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Featured Application: This research has important applications in areas such as smart furniture and human-computer interaction. It will bring people a more efficient and comfortable living experience as well as a new smart experience.  Human action recognition has many application prospects in human-computer interactions, innovative furniture, healthcare, and other fields. The traditional human motion recognition methods have limitations in privacy protection, complex environments, and multi-person scenarios. Millimeter-wave radar has attracted attention due to its ultra-high resolution and all-weather operation. Many existing studies have discussed the application of millimeter-wave radar in single-person scenarios, but only some have addressed the problem of action recognition in multi-person scenarios. This paper uses a commercial millimeter-wave radar device for human action recognition in multi-person scenarios. In order to solve the problems of severe interference and complex target segmentation in multiplayer scenarios, we propose a filtering method based on millimeter-wave inter-frame differences to filter the collected human point cloud data. We then use the DBSCAN algorithm and the Hungarian algorithm to segment the target, and finally input the data into a neural network for classification. The classification accuracy of the system proposed in this paper reaches 92.2% in multi-person scenarios through experimental tests with the five actions we set. [ABSTRACT FROM AUTHOR]

Breathing is one of the body's most basic functions and abnormal breathing can indicate underlying cardiopulmonary problems. Monitoring respiratory abnormalities can help with early detection and reduce the risk of cardiopulmonary diseases. In this study, a 77 GHz frequency-modulated continuous wave (FMCW) millimetre-wave (mmWave) radar was used to detect different types of respiratory signals from the human body in a non-contact manner for respiratory monitoring (RM). To solve the problem of noise interference in the daily environment on the recognition of different breathing patterns, the system utilised breathing signals captured by the millimetre-wave radar. Firstly, we filtered out most of the static noise using a signal superposition method and designed an elliptical filter to obtain a more accurate image of the breathing waveforms between 0.1 Hz and 0.5 Hz. Secondly, combined with the histogram of oriented gradient (HOG) feature extraction algorithm, K-nearest neighbours (KNN), convolutional neural network (CNN), and HOG support vector machine (G-SVM) were used to classify four breathing modes, namely, normal breathing, slow and deep breathing, quick breathing, and meningitic breathing. The overall accuracy reached up to 94.75%. Therefore, this study effectively supports daily medical monitoring. [ABSTRACT FROM AUTHOR]

Sentence Boundary Disambiguation (SBD) is crucial for building datasets for tasks such as machine translation, syntactic analysis, and semantic analysis. Currently, most automatic sentence segmentation in Tibetan adopts the methods of rule-based and statistical learning, as well as the combination of the two, which have high requirements on the corpus and the linguistic foundation of the researchers and are more costly to annotate manually. In this study, we explore Tibetan SBD using deep learning technology. Initially, we analyze Tibetan characteristics and various subword techniques, selecting Byte Pair Encoding (BPE) and Sentencepiece (SP) for text segmentation and training the Bidirectional Encoder Representations from Transformers (BERT) pre-trained language model. Secondly, we studied the Tibetan SBD based on different BERT pre-trained language models, which mainly learns the ambiguity of the shad ("།") in different positions in modern Tibetan texts and determines through the model whether the shad ("།") in the texts has the function of segmenting sentences. Meanwhile, this study introduces four models, BERT-CNN, BERT-RNN, BERT-RCNN, and BERT-DPCNN, based on the BERT model for performance comparison. Finally, to verify the performance of the pre-trained language models on the SBD task, this study conducts SBD experiments on both the publicly available Tibetan pre-trained language model TiBERT and the multilingual pre-trained language model (Multi-BERT). The experimental results show that the F1 score of the BERT (BPE) model trained in this study reaches 95.32% on 465,669 Tibetan sentences, nearly five percentage points higher than BERT (SP) and Multi-BERT. The SBD method based on pre-trained language models in this study lays the foundation for establishing datasets for the later tasks of Tibetan pre-training, summary extraction, and machine translation. [ABSTRACT FROM AUTHOR]

