A Study of the Relation between the Mechanical Properties and the Adhesion Level in a Laminated Packaging Material

The mechanical properties of a laminate consisting of aluminum-foil, adhesive, and polymer layers were studied in relation to the adhesion level. A special application for liquid-food packaging materials was considered. In experiments, laminates with and without adhesive layers were tested. Tensile tests were first run for every layer of the laminate, and the data obtained were then used in analyzing the results of tensile tests on the entire laminate, as well as in theoretical and finite-element calculations. Relations between different mechanical properties (such as Young's modulus, the peak stress, and the strain at the peak stress) and the adhesion level were analyzed. It was found that the tensile strength and the strain at the peak stress increased with adhesion level. Only slight differences in Young's modulus were observed at different adhesion levels.     

Dynamical effects of biocontrol on the ecosystem: Benefits or harm?

Motivated by recent field studies on the effects of biocontrol beetle on invasive plants (Tamarix) on riparian ecosystems, we develop a simple three-species (the native consumer, the biocontrol agent, and their predator) model to explore potential dynamical benefits and harm of introducing a non-indigenous species into varied ecosystems with different habitat structures. Our proposed model assumes that (1) habitat consists of both native plants that are main food resources of the native consumer and invasive plants which are targeted food resources of the biocontrol agents; (2) the habitat structure is stable which has a fixed ratio of invasive plants; (3) biocontrol agents can defoliate invasive plants whose defoliations provide as additional food resources for the native consumer that could potentially increase the native consumer’s abundance; and (4) the predators feed on both the native consumer and the biocontrol agents. Our study shows that introducing biocontrol agents into the ecosystem can generate complicated dynamics that could be beneficial or harmful to the ecosystem depending on the environments: biocontrol agents could be beneficial by promoting its biodiversity such that all species could coexist; on the other hand, biocontrol agents could also be harmful by eliminating the native consumer or the predator. In addition, biocontrol agents could stabilize or destabilize the ecosystem depending on the habit structure and the local ecological environments. These theoretical results provide us potential decision-making tools that allow managers to better predict both safety and efficacy of candidate biological control agents are needed.     

On the antiplane shear deformation of finite wedges

Antiplane shear deformation of finite wedges is considered under different boundary conditions. First, the assertions and results of a recent paper, namely Chue and Liu [C.H. Chue, W.J. Liu, Comments on “Analysis of an isotropic finite wedge under antiplane deformation”, Int. J. Solids Struct. 41 (2004) 5023–5034] are invalidated. Then, closed form solutions are extracted for the stress distribution in the wedge. These closed forms have the advantages of showing the possible geometric stress singularity as well as the load singularity explicitly, in addition to the continuity or discontinuity as well as the convergence of the results in the entire region. Finally, the stress intensity factors are extracted in the special case of a circular shaft containing an edge crack under different boundary conditions.     

Effect of local information within network layers on the evolution of cooperation in duplex public goods games

Traditional works of public goods game (PGG) are often studied in simplex networks where agents play games through the same type of social interactions. In order to promote cooperation against the defection in PGGs in simplex network environment, many mechanisms have been proposed from different perspectives, such as the volunteering mechanisms, and the punishment and reward approaches. However, due to diverse types of interactions between agents in reality, the study of PGG should also consider the characteristic of multiplexity of networks. Hence, we firstly model the public goods game in the duplex network (for simplification of analysis, the duplex network is considered), in which agents have two types of social interactions, and thus the network is modeled as two network layers. This type of PGG is naturally named as duplex public goods game (D-PGG), in which agents can select one of the network layers to allocate their limited resources. Then for the new game environment (D-PGG), we propose a novel perspective to promote cooperation: degrading the information integrity, i.e., agents get information just from one network layer (local information) rather than from the whole duplex network (global information) in the evolution process. Finally, through theoretical analyses and simulations, we find that if agents imitate based on the local information of the payoff in the evolution, cooperation can be generally promoted; and the extent of promotion depends on both the network structure and the similarity of the network layers.     

