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arXiv 2024年

作者： Ahmad, Alwani Liyana Sanchez-Bornot, Jose Sotero, Roberto C. Coyle, Damien Idris, Zamzuri Faye, Ibrahima Department of Fundamental and Applied Sciences Faculty of Science and Information Technology Universiti Teknologi PETRONAS Perak Malaysia Department of Neurosciences Hospital Universiti Sains Malaysia Kelantan Malaysia Brain and Behaviour Cluster School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Intelligent Systems Research Centre School of Computing Engineering and Intelligent Systems Ulster University Magee campus Derry LondonderryBT48 7JL United Kingdom Department of Radiology Hotchkiss Brain Institute University of Calgary CalgaryAB Canada The Bath Institute for the Augmented Human University of Bath BathBA2 7AY United Kingdom Department of Neurosciences School of Medical Sciences Universiti Sains Malaysia Kelantan Malaysia Universiti Teknologi PETRONAS Perak Malaysia Universiti Teknologi PETRONAS Perak32610 Malaysia

Background. Alzheimer's Disease (AD) represents a significant challenge in neurodegenerative disorders, necessitating early detection for effective intervention. Among neuroimaging methods, magnetic resonance imaging (MRI) is widely used because it is easy to apply in clinical practice and cost-effective, making it crucial for studying AD. Objective. This study aims to perform a comprehensive analysis of machine learning (ML) methods used in MRI-based biomarker selection and classification analysis. The goal is to study AD-related early cognitive decline by discriminating between healthy control (HC) participants who stayed stable and those unstable (uHC) who developed mild cognitive impairment (MCI) within five years. Methods. We utilized 3-Tesla (3T) MRI data from the Alzheimer's Disease Neuroinformatic Initiative (ADNI) and the Open Access Series of Imaging Studies 3 (OASIS-3), focusing on HC and uHC. Freesurfer’s recon-all, among other tools, was used to extract MRI-based anatomical biomarkers corresponding to semi-automatic segmented subcortical and cortical brain regions. We applied various ML techniques to select features and classify the data. These included methods from preliminary analysis performed in the MATLAB Classification Learner (MCL) app and more sophisticated methods like nested cross-validation and Bayesian optimization implemented in a customized pipeline to enhance classification performance for balanced and imbalanced datasets. Our pipeline was applied to both original imbalanced and randomly balanced datasets within a Monte Carlo analysis. Moreover, we implemented data harmonization approaches based on polynomial regression that enhanced the performance of ML and statistical methods. Complementary performance metrics, such as Accuracy (Acc), area under receiver operating characteristic curve (AROC), F1 score, and Matthew’s correlation coefficient (MCC), were used to evaluate the assessed methodologies. Results. In feature selection analyses,

关键词： Neurodegenerative diseases

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QSSOR and cubic non-polynomial spline method for the solution of two-point boundary value problems

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Journal of Physics: Conference Series 2021年第1期2000卷

作者： A Sunarto P Agarwal J V L Chew H Justine J Sulaiman Department of Tadris Mathematics IAIN Bengkulu Jl Raden Fatah Pagar Dewa Kota Bengkulu Indonesia Department of Mathematics Anand International College of Engineering Jaipur-303012 India Nonlinear Dynamics Research Center (NDRC) Ajman University Ajman UAE Faculty of Computing and Informatics Universiti Malaysia Sabah Labuan International Campus Malaysia Algonquin College of Applied Arts and Technology Ottawa ON Canada Faculty of Science and Natural Resources Universiti Malaysia Sabah Malaysia

Two-point boundary value problems are commonly used as a numerical test in developing an efficient numerical method. Several researchers studied the application of a cubic non-polynomial spline method to solve the two-point boundary value problems. A preliminary study found that a cubic non-polynomial spline method is better than a standard finite difference method in terms of the accuracy of the solution. Therefore, this paper aims to examine the performance of a cubic non-polynomial spline method through the combination with the full-, half-, and quarter-sweep iterations. The performance was evaluated in terms of the number of iterations, the execution time and the maximum absolute error by varying the iterations from full-, half- to quarter-sweep. A successive over-relaxation iterative method was implemented to solve the large and sparse linear system. The numerical result showed that the newly derived QSSOR method, based on a cubic non-polynomial spline, performed better than the tested FSSOR and HSSOR methods.

