomes 1.5 standard deviations. The standard model Higgs boson is excluded at 95% confidence level in the mass ranges of 113–115 GeV and 134.5–136 GeV

Performance of the ATLAS Trigger System in 2010

Proton-proton collisions at sqrt{s} = 7 TeV and heavy ion collisions at sqrt{s_NN} = 2.76 TeV were produced by the LHC and recorded using the ATLAS experiment's trigger system in 2010. The LHC is designed with a maximum bunch crossing rate of 40 MHz and the ATLAS trigger system is designed to record approximately 200 of these per second. The trigger system selects events by rapidly identifying signatures of muon, electron, photon, tau lepton, jet, and B meson candidates, as well as using global event signatures, such as missing transverse energy. An overview of the ATLAS trigger system, the evolution of the system during 2010 and the performance of the trigger system components and selections based on the 2010 collision data are shown. A brief outline of plans for the trigger system in 2011 is presente

Measurement of the transverse momentum distribution of [Z over γ*] bosons in proton-proton collisions at √s = 7 TeV with the ATLAS detector

A measurement of the [Z over γ*] transverse momentum (p[Z over T]) distribution in proton–proton collisions at √s = 7 TeV is presented using [Z over γ*] →e[superscript +]e[superscript −] and [Z over γ*] →μ[superscript +]μ[superscript −] decays collected with the ATLAS detector in data sets with integrated luminosities of 35 pb[superscript −1] and 40 pb[superscript −1], respectively. The normalized differential cross sections are measured separately for electron and muon decay channels as well as for their combination up to p[Z over T] of 350 GeV for invariant dilepton masses 66 GeV<m[subscript ℓℓ]<116 GeV. The measurement is compared to predictions of perturbative QCD and various event generators. The prediction of resummed QCD combined with fixed order perturbative QCD is found to be in good agreement with the data.United States. Dept. of EnergyNational Science Foundation (U.S.)Brookhaven National LaboratoryEuropean Organization for Nuclear Researc