Surface infrastructure functions, requirements and subsystems for a manned Mars mission

Planning and development for a permanently manned scientific outpost on Mars requires an in-depth understanding and analysis of the functions the outpost is expected to perform. The optimum configuration that accomplishes these functions then arises during the trade studies process. In a project this complex, it becomes necessary to use a formal methodology to document the design and planning process. The method chosen for this study is called top-down functional decomposition. This method is used to determine the functions that are needed to accomplish the overall mission, then determine what requirements and systems are needed to do each of the functions. This method facilitates automation of the trades and options process. In the example, this was done with an off-the shelf software package called TK! olver. The basic functions that a permanently manned outpost on Mars must accomplish are: (1) Establish the Life Critical Systems; (2) Support Planetary Sciences and Exploration; and (3) Develop and Maintain Long-term Support Functions, including those systems needed towards self-sufficiency. The top-down functional decomposition methology, combined with standard spread sheet software, offers a powerful tool to quickly assess various design trades and analyze options. As the specific subsystems, and the relational rule algorithms are further refined, it will be possible to very accurately determine the implications of continually evolving mission requirements

Report of the In Situ Resources Utilization Workshop

The results of a workshop of 50 representatives from the public and private sector which investigated the potential joint development of the key technologies and mechanisms that will enable the permanent habitation of space are presented. The workshop is an initial step to develop a joint public/private assessment of new technology requirements of future space options, to share knowledge on required technologies that may exist in the private sector, and to investigate potential joint technology development opportunities. The majority of the material was produced in 5 working groups: (1) Construction, Assembly, Automation and Robotics; (2) Prospecting, Mining, and Surface Transportation; (3) Biosystems and Life Support; (4) Materials Processing; and (5) Innovative Ventures. In addition to the results of the working groups, preliminary technology development recommendations to assist in near-term development priority decisions are presented. Finally, steps are outlined for potential new future activities and relationships among the public, private, and academic sectors

Forecasting the 2013--2014 Influenza Season using Wikipedia

Infectious diseases are one of the leading causes of morbidity and mortality around the world; thus, forecasting their impact is crucial for planning an effective response strategy. According to the Centers for Disease Control and Prevention (CDC), seasonal influenza affects between 5% to 20% of the U.S. population and causes major economic impacts resulting from hospitalization and absenteeism. Understanding influenza dynamics and forecasting its impact is fundamental for developing prevention and mitigation strategies. We combine modern data assimilation methods with Wikipedia access logs and CDC influenza like illness (ILI) reports to create a weekly forecast for seasonal influenza. The methods are applied to the 2013--2014 influenza season but are sufficiently general to forecast any disease outbreak, given incidence or case count data. We adjust the initialization and parametrization of a disease model and show that this allows us to determine systematic model bias. In addition, we provide a way to determine where the model diverges from observation and evaluate forecast accuracy. Wikipedia article access logs are shown to be highly correlated with historical ILI records and allow for accurate prediction of ILI data several weeks before it becomes available. The results show that prior to the peak of the flu season, our forecasting method projected the actual outcome with a high probability. However, since our model does not account for re-infection or multiple strains of influenza, the tail of the epidemic is not predicted well after the peak of flu season has past.Comment: Second version. In previous version 2 figure references were compiling wrong due to error in latex sourc

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)

<i>S</i>^<i>ν</i><i>EIR</i> with enKS vs. straw man forecast for the 2013–2014 U.S. ILI data.

<p>The M-distance between U.S. 2013–2014 ILI data and the two forecasts is plotted. The M-distance between the forecast and ILI data is calculated for each epidemiological week until the end of the influenza season. The M-distances at week 36 uses the forecast observations from week 36 of 2013 to week 20 of 2014 and the ILI data from week 36 of 2013 to week 20 of 2014. The M-distances plotted for the straw man prediction use sample covariances and means calculated from 300 time series draws of the straw man forecast. Due to the lack of causal relations included in the straw man model this measure of accuracy is significantly lower in the early season for the straw man prediction. This figure shows that the data assimilation forecast has a noticeably smaller M-distance, and therefore is quantitatively better, than the straw man model for the early influenza season. Once the influenza season peaks the success of the forecast breaks down due to model error. It is interesting to note that due to the enKS data assimilation our <i>S</i>^<i>ν</i><i>EIR</i> forecast seems to attempt self-correction, i.e. the M-distance is increasing and then decreases.</p

Histogram of the marginal distribution for the average recovery time, measured in days.

<p>The rate parameter in our <i>S</i>^<i>ν</i><i>EIR</i> model, <i>γ</i>, is the inverse of this average time. We see that this distribution is concentrated over 6–7 days and skewed toward longer incubation times. The prior distribution for <i>γ</i> is more concentrated than the distributions for <i>θ</i> and <i>β</i>₀ which means that the ILI data determine this parameter more exactly.</p

Defining a maximal influenza season.

<p>We highlight the weeks corresponding to our maximal influenza season over which we parameterize our forecast. Since our model does not include re-infection or loss of immunity we can only hope to forecast one pre-defined season at a time.</p

<i>S</i>^<i>ν</i><i>EIR</i> peak quantiles for 2013–2014 U.S. ILI.

<p>50% and 90% credible interval estimates of the influenza season peak are plotted along with the median. Forecasts for the size of the ILI peak were widely varying in the 90% credible interval. This could possibly be reduced by the elimination of high peak outliers such as the 2009 H1N1 emergence and through adjustment of low forecasts in our prior. However, even with these draw backs the 50% credible region has a width of only 1%–2%.</p

<i>S</i>^<i>ν</i><i>EIR</i> start week quantiles for 2013–2014 U.S. ILI.

<p>50% and 90% credible interval estimates of the influenza season start week are plotted along with the median. Each week, as new ILI data become available the forecast is revised. This causes the uncertainty in our forecast to diminish. However, due to the model’s inability to maintain an elevated ILI level past the peak, we see that late in the flu season, the model adjusts by pushing the start week later into the season. This causes an overestimation of the start week that worsens as the season progresses. In practice, once the start week has been observed the <i>forecast</i> would be fixed. However, adjustment of the model parameterization using the enKS would continue to affect the model simulation start date.</p

Histogram of the marginal distribution for the duration of heightened transmissibility.

<p>The parameter <i>w</i> is represented in weeks. A value <i>w</i> = 14 corresponds to 16 weeks of elevated transmission. We see that this distribution is concentrated over 14–20 weeks and skewed toward longer periods of elevated transmission.</p