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Evaluation of soil degradation caused by wildfire through integration of remote sensing soil quality indices and micromorphological analyses
Performance enhancement of hybrid shake table via passive load balancing and nonlinear system identification using coupled ODE modelling
Abstract This study aims to identify a suitable passive load-balancing mechanism for a hybrid shake table, which decouples spatial and planar motion for enhanced control. By minimizing reliance on extensive experimental data, the research seeks to improve system stiffness, compensate for heavy payloads, and enhance the overall performance of shake tables. A regression-based nonlinear least-squares (NLS) technique enhanced by the trust-region-reflective (TRR) algorithm is proposed for system identification. A fusion-based approach is adopted, in which experimental data is used to develop a reduced-fidelity state-space model (SSM) of the shake table, while simulation data is used to evaluate passive load-balancing mechanisms. The study solves higher-order coupled ordinary differential equations (ODEs) to estimate stiffness and damping parameters for various configurations. Among the four vertical motion actuator (VMA) assembly configurations, the configuration equipped with a passive hydraulic damper exhibited superior performance. It attained the highest fit percentages (98.01% and 87.66%), the highest coefficient of determination R 2 values (0.9996 and 0.9847), the lowest normalized root mean square error (NRMSE) values (0.0022 and 0.0083), minimal amplitude errors (0.0338 and 0.1350), and the most negligible phase errors (0 and 0.08) for displacement and velocity responses, respectively. Among the passive mechanisms analyzed, a hydraulic damper proved to be the most effective load-balancing mechanism, demonstrating high stiffness, superior damping, and optimal force compensation. This enhancement is attributed to its nonlinear spring and damping characteristics, which enhance stability under heavy loads. This research presents a standardized approach for solving coupled ODEs in dynamic systems. The findings provide a novel framework for improving shake table performance, with potential applications in earthquake simulation, robotics, and industrial motion platforms.
Dynamics and modulation of weakly nonlinear fast magnetosonic waves in pulsar magnetosphere
Short term associations of weather and air quality with ophthalmic outpatient attendance in eastern China
Abstract The magnitude and timing of short-term associations of weather and air quality with ophthalmic attendance remain poorly quantified. We analysed completed attendances from a tertiary ophthalmology department in Jiangsu, China (2015–2025; 3,788 days) using a time-stratified case-crossover design with conditional Poisson regression. Exposures included catchment-weighted meteorology, nitrogen dioxide (NO₂), and fine particulate matter (PM₂.₅); extreme heat, cold, and heavy rainfall were accumulated over 0–3 and 0–7 days. Over 0–3 days, each additional extreme-heat or heavy-rainfall day was associated with lower completed attendance (rate ratios 0.961 [95% CI 0.954–0.967] and 0.937 [95% CI 0.923–0.952], respectively; both p < 0.0001), whereas extreme cold showed no clear association. From a baseline of 293 visits/day, two heat days and two heavy-rainfall days corresponded to approximately 23 and 36 fewer visits/day, respectively. Lag analyses suggested delayed positive rainfall associations compatible with partial compensation, but no comparable heat pattern within 21 days. The retrospective trigger simulation had 39% precision and 59% recall; approximately 61% of triggered days were false alarms. Locally derived heat thresholds and rainfall amount-related estimates may support preparedness planning. Because completed attendance—not underlying ophthalmic need—was measured, real-time use requires prospective validation of safety, equity, and unmet-need outcomes.
YOLOv8-based real-time obstacle detection in farmland environments for heavy-load agricultural UAVs
Abstract Heavy-load agricultural UAVs operating at low altitude over farmland often encounter three major difficulties: unreliable recognition of distant small obstacles, unstable localization of elongated targets, and strong interference from cluttered backgrounds. To cope with these challenges, this work introduces a lightweight real-time obstacle detection framework by redesigning YOLOv8n for farmland scenes. In the backbone, SPD-Conv, retained high-resolution $$\:{P}_{2}$$ features, and the DGA-C2f module are jointly used to preserve fine-grained cues for small and slender obstacles. In the feature aggregation stage, a lightweight scale-difference fusion network is constructed, where the LSDF module is embedded into a bidirectional interaction scheme to strengthen cross-level feature collaboration. In the prediction stage, a direction-aware decoupled head is adopted so that orientation modeling can assist localization and improve the regression quality of elongated targets. Experiments on a self-built farmland obstacle dataset show that the resulting model reaches 89.3% Precision, 88.0% Recall, 91.8% mAP@0.5, and 85.0% mAP@0.5:0.95. Compared with YOLOv8n, the proposed model improves Precision, Recall, mAP@0.5, and mAP@0.5:0.95 by 1.5, 1.8, 2.4, and 2.3% points, respectively. It also maintains real-time inference performance with appropriate parameters, 8.9 GFLOPs, and 123 FPS, demonstrating a clear balance among detection accuracy, lightweight complexity, and deployment efficiency for farmland obstacle perception.
