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1. CURE: Privacy-Preserving Split Learning Done Right

Author

Kanpak, Halil Ibrahim, Shabbir, Aqsa, Genç, Esra, Küpçü, Alptekin, and Sav, Sinem

Subjects
Computer Science - Cryptography and Security
Abstract

Training deep neural networks often requires large-scale datasets, necessitating storage and processing on cloud servers due to computational constraints. The procedures must follow strict privacy regulations in domains like healthcare. Split Learning (SL), a framework that divides model layers between client(s) and server(s), is widely adopted for distributed model training. While Split Learning reduces privacy risks by limiting server access to the full parameter set, previous research has identified that intermediate outputs exchanged between server and client can compromise client's data privacy. Homomorphic encryption (HE)-based solutions exist for this scenario but often impose prohibitive computational burdens. To address these challenges, we propose CURE, a novel system based on HE, that encrypts only the server side of the model and optionally the data. CURE enables secure SL while substantially improving communication and parallelization through advanced packing techniques. We propose two packing schemes that consume one HE level for one-layer networks and generalize our solutions to n-layer neural networks. We demonstrate that CURE can achieve similar accuracy to plaintext SL while being 16x more efficient in terms of the runtime compared to the state-of-the-art privacy-preserving alternatives.

Published
2024

15. Enhancement in performance of RC beam-column joint under cyclic loading by using various grades of engineered cementitious composites in the joint zone-a sustainable numerical study.

Author

Muhammad, Atta, Sahar, Umbreen Us, Mughal, Ubaid Ahmad, Siddique, Iffat, and Shabbir, Aqsa

Subjects
CEMENT composites, CYCLIC loads, BEAM-column joints, FINITE element method, CRACK propagation (Fracture mechanics), CONCRETE fatigue
Abstract

In this research, the response of the RC Beam-Column Joint under the cyclic loads was analyzed with a FEM solver (Abaqus) by using various grades of Engineered Cementitious Composite (ECC) in the joint zone. The concrete damage plasticity model for concrete and ECC material under cyclic loading was proposed. Accordingly, the Finite Element Analysis (FEA) model was established with the consideration of different constitutive material models for concrete and Engineered Cementitious Composites. The accuracy of the proposed FEA model was validated with the experimental results. Failure mechanism, crack propagation, moment rotation relationship, and energy absorption under cyclic loading were investigated. Under controlled deformation, cyclic loads were applied to the samples at the end of the beam up to the failure point. It was found that the use of various grades of Engineered Cementitious Composites in the joint zone was effective in enhancing the ultimate moment capacity upto 38.50%, ductility upto 13.72%, and siffniness degradation upto 28.57% respectively of the joint up to the failure stage alongwith significant reduction in crack propagation. [ABSTRACT FROM AUTHOR]

Published
2024
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16. EVALUATING THE WORK DESIGN READINESS FOR INDUSTRY 4.0 BASED ON PERSONAL CHARACTERISTICS OF PRODUCTION WORKERS.

Author

Bugvi, Salman Abubakar, Mughal, Khurram Hameed, Jamil, Muhammad Fawad, Kazim, Ali Hussain, Shabbir, Aqsa, Shahid, Muhammad Usama, Dar, Muhammad Uzair, Musa, Muhammad, and Imran, Hassan

Subjects
WORK design, ORGANIZATIONAL commitment, INDUSTRY 4.0, JOB rotation, LABOR productivity, SELF-efficacy, FACTORIES, INTELLIGENT buildings
Abstract

