AccEq-DRT: Planning Demand-Responsive Transit to reduce inequality of accessibility

Accessibility measures how well a location is connected to surrounding opportunities. We focus on accessibility provided by Public Transit (PT). There is an evident inequality in the distribution of accessibility between city centers or close to main transportation corridors and suburbs. In the latter, poor PT service leads to a chronic car-dependency. Demand-Responsive Transit (DRT) is better suited for low-density areas than conventional fixed-route PT. However, its potential to tackle accessibility inequality has not yet been exploited. On the contrary, planning DRT without care to inequality (as in the methods proposed so far) can further improve the accessibility gap in urban areas. To the best of our knowledge this paper is the first to propose a DRT planning strategy, which we call AccEq-DRT, aimed at reducing accessibility inequality, while ensuring overall efficiency. To this aim, we combine a graph representation of conventional PT and a Continuous Approximation (CA) model of DRT. The two are combined in the same multi-layer graph, on which we compute accessibility. We then devise a scoring function to estimate the need of each area for an improvement, appropriately weighting population density and accessibility. Finally, we provide a bilevel optimization method, where the upper level is a heuristic to allocate DRT buses, guided by the scoring function, and the lower level performs traffic assignment. Numerical results in a simplified model of Montreal show that inequality, measured with the Atkinson index, is reduced by up to 34\%. Keywords: DRT Public, Transportation, Accessibility, Continuous Approximation, Network DesignComment: 15 page

ON A STABILIZED FINITE ELEMENT METHOD WITH MESH ADAPTIVE PROCEDURE FOR CONVECTION--DIFFUSION PROBLEMS

International audienc

Complex networks and deep learning for copper flow across countries

In this paper, by using a lifecycle perspective, four stages related to the extraction, refining and processing of copperwere identified. The different behaviors of countries in the import/export networks at the four stages synthetically reflect their position in the global network of copper production and consumption. The trade flows of four commodities related to the extraction, refining and processing of copper of 142 nations with population above 2millions based on the UN Comtrade website (https:// comtrade.un.org/ data/), in five years from 2017 to 2021, were considered. The observed trade flows in each year have been modelled as a directed multilayer network. Then the countries have been grouped according to their structural equivalence in the international copper flow by using a Multilayer Stochastic Block Model. To put further insight in the obtained community structure of the countries, a deep learning model based on adapting the node2vec to a multilayer setting has been used to embed the countries in an Euclidean plane. To identify groups of nations that play the same role across time, some distances between the parameters obtained in consecutive years were introduced. We observe that 97 countries out of 142 consistently occupy the same position in the copper supply chain throughout the five years, while the other 45 move through different roles in the copper supply chain

Type 2 diabetes detection with light CNN from single raw PPG wave

International audiencePhotoplethysmography (PPG) is a non-invasive and cost-efficient optical technique used to assess blood volume variations in the microcirculation. PPG technology is widely used in a variety of wearable sensors to investigate the cardiovascular system. Recent studies have demonstrated the utility of PPG analysis for carrying out large-scale screening to prevent and detect diabetes. However, most of these studies require feature extraction and/or several pre-processing steps. Over the past few years, the advent of deep learning has significantly impacted the analysis of biomedical signals. Despite their success in other fields, however, very few studies have focused on the application of deep learning to raw PPG signals for detecting diabetes. Existing studies have proposed large models trained on large amounts of data. In this paper, we present a Light CNN-based model for screening the presence of type 2 diabetes using a single raw pulse extracted from photoplethysmographic signals. In addition to the baseline architecture, we evaluate different model architectures that take as input age and biological sex or PPG handcrafted features. Furthermore, we apply transfer learning to all the tested architectures to evaluate the effectiveness of harnessing pre-trained models in detecting diabetes. We tested a model pre-trained on a general PPG shape dataset and another model pre-trained on a dataset containing hypertension PPG signals. Our model scored an AUC of 75.5 when trained with raw PPG waves, age, and biological sex without applying transfer learning, which is competitive with current state of the art

The influence of some new 2,5-disubstituted 1,3,4-thiadiazoles on the corrosion behaviour of mild steel in 1M HCl solution: AC impedance study and theoretical approach

International audienceThe new 2,5-disubstituted 1,3,4-thiadiazoles were investigated as corrosion inhibitors of mild steel in 1 M HCl using AC impedance technique. Four of these compounds exhibit good inhibition properties, while two of them, 2,5-bis(4-nitrophenyl)-1,3,4-thiadiazole and 2,5-bis(4-chlorophenyl)-1,3,4-thiadiazole, stimulate the corrosion process especially at low concentrations. The experimental data obtained from this method show a frequency distribution and therefore a modelling element with frequency dispersion behaviour, a constant phase element (CPE) has been used. Possible correlations between experimental inhibition efficiencies and quantum chemical parameters such as dipole moment (μ), highest occupied (EHOMO) and lowest unoccupied (ELUMO) molecular orbitals were investigated. The models of the inhibitors were optimised with the Density Functional Theory formalism (DFT) using hybrid B3LYP/6-31G (2d,2p) as a higher level of theory. The Quantitative Structure Activity Relationship (QSAR) approach has been used and composite index of some quantum chemical parameters were constructed in order to characterize the inhibition performance of the tested molecules

Clustered photoplethysmogram pulse wave shapes and their associations with clinical data.

Peer reviewed: TruePhotopletysmography (PPG) is a non-invasive and well known technology that enables the recording of the digital volume pulse (DVP). Although PPG is largely employed in research, several aspects remain unknown. One of these is represented by the lack of information about how many waveform classes best express the variability in shape. In the literature, it is common to classify DVPs into four classes based on the dicrotic notch position. However, when working with real data, labelling waveforms with one of these four classes is no longer straightforward and may be challenging. The correct identification of the DVP shape could enhance the precision and the reliability of the extracted bio markers. In this work we proposed unsupervised machine learning and deep learning approaches to overcome the data labelling limitations. Concretely we performed a K-medoids based clustering that takes as input 1) DVP handcrafted features, 2) similarity matrix computed with the Derivative Dynamic Time Warping and 3) DVP features extracted from a CNN AutoEncoder. All the cited methods have been tested first by imposing four medoids representative of the Dawber classes, and after by automatically searching four clusters. We then searched the optimal number of clusters for each method using silhouette score, the prediction strength and inertia. To validate the proposed approaches we analyse the dissimilarities in the clinical data related to obtained clusters