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Interconnectivity and interoperability are very important features in the development of integrated energy management systems for industrial facilities. A simple and common strategy for exchanging energy-related information among the entities in a facility is currently lacking. To this end, the purpose of this study is to present an IoT-based communication framework with a common information model to facilitate the development of a demand response (DR) energy management system for industrial customers. Additionally, we developed and implemented an IoT-based energy-management platform based on a common information model and open communication protocols, which takes advantage of integrated energy supply networks to deploy DR energy management in an industrial facility. The experimental results of this study demonstrate that the proposed platform can not only improve the interconnectivity of the entities in industrial energy management systems but also reduce the energy costs of industrial facilities.

Cu2CdSn(S,Se)(4) is an important candidate material for thin film solar cell absorber layers. In this work, low-cost Cu2CdSnS4 nanocrytal thin film with a stannite structure has been successfully fabricated by a butyldithiocarbamate-based ethanol solution approach. The selenized Cu2CdSn(S,Se)(4) thin film shows large densely packed grains and has a suitable band gap value of 1.01 eV. The Cu2CdSn(S,Se)(4) thin film solar cell with a proof-of-concept power conversion efficiency of 3.1% was fabricated. (C) 2014 Elsevier B.V. All rights reserved.

In this paper, a three-rotor turboprop engine (with a fixed-speed free turbine) is set as the studied object. Under the certain flight conditions and the transient state control law, the main influencing factors affecting the position of low-pressure rotor transient state line are studied. They are the ratio of MIL (moment of inertia of low pressure rotor) to MIH (moment of inertia of high pressure rotor), the ratio of HPC (high pressure compressor) pressure ratio to LPC (low pressure compressor) pressure ratio and the ratio of design point HPR (high pressure rotor) speed to design point LPR (low pressure rotor) speed. The influence of these three factors on the position of low-pressure rotor transient state line is obtained and the mechanism is analyzed.

The rapid fabrication of starch gels with excellent mechanical properties still presents a challenge. This study aimed to develop a simple alcohol soaking method to fabricate strong starch gels in a short time. The mechanical properties, crystallization behaviors, and structural characteristics of starch gels were investigated by rheological testing, texture profile analysis, differential scanning calorimetry (DSC), X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), and low-field nuclear magnetic resonance (LF-NMR). The mechanical strength of the starch gel increased from 170.21 g to 666.16 g. The gelation time of starch gel decreased from 12 h (traditional retrogradation) to 2 h. DSC indicated that the enthalpy of starch gel increased from 0.88 J/g to 2.90 J/g. XRD analysis showed that the crystallinity of starch gel increased from 3.8% to 8.9% after alcohol soaking. Alcohol dehydrated the starch gels system, so that amylopectins were arranged to form more crystals. This new simple approach, alcohol soaking to form strong starch gels, can expand the applications of starch in food, agriculture, and textiles.

Abstract Accurate estimation of the remaining useful life of lithium-ion batteries is critically important for electronic devices. In the existing literature, the widely applied model-based approaches for remaining useful battery life estimation are limited by the complexity of the electrochemical modeling required. In addition, data-driven approaches for remaining useful battery life estimation commonly define unreliable sliding window sizes empirically and the prediction accuracy of these approaches needs to be improved. To address the above issues, use of a hybrid neural network with the false nearest neighbors method is proposed in this paper. First, the false nearest neighbors method is used to calculate the sliding window size required for prediction. Second, a hybrid neural network that combines the advantages of a convolutional neural network with those of long short-term memory is designed for model training and prediction. Remaining useful life prediction experiments for batteries with various rated capacities are performed to verify the effectiveness of the proposed approach, and the results demonstrate that the proposed approach offers wide generality and reduced errors when compared with the other state-of-the-art methods.