Detection of maize leaf diseases using improved MobileNet V3-small

Cobalt oxides have been intensely explored as anodes of lithium-ion batteries to resolve the intrinsic disadvantages of low electrical conductivity and volume change. However, as a precursor of preparing cobalt oxides, Co(OH)₂ has rarely been investigated as the anode material of lithium-ion batteries, perhaps because of the complexity of hydroxides. Hybridized Co(OH)₂ nanomaterial structures were synthesized by the water bath method and exhibited high electrochemical performance. The initial discharge and charge capacities were 1703.2 and 1262.9 mAh/g at 200 mA/g, respectively. The reversible capacity was 1050 mAh/g after 150 cycles. The reversible capability was 1015 mAh/g at 800 mA/g and increased to 1630 mAh/g when driven back to 100 mA/g. The electrochemical reaction kinetics study shows that the lithium-ion diffusion-controlled contribution is dominant in the energy storage mechanism. The superior electrochemical performance could result from the water bath method and the hybridization of nanosheets and nanoparticles structures. These hybridized Co(OH)₂ nanomaterial structures with high electrochemical performance are promising anodes for lithium-ion batteries.

Numerous experiments have revealed that fullerene (C₆₀) and its derivatives can bind to proteins and affect their biological functions. In this study, we explored the interaction between fullerine and the β₂-adrenergic receptor (β₂AR). The MD simulation results show that fullerene binds with the extracellular loop 2 (ECL2) and intracellular loop 2 (ICL2) of β₂AR through hydrophobic interactions and π–π stacking interactions. In the C₆₀_in1 trajectory, due to the π–π stacking interactions of fullerene molecules with PHE and PRO residues on ICL2, ICL2 completely flipped towards the fullerene direction and the fullerene moved slowly into the lipid membrane. When five fullerene molecules were placed on the extracellular side, they preferred to stack into a stable fullerene cluster (a deformed tetrahedral aggregate), and had almost no effect on the structure of β₂AR. The hydroxyl groups of fullerene derivatives (C₆₀(OH)_X, X represents the number of hydroxyl groups, X = 4, 8) can form strong hydrogen bonds with the ECL2, helix6, and helix7 of β₂AR. The hydroxyl groups firmly grasp the β₂AR receptor like several claws, blocking the binding entry of ligands. The simulation results show that fullerene and fullerene derivatives may have a significant effect on the local structure of β₂AR, especially the distortion of helix4, but bring about no great changes within the overall structure. It was found that C₆₀ did not compete with ligands for binding sites, but blocked the ligands’ entry into the pocket channel. All the above observations suggest that fullerene and its derivatives exhibit certain cytotoxicity.

The damaged and deformed fruits of fresh berries severely restrict the economic value of produce, and accurate identification and grading methods have become a global research hotspot. To address the challenges of rapid and accurate defect detection in intelligent cherry sorting systems, this study proposes an enhanced YOLOv8n-based framework for sweet cherry defect identification. First, the dilation-wise residual (DWR) module replaces the conventional C2f structure, allowing for the adaptive capture of both local and global features through multi-scale convolution. This enhances the recognition accuracy of subtle surface defects and large-scale damages on cherries. Second, a channel attention feature fusion mechanism (CAFM) is incorporated at the front end of the detection head, which enhances the model’s ability to identify fine defects on the cherry surface. Additionally, to improve bounding box regression accuracy, powerful-IoU (PIoU) replaces the traditional CIoU loss function. Finally, self-distillation technology is introduced to further improve the mode’s generalization capability and detection accuracy through knowledge transfer. Experimental results show that the YOLOv8-DCPF model achieves precision, mAP, recall, and F1 score rates of 92.6%, 91.2%, 89.4%, and 89.0%, respectively, representing improvements of 6.9%, 5.6%, 6.1%, and 5.0% over the original YOLOv8n baseline network. The proposed model demonstrates high accuracy in cherry defect detection, providing an efficient and precise solution for intelligent cherry sorting in agricultural engineering applications.

With the extension of the applications of sandwich panels with corrugated core, sound insulation performance has been a great concern for acoustic comfort design in many industrial fields. This paper presents a numerical and experimental study on the vibro-acoustic optimization of a finite size sandwich panel with corrugated core for maximizing the sound transmission loss. The numerical model is established by using the wave-based method, which shows a great improvement in the computational efficiency comparing to the finite element method. Constrained by the fundamental frequency and total mass, the optimization is performed by using a genetic algorithm in three different frequency bands. According to the optimization results, the frequency averaged sound transmission of the optimized models in the low, middle, and high-frequency ranges has increased, respectively, by 7.6 dB, 7.9 dB, and 11.7 dB compared to the baseline model. Benefiting from the vast number of the evolution samples, the correlation between the structural design parameters and the sound transmission characteristics is analyzed by introducing the coefficient of determination, which gives the variation of the importance of each design parameter in different frequency ranges. Finally, for validation purposes, a sound insulation test is conducted to validate the optimization results in the high-frequency range, which proves the feasibility of the optimization method in the practical engineering design of the sandwich panel.

Apple leaf diseases without timely control will affect fruit quality and yield, intelligent detection of apple leaf diseases was especially important. So this paper mainly focuses on apple leaf disease detection problem, proposes a machine vision algorithm model for fast apple leaf disease detection called LALNet (High-speed apple leaf network). First, an efficient sacked module for apple leaf detection, known as EALD (efficient apple leaf detection stacking module), was designed by utilizing the multi-branch structure and depth-separable modules. In the backbone network of LALNet, (High-speed apple leaf network) four layers of EALD modules were superimposed and an SE(Squeeze-and-Excitation) module was added in the last layer of the model to improve the attention of the model to important features. A structural reparameterization technique was used to combine the outputs of two layers of deeply separable convolutions in branch during the inference phase to improve the model’s operational speed. The results show that in the test set, the detection accuracy of the model was 96.07%. The total precision was 95.79%, the total recall was 96.05%, the total F1 was 96.06%, the model size was 6.61 MB, and the detection speed of a single image was 6.68 ms. Therefore, the model ensures both high detection accuracy and fast execution speed, making it suitable for deployment on embedded devices. It supports precision spraying for the prevention and control of apple leaf disease.