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Twofold saddle-point formulation of biot poroelasticity with stress-dependent diffusion

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arXiv 2021年

作者： Gómez-Vargas, Bryan Mardal, Kent-André Ruiz-Baier, Ricardo Vinje, Vegard Sección de Matemática Sede de Occidente Universidad de Costa Rica San Ramón Ala juela Costa Rica Department of Mathematics Division of Mechanics University of Oslo Norway Simula Research Laboratory Lysaker1325 Norway School of Mathematics Monash University 9 Rainforest Walk MelbourneVIC3800 Australia Institute of Computer Science and Mathematical Modelling Sechenov University Moscow Russia Universidad Adventista de Chile Casilla 7-D Chillán Chile Department for Numerical Analysis and Scientific Computing Simula Research Laboratory Lysaker Norway

We introduce a stress/total-pressure formulation for poroelasticity that includes the coupling with steady nonlinear diffusion modified by stress. The nonlinear problem is written in mixed-primal form, coupling a perturbed twofold saddle-point system with an elliptic problem. The continuous formulation is analysed in the framework of abstract fixed-point theory and Fredholm alternative for compact operators. A mixed finite element method is proposed and its stability and convergence analysis are carried out. We also include a few illustrative numerical tests. The resulting model can be used to study waste removal in the brain parenchyma, where diffusion of a tracer alone or a combination of advection and diffusion are not sufficient to explain the alterations in rates of filtration observed in porous media *** Codes 65N60, 92C50 © 2021, CC BY.

关键词： Diffusion

Preventing Black Hole Attack in Wireless Sensor Network Using HMM

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Procedia computer science 2019年 148卷 552-561页

作者： Hanane Kalkha Hassan Satori Khalid Satori Laboratory Computer Science Image processing and Numerical Analysis Faculty of Sciences Dhar MahrazSidi Mohammed Ben Abbdallah University Fez Morocco Artificial Intelligence Complex Systems and modeling Department of Mathematics and Computer Science Faculty Polydisciplinary of Nador Mohammed Premier University 300 Selouane 62700 Nador Morocco

Secure routing is a difficult task because of the limited nature of wireless sensor network resources. This paper provides a Hidden Markov Model solution to identify malicious nodes in wireless sensor networks through prevention of black hole attack. Our proposed approach based on a new routing algorithm which analyses shortest path in order to avoid malicious node path. Our results demonstrate the success and the efficiency of our proposed routing algorithm.

关键词： WSN HMM Black Hole Malicious Shortest path

Acute Genital Peritonitis in Brazzaville, Congo

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Surgical science 2019年第10期10卷 368-376页

作者： Massamba Miabaou Didace Mbongo Jean Alfred Note Madzélé Murielle Motoula Latou Noé Itoua Clautaire Massamba Alphonse Digestive Surgery Department Brazzaville University Hospital Brazzaville Congo Gynecology-Obstetrics Department Brazzaville University Hospital Brazzaville Congo Laboratory of Numerical Analysis Computer Science and Applications (LANIA) Faculty of Sciences and Technology Marien Ngouabi University Brazzaville Congo.

Genital peritonitis is rare in daily surgical practice in Congo-Brazzaville. Clandestine abortions are incriminated. The purpose of the study is to analyze the epidemiological, etiological, diagnostic and therapeutic aspects of genital peritonitis. A retrospective and case series study was realized in departments of Digestive Surgery and Gynecology-Obstetrics of the University Hospital of Brazzaville. The inclusion criteria for the diagnosis of peritonitis were abdominal pain, fever, transit disturbances and signs of peritoneal irritation. The parameters studied were: age, etiological circumstances, anatomical lesions, type of surgical treatment and evolution. During the study period (July 1, 2015-December 31, 2017), 306 patients were admitted to both departments for acute generalized peritonitis. Among them, a genital cause was incriminated in 18 (5.9%) patients. The mean age was 27.6 ± 3.1 years. At the parity and gestational level, 93% of patients had at least two pregnancies, but not more than the second trimester. In addition, 50% of the patients had an induced miscarriage, due to uterine and intestinal lesions. Induced miscarriages accounted for half of etiological circumstances. Physical examination of the abdomen revealed abdominal contracture in 61.1% of cases. Main visceral lesions were uterine perforation (55.5%) followed by rupture of tubo-ovarian abscess (38.9%). The operative follow-up was simple in 83.33% of cases. In conclusion, genital peritonitis remains unfrequented. Median laparotomy has been the main therapeutic approach in our context where emergency laparoscopic surgery is not yet common.

