Raman spectroscopy is a non-destructive analytical technique based on molecular vibrational properties. However, its practical application is often challenged by weak scattering signals, complex spectra, and the high-dimensional nature of the data, which complicates accurate interpretation. Traditional chemometric methods are limited in handling complex, nonlinear Raman data and rely on tedious, expert-knowledge-based feature engineering. The fusion of data-driven Machine Learning (ML) and Deep Learning (DL) methods offers a robust solution, enabling the automatic learning of complex features from raw data and achieving high-accuracy classification and prediction. The present study employed a structured narrative review methodology to capture the research progress, current trends, and future directions in the field of ML-assisted Raman spectral classification. This review provides a comprehensive overview of the application of traditional ML models and advanced DL architectures in Raman spectral analysis. It highlights the latest applications of this technology across several key domains, including biomedical diagnostics, food safety and authentication, mineralogical classification, and plastic and microplastic identification. Despite recent progress, several challenges remain: limited training data, weak cross-dataset generalization, poor reproducibility, and limited interpretability of deep models. We also outline practical directions for future research.

Smart sensors represent a significant advancement in modern sports science, and their effective use enhances the ability to monitor and analyze athlete performance in real time. The integration of these sensors has enhanced the accuracy of data collection related to physical activity, biomechanics, and physiological responses, thus providing valuable insights for performance optimization, injury prevention, and rehabilitation. This paper provides an overview of the research progress in the application of smart sensors in the field of sports science; highlights the current advances, challenges, and future directions in the deployment of smart sensor technologies; and anticipates their transformative impact on sports science and athlete development.

As a necessary part of all electronic devices, equipment and systems, electronic components play a vital role in the global economy. Since the corrosion of a single electronic component may directly affect the normal operation of the entire electronic system, the failure of electronic components has now become the most important cause of electrical system failure and has become a major obstacle to China’s transformation into a scientific and technological power. Therefore, it is urgent to study the corrosion failure process of electronic components and the means of effective protection. In this paper, starting from the corrosion types and influencing factors of electronic components, especially chips, we introduce the influence of humidity, temperature, salt spray, and environmental particles, as well as the device’s own surface roughness, material adhesion, semiconductor materials, metal coupling system, and lead-free solder system on corrosion performance in the environment. Subsequently, this paper summarizes how to protect electronic components during processing, and sums up the types of electronic component protections, and the specific corrosion protection process for the three commonly used types of chips, namely, the indium antimonide InSb chip, the IC chip, and the Sn–Zn solder chip, for reference. Finally, future development trends in the corrosion protection of electronic components are anticipated and summarized.