There are many data mining and machine learning techniques to manage large sets of complex energy supply and demand data for building, organization and city. As the amount of data continues to grow, new data analysis methods are needed to address the increasing complexity. Using data from the energy loss between the supply (energy production sources) and demand (buildings and cities consumption), this paper proposes a Semi-Supervised Energy Model (SSEM) to analyse different loss factors for a building cluster. This is done by deep machine learning by training machines to semi-supervise the learning, understanding and manage the process of energy losses. Semi-Supervised Energy Model (SSEM) aims at understanding the demand-supply characteristics of a building cluster and utilizes the confident unlabelled data (loss factors) using deep machine learning techniques. The research findings involves sample data from one of the university campuses and presents the output, which provides an estimate of losses that can be reduced. The paper also provides a list of loss factors that contributes to the total losses and suggests a threshold value for each loss factor, which is determined through real time experiments. The conclusion of this paper provides a proposed energy model that can provide accurate numbers on energy demand, which in turn helps the suppliers to adopt such a model to optimize their supply strategies.

The Operator Product Expansion for null polygonal Wilson loop in planar maximally supersymmetric Yang–Mills theory runs systematically in terms of multi-particle pentagon transitions which encode the physics of excitations propagating on the color flux tube ending on the sides of the four-dimensional contour. Their dynamics was unraveled in the past several years and culminated in a complete description of pentagons as an exact function of the 't Hooft coupling. In this paper we provide a solution for the last building block in this program, the SU(4) matrix structure arising from internal symmetry indices of scalars and fermions. This is achieved by a recursive solution of the Mirror and Watson equations obeyed by the so-called singlet pentagons and fixing the form of the twisted component in their tensor decomposition. The non-singlet, or charged, pentagons are deduced from these by a limiting procedure.

Small and medium office buildings consume a significant parcel of the U.S. building stock energy consumption. Still, owners lack resources and experience to conduct detailed energy audits and retrofit analysis. We present an eight-steps framework for an energy retrofit assessment in small and medium office buildings. Through a bottom-up approach and a web-based retrofit toolkit tested on a case study in Arizona, this methodology was able to save about 50% of the total energy consumed by the case study building, depending on the adopted measures and invested capital. While the case study presented is a deep energy retrofit, the proposed framework is effective in guiding the decision-making process that precedes any energy retrofit, deep or light.

In this paper we study the four-point correlation function of the energy–momentum supermultiplet in theories with N = 4 superconformal symmetry in four dimensions. We present a compact form of all component correlators as an invariant of a particular abelian subalgebra of the N = 4 superconformal algebra. This invariant is unique up to a single function of the conformal cross-ratios which is fixed by comparison with the correlation function of the lowest half-BPS scalar operators. Our analysis is independent of the dynamics of a specific theory, in particular it is valid in N = 4 super Yang–Mills theory for any value of the coupling constant. We discuss in great detail a subclass of component correlators, which is a crucial ingredient for the recent study of charge-flow correlations in conformal field theories. We compute the latter explicitly and elucidate the origin of the interesting relations among different types of flow correlations previously observed in arXiv:1309.1424.

Chemical composition affects virtually all aspects of astrobiology, from stellar astrophysics to molecular biology. We present a synopsis of the research results presented at the “Stellar Stoichiometry” Workshop Without Walls hosted at Arizona State University April 11–12, 2013, under the auspices of the NASA Astrobiology Institute. The results focus on the measurement of chemical abundances and the effects of composition on processes from stellar to planetary scales. Of particular interest were the scientific connections between processes in these normally disparate fields. Measuring the abundances of elements in stars and giant and terrestrial planets poses substantial difficulties in technique and interpretation. One of the motivations for this conference was the fact that determinations of the abundance of a given element in a single star by different groups can differ by more than their quoted errors.

The problems affecting the reliability of abundance estimations and their inherent limitations are discussed. When these problems are taken into consideration, self-consistent surveys of stellar abundances show that there is still substantial variation (factors of ∼2) in the ratios of common elements (e.g., C, O, Na, Al, Mg, Si, Ca) important in rock-forming minerals, atmospheres, and biology. We consider how abundance variations arise through injection of supernova nucleosynthesis products into star-forming material and through photoevaporation of protoplanetary disks. The effects of composition on stellar evolution are substantial, and coupled with planetary atmosphere models can result in predicted habitable zone extents that vary by many tens of percent. Variations in the bulk composition of planets can affect rates of radiogenic heating and substantially change the mineralogy of planetary interiors, affecting properties such as convection and energy transport.