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In this paper, a new multifunctional compound, 1,1′-(oxalylbis(4,1-phenylene))bis(1H-pyrrole-2,5-dione) (BVM), is grafted onto crosslinked polyethylene (XLPE) by radical-initiated grafting to play triple roles as a voltage stabilizer, space-charge inhibitor and crosslinking auxiliary and to achieve the purpose of comprehensively enhancing the DC dielectric properties of polymers while decreasing the type and number of additives. By analyzing the DC breakdown field strength, current density and space-charge distribution of the materials at different temperatures, it is demonstrated that BVM grafting can comprehensively and effectively enhance the electrical properties of the materials, with little dependence on temperature. The BVM molecule has two polar groups and an effective molecular structure that acts as a voltage stabilizer, thus enabling the introduction of dense, uniform, deeply trapped energy levels within the material to inhibit the space charge and to capture high-energy electrons to prevent damage to the material structure; however, the two functions do not affect each other. This is also consistent with first-principles calculations and quantum-chemical calculations. Gel content testing shows no effect on polymer crosslinking, even with a 27.8% reduction in the amount of the crosslinking agent di-isopropyl peroxide (DCP), which reduces the damage to the polymer's electrical resistance caused by the byproducts of DCP decomposition. Therefore, grafting multifunctional BVM compounds to improve the dielectric characteristics of polymers is a viable area of study in the development of high-voltage direct current (HVDC) cable materials. [ABSTRACT FROM AUTHOR]

This paper addresses semi-supervised semantic segmentation by exploiting a small set of images with pixel-level annotations (strong supervisions) and a large set of images with only image-level annotations (weak supervisions). Most existing approaches aim to generate accurate pixel-level labels from weak supervisions. However, we observe that those generated labels still inevitably contain noisy labels. Motivated by this observation, we present a novel perspective and formulate this task as a problem of learning with pixel-level label noise. Existing noisy label methods, nevertheless, mainly aim at image-level tasks, which can not capture the relationship between neighboring labels in one image. Therefore, we propose a graph-based label noise detection and correction framework to deal with pixel-level noisy labels. In particular, for the generated pixel-level noisy labels from weak supervisions by Class Activation Map (CAM), we train a clean segmentation model with strong supervisions to detect the clean labels from these noisy labels according to the cross-entropy loss. Then, we adopt a superpixel-based graph to represent the relations of spatial adjacency and semantic similarity between pixels in one image. Finally we correct the noisy labels using a Graph Attention Network (GAT) supervised by detected clean labels. We comprehensively conduct experiments on PASCAL VOC 2012, PASCAL-Context, MS-COCO and Cityscapes datasets. The experimental results show that our proposed semi-supervised method achieves the state-of-the-art performances and even outperforms the fully-supervised models on PASCAL VOC 2012 and MS-COCO datasets in some cases. [ABSTRACT FROM AUTHOR]

The goal of our work is to discover dominant objects in a very general setting where only a single unlabeled image is given. This is far more challenge than typical co-localization or weakly-supervised localization tasks. To tackle this problem, we propose a simple but effective pattern mining-based method, called Object Location Mining (OLM), which exploits the advantages of data mining and feature representation of pre-trained convolutional neural networks (CNNs). Specifically, we first convert the feature maps from a pre-trained CNN model into a set of transactions, and then discovers frequent patterns from transaction database through pattern mining techniques. We observe that those discovered patterns, i.e., co-occurrence highlighted regions, typically hold appearance and spatial consistency. Motivated by this observation, we can easily discover and localize possible objects by merging relevant meaningful patterns. Extensive experiments on a variety of benchmarks demonstrate that OLM achieves competitive localization performance compared with the state-of-the-art methods. We also evaluate our approach compared with unsupervised saliency detection methods and achieves competitive results on seven benchmark datasets. Moreover, we conduct experiments on fine-grained classification to show that our proposed method can locate the entire object and parts accurately, which can benefit to improving the classification results significantly. [ABSTRACT FROM AUTHOR]

