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As one of the main causes of sickness and mortality, heart disease, also known as cardiovascular disease, must be detected early in order to be prevented and treated. The rapid development of computer technology presents an opportunity for the cross-combination of medicine and informatics. A novel stacking model called SDKABL is presented in this work. It uses three classifiers, namely K-Nearest Neighbor (KNN), Decision Tree (DT), and Support Vector Machine (SVM) at the base layer and the Bidirectional Long Short-Term Memory based on Attention Mechanisms (ABiLSTM) model at the meta layer for the ultimate prediction. For lowering the temporal complexity and enhancing the model's accuracy, the dimensionality reduction approach is seen to be crucial. Principal Component Analysis (PCA) was utilized in SDKABL to minimize dimensionality and facilitate feature fusion. Using several performance measures, including precision, F1-score, accuracy, recall, and Receiver Operating Characteristic (ROC) score, the performance of SDKABL was compared to that of other independent classifiers. The experimental findings demonstrate that our proposed model combining individual classifiers with the stacking method helps improve the prediction model's accuracy. [ABSTRACT FROM AUTHOR]

This study investigated the navigation problem for cellular-connected unmanned aerial vehicles (UAVs), particularly in highly dynamic urban environments. To address this problem, the UAV is required not only to evade high-speed obstacles in the airspace but also to avoid the coverage holes of cellular base stations (BS). Moreover, the UAV needs to reach the destination to complete the navigation task. Hence, it is imperative to design the trade-off in action selections between collision evasion and destination-approaching scenarios, while also considering the expected communication outage duration as a crucial reference. To overcome this multiobjective optimization challenge, we propose a deep reinforcement learning (DRL)-based algorithm aimed at enabling the UAV to acquire an optimal decision-making policy. Specifically, we formulated the navigation problem as a Markov decision process (MDP) and developed a layered recurrent soft actor–critic (RSAC)-based DRL framework, stimulating the UAV to resolve two fundamental subtasks of UAV navigation. Furthermore, we develop a multilayer perception (MLP)-based integrated evaluation network to select a particular action from the two subsolutions, satisfying the demands for the entire navigation problem. The layered architecture simplifies the navigation problem, thereby enhancing the convergence speed of the proposed algorithm. Numerical results indicate that the layered-RSAC-based UAV can autonomously perform scheduled navigation tasks in our designed simulated urban environments with superior effectiveness. [ABSTRACT FROM AUTHOR]

Robots assist emergency responders by collecting critical information remotely. Deploying multiple cooperative unmanned ground vehicles (UGVs) for a response can reduce the response time, improve situational awareness, and minimize costs. Reliable communication is critical for multiple UGVs for environmental response because multiple robots need to share information for cooperative navigation and data collection. In this work, we investigate a control policy for optimal communication among multiple UGVs and base stations (BSs). A multi-agent deep deterministic policy gradient (MADDPG) algorithm is proposed to update the control policy for the maximum signal-to-interference ratio. The UGVs communicate with both the fixed BSs and a mobile BS. The proposed control policy can navigate the UGVs and mobile BS to optimize communication and signal strength. Finally, a genetic algorithm (GA) is proposed to optimize the hyperparameters of the MADDPG-based training. Simulation results demonstrate the computational efficiency and robustness of the GA-based MADDPG algorithm for the control of multiple UGVs. [ABSTRACT FROM AUTHOR]

In the field of photovoltaic (PV) power prediction, long-term forecasting, which is more challenging than short-term forecasting, can provide more comprehensive and forward-looking guidance. Currently, significant achievements have been made in the field of short-term forecasting for PV power, but inadequate attention has been paid to long-term forecasting. Additionally, multivariate global forecasting across multiple sites and the limited historical time series data available further increase the difficulty of prediction. To address these challenges, we propose a variable–adaptive channel-independent architecture (VACI) and design a deep tree-structured multi-scale gated component named DTM block for this architecture. Subsequently, we construct a specific forecasting model called DTMGNet. Unlike channel-independent modeling and channel-dependent modeling, the VACI integrates the advantages of both and emphasizes the diversity of training data and the model's adaptability to different variables across channels. Finally, the effectiveness of the DTM block is empirically validated using the real-world solar energy benchmark dataset. And on this dataset, the multivariate long-term forecasting performance of DTMGNet achieved state-of-the-art (SOTA) levels, particularly making significant breakthroughs in the 720-step ultra-long forecasting window, where it reduced the MSE metric below 0.2 for the first time (from 0.215 to 0.199), representing a reduction of 7.44%. [ABSTRACT FROM AUTHOR]

