564 research outputs found

    A Study on the Translation of Four-Character Structure Based on Communicative Translation Theory: Taking the Art of Silk Pattern as an Example

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    There are many four-character structures in Chinese, which are widely used. Its translation should follow specific rules and also contain many techniques. Taking The Art of Silk Pattern as an example, this article is based on the communicative translation theory, categorizing the four-character structures and exploring the translation strategies to achieve the translation goals. The author hopes to provide a reference for the subsequent translation of the four-character structure


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    The overall goal of this project was to investigate the levels of selected chemical components of faba bean (Vicia faba) seeds produced in North America, and the performance of ingredients derived from faba bean in low-fat pork bologna. This project was divided into two studies. In study I, protein, fat, ash, total starch, and total dietary fibre (TDF) were determined in five faba bean cultivars: Snowdrop, Snowbird, Taboar, Fabelle, and Malik, that were produced in North America from three lots representing biological replicates. The effect of short-term germination and autoclaving on the level of undesired compounds, including phytic acid, vicine/convicine, oligosaccharides, and total phenolics, were also evaluated. Faba bean varieties had 30-33% protein, 3-4% ash, ~1% fat, 35-40% starch, and 25-27% total dietary fibre. Phytic acid in faba bean was 1.6-1.8% with no statistically significant difference between varieties. Seeds of Fabelle variety had the lowest (p<0.05) vicine and convicine content. No significant difference was found among cultivars for the levels of oligosaccharides. The contents of verbascose (37.5-64.0 mg/g) and stachyose (20.4-25.5 mg/g) were higher than raffinose (8.7-8.9 mg/g) content for all faba bean cultivars. Content of total phenolics among faba bean varieties was significantly (p<0.05) different. Snowdrop and Snowbird had the highest content of total phenolics, while Fabelle had the lowest values for total phenolic content. Germination had no significant effect on phytic acid, vicine (except Snowbird), and convicine levels in faba bean seeds. The oligosaccharides showed significant (p<0.05) reduction upon germination: raffinose by 100%, stachyose by 60%, verbascose by 80% at 72-hour of germination. Upon germination, total phenolic content in Snowdrop and Snowbird showed significant reduction but not Taboar, Fabelle, and Malik varieties. Autoclaving did not present a significant (p<0.05) effect on phytic acid, vicine /convicine, and oligosaccharides while the total phenolic content showed a significant reduction (15-23%) after autoclaving, especially for the varieties of Taboar, Snowbird, and Malik. In study II, six binders, including wheat flour, pea starch, cotyledon flour (Malik), cotyledon flour (Fabelle), faba bean starch fraction (commercial), and faba bean protein fraction (commercial), were used in production of low-fat pork bologna with two (1.5 and 3%) incorporation levels. Further their chemical composition and functional properties were investigated, and the physicochemical, textural, and sensory properties of bologna were evaluated. All binders significantly (p < 0.05) increased viscosity of the raw meat batters. The cook loss of all bologna products with binders was low (0.5%, w/w) and no significant difference on purge loss among all binders was observed. Products with 3% wheat flour and faba bean protein fraction had significantly lower expressible moisture values when compared to the control. The colour parameters of cooked bologna surface were in a narrow range of L* (69-70), a* (16-18), or b* value (14-16). The texture profile analysis (TPA) showed binders did not change the hardness, cohesiveness, springiness, and chewiness of bologna compared to the control. Bologna with 3% of wheat flour significantly (p<0.05) increased the torsional true stress at failure compared to the control product without a binder while only a limited effect on shear strain was observed. Sensory evaluation of these products by a 12-member trained panel indicated that that the addition of 3% wheat flour resulted in significantly higher sensory firmness scores than the control. Except the products containing Malik cotyledon flour, addition of other binders at 3% significantly (p<0.05) reduced the perception of juiciness of bologna. Products containing binders at 1.5 or 3% showed no effect on graininess and overall flavour intensity scores except the bologna with 3% of Fabelle cotyledon flour which had significantly (p<0.05) increased intensity of foreign flavour. No significant difference was found in overall acceptability among bologna with any of the binders at either level. Faba bean ingredients showed similar effect on textural and sensory properties of bologna products as compared to reference binder ingredients, showing their potential in low-fat meat products

    Continual Event Extraction with Semantic Confusion Rectification

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    We study continual event extraction, which aims to extract incessantly emerging event information while avoiding forgetting. We observe that the semantic confusion on event types stems from the annotations of the same text being updated over time. The imbalance between event types even aggravates this issue. This paper proposes a novel continual event extraction model with semantic confusion rectification. We mark pseudo labels for each sentence to alleviate semantic confusion. We transfer pivotal knowledge between current and previous models to enhance the understanding of event types. Moreover, we encourage the model to focus on the semantics of long-tailed event types by leveraging other associated types. Experimental results show that our model outperforms state-of-the-art baselines and is proficient in imbalanced datasets.Comment: Accepted in the 2023 Conference on Empirical Methods in Natural Language Processing (EMNLP 2023

