Orbital Deflection of Comets by Directed Energy

Cometary impacts pose a long-term hazard to life on Earth. Impact mitigation techniques have been studied extensively, but they tend to focus on asteroid diversion. Typical asteroid interdiction schemes involve spacecraft physically intercepting the target, a task feasible only for targets identified decades in advance and in a narrow range of orbits---criteria unlikely to be satisfied by a threatening comet. Comets, however, are naturally perturbed from purely gravitational trajectories through solar heating of their surfaces which activates sublimation-driven jets. Artificial heating of a comet, such as by a laser, may supplement natural heating by the Sun to purposefully manipulate its path and thereby avoid an impact. Deflection effectiveness depends on the comet's heating response, which varies dramatically depending on factors including nucleus size, orbit and dynamical history. These factors are incorporated into a numerical orbital model to assess the effectiveness and feasibility of using high-powered laser arrays in Earth orbit and on the ground for comet deflection. Simulation results suggest that a diffraction-limited 500 m orbital or terrestrial laser array operating at 10 GW for 1% of each day over 1 yr is sufficient to fully avert the impact of a typical 500 m diameter comet with primary nongravitational parameter A1 = 2 x 10^-8 au d^-2. Strategies to avoid comet fragmentation during deflection are also discussed.Comment: 13 pages, 12 figures; AJ, in pres

Biological Characteristics and Fishery Assessment of Alaska Plaice, Pleuronectes quadrituberculatus, in the Eastern Bering Sea

Alaska plaice, Pleuronectes quadrituberculatus, is one of the major flatfishes in the eastern Bering Sea ecosystem and is most highly concentrated in the shallow continental shelf of the eastern Bering Sea. Annual commercial catches have ranged from less than 1,000 metric tons (t) in 1963 to 62,000 t in 1988. Alaska plaice is a relatively large flatfish averaging about 32 cm in length and 390 g in weight in commercial catches. They are distributed from nearshore waters to a depth of about 100 m in the eastern Bering Sea during summer, but move to deeper continental shelf waters in winter to escape sea ice and cold water temperatures. Being a long-lived species (>30 years), they have a relatively low natural mortality rate estimated at 0.20. Maturing at about age 7, Alaska plaice spawn from April through June on hard sandy substrates of the shelf region, primarily around the 100 m isobath. Prey items primarily include polychaetes and other marine worms. In comparison with other flatfish, Alaska plaice and rock sole, Pleuronectes bilineatus, have similar diets but different habitat preferences with separate areas of peak population density which may minimize interspecific competition. Yellowfin sole, Pleuronectes asper, while sharing similar habitat, differs from these two species because of the variety of prey items in its diet. Competition for food resources among the three species appears to be low. The resource has experienced light exploitation since 1963 and is currently in good condition. Based on the results of demersal trawl surveys and age-structured analyses, the exploitable biomass increased from 1971 through the mid-1980’s before decreasing to the 1997 level of 500,000 t. The recommended 1998 harvest level, Allowable Biological Catch, was calculated from the Baranov catch equation based on the FMSY harvest level and the projected 1997 biomass, resulting in a commercial harvest of 69,000 t, or about 16% of the estimated exploitable biomass

A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification

Convolutional auto-encoders have shown their remarkable performance in stacking to deep convolutional neural networks for classifying image data during past several years. However, they are unable to construct the state-of-the-art convolutional neural networks due to their intrinsic architectures. In this regard, we propose a flexible convolutional auto-encoder by eliminating the constraints on the numbers of convolutional layers and pooling layers from the traditional convolutional auto-encoder. We also design an architecture discovery method by using particle swarm optimization, which is capable of automatically searching for the optimal architectures of the proposed flexible convolutional auto-encoder with much less computational resource and without any manual intervention. We use the designed architecture optimization algorithm to test the proposed flexible convolutional auto-encoder through utilizing one graphic processing unit card on four extensively used image classification datasets. Experimental results show that our work in this paper significantly outperform the peer competitors including the state-of-the-art algorithm.Comment: Accepted by IEEE Transactions on Neural Networks and Learning Systems, 201