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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]

A rapid and sensitive gas chromatography with mass spectrometry method for the determination of venlafaxine in rat plasma has been developed and applied to a drug-drug interaction study of fluoxetine on pharmacokinetics of venlafaxine in rats. Rat plasma was spiked with 2% aqueous ammonia before subjected to preactivated C18 solid-phase extraction columns and eluted with methanol. No endogenous interferences were observed under optimal condition. The calibration curve was linear ( R2 = 0.9994) in the range of 10-1000 ng/mL. The quantification limit of venlafaxine in rat plasma was 10 ng/mL. The accuracy was in the range of 85-110%, and the extraction recovery was no less than 50%. Both the intra- and interday precision were 5.0-10.7%. The concentration-time curve showed that plasma concentrations of the coadministration group (group B) were higher than that of single dose group (group A). Both values of Cmax (0.069 mg/L) and AUC0→∞ (0.291 mg h/L) in group B were statistically greater than that of Cmax (0.046 mg/L) and AUC0→∞ (0.181 mg·h/L) in group A ( P < 0.05). The results indicated that a significant effect of fluoxetine was shown on the pharmacokinetics of venlafaxine, suggesting that drug-drug interactions are of concern for the treatment of depression with the combined use of venlafaxine and fluoxetine. [ABSTRACT FROM AUTHOR]

Morinda morindoidesis an important Liberian traditional medicine for the treatment of malaria, fever, worms etc. The plant was subjected to integrated approaches including phytochemical screening and gas chromatography mass spectrometry (GC–MS) analyses. Phytochemical investigation of the powdered plant revealed the presence of phenolics, tannins, flavonoids, saponins, terpenes, steroidal compounds and volatile oil. Steam distillation followed by GC–MS resulted in the identification of 47 volatiles in its aerial parts: 28 were in common including various bioactive volatiles. Major constituents of leaves were phytol (43.63%), palmitic acid (8.55%) and geranyl linalool (6.95%) and stem were palmitic acid (14.95%), eicosane (9.67%) and phytol (9.31%), and hence, a significant difference in the percentage composition of aerial parts was observed. To study seasonal changes, similarity analysis was carried out by calculating correlation coefficient (r) and vector angle cosine (z) that were more than 0.91 for stem-to-stem and leaf-to-leaf batches indicating considerable consistency. [ABSTRACT FROM PUBLISHER]

A field sampling method based on magnetic core–shell silica nanoparticles was developed for field sampling and the enrichment of low concentrations of pesticides in aqueous samples. The magnetic nanoparticles could be easily extracted from water samples by a custom-made magnetic nanoparticle collector. The recovery of 15 mg of magnetic particles from a 500 mL water sample was 90.8%. Mixtures of seven pesticides spiked into pure water and pond water were used as marker samples to evaluate the field sampling method. The average recoveries at three levels of spiking were in the range 60.0–104.7% with relative standard deviations <7.1%. The proposed method has good linearity with a correlation coefficient >0.9990 in the concentration range 0.5–15 μg L−1. The results of the analysis of a sample of poisoned pond water indicate that this method is fast, convenient and efficient for the field sampling and enrichment of pesticides in aqueous samples. [ABSTRACT FROM AUTHOR]

Sulfite and sulfide share several similarities in terms of chemical properties, such as nucleophilic and reducing reactivities. Therefore, they may disturb the detection of each other. In order to discriminate between these two kinds of sulfur-containing species, a new probe TSSP-N3 was developed, in which para-azidobenzenyl ketone was covalently incorporated to a coumarin fluorophore linked by a C=C double bond. Sulfite and sulfide can respectively react with the C=C double bond and the azido group to give different products, consequently, they can be differentially identified by UV-vis and fluorescence spectroscopy as well as by the naked eye. Selectivity and competition results reveal that TSSP-N3 is a good candidate for the detection of sulfide and sulfite. The bioimaging experiment demonstrates the potential of the TSSP-N3 probe for the differential imaging of sulfide and sulfite in living cells. [ABSTRACT FROM AUTHOR]

Network tomography is a promising inference technique for network topology from end-to-end measurements. In this letter, we propose a novel binary tree pruning algorithm based on t-test to infer the network topology. A binary tree topology is first inferred using the existing Agglomerative Likelihood Tree (ALT) method, and then two samples t-test is applied to prune the binary tree, thus a general tree corresponding to the real topology is obtained. A lower bound on the correctly identified probability of the proposed method is derived. Simulation results show that the pruning method based on t-test outperforms the method which prunes the binary tree using a fixed threshold. [ABSTRACT FROM PUBLISHER]