The design of work is a crucial consideration for production workers in terms of performance, satisfaction, and motivation. This aspect has garnered increasing attention in research, particularly with the rise of automation and the transition to Industry 4.0-enabled smart factories. Work design is influenced by both internal and external performance-shaping factors (PSFs). Internal factors include human, machine and task elements and traits, which HR specialists can structure to enhance efficiency and productivity in manufacturing. External PSFs, such as physical, psychological, social and organizational factors, also play a significant role in shaping production work. This study aims to understand the impact of personal characteristics of production workers in an automated factory. The research findings are extrapolated on an Industry 4.0 framework to establish a productive work design. Data collection is conducted through a quantitative survey, and non-parametric analysis of variance tests are employed to assess significant relationships. The results indicate a better perception of the pace of operations (µ = 4.19) and feedback (µ = 4.47) among elder workers. Additionally, educated workers express significant perceptions regarding external PSFs such as supervision (µ = 4.39), health and safety (µ = 4.25), job rotation (µ = 3.91) and pay and welfare (µ = 3.58). Aspiration, motivation and organizational commitment emerge as crucial work design attributes for experienced and married workers. These findings contribute to understanding the structure of modern work design in the context of Industry 4.0, aiming to enhance performance and worker productivity. The study proposes an advanced Industry 4.0 work design framework, empowering HR specialists to develop effective strategies and optimize work distribution. [ABSTRACT FROM AUTHOR]

Published
2024
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17. Corruption in infrastructure procurement : a study based on procurement of infrastructure projects in Pakistan

Author

Shabbir, Aqsa and Fenn, Francis

Subjects
364.1, Infrastructure, Procurement, Corruption, Public-Private-Partnership, Pakistan
Abstract

The main purpose of this research project is to bridge the existing knowledgegap in the empirical identification and understanding of the most frequentcorrupt actions and the causes behind during procurement of infrastructureprojects in Pakistan, in addition to exploring the ways to enhance institutionalbasedtrust between the participants of the procurement process. Consequentlythe study aims to provide a conceptual framework to control corruption ininfrastructure procurement while proposing the institutional trust-buildingmechanisms. Both qualitative and quantitative approaches are utilised in thisstudy to achieve this research aim. Quantitative research data is collected usinga questionnaire survey. A total of 450 questionnaires were sent to variouspeople engaged in procurement of infrastructure projects in Pakistan. Theresponse rate was 36.7% (n=165). The questionnaire comprises of two mainquestions; one is about the most frequent corrupt actions in traditional andPublic Private Partnership (PPP) infrastructure procurement processes whileother question asks about the perceived institutional trust-building mechanismsin context of infrastructure procurement market in Pakistan. Various appropriatestatistical methods, including Mean Ranking and ANOVA were utilised toanalyse the collected data. The questionnaire survey was followed by 15 indepth semi-structured interviews with a variety of stakeholders. Theseinterviews provided information on various causes of corruption and reasons asto why people do not for example report a known incident of corruption. A traditional content analysis approach was used to analyse the data collectedusing interviews. From the analysis a cyclical framework of corruption controlemerged, and this is outlined within the thesis. The goal of this framework is tofacilitate procurement stakeholders (individuals, groups, or organisations), toimprove their anti-corruption plans from project to project. This research studyhas filled the knowledge gap through identifying the top twenty potentialcorrupt practices in traditional and PPP infrastructure procurement processes inPakistan and explored the causes behind their occurrence. The study alsorecommends the solutions to mitigate this problem throughout the life cycle ofprocurement process. In addition, the study proposes the institutional trustbuildingmechanisms in the context of infrastructure procurement market inPakistan to cater for the likely loss in trust due to perceived level of corruptionin this sector. The study has also introduced a conceptual framework to controlcorruption in infrastructure procurement process in general and particularly inPakistan. The framework does not intend to introduce new alternatives butinstead builds on existing practices so that users can more easily adapt to theimprovement. The findings of this research are believed to be useful for allpractitioners who are either considering or currently involved in infrastructureprocurement process in Pakistan and trying to avoid or minimise the influenceof corruption.

Published
2015

20. Color Texture Discrimination Using the Principal Geodesic Distance on a Multivariate Generalized Gaussian Manifold

Author

Verdoolaege, Geert, Shabbir, Aqsa, Hutchison, David, Series editor, Kanade, Takeo, Series editor, Kittler, Josef, Series editor, Kleinberg, Jon M., Series editor, Mattern, Friedemann, Series editor, Mitchell, John C., Series editor, Naor, Moni, Series editor, Pandu Rangan, C., Series editor, Steffen, Bernhard, Series editor, Terzopoulos, Demetri, Series editor, Tygar, Doug, Series editor, Weikum, Gerhard, Series editor, Nielsen, Frank, editor, and Barbaresco, Frédéric, editor

Published
2015
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34. Characterization of the viscosity of bio-oil produced by fast pyrolysis of the wheat straw.

