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Background Low anterior resection syndrome (LARS) is a common postoperative complication in patients with colorectal cancer, which seriously affects their postoperative quality of life. At present, the aetiology of LARS is still unclear, but some risk factors have been studied. Accurate prediction and early management of medical intervention are keys to improving the quality of life of such high‐risk patients. Objectives Based on machine‐learning methods, this study used the follow‐up results of postoperative patients with colorectal cancer to develop prediction models for LARS and conducted a comparative analysis between the different models. Methods A total of 382 patients diagnosed with colorectal cancer and undergoing surgery at West China Hospital from April 2017 to December 2020 were retrospectively selected as the development cohort. Logistic regression, support vector machine, decision tree, random forest and artificial neural network algorithms were used to construct the prediction models of the obtained dataset. The models were internally validated using cross‐validation. The area under the curve and Brier score measures were used to evaluate and compare the differentiation and calibration degrees of the models. The sensitivity, specificity, positive predictive value and negative predictive value of the different models were described for clinical use. Results A total of 342 patients were included, the incidence of LARS being 47.4% (162/342) during the six‐month follow‐up. After feature selection, the factors influencing the occurrence of LARS were found to be location, distance, diverting stoma, exsufflation and surgical type. The prediction models based on five machine‐learning methods all showed acceptable performance. Conclusions The five models developed based on the machine‐learning methods showed good prediction performance. However, considering the simplicity of clinical use of the model results, the logistic regression model is most recommended. The clinical applicability of these models will also need to be evaluated with external cohort data.

Survival and germination of overwintering oospores of two strains from Gansu,China compared with two standard from Wageningen, the Netherlands of Phytophthora infestans were determined in 2010-2011. It was found that compatible mating strains of P. infestans A1 and A2 produce oospores abundantly in paired cultures on tomato-rye agar medium.The survival rate of overwintering was ranged from19.57% to 24.59%.The germination rate of overwintering was ranged from 3.58% to 8.49%, and there were no significant differences in germination rate of oospores between alternating light at day/night and whole darkness at day/night. Furthermore, the soil extract liquid was more suitable for oospore germination than distilled water.

A new kind of combined finite element named three-dimensional virtual laminated element and its deduce process is introduced. The new finite element was proved to be convenient, accurate and efficient in the calculation of bridge structures by a modal test and corresponding numerical example. Then some research and discussion were done, which show that with small modal construction scale being comparative with beam element, the static and dynamic spatial behavior of complicated structures can be simulated precisely. Furthermore, it is sensitive to structural damages, and more damage information can be obtained than corresponding spatial beam element, which means it is highly promising in the application of structural damage detection.

We develop a reduced-complexity approach to the detection of SOQPSK-TG, a highly bandwidth-efficient constant-envelope waveform. The optimal detector for SOQPSK-TG requires of a bank of 2784 matched filters and 512 states, which is impractical and highly complex. In this paper, a practical detector was developed based on Laurent decomposition and frequency pulse truncation (PT) technology, with the number of correlators reduced to only 2 and states reduced to 4 at the expanse of less than 0.2dB at BER of 10-4.