Linearly polarized GHz magnetization dynamics of spin helix modes in the ferrimagnetic insulator Cu2_{2}OSeO3_{3}

Linear dichroism -- the polarization dependent absorption of electromagnetic waves -- is routinely exploited in applications as diverse as structure determination of DNA or polarization filters in optical technologies. Here filamentary absorbers with a large length-to-width ratio are a prerequisite. For magnetization dynamics in the few GHz frequency regime strictly linear dichroism was not observed for more than eight decades. Here, we show that the bulk chiral magnet Cu$_{2}$OSeO$_{3}$ exhibits linearly polarized magnetization dynamics at an unexpectedly small frequency of about 2 GHz. Unlike optical filters that are assembled from filamentary absorbers, the magnet provides linear polarization as a bulk material for an extremely wide range of length-to-width ratios. In addition, the polarization plane of a given mode can be switched by 90$^\circ$ via a tiny variation in width. Our findings shed a new light on magnetization dynamics in that ferrimagnetic ordering combined with anisotropic exchange interaction offers strictly linear polarization and cross-polarized modes for a broad spectrum of sample shapes. The discovery allows for novel design rules and optimization of microwave-to-magnon transduction in emerging microwave technologies.Comment: 20 pages, 4 figure

Low spin wave damping in the insulating chiral magnet Cu2_{2}OSeO3_{3}

Chiral magnets with topologically nontrivial spin order such as Skyrmions have generated enormous interest in both fundamental and applied sciences. We report broadband microwave spectroscopy performed on the insulating chiral ferrimagnet Cu$_{2}$OSeO$_{3}$. For the damping of magnetization dynamics we find a remarkably small Gilbert damping parameter of about $1\times10^{-4}$ at 5 K. This value is only a factor of 4 larger than the one reported for the best insulating ferrimagnet yttrium iron garnet. We detect a series of sharp resonances and attribute them to confined spin waves in the mm-sized samples. Considering the small damping, insulating chiral magnets turn out to be promising candidates when exploring non-collinear spin structures for high frequency applications.Comment: 5 pages, 5 figures, and supplementary materia

Changes in phenology and abundance of suction‐trapped Diptera from a farmland site in the UK over four decades

1. Recently documented insect declines have caused major concerns and an increased interest in studies using long-term population-monitoring data. 2. Samples from a 12.2-m suction trap were used to examine trends in phenology and abundance of Diptera over four decades. 3. The timing of peak flight has advanced by an average of 17 days, from 23 July in 1974 to 6 July in 2014. 4. The abundance of flies has decreased by 37% over the studied period (from April to September), and peak abundance has decreased by 48%. The flight period has started earlier in recent years, and in 2014, the number of flies was higher in spring until the 31st of May than in 1974. Possible causes and impacts of these changes are discussed

Long-range forecasting of intermittent streamflow

Long-range forecasting of intermittent streamflow in semi-arid Australia poses a number of major challenges. One of the challenges relates to modelling zero, skewed, non-stationary, and non-linear data. To address this, a statistical model to forecast streamflow up to 12 months ahead is applied to five semi-arid catchments in South Western Queensland. The model uses logistic regression through Generalised Additive Models for Location, Scale and Shape (GAMLSS) to determine the probability of flow occurring in any of the systems. We then use the same regression framework in combination with a right-skewed distribution, the Box-Cox t distribution, to model the intensity (depth) of the non-zero streamflows. Time, seasonality and climate indices, describing the Pacific and Indian Ocean sea surface temperatures, are tested as covariates in the GAMLSS model to make probabilistic 6 and 12-month forecasts of the occurrence and intensity of streamflow. The output reveals that in the study region the occurrence and variability of flow is driven by sea surface temperatures and therefore forecasts can be made with some skill

Gaussian Markov random field spatial models in GAMLSS

This paper describes the modelling and fitting of Gaussian Markov random field spatial components within a Generalized Additive-Model for Location, Scale and Shape (GAMLSS) model. This allows modelling of any or all the parameters of the distribution for the response variable using explanatory variables and spatial effects. The response variable distribution is allowed to be a non-exponential family distribution. A new package developed in R to achieve this is presented. We use Gaussian Markov random fields to model the spatial effect in Munich rent data and explore some features and characteristics of the data. The potential of using spatial analysis within GAMLSS is discussed. We argue that the flexibility of parametric distributions, ability to model all the parameters of the distribution and diagnostic tools of GAMLSS provide an ideal environment for modelling spatial features of data

Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications

An emerging field in statistics, distributional regression facilitates the modelling of the complete conditional distribution, rather than just the mean. This book introduces generalized additive models for location, scale and shape (GAMLSS) – one of the most important classes of distributional regression. Taking a broad perspective, the authors consider penalized likelihood inference, Bayesian inference, and boosting as potential ways of estimating models and illustrate their usage in complex applications. Written by the international team who developed GAMLSS, the text's focus on practical questions and problems sets it apart. Case studies demonstrate how researchers in statistics and other data-rich disciplines can use the model in their work, exploring examples ranging from fetal ultrasounds to social media performance metrics. The R code and data sets for the case studies are available on the book's companion website, allowing for replication and further study

Spirometry reference equations for central European populations from school age to old age.

Spirometry reference values are important for the interpretation of spirometry results. Reference values should be updated regularly, derived from a population as similar to the population for which they are to be used and span across all ages. Such spirometry reference equations are currently lacking for central European populations. To develop spirometry reference equations for central European populations between 8 and 90 years of age. We used data collected between January 1993 and December 2010 from a central European population. The data was modelled using "Generalized Additive Models for Location, Scale and Shape" (GAMLSS). The spirometry reference equations were derived from 118'891 individuals consisting of 60'624 (51%) females and 58'267 (49%) males. Altogether, there were 18'211 (15.3%) children under the age of 18 years. We developed spirometry reference equations for a central European population between 8 and 90 years of age that can be implemented in a wide range of clinical settings

Gross motor coordination and weight status of Portuguese children aged 6-14 years

Objectives: To construct age- and gender-specific percentiles for gross motor coordination (MC) tests and to explore differences in gross MC in normal-weight, overweight and obese children. Methods: Data are from the "Healthy Growth of Madeira Study", a cross-sectional study carried out in children, aged 6–14 years. All 1,276 participants, 619 boys and 657 girls, were assessed for gross MC (Korperkoordinations Test fur Kinder, KTK), anthropometry (height and body mass), physical activity (Baecke questionnaire) and socioeconomic status (SES). Centile curves for gross MC were obtained for boys and girls separately using generalized additive models for location, scale and shape. Results: A significant main effect for age was found in walking backwards and moving sideways. Boys performed significantly better than girls on moving sideways. At the upper limit of the distributions, interindividual variability was higher in hopping on one leg (girls) and jumping and moving sideways (boys and girls). One-way ANCOVA, controlling for age, physical activity and SES, indicated that normal-weight children scored significantly better than their obese peers in all gross MC tests. Overweight boys and girls also scored significantly better than their obese colleagues in some MC tests. Conclusions: These centile curves can be used as reference data in Portuguese children and youth, aged 6–14 years. Being overweight or obese was a major limitation in MC tests and, therefore, of the children’s health- and performance related physical fitness

Modeling arsenic in European topsoils with a coupled semiparametric (GAMLSS-RF) model for censored data

Arsenic (As) is a versatile heavy metalloid trace element extensively used in industrial applications. As is carcinogen, poses health risks through both inhalation and ingestion, and is associated with an increased risk of liver, kidney, lung, and bladder tumors. In the agricultural context, the repeated application of arsenical products leads to elevated soil concentrations, which are also affected by environmental and management variables. Since exposure to As poses risks, effective assessment tools to support environmental and health policies are needed. However, the most comprehensive soil As data available, the Land Use/Cover Area frame statistical Survey (LUCAS) database, contains severe limitations due to high detection limits. Although within International Or- ganization for Standardization standards, the detection limits preclude the adoption of standard methodologies for data analysis. The present work focused on developing a new method to model As contamination in European soils using LUCAS soil samples. We introduce the GAMLSS-RF model, a novel approach that couples Random Forests with Generalized Additive Models for Location, Scale, and Shape. The semiparametric model can capture non-linear interactions among input variables while accommodating censored and non-censored observations and can be calibrated to include information from other campaign databases. After fitting and validating a spatial model, we produced European-scale As concentration maps at a 250 m spatial resolution and evaluated the patterns against reference values (i.e., two action levels and a background concentration). We found a significant variability of As concentration across the continent, with lower concentrations in Northern countries and higher concentrations in Portugal, Spain, Austria, France and Belgium. By overcoming limitations in existing databases and methodologies, the present approach provides an alternative way to handle highly censored data. The model also consists of a valuable probabilistic tool for assessing As contamination risks in soils, contributing to informed policy-making for environmental and health protection