This study was designed to develop a sensitive and reliable RP-HPLC method for the separation of aerial parts ofJologbo. Different extraction methods were used and compared to obtain the maximum number of constituents. Different chromatographic conditions were optimized to achieve optimum separation. A total of 98 peaks were obtained in the optimized separation method. Seasonal variation and difference in chemical composition among aerial parts of the plant were evaluated. Significant difference in the chemical composition was observed and statistical data processing supported the results. The correlation coefficient and vector cosine were calculated by chemometric analysis to evaluate peak retention times, peak areas, and peak heights between various batches (using selected peaks) which showed good repeatability. Gallic and protocatechuic acids were identified from the plant and their intra-day and inter-day repeatability values were calculated and convincing results were obtained. [ABSTRACT FROM AUTHOR]

A simple and effective preconcentration method based on magnetic core-shell silica nanoparticles with C18-modified surface was developed for the analysis of pesticide residues in environmental water samples by gas chromatography-mass spectrometry. Several kinds of organophosphorous and pyrethroid pesticides including methamidophos, dichlorvos, orthene, phorate, dimethoate, carbofuran, bifenthrin, fenpropathrin, cypermethrin, fenvalerate, and deltamethrin were used as model compounds to systematically evaluate the method. Various parameters, including the amounts of magnetic nanoparticles absorbents, extraction time, eluting solvent, eluting volume, and sample p H values were optimized. The optimized method affords low detection limits (signal-to-noise ratio = 3) from 0.001 to 0.008 μg/L, and shows good linearity with correlation coefficients over 0.9990 in the concentration range of 0.025-0.5 μg/L. Average recoveries at three spiked levels were in the range of 70.2-110.2% with relative standard deviations below 9.6%. A maximum enrichment factor of 1015 times was achieved. Analysis results of poisoned pond water indicate that this method is fast, convenient, and efficient for the detections of low-concentration pesticides in aqueous samples. [ABSTRACT FROM AUTHOR]

The traditional Langmuir equation displays drawback in accurately characterizing the methane adsorption behavior in coal, due to it assuming the uniform surface of coal pores. Additionally, the decay law of gas adsorption capacity with an increasing coal reservoir temperature remains unknown. In this study, the fractal adsorption model is proposed based on the fractal dimension (Df) of coal pores and the attenuation coefficient (n) of the adsorption capacity. The principles and methods of this fractal adsorption model are deduced and summarized in detail. The results show that the pore structures of the two coal samples exhibit obvious fractal characteristics, with the values of fractal dimensions (Df) being 2.6279 and 2.93. The values of adsorption capacity attenuation coefficients (n) are estimated as −0.006 and −0.004 by the adsorption experiments with different temperatures. The proposed fractal adsorption model presents a greater theoretical significance and higher accuracy than that of the Langmuir equation. The accuracy of the fractal adsorption model with temperature effect dependence is verified, establishing a prediction method for methane adsorption capacity in deep coal reservoirs. This study can serve as a theoretical foundation for coalbed methane exploration and development, as well as provide valuable insights for unconventional natural gas exploitation. [ABSTRACT FROM AUTHOR]

Zheng Guanying paid close attention to the issue of the spread of Christianity in China since his youth. Over a period of more than three decades, he produced five monographs specifically addressing Christianity, from unequivocally opposing the spread of Christianity in China in the mid-to-late 1870s, to advocating for the adoption of the preaching form of the Christian "gatherings every seven days" to disseminate the village covenants and sacred edicts among the Chinese people in the 1890s. He proposed that the Chinese people should hold the right to spread Christianity. In 1906, he advocated for the establishment of a "common religion" and proposed "one religion for all nations" to eliminate wars around the world. In his later years, he proposed the "Five great wishes" to integrate and govern the various religions of the world with Taoism as the core, attempting to reconstruct global order from the perspective of religious unity. He envisioned the establishment of a sacred Taoist monastery, the dissemination of religious concepts, and the cultivation of talents, which drew on the organizational structure and missionary methods of Christianity, reflecting the thinking and efforts of modern Chinese intellectuals to bridge the Chinese and Western civilizations, seek solutions for modern China, reconcile conflicts between China and the West, and pursue global unity. [ABSTRACT FROM AUTHOR]

