A theory of international conflict management and sanctioning

In this paper we analyze sanctioning policies in international law. We develop a model of international military conflict where the conflicting countries can be a target of international sanctions. These sanctions constitute an equilibrium outcome of an international political market for sanctions, where different countries trade political influence. We show that the level of sanctions in equilibrium is strictly positive but limited, in the sense that higher sanctions would exacerbate the military conflict, not reduce it. We then propose an alternative interpretation to the perceived lack of effectiveness of international sanctions, by showing that the problem might not be one of undersanctioning but of oversanctioning.Conflict management, international sanctions, arms embargo, international political market, pressure groups

Competitiveness of U.S. Meats in Japan and South Korea: A Source Differentiated Market Study

The restricted source differentiated almost ideal demand system (RSDAIDS) is used to estimate the parameters of the Japanese and South Korean source differentiated meat demand models. Expenditure and own-price elasticities indicate that Japanese beef, Canadian and Danish pork, and Brazilian and Thai poultry have a competitive advantage in Japan. The BSE outbreak in Japan decreased the shares of Japanese and U.S. beef. Regarding South Korea, the results indicate that imported beef from the U.S. and Australia, Danish pork, and South Korean and Thai poultry have a competitive advantage. The U.S. BSE outbreak decreased the market shares of U.S. beef in the South Korean beef market.BSE, competitive advantage, FMD, Japanese meat demand, RSDAIDS, South Korean Meat Demand, Demand and Price Analysis,

NAFTA Impacts on the U.S. Competitiveness and Trade: Beef, Pork, and Poultry

The restricted source differentiated almost ideal demand system (RSDAIDS) is used to estimate source differentiated meat demand for U.S. NAFTA partners. In the Canadian meat market, the estimated price and expenditure elasticities indicate that Canadian beef has a competitive advantage compared to U.S. beef, while U.S. pork has a competitive advantage compared to Canadian pork. In the Mexican meat market, the estimated expenditure elasticities indicate that an increase in Mexican meat expenditures would lead to an increase in the demand for meats from all sources. Seasonality and Canadian and U.S. BSE outbreaks had small impacts on Canadian and Mexican meat demand.AIDS, BSE impacts, Competitive advantage, Canadian meat demand, Mexican meat demand, source differentiation, International Relations/Trade,

KamLAND and Solar Antineutrino Spectrum

We use the recent KamLAND observations to predict the solar antineutrino spectrum at some confidence limits. We find that a scaling of the antineutrino probability with respect to the magnetic field profile --in the sense that the same probability function can be reproduced by any profile with a suitable peak field value-- can be utilised to obtain a general shape of the solar antineutrino spectrum. This scaling and the upper bound on the solar antineutrino event rate, that can be derived from the data, lead to: 1) an upper bound on the solar antineutrino flux, 2) the prediction of their energy spectrum, as the normalisation of the spectrum can be obtained from the total number of antineutrino events recorded in the experiment. We get $\phi_{\bar\nu}<3.8\times 10^{-3}\phi(^8B)$ or $\phi_{\bar\nu} <5.5\times 10^{-3}\phi(^8B)$ at 95% CL, assuming Gaussian or Poissonian statistics, respectively. And for 90% CL these become $\phi_{\bar\nu}<3.4 \times 10^{-3}\phi(^8B)$ and $\phi_{\bar\nu}<4.9\times 10^{-3}\phi(^8B)$. It shows an improvement by a factor of 3-5 with respect to existing bounds. These limits are quite general and independent of the detailed structure of the magnetic field in the solar interior.Comment: Based on talk given at NANP'03, JINR Dubna, Russia, June 2003. To be published in "Physics of Atomic Nuclie

Food Labels: Implications for U.S. Agricultural Imports

Labels have been used to make food attributes transparent and to satisfy the increasing consumer demand for information about food credence values. Several types of prevalent U.S. food labels, their contributions, and the regulatory agencies behind them are examined in this paper. Additionally, studies dealing with the willingness-to-pay (WTP) for labeled products and the use of food labels as nontariff trade barriers are discussed. While unilateral labeling requirements are identified as a major form of non-tariff trade barriers, positive media influence and trust in the government and science are important factors that affect consumer WTP for food credence characteristics.consumer willingness-to-pay, food credence characteristics, food labeling, non-tariff trade barrier, Agribusiness, Consumer/Household Economics,

Automatic Estimation of the Seafloor Geomorphology of the Santos Basin, Brazil

The bathymetry and acoustic backscatter of Santos Basin, Brazil were mapped using a SeaBeam 2112 (12 kHz, 151 beam) Multibeam Echosounder (MBES) aboard the R/V Falcon Explorer. This MBES data was acquired from January-November, 2000, during a high-resolution multi-channel 3D seismic survey, resulting in 380 parallel lines of 90 km length, spaced 250 m apart. The final survey mapped an area of 5,000 km${}^2$ in water depths of 900--2000 m. These closely spaced multibeam tracks resulted in an average overlap between swaths of 1000%, thereby ensonifying most areas of the seafloor at least ten times. Traditional (hand) processing of a dataset this dense is time-consuming and tedious, and is prone to subjective decisions and operator fatigue. However, the density of the survey makes it ideal for automatic processing methods. Recently, we have developed an algorithm called CUBE that addresses the twin concerns of robustness and reliability that are often raised about automatic processing methods. Based on a very robust multiple hypothesis Bayesian estimator, CUBE processes MBES bathymetry directly into a set of gridded products representing the best estimate of probable depth, and a measure of the uncertainty associated with this estimate. We apply CUBE to the Santos Basin data, illustrating in terms of processing time and human effort the advantages of processing such data automatically. We compare the automatically generated data with a hand-processed set, showing that the results agree to within the estimated experimental uncertainty. We next illustrate the use of CUBE as a data quality measure, indicating areas of concern in the data. Finally, we utilize the bathymetric grid resulting from CUBE to investigate the seafloor morphology, which includes a set of linear depressions parallel and perpendicular to the Shelf break. These linear depressions are the surface expression of fault planes related to subsurface salt walls. In the shallowest part, the detailed bathymetry also shows various pockmarks (350 m wide) possibly associated with fluid expulsion, while in the deeper portion we observe a small number of larger ones (2500 m wide), which are clearly inactive as they are partially filled with recent sediments. Some pockmarks are aligned with fault planes, suggesting a preferential pathway for fluid expulsion. The acquisition geometry for this survey allowed us to analyze the behavior of the backscatter response as a function of grazing angle for any given piece of seafloor, thus eliminating the need to assume a homogeneous seafloor across the swath. Although the backscatter is not calibrated, the variation in response can be used to investigate the effects of gas in shallow sediments of the survey area

Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks

Computational complexity has been the bottleneck of applying physically-based simulations on large urban areas with high spatial resolution for efficient and systematic flooding analyses and risk assessments. To address this issue of long computational time, this paper proposes that the prediction of maximum water depth rasters can be considered as an image-to-image translation problem where the results are generated from input elevation rasters using the information learned from data rather than by conducting simulations, which can significantly accelerate the prediction process. The proposed approach was implemented by a deep convolutional neural network trained on flood simulation data of 18 designed hyetographs on three selected catchments. Multiple tests with both designed and real rainfall events were performed and the results show that the flood predictions by neural network uses only 0.5 % of time comparing with physically-based approaches, with promising accuracy and ability of generalizations. The proposed neural network can also potentially be applied to different but relevant problems including flood predictions for urban layout planning