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

Reinforcement Learning for Planning Heuristics

Informed heuristics are essential for the success of heuristic search algorithms. But, it is difficult to develop a new heuris- tic which is informed on various tasks. Instead, we propose a framework that trains a neural network as heuristic for the tasks it is supposed to solve. We present two reinforcement learning approaches to learn heuristics for fixed state spaces and fixed goals. Our first approach uses approximate value iteration, our second ap- proach uses searches to generate training data. We show that in some domains our approaches outperform previous work, and we point out potentials for future improvements

Exploring family life circumstances and their relationship to a child’s school achievement – an econometric analysis in large data contexts

This thesis is motivated by the need to find parental characteristics other than demographic indicators which exhibit substantial links to a child’s success in school. Therefore, the associations between various indicators of family background and a child’s school achievement are empirically explored. A latent variable model is assumed to describe the underlying relationships. A data set, large in terms of selectable variables, gives rise to the need to use statistical methods that reduce its dimensionality. Since there many possible methods, simulations comparing their predictive ability are conducted. A regularized version of Factor Regression turns out to be most suitable. Using data from the German Socio-Economic Panel, the method extracts several factors with relevant associations to the outcome. While factors related to demographic indicators play a major role, other factors related to participation in cultural life or the interest and participation in politics or in further education substantially contribute to explaining variance in the outcome. The results suggest that demographic indicators alone might not be sufficient to describe the relevant facets of family background

TO ROW TOGETHER OR PADDLE ONE\u27S OWN CANOE? SIMULATING STRATEGIES TO SPUR DIGITAL PLATFORM GROWTH

This study provides a novel perspective on digital platform dynamics by applying a stochastic cellular automaton (CA) as a promising instrument of inquiry to investigate the impact of social and technical openness on platform growth. Owing to the dynamism of digital platforms caused by technological complexity, network effects, and developer-level factors, there is limited under-standing of how early-stage platform owners can successfully sustain platform growth. Research suggests two growth strategies: Adjusting the openness of technical platform resources and gov-erning the developers’ accessibility of the distribution channel. Based on experiments that lev-erage a stochastic CA, we show that platform growth can be achieved through three disparate growth strategy configurations. Our paper contributes to research by synthesizing the technolo-gy, market, and individual levels of platform growth analyses through a novel methodological account, and by offering theoretical propositions for future research. Our results can guide platform owners to scrutinize their growth strategies

Neural Network Heuristic Functions for Classical Planning: Reinforcement Learning and Comparison to Other Methods

How can we train neural network (NN) heuristic functions for classical planning, using only states as the NN input? Prior work addressed this question by (a) supervised learning and/or (b) per-domain learning generalizing over problem in- stances. The former limits the approach to instances small enough for training data generation, the latter to domains and instance distributions where the necessary knowledge generalizes across instances. Clearly, reinforcement learning (RL) on large instances can potentially avoid both difficul- ties. We explore this here in terms of three methods drawing on previous ideas relating to bootstrapping and approximate value iteration, including a new bootstrapping variant that es- timates search effort instead of goal distance. We empirically compare these methods to (a) and (b), aligning three differ- ent NN heuristic function learning architectures for cross- comparison in an experiment of unprecedented breadth in this context. Key lessons from this experiment are that our meth- ods and supervised learning are highly complementary; that per-instance learning often yields stronger heuristics than per- domain learning; and that LAMA is still dominant but is out- performed by our methods in one benchmark domain

A survey of patient motion in disorders of consciousness and optimization of its retrospective correction.

Functional magnetic resonance imaging (fMRI) can be seriously impaired by patient motion. The purpose of this study was to characterize the typical motion in a clinical population of patients in disorders of consciousness and compare the performance of retrospective correction with rigid-body realignment as implemented in widely used software packages. 63 subjects were scanned with an fMRI visual checkerboard paradigm using a 3T scanner. Time series were corrected for motion, and the resulting transformations were used to calculate a motion score. SPM, FSL, AFNI and AIR were evaluated by comparing the motion obtained by re-running the tool on the corrected data. A publicly available sample fMRI dataset was modified with the motion detected in each patient with each tool. The performance of each tool was measured by comparing the number of supra-threshold voxels after standard fMRI analysis, both in the sample dataset and in simulated fMRI data. We assessed the effect of user-changeable parameters on motion correction in SPM. We found the equivalent motion in the patient population to be 1.4mm on average. There was no significant difference in performance between SPM, FSL and AFNI. AIR was considerably worse, and took more time to run. We found that in SPM the quality factor and interpolation method have no effect on the cluster size, while higher separation and smoothing reduce it. We showed that the main packages SPM, FSL and AFNI are equally suitable for retrospective motion correction of fMRI time series. We show that typically only 80% of activated voxels are recovered by retrospective motion correction

Detection and Typing of Highly Pathogenic Porcine Reproductive and Respiratory Syndrome Virus by Multiplex Real-Time RT-PCR

Porcine reproductive and respiratory syndrome (PRRS) causes economic losses in the pig industry worldwide, and PRRS viruses (PRRSV) are classified into the two distinct genotypes “North American (NA, type 2)” and “European (EU, type 1)”. In 2006, a highly pathogenic NA strain of PRRSV (HP-PRRSV), characterized by high fever as well as high morbidity and mortality, emerged in swine farms in China. Therefore, a real-time reverse transcription polymerase chain reaction (RT-qPCR) assay specific for HP-PRRSV was developed and combined with type 1- and type 2-specific RT-qPCR systems. Furthermore, an internal control, based on a heterologous RNA, was successfully introduced. This final multiplex PRRSV RT-qPCR, detecting and typing PRRSV, had an analytical sensitivity of less than 200 copies per µl for the type 1-assay and 20 copies per µl for the type 2- and HP assays and a high diagnostic sensitivity. A panel of reference strains and field isolates was reliably detected and samples from an animal trial with a Chinese HP-PRRS strain were used for test validation. The new multiplex PRRSV RT-qPCR system allows for the first time the highly sensitive detection and rapid differentiation of PRRSV of both genotypes as well as the direct detection of HP-PRRSV

Охорона праці при геологорозвідувальних роботах

Розглянуто питання, пов’язані з управлінням охороною праці в галузі, аналізом шкідливих та небезпечних виробничих чинників та забезпечення до- пустимих санітарно-гігієнічних умов праці на підприємствах геологічного про- філю. Значну увагу приділено питанням техніки безпеки та пожежної безпеки як при польових, так і при камеральних роботах. Навчальний посібник відповідає вимогам програми дисципліни «Охорона праці в галузі» і призначений для студентів спеціальностей 04010301 Геологія, 04010302 Гідрогеологія, 04010303 Геофізика, 05030103 Буріння свердловин. Може бути корисним широкому колу читачів, які цікавляться проблемами охо- рони праці