ObjectivesTo compare the accuracy of an original and two newly designed CAD/CAM scan bodies used in digital impressions with one another as well as conventional implant impressions.Material and methodsA reference model containing four implants was fabricated. Digital impressions were taken using an intraoral scanner with different scan bodies: original scan bodies for Group I (DO), CAD/CAM scan bodies without extensional structure for Group II (DC), and CAD/CAM scan bodies with extensional structure for Group III (DCE). For Group IV, conventional splinted open‐tray impressions (CI) were taken. The reference model and conventional stone casts were digitalized with a laboratory reference scanner. The Standard Tessellation Language datasets were imported into an inspection software for trueness and precision assessment. Statistical analysis was performed with a Kruskal–Wallis test and Dunn–Bonferroni test. The level of significance was set at α = .05.ResultsThe median of trueness was 35.85, 38.50, 28.45, and 25.55 μm for Group I, II, III, and IV, respectively. CI was more accurate than DO (p = .015) and DC (p = .002). The median of precision was 48.40, 48.90, 27.30, and 19.00 for Group I, II, III, and IV, respectively. CI was more accurate than DO (p < .001), DC (p < .001), and DCE (p = .007). DCE was more accurate than DC (p < .001) and DO (p < .001).ConclusionsThe design of the extensional structure could significantly improve scanning accuracy. Conventional splinted open‐tray impressions were more accurate than digital impressions for full‐arch implant rehabilitation.

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Abstract Early warning of debris flow is one of the core contents of disaster prevention and mitigation work for debris flow disasters. There are few early warning methods based on the combination of rainfall threshold and geological environment conditions. In this paper, we presented an early warning method for debris flow based on infinite irrelevance method (IIM) and self-organizing feature mapping (SOFM), and applied it to Liaoning Province, China. The proposed model consisted of three stages. Firstly, eight geological environmental conditions and two rainfall-inducing conditions were selected by analyzing the factors affecting the development of debris flow in the study area, the rainfall threshold for debris flow outbreak was 150 mm. Secondly, the correlation between various factors was analyzed by IIM, which prevented the blindness of parameter selection and improved the prediction accuracy of the model. Finally, the SOFM was employed to predict the test data. Experimental results showed that the IIM-SOFM model had a strong early warning ability. When 25 samples of low- frequency debris flow area were selected, the accuracy rate of IIM-SOFM model with optimized network structure parameters was 100%, which it was obviously superior to rainfall threshold method, BP neural network and competitive neural network. Consequently, it is feasible to use IIM-SOFM model to early warning for debris flow, outperforming traditional machine learning methods.

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