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Harmonized-Multinational qEEG norms (HarMNqEEG)

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NEUROIMAGE 2022年 256卷 119190-119190页

作者： Li, Min Wang, Ying Lopez-Naranjo, Carlos Hu, Shiang Reyes, Ronaldo Cesar Garcia Paz-Linares, Deirel Areces-Gonzalez, Ariosky Hamid, Aini Ismafairus Abd Evans, Alan C. Savostyanov, Alexander N. Calzada-Reyes, Ana Villringer, Arno Tobon-Quintero, Carlos A. Garcia-Agustin, Daysi Yao, Dezhong Dong, Li Aubert-Vazquez, Eduardo Reza, Faruque Razzaq, Fuleah Abdul Omar, Hazim Abdullah, Jafri Malin Galler, Janina R. Ochoa-Gomez, John F. Prichep, Leslie S. Galan-Garcia, Lidice Morales-Chacon, Lilia Valdes-Sosa, Mitchell J. Trondle, Marius Zulkifly, Mohd Faizal Mohd Rahman, Muhammad Riddha Bin Abdul Milakhina, Natalya S. Langer, Nicolas Rudych, Pavel Koenig, Thomas Virues-Alba, Trinidad A. Lei, Xu Bringas-Vega, Maria L. Bosch-Bayard, Jorge F. Valdes-Sosa, Pedro Antonio [a]The Clinical Hospital of Chengdu Brain Science Institute MOE Key Lab for Neuroinformation School of Life Science and Technology University of Electronic Science and Technology of China Chengdu China [b]Cuban Center for Neurocience La Habana Cuba [c]McGill Centre for Integrative Neuroscience Ludmer Centre for Neuroinformatics and Mental Health Montreal Neurological Institute Canada [d]Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [e]Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [f]Hospital Universiti Sains Malaysia Universiti Sains Malaysia Health Campus Kota Bharu Kelantan 16150 Malaysia [g]Humanitarian Institute Novosibirsk State University Novosibirsk 630090 Russia [h]Laboratory of Psychophysiology of Individual Differences Federal State Budgetary Scientific Institution Scientific Research Institute of Neurosciences and Medicine Novosibirsk 630117 Russia [i]Laboratory of Psychological Genetics at the Institute of Cytology and Genetics Siberian Branch of the Russian Academy of Sciences Novosibirsk 630090 Russia [j]University of Pinar del Río “Hermanos Saiz Montes de Oca” Pinar del Río Cuba [k]Department of Neurology Max Planck Institute for Human Cognitive and Brain Sciences Leipzig Germany [l]Department of Cognitive Neurology University Hospital Leipzig Leipzig Germany [m]Center for Stroke Research Charité-Universitätsmedizin Berlin Berlin Germany [n]Grupo Neuropsicología y Conducta - GRUNECO Faculty of Medicine Universidad de Antioquia Colombia [o]Research Department Institución Prestadora de Servicios de Salud IPS Universitaria Colombia [p]The Cuban center aging longevity and health Havana Cuba [q]Research Unit of NeuroInformation Chinese Academy of Medical Sciences Chengdu 2019RU035 China [r]School of Electrical Engineering Zhengzhou University Zhengzhou 4500

This paper extends frequency domain quantitative electroencephalography (qEEG) methods pursuing higher sensitivity to detect Brain Developmental Disorders. Prior qEEG work lacked integration of cross-spectral information omitting important functional connectivity descriptors. Lack of geographical diversity precluded accounting for site-specific variance, increasing qEEG nuisance variance. We ameliorate these weaknesses. (i) Create lifespan Riemannian multinational qEEG norms for cross-spectral tensors. These norms result from the HarMNqEEG project fostered by the Global Brain Consortium. We calculate the norms with data from 9 countries, 12 devices, and 14 studies, including 1564 subjects. Instead of raw data, only anonymized metadata and EEG cross spectral tensors were shared. After visual and automatic quality control, developmental equations for the mean and standard deviation of qEEG traditional and Riemannian DPs were calculated using additive mixed-effects models. We demonstrate qEEG "batch effects " and provide methods to calculate harmonized z-scores. (ii) We also show that harmonized Riemannian norms produce z-scores with increased diagnostic accuracy predicting brain dysfunction produced by malnutrition in the first year of life and detecting COVID induced brain dysfunction. (iii) We offer open code and data to calculate different individual z-scores from the HarMNqEEG dataset. These results contribute to developing bias-free, low-cost neuroimaging technologies applicable in various health settings.

