Implementation and Evaluation of Spatial Attention Mechanism in Apricot Disease Detection Using Adaptive Sampling Latent Variable Network.

In this study, an advanced method for apricot tree disease detection is proposed that integrates deep learning technologies with various data augmentation strategies to significantly enhance the accuracy and efficiency of disease detection. A comprehensive framework based on the adaptive sampling latent variable network (ASLVN) and the spatial state attention mechanism was developed with the aim of enhancing the model's capability to capture characteristics of apricot tree diseases while ensuring its applicability on edge devices through model lightweighting techniques. Experimental results demonstrated significant improvements in precision, recall, accuracy, and mean average precision (mAP). Specifically, precision was 0.92, recall was 0.89, accuracy was 0.90, and mAP was 0.91, surpassing traditional models such as YOLOv5, YOLOv8, RetinaNet, EfficientDet, and DEtection TRansformer (DETR). Furthermore, through ablation studies, the critical roles of ASLVN and the spatial state attention mechanism in enhancing detection performance were validated. These experiments not only showcased the contributions of each component for improving model performance but also highlighted the method's capability to address the challenges of apricot tree disease detection in complex environments. Eight types of apricot tree diseases were detected, including Powdery Mildew and Brown Rot, representing a technological breakthrough. The findings provide robust technical support for disease management in actual agricultural production and offer broad application prospects.

Research on Motion Control and Wafer-Centering Algorithm of Wafer-Handling Robot in Semiconductor Manufacturing.

This paper studies the AWC (Active Wafer Centering) algorithm for the movement control and wafer calibration of the handling robot in semiconductor manufacturing to prevent wafer surface contact and contamination during the transfer process. The mechanical and software architecture of the wafer-handling robot is analyzed first, which is followed by a description of the experimental platform for semiconductor manufacturing methods. Secondly, the article utilizes the geometric method to analyze the kinematics of the semiconductor robot, and it decouples the motion control of the robot body from the polar coordinates and joint space. The wafer center position is calibrated using the generalized least-square inverse method for AWC correction. The AWC algorithm is divided into calibration, deviation correction, and retraction detection. These are determined by analyzing the robot's wafer calibration process. In conclusion, the semiconductor robot's motion control and AWC algorithm are verified through experiments for correctness, feasibility, and effectiveness. After the wafer correction, the precision of AWC is <± 0.15 mm, which meets the requirements for transferring robot wafers.

PVP-stabilized tungsten oxide nanoparticles: pH sensitive anti-cancer platform with high cytotoxicity.

Photochromic tungsten oxide (WO3) nanoparticles stabilized by polyvinylpyrrolidone (PVP) were synthesized to evaluate their potential for biomedical applications. PVP-stabilized tungsten oxide nanoparticles demonstrated a highly selective cytotoxic effect on normal and cancer cells in vitro. WO3 nanoparticles were found to induce substantial cell death in osteosarcoma cells (MNNG/HOS cell line) with a half-maximal inhibitory concentration (IC50) of 5 mg/mL, while producing no, or only minor, toxicity in healthy human mesenchymal stem cells (hMSc). WO3 nanoparticles induced intracellular oxidative stress, which led to apoptosis type cell death. The selective anti-cancer effects of WO3 nanoparticles are due to the pH sensitivity of tungsten oxide and its capability of reactive oxygen species (ROS) generation, which is expressed in the modulation of genes involved in reactive oxygen species metabolism, mitochondrial dysfunction, and apoptosis.