Social ski driver conditional autoregressive-based deep learning classifier for flight delay prediction

The importance of robust flight delay prediction has recently increased in the air transportation industry. This industry seeks alternative methods and technologies for more robust flight delay prediction because of its significance for all stakeholders. The most affected are airlines that suffer from monetary and passenger loyalty losses. Several studies have attempted to analysed and solve flight delay prediction problems using machine learning methods. This research proposes a novel alternative method, namely social ski driver conditional autoregressive-based (SSDCA-based) deep learning. Our proposed method combines the Social Ski Driver algorithm with Conditional Autoregressive Value at Risk by Regression Quantiles. We consider the most relevant instances from the training dataset, which are the delayed flights. We applied data transformation to stabilise the data variance using Yeo-Johnson. We then perform the training and testing of our data using deep recurrent neural network (DRNN) and SSDCA-based algorithms. The SSDCA-based optimisation algorithm helped us choose the right network architecture with better accuracy and less error than the existing literature. The results of our proposed SSDCA-based method and existing benchmark methods were compared. The efficiency and computational time of our proposed method are compared against the existing benchmark methods. The SSDCA-based DRNN provides a more accurate flight delay prediction with 0.9361 and 0.9252 accuracy rates on both dataset-1 and dataset-2, respectively. To show the reliability of our method, we compared it with other meta-heuristic approaches. The result is that the SSDCA-based DRNN outperformed all existing benchmark methods tested in our experiment

Fortran coarray implementation of semi-lagrangian convected air particles within an atmospheric model

This work added semi-Lagrangian convected air particles to the Intermediate Complexity Atmospheric Research (ICAR) model. The ICAR model is a simplified atmospheric model using quasi-dynamical downscaling to gain performance over more traditional atmospheric models. The ICAR model uses Fortran coarrays to split the domain amongst images and handle the halo region communication of the image’s boundary regions. The newly implemented convected air particles use trilinear interpolation to compute initial properties from the Eulerian domain and calculate humidity and buoyancy forces as the model runs. This paper investigated the performance cost and scaling attributes of executing unsaturated and saturated air particles versus the original particle-less model. An in-depth analysis was done on the communication patterns and performance of the semi-Lagrangian air particles, as well as the performance cost of a variety of initial conditions such as wind speed and saturation mixing ratios. This study found that given a linear increase in the number of particles communicated, there is an initial decrease in performance, but that it then levels out, indicating that over the runtime of the model, there is an initial cost of particle communication, but that the computational benefits quickly offset it. The study provided insight into the number of processors required to amortize the additional computational cost of the air particles

A Deep Feedforward Neural Network and Shallow Architectures Effectiveness Comparison: Flight Delays Classification Perspective

Flight delays have negatively impacted the socio-economics state of passengers, airlines and airports, resulting in huge economic losses. Hence, it has become necessary to correctly predict their occurrences in decision-making because it is important for the effective management of the aviation industry. Developing accurate flight delays classification models depends mostly on the air transportation system complexity and the infrastructure available in airports, which may be a region-specific issue. However, no specific prediction or classification model can handle the individual characteristics of all airlines and airports at the same time. Hence, the need to further develop and compare predictive models for the aviation decision system of the future cannot be over-emphasised. In this research, flight on-time data records from the United State Bureau of Transportation Statistics was employed to evaluate the performances of Deep Feedforward Neural Network, Neural Network, and Support Vector Machine models on a binary classification problem. The research revealed that the models achieved different accuracies of flight delay classifications. The Support Vector Machine had the worst average accuracy than Neural Network and Deep Feedforward Neural Network in the initial experiment. The Deep Feedforward Neural Network outperformed Support Vector Machines and Neural Network with the best average percentage accuracies. Going further to investigate the Deep Feedforward Neural Network architecture on different parameters against itself suggest that training a Deep Feedforward Neural Network algorithm, regardless of data training size, the classification accuracy peaks. We examine which number of epochs works best in our flight delay classification settings for the Deep Feedforward Neural Network. Our experiment results demonstrate that having many epochs affects the convergence rate of the model; unlike when hidden layers are increased, it does not ensure better or higher accuracy in a binary classification of flight delays. Finally, we recommended further studies on the applicability of the Deep Feedforward Neural Network in flight delays prediction with specific case studies of either airlines or airports to check the impact on the model's performance

Publisher correction : The duality between particle methods and artificial neural networks

This Article contains a typographical error in the Code availability section. "The code used for the simulations is freely available under the GNU General Public License v3 and can be downloaded from the Cranfield repository https ://publi c.cranf​ield.ac.uk/e1020 81/DeepM P/." should read: "The code used for the simulations is freely available under the GNU General Public License v3 and can be downloaded from the Cranfield repository http://publi c.cranf​ield.ac.uk/e1020 81/DeepM P/.

