8,696 research outputs found

    Farming Shrimp for the Future: A Sustainability Analysis of Shrimp Farming in China.

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    The intensification of the shrimp farming industry has generated much concern over its environmental, social and economic sustainability. The objective of this dissertation was to conduct a comprehensive sustainability analysis for Chinese shrimp farming. My results could be utilized to evaluate and improve shrimp production systems in terms of environmental sustainability, economic profitability, and social acceptability. Life cycle assessment was conducted to evaluate environmental performance of different shrimp farming systems. Intensive systems had higher environmental impacts per unit production than semi-intensive. The grow-out stage contributed on average 95% of the overall impacts, mainly caused by feed production, electricity use and effluents. To produce 1 tonne live-weight of shrimp in China, 38.3±4.3 GJ of energy and 40.4±1.7 tonnes of net primary productivity were required, and 23.1±2.6 kg of SO2 equivalents (eq), 36.9±4.3 kg of PO4 eq, and 3.1±0.4 tonnes of CO2 eq were generated. Changes in feed composition, farm management, electricity generating sources, and effluent treatment may result in future improvement. Mathematical models were developed to study nutrient dynamics and the effects of management strategies on nutrient dynamics and discharge. Management strategies had significant impacts on nutrient dynamics. Nutrient loading increased with farm intensity. On average, approximately 701 kg N ha-1 cycle-1 (100 days/cycle) and 176 kg P ha-1 cycle-1 were unutilized and wasted. Of them, 120 kg N ha-1 cycle-1 in dissolved form and 62 kg P ha-1 cycle-1 were discharged with effluents. Moderate stocking density and reduced water exchange could minimize environmental impacts of pond effluents and achieve high production. A socioeconomic survey of 100 shrimp farms was conducted to evaluate system profitability, disease risk, and changes in quality of life. Production costs per kilogram of shrimp were highest in intensive systems (2.70),followedbysemi−intensive(2.70), followed by semi-intensive (2.10) and polyculture (1.05)systems.Intensivesystemshadsignificantlyhigherprofits(1.05) systems. Intensive systems had significantly higher profits (9,500 ha-1 crop-1) than the other two systems (< $7,300 ha-1 crop-1). If disease occurred, an average of 78% and 36% of shrimp would die in the worst and most probable cases, respectively. Disease had highest influence on the intensive systems. Quality of life of farmers was significantly improved by shrimp farming.Ph.D.Natural Resources and EnvironmentUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/91554/1/caoling_1.pd

    Linear vs Nonlinear Extreme Learning Machine for Spectral-Spatial Classification of Hyperspectral Image

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    As a new machine learning approach, extreme learning machine (ELM) has received wide attentions due to its good performances. However, when directly applied to the hyperspectral image (HSI) classification, the recognition rate is too low. This is because ELM does not use the spatial information which is very important for HSI classification. In view of this, this paper proposes a new framework for spectral-spatial classification of HSI by combining ELM with loopy belief propagation (LBP). The original ELM is linear, and the nonlinear ELMs (or Kernel ELMs) are the improvement of linear ELM (LELM). However, based on lots of experiments and analysis, we found out that the LELM is a better choice than nonlinear ELM for spectral-spatial classification of HSI. Furthermore, we exploit the marginal probability distribution that uses the whole information in the HSI and learn such distribution using the LBP. The proposed method not only maintain the fast speed of ELM, but also greatly improves the accuracy of classification. The experimental results in the well-known HSI data sets, Indian Pines and Pavia University, demonstrate the good performances of the proposed method.Comment: 13 pages,8 figures,3 tables,articl

    Signature of Pseudo Nambu-Goldstone Higgs boson in its Decay

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    If the Higgs boson is a pseudo Nambu-Goldstone boson (PNGB), the hZγhZ\gamma contact interaction induced by the O(p4)\mathcal{O}(p^4) invariants of the non-linear sigma model is free from its nonlinearity effects. The process h→Zγh\rightarrow Z\gamma can be used to eliminate the universal effects of heavy particles, which can fake the nonlinearity effects of the PNGB Higgs boson in the process h→V∗Vh\rightarrow V^*V (V=W±V=W^\pm,\ ZZ). We demonstrate that the ratio of the signal strength of h→Zγh\rightarrow Z\gamma and h→V∗Vh\rightarrow V^*V is good to distinguish the signature of the PNGB Higgs boson from Higgs coupling deviations
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