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Advancing spatially resolved transcriptomics (ST) technologies help biologists comprehensively understand organ function and tissue microenvironment. Accurate spatial domain identification is the foundation for delineating genome heterogeneity and cellular interaction. Motivated by this perspective, a graph deep learning (GDL) based spatial clustering approach is constructed in this paper. First, the deep graph infomax module embedded with residual gated graph convolutional neural network is leveraged to address the gene expression profiles and spatial positions in ST. Then, the Bayesian Gaussian mixture model is applied to handle the latent embeddings to generate spatial domains. Designed experiments certify that the presented method is superior to other state-of-the-art GDL-enabled techniques on multiple ST datasets. The codes and dataset used in this manuscript are summarized at https://github.com/narutoten520/SCGDL. [ABSTRACT FROM AUTHOR]

Precision spraying can significantly reduce herbicide input for turf weed management. A major challenge for autonomous precision herbicide spraying is to accurately and reliably detect weeds growing in turf. Deep convolutional neural networks (DCNNs), an important artificial intelligent tool, demonstrated extraordinary capability to learn complex features from images. The feasibility of using DCNNs, including various image classification or object detection neural networks, has been investigated to detect weeds growing in turf. Due to the high level of performance of weed detection, DCNNs are suitable for the ground-based detection and discrimination of weeds growing in turf. However, reliable weed detection may be subject to the influence of weeds (e.g., biotypes, species, densities, and growth stages) and turf factors (e.g., turf quality, mowing height, and dormancy vs. non-dormancy). The present review article summarizes the previous research findings using DCNNs as the machine vision decision system of smart sprayers for precision herbicide spraying, with the aim of providing insights into future research. [ABSTRACT FROM AUTHOR]

Aiming at promoting the intelligent development of control technology for new energy vehicles and showing the outstanding advantages of deep reinforcement learning (DRL), this paper trained a VGG16-based road recognition convolutional neural network (CNN) at first. Lots of high-definition images of five typical roads are collected by the racing game Dust Rally 2.0, including dry asphalt, wet asphalt, snow, dry cobblestone, and wet cobblestone. Then, a time-varying driving environment model was established, involving driving images, road slope, longitudinal speed, and the number of passengers. Finally, a stereoscopic control network suitable for nine-dimensional state space and three-dimensional action space was built, and for parallel hybrid electric vehicles (HEVs) with the P3 structure, a deep q-network (DQN)-based energy management strategy (EMS) achieving multi-objective control was proposed, including the fine-tuning strategy of the motor speed to maintain the optimal slip rate during braking, the engine power control strategy and the continuously variable transmission (CVT) gear ratio control strategy. Simulation results show under the influence of some factors such as tree shade and image compression, the road recognition network has the highest accuracy for snow roads and wet asphalt roads. Three types of control strategies learned simultaneously by the stereoscopic control network not only maintain the near-optimal slip rate in the braking period but also achieve a fuel consumption of 4788.93 g, while dynamic programming (DP)-based EMS gets a fuel consumption of 4295.61 g. Moreover, even DP-based EMS only contains three states and two actions, the time consumed for DP-based EMS and DQN-based EMS to run the speed cycle of 3602s is about 4911s and 10s, respectively. Therefore, the optimization and real-time performance of DRL-based EMS can be guaranteed. • A VGG16-based neural network for road recognition is trained. • A complex and time-varying road model with idealized characteristics is established. • Aiming at achieving integrated control, a stereoscopic control network is modeled. • A DQN-based multi-objective control strategy is proposed. [ABSTRACT FROM AUTHOR]