Comparing the contribution of visible-light irradiation, gold nanoparticles, and titania supports in photocatalytic nitroaromatic coupling and aromatic alcohol oxidation

Under visible-light irradiation, gold nanoparticles (Au NPs) supported by titania (TiO₂) nanofibers show excellent activity and high selectivity for both reductive coupling of nitroaromatics to corresponding azobenzene or azoxylbenzene and selective oxidation of aromatic alcohols to corresponding aldehydes. The Au NPs act as active centers mainly due to their localized surface plasmon resonance (LSPR) effect. They can effectively couple the photonic energy and thermal energy to enhance reaction efficiency. Visible-light irradiation has more influence on the reduction than on the oxidation, lowering the activation energy by 24.7 kJ mol⁻¹ and increasing the conversion rate by 88% for the reductive coupling, compared to merely 8.7 kJ mol⁻¹ and 46% for the oxidation. Furthermore, it is found that the conversion of nitroaromatics significantly depends on the particle size and specific surface area of supported Au NPs; and the catalyst on TiO₂(B) support outperforms that on anatase phase with preferable ability to activate oxygen. In contrast, for the selective oxidation, the effect of surface area is less prominent and Au NPs on anatase exhibit higher photo-catalytic activity than other TiO₂ phases. The catalysts can be recovered efficiently because the Au NPs stably attach to TiO₂ supports by forming a well-matched coherent interface observed via high-resolution TEM

An Exotic Species Is the Favorite Prey of a Native Enemy

Although native enemies in an exotic species' new range are considered to affect its ability to invade, few studies have evaluated predation pressures from native enemies on exotic species in their new range. The exotic prey naiveté hypothesis (EPNH) states that exotic species may be at a disadvantage because of its naïveté towards native enemies and, therefore, may suffer higher predation pressures from the enemy than native prey species. Corollaries of this hypothesis include the native enemy preferring exotic species over native species and the diet of the enemy being influenced by the abundance of the exotic species. We comprehensively tested this hypothesis using introduced North American bullfrogs (Lithobates catesbeianus, referred to as bullfrog), a native red-banded snake (Dinodon rufozonatum, the enemy) and four native anuran species in permanent still water bodies as a model system in Daishan, China. We investigated reciprocal recognition between snakes and anuran species (bullfrogs and three common native species) and the diet preference of the snakes for bullfrogs and the three species in laboratory experiments, and the diet preference and bullfrog density in the wild. Bullfrogs are naive to the snakes, but the native anurans are not. However, the snakes can identify bullfrogs as prey, and in fact, prefer bullfrogs over the native anurans in manipulative experiments with and without a control for body size and in the wild, indicating that bullfrogs are subjected to higher predation pressures from the snakes than the native species. The proportion of bullfrogs in the snakes' diet is positively correlated with the abundance of bullfrogs in the wild. Our results provide strong evidence for the EPNH. The results highlight the biological resistance of native enemies to naïve exotic species

Bidirectional Graph Reasoning Network for Panoptic Segmentation

Recent researches on panoptic segmentation resort to a single end-to-end network to combine the tasks of instance segmentation and semantic segmentation. However, prior models only unified the two related tasks at the architectural level via a multi-branch scheme or revealed the underlying correlation between them by unidirectional feature fusion, which disregards the explicit semantic and co-occurrence relations among objects and background. Inspired by the fact that context information is critical to recognize and localize the objects, and inclusive object details are significant to parse the background scene, we thus investigate on explicitly modeling the correlations between object and background to achieve a holistic understanding of an image in the panoptic segmentation task. We introduce a Bidirectional Graph Reasoning Network (BGRNet), which incorporates graph structure into the conventional panoptic segmentation network to mine the intra-modular and intermodular relations within and between foreground things and background stuff classes. In particular, BGRNet first constructs image-specific graphs in both instance and semantic segmentation branches that enable flexible reasoning at the proposal level and class level, respectively. To establish the correlations between separate branches and fully leverage the complementary relations between things and stuff, we propose a Bidirectional Graph Connection Module to diffuse information across branches in a learnable fashion. Experimental results demonstrate the superiority of our BGRNet that achieves the new state-of-the-art performance on challenging COCO and ADE20K panoptic segmentation benchmarks.Comment: CVPR202

