Abstract: This paper proposes the use of Bayesian belief
networks (BBN) as a higher level of health risk assessment for a
dumping site of lead battery smelter factory. On the basis of the
epidemiological studies, the actual hospital attendance records and
expert experiences, the BBN is capable of capturing the probabilistic
relationships between the hazardous substances and their adverse
health effects, and accordingly inferring the morbidity of the adverse
health effects. The provision of the morbidity rates of the related
diseases is more informative and can alleviate the drawbacks of
conventional methods.
Abstract: The sanitary sewerage connection rate becomes an
important indicator of advanced cities. Following the construction of
sanitary sewerages, the maintenance and management systems are
required for keeping pipelines and facilities functioning well. These
maintenance tasks often require sewer workers to enter the manholes
and the pipelines, which are confined spaces short of natural
ventilation and full of hazardous substances. Working in sewers could
be easily exposed to a risk of adverse health effects. This paper
proposes the use of Bayesian belief networks (BBN) as a higher level
of noncarcinogenic health risk assessment of sewer workers. On the
basis of the epidemiological studies, the actual hospital attendance
records and expert experiences, the BBN is capable of capturing the
probabilistic relationships between the hazardous substances in sewers
and their adverse health effects, and accordingly inferring the
morbidity and mortality of the adverse health effects. The provision of
the morbidity and mortality rates of the related diseases is more
informative and can alleviate the drawbacks of conventional methods.
Abstract: Testing accounts for the major percentage of technical
contribution in the software development process. Typically, it
consumes more than 50 percent of the total cost of developing a
piece of software. The selection of software tests is a very important
activity within this process to ensure the software reliability
requirements are met. Generally tests are run to achieve maximum
coverage of the software code and very little attention is given to the
achieved reliability of the software. Using an existing methodology,
this paper describes how to use Bayesian Belief Networks (BBNs) to
select unit tests based on their contribution to the reliability of the
module under consideration. In particular the work examines how the
approach can enhance test-first development by assessing the quality
of test suites resulting from this development methodology and
providing insight into additional tests that can significantly reduce
the achieved reliability. In this way the method can produce an
optimal selection of inputs and the order in which the tests are
executed to maximize the software reliability. To illustrate this
approach, a belief network is constructed for a modern software
system incorporating the expert opinion, expressed through
probabilities of the relative quality of the elements of the software,
and the potential effectiveness of the software tests. The steps
involved in constructing the Bayesian Network are explained as is a
method to allow for the test suite resulting from test-driven
development.