In order to improve the accuracy and anti-interference of underground environment positioning, a coal mine underground ultra wideband based positioning system is designed. Aiming at the problems of large accidental errors and poor robustness in traditional UWB positioning, the extended Kalman filter algorithm is added to improve the accuracy of the positioning system. Aiming at the problems of large accidental errors and poor robustness in traditional UWB positioning, the extended Kalman filter algorithm is added to improve the accuracy of the positioning system. The experimental results show that the Kalman filter can improve the positioning accuracy and reduce the system accidental error.

In order to meet the requirements of multi-crop harvesting, reduce the loss of harvesting, and improve the quality of harvesting, the device and method of matching and adjusting the rotation speed and forward speed of the pulling reel were designed. On the premise of satisfying the matching regulation, the parameters of the rotary wheel and the cutting table were adjusted so that the rotational speed ratio λ of the harvest crop was in the suitable range of the rotational speed ratio of the crop. The speed ratio was designed as a suitable interval of different crops by experiments and experience, and, in order to meet the requirements, it was designed to optimize the wheel speed. The speed-matching was designed, and through the experiments on the wheel-speed-adjustment error, which was less than 2%, it was designed to meet the design requirements. Crop-harvesting experiments were carried out under rotational speed-matching and a random speed, and the experimental results showed that the loss rate under rotational speed-matching was significantly lower than that under random speeds; the tests showed that the wheel speed designed with speed-matching can effectively reduce the loss rate.

Food contaminants, including harmful microbes, pesticide residues, heavy metals and illegal additives, pose significant public health risks. While traditional detection methods are effective, they are often slow and require complex equipment, which limits their application in real-time monitoring and rapid response. Surface-enhanced Raman scattering (SERS) technology has gained widespread use in related research due to its hypersensitivity, non-destructibility and molecular fingerprinting capabilities. In recent years, Au-Ag bimetallic nanoparticles (Au-Ag BNPs) have emerged as novel SERS substrates, accelerating advancements in SERS detection technology. Au-Ag BNPs can be classified into Au-Ag alloys, Au-Ag core–shells and Au-Ag aggregates, among which the Au-Ag core–shell structure is more widely applied. This review discusses the types, synthesis methods and practical applications of Au-Ag BNPs in food contaminants. The study aims to provide valuable insights into the development of new Au-Ag BNPs and their effective use in detecting common food contaminants. Additionally, this paper explores the challenges and future prospects of SERS technology based on Au-Ag BNPs for pollutant detection, including the development of functional integrated substrates, advancements in intelligent algorithms and the creation of portable on-site detection platforms. These innovations are designed to streamline the detection process and offer guidance in selecting optimal sensing methods for the on-site detection of specific pollutants.

The inherent complexity of cellulose, hemicellulose, and lignin contributes to the recalcitrance of lignocellulosic biomass, resulting in a low conversion efficiency and high cost of bioethanol conversion. Pretreatment methods that disrupt the plant cell structure of lignocellulose, such as straw, can significantly enhance the conversion efficiency. In this study, we utilized an acid-assisted mechanocatalytic depolymerization technique to pretreat rice straw, and the results demonstrated a significant disruption of the cellulose structure of the straw. Compared to the untreated straw, the particle size of pretreated straw reduced from 279 μm to 11.8 μm, the crystallinity of cellulose decreased from 43.05% to 22.71%, the specific surface area increased by 177%, and the surface oxygen-to-carbon ratio (O/C) ratio was enhanced by 75%. The changes in microstructure enabled the pretreated straw to achieve a total sugar yield of over 95% within 12 h of enzymatic hydrolysis, significantly superior to the 36.24% yield from untreated straw, the 45.20% yield from acid impregnated straw, and the 73.25% yield from ball milled straw. Consequently, acid-assisted mechanocatalytic depolymerization emerges as a highly effective pretreatment strategy to enhance both the enzymatic hydrolysis and the overall conversion efficiency of rice straw.

The structural integrity of poultry eggs is essential for food safety, economic value, and hatchability. External eggshell quality—measured by thickness, strength, cracks, color, and cleanliness—is a key criterion for grading and sorting. Traditional assessment methods, although simple, suffer from subjectivity, low efficiency, and destructive nature. In contrast, recent developments in nondestructive testing (NDT) technologies have enabled precise, automated, and real-time evaluation of eggshell characteristics. This review systematically summarizes state-of-the-art NDT techniques including acoustic resonance, ultrasonic imaging, terahertz spectroscopy, machine vision, and electrical property sensing. Deep learning and sensor fusion methods are highlighted for their superior accuracy in microcrack detection (up to 99.4%) and shell strength prediction. We further discuss emerging challenges such as noise interference, signal variability, and scalability for industrial deployment. The integration of explainable AI, multimodal data acquisition, and edge computing is proposed as a future direction to develop intelligent, scalable, and cost-effective eggshell inspection systems. This comprehensive analysis provides a valuable reference for advancing nondestructive quality control in poultry product supply chains.