Depression is a complex mental health disorder that presents significant challenges in diagnosis and treatment. This study proposes an innovative approach, leveraging artificial intelligence advancements, to enhance multimodal depression diagnosis. The diagnosis of depression often relies on subjective assessments and clinical interviews, leading to potential biases and inaccuracies. Additionally, integrating diverse data modalities, such as textual, imaging, and audio information, poses technical challenges due to data heterogeneity and high dimensionality. To address these challenges, this paper proposes the RLKT-MDD (Representation Learning and Knowledge Transfer for Multimodal Depression Diagnosis) model framework. Representation learning enables the model to autonomously discover meaningful patterns and features from diverse data sources, surpassing traditional feature engineering methods. Knowledge transfer facilitates the effective transfer of knowledge from related domains, improving the model's performance in depression diagnosis. Furthermore, we analyzed the interpretability of the representation learning process, enhancing the transparency and trustworthiness of the diagnostic process. We extensively experimented with the DAIC-WOZ dataset, a diverse collection of multimodal data from clinical settings, to evaluate our proposed approach. The results demonstrate promising outcomes, indicating significant improvements over conventional diagnostic methods. Our study provides valuable insights into cutting-edge techniques for depression diagnosis, enabling more effective and personalized mental health interventions.

The microbial fermentation process often involves variousbiologicalmetabolic reactions and chemical processes. The mixed bacterial cultureprocess of 2-keto-l-gulonic acid has strong nonlinear andtime-varying characteristics. In this study, a probabilistic Bayesiandeep learning approach is proposed to obtain a highly accurate androbust prediction of product formation. The Bayesian optimized deepneural network (BODNN) is utilized as basic model for prediction,the structural parameters of which are optimized. Then, the trainingdatasets are classified into different categories according to theprior evaluation of prediction error. The final forecasting is a weightedcombination of BODNN models based on the Bayesian hybrid method. Theweights can be interpreted as Bayesian posterior probabilities andare computed recursively. The validation of 95 industrial batchesis carried out, and the average root mean square errors are 1.51 and2.01% for 4 and 8 h ahead prediction, respectively. The results illustratethat the proposed approach can capture the dynamics of fermentationbatches and is suitable for online process monitoring.

An optimized feeding strategy for the fed-batch fermentation of 2-keto-L-gulonic acid (2-KGA) is proposed to enhance the total profit of a workshop. Instead of assigning the same quantity of substrate to each batch in the empirical scheduling, the online feeding strategy offers a practical approach that allows for the precise management of substrate feeding to maximize economic performance. The forecasting of the profit function has been achieved through the proposed similarity measurement and multidimensional time series aggregation method. This method solves the problem of predicting the total economic benefit at the end of fermentation, in the absence of fermentation substrate measurement data. Pseudo-online simulation has been conducted, utilizing data derived from ninety industrial batches. A 6% increase in total profit is observed, which indicates that the optimized strategy utilizes the substrate and other related resources more efficiently and leads to a higher overall profit.