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An environment for sustainable research software in Germany and beyond: Current state, open challenges, and call for action

arXiv

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arXiv 2020年

作者： Anzt, Hartwig Bach, Felix Druskat, Stephan Löffler, Frank Loewe, Axel Renard, Bernhard Y. Seemann, Gunnar Struck, Alexander Achhammer, Elke Aggarwal, Piush Appel, Franziska Bader, Michael Brusch, Lutz Busse, Christian Chourdakis, Gerasimos Dabrowski, Piotr W. Ebert, Peter Flemisch, Bernd Friedl, Sven Fritzsch, Bernadette Funk, Maximilian D. Gast, Volker Goth, Florian Grad, Jean-Noël Hermann, Sibylle Hohmann, Florian Janosch, Stephan Kutra, Dominik Linxweiler, Jan Muth, Thilo Peters-Kottig, Wolfgang Rack, Fabian Raters, Fabian H.C. Rave, Stephan Reina, Guido Reißig, Malte Ropinski, Timo Schaarschmidt, Joerg Seibold, Heidi Thiele, Jan P. Uekerman, Benjamin Unger, Stefan Weeber, Rudolf Germany Innovative Computing Lab University of Tennessee KnoxvilleTN United States Department of English Studies Friedrich Schiller University Jena Germany Germany Department of Computer Science Humboldt-Universität zu Berlin Germany Heinz Nixdorf Chair for Distributed Information Systems Friedrich Schiller University Jena Germany Michael Stifel Center Jena Germany Center for Computation and Technology Louisiana State University Baton Rouge United States Germany Robert Koch Institute Berlin Germany Hasso Plattner Institute Faculty of Digital Engineering University of Potsdam Germany Institute for Experimental Cardiovascular Medicine University Heart Centre Freiburg Bad Krozingen Germany Faculty of Medicine University of Freiburg Freiburg Germany Matters of Activity. Image Space Material. Cluster of Excellence at Humboldt-Universität zu Berlin Germany Technische Universität München Germany Language Technology Lab Universität Duisburg-Essen Germany Germany Department of Informatics Technical University of Munich Germany Technische Universität Dresden Germany Deutsches Krebsforschungszentrum Heidelberg Germany Scientific Computing in Computer Science Technical University Munich Germany School of Computing Communication and Business Hochschule für Technik und Wirtschaft Berlin Germany Center for Bioinformatics Saar Saarland Informatics Campus Germany Institute for Modelling Hydraulic and Environmental Systems University of Stuttgart Germany Berlin Institute of Health Germany Scientific Computing Alfred Wegener Institute Helmholtz Center for Polar and Marine Research Bremerhaven Germany Max-Planck-Gesellschaft e.V. Institut für Theoretische Physik und Astrophysik Universität Würzburg Germany Institut für Computerphysik University of Stuttgart Germany University Library University of Stuttgart Germany Universität Bremen Germany Max Planck Institute of Molecular Cell Biology and Genetics Dresden Germany European Molecular Biology Laboratory Heidelb

