Electronic nose systems are widely used in environmental monitoring and other related fields. In recent years, systems based on gas sensor arrays have attracted considerable attention. However, relying solely on improvements in gas-sensitive materials has struggled to break through the bottleneck in recognition accuracy. To address this challenge, this study designs and validates an integrated machine learning framework for enhanced gas identification in electronic nose systems. Specifically, (1) a Butterworth low-pass filter is combined with principal component analysis (PCA) to suppress sensor noise; (2) the synthetic minority over-sampling technique (SMOTE) is utilized for training set data augmentation to further enhance the classification accuracy of the support vector machine (SVM); and (3) the relationship between single-component and mixed-gas responses is analyzed to construct an artificial neural network (ANN) regression model. Experimental results demonstrate that the SMOTE-augmented, PCA-optimized SVM model achieves a recognition accuracy of 0.93 ± 0.08 for most target gases, representing improvements of 19% and 7% over decision tree and ANN classifiers, respectively, and that the ANN regression model attains a correlation coefficient of 99.55% between predicted and measured values in mixed-gas experiments. Overall, the construction and optimization of this system demonstrate significant practical value for intelligent gas identification and the development of advanced e-nose devices.

In this paper, we proposed an eye-tracking system featuring a small size and high scanning frequency, utilizing an electrostatic biaxial scanning mirror fabricated through a micro-electro-mechanical system (MEMS) process. A laser beam is directed onto the mirror, and the two axes of the mirror generate a Lissajous scanning pattern within an artificial eyeball. The scanning pattern reflected from the eyeball is detected by a linear photodiode sensor array (LPSA). The direction and rotation angle of the artificial eyeball result in varying grayscale values across a series of pixels detected by the LPSA, in which the average grayscale values change accordingly. By performing a linear fit between different rotation angles of the same eye movement direction and the corresponding grayscale values, we can determine the correlation between the direction of eye movement and the signal magnitude received by the LPSA, thereby enabling precise eye tracking. The results demonstrated that the minimum resolution was 0.6°. This preliminary result indicates that the system has good accuracy. In the future, this eye-tracking system can be integrated into various wearable glasses devices and applied in various fields, including medicine and psychology.

Dry eye disease (DED) is a chronic inflammatory disorder of the ocular surface, characterized by tear film homeostasis imbalance, with aging being identified as a crucial independent risk factor. Oxidative stress, which refers to the excessive production of reactive oxygen species (ROS) and reactive nitrogen substances during mitochondrial metabolism and the weakened protective effect of antioxidants, plays a central role in this process. With aging, the mitochondrial function of ocular surface tissues, such as the corneal epithelium, meibomian glands, and lacrimal glands, declines. Concurrently, the activity of endogenous antioxidant enzymes (such as superoxide dismutase and glutathione peroxidase) decreases, and the levels of tear antioxidants such as lactoferrin also decrease. These age-related changes collectively lead to excessive accumulation of ROS, triggering oxidative stress that directly damages biomacromolecules in ocular surface cells and impairs the stability of the tear film. Furthermore, we have summarized the current therapeutic strategies for oxidative stress in DED, including both conventional antioxidants and emerging approaches such as eye drops based on nanoenzymes, thermosensitive hydrogels, intense pulsed light therapy, and drug-eluting contact lenses. By combining the new progress in the delivery systems of biomaterials-based drugs with mechanism-guided interventions, this review systematically establishes the intimate functional linkages between mitochondrial dysfunction, oxidative stress, and the pathogenesis of DED and focuses on elaborating the translational potential of advanced biomaterials-based antioxidant regimens, aiming to provide novel foundations and insights theoretical for the development of more effective and precise therapeutic strategies for DED.