RNA inverse folding, designing sequences to form specific 3D structures, is critical for therapeutics, gene regulation, and synthetic biology. Current methods, focused on sequence recovery, struggle to address structural objectives like secondary structure consistency (SS), minimum free energy (MFE), and local distance difference test (LDDT), leading to suboptimal structural accuracy. To tackle this, we propose a reinforcement learning (RL) framework integrated with a latent diffusion model (LDM). Drawing inspiration from the success of diffusion models in RNA inverse folding, which adeptly model complex sequence-structure interactions, we develop an LDM incorporating pre-trained RNA-FM embeddings from a large-scale RNA model. These embeddings capture co-evolutionary patterns, markedly improving sequence recovery accuracy. However, existing approaches, including diffusion-based methods, cannot effectively handle non-differentiable structural objectives. By contrast, RL excels in this task by using policy-driven reward optimization to navigate complex, non-gradient-based objectives, offering a significant advantage over traditional methods. In summary, we propose the Step-wise Optimization of Latent Diffusion Model (SOLD), a novel RL framework that optimizes single-step noise without sampling the full diffusion trajectory, achieving efficient refinement of multiple structural objectives. Experimental results demonstrate SOLD surpasses its LDM baseline and state-of-the-art methods across all metrics, establishing a robust framework for RNA inverse folding with profound implications for biotechnological and therapeutic applications.

It is essential to quantitatively evaluate road safety compliance through the modeling of three-dimensional geometric information of in-service roads to automatically guide the determination of driving speeds in intelligent transportation. A novel methodology is presented for reliably estimating 3D geometric elements and assessing geometry-related safety. A point-octree-normal-vector (PONV) network is developed to cope with the heterogeneous density of point clouds from mobile and terrestrial laser scanning and reduce possible failures caused by the disordered rotation of points. The points on road surfaces are adaptively segmented into stripes without data loss. Road horizontal elements, cross-sectional slopes and longitudinal slopes are estimated independent of alignment variations based on a concave hull algorithm, a cubic B-spline algorithm and a total least-squares method. A road safety compliance assessment related to the radius of curvature, average gradient, rate of curvature changes and side friction factors is conducted. Warning and suggested driving speeds are thus provided. Experiments show that the PONV network achieved superior performance in extracting road surfaces, with accuracy, precision, recall, and mean intersection-over-union scores of 98.1%, 89.7%, 89.3% and 82.5%, respectively. The geometric elements with neighboring distances in meters were basically consistent with those in traditional surveys and raw point clouds. Through comparison with safety guidelines, suitable vehicle operation speeds were suggested in road sections with unreasonable rates of curvature changes and side friction factors. This work can be applied for constructing road information models and automatically guiding driving speeds with high-definition maps on autonomous vehicles.

The acquisition of road information from remote sensing images is of significant value with regard to intelligent transportation research. This study focuses on enhancing the contour-learning ability to mitigate the phenomenon of fragmented road segments and missing connections in road extraction. A novel Deep Feature-Review (FR) Transmit Network (TransNet) is proposed to review and facilitate the flow of contour features into an encoder network. Meanwhile, multiscale features are linked via a bridge between the encoder and the decoder. Compared with the state-of-the-art models such as fully convolutional network (FCN), SegNet, DeepLabv3, D-LinkNet, spatial consistency-FCN, and generative adversarial network (GAN), the proposed network achieves better overall performance for the Massachusetts Roads data set, with accuracy, precision, recall, and mean intersection-over-union (IoU) scores of 97.48%, 83.72%, 78.13%, and 0.6286%, respectively. For the DeepGlobe Road Extraction data set, the proposed network outperforms FCN, SegNet, DeepLabv3, D-LinkNet, and Deep TransNet, achieving accuracy, precision, recall, and mean IoU scores of 98.70%, 87.30%, 81.15%, and 0.7244%, respectively. Overall, these experiments indicate that the proposed network can effectively address the phenomenon of fragmented road segments and poor connectivity in remote sensing images, indicating its potential for utilization in practical intelligent transportation scenarios.

The heterogeneous vehicular network (HetVNET), which consists of multiple radio access networks (RANs), is a promising paradigm to provide a variety of services on the road. However, with the diverse quality of experience (QoE) requirements for vehicles, how to choose the optimal network for the vehicles pose great challenges. In this paper, we investigate the network selection problem in a HetVNET, which includes LTE-vehicle-to-anything (LTE-V2X), dedicated short-range communications (DSRC), WiFi. The network selection problem is formulated as a stable matching between the vehicles and different RANs. A two-sided one-to-many matching algorithm is presented based on the preference lists of both vehicles and RANs. Numerical simulation results show that the proposed method can improve the total throughput of the system and reduce the total network switch time compared to some existing algorithms.

Compared with traditional cloud computing, service providers in edge computing are closer to the requester of the demand, and can better meet the requirements of communication transmission delay. The Internet of Vehicles is one of the important application scenarios of 5G, the vehicle edge nodes of it have a large number of free resources, but its highly dynamic characteristics will lead to environmental uncertainty, which will affect task offloading decisions. In this article, in the vehicle edge cloud computing system, a V2V or V2I communication method is selected based on probability to achieve low-latency and high-reliability communication requirements, and the Multi-Armed Bandit (MAB) method is used to learn the surrounding environment of the vehicle so that it can adapt dynamically changing environments to make task offloading decisions. After a large number of simulations, our solution can adaptively offload tasks to minimize the expected delay and obtain a sub-linearly increasing learning regret curve in a high-speed mobile vehicle networking scenario.

This letter presents a methodology for urban area mapping with density-based spatial clustering of applications with noise (DBSCAN) using the Advanced Synthetic Aperture Radar (ASAR), Sentinel-1A, and HuanJing-1C data. Urban areas have a diversity of shapes, including circles, squares, strips, and other irregular shapes, and the DBSCAN clustering algorithm is suitable for identifying clusters of arbitrary shapes. Exploiting DBSCAN to extract urban areas is a key aspect of this method, and improvements via the incorporation of synthetic aperture radar data preprocessing and postprocessing also play important roles in optimizing the extractions. Different test site sizes were chosen to demonstrate the effectiveness and feasibility of the proposed method, and the validation results showed that the method is efficient and accurately extracts urban areas ranging from small towns to super metropolitan areas.