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Pacific sardine (Sardinops sagax) in the northwest Pacific Ocean (NPO) not only preserve the equilibrium of the NPO food chain, but also serve as a source of nutrition for humans. In order to evaluate the levels of various elements in S. sagax, we collected specimens from NWP waters and ascertained the quantities of four macronutrients, nine essential trace elements, and twelve non-essential trace elements. The factors (fatness, sex, sexual maturity, body length, body weight, and age) influencing the concentration were investigated, and the correlations of trace elements were examined. Additionally, the Estimated Daily Intake (EDI) approach and the Target Hazard Quotient (THQ) methodology were used to assess contamination levels and associated consumption risks. The results revealed that the macronutrient content (54–10,500 mg kg−1) was higher than the necessary trace elements (0–488 mg kg−1) and the non-essential trace elements (0–448 mg kg−1), and most of the elements showed positive correlation with each other. Almost all of the element concentrations were below the maximum permitted levels (MPLs) recommended by the FAO/WHO, except for As and Cd. The factors, including fatness, sexual maturity, body length, body weight, and age, showed a negative correlation with most of the trace elements. The outcomes of the stepwise regression analysis showed that sex and stage of sexual maturation were the principal biological factors influencing elemental content. The risk assessment and standard dietary intake levels indicated that the potential health issues associated with the consumption of S. sagax were negligible. [ABSTRACT FROM AUTHOR]

Tuna are economically important fish species. The automated identification of tuna species is of importance in fishery production and resource assessment in that it would facilitate the informed monitoring of tuna fishing vessels and the establishment of electronic observer systems. As morphological characteristics are important for tuna identification, this study aims to verify the performance of the automated identification of three Thunnus species through morphological characteristics based on different machine learning algorithms. Firstly, morphological outlines were visually analyzed using EFT (elliptic Fourier transform) and CNN (convolutional neural network). Then, the EFT feature data and deep feature data of the tuna outline images were extracted, and principal component analysis of the two different morphological characteristics was performed. Finally, different machine learning algorithms were used to analyze the identification performance of tuna of the same genus and different species. The experimental results showed that EFT features had the highest identification accuracy in KNN (K-nearest neighbor), with 90% for T. obesus, 90% for T. albacores, and 85% for T. alalunga. Deep features had the best identification performance in SVM (support vector machine), with 80% for T. obesus, 90% for T. albacores, and 100% for T. alalunga. Deep features were better than EFT features in identification performance. The biodiversity and intergeneric differences among tuna species can be well analyzed using these two different morphological characteristics. Machine learning algorithms open up the way for rapid near-real-time electronic observer systems in these important international fisheries. [ABSTRACT FROM AUTHOR]

The accurate prediction of internal cracks in steel billets is of great importance for the stable production of continuous casting. However, it is challenging, owing to the strong nonlinearity, and coupling among continuous casting process parameters. In this study, an internal crack prediction model based on the principal component analysis (PCA) and deep neural network (DNN) was proposed by collecting sufficient industrial data. PCA was used to reduce the dimensionality of the factors influencing the internal cracks, and the obtained principal components were used as DNN input variables. The 5-fold cross-validation results demonstrate that the prediction accuracy of the DNN model is 92.2%, which is higher than those of the decision tree (DT), extreme learning machine (ELM), and backpropagation (BP) neural network models. Moreover, the variance analysis showed that the prediction results of the DNN model were more stable. The PCA-DNN model can provide a useful reference for real production, owing to its strong learning ability and fault-tolerant ability. [ABSTRACT FROM AUTHOR]

Central carbon segregation is a typical internal defect of continuous cast steel billets. Real-time and accurate carbon segregation prediction is of great significance for lean control of the production quality in continuous casting processes. In this paper, a data-driven regularized extreme learning machine (R-ELM) model is proposed for the prediction of carbon segregation index (CSI). To improve model performance, outliers in industrial data were eliminated by means of boxplot tool. Besides, Pearson correlation combined with grey relational analysis (GRA) was conducted to avoid multicollinearity and redundancy in input variables. The new model shows potential to evaluate online quality of steel billets. When predictive errors were within ±0.03 and ±0.025, the prediction accuracy of the R-ELM model was 94% and 89%, respectively, which was higher than that of the multiple linear regression (MLR) model and ELM model. Moreover, the effects of several key continuous casting process parameters on CSI were investigated based on the predictions of the R-ELM model via response surface analysis. The conclusions are consistent with the metallurgical mechanism, and the predictive values of the R-ELM model agree well with experimental values, which further verifies the correctness and generalization ability of the R-ELM model. [ABSTRACT FROM AUTHOR]