Human health impacts for renewable energy scenarios from the EnerGEO Platform of Integrated Assessment (PIA)

This article reports impact results from running the EnerGEO Platform of Integrated Assessment (PIA) related to human health for different scenarios in Europe. The scenarios were prepared within the EnerGEO project. The idea of this European project is to determine how low carbon scenarios, and in particular scenarios with a high share of renewable energy, affect concentrations of air pollutants and as a consequence affect human health. PM2.5 concentrations were estimated with the IIASA Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS) model on a time horizon up to the year 2050 for different scenarios. We analyse here the estimation of the Loss of Life Expectancy due to PM2.5 concentrations for the Baseline scenario taken as a reference and the Maximum renewable power scenario

The EnerGEO Platform of Integrated Assessment (PIA): Environmental assessment of scenarios as a web service

With the International Energy Agency estimating that global energy demand will increase between 40 and 50 percent by 2030 (compared to 2003), scientists and policymakers are concerned about the sustainability of the current energy system and what environmental pressures might result from the development of future energy systems. EnerGEO is an ongoing FP7 Project (2009-2013) which assesses the current and future impact of energy use on the environment by linking environmental observation systems with the processes involved in exploiting energy resources. The idea of this European project is to determine how low carbon scenarios, and in particular scenarios with a high share of renewable electricity, affect emissions of air pollutants and greenhouse gases (GHG) and contribute to mitigation of negative energy system impacts on human health and ecosystems. A Platform of Integrated Assessment (PIA) has been elaborated to provide impact results for a selection of scenarios via a set of models (large-scale energy models, Life Cycle Assessment models, ...). This PIA is currently available through a web service. The concept of the PIA is detailed and to illustrate its interest, a set of results is given with the use of the simulation mode of the European version of GAINS for a selection of scenarios

How Advanced Change Patterns Impact the Process of Process Modeling

Process model quality has been an area of considerable research efforts. In this context, correctness-by-construction as enabled by change patterns provides promising perspectives. While the process of process modeling (PPM) based on change primitives has been thoroughly investigated, only little is known about the PPM based on change patterns. In particular, it is unclear what set of change patterns should be provided and how the available change pattern set impacts the PPM. To obtain a better understanding of the latter as well as the (subjective) perceptions of process modelers, the arising challenges, and the pros and cons of different change pattern sets we conduct a controlled experiment. Our results indicate that process modelers face similar challenges irrespective of the used change pattern set (core pattern set versus extended pattern set, which adds two advanced change patterns to the core patterns set). An extended change pattern set, however, is perceived as more difficult to use, yielding a higher mental effort. Moreover, our results indicate that more advanced patterns were only used to a limited extent and frequently applied incorrectly, thus, lowering the potential benefits of an extended pattern set

Using ERA-Interim reanalysis for creating datasets of energy-relevant climate variables

The construction of a bias-adjusted dataset of climate variables at the near surface using ERA-Interim reanalysis is presented. A number of different, variable-dependent, bias-adjustment approaches have been proposed. Here we modify the parameters of different distributions (depending on the variable), adjusting ERA-Interim based on gridded station or direct station observations. The variables are air temperature, dewpoint temperature, precipitation (daily only), solar radiation, wind speed, and relative humidity. These are available on either 3 or 6 h timescales over the period 1979–2016. The resulting bias-adjusted dataset is available through the Climate Data Store (CDS) of the Copernicus Climate Change Data Store (C3S) and can be accessed at present from ftp://ecem.climate.copernicus.eu. The benefit of performing bias adjustment is demonstrated by comparinginitial and bias-adjusted ERA-Interim data against gridded observational fields

Retrieval of surface solar irradiance from satellite imagery using machine learning: pitfalls and perspectives

