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Contrasting land use systems regulate active and passive soil carbon pools and the carbon management index across soil depths
Abstract Understanding how soil carbon pools respond to contrasting land use systems is essential for evaluating soil functioning and land sustainability. Here, we examined the response of total organic carbon (TOC), oxidizable carbon fractions, microbial biomass carbon (MBC), soil organic carbon (SOC) stocks, and the carbon management index (CMI) across four contrasting land use types (forest, orchard, cropland, and abandoned land) at two soil depths (0–10 and 10–20 cm) in a sub-humid watershed. Oxidizable carbon fractions were grouped into active pools (very labile + labile) and passive pools (less labile + non-labile). Forest soils showed the highest TOC and MBC, whereas conversion to cropland and abandoned land reduced surface TOC by 36.0% and 47.4%, respectively, and MBC declined markedly under non-forest uses. Surface SOC stocks also decreased by 30.8% in cropland and 41.3% in abandoned land relative to forest. Active carbon pools declined substantially in the 0–10 cm layer, with reductions of 50.0% in cropland and 45.2% in abandoned land, while passive fractions accounted for a greater proportion of total SOC under these land uses, indicating a shift toward more stable carbon forms. CMI values further highlighted relative differences in SOC status among land uses: at 0–10 cm, CMI was 85.05 in orchard, 56.18 in abandoned land, and 44.16 in cropland, while at 10–20 cm the corresponding values were 77.25, 40.28, and 63.99, respectively. Integrating SOC fractionation with CMI provides a useful comparative framework for detecting relative changes in soil carbon status across contrasting land use systems.
Single station seismic observations enable high resolution localization of tectonic tremor sources using a vision transformer
Abstract Precisely locating tectonic tremor events is essential for understanding subduction zone dynamics. However, the lack of impulsive phase arrivals typically necessitates multi-station observations, posing a fundamental challenge for high-resolution monitoring using limited seismic records. Here, we show that single-station three-component wavefields can encode sufficient information to constrain tremor source locations using TremorViT, a vision transformer-based framework designed to estimate three-dimensional coordinates together with aleatoric uncertainties. Applied to the Nankai subduction zone, TremorViT achieves a mean epicentral error of 4.8 km using a single station, which improves to 2.5 km when independent station-level estimates are integrated. Using this approach, we localized 68,101 tremor events between January and September 2016, corresponding to approximately 1135.0 h of cumulative tremor activity. These metrics are substantially larger than those reported in existing network-based catalogs. This discrepancy likely reflects differences in detection sensitivity, event definition, and temporal windowing, even as the model preserves previously documented spatiotemporal patterns. This framework enables high-resolution tremor detection and offers a more granular view of tectonic activity, including potential signals associated with geodetically undetected slow slip events.
Machine learning prediction of customer satisfaction in fitness centres
Abstract Customer satisfaction in fitness centres is critical for fostering loyalty, higher spending, cross-buying, and positive recommendations. This study seeks to develop a predictive model of gym users’ satisfaction, identify its main determinants, and optimise predictive accuracy through machine learning techniques. Data from 10,368 users across five Spanish fitness centre chains were analysed. Five machine learning algorithms were applied: decision tree, random forest, logistic regression, gradient boosting, and Naïve Bayes. Model performance was evaluated using AUC, sensitivity, specificity, F-measure, Cohen’s Kappa, and overall accuracy. The random forest model showed the highest accuracy (AUC = 0.954, sensitivity = 0.933, specificity = 0.825, F-measure = 0.91, Cohen’s Kappa = 0.767, overall accuracy = 0.889). The most influential factors for satisfaction were the overall environment of the centre, employee trustworthiness, staff quality, and management of waiting times. This study extends prior research by applying machine learning algorithms to explain customer satisfaction in fitness centres, positioning satisfaction as the primary predictive outcome and providing interpretable insights into how environmental and service-related factors shape satisfaction beyond traditional retention-focused approaches.
