Perturbed and non-perturbed brownian taboo processes

ABSTRACT. – In this paper we study the Brownian taboo process, which is a version of Brownian motion conditioned to stay within a finite interval, and the α-perturbed Brownian taboo process, which is an analogous version of an α-perturbed Brownian motion.We are particularly interested in the asymptotic behaviour of the supremum of the taboo process, and our main results give integral tests for upper and lower functions of the supremum as t →∞. In the Brownian case these include extensions of recent results in Lambert [4], but are proved in a quite different way. 2001 Éditions scientifiques et médicales Elsevier SAS AMS classification: 60K05; 60J15 RÉSUMÉ. – Dans cet article, nous étudions le processus Brownien tabou qui est une version du mouvement Brownien, conditionné à rester dans un intervalle fini, et le processus Brownien tabou α-perturbé qui est une version semblable du mouvement Brownien α-perturbé. Nous sommes particulièrement intéressés par le comportement asymptotique du supremum du processus tabou et nos principaux résultats fournissent des intégrales tests pour des fonctions majorantes et minorantes du supremum lorsque t → ∞. Dans le cas Brownien, ces résultats incluent des extensions de résultats récents de Lambert [4], mais ceux-ci sont prouvés de maníère différente. 2001 Éditions scientifiques et médicales Elsevier SAS 1

International Energy Agency Building Energy Simulation Test and Diagnostic Method (IEA BESTEST): In-Depth Diagnostic Cases for Ground Coupled Heat Transfer Related to Slab-on-Grade Construction

This report documents a set of idealized in-depth diagnostic test cases for use in validating ground-coupled floor slab heat transfer models. These test cases represent an extension to IEA BESTEST

Diabesity in the Arabian Gulf: Challenges and Opportunities

Diabesity (diabetes associated with obesity) is a major global and local public health concern, which has almost reached an epidemic order of magnitude in the countries of the Arabian Gulf and worldwide. We sought to review the lifestyle trends in this region and to highlight the challenges and opportunities that health care professionals face and attempt to address and correct them. In this regard, we aimed to review the regional data and widely held expert opinions in the Arabian Gulf and provide a thematic review of the size of the problem of diabesity and its risk factors, challenges, and opportunities. We also wished to delineate the barriers to health promotion, disease prevention, and identify social customs contributing to these challenges. Lastly, we wished to address specific problems with particular relevance to the region such as minimal exercise and unhealthy nutrition, concerns during pregnancy, the subject of childhood obesity, the impact of Ramadan fasting, and the expanding role of bariatric surgery. Finally, general recommendations for prevention, evidence-based, and culturally competent management strategies are presented to be considered at the levels of the individual, community, and policymakers

Machine learning for estimation of building energy consumption and performance:a review

Ever growing population and progressive municipal business demands for constructing new buildings are known as the foremost contributor to greenhouse gasses. Therefore, improvement of energy eciency of the building sector has become an essential target to reduce the amount of gas emission as well as fossil fuel consumption. One most eective approach to reducing CO2 emission and energy consumption with regards to new buildings is to consider energy eciency at a very early design stage. On the other hand, ecient energy management and smart refurbishments can enhance energy performance of the existing stock. All these solutions entail accurate energy prediction for optimal decision making. In recent years, articial intelligence (AI) in general and machine learning (ML) techniques in specic terms have been proposed for forecasting of building energy consumption and performance. This paperprovides a substantial review on the four main ML approaches including articial neural network, support vector machine, Gaussian-based regressions and clustering, which have commonly been applied in forecasting and improving building energy performance

Fourier and Biot numbers and the accuracy of conduction modelling

Recent validation work has shown the importance of accurate modelling in order to bring together experimental results and predictions. A number of important areas were identified: heat transfer coefficient, edge effects, etc. This paper dwells on the latter. After a short review of the associated theory the paper concentrates on the effects of Fourier and Biot numbers on the accuracy of the numerical approximation of the diffusion equation for heat flow through solid elements of buildings. Since this is the most widely used method, the paper focusses on finite-difference modelling. However, the main conclusions are applicable to other numerical approaches as well. By practical examples, representing typical building applications, it is demonstrated that it is easy to introduce - but also relatively easy to minimalize - errors due to space and time discretisation of building structures

Fourier and Biot numbers and the accuracy of conduction modelling

Recent validation work has shown the importance of accurate modelling in order to bring together experimental results and predictions. A number of important areas were identified: heat transfer coefficient, edge effects, etc. This paper dwells on the latter. After a short review of the associated theory the paper concentrates on the effects of Fourier and Biot numbers on the accuracy of the numerical approximation of the diffusion equation for heat flow through solid elements of buildings. Since this is the most widely used method, the paper focusses on finite-difference modelling. However, the main conclusions are applicable to other numerical approaches as well. By practical examples, representing typical building applications, it is demonstrated that it is easy to introduce - but also relatively easy to minimalize - errors due to space and time discretisation of building structures

Energy conservation in buildings through efficient A/C control using neural networks

General regression neural networks (GRNNs) were used to optimize air conditioning setback scheduling in public buildings. To save energy, the temperature inside these buildings is allowed to rise after business hours by setting back the thermostat. The objective is to predict the time of the end of thermostat setback (EoS) such that the design temperature inside the building is restored in time for the start of business hours. State-of-the-art building simulation software, ESP-r, was used to generate a database that covered the past 5 years. The software was used to calculate EoS for two office buildings using the climate records in Kuwait. The EoS data for 1995 and 1996 were used for training and testing the neural networks (NNs). The robustness of the trained NN was tested by applying them to a "production" data set (1997-1999) which the networks have never "seen" before. A parametric study showed that the optimum GRNN design is one that uses a genetic adaptive algorithm, a so-called City Block distance metric, and a linear scaling function for the input data. External hourly temperature readings were used as network inputs, and the thermostat end of setback (EoS) is the output. The NN predictions were improved by developing a neural control-scheme. This scheme is based on using the temperature readings as they become available. Six NNs were designed and trained for this purpose. The performance of the NN analysis was evaluated using a statistical indicator (the coefficient of multiple determination), and by examination of the error patterns. The results show that the neural control-scheme is a powerful instrument for optimizing air conditioning setback scheduling based on external temperature records.Neural networks Energy conservation Air conditioning Control General regression Building simulation