Mobile user equipment (UE) which can connect to satellites directly attracts increasing attention. Power consumption is one of the most significant challenges for mobile UEs. In existing satellite communication systems, beam hopping technology has been utilized to improve the satellite efficiency, but the power consumption of UEs is ignored. Discontinuous reception (DRX) mechanism, wherein UEs turn off their receivers intermittently to save power, has been researched and utilized in terrestrial communication systems. However, the satellite efficiency will decrease if the DRX mechanism is applied to beam hopping satellite systems directly. In this paper, we propose a novel DRX mechanism for beam hopping satellite systems (DRX-BHS) and a new beam hopping operation strategy combining traffic-driven strategy and pre-scheduled strategy. Besides, a new pre-scheduled beam hopping strategy based on UE grouping is proposed, which can overcome the limitation of the satellite efficiency in traditional pre-scheduled beam hopping strategy. A semi-Markov model is established to evaluate the performance of the proposed DRX-BHS mechanism. The numerical results reveal the relationship between the performance and the number of UEs in each group, and show that our proposed DRX-BHS mechanism can balance the satellite efficiency and UE power saving.

OBJECTIVE: The recent surge in large language models (LLMs) across various fields has yet to be fully realized in traditional Chinese medicine (TCM). This study aims to bridge this gap by developing a large language model tailored to TCM knowledge, enhancing its performance and accuracy in clinical reasoning tasks such as diagnosis, treatment, and prescription recommendations. MATERIALS AND METHODS: This study harnessed a wide array of TCM data resources, including TCM ancient books, textbooks, and clinical data, to create 3 key datasets: the TCM Pre-trained Dataset, the Traditional Chinese Patent Medicine (TCPM) Question Answering Dataset, and the Spleen and Stomach Herbal Prescription Recommendation Dataset. These datasets underpinned the development of the Lingdan Pre-trained LLM and 2 specialized models: the Lingdan-TCPM-Chat Model, which uses a Chain-of-Thought process for symptom analysis and TCPM recommendation, and a Lingdan Prescription Recommendation model (Lingdan-PR) that proposes herbal prescriptions based on electronic medical records. RESULTS: The Lingdan-TCPM-Chat and the Lingdan-PR Model, fine-tuned on the Lingdan Pre-trained LLM, demonstrated state-of-the art performances for the tasks of TCM clinical knowledge answering and herbal prescription recommendation. Notably, Lingdan-PR outperformed all state-of-the-art baseline models, achieving an improvement of 18.39% in the Top@20 F1-score compared with the best baseline. CONCLUSION: This study marks a pivotal step in merging advanced LLMs with TCM, showcasing the potential of artificial intelligence to help improve clinical decision-making of medical diagnostics and treatment strategies. The success of the Lingdan Pre-trained LLM and its derivative models, Lingdan-TCPM-Chat and Lingdan-PR, not only revolutionizes TCM practices but also opens new avenues for the application of artificial intelligence in other specialized medical fields. Our project is available at https://github.com/TCMAI-BJTU/LingdanLLM.

Objective: To study the phenotypic characteristics of the combination of disease and syndrome of COPD. Methods: Structured clinical EMRs of inpatients in the respiratory department of the First Affiliated Hospital of Henan University of Chinese Medicine for a total of 10 years from 2010 to 2019 were collected. Patients with COPD in Western medicine diagnosis on admission or discharge were used as the study subjects. A Heterogeneous Medical Record network based on COPD patients was constructed. Then, the K-means clustering algorithm was used to divide the supplemented patient medical record feature matrix into different modules (ie, subtypes), and enrichment analysis was performed on the phenotypes characteristics of different modules to determine the typical clinical phenotypes characteristics of different modules. Results: After the analysis of different phenotypic characteristics of COPD patients, it was found that the phenotypic characteristics of COPD patients were diverse. The prominent manifestation is that COPD patients often have diseases and symptoms other than respiratory system, and more than 70% of them have 2-6 different diseases. Through cluster study and analysis of each subtype of COPD patients revealed that each module has its own unique core characteristics. Conclusions: After the analysis of different phenotypic characteristics of COPD patients, it was found that the phenotypic characteristics of COPD patients were diverse, and each subtype has its own unique core characteristics.

