Perceptions of CAI tools in English/Chinese Interpreting Practice, perspectives of professional interpreters and trainers

This article analyses the perceptions of computer-assisted interpreting tools in interpreting practice and training based on the findings of a survey distributed to English/Chinese interpreters and trainers. Results analysis show that most respondents are positive about the application of CAI tools albeit without much application experience yet. Professional interpreters and trainers are optimistic about the existing CAI tools but mainly used them in preparation and post-interpreting stages. Secondly, user feedback shows CAI assists mainly in the science & technology domain. Thirdly, CAI tools are welcomed in interpreter training but trainers insist on the acquisition of skills before integration of technologies

Electromagnetic modelling using T-A formulation for high-temperature superconductor (RE)Ba2Cu3Oxhigh field magnets

Second generation (2G) high-temperature superconductor (HTS) (RE)Ba2Cu3Ox(REBCO) shows a great potential in building high field magnets beyond 23.5 T. The electromagnetic modelling is vital for the design of HTS magnet, however, this always suffers the challenge of huge computation for high field magnets with large number of turns. This study presents a novel electromagnetic modelling based on T-A formulation for REBCO magnets with thousands of turns. An equivalent turn method is proposed to reduce the number of turns in calculation, so that the computation cost can be reduced significantly, and meanwhile the key electromagnetic behaviour of HTS magnet can be simulated with enough accuracy. The ramping operation of a fully HTS magnet with 12,000 turns are analysed using both the original T-A model with actual turns and improved T-A model with equivalent turns. The two models show a good agreement on the key electromagnetic behaviours of the magnet: distribution of current density, magnetic fields, screen current induced field and magnetisation loss, so that this improved T-A model using equivalent turns is validated. The T-A modelling of REBCO magnet is a powerful tool for the electromagnetic analysis of industry-scale high field magnets

Evaluation of n-Butane Gas Adsorption Performance of Composite Adsorbents Used for Carbon Canister

AbstractA novel adsorbent design technique was proposed to composite adsorbent used for carbon canister for improving the adsorption performance of n-butane gas. Two kinds of activated carbons were tested to produce composite adsorbents and evaluate the performance by measuring the adsorption isotherms of butane and pore structure characteristics. The volume-based amount of adsorption for the adsorbents prepared at sodium silicate solution concentration of 0.1wt% is 1.04 and 1.53 times that of the raw activated carbons (AC1 and AC2). The packing density of the composite adsorbent increased with the increase of sodium silicate solution concentration

Fabrication and Characterization of In Situ Synthesized SiC/Al Composites by Combustion Synthesis and Hot Press Consolidation Method

The in situ SiC/Al composites were fabricated in Al-Si-C systems with different Si/C mass ratios and holding time by the method of combustion synthesis and hot press consolidation. The influences of Si/C mass ratio and holding time on the phase constitution, microstructure, and hardness of the composites were investigated. The results indicate that the increase of Si/C mass ratio leads to more uniform size distribution of the SiC particles in the Al matrix. Moreover, by improving the Si/C mass ratio from 4 : 1 to 5 : 1, the maximum size of SiC particle was reduced from 4.1 μm to 2.0 μm. Meanwhile, the percentage of submicroparticles was increased from 22% to 63%, and the average hardness value of the composites was increased by 13%. In addition, when the holding time is set to be fifteen minutes, the Al4C3 phase did not exist in the composites because of its total reactions with Si atoms to form SiC particles, and the average hardness value was 73.8 HB

Application of Permutation Entropy and Permutation Min-Entropy in Multiple Emotional States Analysis of RRI Time Series

