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Objective To evaluate predictive factors affecting the short-term efficacy of PD-1 inhibitors in non-small cell lung cancer (NSCLC) and to construct a prediction model. Methods From October 2019 to November 2021, 221 patients with advanced NSCLC who met the inclusion criteria and were treated with PD-1 inhibitors were prospectively enrolled. Patients who were enrolled before May 1st, 2021 were included inthe modeling group (n=149), whereas those who enrolled thereafter were included in the validation group (n=72). The general clinical data of patients, information of the four TCM diagnoses were collected, and TCM syndrome elements were identified. R software version 4.0.4 was used in constructing a nomogram clinical prediction model of objective response rate. The predictive ability and discrimination of the model were evaluated and externally validated by using a validation group. Results After two to four cycles of PD-1 inhibitor therapy in 221 patients, the overall objective response rate was 44.80%. Multivariate logistic regression analysis of the modeling group showed that the TPS score (OR=0.261, P=0.001), number of treatment lines (OR=3.749, P=0.002), treatment mode (OR=2.796, P=0.019), qi deficiency disease syndrome elements (OR=2.296, P=0.043), and syndrome elements of yin deficiency disease (OR=3.228, P=0.005) were the independent predictors of the short-term efficacy of PD-1 inhibitors. Based on the above five independent predictors, a nomogram prediction model for the short-term efficacy of PD-1 inhibitors was constructed. The AUC values of the modeling and validation groups were 0.8317 and 0.7535, respectively. The calibration curves of the two groups showed good agreement between the predicted and true values. The mean absolute errors were 0.053 and 0.039, indicating that the model has good predictive performance. Conclusion The nomogram model constructed on the basis of the syndrome elements of Qi-deficiency disease and Yin-deficiency syndrome of TCM, as well as TPS score, number of treatment lines and treatment mode, is a stable and effective tool for predicting the short-term efficacy of PD-1 inhibitors in advanced non-small cell lung cancer.

Objective To construct a nomogram prediction model for the treatment effect of anlotinib with the participation of traditional Chinese medicine syndrome elements on the patients with extensive-stage small cell lung cancer (ES-SCLC) who previously received multiple lines of chemotherapy. Methods The clinical data of 127 patients with ES-SCLC who received at least two cycles of anlotinib treatment were retrospectively studied. Kaplan-Meier method was used to analyze the relationship between each factor and the overall survival time. Cox regression analysis was applied to screen the independent influencing factors of the prognosis of patients with ES-SCLC. R language was employed to build a nomogram prediction model, C-index was used to evaluate the model, and calibration curve was adopted to verify the accuracy of the model. Results Age, PS score, brain metastases, qi deficiency syndrome, yin deficiency syndrome, and blood stasis syndrome were related risk factors for ES-SCLC treated with anlotinib. PS score, brain metastasis, and blood stasis syndrome were independent prognostic factors. On the basis of these three independent influencing factors, a nomogram model was established to predict the prognosis of patients with ES-SCLC treated with anlotinib. The predicted risk was close to the actual risk, showing a high degree of coincidence. Conclusion The nomogram model established with PS score, blood stasis syndrome elements, and brain metastasis as independent factors can predict the prognosis of patients with ES-SCLC receiving second- and third-line treatment of anlotinib.

In order to detect abnormal wind turbine generator and reduce the occurrence of outages, a deep learning framework combining K-CNN and N-GRU is proposed based on multi-dimensional sensor parameters recorded in real wind farm SCADA system, and a wind turbine generator state prediction model is established. Firstly, the correlation of state parameters was analyzed by Pearson correlation coefficient, and then the one-dimensional fusion parameters were weighted by weight coefficient. Secondly, to solve the problem of ignoring shallow features in traditional feature extraction, CNN was used to extract the features of one-dimensional fusion parameters in layers, and KPCA was used to reduce the feature extraction results of different layers to one dimension. Then, to solve the problem of parameter optimization of the traditional GRU algorithm, the neural network architecture search was used to improve the GRU algorithm, and the N-GRU model was obtained. The feature extraction results after dimensionality reduction were input into N-GRU for prediction and reconstruction error was obtained, then the state evaluation was realized by setting the alarm threshold. Finally, a 2 MW wind turbine in a wind farm in Xinjiang was taken as an example to verify the model validity and model accuracy.

To address the issues of workpiece distortion and excessive material melting caused by heat accumulation during laser cutting of thin-walled sheet metal components, this paper proposes a segmented optimization method for process parameters in sheet metal laser cutting considering thermal effects. The method focuses on predetermined perforation points and machining paths. Firstly, an innovative temperature prediction model T p r , t is established for the nth perforation point during the cutting process, with a prediction error of less than 10%. Secondly, using the PSO-BP-constructed prediction model for laser cutting quality features and an empirical model for processing efficiency features, a multi-objective model for quality and efficiency is generated. The NSGA II algorithm is employed to solve the objective optimization model and obtain the Pareto front. Next, based on the predicted temperature at the perforation point using the model T p r , t , the TOPSIS decision-making method is applied. Different weights for quality and efficiency are set during the cutting stages where the temperature is below the lower threshold and above the upper threshold. Various combinations of machining parameters are selected, and by switching the parameters during the cutting process, the thermal accumulation (i.e., temperature) during processing is controlled within a given range. Finally, the effectiveness of the proposed approach is verified through actual machining experiments. [ABSTRACT FROM AUTHOR]