Ⅰ型试件裂端三维应力结构分析之二——应力结构分析

在文献[1]的基础上,本文分别对试件纤维区和剪切唇的应力结构进行考察.纤维区的应力结构特点为:可以进行变量z分离;对称面上的应力结构可由平面应变FEM解或HRR场解经应力三维度修正获得;载荷水平及试样厚度对应力结构的影响,可由厚度方向的CTOD得到反映,所得到应力表达式大为简便与直观.对剪切唇的应力结构进行考察,发现满足一定的精度可由插值法近似获得.本文提出了一种平面应变近似程度系数,并对此进行分析,该系数可较好地反映试样厚度、型式及载荷水平.本文也对断裂参量进行了分析,指出可对CTOD进行应力三维度修正获得.     

Endogenous formation of networks for local public goods

The paper studies a situation in which agents can make a binding agreement both on the amount of local public goods and on the structure of networks through which they share the benefits of public goods. An agent enjoys the benefit of public goods produced by other agents who are (directly or indirectly) connected to him. There is a cost to maintain a link as well as to produce a public good. Because agents can choose the amount of public goods, the value of a link is endogenously determined. We consider two different models of sequential bargaining games through which a contract on allocations is established. In the first model, we allow agents to propose a pure allocation and show that there is no symmetric stationary perfect equilibrium for sufficiently patient agents. In the second model, agents are allowed to propose a distribution on allocations. As a result, we find a symmetric stationary perfect equilibrium in which probabilistic choices are made on an equivalent class of allocations. Subsequently, we characterize core allocations, which consist of a minimally connected network and an effort profile, in which at most one agent does not produce the maximum amount of public good.     

Decentralized and dynamic group formation of reconfigurable agents

Adaptive group formation in dynamic environments performed by heterogeneous swarms of simple agents is an interesting research topic. In this paper we consider an unsupervised scenario where the individuals of the swarm have limited information about their environment as well as limited communication capabilities. The particular case of a multi-agent model with self-organized reconfigurable agents where the agents are confronted with a resource collection task, different movement, and group formation tactics are analyzed experimentally. It is shown that cooperation in groups is profitable for the group members and the optimal group size depends on environmental parameters. Moreover, a simple strategy based on the agents ability to measure their own workload results in an adaptive behavior that influences the size of the groups and increases the performance of the overall system.     

Stable and Pareto optimal group activity selection from ordinal preferences

In several situations agents need to be assigned to activities on basis of their preferences, and each agent can take part in at most one activity. Often, the preferences of the agents do not depend only on the activity itself but also on the number of participants in the respective activity. In the setting we consider, the agents hence have preferences over pairs “(activity, group size)” including the possibility “do nothing”; in this work, these preferences are assumed to be strict orders. The task will be to find stable assignments of agents to activities, for different concepts of stability such as Nash or core stability, and Pareto optimal assignments respectively. In this respect, particular focus is laid on two natural special cases of agents’ preferences inherent in the considered model, namely increasing and decreasing preferences, where agents want to share an activity with as many (as few, respectively) agents as possible.     

The effect of market power on risk-sharing

The paper studies an oligopolistic equilibrium model of financial agents who aim to share their random endowments. The risk-sharing securities and their prices are endogenously determined as the outcome of a strategic game played among all the participating agents. In the complete-market setting, each agent’s set of strategic choices consists of the security payoffs and the pricing kernel that are consistent with the optimal-sharing rules; while in the incomplete setting, agents respond via demand functions on a vector of given tradeable securities. It is shown that at the (Nash) risk-sharing equilibrium, the sharing securities are suboptimal, since agents submit for sharing different risk exposures than their true endowments. On the other hand, the Nash equilibrium prices stay unaffected by the game only in the special case of agents with the same risk aversion. In addition, agents with sufficiently lower risk aversion act as predatory traders, since they absorb utility surplus from the high risk averse agents and reduce the efficiency of sharing. The main results of the paper also hold under the generalized models that allow the presence of noise traders and heterogeneity in agents’ beliefs.     

