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International Journal of Thermofluids 2025年 28卷

作者： Hawzhen Fateh M. Ameen Ali B.M. Ali Ahmed Shawqi Sadeq Narinderjit Singh Sawaran Singh Soheil Salahshour Sh. Baghaei Department of Petroleum Technology Erbil Technology College Erbil Polytechnic University Erbil Kurdistan Region Iraq Department of Petroleum Engineering Knowledge University Erbil Iraq Air Conditioning Engineering Department College of Engineering University of Warith Al-Anbiyaa Karbala Iraq Chemical Engineering Department University of Basrah Basrah Iraq Faculty of Data Science and Information Technology INTI International University Persiaran Perdana BBN Putra Nilai Nilai 71800 Malaysia Faculty of Engineering and Natural Sciences Istanbul Okan University Istanbul Turkey Faculty of Engineering and Natural Sciences Bahcesehir University Istanbul Turkey Research Center of Applied Mathematics Khazar University Baku Azerbaijan Fast Computing Center Shabihsazan Ati Pars Tehran Iran

Nanofluids are mixtures of a base fluid and nanoparticles (also known as nano-scaled particles), and they were used within advanced heat transfer applications with known aggregation issues as well as unreliability in performance. Molecular dynamics simulations can effectively look at nanofluid behavior with no disruptions, especially when considering the complications and limitations involved with performing experiments at the nano-scale. We conducted molecular dynamics simulations that investigate the thermal and atomic behaviors of a nanofluid, which involved ammonia nanofluids with copper nanoparticles in aluminum nanochannels. Our results focused on evaluating the outflow of the nanofluid and on determining the primary factors including maximum velocity, temperature heat flux and nanoparticle aggregation time while modifying the initial conditions of temperature (300-350 K), and pressure (1-5 bar). Furthermore, we found the thermophysical properties of the nanofluids were heavily dependent on the initial temperature and pressure. By improving the initial temperature and pressure, thermal systems can support the promotion of efficiency and sustainability. We also measured the kinetic and potential energies, with the potential energies measuring -8399.15 eV and 80.69 eV after 5 ns with no indications of structural instabilities. The results indicated that as the initial temperature was increased, maximum velocity increased from 0.00086 to 0.00099 Å/ps and maximum temperature increased from 240 to 258 K. Furthermore, heat flux decreased from 1411 to 1397 W/m² and aggregation time decreased from 3.96 to 3.93 ns. On the other hand, maximum velocity decreased to 0.00078 Å/ps and maximum temperature decreased to 234 K, as well as heat flux increased to 1436 W/m² and aggregation time increasing time was increased to 4.07 ns, with the increasing initial pressure. These results provided some insight into the optimization of nanofluids for energy conserving thermal control

关键词： Initial temperature Initial pressure Heat flux Nanofluids Thermal properties Aggregation time Maximum velocity

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MDPL-net: Multi-layer Dictionary Learning Network with Added Skip Dense Connections

MDPL-net: Multi-layer Dictionary Learning Network with Added...

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IEEE International Conference on data Mining (ICDM)

作者： Zhao Zhang Yulin Sun Zheng Zhang Yang Wang Lin Wu Meng Wang School of Computer Science and Technology Soochow University Suzhou China Ministry of Education Key Laboratory of Knowledge Engineering with Big Data School of Computer Science and Information Engineering Hefei University of Technology Hefei China Bio-Computing Research Center Harbin Institute of Technology (Shenzhen) Shenzhen China

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Dictionary learning (DL) is powerful for representation learning, while it fails to capture the deep hierarchical information hidden in data. In this paper, we propose a new generalized end-to-end mulita-layer representation learning architecture referred to as Multi-layer Dictionary Pair Learning Network (MDPL-net) for the deep sparse and hierarchical representation of images. To enable MDPL-net to conduct accurate classification, MDPL-net clearly integrates the skip connection end-to-end network and multi-layer deep sparse dictionary learning into a unified architecture. The representation learning module has several hidden DL blocks, where each hidden DL block has a dictionary pair learning (DPL) layer, a batch-norm layer and an activation function layer, and the DL blocks are connected in a feed-forward manner. To further improve the information flow and maintain the privileged features between different DL blocks, a novel skip dense connectivity pattern is deployed between hidden DL blocks, which can obtain more stable and discriminative features. The DPL layer jointly formulates the discriminative synthesis dictionary and analysis dictionary by minimizing reconstruction error within each batch over the feature maps from front layers. Extensive results on benchmark databases demonstrate the effectiveness of MDPL-net for discriminative representation and robust image classification.

关键词： Dictionaries databases Conferences Machine learning data mining Image reconstruction Image classification

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DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NLP Prototyping

DeepKAF: A Heterogeneous CBR & Deep Learning Approach for NL...

