Domain Walls in a Tetragonal Chiral p-Wave Superconductor

Domain walls in a tetragonal chiral p-wave superconductors with broken time reversal symmetry are analyzed in the framework of the Ginsburg-Landau theory. The energy and the jump of the magnetic induction on the wall were determined for different types of walls as functions of the parameters of the Ginzburg-Landau theory and orientation of the domain wall with respect to the crystallographic axes. We discuss implications of the analysis for $Sr_{2}RuO_{4}$, where no stray magnetic fields from domain walls were detected experimentally.Comment: 8 pages, 2 figure

Critical dynamics of diluted relaxational models coupled to a conserved density (diluted model C)

We consider the influence of quenched disorder on the relaxational critical dynamics of a system characterized by a non-conserved order parameter coupled to the diffusive dynamics of a conserved scalar density (model C). Disorder leads to model A critical dynamics in the asymptotics, however it is the effective critical behavior which is often observed in experiments and in computer simulations and this is described by the full set of dynamical equations of diluted model C. Indeed different scenarios of effective critical behavior are predicted.Comment: 4 pages, 5 figure

Is Using Ornaments Still a Crime? Package Design Complexity and Brand Perception with Application to Champagne Labels

This article investigates the impact of the package design complexity on brand perception. This variable is particularly interesting because it is a choice which must be done by each brand manager no matter the product category. The packaging has been studied in many ways, and we already know its importance. A lot of existing researches are focused on the importance of its shape, its colours or its letter type. But only few studies have been done about the impact of the stylistic choice between simple and complex design on the brand perception. Furthermore, the consumer behaviour and design research both agree that the degree of simplicity of the packaging design has a significant impact on consumer’s attitudes towards a brand. In the case of this study, we defined two overall stylistic trends which come from the art literature: simple design versus overloaded design. In order to study the impact of the complexity degree, we created three labels: two representing the previously exposed styles and another one to study the relevance of a medium-loaded design. These labels were created in partnership with a printing company, present in Champagne since 1910. The three labels have the same text but different graphic designs in order to vary the degree of simplicity/complexity of the packaging observed on the market. Then, they were tested among 305 consumers according to a between-subjects experiment. The results allow the verification of different proposals from the literature: previous researches show that a simple design communicates an authentic and honest value which is also demonstrated in our study as the bottle with the simplest design is perceived as the most successful. Also, the bottle with an overloaded design is perceived as cheerful, imaginative and feminine as demonstrated by previous researches. This study demonstrates a significant impact of the package design’s level of simplicity on the brand perception as well as on consumer’s buying choices

The impact of near-term climate policy choices on technology and emission transition pathways

This paper explores the implications of delays (to 2030) in implementing optimal policies for long-term transition pathways to limit climate forcing to 450 ppm CO2e on the basis of the AMPERE Work Package 2 model comparison study. The paper highlights the critical importance of the period 2030-2050 for ambitious mitigation strategies. In this period, the most rapid shift to low greenhouse gas emitting technology occurs. In the delayed response emission mitigation scenarios, an even faster transition rate in this period is required to compensate for the additional emissions before 2030. Our physical deployment measures indicate that the availability of CCS technology could play a critical role in facilitating the attainment of ambitious mitigation goals. Without CCS, deployment of other mitigation technologies would become extremely high in the 2030-2050 period. Yet the presence of CCS greatly alleviates the challenges to the transition particularly after the delayed climate policies, lowering the risk that the long-term goal becomes unattainable. The results also highlight the important role of bioenergy with CO2 capture and storage (BECCS), which facilitates energy production with negative carbon emissions. If BECCS is available, transition pathways exceed the emission budget in the mid-term, removing the excess with BECCS in the long term. Excluding either BE or CCS from the technology portfolio implies that emission reductions need to take place much earlier

Early transformation of the Chinese power sector to avoid additional coal lock-in

