The rapid growth of flexible Internet of Things devices and multisensory human-computer interaction necessitates advanced systems capable of multimodal data sensing, processing, and feedback in an intelligent and energy-efficient manner. However, conventional architectures face limitations in energy inefficiency and interface mismatch due to the fragmented architecture of sensing, processing, and feedback units. Inspired by biological sensory systems, we introduce an MXene-based flexible dual-mode sensing-processing-visualizing integrated system into a single wearable device through a hierarchical MXene platform combining piezoelectric nanogenerators (mechanosensation), optoelectronic synapses (visual processing), and color-shifting quantum dot light-emitting diodes (optical feedback) with optimized interfaces. The wearable integrated system demonstrates tactile-visual signal recognition capabilities, biological self-protection behavior to adapt to environmental stimuli, dynamic trajectory recognition, and spatial positioning for motion recognition, which are highly desired in multisensory interaction. The bio-inspired material-architecture-function co-design strategy and integrated system promote the development of wearable neuromorphic hardware, edge computing, and intelligent human-machine interaction.

Existing studies indicate that dysregulation or abnormal expression of small nucleolar RNA (snoRNA) is closely associated with various diseases, including lung cancer. Furthermore, these diseases often involve multiple targets, making the redevelopment of traditional medicines highly promising. Accurate prediction of potential snoRNA therapeutic targets is essential for early disease intervention and the redevelopment of traditional medicines. Additionally, researchers have developed artificial intelligence (AI)-based methods to screen and predict potential snoRNA therapeutic targets, thereby advancing traditional drug redevelopment. However, existing methods face challenges such as imbalanced datasets and the dominance of high-degree nodes in graph neural networks (GNNs), which compromise the accuracy of node representations. To address these challenges, we propose an AI model based on variational graph autoencoders (VGAEs) that integrates decoupling and Kolmogorov-Arnold Network (KAN) technologies. The model reconstructs snoRNA-disease graphs by learning snoRNA and disease representations, accurately identifying potential snoRNA therapeutic targets. By decoupling similarity from node degree, the model mitigates the dominance of high-degree nodes, enhances prediction accuracy in scenarios like lung cancer, and leverages KAN technology to improve adaptability and flexibility to new data. Case studies revealed that snoRNA SNORA21 and SNORD33 are abnormally expressed in lung cancer patients and are strong candidates for potential therapeutic targets. These findings validate the proposed model’s effectiveness in identifying therapeutic targets for diseases like lung cancer, supporting early screening and treatment, and advancing the redevelopment of traditional medicines. Data and experimental findings are archived in: https://github.com/shmildsj/data.

Light-emitting electrochemical cells (LECs) are one of the most promising technologies for solid-sate lighting and display. We present a hybrid device concept for achieving higher performance by combining red colloidal quantum dots (QDs) and Ir-based ionic transition metal complex (iTMC). The utilized QDs exhibit a core–shell structure of CdSe/ZnS. By this method, the resultant devices exhibit a maximum luminance of 14 383 cd/m2 and current efficiency (CE) of 2.00 cd/A, almost fourfold higher than those (3475 cd/m2 and 0.55 cd/A) of the control device. In conclusion, QDs exert a regulatory effect on charge carrier transport in hybrid devices, gradually aligning the electron and hole injection of the device, thus enhancing its performance. Red QD (RQD) not only has an electron injection buffering effect but also plays a certain role in hole blocking.

Electrohydrodynamic (EHD) printing is a promising method for manufacturing high-resolution quantum dot light-emitting diodes (QLEDs). The stability of the EHD printing process and the morphology of the final quantum dot film are highly dependent on the ink formulation. To solve this problem, we selected a ternary solvent (decahydronaphthalene, tetradecane and nonane) ink for cadmium-based quantum dots (CdSe/ZnS), to achieve excellent quantum dot dispersion while eliminating the "coffee ring" effect, resulting in high quality quantum dot films. We also fabricated a complete QLED device by printing a light-emitting layer formed by linearly aligned strips of quantum dots, achieving an external quantum efficiency (EQE) of 19.2 %, which is one of the highest levels of printing devices. On this basis, we introduced patterned PMMA structures prepared by nanoimprinting method to achieve ultra-high resolution devices. The pixel density achieved was 8,758 pixels per inch (PPI), with a maximum EQE of 10.5 %. The luminance is 9530.4 cd/m² at a voltage of 4V. This work shows promising potential in realizing ultra-high resolution and high-performance QLEDs.

Cold-start compound-protein interaction (CPI) prediction remains a major challenge in drug discovery, as models must generalize to unseen compounds, targets, or their combinations beyond the training distribution. Most existing methods rely on shallow feature fusion and primarily fit observed interaction patterns, failing to capture semantic correspondence between heterogeneous molecular entities and thus generalize poorly under cold-start conditions. We propose SACS-CPI, a multimodal framework for cold-start CPI prediction based on large language model (LLM)-driven semantic alignment. By mapping compounds and proteins into a shared semantic space, SACS-CPI strengthens semantic consistency between interacting entities, reduces dependence on observed interaction pairs, and improves prediction for unseen compound-protein combinations. The framework further integrates general semantic knowledge learned by large language models with task-specific features through dynamic gating, while bidirectional cross-attention and bilinear pooling are used to capture high-order interaction patterns. Extensive evaluations across multiple benchmark datasets demonstrate that SACS-CPI consistently outperforms state-of-the-art methods, with robust gains under compound cold-start, protein cold-start, and blind-start settings. These results indicate that semantic alignment provides an effective inductive bias for cold-start CPI prediction. The code and data are available at: https://github.com/yangkun021224/SACS-CPI/.