Weld Seam Tracking and Detection Robot Based on Artificial Intelligence Technology.

The regular detection of weld seams in large-scale special equipment is crucial for improving safety and efficiency, and this can be achieved effectively through the use of weld seam tracking and detection robots. In this study, a wall-climbing robot with integrated seam tracking and detection was designed, and the wall climbing function was realized via a permanent magnet array and a Mecanum wheel. The function of weld seam tracking and detection was realized using a DeepLabv3+ semantic segmentation model. Several optimizations were implemented to enhance the deployment of the DeepLabv3+ semantic segmentation model on embedded devices. Mobilenetv2 was used to replace the feature extraction network of the original model, and the convolutional block attention module attention mechanism was introduced into the encoder module. All traditional 3×3 convolutions were substituted with depthwise separable dilated convolutions. Subsequently, the welding path was fitted using the least squares method based on the segmentation results. The experimental results showed that the volume of the improved model was reduced by 92.9%, only being 21.8 Mb. The average precision reached 98.5%, surpassing the original model by 1.4%. The reasoning speed was accelerated to 21 frames/s, satisfying the real-time requirements of industrial detection. The detection robot successfully realizes the autonomous identification and tracking of weld seams. This study remarkably contributes to the development of automatic and intelligent weld seam detection technologies.

Exploring the relationship between age and prognosis in glioma: rethinking current age stratification.

BACKGROUND: The age of glioma plays a unique role in prognosis. We hypothesized that age is not positively correlated with survival prognosis and explored its exact relationship. METHODS: Glioma was identified from the SEER database (between 2000 and 2018). A multivariate Cox proportional regression model and restricted cubic spline (RCS) plot were used to assess the relationship between age and prognosis. RESULTS: A total of 66465 patients with glioma were included. Hazard ratios (HR) for ten-year by age: 0-9 years, HR 1.06 (0.93-1.20); 10-19 years: reference; 20-29 years, HR 0.90 (0.82-1.00); 30-39 years, HR 1.14 (1.04-1.25); 40-49 years, HR 2.09 (1.91-2.28); 50-59 years, HR 3.48 (3.19-3.79); 60-69 years, HR 4.91 (4.51-5.35);70-79 years, HR 7.95 (7.29-8.66); 80-84 years, HR 12.85 (11.74-14.06). After adjusting for covariates, the prognosis was not positively correlated with age. The smooth curve of RCS revealed this non-linear relationship: HR increased to 10 years first, decreased to 23 years, reached its lowest point, and became J-shaped. CONCLUSION: The relationship between age and glioma prognosis is non-linear. These results challenge the applicability of current age groupings for gliomas and advocate the consideration of individualized treatment guided by precise age.

Development and Validation of Prognostic Nomogram in Patients With WHO Grade III Meningioma: A Retrospective Cohort Study Based on SEER Database.

INTRODUCTION: World Health Organization (WHO) Grade III meningioma is a central nervous system tumor with a poor prognosis. In this retrospective cohort study, the authors constructed a nomogram for predicting the prognosis of WHO Grade III meningioma. METHODS: The patients of this nomogram were based on the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2018. All patients were randomly divided into a development cohort (964 patients) and a validation cohort (410 patients) in a 7:3 ratio. The least absolute shrinkage and selection operator (LASSO) regression was used to screen the predictors. The Cox hazards regression model was constructed and the prognosis was visualized by nomogram. The performance of the prognostic nomogram was determined by consistency index (C-index), clinical net benefit, and calibration. RESULTS: Eight variables were included in the nomogram: gender, race, age at diagnosis, histology, tumor site, tumor size, laterality, and surgical method. The C-index of the training set and verification set were 0.654 and 0.628. The calibration plots showed that the nomogram was in good agreement with the actual observation. The clinical decision curve indicates that the nomogram has a good clinical net benefit in WHO Grade III meningioma. CONCLUSIONS: A prognostic nomogram of a large cohort of WHO Grade III meningioma was established and verified based on the SEER database. The nomogram we established may help clinicians provide personalized treatment services and clinical decisions for patients.

Development and validation of prognostic nomogram in ependymoma: A retrospective analysis of the SEER database.

BACKGROUND: The prognostic factors for survival in patients with ependymoma (EPN) remain controversial. The aim of this study was to establish a prognostic model for 5- and 10-year survival probability nomograms for patients with EPN. METHODS: Clinical data from the Surveillance, Epidemiology, and End Results (SEER) database were used for patients diagnosed with ependymoma between 2000 and 2018 and were randomized 7:3 into a development set and a validation set. Factors significantly associated with prognosis were screened out using the least absolute shrinkage and selection operator (LASSO) regression. The calibration chart and consistency index (C-index) are used to evaluate the discrimination and consistency of the prediction model. Decision curve analysis (DCA) was used to further evaluate the established model. Finally, prognostic factors selected by LASSO regression were evaluated using Kaplan-Meier (KM) survival curves. RESULTS: A total of 3820 patients were included in the prognostic model. Seven survival predictors were obtained by LASSO regression screening, including age, gender, morphology, location, size, laterality, and resection. The prognostic model of the nomogram showed moderate discriminative ability in the development group and the validation group, with a C-index of 0.642 and 0.615, respectively. In the development set and validation set survival curves, the prognosis index of high risk was less effective than low risk (p < 0.001). CONCLUSIONS: Our nomograms may play an important role in predicting 5 and 10-year outcomes for patients with ependymoma. This will help assist clinicians in personalized medicine.