The Environmental Fate of polybrominated diphenyl ethers in western Taiwan and coastal waters: evaluation with a fugacity based model

The environmental fate of polybrominated diphenyl ethers (PBDEs), a group of flame retardants that are considered to be persistent organic pollutants (POPs), around the Zhuoshui River and Changhua County regions of Taiwan was assessed. An investigation into emissions, partitioning, and fate of selected PBDEs was conducted based on the equilibrium constant (EQC) fugacity model developed at Trent University, Canada. Emissions for congeners PBDE 47, PBDE 99, and PBDE 209 to air (4.9–92 × 10−3 kg/h), soil (0.91–17.4 × 10−3 kg/h), and water (0.21–4.04 × 10−3 kg/h), were estimated by modifying previous models on PBDE emission rates by considering both industrial and domestic rates. It was found that fugacity modeling can give a reasonable estimation of the behavior, partitioning, and concentrations of PBDE congeners in and around Taiwan. Results indicate that PBDE congeners have a high affinity for partitioning into sediments then soils. As congener number decreases, the PBDEs then partition more readily into air. As the degree of bromination increases, congeners more readily partition to sediments. Sediments may then act as a long-term source of PBDEs which can be released back into the water column due to resuspension during storm events.

Testing the channel-based model of duration perception

The channel-based model of duration perception postulates the existence of neural mechanisms that respond selectively to a narrow range of stimulus durations centred on their preferred duration (Heron et al Proceedings of the Royal Society B 279 690–698). In principle the channel-based model could
explain recent reports of adaptation-induced, visual duration compression effects (Johnston et al Current Biology 16 472–479; Curran and Benton Cognition 122 252–257); from this perspective duration compression is a consequence of the adapting stimuli being presented for a longer duration than the test stimuli. In the current experiment observers adapted to a sequence of moving random dot patterns at the same retinal position, each 340ms in duration and separated by a variable (500–1000ms) interval. Following adaptation observers judged the duration of a 600ms test stimulus at the same location. The test stimulus moved in the same, or opposite, direction as the adaptor. Contrary to the channel-based
model’s prediction, test stimulus duration appeared compressed, rather than expanded, when it moved in the same direction as the adaptor. That test stimulus duration was not distorted when moving in the opposite direction further suggests that visual timing mechanisms are influenced by additional neural processing associated with the stimulus being timed.

Aplication of a morphodinamic process based model in long-term climate change studies

Climate changes are foreseen to produce a large impact in the morphology of estuaries and coastal systems. The morphology changes will subsequently drive changes in the biologic compartments of the systems and ultimately in their ecosystems. Sea level rise is one of the main factors controlling these changes. Morphologic changes can be better understood with the use of long term morphodynamic mathematical models.

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.

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.

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.

Personality, emotion, and mood in agent-based group decision making

Group decision making plays an important role in organizations, especially in the present-day economy that demands high-quality, yet quick decisions. Group decision-support systems (GDSSs) are interactive computer-based environments that support concerted, coordinated team efforts toward the completion of joint tasks. The need for collaborative work in organizations has led to the development of a set of general collaborative computer-supported technologies and specific GDSSs that support distributed groups (in time and space) in various domains. However, each person is unique and has different reactions to various arguments. Many times a disagreement arises because of the way we began arguing, not because of the content itself. Nevertheless, emotion, mood, and personality factors have not yet been addressed in GDSSs, despite how strongly they influence results. Our group’s previous work considered the roles that emotion and mood play in decision making. In this article, we reformulate these factors and include personality as well. Thus, this work incorporates personality, emotion, and mood in the negotiation process of an argumentbased group decision-making process. Our main goal in this work is to improve the negotiation process through argumentation using the affective characteristics of the involved participants. Each participant agent represents a group decision member. This representation lets us simulate people with different personalities. The discussion process between group members (agents) is made through the exchange of persuasive arguments. Although our multiagent architecture model4 includes two types of agents—the facilitator and the participant— this article focuses on the emotional, personality, and argumentation components of the participant agent.

Distributed, Agent-Based Intelligent System for Demand Response Program Simulation in Smart Grids

A distributed, agent-based intelligent system models and simulates a smart grid using physical players and computationally simulated agents. The proposed system can assess the impact of demand response programs.

Agent-based parking occupancy simulation

The existing parking simulations, as most simulations, are intended to gain insights of a system or to make predictions. The knowledge they have provided has built up over the years, and several research works have devised detailed parking system models. This thesis work describes the use of an agent-based parking simulation in the context of a bigger parking system development. It focuses more on flexibility than on fidelity, showing the case where it is relevant for a parking simulation to consume dynamically changing GIS data from external, online sources and how to address this case. The simulation generates the parking occupancy information that sensing technologies should eventually produce and supplies it to the bigger parking system. It is built as a Java application based on the MASON toolkit and consumes GIS data from an ArcGis Server. The application context of the implemented parking simulation is a university campus with free, on-street parking places.

An agent-based simulator to estimate domestic energy use

Throughout recent years, there has been an increase in the population size, as well as a fast economic growth, which has led to an increase of the energy demand that comes mainly from fossil fuels. In order to reduce the ecological footprint, governments have implemented sustainable measures and it is expected that by 2035 the energy produced from renewable energy sources, such as wind and solar would be responsible for one-third of the energy produced globally. However, since the energy produced from renewable sources is governed by the availability of the respective primary energy source there is often a mismatch between production and demand, which could be solved by adding flexibility on the demand side through demand response (DR). DR programs influence the end-user electricity usage by changing its cost along the time. Under this scenario the user needs to estimate the energy demand and on-site production in advance to plan its energy demand according to the energy price. This work focuses on the development of an agent-based electrical simulator, capable of: (a) estimating the energy demand and on-site generation with a 1-min time resolution for a 24-h period, (b) calculating the energy price for a given scenario, (c) making suggestions on how to maximize the usage of renewable energy produced on-site and to lower the electricity costs by rescheduling the use of certain appliances. The results show that this simulator allows reducing the energy bill by 11% and almost doubling the use of renewable energy produced on-site.

Intelligent Agent-based Multilingual Information Retrieval System

The goal of this work is to develop an Open Agent Architecture for Multilingual information retrieval from Relational Database. The query for information retrieval can be given in plain Hindi or Malayalam; two prominent regional languages of India. The system supports distributed processing of user requests through collaborating agents. Natural language processing techniques are used for meaning extraction from the plain query and information is given back to the user in his/ her native language. The system architecture is designed in a structured way so that it can be adapted to other regional languages of India