Here, we describe a fluorination strategy for semiconducting polymers for the development of highly bright second near‐infrared region (NIR‐II) probes. Tetrafluorination yielded a fluorescence QY of 3.2 % for the polymer dots (Pdots), over a 3‐fold enhancement compared to non‐fluorinated counterparts. The fluorescence enhancement was attributable to a nanoscale fluorous effect in the Pdots that maintained the molecular planarity and minimized the structure distortion between the excited state and ground state, thus reducing the nonradiative relaxations. By performing through‐skull and through‐scalp imaging of the brain vasculature of live mice, we quantitatively analyzed the vascular morphology of transgenic brain tumors in terms of the vessel lengths, vessel branches, and vessel symmetry, which showed statistically significant differences from the wild type animals. The bright NIR‐II Pdots obtained through fluorination chemistry provide insightful information for precise diagnosis of the malignancy of the brain tumor.
In order to improve the accuracy of predicting the air pollutants in Shenzhen, a hybrid model based on ARIMA (Autoregressive Integrated Moving Average model) and prophet for mixing time and space relationships was proposed. First, ARIMA and Prophet method were applied to train the data from 11 air quality monitoring stations and gave them different weights. Then, finished the calculation about weight of impact in each air quality monitoring station to final results. Finally, built up the hybrid model and did the error evaluation. The result of the experiments illustrated that this hybrid method can improve the air pollutants prediction in Shenzhen.
Here, we describe a fluorination strategy for semiconducting polymers for the development of highly bright second near‐infrared region (NIR‐II) probes. Tetrafluorination yielded a fluorescence QY of 3.2 % for the polymer dots (Pdots), over a 3‐fold enhancement compared to non‐fluorinated counterparts. The fluorescence enhancement was attributable to a nanoscale fluorous effect in the Pdots that maintained the molecular planarity and minimized the structure distortion between the excited state and ground state, thus reducing the nonradiative relaxations. By performing through‐skull and through‐scalp imaging of the brain vasculature of live mice, we quantitatively analyzed the vascular morphology of transgenic brain tumors in terms of the vessel lengths, vessel branches, and vessel symmetry, which showed statistically significant differences from the wild type animals. The bright NIR‐II Pdots obtained through fluorination chemistry provide insightful information for precise diagnosis of the malignancy of the brain tumor.
Brain decoding, aiming to identify the brain states using neural activity, is important for cognitive neuroscience and neural engineering. However, existing machine learning methods for fMRI‐based brain decoding either suffer from low classification performance or poor explainability. Here, we address this issue by proposing a biologically inspired architecture, Spatial Temporal‐pyramid Graph Convolutional Network (STpGCN), to capture the spatial–temporal graph representation of functional brain activities. By designing multi‐scale spatial–temporal pathways and bottom‐up pathways that mimic the information process and temporal integration in the brain, STpGCN is capable of explicitly utilizing the multi‐scale temporal dependency of brain activities via graph, thereby achieving high brain decoding performance. Additionally, we propose a sensitivity analysis method called BrainNetX to better explain the decoding results by automatically annotating task‐related brain regions from the brain‐network standpoint. We conduct extensive experiments on fMRI data under 23 cognitive tasks from Human Connectome Project (HCP) S1200. The results show that STpGCN significantly improves brain‐decoding performance compared to competing baseline models; BrainNetX successfully annotates task‐relevant brain regions. Post hoc analysis based on these regions further validates that the hierarchical structure in STpGCN significantly contributes to the explainability, robustness and generalization of the model. Our methods not only provide insights into information representation in the brain under multiple cognitive tasks but also indicate a bright future for fMRI‐based brain decoding.
Memetic algorithm (MA) is widely applied to optimize routing problems as it provides one way to combine local search with global search. However, the local search in MA needs to be carefully designed according to the problem's characteristics. In this article, we consider a real-world large-scale waste collection problem with multiple depots, multiple disposal facilities, multiple trips, and working time constraints. Vehicles with a limited capacity and working time can start from different depots, collect waste at different sites, and make multiple trips to different disposal facilities to empty the waste and return to its origin. While the existing work considered problems with multiple trips and time constraints, none have tackled problems with multiple depots, multiple disposal facilities, multiple trips, as well as working time constraints. The change from "singledepot" to "multidepot" not only reflects better the situation in real life but also leads to a qualitative different and more complex problem. In this article, we first model this complex problem mathematically. Then, a novel region-focused MA is proposed to tackle this new challenge. Compared to classic MA, this region-focused one is enhanced by two major components: 1) a new heuristic-assisted solution initialization algorithm and 2) a region-focused local search with novel heuristics. Comprehensive computational studies show that our proposed approaches significantly outperform several state-of-the-arts on our real problem of thousands of tasks. The new local search procedure and solution initialization method significantly improve the search ability in combination with global search ability of MA.
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