A machine learning methodology to quantify the potential of urban densification in the Oxford-Cambridge Arc, United Kingdom

Regional-scale urban residential densification provides an opportunity to tackle multiple challenges of sustainability in cities. But framework for detailed large-scale analysis of densification potentials and their integration with natural capital to assess the housing capacity is lacking. Using a combination of Machine Learning Random Forests algorithm and exploratory data analysis (EDA), we propose density scenarios and housing-capacity estimates for the potential residential lands in the Oxford–Cambridge Arc region (whose current population of 3.7 million is expected to increase up to 4.7 million in 2035) in the UK. A detailed analysis was done for Oxfordshire, assuming different densities in urban and rural areas and protecting lands with high-value natural capital from development. For a 30,000 dwellings-per-year scenario, the land allocated in Local Plans could cover housing growth in the four districts but not in Oxford City itself (which accounts for 48% of the demand); only 19% of the need would be covered in low but 59% in high housing density scenarios. Our study suggests a decision-support method for quantifying how the impact of housing growth on natural capital can be significantly reduced using more compact development patterns, protection of land with high-value natural capital, and use of low-biodiversity brownfield sites where available

Search for contact interactions and large extra dimensions in dilepton events from pppp collisions at s=7\sqrt{s} = 7 TeV with the ATLAS detector

A search for non-resonant new phenomena, originating from either contact interactions or large extra spatial dimensions, has been carried out using events with two isolated electrons or muons. These events, produced at the LHC in proton-proton collisions at sqrt(s) = 7 TeV, were recorded by the ATLAS detector. The data sample, collected throughout 2011, corresponds to an integrated luminosity of 4.9 and 5.0 fb-1 in the e+e- and mu+mu- channels, respectively. No significant deviations from the Standard Model expectation are observed. Using a Bayesian approach, 95% confidence limit lower limits ranging from 9.0 to 13.9 TeV are placed on the energy scale of llqq contact interactions in the left-left isoscalar model. Lower limits ranging from 2.4 to 3.9 TeV are also set on the string scale in large extra dimension models. After combination of these limits with results from a similar search in the diphoton channel, slightly more stringent limits are obtained.Comment: 12 pages plus author list (25 pages in total), 3 figures, 8 tables, published in Phys.Rev.D 87, 015010 (2013), all figures including auxiliary figures are available at http://atlas.web.cern.ch/Atlas/GROUPS/PHYSICS/PAPERS/EXOT-2012-17/; revision corresponds to published version and corrects units for M_S in the legend of Figs. 2 and 3, as well as M_S limit values in the main tex

Search for resonant top plus jet production in ttˉt\bar{t} + jets events with the ATLAS detector in pppp collisions at s=7\sqrt{s}=7 TeV

This paper presents a search for a new heavy particle produced in association with a top or antitop quark. Two models in which the new heavy particle is a color singlet or a color triplet are considered, decaying, respectively, to t̅ q or tq, leading to a resonance within the tt̅ +jets signature. The full 2011 ATLAS pp collision data set from the LHC (4.7  fb-1) is used to search for tt̅ events produced in association with jets, in which one of the W bosons from the top quarks decays leptonically and the other decays hadronically. The data are consistent with the Standard Model expectation, and a new particle with mass below 430 Gev for both W′ boson and color triplet models is excluded at 95% confidence level, assuming unit right-handed coupling.United States. Dept. of EnergyNational Science Foundation (U.S.