Temporal knowledge graphs (TKGs) are used for dynamically modeling facts in the temporal dimension, and are widely used in various fields. However, existing reasoning models often fail to consider the similarity features between entity relationships and static attributes, making it difficult for them to effectively handle these temporal attributes. Therefore, these models have limitations in dealing with previously invisible entities that appear over time and the implicit associations of static attributes between entities. To address this issue, we propose a temporal knowledge graph reasoning model based on Entity Relationship Similarity Perception, known as ERSP. This model employs the similarity measurement method to capture the similarity features of entity relationships and static attributes, and then fuses these features to generate structural representations. Finally, we provide a decoder with entity relationship representation, static attribute representation, and structural representation information to form a quadruple. Experiments conducted on five common benchmark datasets show that ERSP surpasses the majority of TKG reasoning methods. [ABSTRACT FROM AUTHOR]

Background: The study aimed to observe the internal structure of coconuts from two locations (coastal and non-coastal) using computed tomography (CT)., Methods: Seventy-six mature coconuts were collected from Wenchang and Ding'an cities in Hainan Province. These coconuts were scanned four times using CT, with a two-week interval between each scan. CT data were post-processed to reconstruct two-dimensional slices and three-dimensional models. The density and morphological parameters of coconut structures were measured, and the differences in these characteristics between the two groups and the changes over time were analyzed., Results: Time and location had interactive effects on CT values of embryos, solid endosperms and mesocarps, morphological information such as major axis of coconut, thickness of mesocarp, volume of coconut water and height of bud ( p  < 0.05)., Conclusions: Planting location and observation time can affect the density and morphology of some coconut structures., Competing Interests: Li’an Luo is an employee of Siemens Healthineers, but she was not involved in the analysis of the data and had no control of data or information submitted for publication. There is no conflict of interest or industry support in this study. The other authors declare that they have no competing interests., (©2024 Lin et al.)

Medical imaging is indispensable for accurate diagnosis and effective treatment, with modalities like MRI and CT providing diverse yet complementary information. Traditional image fusion methods, while essential in consolidating information from multiple modalities, often suffer from poor image quality and loss of crucial details due to inadequate handling of semantic information and limited feature extraction capabilities. This paper introduces a novel medical image fusion technique leveraging unsupervised image segmentation to enhance the semantic understanding of the fusion process. The proposed method, named DUSMIF, employs a multi-branch, multi-scale deep learning architecture that integrates advanced attention mechanisms to refine the feature extraction and fusion processes. An innovative approach that utilizes unsupervised image segmentation to extract semantic information is introduced, which is then integrated into the fusion process. This not only enhances the semantic relevance of the fused images but also improves the overall fusion quality. The paper proposes a sophisticated network structure that extracts and fuses features at multiple scales and across multiple branches. This structure is designed to capture a comprehensive range of image details and contextual information, significantly improving the fusion outcomes. Multiple attention mechanisms are incorporated to selectively emphasize important features and integrate them effectively across different modalities and scales. This approach ensures that the fused images maintain high quality and detail fidelity. A joint loss function combining content loss, structural similarity loss, and semantic loss is formulated. This function not only guides the network in preserving image brightness and texture but also ensures that the fused image closely resembles the source images in both content and structure. The proposed method demonstrates superior performance over existing fusion techniques in objective assessments and subjective evaluations, confirming its effectiveness in enhancing the diagnostic utility of fused medical images., (© 2024. The Author(s).)