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The present study experimentally investigates the effect of metal oxide nanoparticles on the ignition probability, ignition zone, and burning time of the diesel fuel. For this purpose, a series of hot plate ignition tests are conducted on the diesel fuel droplets with and without nanoparticles. The comparative performances of the pure diesel and diesel fuel containing 0.03 and 0.05 wt% of Al2O3, CeO2, Fe3O4, and TiO2 nanoparticles are examined. The thermo-physical properties of the nanofuels, including thermal conductivity, viscosity, and heat capacity are also measured. The experimental results show that in the presence of metal oxide nanoparticles, the ignition probability significantly increases. It is observed that cerium oxide nanoparticles show the greatest effect on the ignition probability. In addition, it is found that the minimum hot plate ignition temperature decreases. Results show that the burning time and the ignition zone decrease by adding metal oxide nanoparticles.

© 2017 ACM. Ontologies describe complex world knowledge in that they consist of hierarchical relations, such as is-a, which can be expressed by quantifiers or sets, and various binary relations, which can be expressed by links or networks. Should hierarchical relations be distinguished from other binary relations as essentially different ones in building cognitively accessible systems of ontologies? In this study, two kinds of ontology visualizations, a network-based visualization (SOVA) and a set-based visualization (concept diagrams), are empirically compared in the case of consistency checking. Participants were presented with one diagram and then asked to answer the question of whether the meaning of the diagram was contradictory. Our results showed that SOVA is more effective than concept diagrams, suggesting that to represent hierarchical and binary relations of ontologies in a way based on networks suits human cognition when checking ontologies' consistencies.

Analysis and evaluation of land use patterns are of prime importance for natural resources management. Recent studies on land use allocation have been mainly based on linear programming optimization. Although these methods have the ability to solve multi-objective problems, spatial aspects of optimization are not considered when they are used for land use management. This study applied the non-dominated sorting genetic algorithm II (NSGA-II) to optimize land-use allocation in the Taleghan watershed, northwest of Karaj, Iran. The four land use classes of irrigated farming, dry farming, rangeland, and other uses were extracted from the ETM+ image. The objective functions of the proposed model were erosion, economic return, suitability, and compactness-compatibility. A novel crossover operator called exchange randomly block (ERB) was used to exchange information between individuals. Results showed that the optimization model can find a set of optimal land use combinations in accordance with the proposed conditions. For comparison purposes, land use allocation was also done using the combined goal attainment-multi-objective land allocation (GoA-MOLA) approach. The results showed that NSGA-II performance acceptably when compared to GoA-MOLA.

The solution of an inverse, conduction-radiation problem in a two-dimensional rectangle is analyzed to determine the temperature-dependent emissivity at the boundary. The medium is gray, absorbing, emitting and isotropically scattering. The bounding surfaces are assumed to be opaque and diffuse. The inverse problem is solved by minimizing the performance function, which is expressed by the sum of square residuals between estimated and exact heat fluxes, using a combined method of genetic algorithm and conjugate gradient. The emissivity is assumed to be represented as a function of boundary temperature with unknown variables. Therefore, the inverse problem is treated by the estimation of these variables. Finally, four examples are presented to show the accuracy of the algorithm. The effect of the measurement errors on the accuracy of the inverse analysis is also investigated. Results show the algorithm can estimate the unknown emissivity when the measurement errors are neglected. Also it is found that increasing the measurement error decreases the accuracy of estimation of temperature-dependent emissivity.

In this paper we propose a labelling based dialogue game for determining whether a single argument within a Dung argumentation framework is skeptically preferred. Our game consists of two phases, and determines the membership of a single argument within the extension, assuming optimal play by dialogue participants. In the first phase, one player attempts to advance arguments to construct an extension not containing the argument under consideration, while the second phase verifies that the extension is indeed a preferred one. Correctness within this basic game requires perfect play by both players, and we therefore also introduce an overarching game to overcome this limitation.

Autonomous agents operating in a dynamic environment need constantly to reason about actions in pursuit of their goals, while taking into consideration possible norms imposed on those actions. Normative practical reasoning supports agents decision making about what is best for an agent to do in a given situation. What makes practical reasoning challenging is the conflict between goals that the agent is pursuing and the norms that the agent is trying to uphold. We offer a formal model that allows the agents to plan for conflicting goals and norms in presence of durative actions that can be executed concurrently. We compare plans based on decision-theoretic notions (i.e. utility) such that the utility gain of goals and utility loss of norm violations are the basis of this comparison. The set of optimal plans consists of plans that maximise the overall utility, each of which can be chosen by the agent to execute. The formal model is implemented computationally using answer set programming, which in turns permits the statement of the problem in terms of a logic program that can be queried for solutions with specific properties. We demonstrate how a normative practical reasoning problem can be mapped into an answer set program such that the optimal plans of the former can be obtained as the answer sets of the latter.

Reasoning about what is best for an agent to do in a particular situation is a challenging task. What makes it even more challenging in a dynamic environment is the existence of norms that aim to regulate a self-interested agent’s behaviour. Practical reasoning is reasoning about what to do in a given situation, particularly in the presence of conflicts between the agent’s practical attitude such as goals, plans and norms. In this paper we: (i) introduce a formal model for normative practical reasoning that allows an agent to plan for multiple and potentially conflicting goals and norms at the same time (ii) identify the best plan(s) for the agent to execute by means of argumentation schemes and critical questions (iii) justify the best plan(s) via an argumentation-based persuasion dialogue for grounded semantics.

In this paper we propose ArgPROLEG, a normative framework for legal reasoning based on PROLEG, an implementation of the Japanese “theory of presupposed ultimate facts”(JUF). This theory was mainly developed with the purpose of modelling the process of decision making by judges in the court. Not having complete and accurate information about each case, makes uncertainty an unavoidable part of decision making for judges. In the JUF theory each party that puts forward a claim, due to associated burden of proof to each claim, it needs to prove it as well. Not being able to provide such a proof for a claim, enables the judges to discard that claim although they might not be certain about the truth. The framework that we offer benefits from the use of argumentation theory as well as normative framework in multi-agent systems, to bring the reasoning closer to the user. The nature of argumentation in dealing with incomplete information on the one hand and being presentable in the form of dialogues on the other hand, has furthered the emergence and popularity of argumentation in modelling legal disputes. In addition, the use of multiple agents allows more flexibility for the behaviour of the parties involved.