Evaluation of Sub-National Population Projections: a Case Study for London and the Thames Valley

Sub-national population projections help allocate national funding to local areas for planning local services. For example, water utilities prepare plans to meet future water demand over long-term horizons. Future demand depends on projected populations and households and forecasts of per household and per capita domestic water consumption in supply zones. This paper reports on population projections prepared for a water utility, Thames Water, which supplies water to over nine million people in London and the Thames Valley. Thames Water required an evaluation of the accuracy of the delivered projections against alternatives and estimates of uncertainty. The paper reviews how such evaluations have been made by researchers. The factors leading to variation in sub-national projections are identified. The methods, assumptions and results for English sub-national areas, used in five sets of projections, are compared. There is a consensus across projections about the future fertility and mortality but varying views about the future impact of internal and international migration flows. However, the greatest differences were between projections using ethnic populations. and those using homogeneous populations. Areas with high populations of ethnic minorities were projected to grow faster when an ethnic-specific model was used. This result is important for assessing projections for countries housing diverse populations with different demographic profiles. Historic empirical prediction intervals are used to assess the uncertainty of the London and the Thames Valley projections. By 2101 the preferred projection suggests that the population of the Thames Water region will have grown by 85% within an 80% empirical prediction interval between 45 and 125%

A "hair-raising" history of alopecia areata

YesA 3500‐year‐old papyrus from ancient Egypt provides a list of treatments for many diseases including “bite hair loss,” most likely alopecia areata (AA). The treatment of AA remained largely unchanged for over 1500 years. In 30 CE, Celsus described AA presenting as scalp alopecia in spots or the “windings of a snake” and suggested treatment with caustic compounds and scarification. The first “modern” description of AA came in 1813, though treatment still largely employed caustic agents. From the mid‐19th century onwards, various hypotheses of AA development were put forward including infectious microbes (1843), nerve defects (1858), physical trauma and psychological stress (1881), focal inflammation (1891), diseased teeth (1902), toxins (1912) and endocrine disorders (1913). The 1950s brought new treatment developments with the first use of corticosteroid compounds (1952), and the first suggestion that AA was an autoimmune disease (1958). Research progressively shifted towards identifying hair follicle‐specific autoantibodies (1995). The potential role of lymphocytes in AA was made implicit with immunohistological studies (1980s). However, studies confirming their functional role were not published until the development of rodent models (1990s). Genetic studies, particularly genome‐wide association studies, have now come to the forefront and open up a new era of AA investigation (2000s). Today, AA research is actively focused on genetics, the microbiome, dietary modulators, the role of atopy, immune cell types in AA pathogenesis, primary antigenic targets, mechanisms by which immune cells influence hair growth, and of course the development of new treatments based on these discoveries.Alopecia UK

Semiconcavity results for optimal control problems admitting no singular minimizing controls

Semiconcavity results have generally been obtained for optimal control problems in absence of state constraints. In this paper, we prove the semiconcavity of the value function of an optimal control problem with end-point constraints for which all minimizing controls are supposed to be nonsingular

Modeling the Zeeman effect in high-altitude SSMIS channels for numerical weather prediction profiles: Comparing a fast model and a line-by-line model

We present a comparison of a reference and a fast radiative transfer model using numerical weather prediction profiles for the Zeeman-affected high-altitude Special Sensor Microwave Imager/Sounder channels 19-22. We find that the models agree well for channels 21 and 22 compared to the channels\u27 system noise temperatures (1.9 and 1.3 K, respectively) and the expected profile errors at the affected altitudes (estimated to be around 5 K). For channel 22 there is a 0.5 K average difference between the models, with a standard deviation of 0.24 K for the full set of atmospheric profiles. Concerning the same channel, there is 1.2 K on average between the fast model and the sensor measurement, with 1.4 K standard deviation. For channel 21 there is a 0.9 K average difference between the models, with a standard deviation of 0.56 K. Regarding the same channel, there is 1.3 K on average between the fast model and the sensor measurement, with 2.4 K standard deviation. We consider the relatively small model differences as a validation of the fast Zeeman effect scheme for these channels. Both channels 19 and 20 have smaller average differences between the models (at below 0.2 K) and smaller standard deviations (at below 0.4 K) when both models use a two-dimensional magnetic field profile. However, when the reference model is switched to using a full three-dimensional magnetic field profile, the standard deviation to the fast model is increased to almost 2 K due to viewing geometry dependencies, causing up to \ub17 K differences near the equator. The average differences between the two models remain small despite changing magnetic field configurations. We are unable to compare channels 19 and 20 to sensor measurements due to limited altitude range of the numerical weather prediction profiles. We recommended that numerical weather prediction software using the fast model takes the available fast Zeeman scheme into account for data assimilation of the affected sensor channels to better constrain the upper atmospheric temperatures

Modeling the Zeeman effect in high-altitude SSMIS channels for numerical weather prediction profiles: Comparing a fast model and a line-by-line model

We present a comparison of a reference and a fast radiative transfer model using numerical weather prediction profiles for the Zeeman-affected high-altitude Special Sensor Microwave Imager/Sounder channels 19-22. We find that the models agree well for channels 21 and 22 compared to the channels\u27 system noise temperatures (1.9 and 1.3 K, respectively) and the expected profile errors at the affected altitudes (estimated to be around 5 K). For channel 22 there is a 0.5 K average difference between the models, with a standard deviation of 0.24 K for the full set of atmospheric profiles. Concerning the same channel, there is 1.2 K on average between the fast model and the sensor measurement, with 1.4 K standard deviation. For channel 21 there is a 0.9 K average difference between the models, with a standard deviation of 0.56 K. Regarding the same channel, there is 1.3 K on average between the fast model and the sensor measurement, with 2.4 K standard deviation. We consider the relatively small model differences as a validation of the fast Zeeman effect scheme for these channels. Both channels 19 and 20 have smaller average differences between the models (at below 0.2 K) and smaller standard deviations (at below 0.4 K) when both models use a two-dimensional magnetic field profile. However, when the reference model is switched to using a full three-dimensional magnetic field profile, the standard deviation to the fast model is increased to almost 2 K due to viewing geometry dependencies, causing up to \ub17 K differences near the equator. The average differences between the two models remain small despite changing magnetic field configurations. We are unable to compare channels 19 and 20 to sensor measurements due to limited altitude range of the numerical weather prediction profiles. We recommended that numerical weather prediction software using the fast model takes the available fast Zeeman scheme into account for data assimilation of the affected sensor channels to better constrain the upper atmospheric temperatures