    Dual-Functional-Tag-Facilitated Protein Labeling and Immobilization

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    An important strategy in the construction of biomimetic membranes and devices is to use natural proteins as the functional components for incorporation in a polymeric or nanocomposite matrix. Toward this goal, an important step is to immobilize proteins with high efficiency and precision without disrupting the protein function. Here, we developed a dual-functional tag containing histidine and the non-natural amino acid azidohomoalanine (AHA). AHA is metabolically incorporated into the protein, taking advantage of the Met-tRNA and Met-tRNA synthetase. Histidine in the tag can facilitate metal-affinity purification, whereas AHA can react with an alkyne-functionalized probe or surface via well-established click chemistry. We tested the performance of the tag using two model proteins, green fluorescence protein and an enzyme pyrophosphatase. We found that the addition of the tag and the incorporation of AHA did not significantly impair the properties of these proteins, and the histidine‚ÄďAHA tag can facilitate protein purification, immobilization, and labeling

    Cryptanalysis and improvement of an efficient certificateless signature scheme

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    In traditional digital signature schemes, certificates signed by a trusted party are required to ensure the authenticity of the public key. In Asiacrypt 2003, the concept of certificateless signature scheme was introduced. The advantage of certificate-less public key cryptography successfully eliminates the necessity of certificates in the traditional public key cryptography and simultaneously solves the inherent key escrow problem suffered in identity-based cryptography. Recently, Yap et al. proposed an efficient certificateless signature scheme and claimed that their scheme is existentially unforgeable in the random oracle model. In this paper, we show that the certificateless signature scheme proposed by Yap et al. is insecure against public key replacement attacks. Furthermore, we propose an improved certificateless signature scheme, which is existentially unforgeable against adaptive chosen message attacks under the computational Diffie-Hellman assumption in the random oracle model and provide the security proof of the proposed scheme

    A lightweight network for photovoltaic cell defect detection in electroluminescence images based on neural architecture search and knowledge distillation

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    Nowadays, the rapid development of photovoltaic(PV) power stations requires increasingly reliable maintenance and fault diagnosis of PV modules in the field. Due to the effectiveness, convolutional neural network (CNN) has been widely used in the existing automatic defect detection of PV cells. However, the parameters of these CNN-based models are very large, which require stringent hardware resources and it is difficult to be applied in actual industrial projects. To solve these problems, we propose a novel lightweight high-performance model for automatic defect detection of PV cells in electroluminescence(EL) images based on neural architecture search and knowledge distillation. To auto-design an effective lightweight model, we introduce neural architecture search to the field of PV cell defect classification for the first time. Since the defect can be any size, we design a proper search structure of network to better exploit the multi-scale characteristic. To improve the overall performance of the searched lightweight model, we further transfer the knowledge learned by the existing pre-trained large-scale model based on knowledge distillation. Different kinds of knowledge are exploited and transferred, including attention information, feature information, logit information and task-oriented information. Experiments have demonstrated that the proposed model achieves the state-of-the-art performance on the public PV cell dataset of EL images under online data augmentation with accuracy of 91.74% and the parameters of 1.85M. The proposed lightweight high-performance model can be easily deployed to the end devices of the actual industrial projects and retain the accuracy.Comment: 12 pages, 7 figure

    Layer-by-Layer Assembled Membranes with Immobilized Porins

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    New and advanced opportunities are arising for the synthesis and functionalization of membranes with selective separation, reactivity, and stimuli-responsive behavior. One such advancement is the integration of bio-based channels in membrane technologies. By a layer-by-layer (LbL) assembly of polyelectrolytes, outer membrane protein F trimers (OmpF) or ‚Äúporins‚ÄĚ from Escherichia coli with central pores ‚ąľ2 nm in diameter at their opening and ‚ąľ0.7 √ó 1.1 nm at their constricted region are immobilized within the pores of poly(vinylidene fluoride) microfiltration membranes, in contrast to traditional ruptured lipid bilayer or vesicle processes. These OmpF-membranes demonstrate selective rejection of non-charged organics over ionic solutes, allowing the passage of up to 2 times more salts than traditional nanofiltration membranes starting with rejections of 84% for 0.4 to 1.0 kDa organics. The presence of charged groups in OmpF-membranes also leads to pH-dependent salt rejection through Donnan exclusion. These OmpF-membranes also show exceptional durability and stability, delivering consistent and constant permeability and recovery for over 160 h of operation. Characterization of the solutions containing OmpF and the membranes was conducted during each stage of the process, including detection by fluorescence labelling (FITC), zeta potential, pH responsiveness, flux changes, and rejection of organic‚Äďinorganic solutions

    Aero Engine Fault Diagnosis Using an Optimized Extreme Learning Machine

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    A new extreme learning machine optimized by quantum-behaved particle swarm optimization (QPSO) is developed in this paper. It uses QPSO to select optimal network parameters including the number of hidden layer neurons according to both the root mean square error on validation data set and the norm of output weights. The proposed Q-ELM was applied to real-world classification applications and a gas turbine fan engine diagnostic problem and was compared with two other optimized ELM methods and original ELM, SVM, and BP method. Results show that the proposed Q-ELM is a more reliable and suitable method than conventional neural network and other ELM methods for the defect diagnosis of the gas turbine engine
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