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Evaluating the impact of COVID-19 pandemic lockdown on education in Nigeria: Insights from teachers and students on virtual/online learning

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Bulletin of the National Research Centre 2021年第1期45卷 76页

作者： Osamudiamen Ebohon Anayochukwu Chukwunonso Obienu Francis Irabor Frank Iwebuke Amadin Ehimwenma Sheena Omoregie Biochemistry Program Department of Biological and Chemical Sciences Faculty of Natural and Applied Sciences Michael and Cecilia Ibru University P.M.B. 100 Agbarha-Otor Delta State Nigeria. Department of Information Technology Bayelsa Medical University Yenagoa Bayelsa State Nigeria. Department of Computer Science University of Benin P.M.B 1154 Benin City Nigeria. Malaria Research Molecular Biology and Toxicology Unit Department of Biochemistry Faculty of Life Sciences University of Benin P.M.B 1154 Benin City Nigeria.

BACKGROUND:As in many countries worldwide, due to the COVID-19 pandemic lockdown, higher institutions in Nigeria closed in March 2020 and only began reopening in October of the same year. As a result of the lockdown, some higher institutions had to quickly move from the traditional face-to-face teaching method to virtual learning. This study aimed to investigate the impact of COVID-19 lockdown on education in Nigeria and also provide recommendations that may be useful in developing remote teaching contingency strategies. Five-point Likert-scale questionnaire targeting students and teachers separately was designed to get feedbacks from both students and teachers on their experiences, issues and successes. The questionnaires were divided into five categories: virtual classrooms, course learning outcomes, alternative method of assessment, impact of online teaching and satisfaction. RESULTS:A total of 703 students and 60 teachers from five different local universities participated in this study. All participants (> 50%) had difficulties with Internet connection. Students (67%) as well as teachers (59%) agree that they had limited interactions with one another and this negatively influence student's satisfaction ( < 0.01). While students were split on the most appropriate method of assessment, teachers (63%) believe assignments and oral examinations are more suitable for online teaching. Many teachers (66%) admitted that it was difficult assessing students' abilities and performance. Some students (> 40%) were concerned about the number of assignments given. Most teachers (84%) believe there is an increase in tendency for examination malpractice when assessment was conducted virtually. Students had significantly ( < 0.05) higher marks in all courses during online assessment compared to previous session involving face-to-face teaching. About 83% of teachers admitted it was difficult explaining complex scientific concepts. CONCLUSION:Based on the results of this study, we

关键词： COVID-19 Coronavirus Education Lockdown Nigeria Online teaching Pandemic Survey Virtual learning

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Lithium-Ion Batteries: Operando Magnetometry Probing the Charge Storage Mechanism of CoO Lithium-Ion Batteries (Adv. Mater. 12/2021)

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Advanced Materials 2021年第12期33卷

作者： Hongsen Li Zhengqiang Hu Qingtao Xia Hao Zhang Zhaohui Li Huaizhi Wang Xiangkun Li Fengkai Zuo Fengling Zhang Xiaoxiong Wang Wanneng Ye Qinghao Li Yunze Long Qiang Li Shishen Yan Xiaosong Liu Xiaogang Zhang Guihua Yu Guo-Xing Miao College of Physics Center for Marine Observation and Communications Qingdao University Qingdao 266071 China School of Physics State Key Laboratory of Crystal Materials Shandong University Jinan 250100 China State Key Laboratory of Functional Materials for Informatics Shanghai Institute of Microsystem and Information Technology (SIMIT) Chinese Academy of Sciences Shanghai 200050 China College of Material Science and Engineering Nanjing University of Aeronautics and Astronautics Nanjing 210016 China Materials Science and Engineering Program and Department of Mechanical Engineering The University of Texas at Austin Austin TX 78712 USA Department of Electrical and Computer Engineering & Institute for Quantum Computing University of Waterloo Waterloo ON N2L 3G1 Canada

