Phytochemical investigation of the root extract of Phyllanthus reticulatus Poir. (Phyllanthaceae) led to the isolation and identification of one previously undescribed ent-cleistanthane-type diterpenoid glucoside, 19-hydroxycleistanthol 19-O-β-d-glucopyranoside (1), together with ten known compounds (2–11). The structure of the new compound was established by HRESIMS, 1D and 2D NMR spectroscopy, ECD calculations, and GIAO NMR/DP4+ analysis. The known compounds were identified by comparison of their NMR data with those reported in the literature. Among these, compounds 6, 7 and 9 are reported for the first time from this species. In addition, compounds 1–10 were evaluated for their in vitro cytotoxic activities against MDA-MB-231, A549, MCF-7, and HCT-116 cell lines. As a result, phyllanthusmin C (5), an arylnaphthalene lignan, exhibited the most potent inhibitory activity against HCT-116 with IC50 value of 5.73 ± 0.34 μM, comparable to that of paclitaxel (5.03 ± 0.69 μM). The chemotaxonomic significance of all compounds was also discussed.
Visual–inertial odometry (VIO) can be viewed as a structured multimodal fusion problem, where heterogeneous sensory streams with time-varying reliability must be integrated to estimate ego-motion. Most learning-based VIO methods implicitly assume that all modalities are equally reliable at inference time and perform unconditional or softly gated fusion. However, when reliability becomes asymmetric or intermittently uncertain, such strategies may propagate corrupted information across modalities and degrade estimation stability. In this work, we propose WormVIO, a reliability-aware deep VIO framework motivated by compact sensorimotor decision processes, which formulates adaptive inference as a hierarchical discrete fusion problem. At its core, the Instinct-Bias Module is a hierarchical reliability-aware fusion mechanism that explicitly disentangles two decision factors: (i) fusion participation, which determines whether multimodal aggregation is beneficial at a given time step, and (ii) modality dominance, which determines which modality should guide estimation when reliability is imbalanced. These discrete decisions are implemented using a compact Neural Circuit Policy (NCP) combined with differentiable Gumbel-Softmax sampling, enabling end-to-end optimization of structured fusion control. Experiments on the KITTI benchmark demonstrate that the proposed decoupled fusion mechanism improves trajectory stability under intermittent visual and inertial perturbations while preserving competitive performance under nominal conditions. Furthermore, without fine-tuning, WormVIO transfers more consistently to the EuRoC MAV dataset, indicating that disentangling fusion participation and modality dominance enhances robustness to reliability shifts and cross-domain motion variations. The source code is available at: https://github.com/zRiverBird/worm_vio.git.
Floating photovoltaics (FPV) in shallow waters are highly susceptible to water level variations, posing significant challenges to the mooring system design and operational safety of the FPV system. To address this issue, we numerically investigate the dynamic response of a hexagon-type FPV assembly subjected to continuous water level variations. Towards this end, a multi-body coupled model is established by integrating potential flow theory, the Morison equation and the lumped mass method. Comparative analyses are conducted to evaluate two distinct mooring configurations, catenary and taut mooring. Both time- and frequency-domain characteristics of the module motions and mooring line tensions are examined. The results indicate that extreme ebb tide substantially changes the hydrodynamic performance of the FPV assembly, primarily driven by the variations of mooring stiffness. The taut mooring system is more sensitive to water level variations, while the catenary mooring system exhibits better adaptability and robustness. Moreover, reduced water levels generally exacerbate fluctuations in response, while elevated water levels increase mooring line tensions.
ZIF-8-derived materials showed great potential for adsorbing and decomposing ozone, making them suitable for application in catalytic ozonation. This study synthesized four X-Zn-MOF-C (X = 4, 6, 8, 10) materials, among which 10-Zn-MOF-C exhibited the best catalytic performance, possibly due to its superior promotion of ozone decomposition into ∙OH. A probe method was employed to quantify ROS (∙OH, O2−· and 1O2) in the four X-Zn-MOF-C/O3 systems. The results showed that the yield of ∙OH was lower than that of O2−· and 1O2, however, ∙OH played a more critical role in IBP degradation. ROS quenching experiments further confirmed the significant contribution of ∙OH to pollutant removal. Further the investigation into the sources of ∙OH generation was conducted from two dimensions, catalyst sites and solution properties. Compared to the surface hydroxyl protonation of the catalyst, the synergistic effect between the exposed Zn2+ and oxygen vacancy on the catalyst surface was more favorable for ∙OH generation on the catalyst surface, which mainly resulted from the formations of metal and vacancy hydroxyl groups. Meanwhile, the generation of ∙OH in the solution relied on the contribution of H2O2 produced from pollutant degradation.
We demonstrate the tunability of electrical transport properties in Fe70Ga30/Hf0.5Zr0.5O2 thin films via both bias voltage and magnetic field. The current–voltage (I–V) characteristics are modulated by applying a bias voltage across the heterojunction thickness, yielding various resistive states. A 5 V bias voltage induces a maximum resistance change of 90
