Bipedal robots, due to their anthropomorphic design, offer substantial potential across various applications, yet their control is hindered by the complexity of their structure. Currently, most research focuses on proprioception-based methods, which lack the capability to overcome complex terrain. While visual perception is vital for operation in human-centric environments, its integration complicates control further. Recent reinforcement learning (RL) approaches have shown promise in enhancing legged robot locomotion, particularly with proprioception-based methods. However, terrain adaptability, especially for bipedal robots, remains a significant challenge, with most research focusing on flat-terrain scenarios. In this paper, we introduce a novel mixture of experts teacher-student network RL strategy, which enhances the performance of teacher-student policies based on visual inputs through a simple yet effective approach. Our method combines terrain selection strategies with the teacher policy, resulting in superior performance compared to traditional models. Additionally, we introduce an alignment loss between the teacher and student networks, rather than enforcing strict similarity, to improve the student’s ability to navigate diverse terrains. We validate our approach experimentally on the Limx Dynamic P1 bipedal robot, demonstrating its feasibility and robustness across multiple terrain types.

OBJECTIVE: To evaluate an automated reporting checklist generation tool using large language models and retrieval augmentation generation technology, called RAPID. MATERIALS AND METHODS: This study utilized large language models to develop a retrieval augmentation generation architecture. To assess its performance, a total of 91 published journal articles were collected and manually annotated in accordance with the CONSORT and CONSORT-AI medical reporting guidelines. These articles comprised 50 randomized controlled trials conducted without AI intervention and 41 randomized controlled trials that incorporated AI tools. RESULTS: Fifty RCT articles without the intervention of AI tools and 41 RCT articles with the intervention of AI tools were collected as CONSORT and CONSORT-AI datasets. All of the CONSORT reporting items (37) were included in the tool. RAPID achieved a high average accuracy rate of 92.11% and a content consistency score of 81.14% on the CONSORT dataset. Of the CONSORT-AI reporting items, 11 items related to the intervention of AI tools were included in the tool. RAPID achieved an average accuracy of 83.81% with a content consistency score of 72.51% on the CONSORT-AI dataset. DISCUSSION: RAPID may effectively save time and improve working efficiency for different user groups such as medical authors, researchers, editors, and reviewers. CONCLUSION: RAPID has strong scalability, which can be easily adapted to different medical reporting guidelines without transfer learning on a large dataset. RAPID got state-of-the-art performance on 2 datasets for 2 different checklists compared to other methods.

With the rapid development of global retail e-commerce, the critical role of warehouses in logistics systems has become increasingly prominent. Current state-of-the-art solutions typically address only one or two warehouse tasks, including object recognition, sorting, and transportation. This paper presents the development of an integrated autonomous warehouse robot system capable of performing the three functions. The system mimics human capabilities by integrating various components, including advanced computer vision technologies using YOLOv5, a mobile robot, and a robotic arm. The YOLOv5 algorithm is employed for object detection and size estimation, enabling the system to classify goods-containing boxes into small, medium, and large categories. These classified goods are then delivered to predetermined locations by the mobile robot. Finally, the robot arm is employed to pick and place boxes in their storage location based on size. Experimental results demonstrate the system’s robustness and reliability, highlighting its significant potential to enhance warehouse management through robotic automation.

MOTIVATION: Liquid chromatography retention times prediction can assist in metabolite identification, which is a critical task and challenge in nontargeted metabolomics. However, different chromatographic conditions may result in different retention times for the same metabolite. Current retention time prediction methods lack sufficient scalability to transfer from one specific chromatographic method to another. RESULTS: Therefore, we present RT-Transformer, a novel deep neural network model coupled with graph attention network and 1D-Transformer, which can predict retention times under any chromatographic methods. First, we obtain a pre-trained model by training RT-Transformer on the large small molecule retention time dataset containing 80 038 molecules, and then transfer the resulting model to different chromatographic methods based on transfer learning. When tested on the small molecule retention time dataset, as other authors did, the average absolute error reached 27.30 after removing not retained molecules. Still, it reached 33.41 when no samples were removed. The pre-trained RT-Transformer was further transferred to 5 datasets corresponding to different chromatographic conditions and fine-tuned. According to the experimental results, RT-Transformer achieves competitive performance compared to state-of-the-art methods. In addition, RT-Transformer was applied to 41 external molecular retention time datasets. Extensive evaluations indicate that RT-Transformer has excellent scalability in predicting retention times for liquid chromatography and improves the accuracy of metabolite identification. AVAILABILITY AND IMPLEMENTATION: The source code for the model is available at https://github.com/01dadada/RT-Transformer. The web server is available at https://huggingface.co/spaces/Xue-Jun/RT-Transformer.