Motivation There are many clustered transcriptionally active regions in the human genome, in which the transcription complex cannot immediately terminate transcription at the upstream gene termination site, but instead continues to transcribe intergenic regions and downstream genes, resulting in read-through transcripts. Several studies have demonstrated the regulatory roles of read-through transcripts in tumorigenesis and development. However, limited by the read length of next-generation sequencing, discovery of read-through transcripts has been slow. For long but also erroneous third-generation sequencing data, this study developed a novel minimizer sketch algorithm to accurately and quickly identify read-through transcripts. Results Readon initially splits the reference sequence into distinct active regions. It employs a sliding window approach within each region, calculates minimizers, and constructs the specialized structured arrays for query indexing. Following initial alignment anchor screening of candidate read-through transcripts, further confirmation steps are executed. Comparative assessments against existing software reveal Readon's superior performance on both simulated and validated real data. Additionally, two downstream tools are provided: one for predicting whether a read-through transcript is likely to undergo nonsense-mediated decay or encodes a protein, and another for visualizing splicing patterns. Availability and implementation Readon is freely available on GitHub (https://github.com/Bulabula45/Readon). [ABSTRACT FROM AUTHOR]

Copyright of Forensic Science & Technology is the property of Institute of Forensic Science, Ministry of Public Security and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A coupling mechanism between flicker noise and hot carrier degradation (HCD) is revealed in this work. Predicting the flicker noise properties of fresh and aged devices is becoming essential for circuit designs, requiring an understanding of the fundamental noise behaviors. While certain models for fresh devices have been proposed, those for aged devices have not been reported yet because of the lack of a clear mechanism. The flicker noise of aged FinFETs is characterized based on the measure-stress-measure (MSM) method and analyzed from the device physics. It is found that both the mean and deviations of the noise power spectral density increase compared with the fresh counterparts. A coupling mechanism is proposed to explain the trap time constants, leading to the trap characterizations in their energy profiles. The amplitude and number of contributing traps are also changing and are dependent on the mode of HCD and determined by the position of the induced traps. A microscopic picture is developed from the perspective of trap coupling, reproducing well the measured noise of advanced nanoscale FinFETs. The finding is important for accurate flicker noise calculations and aging-aware circuit designs. [ABSTRACT FROM AUTHOR]

There is a great threat to the production safety of coal mines caused by mine water disasters. Traditional identification methods are not adapted to the efficiency of today's coal mining and do not offer the advantage of accurate detection in real-time. In this study, the Mayfly Algorithm (MA) was used to optimize the Long Short-Term Memory (LSTM) network, combined with laser-induced fluorescence technology, to apply it to the identification of mine water sources for the prevention of mine water disasters and post-disaster relief work. Taking sandstone water and goaf water as the original samples, five mixed water samples were also prepared by mixing the sandstone water and goaf water in different proportions, giving a total of seven water samples to be tested. Laser-induced fluorescence technology was used to obtain the fluorescence spectral data of water samples, and then the Linear Discriminant Analysis (LDA) dimensionality reduction algorithm and the Principal Component Analysis (PCA) dimensionality reduction algorithm were used to reduce the dimensions of the original spectral data. Then, three architectures, including LSTM, GA-LSTM (optimization of the LSTM by genetic algorithm) and MA-LSTM were designed to identify mine water sources. Finally, from the results' analysis, MA-LSTM performs best in many aspects after PCA dimensionality reduction and has the best identification effect. These results supported the feasibility of the novel method. [ABSTRACT FROM AUTHOR]

Haiyang-2 (HY-2) missions have accumulated sea surface height (SSH) observations on a global scale for more than 10 years. Four satellites, HY-2A, HY-2B, HY-2C and HY-2D, provide even but differently distributed data, which play a complementary role in marine gravity studies with other missions. Therefore, this paper evaluates the performances of HY-2 altimetric data in marine gravity modeling from the following four perspectives: SSH accuracy, geoid signal resolution ability, vertical deflections and gravity anomaly. First, the centimeter-magnitude accuracy level of HY-2 data is proved by analyzing SSH discrepancies at crossover points within a certain time limit. Second, the spectral analysis of repetitive along-track data sequences in a time domain shows a geoid resolution range from 18 to 24 km. Taking HY-2 exact repeat missions (ERM), for example, the resolution could be remarkably enhanced by stacking repetitive cycles. Third, validation with an XGM2019 model showed that vertical deflections were reliably computed for all HY-2 missions, but HY-2A performed slightly worse than the other HY-2 missions. Meanwhile, HY-2C and HY-2D with a ~66° orbital inclination obviously had an improved ability to capture east–west signals compared to HY-2A and HY-2B. Finally, we constructed global marine gravity results based on three input datasets, HY-2 dataset only, multi-satellite dataset without HY-2 and multi-satellite dataset with HY-2. Validations were performed using published models and shipborne gravimetric data. The results showed that the HY-2 dataset is capable of improving marine gravity anomaly recoveries and that the accuracy of NSOAS22 with incorporated HY-2 data is comparable to DTU21 and SS V31.1. Furthermore, HY-2 observations should not be the only input dataset to construct a 1' × 1' resolution marine gravity model. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Signal Processing is the property of Journal of Signal Processing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This paper aims to summarize the influence law of hybrid deposited and micro-rolling (HDMR) technology on the bead morphology and overlapping coefficient. A better bead topology positively supports the overlapping deposited in multi-beads between layers while actively assisting the subsequent layer's deposition in the wire and arc additive manufacturing (WAAM). Hybrid-deposited and micro-rolling (HDMR) additive manufacturing (AM) technology can smooth the weld bead for improved surface quality. However, the micro-rolling process will change the weld bead profile fitting curve to affect the overlapping coefficient. Design/methodology/approach: Weld bead contours for WAAM and HDMR were extracted using line lasers. A comparison of bead profile curves was conducted to determine the influence law of micro-zone rolling on the welding bead contour and fitting curve. Aiming at the optimized overlapping coefficient of weld bead in HDMR AM, the optimal HDMR overlapping coefficient curve was proposed which varies with the reduction based on the best surface flatness. The mathematical model for overlapping in HDMR was checked by comparing the HDMR weld bead contours under different rolling reductions. Findings: A fitting function of the bead forming by HDMR AM was proposed based on the law of conservation of mass. The change rule of the HDMR weld bead overlapping spacing with the degree of weld bead rolling reduction was generated using the flat-top transition calculation for this model. Considering the damming-up impact of the first bead, the overlapping coefficient was examined for its effect on layer surface flatness. Originality/value: Using the predicted overlapping model, the optimal overlapping coefficients for different rolling reductions can be achieved without experiments. These conclusions can encourage the development of HDMR technology. [ABSTRACT FROM AUTHOR]

