We have developed an extension of the inert doublet model in which the CP phases in the weak sector are generated from one-loop level corrections mediated by dark fields, while the strong CP phase remains vanishing at three loops. In this framework, the tiny masses of the active neutrinos are produced through a radiative inverse seesaw mechanism at a two-loop level, the masses of the first and second families of Standard Model (SM) charged fermions arise from a one-loop level radiative seesaw mechanism, and the third generation of SM charged fermion masses are generated at tree level. We have demonstrated that the proposed model successfully accounts for SM fermion masses and mixings. The radiative nature of the seesaw mechanisms is attributed to preserved discrete symmetries, which are required for ensuring the stability of fermionic and scalar dark matter candidates. The preserved discrete symmetries also allow for multicomponent dark matter, whose annihilation processes permit one to successfully reproduce the measured amount of dark matter relic abundance for an appropriate region of parameter space, which has been shown to be compatible with current dark matter direct detection limits. Besides that, we explore the model's ability to explain the 95 GeV diphoton excess observed by the CMS Collaboration, showing that it readily accommodates this anomaly. We also have shown that charged lepton flavor violating decays acquire rates within the current experimental sensitivity.
Land use/land cover change (LULCC) in general and green space change in particular play an important role in flood control. Therefore, understanding the effects of LULCC and green space change is considered essential for sustainable land use planning, especially in the context of climate change and urban growth. The objective of this study is to evaluate the effects of green space dynamics on flood susceptibility using machine learning and remote sensing, namely the multilayer perceptron (MLP) and the recurrent neural network (RNN) in Vietnam’s Thanh Hoa Province. The novelty of this study lies in the integration of multi-temporal remote sensing data (2017 and 2024) with deep learning to evaluate the effects of green space dynamics on flood susceptibility, a problem that has not been sufficiently addressed in previous studies, which have mainly focused on single-period analysis. A total, 1164 flood inventory points and 14 conditioning factors in 2017 and 2024 were used as input data for the machine-learning model. Various statistical indices—namely root mean square error (RMSE), mean absolute error (MAE), area under the curve (AUC), and coefficient of determination (R2)—were applied to evaluate the computational performance of the MLP and the RNN. The results showed that the MLP model performed better than the RNN model with an R2 value of 0.812. The results also highlighted that the green space area decreased from about 71
A multifunctional advanced material that integrates antibacterial activity, anti-inflammatory effects, and tissue regenerative capability represents a highly effective platform for the treatment of infected wounds. Among various candidates, in situ–forming hydrogels have gained significant attention owing to their favorable physicochemical characteristics and biological performance. Chromolaena odorata (CO) leaves, traditionally used for their antibacterial and wound healing effects, contain abundant phenolic and polyphenolic compounds that also enable them to function as effective reducing and capping agents for silver nanoparticle (AgNPs) synthesis. In this study, we developed a multifunctional thermoresponsive nanocomposite hydrogel by incorporating AgNPs and CO extract into a chitosan-based polymer matrix. This integration improved the mechanical robustness of the system and conferred multiple bioactivities, including antioxidant, antimicrobial, anti-inflammatory, and cell proliferative effects, while preserving its injectability and temperature-triggered gelation behavior. The hydrogel demonstrated enhanced proliferation of human fibroblasts, strong free-radical scavenging capacity, and potent antibacterial activity against both Staphylococcus aureus and Pseudomonas aeruginosa. These findings support the potential of this multifunctional hydrogel platform for further development and application in the treatment of infected skin wounds.
Rising environmental pollution poses a significant threat to bat populations worldwide, raising serious ecological and conservation concerns. Here, we present the first assessment of cytogenotoxic stress responses in cave-dwelling insectivorous bats from the karst island of Cat Ba in northern Vietnam, in relation to anthropogenic pollution. The study aimed to evaluate the association between exposure to heavy metals (HMs) and DNA damage, as well as blood alterations in bats, using minimally -invasive biomarkers. We applied an integrated approach combining three indicators: HM accumulation in guano (a hallmark of exposure), micronucleus (MN) frequency (a measure of genotoxicity), and the polychromatic-to-normochromatic erythrocyte (PCEs/NCES) ratio (a measure of cytotoxicity). Guano-derived cadmium (Cd) and lead (Pb) levels correlated strongly with genotoxic markers, confirming guano as a reliable indicator of HM contamination at the population level. Species- and site- specific cytogenotoxic responses to HM stress were manifested through the formation of MNs and the reduction of the PCEs/NCEs ratio, signifying the presence of DNA damage, suppression of bone marrow activity, and disruption of erythropoiesis. Combining guano chemistry with cytogenotoxic biomarkers provides a sensitive and non-invasive tool for monitoring environmental stress in bat populations in karst ecosystems.
3D part amodal segmentation--decomposing a 3D shape into complete, semantically meaningful parts, even when occluded--is a challenging but crucial task for 3D content creation and understanding. Existing 3D part segmentation methods only identify visible surface patches, limiting their utility. Inspired by 2D amodal segmentation, we introduce this novel task to the 3D domain and propose a practical, two-stage approach, addressing the key challenges of inferring occluded 3D geometry, maintaining global shape consistency, and handling diverse shapes with limited training data. First, we leverage existing 3D part segmentation to obtain initial, incomplete part segments. Second, we introduce HoloPart, a novel diffusion-based model, to complete these segments into full 3D parts. HoloPart utilizes a specialized architecture with local attention to capture fine-grained part geometry and global shape context attention to ensure overall shape consistency. We introduce new benchmarks based on the ABO and PartObjaverse-Tiny datasets and demonstrate that HoloPart significantly outperforms state-of-the-art shape completion methods. By incorporating HoloPart with existing segmentation techniques, we achieve promising results on 3D part amodal segmentation, opening new avenues for applications in geometry editing, animation, and material assignment.