关键词： Quantitative EEG EEG cross-spectrum Riemannian geometry Batch effects Z-score Harmonization Malnutrition Covid induced brain dysfunction Developmental Brain Chart

Guiding students towards an understanding of the electromotive force concept in electromagnetic phenomena through a teaching-learning sequence

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Physical Review Physics Education Research 2020年第2期16卷 020110-020110页

作者： Kristina Zuza Mieke De Cock Paul van Kampen Thomas Kelly Jenaro Guisasola Department of Applied Physics & DoPER University of the Basque Country (UPV/EHU) San Sebastian E-20018 Spain Department of Physics and Astronomy & LESEC KU Leuven Leuven B-3000 Belgium Centre for the Advancement of STEM Teaching and Learning & School of Physical Sciences Dublin City University (dcU) Dublin 9 Ireland Department of Computer Science & Applied Physics School of Science & Computing Galway-Mayo Institute of Technology Galway Ireland

In this work we present the application of design based research (DBR) methodology to conduct a systematic iterative study of the design and implementation of a teaching-learning sequence (TLS) on emf (electromotive force). This work is the final part of a broader study that started with the analysis of students’ difficulties with emf in the contexts of transitory current, direct current, and electromagnetic induction. We complete our research by investigating to what extent students see emf as a cross-cutting concept. We establish the design parameters based on an epistemological analysis and the empirical data, and describe how we devised and implemented the first version of the TLS that takes 4.5 h of classroom time. We analyze the data obtained in the first implementation and describe the redesign process that resulted in a second version of the TLS that takes 1.5 h of classroom time. We conclude that both versions of the TLS effect a similar boost in student attainment of learning objectives compared to the control group.

关键词： Concepts & principles Epistemology, attitudes, & beliefs Instructional materials development Scientific reasoning & problem solving

Decision support system for diagnosing Rheumatic-Musculoskeletal Disease using fuzzy cognitive map technique

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Informatics in Medicine Unlocked 2020年 18卷

作者： Akinnuwesi, Boluwaji A. Adegbite, Blessing A. Adelowo, Femi Ima-Edomwonyi, U. Fashoto, Gbenga Amumeji, Olaseni T. Department of Computer Science Faculty of Science Lagos State University Ojo Lagos State Nigeria ICT Division Planning and Technology Transfer Information Management (PTTIM) Federal Institute of Industrial Research Oshodi (FIIRO) Lagos State Nigeria Consultant Rheumatologist & Head of Rheumatology Unit Department of Medicine Lagos State University Teaching Hospital (LASUTH) Ikeja Lagos State Nigeria Consultant Rheumatologist Department of Medicine Lagos University Teaching Hospital (LUTH) Idi-Araba Lagos State Nigeria Department of Computer Science University of Eswatini Kwaluseni M201 Eswatini Swaziland Department of Mathematical Sciences Faculty of Science Ondo State University of Science & Technology Okitipupa Ondo State Nigeria