Author

Younis, Muhammad Rizwan, Farooq, Muhammad, Imran, Muhammad, Kazim, Ali Hussain, and Shabbir, Aqsa

Subjects
HEAT transfer coefficient, VISCOSITY, NUSSELT number, HEAT exchangers, HEAT transfer, WHEAT straw, HEAVY oil
Abstract

The aim of this work is to evaluate the role of viscosity of the organic condensate obtained from fast pyrolysis of wheat straw. Based on the experimental data, a model has been developed that is able to adequately describe the viscosity as a function of relevant process parameters. The developed model is helpful to investigate the reduction of heat transfer in the heat exchangers of the organic condensate cycle when glycol is replaced by an organic condensate. Temperature, solid contents, and water contents play a significant role and affect the viscosity of bio-oil. As temperature increases, the viscosity of oil reduces rapidly. Viscosity-temperature profile follows an Arrhenius-type-relationship, where the viscosity of the bio-oil decreases exponentially with increasing temperature. As solid content increases, exponential increase of viscosity takes place. However, the increase in water content results in decrease of viscosity on logarithmic scale. The range of water addition in organic condensate is possible up to a certain limit, after certain limit it starts to separate out in two phases. Investigation of reduction of heat transfer in heat exchanger shows that Nusselt number decreases with the increase of viscosity. Therefore, the overall heat transfer coefficient will decrease in case of organic condensate as compared to glycol. [ABSTRACT FROM AUTHOR]

Published
2021
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35. Self-consistent pedestal prediction for JET-ILW in preparation of the DT campaign