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IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA)

作者： Kareem Amin Stelios Kapetanakis Nikolaos Polatidis Klaus-Dieter Althoff Andreas Dengel Smart Data and Knowledge Services German Research Center for Artificial Intelligence Technische Universität Kaiserslautern Kaiserslautern Germany School of Computing Engineering and Mathematics University of Brighton Brighton United Kingdom Intelligent Information Systems Lab Institute of Computer Science University of Hildesheim Hildesheim Germany

ISBN: (数字)9781728167992

ISBN: (纸本)9781728168005

With widespread modernization, digitization and transformations of most of industries, Artificial Intelligence (AI) has become the key enabler in that modernization journey. AI offers substantial capabilities to solve new problems and optimise existing solutions specialising on specific problems and learning from different domains. AI solutions can be either explainable or black box ones with the latter being urged to improve since they cannot trust. Case-based Reasoning (CBR) is an explainable AI approach where solutions are provided along with relevant explanations in terms of why a solution was selected. However, CBR, like most other explainable approaches, has several limitations in terms of scalability, large data volumes, domain complexity, that reduce its ability to scale any CBR system in industrial applications. In this paper, we provide a heterogeneous CBR framework - DeepKAF where we combine CBR paradigm with Deep Learning architectures to solve complicated Natural Language Processing (NLP) problems (eg. mixed language and grammatically incorrect text).DeepKAF is built based on continuous research in the area of Deep Learning and CBR. DeepKAF has been implemented and used across different domains, test use cases and research models as an ensemble deep learning and CBR Architecture.

关键词： Machine learning Training Neural networks Natural language processing Cognition Buildings Task analysis

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Myocardial Ischemia Diagnosis Using a Reduced Lead System 40

Myocardial Ischemia Diagnosis Using a Reduced Lead System

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40th Annual International Conference of the IEEE engineering in Medicine and Biology Society, EMBC 2018

作者： Aranda Hernandez, A. Bonizzi, P. Karel, J. Peeters, R. Department of Research and Technology Bakken Research Center Medtronic Maastricht Netherlands Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands

ISBN: (纸本)9781538636466

This research presents a novel statistical model for diagnosing acute myocardial infarction (AMI). The model is based on features extracted from a reduced lead system consisting of a subset of three leads from the standard 12-lead ECG. We selected a set of relevant parameters commonly used in the clinical practice for ECG-based AMI diagnosis, namely ST elevation and T-wave maximum. We also selectedfeatures, not used in clinical practice, that were derived from vectorcardiography and computed on the reduced three-lead system (pseudo-VCG parameters). To validate the model, we used 104 patients coming from the Physionet STAFF III database which contains 12-lead ECG recordings at baseline and in coronary artery occlusion condition during angioplasty (PTCA). Results show that pseudo-VCG features are able to diagnose AMI slightly better than ST elevation and T-wave maximum features together (area under the ROC curve (AUC) 0.87 vs AUC 0.85). When combining pseudo-VCG features together with ST elevation, and T-wave maximum, the performance improved significantly (AUC 0.95, sensitivity 89.6% and specificity 82.7%). Results indicate a potential for diagnosing AMI using the proposed reduced lead system and the selected set of features. We suggest its possible use for diagnosing AMI in long-term, ambulatory and home monitoring situations, allowing an earlier and faster diagnosis. © 2018 IEEE.

关键词： Electrocardiography Feature extraction Heart Myocardium databases Lead Standards

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FCA2VEC: Embedding techniques for formal concept analysis

arXiv

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arXiv 2019年

作者： Dürrschnabel, Dominik Hanika, Tom Stubbemann, Maximilian Knowledge and Data Engineering Group University of Kassel Kassel Interdisciplinary Research Center for Information System Design University of Kassel Kassel L3S Research Center Leibniz University Hannover Hannover

Embedding large and high dimensional data into low dimensional vector spaces is a necessary task to computationally cope with contemporary data sets. Superseding 'latent semantic analysis' recent approaches like 'word2vec' or 'node2vec' are well established tools in this realm. In the present paper we add to this line of research by introducing 'fca2vec', a family of embedding techniques for formal concept analysis (FCA). Our investigation contributes to two distinct lines of research. First, we enable the application of FCA notions to large data sets. In particular, we demonstrate how the cover relation of a concept lattice can be retrieved from a computational feasible embedding. Secondly, we show an enhancement for the classical node2vec approach in low dimension. For both directions the overall constraint of FCA of explainable results is preserved. We evaluate our novel procedures by computing fca2vec on different data sets like, wiki44 (a dense part of the Wikidata knowledge graph), the Mushroom data set and a publication network derived from the FCA *** Codes 68T30, 03G10, 06A06 Copyright © 2019, The Authors. All rights reserved.