Computational investigation of metal doped coronene for acetophenone adsorption and sensing in wastewater treatment
Abstract Acetophenone (ACT) is a persistent organic pollutant in industrial wastewaters that poses serious risks to human health and the environment. In this study, we evaluated pristine coronene (CRO) and its aluminum- and zinc-doped forms (Al-CRO and Zn-CRO) as adsorbents and potential colorimetric/electrochemical sensing platforms for ACT using B97D/6-31G(d), B97D/LANL2DZ, and ωB97XD/6-31G(d). Statistical comparisons based on mean absolute deviation, root mean square deviation, and Pearson correlation coefficient confirmed the consistency of the main qualitative results at computational levels. Adsorption energy, electronic properties, optical response, and interaction mechanisms were examined using density functional theory (DFT), time-dependent DFT (TD-DFT), quantum theory of atoms in molecules (QTAIM), and NBO, NCI, and ELF/LOL analyses. QTAIM, NCI/RDG, and ELF/LOL analyses consistently identified weak physisorption in CRO@ACT, predominantly noncovalent adsorption with a localized Zn-O contribution in Zn-CRO@ACT, and a strong polarized coordination-type interaction in Al-CRO@ACT. Al-CRO exhibits strong charge-transfer-assisted, coordination-type adsorption, with Eads values from − 47.52 to -48.73 kcal.mol − 1 , accompanied by a reduced HOMO-LUMO gap and a predicted shift in the absorption maximum from 391 to 596 nm upon ACT binding. These results identify Al-CRO as a candidate for strong ACT adsorption/removal and for disposable adsorption-coupled colorimetric/electrochemical sensing due to its extremely long recovery time. Also, according to the theoretical results, Zn-CRO with Eads values from − 10.64 to -12.40 kcal.mol − 1 and faster desorption than Al-CRO (which may facilitate regeneration), along with a shift in the absorption wavelength from 346 to 410 (in the presence of ACT), is a candidate for experimental evaluation in the future.
Comparative investigation on dry sliding wear behaviour of L-PBF printed tool steel for stamping dies
Neutrino mass predictions with metaheuristic optimization under $$A_4$$ modular symmetry
Deep learning-based adaptive recommendation algorithm for personalized music teaching
Sulfonic acid-functionalized chitosan-Fe3O4/covalent triazine framework as an efficient and reusable magnetic nano-catalyst for fructose conversion into 5-hydroxymethylfurfural
Greenness challenge: a game based learning approach for sustainable analytical chemistry education
Association of trajectories and cumulative exposure of modified cardiometabolic index with cardiovascular disease in middle and older adults
Automated conceptual earned value management
NLRP3 participates in IL-17 A-induced epithelial-mesenchymal transition in human nasal epithelial cells of chronic rhinosinusitis with nasal polyps
Life cycle assessment of renewable hydrogen and bioenergy pathways transitioning from conventional to sustainable power generation
Application of multiscale token fusion and pruning in CNN–Transformer hybrids under low-data training for image recognition
Abstract In this paper, a hybrid convolutional neural network (CNN) and transformer architecture for image classification that explicitly exploits multiscale spatial representations while maintaining computational efficiency is proposed. A convolutional backbone is first used to extract hierarchical feature maps, which are subsequently tokenized and fused through a cross-scale token fusion (CSTF) mechanism. In addition, several token-level and CNN-level pruning strategies are evaluated to examine whether redundant spatial tokens or convolutional features can be removed without substantially degrading performance. Extensive experiments on Caltech-101 and Oxford-IIIT Pets under low-data training conditions show that the best proposed configurations are dataset-dependent: the single 14 $$\:\times\:14$$ scale achieves 70.28 $$\:\pm\:1.91$$ % accuracy on Caltech-101, while the single 7 $$\:\times\:$$ 7 scale achieves 28.35 $$\:\pm\:1.62$$ % accuracy on Oxford-IIIT Pets. Among the multi-scale fusion models, 7 $$\:\times\:$$ 7+14 $$\:\times\:$$ 14 performs best on Caltech-101 69.68 $$\:\pm\:$$ 1.77%, whereas 7 $$\:\times\:$$ 7+14 $$\:\times\:$$ 14+28 $$\:\times\:$$ 28 performs best on Oxford-IIIT Pets 27.57 $$\:\pm\:$$ 1.05%. CNN kernel pruning achieves the strongest pruning performance on both datasets, reaching 68.37 $$\:\pm\:$$ 1.06% on Caltech-101 and 28.15 $$\:\pm\:$$ 1.15% on Oxford-IIIT Pets. These results indicate that multi-scale token fusion can provide competitive performance, but careful scale selection is more important than simply increasing the number of spatial token scales.
Intravenous hydrogen-rich acetated Ringer’s solution attenuates liver injury in heatstroke rats through endothelial glycocalyx preservation
Abstract Heatstroke is a life-threatening systemic disorder characterized by hyperthermia, inflammatory activation, endothelial injury, and multiple organ dysfunction. The liver is particularly vulnerable, but effective post-onset therapeutic strategies remain limited. This study examined whether intravenous hydrogen-rich acetated Ringer’s solution (H-AR) attenuates liver injury after heatstroke onset in rats, in comparison with acetated Ringer’s solution alone (Control) and 2% hydrogen inhalation (HI). Liver injury was assessed 60 min after onset using histological scoring and liver enzyme measurements. Circulating markers of inflammation and endothelial injury, endothelial glycocalyx ultrastructure, and 24-h survival were also evaluated. Thermal exposure was comparable among the heatstroke groups. Both H-AR and HI significantly reduced histological liver injury scores compared with Control; H-AR showed significantly greater attenuation than HI and was the only intervention that significantly reduced alanine aminotransferase levels. Both hydrogen-based interventions suppressed high-mobility group box 1 and syndecan-1, preserved endothelial glycocalyx structure, and improved 24-h survival compared with Control. These findings suggest that post-onset intravenous H-AR attenuates heatstroke-associated liver injury, potentially through reduced inflammatory activation and endothelial glycocalyx preservation, and may offer advantages over hydrogen inhalation with respect to hepatoprotection.