Simultaneous localization and mapping (SLAM) has emerged as a promising solution to address the localization challenges faced by underwater robots. This work proposes a factor graph optimization-based Visual-Inertial-Doppler Velocity Log (DVL) tightly-coupled SLAM method designed for underwater robot localization. The approach introduces a novel DVL residual construction method to maximize the utility of DVL measurements. IMU data is integrated to detect anomalies in DVL measurements, and a DVL synchronization marking strategy is developed to enhance the method’s applicability and strengthen data associations. To improve system robustness in scenarios where visual tracking fails, a new sliding window strategy is incorporated. Experimental results on underwater datasets and simulations demonstrate that the proposed method achieves significant improvements in localization accuracy and robustness compared to existing underwater localization algorithms. The implementation code of the proposed method and a simulated underwater dataset can be accessed at https://github. com/uestc-icsp/VID-SLAM.

Time series anomaly detection (TSAD) is crucial for ensuring the reliability of safety-critical systems. While recent multi-view approaches combining time and frequency domains have advanced performance, they still face important representation and fusion bottlenecks. Specifically, conventional linear spectral mappings may introduce representation distortion when decoding complex non-linear frequency shifts. Furthermore, by restricting the feature space to purely numerical modalities, existing deterministic models lack the global operational semantics required to contextualize operational shifts. This semantic void can lead to inter-modal cognitive conflicts and overconfident misclassifications under noisy environments, whereas traditional probabilistic uncertainty estimation methods remain computationally expensive for real-time TSAD. To address these limitations, this paper proposes a Tri-modal Evidential Synergy Network (TES-Net). First, TES-Net leverages a multi-order Kolmogorov-Arnold network alongside large language model (LLM)-extracted global semantics to adaptively decode non-linear spectral fluctuations and bridge the heterogeneous semantic gap. Second, to resolve inter-modal conflicts efficiently, we design a Tri-modal Evidential Fusion module grounded in Dempster-Shafer evidence theory. This mechanism explicitly quantifies modality-level epistemic uncertainty via mass functions in a single deterministic forward pass, dynamically discounting corrupted modalities through expectation aggregation. Finally, a semantic-gated correlation mechanism employs the global textual prior to modulate local inter-variate physical topologies, differentiating genuine anomalies from benign operational transitions. Extensive experiments on benchmark datasets demonstrate that the proposed method achieves competitive performance compared with recent baselines.

In the era of Industry 4.0, distributed storage systems face significant challenges in terms of reliability, data security, privacy, and maintenance. While traditional solutions like HDFS rely on centralized nodes that create single points of failure, existing blockchain-based alternatives often lack efficient mechanisms to dynamically evaluate node reliability in untrusted environments, leading to potential data unavailability.This study introduces a secure, blockchain-based distributed storage system using the Trusted Adaptive File Allocation (TAFA) Algorithm for efficient file distribution. The system uses smart contracts to automatically incentivize and monitor nodes, rewarding or penalizing them to boost long-term availability. Extensive simulation experiments demonstrate that this system achieves an optimal Load Balance Factor (LBF) of nearly 1.00 and maintains high storage reliability. It is noteworthy that, even in environments with up to 90% malicious nodes performing tampering or denial-of-service attacks, the file availability of this system remains at least 30%, which is up to 5.7 times higher than that of other distributed storage algorithms.

Overall survival (OS) analysis has a significant role in clinical treatment and prognosis. A singular data source’s samples may be biased, leading to low generalisable models. Multi-source data, however, may suffer from the problem of missing modalities due to different equipment and regional disparity. To address this issue, we propose a OS prediction model for gastric cancer called preference-based multimodal deep forest (PMDF). Simulating the diagnostic process of a physician, the initial input contains only basic modalities instead of complete data. Subsequently, PMDF learns from the doctor’s expertise and calculates the required supplementary examinations for each patient with these basic modalities. Finally, additional modalities are fused based on the cascade forest architecture. The efficiency of the proposed model is verified on the publicly accessible SEER database, and its applicability in clinical settings is assessed.

With the combination of natural language processing and artificial intelligence techniques, medical extractive question-answering (Q&A) provides valuable insights and assists medical professionals in daily work and scientific research, answering medical questions rapidly and accurately, thus holding significant practical significance. Therefore, research on medical extractive Q&A holds significant practical significance. However, the current state of medical extractive question-answering lacks attention to the interaction and prediction layers in the model structure. To address these issues, this paper proposes the Integrating pre-trained multi-layer structural feature information based Bio-BERT (IPMF-Bio-BERT) approach. This method leverages the rich word vector representations generated by the pre-trained Bio-BERT model, incorporating semantic and syntactic structural information to obtain multi-dimensional and complementary interactive feature information. Additionally, we introduce a flexible guidance network based on interactive information, which combines iterative and pointer network techniques to enhance the predictive performance of the question-answering model. We evaluate our proposed model on the specialized biomedical extractive question-answering BioASQ corpus. Experimental results demonstrate that the IPMF-Bio-BERT training strategy enhances the recognition and predictive capabilities of medical extractive Q&A, we establish new state-of-the-art results by outperforming existing approaches.