On the potential of norm-governed behavior in different categories of artificial societies

Based on a classification of artificial societies and the identification of four different types of stakeholders in such societies, we investigate the potential of norm-governed behavior in different types of artificial societies. The basis of the analysis is the preferences of the stakeholders and how they influence the state of the society. A general conclusion drawn is that the more open a society is the more it has to rely on agent owners and designers to achieve norm-governed behavior, whereas in more closed societies the environment designers and owners may control the degree of norm-governed behavior. Paul Davidsson is professor at the Department of Systems and Software Engineering, School of Engineering, Blekinge Institute of Technology, Sweden. He received his Ph.D. in Computer Science in 1996 from Lund University, Sweden. His research interests include the theory and application of multi-agent systems, autonomous agents, and machine learning. Application areas include logistics, transport systems, district heating systems, building automation, and telecommunications systems. The results of this work have been reported in more than 75 peer-reviewed scientific articles published in international journals and conference proceedings. Moreover, he has been the co-editor of three books on Multi Agent Based Simulation and member of program committees of numerous international conferences, such as the International Joint Conference on Autonomous Agents and Multi-Agent Systems Stefan Johansson is an assistant professor at Department of Systems and Software Engineering, Blekinge Institute of Technology, Sweden, where he also finished his PhD in 2002. The main research areas cover coordination issues in multi-agent systems and theories of autonomous agents. Applications of special interests are agents in game ai, robotics, telecommunication networks. On his list of publications are more than 35 peer-reviewed papers published in conference proceedings and scientific journals in the areas of agents, ai, robotics and games. He has also been a member of a variety of programme committees of scientific conferences, including e.g. Intelligent Agent Technology.     

多晶体光滑表面疲劳微裂纹形核机理研究

建立晶体学模型来研究多晶体承受均匀外载时光滑表面疲劳微裂纹形核的机理．由于晶粒取向的不相同,即使承受均匀外载,多晶体内将产生不均匀的应力,变形也不均匀A·D2在疲劳载荷的作用下,表面粗糙度随疲劳周次逐渐增加,变形逐渐集中到若干部位,即局部化,形成微裂纹．     

Calculation of deformations in nanocomposites using the block multipole method with the analytical-numerical account of the scale effects

Local scale effects for linear continuous media are investigated as applied to the composites reinforced by nanoparticles. A mathematical model of the interphase layer is proposed that describes the specific nature of deformations in the neighborhood of the interface between different phases in an inhomogeneous material. The characteristic length of the interphase layer is determined formally in terms of the parameters of the mathematical model. The local stress state in the neighborhood of the phase boundaries in the interphase layer is examined. This stress can cause a significant change of the integral macromechanical characteristics of the material as a whole if the interphase boundaries are long. Such a situation is observed in composite materials reinforced by microparticles and nanoparticles even when the volume concentration of the inclusions is small. A numerical simulation of the stress state is performed on the basis of the block analytical-numerical multipole method with regard for the local effects related to the special nature of the deformation of the interphase layer in the vicinity of the interface.     

Evolutionary computation and agent-based modeling: biologically-inspired approaches for understanding complex social systems

Computational social science in general, and social agent-based modeling (ABM) simulation in particular, are challenged by modeling and analyzing complex adaptive social systems with emergent properties that are hard to understand in terms of components, even when the organization of component agents is know. Evolutionary computation (EC) is a mature field that provides a bio-inspired approach and a suite of techniques that are applicable to and provide new insights on complex adaptive social systems. This paper demonstrates a combined EC-ABM approach illustrated through the RebeLand model of a simple but complete polity system. Results highlight tax rates and frequency of public issue that stress society as significant features in phase transitions between stable and unstable governance regimes. These initial results suggest further applications of EC to ABM in terms of multi-population models with heterogeneous agents, multi-objective optimization, dynamic environments, and evolving executable objects for modeling social change.     

Game theoretic formalization of the concept of sustainable development in the hierarchical control systems

The resolution of numerous ecological problems on different levels must be implemented on the base of sustainable development concept that determines the conditions to the state of ecological-economic systems and impacting control actions. Those conditions can’t be realized by themselves and require special collaborative efforts of different agents using both cooperation and hierarchical control. To formalize the inevitable trade-offs it is natural to use game theoretic models. Unfortunately, the main optimality principles of hierarchical control (compulsion, impulsion) are not time consistent and therefore can’t be recommended as the direct base for collective solutions. The most prospective is the conviction method which is formalized as a transition from hierarchy to cooperation and allows a regularization that provides the time consistency. However, in current social conditions other methods of hierarchical control also keep their actuality. To ensure the time consistency of those optimality principles it is necessary to build cooperative differential games on their base. An example of the approach is considered in this paper.     

