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Using high temperature CO 2 sorbent for carbon capture is an effective approach to reduce carbon footprint. Copper slag, as an abundant metallurgical waste containing substantial amount of Si and Fe resources, was being explored as cheap raw material for the preparation of lithium-based sorbent. The results shows that the lithiation is initiated at 450–600 °C, where fayalite and magnetite in slag are firstly oxidized by released water vapor from the decomposition of LiOH, then further react with Li 2 O to form Li 4 SiO 4 and LiFeO 2. As the temperature rises, the conversion of LiFeO 2 to Li 5 FeO 4 and incorporation of Fe3+, Al3+ into Li 4 SiO 4 lattice occur in parallel. The resulting sorbent exhibited excellent CO 2 chemisorption ability with a capture capacity of 420 mg/g material at 725 °C. Particularly, even in the gas stream containing 5 vol% CO 2 , it showed a CO 2 uptake capacity of 383 mg/g material. In the CO 2 cycling tests, the sorbent suffered a decay in sorption capacity, which was ascribed to the poor regeneration ability of Li 5 FeO 4. Comparatively, it possesses a relatively high capacity of 241 mg/g material in 20 vol% CO 2 after 20 cycles, which is higher than those of previously reported Li-silicate sorbents derived from wastes. [ABSTRACT FROM AUTHOR]

A physics-informed graph convolutional network (PINN-GCN) data-driven model was proposed to solve steady-state fluid flows with various geometries. A GCN guarantees a data-driven model that adapts to irregularly structured or unstructured mesh data. In addition, the mean squared residuals of the governing equations in partial differential forms and boundary conditions are introduced into the loss function of the network model, the Physics-Informed Neural Network (PINN) increases the knowledge of the physical rules of the model, enhances the model accuracy even with sparse training data. With nine training cases containing approximately 2000 spatial point data individually, PINN-GCN could accurately predict the physical flow field over a two-dimensional object of variable size, achieving above 98.6% for mean accuracy. With five training data cases, the model could predict flow over a cylinder with variable position, achieving above 98.2% for mean accuracy. Moreover, the PINN-GCN framework can produce high-resolution predictions, even with low-resolution field training data. Furthermore, the PINN-GCN prediction speed was approximately 1000 times faster than that of the numerical simulations. Thus, the proposed model can rapidly predict high-resolution flow fields using a physics-informed, grid- and geometry-adaptive, data-driven model. • The proposed PINN-GCN model obtains the target PDEs solutions in the irregular domains with unstructured grids and without the pixelated preprocessing. • The proposed model can be trained on sparse data (low-resolution) and then predictions can be made on dense data (high-resolution). • The trained model can achieve direct predictions for domains with unseen and new geometries, which fully reflects its scalability. [ABSTRACT FROM AUTHOR]

• We test when and how emotional intelligence (EI) affects safety performance (SP). • We use two-wave survey data collected from airplane pilots to test our hypotheses. • Training inadequacy strengthens the effect of EI on situational awareness (SA). • SA is more likely to mediate the EI–SP link as training inadequacy worsens. Introduction: Safety outcomes in the workplace require individual employees to perform (behave) safely in everyday duties. While the literature suggests that emotional management capabilities or traits can be positively related to individual performance in certain conditions, it is not clear how they can influence safety-related performance in high-risk work contexts. Drawing upon trait activation theory, this paper aims to examine when emotional intelligence (EI) benefits employees' safety performance. We propose that when employees receive inadequate safety training, EI is more likely to trigger their situational awareness and consequently promote their safety performance. Method: We collected time-lagged data from 133 full-time airplane pilots working in commercial aviation industry. Hierarchical moderated regression analysis was conducted to test the moderating effect of safety training inadequacy on the EI–situational awareness relationship. The moderated mediation model, which involves conditional indirect effects of EI on safety performance via situational awareness across different levels of safety training inadequacy, was tested using the PROCESS-based bootstrap confidence interval. Results: Safety training inadequacy negatively moderated the relationship between EI and situational awareness, such that EI was significantly related to situational awareness only when safety training inadequacy was more salient. The more inadequate safety training was, the greater the indirect effect of EI on safety performance via situational awareness was. Conclusions: Inadequate safety training, as a negative situational cue, can activate individuals' EI to drive their safety-related cognitions (e.g., situational awareness) and behaviors. Effective safety training may be able to complement employees' low EI in shaping their situational awareness and safety behaviors. Practical Applications : Aviation managers should monitor the adequacy and effectiveness of safety training; this could make pilots' situational awareness and safety performance depend less on personal attributes (e.g., EI), which organizations are less able to control. When training capacity is temporarily limited, priority might be given to those with low EI. [ABSTRACT FROM AUTHOR]

