Pesiqta Rabbati: a text-linguistic and form-analytical analysis of the rabbinic homily

Pesiqta Rabbati is a unique homiletic midrash that follows the liturgical calendar in its presentation of homilies for festivals and special Sabbaths. This article attempts to utilize Pesiqta Rabbati in order to present a global theory of the literary production of rabbinic/homiletic literature. In respect to Pesiqta Rabbati it explores such areas as dating, textual witnesses, integrative apocalyptic meta-narrative, describing and mapping the structure of the text, internal and external constraints that impacted upon the text, text linguistic analysis, form-analysis: problems in the texts and linguistic gap-filling, transmission of text, strict formalization of a homiletic unit, deconstructing and reconstructing homiletic midrashim based upon form-analytic units of the homily, Neusner’s documentary hypothesis, surface structures of the homiletic unit, and textual variants. The suggested methodology may assist scholars in their production of editions of midrashic works by eliminating superfluous material and in their decoding and defining of ancient texts.

Assessing the Validity of Brain Glucose Concentration Measurement Using Microdialysis

Brain functions, such as learning, orchestrating locomotion, memory recall, and processing information, all require glucose as a source of energy. During these functions, the glucose concentration decreases as the glucose is being consumed by brain cells. By measuring this drop in concentration, it is possible to determine which parts of the brain are used during specific functions and consequently, how much energy the brain requires to complete the function. One way to measure in vivo brain glucose levels is with a microdialysis probe. The drawback of this analytical procedure, as with many steadystate fluid flow systems, is that the probe fluid will not reach equilibrium with the brain fluid. Therefore, brain concentration is inferred by taking samples at multiple inlet glucose concentrations and finding a point of convergence. The goal of this thesis is to create a three-dimensional, time-dependent, finite element representation of the brainprobe system in COMSOL 4.2 that describes the diffusion and convection of glucose. Once validated with experimental results, this model can then be used to test parameters that experiments cannot access. When simulations were run using published values for physical constants (i.e. diffusivities, density and viscosity), the resulting glucose model concentrations were within the error of the experimental data. This verifies that the model is an accurate representation of the physical system. In addition to accurately describing the experimental brain-probe system, the model I created is able to show the validity of zero-net-flux for a given experiment. A useful discovery is that the slope of the zero-net-flux line is dependent on perfusate flow rate and diffusion coefficients, but it is independent of brain glucose concentrations. The model was simplified with the realization that the perfusate is at thermal equilibrium with the brain throughout the active region of the probe. This allowed for the assumption that all model parameters are temperature independent. The time to steady-state for the probe is approximately one minute. However, the signal degrades in the exit tubing due to Taylor dispersion, on the order of two minutes for two meters of tubing. Given an analytical instrument requiring a five μL aliquot, the smallest brain process measurable for this system is 13 minutes.

Design Simulation for Solar Energy Research and Education

Solar research is primarily conducted in regions with consistent sunlight, severely limiting research opportunities in many areas. Unfortunately, the unreliable weather in Lewisburg, PA, can prove difficult for such testing to be conducted. As such, a solar simulator was developed for educational purposes for the Mechanical Engineering department at Bucknell University. The objective of this work was to first develop a geometric model to evaluate a one sun solar simulator. This was intended to provide a simplified model that could be used without the necessity of expensive software. This model was originally intended to be validated experimentally, but instead was done using a proven ray tracing program, TracePro. Analyses with the geometrical model and TracePro demonstrated the influence the geometrical properties had results, specifically the reflector (aperture) diameter and the rim angle. Subsequently, the two were approaches were consistent with one another for aperture diameters 0.5 m and larger, and for rim angles larger than 45°. The constructed prototype, that is currently untested, was designed from information provided by the geometric model, includes a metal halide lamp with a 9.5 mm arc diameter and parabolic reflector with an aperture diameter of 0.631 meters. The maximum angular divergence from the geometrical model was predicted to be 30 mRadians. The average angular divergence in TraceProof the system was 19.5 mRadians, compared to the sun’s divergence of 9.2 mRadians. Flux mapping in TracePro showed an intensity of 1000 W/m2 over the target plane located 40 meters from the lamp. The error between spectrum of the metal halide lamp and the solar spectrum was 10.9%, which was found by comparing their respective Plank radiation distributions. The project did not satisfy the original goal of matching the angular divergence of sunlight, although the system could still to be used for optical testing. The geometric model indicated performance in this area could be improved by increasing the diameter of the reflector, as well as decreasing the source diameter. Although ray tracing software provides more information to analyze the simulator system, the geometrical model is adequate to provide enough information to design a system.

Advantages and Applications of Transforming Empirical Model Input Space with Dimensional Models

Model-based calibration of steady-state engine operation is commonly performed with highly parameterized empirical models that are accurate but not very robust, particularly when predicting highly nonlinear responses such as diesel smoke emissions. To address this problem, and to boost the accuracy of more robust non-parametric methods to the same level, GT-Power was used to transform the empirical model input space into multiple input spaces that simplified the input-output relationship and improved the accuracy and robustness of smoke predictions made by three commonly used empirical modeling methods: Multivariate Regression, Neural Networks and the k-Nearest Neighbor method. The availability of multiple input spaces allowed the development of two committee techniques: a 'Simple Committee' technique that used averaged predictions from a set of 10 pre-selected input spaces chosen by the training data and the "Minimum Variance Committee" technique where the input spaces for each prediction were chosen on the basis of disagreement between the three modeling methods. This latter technique equalized the performance of the three modeling methods. The successively increasing improvements resulting from the use of a single best transformed input space (Best Combination Technique), Simple Committee Technique and Minimum Variance Committee Technique were verified with hypothesis testing. The transformed input spaces were also shown to improve outlier detection and to improve k-Nearest Neighbor performance when predicting dynamic emissions with steady-state training data. An unexpected finding was that the benefits of input space transformation were unaffected by changes in the hardware or the calibration of the underlying GT-Power model.