Zizhen Tang

Accurate short-term wind-power forecasting supports renewable-energy integration. This study presents a reproducibility and implementation audit of MoRAM, an integrated architecture that combines feature-token attention, a residual Mamba layer, dense two-expert blending, and a one-dimensional convolution (Conv1D)–linear residual output path to forecast 12 h of normalized turbine power from 48 h multivariate histories in Dataset A (200 turbines; 8760 hourly records). Source reconstruction found that the historical PyTorch attention layer used its default sequence-major interface on batch-major data, thereby mixing samples; the dense-window record is therefore retained only as an implementation audit. We corrected the layer to batch-first feature-token semantics and ran an attention-only ablation using all turbines, a 12 h window-start stride, and five paired seeds. Corrected MoRAM achieved mean absolute error (MAE) 0.2428 ± 0.0029 per unit (p.u.) and root-mean-square error (RMSE) 0.3035 ± 0.0059 p.u.; bypassing only attention achieved MAE 0.2298 ± 0.0027 p.u. and RMSE 0.2839 ± 0.0028 p.u. (mean ± sample standard deviation). The attention-free condition had lower overall MAE and RMSE in every seed and, after averaging across seeds, at every forecast horizon. The principal validated contribution is therefore an auditable reconstruction—complete data protocol, source/log mapping, corrected tensor semantics, and a reproducible five-seed negative ablation—rather than evidence that every constituent module or the integrated architecture is superior.

Age-related macular degeneration (AMD) is a prevalent retinal degenerative disease among the elderly and is a major cause of irreversible vision loss worldwide. Although color fundus photography (CFP) and optical coherence tomography (OCT) are widely used for AMD diagnosis, information from a single modal is inadequate to fully capture the complex pathological features of AMD. To address this, this study proposes an innovative multi-modal deep learning framework that fine-tunes pre-trained single-modal retinal models for efficient application in multi-modal AMD categorization tasks. Specifically, two independent vision transformer models are used to extract features from CFP and OCT images, followed by deep canonical correlation analysis (DCCA) to perform nonlinear mapping and fusion of features from both modalities, maximizing cross-modal feature correlation. Moreover, to reduce the computational complexity of multi-modal integration, we introduce the low-rank adaptation (LoRA) technique, which uses low-rank decomposition of parameter matrices, achieving superior performance compared to full fine-tuning with only about 0.49% of the trainable parameters. Experimental results on the public dataset MMC-AMD validate the framework’s effectiveness. The proposed model achieves an overall F1-score of 0.948, AUC-ROC of 0.991, and accuracy of 0.949, significantly outperforming existing single-modal and multi-modal baseline models, particularly excelling in recognizing complex pathological categories.