This study aims to solve the problem of named entity recognition of complex mechanical equipment faults, especially the problems of many professional terms, long sentences, fuzzy entity boundaries, entity nesting, and abbreviation ambiguity, in mine hoist fault text. Therefore, this study proposes a named entity recognition method based on domain dictionary embedding. The method first uses the fault domain knowledge of the mine hoist to construct a domain-specialized dictionary and generate a word vector of characteristic words. Secondly, the BERT pre-trained language model is used to obtain dynamic word vectors, and a dictionary adapter is loaded to obtain contextual domain lexical features to improve recognition accuracy. Finally, the conditional random field (CRF) is the model classifier to output the annotation sequence with the highest score. The experimental results show that this model achieves better than several baseline models and effectively improves the accuracy of fault named entity identification for mine hoists. The innovation of this study is the combination of domain dictionary embedding and a BERT pre-trained language model, which improves the accuracy and robustness of named entity recognition. Therefore, the results of this study have essential research significance for improving the accuracy of fault named entity identification of mine hoists and the construction of fault knowledge maps. [ABSTRACT FROM AUTHOR]

Due to their individual advantages, the integration of lexicon information and pre-trained models like BERT has been widely adopted in Chinese sequence labeling tasks. However, given their high demand for training data, efforts have been made to enhance their performance in low-resource scenarios. Currently, certain specialized domains, such as agriculture, the industrial sector, and the metallurgical industry, suffer from a scarcity of data. Consequently, there is a dearth of effective models for entity relationship recognition when faced with limited data availability. Inspired by this, we constructed a suitable small balanced dataset and proposed a based-domain-NER model. Firstly, we construct a domain-specific dictionary based on mine hoist equipment and fault text and generate a dictionary tree to obtain word vector information. Secondly, we use a Lexicon Adapter to obtain the vector information of the domain-specific dictionary feature words matched using the characters and calculate the weights between their word vectors, integrating position encoding to enhance the positional information of the word vectors. Finally, we incorporate word vector information into the feature extraction layer to enhance the boundary information of domain entities and mitigate the semantic loss problem caused via using only character feature representation. Experimental results on a manually annotated dataset of mine hoist fault texts show that this method outperforms BiLSTM, BiLSTM-CRF, BERT, BERT-BiLSTM-CRF, and LEBERT, effectively improving the accuracy of named entity recognition (NER) for mine hoist faults. [ABSTRACT FROM AUTHOR]

Triplet extraction is the key technology to automatically construct knowledge graphs. Extracting the triplet of mechanical equipment fault relationships is of great significance in constructing the fault diagnosis of a mine hoist. The pipeline triple extraction method will bring problems such as error accumulation and information redundancy. The existing joint learning methods cannot be applied to fault texts with more overlapping relationships, ignoring the particularity of professional knowledge in the field of complex mechanical equipment faults. Therefore, based on the Chinese pre-trained language model BERT Whole Word Masking (BERT-wwm), this paper proposes a joint entity and relation extraction model MHlinker (Mine Hoist linker, MHlinker) for the mine hoist fault field. This method uses BERT-wwm as the underlying encoder. In the entity recognition stage, the classification matrix is constructed using the multi-head extraction paradigm, which effectively solves the problem of entity nesting. The results show that this method enhances the model's ability to extract fault relationships as a whole. When the small-scale manually labeled mine hoist fault text data set is tested, the extraction effect of entities and relationships is significantly improved compared with several baseline models. [ABSTRACT FROM AUTHOR]

The complex structure of the blood–brain barrier (BBB), which blocks nearly all large biomolecules, hinders drug delivery to the brain and drug assessment, thus decelerating drug development. Conventional in vitro models of BBB cannot mimic some crucial features of BBB in vivo including a shear stress environment and the interaction between different types of cells. There is a great demand for a new in vitro platform of BBB that can be used for drug delivery studies. Compared with in vivo models, an in vitro platform has the merits of low cost, shorter test period, and simplicity of operation. Microfluidic technology and microfabrication are good tools in rebuilding the BBB in vitro. During the past decade, great efforts have been made to improve BBB penetration for drug delivery using biochemical or physical stimuli. In particular, compared with other drug delivery strategies, sonoporation is more attractive due to its minimized systemic exposure, high efficiency, controllability, and reversible manner. BBB on chips (BOC) holds great promise when combined with sonoporation. More details and mechanisms such as trans-endothelial electrical resistance (TEER) measurements and dynamic opening of tight junctions can be figured out when using sonoporation stimulating BOC, which will be of great benefit for drug development. Herein, we discuss the recent advances in BOC and sonoporation for BBB disruption with this in vitro platform. [ABSTRACT FROM AUTHOR]

