Search results

The public’s awareness of noise and vibration forms a significant barrier to further development of railways. This chapter begins with a short introduction to the main fundamental aspects of acoustics, including decibels, frequency analysis, the propagation of sound with distance and common measurement quantities. The main sources of railway noise are discussed, including rolling noise, impact noise, curve squeal and aerodynamic noise. Simple calculation procedures are described that can be used to assess the impact of railway noise and to compare it with legal limits. The final section is devoted to ground vibration, which is a related form of environmental disturbance.

We develop a Bayesian approach for modeling brain activation and connectivity from functional magnetic resonance image (fMRI) data. Our approach simultaneously estimates local hemodynamic response functions (HRFs) and activation parameters, as well as global effective and functional connectivity parameters. Existing methods assume identical HRFs across brain regions, which may lead to erroneous conclusions in inferring activation and connectivity patterns. Our approach addresses this limitation by estimating region-specific HRFs. Additionally, it enables neuroscientists to compare effective connectivity networks for different experimental conditions. Furthermore, the use of spike and slab priors on the connectivity parameters allows us to directly select significant effective connectivities in a given network.

We include a simulation study that demonstrates that, compared to the standard generalized linear model (GLM) approach, our model generally has higher power and lower type I error and bias than the GLM approach, and it also has the ability to capture condition-specific connectivities. We applied our approach to a dataset from a stroke study and found different effective connectivity patterns for task and rest conditions in certain brain regions of interest (ROIs).

Information is often defined in terms of meaning. Traditional theories of meaning, each with some drawbacks, have been rooted in language; but a more satisfactory theory of meaning may be rooted in information. Meaning can be defined as coordinated action toward some end. In this sense, the meaning of something is the way it affords and constrains actions, and it is therefore inextricable from its context. Meaning can be discussed in several senses, including personal, social, environmental, historical, political, etc. Because information studies is concerned with the intersection of people and information, two key conceptualizations of meaning are personal meaning and social meaning. When activities have this meaningful dimension, they make a person's life feel more valuable and worth living, as a person and/or as a member of a group. In general, personal and social meaning include aspects such as purpose and connection with others.

The need to design buildings with due consideration for bioclimatic and passive design is central to promoting sustainability in the built environment from an energy perspective. Indeed, the energy and atmosphere considerations in building design, construction and operation have received the highest consideration in green building frameworks such as LEED and BREEAM to promote SDG 9: Industry, Innovation and Infrastructure and SDG 11: Sustainable Cities and Communities and contributing directly to support SDG 13: Climate Action. The research literature is rich of findings on the efficacy of passive measures in different climate contexts, but given that these measures are highly dependent on the prevailing weather conditions, which is constantly in evolution, disturbed by the climate change phenomenon, there is pressing need to be able to accurately predict such changes in the short (to the minute) and medium (to the hour and day) terms, where AI algorithms can be effectively applied. The dynamics of the weather patterns over seasons, but more crucially over a given season means that optimum response of building envelope elements, specifically through the passive elements, can be reaped if these passive measures can be adapted according to the ambient weather conditions. The use of representative mechatronics systems to intelligently control certain passive measures is presented, together with the potential use of artificial intelligence (AI) algorithms to capture the complex building physics involved to predict the expected effect of weather conditions on the indoor environmental conditions.