We present **Brain Harmony (BrainHarmonix)**, the first multimodal brain foundation model that unifies structural morphology and functional dynamics into compact 1D token representations. The model was pretrained on two of the largest neuroimaging datasets to date, encompassing 64,594 T1-weighted structural MRI 3D volumes (～ 14 million images) and 70,933 functional MRI (fMRI) time series. BrainHarmonix is grounded in two foundational neuroscience principles: *structure complements function* - structural and functional modalities offer distinct yet synergistic insights into brain organization; *function follows structure* - brain functional dynamics are shaped by cortical morphology. The modular pretraining process involves single-modality training with geometric pre-alignment followed by modality fusion through shared brain hub tokens. Notably, our dynamics encoder uniquely handles fMRI time series with heterogeneous repetition times (TRs), addressing a major limitation in existing models. BrainHarmonix is also the first to deeply compress high-dimensional neuroimaging signals into unified, continuous 1D tokens, forming a compact latent space of the human brain. BrainHarmonix achieves strong generalization across diverse downstream tasks, including neurodevelopmental and neurodegenerative disorder classification and cognition prediction - consistently outperforming previous approaches. Our models - pretrained on 8 H100 GPUs - aim to catalyze a new era of AI-driven neuroscience powered by large-scale multimodal neuroimaging.

One of the extensively studied coverage models in wireless sensor networks is barrier coverage, which guarantees that any movement crossing the given belt must be detected, while the direction of the movement is not required. For some intrusion detection applications, it may be the case that only one direction of crossing (the belt) is illegal such as border guarding. Therefore, we introduce a new coverage model called one-way barrier coverage, which requires that the network reports illegal intruders while ignores legal intruders. We propose an appropriate definition for one-way barrier coverage. We deeply investigate one-way barrier coverage with binary sensors. Our research illustrates that it is not straightforward to provide oneway barrier coverage even though there is only one intruder. When there are multiple intruders, we introduce the concept of neighboring barriers and design different protocols to provide one-way barrier coverage for different sensor models based on neighboring barriers.

Position-based routing protocols use location information to refine the traditional packet flooding method in mobile ad hoc networks. They mainly focus on densely and evenly distributed network scenarios, but their performances degrade quickly as networks become sparse. To overcome these shortcomings, we introduce the network density concept in the routing process, and propose a density adaptive routing protocol (DAR) to optimize the packet forwarding process. DAR algorithm utilizes the local network density to determine the packet forwarding zone; in dense areas, it narrows the forwarding range to reduce the total number of participants in flooding; in sparse area, it enlarges the forwarding scope to enclose enough nodes for packet relaying. Compared to the existing protocols, DAR yields lower flooding overhead and higher route discovery rate. Theoretical analysis proves that it works well in both densely and sparsely distributed network scenarios, and is more scalable than traditional routing protocols. Simulation results are presented in this paper to demonstrate the effectiveness of the proposed protocol.