Rheumatic-Musculoskeletal Disease (RMD) is a leading cause of disability worldwide. It causes inflammation of the connecting body structures, affects joints, tendons, ligaments, bones, and muscles, and is responsible for thousands of deaths per year worldwide. It is prevalent in Africa. In Nigeria, RMD constitutes 10–15% of rheumatology cases in most clinics, with a ratio of 2.4:1 (Female:Male), killing over 1652 people per year. The similarity of its symptoms with other diseases often cause misdiagnosis at an early stage among infected patients, and existing computational techniques used for diagnosis cannot address the confusability of the symptoms. Hence, there is an inability to determine the causality of symptoms. Moreover, a dearth of Rheumatologists prevents many RMD infected persons from obtaining an early and accurate diagnosis. Therefore, the need arises to develop a decision support system (DSS) for diagnosing RMD, such as by using a fuzzy cognitive map (FCM) technique. Our study focuses on the development and implementation of FCM-based DSS for RMD diagnosis (RMD-FCMDSS). RMD-FCMDSS serves as a software tool complementing physician decision-making. Our evaluation of RMD-FCMDSS suggests that it has an improved diagnostic value as compared to earlier traditional and conventional medical methods. Performance results indicate an 87% accuracy, 90% sensitivity, and 80% specificity. RMD-FCMDSS was tested on limited data due to a dearth of Rheumatologists in Nigeria and a limited population of rheumatic patients interacted with;thus in future work we shall need to increase the test sample. Hybridization of Fuzzy-Logic and Cognitive Mapping techniques helps to guarantee accuracy, specificity, sensitivity, and also speed diagnosis. © 2019

关键词： Causality Confusability Decision support system Fuzzy cognitive map Rheumatic-musculoskeletal disease

Groups, platonic solids and bell inequalities

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arXiv 2020年

作者： Bolonek-Lasoń, Katarzyna Kosiński, Piotr Department of Statistical Methods Faculty of Economics and Sociology University of Lodz 41/43 Rewolucji 1905 St. Lodz90-214 Poland Department of Computer Science Faculty of Physics and Applied Informatics University of Lodz 149/153 Pomorska St. Lodz90-236 Poland

The construction of Bell inequalities based on Platonic and Archimedean solids (Quantum 4 (2020), 293) is generalized to the case of orbits generated by the action of some finite groups. A number of examples with cons...

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A DG-IMEX method for two-moment neutrino transport: Nonlinear solvers for neutrino-matter coupling

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arXiv 2021年

作者： Laiu, M. Paul Endeve, Eirik Chu, Ran Harris, J. Austin Messer, O.E. Bronson Multiscale Methods Group Computer Science and Mathematics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Physics and Astronomy University of Tennessee Knoxville KnoxvilleTN37996 United States National Center for Computational Sciences Oak Ridge National Laboratory Oak RidgeTN37831 United States Physics Division Oak Ridge National Laboratory Oak RidgeTN37831 United States

Neutrino-matter interactions play an important role in core-collapse supernova (CCSN) explosions as they contribute to both lepton number and/or four-momentum exchange between neutrinos and matter, and thus act as the agent for neutrino-driven explosions. Due to the multiscale nature of neutrino transport in CCSN simulations, an implicit treatment of neutrino-matter interactions is desired, which requires solutions of coupled nonlinear systems in each step of the time integration scheme. In this paper we design and compare nonlinear iterative solvers for implicit systems with energy coupling neutrino-matter interactions commonly used in CCSN simulations. Specifically, we consider electron neutrinos and antineutrinos, which interact with static matter configurations through the Bruenn 85 opacity set. The implicit systems arise from the discretization of a non-relativistic two-moment model for neutrino transport, which employs the discontinuous Galerkin (DG) method for phase-space discretization and an implicit-explicit (IMEX) time integration scheme. In the context of this DG-IMEX scheme, we propose two approaches to formulate the nonlinear systems - a coupled approach and a nested approach. For each approach, the resulting systems are solved with Anderson-accelerated fixed-point iteration and Newton's method. The performance of these four iterative solvers has been compared on relaxation problems with various degree of collisionality, as well as proto-neutron star deleptonization problems with several matter profiles adopted from spherically symmetric CCSN simulations. Numerical results suggest that the nested Anderson-accelerated fixed-point solver is more efficient than other tested solvers for solving implicit nonlinear systems with energy coupling neutrino-matter interactions. Copyright © 2021, The Authors. All rights reserved.

关键词： Galerkin methods

The COVID-19 Data Exchange Platform of the German University Medicine 32

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The COVID-19 Data Exchange Platform of the German University...

