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Factors Affecting Lecturers Motivation in Using Online Teaching Tools

Procedia - Social and Behavioral Sciences 2015年 195卷 1778-1784页

作者： Siti Nurul Mahfuzah Mohamad Mohd Azran Mohd Salleh Sazilah Salam Computer System and Support Unit Kolej Komuniti Selandar (KKSL) Malaysia Computer System and Support Unit Kolej Komuniti Masjid Tanah (KKMT) Malaysia Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka (UTeM) Malaysia

Motivation is the drive to will and desire to succeed or achieve something. Motivation in this treatment refers to when the lecturers feel the motivation in using online teaching tools in the classroom. Self-motivation of lecturers to change their teaching styles or methods is one of the problems stated by few researchers. The objective of this paper is to identify the factors that motivate lecturers to use online teaching tools, such as Web 2.0 tools. There are a variety of online teaching tools available to the lecturers. The popular tools are online whiteboard, WordPress, YouTube, Facebook, Edmodo and others. There are not many lecturers who apply the Internet as an alternative to provide access to information that can help to enhance teaching and learning materials. An evaluation of the lecturer's motivation had been completed by 30 lecturers from different subject matter experts. The instruments used were interview sessions and classroom observation, which were used to measure four motivation variables: ease of use, enjoyment, usefulness and self-confidence, based on the ARCS Motivation model. Based on the findings, factors that can affect lecturers’ motivation in using online teaching tools are knowledge, perceptions and skills.

关键词： Motivation Online Teaching Tools Web 2.0

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Author Correction: Federated learning enables big data for rare cancer boundary detection

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Nature communications 2023年第1期14卷 436页

作者： Sarthak Pati Ujjwal Baid Brandon Edwards Micah Sheller Shih-Han Wang G Anthony Reina Patrick Foley Alexey Gruzdev Deepthi Karkada Christos Davatzikos Chiharu Sako Satyam Ghodasara Michel Bilello Suyash Mohan Philipp Vollmuth Gianluca Brugnara Chandrakanth J Preetha Felix Sahm Klaus Maier-Hein Maximilian Zenk Martin Bendszus Wolfgang Wick Evan Calabrese Jeffrey Rudie Javier Villanueva-Meyer Soonmee Cha Madhura Ingalhalikar Manali Jadhav Umang Pandey Jitender Saini John Garrett Matthew Larson Robert Jeraj Stuart Currie Russell Frood Kavi Fatania Raymond Y Huang Ken Chang Carmen Balaña Jaume Capellades Josep Puig Johannes Trenkler Josef Pichler Georg Necker Andreas Haunschmidt Stephan Meckel Gaurav Shukla Spencer Liem Gregory S Alexander Joseph Lombardo Joshua D Palmer Adam E Flanders Adam P Dicker Haris I Sair Craig K Jones Archana Venkataraman Meirui Jiang Tiffany Y So Cheng Chen Pheng Ann Heng Qi Dou Michal Kozubek Filip Lux Jan Michálek Petr Matula Miloš Keřkovský Tereza Kopřivová Marek Dostál Václav Vybíhal Michael A Vogelbaum J Ross Mitchell Joaquim Farinhas Joseph A Maldjian Chandan Ganesh Bangalore Yogananda Marco C Pinho Divya Reddy James Holcomb Benjamin C Wagner Benjamin M Ellingson Timothy F Cloughesy Catalina Raymond Talia Oughourlian Akifumi Hagiwara Chencai Wang Minh-Son To Sargam Bhardwaj Chee Chong Marc Agzarian Alexandre Xavier Falcão Samuel B Martins Bernardo C A Teixeira Flávia Sprenger David Menotti Diego R Lucio Pamela LaMontagne Daniel Marcus Benedikt Wiestler Florian Kofler Ivan Ezhov Marie Metz Rajan Jain Matthew Lee Yvonne W Lui Richard McKinley Johannes Slotboom Piotr Radojewski Raphael Meier Roland Wiest Derrick Murcia Eric Fu Rourke Haas John Thompson David Ryan Ormond Chaitra Badve Andrew E Sloan Vachan Vadmal Kristin Waite Rivka R Colen Linmin Pei Murat Ak Ashok Srinivasan J Rajiv Bapuraj Arvind Rao Nicholas Wang Ota Yoshiaki Toshio Moritani Sevcan Turk Joonsang Lee Snehal Prabhudesai Fanny Morón Jacob Mandel Konstantinos Kamnitsas Ben Glocker Luke V M Dixon Matthew Williams Peter Zamp Center for Biomedical Image Computing and Analytics (CBICA) University of Pennsylvania Philadelphia PA USA. Department of Radiology Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania Philadelphia PA USA. Department of Informatics Technical University of Munich Munich Bavaria Germany. Intel Corporation Santa Clara CA USA. Department of Neuroradiology Heidelberg University Hospital Heidelberg Germany. Clinical Cooperation Unit Neuropathology German Cancer Consortium (DKTK) within the German Cancer Research Center (DKFZ) Heidelberg Germany. Department of Neuropathology Heidelberg University Hospital Heidelberg Germany. Division of Medical Image Computing German Cancer Research Center Heidelberg Germany. Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany. Neurology Clinic Heidelberg University Hospital Heidelberg Germany. Department of Radiology & Biomedical Imaging University of California San Francisco San Francisco CA USA. Symbiosis Center for Medical Image Analysis Symbiosis International University Pune Maharashtra India. Department of Neuroimaging and Interventional Radiology National Institute of Mental Health and Neurosciences Bangalore Karnataka India. Department of Radiology School of Medicine and Public Health University of Wisconsin Madison WI USA. Department of Medical Physics School of Medicine and Public Health University of Wisconsin Madison WI USA. Leeds Teaching Hospitals Trust Department of Radiology Leeds UK. Department of Radiology Brigham and Women's Hospital Harvard Medical School Boston MA USA. Athinoula A. Martinos Center for Biomedical Imaging Massachusetts General Hospital Charlestown MA USA. Catalan Institute of Oncology Badalona Spain. Consorci MAR Parc de Salut de Barcelona Catalonia Spain. Department of Radiology (IDI