关键词： Peritonitis Abortion Uterine Perforation

Modeling eye gaze velocity trajectories using GANs with spectral loss for enhanced fidelity

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Scientific reports 2025年第1期15卷 19929页

作者： Shailendra Bhandari Pedro Lencastre Rujeena Mathema Alexander Szorkovszky Anis Yazidi Pedro G Lind Department of Computer Science OsloMet - Oslo Metropolitan University P.O. Box 4 St. Olavs plass N-0130 Oslo Norway. shailendra.bhandari@oslomet.no. OsloMet Artificial Intelligence Lab Pilestredet 52 N-0166 Oslo Norway. shailendra.bhandari@oslomet.no. Department of Computer Science OsloMet - Oslo Metropolitan University P.O. Box 4 St. Olavs plass N-0130 Oslo Norway. OsloMet Artificial Intelligence Lab Pilestredet 52 N-0166 Oslo Norway. Simula Research Laboratory Numerical Analysis and Scientific Computing Oslo 0164 Norway. Department of Informatics University of Oslo Oslo Norway. School of Economics Innovation and Technology Kristiania University of Applied Sciences Kirkegata 24-26 N-0153 Oslo Norway.

Accurate modeling of eye gaze dynamics is essential for advancement in human-computer interaction, neurological diagnostics, and cognitive research. Traditional generative models like Markov models often fail to capture the complex temporal dependencies and distributional nuance inherent in eye gaze trajectories data. This study introduces a Generative Adversarial Network (GAN) framework employing Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) generators and discriminators to generate high-fidelity synthetic eye gaze velocity trajectories. We conducted a comprehensive evaluation of four GAN architectures: CNN-CNN, LSTM-CNN, CNN-LSTM, and LSTM-LSTM-trained under two conditions: using only adversarial loss ([Formula: see text]) and using a weighted combination of adversarial and spectral losses. Our findings reveal that the LSTM-CNN architecture trained with this new loss function exhibits the closest alignment to the real data distribution, effectively capturing both the distribution tails and the intricate temporal dependencies. The inclusion of spectral regularization significantly enhances the GANs' ability to replicate the spectral characteristics of eye gaze movements, leading to a more stable learning process and improved data fidelity. Comparative analysis with a Hidden Markov Model (HMM) optimized to four hidden states further highlights the advantages of the LSTM-CNN GAN. Statistical metrics show that the HMM-generated data significantly diverges from the real data in terms of mean, standard deviation, skewness, and kurtosis. In contrast, the LSTM-CNN model closely matches the real data across these statistics, affirming its capacity to model the complexity of eye gaze dynamics effectively. These results position the spectrally regularized LSTM-CNN GAN as a robust tool for generating synthetic eye gaze velocity data with high fidelity. Its ability to accurately replicate both the distributional and temporal properties of real data holds

关键词： Eye-gaze trajectories Generative Adversarial Networks Hidden Markov Models Stochastic Processes

Evaluating associative classification algorithms for Big Data

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Big Data Analytics 2019年第1期4卷 1-27页

