van der Waals layered structures, notably the transitional metal dichalcogenides (TMDs) and TMD-based heterostructures, have recently attracted immense interest due to their unique physical properties and potential applications in electronics, optoelectronics, and energy harvesting. Despite the recent progress, it is still a challenge to perform comprehensive characterizations of critical properties of these layered structures, including crystal structures, chemical dynamics, and interlayer coupling, using a single characterization platform. In this study, we successfully developed a multimodal nonlinear optical imaging method to characterize these critical properties of molybdenum disulfide (MoS2) and MoS2-based heterostructures. Our results demonstrate that MoS2 layers exhibit strong four-wave mixing (FWM), sum-frequency generation (SFG), and second-harmonic generation (SHG) nonlinear optical characteristics. We believe this is the first observation of FWM and SFG from TMD layers. All three kinds of optical nonlinearities are sensitive to layer numbers, crystal orientation, and interlayer coupling. The combined and simultaneous SHG/SFG-FWM imaging not only is capable of rapid evaluation of crystal quality and precise determination of odd-even layers but also provides in situ monitoring of the chemical dynamics of thermal oxidation in MoS2 and interlayer coupling in MoS2-graphene heterostructures. This method has the advantages of versatility, high fidelity, easy operation, and fast imaging, enabling comprehensive characterization of van der Waals layered structures for fundamental research and practical applications.

7539c is not very small, the decay of the IP can be reproduced approximately by a single exponential. In this case, the rate of C R deactivation of CIP and its solvation to form LIP are rather slow and may be close to the rate of CR deactivation and dissociation of LIP, which seems to make difficult the detection of the double-exponential decay of IP.(d) The C R rate constant of CIP (k,) is larger than that of LIP (k,) in the case of TCNB-toluene and -xylene complexes in acetonitrile solution where both kinds of CR rate constants have been obtained, in agreement with the general tendency observed thus far in the case of various donor-acceptor systems.I*I5 The energy gap dependence of the CR rate constant of CIP formed by excitation of TCNB complexes in acetonitrile solution including a wide range of aromatic hydrocarbon donors of various strength has been confirmed to agree qualitatively with the previous result that the C R rate increases exponentially with decrease of the energyIn this way, we have examined a wide range of aromatic hydrocarbon donors by changing their oxidation potentials, molecular structures including their size, etc., in the case of TCNB complexes in relation to their photoinduced CS and ionic dissociation mechanisms, and have established the existence of several different cases concerning the behaviors of IP by referring also to our previous results.615In the above discussions, we have assumed the existence of SSIP in addition to CIP in the course of ionic dissociation. The results in case a seem to indicate strongly the existence of such an intermediate state of ionic dissociation. In case c, however, the existence of such an intermediate state in the course of ionic dissociation is not so evident as in case a. Moreover, some model calculations21*22 on ion pair-solvent dipole systems indicate the existence of LIPS with more intervening solvents between ions in the pair than SSIP, that is, the existence of the multiple kinds of IPS in the course of the dissociation. Actually, we have obtained some results that indicated the existence of multiple kinds of IPS in the course of CR and d i s s o c i a t i~n ,~~ which will be discussed elsewhere. N.M. acknowledges the support by a Grant-in-Aid (No. 62065006) from the Japanese Ministry of Education, Science and Culture. Acknowledgment. ~ (21) Levesque, D.; Weis, J. J.; Patey, G. N. J. Chem. Phys. 1980, 72, 1887. (22) Salem, L. Electrons in Chemical Reactions: First Principles; Wi-(23) Mataga, N.; Kanda, Y.; Asahi, T.; Hirata, Y.; Okada, T., manuscript (24) Gould, I. R.; Moser, J. E.; Ege, D.; Farid, S. J. Am. Chem. Soc. 1988.Photophysical properties of cis-stilbene and several homologues are investigated in supersonic expansions as free molecules and in large Ar clusters. Assignment of sharp vibrational structure in the spectra of 1,2-diphenylcyclobutene to long progressions in phenyl twisting and bending vibrations shows that in the SI state cis-stilbene phenyl twist angles are more planar and the phenyl-ethylene bend angles are more bent than i...

