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To increase coalbed methane extraction, sealing material features are examined and adjusted to improve borehole sealing. This research developed a cement-based hole sealing material employing silicate cement (PC) and sulfoaluminate cement (SAC) as the substrate, nano-SiO2as the primary ingredient, and fly ash, plastic expander, and water reducer as additives. Orthogonal tests examined the impacts of these additives on PC-SAC compounded cementitious pore sealing material's fluidity, setting time, and compressive strength, while XRD, TGA, SEM, and BET examined the hydration process and products. Results show that nano-SiO2and fly ash have a better synergistic effect, effectively improve compressive strength, microstructure, and harmful holes; through the orthogonal test method to get the optimal group, the optimal group compared to the comparison group strength increased by 18.52 Mpa, the expansion rate increased by 12.9%; and through the engineering field t The engineering field test shows that the self-developed substance extracts gas 3.5% more efficiently than the mining material.

BackgroundUltrasound imaging is suitable for detecting and diagnosing ophthalmic abnormalities. However, a shortage of experienced sonographers and ophthalmologists remains a problem. This study aims to develop a multibranch transformer network (MBT-Net) for the automated classification of multiple ophthalmic diseases using B-mode ultrasound images.MethodsUltrasound images with six clinically confirmed categories, including normal, retinal detachment, vitreous haemorrhage, intraocular tumour, posterior scleral staphyloma and other abnormalities, were used to develop and evaluate the MBT-Net. Images were derived from five different ultrasonic devices operated by different sonographers and divided into training set, validation set, internal testing set and temporal external testing set. Two senior ophthalmologists and two junior ophthalmologists were recruited to compare the model’s performance.ResultsA total of 10 184 ultrasound images were collected. The MBT-Net got an accuracy of 87.80% (95% CI 86.26% to 89.18%) in the internal testing set, which was significantly higher than junior ophthalmologists (95% CI 67.37% to 79.16%; both p<0.05) and lower than senior ophthalmologists (95% CI 89.45% to 92.61%; both p<0.05). The micro-average area under the curve of the six-category classification was 0.98. With reference to comprehensive clinical diagnosis, the measurements of agreement were almost perfect in the MBT-Net (kappa=0.85, p<0.05). There was no significant difference in the accuracy of the MBT-Net across five ultrasonic devices (p=0.27). The MBT-Net got an accuracy of 82.21% (95% CI 78.45% to 85.44%) in the temporal external testing set.ConclusionsThe MBT-Net showed high accuracy for screening and diagnosing multiple ophthalmic diseases using only ultrasound images across mutioperators and mutidevices.

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