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Systematic Biology is the bimonthly journal of the Society of Systematic Biologists. Papers for the journal are original contributions to the theory, principles, and methods of systematics as well as phylogeny, evolution, morphology, biogeography, paleontology, genetics, and the classification of all living things. A Points of View section offers a forum for discussion, while book reviews and announcements of general interest are also featured. For more information about the journal, see http://sysbio.oxfordjournals.org/.

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Arslan Tariq's Curriculum Vitae Created with the Deedy résumé template

Energy consumption is one of the most critical issues in the manufacturing industry. The modeling, analysis and improvement of energy cost and consumption in multistage production system have been widely studied in many research works. To summarize the latest development of research of energy consumption, a large amount of research work have been investigated. The review work includes effect of design for the energy consumption and interactions between many aspects related to industry. This research research combines energy systems from microscopic to macroscopic, which includes machine, manufacturing line and factory level. This document begins with a review of energy consumption on machine level. Including the schema for machine states and transition of energy, the process model for energy analysis and improvement methods. These topics are further discussed in detail for different processes, eg. forming process, additive processes, etc. In the next part of the review the researches on energy consumption for the multi-machine/manufacturing line level are introduced. Include the define of manufacturing line level, the logical benchmarking of manufacturing lines, and the utilization of energy flows. At last, the detailed way for the factory level energy management are studied. Including the factory energy management system and the method of reduce energy consumption.

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Thesis template for the MME program at Saint Martin's University based on Dissertate.

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Master Thesis Template Computer Science, University of Genova

About Adversarial Machine Learning