32nd Medical Informatics Europe Conference, MIE 2022

作者： Prokosch, Hans-Ulrich Bahls, Thomas Bialke, Martin Eils, Jürgen Fegeler, Christian Gruendner, Julian Haarbrandt, Birger Hampf, Christopher Hoffmann, Wolfgang Hund, Hauke Kampf, Marvin Kapsner, Lorenz A. Kasprzak, Piotr Kohlbacher, Oliver Krefting, Dagmar Mang, Jonathan M. Marschollek, Michael Mate, Sebastian Müller, Armin Prasser, Fabian Sass, Julian Semler, Sebastian Stenzhorn, Holger Thun, Sylvia Zenker, Sven Eils, Roland Chair of Medical Informatics Friedrich-Alexander-Universität Erlangen-Nürnberg Wetterkreuz 15 Erlangen91058 Germany Institute for Community Medicine University Medicine Greifswald Greifswald Germany Berlin Institute of Health at Charité-Universitätsmedizin Berlin Berlin Germany Gecko Institute Heilbronn University of Applied Sciences Heilbronn Germany Peter L. Reichertz Institute for Medical Informatics of Tu Braunschweig and Hannover Medical School Hannover Germany Medical Center for Information and Communication Technology Universitätsklinikum Erlangen Erlangen Germany Institute for Translational Bioinformatics University Medical Center Tübingen Germany Department of Medical Informatics University Medical Center Göttingen Göttingen Germany Tmf-Technology Methods and Infrastructure for Networked Medical Research Berlin Germany Institute for Medical Biometry Epidemiology und Medical Informatics Saarland University Medical Center Homburg Germany Commercial DirectorateInstitute for Medical Biometry Informatics & EpidemiologyDepartment of Anesthesiology and Intensive Care Medicine University Hospital Bonn Bonn Germany Institute for Bioinformatics and Medical Informatics University of Tübingen Tübingen Germany Department of Computer Science University of Tübingen Tübingen Germany Gesellschaft für Wissenschaftliche Datenverarbeitung MbH Göttingen Germany

ISBN: (纸本)9781643682846

COVID-19 has challenged the healthcare systems worldwide. To quickly identify successful diagnostic and therapeutic approaches large data sharing approaches are inevitable. Though organizational clinical data are abundant, many of them are available only in isolated silos and largely inaccessible to external researchers. To overcome and tackle this challenge the university medicine network (comprising all 36 German university hospitals) has been founded in April 2020 to coordinate COVID-19 action plans, diagnostic and therapeutic strategies and collaborative research activities. 13 projects were initiated from which the CODEX project, aiming at the development of a Germany-wide Covid-19 Data Exchange Platform, is presented in this publication. We illustrate the conceptual design, the stepwise development and deployment, first results and the current status. © 2022 European Federation for Medical Informatics (EFMI) and IOS Press.

关键词： Electronic data interchange

Evolution of AI in Education: Agentic Workflows

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arXiv 2025年

作者： Kamalov, Firuz Calonge, David Santandreu Smail, Linda Azizov, Dilshod Thadani, Dimple R. Kwong, Theresa Atif, Amara Department of Electrical Engineering Canadian University Dubai Dubai United Arab Emirates Centre for Teaching and Learning Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates College of Interdisciplinary Studies Zayed University Dubai United Arab Emirates Mohamed bin Zayed University of Artificial Intelligence Abu Dhabi United Arab Emirates Department of Management Marketing and Information Systems Hong Kong Baptist University Hong Kong Centre for Holistic Teaching and Learning Hong Kong Baptist University Hong Kong School of Computer Science University of Technology Sydney Sydney Australia

Artificial intelligence (AI) has transformed various aspects of education, with large language models (LLMs) driving advancements in automated tutoring, assessment, and content generation. However, conventional LLMs are constrained by their reliance on static training data, limited adaptability, and lack of reasoning. To address these limitations and foster more sustainable technological practices, AI agents have emerged as a promising new avenue for educational innovation. In this review, we examine agentic workflows in education according to four major paradigms: reflection, planning, tool use, and multi-agent collaboration. We critically analyze the role of AI agents in education through these key design paradigms, exploring their advantages, applications, and challenges. To illustrate the practical potential of agentic systems, we present a proof-of-concept application: a multi-agent framework for automated essay scoring. Preliminary results suggest this agentic approach may offer improved consistency compared to stand-alone LLMs. Our findings highlight the transformative potential of AI agents in educational settings while underscoring the need for further research into their interpretability, trustworthiness, and sustainable impact on pedagogical impact. Copyright © 2025, The Authors. All rights reserved.

