A Comparison of the Code of Hammurabi and Mosaic Law

As the title of this thesis indicates, the problem we wish to explore is the apparent similarities between the Code of Hammurabi and the Mosaic Law. We want to explore both bodies of law and take note of the apparent similarities, and then try to account for them. In the search for a better understanding of the similarities, we shall not limit ourselves to the texts of the two codes but we shall also consider the historical background, cultures, and personalities responsible for them

Dry heat effects on survival of indigenous soil particle microflora and particle viability studies of Kennedy Space Center soil

Research efforts were concentrated on attempts to obtain data concerning the dry heat resistance of particle microflora in Kennedy Space Center soil samples. The in situ dry heat resistance profiles at selected temperatures for the aggregate microflora on soil particles of certain size ranges were determined. Viability profiles of older soil samples were compared with more recently stored soil samples. The effect of increased particle numbers on viability profiles after dry heat treatment was investigated. These soil particle viability data for various temperatures and times provide information on the soil microflora response to heat treatment and are useful in making selections for spacecraft sterilization cycles

Assessing current and future impacts of climate-related extreme events. The case of Bangladesh

Extreme events and options for managing these risks are receiving increasing attention in research and policy. In order to cost these extremes, a standard approach is to use Integrated Assessment Models with global or regional resolution and represent risk using add-on damage functions that are based on observed impacts and contingent on gradual temperature increase. Such assessments generally find that economic development and population growth are likely to be the major drivers of natural disaster risk in the future; yet, little is said about changes in vulnerability, generally considered a key component of risk. As well, risk is represented by an estimate of average observed impacts using the statistical expectation. Explicitly accounting for vulnerability and using a fuller risk-analytical framework embedded in a simpler economic model, we study the case of Bangladesh, the most flood prone country in the world, in order to critically examine the contribution of all drivers to risk. Specifically, we assess projected changes in riverine flood risk in Bangladesh up to the year 2050 and attempt to quantitatively assess the relative importance of climate change versus socio-economic change in current and future disaster risk. We find that, while flood frequency and intensity, based on regional climate downscaling, are expected to increase, vulnerability, based on observed behaviour in real events over the last 30 years, can be expected to decrease. Also, changes in vulnerability and hazard are roughly of similar magnitudes, while uncertainties are large. Overall, we interpret our findings to corroborate the need for taking a more risk-based approach when assessing extreme events impacts and adaptation - cognizant of the large associated uncertainties and methodological challenges -

Metabolic and Molecular Regulation of Dietary Polyunsaturated Fatty Acids on Prostate Cancer

Purpose The aim of this study is to investigate the role of n-3 and n-9 fatty acids in crucial processes involved in prostate cancer cell growth through a large-scale proteomic analysis. Experimental design We used a label-free protein quantification method to profile global protein expression of fish oil and oleic acid treated PCa cells and validated a panel of differentially expressed proteins by either Western blot or multiple reaction monitoring. Bioinformatic analysis was also performed to uncover the pathways involved in fatty acid metabolism. Results Fish oil, not oleic acid, suppresses prostate cancer cell viability. Assessment of fatty acid synthesis pathway activity also shows that oleic acid is a more potent inhibitor than fish oil on de novo fatty acid synthesis. Although fatty acid synthase activity decreases with fish oil treatment, the inhibition of its activity occurs over time while reduction in viability occurs within 24 h. Bioinformatic analysis revealed the pathways altered by these fatty acid treatments. Conclusions and clinical relevance This study suggests that suppression of cell viability by fish oil is independent of fatty acid synthase and fish oil regulates prostate cancer cells through activation of other pathways depending upon length of exposure to fish oil

Plasma polymerization of cyclopropylamine in a low-pressure cylindrical magnetron reactor: A PIC-MC study of the roles of ions and radicals

A study of plasma polymerization of cyclopropylamine in a low-pressure cylindrical magnetron reactor is presented. Both experimental and numerical approaches are used to investigate thin film growth mechanisms and polymer film properties depending on the magnetic field strength. Combining both approaches enables the consistency of the numerical model to be checked while acquiring data for understanding the observed phenomena. Samples are first analyzed by x-ray photoelectron spectroscopy, time of flight secondary ion mass spectrometry, and ion beam analysis to illustrate the differences in degrees of chemical functionalization and cross-linking between the regions of high and low magnetic fields. 3D particle-in-cell Monte Carlo collision simulations are then performed to shed light on experimental results, after implementing a set of electron-cyclopropylamine collision cross sections computed using the R-matrix method. The simulations enable the main radicals produced in the discharge to be tracked by determining their production rates, how they diffuse in the plasma, and how they absorb on the reactor walls. Additionally, the cyclopropylamine ion (C₃H₇N⁺) behavior is followed to bring insights into the respective roles of ions and radicals during the plasma polymerization process

Evolutionary multi-stage financial scenario tree generation

Multi-stage financial decision optimization under uncertainty depends on a careful numerical approximation of the underlying stochastic process, which describes the future returns of the selected assets or asset categories. Various approaches towards an optimal generation of discrete-time, discrete-state approximations (represented as scenario trees) have been suggested in the literature. In this paper, a new evolutionary algorithm to create scenario trees for multi-stage financial optimization models will be presented. Numerical results and implementation details conclude the paper

Entire curves avoiding given sets in C^n

Let $F\subset\Bbb C^n$ be a proper closed subset of $\Bbb C^n$ and $A\subset\Bbb C^n\setminus F$ at most countable ($n\geq 2$). We give conditions of $F$ and $A$, under which there exists a holomorphic immersion (or a proper holomorphic embedding) $\phi:\Bbb C\to\Bbb C^n$ with $A\subset\phi(\Bbb C)\subset\Bbb C^n\setminus F$.Comment: 10 page

HMM based scenario generation for an investment optimisation problem

This is the post-print version of the article. The official published version can be accessed from the link below - Copyright @ 2012 Springer-Verlag.The Geometric Brownian motion (GBM) is a standard method for modelling financial time series. An important criticism of this method is that the parameters of the GBM are assumed to be constants; due to this fact, important features of the time series, like extreme behaviour or volatility clustering cannot be captured. We propose an approach by which the parameters of the GBM are able to switch between regimes, more precisely they are governed by a hidden Markov chain. Thus, we model the financial time series via a hidden Markov model (HMM) with a GBM in each state. Using this approach, we generate scenarios for a financial portfolio optimisation problem in which the portfolio CVaR is minimised. Numerical results are presented.This study was funded by NET ACE at OptiRisk Systems

Mean-risk models using two risk measures: A multi-objective approach

This paper proposes a model for portfolio optimisation, in which distributions are characterised and compared on the basis of three statistics: the expected value, the variance and the CVaR at a specified confidence level. The problem is multi-objective and transformed into a single objective problem in which variance is minimised while constraints are imposed on the expected value and CVaR. In the case of discrete random variables, the problem is a quadratic program. The mean-variance (mean-CVaR) efficient solutions that are not dominated with respect to CVaR (variance) are particular efficient solutions of the proposed model. In addition, the model has efficient solutions that are discarded by both mean-variance and mean-CVaR models, although they may improve the return distribution. The model is tested on real data drawn from the FTSE 100 index. An analysis of the return distribution of the chosen portfolios is presented