关键词： Vector spaces

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Seen the villains: Detecting social engineering attacks using case-based reasoning and deep learning 27

Seen the villains: Detecting social engineering attacks usin...

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27th International Conference on Case-Based Reasoning Workshops, ICCBR-WS 2019

作者： Lansley, Merton Polatidis, Nikolaos Kapetanakis, Stelios Amin, Kareem Samakovitis, George Petridis, Miltos School of Computing Engineering and Mathematics University of Brighton BrightonBN2 4GJ United Kingdom German Research Center for Artificial Intelligence Smart Data and Knowledge Services Trippstadter Strasse 122 Kaiserslautern67663 Germany School of Computing & Mathematical Sciences University of Greenwich London United Kingdom Department of Computing University of Middlesex London United Kingdom

Social engineering attacks are frequent, well-known and easy-toapply attacks in the cyber domain. Historical evidence of such attacks has shown that the vast majority of malicious attempts against both physical and virtual IT systems were based or been initiated using social engineering methods. By identifying the importance of tackling efficiently cybersecurity threats and using the recent developments in machine learning, case-based reasoning and cybersecurity we propose and demonstrate a two-stage approach that detects social engineering attacks and is based on natural language processing, case-based reasoning and deep learning. Our approach can be applied in offline texts or real time environments and can identify whether a human, chatbot or offline conversation is a potential social engineering attack or not. Initially, the conversation text is parsed and checked for grammatical errors using natural language processing techniques and case-based reasoning and then deep learning is used to identify and isolate possible attacks. Our proposed method is being evaluated using both real and semi-synthetic conversation points with high accuracy results. Comparison benchmarks are also presented for comparisons in both datasets. © Copyright 2019 for this paper by its authors.

关键词： Cybersecurity

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Correction to: Non-extensitivity and criticality of atomic hydropathicity around a voltage-gated sodium channel's pore: a modeling study

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Journal of biological physics 2022年第4期48卷 477-478页

作者： Markos N Xenakis Dimos Kapetis Yang Yang Jordi Heijman Stephen G Waxman Giuseppe Lauria Catharina G Faber Hubert J Smeets Patrick J Lindsey Ronald L Westra Department of Toxicogenomics Section Clinical Genomics Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. mrksxenakis@***. Research School for Mental Health and Neuroscience (MHeNS) Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. mrksxenakis@***. Neuroalgology Unit Fondazione IRCCS Istituto Neurologico "Carlo Besta" Via Celoria 11 20133 Milan Italy. Department of Medicinal Chemistry and Molecular Pharmacology Purdue University College of Pharmacy IN 47907 West Lafayette USA. Purdue Institute for Integrative Neuroscience IN 47907 West Lafayette USA. Department of Cardiology CARIM School for Cardiovascular Diseases Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. Department of Neurology and Center for Neuroscience and Regeneration Research Yale University School of Medicine New Haven CT 06510 USA. Rehabilitation Research Center Veterans Affairs Connecticut Healthcare System West Haven CT 06516 USA. Department of Biomedical and Clinical Sciences "Luigi Sacco" University of Milan Via G.B. Grassi 74 20157 Milan Italy. Department of Neurology Maastricht University Medical Center PO Box 5800 6202 AZ Maastricht The Netherlands. Department of Toxicogenomics Section Clinical Genomics Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. Research School for Mental Health and Neuroscience (MHeNS) Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. Research School for Oncology and Developmental Biology (GROW) Maastricht University PO Box 616 6200 MD Maastricht The Netherlands. Department of Data Science and Knowledge Engineering Maastricht University PO Box 616 6200 MD Maastricht The Netherlands.

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Deep learning - A first meta-survey of selected reviews across scientific disciplines, their commonalities, challenges and research impact

arXiv

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arXiv 2020年

作者： Egger, Jan Pepe, Antonio Gsaxner, Christina Jin, Yuan Li, Jianning Kern, Roman Institute of Computer Graphics and Vision Faculty of Computer Science and Biomedical Engineering Graz University of Technology Graz Austria Computer Algorithms for Medicine Laboratory Graz Austria Department of Oral and Maxillofacial Surgery Medical University of Graz Graz Austria University Medicine Essen Essen Germany Research Center for Connected Healthcare Big Data Zhejiang Lab Zhejiang Hangzhou China Research Unit Experimental Neurotraumatology Department of Neurosurgery Medical University of Graz Graz Austria Knowledge Discovery Know-Center Graz Austria Institute of Interactive Systems and Data Science Graz University of Technology Graz Austria