In this article, we exploit Semi-Supervised Learning (SSL) to increase the amount of training data to improve the performance of Fine-Grained Visual Categorization (FGVC). This problem has not been investigated in the past in spite of prohibitive annotation costs that FGVC requires. Our approach leverages unlabeled data with an adversarial optimization strategy in which the internal features representation is obtained with a second-order pooling model. This combination allows one to back-propagate the information of the parts, represented by second-order pooling, onto unlabeled data in an adversarial training setting. We demonstrate the effectiveness of the combined use by conducting experiments on six state-of-the-art finegrained datasets, which include Aircrafts, Stanford Cars, CUB-200-2011, Oxford Flowers, Stanford Dogs, and the recent Semi-Supervised iNaturalist-Aves. Experimental results clearly show that our proposed method has better performance than the only previous approach that examined this problem; it also obtained higher classification accuracy with respect to the supervised learning methods with which we compared. [ABSTRACT FROM AUTHOR]

Purpose: This paper aims to present a series of approaches for three-related issues in multiaxis in wire and arc additive manufacturing (WAAM) as follows: how to achieve a stable and robust deposition process and maintain uniform growth of the part; how to maintain consistent formation of a melt pool on the surface of the workpiece; and how to fabricate an overhanging structure without supports. Design/methodology/approach: The principal component analysis-based path planning approach is proposed to compute the best scanning directions of slicing contours for the generation of filling paths, including zigzag paths and parallel skeleton paths. These printing paths have been experimented with in WAAM. To maintain consistent formation of a melt pool at overhanging regions, the authors introduce definitions for the overhanging point, overhanging distance and overhanging vector, with which the authors can compute and optimize the multiaxis motion. A novel fabricating strategy of depositing the overhanging segments as a support for the deposition of filling paths is presented. Findings: The second principal component of a planar contour is a reasonable scanning direction to generate zigzag filling paths and parallel skeleton filling paths. The overhanging regions of a printing layer can be supported by pre-deposition of overhanging segments. Large overhangs can be successfully fabricated by the multiaxis WAAM process without supporting structures. Originality/value: An intelligent approach of generating zigzag printing paths and parallel skeleton printing paths. Optimizations of depositing zigzag paths and parallel skeleton paths. Applications of overhanging point overhanging distance and overhanging vector for multiaxis motion planning. A novel fabricating strategy of depositing the overhanging segments as a support for the deposition of filling paths. [ABSTRACT FROM AUTHOR]

The horizontal features of vehicle bottom and the vertical features of both sides of vehicle are selected, and the local features of gray scale and its gradient and variation, which are less affected by global gray scale, are extracted. The horizontal and vertical features of vehicle are fused respectively by applying weighted evidence theory, and the weight of each feature is adjusted according to different light intensity. For improving the realtimeness of identification, artificial fish swarm algorithm is applied, and a vehicle identification module is added to enhance the guiding ability of artificial fishes. Based on search results and further judgment by symmetric feature, the accurate positioning of front vehicle is realized. Experiment results show that the technique proposed can accurately identify front vehicles under different lighting conditions with good adaptability, accuracy and realtimeness. [ABSTRACT FROM AUTHOR]