Research software has become a central asset in academic research. It optimizes existing and enables new research methods, implements and embeds research knowledge, and constitutes an essential research product in itself. Research software must be sustainable in order to understand, replicate, reproduce, and build upon existing research or conduct new research effectively. In other words, software must be available, discoverable, usable, and adaptable to new needs, both now and in the future. Research software therefore requires an environment that supports sustainability. Hence, a change is needed in the way research software development and maintenance are currently motivated, incentivized, funded, structurally and infrastructurally supported, and legally treated. Failing to do so will threaten the quality and validity of research. In this paper, we identify challenges for research software sustainability in Germany and beyond, in terms of motivation, selection, research software engineering personnel, funding, infrastructure, and legal aspects. Besides researchers, we specifically address political and academic decision-makers to increase awareness of the importance and needs of sustainable research software practices. In particular, we recommend strategies and measures to create an environment for sustainable research software, with the ultimate goal to ensure that software-driven research is valid, reproducible and sustainable, and that software is recognized as a first class citizen in research. This paper is the outcome of two workshops run in Germany in 2019, at deRSE19 - the first International Conference of Research Software Engineers in Germany - and a dedicated DFG-supported follow-up workshop in Berlin. Copyright © 2020, The Authors. All rights reserved.

关键词： Software design

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Parallel Implementation of RC4 Data Encryption Method for Cloud computing

Computing Open

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computing Open 2023年第1期computing-open卷

作者： Arif Ullah Tanweer Alam Fawad Salam Khan Hanane Aznaoui https://***/0000-0002-7740-2206 Department of Computer Science Faculty of Computing and Artificial Intelligent Air University Islamabad Pakistan https://***/0000-0002-6350-0761 Faculty of Computer and Information Systems Islamic University of Madinah Medina Saudi Arabia https://***/0009-0005-7014-9470 Department of Creative Technology and Artificial Intelligent Air University Islamabad Pakistan https://***/0000-0002-3524-7869 Department of Computer Science Laboratory of Applied Mathematics and Computer Science Morocco

Cloud computing is an on-demand availability of computing resources. The current cloud computing environment uses different algorithms for its security; RC4 is one of them. However, due to the serial implementation of RC4 over cloud computing, the process of encryption and data transmission is badly affected by the factors of poor latency. This research is proposed to cover this factor by implementing a parallel RC4 algorithm in a cloud computing environment. The parallel RC4 model has been developed in web-based architecture relevant to the cloud computing environment. The method processed data simultaneously in four parallel pipelines encrypted through the RC4 algorithm. The proposed method used RC4 algorithms’ parallel implementation to encrypt and decrypt the data. The proposed method increases the efficiency of data encryption and decryption and transmission over a cloud environment using four pipelines at once. These four pipelines receive data and encrypt them using a key and then merge those four streams into a single cipher. These four pipelines enhance the speed of communication over cloud and solve the latency issue majorly. Data sending from node devices to cloud servers is divided into four equal streams and then encrypted using RC4 distinctly but parallel. Then after the roaming, the data are again first combined and then decrypted using the RC4 algorithm. The sample data sets are evaluated on the criteria of the starting time and ending time of the encryption and decryption of data. Results show that the proposed method is able to enhance the speed of the whole process up to average 3.7% due to the implementation of parallel technique and had reduced the time notably. The future work on this research involves enhancement of security process of RC4 algorithm since it can be breached easily.

关键词： Cloud computing RC4 algorithm encryption parallel latency

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Divertor shaping with neutral baffling as a solution to the tokamak power exhaust challenge

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Communications Physics 2025年第1期8卷 1-15页

