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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Joanna Tobiasz Christos Hatzis Joanna Polanska Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Gliwice Poland Department of Medicine Yale School of Medicine New Haven USA

ISBN: (纸本)9781728146188

Breast cancer, a frequent cause of mortality among women, is a heterogeneous disease with multiple subtypes, which identification has a great impact on the prognosis and the choice of therapy. Various attempts are being made worldwide to classify breast tumors and to discover differences between subtypes. The purpose of this study is to divide a diverse group of patients with a multiple k-means clustering approach in order to investigate the complex structure of the dataset. Primary tumor samples collected from 859 females within The Cancer Genome Atlas Breast Invasive Carcinoma project served as the material for an RNA-Seq experiment. Expression levels measured with the RNA-Seq procedure were downloaded from the Genomic Data Commons Data Portal and used for this study. The proposed multilevel clustering algorithm is based on the idea of treating each resulting cluster as an input for a new k-means clustering as long as a given cluster is not too small or integrated. Each clustering is preceded with feature filtration and selection, which are based on the Gaussian Mixture Model decomposition of gene expression level amplitude or variance, respectively. The gap statistic is used for the selection of the optimal number of clusters as well as for deciding whether a group should be divided. Results were compared to the subtype labels either obtained from the PAM50 classifier or based on the HER2 and hormone-receptor markers. The performed multilevel clustering produced a large number of small clusters. During the first division five clusters were obtained, three of them containing almost only hormone-receptor positive (HR+) tumors. One cluster consisted of similar numbers of HER2-enriched, triple-negative breast cancer (TNBC), and HR+ cases. Obtained results confirm a great heterogeneity of breast cancer. However, they also suggest that commonly used breast tumor classification corresponding to the receptor markers may not entirely reflect the complexity of subtypes of

关键词： Breast cancer Clustering algorithms Tumors Filtration Gene expression Bioinformatics

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Designing Cyclic Schedules for Streaming Repetitive Job-Shop Manufacturing Systems with Blocking and No-Wait Constraints

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IFAC-PapersOnLine 2019年第10期52卷 73-78页

作者： R. Wójcik J. Pempera Department of Computer Engineering Faculty of Electronics Wrocław University of Science and Technology Wrocław Poland Department of Control Systems and Mechatronics Faculty of Electronics Wrocław University of Science and Technology Wrocław Poland

We consider a problem of designing no-wait cyclic schedules for job-shop manufacturing system with repetitive transportation tasks that are defined by sequences of operations. Each operation is performed in a sector of manufacturing system using its resources, e.g. vehicles, machines, and takes some time for manufacturing and transportation. The sectors and their resources can be shared by several tasks in the exclusive mode. There are no buffers between sectors thus when a transportation car is in a sector it blocks this sector to other cars and cannot leave it until a sector which is required by the following operation in its transportation route is free. The repetitive transportation tasks performed in a manufacturing system are modelled as a system of cyclic processes executing sequences of operations and sharing common resources according to a mutual exclusion protocol. The problem consists in finding no-wait cyclic schedules for which no process (tasks) that started execution will wait for resources (sectors) required to execute its operations and each operation will be started immediately after finishing the previous one. The casade-like structure (C-LS) system of processes is considered. This system is structurally deadlock-free therefore no-buffer (resource blocking) constraint will never lead to deadlock state. The C-LS system of processes can be seen as a composition of subsystems with two cyclic processes sharing a single resource, namely 2-P subsystems. Using our recurrent integer modulus equations model (developed in our previous works) we find for each 2-P subsystem all possible start times of the processes (if exist) that belong to a no-wait schedule and construct all possible no-wait schedules for each component 2-P. Finally, we design cyclic no-wait schedules for the C-LS system by joining the cyclic schedules designed for its component 2-P subsystems.

关键词： Repetitive tasks cyclic processes job-shop systems blocking no-wait constraints cyclic scheduling two processes sharing resource cascade-like structured systems cyclic schedules integration

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Benchmark problems for transcranial ultrasound simulation: Intercomparison of compressional wave models

