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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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Correction: Assessing real-world gait with digital technology? Validation, insights and recommendations from the Mobilise-D consortium

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Journal of neuroengineering and rehabilitation 2024年第1期21卷 71页

作者： M Encarna Micó-Amigo Tecla Bonci Anisoara Paraschiv-Ionescu Martin Ullrich Cameron Kirk Abolfazl Soltani Arne Küderle Eran Gazit Francesca Salis Lisa Alcock Kamiar Aminian Clemens Becker Stefano Bertuletti Philip Brown Ellen Buckley Alma Cantu Anne-Elie Carsin Marco Caruso Brian Caulfield Andrea Cereatti Lorenzo Chiari Ilaria D'Ascanio Bjoern Eskofier Sara Fernstad Marcel Froehlich Judith Garcia-Aymerich Clint Hansen Jeffrey M Hausdorff Hugo Hiden Emily Hume Alison Keogh Felix Kluge Sarah Koch Walter Maetzler Dimitrios Megaritis Arne Mueller Martijn Niessen Luca Palmerini Lars Schwickert Kirsty Scott Basil Sharrack Henrik Sillén David Singleton Beatrix Vereijken Ioannis Vogiatzis Alison J Yarnall Lynn Rochester Claudia Mazzà Silvia Del Din Translational and Clinical Research Institute Faculty of Medical Sciences Newcastle University Newcastle Upon Tyne UK. Department of Mechanical Engineering and Insigneo Institute for in Silico Medicine The University of Sheffield Sheffield UK. Laboratory of Movement Analysis and Measurement Ecole Polytechnique Federale de Lausanne Lausanne Switzerland. Machine Learning and Data Analytics Lab Department of Artificial Intelligence in Biomedical Engineering Friedrich-Alexander-Universität Erlangen Nürnberg Erlangen Germany. Center for the Study of Movement Cognition and Mobility Neurological Institute Tel Aviv Sourasky Medical Center Tel Aviv Israel. Department of Biomedical Sciences University of Sassari Sassari Italy. Department of Electronics and Telecommunications Politecnico di Torino Turin Italy. National Institute for Health and Care Research (NIHR) Newcastle Biomedical Research Centre (BRC) Newcastle University and The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne UK. Robert Bosch Gesellschaft für Medizinische Forschung Stuttgart Germany. The Newcastle Upon Tyne Hospitals NHS Foundation Trust Newcastle Upon Tyne UK. School of Computing Newcastle University Newcastle Upon Tyne UK. Barcelona Institute for Global Health (ISGlobal) Barcelona Spain. Universitat Pompeu Fabra Barcelona Catalonia Spain. CIBER Epidemiología y Salud Pública (CIBERESP) Madrid Spain. Insight Centre for Data Analytics University College Dublin Dublin Ireland. School of Public Health Physiotherapy and Sports Science University College Dublin Dublin Ireland. Department of Electrical Electronic and Information Engineering «Guglielmo Marconi» University of Bologna Bologna Italy. Health Sciences and Technologies-Interdepartmental Center for Industrial Research (CIRI-SDV) University of Bologna Bologna Italy. Grünenthal GmbH Aachen Germany. Department of Neurology University Medical Center Schleswig-Holstein Campus Kiel Kiel Germany. Sagol School of Neuroscien

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Wind disturbance elimination on dual axis solar tracker using fuzzy logic control

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IOP Conference Series: Earth and Environmental science 2019年第1期364卷

作者： M Ikhwan S Rizal M Ramli Z A Muchlisin M Mardlijah Graduate School of Mathematics and Applied Science Universitas Syiah Kuala Banda Aceh 23111 Indonesia. Department of Marine Sciences Faculty of Marine and Fisheries Universitas Syiah Kuala Banda Aceh 23111 Indonesia. Department of Mathematics Faculty of Mathematics and Natural Science Universitas Syiah Kuala Banda Aceh 23111 Indonesia. Department of Aquacultures Faculty of Marine and Fisheries Universitas Syiah Kuala Banda Aceh 23111 Indonesia. Department of Mathematics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya 60111 Indonesia.

The use of solar energy using solar panels began to be encouraged. Solar panels utilize solar radiation and convert it into clean and renewable energy. The problems of the accuracy of the panel facing right towards radiation are a challenge in the development of a driving system. Solar panels have limited motion, which is a maximum of phi, so that wind disturbances are very dangerous for motor drives and solar panels. This paper aims to eliminate wind disturbances in solar panel drive systems. The fuzzy logic control is used to determine the amount of power given to the driving motor to direct the panel towards solar radiation. Inputs and outputs of fuzzy controllers are arranged by dividing into each of the five members. Simulation results show that the position error of solar panels without wind disturbances reaches 0.3325° in zonal and 0.5210° in meridional. By using wind disturbances, this system managed to maintain errors smaller than 0.4285° in zonal and 0.6116° in meridional. After modifying the rule base and membership function, systems show decreasing of error half of recent trial.

