Search

The galvanic dissolved oxygen sensor finds widespread applications in multiple critical fields due to its high precision and excellent stability. As its core sensing components, the oxygen-permeable membrane, electrode, and electrolyte significantly impact the sensor's performance. To systematically investigate the comprehensive effects of these core sensing components on the performance of galvanic dissolved oxygen sensors, this study selected six types of oxygen-permeable membranes made from two materials (Perfluoroalkoxy Polymer (PFA) and Fluorinated Ethylene Propylene Copolymer (FEP)) with three thicknesses (0.015 mm, 0.03 mm, and 0.05 mm). Additionally, five concentrations of KCl electrolyte were configured, and four different proportions of lead–tin alloy electrodes were chosen. Single-factor and crossover experiments were conducted using the OxyGuard dissolved oxygen sensor as the experimental platform. The experimental results indicate that under the same membrane thickness conditions, PFA membranes provide a higher output voltage compared to FEP membranes. Moreover, the oxygen permeability of FEP membranes is more significantly affected by temperature. Furthermore, the oxygen permeability of the membrane is inversely proportional to its thickness; the thinner the membrane, the better the oxygen permeability, resulting in a corresponding increase in sensor output voltage. When the membrane thickness is reduced from 0.05 mm to 0.015 mm, the sensor output voltage for PFA and FEP membranes increases by 86% and 74.91%, respectively. However, this study also observed that excessively thin membranes might compromise measurement accuracy. In a saturated, dissolved oxygen environment, the sensor output voltage corresponding to the six oxygen-permeable membranes used in the experiment exhibits a highly linear inverse relationship with temperature (correlation coefficient ≥ 98%). Meanwhile, the lead–tin ratio of the electrode and electrolyte concentration have a relatively minor impact on the sensor output voltage, demonstrating good stability at different temperatures (coefficient of variation ≤ 0.78%). In terms of response time, it is directly proportional to the thickness of the oxygen-permeable membrane, especially for PFA membranes. When the thickness increases from 0.015 mm to 0.05 mm, the response time extends by up to 2033.33%. In contrast, the electrode material and electrolyte concentration have a less significant effect on response time. To further validate the practical value of the experimental results, the best-performing combination of core sensing components from the experiments was selected to construct a new dissolved oxygen sensor. A performance comparison test was conducted between this new sensor and the OxyGuard dissolved oxygen sensor. The results showed that both sensors had the same response time (49 s). However, in an anaerobic environment, the OxyGuard sensor demonstrated slightly higher accuracy by 2.44%. This study not only provides a deep analysis of the combined effects of oxygen-permeable membranes, electrodes, and electrolytes on the performance of galvanic dissolved oxygen sensors but also offers scientific evidence and practical guidance for optimizing sensor design. [ABSTRACT FROM AUTHOR]

