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Recently, a lot of Chinese patients consult treatment plans through social networking platforms, but the Chinese medical text contains rich information, including a large number of medical nomenclatures and symptom descriptions. How to build an intelligence model to automatically classify the text information consulted by patients and recommend the correct department for patients is very important. In order to address the problem of insufficient feature extraction from Chinese medical text and low accuracy, this paper proposes a dual channel Chinese medical text classification model. The model extracts feature of Chinese medical text at different granularity, comprehensively and accurately obtains effective feature information, and finally recommends departments for patients according to text classification. One channel of the model focuses on medical nomenclatures, symptoms and other words related to hospital departments, gives different weights, calculates corresponding feature vectors with convolution kernels of different sizes, and then obtains local text representation. The other channel uses the BiGRU network and attention mechanism to obtain text representation, highlighting the important information of the whole sentence, that is, global text representation. Finally, the model uses full connection layer to combine the representation vectors of the two channels, and uses Softmax classifier for classification. The experimental results show that the accuracy, recall and F1-score of the model are improved by 10.65%, 8.94% and 11.62% respectively compared with the baseline models in average, which proves that our model has better performance and robustness.

The Mesozoic gold deposits in the North China Craton (NCC) were hosted by the Precambrian basement and Mesozoic intrusions. Thus, most researchers consider that these gold deposits were genetically linked to the Mesozoic intrusions. However, we suggest that a metamorphic devolatilization model provides an alternative based on a combined Fe and in-situ S isotopes study on auriferous pyrites from the Baiyun gold deposit in the NCC. The Triassic Baiyun gold deposit contains the quartz vein and altered rock ores that were developed in the Paleoproterozoic metavolcanic-sedimentary rocks (the Liaohe Group). Our in-situ S isotopic analyses show that pyrites from the quartz vein ores are characterized by negative δ34S values (-10.7 ∼ -5.5‰), while those from the altered rock ores have two distinct groups of δ34S values, one being positive (+13.5 ∼ +16.2‰) and the other negative (-10.6 ∼ -3.0‰). We suggest that pyrite grains with positive δ34S values should be relicts from the host rocks, because they show comparable δ34S values with those from the host rocks schists (+3.3 ∼ +16.1‰). Thus, only the negative δ34S values of pyrites in ores (-10.7 ∼ -3.0‰) and the Fe isotopes of the quartz vein ores (δ56Fe = +0.30 ∼ +0.48‰) can represent the isotopic characteristics of ore-forming fluids at Baiyun. Our study shows that the sulfur were probably from the pyritic volcanic-sedimentary sequences of the Liaohe Group, rather than from magmas. The calculated δ56Fe values of the ore-forming fluids (-0.78 ∼ -0.37‰; pyrite-fluid isotope fractionation) could be modelled in a metamorphic devolatilization model with Fe-species (pyrite&magnetite) of the Liaohe Group as sources. Therefore, our combined S- and Fe- isotope data indicate that the metamorphic devolatilization of the Liaohe Group could account for the genesis of the Baiyun gold deposit.

Text classification is an important topic in natural language processing, with the development of social network, many question-and-answer pairs regarding health-care and medicine flood social platforms. It is of great social value to mine and classify medical text and provide targeted medical services for patients. The existing algorithms of text classification can deal with simple semantic text, especially in the field of Chinese medical text, the text structure is complex and includes a large number of medical nomenclature and professional terms, which are difficult for patients to understand. We propose a Chinese medical text classification model using a BERT-based Chinese text encoder by N-gram representations (ZEN) and capsule network, which represent feature uses the ZEN model and extract the features by capsule network, we also design a N-gram medical dictionary to enhance medical text representation and feature extraction. The experimental results show that the precision, recall and F1-score of our model are improved by 10.25%, 11.13% and 12.29%, respectively, compared with the baseline models in average, which proves that our model has better performance.

Convolutional neural network (CNN) model based on deep learning has excellent performance for target detection. However, the detection effect is poor when the object is circular or tubular because most of the existing object detection methods are based on the traditional rectangular box to detect and recognize objects. To solve the problem, we propose the circular representation structure and RepVGG module on the basis of CenterNet and expand the network prediction structure, thus proposing a high-precision and high-efficiency lightweight circular object detection method RebarDet. Specifically, circular tubular type objects will be optimized by replacing the traditional rectangular box with a circular box. Second, we improve the resolution of the network feature map and the upper limit of the number of objects detected in a single detect to achieve the expansion of the network prediction structure, optimized for the dense phenomenon that often occurs in circular tubular objects. Finally, the multibranch topology of RepVGG is introduced to sum the feature information extracted by different convolution modules, which improves the ability of the convolution module to extract information. We conducted extensive experiments on rebar datasets and used AB-Score as a new evaluation method to evaluate RebarDet. The experimental results show that RebarDet can achieve a detection accuracy of up to 0.8114 and a model inference speed of 6.9 fps while maintaining a moderate amount of parameters, which is superior to other mainstream object detection models and verifies the effectiveness of our proposed method. At the same time, RebarDet’s high precision detection of round tubular objects facilitates enterprise intelligent manufacturing processes.

Artists are increasingly using blockchain as a tool for trading digital artwork as non-fungible tokens (NFTs); however, some are also beginning to experiment with the blockchain as a medium for generative art, using it as a seed for a generative process or to continuously modify an evolving piece. This paper surveys, reviews, and classifies the state-of-the-art in blockchain-interactive NFTs and presents a liberal-arts critique of the opportunities and threats posed by this technology, whilst addressing existing criticism on the broader topic of art-related NFTs. The paper examines some of the most experimental pieces minted on the Hic et Nunc (HEN) and Teia NFT marketplaces, for which a purpose-built research tool was developed. The survey reveals some reliance on centralised infrastructure, namely blockchain indexers, placing undesired trust on third parties which undermines the potential longevity of the artwork. The paper concludes with recommendations for artists and NFT platform designers for developing more resilient and economically sustainable architectures.

This contribution to the special issue is an historical account of Paulo Freire’s pedagogical and administrative praxis before his forced exile in 1964. It relies on interviews collected during a field trip in 1976, a conversation with Paulo Freire in Geneva one year later and on the secondary literature up to date. Being the head of the first Extension Service of a major Brazilian university in the early 1960s gave Freire and his collaborators the space and time to experiment with the today world famous literacy method bearing his name. The concept of ‘Field of Cultural Production’ (Bourdieu) is used to elucidate better Freire and his team’s avant-gardist production within the spaces opened up by Brazil’s popular movements in the early sixties. The contribution shows how the ‘Paulo Freire System’ developed in the praxis of a cultural movement and received its academic consecration in an incremental and eclectic style.