Encoded microparticles (EMPs) have shown demonstrative
value for
multiplexed high-throughput bioassays such as drug discovery and diagnostics.
Herein, we propose for the first time the incorporation of thermally
activated delayed fluorescence (TADF) dyes with low-cost, heavy metal-free,
and long-lived luminescence properties into polymer matrices via a
microfluidic droplet-facilitated assembly technique. Benefiting from
the uniform droplet template sizes and polymer-encapsulated structures,
the resulting composite EMPs are highly monodispersed, efficiently
shield TADF dyes from singlet oxygen, well preserve TADF emission,
and greatly increase the delayed fluorescence lifetime. Furthermore,
by combining with phase separation of polymer blends in the drying
droplets, TADF dyes with distinct luminescent colors can be spatially
separated within each EMP. It eliminates optical signal interference
and generates multiple fluorescence colors in a compact system. Additionally,
in vitro studies reveal that the resulting EMPs show good biocompatibility
and allow cells to adhere and grow on the surface, thereby making
them promising optically EMPs for biolabeling.
Smart structural systems require the electronic control systems which are integrated into the structures to be small, light weight and power-efficient. The field programmable gate array (FPGA) is a good platform to implement such controllers. In our previous work, FPGA-based digital controllers were built and tested on a simple structural system. In order to implement multivariable controllers, the hardware resources for FPGA-based architecture need to be further reduced. Distributed arithmetic (DA) has long been proven to be a very efficient means to mechanize computations that are dominated by inner products involving constant multiplicand. The computational requirements of the smart structural controllers match this type very well. In this paper various DA structure controllers are designed and results are compared with multiply-and-accumulate structure controllers. Single-and multi-variable controllers are implemented and tested on a cantilevered beam.
In recent years, semi-supervised graph learning with data augmentation (DA) has been the most commonly used and best-performing method to improve model robustness in the sparse scenarios with few labeled samples. However, most of existing DA methods are based on the homogeneous graph while none are specific for the heterogeneous graph. Differing from the homogeneous graph, DA in heterogeneous graph faces greater challenges: heterogeneity of information requires DA strategies to effectively handle heterogeneous relations, which considers the information contribution of different types of neighbors and edges to the target nodes. Furthermore, over-squashing of information is caused by the negative curvature that formed by the non-uniformity distribution and the strong clustering in complex graph. To address these challenges, this paper presents a novel method named Semi-Supervised Heterogeneous Graph Learning with Multi-level Data Augmentation (HG-MDA). For the problem of heterogeneity of information in DA, node and topology augmentation strategies are proposed for the characteristics of heterogeneous graph. And meta-relation-based attention is applied as one of the indexes for selecting augmented nodes and edges. For the problem of over-squashing of information, triangle based edge adding and removing are designed to alleviate the negative curvature and bring the gain of topology. Finally, the loss function consists of the cross-entropy loss for labeled data and the consistency regularization for unlabeled data. In order to effectively fuse the prediction results of various DA strategies, the sharpening is used. Existing experiments on public datasets, i.e., ACM, DBLP, OGB, and industry dataset MB show that HG-MDA outperforms current SOTA models. Additionly, HG-MDA is applied to user identification in internet finance scenarios, helping the business to add 30% key users, and increase loans and balances by 3.6%, 11.1%, and 9.8%.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations –citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.