作者： Kevin Verhaegh James Harrison David Moulton Nicola Lonigro Nick Osborne Peter Ryan Cyd Cowley Stuart Henderson Andrew Thornton Lingyan Xiang S. Aleiferis S. Allan J. Allcock L. Appel W. Arter I. Balboa O. Bardsley R. Bianchetti Morales A. Bleasdale A. Boboc C. Bowman D. Brunetti I. Carvalho F. J. Casson C. D. Challis B. Chapman N. J. Conway Y. Damizia S. Dowson S. Elmore F. Eriksson T. Farley A. R. Field N. Fil M. Fitzgerald M. Fontana R. Futtersack L. Garzotti S. N. Gerasimov Z. Ghani S. Gibson C. Giroud V. Gopakumar C. J. Ham J. Harrison N. C. Hawkes S. Henderson R. Henriques J. C. Hillesheim L. Horvath Z. Huang P. Jacquet I. Jepu D. Keeling H. T. Kim D. B. King V. G. Kiptily K. K. Kirov F. Köchl G. Kocsis L. Kogan D. Kos K. D. Lawson M. Lennholm E. LitherlandSmith P. J. Lomas C. Lowry C. F. Maggi J. Mailloux S. Marsden M. Maslov G. F. Matthews M. L. Mayoral G. McArdle A. G. Meigs S. Menmuir H. Meyer F. Militello J. Mitchell D. Moulton C. Olde H. J. C. Oliver N. Osborne R. Otin V. V. Parail A. Parrott A. Patel B. Patel M. Poradzinski F. G. Rimini C. Roach P. Ryan D. Ryan S. Saarelma S. Scully S. E. Sharapov R. Sharma A. Shaw S. A. Silburn J. Simpson S. Smith C. Srinivasan Z. Stancar C. I. Stuart H. J. Sun G. Szepesi D. Taylor E. Tholerus B. Thomas A. Thornton A. Tookey D. F. Valcarcel M. Valovic K. Verhaegh I. Voitsekhovitch D. Weston A. Widdowson T. Wilson L. Xiang Y. Zayachuk J. R. Harrison M. Aljunid S. Y. Allan L. C. Appel A. Ash J. Ashton O. Bachmann B. Barrett D. Beckett J. Bennett J. Buchanan N. Bulmer A. Carruthers J. Clark M. Coy G. Cunningham T. Da Assuncao P. Davies I. E. Day S. Dixon T. Eagles J. Edmond H. El-Haroun S. D. Elmore I. Fitzgerald S. Gee R. Harrison S. S. Henderson C. Hickling R. Hussain P. Jepson B. Kandan I. Katramados D. King R. King A. Kirk M. Kochan M. Lees G. Liddiard C. Man-Friel F. Mansfield R. Martin R. McAdams K. G. McClements D. McConville C. McKnight P. McKnight A. McShee J. Measures N. Mehay D. Morbey O. Myatra M. Nicassio E. Parr B. Parry B. S. Patel D. Payne M. Price R. Pr United Kingdom Atomic Energy Authority Culham UK Eindhoven University of Technology Eindhoven The Netherlands York Plasma Institute University of York York UK University of Liverpool Liverpool UK ITER Organization St-Paul-lez-Durance Cedex France Nat. Inst. for Laser Plasma & Radiation Physics Măgurele Romania EUROfusion PMU Garching Germany Dutch Institute for Fundamental Energy Research DIFFER Eindhoven The Netherlands Swiss Plasma Centre École Polytechnique Fédérale de Lausanne Lausanne Switzerland Oak Ridge National Laboratory Oak Ridge TN USA Universidad de Sevilla Seville Spain Electromagnetic Engineering & Fusion Science KTH Royal Institute of Technology Stockholm Sweden UniversitÃ degli Studi della Tuscia DEIM Viterbo Italy Aix-Marseille Univ. CNRS PIIM UMR 7345 Marseille France Politecnico di Milano Milan Italy Center for Energy Research UC San Diego La Jolla CA USA V. N. Karazin Kharkiv National University Kharkiv Ukraine Max Planck Institute for Plasma Physics Garching Germany Institute of Plasma Physics of the CAS Prague 8 Czechia Dublin City University Dublin Ireland VTT Technical Research Centre of Finland Espoo Finland Consorzio RFX Padova Italy University of California Irvine Irvine CA USA Istituto per la Scienza e la Tecnologia dei Plasmi CNR Padova Italy Graz University of Technology Graz Austria Aalto University Aalto Finland Institute for Plasma Science & Technology CNR Milan Italy Dept. of Applied Physics Ghent University Ghent Belgium ENEA Fusion & Nuclear Safety Dept. C.R. Frascati Frascati (RM) Italy National Technical University of Athens Zografou Greece Dept. of Physics & Astronomy Uppsala University Uppsala Sweden Dept. of Physics Durham University Durham UK Consorzio CREATE Naples Italy Aix-Marseille Univ. CNRS PIIM F-13397 Marseille France IRFM-CEA Centre de Cadarache St-Paul-lez-Durance France Centre for Energy Research Budapest Hungary University of Milano-Bicocca Milan Italy Dept. of Ele

