Explore the vast range of titles available.

Metallic magnetic calorimeters (MMCs) are calorimetric low-temperature particle detectors that are currently strongly advancing the state of the art in energy-dispersive single particle detection. They are typically operated at temperatures below 100 mK and make use of a metallic, paramagnetic temperature sensor to transduce the temperature rise of the detector upon the absorption of an energetic particle into a change of magnetic flux which is sensed by a superconducting quantum interference device. This outstanding interplay between a high-sensitivity thermometer and a near quantum-limited amplifier results in a very fast signal rise time, an excellent energy resolution, a large dynamic range, a quantum efficiency close to 100% as well as an almost ideal linear detector response. For this reason, a growing number of groups located all over the world is developing MMC arrays of various sizes which are routinely used in a variety of applications. Within this paper, we briefly review the state of the art of metallic magnetic calorimeters. This includes a discussion of the detection principle, sensor materials and detector geometries, readout concepts, the structure of modern detectors as well as the state-of-the-art detector performance.

In a commentary on our paper (Renner et al., Oecologia 195:825–831, 2021), Harder and Miksha lay out why they think that our finding of higher honeybee abundances reducing wild bee abundances in an urban botanical garden is not statistically supported. Here, we explain the statistical test provided in our paper, which took advantage of a natural experiment offered by 2019 being a poorer year for bee keeping than 2020.

Satellite remote sensing enhances model predictions by providing insights into terrestrial and hydrological processes. While data assimilation techniques have proven promising, there is a lack of standardized and effective approaches for integrating multiple observations simultaneously. This study presents a novel assimilation framework, the multi‐observation local ensemble‐Kalman‐filter (MoLEnKF), designed to effectively integrate multiple variables, even at scales different than the model. Evaluation of MoLEnKF in the Amazon River basin includes assimilation experiments with remote sensing data only, including water surface elevation (WSE), terrestrial water storage (TWS), flood extent (FE), and soil moisture (SM). MoLEnKF demonstrates improvements in a scenario where regions lack in‐situ hydroclimatic records and when assuming uncertainties of large‐scale hydrologic‐hydrodynamic models. Assimilating WSE outperforms daily discharge and water‐level estimations, achieving 38% and 36% error reduction, respectively. However, the monthly evapotranspiration estimate achieves the greatest error reduction by assimilating SM with 11%. MoLEnKF always remains in second position in a ranking of error and uncertainty reduction, providing an intermediate condition, being able to holistically outperform univariate experiments. MoLEnKF also outperform state‐of‐the‐art models in many cases. This study suggests potential improvements, urging exploration of correlations between assimilated variables and adaptive localization methods based on seasonality. The flexibility and the elegant way of expressing the LEnKF equations by MoLEnKF facilitates their application with different types of variables, compatible with large‐scale hydrologic‐hydrodynamic models and missions such as SWOT. Its robustness ensures easy replicability worldwide, facilitating hydrological reanalysis and improved forecasting, establishing MoLEnKF as a valuable tool for the scientific community in hydrological research. Plain Language Summary The use of satellites to collect information from far away helps us to understand how water behaves on the continents. But combining all this data with uncertain computer models is complicated. This study introduces a new method called multi‐observation local ensemble‐Kalman‐filter (MoLEnKF) to combine many different kinds of data at once. We tested MoLEnKF in the Amazon River basin, using satellite data on water levels, terrestrial water storage, flood extent and soil moisture. MoLEnKF by using all these observations at the same time obtained better results holistically than the individual experiments, improving our ability to predict aspects such as the amount of discharge, water level and evapotranspiration. This study is a step forward and could be really useful for understanding and predicting water‐related phenomena worldwide, especially in a context of scarce or no availability of in‐situ observations. Key Points Multi‐observation local ensemble‐Kalman‐filter (MoLEnKF) advances multi‐observation and multi‐scale assimilation, overcoming holistically univariate experiments MoLEnKF improves the simulation of large‐scale hydrologic‐hydrodynamic uncertain models using only remotely sensed data MoLEnKF flexibility for global applications: Simplicity and compatibility with various data types make it a robust tool, for example, SWOT mission

