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Presenting an analysis of different approaches for predicting the
service life of buildings, this monograph discusses various
statistical tools and mathematical models, some of which have
rarely been applied to the field. It explores methods including
deterministic, factorial, stochastic and computational models and
applies these to facade claddings. The models allow (i)
identification of patterns of degradation, (ii) estimation of
service life, (iii) analysis of loss of performance using
probability functions, and (iv) estimation of service life using a
probability distribution. The final chapter discusses the
differences between the different methodologies and their
advantages and limitations. The authors also argue that a better
understanding of the service life of buildings results in more
efficient building maintenance and reduced environmental costs. It
not only provides an invaluable resource to students, researchers
and industry professionals interested in service life prediction
and sustainable construction, but is also of interest to
environmental and materials scientists.
The editors draw on a 3-year project that analyzed a Portuguese
area in detail, comparing this study with papers from other
regions. Applications include the estimation of technical
efficiency in agricultural grazing systems (dairy, beef and mixed)
and specifically for dairy farms. The conclusions indicate that it
is now necessary to help small dairy farms in order to make them
more efficient. These results can be compared with the technical
efficiency of a sample of Spanish dairy processing firms presented
by Magdalena Kapelko and co-authors.
This book explains the minimum error entropy (MEE) concept applied
to data classification machines. Theoretical results on the inner
workings of the MEE concept, in its application to solving a
variety of classification problems, are presented in the wider
realm of risk functionals. Researchers and practitioners also find
in the book a detailed presentation of practical data classifiers
using MEE. These include multi layer perceptrons, recurrent neural
networks, complexvalued neural networks, modular neural networks,
and decision trees. A clustering algorithm using a MEE like concept
is also presented. Examples, tests, evaluation experiments and
comparison with similar machines using classic approaches,
complement the descriptions.
Presenting an analysis of different approaches for predicting the
service life of buildings, this monograph discusses various
statistical tools and mathematical models, some of which have
rarely been applied to the field. It explores methods including
deterministic, factorial, stochastic and computational models and
applies these to facade claddings. The models allow (i)
identification of patterns of degradation, (ii) estimation of
service life, (iii) analysis of loss of performance using
probability functions, and (iv) estimation of service life using a
probability distribution. The final chapter discusses the
differences between the different methodologies and their
advantages and limitations. The authors also argue that a better
understanding of the service life of buildings results in more
efficient building maintenance and reduced environmental costs. It
not only provides an invaluable resource to students, researchers
and industry professionals interested in service life prediction
and sustainable construction, but is also of interest to
environmental and materials scientists.
The editors draw on a 3-year project that analyzed a Portuguese
area in detail, comparing this study with papers from other
regions. Applications include the estimation of technical
efficiency in agricultural grazing systems (dairy, beef and mixed)
and specifically for dairy farms. The conclusions indicate that it
is now necessary to help small dairy farms in order to make them
more efficient. These results can be compared with the technical
efficiency of a sample of Spanish dairy processing firms presented
by Magdalena Kapelko and co-authors.
This book explains the minimum error entropy (MEE) concept applied
to data classification machines. Theoretical results on the inner
workings of the MEE concept, in its application to solving a
variety of classification problems, are presented in the wider
realm of risk functionals. Researchers and practitioners also find
in the book a detailed presentation of practical data classifiers
using MEE. These include multi-layer perceptrons, recurrent neural
networks, complexvalued neural networks, modular neural networks,
and decision trees. A clustering algorithm using a MEE-like concept
is also presented. Examples, tests, evaluation experiments and
comparison with similar machines using classic approaches,
complement the descriptions.
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