The knowledge of phenotype-genotype associations is crucial for the understanding of disease mechanisms. Numerous studies have focused on developing efficient and accurate computing approaches to predict disease genes. However, owing to the sparseness and complexity of medical data, developing an efficient deep neural network model to identify disease genes remains a huge challenge. Therefore, we develop a novel deep neural network model that fuses the multi-view features of phenotypes and genotypes to identify disease genes (termed PDGNet). Our model integrated the multi-view features of diseases and genes and leveraged the feedback information of training samples to optimize the parameters of deep neural network and obtain the deep vector features of diseases and genes. The evaluation experiments on a large data set indicated that PDGNet obtained higher performance than the state-of-the-art method (precision and recall improved by 9.55 and 9.63 percent). The analysis results for the candidate genes indicated that the predicted genes have strong functional homogeneity and dense interactions with known genes. We validated the top predicted genes of Parkinson's disease based on external curated data and published medical literatures, which indicated that the candidate genes have a huge potential to guide the selection of causal genes in the 'wet experiment'. The source codes and the data of PDGNet are available at https://github.com/yangkuoone/PDGNet.

Traditional Chinese medicine (TCM) has played an indispensable role in clinical diagnose and treatment. Based on patient’s symptom phenotypes, computation-based prescription recommendation methods can recommend personalized TCM prescription using machine learning and artificial intelligence technologies. However, owing to the complexity and individuation of patient’s clinical phenotypes, current prescription recommendation methods cannot obtain good performance. Meanwhile, it’s very difficult to conduct effective representation for unrecorded symptom terms in existing knowledge base. In this study, we proposed a subnetwork-based symptom term mapping method (SSTM), and constructed a SSTM-based TCM prescription recommendation method (termed TCMPR). Our SSTM can extract the subnetwork structure between symptoms from knowledge network to effectively represent the embedding features of clinical symptom terms (especially, the unrecorded terms). The experimental results showed that our method performs better than state-of-the-art methods. In addition, the comprehensive experiments of TCMPR with different hyper parameters (i.e., feature embedding, feature dimension and feature fusion) that demonstrates that our method has high performance on TCM prescription recommendation and potentially promote clinical diagnosis and treatment of TCM precision medicine.

Biomedical named entity recognition from clinical texts is a fundamental task for clinical data analysis due to the availability of large volume of electronic medical record data, which are mostly in free text format, in real-world clinical settings. Clinical text data incorporates significant phenotypic medical entities, which could be used for profiling the clinical characteristics of patients in specific disease conditions. However, general approaches mostly rely on the coarse-grained annotations (e.g. mentions of symptom terms) of phenotypic entities in benchmark text dataset. Owing to the numerous negation expressions of phenotypic entities (e.g. “no fever”, “no cough” and “no hypertension”) in clinical texts, this could not feed the subsequent data analysis process with well-prepared structured clinical data. Thus, we constructed a fine-grained Chinese clinical corpus. Thereafter, we proposed a phenotypic named entity recognizer (Phenonizer). The results on the test set show that Phenonizer outperform those methods based on Word2Vec with Fl-score of 0.896. By comparing character embeddings from different data, it is found that character embeddings trained by clinical corpora can improve F-score by 0.0103. Furthermore, the fine-grained dataset enables methods to distinguish between negated symptoms and presented symptoms, and avoids the interference of negated symptoms. Finally, we tested the generalization performance of Phenonier, achieving a superior F1-score of 0.8389. In summary, together with fine-grained annotated benchmark dataset, Phenonier proposes a feasible approach to effectively extract symptom information from Chinese clinical texts with acceptable performance.

Microwave power transmission (MPT) is the wireless transfer of large amounts of power at microwave frequencies from one location to another. MPT research has been driven primarily by the desire to remotely power unmanned aerial vehicles (UAVs) and by the concept of space solar power (SSP) first conceived by Dr. Peter Glaser of the Arthur D. Little Company in 1968. This paper attempts to reveal, in adequate chronological detail, many of the MPT milestones reached over the past 50 years, including those related to SSP. Key components to various MPT systems are presented as well as design schemes for achieving efficient MPT. Special focus is given to rectenna design since this particular MPT component has received the most attention from researchers over the last couple of decades.