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Rosa Bitamina (organic fertilizer) Effects in Enhancing Rosa Centifolia Resistancy and Flowering Process

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Procedia - Social and Behavioral Sciences 2015年 195卷 1715-1718页

作者： Nurhidayati Mad Noh Nur Azlin Azhari Siti Nurul Mahfuzah Mohamad Landscape Unit Engineering and Technical Department Kolej Komuniti Selandar Melaka Malaysia Computer System and Support Unit Engineering and Technical DepartmentKolej Komuniti Selandar Melaka Malaysia

Rosa Bitamina (RB) is an organic fertilizer made from marine-waste product such as shrimp shells, fish carcasses and anchovy heads. This product applied a 3R waste management system which reduced waste products by reused it in production of a new product. Objective of this study is to observe effects of RB towards Rosa centifolia resistancy and flowering process. This fertilizer was produced by grinding dried marine-waste product to small particles. Rose plant with same maturity were used and divided into three different treatments which are T1; control (soil only), T2; soil +commercial fertilizer, T3; soil +RB and T3; soil +RB +charcoal. All samples were placed under plant house condition with 12 hours light exposure for 8 weeks observation. Assessment was conducted on physical assessment through an observation. On week 2, all samples for T3 and T4 showed changes except sample from treatment T1 and T2. T1 and T2 only showed a development on week 4. All leaves remains green and dewy throughout the observation. Thus, RB can enhance the flowering process of R. centifolia up to 2 weeks earlier compared to the untreated R. centifolia . Plant resistency also improved by applying Rosa Bitamina since all sample remains fresh until day 8 weeks of observation.

关键词： Organic fertilizer 3R waste management system and Rosa centifolia

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Identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the BRATS challenge