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Eavesdropper’s ability to attack a free-space quantum-key-distribution receiver in atmospheric turbulence

arXiv

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arXiv 2019年

作者： Chaiwongkhot, Poompong Kuntz, Katanya B. Zhang, Yanbao Huang, Anqi Bourgoin, Jean-Philippe Sajeed, Shihan Lütkenhaus, Norbert Jennewein, Thomas Makarov, Vadim Institute for Quantum Computing University of Waterloo WaterlooONN2L 3G1 Canada Department of Physics and Astronomy University of Waterloo WaterlooONN2L 3G1 Canada NTT Basic Research Laboratories NTT Corporation AtsugiKanagawa Japan NTT Research Center for Theoretical Quantum Physics NTT Corporation AtsugiKanagawa Japan Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer National University of Defense Technology Changsha410073 China Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada Aegis Quantum WaterlooON Canada Department of Electrical and Computer Engineering University of Toronto M5S 3G4 Canada Quantum Information Science Program Canadian Institute for Advanced Research TorontoONM5G 1Z8 Canada Russian Quantum Center Skolkovo Moscow143025 Russia Shanghai Branch National Laboratory for Physical Sciences at Microscale and CAS Center for Excellence in Quantum Information University of Science and Technology of China Shanghai201315 China NTI Center for Quantum Communications National University of Science and Technology MISiS Moscow119049 Russia

The ability of an eavesdropper (Eve) to perform an intercept-resend attack on a free-space quantum key distribution (QKD) receiver by precisely controlling the incidence angle of an attack laser has been previously demonstrated. However, such an attack could be ineffective in the presence of atmospheric turbulence due to beam wander and spatial mode aberrations induced by the air’s varying index of refraction. We experimentally investigate the impact turbulence has on Eve’s attack on a free-space polarization-encoding QKD receiver by emulating atmospheric turbulence with a spatial light modulator. Our results identify how well Eve would need to compensate for turbulence to perform a successful attack by either reducing her distance to the receiver, or using beam wavefront correction via adaptive optics. Furthermore, we use an entanglement-breaking scheme to find a theoretical limit on the turbulence strength that hinders Eve’s attack. Copyright © 2019, The Authors. All rights reserved.

关键词： Atmospheric turbulence

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Assessing the Potential of GPT-4 to Annotate Medical Notes Across Different Languages and Countries: A Model Evaluation Study

SSRN

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SSRN 2024年

作者： Saad Menezes, Maria Clara Hoffman, Alexander F. Tan, Amelia L.M. Nalbandyan, Mariné Omenn, Gilbert S. Mazzotti, Diego R. Hernández-Arango, Alejandro Visweswaran, Shyam Venkatesh, Shruthi Mandl, Kenneth D. Bourgeois, Florence T. Lee, James W.K. Makmur, Andrew Hanauer, David A. Semanik, Michael G. Kerivan, Lauren T. Hill, Terra Forero, Julian Restrepo Castro, Carlos Andrés C.R. Vigna, Matteo Ceriana, Piero Abu-El-rub, Noor Avillach, Paul Bellazzi, Riccardo Callaci, Thomas Gutiérrez-Sacristán, Alba Malovini, Alberto Mathew, Jomol P. Morris, Michele Murthy, Venkatesh Parimbelli, Enea Patel, Lav P. Sáez, Carlos Samayamuthu, Malarkodi Jebathilagam Thompson, Jeffrey A. Tibollo, Valentina Xia, Zongqi Kohane, Isaac S. Harvard Medical School Department of Biomedical Informatics 10 Shattuck Street BostonMA02115 United States School of Medicine and Public Health University of Wisconsin-Madison Office of Informatics and Information Technology 750 Highland Ave MadisonWI53705 United States University of Michigan Department of Computational Medicine & Bioinformatics Internal Medicine Human Genetics Environmental Health 100 Washtenaw Avenue Ann ArborMI48109 United States University of Kansas Medical Center Division of Medical Informatics Division of Pulmonary Critical Care and Sleep Medicine Department of Internal Medicine 3901 Rainbow Blvd Kansas CityKS66160 United States University of Antioquia Hospital Alma Máter de Antioquia Department of Internal Medicine Calle 64 N°51 D - 154 Medellín Colombia University of Pittsburgh Department of Biomedical Informatics 5607 Baum Blvd PittsburghPA15206 United States University of Pittsburgh Department of Neurology 3501 5th Avenue PittsburghPA15206 United States Boston Children's Hospital Computational Health Informatics Program 300 Longwood Avenue BostonMA02115 United States Boston Children's Hospital Department of Pediatrics 300 Longwood Avenue BostonMA02115 United States National University Health System Department of Surgery 1E Kent Ridge Rd Singapore119228 Singapore National University Health System Department of Diagnostic Imaging 1E Kent Ridge Rd Singapore119228 Singapore University of Michigan Department of Learning Health Sciences NCRC 100 2800 Plymouth Road Ann ArborMI48109 United States University of Kansas Medical Center Department of Surgery 3901 Rainbow Blvd Kansas CityKS66160 United States Istituti Clinici Scientifici Maugeri IRCCS Respiratory rehabilitation unit Via Maugeri 10 Pavia27100 Italy University of Kansas Medical Center 3901 Rainbow Blvd Kansas CityKS66160 United States University of Pavia Department of Electrical Computer and Biomedical Engineering via Ferrata 5 Pavia2710