Deep reinforcement learning (DRL) has gradually emerged as a novel and effective method for intelligent control of conjugate heat transfer. Through proper training, DRL agent usually can find a better control strategy than the one optimized manually. For numerous numerical simulation-based investigations, the promising results have only been obtained on 2-dimensional models due to the heavy burden of data acquisition. This paper proposes a novel strategy of transferring the control policy learned in 2-dimensional environment into a new 3-dimensional environment. PDD-DQN (Prioritized Dueling Double Deep Q-Networks) algorithm is used to recognize the underlying relationship between the forced fluid flow and heat transfer performance to produce a control strategy. The DRL controller is first trained on a simple 2-dimensional cavity with one heat source, and the DRL controller is able to reduce the maximum temperature to 315 K which is 2 K lower than the manually optimized control strategy; then the control strategy is transferred to 3-dimensional models where the maximum temperature is further reduced to 308 K; and compared to training the DRL agent directly on 3-dimensional model, the policy-transfer strategy requires only 10% computational expenditure. The proposed strategy is then applied to a more complex testbed with multiple heat sources for further investigating its ability; in both 2D and 3D environment, the DRL controller can cool the maximum temperature to be 307 K which is 8 K lower than the manually optimized control policy; and the computational cost drop to be 3%. The generalization ability of the trained DRL agent in inter-dimensional geometries is confirmed. For the cases with huge grid size, the policy-transfer strategy can economize exponential computational cost, which is of great significance for applying DRL methods to practical thermal control problems. [ABSTRACT FROM AUTHOR]

• The fluid dynamic and energy equations are embedded into the neural network. • The proposed model can accurately solve natural convection with various geometries. • The model can accurately predict the physical information at the boundaries. • The model training only requires a small amount of dataset. This paper presents a novel deep learning-based surrogate model for steady-state natural convection problem with variable geometry. Traditional deep learning based surrogate models are more or less limited by the requirement for large amounts of training data, loss of accuracy due to pixelization of the original data, the low accurate prediction near the boundaries and low geometry adaptive capability. To overcome the above challenges, the proposed natural convection prediction framework is mainly composed of a physics-informed neural network (PINN) and a graph convolutional neural network (GCN), called natural convection prediction model based on physics-informed graph convolutional network (NCV-PIGN). The GCN serves as the prediction module, inferring and predicting natural convection phenomena by considering the interactions between unstructured nodes and their neighbor; the PINN incorporates the governing equations of natural convection into the loss function of the neural network, allowing the predictions from GCN to satisfy the constraints imposed by the physical laws. The advantages of this framework are twofold: the operation principles of the GCN better align with the development of the temperature field in real situations, and the embedding of physical information strengthens the model's understanding of the flow field, accurately describing the variations of temperature gradients at the boundary positions while reducing the model's reliance on training data. Finally, to validate the superiority of the NCV-PIGN, we analyze its geometric adaptability and accuracy of prediction using single and dual heat source cases. We compare the model's prediction results at different sampling point quantities and contrast them with those of purely data-driven models. The results demonstrate that the excellent geometric adaptability and prediction capability of the proposed model can be achieved with only 20 training data and once the fully trained the model can solve natural convection problems within 3 ms. The max and mean relative errors in predicting the temperature field are less than 2% and 0.4% for both single and dual heat source cases. Compared to the pure data-driven model, the proposed model has reduced the maximum error by 65.5% and the mean error by 72%. These results validate the effectiveness of the developed NCV-PIGN model, enabling better performance of the deep learning-based surrogate models for natural convection problems. [ABSTRACT FROM AUTHOR]

To detect arsenic geochemistry and evaluate its environmental risk, 30 geothermal water samples were collected from Rehai and Ruidian geothermal fields of Tengchong, China. There were three main water types including SO 4 and Na-Cl-HCO 3 types in Rehai and Na-HCO 3 type in Ruidian. Arsenic concentrations ranged from 22.1 to 1150.3 μg L −1 . Relative high concentrations of arsenic with an average of 495.1 μg L −1 , as well as K, Na, F, Cl, Li and B, were detected in neutral or alkaline geothermal water samples, which suggested that rock leaching in geothermal reservoir was the main resource of arsenic enrichment in geothermal water. Comparatively, those acid geothermal water samples which were formed by vapor rich in H 2 S condensing and oxidizing in the shallow groundwater or surface water had lower arsenic concentrations with an average of 77.1 μg L −1 . The ratios of arsenite to total arsenic widely ranged from 0.01 to 0.97 and were determined by mixing proportions of geothermal water and shallow groundwater or surface water. Furthermore, there was a significantly positive correlation between arsenic with HCO 3 (R = 0.81, P = 0.01). These results implied that shallow groundwater or surface water in Tengchong geothermal area had been probably contaminated by arsenic due to the mixing of geothermal water. [ABSTRACT FROM AUTHOR]