作者： Padillo, Francisco Luna, José María Ventura, Sebastián Department of Computer Science and Numerical Analysis University of Cordoba Cordoba Spain Knowledge Discovery and Intelligent Systems in Biomedicine Laboratory Maimonides Biomedical Research Institute of Cordoba Cordoba Spain Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Associative Classification, a combination of two important and different fields (classification and association rule mining), aims at building accurate and interpretable classifiers by means of association rules. A major problem in this field is that existing proposals do not scale well when Big Data are considered. In this regard, the aim of this work is to propose adaptations of well-known associative classification algorithms (CBA and CPAR) by considering different Big Data platforms (Spark and Flink). An experimental study has been performed on 40 datasets (30 classical datasets and 10 Big Data datasets). Classical data have been used to find which algorithms perform better sequentially. Big Data dataset have been used to prove the scalability of Big Data proposals. Results have been analyzed by means of non-parametric tests. Results proved that CBA-Spark and CBA-Flink obtained interpretable classifiers but it was more time consuming than CPAR-Spark or CPAR-Flink. In this study, it was demonstrated that the proposals were able to run on Big Data (file sizes up to 200 GBytes). The analysis of different quality metrics revealed that no statistical difference can be found for these two approaches. Finally, three different metrics (speed-up, scale-up and size-up) have also been analyzed to demonstrate that the proposals scale really well on Big Data. The experimental study has revealed that sequential algorithms cannot be used on large quantities of data and approaches such as CBA-Spark, CBA-Flink, CPAR-Spark or CPAR-Flink are required. CBA has proved to be very useful when the main goal is to obtain highly interpretable results. However, when the runtime has to be minimized CPAR should be used. No statistical difference could be found between the two proposals in terms of quality of the results except for the interpretability of the final classifiers, CBA being statistically better than CPAR.

关键词：

Feasibility of Acousto-Electric Tomography

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arXiv 2019年

作者： Jensen, Bjørn Kirkeby, Adrian Knudsen, Kim Faculty of Information Technology University of Jyväskylä Finland Department of Numerical Analysis and Scientific Computing Simula Research Laboratory Oslo Norway Department of Applied Mathematics and Computer Science Technical University of Denmark Kgs Lyngby Denmark

In acousto-electric tomography the goal is to reconstruct the electric conductivity in a domain from electrostatic boundary measurements of corresponding currents and voltages, while the domain is penetrated by a time-dependent acoustic wave. In this paper we provide an in-silico model of the entire coupled physics scenario and perform computational experiments. We propose a complete inversion framework for acousto-electric tomography in two steps: First the interior power density is obtained from boundary measurements by solving a linear inverse problem;second the interior conductivity is reconstructed from the power density by solving a non-linear, fairly well-posed problem. We develop a numerical model with realistically chosen parameters inspired by medical imaging. The critical signal strength is analyzed and the omnipresent Johnson-Nyquist noise is estimated. With this setup we perform numerical experiments on synthetic data with noise and demonstrate the feasibility of the method. However, our findings are a mix of positive and negative. We reconstruct features even under severe noise conditions, but we also find that the required Signal-to-Noise ratio yet remains infeasible for practical *** Codes 35R30, 65N21 Copyright © 2019, The Authors. All rights reserved.

关键词： Inverse problems

Strengthening some complexity results on toughness of graphs

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arXiv 2019年

作者： Katona, Gyula Y. Varga, Kitti Department of Computer Science and Information Theory Budapest University of Technology and Economics Hungary MTA-ELTE Numerical Analysis and Large Networks Research Group Hungary HAS Alfréd Rényi Institute of Mathematics Hungary

Let t be a positive real number. A graph is called t-tough if the removal of any vertex set S that disconnects the graph leaves at most |S|/t components. The toughness of a graph is the largest t for which the graph is t-tough. The main results of this paper are the following. For any positive rational number t ≤ 1 and for any k ≥ 2 and r ≥ 6 integers recognizing t-tough bipartite graphs is coNP-complete (the case t = 1 was already known), and this problem remains coNP-complete for k-connected bipartite graphs, and so does the problem of recognizing 1-tough r-regular bipartite graphs. To prove these statements we also deal with other related complexity problems on toughness. Copyright © 2019, The Authors. All rights reserved.

