Nitrogen-Rich Perylene Nanosheet Enhanced Bismaleimide Resin

The low toughness of bismaleimide resin (BMI) hinders its application in the aerospace field. In order to improve the strength and toughness of BMI resin simultaneously, this study proposes to introduce perylene-dicyandiamide (P-DCD) nanosheets with an ultra-s rigid conjugated planar structure into the polymer matrix of bismaleimide resin through hydrogen bonding and cross-linking to construct modified composites. The research results showed that the modified cured composites exhibited excellent mechanical properties, with a significant increase in impact strength of 135.8%, flexural strength and flexural modulus of 87.1% and 44.6%, respectively. The thermal properties of the resin were maintained before and after modification, with the glass transition temperature (Tg) of 284.0 ˚C and decomposition temperature > 520 ˚C. Meanwhile, the strengthening and toughening mechanism of the bismaleimide-based system modified by additive P-DCD were also explored. The results showed that the functional group of dicyandiamide in nanosheets and the hydrogen bonding effect in P-DCD synergically increased the cross-linking network and compatibility between P-DCD and the matrix resin

Efficient Segmentation with Texture in Ore Images Based on Box-supervised Approach

Image segmentation methods have been utilized to determine the particle size distribution of crushed ores. Due to the complex working environment, high-powered computing equipment is difficult to deploy. At the same time, the ore distribution is stacked, and it is difficult to identify the complete features. To address this issue, an effective box-supervised technique with texture features is provided for ore image segmentation that can identify complete and independent ores. Firstly, a ghost feature pyramid network (Ghost-FPN) is proposed to process the features obtained from the backbone to reduce redundant semantic information and computation generated by complex networks. Then, an optimized detection head is proposed to obtain the feature to maintain accuracy. Finally, Lab color space (Lab) and local binary patterns (LBP) texture features are combined to form a fusion feature similarity-based loss function to improve accuracy while incurring no loss. Experiments on MS COCO have shown that the proposed fusion features are also worth studying on other types of datasets. Extensive experimental results demonstrate the effectiveness of the proposed method, which achieves over 50 frames per second with a small model size of 21.6 MB. Meanwhile, the method maintains a high level of accuracy compared with the state-of-the-art approaches on ore image dataset. The source code is available at \url{https://github.com/MVME-HBUT/OREINST}.Comment: 14 pages, 8 figure

Proposing a New Research Framework for Loan Allocation Strategies in P2P Lending

One of the frontier Web 2.0 applications is online peer-to-peer (P2P) lending marketplace, where individual lenders and borrowers can virtually meet for loan transactions. From a lender’s perspective, she not only wants to lower investment risk but also to gain as much return as possible. However, P2P lenders possess the inherent problem of information asymmetry that they don’t really know if a borrower has capability to pay the loan or is truthfully willing to pay it in due time, leading them to a disadvantaged situation when making the decision of lending money to the borrower. This study intends to consider the loan allocation as an optimization research problem using the research framework based upon modern portfolio theory with the aim of helping lenders achieve the two goals of gaining high return and lowering risk at the same time. The expected results of this research are twofold: 1) compared to a logistic regression based credit scoring method, we expect to make more profits for lenders with risk level unchanged, and 2) compared to a linear regression based profit scoring method, we expect to lower risk without lowering return. Our proposed new model could offer insights into how individual lenders can optimize their loan allocation strategies when considering return and risk simultaneously

Faster OreFSDet : A Lightweight and Effective Few-shot Object Detector for Ore Images

For the ore particle size detection, obtaining a sizable amount of high-quality ore labeled data is time-consuming and expensive. General object detection methods often suffer from severe over-fitting with scarce labeled data. Despite their ability to eliminate over-fitting, existing few-shot object detectors encounter drawbacks such as slow detection speed and high memory requirements, making them difficult to implement in a real-world deployment scenario. To this end, we propose a lightweight and effective few-shot detector to achieve competitive performance with general object detection with only a few samples for ore images. First, the proposed support feature mining block characterizes the importance of location information in support features. Next, the relationship guidance block makes full use of support features to guide the generation of accurate candidate proposals. Finally, the dual-scale semantic aggregation module retrieves detailed features at different resolutions to contribute with the prediction process. Experimental results show that our method consistently exceeds the few-shot detectors with an excellent performance gap on all metrics. Moreover, our method achieves the smallest model size of 19MB as well as being competitive at 50 FPS detection speed compared with general object detectors. The source code is available at https://github.com/MVME-HBUT/Faster-OreFSDet.Comment: 18 pages, 11 figure

Structures of human gastrin-releasing peptide receptors bound to antagonist and agonist for cancer and itch therapy

Gastrin releasing peptide receptor (GRPR), a member of the bombesin (BBN) G protein-coupled receptors, is aberrantly overexpressed in several malignant tumors, including those of the breast, prostate, pancreas, lung, and central nervous system. Additionally, it also mediates non-histaminergic itch and pathological itch conditions in mice. Thus, GRPR could be an attractive target for cancer and itch therapy. Here, we report the inactive state crystal structure of human GRPR in complex with the non-peptide antagonist PD176252, as well as two active state cryo-electron microscopy (cryo-EM) structures of GRPR bound to the endogenous peptide agonist gastrin-releasing peptide and the synthetic BBN analog [D-Ph

An Efficient MLP-based Point-guided Segmentation Network for Ore Images with Ambiguous Boundary

The precise segmentation of ore images is critical to the successful execution of the beneficiation process. Due to the homogeneous appearance of the ores, which leads to low contrast and unclear boundaries, accurate segmentation becomes challenging, and recognition becomes problematic. This paper proposes a lightweight framework based on Multi-Layer Perceptron (MLP), which focuses on solving the problem of edge burring. Specifically, we introduce a lightweight backbone better suited for efficiently extracting low-level features. Besides, we design a feature pyramid network consisting of two MLP structures that balance local and global information thus enhancing detection accuracy. Furthermore, we propose a novel loss function that guides the prediction points to match the instance edge points to achieve clear object boundaries. We have conducted extensive experiments to validate the efficacy of our proposed method. Our approach achieves a remarkable processing speed of over 27 frames per second (FPS) with a model size of only 73 MB. Moreover, our method delivers a consistently high level of accuracy, with impressive performance scores of 60.4 and 48.9 in~$AP_{50}^{box}$ and~$AP_{50}^{mask}$ respectively, as compared to the currently available state-of-the-art techniques, when tested on the ore image dataset. The source code will be released at \url{https://github.com/MVME-HBUT/ORENEXT}.Comment: 10 pages, 8 figure