Indoor air quality (IAQ) is referred to as “the air quality within and around buildings and structures, especially as it relates to the health and comfort of building occupants” (US EPA, 2015). Indoor pollutant levels further determine the quality of indoor air, and one of the indicators used to measure IAQ is carbon dioxide (CO2). Drawing on data collected from a classroom, auditorium, and gym setting in the Mount Royal University campus, the aim of this report is to determine if CO2 levels present are within established margins substantial to result in adverse health effects. Environmental factors that are considered in this report include: room size, supply air, and occupant load in the specified spaces on the campus. The results of this study suggest that there are a myriad of factors that may affect IAQ and that CO2 is merely an indicator of poor air quality. Overall, peak indoor CO2 levels can further be used to determine appropriate ventilation rates in an indoor space.
Sermão Baseado na obra de Tozer, A.W. Cinco votos para obter poder espiritual / A.W. Tozer - São Paulo: Editora dos Clássicos, 1a edição: julho de 2004.
A dual energy radiography method using basis decomposition was developed, the process to do it is shown and it is compared against an alternate more direct method of analyzing the data using the logarithm of the original data, concluding that this second method does work but it is not better than basis decomposition.
Gene regulatory networks have an important role to study the behaviour of genes. By analysing
these Gene Regulatory Networks we can get the detailed information i.e. the occurrence of diseases by
changing behaviour of GRNs. Many different approaches are used (i.e. qualitative modelling and hybrid
modelling) and various tools (i.e. GenoTech, GINsim) have been developed to model and simulate gene
regulatory networks. GenoTech allows the user to specify a GRN on Graphical User Interface (GUI) according
to the asynchronous multivalued logical functions of René Thomas, and to simulate and/or analyse its
qualitative dynamical behaviour. René
Thomas discrete modelling of gene regulatory network (GRN) is a
well known approach to study the dynamics of genes. It deals with some parameters which reflect the possible
targets of trajectories. Those parameters are priory unknown. These unknown parameters are fetched using
another model checking tool SMBioNet. SMBioNet produces all the possible parameters satisfying the given
Computational Logic Tree (CTL) formula as input. This approach involving logical parameters and conditions
also known as qualitative modelling of GRN. However, this approach neglects the time delays for a gene to
pass from one level of expression to another one i.e. inhibition to activation and vice versa. To find out these
time delays, another modelling tool HyTech is used to perform hybrid modelling of GRN.
We have developed a Java based tool called GenNet http://asanian.com/gennet to facilitate the
model checking user by providing a unique GUI layout for both qualitative and quantitative modelling of GRNs.
As we discussed, three separate modelling tools are used for complete modelling and analysis of a GRN. This
process is much lengthy and takes too much time. GenNet assists the modelling users by providing some extra
features i.e. CTL editor, parameters filtering and input/output files management.
GenNet takes a GRN network as input and does all the rest of computations i.e. CTL verification,
K-parameters generation, parameter implication to GRN, state graph, hybrid modelling and parameter
filtration automatically. GenNet serves the user by computing the results within seconds that were taking hours
and days of manual computation
LEEPIANS
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