Algorithm for calculating parameters of mold temperature controller

In the problem of mold heating proper parameters of mold temperature controller are important for mold temperature, but the parameters are hard to calculate for numerous computing amount, and simulating its state during heating is also difficult for there is a limitation for flow velocity of hot water that simulating will be failure when large velocity. To solve this problem the paper presents an algorithm based on the fast algorithm for heating injection mold by water. A mold temperature equation is established, and the dichotomy method based on the fast algorithm as a core is used to numerically solve the mold temperature equation. The first step of this process is to construct a similar model having the same solution with the original model of the mold heating problem. The similar model is obtained by similarity transformation to the original model. The second step is to solve the similar model, which is fast than solving the original model. The third step is to find solution of the mold temperature equation meeting the heating conditions. An example is given to show that parameters of mold temperature controller are easy to calculate

Prediction of coronary artery lesions in children with Kawasaki syndrome based on machine learning

Abstract Objective Kawasaki syndrome (KS) is an acute vasculitis that affects children < 5 years of age and leads to coronary artery lesions (CAL) in about 20-25% of untreated cases. Machine learning (ML) is a branch of artificial intelligence (AI) that integrates complex data sets on a large scale and uses huge data to predict future events. The purpose of the present study was to use ML to present the model for early risk assessment of CAL in children with KS by different algorithms. Methods A total of 158 children were enrolled from Women and Children’s Hospital, Qingdao University, and divided into 70–30% as the training sets and the test sets for modeling and validation studies. There are several classifiers are constructed for models including the random forest (RF), the logistic regression (LR), and the eXtreme Gradient Boosting (XGBoost). Data preprocessing is analyzed before applying the classifiers to modeling. To avoid the problem of overfitting, the 5-fold cross validation method was used throughout all the data. Results The area under the curve (AUC) of the RF model was 0.925 according to the validation of the test set. The average accuracy was 0.930 (95% CI, 0.905 to 0.956). The AUC of the LG model was 0.888 and the average accuracy was 0.893 (95% CI, 0,837 to 0.950). The AUC of the XGBoost model was 0.879 and the average accuracy was 0.935 (95% CI, 0.891 to 0.980). Conclusion The RF algorithm was used in the present study to construct a prediction model for CAL effectively, with an accuracy of 0.930 and AUC of 0.925. The novel model established by ML may help guide clinicians in the initial decision to make a more aggressive initial anti-inflammatory therapy. Due to the limitations of external validation and regional population characteristics, additional research is required to initiate a further application in the clinic

Causal associations between erectile dysfunction and high blood pressure, negative psychology: a Mendelian randomization study

Erectile dysfunction (ED) has been closely associated with both high blood pressure (HBP) and psychological traits, but the causal relationship between them remains unclear. Herein, we aimed to identify the causal risk factors for ED. We conducted univariable and multivariable Mendelian randomization (MR) analyses using genetic variants associated with metabolic syndrome and psychology traits at the genome-wide significance (p < 5 × 10−8) level obtained from corresponding genome-wide association studies. We used summary-level statistical data for ED from the European Bioinformatics Institute (EBI) database of complete Genome-Wide Association Studies (GWAS) summary data. We also conducted reverse causality and performed power calculations for MR. Our results showed that HBP was associated with increased odds of ED (odds ratio (OR) = 1.66 (95% confidence interval (CI), 1.13–2.45), a p-value for the inverse variance-weighted method (PIVW) = 1.06 × 10−2, Power = 100%), as were myocardial infarction (OR = 1.09 (95% CI, 1.02–1.17), PIVW = 1.18 × 10−2, Power = 56%) and ischemic stroke (OR = 1.21 (95% CI, 1.02–1.43), PIVW = 2.87 × 10−2, Power = 10%). In terms of psychological traits, irritable mood (OR = 1.86 (95% CI, 1.14–3.02), PIVW = 1.30 × 10−2, Power = 96%) and neuroticism (OR = 1.36 (95% CI, 1.04–1.79), PIVW = 2.66 × 10−2, Power = 80%) were associated with increased odds of ED. Mendelian randomization pleiotropy residual sum and outlier (MR-PRESSO) showed no evidence of pleiotropic bias, and sensitivity analyses confirmed the robustness of our results. We have established a causal link between HBP and ED, and we have also found evidence suggesting a causal relationship between irritable mood and ED

Long-Term (1990&ndash;2013) Changes and Spatial Variations of Cropland Runoff across China

Quantitative information on regional cropland runoff is important for sustainable agricultural water quantity and quality management. This study combined the Soil Conservation Service Curve Number (SCS-CN) method and geostatistical approaches to quantify long-term (1990&ndash;2013) changes and regional spatial variations of cropland runoff in China. Estimated CN values from 17 cropland study sites across China showed reasonable agreement with default values from the National Engineering Handbook (R2 = 0.76, n = 17). Among four commonly used geostatistical interpolation methods, the inverse distance weighting (IDW) method achieved the highest accuracy (R2 = 0.67, n = 209) for prediction of cropland runoff. Using default CN values and the IDW method, estimated national annual cropland runoff volume and runoff depth in 1990&ndash;2013 were 253 &plusmn; 25 km3 yr&minus;1 and 182 &plusmn; 15 mm yr&minus;1, respectively. Estimated cropland runoff depth gradually increased from the drier northwest inland region to the wetter southeast coastal region (range: 2&ndash;1375 mm yr&minus;1). Regionally, eastern, central and southern China accounted for 39% of the cultivated area and 53% of the irrigated land area and contributed to 68% of the national cropland runoff volume. In contrast, northwestern, northern, southwestern and northeastern China accounted for 61% of the cultivated area and 47% of the irrigated land area and contributed to 32% of the runoff volume. Rainfall was the main source (72%) of cropland runoff for the entire nation, while irrigation became the main source of cropland runoff in drier regions (northwestern and southwestern China). Over the 24-year study period, estimated cropland runoff depth showed no significant trends, whereas cropland runoff volume and irrigation-contributed percentages decreased by 7% and 35%, respectively, owing to implementation of water-saving irrigation technologies. To reduce excessive runoff and increase water utilization efficiencies, regionally specific water management strategies should be further promoted. As the first long-term national estimate of cropland runoff in China, this study provides a simple framework for estimating regional cropland runoff depth and volume, providing critical information for guiding developments of management practices to mitigate agricultural nonpoint source pollution, soil erosion and water scarcity