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Engineering

Neural correlates of economic

Our daily lives are shaped by a series of decision processes, ranging from very unimportant choices to life-changing judgments. The complexity of the decision processes increases tremendously when the decision-making takes place in a social context, i.e., when other human beings are directly involved in the decision. In such conditions the decision-maker not only tries to maximize his own utility, but also needs to take into account the interdependent nature of the situation. Information about others’ preferences, characteristics, and actions play an important role, and need to be thoroughly evaluated and predicted before making a decision. In this thesis we explore the neural correlates of two different types of social decision-making.

Modeling artificial, mobile swarm systems

Swarm intelligence is a new research paradigm that offers novel approaches for studying and solving distributed problems using solutions inspired by social insects and other natural behaviors of vertebrates. In this thesis, we present methodologies for modeling artificial, mobile systems within the swarm intelligence framework. The proposed methodologies provide guidelines in the study and design of artificial swarm systems for the following two classes of experiments: distributed sensing and distributed manipulation.

Event discovery and information dissemination through local communication in artificial swarm systems present similar characteristics as natural phenomena such as foraging and food discovery in insect colonies and the spread of infectious diseases in animal populations, respectively. We show that the artificial systems can be described in similar mathematical terms as those used to describe the natural systems. The proposed models can be classified in two main categories: non-embodied and embodied models. In the first category agents are modeled as mobile bodiless points, whereas the other models take into account the physical interference between agents resulting from embodiment. Furthermore, within each category, we distinguish two subcategories: spatial and nonspatial models. In the spatial models we keep track of the trajectory of each agent, the correlation between the positions occupied by the agents over consecutive time steps, or make use of the spatial distribution resulting from the movement pattern of the agents. In the nonspatial models we assume that agents hop around randomly and occupy independent positions over consecutive time steps.

Bandwidth limitations and synthesis procedures for negative resistance and variable reactance amplifiers

The bandwidth limitation on the reflection coefficient of circuits containing a reactance limited negative conductance such as a tunnel diode is derived, and the insertion loss method of modern network theory is adapted to the synthesis of low pass ladder equivalents of amplifiers containing these elements. Amplifiers which have a considerable bandwidth advantage over simple single tuned circuits, and which approach the ultimate bandwidth limit as rapidly as possible as the number of passive components is increased, are demonstrated.

Neural correlates of economic and moral decision-making

Our daily lives are shaped by a series of decision processes, ranging from very unimportant choices to life-changing judgments. The complexity of the decision processes increases tremendously when the decision-making takes place in a social context, i.e., when other human beings are directly involved in the decision. In such conditions the decision-maker not only tries to maximize his own utility, but also needs to take into account the interdependent nature of the situation. Information about others’ preferences, characteristics, and actions play an important role, and need to be thoroughly evaluated and predicted before making a decision. In this thesis we explore the neural correlates of two different types of social decision-making.

Broadband modeling of earthquake source and mantle structures

Broadband seismic arrays have provided unprecedented data sets for seismologists to image the slips on faults and velocity structure beneath Earth's surface at all scales. In particular, plate boundary zones are the most complicated regions on the surface and full of complexities. Often that great earthquakes occur and rapid structural changes take place.

Dynamic compression of minerals in the MgO-FeO-SiO2 system

The first shock wave experiments performed on silicate materials were reported for quartz in 1962. The intervening forty years have allowed for extensive investigation of SiO2 by dynamic, static and theoretical means. Previous studies have concluded that quartz transforms completely to stishovite at ~40 GPa and melts at ~115 GPa along its Hugoniot. Recent discoveries that SiO2 transforms to phases slightly more dense than stishovite have led to a reexamination of the dynamic compression of SiO2 in this thesis. Based on comparing calculated Hugoniots to data for multiple initial SiO2 phases, it is proposed that, in addition to the stishovite and melt transitions, quartz is completely transformed to the CaCl2 structure at ~70 GPa. Coesite shows evidence of complete transformation to stishovite at ~ 50 GPa, and to the CaCl2 structure at ~65 GPa. Due to the higher temperature achieved in the quartz samples the slope of the stishovite-CaCl2 phase boundary is constrained to be ~180 K/GPa.

Transient response of uniform beams

Several special topics relating to the transient flexural vibrations of a uniform beam predicted by the usual elementary or Bernoulli-Euler equation are discussed. The effect on the beam response of the concentration of an applied transient force in space and in time is studied. In the case of an applied step force, it is shown that the dynamic team response can be larger than twice the response to an equal force statically applied. It is demonstrated that the beam response in the higher modes is independent of the boundary conditions.

Modeling artificial, mobile swarm systems

Swarm intelligence is a new research paradigm that offers novel approaches for studying and solving distributed problems using solutions inspired by social insects and other natural behaviors of vertebrates. In this thesis, we present methodologies for modeling artificial, mobile systems within the swarm intelligence framework. The proposed methodologies provide guidelines in the study and design of artificial swarm systems for the following two classes of experiments: distributed sensing and distributed manipulation.

Robotics training algorithms for optimizing motor learning in spinal cord injured subjects

The circuitries within the spinal cord are remarkably robust and plastic. Even in the absence of supraspinal control, such circuitries are capable of generating functional movements and changing their level of excitability based on a specific combination of properceptive inputs going into the spinal cord. This has led to an increase in locomotor training, such as Body Weight Support Treadmill training (BWST) for spinal cord injured (SCI) patients. However, today, little is known about the underlying physiological mechanisms responsible for the locomotor recovery achieved with this type of rehabilitative training, and the optimal rehabilitative strategy is still unknown.

Alternative models for air pollutant effects on visibility

Air pollution causes visibility reduction in urban areas such as Los Angeles as well as in national parks and wilderness areas. In this work, alternative mathematical models are formulated that relate air pollutant emissions or ambient air pollutant concentrations to visual range or to changes in the appearance of a scenic vista.

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