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Duckweed can be utilised to produce ethanol, butanol and biogas, which are promising alternative energy sources to minimise dependence on limited crude oil and natural gas. The advantages of this aquatic plant include high rate of nutrient (nitrogen and phosphorus) uptake, high biomass yield and great potential as an alternative feedstock for the production of fuel ethanol, butanol and biogas. The objective of this article is to review the published research on growing duckweed for the production of the biofuels, especially starch enrichment in duckweed plants. There are mainly two processes affecting the accumulation of starch in duckweed biomass: photosynthesis for starch generation and metabolism-related starch consumption. The cost of stimulating photosynthesis is relatively high based on current technologies. Considerable research efforts have been made to inhibit starch degradation. Future research need in this area includes duckweed selection, optimisation of duckweed biomass production, enhancement of starch accumulation in duckweeds and use of duckweeds for production of various biofuels.

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The optimal scheduling of interfering links in a dense wireless network with full frequency reuse is a challenging task. The traditional method involves first estimating all the interfering channel strengths then optimizing the scheduling based on the model. This model-based method is however resource intensive and computationally hard, because channel estimation is expensive in dense networks; further, finding even a locally optimal solution of the resulting optimization problem may be computationally complex. This paper shows that by using a deep learning approach, it is possible to bypass channel estimation and to schedule links efficiently based solely on the geographic locations of transmitters and receivers for networks in which the channels are largely functions of distance dependent path-losses. This is accomplished by unsupervised training over randomly deployed networks, and by using a novel neural network architecture that takes the geographic spatial convolutions of the interfering or interfered neighboring nodes as input over multiple feedback stages to learn the optimum solution. The resulting neural network gives near-optimal performance for sum-rate maximization and is capable of generalizing to larger deployment areas and to deployments of different link densities. Moreover, to provide fairness, this paper proposes a novel scheduling approach that utilizes the sum-rate optimal scheduling algorithm over judiciously chosen subsets of links for maximizing a proportional fairness objective over the network. The proposed approach shows highly competitive and generalizable network utility maximization results.