Dark matter scenarios in a constrained model with Dirac gauginos

We perform the first analysis of Dark Matter scenarios in a constrained model with Dirac Gauginos. The model under investigation is the Constrained Minimal Dirac Gaugino Supersymmetric Standard model (CMDGSSM) where the Majorana mass terms of gauginos vanish. However, $R$-symmetry is broken in the Higgs sector by an explicit and/or effective $B_\mu$-term. This causes a mass splitting between Dirac states in the fermion sector and the neutralinos, which provide the dark matter candidate, become pseudo-Dirac states. We discuss two scenarios: the universal case with all scalar masses unified at the GUT scale, and the case with non-universal Higgs soft-terms. We identify different regions in the parameter space which fullfil all constraints from the dark matter abundance, the limits from SUSY and direct dark matter searches and the Higgs mass. Most of these points can be tested with the next generation of direct dark matter detection experiments.Comment: 28 pages, 11 figures; v2: minor changes, title modified; matches published versio

Thermodynamic models for a concentration and electric field dependent susceptibility in liquid electrolytes

The dielectric susceptibility $chi$ is an elementary quantity of the electrochemical double layer and the associated Poisson equation. While most often $chi$ is treated as a material constant, its dependency on the salt concentration in liquid electrolytes is demonstrated by various bulk electrolyte experiments. This is usually referred to as dielectric decrement. Further, it is theoretically well accepted that the susceptibility declines for large electric fields. This effect is frequently termed dielectric saturation. We analyze the impact of a variable susceptibility in terms of species concentrations and electric fields based on non-equilibrium thermodynamics. This reveals some non-obvious generalizations compared to the case of a constant susceptibility. In particular the consistent coupling of the Poisson equation, the momentum balance and the chemical potentials functions are of ultimate importance. In a numerical study, we systematically analyze the effects of a concentration and field dependent susceptibility on the double layer of a planar electrode electrolyte interface. We compute the differential capacitance and the spatial structure of the electric potential, solvent concentration and ionic distribution for various non-constant models of $chi$

Eco-Efficiency: A Prerequisite for Future Success

At Roche, eco-efficiency – the optimal use of material, energy, human resources, and capital to supply innovative products to the market – is considered as a prerequisite for business success in the future. Group-wide activities to rise eco-efficiency are focussed on manufacturing processes rather than on product design, since chemical composition and structure of the Roche pharmaceutical products are defined by the desired therapeutic effect, in contrast to commodities. Three examples of currently operating eco-efficient processes at Roche are described. They cover main areas for further improvement of both environmental performance and economical benefit: less material intensity and waste for disposal, energy recovery, minimization of water consumption. Furthermore, four different indicators currently used at Roche to track environmental performance and eco-efficiency are presented: the Roche Environmental Impact Figure (REIF), the Roche Energy Rate (RER), the Roche contribution to man-made global warming (CO2 equivalents), and the Eco-Efficiency Rate (EER). These key indicators are used as a basis to recognize weaknesses and strengths, to take decisions for improvement, to set environmental targets, and as management information

Visual Analysis of Spatio-Temporal Event Predictions: Investigating the Spread Dynamics of Invasive Species

Invasive species are a major cause of ecological damage and commercial losses. A current problem spreading in North America and Europe is the vinegar fly Drosophila suzukii. Unlike other Drosophila, it infests non-rotting and healthy fruits and is therefore of concern to fruit growers, such as vintners. Consequently, large amounts of data about infestations have been collected in recent years. However, there is a lack of interactive methods to investigate this data. We employ ensemble-based classification to predict areas susceptible to infestation by D. suzukii and bring them into a spatio-temporal context using maps and glyph-based visualizations. Following the information-seeking mantra, we provide a visual analysis system Drosophigator for spatio-temporal event prediction, enabling the investigation of the spread dynamics of invasive species. We demonstrate the usefulness of this approach in two use cases

Spatial and temporal correlations in human cortex are inherently linked and predicted by functional hierarchy, vigilance state as well as antiepileptic drug load