Author

Saarelma, S., Frassinetti, L., Bilkova, P., Challis, C. D., Chankin, A., Fridstrom, R., Garzotti, L., Horvath, L., Maggi, C. F., Abduallev, S., Abhangi, M., Abreu, P., Afanasev, V, Afzal, M., Aggarwal, K. M., Ahlgren, T., Aho-Mantila, L., Aiba, N., Airila, M., Alarcon, T., Albanese, R., Alegre, D., Aleiferis, S., Alessi, E., Aleynikov, P., Alkseev, A., Allinson, M., Alper, B., Alves, E., Ambrosino, G., Ambrosino, R., Amosov, V, Sunden, E. Andersson, Andrews, R., Angelone, M., Anghel, M., Angioni, C., Appel, L., Appelbee, C., Arena, P., Ariola, M., Arshad, S., Artaud, J., Arter, W., Ash, A., Ashikawa, N., Aslanyan, V, Asunta, O., Asztalos, O., Auriemma, F., Austin, Y., Avotina, L., Axton, M., Ayres, C., Baciero, A., Baiao, D., Balboa, I, Balden, M., Balshaw, N., Bandaru, V. K., Banks, J., Baranov, Y. F., Barcellona, C., Barnard, T., Barnes, M., Barnsley, R., Wiechec, A. Baron, Orte, L. Barrera, Baruzzo, M., Basiuk, V, Bassan, M., Bastow, R., Batista, A., Batistoni, P., Baumane, L., Bauvir, B., Baylor, L., Beaumont, P. S., Beckers, M., Beckett, B., Bekris, N., Beldishevski, M., Bell, K., Belli, F., Belonohy, E., Benayas, J., Bergsaker, H., Bernardo, J., Bernert, M., Berry, M., Bertalot, L., Besiliu, C., Betar, H., Beurskens, M., Bielecki, J., Biewer, T., Bilato, R., Biletskyi, O., Binda, F., Birkenmeier, G., Bizarro, J. P. S., Bjorkas, C., Blackburn, J., Blackman, T. R., Blanchard, P., Blatchford, P., Bobkov, V, Boboc, A., Bogar, O., Bohm, P., Bohm, T., Bolshakova, I, Bolzonella, T., Bonanomi, N., Boncagni, L., Bonfiglio, D., Bonnin, X., Boom, J., Borba, D., Borodin, D., Borodkina, I, Boulbe, C., Bourdelle, C., Bowden, M., Bowman, C., Boyce, T., Boyer, H., Bradnam, S. C., Braic, V, Bravanec, R., Breizman, B., Brennan, D., Breton, S., Brett, A., Brezinsek, S., Bright, M., Brix, M., Broeckx, W., Brombin, M., Broslawski, A., Brown, B., Brunetti, D., Bruno, E., Buch, J., Buchanan, J., Buckingham, R., Buckley, M., Bucolo, M., Budny, R., Bufferand, H., Buller, S., Bunting, P., Buratti, P., Burckhart, A., Burroughes, G., Buscarino, A., Busse, A., Butcher, D., Butler, B., Bykov, I, Cahyna, P., Calabro, G., Calacci, L., Callaghan, D., Callaghan, J., Calvo, I, Camenen, Y., Camp, P., Campling, D. C., Cannas, B., Capat, A., Carcangiu, S., Card, P., Cardinali, A., Carman, P., Carnevale, D., Carr, M., Carralero, D., Carraro, L., Carvalho, B. B., Carvalho, I, Carvalho, P., Carvalho, D. D., Casson, F. J., Castaldo, C., Catarino, N., Causa, F., Cavazzana, R., Cave-Ayland, K., Cavedon, M., Cecconello, M., Ceccuzzi, S., Cecil, E., Chandra, D., Chang, C. S., Chapman, I. T., Chapman, B., Chapman, S. C., Chernyshova, M., Chiariello, A., Chitarin, G., Chmielewski, P., Chone, L., Ciraolo, G., Ciric, D., Citrin, J., Clairet, F., Clark, M., Clark, E., Clarkson, R., Clay, R., Clements, C., Coad, J. P., Coates, P., Cobalt, A., Coccorese, V, Cocilovo, V, Coelho, R., Coenen, J. W., Coffey, I, Colas, L., Colling, B., Collins, S., Conka, D., Conroy, S., Conway, N., Coombs, D., Cooper, S. R., Corradino, C., Corre, Y., Corrigan, G., Coster, D., Craciunescu, T., Cramp, S., Crapper, C., Crisanti, F., Croci, G., Croft, D., Crombé, Kristel, Cruz, N., Cseh, G., Cufar, A., Cullen, A., Curson, P., Curuia, M., Czarnecka, A., Czarski, T., Cziegler, I, Dabirikhah, H., Dal