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ACQUISITION REFORM AND BEST PRODUCT PROCUREMENT - AN engineering VIEW

NAVAL ENGINEERS JOURNAL 1994年第6期106卷 41-57页

作者： FISHER, DA SKOLNICK, A He has been involved with several weapon system developments. Among these were the Trident II fire control and navigation systems the BSY-I Anti-Submarine Warfare System and the Navy standard computers (UYK44 and EMSP). He served as manager of the Digital Circuits Engineering Branch and was then appointed as manager of the Automatic Test Equipment (ATE) Technology Division. As manager of the ATE Technology Division he was responsible for the SP-23 Fire Control System Support Project the SP-24 Navigation Project and the Fleet Progressive Maintenance Program (2M/ATE). This Division was responsible for selection and application of electronic product technology and for developing fleet test and repair techniques. He holds a B.S. in Electrical Engineering from the University of Evansville and is currently pursuing a Masters of Public Administration at Indiana University-Purdue University Indianapolis. Dr. Alfred Skolnick:retired from the Navy in 1983 with the rank of captain. He is president of System Science Consultants (SSC) specializing in strategic planning technical program definition technology assessment and engineering analyses on selected matters of national interest. Dr. Skolnick taught mathematics and management sciences at University of Virginia and Marymount University. He is adjunct faculty at Northern Virginia Community College. From 1985 to 1989 he was president of the American Society of Naval Engineers. In the Navy he served at Applied Physics Laboratory/The Johns Hopkins University then aboard USSBoston(CAG-1) and played leading roles in several weapon system developments inertial navigation (Polaris) deep submergence (DSRV) and advanced ship designs (SES). He later was director Combat System Integration Naval Sea Systems Command and head Combat Projects Naval Ship Engineering Center. In 1975 he became a major project manager and led the Navy's High Energy Lasers Program in 1981 he was assigned all Navy Directed Energy Weapons development efforts. He was vice president advanced tech

The military services are being moved in the direction of performance-based specifications and standards. They are being steered against dictating ''how to'' produce an item since such action forecloses on the ability to gain access to components or technology that may have a commercial equivalent. Why should the engineering community embrace the new approach? Aside from the obvious weight of it being approved policy, therefore currently mandated, it warrants examination because it is the correct approach at this time when applied to appropriate products. Military specifications and standards are to be displaced then, by acceptable alternative contractor design solutions. Industry bidders will be allowed to propose the particular design details, permitting procurement flexibility by contractually citing only system level or interface requirements, both physical and functional. Hopefully, this can broaden the industrial base and increase competition with reduced costs to follow. Conceptually, the approach appears both performance-sensible and cost-attractive (there are, of course, consequent risks) but how does implementation proceed? Is it possible to pursue the goals envisioned along paths that are not in themselves experimental? Can the American postulate, minimal loss of life and limb to U.S. military people, continue to be honored? Experience and track record elsewhere imply encouraging possibilities in select situations-useful prospects are identified and discussed in practical terms.

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The 2024 Active Metamaterials Roadmap

arXiv

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arXiv 2024年

作者： Pope, Simon A. Roth, Diane J. Bansal, Aakash Mousa, Mostafa Rezanejad, Ashkan Forte, Antonio E. Nash, Geoff.R. Singleton, Lawrence Langfeldt, Felix Cheer, Jordan Henthorn, Stephen Hooper, Ian R. Hendry, Euan Powell, Alex W. Souslov, Anton Plum, Eric Sun, Kai de Groot, C.H. Muskens, Otto L. Shields, Joe De Galarreta, Carlota Ruiz Wright, C. David Kocabas, Coskun Ergoktas, M. Said Xiao, Jianling Schulz, Sebastian A. Di Falco, Andrea Krasavin, Alexey V. Zayats, Anatoly V. Galiffi, Emanuele Department of Automatic Control and Systems Engineering The University of Sheffield Amy Johnson Building Portobello Street SheffieldS1 3JD United Kingdom Department of Physics London Centre for Nanotechnology King’s College London Strand WC2R 2LS United Kingdom Wolfson School of Mechanical Electrical and Manufacturing Engineering Loughborough University United Kingdom Department of Engineering King’s College London LondonWC2R 2LS United Kingdom Natural Sciences University of Exeter Harrison Building North Park Road Exeter United Kingdom Institute of Sound & Vibration Research University of Southampton University Road Southampton United Kingdom Department of Electronic and Electrical Engineering University of Sheffield Sheffield United Kingdom Centre for Metamaterial Research and Innovation University of Exeter Stoker Road ExeterEX4 4QL United Kingdom T.C.M. Group Cavendish Laboratory University of Cambridge CambridgeCB3 0HE United Kingdom Optoelectronics Research Centre Centre for Photonic Metamaterials University of Southampton Highfield SouthamptonSO17 1BJ United Kingdom School of Physics and Astronomy University of Southampton Southampton United Kingdom Electronics and Computer Science University of Southampton Southampton United Kingdom Institute of Materials Science of Barcelona Universitat Autònoma de Barcelona Bellaterra Barcelona08193 Spain Department of Materials National Graphene Institute University of Manchester United Kingdom Schoolof Physics and Astronomy University of St Andrews North Haugh Fife St. AndrewsKY16 9SS United Kingdom Department of Electrical and Computer Engineering University of Waterloo 200 University Avenue West WaterlooONN2L 3G1 Canada Photonics Initiative Advanced Science Research Centre City University of New York 85 St. Nicholas Terrace New YorkNY10027 United States