Waterbird guilds function as dynamic cross-ecosystem energy vectors in unique soda pan model systems
Abstract Migratory animals play a key role in coupling ecosystems through the redistribution of energy, to date, energy transport by functional guilds has not been comprehensively quantified. Here, I present a generalized, own guild-based framework that extends established waterbird nutrient-cycling guild concepts into the energy flow across ecosystems, enabling the quantification of energy import, export and net balance mediated by waterbirds. Using long-term (1986–2017) waterbird census data from two internationally important (Ramsar-sites) intermittent soda pans in Central Europe, as simplified unique model systems. I estimated avian energy fluxes by integrating guild-specific foraging behaviour, habitat use, metabolic demand and residence time. Waterbird guilds were classified as net importers, importer–exporters or net exporters of energy based on Boros’s method. Estimated annual energy fluxes varied across several orders of magnitude, reflecting pronounced interannual variability in the studied ecosystem. Net energy import ranged widely from 574,712 to 57,022,011 kJ/ha/yr, while net energy export from 361,206 to 16,171,538 kJ/ha/yr. The net energy import consistently and significantly exceeded net energy export. Consequently, the average community-level energy balance was significantly positive (+ 51.3) regarding waterbird-mediated nutrient transport. The functional asymmetry between guilds ensures that energy import remains dominant over export regardless of species richness, fundamentally defining the ecosystem’s role as a significant energy sink in the studied system. Temporal analyses revealed significant dynamics in net energy import, whereas variation in open water area did not predict exactly energy balance. I partly reject the hypothesis that net energy import responds predictably to long-term trends in open water area, but partly confirm as it is rather depends on guild-structure dynamics. These results demonstrate that cross-ecosystem energy transport by waterbirds is governed primarily by functional guild composition and behaviour rather than habitat extent alone. The proposed framework provides a scalable tool for integrating avian-mediated energy transport into ecosystem energetics and spatial food-web theory.
Exploring the immunomodulatory potential of Lilium lancifolium in pneumonia: a combined study of network pharmacology, molecular docking, and in vitro experiments
Preterm birth and birth weight extremes are associated with periodontal disease and tooth loss in the ARIC study
Halodule uninervis ethanolic extract reduces inflammation in LPS-stimulated RAW 264.7 macrophages via NF-κB, STAT3, and MAPK modulation
The effects of meteorological factors on hemorrhagic fever with renal syndrome in Yichun, China: a major Apodemus-type endemic city
Hydrogen suppressed tumor growth during chronic intermittent hypoxia via modulating macrophage polarization
Synthesis and characterization of selenium-containing sodium phosphate glasses with enhanced physical and radiation shielding performance
Training set augmentation and biology-aware harmonization improve radiomic models for lung cancer prediction in indeterminate nodules
Abstract CT radiomics-based machine learning has potential to predict lung cancer in pulmonary nodules (PNs) earlier than standard-of-care methods. Low malignancy rates in early-development PNs and variable image acquisition hinder development of radiomic models for diagnosing these PNs. To address these challenges, we augmented training using later-development PNs and harmonized for acquisition effects. We examine early-development benign and malignant PNs (n = 106) below the sensitivity of standard-of-care diagnosis. Classifiers predicting malignancy performed near chance when trained on ComBat-harmonized radiomic features from only early-development PNs. We then augmented training with later-development benign and malignant PNs (n = 225). We evaluated whether harmonization must incorporate biology that impacts acquisition effects in added training data. To correct variability from four acquisition protocols, we compared: (1) biology-unaware harmonization, (2) harmonizing with a covariate distinguishing early-development, later-development benign, later-development malignant datasets, (3) harmonizing each dataset separately. Models trained using augmentation, but biology-unaware harmonization, failed to improve consistently. Augmented training data harmonized with a covariate (ROC-AUC 0.74 [0.69–0.79]) or separately (ROC-AUC 0.71 [0.66–0.77]) yielded higher test ROC-AUC (Delong, p ≤ 0.05) and PR-AUC (Wilcoxon, p ≤ 0.05). In a proof-of-principle methodological study, we demonstrate with a small single-center dataset that combining radiomic features from later-development benign and malignant PNs requires biology-aware harmonization.
