Microplastic pollution in global aquatic ecosystems poses an imminent threat to both ecological integrity and human wellbeing. These minuscule particles (<5 mm), derived from anthropogenic activities, accumulate organic pollutants and heavy metals, permeating the food chain and triggering reproductive abnormalities and endocrine disruption in organisms. Particles with diameters smaller than 100 μm are particularly insidious, owing to their diminutive size, which facilitates greater bioaccumulation while rendering them significantly more challenging to collect and detect. Drawing inspiration from the highly efficient filtration mechanism of sabellid worms, this study proposes the design of an aquatic microplastic adsorption robot that mimics the feather-like radiolar crown structure of these organisms. The robot incorporates a flexible polymeric vibrating membrane system, a solid monolithic magnetic porous polymer material (PDVB-Fe₃O₄), and underwater adsorption suction cups to achieve efficient capture of minute microplastics (diameters less than 100 μm). Post-collection, the adsorption module enables rapid desorption, thereby facilitating facile onshore analysis and detection. The authors adopted computational fluid dynamics (CFD) methods to develop a fluid-solid coupling model, simulating five water environments with varying flow velocities: 0.05 m/s, 0.07 m/s, 0.09 m/s, 0.2 m/s, and 0.5 m/s. The results validated the robotic system's in-water performance, revealing low energy consumption and favorable stability (data). This design offers a scalable technical solution for achieving the Sustainable Development Goal 14 (SDG14) target.

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Social biases in text generation models severely compromise technical fairness and social inclusiveness. Existing studies mostly focus on debiasing at the word embedding level, ignoring dynamic biases in generated text, with limitations like low training efficiency and poor generalization. To address this, we propose Debias-Adapter, a bias mitigation method based on text diffusion models. Built on the Latent Diffusion Model (LDM) framework, it introduces a decoupled cross-attention mechanism and adds an adapter module to the Transformer-Encoder, optimizing only <5% of adapter parameters while freezing the pre-trained backbone to boost efficiency. Targeted data filtering and adaptation ensure input validity. Tested on the BBQ dataset, it outperforms baselines like Qwen3 and LD4LG, achieving a top accuracy of 73.7% and a maximum accuracy difference of 9.6% in non-ambiguous contexts, with significantly lower bias scores in both ambiguous and disambiguated contexts. This method exhibits strong debiasing effectiveness across multiple bias scenarios, supporting ethical optimization and fairness improvement of text generation models.

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In the digital age, where the amount of data is growing exponentially, traditional big data visualization methods often fall short, especially when it comes to processing high-dimensional data or matching visualization results to user needs. This highlights the need for better integration between artificial intelligence (AI) and big data visualization. AI has brought about significant changes in this field. This paper reviews research from the past five years to explore the integration of AI with big data visualization. It looks at the core technologies, common use cases, and challenges of this combination. The review finds that AI can address many issues in traditional visualization techniques, such as handling complex data and improving interaction and evaluation. However, challenges like model interpretability, data privacy concerns, and inconsistent standards remain. In the future, this integration is expected to move toward more transparent models, stronger privacy protection, and standardized systems. This will help unlock the full value of data in real-world applications.

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This paper systematically reviews the latest advances in deep learning-based path planning for autonomous mobile robots, addressing the limitations of traditional methods (e.g., A*, RRT) in dynamic, high-dimensional, and unstructured environments. We comprehensively analyze five major deep learning model categories: Convolutional Neural Networks (CNNs) for spatial feature extraction, Graph Neural Networks (GNNs) for multi-agent collaboration, Recurrent Neural Networks (RNNs) for temporal modeling, Transformers for long-range dependency and complex instruction understanding, and generative models (e.g., GANs, Diffusion Models) for creative path generation. Our analysis covers technical principles, advantages, limitations, application scenarios, and development trends of these methods. The review reveals that deep learning has fundamentally transformed path planning from perception enhancement to decision substitution, from isolated agents to multi-agent collaboration, and from search-based to generative paradigms. Key findings indicate significant performance improvements: GNN-based distributed planning triples multi-robot collaboration efficiency, and generative models increase complex instruction planning success rates to 78.1%. Future directions include cross-modal integration, lightweight deployment, simulation-to-reality transfer, and verifiable safety assurance, which will be crucial for advancing next-generation intelligent mobile robot navigation systems.

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