Geochemische Modellierung - Radiotoxische und chemisch-toxische Stoffe in natürlichen aquatischen Systemen. Workshop im Forschungszentrum Karlsruhe am 23. und 24. April 1997

Der Workshop "Geochemische Modellierung" diente dem Erfahrungsaustausch von Wissenschaftlern aus Universitäten, Forschungszentren und Behörden bei der Entwicklung geochemischer Instrumentarien zur sicherheitstechnischen Bewertung von kontaminierten Böden, von Untertagedeponien und von Endlagern. Hier soll insbesondere modelliert werden, welcher Anteil der eingebrachten Schadstoffe durch Grundwässer oder Laugen mobilisiert werden kann. Durch Kopplung mit Transport- und Sorptionsmodellen kann das Migrationsverhalten der Schadstoffe berechnet werden. Da viele Prozesse nur unzureichend verstanden sind, ist für jeden Einzelfall zu prüfen, ob diese Modelle bereits zu belastbaren Aussagen über die Gefährdung der Umwelt und des Menschen durch mobilisierbare Schadstoffe herangezogen werden können. Trotz der Entwicklung erfolgversprechender Modellbausteine stehen belastbare geochemische Modelle für den Kolloideinfluß und von Oberflächenkomplexierung auf das Migrationsverhalten etc. heute noch nicht zur Verfügung

Seasonal Variations in Air Pollution Particle-Induced Inflammatory Mediator Release and Oxidative Stress

Health effects associated with particulate matter (PM) show seasonal variations. We hypothesized that these heterogeneous effects may be attributed partly to the differences in the elemental composition of PM. Normal human bronchial epithelial (NHBE) cells and alveolar macrophages (AMs) were exposed to equal mass of coarse [PM with aerodynamic diameter of 2.5–10 μm (PM(2.5–10))], fine (PM(2.5)), and ultrafine (PM (< 0.1)) ambient PM from Chapel Hill, North Carolina, during October 2001 (fall) and January (winter), April (spring), and July (summer) 2002. Production of interleukin (IL)-8, IL-6, and reactive oxygen species (ROS) was measured. Coarse PM was more potent in inducing cytokines, but not ROSs, than was fine or ultrafine PM. In AMs, the October coarse PM was the most potent stimulator for IL-6 release, whereas the July PM consistently stimulated the highest ROS production measured by dichlorofluorescein acetate and dihydrorhodamine 123 (DHR). In NHBE cells, the January and the October PM were consistently the strongest stimulators for IL-8 and ROS, respectively. The July PM increased only ROS measured by DHR. PM had minimal effects on chemiluminescence. Principal-component analysis on elemental constituents of PM of all size fractions identified two factors, Cr/Al/Si/Ti/Fe/Cu and Zn/As/V/Ni/Pb/Se, with only the first factor correlating with IL-6/IL-8 release. Among the elements in the first factor, Fe and Si correlated with IL-6 release, whereas Cr correlated with IL-8 release. These positive correlations were confirmed in additional experiments with PM from all 12 months. These results indicate that elemental constituents of PM may in part account for the seasonal variations in PM-induced adverse health effects related to lung inflammation

Quantitative description and local structures of trivalent metal ions Eu(III) and Cm(III) complexed with polyacrylic acid

The trivalent metal ion (M(III) = Cm, Eu)/polyacrylic acid (PAA) system was studied in the pH range between 3 and 5.5 for a molar PAA-to-metal ratio above 1. The interaction was studied for a wide range of PAA (0.05 mg L−1–50 g L−1) and metal ion concentrations (2×10−9–10−3 M). This work aimed at 3 goals (i) to determine the stoichiometry of M(III)–PAA complexes, (ii) to determine the number of complexed species and the local environment of the metal ion, and (iii) to quantify the reaction processes. Asymmetric flow-field-flow fractionation (AsFlFFF) coupled to ICP-MS evidenced that size distributions of Eu–PAA complexes and PAA were identical, suggesting that Eu bound to only one PAA chain. Time-resolved laser fluorescence spectroscopy (TRLFS) measurements performed with Eu and Cm showed a continuous shift of the spectra with increasing pH. The environment of complexed metal ions obviously changes with pH. Most probably, spectral variations arose from conformational changes within the M(III)–PAA complex due to pH variation. Complexation data describing the distribution of complexed and free metal ion were measured with Cm by TRLFS. They could be quantitatively described in the whole pH-range studied by considering the existence of only a single complexed species. This indicates that the slight changes in M(III) speciation with pH observed at the molecular level do not significantly affect the intrinsic binding constant. The interaction constant obtained from the modelling must be considered as a mean interaction constant