Molin, A., Dalgliesh, P., Dalley, S., Dankowski, J., Darrow, D., David, P., Davies, A., Davis, W., Dawson, K., Day, I, Day, C., De Bock, M., de Castro, A., De Dominici, G., de la Cal, E., de la Luna, E., De Masi, G., De Temmerman, G., De Tommasi, G., de Vries, P., Deane, J., Dejarnac, R., Del Sarto, D., Delabie, E., Demerdzhiev, V, Dempsey, A., den Harder, N., Dendy, R. O., Denis, J., Denner, P., Devaux, S., Devynck, P., Di Maio, F., Di Siena, A., Di Troia, C., Dickinson, D., Dinca, P., Dittmar, T., Dobrashian, J., Doerk, H., Doerner, R. P., Domptail, F., Donne, T., Dorling, S. E., Douai, D., Dowson, S., Drenik, A., Dreval, M., Drewelow, P., Drews, P., Duckworth, Ph, Dumont, R., Dumortier, P., Dunai, D., Dunne, M., Duran, I, Durodie, F., Dutta, P., Duval, B. P., Dux, R., Dylst, K., Edappala, P., V, Edwards, A. M., Edwards, J. S., Eich, Th, Eidietis, N., Eksaeva, A., Ellis, R., Ellwood, G., Elsmore, C., Emery, S., Enachescu, M., Ericsson, G., Eriksson, J., Eriksson, F., Eriksson, L. G., Ertmer, S., Esquembri, S., Esquisabel, A. L., Esser, H. G., Ewart, G., Fable, E., Fagan, D., Faitsch, M., Falie, D., Fanni, A., Farahani, A., Fasoli, A., Faugeras, B., Fazinic, S., Felici, F., Felton, R. C., Feng, S., Fernades, A., Fernandes, H., Ferreira, J., Ferreira, D. R., Ferro, G., Fessey, J. A., Ficker, O., Field, A., Fietz, S., Figini, L., Figueiredo, J., Figueiredo, A., Fil, N., Finburg, P., Fischer, U., Fittill, L., Fitzgerald, M., Flammini, D., Flanagan, J., Flinders, K., Foley, S., Fonnesu, N., Fontdecaba, J. M., Formisano, A., Forsythe, L., Fortuna, L., Fransson, E., Frasca, M., Freisinger, M., Fresa, R., Frigione, D., Fuchs, V, Fusco, V, Futatani, S., Gal, K., Galassi, D., Galazka, K., Galeani, S., Gallart, D., Galvao, R., Gao, Y., Garcia, J., Garcia-Carrasco, A., Garcia-Munoz, M., Gardener, M., Gaspar, J., Gaudio, P., Gear, D., Gebhart, T., Gee, S., Geiger, B., Gelfusa, M., George, R., Gerasimov, S., Gervasini, G., Gethins, M., Ghani, Z., Ghate, M., Gherendi, M., Ghezzi, F., Giacalone, J. C., Giacomelli, L., Giacometti, G., Gibson, K., Giegerich, T., Gil, L., Gilbert, M. R., Gin, D., Giovannozzi, E., Giroud, C., Gloeggler, S., Goff, J., Gohil, P., Goloborod'ko, V, Gomes, R., Goncalves, B., Goniche, M., Goodyear, A., Gorini, G., Goerler, T., Goulding, R., Goussarov, A., Graham, B., Graves, J. P., Greuner, H., Grierson, B., Griffiths, J., Griph, S., Grist, D., Groth, M., Grove, R., Gruca, M., Guard, D., Guerard, C., Guillemaut, C., Guirlet, R., Gulati, S., Gurl, C., Gutierrez-Milla, A., Utoh, H. H., Hackett, L., Hacquin, S., Hager, R., Hakola, A., Halitovs, M., Hall, S., Hallworth-Cook, S., Ham, C., Hamed, M., Hamilton, N., Hamlyn-Harris, C., Hammond, K., Hancu, G., Harrison, J., Harting, D., Hasenbeck, F., Hatano, Y., Hatch, D. R., Haupt, T., Hawes, J., Hawkes, N. C., Hawkins, J., Hawkins, P., Hazel, S., Heesterman, P., Heinola, K., Hellesen, C., Hellsten, T., Helou, W., Hemming, O., Hender, T. C., Henderson, S. S., Henderson, M., Henriques, R., Hepple, D., Herfindal, J., Hermon, G., Hidalgo, C., Higginson, W., Highcock, E. G., Hillesheim, J., Hillis, D., Hizanidis, K., Hjalmarsson, A., Ho, A., Hobirk, J., Hogben, C. H. A., Hogeweij, G. M. D., Hollingsworth, A., Hollis, S., Hoelzl, M., Honore, J-J, Hook, M., Hopley, D., Horacek, J., Hornung, Grégoire, Horton, A., Horton, L. D., Hotchin, S. P., Howell, R., Hubbard, A., Huber, A., Huber, V, Huddleston, T. M., Hughes, M., Hughes, J., Huijsmans, G. T. A., Huynh, P., Hynes, A., Igaune, I., Iglesias, D., Imazawa, N., Imrisek, M., Incelli, M., Innocente, P., Ivanova-Stanik, I., Ivings, E., Jachmich, S., Jackson, A., Jackson, T., Jacquet, P., Jansons, J., Jaulmes, F., Jednorog, S., Jenkins, I, Jepu, I, Johnson, T., Johnson, R., Johnston, J., Joita, L., Joly, J., Jonasson, E., Jones, T., Jones, C., Jones, L., Jones, G., Jones, N., Juvonen, M., Hoshino, K. K., Kallenbach, A., Kalsey, M., Kaltiaisenaho, T., Kamiya, K., Kaniewski, J., Kantor, A., Kappatou, A., Karhunen, J., Karkinsky, D., Kaufman, M., Kaveney, G., Kazakov, Y., Kazantzidis, V, Keeling, D. L., Keenan, F. P., Kempenaars, M., Kent, O., Kent, J., Keogh, K., Khilkevich, E., Kim, H-T, Kim, H. T., King, R., King, D., Kinna, D. J., Kiptily, V, Kirk, A., Kirov, K., Kirschner, A., Kizane, G., Klas, M., Klepper, C., Klix, A., Knight, M., Knight, P., Knipe, S., Knott, S., Kobuchi, T., Kochl, F., Kocsis, G., Kodeli, I, Koechl, F., Kogut, D., Koivuranta, S., Kolesnichenko, Y., Kollo, Z., Kominis, Y., Koeppen, M., Korolczuk, S., Kos, B., Koslowski, H. R., Kotschenreuther, M., Koubiti, M., Kovaldins, R., Kovanda, O., Kowalska-Strzeciwilk, E., Krasilnikov, A., Krasilnikov, V, Krawczyk, N., Kresina, M., Krieger, K., Krivska, A., Kruezi, U., Ksiazek, I, Kukushkin, A., Kundu, A., Kurki-Suonio, T., Kwak, S., Kwon, O. J., Laguardia, L., Lahtinen, A., Laing, A., Lalousis, P., Lam, N., Lamb, C., Lambertz, H. T., Lang, P. T., Lanthaler, S., Neto, E. Lascas, Laszynska, E., Lawless, R., Lawson, K. D., Lazaros, A., Lazzaro, E., Leach, R., Learoyd, G., Leerink, S., Lefebvre, X., Leggate, H. J., Lehmann, J., Lehnen, M., Leichauer, P., Leichtle, D., Leipold, F., Lengar, I, Lennholm, M., Lepiavko, B., Leppanen, J., Lerche, E., Lescinskis, A., Lescinskis, B., Lesnoj, S., Leyland, M., Leysen, W., Li, Y., Li, L., Liang, Y., Likonen, J., Linke, J., Linsmeier, Ch, Lipschultz, B., Litaudon, X., Liu, G., Lloyd, B., Lo Schiavo, V. P., Loarer, T., Loarte, A., Lomanowski, B., Lomas, P. J., Lonnroth, J., Lopez, J. M., Lorenzini, R., Losada, U., Loughlin, M., Lowry, C., Luce, T., Lucock, R., Lukin, A., Luna, C., Lungaroni, M., Lungu, C. P., Lungu, M., Lunniss, A., Lunt, T., Lupelli, I, Lutsenko, V, Lyssoivan, A., Macheta, P., Macusova, E., Magesh, B., Maggi, C., Maggiora, R., Mahesan, S., Maier, H., Mailloux, J., Maingi, R., Makwana, R., Malaquias, A., Malinowski, K., Malizia, A., Manas, P., Manduchi, G., Manso, M. E., Mantica, P., Mantsinen, M., Manzanares, A., Maquet, Ph, Marandet, Y., Marcenko, N., Marchetto, C., Marchuk, O., Marconato, N., Mariani, A., Marin, M., Marinelli, M., Marinucci, M., Markovic, T., Marocco, D., Marot, L., Marsh, J., Martin, A., Martin