arXiv

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arXiv 2022年

作者： Aubry, Jean-Francois Bates, Oscar Boehm, Christian Pauly, Kim Butts Christensen, Douglas Cueto, Carlos Gélat, Pierre Guasch, Lluis Jaros, Jiri Jing, Yun Jones, Rebecca Li, Ningrui Marty, Patrick Montanaro, Hazael Neufeld, Esra Pichardo, Samuel Pinton, Gianmarco Pulkkinen, Aki Stanziola, Antonio Thielscher, Axel Treeby, Bradley Van't Wout, Elwin Physics for Medicine Paris INSERM U1273 ESPCI Paris PSL University CNRS UMR 8063 France Department of Bioengineering Imperial College London Exhibition Road LondonSW7 2AZ United Kingdom Institute of Geophysics ETH Zürich Sonneggstrasse 5 Zürich8092 Switzerland Department of Radiology Stanford University StanfordCA United States Department of Biomedical Engineering Department of Electrical and Computer Engineering University of Utah United States Department of Surgical Biotechnology Division of Surgery and Interventional Science University College London LondonNW3 2PF United Kingdom Earth Science and Engineering Department Imperial College London London United Kingdom Centre of Excellence IT4Innovations Faculty of Information Technology Brno University of Technology Bozetechova 2 Brno612 00 Czech Republic Graduate Program in Acoustics The Pennsylvania State University University Park PA16802 United States Joint Department of Biomedical Engineering University of North Carolina at Chapel Hill North Carolina State University NC United States Department of Electrical Engineering Stanford University StanfordCA United States Zurich Switzerland Laboratory for Acoustics / Noise control Empa Swiss Federal Laboratories for Materials Science and Technology Dubendorf Switzerland Zurich Switzerland Radiology and Clinical Neurosciences Departments Cumming School of Medicine University of Calgary Canada Department of Applied Physics University of Eastern Finland Kuopio70211 Finland Department of Medical Physics and Biomedical Engineering University College London Gower Street LondonWC1E 6BT United Kingdom Technical University of Denmark Denmark Danish Research Center for Magnetic Resonance Copenhagen University Hospital Hvidovre Denmark Institute for Mathematical and Computational Engineering School of Engineering and Faculty of Mathematics Pontificia Universidad Catolica de Chile Santiago Chile

Computational models of acoustic wave propagation are frequently used in transcranial ultrasound therapy, for example, to calculate the intracranial pressure field or to calculate phase delays to correct for skull distortions. To allow intercomparison between the different modeling tools and techniques used by the community, an international working group was convened to formulate a set of numerical benchmarks. Here, these benchmarks are presented, along with intercomparison results. Nine different benchmarks of increasing geometric complexity are defined. These include a single-layer planar bone immersed in water, a multi-layer bone, and a whole skull. Two transducer configurations are considered (a focused bowl and a plane piston), giving a total of 18 permutations of the benchmarks. Eleven different modeling tools are used to compute the benchmark results. The models span a wide range of numerical techniques, including the finite-difference time-domain method, angular-spectrum method, pseudospectral method, boundary-element method, and spectral-element method. Good agreement is found between the models, particularly for the position, size, and magnitude of the acoustic focus within the skull. When comparing results for each model with every other model in a cross comparison, the median values for each benchmark for the difference in focal pressure and position are less than 10% and 1 mm, respectively. The benchmark definitions, model results, and intercomparison codes are freely available to facilitate further comparisons. Copyright © 2022, The Authors. All rights reserved.

关键词： Bone

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User-configurable decision support system for clinical risk management 11th

User-configurable decision support system for clinical risk ...

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2020 Annual International Conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence: Eleventh Annual Meeting of the BICA Society, BICA*AI 2020

作者： Kleymenova, Elena Dronov, Mikhail Yashina, Liubov Matrosova, Elena Payushchik, Svetlana Nigmatkulova, Maria Otdelenov, Vitaly General Medical Center of the Bank of Russia Sevastopolsky pr.66 Moscow117593 Russia National Research Nuclear University MEPhI Moscow Engineering Physics Institute Kashirskoe shosse 31 Moscow115409 Russia Faculty of Physics M.V. Lomonosov Moscow State University Leninskie Gory GSP-1 Moscow119991 Russia Information Technology Department Moscow City University Vtoroy Selskohoziajstvenny proezd 4 Moscow129226 Russia Federal Research Center Computer Science and Control Russian Academy of Sciences Vavilova str. 44 Moscow119333 Russia Russian Medical Academy of Continuous Professional Education Barrikadnaya st. 2/1 Moscow125993 Russia

The paper describes an application of relational structure-independent database for the design of user-configurable information system aimed at the intellectual support of clinical risk management in a hospital. The system represents a clinical decision support system (CDSS) and includes three modules: prevention of hospital-acquired adverse events (HA-AE), HA-AE prevention processes and audit of HA-AE cases. Thanks to flexible system configuration and graphic user interface, the clinical experts are able to compose complex knowledge artifacts directly working with the system, without engagement of knowledge engineers for supporting knowledge formalization or highly skilled professionals to ensure the physical layer performance. The successful practical approval of CDSS in a medical facility opens up prospects for a new level of cognitive support for diagnostic and treatment processes, patient safety improvement and healthcare costs reduction. © 2020 Elsevier B.V.. All rights reserved.

