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Tool wear state recognition is an important part of tool condition monitoring (TCM). Online tool wear monitoring can avoid wasteful early tool changes and degraded workpiece quality due to later tool changes. This study incorporated an attention mechanism implemented by one-dimensional convolution in a convolutional neural network for improving the performance of the tool wear recognition model (1DCCA-CNN). The raw multichannel cutting signals were first preprocessed and three time-domain features were extracted to form a new time-domain sequence. CNN was used for deep feature extraction of temporal sequences. A novel 1DCNN-based channel attention mechanism was proposed to weigh the channel dimensions of deep features to enhance important feature channels and capture key features. Compared with the traditional squeeze excitation attention mechanism, 1DCNN can enhance the information interaction between channels. The performance of the model was validated on the PHM2010 public cutting dataset. The excellent performance of the proposed 1DCCA-CNN was verified by the improvement of 4% and 5% compared to the highest level of existing research results on T1 and T3 datasets, respectively.

Currently, oil spill pollution is one of the major environmental concerns for sea cucumber (Apostichopus japonicus) aquaculture. During oil spills, spraying chemical dispersants is generally considered an efficient oil spill response. However, the impact of chemical dispersant deployment during oil spills on sea cucumbers is still less known. In this study, we treated sea cucumbers with physically and chemically (by GM-2 chemical dispersant) dispersed Oman crude oil for 24 h. For antioxidant defense capacities, our results showed that physically dispersed crude oil caused a significant elevation on superoxide dismutase (SOD) and catalase (CAT) activities, and glutathione (GSH) content, while chemically dispersed crude oil caused a significant decrease in SOD activity and GSH content with no apparent change in CAT activity. As for non-specific immune responses, our results indicated that physically dispersed crude oil up-regulated acid phosphatase (ACP) and lysozyme (LZM) activities but had no obvious impact on alkaline phosphatase (ALP) activity. Differently, chemically dispersed crude oil down-regulated ACP and LZM activities while up-regulating ALP activity. Based on the integrated biomarker response analysis, the overall impact of chemically dispersed crude oil on antioxidant defense capacities and non-specific immune responses of sea cucumbers was more severe than physically dispersed crude oil.

The traditional photovoltaic (PV) forecasting method depends on sufficient historical data (PV power station historical power generation data and numerical weather prediction meteorological data), which is not suitable for a newly built PV power plant. In order to calculate the PV array irradiance and to predict the PV power, a physical prediction approach based on solar irradiance on inclined surfaces is proposed. This method selects three decomposition models and four transposition models to be combined into 12 combination forecasting models. Furthermore, solar spectral response, incidence angle, and soiling factor are taken into account in the modified model. The results show that the methods combining the Liu–Jordan transposition model have higher forecasting accuracy under the different weather types. Among them, the Erbs + Liu–Jordan model predictions are the most accurate.