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Rapid simulation of glycoprotein structures by grafting and steric exclusion of glycan conformer libraries

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Cell 2024年第5期187卷 1296-1311.e26页

作者： Yu-Xi Tsai Ning-En Chang Klaus Reuter Hao-Ting Chang Tzu-Jing Yang Sören von Bülow Vidhi Sehrawat Noémie Zerrouki Matthieu Tuffery Michael Gecht Isabell Louise Grothaus Lucio Colombi Ciacchi Yong-Sheng Wang Min-Feng Hsu Kay-Hooi Khoo Gerhard Hummer Shang-Te Danny Hsu Cyril Hanus Mateusz Sikora Institute of Biological Chemistry Academia Sinica Taipei 11529 Taiwan Institute of Biochemical Sciences National Taiwan University Taipei 10617 Taiwan. Max Planck Computing and Data Facility 85748 Garching Germany. Department of Theoretical Biophysics Max Planck Institute for Biophysics 60438 Frankfurt Germany. Department of Theoretical Biophysics Max Planck Institute for Biophysics 60438 Frankfurt Germany Malopolska Centre of Biotechnology Jagiellonian University 31-007 Kraków Poland. Institute of Psychiatry and Neurosciences of Paris Inserm UMR1266 Université Paris-Cité 75014 Paris France. Hybrid Materials Interfaces Group Faculty of Production Engineering Bremen Center for Computational Materials Science and MAPEX Center for Materials and Processes University of Bremen 28359 Bremen Germany. Institute of Biological Chemistry Academia Sinica Taipei 11529 Taiwan. Institute of Biophysics Goethe University 60438 Frankfurt Germany. Institute of Biochemical Sciences National Taiwan University Taipei 10617 Taiwan International Institute for Sustainability with Knotted Chiral Meta Matter (WPI-SKCM(2)) Hiroshima University Hiroshima 739-8526 Japan. Electronic address: sthsu@gate.sinica.edu.tw. Institute of Psychiatry and Neurosciences of Paris Inserm UMR1266 Université Paris-Cité 75014 Paris France GHU Psychiatrie et Neurosciences de Paris 75014 Paris France. Electronic address: cyril.hanus@inserm.fr. Malopolska Centre of Biotechnology Jagiellonian University 31-007 Kraków Poland. Electronic address: mateusz.sikora@uj.edu.pl.

Most membrane proteins are modified by covalent addition of complex sugars through N- and O-glycosylation. Unlike proteins, glycans do not typically adopt specific secondary structures and remain very mobile, shielding potentially large fractions of protein surface. High glycan conformational freedom hinders complete structural elucidation of glycoproteins. Computer simulations may be used to model glycosylated proteins but require hundreds of thousands of computing hours on supercomputers, thus limiting routine use. Here, we describe GlycoSHIELD, a reductionist method that can be implemented on personal computers to graft realistic ensembles of glycan conformers onto static protein structures in minutes. Using molecular dynamics simulation, small-angle X-ray scattering, cryoelectron microscopy, and mass spectrometry, we show that this open-access toolkit provides enhanced models of glycoprotein structures. Focusing on N-cadherin, human coronavirus spike proteins, and gamma-aminobutyric acid receptors, we show that GlycoSHIELD can shed light on the impact of glycans on the conformation and activity of complex glycoproteins.

关键词： GABAA receptor N-cadherin SAXS coronavirus S protein cryo-EM glycans glycoproteins mass spectrometry molecular dynamics

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Distinguishing unitary gates on the IBM quantum processor

arXiv

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arXiv 2018年

作者： Liu, Shusen Li, Yinan Duan, Runyao School of Data and Computer Science Sun Yat-sen University Guangzhou Guangdong510006 China Centre for Quantum Software and Information Faculty of Engineering and Information Technology University of Technology SydneyNSW2007 Australia Centrum Wiskunde & Informatica and Research Center for Quantum Software Netherlands Institute for Quantum Computing Baidu Inc. Beijing100193 China

An unknown unitary gates, which is secretly chosen from several known ones, can always be distinguished perfectly. In this paper, we implement such a task on IBM's quantum processor. More precisely, we experimentally demonstrate the discrimination of two qubit unitary gates, the identity gate and the 2/3π-phase shift gate, using two discrimination schemes - the parallel scheme and the sequential scheme. We program these two schemes on the ibmqx4, a 5-qubit superconducting quantum processor via IBM cloud, with the help of the QSI modules [S. Liu et al., arXiv:1710.09500, 2017]. We report that both discrimination schemes achieve success probabilities at least 85%. Copyright © 2018, The Authors. All rights reserved.

