Hidden costs and benefits in two-tier welfare states

We apply a monopoly trade union model and analyze employment, wage and budgetary effects of (i) an inflow of migrant workers and (ii) an increase in the labor market participation rate of migrants. Per assumption, natives and migrants solely differ with respect to the level of benefit claims in a two- tier welfare system. Furthermore, the labor effects are studied under two types of union behavior. Analyzing the ceteris paribus labor market effects, we identify hidden costs and benefits of intensified integration that emerge from the design of the welfare program. We support previous findings in case of an inflow of migrant workers. More interesting, though, it is shown that a larger share of migrants in the workforce increases (decreases) the employment level, if the union represents (does not represent) migrant workers

Heterologous Expression, Purification, and Biochemical Characterization of α-Humulene Synthase from Zingiber zerumbet Smith

The α-humulene synthase from Zingiber zerumbet Smith was expressed as a polyhistidine-tagged protein in an E. coli BL21(DE3) strain. Induction time and inductor (isopropyl-β-D-thiogalactopyranoside) concentration were optimized. The enzyme was successfully purified directly from cell lysate by NTA affinity column chromatography and careful selection of coordinated metal ion and imidazole elution conditions. Bioactivity assays were conducted with the natural substrate farnesyl diphosphate (FDP) in a two-phase system with in situ extraction of products. The conversion of FDP to α-humulene (~94.5 %) and β-caryophyllene (~5.5 %) could be monitored by gas chromatography-flame ionization detection (GC-FID). Optimal pH and temperature as well as kinetic parameters KM and kcat were determined using a discontinuous kinetic assay. The final publication is available at Springer via http://dx.doi.org/10.1007/s12010-015-1888-4.EFRE/ZW-8-8013094

Improving ICD-based semantic similarity by accounting for varying degrees of comorbidity

Finding similar patients is a common objective in precision medicine, facilitating treatment outcome assessment and clinical decision support. Choosing widely-available patient features and appropriate mathematical methods for similarity calculations is crucial. International Statistical Classification of Diseases and Related Health Problems (ICD) codes are used worldwide to encode diseases and are available for nearly all patients. Aggregated as sets consisting of primary and secondary diagnoses they can display a degree of comorbidity and reveal comorbidity patterns. It is possible to compute the similarity of patients based on their ICD codes by using semantic similarity algorithms. These algorithms have been traditionally evaluated using a single-term expert rated data set. However, real-word patient data often display varying degrees of documented comorbidities that might impair algorithm performance. To account for this, we present a scale term that considers documented comorbidity-variance. In this work, we compared the performance of 80 combinations of established algorithms in terms of semantic similarity based on ICD-code sets. The sets have been extracted from patients with a C25.X (pancreatic cancer) primary diagnosis and provide a variety of different combinations of ICD-codes. Using our scale term we yielded the best results with a combination of level-based information content, Leacock & Chodorow concept similarity and bipartite graph matching for the set similarities reaching a correlation of 0.75 with our expert's ground truth. Our results highlight the importance of accounting for comorbidity variance while demonstrating how well current semantic similarity algorithms perform.Comment: 11 pages, 6 figures, 1 tabl

IEA SHC Task 42/ECES Annex 29 – A Simple Tool for the Economic Evaluation of Thermal Energy Storages

Proceedings of the 4th International Conference on Solar Heating and Cooling for Buildings and Industry (SHC 2015)Within the framework of IEA SHC Task 42 / ECES Annex 29, a simple tool for the economic evaluation of thermal energy storages has been developed and tested on various existing storages. On that account, the storage capacity costs (costs per installed storage capacity) of thermal energy storages have been evaluated via a Top-down and a Bottom-up approach. The Top-down approach follows the assumption that the costs of energy supplied by the storage should not exceed the costs of energy from the market. The maximum acceptable storage capacity costs depend on the interest rate assigned to the capital costs, the intended payback period of the user class (e.g. industry or building), the reference energy costs, and the annual number of storage cycles. The Bottom-up approach focuses on the realised storage capacity costs of existing storages. The economic evaluation via Top-down and Bottom-up approach is a valuable tool to make a rough estimate of the economic viability of an energy storage for a specific application. An important finding is that the annual number of storage cycles has the largest influence on the cost effectiveness. At present and with respect to the investigated storages, seasonal heat storage is only economical via large sensible hot water storages. Contrary, if the annual number of storage cycles is sufficiently high, all thermal energy storage technologies can become competitive.This study is part of IEA SHC Task 42 / ECES Annex 29 „Compact Thermal Energy Storage - Material Development and System Integration“ (http://task42.iea-shc.org). The work of ZAE Bayern is part of the project PC-Cools_V and supported by the German Federal Ministry for Economic Affairs and Energy under the project code 03ESP138A. University of Zaragoza thanks the Spanish Government for the funding of their work under the projects ENE2008-06687-C02-02, ENE2011-28269-C03-01 and ENE2014-57262-R. University of Lleida would like to thank the Catalan Government for the quality accreditation given to their research group (2014 SGR 123). The research leading to these results has received funding from the European Union's Seventh Framework Program (FP7/2007-2013) under grant agreement n° PIRSES-GA-2013-610692 (INNOSTORAGE) and European Union’s Horizon 2020 research and innovationprogramme under grant agreement No 657466 (INPATH-TES). Laia Miró would like to thank the Spanish Government for her research fellowship (BES-2012-051861). The University of the Basque Country acknowledges the financial support of the Spanish’s Ministry of Economy and Competitiveness through the MicroTES (ENE2012- 38633) research project. The responsibility for the content of this publication is with the author