Knowledge of the spatial and temporal characteristics of solar surface irradiance (SSI) is critical in many domains. While meteorological ground stations can provide accurate measurements of SSI locally, they are sparsely distributed worldwide. SSI estimations derived from satellite imagery are thus crucial to gain a finer understanding of the solar resource. Inferring SSI from satellite images is, however, not straightforward, and it has been the focus of many researchers in the past 30 to 40 years. For long, the emphasis has been on models grounded in physical laws with, in some cases, simple statistical parametrizations. Recently, new satellite SSI retrieval methods have been emerging, which directly infer the SSI from the satellite images using machine learning. Although only a few such works have been published, their practical efficiency has already been questioned. The objective of this paper is to better understand the potential and the pitfalls of this new family of methods. To do so, simple multi-layer-perceptron (MLP) models are constructed with different training datasets of satellite-based radiance measurements from Meteosat Second Generation (MSG) with collocated SSI ground measurements from Météo-France. The performance of the models is evaluated on a test dataset independent from the training set in both space and time and compared to that of a state-of-the-art physical retrieval model from the Copernicus Atmosphere Monitoring Service (CAMS). We found that the data-driven model's performance is very dependent on the training set. Provided the training set is sufficiently large and similar enough to the test set, even a simple MLP has a root mean square error (RMSE) that is 19 % lower than CAMS and outperforms the physical retrieval model at 96 % of the test stations. On the other hand, in certain configurations, the data-driven model can dramatically underperform even in stations located close to the training set: when geographical separation was enforced between the training and test set, the MLP-based model exhibited an RMSE that was 50 % to 100 % higher than that of CAMS in several locations.</p

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

A peripheral epigenetic signature of immune system genes is linked to neocortical thickness and memory

Increasing age is tightly linked to decreased thickness of the human neocortex. The biological mechanisms that mediate this effect are hitherto unknown. The DNA methylome, as part of the epigenome, contributes significantly to age-related phenotypic changes. Here, we identify an epigenetic signature that is associated with cortical thickness (P=3.86 × 10(-8)) and memory performance in 533 healthy young adults. The epigenetic effect on cortical thickness was replicated in a sample comprising 596 participants with major depressive disorder and healthy controls. The epigenetic signature mediates partially the effect of age on cortical thickness (P&lt;0.001). A multilocus genetic score reflecting genetic variability of this signature is associated with memory performance (P=0.0003) in 3,346 young and elderly healthy adults. The genomic location of the contributing methylation sites points to the involvement of specific immune system genes. The decomposition of blood methylome-wide patterns bears considerable potential for the study of brain-related traits

Change Patterns in Use: A Critical Evaluation

Process model quality has been an area of considerable research efforts. In this context, the correctness-by-construction principle of change patterns provides promising perspectives. However, using change patterns for model creation imposes a more structured way of modeling. While the process of process modeling (PPM) based on change primitives has been investigated, little is known about this process based on change patterns. To obtain a better understanding of the PPM when using change patterns, the arising challenges, and the subjective perceptions of process designers, we conduct an exploratory study. The results indicate that process designers face little problems as long as control-flow is simple, but have considerable problems with the usage of change patterns when complex, nested models have to be created. Finally, we outline how effective tool support for change patterns should be realized.This research is supported by Austrian Science Fund (FWF): P23699-N23.Weber, B.; Pinggera, J.; Torres Bosch, MV.; Reichert, M. (2013). Change Patterns in Use: A Critical Evaluation. En Enterprise, Business-Process and Information Systems Modeling, BPMDS 2013. Springer Verlag. 261-276. https://doi.org/11007/978-3-642-38484-4_19S26127

Coordinated effects of sequence variation on DNA binding, chromatin structure, and transcription.

DNA sequence variation has been associated with quantitative changes in molecular phenotypes such as gene expression, but its impact on chromatin states is poorly characterized. To understand the interplay between chromatin and genetic control of gene regulation, we quantified allelic variability in transcription factor binding, histone modifications, and gene expression within humans. We found abundant allelic specificity in chromatin and extensive local, short-range, and long-range allelic coordination among the studied molecular phenotypes. We observed genetic influence on most of these phenotypes, with histone modifications exhibiting strong context-dependent behavior. Our results implicate transcription factors as primary mediators of sequence-specific regulation of gene expression programs, with histone modifications frequently reflecting the primary regulatory event

A genome-wide survey of human short-term memory

Recent advances in the development of high-throughput genotyping platforms allow for the unbiased identification of genes and genomic sequences related to heritable traits. In this study, we analyzed human short-term memory, which refers to the ability to remember information over a brief period of time and which has been found disturbed in many neuropsychiatric conditions, including schizophrenia and depression. We performed a genome-wide survey at 909 622 polymorphic loci and report six genetic variations significantly associated with human short-term memory performance after genome-wide correction for multiple comparisons. A polymorphism within SCN1A (encoding the α subunit of the type I voltage-gated sodium channel) was replicated in three independent populations of 1699 individuals. Functional magnetic resonance imaging during an n-back working memory task detected SCN1A allele-dependent activation differences in brain regions typically involved in working memory processes. These results suggest an important role for SCN1A in human short-term memory