This article presents an on-body antenna characterized by low structural complexity, low spatial power density, and high robustness. This antenna features unique dual-wideband capability, offering ultrawideband operating frequency bands along with dynamic wideband notch tunability. This design aims to support simultaneous multiuser IoT operation while intelligently mitigating in-band interference from unwanted users. This achievement is realized through a novel approach using a reconfigurable quasi-spoof surface plasmon polaritons waveguide filter as the feedline for a substrate-integrated waveguide (SIW) antenna, allowing tuning of band-notched characteristics via varactor diodes on the SSPP element. Enhanced bandwidth performance is achieved by etching gradient rectangular slots on the open-ended SIW and implementing a transition from grounded coplanar waveguide to SIW with tapered coupling slots. Conductive fibers effectively bond the flexible foam substrate with a conductive thin copper film, forming the prototype. The prototype antenna operates across 9.4 to 29 GHz with an average gain of 9.0 dBi and features a dynamically tunable 2 GHz rejection bandwidth within the 14 to 26 GHz range, adjusted via variable capacitors in loaded varactors. The antenna maintains consistent performance under deformation and exhibits reliable wireless signal transmission in experiments.

MOTIVATION: Deep learning-based molecule generation becomes a new paradigm of de novo molecule design since it enables fast and directional exploration in the vast chemical space. However, it is still an open issue to generate molecules, which bind to specific proteins with high-binding affinities while owning desired drug-like physicochemical properties. RESULTS: To address these issues, we elaborate a novel framework for controllable protein-oriented molecule generation, named CProMG, which contains a 3D protein embedding module, a dual-view protein encoder, a molecule embedding module, and a novel drug-like molecule decoder. Based on fusing the hierarchical views of proteins, it enhances the representation of protein binding pockets significantly by associating amino acid residues with their comprising atoms. Through jointly embedding molecule sequences, their drug-like properties, and binding affinities w.r.t. proteins, it autoregressively generates novel molecules having specific properties in a controllable manner by measuring the proximity of molecule tokens to protein residues and atoms. The comparison with state-of-the-art deep generative methods demonstrates the superiority of our CProMG. Furthermore, the progressive control of properties demonstrates the effectiveness of CProMG when controlling binding affinity and drug-like properties. After that, the ablation studies reveal how its crucial components contribute to the model respectively, including hierarchical protein views, Laplacian position encoding as well as property control. Last, a case study w.r.t. protein illustrates the novelty of CProMG and the ability to capture crucial interactions between protein pockets and molecules. It's anticipated that this work can boost de novo molecule design. AVAILABILITY AND IMPLEMENTATION: The code and data underlying this article are freely available at https://github.com/lijianing0902/CProMG.