Eleven strategies for making reproducible research and open science training the norm at research institutions

Across disciplines, researchers increasingly recognize that open science and reproducible research practices may accelerate scientific progress by allowing others to reuse research outputs and by promoting rigorous research that is more likely to yield trustworthy results. While initiatives, training programs, and funder policies encourage researchers to adopt reproducible research and open science practices, these practices are uncommon inmanyfields. Researchers need training to integrate these practicesinto their daily work. We organized a virtual brainstorming event, in collaboration with the German Reproducibility Network, to discuss strategies for making reproducible research and open science training the norm at research institutions. Here, weoutline eleven strategies, concentrated in three areas:(1)offering training, (2)adapting research assessment criteria and program requirements, and (3) building communities. We provide a brief overview of each strategy, offer tips for implementation,and provide links to resources. Our goal is toencourage members of the research community to think creatively about the many ways they can contribute and collaborate to build communities,and make reproducible research and open sciencetraining the norm. Researchers may act in their roles as scientists, supervisors, mentors, instructors, and members of curriculum, hiring or evaluation committees. Institutionalleadership and research administration andsupport staff can accelerate progress by implementing change across their institution

Mobility in a Globalised World 2016

The term mobility has different meanings in the following science disciplines. In economics, mobility is the ability of an individual or a group to improve their economic status in relation to income and wealth within their lifetime or between generations. In information systems and computer science, mobility is used for the concept of mobile computing, in which a computer is transported by a person during normal use. Logistics creates by the design of logistics networks the infrastructure for the mobility of people and goods. Electric mobility is one of today’s solutions from engineering perspective to reduce the need of energy resources and environmental impact. Moreover, for urban planning, mobility is the crunch question about how to optimise the different needs for mobility and how to link different transportation systems. In this publication we collected the ideas of practitioners, researchers, and government officials regarding the different modes of mobility in a globalised world, focusing on both domestic and international issues

Eleven strategies for making reproducible research and open science training the norm at research institutions

Across disciplines, researchers increasingly recognize that open science and reproducible research practices may accelerate scientific progress by allowing others to reuse research outputs and by promoting rigorous research that is more likely to yield trustworthy results. While initiatives, training programs, and funder policies encourage researchers to adopt reproducible research and open science practices, these practices are uncommon inmanyfields. Researchers need training to integrate these practicesinto their daily work. We organized a virtual brainstorming event, in collaboration with the German Reproducibility Network, to discuss strategies for making reproducible research and open science training the norm at research institutions. Here, weoutline eleven strategies, concentrated in three areas:(1)offering training, (2)adapting research assessment criteria and program requirements, and (3) building communities. We provide a brief overview of each strategy, offer tips for implementation,and provide links to resources. Our goal is toencourage members of the research community to think creatively about the many ways they can contribute and collaborate to build communities,and make reproducible research and open sciencetraining the norm. Researchers may act in their roles as scientists, supervisors, mentors, instructors, and members of curriculum, hiring or evaluation committees. Institutionalleadership and research administration andsupport staff can accelerate progress by implementing change across their institution

FAIRagro: Ein Konsortium in der Nationalen Forschungsdateninfrastruktur (NFDI) für Forschungsdaten in der Agrosystemforschung : Herausforderungen und Lösungsansätze für den Aufbau einer FAIRen Forschungsdateninfrastruktur

FAIRagro ist ein Konsortium in der Nationalen Forschungsdateninfrastruktur (NFDI) in Deutschland um Forschungsdaten der Agrosystemforschung FAIR – d. h. auffindbar (F), zugänglich (A), interoperabel (I) und für andere Forschende domänenübergreifend nachnutzbar (R) zu machen. In der deutschen Forschungslandschaft rund um nachhaltige Agrosysteme werden heterogene Forschungsdaten erhoben und nur zum Teil in existierenden Forschungsdatenrepositorien veröffentlicht. Das Spektrum der Datenformate erstreckt sich beispielsweise von Laborergebnissen, über Satellitenbilder bis hin zu qualitativen Interviews mit Landwirt:innen. Um diese Daten zukünftig für Forschende verschiedener Disziplinen besser auffindbar und nachnutzbar zu machen, wird FAIRagro eine Forschungsdateninfrastruktur (FDI) für die Agrosystemforschung einrichten, in der disziplinäre Dateninfrastrukturen miteinander verknüpft werden. Spezifische Herausforderungen im Forschungsdatenmanagement (FDM) fachlicher Disziplinen wie Pflanzenzüchtung, integrierter Pflanzenschutz oder Agrarrobotik werden als Use Cases in FAIRagro adressiert und für diese Lösungen entwickelt. Darüber hinaus wird FAIRagro ein Netzwerk aus direkten Ansprechpersonen für Fragen zum Forschungsdatenmanagement in der Agrosystem-Community bereitstellen. In Übereinstimmung mit den Zielsetzungen der NFDI und der European Open Science Cloud ist FAIRagro aktiv an der konzeptionellen Implementierung eines interoperablen Datenraums beteiligt