Highlights d Long-term in vivo imaging of dendrite differentiation with automated quantitation d Anterograde-polymerizing microtubules subdivide the dendrite tip to create branches d Actin motor Myosin6 stabilizes filopodia-derived actin tails to guide microtubules d Transcription factor Knot regulates Myosin6 for dendrite arbor diversification

Abstract-Energy efficiency in mobile radio networks has recently gained great interest due to escalating energy cost and environmental concerns. Rapidly growing demand for capacity will require denser and denser networks which further increase the energy consumption. In this regard, the deployment of small cells under macro-cellular umbrella coverage appears a promising solution to cope with the explosive demand in an energy efficient manner. In this paper, we investigate the impact of joint macro-and femtocell deployment on energy efficiency of wireless access networks, based on varying area throughput requirements. We take into account the the co-channel interference, fraction of indoor users, femto base station density and backhaul power consumption. It is shown that utilizing indoor base stations provide significant energy savings compared to traditional macro only network in urban areas with medium and high user demand where the gain increases up to 75 percent as more data traffic is offloaded to femtocells.

Combustion is the main source of energy and environmental pollution. The objective of the combustion study is to improve combustion efficiency and to reduce pollution emissions. In the past decades, machine learning (ML), as a branch of artificial intelligence, has attracted increasing interests, especially in the combustion field. In the present work, the definition, current status and recent progress in the applications of ML on researches related to combustion are briefly reviewed. Combustion studies combined with ML can be divided into theoretical and industrial aspects. Studies of combustion theory include computational fluid dynamics (CFD) simulation, combustion phenomenon and fuel. ML is used to reduce the cost of CFD, including reducing the scale of combustion mechanism, saving the memory storage of the probability density function table and optimizing Large Eddy Simulation. ML helps in the research of combustion phenomena, such as detecting thermoacoustic combustion oscillation, portioning regimes of ignition and detonation, and reconstructing cellular surface of gaseous detonation. ML has been also applied to study physicochemical properties of fuels and to design the next generation fuels.In the industrial research with respect to combustion, ML is mainly applied to produce electricity and power by power plants or engines, and less to other fields. ML could figure out problems of combustion in various kinds of furnaces and postcombustion emissions in power plants. In addition, ML plays important roles in biodiesel engine, Homogenous Charge Compression Ignition (HCCI), and operation control or monitoring in the engines. Moreover, ML can also be applied to other industrial studies related to combustion, mainly to particulate matters. The methods of the mentioned studies are summarized in details and the potential applications of ML in combustion community are proposed.

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