Background: Physicians invest hours creating patient notes, which are rich in information but difficult for computers to analyze due to their unstructured format. GPT-4 reshaped our ability to process text, yet it is unknown how well this model can handle medical notes. This project aims to compare GPT-4’s ability to annotate medical notes against experienced physicians across three different languages at multiple institutions and countries. Methods: This study included eight sites from four countries - the United States, Colombia, Singapore, and Italy. Each site contributed seven de-identified notes (admission, progress, or consult) from hospitalized patients. GPT-4 assessed each note by answering 14 questions, including demographic information, clinical judgments, data quality, and patients’ eligibility for a hypothetical study enrollment. For validation, two physicians from each site independently evaluated GPT-4's responses. Findings: Overall, 56 medical notes, written in English, Italian, and Spanish, were analyzed. A total of 784 responses from GPT-4 were generated. Both physicians agreed with GPT-4’s response 79% of the time (622/784, 95%CI 76-82%). Only one of the two physicians agreed with GPT-4’s response 10% of the time (82/784, 95%CI 8-13%). Neither physician agreed with GPT-4’s response 10% of the time (80/784, 95%CI 8-13%). Both physicians agreed with GPT-4 more often in notes written in Spanish and Italian than in English, with agreement rates of 88% (86/98, 95%CI 79-93%), 84% (82/98, 95%CI 75-90%), and 77% (454/588, 95%CI 74-80%), respectively. Hallucinations were rare (10/784, 95%CI 0-2%). GPT-4 correctly selected patients for a hypothetical study enrollment based on three criteria 90% of the time (95%CI 81-98%). Interpretation: The findings indicate that GPT-4 annotations demonstrated a high agreement rate with physicians across all languages. We also demonstrate GPT-4's potential to assist in patient selection for studies. Funding: None. Declarati

关键词： Medical computing

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On Solving a class of stochastic multiobjective integer linear programming problems with Interactive Based Approach

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Journal of Physics: Conference Series 2019年第1期1255卷

作者： Suparni Herman Mawengkang Opim Salim Sitompul Saib Suwilo Mathematics Doctorate Program University of Sumatera Utara Medan Indonesia Department of Mathematics University of Sumatera Utara Medan Indonesia Faculty of Computer Science and Information Technology University of Sumatera Utara Medan Indonesia

Decision problems of stochastic or probabilistic optimization arise when certain coefficient of an optimization model are not fixed or known but are instead, to some extent, stochastic(or random or probabilistic) quantities. This paper focused on multiobjective stochastic optimization. We propose a method for solving a multiobjective chance constraints integer programming problem based on interactive approach. We assume that there is randomness in the right-hand sides of the constraints only and that the random variables are normally distributed. Some examples are presented.

