Search

This paper proposes an integrated approach combining computer networks and artificial neural networks to construct an intelligent network operator, functioning as an AI model. State information from computer networks is transformed into embedded vectors, enabling the operator to efficiently recognize different pieces of information and accurately output appropriate operations for the computer network at each step. The operator has undergone comprehensive testing, achieving a 100% accuracy rate, thus eliminating operational risks. Furthermore, a novel algorithm is proposed to emphasize crucial training losses, aiming to enhance the efficiency of operator training. Additionally, a simple computer network simulator is created and encapsulated into training and testing environment components, enabling automation of the data collection, training, and testing processes. This abstract outlines the core contributions of the paper while highlighting the innovative methodology employed in the development and validation of the AI-based network operator.

The medical field is one of the important fields in the application of artificial intelligence technology. With the explosive growth and diversification of medical data, as well as the continuous improvement of medical needs and challenges, artificial intelligence technology is playing an increasingly important role in the medical field. Artificial intelligence technologies represented by computer vision, natural language processing, and machine learning have been widely penetrated into diverse scenarios such as medical imaging, health management, medical information, and drug research and development, and have become an important driving force for improving the level and quality of medical services.The article explores the transformative potential of generative AI in medical imaging, emphasizing its ability to generate syntheticACM-2 data, enhance images, aid in anomaly detection, and facilitate image-to-image translation. Despite challenges like model complexity, the applications of generative models in healthcare, including Med-PaLM 2 technology, show promising results. By addressing limitations in dataset size and diversity, these models contribute to more accurate diagnoses and improved patient outcomes. However, ethical considerations and collaboration among stakeholders are essential for responsible implementation. Through experiments leveraging GANs to augment brain tumor MRI datasets, the study demonstrates how generative AI can enhance image quality and diversity, ultimately advancing medical diagnostics and patient care.

The paragraph is grammatically correct and logically coherent. It discusses the importance of mobile terminal cloud computing migration technology in meeting the demands of evolving computer and cloud computing technologies. It emphasizes the need for efficient data access and storage, as well as the utilization of cloud computing migration technology to prevent additional time delays. The paragraph also highlights the contributions of cloud computing migration technology to expanding cloud computing services. Additionally, it acknowledges the role of virtualization as a fundamental capability of cloud computing while emphasizing that cloud computing and virtualization are not inherently interconnected. Finally, it introduces machine learning-based virtual machine migration optimization and dynamic resource allocation as a critical research direction in cloud computing, citing the limitations of static rules or manual settings in traditional cloud computing environments. Overall, the paragraph effectively communicates the importance of machine learning technology in addressing resource allocation and virtual machine migration challenges in cloud computing.

A new extended solid nitrogen, referred to post-layered-polymeric nitrogen (PLP-N), was observed by further heating the layered-polymeric nitrogen (LP-N) to above 2300 K at 161 GPa. The new phase is found to be very transparent and exhibits ultra-large d-spacings ranging from 2.8 to 4.9 {\AA} at 172 GPa, suggesting a possible large-unit-cell 2D chain-like or 0D cluster-type structure with wide bandgap. However, the observed X-ray diffraction pattern and Raman scattering data cannot match any predicted structures in the published literature. This finding further complicates the phase diagram of nitrogen and also highlights the path dependence of the high-pressure dissociative transition in nitrogen. In addition, the forming boundary between cg-N and LP-N has been determined., Comment: 13 pages, 4 figures, 1 table

Understanding high-pressure transitions in prototypical linear diatomic molecules, such as hydrogen, nitrogen, and oxygen, is an important objective in high-pressure physics. Recent ultrahigh-pressure study on hydrogen revealed that there exists a molecular-symmetry-breaking isostructural electronic transition in hydrogen. The pressure-induced symmetry breaking could also lead to a series of solid molecular phases in nitrogen associated with the splitting of vibron, walking a path from fluid nitrogen to molecular solids and then to polymetric nitrogen phases. However, isostructural transition in nitrogen has not been reported so far, which may provide crucial information on the crystallographic nature of the pressure-induced dissociation transitions in nitrogen. Here we present a high-pressure Raman scattering and high-pressure angle dispersive X-ray diffraction study of the previously reported lambda-N2 and reveal an isostructural transition from the lambda-N2 to a new derivative phase, lambda-N2. Under compression, lambda-N2 remains in the monoclinic crystal lattice, accompanied by a monotonic increase in anisotropy. The pressure-dependent decrease of the unit cell parameters exhibits a discontinuity at the pressure of 54 GPa, accompany with sudden broadening and turnover of vibron., Comment: 19 pages, 5 figures