Exhausting power from the hot fusion core to the plasma-facing components is one fusion energy’s biggest challenges. The MAST Upgrade tokamak uniquely integrates strong containment of neutrals within the exhaust area (divertor) with extreme divertor shaping capability. By systematically altering the divertor shape, this study shows the strongest evidence to date to our knowledge that long-legged divertors with a high magnetic field gradient (total flux expansion) deliver key power exhaust benefits without adversely impacting the hot fusion core. These benefits are already achieved with relatively modest geometry adjustments that are more feasible to integrate in reactor designs. Benefits include reduced target heat loads and improved access to, and stability of, a neutral gas buffer that ‘shields’ the target and enhances power exhaust (detachment). Analysis and model comparisons shows these benefits are obtained by combining multiple shaping aspects: long-legged divertors have expanded plasma-neutral interaction volume that drive reductions in particle and power loads, while total flux expansion enhances detachment access and stability. Containing the neutrals in the exhaust area with physical structures further augments these shaping benefits. These results demonstrate strategic variation in the divertor geometry and magnetic topology is a potential solution to one of fusion’s power exhaust challenge.

关键词： Fusion reactor divertors

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CHAOS Challenge - Combined (CT-MR) healthy abdominal organ segmentation

arXiv

引用

arXiv 2020年

作者： Kavur, A. Emre Gezer, N. Sinem Barış, Mustafa Conze, Pierre-Henri Groza, Vladimir Pham, Duc Duy Chatterjee, Soumick Ernst, Philipp Özkan, Savaş Baydar, Bora Lachinov, Dmitry Han, Shuo Pauli, Josef Isensee, Fabian Perkonigg, Matthias Sathish, Rachana Rajan, Ronnie Aslan, Sinem Sheet, Debdoot Dovletov, Gurbandurdy Speck, Oliver Nürnberger, Andreas Maier-Hein, Klaus H. Akar, Gözde Bozdagı Ünal, Gözde Dicle, Oguz Selver, M. Alper Graduate School of Natural and Applied Sciences Dokuz Eylul University Izmir Turkey Department of Radiology Faculty Of Medicine Dokuz Eylul University Izmir Turkey Dept. of Ophthalmology and Optometry Medical Uni. of Vienna Austria Intelligent Systems Faculty of Engineering University of Duisburg-Essen Germany Division of Medical Image Computing German Cancer Research Center Heidelberg Germany CIR Lab Dept of Biomedical Imaging and Image-guided Therapy Medical Uni. of Vienna Austria IMT Atlantique LaTIM UMR 1101 Brest France Department of Electrical and Electronics Engineering Middle East Technical University Ankara Turkey Department of Electrical Engineering Indian Institute of Technology Kharagpur India School of Medical Science and Technology Indian Institute of Technology Kharagpur India Ca’ Foscari University of Venice ECLT DAIS Venice Italy Ege University International Computer Institute Izmir Turkey Johns Hopkins University Baltimore United States Data and Knowledge Engineering Group Otto von Guericke University Magdeburg Germany Magnetic Resonance Otto von Guericke University Magdeburg Germany Median Technologies Valbonne France Department of Computer Engineering Istanbul Technical University Istanbul Turkey Department of Electrical and Electronics Engineering Dokuz Eylul University Izmir Turkey

