Automatic labeling algorithms and their applications in weld seam of oil pipeline

Building information model (BIM) technology has become an important tool for construction practitioners to improve the engineering design, construction and management. However, it has not been widely used in petrochemical industry, such as oil pipeline construction. This paper presents an automatic labeling algorithm based on BIM technology. We used simulated annealing algorithm to optimize the overlap of pipe weld label positions, resulting in the sequential pipe welds. We used this algorithm in oil pipeline construction and verified its automatic labeling effect on pipe welds

Development and validation of a cancer cachexia risk score for digestive tract cancer patients before abdominal surgery

Abstract Background Cancer cachexia is prevalent in digestive tract cancer patients and has significant impacts on prognosis; it is vital to identify individuals who are at risk of cancer cachexia to allow for appropriate evaluation and treatment. This study evaluated whether digestive tract cancer patients with a risk of cancer cachexia and who had a risk of adverse survival could be identified before abdominal surgery. Methods This large‐scale cohort study involved patients who underwent abdominal surgery between January 2015 and December 2020 to treat digestive tract cancer. Participants were allocated to the development cohort, the validation cohort, or the application cohort. Univariate and multivariate analyses of the development cohort were performed to detect distinct risk variables for cancer cachexia to create a cancer cachexia risk score. The performance of the risk score across all the three cohorts was assessed through calculating the area under the receiver operating characteristic curve (AUC), as well as calibration and decision curves. We tested how well the score predicted survival outcomes in the application cohort. Results A total of 16 264 patients (median 64 years of age; 65.9% male) were included, with 8743 in the development cohort, 5828 in the validation cohort, and 1693 in the application cohort. Seven variables were identified as independent predictive factors and were included in the cancer cachexia risk score: cancer site, cancer stage, time from symptom onset to hospitalization, appetite loss, body mass index, skeletal muscle index, and neutrophil‐lymphocyte ratio. The risk score predicting cancer cachexia owns a good discrimination, with the mean AUC of 0.760 (P  0.05). The decision curve analysis revealed net benefits of the risk score across a range of risk thresholds in the three cohorts. In the application cohort, compared with the high‐risk group, the low‐risk group experienced significantly longer overall survival [hazard ratio (HR) 2.887, P < 0.001] as well as relapse‐free survival (HR 1.482, P = 0.01). Conclusions The cancer cachexia risk score constructed and validated demonstrated good performance in identifying those digestive tract cancer patients before abdominal surgery at a higher risk of cancer cachexia and unfavourable survival. This risk score can help clinicians to enhance their capabilities to screen for cancer cachexia, assess patient prognosis, and strengthen early decision‐making on targeted approaches to attune cancer cachexia for digestive tract cancer patients before abdominal surgery