A Review of Marcus Borg\u27s Meeting Jesus Again for the First Time

In lieu of an abstract, below is the essay\u27s first paragraph. Meeting Jesus Again for the First Time was written by Marcus J. Borg and was first published in 1994. Much of the information and theories found within the book came from and annual lecture Borg gave at the Northern California Conference of the United Church of Christ at Asilomar, California in 1992. The main idea of Borg’s novel is that “there is a strong connection between images of Jesus and images of the Christian life, between how we think of Jesus and how we think of the Christian life “(Howard 1-2)

Dating 101

In lieu of an abstract, below is the essay\u27s first paragraph. It was recently brought to my attention that race car drivers use special gas which costs them 8 dollars a gallon. That got me thinking about how great it would to have a race car. Forget that it costs 8 dollars a gallon because you would be able to go very fast- Getting places would be so much easier traveling at over 200 miles an hour. However, there would be some drawbacks such as

Breaking degeneracy in jet dynamics: multi-epoch joint modelling of the BL Lac PKS 2155-304

Supermassive black holes can launch powerful jets which can be some of the most luminous multi-wavelength sources; decades after their discovery their physics and energetics are still poorly understood. The past decade has seen a dramatic improvement in the quality of available data, but despite this improvement the semi-analytical modelling of jets has advanced slowly: simple one-zone models are still the most commonly employed method of interpreting data, in particular for AGN jets. These models can roughly constrain the properties of jets but they cannot unambiguously couple their emission to the launching regions and internal dynamics, which can be probed with simulations. However, simulations are not easily comparable to observations because they cannot yet self-consistently predict spectra. We present an advanced semi-analytical model which accounts for the dynamics of the whole jet, starting from a simplified parametrization of Relativistic Magnetohydrodynamics in which the magnetic flux is converted into bulk kinetic energy. To benchmark the model we fit six quasisimultaneous, multi-wavelength spectral energy distributions of the BL Lac PKS 2155-304 obtained by the TANAMI program, and we address the degeneracies inherent to such a complex model by employing a state-of-the-art exploration of parameter space, which so far has been mostly neglected in the study of AGN jets. We find that this new approach is much more effective than a single-epoch fit in providing meaningful constraints on model parameters.Comment: Accepted for publication on MNRA

A statistical method for estimating predictable differences between daily traffic flow profiles

It is well known that traffic flows in road networks may vary not only within the day but also between days. Existing models including day-to-day variability usually represent all variability as unpredictable fluctuations. In reality, however, some of the differences in flows on a road may be predictable for transport planners with access to historical data. For example, flow profiles may be systematically different on Mondays compared to Fridays due to predictable differences in underlying activity patterns. By identifying days of the week or times of year where flows are predictably different, models can be developed or model inputs can be amended (in the case of day-to-day dynamical models) to test the robustness of proposed policies or to inform the development of policies which vary according to these predictably different day types. Such policies could include time-of-day varying congestion charges that themselves vary by day of the week or season, or targeting public transport provision so that timetables are more responsive to the day of the week and seasonal needs of travellers. A statistical approach is presented for identifying systematic variations in daily traffic flow profiles based on known explanatory factors such as the day of the week and the season. In order to examine day-to-day variability whilst also considering within-day dynamics, the distribution of flows throughout a day are analysed using Functional Linear Models. F-type tests for functional data are then used to compare alternative model specifications for the predictable variability. The output of the method is an average flow profile for each predictably different day type, which could include day of the week or time of year. An application to real-life traffic flow data for a two-year period is provided. The shape of the daily profile was found to be significantly different for each day of the week, including differences in the timing and width of peak flows and also the relationship between peak and inter-peak flows. Seasonal differences in flow profiles were also identified for each day of the week