Identification of Streptococcus gallolyticus subsp. macedonicus as the etiological agent in a case of culture-negative multivalve infective endocarditis by 16S rDNA PCR analysis of resected valvular tissue

Today, PCR using broad-range primers is being used increasingly to detect pathogens from resected heart valves. Herein is described the first case of multivalve infective endocarditis where 16S rDNA PCR was used to detect a single pathogen from two affected valves in a 61-year-old man. Triple heart valve replacement was required despite six weeks of appropriate antimicrobial therapy. The organism was confirmed as Streptococcus gallolyticus subsp. macedonicus, a member of the 'S. equinus/S. bovis' complex. To date, only one report has been made of human infection due to this organism. This may be due to the limited resolution of the routine diagnostic methods used and/or as a consequence of the complex nomenclature associated with this group of organisms.

A pyrophosphate-responsive gadolinium(III) MRI contrast agent

This study shows that the relaxivity and optical properties of functionalised lanthanide-DTPA-bis-amide complexes (lanthanide=Gd3+ and Eu3+, DTPA=diethylene triamine pentaacetic acid) can be successfully modulated by addition of specific anions, without direct Ln3+/anion coordination. Zinc(II)-dipicolylamine moieties, which are known to bind strongly to phosphates, were introduced in the amide “arms” of these ligands, and the interaction of the resulting Gd–Zn2 complexes with a range of anions was screened by using indicator displacement assays (IDAs). Considerable selectivity for polyphosphorylated species (such as pyrophosphate and adenosine-5′-triphosphate (ATP)) over a range of other anions (including monophosphorylated anions) was apparent. In addition, we show that pyrophosphate modulates the relaxivity of the gadolinium(III) complex, this modulation being sufficiently large to be observed in imaging experiments. To establish the binding mode of the pyrophosphate and gain insight into the origin of the relaxometric modulation, a series of studies including UV/Vis and emission spectroscopy, luminescence lifetime measurements in H2O and D2O, 17O and 31P NMR spectroscopy and nuclear magnetic resonance dispersion (NMRD) studies were carried out.

A multi-agent-based evolution model of innovation networks in dynamic environments

An innovation network can be considered as a complex adaptive system with evolution affected by dynamic environments. This paper establishes a multi-agent-based evolution model of innovation networks under dynamic settings through computational and logical modeling, and a multi-agent system paradigm. This evolution model is composed of several sub-models of agents' knowledge production by independent innovations in dynamic situations, knowledge learning by cooperative innovations covering agents' heterogeneities, decision-making for innovation selections, and knowledge update considering decay factors. On the basis of above-mentioned sub-models, an evolution rule for multi-agent based innovation network system is given. The proposed evolution model can be utilized to simulate and analyze different scenarios of innovation networks in various dynamic environments and support decision-making for innovation network optimization.

Distributed Agent-Based Load Balancer for Cloud Computing

In this paper we present a concept of an agent-based strategy to allocate services on a Cloud system without overloading nodes and maintaining the system stability with minimum cost. To provide a base for our research we specify an abstract model of cloud resources utilization, including multiple types of resources as well as considerations for the service migration costs. We also present an early version of simulation environment and a prototype of agent-based load balancer implemented in functional language Scala and Akka framework.

Agent-Based Modeling of Deshopping Behaviour: A Single Shop Model with Multiple Deshoppers

Deshopping is rapidly turning into a modern day scourge for the retailers worldwide due to its prevalence and regularity. The presence of flexible return policies have made retail return management a real challenging issue for both the present and the future. In this study, we propose and develop a multi-agent simulation model for deshopper behavior in a single shop context. The background, theoretical underpinning, logical and computational model, experiment design and simulation results are reported and discussed in the paper.

Electrospun Contrast Agent-Loaded Fibers for Colon-Targeted MRI

Magnetic resonance imaging is a diagnostic tool used for detecting abnormal organs and tissues, often using Gd(III) complexes as contrast-enhancing agents. In this work, core–shell polymer fibers have been prepared using coaxial electrospinning, with the intent of delivering gadolinium (III) diethylenetriaminepentaacetate hydrate (Gd(DTPA)) selectively to the colon. The fibers comprise a poly(ethylene oxide) (PEO) core loaded with Gd(DTPA), and a Eudragit S100 shell. They are homogeneous, with distinct core–shell phases. The components in the fibers are dispersed in an amorphous fashion. The proton relaxivities of Gd(DTPA) are preserved after electrospinning. To permit easy visualization of the release of the active ingredient from the fibers, analogous materials are prepared loaded with the dye rhodamine B. Very little release is seen in a pH 1.0 buffer, while sustained release is seen at pH 7.4. The fibers thus have the potential to selectively deliver Gd(DTPA) to the colon. Mucoadhesion studies reveal there are strong adhesive forces between porcine colon mucosa and PEO from the core, and the dye-loaded fibers can be successfully used to image the porcine colon wall. The electrospun core–shell fibers prepared in this work can thus be developed as advanced functional materials for effective imaging of colonic abnormalities.

Agent-Based Simulation of the Evolution of Enterprise Carbon Assets

Carbon assets have the value of carbon emission reduction in enterprises and are closely relevant to business images and competitiveness. In this paper, the connotation of carbon assets is clarified. The definition of carbon assets in enterprise business contexts are also provided. In addition, an interactive evolution framework is established to demonstrate the emergent property of carbon assets using multi-agent-based simulation, which can bring a new perspective for enterprises to manage their carbon assets and improve low-carbon competitiveness.

