Yu Luan

Maturity is a crucial indicator in assessing the quality of tomatoes, and it is closely related to lycopene content. Using hyperspectral imaging, this study aimed to monitor tomato maturity and predict its lycopene content at different maturity stages. Standard normal variable (SNV) transformation was applied to preprocess the hyperspectral data. Then, using competitive adaptive reweighted sampling (CARS), the characteristic wavelengths were selected to simplify the calibration models. Based on the full and characteristic wavelengths, a support vector classifier (SVC) model was developed to determine tomato maturity qualitatively. The results demonstrated that the classification accuracy using the characteristic wavelength led to the obtention of better results with an accuracy of 95.83%. In addition, the support vector regression (SVR) and partial least squares regression (PLSR) models were utilized to predict lycopene content. With a coefficient of determination for prediction (R2P) of 0.9652 and a root mean square error for prediction (RMSEP) of 0.0166 mg/kg, the SVR model exhibited the best quantitative prediction capacity based on the characteristic wavelengths. Following this, a visual distribution map was created to evaluate the lycopene content in tomato fruit intuitively. The results demonstrated the viability of hyperspectral imaging for detecting tomato maturity and quantitatively predicting the lycopene content during storage.

Abstract As a pivotal element within the drive system of mechanical equipment, the remaining useful life (RUL) of rolling bearings not only dictates the lifespan of the equipment’s drive system but also the overall machine. An inaccurate prediction of the RUL of rolling bearings could hinder the formulation of maintenance strategies and lead to a chain of failures stemming from bearing malfunction, culminating in potentially catastrophic accidents. This paper designs a novel temporal convolutional network-multi-head self-attention (TCN-MSA) model for predicting the RUL of rolling bearings. This model considers the intricate non-linearity and complexity of mechanical equipment systems. It captures long-term dependencies using the causally inflated convolutional structure within the temporal convolutional network (TCN) and simultaneously extracts features from the frequency domain signal. Subsequently, by employing the multi-head self-attention (MSA) mechanism, the model discerns the significance of different features throughout the degradation process of rolling bearings by analyzing global information. The final prediction for rolling bearings’ RUL has been successfully attained. To underline the excellence of the method presented in this paper, a comparative analysis was performed with existing methods, such as convolutional neural network, gate recurrent unit, and TCN. The results highlight that the model designed in this paper surpasses other existing methods in predicting the RUL of rolling bearings, demonstrating superior prediction accuracy and robust generalization capability.

BACKGROUND: Cancer development is closely associated with complex alterations in the tumor microenvironment (TME). Among these, immune cells within the TME play a huge role in personalized tumor diagnosis and treatment. OBJECTIVES: This review aims to summarize the diversity of immune cells in the TME, their impact on patient prognosis and treatment response, and the contributions of single-cell RNA sequencing (scRNA-seq) in understanding their functional heterogeneity. METHODS: We analyzed recent studies utilizing scRNA-seq to investigate immune cell populations in the TME, focusing on their interactions and regulatory mechanisms. RESULTS: ScRNA-seq reveals the functional heterogeneity of immune cells, enhances our understanding of their role in tumor antibody responses, and facilitates the construction of immune cell interaction networks. These insights provide guidance for the development of cancer immunotherapies and personalized treatment approaches. CONCLUSION: Applying scRNA-seq to immune cell analysis in the TME offers a novel pathway for personalized cancer treatment. Despite its promise, several challenges remain, highlighting the need for further advancements to fully integrate scRNA-seq into clinical applications.