Multi-modality image fusion (MMIF) in adverse weather aims to address the loss of visual information caused by weather-related degradations, providing clearer scene representations. Although a few studies have attempted to incorporate textual information to improve semantic perception, they often lack effective categorization and thorough analysis of textual content. To address these limitations, we propose AWM-Fuse, a unified fusion framework that handles diverse weather degradations via global and local text perception with shared parameters. In particular, a global text perception module leverages BLIP-generated captions to extract overall scene features and identify primary degradation types, thus promoting generalization across various adverse weather conditions. Complementing this, the local module employs detailed scene descriptions produced by ChatGPT to concentrate on specific degradation effects through concrete textual cues, enabling the recovery of subtle details. Furthermore, textual descriptions are used to constrain the generation of fused images, effectively steering the network learning process toward better alignment with semantic labels, thereby promoting the learning of more meaningful visual features. To facilitate text-guided fusion under adverse weather, we construct AWMM-Text, a large-scale benchmark providing paired global and local annotations for multi-modality image pairs. Extensive experiments demonstrate that AWM-Fuse consistently outperforms state-of-the-art methods under complex weather conditions and on multiple downstream tasks. Our code is available at https://github.com/Feecuin/AWM-Fuse.

As a transport layer protocol, TCP is widely used in the various applications especially for video transmission in the mobile network. The TCP protocol not only provides an end-to-end connection to guarantee the reliability and in-order delivery of packets, but also shapes the transmission rate from the server to client through the mobile network. In wireless link, the available bandwidth varies significantly due to the changes in the underlying radio channel conditions. Traditional TCP congestion control has difficulties in adapting to these rapidly varying conditions which leads to low throughput and long delay. This paper analyzes the TCP issues in the mobile wireless network, and proposes two radio-aware TCP optimization solutions. The loose coupled radio-aware TCP optimization solution controls the parameters of TCP layer according to the cross-layer information which includes the client predicted rate and low layer's assisted information, while the close coupled radio- aware TCP solution modifies the traditional TCP congestion control mechanism to a novel TCP request- grant mechanism. The simulation proves the solutions can acquire significant TCP performance gain with extreme low latency.