On some Mixture Distributions

The aim of this paper is to establish some mixture distributions that arise in stochastic processes. Some basic functions associated with the probability mass function of the mixture distributions, such as k-th moments, characteristic function and factorial moments are computed. Further we obtain a three-term recurrence relation for each established mixture distribution

History and development of validation with the ESP-r simulation program

It is well recognised that validation of dynamic building simulation programs is a long-term complex task. There have been many large national and international efforts that have led to a well-established validation methodology comprising analytical, inter-program comparison and empirical validation elements, and a significant number of tests have been developed. As simulation usage increases, driven by such initiatives as the European Energy Performance of Buildings Directive, such tests are starting to be incorporated into national and international standards. Although many program developers have run many of the developed tests, there does not appear to have been a systematic attempt to incorporate such tests into routine operation of the simulation programs. This paper reports work undertaken to address this deficiency. The paper summarizes the tests that have been applied to the simulation program ESP-r. These tests have been developed within International Energy Agency Annexes, within CEN standards, within various large-scale national projects, and by the UK's Chartered Institution of Building Services Engineers. The structure used to encapsulate the tests allows developers to ensure that recent code modifications have not resulted in unforeseen impacts on program predictions, and allows users to check for themselves against benchmarks

A generalized beta function and associated probability density

We introduce and establish some properties of a generalized form of the beta function. Corresponding generalized incomplete beta functions are also defined. Moreover, we define a new probability density function (pdf) involving this new generalized beta function. Some basic functions associated with the pdf, such as moment generating function, mean residue function, and hazard rate function are derived. Some special cases are mentioned. Some figures for pdf, hazard rate function, and mean residue life function are given. These figures reflect the role of shape and scale parameters

Probability density functions involving a generalized r–Gauss hypergeometric function

The aim of this paper is to study r–generalized gamma functions of a particular form.Moreover, we define a new probability density function (p.d.f) involving these new generalized functions. Some basic functions associated with the p.d.f’s, such as moment generating functions, mean residue functions and hazard rate functions are derived

Performance assessment of thermal bridge elements into a full scale experimental study of a building façade

In this paper, an experimental and numerical approach to the characterization of thermal bridges is presented. The need for this characterization was found within an experimental study in a 2 floor high facade. This facade was constructed with 3 concrete elements which were placed in it to produce a similar thermal bridge effect to the one created by floor slabs traditional building construction in Spain. Commonly applied thermal assessments perform one-dimensional heat transfer analysis over planar elements such as the facades studied in this experiment. However, it is well known that thermal bridges are locations in buildings where one-dimensional heat transfer analysis cannot be applied. This problem was approached by creating a numerical 2D thermal model which was calibrated against experimental data from several temperature and heat flux sensors which were located at specific points in the thermal bridge elements.Government of the Basque Countr

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