Decision support model for the selection of asphalt wearing courses in highly trafficked roads

The suitable choice of the materials forming the wearing course of highly trafficked roads is a delicate task because of their direct interaction with vehicles. Furthermore, modern roads must be planned according to sustainable development goals, which is complex because some of these might be in conflict. Under this premise, this paper develops a multi-criteria decision support model based on the analytic hierarchy process and the technique for order of preference by similarity to ideal solution to facilitate the selection of wearing courses in European countries. Variables were modelled using either fuzzy logic or Monte Carlo methods, depending on their nature. The views of a panel of experts on the problem were collected and processed using the generalized reduced gradient algorithm and a distance-based aggregation approach. The results showed a clear preponderance by stone mastic asphalt over the remaining alternatives in different scenarios evaluated through sensitivity analysis. The research leading to these results was framed in the European FP7 Project DURABROADS (No. 605404).The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013) under Grant Agreement No. 605404

Kullback-leibler entropy analysis of the electroencephalogram background activity in alzheimer's disease patients

Alzheimer's disease (AD) is the most frequent form of dementia in western countries. An early detection would be beneficial, but currently diagnostic accuracy is relatively poor. In this study, differences in information content between cortical areas in 12 AD patients and 11 control subjects were assessed with Kullback-Leibler (KL) entropy. KL entropy measures the degree of similarity between two probability distributions. EEGs were recorded from 19 scalp electrodes and KL entropy values of the EEGs in both groups were estimated for the local, distant and interhemispheric electrodes. KL entropy values were lower in AD patients than in age-matched control subjects, with significant effects for diagnosis and brain region (p < 0.05, two-way ANOVA). No significant interaction for diagnosis X region was found (p = 0.7671). Additionally a one-way ANOVA showed that KL entropy values were significantly lower in AD patients (p < 0.05) for the distant electrodes on the right hemisphere. These results suggest that KL entropy highlights information content changes in the EEG due to AD. However, further studies are needed to address the possible usefulness of KL entropy in the characterisation and early detection of AD

Teaching Social & Cultural Concepts Through Embodied Practices (Dance) & Specific Language Purpose (Hispanic Dance Vocabulary & Metaphors): A Preview of Early Survey Data

Social and cultural concepts are often problematic to teach to students academically. These concepts often seem abstract, dry, detached, and counter-intuitive to students\u27 experiences. This is particularly the case in teaching concepts related to Diversity (DEIB), including issues related to Colonialism, Slavery, Ethnicity, Social Class, Gender, Marginalization, etc.). Teaching these difficult social concepts through the embodied, cultural practices, such as Music and Hispanic Dance, makes these concepts more approachable, less threatening, non-confrontational, more relevant, and more tangible to students. Theory and research continues to point to the richness of embodied learning (English, Johnson, Mead, Minton, Sloat & Faber). Evidence indicates that embodied learning makes academic content more absorbable , as well as making information more resilient, more memorable, and more meaningful to students. Embodied activities (such as Dance) stimulate the entire brain, create new neurons, and make connections between previously learned material and new material (Hannah, Minton). Relating content to the human body makes information more meaningful and relevant (English, Johnson). Additionally, approaching difficult social concepts through the lens/focus of Dance and Music, makes them more neutral and more enjoyable to learn about, increasing students\u27 enjoyment, interest, focus, concentration, and understanding. Similarly, Dance can be used to teach language and cultural concepts. We are currently in the process of doing preliminary survey research to give us more feedback about what students remember learning in the Hispanic Dance Sessions. This poster is a preview of that survey data. All surveys indicated that some learning occurred

Analysis of electroencephalograms in Alzheimer's disease patients with multiscale entropy

The aim of this study was to analyse the electroencephalogram (EEG) background activity of Alzheimer’s disease (AD) patients using the Multiscale Entropy (MSE). The MSE is a recently developed method that quantifies the regularity of a signal on different time scales. These time scales are inspected by means of several coarse-grained sequences formed from the analysed signals. We recorded the EEGs from 19 scalp electrodes in 11 AD patients and 11 age-matched controls and estimated the MSE profile for each epoch of the EEG recordings. The shape of the MSE profiles reveals the EEG complexity, and it suggests that the EEG contains information in deeper scales than the smallest one. Moreover, the results showed that the EEG background activity is less complex in AD patients than control subjects. We found significant difference