Ground weather observations are scarce in many parts of the globe, hampering effective climate monitoring and disaster management. In the Amazon basin, this occurs due to its remoteness and the challenging measurement of rainfall within the forest. Innovative rainfall estimation methods are thus requested to fill this gap. Here we present an approach to estimate rainfall based on sound measurements. We identified the best frequency range to estimate rainfall occurrence and intensity, trained classification and regression models with sound and rain gauge data collected in the Central Amazon during 9 months. By training a random forest classifier/regression model based on power spectrum values it was possible to identify and satisfactorily estimate hourly rainfall rates in two vegetation environments distinct from the training site, located 30 km from it. The proposed method is a promising approach for future weather monitoring in remote tropical areas. Plain Language Summary Understanding and predicting rainfall is a complex task, especially in areas where the availability of data from surface stations is limited, a common feature in many developing regions with insufficient rain gauge coverage. Recently, new opportunistic methods of rainfall measurement have emerged. Among them, is the use of the relationship between rainfall intensity and the sound produced by droplets hitting a surface. Sound recorders offer a low‐cost solution and could provide an interesting means to increase spatial coverage of rainfall measurements, but also to fill information gaps under dense forests where conventional devices do not work. Our study developed a new technique and applied it to the Central Amazon region, by training a supervised machine learning model applied to sound recordings obtained in a tropical rainforest. To our knowledge, for the first time, such techniques are validated in locations far from the calibration site. We showed that reasonable results can be obtained for sites with distinct vegetation types and up to 30 km of distance from where the training data was acquired. Our findings demonstrate a strong capability for estimating hourly rainfall rates. Key Points Rainfall intensity estimated from sound measurements in the Amazon rainforest, tipping bucket rain gauge, and machine learning models The best model successfully detects rainfall in 88% of the cases, with R2 > 0.87 for hourly rainfall rates on the training site Model validated over two sites in the Amazon, 97% accuracy identifying rainfall events, R2 of 0.69 and 0.93 for hourly rainfall rate

Dengue is one of the most common diseases in the tropics and subtropics. Whilst mortality is a rare event when adequate supportive care can be provided, a large number of patients get hospitalised with dengue every year that places a heavy burden on local health systems. A better understanding of the support required at the time of hospitalisation is therefore of critical importance for healthcare planning, especially when resources are limited during major outbreaks. Here we performed a retrospective analysis of clinical data from over 1500 individuals hospitalised with dengue in Vietnam between 2017 and 2019. Using a broad panel of potential biomarkers, we sought to evaluate robust predictors of prolonged hospitalisation periods. Our analyses revealed a lead-time bias, whereby early admission to hospital correlates with longer hospital stays - irrespective of disease severity. Importantly, taking into account the symptom duration prior to hospitalisation significantly affects observed associations between hospitalisation length and previously reported risk markers of prolonged stays, which themselves showed marked inter-annual variations. Once corrected for symptom duration, age, temperature at admission and elevated neutrophil-to-lymphocyte ratio were found predictive of longer hospitalisation periods. This study demonstrates that the time since dengue symptom onset is one of the most significant predictors for the length of hospital stays, independent of the assigned severity score. Pre-hospital symptom durations need to be accounted for to evaluate clinically relevant biomarkers of dengue hospitalisation trajectories.

PurposeThe purpose of this paper is to investigate how professional football clubs from the English Premier League, German Bundesliga and Spanish Primera División use digital media to expand their international reach in emerging football markets (EFM) outside of Europe. Based on the EPRG framework and Rugman’s home-region hypothesis, the aim is to broaden the perspective where “sports go global” for a further understanding of actors’ international orientation in the digital sphere.Design/methodology/approachThe study is based on data from desk research and a qualitative survey, comprising information on international digital media activities of 58 European clubs. Cluster analysis is used to identify different international orientations with regard to digital media activities.FindingsThe data provide evidence that clubs differ strongly in their orientations towards EFM. While some global players that provide digital media content in several EFM languages and attract a large share of Facebook followers from EFM exist, other clubs focus on their home region. League-specific differences become apparent.Originality/valueThis study determines the international online orientations of European football clubs by combining two previously separated research streams in football management studies: internationalisation and digital media activities. Most clubs with a strong EFM fan base choose polycentric, multi-language digital media strategies, followed by geocentric, standardised approaches. By offering a novel angle on internationalisation in professional football, this study contributes towards optimising clubs’ international online strategies for EFM, which are markets that promise high growth rates.

