Fuzhou University

Ozone concentration forecasting now runs on two-stage decomposition and two-scale feature selection. The raw series is first decomposed by ICEEMDAN, the most complex component IMF1 is decomposed again by VMD, and the remaining subsequences are reconstructed by fuzzy entropy; partial autocorrelation and random forest then select input dimensions before a temporal convolutional network predicts. On daily ozone data from Beijing and Shanghai, the combined model reaches MAE, RMSE and SMAPE of 0.53509, 0.73346 and 0.8433%, the best among four comparison experiments.

Ozone concentration forecasting now runs on two-stage decomposition and two-scale feature selection. The raw series is first decomposed by ICEEMDAN, the most complex component IMF1 is decomposed again by VMD, and the remaining subsequences are reconstructed by fuzzy entropy; partial autocorrelation and random forest then select input dimensions before a temporal convolutional network predicts. On daily ozone data from Beijing and Shanghai, the combined model reaches MAE, RMSE and SMAPE of 0.53509, 0.73346 and 0.8433%, the best among four comparison experiments.

The work proposes CARing, which compresses disease names and ICD ontology paths into compositional medical Semantic IDs (SIDs) via residual quantization, grounds them through six alignment tasks plus teacher-generated semantic-enrichment corpora and reasoning activation so a single autoregressive model reasons before emitting a diagnosis SID, and optimizes multi-label coverage with a coverage reward and multi-positive supervision; on MIMIC-III and MIMIC-IV it exceeds all EHR-trained baselines in weighted score and attains the highest recall at every reported cutoff, with R@30 of 46.04% and 46.52% in reasoning mode.

The work proposes CARing, which compresses disease names and ICD ontology paths into compositional medical Semantic IDs (SIDs) via residual quantization, grounds them through six alignment tasks plus teacher-generated semantic-enrichment corpora and reasoning activation so a single autoregressive model reasons before emitting a diagnosis SID, and optimizes multi-label coverage with a coverage reward and multi-positive supervision; on MIMIC-III and MIMIC-IV it exceeds all EHR-trained baselines in weighted score and attains the highest recall at every reported cutoff, with R@30 of 46.04% and 46.52% in reasoning mode.

Using PTR-ToF-MS headspace volatile fingerprints plus HPLC- and amino-acid-analysis-quantified non-volatile metabolites, this study built a two-stream Transformer that encodes each modality separately and fuses them for four-class grading of Baimudan white tea, reaching 95.8% accuracy (23/24) and a 0.958 macro-F1 on an independent prediction set drawn from 120 samples (30 per grade; 96 training, 24 prediction), with a single Special-grade sample misclassified as Grade I, mean cross-validated accuracy of 0.979±0.026 within the training set, and SHAP/attention analyses linking high grades to floral/sweet volatile ions plus higher amino acids and soluble sugars and lower grades to greener/woody volatile ions and kaempferol-related markers.

Using PTR-ToF-MS headspace volatile fingerprints plus HPLC- and amino-acid-analysis-quantified non-volatile metabolites, this study built a two-stream Transformer that encodes each modality separately and fuses them for four-class grading of Baimudan white tea, reaching 95.8% accuracy (23/24) and a 0.958 macro-F1 on an independent prediction set drawn from 120 samples (30 per grade; 96 training, 24 prediction), with a single Special-grade sample misclassified as Grade I, mean cross-validated accuracy of 0.979±0.026 within the training set, and SHAP/attention analyses linking high grades to floral/sweet volatile ions plus higher amino acids and soluble sugars and lower grades to greener/woody volatile ions and kaempferol-related markers.