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Canopy characterization detection is essential for target-oriented spray, which minimizes pesticide residues in fruits, pesticide wastage, and pollution. In this study, a novel canopy meshing-profile characterization (CMPC) method based on light detection and ranging (LiDAR)point-cloud data was designed for high-precision canopy volume calculations. First, the accuracy and viability of this method were tested using a simulated canopy. The results show that the CMPC method can accurately characterize the 3D profiles of the simulated canopy. These simulated canopy profiles were similar to those obtained from manual measurements, and the measured canopy volume achieved an accuracy of 93.3%. Second, the feasibility of the method was verified by a field experiment where the canopy 3D stereogram and cross-sectional profiles were obtained via CMPC. The results show that the 3D stereogram exhibited a high degree of similarity with the tree canopy, although there were some differences at the edges, where the canopy was sparse. The CMPC-derived cross-sectional profiles matched the manually measured results well. The CMPC method achieved an accuracy of 96.3% when the tree canopy was detected by LiDAR at a moving speed of 1.2 m/s. The accuracy of the LiDAR system was virtually unchanged when the moving speeds was reduced to 1 m/s. No detection lag was observed when comparing the start and end positions of the cross-section. Different CMPC grid sizes were also evaluated. Small grid sizes (0.01 m × 0.01 m and 0.025 m × 0.025 m) were suitable for characterizing the finer details of a canopy, whereas grid sizes of 0.1 m × 0.1 m or larger can be used for characterizing its overall profile and volume. The results of this study can be used as a technical reference for the development of a LiDAR-based target-oriented spray system.

Variable application by wind is an efficient application technology recommended by the Food and Agriculture Organization (FAO) of the United Nations that can effectively improve the deposition effect of liquid medicine in a canopy and reduce droplet drift. In view of the difficulty of modelling wind forces in orchard tree canopies and the lack of a wind control model, the wind loss model for a canopy was studied. First, a three-dimensional wind measurement test platform was built for an orchard tree canopy. The orchard tree was located in three-dimensional space, and the inner leaf areas of the orchard tree canopy and the wind force in different areas were measured. Second, light detection and ranging (LiDAR) point cloud data of the orchard tree canopy were obtained by LiDAR scanning. Finally, classic regression, partial least squares regression (PLSR), and back propagation (BP) neural network algorithms were used to build wind loss models in the canopy. The research showed that the BP neural network algorithm can significantly improve the fitting accuracy of the model. Under different fan speeds of 1,381 r/min, 1,502 r/min, and 1,676 r/min, the coefficient of determination (R 2 ) of the model were 81.78, 72.85, and 69.20%, respectively, which were 19.38, 7.55, and 12.3% higher than those of the PLSR algorithm and 21.48, 22.25, and 24.3% higher than those of multiple regression analysis. The comparison showed that the BP neural network algorithm obtains the highest model accuracy, but because the model is not intuitive, PLSR has the advantages of intuitive and simple models in the three algorithms. In practical applications, the wind loss model based on a BP neural network or PLSR can be selected according to the operational requirements and software and hardware conditions. This study can provide a basis for wind control in precise variable spraying and promote the development of wind control technologies.

High-air temperature stress inhibits the growth of hydroponic lettuce. The practical application of conventional air cooling is constrained by high cost and moderate efficacy. However, root-zone cooling represents a more promising temperature regulation strategy for vegetable production, offering advantages such as ease of integration and lower cost. This study used lettuce (Spanish Green) as the plant material under four RZT treatments: T0 (control: 24.65~31.65 °C), T1 (24.5 °C), T2 (20.5 °C), and T3 (16.5 °C). Growth parameters and nutritional quality indicators under each treatment were systematically monitored, and a comprehensive evaluation was performed using the fuzzy membership function method. All cooling treatments (T1–T3) enhanced lettuce plant height, leaf area, and shoot dry weight. According to the fuzzy membership function analysis, the T1 treatment was found to exhibit the highest overall nutritional value. Although the T0 control group displayed the poorest growth performance, with a shoot dry weight 47.24% lower than that of T1, it accumulated significantly higher levels of P, Ca, and Zn. These findings demonstrate that regulating RZT to approximately 24.5 °C synergistically enhances both biomass and quality in lettuce, providing theoretical and practical support for optimizing hydroponic production in summer conditions.

Different fruit tree canopies have different requirements for air speed and air volume. Due to the strong relationship between air speed and air volume, the decoupled control of air speed and air volume cannot be achieved using the existing sprayers. In this study, an innovative air-assisted sprayer that supports the independent adjustment of fan speed (0-2940 r/min) and air outlet area (1022.05-2248.51 cm 2 ) is developed, and the maximum air speed and air volume of the sprayer outlet are 45.98 m/s and 37239.94 m 3 /h, respectively. An independent adjustment test of the fan speed and air outlet area was carried out. The results indicated that the fan speed and air outlet area have opposing adjustment effects on air speed and air volume; decoupled control of the outlet air speed and air volume can thus be achieved through combined control of the fan speed and air outlet area. A test was carried out on combined fan speed and air outlet area control. Two decoupled air speed and air volume adjustment models were established, one with a constant air speed and variable air volume and the other with a constant air volume and variable air speed. The test results show that the air volume adjustment model with constant air speed had a maximum mean error of 1.13%, and the air speed adjustment model with constant air volume had a maximum mean error of 1.67%. The results will provide theoretical and methodological support for the development of airflow adjustment systems for orchard air-assisted sprayer.

