Background: Clinical named entity recognition (CNER), whose goal is to automatically identify clinical entities in electronic medical records (EMRs), is an important research direction of clinical text data mining and information extraction. The promotion of CNER can provide support for clinical decision making and medical knowledge base construction, which could then improve overall medical quality. Compared with English CNER, and due to the complexity of Chinese word segmentation and grammar, Chinese CNER was implemented later and is more challenging. Objective: With the development of distributed representation and deep learning, a series of models have been applied in Chinese CNER. Different from the English version, Chinese CNER is mainly divided into character-based and word-based methods that cannot make comprehensive use of EMR information and cannot solve the problem of ambiguity in word representation. Methods: In this paper, we propose a lattice long short-term memory (LSTM) model combined with a variant contextualized character representation and a conditional random field (CRF) layer for Chinese CNER: the Embeddings from Language Models (ELMo)-lattice-LSTM-CRF model. The lattice LSTM model can effectively utilize the information from characters and words in Chinese EMRs; in addition, the variant ELMo model uses Chinese characters as input instead of the character-encoding layer of the ELMo model, so as to learn domain-specific contextualized character embeddings. Results: We evaluated our method using two Chinese CNER datasets from the China Conference on Knowledge Graph and Semantic Computing (CCKS): the CCKS-2017 CNER dataset and the CCKS-2019 CNER dataset. We obtained F1 scores of 90.13% and 85.02% on the test sets of these two datasets, respectively. Conclusions: Our results show that our proposed method is effective in Chinese CNER. In addition, the results of our experiments show that variant contextualized character representations can significantly improve the performance of the model.

Detecting pollution timely and locating the pollution source is of great importance in environmental protection. Considering advantages of the sensor network technology, sensor networks have been adopted in pollution monitoring works. In this paper, a survey on researches of pollution monitoring using sensor networks in environment protection is given. Firstly, sensors and pollution monitoring network systems are studied. Secondly, different pollution detection methods are analyzed and compared. Thirdly, an overview of state-of-art technologies on pollution source localization is given. Finally, challenges on pollution monitoring using sensor networks are presented.

Background: Incentive spirometry (IS) as a routine respiratory therapy during the perioperative period has been widely used in clinical practice. However, the impact of IS on patients with perioperative lung cancer remains controversial. This review aimed to evaluate the efficacy of IS in perioperative pulmonary rehabilitation for patients with lung cancer. Methods: Cochrane Library, PubMed, Web of Science, Ovid, CINAHL, Chinese National Knowledge Infrastructure, Weipu, and Wanfang Databases were searched from inception to 30 November 2023. Only randomized controlled trials were included in this systematic review. The PRISMA checklist served as the guidance for conducting this review. The quality assessment of the included studies was assessed by the Cochrane risk-of-bias tool. The meta-analysis was carried out utilizing Review Manager 5.4. Furthermore, sensitivity analysis and subgroup analysis were also performed. Results: Nine studies recruited 1209 patients met our inclusion criteria. IS combined with other respiratory therapy techniques was observed to reduce the incidence of postoperative pulmonary complications, enhance pulmonary function, curtail the length of hospital stay, and lower the Borg score. Nevertheless, no improvements were found in the six-minute walk distance or quality of life score. Conclusions: Although IS demonstrates benefits as a component of comprehensive intervention measures for perioperative patients with lung cancer, it proves challenging to determine the precise impact of IS as a standalone component within the comprehensive intervention measures. Therefore, further researches are required to better understand the effectiveness of IS isolation and its interactions when integrated with additional respiratory therapies for these patients. Clinical trial registration: PROSPERO, https://www.crd.york.ac.uk/prospero/, registry number: CRD42022321044.

Water pollution detection is of great importance in water conservation. In this paper, the water pollution detection problems of the network and of the node in sensor networks are discussed. The detection problems in both cases of the distribution of the monitoring noise being normal and nonnormal are considered. The pollution detection problems are analyzed based on hypothesis testing theory firstly; then, the specific detection algorithms are given. Finally, two implementation examples are given to illustrate how the proposed detection methods are used in the water pollution detection in sensor networks and prove the effectiveness of the proposed detection methods.

Background: With the rapid expansion of biomedical literature, biomedical information extraction has attracted increasing attention from researchers. In particular, relation extraction between 2 entities is a long-term research topic. Objective: This study aimed to perform 2 multiclass relation extraction tasks of Biomedical Natural Language Processing Workshop 2019 Open Shared Tasks: relation extraction of Bacteria-Biotope (BB-rel) task and binary relation extraction of plant seed development (SeeDev-binary) task. In essence, these 2 tasks are aimed at extracting the relation between annotated entity pairs from biomedical texts, which is a challenging problem. Methods: Traditional research methods adopted feature- or kernel-based methods and achieved good performance. For these tasks, we propose a deep learning model based on a combination of several distributed features, such as domain-specific word embedding, part-of-speech embedding, entity-type embedding, distance embedding, and position embedding. The multi-head attention mechanism is used to extract the global semantic features of an entire sentence. Meanwhile, we introduced a dependency-type feature and the shortest dependency path connecting 2 candidate entities in the syntactic dependency graph to enrich the feature representation. Results: Experiments show that our proposed model has excellent performance in biomedical relation extraction, achieving F1 scores of 65.56% and 38.04% on the test sets of the BB-rel and SeeDev-binary tasks. Especially in the SeeDev-binary task, the F1 score of our model is superior to that of other existing models and achieves state-of-the-art performance. Conclusions: We demonstrated that the multi-head attention mechanism can learn relevant syntactic and semantic features in different representation subspaces and different positions to extract comprehensive feature representation. Moreover, syntactic dependency features can improve the performance of the model by learning dependency relation between the entities in biomedical texts.

Magnetically-coupled resonant wireless power transfer (MCR-WPT) technology is one of the most promising technologies to power embedded sensors. In many practical situations, embedded sensors are located in a metal environment. The nearby metal materials will affect the performance of the MCR-WPT system. In this paper, the equivalent circuit model of the MCR-WPT system in metal environment is established and the factors affecting the transmission efficiency is analyzed. Then, simulation analysis is performed using Maxwell software and Simplorer software. Two cases are considered in the simulation study: the coil is close to a metal plate and the coil is close to a metal shaft. Simulation results show that metal materials will change the coil inductance and interfere with the magnetic field distribution of the MCR-WPT system, which in turn affects the transmission efficiency of the system.