• Energy Research
• Closed Access close
• Restricted close
• Open Source close
• CN close
• GB close
• AU close

Low-salinity water injection emerges to be a cost-effective and environmentally friendly enhanced oil recovery technique. Furthermore, additives, such as the surfactant and nanoparticles in combina...

SummaryNumerous wind turbines form large‐scale wind farms, which are complex nonlinear systems with uncertain parameters. The issue of maximum wind energy capture and coordinated control has always been a research hotspot. In this article, under the condition of limited input and uncertain parameters, the preset controller and the adaptive cooperation control are designed to realize the maximum wind energy capture for every wind turbine and the adaptive cooperation control of multiple wind turbines. The research gap lies in that the Hamilton model of wind power generation system is established with uncertain parameters, and the preset controller (method) is designed to capture the maximum wind energy. Under the hypothesis that the uncertain part can be expressed as a linear form about unknown parameter, and using the saturation function processing method in the diagonal matrix, an adaptive feedback controller with limited input is designed to realize the adaptive cooperation control of multiple wind turbines. The simulation results show that under the conditions such as variable wind speed, limited input and uncertain parameters, the wind turbine remain normal operation at the desired angular velocity. It can be concluded that not only the maximum wind energy capture is realized under the condition of variable wind speed, in which the wind turbine can operate on the optimal power curve to improve the utilization of wind energy, but also the adaptive cooperation control of multiple wind turbines can be achieved with limited input and parameter perturbation.

AbstractThe brief exposure of recently ovulated mouse oocytes to a dilute solution of ethanol in vitro for 1, 3, or 5 min induced a uniform high incidence of parthenogenetic activation. The majority of parthenogenones developed a single haploid pronucleus after the extrusion of a second polar body. The proportionate incidence of this parthenogenetic class was significantly reduced as the duration of ethanol exposure increased from 1 min to 5 min. There was a concomitant increase in the incidence of parthenogenones that developed two haploid pronuclei following failure of extrusion of the second polar body. Cytogenetic analysis of the ethanol‐induced single‐pronuclear haploid parthenogenones at metaphase of the first cleavage division clearly demonstrated that a significant proportion were aneuploid. The incidence of aneuploidy observed was directly related to the duration of ethanol exposure. G‐band analysis of the aneuploid metaphases revealed that the chromosomes were not randomly involved in the malsegregation events. This observation may be a reflection of the relationship of particular chromosomes to the meiotic spindle apparatus rather than on any specific property of the agent to which they were exposed. It is believed that ethanol disrupts the organisation of cytoskeletal elements and, in particular, interferes with the processes of chromosome segregation at the second meiotic division.

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.