DITRANS - a multi-agent system for integrated diagnosis of power transformers

This paper describes the development and the implementation of a multi-agent system for integrated diagnosis of power transformers. The system is divided in layers which contain a number of agents performing different functions. The social ability and cooperation between the agents lead to the final diagnosis and to other relevant conclusions through integrating various monitoring technologies, diagnostic methods and data sources, such as the dissolved gas analysis.

MASCEM improvement for agent based coalitions

This paper presents MASCEM - Multi-Agent Simulator for Electricity Markets improvement towards an enlarged model for Seller Agents coalitions. The simulator has been improved, both regarding its user interface and internal structure. The OOA, used as development platform, version was updated and the multi-agent model was adjusted for implementing and testing several negotiations regarding Seller agents’ coalitions. Seller coalitions are a very important subject regarding the increased relevance of Distributed Generation under liberalised electricity markets.

MASGriP – a Multi-Agent Smart Grid Simulation Platform

The increasing number of players that operate in power systems leads to a more complex management. In this paper a new multi-agent platform is proposed, which simulates the real operation of power system players. MASGriP – A Multi-Agent Smart Grid Simulation Platform is presented. Several consumer and producer agents are implemented and simulated, considering real characteristics and different goals and actuation strategies. Aggregator entities, such as Virtual Power Players and Curtailment Service Providers are also included. The integration of MASGriP agents in MASCEM (Multi-Agent System for Competitive Electricity Markets) simulator allows the simulation of technical and economical activities of several players. An energy resources management architecture used in microgrids is also explained.

A multi-agent based approach for intelligent smart grid management

The spread and globalization of distributed generation (DG) in recent years has should highly influence the changes that occur in Electricity Markets (EMs). DG has brought a large number of new players to take action in the EMs, therefore increasing the complexity of these markets. Simulation based on multi-agent systems appears as a good way of analyzing players’ behavior and interactions, especially in a coalition perspective, and the effects these players have on the markets. MASCEM – Multi-Agent System for Competitive Electricity Markets was created to permit the study of the market operation with several different players and market mechanisms. MASGriP – Multi-Agent Smart Grid Platform is being developed to facilitate the simulation of micro grid (MG) and smart grid (SG) concepts with multiple different scenarios. This paper presents an intelligent management method for MG and SG. The simulation of different methods of control provides an advantage in comparing different possible approaches to respond to market events. Players utilize electric vehicles’ batteries and participate in Demand Response (DR) contracts, taking advantage on the best opportunities brought by the use of all resources, to improve their actions in response to MG and/or SG requests.

Multi-agent simulation of continental, regional, and micro electricity markets

Renewable based power generation has significantly increased over the last years. However, this process has evolved separately from electricity markets, leading to an inadequacy of the present market models to cope with huge quantities of renewable energy resources, and to take full advantage of the presently existing and the increasing envisaged renewable based and distributed energy resources. This paper proposes the modelling of electricity markets at several levels (continental, regional and micro), taking into account the specific characteristics of the players and resources involved in each level and ensuring that the proposed models accommodate adequate business models able to support the contribution of all the resources in the system, from the largest to the smaller ones. The proposed market models are integrated in MASCEM (Multi- Agent Simulator of Competitive Electricity Markets), using the multi agent approach advantages for overcoming the current inadequacy and significant limitations of the presently existing electricity market simulators to deal with the complex electricity market models that must be adopted.

Agent-based simulation of electronic marketplaces with decision support

This paper presents a Multi-Agent Market simulator designed for analyzing agent market strategies based on a complete understanding of buyer and seller behaviors, preference models and pricing algorithms, considering user risk preferences and game theory for scenario analysis. The system includes agents that are capable of improving their performance with their own experience, by adapting to the market conditions, and capable of considering other agents reactions.

Multi-agent electricity market simulation with dynamic strategies & virtual power producers

Distributed energy resources will provide a significant amount of the electricity generation and will be a normal profitable business. In the new decentralized grid, customers will be among the many decentralized players and may even help to co-produce the required energy services such as demand-side management and load shedding. So, they will gain the opportunity to be more active market players. The aggregation of DG plants gives place to a new concept: the Virtual Power Producer (VPP). VPPs can reinforce the importance of these generation technologies making them valuable in electricity markets. In this paper we propose the improvement of MASCEM, a multi-agent simulation tool to study negotiations in electricity spot markets based on different market mechanisms and behavior strategies, in order to take account of decentralized players such as VPP.

A new approach for multi-agent coalition formation and management in the scope of electricity markets

This paper presents a new methodology for the creation and management of coalitions in Electricity Markets. This approach is tested using the multi-agent market simulator MASCEM, taking advantage of its ability to provide the means to model and simulate VPP (Virtual Power Producers). VPPs are represented as coalitions of agents, with the capability of negotiating both in the market, and internally, with their members, in order to combine and manage their individual specific characteristics and goals, with the strategy and objectives of the VPP itself. The new features include the development of particular individual facilitators to manage the communications amongst the members of each coalition independently from the rest of the simulation, and also the mechanisms for the classification of the agents that are candidates to join the coalition. In addition, a global study on the results of the Iberian Electricity Market is performed, to compare and analyze different approaches for defining consistent and adequate strategies to integrate into the agents of MASCEM. This, combined with the application of learning and prediction techniques provide the agents with the ability to learn and adapt themselves, by adjusting their actions to the continued evolving states of the world they are playing in.