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Applying any sustainable intervention in the urban energy system requires fundamental knowledge of the energy demand dynamics. Only when we can predict the users' energy demand at any given time with accuracy, we can redesign the urban energy system. Accordingly, the main objective of this paper is to determine the annual electricity usage of the building connections in the urban built environment. In this paper firstly through a literature review, the important electricity usage explanatory variables of the built environment are recognized. For each building, besides the annual electricity usage, three major categories of explanatory variables, including physical, socioeconomic, and geospatial characteristics are determined. Based on the available data sources, a building electricity usage database is created. The database is categorized based on the two most frequently used building sectors including residential and non-residential. Ordinary Least Squares (OLS) technique is applied to the constructed database to estimate the predicting model parameters establishing a relationship between the annual electricity usage as a dependent variable and physical, socioeconomic, and geospatial variables as independent variables. In this research, to determine the contribution of geospatial characteristics in the annual electricity usage variability, regression analysis is performed in two consecutive steps. In the first step only, the geospatial characteristics were implemented in the multiple linear regression analysis. Following that, in the second step, the other categories including physical and socioeconomic characteristics are added to the model. The result revealed that in both building sectors most of the predictors are statistically significant at the 0.05 level. While for the residential buildings the geospatial characteristics account for 9.7% of the electricity usage variation, these values for the service and industry sub-sectors are 9.9% and 8.7% respectively. In total, all variables explain 28.1%, 39.4%, and 42.9% of the electricity usage variability of residential, service, and industrial buildings respectively.

A measurement of the underlying activity in scattering processes with a hard scale in the several GeV region is performed in proton-proton collisions at sqrt(s) = 0.9 and 7 TeV, using data collected by the CMS experiment at the LHC. The production of charged particles with pseudorapidity |eta| < 2 and transverse momentum pT > 0.5 GeV/c is studied in the azimuthal region transverse to that of the leading set of charged particles forming a track-jet. A significant growth of the average multiplicity and scalar-pT sum of the particles in the transverse region is observed with increasing pT of the leading track-jet, followed by a much slower rise above a few GeV/c. For track-jet pT larger than a few GeV/c, the activity in the transverse region is approximately doubled with a centre-of-mass energy increase from 0.9 to 7 TeV. Predictions of several QCD-inspired models as implemented in PYTHIA are compared to the data. Journal of high energy physics 2011(9), 109(2011). doi:10.1007/JHEP09(2011)109 Published by Springer, Berlin

Abstract A search has been made for events containing an energetic jet and an imbalance in transverse momentum using a data sample of pp collisions at a center-of-mass energy of 7 TeV. This signature is common to both dark matter and extra dimensions models. The data were collected by the CMS detector at the LHC and correspond to an integrated luminosity of 5.0 fb−1. The number of observed events is consistent with the standard model expectation. Constraints on the dark matter-nucleon scattering cross sections are determined for both spin-independent and spin-dependent interaction models. For the spin-independent model, these are the most constraining limits for a dark matter particle with mass below 3.5 GeV/c 2, a region unexplored by direct detection experiments. For the spin-dependent model, these are the most stringent constraints over the 0.1–200 GeV/c 2 mass range. The constraints on the Arkani-Hamed, Dimopoulos, and Dvali model parameter M D determined as a function of the number of extra dimensions are also an improvement over the previous results.