关键词： Graphic methods

A mixture of experts model for predicting persistent weather patterns

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arXiv 2019年

作者： Pérez-Ortiz, M. Gutiérrez, P.A. Tino, P. Casanova-Mateo, C. Salcedo-Sanz, S. Computer Laboratory University of Cambridge United Kingdom Dpt. of Computer Science and Numerical Analysis University of Córdoba Spain School of Computer Science University of Birmingham United Kingdom Department of Civil Engineering: Construction Infrastructure and Transport Universidad Politécnica de Madrid Madrid Spain Department of Signal Processing and Communications University of Alcalá Spain

Weather and atmospheric patterns are often persistent. The simplest weather forecasting method is the so-called persistence model, which assumes that the future state of a system will be similar (or equal) to the present state. Machine learning (ML) models are widely used in different weather forecasting applications, but they need to be compared to the persistence model to analyse whether they provide a competitive solution to the problem at hand. In this paper, we devise a new model for predicting low-visibility in airports using the concepts of mixture of experts. Visibility level is coded as two different ordered categorical variables: cloud height and runway visual height. The underlying system in this application is stagnant approximately in 90% of the cases, and standard ML models fail to improve on the performance of the persistence model. Because of this, instead of trying to simply beat the persistence model using ML, we use this persistence as a baseline and learn an ordinal neural network model that refines its results by focusing on learning weather fluctuations. The results show that the proposal outperforms persistence and other ordinal autoregressive models, especially for longer time horizon predictions and for the runway visual height variable. Copyright © 2019, The Authors. All rights reserved.

关键词： Weather forecasting

Changes Induced by Physical Activity, Weight Loss and Calorie Restriction in Body Composition, Lipoproteins and Functional Capacity in Obese Congolese Women

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Health 2020年第6期12卷 548-571页

作者： Moulongo Jean Georges André Makosso Vheiye Georges Eouani Max Lévy Eméry Mouanda Richard Le Savant Ndalla Mathias Cyriaque Packa Tchissambou Bernard Massamba Alphonse Laboratory of Sports Biosciences Higher Institute of Physical Education and Sports Marien NGOUABI University Brazzaville Congo Unit of Nutritional Science and Metabolism Higher Institute of Physical Education and Sports Marien NGOUABI University Brazzaville Congo Department of Gynecology & Obstetrics Loandjili Hospital Pointe-Noire (Congo) and Faculty of Health Sciences Marien NGOUABI University Brazzaville Congo Clinical Psychology Research Division Marien NGOUABI University Brazzaville Congo Biomedical Analyses Center Makelekele Brazzaville Congo Laboratory of Numerical Analysis Computer Science and Applications Faculty of Sciences and Technology Marien NGOUABI University Brazzaville Congo

Background. The effects of physical exercises combined with a low-calorie diet on weight loss, body composition, lipoproteins profile, and physical fitness had been well described. However, Central Africa’s studies investigating these kinds of diets and exercise regimens are lacking. Objective. To investigate the effects of adding 14-weeks exercises to a hypocaloric diet on changes in body composition, lipoproteins concentrations, and physical capacities in obese Congolese women. Population and Methods. In total, 34 obese women aged 30 - 39 years (mean age: 33.7 ± 2.4 years) assigned to 14-weeks training program and low energy ketogenic diet. Body composition was assessed using classic methods and impedancemetry. Fasting plasma glucose (FPG) and fasting serum insulin were assessing using enzymatic colorimetric and radioim-munoradiometric methods. HOMA-IR and lipoproteins concentrations were assessed using standardized laboratory methods. VO2peak was measured on a treadmill during a progressive exercise test. Speed, cadence and stride length were measured along the 10-m level walkway. Muscular endurance was measured using the tests of sit-up and inflections-extensions of elbows. All the variables of the study were assessed at the beginning, in the 7-weeks, and in the 14-weeks of training methods. Results. Declines in body weight (16%), percent fat (12.1%), fat weight (26.4%), abdominal fat (34.2%), and waist circumference (10.4%) were found. A significant decrease in FPG (13%), fasting serum insulin (60.9%), HOMA-IR (64.7%), total cholesterol (12.2%), LDL-cholesterol (20.3%), triglycerides (92.8%), and VLDL-triglycerides (17.5%) was shown. In contrast, significant increase in HDL-cholesterol (27.13%) was found. The peak oxygen consumption VO2peak relative to body weight improved more in the 14-weeks training program (13.4%). Obese women exhibited higher values in the 14-weeks training program for speed gait (16.5%), cadence (9.1%), and stride length (15.7%) during no

关键词： Lipoproteins Body Composition Calorie Restriction Physical Exercise Obese Congolese Women