Aeroengine is a vital component of the aircraft, and its health monitoring is considered a critical aspect. Electrostatic (ES) sensors provide a feasible manner to assess the online health monitoring (OHM) of aeroengines by real-time evaluating exhaust emissions. Effective performance prediction of ES sensors is desired in the research and development of ES sensors for OHM systems. Various theoretical and numerical studies have been carried out to model and analyze the ES sensing process. However, the existing models are oversimplified for practical circumstances. There is a crucial need for an advanced model that can predict the performance of the ES sensors under realistic conditions, which is not yet available. To this end, an effort is presented to model and evaluate the performance of the ES sensors. The physical processes involved in ES sensing are analyzed, and the underlying interactions among different physical processes are investigated. Afterward, an advanced multiphysics coupling model is established, reflecting the practical phenomena that have profound effects on ES sensing, including the random release of the particles, temperature-dependent particle charge, nonuniformity of particle trajectories caused by high-speed exhaust gas, and complicated gas path geometry. By reasonably predicting the response of an ES sensor under practical conditions, the proposed model provides a feasible manner to obtain a comprehensive understanding of how design parameters, inherent parameters of particles, and application conditions influence the performance of the ES sensor. Thereby, the developed advanced model facilitates the processes of research and development in the field of sensors.

Cold plasma has been widely reported to enhance seed germination and plant growth, as demonstrated in both previous research and our earlier studies. However, the molecular mechanisms underlying these physiological improvements remain poorly understood. To address this gap, we investigated the transcriptomic responses of papaya seeds (Carica papaya) following a 10-second argon plasma treatment. Optical emission spectroscopy (OES) identified argon and oxygen species as the predominant reactive components during plasma discharge, suggesting their involvement in regulating cellular functions. RNA sequencing indicated significant upregulation of genes involved in sucrose metabolism, redox homeostasis, and hormone regulation. Notably, PHYB expression increased 8.5-fold, suggesting plasma-mediated modulation of ABA signaling, while RGL homolog expression decreased 7.8-fold, releasing inhibition on TCP14 to promote seedling growth. Significant increases were also observed in key metabolic enzymes, including alkaline/neutral invertase and purple acid phosphatase. This comprehensive analysis provides insights into the molecular pathways influenced by cold plasma, underscoring its potential for agricultural applications. Key metabolic enzymes, including alkaline/neutral invertase and purple acid phosphatase, were also significantly elevated. These results provide molecular evidence that cold plasma treatment enhances seed readiness for germination through coordinated regulation of metabolism, hormonal pathways, and cell growth, highlighting its potential for agricultural applications.

This work presents and validates an eye-tracking-based visual system for driving the delta robot. A delta robot is tracked by image processing based on vision servo control. The vision servo program is developed in C++ to perform image processing-based object detection. For image processing, Haar classifier-based methods are used. Finally, image processing and motion controller movements are integrated into one system to perform the visual servo-based motion of the end effector of the delta robot. Experiments are performed to validate the proposed method from the perspective of image processing. Moreover, this paper validates the kinematic analysis, which is vital for obtaining 3D information on the end-effector of the delta robot. The presented model can be implemented in eye clinics to facilitate ophthalmologists by replacing manual eye-checking equipment with automatic, unattended, computerized eye checkups.

Electrostatic (ES) sensors have gained significant use in Engine Health Monitoring (EHM) system due to their real-time particle detection, non-contact operation, high- temperature resistance, and cost-effective operation in the complex environment of the exhaust gas path. The response of an ES sensor is dependent on a series of uncertain inherent parameters of gas particles, including their trajectory, velocity, size, concentration, temperature, and release conditions. This series of dependencies on uncertain parameters leads to uncertainty in the performance of ES sensors and increases the chances of false alarms in real-time EHM systems. To this end, herein, a 3-D Multiphysics approach is presented by integrating heat transfer, fluid flow, particle tracing, and electrostatic sensing models. The Multiphysics numerical model demonstrates good accuracy compared to existing models while being validated with experimental results. The 3-D Multiphysics simulations have shown coupling effect of models, the influence of inherent uncertain parameters on charge accumulation, and the induced charge on ES sensor. Thereby, the developed 3-D Multiphysical model facilitates to avoid false alarms in the EHM system due to uncertain inherent parameters of exhaust gas.