Health indicators (HIs) are fundamental to machinery condition monitoring and remaining useful life prediction. However, obtaining condition-invariant HIs under varying operating conditions remains a significant challenge. To address this issue, an HI construction model that integrates degradation feature disentanglement with a dynamic prior distribution guidance strategy is proposed. First, an optimization objective that enables feature disentanglement is formulated through variational inference, grounded in mutual information theory. Second, departing from conventional static prior distributions, a dynamic prior distribution guidance mechanism utilizing a complete partition scheme is introduced. Finally, an HI construction model applicable to variable-speed operating conditions is developed. Degradation feature disentanglement and dynamic prior distribution guidance respectively ensure from macroscopic and microscopic perspectives that the constructed HIs preserve pure degradation features without being affected by varying operating conditions. Additionally, the effectiveness of the model is validated through variable-speed accelerated life tests and real wind turbine generator bearing monitoring data. Multiple comparative experiments further demonstrate the considerable potential of the model for machinery condition monitoring and prediction under complex operating conditions.
Single wall carbon nanotubes (SWCNTs) are promising building blocks for nanoelectronic and optoelectronic devices, yet reliable and stable doping, particularly n type, remains challenging due to strong environmental sensitivity and competing extrinsic effects. Encapsulation of charge transfer molecules within the SWCNT cavity offers a promising route to stable doping while preserving the nanotubes outer surface for subsequent processing. Here, we systematically investigate the filling of arc discharge SWCNTs with the electron donor tetrathiafulvalene and electron acceptor tetracyanoquinodimethane, comparing different methods for filling, including melt filling, solution reflux, and vacuum phase sublimation. We follow the entire processing workflow from raw, unfilled powders to aqueous dispersions and employ density gradient ultracentrifugation to separate filled from empty nanotubes as well as metallic from semiconducting ones. Encapsulation efficiency and electronic modification are assessed using absorption spectroscopy, resonant Raman scattering, thermogravimetric analysis, X-ray photoelectron spectroscopy and electron paramagnetic resonance. Finally, we introduce a complementary vacuum-phase method that removes externally adsorbed molecules without extensive solvent washing, enabling cleaner encapsulated systems.
Accurate integrated water vapor (IWV) estimates are essential for atmospheric monitoring, yet reanalyses can retain systematic discrepancies relative to in situ observations. This study develops a radiosonde-informed machine learning framework to correct ERA5-derived IWV over Europe and assess spatial and temporal generalization. Radiosonde profiles from the University of Wyoming and the Integrated Global Radiosonde Archive were collocated with ERA5 and VMF3 products at 116 stations from November 2021 to November 2025. CatBoost used spatiotemporal, geographical, meteorological, atmospheric structural, and climate-zone predictors to estimate ERA5 IWV differences. Spatial generalization was assessed by strict station holdout and temporal generalization by an independent one-year test. The station holdout achieved RMSE values of 1.33 and 2.40 mm and R2 values of 0.94 and 0.92 in the cold and warm seasons, respectively. In the temporal test, RMSE decreased to 1.11 and 2.06 mm, with R2 values of 0.96 and 0.93 and near-zero mean biases. Relative to ERA5, RMSE was reduced by 51.5% and 40.1% in the two seasons. The model was then transferred to external GNSS station coordinates to generate radiosonde-informed, spatially collocated IWV estimates. Across 47,418 common observations from 95 valid radiosonde-GNSS pairs, the collocated product reduced RMSE from 3.15 mm for nearby radiosonde observations to 2.25 mm. These results demonstrate robust radiosonde-based correction and spatiotemporal generalization, while highlighting the value of transferring radiosonde observational constraints to locations without collocated sounding measurements.
At high penetration levels, variable renewable energy (VRE) reshapes price formation in wholesale electricity markets by increasing both oversupply and scarcity. While the fundamental mechanisms underlying extreme prices are well documented, the role of bidding behavior in amplifying price spikes under scarcity remains underexplored. This paper develops an empirical framework to characterize how aggregate bidding patterns respond to system scarcity, as proxied by residual load, and embeds the resulting behavioral rules in an agent-based market simulation to assess their implications under a future energy-transition scenario. The framework is applied to the Belgian, Dutch, and German day-ahead markets, testing it across systems with different VRE penetration levels.The results show that aggregate supply curves become significantly steeper with residual load, while bidding volumes shift toward upper price segments. In the 2035 simulation, scarcity-induced strategic bidding increases the annual average price in Germany by 15.49% (7.38 EUR/MWh) relative to marginal-cost bidding, while the peak price rises from 153 to 561 EUR/MWh (+268%), with effects concentrated in a small share of scarcity hours. These results suggest that extreme price formation cannot be understood from fundamental supply conditions alone. As VRE penetration increases and the dispatchable margin becomes more constrained, scarcity-related bidding behavior can substantially amplify price extremes. Navigating these risks requires market monitoring that distinguishes efficient scarcity rents from strategic conduct, alongside careful policy design to manage the underlying structural exposure to scarcity—primarily sufficient cross-border interconnection and dispatchable capacity—without dampening the price signals needed for long-term adequacy.
