The innovation impact of EU emission trading: findings of company case studies in the German power sector

This paper provides a comprehensive analysis of how the European Emission Trading System (EU ETS) as the core climate policy instrument of the European Union has impacted innovation. Towards this end, we investigate the impact of the EU ETS on research, development, and demonstration (RD&D), adoption, and organizational change. In doing so, we pay particular attention to the rela-tive influences of context factors (policy mix, market factors, public acceptance) as well as firm characteristics (value chain position, technology portfolio, size, vision). Empirically, our analysis is based on multiple case studies with 19 power generators, technology providers, and project developers in the German power sector which we conducted from June 2008 until June 2009. We find that the innovation impact of the EU ETS has remained limited so far because of the scheme’s initial lack in stringency and predictability and the relatively greater importance of context factors. Additionally, the impact varies tremendously across technologies, firms, and innovation dimensions, and is most pronounced for RD&D on carbon capture technologies and corporate procedural change. Our analysis suggests that the EU ETS by itself may not provide sufficient incentives for fundamental changes in corporate climate innovation activities at a level adequate for reaching political long-term targets. Based on the study’s findings, we derive a set of policy and research recommendations. --EU ETS,emission trading,innovation,technological change,adoption,diffusion,organizational change,power sector

Investigation of the Mechanical and Electrical Properties of Elastic Textile/Polymer Composites for Stretchable Electronics at Quasi-Static or Cyclic Mechanical Loads

In the last decade, interest in stretchable electronic systems that can be bent or shaped three-dimensionally has increased. The application of these systems is that they differentiate between two states and derive there from the requirements for the materials used: once formed, but static or permanently flexible. For this purpose, new materials that exceed the limited mechanical properties of thin metal layers as the typical printed circuit board conductor materials have recently gained the interest of research. In this work, novel electrically conductive textiles were used as conductor materials for stretchable circuit boards. Three different fabrics (woven, knitted and nonwoven) made of silver-plated polyamide fibers were investigated for their mechanical and electrical behavior under quasi-static and cyclic mechanical loads with simultaneous monitoring of the electrical resistance. Thereto, the electrically conductive textiles were embedded into a thermoplastic polyurethane dielectric matrix and structured by laser cutting into stretchable conductors. Based on the characterization of the mechanical and electrical material behavior, a life expectancy was derived. The results are compared with previously investigated stretchable circuit boards based on thermoplastic elastomer and meander-shaped conductor tracks made of copper foils. The microstructural changes in the material caused by the applied mechanical loads were analyzed and are discussed in detail to provide a deep understanding of failure mechanisms.EC/H2020/825647/EU/Re-Thinking of Fashion in Research and Artist collaborating development for Urban Manufacturing/REFREA

Not All Data Are Created Equal: Lessons From Sampling Theory For Adaptive Machine Learning

In survey methodology, inverse probability weighted (Horvitz-Thompson) estimation has become an indispensable part of statistical inference. This is triggered by the need to deal with complex samples, that is, non-identically distributed data. The general idea is that weighting observations inversely to their probability of being included in the sample produces unbiased estimators with reduced variance. In this work, we argue that complex samples are subtly ubiquitous in two promising subfields of data science: Self-Training in Semi-Supervised Learning (SSL) and Bayesian Optimization (BO). Both methods rely on refitting learners to artificially enhanced training data. These enhancements are based on pre-defined criteria to select data points rendering some data more likely to be added than others. We experimentally analyze the distance from the so-produced complex samples to i.i.d. samples by Kullback-Leibler divergence and maximum mean discrepancy. What is more, we propose to handle such samples by inverse probability weighting. This requires estimation of inclusion probabilities. Unlike for some observational survey data, however, this is not a major issue since we excitingly have tons of explicit information on the inclusion mechanism. After all, we generate the data ourselves by means of the selection criteria. To make things more tangible, consider the case of BO first. It optimizes an unknown function by iteratively approximating it through a surrogate model, whose mean and standard error estimates are scalarized to a selection criterion. The arguments of this criterion's optima are evaluated and added to the training data. We propose to weight them by means of the surrogate model's standard errors at time of selection. For the case of deploying random forests as surrogate models, we refit them by weighted drawing in the bootstrap sampling step. Refitting may be done iteratively aiming at speeding up the optimization or after convergence aiming at providing applicants with a (global) interpretable surrogate model. Similarly, self-training in SSL selects instances from a set of unlabeled data, predicts its labels and adds these pseudo-labeled data to the training data. Instances are selected according to a confidence measure, e.g. the predictive variance. Regions in the feature space where the model is very confident are thus over-represented in the selected sample. We again explicitly exploit the selection criteria to define weights which we use for resampling-based refitting of the model. Somewhat counter-intuitively, the more confident the model is in the self-assigned labels, the lower their weights should be to counteract the selection bias. Preliminary results suggest this can increase generalization performance

Phonon Sidebands in Transition Metal Dichalcogenides

Excitons dominate the optical properties of monolayer transition metal dichalcogenides (TMDs). Besides optically accessible bright exciton states, TMDs exhibit also a multitude of optically forbidden dark excitons. Here, we show that efficient exciton-phonon scattering couples bright and dark states and gives rise to an asymmetric excitonic line shape. The observed asymmetry can be traced back to phonon-induced sidebands that are accompanied by a polaron redshift. We present a joint theory-experiment study investigating the microscopic origin of these sidebands in different TMD materials taking into account intra- and intervalley scattering channels opened by optical and acoustic phonons. The gained insights contribute to a better understanding of the optical fingerprint of these technologically promising nanomaterials