Active metamaterials are engineered structures that possess novel properties that can be changed after the point of manufacture. Their novel properties arise predominantly from their physical structure, as opposed to their chemical composition and can be changed through means such as direct energy addition into wave paths, or physically changing/morphing the structure in response to both a user or environmental input. Active metamaterials are currently of wide interest to the physics community and encompass a range of sub-domains in applied physics (e.g. photonic, microwave, acoustic, mechanical, etc.). They possess the potential to provide solutions that are more suitable to specific applications, or which allow novel properties to be produced which cannot be achieved with passive metamaterials, such as time-varying or gain enhancement effects. They have the potential to help solve some of the important current and future problems faced by the advancement of modern society, such as achieving net-zero, sustainability, healthcare and equality goals. Despite their huge potential, the added complexity of their design and operation, compared to passive metamaterials creates challenges to the advancement of the field, particularly beyond theoretical and lab -based experiments. This roadmap brings together experts in all types of active metamaterials and across a wide range of areas of applied physics. The objective is to provide an overview of the current state of the art and the associated current/future challenges, with the hope that the required advances identified create a roadmap for the future advancement and application of this field. Copyright © 2024, The Authors. All rights reserved.

关键词： Microwave acoustics

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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A Robust Ensemble Algorithm for Ischemic Stroke Lesion Segmentation: Generalizability and Clinical Utility Beyond the ISLES Challenge

arXiv

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arXiv 2024年

作者： de la Rosa, Ezequiel Reyes, Mauricio Liew, Sook-Lei Hutton, Alexandre Wiest, Roland Kaesmacher, Johannes Hanning, Uta Hakim, Arsany Zubal, Richard Valenzuela, Waldo Robben, David Sima, Diana M. Anania, Vincenzo Brys, Arne Meakin, James A. Mickan, Anne Broocks, Gabriel Heitkamp, Christian Gao, Shengbo Liang, Kongming Zhang, Ziji Siddiquee, Md Mahfuzur Rahman Myronenko, Andriy Ashtari, Pooya Van Huffel, Sabine Jeong, Hyun-Su Yoon, Chi-Ho Kim, Chulhong Huo, Jiayu Ourselin, Sebastien Sparks, Rachel Clèrigues, Albert Oliver, Arnau Lladó, Xavier Chalcroft, Liam Pappas, Ioannis Bertels, Jeroen Heylen, Ewout Moreau, Juliette Hatami, Nima Frindel, Carole Qayyum, Abdul Mazher, Moona Puig, Domenec Lin, Shao-Chieh Juan, Chun-Jung Hu, Tianxi Boone, Lyndon Goubran, Maged Liu, Yi-Jui Wegener, Susanne Kofler, Florian Ezhov, Ivan Shit, Suprosanna Hernandez Petzsche, Moritz R. Menze, Bjoern Kirschke, Jan S. Wiestler, Benedikt Department of Quantitative Biomedicine University of Zurich Zurich Switzerland Department of Informatics Technical University Munich Germany Icometrix Leuven Belgium ARTORG Center for Biomedical Research University of Bern Bern Switzerland Department of Radiation Oncology University Hospital Bern University of Bern Switzerland University of Bern Bern Switzerland Chan Division of Occupational Science and Occupational Therapy University of Southern California Los AngelesCA United States Stevens Neuroimaging and Informatics Institute Department of Neurology Keck School of Medicine University of Southern California United States University Institute of Diagnostic and Interventional Neuroradiology Inselspital Bern Switzerland University Institute of Diagnostic and Interventional Neuroradiology University Hospital Bern Inselspital University of Bern Bern Switzerland Department of Diagnostic and Interventional neuroradiology University Medical Center Hamburg-Eppendorf Hamburg Germany Department of Medical Imaging Radboud University Medical Center Institute for Health Sciences Nijmegen Netherlands Deepwise AI Lab Beijing China Beijing University of Posts and Telecommunications Bejing China School of Computing and Augmented Intelligence Arizona State University TempeAZ United States NVIDIA Santa ClaraCA United States STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium Pohang Korea Republic of School of Biomedical Engineering & Imaging Sciences King’s College London United Kingdom Institute of Computer Vision and Robotics University of Girona Spain Wellcome Centre for Human Neuroimaging University College London London United Kingdom Laboratory of Neuro Imaging Stevens Institute for Neuroimaging and Informatics Keck School of Medicine University of Southern California Los Angeles United States KU Leuven Leuven Belgium CREATIS Université Lyon1 CNRS UMR5220 INSERM U1206 INSA-Lyon Villeurbanne696

Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a novel ensemble algorithm derived from the 2022 Ischemic Stroke Lesion Segmentation (ISLES) challenge. ISLES’22 provided 400 patient scans with ischemic stroke from various medical centers, facilitating the development of a wide range of cutting-edge segmentation algorithms by the research community. By assessing them against a hidden test set, we identified strengths, weaknesses, and potential biases. Through collaboration with leading teams, we combined top-performing algorithms into an ensemble model that overcomes the limitations of individual solutions. Our ensemble model combines the individual algorithms’ strengths and achieved superior ischemic lesion detection and segmentation accuracy (median Dice score: 0.82, median lesion-wise F1 score: 0.86) on our internal test set compared to individual algorithms. This accuracy generalized well across diverse image and disease variables. Furthermore, the model excelled in extracting clinical biomarkers like lesion types and affected vascular territories. Notably, in a Turing-like test, neuroradiologists consistently preferred the algorithm’s segmentations over manual expert efforts, highlighting increased comprehensiveness and precision. Validation using a real-world external dataset (N=1686) confirmed the model’s generalizability (median Dice score: 0.82, median lesion-wise F1 score: 0.86). The algorithm’s outputs also demonstrated strong correlations with clinical scores (admission NIHSS and 90-day mRS) on par with or exceeding expert-derived results, underlining its clinical relevance. This study offers two key findings. First, we present an ensemble algorithm that detects and segments ischemic stroke lesions on DWI across diverse scenarios on par with expert (neuro)rad

关键词： Diagnosis

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Erratum to: The whole genome sequence of the Mediterranean fruit fly, Ceratitis capitata (Wiedemann), reveals insights into the biology and adaptive evolution of a highly invasive pest species

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Genome biology 2017年第1期18卷 11页

作者： Alexie Papanicolaou Marc F Schetelig Peter Arensburger Peter W Atkinson Joshua B Benoit Kostas Bourtzis Pedro Castañera John P Cavanaugh Hsu Chao Christopher Childers Ingrid Curril Huyen Dinh HarshaVardhan Doddapaneni Amanda Dolan Shannon Dugan Markus Friedrich Giuliano Gasperi Scott Geib Georgios Georgakilas Richard A Gibbs Sarah D Giers Ludvik M Gomulski Miguel González-Guzmán Ana Guillem-Amat Yi Han Artemis G Hatzigeorgiou Pedro Hernández-Crespo Daniel S T Hughes Jeffery W Jones Dimitra Karagkouni Panagiota Koskinioti Sandra L Lee Anna R Malacrida Mosè Manni Kostas Mathiopoulos Angela Meccariello Monica Munoz-Torres Shwetha C Murali Terence D Murphy Donna M Muzny Georg Oberhofer Félix Ortego Maria D Paraskevopoulou Monica Poelchau Jiaxin Qu Martin Reczko Hugh M Robertson Andrew J Rosendale Andrew E Rosselot Giuseppe Saccone Marco Salvemini Grazia Savini Patrick Schreiner Francesca Scolari Paolo Siciliano Sheina B Sim George Tsiamis Enric Ureña Ioannis S Vlachos John H Werren Ernst A Wimmer Kim C Worley Antigone Zacharopoulou Stephen Richards Alfred M Handler Hawkesbury Institute for the Environment Western Sydney University Sydney Australia. Justus-Liebig-University Giessen Institute for Insect Biotechnology 35394 Giessen Germany. Department of Biological Sciences Cal Poly Pomona Pomona CA 91768 USA. Department of Entomology and Center for Disease Vector Research University of California Riverside Riverside CA 92521 USA. Interdepartmental Graduate Program in Genetics Genomics & Bioinformatics University of California Riverside Riverside CA 92521 USA. Department of Biological Sciences University of Cincinnati Cincinnati OH 45221 USA. Insect Pest Control Laboratory Joint FAO/IAEA Programme of Nuclear Techniques in Food and Agriculture Seibersdorf Vienna Austria. Department of Environmental and Natural Resources Management University of Patras Agrinio Greece. Department of Environmental Biology Centro de Investigaciones Biológicas CSIC 28040 Madrid Spain. Human Genome Sequencing Center Department of Human and Molecular Genetics Baylor College of Medicine 77030 Houston TX USA. National Agricultural Library USDA 20705 Beltsville MD USA. Georg-August-Universität Göttingen Johann-Friedrich-Blumenbach-Institut für Zoologie und Anthropologie 37077 Göttingen Germany. Department of Biology University of Rochester 14627 Rochester NY USA. Department of Biological Sciences Wayne State University 48202 Detroit MI USA. Department of Biology and Biotechnology University of Pavia 27100 Pavia Italy. USDA-ARS Pacific Basin Agricultural Research Center 96720 Hilo HI USA. DIANA-Lab Department of Electrical & Computer Engineering University of Thessaly 382 21 Volos Greece and Hellenic Pasteur Institute 11521 Athens Greece. Department of Entomology University of Illinois at Urbana-Champaign 61801 Urbana IL USA. Department of Biological Sciences Oakland University 48309 Rochester MI USA. Department of Biochemistry and Biotechnology University of Thessaly Larissa Greece. Department of Biology Univer

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