Abstract Diagnosis prediction exploits electronic health records (EHRs) to predict the future diagnoses of patients, further supporting clinical decision making and personalized treatments. However, a patient's EHR is an irregular sequence of visits that contains a large number of medical concepts. The disease progression patterns are closely related to the visits, as well as the contextual knowledge of each visit. The existing diagnosis prediction methods ignore the complex relationships between the visits and the contextual knowledge, and thus cannot achieve satisfactory performance. Therefore, we develop a knowledge‐aware representation learning method to comprehensively model these complex relationships. Specifically, we first construct a medical knowledge graph to model the correlations between medical concepts in EHRs, and project the contextual knowledge into the pre‐learned vectors. We then devise an enhanced gated recurrent unit (GRU) neural network to extract the long‐term intra‐relationships between visits, and design a novel knowledge attention module to capture the complex inter‐relationships between the visits and the contextual knowledge. Armed with these, we provide a powerful and flexible framework to capture the long‐term discriminative disease progression patterns for diagnosis prediction. Intensive experiments are conducted on two real‐world EHR datasets. And the model achieves a competitive performance with Code‐level Accuracy@20 of 0.7465 and Visit‐level Precision@20 of 0.7547 on MIMIC‐III, and Code‐level Accuracy@20 of 0.9337 and Visit‐level Precision@20 of 0.9427 on MIMIC‐IV.

BACKGROUND: Protein secondary structure (PSS) is critical to further predict the tertiary structure, understand protein function and design drugs. However, experimental techniques of PSS are time consuming and expensive, and thus it's very urgent to develop efficient computational approaches for predicting PSS based on sequence information alone. Moreover, the feature matrix of a protein contains two dimensions: the amino-acid residue dimension and the feature vector dimension. Existing deep learning based methods have achieved remarkable performances of PSS prediction, but the methods often utilize the features from the amino-acid dimension. Thus, there is still room to improve computational methods of PSS prediction. RESULTS: We propose a novel deep neural network method, called DeepACLSTM, to predict 8-category PSS from protein sequence features and profile features. Our method efficiently applies asymmetric convolutional neural networks (ACNNs) combined with bidirectional long short-term memory (BLSTM) neural networks to predict PSS, leveraging the feature vector dimension of the protein feature matrix. In DeepACLSTM, the ACNNs extract the complex local contexts of amino-acids; the BLSTM neural networks capture the long-distance interdependencies between amino-acids. Furthermore, the prediction module predicts the category of each amino-acid residue based on both local contexts and long-distance interdependencies. To evaluate performances of DeepACLSTM, we conduct experiments on three publicly available datasets: CB513, CASP10 and CASP12. Results indicate that the performance of our method is superior to the state-of-the-art baselines on three publicly datasets. CONCLUSIONS: Experiments demonstrate that DeepACLSTM is an efficient predication method for predicting 8-category PSS and has the ability to extract more complex sequence-structure relationships between amino-acid residues. Moreover, experiments also indicate the feature vector dimension contains the useful information for improving PSS prediction.

To overcome the problems that homemade CCD camera's resolution and acquisition speed is low. In this paper a design of high speed and resolution CCD camera is given. This system is made up of KSC-100 timing generator, signal processor AD9840A, KODAK KAI0304 640×480CCD. The output of AD9804A is stored in SDRAM storage, and the output data is transfer to computer by USB acquisition system. Recording speeds 40000fps has been obtained by test. This system has characteristics with low cost, low noises and fast speeds.