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The Design of Automatic Bird Data Capture Systems

The Design of Automatic Bird Data Capture Systems

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IEEE International Conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Chia-Yuan Lin Yi-Chun Lin Che-Jui Chang Chi-Feng Chen Ming-Fong Tsai Department of Information Engineering and Computer Science Feng Chia University Taiwan Information and Communications Research Laboratories Industrial Technology Research Institute Taiwan Industrial Ph.D. Program of Internet of Things Feng Chia University Taiwan Departrnent of Electronic Engineering National United University Taiwan

This paper implements an automatic bird data capture system which uploads data captured from social network websites to the server and stores them in the database. When using the Facebook API to capture data of interest, the system needs to filter the captured data to avoid false data storage. This paper takes building an automatic system that includes reinforcing the training database of the bird community as a top priority.

关键词： Birds Databases Facebook Machine learning Servers System implementation

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QU-BraTS: MICCAI BraTS 2020 Challenge on Quantifying Uncertainty in Brain Tumor Segmentation - Analysis of Ranking Scores and Benchmarking Results

arXiv

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arXiv 2021年

作者： Mehta, Raghav Filos, Angelos Baid, Ujjwal Sako, Chiharu McKinley, Richard Rebsamen, Michael Dätwyler, Katrin Meier, Raphael Radojewski, Piotr Murugesan, Gowtham Krishnan Nalawade, Sahil Ganesh, Chandan Wagner, Ben Yu, Fang F. Fei, Baowei Madhuranthakam, Ananth J. Maldjian, Joseph A. Daza, Laura Gómez, Catalina Arbeláez, Pablo Dai, Chengliang Wang, Shuo Reynaud, Hadrien Mo, Yuanhan Angelini, Elsa Guo, Yike Bai, Wenjia Banerjee, Subhashis Pei, Linmin Murat, A.K. Rosas-González, Sarahi Zemmoura, Ilyess Tauber, Clovis Vu, Minh H. Nyholm, Tufve Löfstedt, Tommy Ballestar, Laura Mora Vilaplana, Veronica McHugh, Hugh Talou, Gonzalo Maso Wang, Alan Patel, Jay Chang, Ken Hoebel, Katharina Gidwani, Mishka Arun, Nishanth Gupta, Sharut Aggarwal, Mehak Singh, Praveer Gerstner, Elizabeth R. Kalpathy-Cramer, Jayashree Boutry, Nicolas Huard, Alexis Vidyaratne, Lasitha Rahman, Md Monibor Iftekharuddin, Khan M. Chazalon, Joseph Puybareau, Elodie Tochon, Guillaume Ma, Jun Cabezas, Mariano Llado, Xavier Oliver, Arnau Valencia, Liliana Valverde, Sergi Amian, Mehdi Soltaninejad, Mohammadreza Myronenko, Andriy Hatamizadeh, Ali Feng, Xue Dou, Quan Tustison, Nicholas Meyer, Craig Shah, Nisarg A. Talbar, Sanjay Weber, Marc-André Mahajan, Abhishek Jakab, Andras Wiest, Roland Fathallah-Shaykh, Hassan M. Nazeri, Arash Milchenko, Mikhail Marcus, Daniel Kotrotsou, Aikaterini Colen, Rivka Freymann, John Kirby, Justin Davatzikos, Christos Menze, Bjoern Bakas, Spyridon Gal, Yarin Arbel, Tal McGill University MontrealQC Canada Group University of Oxford Oxford United Kingdom University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine The University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States University Institute of Diagnostic and Interventional Neuroradiology University of Bern Inselspital Bern University Hospital Bern Switzerland Department of Radiology University of Texas Southwestern Medical Center DallasTX United States Department of Bioengineering University of Texas DallasTX United States Advanced Imaging Research Center University of Texas Southwestern Medical Center DallasTX United States Universidad de los Andes Bogotá Colombia Data Science Institute Imperial College London London United Kingdom NIHR Imperial BRC ITMAT Data Science Group Imperial College London London United Kingdom Department of Brain Sciences Imperial College London London United Kingdom Machine Intelligence Unit Indian Statistical Institute Kolkata India Department of CSE University of Calcutta Kolkata India Department of Information Technology Uppsala University Uppsala Sweden Department of Diagnostic Radiology The University of Pittsburgh Medical Center PittsburghPA United States UMR U1253 iBrain Université de Tours Inserm Tours France Department of Radiation Sciences Umeå University Umeå Sweden Department of Computing Science Umeå University Umeå Sweden Signal Theory and Communications Department Universitat Politècnica de Catalunya Barcelona Tech Barcelona Spain Faculty of Medical and Health Sciences University of Auckland Auckland New Zealand Radiology Department Auckland City Hospital Auckland New Zealand Auckland Bioengineering Institute University of Auckland New Zealand Athinoula A. Martinos Center for Biomedical Imaging Department of Radiology