Deep learning belongs to the field of artificial intelligence, where machines perform tasks that typically require some kind of human intelligence. Deep learning tries to achieve this by drawing inspiration from the learning of a human brain. Similar to the basic structure of a brain, which consists of (billions of) neurons and connections between them, a deep learning algorithm consists of an artificial neural network, which resembles the biological brain structure. Mimicking the learning process of humans with their senses, deep learning networks are fed with (sensory) data, like texts, images, videos or sounds. These networks outperform the state-of-the-art methods in different tasks and, because of this, the whole field saw an exponential growth during the last years. This growth resulted in way over 10,000 publications per year in the last years. For example, the search engine PubMed alone, which covers only a sub-set of all publications in the medical field, provides already over 11,000 results in Q3 2020 for the search term 'deep learning', and around 90% of these results are from the last three years. Consequently, a complete overview over the field of deep learning is already impossible to obtain and, in the near future, it will potentially become difficult to obtain an overview over a subfield. However, there are several review articles about deep learning, which are focused on specific scientific fields or applications, for example deep learning advances in computer vision or in specific tasks like object detection. With these surveys as a foundation, the aim of this contribution is to provide a first high-level, categorized meta-survey of selected reviews on deep learning across different scientific disciplines and outline the research impact that they already have during a short period of time. The categories (computer vision, language processing, medical informatics and additional works) have been chosen according to the underlying data sources (image,

关键词： Surveys

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Culture versus Policy: More Global Collaboration to Effectively Combat COVID-19

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The Innovation 2020年第2期1卷 15-16页

作者： Jianping Li Kun Guo Enrique Herrera Viedma Heesoek Lee Jiming Liu Ning Zhong Luiz Flavio Autran Monteiro Gomes Florin Gheorghe Filip Shu-Cherng Fang Mujgan SagirÖzdemir Xiaohui Liu Guoqing Lu Yong Shi School of Economics and Management University of Chinese Academy of SciencesBeijing 100190China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of SciencesBeijing 100190China Research Center on Fictitious Economy&Data Science Chinese Academy of SciencesBeijing 100190China Department of Computer Science and Artificial Intelligence E.T.S.de Ingenieria Informatica y de TelecomunicacionesUniversity of Granada18071 GranadaSpain Department of Information Management Korea Advanced Institute of Science and TechnologySeoul 207-43 Korea Department of Computer Science and HKBU-CSD&NIPD Joint Research Laboratory for Intelligent Disease Surveillance and Control Hong Kong Baptist UniversityHong KongChina Department of Life Science and Informatics Maebashi Institute of TechnologyMaebashi 371-0816Japan Ibmec University Center Av.Presidente Wilson118Office#111020030-020 Rio de JaneiroBrazil The Romanian Academy Bucharest010071Romania Industrial and Systems Engineering Department North Carolina State UniversityRaleighNC 27695USA Department of Industrial Engineering Eskisehir Osmangazi University26480 EskisehirTurkey Department of Computer Science Brunel University LondonLondonUB83PHUK Department of Biology and School of Interdisciplinary Informatics University of Nebraska at OmahaOmahaNE 68182USA College of Information Science and Technology University of Nebraska at OmahaOmahaNE 68182USA

The outbreak of COVID-19 seriously challenges every government with regard to capacity and management of public health systems facing the catastrophic *** and anti-epidemic policy do not necessarily conflict with each *** countries and governments should be more tolerant to each other in seeking cultural and political consensus to overcome this historically tragic pandemic together.

关键词： seeking overcome policy

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A Comparative Study of Two Complex Ontologies in Air Traffic Management

A Comparative Study of Two Complex Ontologies in Air Traffic...

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Conference on Digital Avionics Systems (DASC)

作者： Eduard Gringinger Richard M. Keller Audun Vennesland Christoph G. Schuetz Bernd Neumayr Corporate Research Frequentis AG Vienna Austria Intelligent Systems Division NASA Ames Research Center Moffet Field CA USA Software Engineering Safety and Security SINTEF Trondheim Norway Institute of Business Informatics - Data & Knowledge Engineering Johannes Kepler University Linz Linz Austria

ISBN: (数字)9781728106496

ISBN: (纸本)9781728114972

Over the past 25 years, multiple different data models have been introduced to standardize information management and facilitate data exchange and integration in the aviation domain. As a next step in the evolution of aviation data management, ontologies capturing the semantics (concepts, properties, and relationships) have been produced based on those models. In this paper, we describe a study comparing two recently released and independently-developed complex ontologies focused on Air Traffic Management (ATM) - the NASA ATM Ontology and an ontology derived from the ATM Information Reference Model. We develop a methodology for manually comparing two ontologies and identifying what we describe as exact, light, and mismatches between concepts in the two ontologies. We also describe a classification scheme that characterizes mismatches in terms of the general reason for the mismatch. This approach can be applied to improve existing ATM ontologies and foster interoperability, which will benefit aviation stakeholders.

关键词： Information Exchange Models ATM Information Reference Model NASA ATM Ontology Ontology Matching Ontology Air traffic management Automatic Teller Machines classification scheme MISMATCH aviation data exchange data models Information management

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