Obstructive sleep apnea-hypopnea syndrome (OSAHS) can lead to cognitive impairment, however, its central neural mechanism is still unclear. Resting-state functional magnetic resonance imaging (rs-fMRI) data were collected from 31 severe OSAHS patients and 28 healthy controls (HCs). Both regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuation (fALFF) measures were calculated and compared between groups. Moreover, the correlations between abnormal regions and Montreal Cognitive Assessment (MoCA) scores were evaluated using Pearson correlation analysis. Finally, ROC analysis was performed to evaluate the values of abnormal brain regions for distinguishing OSAHS patients from HCs. OSAHS patients had decreased MoCA scores when compared with HCs. In comparison with HCs, increased ReHo values were found in the left and right rolandic operculum of OSAHS patients. In addition, decreased fALFF values were identified in the right superior frontal gyrus, right postcentral gyrus, left angular gyrus while increased fALFF values were identified in the left thalamus, right thalamus and right putamen of OSAHS patients. Positive relationships were found between fALFF values of the right superior frontal gyrus and MoCA scores in the patient group. The results of ROC analysis showed that the combined model of (ReHo and fALFF values of all abnormal brain regions) could effectively distinguish OSAHS from HCs. Severe OSAHS patients showed decreased brain activities, which were associated with the decreased cognition of patients. In addition, abnormal brain regions could help distinguishing OSAHS patients from HCs. These findings provided new insights about the potential pathogenesis of cognitive impairment caused by OSAHS from the perspective of changes in brain activity.

Nanoparticles (NPs) decorated with functional ligands are promising candidates for cancer diagnosis and treatment. However, numerous studies have shown that chemically coupled targeting moieties on NPs lose their targeting capability in the biological milieu because they are shielded or covered by a \"protein corona\". Herein, we construct a functional magnetosome that recognizes and targets cancer cells even in the presence of protein corona. Magnetosomes (BMPs) were extracted from magnetotactic bacteria, (MSR-1), and decorated with trastuzumab (TZ) via affibody (RA) and glutaraldehyde (GA). The engineered BMPs are referred to as BMP-RA-TZ and BMP-GA-TZ. Their capacities to combine HER2 were detected by ELISA, the quantity of plasma corona proteins was analyzed using LC-MS. The efficiencies of targeting SK-BR-3 were demonstrated by confocal laser scanning microscopy and flow cytometry. Both engineered BMPs contain up to ~0.2 mg TZ per mg of BMP, while the quantity of HER2 binding to BMP-RA-TZ is three times higher than that binding to BMP-GA-TZ. After incubation with normal human plasma or IgG-supplemented plasma, GA-TZ-containing BMPs have larger hydrated radii and more surface proteins in comparison with RA-TZ-containing BMPs. The TZ-containing BMPs all can be targeted to and internalized in the HER2-overexpressing breast cancer cell line SK-BR-3; however, their targeting efficiencies vary considerably: 50-75% for RA-TZ-containing BMPs and 9-19% for GA-TZ-containing BMPs. BMPs were incubated with plasma (100%) and cancer cells to simulate human in vivo environment. In this milieu, BMP-RA-TZ uptake efficiency of SK-BR-3 reaches nearly 80% (slightly lower than for direct interaction with BMP-RA-TZ), whereas the BMP-GA-TZ uptake efficiency is <17%. Application of the RA scaffold promotes and orients the arrangement of targeting ligands and reduces the shielding effect of corona proteins. This strategy improves the targeting capability and drug delivery of NP in a simulated in vivo milieu.

Orchard wind-assisted spraying technology relies on auxiliary airflow to disturb the canopy and improve droplet deposition uniformity. However, there are few effective means of quantitatively assessing the dynamic response of fruit tree leaves to airflow or the changes in airflow patterns within the canopy in real time. To address this, this study proposed an online monitoring method for the aerodynamic characteristics of fruit tree leaves using strain gauge sensors. The flexible strain gauge was affixed to the midribs of leaves from peach, pear and apple trees. Leaf deformations were captured with high-speed video recording (100 fps) alongside electrical signals in controlled wind fields. Bartlett low-pass filtering and Fourier transform were used to extract frequency-domain features spanning between 0 and 50 Hz. The AdaBoost decision tree model was used to evaluate classification performance across frequency bands. The results demonstrated high accuracy in identifying wind exposure (98%) for pear leaf and classifying the three leaf types (κ = 0.98) within the 4–6 Hz band. A comparison with the frame analysis of high-speed video recordings revealed a time error of 2 s in model predictions. This study confirms that strain gauge sensors combined with machine learning could efficiently monitor fruit tree leaf responses to external airflow in real time. It provides novel insights for optimizing wind-assisted spray parameters, reconstructing internal canopy wind field distributions and achieving precise pesticide application.

