Real-time modelling and interpolation of spatio-temporal marine pollution

Due to the complexity of the interactions involved in various dynamic systems, known physical, biological or chemical laws cannot adequately describe the dynamics behind these processes. The study of these systems thus depends on measurements often taken at various discrete spatial locations through time by noisy sensors. For this reason, scientists often necessitate interpolative, visualisation and analytical tools to deal with the large volumes of data common to these systems. The starting point of this study is the seminal research by C. Shannon on sampling and reconstruction theory and its various extensions. Based on recent work on the reconstruction of stochastic processes, this paper develops a novel real-time estimation method for non- stationary stochastic spatio-temporal behaviour based on the Integro-Di erence Equation (IDE). This meth- odology is applied to collected marine pollution data from a Norwegian fjord. Comparison of the results obtained by the proposed method with interpolators from state-of-the-art Geographical Information System (GIS) packages will show, that signifi cantly superior results are obtained by including the temporal evolution in the spatial interpolations.peer-reviewe

Computationally efficient estimation of high-dimension autoregressive models : with application to air pollution in Malta

The modelling and analysis of spatiotemporal behaviour is receiving wide-spread attention due to its applicability to various scientific fields such as the mapping of the electrical activity in the human brain, the spatial spread of pandemics and the diffusion of hazardous pollutants. Nevertheless, due to the complexity of the dynamics describing these systems and the vast datasets of the measurements involved, efficient computational methods are required to obtain representative mathematical descriptions of such behaviour. In this work, a computationally efficient method for the estimation of heterogeneous spatio-temporal autoregressive models is proposed and tested on a dataset of air pollutants measured over the Maltese islands. Results will highlight the computation advantages of the proposed methodology and the accuracy of the predictions obtained through the estimated model.peer-reviewe

Constrained dynamic control of traffic junctions

Excessive traffic in our urban environments has detrimental effects on our health, economy and standard of living. To mitigate this problem, an adaptive traffic lights signalling scheme is developed and tested in this paper. This scheme is based on a state space representation of traffic dynamics, controlled via a dynamic programme. To minimise implementation costs, only one loop detector is assumed at each link. The comparative advantages of the proposed system over optimal fixed time control are highlighted through an example. Results will demonstrate the flexibility of the system when applied to different junctions. Monte Carlo runs of the developed scheme highlight the consistency and repeatability of these results.peer-reviewe

Spatio-temporal analysis of air pollution data in Malta

Air pollution measurements display patterns over space and time allowing for spatio-temporal modelling, through which pollution concentrations and trends can be analysed. In Malta, the MEPA (Malta Environment and Planning Authority) collects monthly averaged data for various pollutants from a network of 123 diffusion tubes located around the Islands (Figure 1). This preliminary study uses data associated with traffic, that is nitrogen dioxide (NO2) and benzene, collected monthly between the period 2004 and 2010 with the objectives to i) develop a computationally efficient method that best describes the data; ii) determine the level of dependency of each site on neighbouring ones and iii) identify any factors that affect the behaviour and patterns of pollution. Results will show that generally there is a low spatial dependency between close sites, thus implying that local sources, rather than diffusion, have a predominant effect on the measurements. This analysis will prove valuable in MEPA’s redistribution exercise of the diffusion tube network to determine which sites are necessary to retain and which sites can be removed without significantly affecting the information gathered.peer-reviewe

Local gradient analysis of human brain function using the Vogt-Bailey Index

In this work, we take a closer look at the Vogt-Bailey (VB) index, proposed in Bajada et al. (NeuroImage 221:117140, 2020) as a tool for studying local functional homogeneity in the human cortex. We interpret the VB index in terms of the minimum ratio cut, a scaled cut-set weight that indicates whether a network can easily be disconnected into two parts having a comparable number of nodes. In our case, the nodes of the network consist of a brain vertex/voxel and its neighbours, and a given edge is weighted according to the affinity of the nodes it connects (as reflected by the modified Pearson correlation between their fMRI time series). Consequently, the minimum ratio cut quantifies the degree of small-scale similarity in brain activity: the greater the similarity, the ‘heavier’ the edges and the more difficult it is to disconnect the network, hence the higher the value of the minimum ratio cut. We compare the performance of the VB index with that of the Regional Homogeneity (ReHo) algorithm, commonly used to assess whether voxels in close proximity have synchronised fMRI signals, and find that the VB index is uniquely placed to detect sharp changes in the (local) functional organization of the human cortex.</p

A travelling heads study investigating qMRI metrics on cortical regions

Technological advances in magnetic resonance imaging (MRI) have facilitated numerous studies on neural architecture, such as studies addressing pathology, behaviour or individual differences in brain activity. It is important, however, to first ascertain what variation can arise due to site-specific scanner properties (hard- and software). A certain amount of noise in MR images can indeed be attributable to such properties, even when the same scanner is used across different sites. Reproducibility across sites is possible with the use of quantitative MRI metrics (qMRI), where physical properties assigned to voxels allow for non-invasive analysis of brain tissue including sensitivity to iron and myelin content. Leutritz et al. (2020) investigated intra-site (scan-rescan) and intersite (between sites) variability on Siemens and Philips scanners through multi-parameter mapping techniques (MPM). The authors found intra-site scan-rescan coefficients of variance (CoV) ranging between 4% and 16% across parameters, with similar results for inter-site CoV. The current study implements a similar strategy to Leutritz et al. (2020) in that it investigates inter-site and interscanner variability in a "travelling heads" type of study. Using scanners by the same manufacturer (but two different models), the study investigates qMRI metrics for inter-site and inter-scanner differences and their corresponding effects on cortical regions.peer-reviewe

A systems approach to spatio-temporal modelling

EThOS - Electronic Theses Online ServiceGBUnited Kingdo

Constrained Dynamic Control of Traffic Junctions

Excessive traffic in our urban environments has detrimental effects on our health, economy and standard of living. To mitigate this problem, an adaptive traffic lights signalling scheme is developed and tested in this paper. This scheme is based on a state space representation of traffic dynamics, controlled via a dynamic programme. To minimise implementation costs, only one loop detector is assumed at each link. The comparative advantages of the proposed system over optimal fixed time control are highlighted through an example. Results will demonstrate the flexibility of the system when applied to different junctions. Monte Carlo runs of the developed scheme highlight the consistency and repeatability of these results. peer-reviewe

Geometric effects of volume-to-surface mapping of fMRI data

In this work, we identify a problem with the process of volume-to-surface mapping of functional Magnetic Resonance Imaging (fMRI) data that emerges in local connectivity analysis. We show that neighborhood correlations on the surface of the brain vary spatially with the gyral structure, even when the underlying volumetric data are uncorrelated noise. This could potentially have impacted studies focusing upon local neighborhood connectivity. We explore the effects of this anomaly across varying data resolutions and surface mesh densities, and propose several measures to mitigate these unwanted effects.· ·peer-reviewe