Preventing the occurrence of depression/anxiety and suicide during adolescence can lead to substantive health gains over the course of an individual person's life. This study set out to identify the expected population-level costs and health impacts of implementing universal and indicated school-based socio-emotional learning (SEL) programs in different country contexts. A Markov model was developed to examine the effectiveness of delivering universal and indicated school-based SEL programs to prevent the onset of depression/anxiety and suicide deaths among adolescents. Intervention health impacts were measured in healthy life years gained (HLYGs) over a 100-year time horizon. Country-specific intervention costs were calculated and denominated in 2017 international dollars (2017 I$) under a health systems perspective. Cost-effectiveness findings were subsequently expressed in terms of I$ per HLYG. Analyses were conducted on a group of 20 countries from different regions and income levels, with final results aggregated and presented by country income group - that is, low and lower middle income countries (LLMICs) and upper middle and high-income countries (UMHICs). Uncertainty and sensitivity analyses were conducted to test model assumptions. Implementation costs ranged from an annual per capita investment of I$0.10 in LLMICs to I$0.16 in UMHICs for the universal SEL program and I$0.06 in LLMICs to I$0.09 in UMHICs for the indicated SEL program. The universal SEL program generated 100 HLYGs per 1 million population compared to 5 for the indicated SEL program in LLMICs. The cost per HLYG was I$958 in LLMICS and I$2,006 in UMHICs for the universal SEL program and I$11,123 in LLMICs and I$18,473 in UMHICs for the indicated SEL program. Cost-effectiveness findings were highly sensitive to variations around input parameter values involving the intervention effect sizes and the disability weight used to estimate HLYGs. The results of this analysis suggest that universal and indicated SEL programs require a low level of investment (in the range of I$0.05 to I$0.20 per head of population) but that universal SEL programs produce significantly greater health benefits at a population level and therefore better value for money (e.g., less than I$1,000 per HLYG in LLMICs). Despite producing fewer population-level health benefits, the implementation of indicated SEL programs may be justified as a means of reducing population inequalities that affect high-risk populations who would benefit from a more tailored intervention approach.

Self-poisoning with pesticides accounts for about a third of all suicides worldwide. To tackle this problem, the World Health Organization announced a global public health initiative in the second half of 2005. Planned approaches were to range from government regulatory action to the development of new treatments for pesticide poisoning. With broad-based support, this strategy should have a major impact on the global burden of suicide.

The determination of the absolute scale of the neutrino masses is one of the most challenging present questions in particle physics. The most stringent limit, \\(m(\\bar{\\nu }_{\\mathrm {e}})< 2\\) eV, was achieved for the electron anti-neutrino mass. Different approaches are followed to reach a sensitivity on neutrino masses in the sub-eV range. Among them, experiments exploring the beta decay or electron capture of suitable nuclides can provide information on the electron neutrino mass value. We present the electron capture \\(163}\\) Ho experiment ECHo, which aims to investigate the electron neutrino mass in the sub-eV range by means of the analysis of the calorimetrically measured energy spectrum following electron capture in \\(163}\\) Ho. A high precision and high statistics spectrum will be measured with arrays of metallic magnetic calorimeters. We discuss some of the essential aspects of ECHo to reach the proposed sensitivity: detector optimization and performance, multiplexed readout, \\(163}\\) Ho source production and purification, as well as a precise theoretical and experimental parameterization of the calorimetric EC spectrum including in particular the value of \\(Q_{\\mathrm {EC}}\\) . We present preliminary results obtained with a first prototype of single channel detectors as well as a first 64-pixel chip with integrated micro-wave SQUID multiplexer, which will already allow to investigate \\(m(\\nu _{\\mathrm {e}})\\) in the eV range.

The options for primary thermometry at ultra-low temperatures are rather limited. In practice, most laboratories are using 195Pt NMR thermometers in the microkelvin range. In recent years, current sensing direct current superconducting quantum interference devices (DC-SQUIDs) have enabled the use of noise thermometry in this temperature range. Such devices have also demonstrated the potential for primary thermometry. One major advantage of noise thermometry is the fact that no driving current is needed to operate the device and thus the heat dissipation within the thermometer can be reduced to a minimum. Ultimately, the intrinsic power dissipation is given by the negligible back action of the readout SQUID. For thermometry in low-temperature experiments, current noise thermometers and magnetic flux fluctuation thermometers have proved to be most suitable. To make use of such thermometers at ultra-low temperatures, we have developed a cross-correlation technique that reduces the amplifier noise contribution to a negligible value. For this, the magnetic flux fluctuations caused by the Brownian motion of the electrons in our noise source are measured inductively by two DC-SQUID magnetometers simultaneously and the signals from these two channels are cross-correlated. Experimentally, we have characterized a thermometer made of a cold-worked high-purity copper cylinder with a diameter of 5 mm and a length of 20 mm for temperatures between 42 μK and 0.8 K. For a given temperature, a measuring time below 1 min is sufficient to reach a precision of better than 1%. The extremely low power dissipation in the thermometer allows continuous operation without heating effects.