DBI equations and holographic DC conductivity

We provide a simple method for writing the Dirac-Born-Infeld (DBI) equations of a Dp-brane in an arbitrary static background whose metric depends only on the holographic radial coordinate z. Using this method we revisit the Karch-O'Bannon's procedure to calculate the DC conductivity in the presence of constant electric and magnetic fields for backgrounds where the boundary is four or three dimensional and satisfies homogeneity and isotropy. We find a frame-independent expression for the DC conductivity tensor. For particular backgrounds we recover previous results on holographic metals and strange metals.Comment: 1+21 pages; v2 : references added, minor typos correcte

On the Nature of Income Inequality Across Nations

In this paper, we investigate the nature of income inequality across nations by first estimating, testing, and distinguishing between two types of aggregate production functions: the extended neoclassical model and a mincerian formulation of schooling-returns to skills. Next, given our panel-data estimates, we proceed in decomposing the variance of the (log) level of output per-worker in 1985 into that of three distinct factors: productivity, human capital, and the dynamic incentives to accumulate capital. Finally, we classify a group of 95 countries according to their relative position (above or below average) for each of these factors. The picture that emerges from these last two exercises is one where countries grew in the past for different reasons, which should be considered for policy design. Although there is not a single-factor explanation for the difference in output per-worker across nations, it seems that productivity differences can explain a considerable portion of income inequality, followed second by dynamic inefficiencies and third by human capital accumulation.

SRML: Space Radio Machine Learning

Space-based communications systems to be employed by future artificial satellites, or spacecraft during exploration missions, can potentially benefit from software-defined radio adaptation capabilities. Multiple communication requirements could potentially compete for radio resources, whose availability of which may vary during the spacecraft\u27s operational life span. Electronic components are prone to failure, and new instructions will eventually be received through software updates. Consequently, these changes may require a whole new set of near-optimal combination of parameters to be derived on-the-fly without instantaneous human interaction or even without a human in-the-loop. Thus, achieving a sufficiently set of radio parameters can be challenging, especially when the communication channels change dynamically due to orbital dynamics as well as atmospheric and space weather-related impairments. This dissertation presents an analysis and discussion regarding novel algorithms proposed in order to enable a cognition control layer for adaptive communication systems operating in space using an architecture that merges machine learning techniques employing wireless communication principles. The proposed cognitive engine proof-of-concept reasons over time through an efficient accumulated learning process. An implementation of the conceptual design is expected to be delivered to the SDR system located on the International Space Station as part of an experimental program. To support the proposed cognitive engine algorithm development, more realistic satellite-based communications channels are proposed along with rain attenuation synthesizers for LEO orbits, channel state detection algorithms, and multipath coefficients function of the reflector\u27s electrical characteristics. The achieved performance of the proposed solutions are compared with the state-of-the-art, and novel performance benchmarks are provided for future research to reference

Evaluating Drill Interseeded Cover Crop Establishment and Nitrogen Impact in Irrigated Corn

The adoption of cover crops as a strategy to improve soil health and cropping systems sustainability is on the rise in the United States. PRE herbicides with soil residual activity are widely applied in corn production systems to prevent early season weed development, crop-weed competition, and yield loss. When preemergence herbicides are applied in the field, the active ingredients remain in the soil rhizosphere for a period of time, killing weed seedlings as they emerge. However, PRE herbicides can also impact the establishment of interseeded cover crops. Greenhouse bioassay was conducted to evaluate the preemergence herbicide carry-over potential to interseeded cover crops. On-farm research was conducted to evaluate nitrogen uptake by interseeded cover crops that could potentially decrease nitrogen losses. The objectives of these studies were: (1) to elucidate the impact of corn PRE herbicides on CC establishment; (2) to identify the ideal time to interseed cover crops following herbicide application; and (3) to evaluate the effect of cover crops on nitrogen uptake, including corn-N, cover crop-N, and soil nitrate in different depths. Advisor: Christopher A. Procto

Reliability-Oriented Strategies for Multichip Module Based Mission Critical Industry Applications

The availability is defined as the portion of time the system remains operational to serve its purpose. In mission critical applications (MCA), the availability of power converters are determinant to ensure continue productivity and avoid financial losses. Multichip Modules (MCM) are widely adopted in such applications due to the high power density and reduced price; however, the high number of dies inside a compact package results in critical thermal deviations among them. Moreover, uneven power flow, inhomogeneous cooling and accumulated degradation, potentially result in thermal deviation among modules, thereby increasing the temperature differences and resulting in extra temperature in specific subset of devices. High temperatures influences multiple failure mechanisms in power modules, especially in highly dynamic load profiles. Therefore, the higher failure probability of the hottest dies drastically reduces the reliability of mission critical power converters. Therefore, this work investigate reliability-oriented solutions for the design and thermal management of MCM-based power converters applied in mission critical applications. The first contribution, is the integration of a die-level thermal and probabilistic analysis on the design for reliability (DFR) procedure, whereby the temperature and failure probability of each die are taken into account during the reliability modeling. It is demonstrated that the dielevel analysis can obtain more realistic system-level reliability of MCM-based power converters. Thereafter, three novel die-level thermal balancing strategies, based on a modified MCM - with more gate-emitter connections - are proposed and investigated. It is proven that the temperatures inside the MCM can be overcame, and the maximum temperate reduced in up to 8 %