In a context of increasing political polarization, ideological bias poses a challenge to evidence-based reasoning and the evaluation of factual information. However, its systematic study is hindered by the lack of reliable and valid measurement tools and by the frequent conflation of ideological bias with ideological stance. Addressing this gap, we introduce the Ideological Bias Expression (IBE) scale, a novel measure grounded in signal detection theory that disentangles discrimination accuracy from ideological bias in individuals' evaluations of evidence. Across two studies using German samples (NStudy1 = 446; NStudy2 = 1,359), we provide initial validation of the IBE scale across two distinct political issue domains (climate change/energy & domestic security/migration). Results indicate that the IBE scale is an effective tool for assessing individual differences in biased evidence evaluation, providing a foundation for future research on its determinants and potential interventions to promote more evidence-based public discourse. Using the scale, we find evidence that ideological bias is empirically distinct from knowledge-related factors, including formal education and political knowledge, indicating that such bias cannot be reduced to deficits in knowledge or reasoning alone. Moderate coherence across two issue domains also suggests that biased evidence evaluation is not purely issue-specific. Finally, in the German political context, ideological bias appears to reflect a combination of social identity dynamics and value-based cognition rather than partisan alignment alone.
This research presents the evaluation of a computer simulation for modelling shapes of paths traversed by various artificial species of worm on a 3D orthogonal grid. These resulting 3D worm trace patterns have not been modelled or analysed before. Simulation software was custom developed in multiple programming languages and platforms, supporting 3D graphics and virtual reality visualisations on various headsets and operating systems. A genetic encoding enables exploration of the full range of 3D worm phenotypes, this research modelled every possible unique worm in simulations up to 53.5 million time-steps. The results show that in this new 3D grid, there are 3239 potentially unique worms. Of the 3239 unique worms, 1882 (58%) terminated at the origin. Also 1352 (41%) of worms entered repeating loops, some complex loops were identified with lengths of up to 296639. Currently 5 worms (0.1%) continue to run chaotically beyond 53.5 million population in simulation runs, and their final outcome still has not been identified yet and remains unknown. Simple rules result in surprisingly complex and intricate movements. Images and unique outlier observations are presented demonstrating complex 3D patterns that emerge.
ObjectivesThis study examined within-player associations between GPS-derived external training load variables and heart rate-based internal training load during in-season training in elite youth soccer players. A secondary aim was to identify which intensity-based external load variables are independently associated with heart rate (HR) exertion per minute.MethodsEleven under-17 male soccer players were monitored across a nine-week in-season period. 266 player-session observations were included. External training load was assessed using GPS units with integrated inertial sensors, including total distance covered, high-speed running distance, explosive efforts, player load, and their time-normalized derivatives. Internal training load was quantified using heart rate exertion derived from individualized HR zones. Within-player associations were examined using repeated-measures correlations. A linear mixed-effects model was used to examine predictors of heart rate exertion per minute.ResultsHR exertion showed nearly perfect within-player associations with total distance covered (r = 0.921, p < 0.001) and PlayerLoadTM (r = 0.924, p < 0.001), while large associations were observed for high-speed running distance (r = 0.662, p < 0.001) and explosive efforts (r = 0.621, p < 0.001). Intensity-based variables, including total distance covered per minute and explosive efforts per minute, were significant positive predictors of HR exertion·min - ¹.ConclusionsIn elite under-17 youth soccer training, cumulative HR exertion primarily reflects accumulated external training volume, whereas HR exertion·min - ¹ is more closely related to locomotor and explosive action intensity. These findings highlight the need to interpret cumulative and intensity-based internal training load metrics separately in within-player training monitoring.
Tumor evolution in pediatric leukemia is shaped by stochastic fluctuations and constraints imposed by developmental programs and therapeutic pressure. However, quantitative characterization of these dynamics remains challenging because of sparse longitudinal sampling and measurement noise in clinical data. In this study, we applied Bayesian calibration of stochastic Ornstein-Uhlenbeck (OU) models to characterize patient-specific tumor evolution trajectories in pediatric leukemia. Using a longitudinal cohort of KMT2A-rearranged leukemias (52 patients, 311 longitudinal observations corresponding to 294 unique patient-time points), we estimated parameters governing mean reversion and stochastic variability and assessed the stability and uncertainty of these estimates through simulation-based validation under empirically derived sampling conditions. A representative single-patient trajectory further illustrated patient-level evolutionary dynamics. Across the cohort, inferred OU parameters showed stage-associated tendencies, including higher average diffusion in refractory and very-early disease groups, although posterior intervals overlapped substantially and did not support sharply separated dynamical regimes. Simulation analyses indicated that Bayesian calibration provided stable posterior computation and interpretable uncertainty quantification under sparse sampling, although recovery remained parameter- and schedule-dependent and predictive performance was limited. These findings show that Bayesian-calibrated stochastic models provide a useful framework for interpreting longitudinal tumor evolution in pediatric leukemia. While not intended for clinical prediction, this approach offers a quantitative perspective on stage-dependent disease dynamics and highlights the importance of uncertainty-aware modeling in sparse clinical datasets.