Emission reduction from the coal-dominated power sector is vital for achieving China's carbon mitigation targets. Although the coal expansion has been slowed down due to the cancellation of and delay in new construction, coal-based power was responsible for over one third of China's energy-related CO2 emissions by 2018. Moreover, with a technical lifetime of over 30 years, current investment in coal-based power could hinder CO2 mitigation until 2050. Therefore, it is important to examine whether the current coal-based power planning aligns with the long-term climate targets. This paper introduces China's Nationally Determined Contribution (NDC) goals and an ambitious carbon budget along with global pathways well-below 2 degrees that are divided into five integrated assessment models, which are two national and three global models. We compare the models' results with bottom-up data on current capacity additions and expansion plans to examine if the NDC targets are in line with 2-degree pathways. The key findings are: 1. NDC goals alone are unlikely to lead to significant reductions in coal-based power generation. On the contrary, more plants may be built before 2030; 2. this would require an average of 187–261 TWh of annual coal-based power capacity reduction between 2030 and 2050 to achieve a 2 °C compatible trajectory, which would lead to the stranding of large-scale coal-based power plants; 3. if the reduction in coal power can be brought forward to 2020, the average annual coal-based power reduction required would be 104–155 TWh from 2020 to 2050 and the emissions could peak earlier; 4. early regulations in coal-based power would require accelerated promotion of alternatives between 2020 and 2030, with nuclear, wind and solar power expected to be the most promising alternatives. By presenting the stranding risk and viability of alternatives, we suggest that both the government and enterprises should remain cautious about making new investment in coal-based power sector

Generalizing with perceptrons in case of structured phase- and pattern-spaces

We investigate the influence of different kinds of structure on the learning behaviour of a perceptron performing a classification task defined by a teacher rule. The underlying pattern distribution is permitted to have spatial correlations. The prior distribution for the teacher coupling vectors itself is assumed to be nonuniform. Thus classification tasks of quite different difficulty are included. As learning algorithms we discuss Hebbian learning, Gibbs learning, and Bayesian learning with different priors, using methods from statistics and the replica formalism. We find that the Hebb rule is quite sensitive to the structure of the actual learning problem, failing asymptotically in most cases. Contrarily, the behaviour of the more sophisticated methods of Gibbs and Bayes learning is influenced by the spatial correlations only in an intermediate regime of $\alpha$, where $\alpha$ specifies the size of the training set. Concerning the Bayesian case we show, how enhanced prior knowledge improves the performance.Comment: LaTeX, 32 pages with eps-figs, accepted by J Phys

Uncertainty in an emissions-constrained world

Our study focuses on uncertainty in greenhouse gas (GHG) emissions from anthropogenic sources, including land use and land-use change activities. We aim to understand the relevance of diagnostic (retrospective) and prognostic (prospective) uncertainty in an emissions-temperature setting that seeks to constrain global warming and to link uncertainty consistently across temporal scales. We discuss diagnostic and prognostic uncertainty in a systems setting that allows any country to understand its national and near-term mitigation and adaptation efforts in a globally consistent and long-term context. Cumulative emissions are not only constrained and globally binding but exhibit quantitative uncertainty; and whether or not compliance with an agreed temperature target will be achieved is also uncertain. To facilitate discussions, we focus on two countries, the USA and China. While our study addresses whether or not future increase in global temperature can be kept below 2, 3, or 4 degrees C targets, its primary aim is to use those targets to demonstrate the relevance of both diagnostic and prognostic uncertainty. We show how to combine diagnostic and prognostic uncertainty to take more educated (precautionary) decisions for reducing emissions toward an agreed temperature target; and how to perceive combined diagnostic and prognostic uncertainty-related risk. Diagnostic uncertainty is the uncertainty contained in inventoried emission estimates and relates to the risk that true GHG emissions are greater than inventoried emission estimates reported in a specified year; prognostic uncertainty refers to cumulative emissions between a start year and a future target year, and relates to the risk that an agreed temperature target is exceeded