The virtual physiological human gets nerves! How to account for the action of the nervous system in multiphysics simulations of human organs

This article shows how to couple multiphysics and artificial neural networks to design computer models of human organs that autonomously adapt their behaviour to environmental stimuli. The model simulates motility in the intestine and adjusts its contraction patterns to the physical properties of the luminal content. Multiphysics reproduces the solid mechanics of the intestinal membrane and the fluid mechanics of the luminal content; the artificial neural network replicates the activity of the enteric nervous system. Previous studies recommended training the network with reinforcement learning. Here, we show that reinforcement learning alone is not enough; the input-output structure of the network should also mimic the basic circuit of the enteric nervous system. Simulations are validated against in vivo measurements of high-amplitude propagating contractions in the human intestine. When the network has the same input-output structure of the nervous system, the model performs well even when faced with conditions outside its training range. The model is trained to optimize transport, but it also keeps stress in the membrane low, which is exactly what occurs in the real intestine. Moreover, the model responds to atypical variations of its functioning with 'symptoms' that reflect those arising in diseases. If the healthy intestine model is made artificially ill by adding digital inflammation, motility patterns are disrupted in a way consistent with inflammatory pathologies such as inflammatory bowel disease

Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

Using Big Data to Compare Classification Models for Household Credit Rating in Kuwait

Credit rating risks have become the backbone of bank performance. They are the reflection of the current status of the bank and the milestone for future planning. A good credit assessment can better anticipate expected losses and will minimize unexpected losses from accumulating. Given advancements in technology as well as the big data available within banks about customers in an oil country such as Kuwait, a built-in model to help in-household credit scoring is at management’s decision. Compared with the current ‘black box’ rating models, we did a comparison between different classification models for two types of banking: conventional and Islamic. The classification models are as follows: Logistic Regression, Fine Decision Tree, Linear Support Vector Machines, Kernel Naïve Bayes, and RUSBoosted. Sufficiently, the last could be used to classify banks’ household customers and determine their default cases

Méthylations de l'histone H3 et contrôle épigénétique des propriétés des cellules souches de gliomes