The ability of neural circuits to integrate information over time and across different cortical areas is believed an essential ingredient for information processing in the brain. Temporal and spatial correlations in cortex dynamics have independently been shown to capture these integration properties in task-dependent ways. A fundamental question remains if temporal and spatial integration properties are linked and what internal and external factors shape these correlations. Previous research on spatio-temporal correlations has been limited in duration and coverage, thus providing only an incomplete picture of their interdependence and variability. Here, we use long-term invasive EEG data to comprehensively map temporal and spatial correlations according to cortical topography, vigilance state and drug dependence over extended periods of time. We show that temporal and spatial correlations in cortical networks are intimately linked, decline under antiepileptic drug action, and break down during slow-wave sleep. Further, we report temporal correlations in human electrophysiology signals to increase with the functional hierarchy in cortex. Systematic investigation of a neural network model suggests that these dynamical features may arise when dynamics are poised near a critical point. Our results provide mechanistic and functional links between specific measurable changes in the network dynamics relevant for characterizing the brain's changing information processing capabilities.Author summary A growing body of research suggests spatial and temporal correlations, which capture the propagation of information in space and time, to be useful characterizations of information processing in the human brain. The criticality hypothesis, the hypothesis that networks in the brain reside in the vicinity of a phase transition, posits that spatial and temporal correlations are intimately linked and maximized near the critical point. Previous research has predominantly focused on spatial and temporal correlations independently and was often restricted in duration, thus limiting our knowledge whether spatial and temporal correlations indeed co-vary and what other factors influence these information integration properties in general. Here, we use long-term invasive EEG data to comprehensively map temporal and spatial correlations according to cortical topography, vigilance state, and drug dependence over extended periods of time. We show that temporal and spatial correlations in cortical networks are strongly linked, decline under antiepileptic drug action, and completely break down during slow-wave sleep. We provide direct electrophysical evidence that temporal correlations follow a gradient which aligns with the functional hierarchy. Systematic investigation alongside a companion neural network model suggests that these findings may arise due to dynamics being poised near a critical point. The ability of neural circuits to integrate information over time and across different cortical areas is believed an essential ingredient for information processing in the brain. Temporal and spatial correlations in cortex dynamics have independently been shown to capture these integration properties in task-dependent ways. A fundamental question remains if temporal and spatial integration properties are linked and what internal and external factors shape these correlations. Previous research on spatio-temporal correlations has been limited in duration and coverage, thus providing only an incomplete picture of their interdependence and variability. Here, we use long-term invasive EEG data to comprehensively map temporal and spatial correlations according to cortical topography, vigilance state and drug dependence over extended periods of time. We show that temporal and spatial correlations in cortical networks are intimately linked, decline under antiepileptic drug action, and break down during slow-wave sleep. Further, we report temporal correlations in human electrophysiology signals to increase with the functional hierarchy in cortex. Systematic investigation of a neural network model suggests that these dynamical features may arise when dynamics are poised near a critical point. Our results provide mechanistic and functional links between specific measurable changes in the network dynamics relevant for characterizing the brain's changing information processing capabilities

A parametric approach to electoral systems

[ES] Se considera un encuadre algorítmico de los sistemas electorales. A partir de ello, se parametriza la especificación de los sistemas electorales principales. [EN] An algorithmic framework of electoral systems is considered. As a result, the specification of the main electoral systems is parametrized

Modeling polycrystalline electrode-electrolyte interfaces: The differential capacitance

Consistent boundary conditions for electrochemical interfaces, which cover double layer charging, pseudo-capacitive effects and transfer reactions, are of high demand in electrochemistry and adjacent disciplines. Mathematical modeling and optimization of electrochemical systems is a strongly emerging approach to reduce cost and increase efficiency of super-capacitors, batteries, fuel cells, and electrocatalysis. However, many mathematical models which are used to describe such systems lack a real predictive value. Origin of this shortcoming is the usage of oversimplified boundary conditions. In this work we derive the boundary conditions for some general electrode-electrolyte interface based on non-equilibrium thermodynamics for volumes and surfaces. The resulting equations are widely applicable and cover also tangential transport. The general framework is then applied to a specific material model which allows the deduction of a current-voltage relation and thus a comparison to experimental data. Some simplified 1D examples show the range of applicability of the new approach