In the process of manufacture and transportation, vials are prone to breakage and cracks. Oxygen (O₂) in the air entering vials can lead to the deterioration of medicine and a reduction in pesticide effects, threatening the life of patients. Therefore, accurate measurement of the headspace O₂ concentration for vials is crucial to ensure pharmaceutical quality. In this invited paper, a novel headspace oxygen concentration measurement (HOCM) sensor for vials was developed based on tunable diode laser absorption spectroscopy (TDLAS). First, a long–optical–path multi–pass cell was designed by optimizing the original system. Moreover, vials with different O₂ concentrations (0%, 5%, 10%, 15%, 20%, and 25%) were measured with this optimized system in order to study the relationship between the leakage coefficient and O₂ concentration; the root mean square error of the fitting was 0.13. Moreover, the measurement accuracy indicates that the novel HOCM sensor achieved an average percentage error of 1.9%. Sealed vials with different leakage holes (4, 6, 8, and 10 mm) were prepared to investigate the variation in the headspace O₂ concentration with time. The results show that the novel HOCM sensor is non-invasive and has a fast response and high accuracy, with prospects in applications for online quality supervision and management of production lines.

Accurate prediction of wind speed at sea is crucial for the site selection of wind farms, the layout of wind turbines, and the estimation of power generation. To improve the accuracy of short-term predictions under limited data conditions, this study proposes a backpropagation (BP) neural network prediction model optimized by the particle swarm optimization algorithm (PSO). This model is trained using hourly wind speed data from meteorological stations along the northeastern coast of China from 2020 to 2022, and two modeling strategies, namely the unified training model over multiple years and the seasonal model, are constructed for comparison. The validation using the measured data from January to July 2023 indicates that the unified model with a root mean square error of 1.235 and an average absolute error of 0.924 demonstrates superior generalization performance, outperforming the seasonal models (such as the spring model with RMSE = 1.243 and the summer model with RMSE = 1.324). Benchmark comparisons against LSTM, ARIMA, and persistence models further confirmed the superiority of the proposed approach. To address the stochastic nature of wind speed and support grid operation, we extended the deterministic forecasts to probabilistic prediction intervals using Monte Carlo Dropout, achieving a prediction interval coverage probability of 81.2% with a mean width of 1.38 m/s. The results indicate that while seasonal modeling offers insights into intra-annual wind variations, it does not exceed the accuracy of the globally trained multi-year model under limited data conditions. In conclusion, the proposed BP-PSO hybrid model provides a robust and low-cost solution for offshore wind speed forecasting, with the probabilistic forecasting framework offering actionable uncertainty information for grid integration. The multi-year training framework demonstrates stronger practical utility, and the findings support the application of hybrid optimization algorithms in real-world wind resource assessment.

This paper proposes a rotating test method to address the limitations of high costs and the inability to replicate high-speed multiphase environments in icing wind tunnels and artificial climate chambers. The method simulates high-speed multiphase in an enclosed space using relative motion and duct regulation at a lower cost. Using the FQJG2-30/16-400 type roof insulator, the Eulerian–Eulerian and CFD (computational fluid dynamics) method was employed to compare the proposed rotating method with traditional linear airflow tests in wind–sand erosion and high-speed icing experiments. Results show maximum differences of 3.23% in the collision rate and 4.34% in the icing mass, indicating good consistency. Validation experiments in an artificial climate chamber further confirmed the feasibility of the rotating test method, with icing mass differences within 5–8%. This study provides a cost-effective approach for high-speed testing in multiphase environments.