This paper proposes a novel high-sensitivity micro-electromechanical system (MEMS) piezoresistive pressure sensor that can be used for rock mass stress monitoring. The entire sensor consists of a cross, dual-cavity, and all-silicon bulk-type (CCSB) structure. Firstly, the theoretical analysis is carried out, and the relationship between the structural parameters of the sensor and the stress is analyzed by finite element simulation and curve-fitting prediction, and then the optimal structural parameters are also analyzed. The simulation results indicate that the sensor with the CCSB structure proposed in this article obtained a high sensitivity of 87.74 μV/V/MPA and a low nonlinearity error of 0.28% full-scale span (FSS) within the pressure range of 0–200 MPa. Compared with All-Si Bulk, grooved All-Si Bulk, Si-Glass Bulk, silicon diaphragm, resistance strain gauge, and Fiber Bragg grating structure pressure sensors, the designed sensor has a significant improvement in sensitivity and nonlinearity error. It can be used as a new sensor for rock disaster (such as collapse) monitoring and forecasting.

In this study, drift of microelectromechanical system (MEMS) accelerometers stored at room temperature for one year is investigated by both finite element analysis (FEA) simulation and experiments. In the simulation, a comprehensive FEA model considering the die-attach adhesive viscoelasticity was established to emulate the deformation of the sensor package structure during storage. Then drift models of scale factor and bias of the accelerometer were built according to the deformed structure. To validate the simulation results, several accelerometers were packaged and their outputs were tested and recorded once per month. The two parameters displayed a continuous drift owing to the release of the packaging stress in the chip. The scale factor drifts were negative and about 2500 ppm for different types of the sensors. However, the bias drifts were positive or negative, and their magnitudes were ranging from 12 mg to 32 mg in storage. Moreover, they reached a steady state after about 10 months (<50 ppm per month for scale factor) and 6 months (<1 mg per month for bias) room temperature storage. The results in this paper provide meaningful guidance for the application of MEMS accelerometers.

Highlights Modify the YOLOV8 backbone network to reduce model calculation complexity and parameter amount. Integrating the Slim-Neck design and the SA attention mechanism into the neck network to enhance the attention of target features and improve the weight of the target. Adoption of the WIoU loss function over the original CIoU to address the sample imbalance problem and accelerate model convergence. Abstract. In response to the challenges posed by the excessive number of parameters and the high computational complexity inherent in existing algorithms for apple leaf disease recognition, this study introduces an enhanced, lightweight detection algorithm, YOLOv8-GSSW. Building upon the YOLOv8n as the foundational model, this research innovates in several key areas. Initially, the algorithm employs a lightweight GSConv convolutional module to replace the standard convolutional modules within the backbone network, significantly reducing both the model’s computational complexity and its parameter count. Furthermore, the incorporation of the Slim-Neck design concept into the neck network module serves to bolster the model’s capabilities in feature processing and learning. This enhancement is further augmented by the integration of the SA attention mechanism within Slim-Neck, which amplifies the attention to target features and increases their weight by merging information across both spatial and channel dimensions. Finally, the original CIoU loss function is substituted with the WIoU loss function to address the issue of sample imbalance and accelerate the model’s convergence. Experimental results underscore the YOLOv8-GSSW algorithm’s superior performance, achieving a mean average precision (mAP) of 87.3% with a parameter volume of 2.7×10⁶ and a model weight of 5.4 MB. Relative to the YOLOv8n benchmark model, this represents a 15.6% reduction in parameter volume and an 11.5% decrease in model size. The modifications introduced in this algorithm not only facilitate more convenient application in practical scenarios for apple leaf disease detection but also render the algorithm well-suited for deployment on edge devices. Keywords: Apple leaves, Disease detection, Lightweight, YOLOv8n.