Air-assisted spraying is the primary method of plant protection in orchards, and precision spraying according to the canopy characteristics of fruit trees can reduce waste and pollution due to pesticide drift. To facilitate targeted pesticide application in the canopy of fruit trees, this study employed a newly developed wind-speed-adjustable orchard sprayer and established a prediction model for deposition based on data from orthogonal trials using a central composite design accounting for the coupling effect of three-dimensional spatial parameters. The experimental design systematically quantified the interaction effects of spray distance (1.5–2.5 m), fan wind speed (10–20 m/s), and deposition height (0.5–3 m) on the spatial distribution of droplets. Model significance was p < 0.0001 and the misfit term was significant (p = 0.2193), supporting its validity. The research found that wind speed and distance significantly interact in influencing deposition. By adjusting fan speed and spray distance, variable applications can be achieved in different canopy zones during plant protection operations. The response surface model developed in this study can be applied to variable-rate spraying control systems, thus providing a quantitative basis for dynamic droplet control guided by canopy characteristics. Validation tests revealed that the model’s accuracy was lower in high canopy regions and upwind spraying scenarios, indicating areas for further research.

Orchard spraying can effectively control pests and diseases. Over-spraying commonly results in excessive pesticide residues on agricultural products and environmental pollution. To avoid these problems, variable spraying technology uses target canopy detection to evaluate the leaf area in a canopy and adjust the application rate accordingly. In this study, a mobile LiDAR detection platform was set up to automatically measure point cloud data for a thick canopy in an apple orchard. A test platform was built, and manual measurements of the canopy leaf area were taken. Then, polynomial regression, back propagation (BP) neural network regression, and partial least squares regression (PLSR) algorithms were used to study the relationship between the orchard tree canopy point clouds and leaf areas. The BP neural network algorithm (86.1% and 73.6% accuracies for the test and verification data, respectively) and the PLSR algorithm (78.46% and 60.3%, respectively) performed better than the Fourier function of the polynomial regression (59.73% accuracy). The leaf area model obtained using PLSR was intuitive and simple, while the BP neural network algorithm was more accurate and could meet the requirements for high-precision variable spraying.

Objective Postinfection cough (PIC) is a lingering cough that occurs after the resolution of a primary respiratory infection, affecting the quality of life and potentially leading to complications, thus necessitating effective treatment. Qingfei Zhisou syrup, which has heat‐clearing, cough‐relieving, and phlegm‐resolving properties, has demonstrated a mitigating effect on PIC. However, the exact mechanisms are largely unknown. The purpose of this study is to investigate the effect of Qingfei Zhisou syrup on the molecular mechanism of PIC. Methods A total of 105 Sprague–Dawley (SD) male rats were used to establish a PIC model via nasal drops of lipopolysaccharide (LPS), smoke, and capsaicin, which were divided into the normal group, model group, western medicine positive drugs dextromethorphan hydrobromide syrup group (3.50 mL/kg), and traditional Chinese medicine positive drugs Jinzhen oral liquid group (5.41 mL/kg), and the high‐, medium‐, and low‐dose children’s Qingfei Zhisou syrup groups (3.66, 1.83, and 0.92 g/kg). The drug was given by continuous intragastric administration at 10 mL/kg for 10 days. The cough sensitivity of rats was determined by capsaicin aerosol‐induced cough, the morphological changes of lung tissue were observed by hematoxylin–eosin staining, and the levels of inflammatory factors tumor necrosis factor‐α (TNF‐α), interleukin‐1β (IL‐1β), and interleukin‐6 (IL‐6) in the homogenate of lung tissue were determined by enzyme‐linked immunosorbent assay. According to the previous literature, the expression levels of transient receptor potential Vanilloid 1 (TRPV1), prostaglandin E Receptor 2 (PTGER2), and protein Kinase A (PKA) were evaluated by immunohistochemistry (IHC), western blot (WB) analysis, and quantitative polymerase chain reaction using TRPV1 as the entry point, combined with key genes and pathway proteins screened by network pharmacology and molecular docking. Results Network analysis predicted Qingfei Zhisou syrup might relieve PIC symptoms through key target genes IL‐1β, IL‐6, and TNF‐α and the action of neuroactive ligand–receptor and control the reaction of LPS and drugs. Animal experiments have shown that Qingfei Zhisou syrup for children decreased cough sensitivity, alleviated pathological changes in rat lung tissues, and inhibited IL‐1β, IL‐6, and TNF‐α levels in lung tissue. Molecular‐level studies revealed that children’s Qingfei Zhisou syrup decreased the expression of TRPV1, PTGER2, and PKA. Conclusion This study shows that pediatric Qingfei Zhisou syrup can inhibit key target genes IL‐1β, IL‐6, and TNF‐α to reduce inflammation, control LPS capsaicin reaction, and affect the TRPV1 channel through the PGE2‐E2 receptor‐PKA pathway to ameliorate PIC. These findings provide a scientific evidence for the use of children’s Qingfei Zhisou syrup in the treatment of PIC.