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Background Influenza A viruses of swine (IAV-S) cause acute and subclinical respiratory disease. To increase our understanding of the etiology of the subclinical form and thus help prevent the persistence of IAV-S in pig populations, we conducted active virologic surveillance in Vietnam, the second-largest pig-producing country in Asia, from February 2010 to December 2013. Results From a total of 7034 nasal swabs collected from clinically healthy pigs at 250 farms and 10 slaughterhouses, we isolated 172 IAV-S from swine at the weaning and early-fattening stages. The isolation rate of IAV-S was significantly higher among pigs aged 3 weeks to 4.5 months than in older and younger animals. IAV-S were isolated from 16 large, corporate farms and 6 family-operated farms from among the 250 farms evaluated. Multivariate logistic regression analysis revealed that “having more than 1,000 pigs” was the most influential risk factor for IAV-S positivity. Farms affected by reassortant IAV-S had significantly larger pig populations than did those where A(H1N1)pdm09 viruses were isolated, thus suggesting that large, corporate farms serve as sites of reassortment events. Conclusions We demonstrate the asymptomatic circulation of IAV-S in the Vietnamese pig population. Raising a large number of pigs on a farm has the strongest impact on the incidence of subclinical IAV-S infection. Given that only some of the corporate farms surveyed were IAV-S positive, further active monitoring is necessary to identify additional risk factors important in subclinical infection of pigs with IAV-S in Vietnam. Electronic supplementary material The online version of this article (doi:10.1186/s12917-016-0844-z) contains supplementary material, which is available to authorized users.

Ground penetrating radar (GPR) systems emit electromagnetic energy into ground and receive reflection signals to process and display images of objects underground. The technology can be applied to variety of fields such as military, constructions, geophysics, ... In the paper, we will propose the prediction deconvolution technique for signal processing in GPR systems. The technique is developed based on the method of Least Square filter and Wiener filter. Our processed results have shown that by applying the proposed technique, received signals will be eliminated interference and give better images with high resolution. In addition, to get good results we see that it is necessary to predict the accuracy of pulse response of environments.

This paper presents the design and evaluation of an effective market-clearing scheme for trading demand response (DR) in a deregulated power system. The proposed scheme is called demand response exchange (DRX), in which DR is treated as a public good to be exchanged between two groups of participating agents, namely DR buyers and DR sellers. While buyers require DR and are willing to pay for it, sellers have the capacity to curtail customer loads to supply DR on request. The DRX market clearing scheme uses Walrasian auctions, where agents update their DR quantity bids in response to prices adjusted by the market operator. This auction is repeated iteratively until market equilibrium is obtained at the point where the market outcome is proven to be Pareto optimal. The proposed scheme is tested on a small power system and its effectiveness substantiated.

In restructured power systems, there are many independent players who benefit from demand response (DR). These include the transmission system operator (TSO), distributors, retailers, and aggregators. This paper proposes a new concept-demand response eXchange (DRX)-in which DR is treated as a public good to be exchanged between DR buyers and sellers. Buyers need DR to improve the reliability of their own electricity-dependent businesses and systems. Sellers have the capacity to significantly modify electricity demand on request. Microeconomic theory is applied to model the DRX in the form of a pool-based market. In this market, a DRX operator (DRXO) collects DR bids and offers from the buyers and sellers, respectively. It then clears the market by maximizing the total market benefit subject to certain constraints including: demand-supply balance, and assurance contracts related to individual buyer contributions for DR. The DRX model is also tested on a small power system, and its efficiency is reported.

The concept of demand response (DR) market, which recently introduced, is a comprehensive approach to schedule DR. DR market is a separate market in which DR is treated as a virtual resource to be exchanged between DR buyers and sellers. The major advantage of the DR market in comparison to other DR proposals is that it allocates benefits and payments across all participants, fairly. However, there are still obstacles to its integration into the existing power markets. This paper tries to address technical and economical issues towards integrating DR market into energy/reserve markets. For this purpose, a bilevel approach is proposed for clearing joint energy/reserve and DR markets whose upper-level problem determines system requirements for reserve using N-1 contingency criteria, and whose lower-level problem clears the DR market. The resulting nonlinear bilevel programming problem is translated into an equivalent single-level mixed-integer linear programming problem by replacing the lower-level problem by its Karush– Kuhn–Tucker optimality conditions and converting a number of nonlinearities to linear equivalents using some well-known techniques. Finally, a simple case study is used to verify the efficiency of the proposed approach.

Load recovery is a major problem in scheduling demand response (DR) in a power system where they significantly increase the cost of electricity supply during off-peak periods. This paper analyses such intertemporal recovery effects on each customer and each involved stakeholder, using a novel financial valuation method termed `securitization'. Based on this local analysis, a global market clearing system is designed with the aim of maximizing a total market surplus, derived from the recovery-based DR, for all customers and stakeholders. The proposed DR scheduling approach via the securitization and market clearing is tested and its efficiency reported. Its potential applications as part of the future smart grid is also discussed.

This paper proposes a novel assessment framework for estimating short-term financial benefits of demand response (DR) to be delivered to a deregulated power system. First, the framework analyzes DR costs and benefits for participants in a DR market, and for generators and retailers in the electricity trading market. Based on these local analyses, a global evaluation is performed to determine whether the optimized DR can produce a positive social surplus. If so, the DR will be dispatched during the time period under consideration. Case study is given on the Roy Billinton test system (RBTS) to show the usefulness of the proposed assessment framework and to examine its scalability.

This paper introduces a new and separated market for the trading of Demand Response (DR) in a restructured power system. This market is called Demand Response eXchange or shortly DRX, in which DR is treated as a virtual resource to be exchanged between DR buyers and sellers. Buyers — including the Transmission System Operator (TSO), distributors, and retailers — need DR to improve the efficiency and reliability of their own businesses and systems. Sellers — including consumers though an aggregator — negotiate DR with those buyers. The DRX is modeled in the form of a pool-based market using microeconomic theory. In this market, a DRX Operator (DRXO) collects bids and offers from DR buyers and sellers respectively. It then clears the market by maximizing the total market benefit subject to certain operational constraints. The DRX model is also tested on a small power system in comparison to conventional DR approaches, and its advantages are reported.