关键词： Program processors

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Random asynchronous iterations in distributed coordination algorithms

arXiv

引用

arXiv 2018年

作者： Chen, Yao Xia, Weiguo Cao, Ming Lü, Jinhu Department of Computer Science Southwestern University of Finance and Economics Chengdu611130 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China Faculty of Science and Engineering ENTEG University of Groningen Groningen9747 AG Netherlands School of Automation Science and Electrical Engineering State Key Laboratory of Software Development Environment Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing100190 China

Distributed coordination algorithms (DCA) carry out information processing processes among a group of networked agents without centralized information fusion. Though it is well known that DCA characterized by an SIA (stochastic, indecomposable, aperiodic) matrix generate consensus asymptotically via synchronous iterations, the dynamics of DCA with asynchronous iterations have not been studied extensively, especially when viewed as stochastic processes. This paper aims to show that for any given irreducible stochastic matrix, even non-SIA, the corresponding DCA lead to consensus successfully via random asynchronous iterations under a wide range of conditions on the transition probability. Particularly, the transition probability is neither required to be independent and identically distributed, nor characterized by a Markov chain. Copyright © 2018, The Authors. All rights reserved.

关键词： Stochastic systems

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AGlobal benchmark of algorithms for segmentinglate gadolinium-enhanced cardiac magnetic resonance imaging

arXiv

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arXiv 2020年

作者： Xiong, Zhaohan Xia, Qing Hu, Zhiqiang Huang, Ning Bian, Cheng Zheng, Yefeng Vesal, Sulaiman Ravikumar, Nishant Maier, Andreas Yang, Xin Heng, Pheng-Ann Ni, Dong Li, Caizi Tong, Qianqian Si, Weixin Puybareau, Elodie Khoudli, Younes Géraud, Thierry Chen, Chen Bai, Wenjia Rueckert, Daniel Xu, Lingchao Zhuang, Xiahai Luo, Xinzhe Jia, Shuman Sermesant, Maxime Liu, Yashu Wang, Kuanquan Borra, Davide Masci, Alessandro Corsi, Cristiana De Vente, Coen Veta, Mitko Karim, Rashed Preetha, Chandrakanth Jayachandran Engelhardt, Sandy Qiao, Menyun Wang, Yuanyuan Tao, Qian Nuñez-Garcia, Marta Camara, Oscar Savioli, Nicolo Lamata, Pablo Zhao, Jichao Auckland Bioengineering Institute University of Auckland Auckland New Zealand State Key Lab of Virtual Reality Technology and Systems Beihang University Beijing China School of Electronics Engineering and Computer Science Peking University Beijing China SenseTime Inc Shenzhen China Tencent Jarvis Laboratory Shenzhen China Pattern Recognition Lab Friedrich-Alexander-Universität Erlangen-Nürnberg Erlangen Germany Department of Computer Science and Engineering Chinese University of Hong Kong Hong Kong National-Regional Key Technology Engineering Laboratory for Medical Ultrasound School of Biomedical Engineering Shenzhen University Shenzhen China School of Computer Science Wuhan University Wuhan China Shenzhen Key Laboratory of Virtual Reality and Human Interaction Technology Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China Epita Research and Development Laboratory Paris France Department of Computing Imperial College London London United Kingdom School of Naval Architecture Shanghai Jiao Tong University Ocean & Civil Engineering Shanghai China School of Data Science Fudan University Shanghai China Epione Research Group Universite Cote d'Azur Inria Sophia Antipolis France Harbin Institute of Technology School of Computer Science and Technology Harbin China Department of Electric University of Bologna Electronic and Information Engineering Cesena Italy Department of Biomedical Engineering Eindhoven University of Technology Eindhoven Netherlands School of Biomedical Engineering and Imaging Sciences Kings College London London United Kingdom Faculty of Electrical Engineering and Information Technology University of Magdeburg Magdeburg Germany Heidelberg University Hospital Germany Biomedical Engineering Center Fudan University Shanghai China Department of Radiology Leiden University Medical Center Leiden Netherlands Department of Information and Communication Technologies Universitat Pompeu Fabra Barcelona Spain D