No Initial Elevation on Personality Self-Reports in an Online Convenience Sample

Research shows that people’s self-reports may be biased by an initial elevation phenomenon in which ratings are higher the first time that people take a survey as compared to the second and subsequent times. Apart from the fact that this phenomenon exists, and that it might bias ratings for negative subjective experiences more strongly than positive ones, little else is known. In the present study, we examined whether the initial elevation phenomenon occurs for commonly used trait measures, such as ratings on personality inventories and life satisfaction. We hypothesized that the initial elevation phenomenon may be associated with the (un)desirability of the content of the self-report items such that scores for undesirable facets would show initial elevation and scores for desirable facets would show the reverse. We tested this in an online convenience sample (N = 3,329) using 5 facets of a personality inventory and a single item measure of life satisfaction. Our hypotheses were not supported. Our findings suggest that at least for online convenience samples, ratings on personality inventories and life satisfaction are not strongly impacted by initial elevation

Inside/Outside : Post-Synthetic Modification of the Zr-Benzophenonedicarboxylate Metal–Organic Framework

The Zr-based metal–organic framework, Zr-bzpdc-MOF, contains the photoreactive linker molecule benzophenone-4,4'-dicarboxylate (bzpdc) which imparts the possibility for photochemical post-synthetic modification. Upon irradiation with UV light, the keto group of the benzophenone moiety will react with nearly every C-H bond-containing molecule. Within this paper, we further explore the photochemical reactivity of the Zr-bzpdc-MOF, especially with regard to which restrictions govern internal versus external reactions. We show that apart from reactions with C-H bond-containing molecules, the MOF reacts also with water. By studying the reactivity versus linear alcohols we find a clear delineation in that shorter alcohol molecules (up to butanol as a borderline case) react with photoexcited keto groups throughout the whole crystals whereas longer ones react only with surface-standing keto groups. In addition, we show that with the alkanes n-butane to n-octane, the reaction is restricted to the outer surface. We hypothesize that the reactivity of the Zr-bzpdc-MOF versus different reagents depends on the accessibility of the pore system which in turn depends mainly on the size of the reagents and on their polarity. The possibility to direct the post-synthetic modification of the Zr-bzpdc-MOF (selective modification of the whole pore system versus surface modification) gives additional degrees of freedom in the design of this metal–organic framework for shaping and for applications. © 2020 The Authors. Published by Wiley-VCH Verlag GmbH & Co. KGaA

A hexapod walker using a heterarchical architecture for action selection

Schilling M, Paskarbeit J, Hoinville T, et al. A hexapod walker using a heterarchical architecture for action selection. Frontiers in Computational Neuroscience. 2013;7:126.Moving in a cluttered environment with a six-legged walking machine that has additional body actuators, therefore controlling 22 DoFs, is not a trivial task. Already simple forward walking on a flat plane requires the system to select between different internal states. The orchestration of these states depends on walking velocity and on external disturbances. Such disturbances occur continuously, for example due to irregular up-and-down movements of the body or slipping of the legs, even on flat surfaces, in particular when negotiating tight curves. The number of possible states is further increased when the system is allowed to walk backward or when front legs are used as grippers and cannot contribute to walking. Further states are necessary for expansion that allow for navigation. Here we demonstrate a solution for the selection and sequencing of different (attractor) states required to control different behaviors as are forward walking at different speeds, backward walking, as well as negotiation of tight curves. This selection is made by a recurrent neural network (RNN) of motivation units, controlling a bank of decentralized memory elements in combination with the feedback through the environment. The underlying heterarchical architecture of the network allows to select various combinations of these elements. This modular approach representing an example of neural reuse of a limited number of procedures allows for adaptation to different internal and external conditions. A way is sketched as to how this approach may be expanded to form a cognitive system being able to plan ahead. This architecture is characterized by different types of modules being arranged in layers and columns, but the complete network can also be considered as a holistic system showing emergent properties which cannot be attributed to a specific module

The Potential of Epigallocatechin-3-gallate (EGCG) as Complementary Medicine for the Treatment of Inflammatory Bowel Disease

Complementary and alternative medicine has the potential to enrich conventional therapy to improve the treatment of various diseases. Patients that suffer from inflammatory bowel disease, which requires a constant need for medication, have to deal with the adverse effects of repeated application. Natural products such as Epigallocatechin-3-gallate (EGCG) possess the potential to improve symptoms of inflammatory diseases. We investigated the efficacy of EGCG on an inflamed co-culture model simulating IBD and compared it to the efficacies of four commonly applied active pharmaceutical ingredients. EGCG (200 µg/mL) strongly stabilized the TEER value of the inflamed epithelial barrier to 165.7 ± 4.6% after 4 h. Moreover, the full barrier integrity was maintained even after 48 h. This corresponds to the immunosuppressant 6-Mercaptopurin and the biological drug Infliximab. The EGCG treatment significantly decreased the release of the pro-inflammatory cytokines IL-6 (to 0%) and IL-8 (to 14.2%), similar to the effect of the corticosteroid Prednisolone. Therefore, EGCG has a high potential to be deployed as complementary medicine in IBD. In future studies, the improvement of EGCG stability is a key factor in increasing the bioavailability in vivo and fully harnessing the health-improving effects of EGCG