In this paper, a new multifunctional compound, 1,1'-(oxalylbis(4,1-phenylene))bis(1H-pyrrole-2,5-dione) (BVM), is grafted onto crosslinked polyethylene (XLPE) by radical-initiated grafting to play triple roles as a voltage stabilizer, space-charge inhibitor and crosslinking auxiliary and to achieve the purpose of comprehensively enhancing the DC dielectric properties of polymers while decreasing the type and number of additives. By analyzing the DC breakdown field strength, current density and space-charge distribution of the materials at different temperatures, it is demonstrated that BVM grafting can comprehensively and effectively enhance the electrical properties of the materials, with little dependence on temperature. The BVM molecule has two polar groups and an effective molecular structure that acts as a voltage stabilizer, thus enabling the introduction of dense, uniform, deeply trapped energy levels within the material to inhibit the space charge and to capture high-energy electrons to prevent damage to the material structure; however, the two functions do not affect each other. This is also consistent with first-principles calculations and quantum-chemical calculations. Gel content testing shows no effect on polymer crosslinking, even with a 27.8% reduction in the amount of the crosslinking agent di-isopropyl peroxide (DCP), which reduces the damage to the polymer's electrical resistance caused by the byproducts of DCP decomposition. Therefore, grafting multifunctional BVM compounds to improve the dielectric characteristics of polymers is a viable area of study in the development of high-voltage direct current (HVDC) cable materials.

A multifunctional biodegradable brush polymer-drug conjugate (BPDC) is developed for the co-delivery of hydrophobic paclitaxel (PTX) and hydrophilic gemcitabine (GEM) chemotherapeutics, as well as a tumor imaging agent. A novel ternary copolymer of conventional, acetylenyl-functionalized and allyl-functionalized lactides is prepared to serve as the backbone precursor of BPDC. Acetylenyl groups of the copolymer are then reacted with poly(ethylene glycol) (PEG) side chains and cyanine5.5, a fluorescent probe, via azide-alkyne click reactions. Subsequently, the allyl groups of the yielded PEG-grafted brush polymer are used to covalently link PTX and GEM onto the backbone via thiol-ene click reactions. The resulting BPDC exhibits an average hydrodynamic diameter of 111 nm. Sustained and simultaneous release of PTX and GEM from the BPDC is observed in phosphate buffered saline, with the release of PTX showing sensitivity in mild acidic conditions. In vitro studies using MIA PaCa-2 human pancreatic cancer cells illustrate the cellular uptake and cytotoxicity of the BPDC. In vivo , the BPDC possesses long blood circulation, tumor accumulation, and enables optical tumor imaging. Further development and testing is warranted for multifunctional conjugated brush polymer systems that integrate combination chemotherapies and imaging., Competing Interests: Conflicts of interest There are no conflicts to declare.

Cyanohydrins, also be called cyanoalcohols, are important industrial precursors to carboxylic acids and some amino acids. Acetone cyanohydrin (ACH) and formaldehyde cyanohydrin (glycolonitrile, FCH), which are the typical examples of cyanohydrins, are classified as extremely hazardous substances. As the cyanohydrins can readily decompose, and it is hard to find cyanohydrins in gastric contents and heart blood, the determination study in biological samples can be divided into two parts: the first is the determination of HCN by using a Prussian blue reaction and the HS-GC-MSD after derivatization by chloramine-T. The second is the determination of acetone or formaldehyde. In this part, headspace gas chromatography with flame ionization detector (HS-GC-FID) and solid phase microextraction (SPME)-gas chromatography with mass spectrometric detectors (GC-MSD) had been used. In this report, we reported two fatal intoxication cases of ACH and FCH; one person was killed by his wife by poisoning his food and the other was suicide by poison. Two real cases of ACH and FCH in human blood and gastric contents have been analyzed by using the above-mentioned method. The Prussian blue reaction was positive in the two cases. The peaks of acetone with retention times of 0.998 min appear in specimens of the deceased are consistent with the retention times of pure acetone. The peaks of formaldehyde with a retention time of 1.658 min appear in heart blood of the deceased, and the retention time of formaldehyde of the liquid is 1.674 min, which are consistent with the retention times of pure formaldehyde (1.673 min)., (© The Author 2016. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.)