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The number of maize leaves is an important indicator for assessing plant growth and regulating population structure. However, the traditional leaf counting method mainly relies on manual work, which is both time-consuming and straining, while the existing image processing methods have low accuracy and poor adaptability, making it difficult to meet the standards for practical application. To accurately detect the growth status of maize, an improved lightweight YOLOv8 maize leaf detection and counting method was proposed in this study. Firstly, the backbone of the YOLOv8 network is replaced using the StarNet network and the convolution and attention fusion module (CAFM) is introduced, which combines the local convolution and global attention mechanisms to enhance the ability of feature representation and fusion of information from different channels. Secondly, in the neck network part, the StarBlock module is used to improve the C2f module to capture more complex features while preserving the original feature information through jump connections to improve training stability and performance. Finally, a lightweight shared convolutional detection head (LSCD) is used to reduce repetitive computations and improve computational efficiency. The experimental results show that the precision, recall, and mAP50 of the improved model are 97.9%, 95.5%, and 97.5%, and the numbers of model parameters and model size are 1.8 M and 3.8 MB, which are reduced by 40.86% and 39.68% compared to YOLOv8. This study shows that the model improves the accuracy of maize leaf detection, assists breeders in making scientific decisions, provides a reference for the deployment and application of maize leaf number mobile end detection devices, and provides technical support for the high-quality assessment of maize growth.

Blood viscosity measurements are crucial for the diagnosis of cardiovascular and hematological diseases. Traditional blood viscosity measurements have obvious limitations because of their expensive equipment usage and large sample consumption. In this study, blood viscosity was measured by the oscillating circuit method and impedance analysis method based on single QCM. In addition, the effectiveness of two methods with high precision and less sample is proved by the experiments. Moreover, compared to the result from a standard rotational viscometer, the maximum relative errors of the proposed oscillating circuit method and impedance analysis method are ±5.2% and ±1.8%, respectively. A reliability test is performed by repeated measurement (N = 5), and the result shows that the standard deviation about 0.9% of impedance analysis is smaller than that of oscillating circuit method. Therefore, the impedance analysis method is superior. Further, the repeatability of impedance analysis method was evaluated by regression analysis method, and the correlation coefficient R2 > 0.965 demonstrated that it had excellent reproducibility.

Singular value decomposition (SVD) methods have aroused wide concern to extract the periodic impulses for bearing fault diagnosis. The state-of-the-art SVD methods mainly focus on the low rank property of the Hankel matrix for the fault feature, which cannot achieve satisfied performance when the background noise is strong. Different to the existing low rank-based approaches, we proposed a simultaneously low rank and group sparse decomposition (SLRGSD) method for bearing fault diagnosis. The major contribution is that the simultaneously low rank and group sparse (SLRGS) property of the Hankel matrix for fault feature is first revealed to improve performance of the proposed method. Firstly, we exploit the SLRGS property of the Hankel matrix for the fault feature. On this basis, a regularization model is formulated to construct the new diagnostic framework. Furthermore, the incremental proximal algorithm is adopted to achieve a stationary solution. Finally, the effectiveness of the SLRGSD method for enhancing the fault feature are profoundly validated by the numerical analysis, the artificial bearing fault experiment and the wind turbine bearing fault experiment. Simulation and experimental results indicate that the SLRGSD method can obtain superior results of extracting the incipient fault feature in both performance and visual quality as compared with the state-of-the-art methods.

We have developed a novel optical biosensor device using recombinant methyl parathion hydrolase (MPH) enzyme immobilized on agarose by metal-chelate affinity to detect organophosphorus (OP) compounds with a nitrophenyl group. The biosensor principle is based on the optical measurement of the product of OP catalysis by MPH (p-nitrophenol). Briefly, MPH containing six sequential histidines (6× His tag) at its N-terminal was bound to nitrilotriacetic acid (NTA) agarose with Ni ions, resulting in the flexible immobilization of the bio-reaction platform. The optical biosensing system consisted of two light-emitting diodes (LEDs) and one photodiode. The LED that emitted light at the wavelength of the maximum absorption for p-nitrophenol served as the signal light, while the other LED that showed no absorbance served as the reference light. The optical sensing system detected absorbance that was linearly correlated to methyl parathion (MP) concentration and the detection limit was estimated to be 4 μM. Sensor hysteresis was investigated and the results showed that at lower concentration range of MP the difference got from the opposite process curves was very small. With its easy immobilization of enzymes and simple design in structure, the system has the potential for development into a practical portable detector for field applications.

Combining near-earth remote sensing spectral imaging technology with unmanned aerial vehicle (UAV) remote sensing sensing technology, we measured the Ningqi No. 10 goji variety under conditions of health, infestation by psyllids, and infestation by gall mites in Shizuishan City, Ningxia Hui Autonomous Region. The results indicate that the red and near-infrared spectral bands are particularly sensitive for detecting pest and disease conditions in goji. Using UAV-measured data, a remote sensing monitoring model for goji pest and disease was developed and validated using near-earth remote sensing hyperspectral data. A fully connected neural network achieved an accuracy of over 96.82% in classifying gall mite infestations, thereby enhancing the precision of pest and disease monitoring in goji. This demonstrates the reliability of UAV remote sensing. The pest and disease remote sensing monitoring model was used to visually present predictive results on hyperspectral images of goji, achieving data visualization.

Wireless sensor networks (WSNs) are widely used in various fields such as military, industrial, and transportation for real-time monitoring, sensing, and data collection of different environments or objects. However, the development of WSNs is hindered by several limitations, including energy, storage space, computing power, and data transmission rate. Among these, the availability of power energy plays a crucial role as it directly determines the lifespan of WSN. To extend the life cycle of WSN, two key approaches are power supply improvement and energy conservation. Therefore, we propose an energy harvesting system and a low-energy-consumption mechanism for WSNs. Firstly, we delved into the energy harvesting technology of WSNs, explored the utilization of solar energy and mechanical vibration energy to ensure a continuous and dependable power supply to the sensor nodes, and analyzed the voltage output characteristics of bistable piezoelectric cantilever. Secondly, we proposed a neighbor discovery mechanism that utilizes a separation beacon, is based on reply to ACK, and can facilitate the identification of neighboring nodes. This mechanism operates at a certain duty cycle ratio, significantly reduces idle listening time and results in substantial energy savings. In comparison to the Disco and U-connect protocols, our proposed mechanism achieved a remarkable reduction of 66.67% and 75% in the worst discovery delay, respectively. Furthermore, we introduced a data fusion mechanism based on integer wavelet transform. This mechanism effectively eliminates data redundancy caused by spatiotemporal correlation, resulting in a data compression rate of 5.42. Additionally, it significantly reduces energy consumption associated with data transmission by the nodes.