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The largest obstacle that suppresses the increase of wind power penetration within the power grid is uncertainties and fluctuations in wind speeds. Therefore, accurate wind power forecasting is a challenging task, which can significantly impact the effective operation of power systems. Wind power forecasting is also vital for planning unit commitment, maintenance scheduling and profit maximisation of power traders. The current development of cost-effective operation and maintenance methods for modern wind turbines benefits from the advancement of effective and accurate wind power forecasting approaches. This paper systematically reviewed the state-of-the-art approaches of wind power forecasting with regard to physical, statistical (time series and artificial neural networks) and hybrid methods, including factors that affect accuracy and computational time in the predictive modelling efforts. Besides, this study provided a guideline for wind power forecasting process screening, allowing the wind turbine/farm operators to identify the most appropriate predictive methods based on time horizons, input features, computational time, error measurements, etc. More specifically, further recommendations for the research community of wind power forecasting were proposed based on reviewed literature.

Effective wind power prediction will facilitate the world's long-term goal in sustainable development. However, a drawback of wind as an energy source lies in its high variability, resulting in a challenging study in wind power forecasting.To solve this issue, a novel data-driven approach is proposed for wind power forecasting by integrating data pre-processing & re-sampling, anomalies detection & treatment, feature engineering, and hyperparameter tuning based on gated recurrent deep learning models, which is systematically presented for the first time. Besides, a novel deep learning neural network of Gated Recurrent Unit (GRU) is successfully developed and critically compared with the algorithm of Long Short-term Memory (LSTM). Initially, twelve features were engineered into the predictive model, which are wind speeds at four different heights, generator temperature, and gearbox temperature. The simulation results showed that, in terms of wind power forecasting, the proposed approach can capture a high degree of accuracy at lower computational costs. It can also be concluded that GRU outperformed LSTM in predictive accuracy under all observed tests, which provided faster training process and less sensitivity to noise in the used Supervisory Control and Data Acquisition (SCADA) datasets.

Accurate wind power forecasting is essential for efficient operation and maintenance (O&M) of wind power conversion systems. Offshore wind power predictions are even more challenging due to the multifaceted systems and the harsh environment in which they are operating. In some scenarios, data from Supervisory Control and Data Acquisition (SCADA) systems are used for modern wind turbine power forecasting. In this study, a deep learning neural network was constructed to predict wind power based on a very high-frequency SCADA database with a sampling rate of 1-second. Input features were engineered based on the physical process of offshore wind turbines, while their linear and non-linear correlations were further investigated through Pearson product-moment correlation coefficients and the deep learning algorithm, respectively. Initially, eleven features were used in the predictive model, which are four wind speeds at different heights, three measured pitch angles of each blade, average blade pitch angle, nacelle orientation, yaw error, and ambient temperature. A comparison between different features shown that nacelle orientation, yaw error, and ambient temperature can be reduced in the deep learning model. The simulation results showed that the proposed approach can reduce the computational cost and time in wind power forecasting while retaining high accuracy.

Wind power plays a key role in reducing global carbon emission. The power curve provided by wind turbine manufacturers offers an effective way of presenting the global performance of wind turbines. However, due to the complicated dynamics nature of offshore wind turbines, and the harsh environment in which they are operating, wind power forecasting is challenging, but at the same time vital to enable condition monitoring (CM). Wind turbine power prediction, using supervisory control and data acquisition (SCADA) data, may not lead to the optimum control strategy as sensors may generate non-calibrated data due to degradation. To mitigate the adverse effects of outliers from SCADA data on wind power forecasting, this paper proposed a novel approach to perform power prediction using high-frequency SCADA data, based on isolate forest (IF) and deep learning neural networks. In the predictive model, wind speed, nacelle orientation, yaw error, blade pitch angle, and ambient temperature were considered as input features, while wind power is evaluated as the output feature. The deep learning model has been trained, tested, and validated against SCADA measurements. Compared against the conventional predictive model used for outlier detection, i.e. based on Gaussian processes, the proposed integrated approach, which coupled IF and deep learning, is expected to be a more efficient tool for anomaly detection in wind power prediction.