Deep learning (DL) models have provided state-of-the-art performance in various medical imaging benchmarking challenges, including the Brain Tumor Segmentation (BraTS) challenges. However, the task of focal pathology multi-compartment segmentation (e.g., tumor and lesion sub-regions) is particularly challenging, and potential errors hinder translating DL models into clinical workflows. Quantifying the reliability of DL model predictions in the form of uncertainties could enable clinical review of the most uncertain regions, thereby building trust and paving the way toward clinical translation. Several uncertainty estimation methods have recently been introduced for DL medical image segmentation tasks. Developing scores to evaluate and compare the performance of uncertainty measures will assist the end-user in making more informed decisions. In this study, we explore and evaluate a score developed during the BraTS 2019 and BraTS 2020 task on uncertainty quantification (QU-BraTS) and designed to assess and rank uncertainty estimates for brain tumor multi-compartment segmentation. This score (1) rewards uncertainty estimates that produce high confidence in correct assertions and those that assign low confidence levels at incorrect assertions, and (2) penalizes uncertainty measures that lead to a higher percentage of under-confident correct assertions. We further benchmark the segmentation uncertainties generated by 14 independent participating teams of QU-BraTS 2020, all of which also participated in the main BraTS segmentation task. Overall, our findings confirm the importance and complementary value that uncertainty estimates provide to segmentation algorithms, highlighting the need for uncertainty quantification in medical image analyses. Finally, in favor of transparency and reproducibility, our evaluation code is made publicly available at https://***/RagMeh11/QU-BraTS. © 2021, CC BY.

关键词： Benchmarking

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Modeling on clinical group decision support system for screening and working diagnosis acute respiratory infections

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IOP Conference Series: Materials science and Engineering 2019年第1期508卷

作者： Muhammad Syaukani Sri Hartati Hari Kusnanto Suryo Guritno Study Program of Information Technology STMIK Indonesia Banjarmasin Indonesia Dept. of Computer Science and Electronics Faculty of Mathematics and Natural Sciences Universitas Gadjah Mada Yogyakarta Indonesia Study Program of Publich Health Interests Health Management Information System Faculty of Medicine Universitas Gadjah Mada Yogyakarta Indonesia

There are cases of patients with Acute Respiratory Infection (ARI) which are not handled quickly and appropriately at emergency department (ED), thus there should be a medium that can assist medical personnel in making decisions. This research proposes the development model of Clinical Group Decision Support Systems (CGDSS) named KLISPA models. KLISPA model can accommodate a screening process to the working diagnosis process of ARI by integrating Case Based Reasoning (CBR) and Groups Decision Support System (GDSS). Screening process completed by CBR using Nearest Neighbor method. This process helps medical personnel such as nurses, co asst doctors, and general practitioners to do screening to patients for determining the early solution to the disease. Meanwhile, the working diagnosis solved by GDSS uses the methods of Eckenrode, Extended TOPSIS and BoostVote. This process accommodates the needs of joint decision-making which involves groups of Decision Maker (DM) consisting of pediatrician, child lung specialist doctor, and the radiologist doctor.