This study’s aim was to apply permutation entropy (PE) and permutation min-entropy (PME) over an RR interval time series to quantify the changes in cardiac activity among multiple emotional states. Electrocardiogram (ECG) signals were recorded under six emotional states (neutral, happiness, sadness, anger, fear, and disgust) in 60 healthy subjects at a rate of 1000 Hz. For each emotional state, ECGs were recorded for 5 min and the RR interval time series was extracted from these ECGs. The obtained results confirm that PE and PME increase significantly during the emotional states of happiness, sadness, anger, and disgust. Both symbolic quantifiers also increase but not in a significant way for the emotional state of fear. Moreover, it is found that PME is more sensitive than PE for discriminating non-neutral from neutral emotional states.Facultad de Ingenierí

Advances in the Isolation of Specific Monoclonal Rabbit Antibodies

The rabbit monoclonal antibodies (mAbs) have advantages in pharmaceuticals and diagnostics with high affinity and specificity. During the past decade, many techniques have been developed for isolating rabbit mAbs, including single B cell antibody technologies. This review describes the basic characterization of rabbit antibody repertoire and summarizes methods of hybridoma technologies, phage display platform, and single B cell antibody technologies. With advances in antibody function and repertoire analysis, rabbit mAbs will be widely used in therapeutic applications in the coming years

Denoising Distantly Supervised Named Entity Recognition via a Hypergeometric Probabilistic Model

Denoising is the essential step for distant supervision based named entity recognition. Previous denoising methods are mostly based on instance-level confidence statistics, which ignore the variety of the underlying noise distribution on different datasets and entity types. This makes them difficult to be adapted to high noise rate settings. In this paper, we propose Hypergeometric Learning (HGL), a denoising algorithm for distantly supervised NER that takes both noise distribution and instance-level confidence into consideration. Specifically, during neural network training, we naturally model the noise samples in each batch following a hypergeometric distribution parameterized by the noise-rate. Then each instance in the batch is regarded as either correct or noisy one according to its label confidence derived from previous training step, as well as the noise distribution in this sampled batch. Experiments show that HGL can effectively denoise the weakly-labeled data retrieved from distant supervision, and therefore results in significant improvements on the trained models.Comment: Accepted to AAAI202

ERRA: An Embodied Representation and Reasoning Architecture for Long-horizon Language-conditioned Manipulation Tasks

This letter introduces ERRA, an embodied learning architecture that enables robots to jointly obtain three fundamental capabilities (reasoning, planning, and interaction) for solving long-horizon language-conditioned manipulation tasks. ERRA is based on tightly-coupled probabilistic inferences at two granularity levels. Coarse-resolution inference is formulated as sequence generation through a large language model, which infers action language from natural language instruction and environment state. The robot then zooms to the fine-resolution inference part to perform the concrete action corresponding to the action language. Fine-resolution inference is constructed as a Markov decision process, which takes action language and environmental sensing as observations and outputs the action. The results of action execution in environments provide feedback for subsequent coarse-resolution reasoning. Such coarse-to-fine inference allows the robot to decompose and achieve long-horizon tasks interactively. In extensive experiments, we show that ERRA can complete various long-horizon manipulation tasks specified by abstract language instructions. We also demonstrate successful generalization to the novel but similar natural language instructions.Comment: Accepted to IEEE Robotics and Automation Letters (RA-L

Comparative study on the synergistic effect of POSS and graphene with melamine phosphate on the flame retardance of poly(butylene succinate)

Flame retardant poly(butylene succinate) (PBS) composites were prepared by melt blending PBS with melamine phosphate (MP), using graphene or polyhedral oligomeric silsesquioxanes (POSS) as synergists. The comparative study on the effect of POSS and graphene on the mechanical, thermal properties and flammability of flame retardant PBS was investigated. The addition of POSS or graphene further improved the LOI values of the flame retardant PBS, and V0 rating was obtained for the formulation containing 18 wt % MP and 2 wt% graphene. The incorporation of POSS and graphene reduced the crystallization of PBS, but improved the tensile strength. The presence of graphene exhibited superior thermal-oxidative resistance of the char layer compared to POSS, which effectively retarded the mass and heat transfer between the flame and the burning substrate, thus the heat release rate and total heat release of the flame retardant PBS composites containing graphene was significantly reduced during combustion