Segmentation of cardiac images, particularly late gadolinium-enhanced magnetic resonance imaging (LGE-MRI)widely used for visualizing diseased cardiacstructures, is a crucial first step for clinical diagnosis and treatment. However, direct segmentation ofLGE-MRIsischallenging due to *** most clinical studies have relied on manual and labor-intensive approaches, automatic methods are of high interest, particularly optimized machine learning *** address this, we organized the "2018 Left Atrium Segmentation Challenge" using 154 3D LGE-MRIs, currently the world's largest cardiac LGE-MRI dataset, and associated labels of the left atriumsegmented by three medical experts,ultimately attractingthe participation of27 international teams. In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis,offeringan overall picture ofthe major design choices of convolutional neural networks(CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation. Results show the top methodachieved a dice score of 93.2%and a mean surface to a surface distance of 0.7mm, significantly outperforming prior state-of-the-art. Particularly, our analysisdemonstrated that double, sequentially usedCNNs, in which a first CNN is used for automatic region-of-interestlocalization and a subsequent CNN is used for refinedregional segmentation, achieved far superiorresults than traditional methods and pipelinescontaining *** findings from this study can potentially be extended to other imaging datasets and modalities, having an impact on the wider medical imaging *** large-scale benchmarking study makes a significant step towards much-improved segmentation methods for cardiac LGE-MRIs, and will serveas animportant benchmark for evaluating and comparing the future works in the field. Copyright © 2020, Th

关键词： Magnetic resonance imaging

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An Energy Efficient VM Migration Algorithm in data Centers

An Energy Efficient VM Migration Algorithm in Data Centers

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2017 16th International Symposium on Distributed computing and Applications to Business,Engineering and science（DCABES 2017）

作者： Xiaodong Wu Yuzhu Zeng Guoxin Lin Faculty of Mathematics and Computer Science Quanzhou Normal University Fujian Provincial Key Laboratory of Data Intensive Computing Key Laboratory of Intelligent Computing and Information ProcessingFujian Province University

In order to reduce operation and maintenance expense and also increase the resource utilization rate,server consolidation and virtualization solutions have been adopted in modern cloud computing data ***,the scheduling policies of virtual machine（VM） migration have been regarded as an effective method for energy *** this paper,we address the problem of VM consolidation in cloud data centers.A power aware scheduling algorithm THRUG,which is combined with a utilization threshold strategy and a VM selection policy,is *** tries to select the most appropriate VMs for migration each time when a physical machine（PM） is considered as being overloaded,such that the utilization of this PM is just not more than the utilization threshold,such that both the energy consumption and the number of VM migration can be *** experimental results show that compared with other algorithm,the proposed algorithm can effectively reduce the number of VM migrations as well as the energy consumption.

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Comparative validation of machine learning algorithms for surgical workflow and skill analysis with the heichole benchmark

arXiv

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arXiv 2021年

作者： Wagner, Martin Müller-Stich, Beat-Peter Kisilenko, Anna Tran, Duc Heger, Patrick Mündermann, Lars Lubotsky, David M. Müller, Benjamin Davitashvili, Tornike Capek, Manuela Reinke, Annika Yu, Tong Vardazaryan, Armine Innocent Nwoye, Chinedu Padoy, Nicolas Liu, Xinyang Lee, Eung-Joo Disch, Constantin Meine, Hans Xia, Tong Jia, Fucang Kondo, Satoshi Reiter, Wolfgang Jin, Yueming Long, Yonghao Jiang, Meirui Dou, Qi Heng, Pheng Ann Twick, Isabell Kirtac, Kadir Hosgor, Enes Bolmgren, Jon Lindström Stenzel, Michael von Siemens, Björn Kenngott, Hannes G. Nickel, Felix von Frankenberg, Moritz Mathis-Ullrich, Franziska Maier-Hein, Lena Speidel, Stefanie Bodenstedt, Sebastian Department for General Visceral and Transplantation Surgery Heidelberg University Hospital Im Neuenheimer Feld 420 Heidelberg69120 Germany Heidelberg Im Neuenheimer Feld 460 Heidelberg69120 Germany Data Assisted Solutions Corporate Research & Technology KARL STORZ SE & Co. KG Dr. Karl-Storz-Str. 34 Tuttlingen78332 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany Im Neuenheimer Feld 223 Heidelberg69120 Germany Faculty of Mathematics and Computer Science Heidelberg University Im Neuenheimer Feld 205 Heidelberg69120 Germany ICube University of Strasbourg CNRS France 300 bd Sébastien Brant - CS 10413 Illkirch CedexF-67412 France IHU Strasbourg France 1 Place de l'hôpital Strasbourg67000 France Sheikh Zayed Institute for Pediatric Surgical Innovation Children's National Hospital 111 Michigan Ave NW WashingtonDC20010 United States University of Maryland College Park 2405 A V Williams Building College ParkMD20742 United States Fraunhofer Institute for Digital Medicine MEVIS Max-von-Laue-Str. 2 Bremen28359 Germany University of Bremen FB3 Medical Image Computing Group ℅ Fraunhofer MEVIS Am Fallturm 1 Bremen28359 Germany Lab for Medical Imaging and Digital Surgery Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Konika Minolta Inc. JP TOWER 2-7-2 Marunouchi Chiyoda-ku Tokyo100-7015 Japan Wintegral GmbH Ehrenbreitsteiner Str. 36 München80993 Germany Department of Computer Science and Engineering Ho Sin-Hang Engineering Building The Chinese University of Hong Kong Sha Tin NT Hong Kong Caresyntax GmbH Komturstr. 18A Berlin12099 Germany Department of Surgery Salem Hospital of the Evangelische Stadtmission Heidelberg Zeppelinstrasse 11-33 Heidelberg69121 Germany Health Robotics and Automation Laboratory Institute for Anthropomatics and Robotics Karlsruhe Institute of Technology Geb. 40.28 KIT Campus Süd Engler-Bunte-Ring 8 Karlsruhe76131 Germany Medical Faculty Heidelberg Uni

