Self-reported burnout complaints have been increasing among young workers. In order to identify starting points for addressing this issue, greater insight is required into the heterogeneity within this group regarding these complaints. This research focuses on burnout complaints among young employees and on relevant factors. Using the National Survey on Working Conditions by TNO/ CBS, we examined (1) how burnout complaints among young employees have developed since 2014, broken down by educational level, and (2) what associations exist between various background and work characteristics and burnout complaints among young employees in general, as well as (3) within subgroups based on educational level. Results show an increase in burnout complaints for all educational levels. Risk groups are identified based on gender, age, and educational level. However, work characteristics such as perceived job insecurity, high job demands, and low autonomy in particular appear to play an important role in explaining differences in burnout complaints. We also observe that higher job demands have a stronger correlation with burnout complaints among young workers with intermediate education, compared with youngworkers higher education. Psychosocial work characteristics therefore provide important starting points for interventions aimed at preventing or reducing burnout complaints among younger employees.
Smartphone bans are gaining popularity in education, with approximately 40
ABSTRACT With the increasingly large volumes of silicon solar panels being decommissioned worldwide, we urgently need to come up with a cheap and efficient recycling strategy that yields high‐value output materials. A crucial step in such recycling is to delaminate the front and back sheets to access the cells and their metallization. In this work, we demonstrate that the adhesion between the encapsulant and the silicon wafers can be weakened, in a fast and effective way, using a picosecond pulsed near‐infrared laser. The glass and encapsulant are then delaminated from the silicon wafer in a thermomechanical step. This method provides direct access to the silicon emitter and bulk as well as the precious and/or toxic metals on its surface, enabling their recycling. Ablation threshold experiments show that the IR laser mostly interacts with the silicon, thereby indirectly ablating the SiN x anti‐reflective coating. We show that laser pattern and laser setting optimization help strike a balance between effective silicon wafer surface ablation and minimal (submicron thick) contamination from the encapsulant, due to lower heat dissipation into the wafer and encapsulant. The SiN x removal, combined with high potential throughput and low OpEx, sets this process apart from existing delamination techniques. The process described in this paper can be crucial to enable rapid and energy‐efficient recycling of silicon PV modules to high‐purity raw materials with a high recovery rate.
Non-destructive test (NDT) methods provide an indirect assessment of the compressive strength of in-situ concrete structures. While traditional static models effectively capture the behaviour of small-scale localised datasets, their accuracy diminishes when applied to larger, aggregated datasets, where increased variability in NDT measurements introduces greater uncertainty in predicting concrete compressive strength. This paper presents three exhaustive, largest-to-date NDT databases on the ultrasonic pulse velocity (UPV), rebound hammer (RH), and SonReb methods, comprising 16,531 test results from 115 studies. First, existing empirical models are evaluated against global dataset trends. New relationships are fitted to reflect the global behaviour of each NDT method, highlighting their innate limitations in capturing large-scale variability. A comprehensive three-phase machine learning (ML) program is then introduced, studying the effects of incomplete features with varying levels of missing data on model performance. Seven diverse ML models are included in Phase 1, while Phase 2 assesses different imputation strategies. Phase 3 integrates the top-performers with a Tree-Structured Parzen estimator (TPE) optimisation algorithm to refine hyperparameters and maximise performance. Across all phases, CatBoost regression emerged as the most robust predictive model due to the high proportion of categorical variables included within the databases. The TPE-CatBoost models achieved final R2 values of 0.928, 0.896, and 0.947 for UPV, RH, and SonReb, respectively. Finally, a Django-based web application was deployed on a cloud server (https://recreate-ndt.onrender.com/), allowing practitioners to generate real-time compressive strength predictions for new NDT results. These novel datasets and ML tools can power future innovation through more advanced data-driven modelling.
Wetlands are the largest natural source of atmospheric methane (CH4), yet comprehensive global budgets are typically delayed by years, preventing a timely understanding of CH4 sources, sinks, and trends. To reduce this delay, we present a model emulator-driven framework and accompanying workflow that enable timely, continuous emission updates using a machine-learning emulator to reconstruct spatially explicit monthly emission fields at 1° × 1° resolution. We apply this framework to a global dataset of natural vegetated wetland CH4 emissions to extend the most recent Global Methane Budget (GMB; Saunois et al., 2025) record that covers the 2000–2020 emissions through 2025. In the test data (∼ 30 % of the total dataset), the emulator achieved a global R2 of 0.65 ± 0.003 (mean ± 95 % CI, hereafter) and an RMSE of 5.49±0.12×10-3 Tg CH4 yr−1. The emulator is trained on 35 GMB model estimates, including 22 process-based models and 13 atmospheric inversions, paired with 10 ensemble realizations of 11 gridded climate predictor variables from atmospheric reanalyses. Our results show that the global mean predicted wetland CH4 emissions for 2021–2025 (157.8 ± 2.4 Tg CH4 yr−1) are not significantly higher (∼ 0.05 Tg CH4 yr−1) than the 2000–2020 baseline. However, this stability masks a significant hemispheric redistribution of emissions. We detect an increase in Northern Hemisphere (NH) emissions in 2021–2025, with mid- and high-latitudes increasing by 0.76 ± 0.07 and 0.35 ± 0.03 Tg CH4 yr−1, respectively, while the tropics and Southern Hemisphere (SH) extratropics show offsetting negative trends (−0.95 ± 0.19 and -0.11±0.02 Tg CH4 yr−1, respectively). The predicted emissions are able to capture the low emissions in 2023 in South America linked to El Niño-related drought, as reported by recent studies (Ciais et al., 2026; Quinn et al., 2025). Furthermore, we identify a distinct seasonal amplification of global emission trends that peaks in late boreal summer. This new modeled dataset and operational framework bridge the gap between the latest updated budgets and low-latency monitoring, providing a scalable capacity to frequently update global emission estimates and critical early warnings of regional wetland feedback loops. The data are publicly available at https://doi.org/10.5281/zenodo.18870108 (Li et al., 2026).
