The accurate prediction of moisture content is crucial for controlling the drying process of agricultural products. While existing studies on drying models often rely on laboratory-scale experiments with limited data, real-time and high-frequency data collection under industrial conditions remains underexplored. This study collected and constructed a multi-dimensional dataset using an industrial-grade data acquisition system specifically designed for heat pump low-temperature circulating dryers. An artificial neural network (ANN) prediction model for moisture content during the rice drying process was developed. To prevent model overfitting, K-fold cross-validation was utilized. The model’s performance was evaluated using the mean squared error (MSE) and the coefficient of determination (R²), which also helped determine the preliminary structure of the ANN model. Bayesian regularization (trainbr) was then employed to train the network. Furthermore, optimization was conducted using neural network weights (RI) analysis and Sobol variance contribution analysis of the input variables to simplify the model structure and improve predictive performance. The experimental results showed that optimizing the model through RI sensitivity analysis simplified its topology to a 5-14-1 structure. The optimized model exhibited not only simplicity but also high prediction accuracy, achieving R² values of 0.969 and 0.966 for the training and testing sets, respectively, with MSEs of 5.6 × 10⁻³ and 6.3 × 10⁻³. Additionally, the residual errors followed a normal distribution, indicating that the predictions were reliable and realistic. Statistical tests such as t-tests, F-tests, and Kolmogorov–Smirnov tests revealed no significant differences between the predicted and actual values of rice moisture content, confirming the high consistency between them.

On-chip spectrometers are key components in many spectral sensing applications owing to their unique advantages in size and in situ detection. In this work, we propose and demonstrate a class of thermally tunable spectrometers by utilizing topological miniaturized bound states in the continuum (mini-BIC) cavities in a photonic crystal (PhC) slab combined with a metal micro-ring heater. We achieve a resolution of 0.19 nm in a spectral range of ∼6 nm, while the device’s footprint is only 42×42μm². The mini-BIC spectrometer works in nearly vertical incidence and is compatible with array operation. Our work sheds light on the new possibilities of high-performance on-chip spectrometers for applications ranging from bio-sensing to medicine.

With the in-depth implementation of national environmental protection policies, gas will be more widely used in our lives, which requires better design to upgrade the gas safety. On the basis of the project, our laboratory constructed a gas monitoring and warning visualization platform through a variety of front-end and back-end technologies, enabling managers to conduct real-time monitoring of equipment status in different gas stations through web pages. The system focuses on the collection and analysis of video information, which can effectively identify the identity and action of the personnel entering the gas station, and then mark the personnel and issue an early warning on the video window of the visual page of the system, thus improving the management ability of the gas company of each gas station.