PURPOSE: Surgical workflow and skill analysis are key technologies for the next generation of cognitive surgical assistance systems. These systems could increase the safety of the operation through context-sensitive warnings and semi-autonomous robotic assistance or improve training of surgeons via data-driven feedback. In surgical workflow analysis up to 91% average precision has been reported for phase recognition on an open data single-center video dataset. In this work we investigated the generalizability of phase recognition algorithms in a multi-center setting including more difficult recognition tasks such as surgical action and surgical skill. METHODS: To achieve this goal, a dataset with 33 laparoscopic cholecystectomy videos from three surgical centers with a total operation time of 22 hours was created. Labels included framewise annotation of seven surgical phases with 250 phase transitions, 5514 occurences of four surgical actions, 6980 occurences of 21 surgical instruments from seven instrument categories and 495 skill classifications in five skill dimensions. The dataset was used in the 2019 international Endoscopic Vision challenge, sub-challenge for surgical workflow and skill analysis. Here, 12 research teams trained and submitted their machine learning algorithms for recognition of phase, action, instrument and/or skill assessment. RESULTS: F1-scores were achieved for phase recognition between 23.9% and 67.7% (n=9 teams), for instrument presence detection between 38.5% and 63.8% (n=8 teams), but for action recognition only between 21.8% and 23.3% (n=5 teams). The average absolute error for skill assessment was 0.78 (n=1 team). CONCLUSION: Surgical workflow and skill analysis are promising technologies to support the surgical team, but are not solved yet, as shown by our comparison of machine learning algorithms. This novel HeiChole benchmark can be used for comparable evaluation and validation of future work. In future studies, it is of utmost impo

关键词： Learning algorithms

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Optimal control of predator-prey mathematical model with infection and harvesting on prey

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Journal of Physics: Conference Series 2018年第1期974卷

作者： R. U. Diva Amalia Fatmawati Windarto Didik Khusnul Arif Department of Mathematics Faculty of Science and Technology Universitas Airlangga Surabaya 60115 Indonesia Department of Mathematics Faculty of Mathematics Computing and Data Science Institut Teknologi Sepuluh Nopember Surabaya 60111 INDONESIA.

This paper presents a predator-prey mathematical model with infection and harvesting on prey. The infection and harvesting only occur on the prey population and it assumed that the prey infection would not infect predator population. We analysed the mathematical model of predator-prey with infection and harvesting in prey. Optimal control, which is a prevention of the prey infection, also applied in the model and denoted as U. The purpose of the control is to increase the susceptible prey. The analytical result showed that the model has five equilibriums, namely the extinction equilibrium (E 0), the infection free and predator extinction equilibrium (E 1), the infection free equilibrium (E 2), the predator extinction equilibrium (E 3), and the coexistence equilibrium (E 4). The extinction equilibrium (E 0) is not stable. The infection free and predator extinction equilibrium (E 1), the infection free equilibrium (E 2), also the predator extinction equilibrium (E 3), are locally asymptotically stable with some certain conditions. The coexistence equilibrium (E 4) tends to be locally asymptotically stable. Afterwards, by using the Maximum Pontryagin Principle, we obtained the existence of optimal control U. From numerical simulation, we can conclude that the control could increase the population of susceptible prey and decrease the infected prey.

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