关键词： teaching

ST-ONCODIAG: A semantic rule-base approach to diagnosing breast cancer base on Wisconsin datasets

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Informatics in Medicine Unlocked 2018年 10卷 117-125页

作者： Oyelade, O.N. Obiniyi, A.A. Junaidu, S.B. Adewuyi, S.A. Department of Computer Science Faculty of Physical Science Ahmadu Bello University Zaria Nigeria Department of Radiotheraphy and Oncology Ahmadu Bello University Teaching Hospital Zaria Nigeria

Breast cancer is a major terminal disease that occurs largely among females. This disease stems from abnormal mutations in the genes of normal cells, thereby resulting in development of cancerous cells. Though there have being several research breakthroughs in the field of medicine in taming this disease, however, computer aided diagnosis on the other hand has proven very supportive in the quest. Techniques such as Machine Learning (ML) and Medical Expert Systems (MES) algorithms have added impetus to the use of artificial intelligence in detecting and diagnosing breast cancer. While MES may seem promising in machine based diagnostic systems, their accuracy is often impaired by inefficient medical reasoning algorithms employed. This paper therefore seeks to address the limitation of one such reasoning algorithm known as Select and Test (ST). The approach in this paper is to first create an efficient input mechanism that enables the system to read, filter and clean input from datasets. Secondly, semantic web languages (ontologies and rule languages) were used to create a coordinated rule set and a knowledge representation framework was created to aid the reasoning algorithm. As a result, the reasoning structures of ST were modified to accommodate this enhancement. Thereafter, the input generating mechanism was used to transform instances of the databases of Breast Cancer Wisconsin Data set retrieved from UCI Learning Repository. The generated inputs were passed into the improved ST algorithm to diagnose breast cancer in patients captured in the datasets. Experiments were carried out, and result show that 26.60%, 56.17%, and 54.05% were diagnosed of breast cancer in Wisconsin Breast Cancer Database (WBCD), Wisconsin Diagnostic Breast Cancer (WDBC), and Wisconsin Prognostic Breast Cancer (WPBC) respectively. © 2017

关键词： Attributes Breast cancer Diagnoses Instances Reasoning algorithm Select and test

If you cheat, I cheat: Cheating on a collaborative task with a social robot

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arXiv 2021年

作者： Ayub, Ali Hu, Huiqing Zhou, Guangwei Fendley, Carter Ramsay, Crystal Jackson, Kathy Lou Wagner, Alan R. Department of Electrical Engineering Pennsylvania State University State CollegePA16802 United States Pennsylvania State University State CollegePA16802 United States Department of Robotics Johns Hopkins University BaltimoreMD21218 United States Department of Computer Science Pennsylvania State University State CollegePA16802 United States Teaching and Learning with Technology Pennsylvania State University State CollegePA16802 United States Department of Aerospace Engineering Pennsylvania State University State CollegePA16802 United States

Robots may soon play a role in higher education by augmenting learning environments and managing interactions between instructors and learners. Little, however, is known about how the presence of robots in the learning environment will influence academic integrity. This study therefore investigates if and how college students cheat while engaged in a collaborative sorting task with a robot. We employed a 2x2 factorial design to examine the effects of cheating exposure (exposure to cheating or no exposure) and task clarity (clear or vague rules) on college student cheating behaviors while interacting with a robot. Our study finds that prior exposure to cheating on the task significantly increases the likelihood of cheating. Yet, the tendency to cheat was not impacted by the clarity of the task rules. These results suggest that normative behavior by classmates may strongly influence the decision to cheat while engaged in an instructional experience with a robot. © 2021, CC BY.

关键词： Robots