To address the limitations of single-modal feature coverage and class distribution imbalance in knee osteoarthritis (KOA) classification, this study proposes a Multimodal Spatial-constraint Contrastive Learning (MSCL) model. First, dynamic and static plantar pressure data and human keypoint trajectories are synchronously acquired. The model first feeds dynamic plantar pressure and keypoint data into a multimodal spatial-temporal fusion branch, where graph convolutional networks and Transformers extract spatial-temporal representations of human keypoints and dynamic pressure patterns respectively, followed by Cross Attention fusion. Subsequently, static plantar pressure is processed through a pyramid CNN architecture to generate coarse-grained spatial constraint vectors, which serve as anatomical priors to regularize the fused representations. Finally, a contrastive learning framework is integrated to establish explicit mapping between the enhanced representations and Kellgren-Lawrence (KL) grading system, enabling precise KOA severity stratification. Experimental results demonstrate that the MSCL model achieves 0.94 macro-average accuracy in KL grading, with 7% improvement in F1-scores for imbalanced categories with limited samples. This work establishes a novel paradigm for accurate KOA assessment through multimodal gait analysis.

Deep learning offers efficient solutions for drug-target interaction prediction, but current methods often fail to capture the full complexity of multi-modal data (i.e., sequence, graphs, and three-dimensional structures), limiting both performance and generalization. Here, we present UnitedDTA, a novel explainable deep learning framework capable of integrating multi-modal biomolecule data to improve the binding affinity prediction, especially for novel (unseen) drugs and targets. UnitedDTA enables automatic learning unified discriminative representations from multi-modality data via contrastive learning and cross-attention mechanisms for cross-modality alignment and integration. Comparative results on multiple benchmark datasets show that UnitedDTA significantly outperforms the state-of-the-art drug-target affinity prediction methods and exhibits better generalization ability in predicting unseen drug-target pairs. More importantly, unlike most "black-box" deep learning methods, our well-established model offers better interpretability which enables us to directly infer the important substructures of the drug-target complexes that influence the binding activity, thus providing the insights in unveiling the binding preferences. Moreover, by extending UnitedDTA to other downstream tasks (e.g., molecular property prediction), we showcase the proposed multi-modal representation learning is capable of capturing the latent molecular representations that are closely associated with the molecular property, demonstrating the broad application potential for advancing the drug discovery process.

$Z$-numbers have been proven to be advantageous in revealing uncertain information. This article focuses on applying uncertain$Z$-numbers to engineering practice. Considering that various uncertain data are inevitable in the process of addressing actual problems, uncertain$Z$-numbers are introduced to express information and measure information reliability. Powerful tools are essential when dealing with uncertain information; then, complex fuzzy sets (CFSs) are employed. First, the parameter$B$in$Z$-numbers is interpreted from the perspective of the phase term, and uncertain$Z$-numbers are transformed to interval-valued CFSs. Next, arithmetic operators, aggregation operator, generalized entropy, distance measure, and similarity measure are defined. Besides, a multicriteria group decision-making method is suggested by combining the proposed entropy, distance measure, and aggregation operator. The algorithm is applied to select the brand of the inertial navigation system. The comparison analysis with other widely used methods is conducted to verify the feasibility and validity of$Z$-information in the application.