arXiv

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arXiv 2018年

作者： Bakas, Spyridon Reyes, Mauricio Jakab, Andras Bauer, Stefan Rempfler, Markus Crimi, Alessandro Shinohara, Russell Takeshi Berger, Christoph Ha, Sung Min Rozycki, Martin Prastawa, Marcel Alberts, Esther Lipkova, Jana Freymann, John Kirby, Justin Bilello, Michel Fathallah-Shaykh, Hassan M. Wiest, Roland Kirschke, Jan Wiestler, Benedikt Colen, Rivka Kotrotsou, Aikaterini Lamontagne, Pamela Marcus, Daniel Milchenko, Mikhail Nazeri, Arash Weber, Marc-Andr Mahajan, Abhishek Baid, Ujjwal Gerstner, Elizabeth Kwon, Dongjin Acharya, Gagan Agarwal, Manu Alam, Mahbubul Albiol, Alberto Albiol, Antonio Albiol, Francisco J. Alex, Varghese Allinson, Nigel Amorim, Pedro H.A. Amrutkar, Abhijit Anand, Ganesh Andermatt, Simon Arbel, Tal Arbelaez, Pablo Avery, Aaron Azmat, Muneeza Pranjal, B. Bai, Wenjia Banerjee, Subhashis Barth, Bill Batchelder, Thomas Batmanghelich, Kayhan Battistella, Enzo Beers, Andrew Belyaev, Mikhail Bendszus, Martin Benson, Eze Bernal, Jose Bharath, Halandur Nagaraja Biros, George Bisdas, Sotirios Brown, James Cabezas, Mariano Cao, Shilei Cardoso, Jorge M. Carver, Eric N. Casamitjana, Adri Castillo, Laura Silvana Cat, Marcel Cattin, Philippe Cérigues, Albert Chagas, Vinicius S. Chandra, Siddhartha Chang, Yi-Ju Chang, Shiyu Chang, Ken Chazalon, Joseph Chen, Shengcong Chen, Wei Chen, Jefferson W. Chen, Zhaolin Cheng, Kun Choudhury, Ahana Roy Chylla, Roger Clrigues, Albert Colleman, Steven Colmeiro, Ramiro German Rodriguez Combalia, Marc Costa, Anthony Cui, Xiaomeng Dai, Zhenzhen Dai, Lutao Daza, Laura Alexandra Deutsch, Eric Ding, Changxing Dong, Chao Dong, Shidu Dudzik, Wojciech Eaton-Rosen, Zach Egan, Gary Escudero, Guilherme Estienne, Tho Everson, Richard Fabrizio, Jonathan Fan, Yong Fang, Longwei Feng, Xue Ferrante, Enzo Fidon, Lucas Fischer, Martin French, Andrew P. Fridman, Naomi Fu, Huan Fuentes, David Gao, Yaozong Gates, Evan Gering, David Gholami, Amir Gierke, Willi Glocker, Ben Gong, Mingming Gonzlez-Vill, Sandra Grosges, T. Guan, Yuanfang Guo, Sheng Gupta, Sudeep Han, Woo-Sup Han, Il Song Harmuth, Ko Center for Biomedical Image Computing and Analytics University of Pennsylvania PhiladelphiaPA United States Department of Radiology Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Pathology and Laboratory Medicine Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Institute for Surgical Technology and Biomechanics University of Bern Bern Switzerland Center for MR-Research University Children's Hospital Zurich Zurich Switzerland Support Centre for Advanced Neuroimaging Inselspital Institute for Diagnostic and Interventional Neuroradiology Bern University Hospital Bern Switzerland University Hospital of Zurich Zurich Switzerland Center for Clinical Epidemiology and Biostatistics University of Pennsylvania Philadelphia United States Image-Based Biomedical Modeling Group Technical University of Munich Munich Germany Icahn School of Medicine Mount Sinai Health System New YorkNY United States Leidos Biomedical Research Inc. Frederick National Laboratory for Cancer Research FrederickMD21701 United States Cancer Imaging Program National Cancer Institute National Institutes of Health BethesdaMD20814 United States Department of Neurology University of Alabama at Birmingham BirminghamAL United States Department of Diagnostic Radiology University of Texas MD Anderson Cancer Center HoustonTX United States Department of Psychology Washington University St. LouisMO United States Neuroimaging Informatics and Analysis Center Washington University St. LouisMO United States Department of Radiology Washington University St. LouisMO United States Institute of Diagnostic and Interventional Radiology Pediatric Radiology and Neuroradiology University Medical Center Rostock Ernst-Heydemann-Str. 6 Rostock18057 Germany Tata Memorial Centre Homi Bhabha National Institute Mumbai India Shri Guru Gobind Singhji Institute of Engineering and Technology Nanded India NVIDIA Santa Clara

Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumor is a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses the state-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross total resection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset. Copyright © 2018, The Authors. All rights reserved.

关键词： Tumors

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Human systems integration and advanced technology in engineering department workload and manpower reduction

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NAVAL ENGINEERS JOURNAL 2003年第1期115卷 57-65页

作者： Lively, KA Seman, AJ Kirkpatrick, M KENNETH A. LIVELY graduated from the University of Colorado with a BS in applied mathematics and an MS in mathematics in 1976 and from the Massachusetts Institute of Technology with an MS in electrical engineering and the degree ocean engineer in naval architecture and marine engineering in 1984. He retired from the U.S. Navy in 1989 after 23 years of service. Assignments included electrical officer on the USS Constellation (CV 64) project engineer for the DDG 51 machinery control system (NAVSEA) and DDG 51 Technical Director (NAVSEA). He was vice president of the PDI Division of Bird-Johnson Company from July 1989 to November 1998 where he managed various gas turbine and machinery controls related development projects. He joined Anteon Corporation's Systems Engineering Group as senior controls engineer in December 1998 where he provided technical support to the integrated power systems program (NAVSEA PMS 510) and managed the Office of Naval Research Afloat Laboratory. DR. MARK KIRKPATRICK is currently an independent consultant in human factors and work-load/manning analysis and modeling. He holds a Ph.D. degree in experimental psychology from The Ohio State University and has 34 years of experience in applied human factors. From 1982 through 2000 Dr. Kirkpatrick served as the senior vice president of Carlow International. Prior to joining Carlow in 1982 Dr. Kirkpatrick served as a member of the technical staff at North American Rockwell's Missiles Division and as a project director and vice president for Essex Corporation. His areas of expertise include workload simulation task analysis operator-in-the-loop simulation human performance experimentation statistical analysis and human factors T&E. He has directed and/or participated in human factors projects for the U.S. Navy U.S. Army NASA Department of Transportation the U.S. Nuclear Regulatory Commission and private industry. ANTHONY J. SEMAN III is the technical manager for the reduced ship's crew by virtual presence (RSVP) advanced technology d