Embodied carbon determination in the transportation stage of prefabricated constructions: A micro-level model using the bin-packing algorithm and modal analysis model

The prefabricated construction generates considerable embodied carbon emissions during the manufacture, transportation, and construction stages. However, the contribution from the transportation stage is usually overlooked, leading to biases in life-cycle sustainability analysis of these projects. This article provides a micro-level transportation CE calculation method that estimates the project-specific emissions according to the features of prefabricated elements. The method simulates the transportation status of prefabricated elements as bin packing (BP) problems. Then, a modal analysis model is employed to calculate the CE of each vehicle based on vehicle type, road condition, and freight weight. Considering the minimum transportation CE as objective, a genetic algorithm is then used to search for the optimal solution and corresponding CE values. The comparative results among different CE calculation methods show that this BP-algorithm-based method provides reliable data across different loading rates, rendering the method suitable for calculating the transportation CE of prefabricated construction projects. Additionally, the BP-algorithm-based method differs the emission characteristics among different element types—the prefabricated floor generates the highest emissions, followed by prefabricated beam, wall, and column—suggesting the need to identify disparate emission factors for different element types and considering the sustainability aspects when selecting prefabricated approaches of projects. The results also highlight the efficiency of considering more prefabricated elements in a single transportation batch and selecting suitable vehicles for the optimisation of embodied carbon emissions. Architects, engineers, and contractors can use the method for project-specific transportation CE calculations and transportation planning. The calculation variables concerning the geometric features of prefabricated elements and vehicles can be adopted in the optimisation of project design and construction management for achieving less embodied carbon

Federated Uncertainty-Aware Aggregation for Fundus Diabetic Retinopathy Staging

Deep learning models have shown promising performance in the field of diabetic retinopathy (DR) staging. However, collaboratively training a DR staging model across multiple institutions remains a challenge due to non-iid data, client reliability, and confidence evaluation of the prediction. To address these issues, we propose a novel federated uncertainty-aware aggregation paradigm (FedUAA), which considers the reliability of each client and produces a confidence estimation for the DR staging. In our FedUAA, an aggregated encoder is shared by all clients for learning a global representation of fundus images, while a novel temperature-warmed uncertainty head (TWEU) is utilized for each client for local personalized staging criteria. Our TWEU employs an evidential deep layer to produce the uncertainty score with the DR staging results for client reliability evaluation. Furthermore, we developed a novel uncertainty-aware weighting module (UAW) to dynamically adjust the weights of model aggregation based on the uncertainty score distribution of each client. In our experiments, we collect five publicly available datasets from different institutions to conduct a dataset for federated DR staging to satisfy the real non-iid condition. The experimental results demonstrate that our FedUAA achieves better DR staging performance with higher reliability compared to other federated learning methods. Our proposed FedUAA paradigm effectively addresses the challenges of collaboratively training DR staging models across multiple institutions, and provides a robust and reliable solution for the deployment of DR diagnosis models in real-world clinical scenarios

Nonparaxiality-triggered Landau-Zener transition in topological photonic waveguides

Photonic lattices have been widely used for simulating quantum physics, owing to the similar evolutions of paraxial waves and quantum particles. However, nonparaxial wave propagations in photonic lattices break the paradigm of the quantum-optical analogy. Here, we reveal that nonparaxiality exerts stretched and compressed forces on the energy spectrum in the celebrated Aubry-Andre-Harper model. By exploring the mini-gaps induced by the finite size of the different effects of nonparaxiality, we experimentally present that the expansion of one band gap supports the adiabatic transfer of boundary states while Landau-Zener transition occurs at the narrowing of the other gap, whereas identical transport behaviors are expected for the two gaps under paraxial approximation. Our results not only serve as a foundation of future studies of dynamic state transfer but also inspire applications leveraging nonparaxial transitions as a new degree of freedom.Comment: 17 pages, 4 figure