Electroencephalogram background activity characterization with Detrended Moving Average in Alzheimer's disease patients

The aim of this study was to analyse the electroencephalogram (EEG) background activity in Alzheimer's disease (AD) with the Detrended Moving Average (DMA) method, a new approach to quantify correlation properties in non-stationary signals with underlying trends. EEGs were recorded from the 19 scalp loci of the international 10-20 system in 11 AD patients and 11 age-matched controls. Our results showed two scaling regions in all subjects' channels, with a clear bend when their corresponding slopes (alpha(1) and alpha(2)) were distinctly different. With the exception of electrode T4, the alpha(1) values were lower in control subjects than in AD patients, with significant differences at TS, P3, P4 and O1 (p < 0.01, Student's t-test). On the other hand, alpha(2) values were higher in control subjects than in AD patients, with significant differences only at F4. Furthermore, we evaluated the ability of alpha(2) to discriminate AD patients from control subjects at these electrodes using ROC plots. We obtained a maximum accuracy of 81.82% at O1 with alpha(1) and at F4 with alpha(2). These findings suggest that the scaling behaviour of the EEG is sensitive to AD and that the DMA method could help to increase our insight into brain dysfunction in AD

Kullback-Leibler Entropy Analysis of the Electroencephalogram Background Activity in Alzheimer's Disease Patients

Alzheimer's disease (AD) is the most frequent form of dementia in western countries. An early detection would be beneficial, but currently diagnostic accuracy is relatively poor. In this study, differences in information content between cortical areas in 12 AD patients and 11 control subjects were assessed with Kullback-Leibler (KL) entropy. KL entropy measures the degree of similarity between two probability distributions. EEGs were recorded from 19 scalp electrodes and KL entropy values of the EEGs in both groups were estimated for the local, distant and interhemispheric electrodes. KL entropy values were lower in AD patients than in age-matched control subjects, with significant effects for diagnosis and brain region (p < 0.05, two-way ANOVA). No significant interaction for diagnosis X region was found (p = 0.7671). Additionally a one-way ANOVA showed that KL entropy values were significantly lower in AD patients (p < 0.05) for the distant electrodes on the right hemisphere. These results suggest that KL entropy highlights information content changes in the EEG due to AD. However, further studies are needed to address the possible usefulness of KL entropy in the characterisation and early detection of AD

Hyperspectral chemical imaging reveals spatially varied degradation of polycarbonate urethane (PCU) biomaterials

Hyperspectral chemical imaging (HCI) is an emerging technique which combines spectroscopy with imaging. Unlike traditional point spectroscopy, which is used in the majority of polymer biomaterial degradation studies, HCI enables the acquisition of spatially localised spectra across the surface of a material in an objective manner. Here, we demonstrate that attenuated total reflectance Fourier transform infra-red (ATR-FTIR) HCI reveals spatial variation in the degradation of implantable polycarbonate urethane (PCU) biomaterials. It is also shown that HCI can detect possible defects in biomaterial formulation or specimen production; these spatially resolved images reveal regional or scattered spatial heterogeneity. Further, we demonstrate a map sampling method, which can be used in time-sensitive scenarios, allowing for the investigation of degradation across a larger component or component area. Unlike imaging, mapping does not produce a contiguous image, yet grants an insight into the spatial heterogeneity of the biomaterial across a larger area. These novel applications of HCI demonstrate its ability to assist in the detection of defective manufacturing components and lead to a deeper understanding of how a biomaterial’s chemical structure changes due to implantation. Statement of Signifance The human body is an aggressive environment for implantable devices and their biomaterial components. Polycarbonate urethane (PCU) biomaterials in particular were investigated in this study. Traditionally one or a few points on the PCU surface are analysed using ATR-FTIR spectroscopy. However the selection of acquisition points is susceptible to operator bias and critical information can be lost. This study utilises hyperspectral chemical imaging (HCI) to demonstrate that the degradation of a biomaterial varies spatially. Further, HCI revealed spatial variations of biomaterials that were not subjected to oxidative degradation leading to the possibility of HCI being used in the assessment of biomaterial formulation and/or component production