—Segmentation of abdominal organs has been a comprehensive, yet unresolved, research field for many years. In the last decade, intensive developments in deep learning (DL) have introduced new state-of-the-art segmentation systems. Despite outperforming the overall accuracy of existing systems, the effects of DL model properties and parameters on the performance is hard to interpret. This makes comparative analysis a necessary tool to achieve explainable studies and systems. Moreover, the performance of DL for emerging learning approaches such as cross-modality and multi-modal tasks have been rarely discussed. In order to expand the knowledge in these topics, CHAOS – Combined (CT-MR) Healthy Abdominal Organ Segmentation challenge has been organized in the IEEE International Symposium on Biomedical Imaging (ISBI), 2019, in Venice, Italy. Despite a large number of the previous abdomen related challenges, the majority of which are focused on tumor/lesion detection and/or classification with a single modality, CHAOS providesboth abdominal CT and MR data from healthy subjects. Five different and complementary tasks have been designed to analyze the capabilities of the current approaches from multiple perspectives. The results are investigated thoroughly, compared with manual annotations and interactive methods. The outcomes are reported in detail to reflect the latest advancements in the field. CHAOS challenge and data will be available online to provide a continuous benchmark resource for segmentation. Copyright © 2020, The Authors. All rights reserved.

关键词： Medical imaging

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RETRACTED ARTICLE: SmartPharma: Blockchain Enabled Internet of Things for Smart Pharmaceutical Traceability System

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IETE Journal of Research 2023年第11期69卷 XLIV-LVII页

作者： M. Kavitha Margret E. Golden Julie Y. Harold Robinson D. Vijayanandh S. Vimal Seifidine Kadry Ahmed M. El-Sherbeeny Mohammed A. El-Meligy 1 Department of Computer Science and Engineering Sri Krishna College of Technology Coimbatore India 2 Department of Computer Science and Engineering Anna University Regional Campus Tirunelveli India 3 School of Information Technology and Engineering Vellore Institute of Technology Vellore India 4 Department of Electrical and Electronics Engineering Hindustan College of Engineering and Technology Coimbatore India 5 Department of Artificial Intelligence and Data Science Ramco Institute of Technology Rajapalayam Tamilnadu India 6 Faculty of Applied Computing and Technology Noroff University College Kristiansand Norway 7 Industrial Engineering Department College of Engineering King Saud University Riyadh11421 Saudi Arabia

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled “Federated Learning for Blockchain Systems and Industrial Internet of Things”:M. Kavitha Margret, E. Golden Julie, Y. Harold Robinson, D. Vijayanandh, S. Vimal, Seifidine Kadry, Ahmed M. El-Sherbeeny & Mohammed A. El-Meligy (2021) SmartPharma: Blockchain Enabled Internet of Things for Smart Pharmaceutical Traceability System, IETE Journal of Research, DOI: 10.1080/03772063.2021.1994041Following publication, concerns were raised about the peer review and decision-making processes for this special *** an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal's peer review standards and *** the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special *** journal has not confirmed if the authors were aware of this compromised peer review *** journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this *** have been informed in our decision-making by our editorial policies and the COPE *** retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as ‘Retracted’.

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Correction: Sign language mobile apps: a systematic review of current app evaluation progress and solution framework

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Evolving Systems 2024年第5期15卷 1989-1989页

作者： David, Dianes Alamoodi, A. H. Albahri, O. S. Garfan, Salem Albahri, A. S. Zaidan, B. B. Chen, Juliana Faculty of Computing and Meta-Technology (FKMT) Universiti Pendidikan Sultan Idris (UPSI) Perak Malaysia MEU Research Unit Middle East University Amman Jordan Applied Science Research Center Applied Science Private University Amman Jordan Department of Computer Science and Information Technology La Trobe University Melbourne Australia Computer Techniques Engineering Department Mazaya University College Nasiriyah Iraq Iraqi Commission for Computers and Informatics (ICCI) Baghdad Iraq Technical College Imam Ja’afar Al-Sadiq University Baghdad Iraq Future Technology Research Center National Yunlin University of Science and Technology Douliou Taiwan ROC Discipline of Nutrition and Dietetics Susan Wakil School of Nursing and Midwifery Faculty of Medicine and Health The University of Sydney Camperdown Australia Charles Perkins Centre The University of Sydney Camperdown Australia

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Identifying factors that affected student enrolment in Additional Mathematics for urban areas of Kuantan district

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Journal of Physics: Conference Series 2021年第1期1988卷

