Economics of Homeland Security: Carcass Disposal and the Design of Animal Disease Defense

In an effort to bolster confidence and protect the nation the U.S. government through agencies like the Department of Homeland Security is identifying vulnerabilities and evolving strategies for protection. Agricultural food supply is one identified vulnerable area, and animal disease defense is one of the major concerns there under. Should an outbreak of animal disease occur, it is likely to have a mass slaughter and disposal of animal carcasses. The current existing policy, mainly including slaughter policy and strict movement bans, may be not sufficient to control disease spread at reasonable cost. We address the issue modeling vaccination as a supporting strategy with later slaughter of animals and argue that vaccination can decrease slaughter and disposal cost in the case of emergency. Our results show that (a) Vaccination gains time to slow down the flow of slaughter, thereafter the disposal operation of animal carcasses. By smoothing slaughter/disposal flow, vaccination likely decreases slaughter and disposal cost; (b) Vaccination likely reduce the total amount of slaughter and disposal of animals mainly because vaccinated animals shed less and disease spread slower; and (c) Vaccination becomes more valuable in reducing slaughter and disposal costs when the marginal cost of vaccination falls, the even size of disease outbreak is larger, the disease is more contagious and spreads faster, and/or vaccines are more effective in controlling disease spread.Food Consumption/Nutrition/Food Safety,

Microstructure evolution under the space-time variational solidification conditions in a melt pool: A multi-scale simulation study

The properties of welded components are dominated by the microstructure evolution in the pool, where the solidification conditions are space-time variational. To represent the variational solidification conditions in the pool, the multi-scale simulation is carried out in this paper, combining microscopic Phase-Field (PF) equations with macroscopic thermal processes. First, two different models, the GR model and TF model, are employed to simulate the single crystal solidification at a local region of pool. Results suggest the TF model is more suitable to reflect the variational conditions than the GR model. Then the single-crystal solidification and poly-crystal solidification at the whole region of pool are carried out through the TF model. The results indicate the space-time variabilities of solidification conditions across the pool. Meanwhile, the variational solidification conditions influence the microstructure evolution significantly, including the onset of initial instability at the epitaxial growth stage and the directional evolutions of the converging grain boundaries (GBs) and diverging GBs at the competitive growth stage. Moreover, the formation of axial grain structures is observed, which can be regarded as the competition between the grains along the axial direction and radial direction. This study indicates the necessity of considering variational conditions in a pool. Meanwhile, the PF model can simulate microstructure evolution under variational conditions accurately, which has a great potential for investigating solidification dynamics in a melt pool.Comment: 30pages, 14 figure

In Vitro Antimicrobial Activities of Organic Acids and Their Derivatives on Several Species of Gram-Negative and Gram-Positive Bacteria.

The objective of this study was to determine the in vitro antimicrobial activity of several organic acids and their derivatives against Gram-positive (G+) and Gram-negative (G-) bacteria. Butyric acid, valeric acid, monopropionin, monobutyrin, monovalerin, monolaurin, sodium formate, and ProPhorce-a mixture of sodium formate and formic acid (40:60 w/v)-were tested at 8 to 16 concentrations from 10 to 50,000 mg/L. The tested bacteria included G- bacteria (Escherichia coli, Salmonella enterica Typhimurium, and Campylobacter jejuni) and G+ bacteria (Enterococcus faecalis, Clostridium perfringens, Streptococcus pneumoniae, and Streptococcus suis). Antimicrobial activity was expressed as minimum inhibitory concentration (MIC) of tested compounds that prevented growth of tested bacteria in treated culture broth. The MICs of butyric acid, valeric acid, and ProPhorce varied among bacterial strains with the lowest MIC of 500-1000 mg/L on two strains of Campylobacter. Sodium formate at highest tested concentrations (20,000 mg/L) did not inhibit the growth of Escherichia coli, Salmonella Typhimurium, and Enterococcus faecalis, but sodium formate inhibited the growth of other tested bacteria with MIC values from 2000 to 18,800 mg/L. The MIC values of monovalerin, monolaurin, and monobutyrin ranged from 2500 to 15,000 mg/L in the majority of bacterial strains. Monopropionin did not inhibit the growth of all tested bacteria, with the exception that the MIC of monopropionin was 11,300 mg/L on Clostridia perfringens. Monolaurin strongly inhibited G+ bacteria, with the MIC value of 10 mg/L against Streptococcus pneumoniae. The MIC tests indicated that organic acids and their derivatives exhibit promising antimicrobial effects in vitro against G- and G+ bacteria that are resistant to antimicrobial drugs. The acid forms had stronger in vitro antimicrobial activities than ester forms, except that the medium chain fatty acid ester monolaurin exhibited strong inhibitory effects on G+ bacteria

A Survey of Geometric Optimization for Deep Learning: From Euclidean Space to Riemannian Manifold

Although Deep Learning (DL) has achieved success in complex Artificial Intelligence (AI) tasks, it suffers from various notorious problems (e.g., feature redundancy, and vanishing or exploding gradients), since updating parameters in Euclidean space cannot fully exploit the geometric structure of the solution space. As a promising alternative solution, Riemannian-based DL uses geometric optimization to update parameters on Riemannian manifolds and can leverage the underlying geometric information. Accordingly, this article presents a comprehensive survey of applying geometric optimization in DL. At first, this article introduces the basic procedure of the geometric optimization, including various geometric optimizers and some concepts of Riemannian manifold. Subsequently, this article investigates the application of geometric optimization in different DL networks in various AI tasks, e.g., convolution neural network, recurrent neural network, transfer learning, and optimal transport. Additionally, typical public toolboxes that implement optimization on manifold are also discussed. Finally, this article makes a performance comparison between different deep geometric optimization methods under image recognition scenarios.Comment: 41 page