Particle ejection during mergers of dark matter halos

Dark matter halos are built from accretion and merging. During merging some of the dark matter particles may be ejected with velocities higher than the escape velocity. We use both N-body simulations and single-particle smooth-field simulations to demonstrate that rapid changes to the mean field potential are responsible for such ejection, and in particular that dynamical friction plays no significant role in it. Studying a range of minor mergers, we find that typically between 5-15% of the particles from the smaller of the two merging structures are ejected. We also find that the ejected particles originate essentially from the small halo, and more specifically are particles in the small halo which pass later through the region in which the merging occurs.Comment: 18 pages, 12 figures. Accepted for publication in JCA

Problem awareness does not predict littering:A field study on littering in the Gambia

Littering is a worldwide problem. Recently, it has become a problem in countries that until now have had much less of some types of trash (e.g., plastics), such as in Africa. Most research on factors influencing littering has been conducted in more industrialized regions and has shown that personal norms and social norms mainly explain why people litter or act in an environmentally unfriendly way. One prominent model, the Norm Activation Model (NAM) postulates and has shown that awareness of consequences (AC), ascription of responsibility (AR), and personal norm (PN) are positively related with behavioral intention (BI) and littering behavior. In this field study we tested the model in the Gambia, West Africa, and offer new insights. We approached 132 people on the street and invited them to a candy tasting to observe their littering behavior of the candy wrapper followed by an interview assessing AC, AR, PN, and BI. Structural equation modeling confirmed an overall fit of the data to the “Western” hypothesized model. However importantly differences emerged, Gambians had a high AC, but this was not related to littering behavior. Moreover, an antilittering PN was low. The results suggest that interventions, which aim to decrease littering, should focus on promoting personal responsibility to strengthen a PN, in this context where trash facilities are not yet overall available. Future research should investigate how developing social norms could also help to keep the streets cleaner

Environmental exposure assessment framework for nanoparticles in solid waste

Information related to the potential environmental exposure of engineered nanomaterials (ENMs) in the solid waste management phase is extremely scarce. In this paper, we define nanowaste as separately collected or collectable waste materials which are or contain ENMs, and we present a five-step framework for the systematic assessment of ENM exposure during nanowaste management. The framework includes deriving EOL nanoproducts and evaluating the physicochemical properties of the nanostructure, matrix properties and nanowaste treatment processes as well as transformation processes and environment releases, eventually leading to a final assessment of potential ENM exposure. The proposed framework was applied to three selected nanoproducts: nanosilver polyester textile, nanoTiO(2) sunscreen lotion and carbon nanotube tennis racquets. We found that the potential global environmental exposure of ENMs associated with these three products was an estimated 0.5–143 Mg/year, which can also be characterised qualitatively as medium, medium, low, respectively. Specific challenges remain and should be subject to further research: (1) analytical techniques for the characterisation of nanowaste and its transformation during waste treatment processes, (2) mechanisms for the release of ENMs, (3) the quantification of nanowaste amounts at the regional scale, (4) a definition of acceptable limit values for exposure to ENMs from nanowaste and (5) the reporting of nanowaste generation data. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11051-014-2394-2) contains supplementary material, which is available to authorized users

Modeling Semantic Encoding in a Common Neural Representational Space

Encoding models for mapping voxelwise semantic tuning are typically estimated separately for each individual, limiting their generalizability. In the current report, we develop a method for estimating semantic encoding models that generalize across individuals. Functional MRI was used to measure brain responses while participants freely viewed a naturalistic audiovisual movie. Word embeddings capturing agent-, action-, object-, and scene-related semantic content were assigned to each imaging volume based on an annotation of the film. We constructed both conventional within-subject semantic encoding models and between-subject models where the model was trained on a subset of participants and validated on a left-out participant. Between-subject models were trained using cortical surface-based anatomical normalization or surface-based whole-cortex hyperalignment. We used hyperalignment to project group data into an individual’s unique anatomical space via a common representational space, thus leveraging a larger volume of data for out-of-sample prediction while preserving the individual’s fine-grained functional–anatomical idiosyncrasies. Our findings demonstrate that anatomical normalization degrades the spatial specificity of between-subject encoding models relative to within-subject models. Hyperalignment, on the other hand, recovers the spatial specificity of semantic tuning lost during anatomical normalization, and yields model performance exceeding that of within-subject models

Modeling Semantic Encoding in a Common Neural Representational Space

Encoding models for mapping voxelwise semantic tuning are typically estimated separately for each individual, limiting their generalizability. In the current report, we develop a method for estimating semantic encoding models that generalize across individuals. Functional MRI was used to measure brain responses while participants freely viewed a naturalistic audiovisual movie. Word embeddings capturing agent-, action-, object-, and scene-related semantic content were assigned to each imaging volume based on an annotation of the film. We constructed both conventional within-subject semantic encoding models and between-subject models where the model was trained on a subset of participants and validated on a left-out participant. Between-subject models were trained using cortical surface-based anatomical normalization or surface-based whole-cortex hyperalignment. We used hyperalignment to project group data into an individual’s unique anatomical space via a common representational space, thus leveraging a larger volume of data for out-of-sample prediction while preserving the individual’s fine-grained functional–anatomical idiosyncrasies. Our findings demonstrate that anatomical normalization degrades the spatial specificity of between-subject encoding models relative to within-subject models. Hyperalignment, on the other hand, recovers the spatial specificity of semantic tuning lost during anatomical normalization, and yields model performance exceeding that of within-subject models