No abstract

The indoor thermal environment has become a critical factor, due to its impact on the energy efficiency of a building and the health and performance of its occupants. It is particularly important for educational buildings, where students and teachers are exposed to these thermal conditions. This study assessed the impact of natural ventilation efficiency and university students’ thermal perception during the cold season. A field monitoring campaign and a questionnaire survey were conducted. A total of 989 students participated in this study. The results show that, although the CO2 concentration in 90% of the evaluated classrooms was below the European recommended value (i.e., 800 ppm), only 18% of the classrooms were within the thermal comfort zone defined by national regulations. These thermal conditions caused 55% of the students surveyed to report that they were dissatisfied, and that this environment interfered with their academic performance. Significant differences were found between thermal sensation votes from female and male students (p < 0.001). The obtained neutral temperature was one degree higher for female students than for males. Our results suggest that ventilation protocols need to be modified by adjusting the window opening strategy, and these findings should be used as guidelines during their redesign.

We propose a Bayesian approach for constructing gene networks based on microarray data. Especially, we focus on Bayesian methods that can provide soft (probabilistic) information. This soft information is attractive not only for its ability to measure the level of confidence of the solution, but also because it can be used to realize Bayesian data integration, an extremely important task in gene network research. We propose a variable selection formulation of gene regulation and develop an inference solution based on a variational Bayesian expectation maximization (VBEM) learning rule. This solution has better performance and lower complexity than the popular Monte Carlo sampling techniques. In addition, we develop a method to incorporate the often needed constraints into the VBEM algorithm, making it much more suitable for common cases of small data size. To further illustrate the advantage of the VBEM algorithm, we demonstrate a Bayesian data integration scheme using the soft information obtained from the VBEM algorithm. The efficacy of the proposed VBEM algorithm and the corresponding Bayesian data integration scheme is evaluated on both simulated data and the yeast cell cycle microarray data sets.

Managing indoor environmental quality (IEQ) is a challenge in educational buildings in the wake of the COVID-19 pandemic. Adequate indoor air quality is essential to ensure that indoor spaces are safe for students and teachers. In fact, poor IEQ can affect academic performance and student comfort. This study proposes a framework for integrating occupants’ feedback into the building information modelling (BIM) methodology to assess indoor environmental conditions (thermal, acoustic and lighting) and the individual airborne virus transmission risk during teaching activities. The information contained in the parametric 3D BIM model and the algorithmic environment of Dynamo were used to develop the framework. The IEQ evaluation is based on sensor monitoring and a daily schedule, so the results show real problems of occupants’ dissatisfaction. The output of the framework shows in which range the indoor environmental variables were (optimal, acceptable and unacceptable) and the probability of infection during each lecture class (whether or not 1% is exceeded). A case study was proposed to illustrate its application and validate it. The outcomes provide key information to support the decision-making process for managing IEQ and controlling individual airborne virus transmission risks. Long-term application could provide data that support the management of ventilation strategies and protocol redesign.

The need to tackle the urban heat island effect demands the implementation of cool surfaces as a mitigation strategy. This study comprehensively reviews the evolution of this research field from a materials perspective. It provides a bibliometric analysis of the relevant literature using the SciMAT software processing of bibliographic records from 1995 to 2020, for the evolution of cool surfaces. The results obtained show an increased interest in the field from 2011 to 2020, particularly for roof applications, and present the scientific evolution of reflective materials. According to the materials dimension adopted by the development of the research field, the study is refined from a bibliometric analysis of 982 selected records for the analysis of five themes: (i) Pigments; (ii) Phase change materials; (iii) Retroreflective materials; (iv) Ceramic materials; and (v) Glass. These materials present promising results in terms of their solar reflectance performances in the mitigation of the urban heat island phenomenon. At the end of this review, recommendations for future studies are provided for the creation of economic and environmentally friendly materials based on waste glass recycling. This study represents a valuable contribution that provides a scientific background with regard to cool surfaces from a materials perspective for future investigations.