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Tunnel settlement commonly occurs during the tunnel construction processes in large cities. Existing forecasting methods for tunnel settlements include model-based approaches and artificial intelligence (AI) enhanced approaches. Compared with traditional forecasting methods, artificial neural networks can be easily implemented, with high performance efficiency and forecasting accuracy. In this study, an extended machine learning framework is proposed combining particle swarm optimization (PSO) with support vector regression (SVR), back-propagation neural network (BPNN), and extreme learning machine (ELM) to forecast the surface settlement for tunnel construction in two large cities of China P.R. Based on real-world data verification, the PSO-SVR method shows the highest forecasting accuracy among the three proposed forecasting algorithms. [ABSTRACT FROM AUTHOR]

Cadmium is one of the major heavy metal pollutions in coastal waters, and it is well known that cadmium at trace concentration is toxic to macroalgae. Change in marine carbonate system and ocean acidification caused by elevated atmospheric CO 2 also alter physiological characteristics of macroalgae. However, less research is focused on the combined impacts of elevated CO 2 and cadmium pollution on the growth and physiology in macroalgae. In this study, the maricultivated macroalga Pyropia haitanensis (Rhodophyta) was cultured at three levels of Cd 2+ (control, 4 and 12 mg L -1 ) and two concentrations of CO 2 , the ambient CO 2 (AC, 410 ppm) and elevated CO 2 (HC, 1100 ppm). The results showed that 12 mg L -1 Cd 2+ significantly suppressed the relative growth rate and superoxide dismutase activity in AC-grown P. haitanensis, while such inhibition extents by Cd 2+ were alleviated in HC-grown algae. Cd 2+ had no effects on efficiency of electron transport (α) and maximum electron transport rate (ETR max ), but α was increased by elevated CO 2 . Cd 2+ dramatically suppressed the maximum net photosynthesis oxygen evolution rate (NPR m ) and the minimum saturation irradiance (I k ) when the algal thalli were grown at AC, while such suppression of NPR m by Cd 2+ was much decreased when the thalli were grown at HC. Collectively, our results suggested that elevated CO 2 would alleviate Cd 2+ toxicity on P. haitanensis.

Hepatocellular carcinoma (HCC) is one of the most common malignant tumors. Clinical symptoms attributable to HCC are usually absent, thus often miss the best therapeutic opportunities. Traditional Chinese Medicine (TCM) plays an active role in diagnosis and treatment of HCC. In this paper, we proposed a particle swarm optimization-based hierarchical feature selection (PSOHFS) model to infer potential syndromes for diagnosis of HCC. Firstly, the hierarchical feature representation is developed by a three-layer tree. The clinical symptoms and positive score of patient are leaf nodes and root in the tree, respectively, while each syndrome feature on the middle layer is extracted from a group of symptoms. Secondly, an improved PSO-based algorithm is applied in a new reduced feature space to search an optimal syndrome subset. Based on the result of feature selection, the causal relationships of symptoms and syndromes are inferred via Bayesian networks. In our experiment, 147 symptoms were aggregated into 27 groups and 27 syndrome features were extracted. The proposed approach discovered 24 syndromes which obviously improved the diagnosis accuracy. Finally, the Bayesian approach was applied to represent the causal relationships both at symptom and syndrome levels. The results show that our computational model can facilitate the clinical diagnosis of HCC.