We present the third release of ULYSSES, a Python package for the numerical evaluation of the baryon asymmetry generated through leptogenesis. This version includes code implementing state-of-the-art density matrix equations for low-scale leptogenesis with three quasi-degenerate right-handed neutrinos. We extend the validity of the code in this scenario beyond the 100 GeV right-handed neutrino mass scale, into the regime of resonant leptogenesis, by including neutrino production rates valid in both the relativistic and non-relativistic regimes. In addition, in the high-scale vanilla scenario, we provide routines for computing ΔL=1 scattering processes, enabling full phase-space evolution of the right-handed neutrino and lepton asymmetry. A new --extended parameter interface allows users to pass model-specific inputs beyond the standard leptogenesis runcard without modifying the core infrastructure and demonstrate its use with a toy module that simultaneously solves the vanilla leptogenesis equations and the freeze-in production of dark matter. On top of these improvements, we introduce an alternative parametrisation of the Casas-Ibarra matrix, update the default neutrino oscillation parameters and report cross-checks of the new low-scale leptogenesis module against published benchmarks and independent codes. ULYSSES is publicly available on GitHub https://github.com/earlyuniverse/ulysses and pip-installable from PyPI. PROGRAM SUMMARY Program title: ULYSSESCPC Library link to program files: https://doi.org/10.17632/4cvdr57t2n.1Developer’s repository link: https://github.com/earlyuniverse/ulyssesLicensing provisions: MIT LicenseProgramming language: Python 3Journal reference of previous version: Comput. Phys. Commun. 291 (2023) 108834Does the new version supersede the previous version?: YesReasons for the new version: Extension of the code capabilities to include low-scale leptogenesis scenarios, complete ΔL=1 scattering processes for high-scale leptogenesis, improved user-defined model support, and enhanced usability through a Python API and Jupyter notebook interface.Summary of revisions: Implementation of a unified treatment of low-scale leptogenesis with three right-handed neutrinos; inclusion of complete ΔL=1 scattering processes in the leptogenesis evolution equations; addition of alternative Casas–Ibarra parametrisations; support for user-specified initial heavy-neutrino abundances; extension of the framework to accommodate custom leptogenesis models and parameters; introduction of a Python API and Jupyter notebook workflow.Nature of problem: Numerical solution of coupled Boltzmann and Density Matrix equations governing thermal, resonant, and low-scale leptogenesis, including the evaluation of computationally demanding thermal integrals and reaction rates.Solution method: The evolution equations are solved using the odeint and solve_ivp integrators available in Python. Integrals are evaluated using a Numba-accelerated wrapper around the cquadpack integration library.
Understanding resilience dynamics remains a central challenge in social-ecological systems research, particularly in marginalised and rapidly transforming local contexts. Although resilience theory offers a compelling framework for how systems absorb disturbances, adapt, and transform, managing system resilience remains difficult without explicitly foregrounding human agency. This paper introduces an enriched model for analysing resilience dynamics at local scales by integrating the adaptive cycle framework with human responses conceptualised as coping, adaptation, and transformation. Applied to one of Ethiopia’s most marginalised local contexts—the agropastoral Lower Omo Valley, which bears the burden of state-led mega-developments such as hydroelectric dams and sugar plantations—the model examines how local actors navigate the externally imposed changes and either sustain or lose resilience. Findings reveal that while national narratives frame these developments as transformative, local responses are predominantly characterised by coping. The study documents declining agricultural, livestock, and decision-making potential, weakened social and economic connectedness, and limited adaptive capacity. It argues that resilience at local scales is undermined by large-scale interventions that exclude community agency, pushing systems toward maladaptive responses and poverty traps. By foregrounding local agency and integrating resilience responses into adaptive cycle analysis, the study provides a nuanced understanding of resilience dynamics in marginalised social-ecological systems highly vulnerable to regime shifts. Our findings have broader implications for resilience-building in marginalised regions, particularly where external interventions reshape local systems without community participation.
Spontaneous thought is often considered an unconstrained, internally generated mode of thinking that is independent of the present perceptual environment, making it difficult to study empirically. However, recent accounts have raised the possibility that the generation and content of spontaneous thought can be shaped by even weak perceptual constraints. We leveraged this new perspective to develop a novel approach using music, a ubiquitous stimulus that permeates our everyday environments, as a window into the mechanisms supporting spontaneous thought content. Adults spanning four age groups listened to music from ten different genres and completed the open-ended task of writing descriptions of any thoughts that arose. By applying natural language processing analysis to the spontaneous thought descriptions, we found that thoughts sustained while hearing music from the same genre shared greater semantic similarity than those sustained while hearing music from different genres, and participants from the same age group experienced more similar thoughts than those from different age groups. Properties that varied naturally with genre, including the music’s auditory features, expressed emotions, and contextual associations, all contributed significantly to explaining the greater semantic similarity of thoughts within genres. These results demonstrate that spontaneous thought is jointly shaped by perceptual and experiential constraints, opening new avenues for understanding when and why particular thoughts come to mind, with implications for creativity and mental health.
Skeleton-based action recognition aims to recognize human actions from sequences of human joint coordinates. Most existing Spatial-Temporal Graph Convolutional Networks (STGCNs) have achieved promising results by modeling skeletal structures with implicit spatial-temporal representations. However, our empirical study reveals a clear performance imbalance across different skeletal modalities, indicating that implicitly coupling spatial and temporal information limits the full exploitation of complementary structural and motion cues. Inspired by the ventral and dorsal pathways in human perception, we propose Dual-Pathway Graph Convolutional Networks (NeuroPath), which adopt a dual-pathway architecture for separate yet collaborative modeling of spatial and temporal information. Specifically, transformation units first convert the input into pathway-specific skeletal representations, allowing each pathway to focus on complementary aspects of human motion. To further capture coordinated joint behaviors and their interrelationships, we introduce a group graph convolution block that dynamically identifies key body parts and models their spatial-temporal dependencies. In addition, inter-pathway dynamic fusion modules integrate complementary inter-modal information across pathways, facilitating higher-level semantic interpretation of actions. Extensive experiments on Kinetics Skeleton 400, NTU RGB+D 60, and NTU RGB+D 120 demonstrate consistent performance improvements, validating the effectiveness of dual-pathway spatial-temporal modeling for skeleton-based action recognition.
Ensuring an adequate truck driver workforce is a key policy concern for governments seeking resilient supply chains. In recent years, workforce contraction has intensified this challenge. In the aftermath of the COVID19 pandemic, many national governments introduced recruitment-focused measures, however worker retention received comparatively less attention. Drawing on a 2023 survey designed and administered to UK truck drivers (N = 1756), Structural Equation Modelling (SEM) is used to examine how job satisfaction, health and wellbeing, flexible work arrangements and workplace discrimination are associated with intention to quit or remain working as a truck driver. Drivers reporting higher job satisfaction and wellbeing were less likely to intend to leave the occupation (inter-occupational turnover). The availability and uptake of diverse work arrangements was associated with better retention outcomes through higher satisfaction and wellbeing, whereas experiencing discrimination was associated with lower retention. Company size and worker age also had direct associations with quitting intentions and retention, while contract type and caring responsibilities operated mainly through their relationships with satisfaction, wellbeing and discrimination. The findings indicate how truck driver retention is shaped by the interaction between working conditions, health and the fit between paid work and life outside work. These findings support a shift from recruitment-led responses towards retention-oriented transport policy, including stronger health and wellbeing provision, more predictable working hours and flexible working arrangements, and effective protection against discrimination. These measures will require support through employment regulation, freight procurement standards and buyer-supplier contracting practices, rather than relying on individual firms alone.