SPEW: Synthetic Populations and Ecosystems of the World

Agent-based models (ABMs) simulate interactions between autonomous agents in constrained environments over time and are often used for modeling the spread of infectious diseases. ABMs use information about agents and their environments as input, together referred to as a “synthetic ecosystem.” Previous approaches for generating synthetic ecosystems have some limitations: they are not open-source, cannot be adapted to new or updated input data sources, or do not allow for alternative methods for sampling agent characteristics and locations. We introduce a general framework for generating synthetic ecosystems, called “Synthetic Populations and Ecosystems of the World” (SPEW). SPEW lets researchers choose from a variety of sampling methods for agent characteristics and locations and is implemented as an open-source R package. We analyze the accuracy and computational efficiency of SPEW, given different sampling methods for agent characteristics and locations, and provide a suite of statistical and graphical tools to screen our generated ecosystems. SPEW has generated over five billion human agents across approximately 100,000 geographic regions in over 70 countries available online.     

Appraisals of harms and injustice trigger an eerie feeling that decreases trust in artificial intelligence systems

As artificial intelligence (AI) becomes more pervasive, the concern over how users can trust artificial agents is more important than ever before. In this research, we seek to understand the trust formation between humans and artificial agents from the morality and uncanny theory perspective. We conducted three studies to carefully examine the effect of two moral foundations: perceptions of harm and perceptions of injustice, as well as reported wrongdoing on uncanniness and examine the effect of uncanniness on trust in artificial agents. In Study 1, we found perceived injustice was the primary determinant of uncanniness and uncanniness had a negative effect on trust. Studies 2 and 3 extended these findings using two different scenarios of wrongful acts involving an artificial agent. In addition to explaining the contribution of moral appraisals to the feeling of uncanny, the latter studies also uncover substantial contributions of both perceived harm and perceived injustice. The results provide a foundation for establishing trust in artificial agents and designing an AI system by instilling moral values in it.

     

Determination of the optimal accelerated burn-in time under Arrhenius–Lognormal distribution assumption

Burn-in has been widely used as an effective procedure for screening out failed electronic products during the early-failure period, before shipment to the customers. Environmental stress such as temperature is increasingly being used to effectively shorten the burn-in time, and this method is usually called an accelerated burn-in test. When different stress levels are chosen for the burn-in operation, the burn-in times must be determined. An Arrhenius–Lognormal distribution can describe the lognormal lifetime of electronic products under different temperature levels. In this paper, the Arrhenius–Lognormal distribution and its mean residual life function are applied to the accelerated burn-in cost model, and a genetic algorithm is used to solve for the optimal burn-in time. We choose a real TFT–LCD module as an example, and determine its optimal accelerated burn-in time. A sensitivity analysis of the TFT–LCD module case shows the effect of model parameters on optimal burn-in time.     

The Effects of Generalized Reciprocal Exchange on the Resilience of Social Networks: An Example from the Prehispanic Mesa Verde Region

The initial version of the model used in this study, Village 1.0, was implemented by Tim Kohler and a team of developers mostly from Washington State University. The original model addressed environmental constraints only and did not attempt to model social interaction. In a recent paper we employed Cultural Algorithms as a framework in which to add selected social considerations. In this paper we extend our previous model by adding the ability of agents to perform symmetrically initiated or asymmetrically initiated generalized reciprocal exchange. We have developed a state model for agents' knowledge and, given agents' different responses based on this knowledge. Experiments have shown that the network structure of the systems without reciprocity was the simplest but least resilient. As we allowed agents more opportunities to exchange resources we produced more complex network structures, larger populations, and more resilient systems. Furthermore, allowing the agents to buffer their requests by using a finite state model improved the relative resilience of these larger systems. Introducing reciprocity that can be triggered by both requestors and donors produced the largest number of successful donations. This represents the synergy produced by using the information from two complementary situations within the network. Thus, the network has more information with which it can work and tended to be more resilient than otherwise.