关键词： Decision support systems

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Data-driven machine learning improves prediction of sulfonamide antibiotic adsorption by biochar in aqueous phase

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Bioresource Technology 2025年 434卷 132773页

作者： Zhai, Mudi Fu, Bomin Wu, Zhaozhong Wang, Junsen Wang, Weijie Wang, Hongtao College of Environmental Science and Engineering Key Laboratory of Yangtze River Water Environment Ministry of Education Tongji University Siping Rd 1239 Shanghai200092 China National Observation and Research Station of Coastal Ecological Environments in Macao Macao Environmental Research Institute Faculty of Innovation Engineering Macau University of Science and Technology Macao SAR China State Key Laboratory of Marine Pollution and Department of Chemistry City University of Hong Kong Hong Kong999077 China School of Computer Science Northwestern Polytechnical University Xi'an710129 China Shanghai Institute of Pollution Control and Ecological Security Shanghai200092 China

Sulfonamide antibiotics (SAs) have attracted much attention due to their environmental risks to aquatic ecosystems. Biochars (BCs), as excellent adsorbent materials, have been used to remove SAs from aqueous phases. To achieve effective evaluation of adsorption, machine learning (ML) strategies are increasingly being developed. However, no applicable data-driven ML models have been studied to predict the adsorption of SAs by BCs in water. Therefore, this study employed an ML approach based on Wasserstein generative adversarial network (WGAN) data augmentation to predict the adsorption of SAs on BCs in the aqueous phase. The results indicated that the WGAN could generate virtual data highly similar to the original adsorption dataset. By expanding the original data using WGAN, the performance of the extreme gradient boosting model in predicting the adsorption amount improved. This study provides new insights into predicting the adsorption behavior of waste-based BCs for SAs in water environments. © 2025 Elsevier Ltd

关键词： Generative adversarial networks

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Investigation of the diagnostic properties of sensors and features in a multiphase flow facility case study ⁎

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IFAC-PapersOnLine 2019年第1期52卷 772-777页

作者： Anna Stief James R. Ottewill Jerzy Baranowski ABB Corporate Research Center ul. Starowislna 13a 31-038 Krakow Poland Department of Automatic Control and Robotics Faculty of Electrical Engineering Automatics Computer Science and Biomedical Engineering AGH University of Science and Technology Al. A. Mickiewicza 30 30-059 Krakow Poland

Feature design and selection is one of the first steps towards successful fault detection and diagnosis. Data from different sources can contain complimentary information about a monitored system. Hence methods which fuse features from multiple sources can often detect and diagnose a greater number of fault modes with higher confidence. However, solutions that require data from multiple sensors as inputs can be susceptible to failure if one or more of those sensors cease to function. Optimally a solution will fuse data from a sufficient number of sensors so that the advantages of sensor fusion are realized, while the robustness of the system is retained. In this paper the authors investigate how the best subset of features might differ for fault detection and fault severity diagnosis in a multiphase flow facility case study. ReliefF, which is a K-nearest neighbors-based feature selection filter, is used to rank the features for different problems. The dataset used for the analysis contains data from various operating conditions and induced faults with various severities. It was found that the optimal subset of features varied for different monitoring problems. It was also shown that including features that are ranked as being uninformative into a fault classifier can also impact the robustness of the classifier to sensor failures.

关键词： Signal processing Feature extraction Feature selection Process monitoring

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Computational fluid dynamic and machine learning modeling of nanofluid flow for determination of temperature distribution and models comparison

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Scientific Reports 2025年第1期15卷 1-9页

作者： Altalbawy, Farag M. A. Alkhayyat, Ahmad Jhala, Ramdevsinh Yadav, Anupam Ramachandran, T. Shankhyan, Aman Karthikeyan, A. Thatoi, Dhirendra Nath Sinitsin, Vladimir Vladimirovich Department of Chemistry University College of Duba University of Tabuk Tabuk Saudi Arabia Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Al Diwaniyah Al Diwaniyah Iraq Department of Computers Techniques Engineering College of Technical Engineering The Islamic University of Babylon Babylon Iraq Marwadi University Research Center Department of Mechanical Engineering Faculty of Engineering & Technology Marwadi University Gujarat Rajkot 360003 India Department of Computer Engineering and Application GLA University Mathura 281406 India Department of Mechanical Engineering School of Engineering and Technology JAIN (Deemed to be University) Karnataka Bangalore India Centre for Research Impact & Outcome Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura 140401 India Department of Mechanical Engineering Sathyabama Institute of Science and Technology Tamil Nadu Chennai India Department of Mechanical Engineering Siksha ’O’ Anusandhan (Deemed to be University) Odisha Bhubaneswar 751030 India Research Laboratory of Technical Self-Diagnostics and Self-Control of Devices and Systems Department of Scientific and Innovative Activities Research Institute of Mechanical Engineering South Ural State University Lenin prospect 76 Chelyabinsk 454080 Russian Federation

