Property-constrained molecular generation and editing are crucial in AI-driven drug discovery but remain hindered by two factors: (i) capturing the complex relationships between molecular structures and multiple properties remains challenging, and (ii) the narrow coverage and incomplete annotations of molecular properties weaken the effectiveness of property-based models. To tackle these limitations, we propose HSPAG, a data-efficient framework featuring hierarchical structure–property alignment. By treating SMILES and molecular properties as complementary modalities, the model learns their relationships at atom, substructure, and whole-molecule levels. Moreover, we select representative samples through scaffold clustering and hard samples via an auxiliary variational auto-encoder (VAE), substantially reducing the required pre-training data. In addition, we incorporate a property relevance-aware masking mechanism and diversified perturbation strategies to enhance generation quality under sparse annotations. Experiments demonstrate that HSPAG captures fine-grained structure–property relationships and supports controllable generation under multiple property constraints. Two real-world case studies further validate the editing capabilities of HSPAG.

Accurate medical image segmentation is a pivotal task in medical image analysis, serving as the foundation for extracting critical clinical information and directly influencing the precision of disease identification, diagnosis, and treatment decision-making. Although Convolutional Neural Networks (CNNs) and Transformers have demonstrated remarkable performance in medical image segmentation, their sensitivity to variations in target size and morphology remains insufficient, resulting in limited capability of single-scale features to comprehensively capture multi-target details. Current approaches also face bottlenecks, including restricted global context modeling capabilities, high computational complexity, and inefficient multilevel feature fusion. To address these challenges in medical image segmentation, this study proposes an innovative architecture named GroupTransUNet. The model implements dual enhancements based on the UNet framework: (i) A novel Next-Generation Transformer Block (NGTB) is designed for the bottleneck layer, which organically integrates Efficient Multi-Head Self-Attention (E-MHSA) with Convolution-Enhanced Multi-Layer Perceptron (MLP) through a complementary mechanism to simultaneously enhance global semantic understanding and local detail characterization. (ii) The Grouped Feature Fusion Module (GFFM) is introduced in skip connections, employing grouped convolutions and multi-dilation rate strategies to construct multi-scale contextual receptive fields while preserving detailed integrity, thereby significantly improving feature fusion efficiency. Experimental results on the Synapse and ACDC datasets validate the effectiveness of our proposed GroupTransUNet. The source code can be obtained at https://anonymous.4open.science/r/GroupTransUNetA3D6.

The smart speakers benefit from the ability to localize and identify users. Specifically, we can analyze the user's habits from the historical trajectory to provide better voice-based services. However, current voice localization method requires the user to actively issue voice commands, which makes smart speakers unable to track and identify silent users most of the time. This paper introducesWSTrack+, a system that combinesWi-Fi andSound to track human movement and recognize gait patterns. In particular, current smart speakers naturally support both Wi-Fi and acoustic functions. As a result, we are able to construct the system using just one router and a smart speaker, which is a more promising approach compared to existing systems that rely on multiple routers for sensing. To track and identify the silent user, our insights are twofold: 1) the smart speakers can hear the sound of the user's footstep, and then extract which direction the user is in; 2) we can extract the reflected path change rate from the Wi-Fi signals, and the acoustic signal can help us convert the path change rate into the actual user's velocity. Our implementation and evaluation on commodity devices demonstrate thatWSTrack+can realize simultaneous tracking and gait recognition, where the median tracking error is$0.34m$and the recognition accuracy is 88.6% for 12 users.

As the predominant trade secret of manufacturing enterprises, industrial data may be monitored and stolen by competitive adversaries during the transmission via open wireless link. Such information leakage will cause severe economic losses. Hence, a VoNR-IPD covert timing steganography based on 5G network is proposed in this paper, in which VoNR traffic is employed as the steganographic carrier of covert communication in Industrial Internet. Interference of network jitter noise is fully considered and the high-order statistical properties of jittered VoNR interpacket delays (IPDs) are imitated during the modulation of confidential industrial data. Thus, the generated covert VoNR IPDs can possess consistent statistical properties with the normal case in order to improve undetectability. Besides, the synchronization mechanism of steganographic embedding mode is designed to control the embedding density of industrial data flexibly. The experimental results show that our scheme can resist statistical-based detections and the network noise effectively, which outperforms the existing methods in terms of undetectability and robustness.

Colonoscopy is an important procedure for the diagnosis of various pathologies, in particular cancer of the colon and of the rectum. However, colonoscopy is a procedure often painful for the patient and complex for the doctor. So in the biomedical field, a wireless microrobot in pipe that can move smoothly in water or aqueous medium has urgently been demanded. In this paper, we developed a new kind of wireless microrobot with symmetrical spiral structure, which also had symmetrical kinematic characteristics. According to the hydromechanical lubrication theory and Newton viscous law, we build the motion model of the microrobot, which will provide a theoretical basis on designing the optimal structure parameters of the microrobot. Through analysis, simulations and experiments, this paper had evaluated the effect of spiral angle, which could realize forward-backward, upward-downward motion and stopping at any position we need in the pipe. In addition, we obtained the moving speeds of forward-backward and upward-downward motion in the pipe. The experimental results indicated that the maximum moving speed is 36.5 mm/s at 14 Hz in the horizontal direction and 4.6 mm/s at 16Hz in the vertical direction with input currents of 0.7A. Finally, we designed a control panel for this system, which can control the microrobot current motion states intuitively and easily, and make our system more portable and compact. The developed wireless microrobot can move smoothly in water and other liquid medium and is very useful in the industrial.