Deformability is shown to correlate with the invasiveness and metastasis of cancer cells. Recent studies suggest epithelial-to-mesenchymal transition (EMT) might enable cancer metastasis. However, the correlation of EMT with cancer cell deformability has not been well elucidated. Cellular deformability could also help evaluate the drug response of cancer cells. Here, we combine hydrodynamic stretching and microsieve filtration to study cellular deformability in several cellular models. Hydrodynamic stretching uses extensional flow to rapidly quantify cellular deformability and size with high throughput at the single cell level. Microsieve filtration can rapidly estimate relative deformability in cellular populations. We show that colorectal cancer cell line RKO with the mesenchymal-like feature is more flexible than the epithelial-like HCT116. In another model, the breast epithelial cells MCF10A with deletion of the TP53 gene are also significantly more deformable compared to their isogenic wildtype counterpart, indicating a potential genetic link to cellular deformability. We also find that the drug docetaxel leads to an increase in the size of A549 lung cancer cells. The ability to associate mechanical properties of cancer cells with their phenotypes and genetics using single cell hydrodynamic stretching or the microsieve may help to deepen our understanding of the basic properties of cancer progression. [ABSTRACT FROM AUTHOR]

(1) Background: The environmentally friendly choline-amino acid ionic liquids (ChAAILs) and deep eutectic solvents (DESs) have been used as excellent alternatives to volatile organic compounds (VOCs) and ionic liquids (ILs) in recent years; (2) Methods: Thus, ChAAILs/salt and DESs/salt aqueous two-phase systems (ATPSs) were developed for the chiral extraction of phenylalanine enantiomers. The optimum ATPS of [Ch][L-Pro]/K3PO4 was chosen, and the influencing parameters were investigated, including ChAAILs concentration, salt concentration, chiral selector concentration, extraction temperature, phenylalanine concentration, and system pH; (3) Results: The phenylalanine enantiomers were mainly extracted into the top phase (ChAAIL-rich phase), meanwhile, the (S)-phenylalanine [(S)-Phe)] was preferentially recognized by the chiral selector in the top phase. The maximum separation factor (α) of 2.05 was obtained under the optimal conditions; and (4) Conclusions: This ATPS that was used for the chiral extraction of enantiomers is much more environmentally friendly, simple, and rapid, and has the potential to be used in the enantioselective extraction of other enantiomers. [ABSTRACT FROM AUTHOR]

The complex structure of the blood-brain barrier (BBB), which blocks nearly all large biomolecules, hinders drug delivery to the brain and drug assessment, thus decelerating drug development. Conventional in vitro models of BBB cannot mimic some crucial features of BBB in vivo including a shear stress environment and the interaction between different types of cells. There is a great demand for a new in vitro platform of BBB that can be used for drug delivery studies. Compared with in vivo models, an in vitro platform has the merits of low cost, shorter test period, and simplicity of operation. Microfluidic technology and microfabrication are good tools in rebuilding the BBB in vitro. During the past decade, great efforts have been made to improve BBB penetration for drug delivery using biochemical or physical stimuli. In particular, compared with other drug delivery strategies, sonoporation is more attractive due to its minimized systemic exposure, high efficiency, controllability, and reversible manner. BBB on chips (BOC) holds great promise when combined with sonoporation. More details and mechanisms such as trans-endothelial electrical resistance (TEER) measurements and dynamic opening of tight junctions can be figured out when using sonoporation stimulating BOC, which will be of great benefit for drug development. Herein, we discuss the recent advances in BOC and sonoporation for BBB disruption with this in vitro platform.

(1) Background: Ionic liquids (ILs) are considered "green" solvents and have been widely used in the extraction and separation field in recent years; (2) Methods: In this study, some common ILs and functionalized magnetic ionic liquids (MILs) were used as adjuvants for the solvent extraction of paclitaxel from Taxus x media ( T. x media ) using methanol solution. The extraction conditions of methanol concentration, IL type and amount, solid-liquid ratio, extraction temperature, and ultrasonic irradiation time were investigated in single factor experiments. Then, three factors of IL amount, solid-liquid ratio, and ultrasonic irradiation time were optimized by response surface methodology (RSM); (3) Results: The MIL [C₄MIM]FeCl₃Br was screened as the optimal adjuvant. Under the optimization conditions of 1.2% IL amount, 1:10.5 solid-liquid ratio, and 30 min ultrasonic irradiation time, the extraction yield reached 0.224 mg/g; and (4) Conclusions: Compared with the conventional solvent extraction, this ultrasonic assisted extraction (UAE) using methanol and MIL as adjuvants can significantly improve the extraction yield, reduce the use of methanol, and shorten the extraction time, which has the potentiality of being used in the extraction of some other important bioactive compounds from natural plant resources., Competing Interests: The authors declare no conflict of interest.