作者： Z L Chuan C-.Y. Liong W N S W Yusoff A S A Aminuddin E H Tan Centre for Mathematical Sciences College of Computing and Applied Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Kuantan Pahang DM Malaysia Department of Mathematical Sciences Faculty of Science and Technology Universiti Kebangsaan Malaysia 43600 UKM Bangi Selangor DE Malaysia Sekolah Menengah Kebangsaan Semambu Depan Qtrs Loji Air Semambu 25350 Kuantan Pahang DM Malaysia

The skilful and qualified Science, technology, Engineering and Mathematics (STEM) workforces are expected to be in high demand in the 21st digital economy. In Malaysia public education system, the principal key to STEM education is Additional Mathematics. However, the statistics depicted that the number of upper secondary students enrolled in Additional Mathematics have been severely delineated. Furthermore, the prerequisite to enrol in STEM and Technical and Vocational Education and Training (TVET) courses in tertiary education is achieving a minimum grade C for Additional Mathematics. Therefore, the principal objective of this article is to identify the significant factors that affected the upper secondary students enrolled in Additional Mathematics using Pearson's chi-square test without Yates continuity correction and unadjusted odds ratio (OR). A validated questionnaire comprised nine potential factors that affected enrolment in additional mathematics was distributed to 389 Form Four students' cohort 2020 from four selected urban government schools to pursue this objective. Based on the finding of this article, several initiatives might be taken by the policymakers, such as the teachers may enhancing and throughout the broad of STEM and TVET careers in the 21st digital economy era, while the students' parents can participate in schools in building the communicating and coordinating mechanism. Consequently, the number of upper secondary enrolled in STEM education might be increased to pipeline the future STEM and TVET human capitals in sustaining and stabilising the national economy in the digital era.

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Analysis of the hydrocarbon seepage detection in oil palm vegetation stress using unmanned aerial vehicle (UAV) multispectral data

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AIP Conference Proceedings 2020年第1期2266卷

作者： Nur Asyatulmaila Mohamad Asri Hamzah Sakidin Mahmod Othman Abd Nasir Matori Asmala Ahmad 1Fundamental and Applied Sciences Department Universiti Teknologi PETRONAS 32610 Seri Iskandar Perak Malaysia 2Fundamental and Applied Sciences Department Universiti Teknologi PETRONAS 32610 Seri Iskandar Perak Malaysia 3Fundamental and Applied Sciences Department Universiti Teknologi PETRONAS 32610 Seri Iskandar Perak Malaysia 4Civil and Environmental Engineering Department Universiti Teknologi PETRONAS 32610 Seri Iskandar Perak Malaysia 5Department of Industrial Computing Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka (UTeM) 76100 Durian Tunggal Melaka Malaysia

Due to difference pressure beneath the earth, oil and gas or known as hydrocarbon most probably rises to the surface. This presence of oil and gas can be identified through their seepage. The hydrocarbon seepage can lead to an abnormality of soil and dynamism of vegetation health on land surface. This phenomenon can be act as indicator for detecting potential onshore of oil and gas reservoir. Through remote sensing spectral reflectance vegetation index, the abnormality and dynamism of plant growth can be detected. In this study, spectral reflectance data originating from Unmanned Aerial Vehicle (UAV) Multispectral is used to monitor and determine the hydrocarbon seepage detection in oil palm vegetation stress. Based on the experimental design, 12 oil palm trees were simulated with crude oil and remaining three trees were left as control sample which these trees are divided into five sample. The spectra acquisition was taken on the first day, 30th day, 60th day and 90th day of experiment. From the data, the potential visible region and Near - Infrared (NIR) reflectance region being investigated by exerted the statistical analysis to vegetation index which is Normalised Difference Vegetation Index (NDVI) data trends to study the impact of hydrocarbon seepage towards oil palm tree. Detecting hydrocarbon seepage using vegetation reflectance is very useful in oil and gas field as this can be one of the alternative approaches in exploring new oil and gas resources.

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