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Investigations have shown that storage bugs seriously harm grains during storage. In the interim, essential oils (EOs) have been proven to be a good botanical pesticide. The anti-Lasioderma serricorne properties of Elsholtzia ciliata essential oil, which was obtained by steam distillation, were evaluated using DL-limonene, carvone, and their two optical isomer components using contact, repelling, and fumigation techniques. Simultaneously, the fumigation, contact, and repellent activities of carvone and its two optical isomers mixed with DL-limonene against L. serruricorne were evaluated. The results showed that E. ciliata, its main components (R-carvone, DL-limonene), and S-carvone exhibited both fumigations (LC50 = 14.47, 4.42, 20.9 and 3.78 mg/L) and contact (LD50 = 7.31, 4.03, 28.62 and 5.63 µg/adult) activity against L.serricorne. A binary mixture (1:1) of R-carvone and DL-limonene displayed an obvious synergistic effect. A binary mixture (1:1) of carvone and its two optical isomers exhibited an obvious synergistic effect, too. Furthermore, the repellent activity of the EO, carvone, and its two optical isomers, DL-limonene, and a combination of them varied. To stop insect damage during storage, E. ciliata and its components can be utilized as bio-insecticides.

As clean and low-carbon energy, wind energy has attracted the attention of many countries. The main bearing in the transmission system of large-scale wind turbines (WTs) is the most important part. The research on the condition monitoring of the main bearing has received more attention from many scholars and the wind industry, and it has become a hot research topic. The existing research on the condition monitoring of the main bearing has the following drawbacks: (1) the existing research assigns the same weight to each condition parameter variable, and the model extracts features indiscriminately; (2) different historical time points of the condition parameter variable are given the same weight, and the influence degree of different historical time points on the current value is not considered; and (3) the existing literature does not consider the operating characteristics of WTs. Different operating conditions have different control strategies, which also determine which condition parameters are artificially controlled. Therefore, to solve the problems above, this paper proposes a novel method for condition monitoring of WT main bearings by applying the dual attention mechanism and Bi-LSTM, named Dual Attention-Based Bi-LSTM (DA-Bi-LSTM). Specifically, two attention calculation modules are designed to extract the important features of different input parameters and the important features of input parameter time series, respectively. Then, the two extracted features are fused, and the Bi-LSTM building block is utilized to perform pre-and post-feature extraction of the fused information. Finally, the extracted features are applied to reconstruct the input data. Extensive experiments verify the performance of the proposed method. Compared with the Bi-LSMT model without adding an attention module, the proposed model achieves 19.78%, 2.17%, and 18.92% improvement in MAE, MAPE, and RMSE, respectively. Compared with the Bi-LSTM model which only considers a single attention mechanism, the proposed model achieves the largest improvement in MAE and RMSE by 28.84% and 30.37%. Furthermore, the proposed model has better stability and better interpretability of the monitoring process.

Condition monitoring and overheating warnings of the main bearing of large-scale wind turbines (WT) plays an important role in enhancing their dependability and reducing operating and maintenance (O&M) costs. The temperature parameter of the main bearing is the key indicator to characterize the normal or abnormal operating condition. Therefore, forecasting the trend of temperature change is critical for overheating warnings. To achieve forecasting with high accuracy, this paper proposes a novel model for the WT main bearing, named stacked long-short-term memory with multi-layer perceptron (SLSTM-MLP) by utilizing supervisory control and data acquisition (SCADA) data. The model is mainly composed of multiple LSTM cells and a multi-layer perceptron regression layer. By combining condition parameters into a characteristic matrix, SLSTM can mine nonlinear, non-stationary dynamic feature relationships between temperature and its related variables. To evaluate the performance of the SLSTM-MLP model, experimental analysis was carried out from three aspects: different sample capacity sizes, different sampling time segments, and different sampling frequencies. Furthermore, the model’s capability of online fault detection was also carried out by simulation. The results of comparative studies and online fault simulation tests show that the proposed SLSTM-MLP has better performance for temperature forecasting of the main bearing of large-scale WTs.

The carbon emissions trading market and direct power purchases by large consumers are two promising directions of power system development. To trace the carbon emission flow in the power grid, the theory of carbon emission flow is improved by allocating power loss to the load side. Based on the improved carbon emission flow theory, an optimal dispatch model is proposed to optimize the cost of both large consumers and the power grid, which will benefit from the carbon emissions trading market. Moreover, to better simulate reality, the direct purchase of power by large consumers is also considered in this paper. The OPF (optimal power flow) method is applied to solve the problem. To evaluate our proposed optimal dispatch strategy, an IEEE 30-bus system is used to test the performance. The effects of the price of carbon emissions and the price of electricity from normal generators and low-carbon generators with regards to the optimal dispatch are analyzed. The simulation results indicate that the proposed strategy can significantly reduce both the operation cost of the power grid and the power utilization cost of large consumers.

In power systems, although the inertia energy in power sources can partly cover power unbalances caused by load disturbance or renewable energy fluctuation, it is still hard to maintain the frequency deviation within acceptable ranges. However, with the vehicle-to-grid (V2G) technique, electric vehicles (EVs) can act as mobile energy storage units, which could be a solution for load frequency control (LFC) in an isolated grid. In this paper, a LFC model of an isolated micro-grid with EVs, distributed generations and their constraints is developed. In addition, a controller based on multivariable generalized predictive control (MGPC) theory is proposed for LFC in the isolated micro-grid, where EVs and diesel generator (DG) are coordinated to achieve a satisfied performance on load frequency. A benchmark isolated micro-grid with EVs, DG, and wind farm is modeled in the Matlab/Simulink environment to demonstrate the effectiveness of the proposed method. Simulation results demonstrate that with MGPC, the energy stored in EVs can be managed intelligently according to LFC requirement. This improves the system frequency stability with complex operation situations including the random renewable energy resource and the continuous load disturbances.

A highly sensitive and specific LC-MS/MS method for the quantitation of largazole thiol, the active species of the marine-derived preclinical histone deacetylase inhibitor, largazole (prodrug), was developed and validated. Largazole thiol was extracted with ethyl acetate from human or rat plasma along with the internal standard, harmine. Samples were separated on an Onyx Monolithic C18 column by a stepwise gradient elution with 0.1% formic acid in methanol and 0.1% aqueous formic acid employing multiple reaction monitoring (MRM) detection. Linear calibration curves were obtained in the range of 12.5–400 ng/mL with 200 µL of human plasma. The overall intra-day precision was from 3.87% to 12.6%, and the inter-day precision was from 7.12% to 9.8%. The accuracy at low, medium and high concentrations ranged from 101.55% to 105.84%. Plasma protein bindings of largazole thiol in human and rat plasma as determined by an ultrafiltration method were 90.13% and 77.14%, respectively. Plasma drug concentrations were measured by this LC-MS/MS method. The pharmacokinetics of largazole thiol in rats was studied following i.v. administration at 10 mg/kg and found to follow a two-compartment model. Largazole thiol was rapidly eliminated from systemic circulation within 2 h. The established LC-MS/MS method is suitable for the analysis of largazole thiol in human plasma, as well.