The Earth's climate has stayed within a window allowing continuous habitability by living organisms for approaching 4 billion years, even though significant changes have occurred to major climate controls such solar energy flux and the geography of land and sea; key features allowing this likely include silicate weathering as a planetary thermostat and long-term reduction in greenhouse gases as the sun has slowly brightened. Within this overall stability, the Earth has oscillated long-term between warm "greenhouse" and colder "icehouse" states, probably reflecting global tectonic changes. Within these, there have been smaller-scale fluctuations that include astronomically based cycles, paced by regular changes in the Earth's spin and orbit, and more irregular episodes such as geologically brief "hyperthermal" events caused by greenhouse gas outbursts. Currently, geologically hyperrapid anthropogenic greenhouse gas release is driving climate warming that, if unchecked, looks set to lead to a "Hothouse Earth" with profound societal and wider planetary implications.
Background Studies in post-COVID-19 condition following hospitalisation have focused on maximal exercise, with limited insight into submaximal responses that better reflect daily activities. We compared central haemodynamic, ventilatory, and peripheral muscle oxygen extraction responses during submaximal exercise between individuals with post-COVID-19 condition and healthy age-matched controls. Methods Twelve participants with post-COVID-19 condition, who were assessed 624 (593, 729) days following hospitalisation (median (IQR) length of stay: 7 (4.7, 11.7) days) and seven age-matched healthy controls (59 ± 11 years) completed a 4-min constant-load exercise test at 80% of peak cycling workload. Cardiac output (CO), oxygen uptake (V̇O₂), and ventilatory efficiency (V̇E/V̇CO₂) were assessed throughout exercise. Arteriovenous oxygen content difference was calculated using the Fick equation (C(a–v)O₂ = V̇O₂/CO). Results Post-COVID-19 condition participants exhibited a lower (p = 0.03) mean±SD C(a–v)O₂ compared with healthy controls (135 ± 29 vs. 171 ± 46 mL O₂·L⁻¹ blood). No significant differences were observed between post-COVID-19 condition and controls for V̇O₂ (1.9 ± 0.3 vs. 2.1 ± 0.7 L·min⁻¹), V̇E/V̇CO₂ (26.6 ± 3.0 vs. 27.5 ± 2.4), or CO (12.7 ± 3.0 vs. 11.1 ± 3.6 L·min⁻¹), indicating similar central cardiovascular and ventilatory responses between groups, despite a numerically greater cardiac output in the post-COVID-19 condition group (11 ± 4% higher). In contrast, RER and V̇E/V̇O₂ were significantly higher in healthy controls than in post-COVID-19 condition participants, indicating divergent ventilatory behaviour despite preserved V̇E/V̇CO₂. Conclusion Post-COVID-19 condition following hospitalisation is characterised by reduced peripheral oxygen extraction during submaximal exercise, alongside no significant difference in cardiovascular responses and preserved ventilatory efficiency with respect to carbon dioxide elimination, compared with healthy controls. Although RER and V̇E/V̇O₂ were higher in healthy controls, indicating some divergence in ventilatory behaviour between groups, this did not reflect impaired ventilatory efficiency in post-COVID-19 condition. These findings indicate that differences in exercise responses at workloads representative of daily living are possibly peripheral rather than central, supporting rehabilitation strategies targeting skeletal muscle oxidative function.
This retrospective single-center study developed and evaluated LiteFDNet, a lightweight deep neural network for intrapartum fetal distress detection from cardiotocography (CTG). All eligible deliveries from April 2014 to December 2024 were included under IRB approval (LGECH-2023-002; consent waived). Labels were refined using umbilical artery blood gas (distress: pH < 7.20; normal: pH 7.20-7.27), with exclusions for multiple gestations, major anomalies, missing CTG/outcomes, or non-analysable signals. Distress cases were matched 1:2 to controls, yielding 4,353 dual-channel CTG segments (1,451 distress; 2,902 normal). For each delivery, 30-60 min fetal heart rate and uterine contraction traces were artefact-screened, short gaps interpolated, z-normalised, lightly smoothed, and rendered as 224 & times;224 images. LiteFDNet was inspired by ResNet-18 and trained with AdamW, cosine annealing, and mild affine augmentation; performance was primarily assessed using stratified 10-fold cross-validation, while an independent held-out test split was retained only for supplementary transparency analyses. The primary outcome was AUC, and secondary outcomes included accuracy, sensitivity, specificity, and F1 score. Among 4,353 women (maternal age 30.3 +/- 4.4 years; gestational age 38.9 +/- 1.6 weeks), LiteFDNet achieved a mean AUC of 0.940 +/- 0.015 across the cross-validation folds and showed favorable performance relative to the compared baselines. Ablation analyses supported the contributions of the task-specific lightweight architectural modifications (t = 2.77; p < 0.01) and squeeze-and-excitation blocks (t = 2.15; p = 0.03). LiteFDNet may provide an accurate and computationally efficient proof-of-concept approach for CTG-based fetal distress screening, but further multi-center validation and comparison with stronger direct temporal baselines are warranted.
Deep learning models using traditional softmax classifiers have achieved remarkable success in various classification tasks. However, their performance degrades significantly on imbalanced datasets. Although Balanced Softmax is widely adopted as a state-of-the-art rebalancing method, it possesses inherent limitations, such as yielding disproportionately lower testing accuracy for tail classes. To mitigate these shortcomings, we propose the Class-Balanced Softmax (CBS). Rooted in a theoretical Bayesian framework and a heuristic power-law assumption, the CBS is a simple logit adjustment that is computationally inexpensive and easily integrated into existing pipelines. Furthermore, we characterise a fundamental phenomenon in models trained on imbalanced data, termed the preference issue, wherein models exhibit higher training error and a larger generalisation gap for classes with limited data. To quantify this issue, we introduce a novel metric and demonstrate that CBS effectively mitigates the preference issue. Extensive experiments on large-scale benchmarks show that CBS is highly scalable and outperforms existing methods, including Balanced Softmax.
Washery middlings (WMC) are abundant by-products of coal preparation, yet their high ash content and associated safety risks hinder their large-scale application as blast-furnace injection fuels. In this study, we identify a beneficiation-induced quality-safety paradox and propose a coordinated regulation strategy that combines coal blending with carbonate additives. We find that while flotation upgrading successfully reduced the ash content from 23.70 wt% to 11.86 wt% - meeting the GB/T 18512-2022 specification - it selectively enriched reactive macerals and increased the volatile matter to 29.13 wt%. Consequently, long-tube explosion tests revealed a concerning shift, exacerbating the dust explosibility from weak to strong. To address this, particle-size control, anthracite (AC) blending, and the use of carbonate inhibitors were systematically evaluated. An optimal blend of 60 wt% WMC and 40 wt% anthracite effectively reverted the mixture to weak explosibility, primarily attributable to the physical heat-sink and barrier effects of the inert anthracite particles. Furthermore, thermogravimetric analyses demonstrated a non-linear, U-shaped variation in apparent activation energy (Ea) during cocombustion. The 60 wt% WMC blend exhibited the minimum Ea (99.74 kJ mol(-1)), indicating a heat-transferand radical-assisted synergy whereby volatile-rich WMC facilitates anthracite ignition. Trace CaCO3 at 2-4 wt % provided effective explosion mitigation while maintaining acceptable combustion performance, consistent with a stage-dependent balance between thermal inhibition and chemical promotion. Ultimately, an optimized injection formulation - 60 wt% WMC blended with 40 wt% anthracite and 2-4 wt% CaCO3 - is recommended to achieve a balance among safety, combustion efficiency, and economic viability.
