Probabilistic weather forecasting with machine learning.

Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather to planning renewable energy use. Traditionally, weather forecasts have been based on numerical weather prediction (NWP), which relies on physics-based simulations of the atmosphere. Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations.

Seasonal Arctic sea ice forecasting with probabilistic deep learning.

Anthropogenic warming has led to an unprecedented year-round reduction in Arctic sea ice extent. This has far-reaching consequences for indigenous and local communities, polar ecosystems, and global climate, motivating the need for accurate seasonal sea ice forecasts. While physics-based dynamical models can successfully forecast sea ice concentration several weeks ahead, they struggle to outperform simple statistical benchmarks at longer lead times.

Psychometric Properties of the Swedish Version of the Brief Repetitive Thinking Questionnaire (RTQ-10): An Internet-Based Study on Degrees of Affective Symptoms and Levels of Distress.

Introduction: Repetitive negative thinking (RNT) is reported in a wide variety of emotional disorders, although it is most often associated with either depression or anxiety disorders, assessed as symptoms of rumination and worry. Early detection of indicators for RNT across disorders is needed. To this end we explored the psychometric properties of a transdiagnostic measure, i.

A Multiscale Modelling Approach for Estimating the Effect of Defects in Unidirectional Carbon Fiber Reinforced Polymer Composites.

A multiscale modelling approach was developed in order to estimate the effect of defects on the strength of unidirectional carbon fiber composites. The work encompasses a micromechanics approach, where the known reinforcement and matrix properties are experimentally verified and a 3D finite element model is meshed directly from micrographs. Boundary conditions for loading the micromechanical model are derived from macroscale finite element simulations of the component in question.

Age, gender, and arousal in recognition of negative and neutral pictures 1 year later.

Compared with nonarousing stimuli, arousing stimuli enhance memory performance. The most robust effects have been reported for negative stimuli, "the negativity effect," although a number of mediating factors prevent definitive conclusions, for example, age, gender, memory task, retention period, and alternative arousal measures. To clarify whether the negativity effect is robust across age, gender, and time, we studied incidental recognition of neutral and negative pictures from the International Affective Picture System (Lang, Bradley, & Cuthbert, 1999) in healthy younger and older adults--women and men--after a 1-year retention interval.

Decoding complex biological networks - tracing essential and modulatory parameters in complex and simplified models of the cell cycle.

Background: One of the most well described cellular processes is the cell cycle, governing cell division. Mathematical models of this gene-protein network are therefore a good test case for assessing to what extent we can dissect the relationship between model parameters and system dynamics. Here we combine two strategies to enable an exploration of parameter space in relation to model output.

Modeling gene associations for virulence classification of verocytotoxin-producing E. coli (VTEC) from patients and beef.

An evaluation of 22 EHEC genes was carried out for virulence classification of VTEC. The data consisted of 116 patient isolates and 42 beef isolates. The symptoms among patients ranged from mild (diarrhea) to severe (bloody diarrhea and HUS).

Exploring voltage-dependent ion channels in silico by hysteretic conductance.

Kinetic models of voltage-dependent ion channels are normally inferred from time records of macroscopic current relaxation or microscopic single channel data. A complementary explorative approach is outlined. Hysteretic conductance refers to conductance delays in response to voltage changes, delays at either macroscopic or microscopic levels of observation.