Abstract
The heating energy demand stated in energy performance certificates (EPC) and in other instruments used in the of evaluation of building's energy performance is usually determined assuming very specific (reference) indoor behavioral/heating patterns. Particularly, they tend to assume that households heat (nearly) the entire house to a "comfort" temperature during (nearly) all the heating season. However, several field studies have shown that there are major niches of the housing stock which do not follow this pattern (even the majority, in some geographical areas). Considering this matter, it would be interesting to build models able to estimate heating energy use values resultant from occupation and heating patterns different from those considered as "reference". This work aimed at producing tools to assess the relationship between heating energy use and indoor temperatures at different levels of occupant behavior (in terms of where, when and at what temperature households heat their dwellings). This relationship was expressed through models while still takes advantage of the information from the certificates. The work developed artificial neural networks (ANN) that characterize the relationship between heating energy use, indoor temperatures and the heating energy demand under reference conditions (typically available from energy rating/certificates) in the residential buildings, for different occupant behaviors heating patterns. Theoretically, these models can be applicable to any national geographical context. The data for building the ANNs was obtained from dynamic thermal building simulations using ESP-r, considering a large number of housing types and hypothetical occupation and heating patterns (i.e., which parts of the house are heated, when and at what temperature). From the analysis performed, it was possible to conclude that the developed ANN models proved to perform well (R-2 > 0.93) in estimating either heating energy use or indoor temperature, both at an individual and at the building stock level. This work may have important contributions in the energy planning practices regarding the residential building stock.

Abstract

Abstract
In several countries, it is a common practice for construction owners to require contractors to submit several types of surety bonds shifting the risk of contractors default to sureties. The process of evaluating contractors' applications for bonding is complicated and time-consuming. Surety underwriters are faced with the challenge of considering tens of objective and subjective criteria to make the bonding recommendation. These criteria include a contractor's financial strength, past performance, and other related aspects. Literature review revealed that there is little guidance to surety underwriters in this regard. As a result, the purpose of this paper is to equip surety underwriters with a decision making model for evaluating contractors' applications for bonding. The proposed model is based on data envelopment analysis (DEA). The DEA is a robust nonparametric linear programming approach that is widely used for benchmarking, performance measurement, and decision making. The proposed DEA model guides surety underwriters throughout the decision making process. The model offers surety underwriters the flexibility to accommodate any number of variables in the evaluation process. The proposed model is deployed based on a database that includes 49 contractors. The validity and applicability of the proposed model are scored positively by industry practitioners in terms of methodology used, evaluation criteria, ease of use, performance results, and overall usefulness compared to existing methods. (C) 2015 American Society of Civil Engineers.

Abstract
Urban form is an important driver of energy demand and therefore of GHG emissions in urban areas. Yet, research on urban form and energy remains sectorial and hasn't been able to deliver a full understanding of the impact of the physical structure of cities upon their energy demand. Most common approaches feature engineering models in buildings, and statistical models in transports. This study aims at contributing to the characterization of the link between urban form and energy considering altogether three distinct energy uses: ambient heating and cooling in buildings, and travel. A high-resolution methodology is proposed. It applies GIS to provide the analysis with a spatially-explicit character, and neural networks to model energy demand based on a set of relevant urban form indicators. The results confirm that the effect of urban form indicators on the overall energy needs is far from being negligible. In particular, the number of floors, the diversity of activities within a walking reach, the floor area and the subdivision of blocks evidenced a significant impact on the overall energy demand of the case study analyzed.

Abstract
This study approaches the internationalization-performance (I-P) relationship following an innovative strategy, using DEA to calculate a financial performance metric that considers several financial indicators. We then apply a truncated regression to evaluate the relationship between financial performance and internationalization for a sample of firms in the footwear Portuguese industry for the period 2010-2013, using several controls, while exploring potential non-linear effects. Results tend to support the conclusion that export participation leads to increased efficiency, eventually through the so-called learning effects. For our case, the relationship is U-shaped. So, beyond a certain level the degree of international engagement might compromise efficiency.

Abstract
Empirical data for residential indoor temperature and its determinants have important implications for policymakers in terms of the assessment of thermal comfort, health of occupants and the use for supporting energy demand models. With the purpose of advancing this knowledge, the indoor temperatures of 141 households in the Northern Portugal were measured at a half-hourly basis during the winter of 2013-2014. The observed mean winter daily indoor temperature at the occupied period was 14.9 °C for the bedrooms and 16.6°C for the living rooms. The results show that indoor temperatures are significantly below the comfort levels generally accepted, which could be an indication of future potential rebound effects. Results also reinforce the idea that 'cold homes' during winter season are a reality even in the southern European countries. Models for predicting the daily mean bedroom and living room temperature were developed using an enhanced linear regression with panel-corrected standard errors. The results showed that climatic conditions, and especially building characteristics, affect significantly the bedroom and living room's indoor temperatures.