Les gliomes sont les tumeurs primitives les plus fréquentes du cerveau et restent de mauvais pronostic en raison de l inefficacité des traitements actuels. Des cellules souches cancéreuses ont été isolées à partir de gliomes de haut grade de l adulte. Ces cellules souches de gliomes (GSC) peuvent fournir tous les sous-types cellulaires qui composent la tumeur. De nombreuses données indiquent que la résistance aux traitements est due en grande partie aux GSC. Cibler les GSC et leurs propriétés souches constitue donc un enjeu thérapeutique important. [...] Une solution pertinente de ciblage thérapeutique est de forcer les GSC à quitter leur état souche. Dans ce cadre, mes principaux travaux ont eu pour but de caractériser les changements épigénétiques des marques d histones qui accompagnent la répression des propriétés des GSC par un groupe de micro-ARN, miR-302-367. [...] L étude de cette plasticité par notre équipe a abouti à l identification de miR-302-367. Son expression forcée, à l aide de lentivirus, bloque de façon irréversible les propriétés souches et initiatrices de tumeur des GSC. L effet suppresseur de tumeur exercé par miR offre la possibilité d identifier les mécanismes qui régulent le maintien ou la perte des propriétés des GSC. A l aide d un modèle formé par une lignée de GSC et de sa contrepartie dépourvue des propriétés souches et tumorigènes GSC-miR-302-367, je me suis attachée à caractériser les méthylations de l histone H3, qui font parties du code d histone associé à une transcription génique respectivement active ou réprimée. Je me suis axée sur la triméthylation de la lysine 4 (H3K4me3) et de la lysine 27 (H3K27me3), respectivement permissive et répressive de la transcription. Une analyse par ChIP-seq (Immunoprécipitation de la chromatine-séquençage) des gènes associés à ces marques a été associée à la caractérisation des transcriptomes des cellules par exon-array. Nos résultats montrent que l expression du groupe de miR-302-367 ne modifie pas de façon globale les taux des marques H3K4me3 et H3K27me3. Par contre, des changements dans des groupes de gènes circonscrits ont pu être identifiés. La corrélation positive observée entre les marques d histones et les taux d expression des gènes montre une conservation du code d histone dans les cellules cancéreuses, au moins pour les marques étudiées. L analyse des termes GO (Gene Ontology) indique que la perte des propriétés induites par miR-302-367 s accompagne d un engagement de GSC dans une voie de différenciation. Les gènes portant la marque répressive dans les GSC-miR-302-367 participent notamment à des catégories fonctionnelles associées à l expression de propriétés souches et tumorigènes. L analyse du groupe de gènes portant une marque permissive dans les GSC et répressive dans les GSC-miR-302-367, a révélé un réseau de facteurs de transcription susceptible de participer au contrôle des propriétés souches des GSC. La répression à l aide de siRNA d un des membres de ce réseau, le facteur de transcription ARNT2, nous a permis de révéler son rôle dans le maintien des capacités prolifératives des GSC issues de gliomes distincts et dans l expression du facteur de transcription Nanog, connu pour son rôle central dans le contrôle des propriétés souches des GSC. Nos résultats montrent que l analyse des changements de marques d histone offre donc non seulement une vue d ensemble des différents réseaux moléculaires associés au maintien ou au contraire à la répression des propriétés des GSC, mais permet d identifier de nouveaux acteurs. L effet stimulateur d ARNT2 sur la croissance cellulaire et l expression de Nanog, dans des GSC dérivées de gliomes différents aux altérations génomiques distinctes, indique que ce facteur de transcription tient une place centrale, insoupçonnée jusqu à présent, dans la hiérarchie des gènes qui gouvernent les propriétés des GSC.Gliomas, the most frequent primary brain tumors, are resistant to current therapies and the survival rate of patients is very low. Within high-grade gliomas, a cell sub-population bearing stem-like properties has been isolated. These cells, called glioma stem cell (GSC), are capable of generating all glioma cellular sub-types. Recent data indicates that resistance of these aggressive tumors to therapies is mostly due to GSCs. Thus, targeting the GSCs and their stem-like properties is imperative in order to improve current therapies. [...] Another effective solution to treat GSCs is to force them to lose their stem-like properties. In this context, the aims of my major project were to characterize the epigenetic modifications of histone marks accompanying the loss of GSC stem-like properties under the influence of a cluster of micro-RNA, miR-302-367. GSCs are endowed with an exceptional plasticity, allowing them to gain or lose their stem-like state in response to modifications in their micro-environment. Our results identified the implication of miR-302-367 in the regulation of GSC plasticity. Its stable expression using lentivirus inhibits in an irreversible manner the stem-like and tumorigenic properties of GSC. The tumor-suppressor effect of this miR offers the possibility to decipher the mechanisms responsible for the maintenance or the loss of GSC stem-like properties. Using the model of GSC and their counterparts, GSC-miR-302-367, who lost their stem-like and tumorigenic properties, my aim was to identify the methylation status of histone H3 of the histone code which is known to be associated either to an active or to a repressive gene transcription. I focused on the trimethylation of lysine 4 (H3K4me3) and lysine 27 (H3K27me3), which are associated with an activation or repression of gene transcription, respectively. We performed a ChIP-seq (Chromatin-immunoprecipitation-sequencing) analysis of the respective associated genes followed by a transcriptomic (exon-array) analysis of both cell lines. Our results show that miR-302-367 expression does not alter in a global manner the expression levels of H3K4me3 and H3K27me3. On the contrary, we were able to detect modifications in a discrete group of genes. At least for the studied marks, the positive correlation between the identified histone marks and the gene expression levels indicates that the histone code is well preserved in cancer. GO (Gene Ontology) analysis indicates that miR-302-367-induced loss of stem-like properties is accompanied with activation of the differentiation process in GSC. Genes implicated in the regulation of stem-like and tumorigenic properties were found to bear the repressive histone mark in GSC-miR-302-367. From our analysis of the group of genes bearing the active histone mark in GSC and the repressive one in GSC-miR-302-367, emerged a network of transcription factors that could possibly participate in the regulation of GSC stem-like properties. Down-regulation using siRNA of a member of this network, namely ARNT2, highlighted its role in the maintenance of the proliferative dynamic, as well as the expression of the transcription factor Nanog (a major regulator of GSC stem-like properties), in GSC derived from distinct gliomas. Our histone mark modification analysis, not only elucidated the molecular pathways implicated in the maintenance or, on the contrary, in the loss of GSC stem-like properties, but also, highlighted the implication of new actors in these processes. The activator effect of ARNT2 on GSC proliferation, as well as on the expression of Nanog, observed in GSC bearing distinct genetic alterations and derived from different glioma, indicates that this transcription factor plays a major role, not documented thus far, in the regulation of GSC stem-like properties.PARIS5-Bibliotheque electronique (751069902) / SudocPARIS-BIUM-Bib. électronique (751069903) / SudocSudocFranceF

Dispersed-phase structural anisotropy in homogeneous magnetohydrodynamic turbulence at low magnetic Reynolds number

A new tensor statistic, the dispersed-phase structure dimensionality Dp, is defined to describe the preferred orientation of clusters of discrete bodies. The evolution of Dp is calculated via direct numerical simulations of passive, Stokesian particles driven by initially isotropic, decaying magnetohydrodynamic turbulence. Results are presented for five magnetic field strengths as characterized by magnetic interaction parameters, N, in the range 0-50. Four field strengths are studied at a grid resolution of 1283. The strongest field strength is also studied at 2563 resolution. In each case, the externally applied magnetic field was spatially uniform and followed a step function in time. Particles with initially uniform distributions were tracked through hydrodynamic turbulence for up to 2800 particle response times before the step change in the magnetic field. In the lower resolution simulation, the particle response time, τp, matched the Kolmogorov time scale at the magnetic field application time t0. The higher-resolution simulation tracked ten sets of particles with τp spanning four decades bracketing the Kolmogorov time scale and the Joule time. The results demonstrate that Dp distinguishes between uniformly distributed particles, those organized into randomly oriented clusters, and those organized into two-dimensional sheets everywhere tangent to the magnetic field lines. Lumley triangles are used to demonstrate that the degree of structural anisotropy depends on τp, N, and the time span over which the magnetic field is applied. © 2008 American Institute of Physics.SCOPUS: ar.jinfo:eu-repo/semantics/publishe