Enhancing modeling and change support for process families through change patterns

The increasing adoption of process-aware information systems (PAISs), together with the variability of business processes (BPs), has resulted in large collections of related process model variants (i.e., process families). To effectively deal with process families, several proposals (e.g., C-EPC, Provop) exist that extend BP modeling languages with variability-specific constructs. While fostering reuse and reducing modeling efforts, respective constructs imply additional complexity and demand proper support for process designers when creating and modifying process families. Recently, generic and language independent adaptation patterns were successfully introduced for creating and evolving single BP models. However, they are not sufficient to cope with the specific needs for modeling and evolving process families. This paper suggests a complementary set of generic and language-independent change patterns specifically tailored to the needs of process families. When used in combination with existing adaptation patterns, change patterns for process families will enable the modeling and evolution of process families at a high-level of abstraction. Further, they will serve as reference for implementing tools or comparing proposals managing process families. © 2013 Springer-Verlag.This work has been developed with the support of MICINN under the Project EVERYWARE TIN2010-18011.Ayora Esteras, C.; Torres Bosch, MV.; Weber, B.; Reichert, M.; Pelechano Ferragud, V. (2013). Enhancing modeling and change support for process families through change patterns. En Enterprise, Business-Process and Information Systems Modeling, BPMDS 2013. Springer Verlag. 246-260. https://doi.org/10.1007/978-3-642-38484-4_18S246260van der Aalst, W.M.P., ter Hofstede, A.H.M., Barros, B.: Workflow Patterns. Distributed and Parallel Databases 14(1), 5–51 (2003)Aghakasiri, Z., Mirian-Hosseinabadi, S.H.: Workflow change patterns: Opportunities for extension and reuse. In: Proc. SERA 2009, pp. 265–275 (2009)Ayora, C., Torres, V., Reichert, M., Weber, B., Pelechano, V.: Towards run-time flexibility for process families: Open issues and research challenges. In: La Rosa, M., Soffer, P. (eds.) BPM 2012 Workshops. LNBIP, vol. 132, pp. 477–488. Springer, Heidelberg (2013)Ayora, C., Torres, V., Weber, B., Reichert, M., Pelechano, V.: Change patterns for process families. Technical Report, PROS-TR-2012-06, http://www.pros.upv.es/technicalreports/PROS-TR-2012-06.pdfDadam, P., Reichert, M.: The ADEPT project: a decade of research and development for robust and flexible process support. Com. Sci. - R&D 23, 81–97 (2009)Dijkman, R., La Rosa, M., Reijers, H.A.: Managing large collections of business process models - Current techniques and challenges. Comp. in Ind. 63(2), 91–97 (2012)Döhring, M., Zimmermann, B., Karg, L.: Flexible workflows at design- and runtime using BPMN2 adaptation patterns. In: Abramowicz, W. (ed.) BIS 2011. LNBIP, vol. 87, pp. 25–36. Springer, Heidelberg (2011)Gottschalk, F.: Configurable process models. Ph.D. thesis, Eindhoven University of Technology, The Netherlands (2009)Grambow, G., Oberhauser, R., Reichert, M.: Contextual injection of quality measures into software engineering processes. Intl. J. Adv. in Software 4, 76–99 (2011)Gschwind, T., Koehler, J., Wong, J.: Applying patterns during business process modeling. