Abstract: The evolution of technology and construction techniques has enabled the upgrading of transport networks. In particular, the high-speed rail networks allow convoys to peak at above 300 km/h. These structures, however, often significantly impact the surrounding environment. Among the effects of greater importance are the ones provoked by the soundwave connected to train transit. The wave propagation affects the quality of life in areas surrounding the tracks, often for several hundred metres. There are substantial damages to properties (buildings and land), in terms of market depreciation. The present study, integrating expertise in acoustics, computering and evaluation fields, outlines a useful model to select project paths so as to minimize the noise impact and reduce the causes of possible litigation. It also facilitates the rational selection of initiatives to contain the environmental damage to the already existing railway tracks. The research is developed with reference to the Italian regulatory framework (usually more stringent than European and international standards) and refers to a case study concerning the high speed network in Italy.
Abstract: The main goal in this paper is to quantify the quality of
different techniques for radiation treatment plans, a back-propagation
artificial neural network (ANN) combined with biomedicine theory
was used to model thirteen dosimetric parameters and to calculate
two dosimetric indices. The correlations between dosimetric indices
and quality of life were extracted as the features and used in the ANN
model to make decisions in the clinic. The simulation results show
that a trained multilayer back-propagation neural network model can
help a doctor accept or reject a plan efficiently. In addition, the
models are flexible and whenever a new treatment technique enters
the market, the feature variables simply need to be imported and the
model re-trained for it to be ready for use.
Abstract: Green- spaces might be very attractive, but
where are the economic benefits? What value do nature and
landscape have for us? What difference will it make to jobs,
health and the economic strength of areas struggling with
deprivation and social problems? [1].There is a need to consider
green spaces from a different perspective. Green planning is not just
about flora and fauna, but also about planning for economic benefits
[2]. It is worth trying to quantify the value of green spaces since
nature and landscape are crucially important to our quality of life and
sustainable development. The reality, however, is that urban
development often takes place at the expense of green spaces.
Urbanization is an ongoing process throughout the world; however,
hyper-urbanization without environmental planning is destructive,
not constructive [3]. Urban spaces are believed to be more valuable
than other land uses, particular green areas, simply because of the
market value connected to urban spaces. However, attractive
landscapes can help raise the quality and value of the urban market
even more. In order to reach these objectives of integrated planning,
the Green-Value-Gap needs to be bridged. Economists have to
understand the concept of Green-Planning and the spinoffs, and
Environmentalists have to understand the importance of urban
economic development and the benefits thereof to green planning. An
interface between Environmental Management, Economic
Development and sustainable Spatial Planning are needed to bridge
the Green-Value-Gap.
Abstract: To evaluate the ability to predict xerostomia after
radiotherapy, we constructed and compared neural network and
logistic regression models. In this study, 61 patients who completed a
questionnaire about their quality of life (QoL) before and after a full
course of radiation therapy were included. Based on this questionnaire,
some statistical data about the condition of the patients’ salivary
glands were obtained, and these subjects were included as the inputs of
the neural network and logistic regression models in order to predict
the probability of xerostomia. Seven variables were then selected from
the statistical data according to Cramer’s V and point-biserial
correlation values and were trained by each model to obtain the
respective outputs which were 0.88 and 0.89 for AUC, 9.20 and 7.65
for SSE, and 13.7% and 19.0% for MAPE, respectively. These
parameters demonstrate that both neural network and logistic
regression methods are effective for predicting conditions of parotid
glands.