Vhf:A Time Series Hybrid Model Based on Variational Decomposition and Prior Knowledge Reconstruction for the Bio-Pharmaceutical Industry

Candlesticks are widely used as an effective technical analysis tool in financial markets. Traditionally, different combinations of candlesticks have formed specific bullish/bearish patterns providing investors with increased opportunities for profitable trades. However, most patterns derived from subjective expertise without quantitative analysis. In this article, combining bullish/bearish patterns with ensemble learning, we present an intelligent system for making stock trading decisions. The Ensemble Classifier through Multimodal Perturbation (ECMP) is designed to generate a diverse set of precise base classifiers to further determine the candlestick patterns. It achieves this by: first, introducing perturbations to the sample space through bootstrap sampling; second, employing an attribute reduction algorithm based on neighborhood rough set theory to select relevant features; third, perturbing the feature space through random subspace selection. Ultimately, the trading decisions are guided by the classification outcomes of this procedure. To evaluate the proposed model, we apply it to empirical investigations within the context of the Chinese stock market. The results obtained from our experiments clearly demonstrate the effectiveness of the approach.

An intelligent stock trading decision system based on ensemble classifier through multimodal perturbation

<title>Abstract</title> In the process of penicillin fermentation, there is a strong nonlinear relationship between the input eigenvector and multiple output vectors, which makes the prediction accuracy of the existing model difficult to meet the requirements of chemical production. Therefore, a local selective integrated learning multi-objective soft sensing modeling strategy is proposed in this paper. Firstly, a localization method based on transfer entropy and K-means is proposed to reconstruct the sample set. Then, based on the reconstructed local samples, the local soft sensing model is established by the multi-objective support vector regression method, and the selective integration of sub-models and the adaptive calculation of prediction weights are realized. At the same time, to reduce the adverse effects caused by improper selection of model parameters, the sparrow search algorithm is used to realize the tuning of the mentioned model parameters. Finally, the proposed modeling strategy is simulated. The results show that, compared with other methods, the proposed local selective integrated learning multi-objective soft sensing modeling strategy has better prediction performance. 

Soft Sensing Modeling of Penicillin Fermentation Process Based on Local Selection Ensemble Learning

Pre-trained large models have emerged as a pivotal technological approach for foundational cell modeling. However, existing deep learning-based foundational models for cells have predominantly focused on human or murine systems, with a relative scarcity of research on model microorganisms such as Saccharomyces cerevisiae. Furthermore, these models often exhibit limitations in the integration of biological prior knowledge. To address this gap, we introduce scYeast, the first foundational cell model specifically designed for yeast that deeply integrates biological priors. scYeast features an innovative asymmetric parallel architecture that embeds transcriptional regulatory prior information directly into the Transformer's attention mechanism, thereby systematically incorporating and leveraging established biological knowledge during model training. After large-scale pre-training on single-cell transcriptomics data from yeast, scYeast demonstrates robust generalization capabilities and strong biological interpretability. It can perform zero-shot tasks, such as inferring specific regulatory relationships and resolving critical cell states, functional types, and developmental trajectories. Moreover, by constructing fine-tuning networks, scYeast excels at tasks including cell type identification, doubling time prediction, and forecasting responses to gene perturbations. Further leveraging transfer learning, scYeast can be adapted to other multi-omics data, such as proteomics, expanding its application boundaries in systems biology research. scYeast not only provides a novel tool for fundamental research in yeast single-cell biology but also establishes a new paradigm for the organic fusion of foundational models with prior knowledge, laying a solid groundwork for synthetic and systems biology. 

scYeast: a Biological-knowledge-guided Foundation Model on Yeast Single-Cell Transcriptomics

Multiple feature fusion transformer for modeling penicillin fermentation process with unequal sampling intervals

An Optimally Selective Ensemble Classifier Based on Multimodal Perturbation and Its Application

Multimodal Perturbation and Cluster Pruning Based Selective Ensemble Classifier and Its Iron Industrial Application

Partially uncertain industrial process monitoring based on hybrid-scale wavelet signal fusion using crossed transformer

Spatial-Temporal relation inference Transformer combined with dynamic relationship and static causality for batch process modeling and the application of erythromycin fermentation

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