Background: The main application of [18F] FDG‐PET (18FDG‐PET) and CT images in oncology is tumor identification and quantification. Combining PET and CT images to mine pulmonary perfusion information for functional lung avoidance radiation therapy (FLART) is desirable but remains challenging. Purpose: To develop a deep‐learning‐based (DL) method to combine 18FDG‐PET and CT images for producing pulmonary perfusion images (PPI). Methods: Pulmonary technetium‐99 m‐labeled macroaggregated albumin SPECT (PPISPECT), 18FDG‐PET, and CT images obtained from 53 patients were enrolled. CT and PPISPECT images were rigidly registered, and registration displacement was subsequently used to align 18FDG‐PET and PPISPECT images. The left/right lung was separated and rigidly registered again to improve the registration accuracy. A DL model based on 3D Unet architecture was constructed to directly combine multi‐modality 18FDG‐PET and CT images for producing PPI (PPIDLM). 3D Unet architecture was used as the basic architecture, and the input was expanded from a single‐channel to a dual‐channel to combine multi‐modality images. For comparative evaluation, 18FDG‐PET images were also used alone to generate PPIDLPET. Sixty‐seven samples were randomly selected for training and cross‐validation, and 36 were used for testing. The Spearman correlation coefficient (rs) and multi‐scale structural similarity index measure (MS‐SSIM) between PPIDLM/PPIDLPET and PPISPECT were computed to assess the statistical and perceptual image similarities. The Dice similarity coefficient (DSC) was calculated to determine the similarity between high‐/low‐ functional lung (HFL/LFL) volumes. Results: The voxel‐wise rs and MS‐SSIM of PPIDLM/PPIDLPET were 0.78 ± 0.04/0.57 ± 0.03, 0.93 ± 0.01/0.89 ± 0.01 for cross‐validation and 0.78 ± 0.11/0.55 ± 0.18, 0.93 ± 0.03/0.90 ± 0.04 for testing. PPIDLM/PPIDLPET achieved averaged DSC values of 0.78 ± 0.03/0.64 ± 0.02 for HFL and 0.83 ± 0.01/0.72 ± 0.03 for LFL in the training dataset and 0.77 ± 0.11/0.64 ± 0.12, 0.82 ± 0.05/0.72 ± 0.06 in the testing dataset. PPIDLM yielded a stronger correlation and higher MS‐SSIM with PPISPECT than PPIDLPET (p < 0.001). Conclusions: The DL‐based method integrates lung metabolic and anatomy information for producing PPI and significantly improved the accuracy over methods based on metabolic information alone. The generated PPIDLM can be applied for pulmonary perfusion volume segmentation, which is potentially beneficial for FLART treatment plan optimization. [ABSTRACT FROM AUTHOR]

In order to reduce the application dosage of pesticides, a targeting air-assisted (TAA) sprayer was developed and tested in this study. Fruit trees were assayed by an infrared detection system to determine if the canopy needs to be sprayed. This TAA sprayer was compared with conventional air-assisted (CAA) sprayers, and the impacts of various fan speeds (0, 800 r/min, 1300 r/min, and 1800 r/min) on spray deposition, coverage, and drift amount were tested. Ponceau 2R was used as tracer to measure spray deposition under each treatment. Droplet coverage and canopy deposition were best when the CAA application fan speed was increased to 1300 r/min, but at higher fan speeds, spray deposition and coverage in canopy did not increase because extra air flow blew droplets from the ground into the air. During TAA spraying, droplet sizes increased at opening and closing moments. Optimal spray effects were achieved when the auxiliary airflow velocity was increased at a fan speed of 1800 r/min. The research provides a useful reference for the design of TAA and parameters optimization method with respect to the relationship between droplets deposition into tree canopy, ground and drift in the air. [ABSTRACT FROM AUTHOR]

OBJECTIVE. The purpose of this study was to develop and validate a radiomics model for evaluating immunohistochemical characteristics in patients with suspected thyroid nodules. MATERIALS AND METHODS. A total of 103 patients (training cohort-to-validation cohort ratio, ≈ 3:1) with suspected thyroid nodules who had undergone thyroidectomy and immunohistochemical analysis were enrolled. The immunohistochemical markers were cytokeratin 19, galectin 3, thyroperoxidase, and high-molecular-weight cytokeratin. All patients underwent CT before surgery, and a 3D slicer was used to analyze images of the surgical specimen. Test-retest and Spearman correlation coefficient (ρ) were used to select reproducible and nonredundant features. The Kruskal-Wallis test ( p < 0.05) was used for feature selection, and a feature-based model was built by support vector machine methods. The performance of the radiomic models was assessed with respect to accuracy, sensitivity, specificity, corresponding AUC, and independent validation. RESULTS. Eighty-six reproducible and nonredundant features selected from the 828 features were used to build the model. The best performance of the cytokeratin 19 model yielded accuracy of 84.4% in the training cohort and 80.0% in the validation cohort. The thyroperoxidase and galectin 3 predictive models yielded accuracies of 81.4% and 82.5% in the training cohort and 84.2% and 85.0% in the validation cohort. The performance of the high-molecular-weight cytokeratin predictive model was not good (accuracy, 65.7%) and could not be validated. CONCLUSION. A radiomics model with excellent performance was developed for individualized noninvasive prediction of the presence of cytokeratin 19, galectin 3, and thyroperoxidase based on CT images. This model may be used to identify benign and malignant thyroid nodules.