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Abstract-This paper presents a robust distributed secondary control (DSC) scheme for inverter-based microgrids (MGs) in a distribution sparse network with uncertain communication links. By using the iterative learning mechanics, two discrete-time DSC controllers are designed, which enable all distributed energy resources (DERs) in a MG to achieve the voltage/frequency restoration and active power sharing accuracy, respectively. In special, the secondary control inputs are merely updated at the end of each round of iteration, and thus each DER only needs to share information with its neighbors intermittently in a lowbandwidth communication manner. This way, the communication costs are greatly reduced, and some sufficient conditions on the system stability and robustness to the uncertainties are also derived by using the tools of Lyapunov stability theory, algebraic graph theory, and matrix inequality theory. The proposed controllers are implemented on local DERs, and thus no central controller is required. Moreover, the desired control objective can also be guaranteed even if all DERs are subject to internal uncertainties and external noises including initial voltage and/or frequency resetting errors and measurement disturbances, which then improves the system reliability and robustness. The effectiveness of the proposed DSC scheme is verified by the simulation of an islanded MG in MATLAB/SimPowerSystems.

The objective of this study is to produce an observationally based monthly evapotranspiration (ET) product using the simple water balance equation across the conterminous United States (CONUS). We adopted the best quality ground and satellite-based observations of the water budget components, i.e., precipitation, runoff, and water storage change, while ET is computed as the residual. Precipitation data are provided by the bias-corrected PRISM observation-based precipitation data set, while runoff comes from observed monthly streamflow values at 592 USGS stream gauging stations that have been screened by strict quality controls. We developed a land surface model-based downscaling approach to disaggregate the monthly GRACE equivalent water thickness data to daily, 0.1258 values. The derived ET computed as the residual from the water balance equation is evaluated against three sets of existing ET products. The similar spatial patterns and small differences between the reconstructed ET in this study and the other three products show the reliability of the observationally based approach. The new ET product and the disaggregated GRACE data provide a unique, important hydro-meteorological data set that can be used to evaluate the other ET products as a benchmark data set, assess recent hydrological and climatological changes, and terrestrial water and energy cycle dynamics across the CONUS. These products will also be valuable for studies and applications in drought assessment, water resources management, and climate change evaluation.

Massive Open Online Courses (MOOCs) is an innovative method in modern education, especially important for autonomous study and the sharing of global excellent education resources. However, it is not easy to implement the teaching process according to the specific characters of students by MOOCs because the number of participants is huge and the teacher cannot identify the characters of students through a face to face interaction. As a new subject combined with different areas, such as economics, sociology, environment, and even engineering, the education of sustainability-related courses requires elaborate consideration of individualized teaching for students from diverse backgrounds and with different learning styles. Although the major MOOC platforms or learning management systems (LMSs) have tried lots of efforts in the design of course system and the contents of the courses for sustainability education, the achievements are still unsatisfied, at least the issue of how to effectively take into account the individual characteristics of participants remains unsolved. A hybrid Neural Network (NN) model is proposed in this paper which integrates a Convolutional Neural Networks (CNN) and with a Gated Recurrent Unit (GRU) based Recurrent Neural Networks (RNN) in an effort to detect individual learning style dynamically. The model was trained by learners' behavior data and applied to predicting their learning styles. With identified learning style for each learner, the power of MOOC platform can be greatly enhanced by being able to offer the capabilities of recommending specific learning path and the relevant contents individually according to their characters. The efficiency of learning can thus be significantly improved. The proposed model was applied to the online study of sustainability-related course based on a MOOC platform with more than 9,400,000 learners. The results revealed that the learners could effectively increase their learning efficiency and quality for the courses when the learning styles are identified, and proper recommendations are made by using our method.

A sound absorption material composed of polymer microparticles and polyurethane (PU) foam with certain geometry cavum has been developed. Its soundabsorbing characteristic was investigated in the impedance tube, according to transfer function method. Measurements show that polymer microparticles have remarkable effect on the absorption performance of the composite material because of their microstructures and features. Several models established for acoustic properties have been adopted to fit the experimental data. The results show that these models fail to predict accurately the acoustic properties of the materials. The sound energy attenuation in polymer microparticles material may most likely consist of two parts, viscous attenuation of air inside the pores and the friction energy caused by the oscillation of polymer particles.

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