DFT studies of doped and encapsulated of B12N12 nanocage using nickel and platinum metals as carriers for Sunvozertinib drug
Social inequality and the mental health of Chinese youth
EMG-based hand gesture recognition using multi-scale deep residual network with SE-module
Solidago canadensis extract fractionation, phytochemical identification, and nematicidal/nematistatic activity against Meloidogyne incognita
Abstract The development of eco-friendly pest management strategies is crucial for sustainable agriculture. In this study, we investigated the bioactivity of Solidago canadensis leaf extract against Meloidogyne incognita juveniles (J2). In vitro assays showed that aqueous extracts exhibited greater nematistatic (immobilizing) activity than less polar solvent extracts. Fractionation of the aqueous decoction extract using column chromatography yielded 11 fractions (F), which were evaluated at 100, 200, and 300 mg L − 1 . The crude extract showed 100% nematistatic activity at all concentrations, while among the fractions, F5, F7, and F9 were the most effective. In the pot experiment, the crude extract and F7 reduced gall formation by 55.3%, whereas F8, despite low in vitro nematistatic activity (21.5%), achieved the highest gall reduction (58.7%). Unexpectedly, some fractions increased gall formation. GC-MS analysis of F7 and F8 revealed 20 phytochemical compounds, including several with reported nematode-suppressive properties. These findings underscore the potential of S. canadensis extracts and specific fractions as botanical nematistatic agents and emphasize the importance of combining the in vitro and pot bioassays when evaluating botanical nematicides.
The impact of Paulownia–buckwheat intercropping on the biodiversity of different living biota
Abstract Intercropping has emerged as a promising strategy to improve agroecosystem biodiversity and mitigate some adverse effects associated with intensive monoculture systems. This study evaluated weed infestation and biodiversity responses in a Paulownia –buckwheat intercropping system compared with buckwheat monoculture under the environmental conditions of southwestern Poland. Weed species composition, abundance, and biomass were assessed at different growth stages of buckwheat. Selected agroecosystem components, including soil microorganisms, soil mesofauna, and pollinator abundance, were evaluated. The intercropping system increased Collembola diversity and Acari abundance, while higher diversity and richness of segetal plant species were also observed compared with monoculture. Higher bacterial abundance and dehydrogenase activity were recorded under intercropping, whereas fungal community composition remained generally stable between cultivation systems. Several melliferous weed species were identified within the intercropping system, potentially supporting pollinator activity. Although no statistically significant differences were observed in pollinator-related parameters, nectar productivity and sugar availability tended to be higher under intercropping conditions, suggesting that the Paulownia –buckwheat system may contribute to maintaining pollinator activity and supporting agroecosystem biodiversity. No significant differences in buckwheat yield or biometric traits were found between cultivation systems. Overall, the results indicate that Paulownia –buckwheat intercropping may enhance selected components of agroecosystem biodiversity without reducing crop productivity.
Design of a Fano-resonance-enhanced dielectric grating for ultralow-filling-factor superconducting nanowire single-photon detector
Abstract Superconducting nanowire single photon detectors (SNSPDs) exhibit excellent performance in the near-infrared band, but their application range is limited by the detection efficiency and detection speed. In this paper, we propose a high-efficient and ultralow-filling-factor design scheme based on Fano resonance which is excited by a one-dimensional silicon dielectric grating. This design can reduce the kinetic inductance of the nanowires while maintaining the active sensing area of SNSPDs, which is beneficial for improving the recovery time of the detector. Meanwhile, the absorption efficiency of the nanowires can be enhanced by the Fano resonance. Taking λ = 1550 nm as an example, numerical simulations are performed using commercial simulation software based on the finite-difference time-domain method. Under idealized structural conditions, the absorption efficiency of the Niobium Nitride (NbN) superconducting nanowires can exceed 98% when the filling factor of the nanowire is only 10%.