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Using a Recurrent Neural Network Model for Classification of Tweets Conveyed Influenza-related information 1

Using a Recurrent Neural Network Model for Classification of...

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1st International Workshop on Digital Disease Detection using Social Media, DDDSM 2017, co-located with the 8th International Joint Conference on Natural Language Processing, IJCNLP 2017

作者： Wang, Chen-Kai Singh, Onkar Tang, Zhao-Li Dai, Hong-Jie Graduate Institute of Biomedical Informatics Taipei Medical University Taipei Taiwan Bioinformatics Program Taiwan International Graduate Program Institute of Information Science Academia Sinica Taipei Taiwan Institute of Biomedical Informatics National Yang-Ming University Taipei Taiwan Department of Computer Science and Information Engineering National Taitung University Taitung Taiwan Interdisciplinary Program of Green and Information Technology National Taitung University Taitung Taiwan

ISBN: (纸本)9781948087070

Traditional disease surveillance systems depend on outpatient reporting and virological test results released by hospitals. These data have valid and accurate information about emerging outbreaks but it's often not timely. In recent years the exponential growth of users getting connected to social media provides immense knowledge about epidemics by sharing related information. Social media can now flag more immediate concerns related to outbreaks in real time. In this paper we apply the long short-term memory recurrent neural network (RNN) architecture to classify tweets conveyed influenza-related information and compare its performance with baseline algorithms including support vector machine (SVM), decision tree, naive Bayes, simple logistics, and naive Bayes multinomial. The developed RNN model achieved an F-score of 0.845 on the MedWeb task test set, which outperforms the F-score of SVM without applying the synthetic minority oversampling technique by 0.08. The F-score of the RNN model is within 1% of the highest score achieved by SVM with oversampling technique. © 2017 AFNLP

关键词： Support vector machines

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Control and Notification Automatic Water Pump with Arduino and SMS Gateway

Control and Notification Automatic Water Pump with Arduino a...

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International Conference on Informatics, Engineering, science and technology, INCITEST 2018

作者： Ratnadewi, R. Nurdiyanto, H. Najmurrokhman, A. Prabowo, C. Idmayanti, R. Eteruddin, H. Sugianto, C.A. Kurniasih, N. Siburian, H.K. Nababan, D. Rahim, R. Electrical Engineering Universitas Kristen Maranatha Bandung Indonesia Department of Informatics STMIK Dharma Wacana Metro Lampung Indonesia Department of Electrical Engineering Universitas Jenderal Achmad Yani Cimahi Indonesia Department of Information Technology Politeknik Negeri Padang Padang Indonesia Department of Electrical Universitas Lancang Kuning Pekanbaru Indonesia Departement of Informatics Politeknik TEDC Bandung Bandung Indonesia Library and Information Science Program Universitas Padjadjaran Bandung Indonesia Department of Informatics Sekolah Tinggi Manajemen Informatika Dan Komputer Medan Indonesia Department of Information System Universitas Pelita Harapan Medan Medan Indonesia School of Computer and Communication Engineering Universiti Malaysia Perlis Kubang Gajah Malaysia

This paper aims to facilitate the owner to control water pump machine from the vacuum, and the condition of the water is it full or not in house or apartment. The current control system still has many limitations in controlling the water pump machine at home or apartment. The water machine control system was designed with device that has a smart surveillance system that can be accessed with Android smartphone and all information from or to machine control system will command by using SMS, this device create a development of Arduino UNO microcontroller technology that allows the reading of the GSM Module and connected with SMS Gateway. Experiment results facilitate real-time water control and much easy for the owner to control water tank. © Published under licence by IOP Publishing Ltd.

关键词： Water tanks

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