Electron mobilities in GaN and InN are calculated, by variational principle, as a function of temperature for carrier concentrations of 1016, 1017, and 1018 cm−3 with compensation ratio as a parameter. Both GaN and InN have maximum mobilities between 100 and 200 K, depending on the electron density and compensation ratio, with lower electron density peaking at lower temperature. This is due to the interplay of piezoelectric acoustic phonon scattering at low carrier concentrations and ionized impurity scattering at higher carrier concentrations. Above 200 K, polar mode optical phonon scattering is the mobility limiting process. The 300 and 77 K electron and Hall mobilities as functions of carrier concentration in the range of 1016–1020 cm−3 and compensation ratio are also calculated. The theoretical maximum mobilities in GaN and InN at 300 K are about 1000 and 4400 cm2 V−1 s−1, respectively, while at 77 K the limits are beyond 6000 and 30 000 cm2 V−1 s−1, respectively. We compare the results with experimental data and find reasonable correlation, but with evidence that structural imperfection and heavy compensation play important roles in the material presently available. Only phonon limited scattering processes are considered in the calculation of the mobility in AlN since it is an insulator of extremely low carrier concentration. We find a phonon limited electron mobility of about 300 cm2 V−1 s−1 at 300 K.

Forecasting models have been influential in shaping decision-making in the COVID-19 pandemic. However, there is concern that their predictions may have been misleading. Here, we dissect the predictions made by four models for the daily COVID-19 death counts between March 25 and June 5 in New York state, as well as the predictions of ICU bed utilisation made by the influential IHME model. We evaluated the accuracy of the point estimates and the accuracy of the uncertainty estimates of the model predictions. First, we compared the "ground truth" data sources on daily deaths against which these models were trained. Three different data sources were used by these models, and these had substantial differences in recorded daily death counts. Two additional data sources that we examined also provided different death counts per day. For accuracy of prediction, all models fared very poorly. Only 10.2% of the predictions fell within 10% of their training ground truth, irrespective of distance into the future. For accurate assessment of uncertainty, only one model matched relatively well the nominal 95% coverage, but that model did not start predictions until April 16, thus had no impact on early, major decisions. For ICU bed utilisation, the IHME model was highly inaccurate; the point estimates only started to match ground truth after the pandemic wave had started to wane. We conclude that trustworthy models require trustworthy input data to be trained upon. Moreover, models need to be subjected to prespecified real time performance tests, before their results are provided to policy makers and public health officials.

Objective : To compare the inference regarding the effectiveness of the various non-pharmaceutical interventions (NPIs) for COVID-19 obtained from different SIR models. Study design and setting: We explored two models developed by Imperial College that considered only NPIs without accounting for mobility (model 1) or only mobility (model 2), and a model accounting for the combination of mobility and NPIs (model 3). Imperial College applied models 1 and 2 to 11 European countries and to the USA, respectively. We applied these models to 14 European countries (original 11 plus another 3), over two different time horizons. Results: While model 1 found that lockdown was the most effective measure in the original 11 countries, model 2 showed that lockdown had little or no benefit as it was typically introduced at a point when the time-varying reproduction number was already very low. Model 3 found that the simple banning of public events was beneficial, while lockdown had no consistent impact. Based on Bayesian metrics, model 2 was better supported by the data than either model 1 or model 3 for both time horizons. Conclusions: Inferences on effects of NPIs are non-robust and highly sensitive to model specification. In the SIR modeling framework, the impacts of lockdown are uncertain and highly model dependent.

Introduction: Assessments of executive functions (EFs) with varying levels of perceptual information or action fidelity are common talent-diagnostic tools in soccer, yet their validity still has to be established. Therefore, a longitudinal development of EFs in high-level players to understand their relationship with increased exposure to training is required. Methods: A total of 304 high-performing male youth soccer players (10–21 years old) in Germany were assessed across three seasons on various sport-specific and non-sport-specific cognitive functioning assessments. Results: The posterior means (90% highest posterior density) of random slopes indicated that both abilities predominantly developed between 10 and 15 years of age. A plateau was apparent for domain-specific abilities during adolescence, whereas domain-generic abilities improved into young adulthood. Conclusion: The developmental trajectories of soccer players’ EFs follow the general populations’ despite long-term exposure to soccer-specific training and game play. This brings into question the relationship between high-level experience and EFs and renders including EFs in talent identification questionable.