Aboard current ships, such as the DDG 51, engineering control and damage control activities are manpower intensive. It is anticipated that, for future combatants, the workload demand arising from operation of systems under conditions of normal steaming and during casualty response will need to be markedly reduced via automated monitoring, autonomous control, and other technology initiatives. Current DDG 51 class ships can be considered as a manpower baseline and under Condition III typical engineering control involves seven to eight watchstanders at manned stations in the Central Control Station, the engine rooms and other machinery spaces. In contrast to this manning level, initiatives such as DD 21 and the integrated engineering plant (IEP) envision a partnership between the operator and the automation system, with more and more of the operator's functions being shifted to the automation system as manning levels decrease. This paper describes some human systems integration studies of workload demand reduction and, consequently, manning reduction that can be achieved due to application of several advanced technology concepts. Advanced system concept studies in relation to workload demand are described and reviewed including. Piecemeal applications of diverse automation and remote control technology concepts to selected high driver tasks in current DDG 51 activities. Development of the reduced ship's crew by virtual presence system that will provide automated monitoring and display to operators of machinery health, compartment conditions, and personnel health. The IEP envisions the machinery control system as a provider of resources that are used by various consumers around the ship. Resource needs and consumer priorities are at all times dependent upon the ship's current mission and the availability of equipment pawnbrokers.

关键词： Naval warfare

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MPCS - THE MANUFACTURING PROCESS-CONTROL system

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AT&T TECHNICAL JOURNAL 1986年第4期65卷 35-45页

作者： DUNIETZ, IS HSU, JLC MCEACHERN, MT STOCKING, JH SWARTZ, MA TROMBLY, RM The authors Irwin S. Dunietz John L.C. Hsu Michael T. McEachern James H. Stocking Mark A. Swartz andRodney M. Tromblyare responsible for design and development of the Manufacturing Process Control System. Mr. Dunietz joined AT&T in 1980. He is a member of the technical staff in the Manufacturing Information Automation department at AT&T Engineering Research Center Princeton New Jersey. He received an A.B. in mathematics from Cornell University and an M.S.E. in computer science from Princeton University. Mr. Hsu who joined AT&T in 1970 is a department head in the Manufacturing Information Automation department at the Engineering Research Center. He received an M.S. in electrical engineering from the University of Missouri. Mr. McEachern joined AT&T in 1962 and is a supervisor in the 5ESS™ Line Unit Manufacturing department at AT&T Technologies in Oklahoma City Oklahoma. He is responsible for the manufacturing process control center in Oklahoma City which provides computerized support for all circuit pack manufacturing. Mr. Stocking who joined AT&T in 1975 is a supervisor in the Manufacturing Information Automation department at the Engineering Research Center. He received a B.S. in chemical engineering from Rensselaer Polytechnic Institute and a Ph.D. in chemical engineering from the University of California Berkeley. Mr. Swartz joined AT&T in 1980 and is a member of the technical staff in the Manufacturing Information Automation department at the Engineering Research Center. He received an A.B. in computer science from Cornell University and an M.S. in computer science from Rutgers—The State University. Mr. Trombly who joined AT&T in 19 78 is an assistant manager at the AT&T Merrimack Valley Works in Massachusetts. Previously he was a supervisor at the Engineering Research Center. He holds a B.S. in computers and systems engineering and an M.S.E.E. from Rensselaer Polytechnic Institute.

The central challenge of all manufacturing is making products to the right standards and delivering them at the right time. AT&T is upgrading its corporate and factory resource planning systems to improve control of day-to-day manufacturing. The Manufacturing Process Control system (MPCS), developed at the AT&T Engineering Research Center (ERC), provides this support. MPCS connects the shop floor with production scheduling, accounting, product data archive, and engineering support systems.

关键词： PRODUCTION CONTROL

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