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 4–19. Springer, Heidelberg (2008)Günther, C.W., Rinderle, S., Reichert, M., van der Aalst, W.M.P.: Change mining in adaptive process management systems. In: Meersman, R., Tari, Z. (eds.) OTM 2006. LNCS, vol. 4275, pp. 309–326. Springer, Heidelberg (2006)Hallerbach, A., Bauer, T., Reichert, M.: Context-based configuration of process variants. In: Proc. TCoB 2008, pp. 31–40 (2008)Hallerbach, A., Bauer, T., Reichert, M.: Capturing variability in business process models: the Provop approach. J. of Software Maintenance 22(6-7), 519–546 (2010)Kitchenham, B., Charters, S.: Guidelines for performing Systematic Literature Reviews in Software Engineering, Technical Report EBSE/EPIC–2007–01 (2007)Kulkarni, V., Barat, S., Roychoudhury, S.: Towards business application product lines. In: France, R.B., Kazmeier, J., Breu, R., Atkinson, C. (eds.) MODELS 2012. LNCS, vol. 7590, pp. 285–301. Springer, Heidelberg (2012)Küster, J.M., Gerth, C., Förster, A., Engels, G.: Detecting and resolving process model differences in the absence of a change log. In: Dumas, M., Reichert, M., Shan, M.-C. (eds.) BPM 2008. LNCS, vol. 5240, pp. 244–260. Springer, Heidelberg (2008)Küster, J.M., Gerth, C., Engels, G.: Dynamic computation of change operations in version management of business process models. In: Kühne, T., Selic, B., Gervais, M.-P., Terrier, F. (eds.) ECMFA 2010. LNCS, vol. 6138, pp. 201–216. Springer, Heidelberg (2010)Lanz, A., Weber, B., Reichert, M.: Time patterns for process-aware information systems. Requirements Engineering, 1–29 (2012)La Rosa, M., van der Aalst, W.M.P., Dumas, M., ter Hofstede, A.H.M.: Questionnaire-based variability modeling for system configuration. Software and System Modeling 8(2), 251–274 (2009)Lerner, B.S., Christov, S., Osterweil, L.J., Bendraou, R., Kannengiesser, U., Wise, A.: Exception Handling Patterns for Process Modeling. IEEE Transactions on Software Engineering 36(2), 162–183 (2010)Li, C., Reichert, M., Wombacher, A.: Mining business process variants: Challenges, scenarios, algorithms. Data Knowledge & Engineering 70(5), 409–434 (2011)Marrella, A., Mecella, M., Russo, A.: Featuring automatic adaptivity through workflow enactment and planning. In: Proc. CollaborateCom 2011, pp. 372–381 (2011)Müller, D., Herbst, J., Hammori, M., Reichert, M.: IT support for release management processes in the automotive industry. In: Dustdar, S., Fiadeiro, J.L., Sheth, A.P. (eds.) BPM 2006. LNCS, vol. 4102, pp. 368–377. Springer, Heidelberg (2006)Reichert, M., Weber, B.: Enabling flexibility in process-aware information systems: challenges, methods, technologies. Springer (2012)Reinhartz-Berger, I., Soffer, P., Sturm, A.: Organizational reference models: supporting an adequate design of local business processes. IBPIM 4(2), 134–149 (2009)Rosemann, M., van der Aalst, W.M.P.: A configurable reference modeling language. Information Systems 32(1), 1–23 (2007)Russell, N., ter Hofstede, A.H.M., Edmond, D., van der Aalst, W.M.P.: Workflow data patterns. Technical Report FIT-TR-2004-01, Queensland Univ. of Technology (2004)Russell, N., ter Hofstede, A.H.M., Edmond, D., van der Aalst, W.M.P.: Workflow resource patterns. Technical Report WP 127, Eindhoven Univ. of Technology (2004)Russell, N., van der Aalst, W.M.P., ter Hofstede, A.H.M.: Workflow Exception Patterns. In: Martinez, F.H., Pohl, K. (eds.) CAiSE 2006. LNCS, vol. 4001, pp. 288–302. Springer, Heidelberg (2006)Smirnov, S., Weidlich, M., Mendling, J., Weske, M.: Object-sensitive action patterns in process model repositories. In: Muehlen, M.z., Su, J. (eds.) BPM 2010 Workshops. LNBIP, vol. 66, pp. 251–263. Springer, Heidelberg (2011)Weber, B., Reichert, M., Rinderle-Ma, S.: Change patterns and change support features - Enhancing flexibility in process-aware information systems. Data Knowledge & Engineering 66, 438–466 (2008)Weber, B., Sadiq, S., Reichert, M.: Beyond rigidity - dynamic process lifecycle support. Computer Science 23, 47–65 (2009)Weber, B., Reichert, M., Reijers, H.A., Mendling, J.: Refactoring large process model repositories. Computers in Industry 62(5), 467–486 (2011