Microstructure and Mechanical Properties of IN690 Ni-Based Alloy/316LN Stainless-Steel Dissimilar Ring Joint Welded by Inertia Friction Welding.

Inertia friction welding (IFW) was used to join large-diameter hollow bars made of Inconel 690 and 316LN successfully. The interfacial characteristics, microstructure, mechanical properties and fracture mechanism of welded joints under different process parameters were investigated. The results indicated that a joining mechanism with mechanical interlocking and metallurgical bonding was found in IFW joints. There was a significant mechanical mixing zone at the welding interface. The elemental diffusion layer was found in the "wrinkles" of the mechanical mixing zone. A tiny quantity of C elements accumulated on the friction and secondary friction surfaces. The tensile strength and impact toughness of the joints increased with the total welding energy input. Increasing the friction pressure could make the grain in all parts of the joint uniformly refined, thus enhancing the mechanical properties of welded joints. The maximum tensile strength and impact toughness of the welded joint were 639 MPa and 146 J/cm2, reaching 94% and 68% of that for Inconel 690, respectively, when the flywheel was initially set at 760 rpm, 200 MPa for friction pressure, and 388 kg/m2 for rotary inertia. Due to the Kirkendall effect in the welded joint, superior metallurgical bonding was at the welding interface close to the Inconel 690 side compared to the 316LN side.

Microstructure and Mechanical Properties of Powder Metallurgy Superalloy Joints Welded by Inertia Friction Welding.

In recent years, for the structural characteristics and design requirements of the integral rotor and disc shaft of the integrated engine, the welding quality and mechanical properties of superalloy weldments have received increasing attention. In this paper, inertia friction welding (IFW) of FGH96 alloy was carried out using different welding parameters, and the homogeneous connection of FGH96 alloy hollow bars was successfully realized. The microstructure evolution, mechanical properties and fracture failure of the welded joints at room and high temperatures were investigated. The FGH96 alloy IFW joints were divided into the weld nugget zone (WNZ), the thermo-mechanically affected zone (TMAZ), the heat-affected zone (HAZ) and the base metal (BM), and there were significant differences in grain structure and distribution of the γ' phase in each of the characteristic zones. The microhardness and tensile properties of the IFW joints were investigated, and the results showed an "M"-shaped curve in the microhardness distribution, with the lowest point of hardness observed in the HAZ. The tensile test results indicated that the fracture position moved from the BM to the WNZ with the increase in temperature, the microstructure at the fracture changed significantly and the tensile strength decreased from 1512.0 MPa at room temperature to 1201.3 MPa at 750 °C. The difference in the mechanical properties of the joints was mainly attributed to the changes in the dissolution and precipitation of the γ' phase.

Composition of major quinochalcone hydroxysafflor yellow A and anhydrosafflor yellow B is associated with colour of safflower (Carthamus tinctorius) during colour-transition but not with overall antioxidant capacity: A study on 144 cultivars.

Yellow pigments in the water-extract of safflower (Carthamus tinctorius L.) belong to quinochalcone flavonoid family and are widely used as food colourants. The aim of the study was to characterize the main quinochalcone compounds in safflower water-extract during blooming period when floret changed colour. Mass-spectrometry results showed that hydroxysafflor yellow A (HSYA) and anhydrosafflor yellow B (AHSYB) were the most abundant. Based on 370 florets samples collected from 144 cultivars, the contents of HSYA and AHSYB were determined, which showed that only AHSYB content had relatively strong positive association with colour indexes. The ratio of HSYA/AHSYB and visual colour exhibited certain patterns: yellow = 2, orange = 3-4, red = more dispersed, mostly falling 5-6. Most of the florets had HSYA increased first and decreased, while AHSYB decreased all the time when floret changed colour as yellow â†’ orange â†’ red. Regardless of the composition of HSYA/AHSYB in florets, the antioxidant capacities of safflower petal water-extracts were the same.

Improving biomedical named entity recognition by dynamic caching inter-sentence information.

MOTIVATION: Biomedical Named Entity Recognition (BioNER) aims to identify biomedical domain-specific entities (e.g. gene, chemical and disease) from unstructured texts. Despite deep learning-based methods for BioNER achieving satisfactory results, there is still much room for improvement. Firstly, most existing methods use independent sentences as training units and ignore inter-sentence context, which usually leads to the labeling inconsistency problem. Secondly, previous document-level BioNER works have approved that the inter-sentence information is essential, but what information should be regarded as context remains ambiguous. Moreover, there are still few pre-training-based BioNER models that have introduced inter-sentence information. Hence, we propose a cache-based inter-sentence model called BioNER-Cache to alleviate the aforementioned problems. RESULTS: We propose a simple but effective dynamic caching module to capture inter-sentence information for BioNER. Specifically, the cache stores recent hidden representations constrained by predefined caching rules. And the model uses a query-and-read mechanism to retrieve similar historical records from the cache as the local context. Then, an attention-based gated network is adopted to generate context-related features with BioBERT. To dynamically update the cache, we design a scoring function and implement a multi-task approach to jointly train our model. We build a comprehensive benchmark on four biomedical datasets to evaluate the model performance fairly. Finally, extensive experiments clearly validate the superiority of our proposed BioNER-Cache compared with various state-of-the-art intra-sentence and inter-sentence baselines. AVAILABILITYAND IMPLEMENTATION: Code will be available at https://github.com/zgzjdx/BioNER-Cache. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

An Improved Sign Language Translation Model with Explainable Adaptations for Processing Long Sign Sentences.

Sign language translation (SLT) is an important application to bridge the communication gap between deaf and hearing people. In recent years, the research on the SLT based on neural translation frameworks has attracted wide attention. Despite the progress, current SLT research is still in the initial stage. In fact, current systems perform poorly in processing long sign sentences, which often involve long-distance dependencies and require large resource consumption. To tackle this problem, we propose two explainable adaptations to the traditional neural SLT models using optimized tokenization-related modules. First, we introduce a frame stream density compression (FSDC) algorithm for detecting and reducing the redundant similar frames, which effectively shortens the long sign sentences without losing information. Then, we replace the traditional encoder in a neural machine translation (NMT) module with an improved architecture, which incorporates a temporal convolution (T-Conv) unit and a dynamic hierarchical bidirectional GRU (DH-BiGRU) unit sequentially. The improved component takes the temporal tokenization information into consideration to extract deeper information with reasonable resource consumption. Our experiments on the RWTH-PHOENIX-Weather 2014T dataset show that the proposed model outperforms the state-of-the-art baseline up to about 1.5+ BLEU-4 score gains.