Expectation-maximization machine learning model for micromechanical evaluation of thermally-cycled solder joints in a semiconductor.

This paper aims to study the microstructural and micromechanical variations of solder joints in a semiconductor under the evolution of thermal-cycling loading. For this purpose, a model was developed on the basis of expectation-maximization machine learning (ML) and nanoindentation mapping. Using this model, it is possible to predict and interpret the microstructural features of solder joints through the micromechanical variations (i.e. elastic modulus) of interconnection. According to the results, the classification of Sn-based matrix, intermetallic compounds (IMCs) and the grain boundaries with specified elastic-modulus ranges was successfully performed through the ML model. However, it was detected some overestimations in regression process when the interfacial regions got thickened in the microstructure. The ML outcomes also revealed that the thermal-cycling evolution was accompanied with stiffening and growth of IMCs; while the spatial portion of Sn-based matrix decreased in the microstructure. It was also figured out that the stiffness gradient becomes intensified in the treated samples, which is consistent with this fact that the thermal cycling increases the mechanical mismatch between the matrix and the IMCs.

Application of wavelet theory to enhance the performance of machine learning techniques in estimating water quality parameters (case study: Gao-Ping River).

There are several methods for modeling water quality parameters, with data-based methods being the focus of research in recent decades. The current study aims to simulate water quality parameters using modern artificial intelligence techniques, to enhance the performance of machine learning techniques using wavelet theory, and to compare these techniques to other widely used machine learning techniques. EC, Cl, Mg, and TDS water quality parameters were modeled using artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS). The study area in the present research is Gao-ping River in Taiwan. In the training state, using hybrid models with wavelet transform improved the accuracy of ANN models from 8.1 to 22.5% and from 25.7 to 55.3% in the testing state. In addition, wavelet transforms increased the ANFIS model's accuracy in the training state from 6.7 to 18.4% and in the testing state from 9.9 to 50%. Using wavelet transform improves the accuracy of machine learning model results. Also, the WANFIS (Wavelet-ANFIS) model was superior to the WANN (Wavelet-ANN) model, resulting in more precise modeling for all four water quality parameters.

The protective effects of quercetin on the physiological responses in malathion-exposed common carp, Cyprinus carpio.

This study aimed to evaluate the protective effects of quercetin on the biochemical parameters, immunity, and growth performance in malathion-exposed common carp, Cyprinus carpio. The methods six experimental groups, including the control group, fish exposed to concentrations of 1.04 and 2.08 mg/l malathion, fish supplemented with quercetin (200 mg/kg diet), and fish treated with quercetin + malathion for 21 days, were considered for the experiment. After the feeding period, in results the activities of catalase (CAT), superoxide dismutase (SOD), glutathione peroxidase (GPx), and glutathione S-transferase (GST) were significantly decreased in the hepatocyte, while malondialdehyde (MDA) content increased in response to malathion. Aspartate aminotransferase (AST) and alanine aminotransferase (ALT) activities and glucose, cortisol, and urea levels significantly increased after exposure to malathion. Exposure of fish to malathion-induced decreases in protease, lysozyme, and alternative complement (ACH50) activities and total immunoglobulin (total Ig) in the mucosa. Changes in other parameters were different depending on malathion concentrations. The supplementation of fish with quercetin had no ameliorating effect on the malathion-related alternations of mucosal lysozyme and protease activities. However, quercetin ameliorated the depressing effects of malathion on biochemical and immunological parameters. Changes in the growth performance and hematological parameters indicated the toxic effect of malathion. In conclusion, quercetin could efficiently reduce the toxic effects of malathion on the biochemical, immune, and hematological parameters of the common carp.

Corrigendum to "Estimation of Isentropic Compressibility of Biodiesel Using ELM Strategy: Application in Biofuel Production Processes".

[This corrects the article DOI: 10.1155/2021/7332776.].

Estimation of Isentropic Compressibility of Biodiesel Using ELM Strategy: Application in Biofuel Production Processes.

Isentropic compressibility is one of the significant properties of biofuel. On the other hand, the complexity related to the experimental procedure makes the detection process of this parameter time-consuming and hard. Thus, we propose a new Machine Learning (ML) method based on Extreme Learning Machine (ELM) to model this important value. A real database containing 483 actual datasets is compared with the outputs predicted by the ELM model. The results of this comparison show that this ML method, with a mean relative error of 0.19 and R 2 values of 1, has a great performance in calculations related to the biodiesel field. In addition, sensitivity analysis exhibits that the most efficient parameter of input variables is the normal melting point to determine isentropic compressibility.