This paper introduces an approach to temperature prediction by employing three distinct machine learning models: K-Nearest Neighbors (KNN), Gaussian Process Regression (GPR), and Multi-layer Perceptron (MLP) which are integrated into Computational Fluid Dynamics (CFD). The dataset consists of inputs, represented by the features x and y, and the corresponding output, which is the temperature. The case study is fluid flow of nanofluid through a pipe and the nanofluid contains CuO particles. To enhance model efficacy, the Political Optimizer (PO) algorithm was utilized for fine-tuning purposes. The findings substantiate the capability of the optimized frameworks in delivering precise temperature estimations. GPR yielded a notable R² value of 0.998, reflecting a strong concordance between the estimated and actual measurements. In a comparable manner, the KNN approach exhibited outstanding predictive performance, also attaining an R² value of 0.998. MLP, although slightly lower in performance compared to the other models, still proves to be a reliable predictor of temperature values, with an R-squared score of 0.984. Overall, the combination of the PO algorithm and the machine learning models showcases promising results in temperature prediction. The study’s findings offer valuable insights for decision-making processes in a variety of domains that rely on accurate temperature forecasting. © The Author(s) 2025.

关键词： Gaussian process regression Heat transfer K-Nearest neighbors Multi-layer perceptron Nanofluid

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Regular path-constrained time-optimal control problems in three-dimensional flow fields

arXiv

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arXiv 2019年

作者： Chertovskih, R. Karamzin, D. Khalil, N.T. Pereira, F.L. Electrical and Computer Engineering Department Faculty of Engineering University of Porto 4200-465 Portugal Federal Research Center "Computer Science and Control" Russian Academy of Science Moscow Russia

This article concerns a class of time-optimal state constrained control problems with dynamics defined by an ordinary differential equation involving a three-dimensional steady flow vector field. The problem is solved via an indirect method based on the maximum principle in Gamkrelidze's form. The proposed computational method essentially uses a certain regularity condition imposed on the data of the problem. The property of regularity guarantees the continuity of the measure multiplier associated with the state constraint, and ensures the appropriate behavior of the corresponding numerical procedure which, in general, consists in computing the entire field of extremals for the problem in question. Several examples of vector fields are considered to illustrate the computational *** Codes 49K15, 49M05 Copyright © 2019, The Authors. All rights reserved.

关键词： Maximum principle

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Authentication by witness functions

arXiv

引用

arXiv 2019年

作者： Fattahi, Jaouhar Mejri, Mohamed Pricop, Emil Department of Computer Science and Software Engineering Université Laval Québec Canada Automatic Control Computers and Electronics Department. Petroleum-Gas University of Ploiesti Romania

Witness functions have recently been introduced in cryptographic protocols’ literature as a new powerful way to prove protocol correctness with respect to secrecy. In this paper, we extend them to the property of authentication. We show how to use them safely and we run an analysis on a modified version of the Woo-Lam protocol. We show that it is correct with respect to authentication. Copyright © 2019, The Authors. All rights reserved.

关键词： Authentication

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IDF2022-1183 The effect of multi-strain probiotics on diarrhea in patients with type 2 diabetes and metformin intolerance

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Diabetes Research and Clinical Practice 2023年 197卷 110340-110340页

作者： Nabrdalik, K. Drożdż, K. Kwiendacz, H. Skonieczna-Żydecka, K. Łoniewski, I. Kaczmarczyk, M. Nalepa, J. Holleman, F. Nieuwdorp, M. Gumprecht, J. Medical University of Silesia in Katowice Faculty of Medical Sciences in Zabrze Department of Internal Medicine Diabetology and Nephrology Zabrze Poland Pomeranian Medical University in Szczecin Department of Human Nutrition and Metabolomics Szczecin Poland Pomeranian Medical University in Szczecin Department of Biochemical Sciences Szczecin Poland Pomeranian Medical University in Szczecin Department of Clinical and Molecular Biochemistry Szczecin Poland Silesian University of Technology Faculty of Automatic Control Electronics and Computer Science Department of Algorithmics and Software Gliwice Poland Universiteit van Amsterdam- Amsterdam UMC Department of Internal Medicine Amsterdam The Netherlands Universiteit van Amsterdam Amsterdam UMC Department of Internal Medicine Section Vascular Medicine Amsterdam The Netherlands

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