de Aguilera, A., Martin-Solis, J. R., Martone, R., Martynova, Y., Maruyama, S., Maslov, M., Matejcik, S., Mattei, M., Matthews, G. F., Matveev, D., Matveeva, E., Mauriya, A., Maviglia, F., May-Smith, T., Mayer, M., Mayoral, M. L., Mazon, D., Mazzotta, C., McAdams, R., McCarthy, P. J., McClements, K. G., McCormack, O., McCullen, P. A., McDonald, D., McHardy, M., McKean, R., McKehon, J., McNamee, L., Meadowcroft, C., Meakins, A., Medley, S., Meigh, S., Meigs, A. G., Meisl, G., Meiter, S., Meitner, S., Meneses, L., Menmuir, S., Mergia, K., Merle, A., Merriman, P., Mertens, Ph, Meshchaninov, S., Messiaen, A., Meyer, H., Michling, R., Milanesio, D., Militello, F., Militello-Asp, E., Milocco, A., Miloshevsky, G., Mink, F., Minucci, S., Miron, I, Mistry, S., Miyoshi, Y., Mlynar, J., Moiseenko, V, Monaghan, P., Monakhov, I, Moon, S., Mooney, R., Moradi, S., Morales, J., Moran, J., Mordijck, S., Moreira, L., Moro, F., Morris, J., Moser, L., Mosher, S., Moulton, D., Mrowetz, T., Muir, A., Muraglia, M., Murari, A., Muraro, A., Murphy, S., Muscat, P., Muthusonai, N., Myers, C., Asakura, N. N., N'Konga, B., Nabais, F., Naish, R., Naish, J., Nakano, T., Napoli, F., Nardon, E., Naulin, V, Nave, M. F. F., Nedzelskiy, I, Nemtsev, G., Nesenevich, V, Nespoli, F., Neto, A., Neu, R., Neverov, V. S., Newman, M., Ng, S., Nicassio, M., Nielsen, A. H., Nina, D., Nishijima, D., Noble, C., Nobs, C. R., Nocente, M., Nodwell, D., Nordlund, K., Nordman, H., Normanton, R., Noterdaeme, J. M., Nowak, S., Nunes, I, O'Gorman, T., O'Mullane, M., Oberkofler, M., Oberparleiter, M., Odupitan, T., Ogawa, M. T., Okabayashi, M., Oliver, H., Olney, R., Omoregie, L., Ongena, J., Orsitto, F., Orszagh, J., Osborne, T., Otin, R., Owen, A., Owen, T., Paccagnella, R., Packer, L. W., Pajuste, E., Pamela, S., Panja, S., Papp, P., Papp, G., Parail, V, Pardanaud, C., Diaz, F. Parra, Parsloe, A., Parsons, N., Parsons, M., Pasqualotto, R., Passeri, M., Patel, A., Pathak, S., Patten, H., Pau, A., Pautasso, G., Pavlichenko, R., Pavone, A., Pawelec, E., Soldan, C. Paz, Peackoc, A., Pehkonen, S-P, Peluso, E., Penot, C., Penzo, J., Pepperell, K., Pereira, R., Cippo, E. Perelli, von Thun, C. Perez, Pericoli, V, Peruzzo, S., Peterka, M., Petersson, P., Petravich, G., Petre, A., Petrzilka, V, Philipps, V, Pigatto, L., Pillon, M., Pinches, S., Pintsuk, G., Piovesan, P., de Sa, W. Pires, dos Reis, A. Pires, Piron, L., Piron, C., Pironti, A., Pisano, F., Pitts, R., Plyusnin, V, Poli, F. M., Pomaro, N., Pompilian, O. G., Pool, P., Popovichev, S., Poradzinski, M., Porfiri, M. T., Porosnicu, C., Porton, M., Possnert, G., Potzel, S., Poulipoulis, G., Powell, T., Prajapati, V, Prakash, R., Predebon, I, Prestopino, G., Price, D., Price, M., Price, R., Primetzhofer, D., Prior, P., Pucella, G., Puglia, P., Puiatti, M. E., Purahoo, K., Pusztai, I, Puetterich, Th, Rachlew, E., Rack, M., Ragona, R., Rainford, M., Raj, P., Rakha, A., Ramogida, G., Ranjan, S., Rapson, C. J., Rasmussen, D., Rasmussen, J. J., Rathod, K., Ratta, G., Ratynskaia, S., Ravera, G., Rebai, M., Reed, A., Refy, D., Regana, J., Reich, M., Reid, N., Reimold, F., Reinhart, M., Reinke, M., Reiser, D., Rendell, D., Reux, C., Cortes, S. D. A. Reyes, Reynolds, S., Ricci, D., Richiusa, M., Rigamonti, D., Rimini, F. G., Risner, J., Riva, M., Rivero-Rodriguez, J., Roach, C., Robins, R., Robinson, S., Robson, D., Rodionov, R., Rodrigues, P., Rodriguez, J., Rohde, V, Romanelli, M., Romanelli, F., Romanelli, S., Romazanov, J., Rowe, S., Rubel, M., Rubinacci, G., Rubino, G., Ruchko, L., Ruset, C., Rzadkiewicz, J., Sabot, R., Saez, X., Safi, E., Sahlberg, A., Saibene, G., Saleem, M., Salewski, M., Salmi, A., Salmon, R., Salzedas, F., Samm, U., Sandiford, D., Santa, P., Santala, M. I. K., Santos, B., Santucci, A., Sartori, F., Sartori, R., Sauter, O., Scannell, R., Schluck, F., Schlummer, T., Schmid, K., Schmuck, S., Schoepf, K., Schweinzer, J., Schworer, D., Scott, S. D., Sergienko, G., Sertoli, M., Shabbir, Aqsa, Sharapov, S. E., Shaw, A., Sheikh, H., Shepherd, A., Shevelev, A., Shiraki, D., Shumack, A., Sias, G., Sibbald, M., Sieglin, B., Silburn, S., Silva, J., Silva, A., Silva, C., Silvagni, D., Simmons, P., Simpson, J., Sinha, A., Sipila, S. K., Sips, A. C. C., Siren, P., Sirinelli, A., Sjostrand, H., Skiba, M., Skilton, R., Skvara, V, Slade, B., Smith, R., Smith, P., Smith, S. F., Snoj, L., Soare, S., Solano, E. R., Somers, A., Sommariva, C., Sonato, P., Sos, M., Sousa, J., Sozzi, C., Spagnolo, S., Sparapani, P., Spelzini, T., Spineanu, F., Sprada, D., Sridhar, S., Stables, G., Stallard, J., Stamatelatos, I, Stamp, M. F., Stan-Sion, C., Stancar, Z., Staniec, P., Stankunas, G., Stano, M., Stavrou, C., Stefanikova, E., Stepanov, I, Stephen, A., V, Stephen, M., Stephens, J., Stevens, B., Stober, J., Stokes, C., Strachan, J., Strand, P., Strauss, H. R., Strom, P., Studholme, W., Subba, F., Suchkov, E., Summers, H. P., Sun, H., Sutton, N., Svensson, J., Sytnykov, D., Szabolics, T., Szepesi, G., Suzuki, T. T., Tabares, F., Tadic, T., Tal, B., Tala, T., Taliercio, C., Tallargio, A., Tanaka, K., Tang, W., Tardocchi, M., Tatali, R., Taylor, D., Tegnered, D., Telesca, Giuseppe, Teplova, N., Teplukhina, A., Terranova, D., Terry, C., Testa, D., Tholerus, E., Thomas, J., Thompson, V. K., Thornton, A., Tierens, W., Tiseanu, I, Tojo, H., Tokitani, M., Tolias, P., Tomes, M., Trimble, P., Tripsky, M., Tsalas, M., Tsavalas, P., Tskhakaya, D., Jun, D. Tskhakaya, Turner, I, Turner, M. M., Turnyanskiy, M., Tvalashvili, G., Tyshchenko, M., Uccello, A., Uljanovs, J., Urano, H., Urban, A., Urbanczyk, G., Uytdenhouwen, I, Vadgama, A., Valcarcel, D., Vale, R., Valentinuzzi, M., Valerii, K., Valisa, M., Olivares, P. Vallejos, Valovic, M., Van Eester, D., Van Renterghem, W., van Rooij, G. J., Varje, J., Vartanian, S., Vasava, K., Vasilopoulou, T., Vecsei, M., Vega, J., Ventre, S., Verdoolaege, Geert, Verona, C., Rinati, G. Verona, Veshchev, E., Vianello, N., Vicente, J., Viezzer, E., Villari, S., Villone, F., Vincent, M., Vincenzi, P., Vinyar, I, Viola, B., Vitins, A., Vizvary, Z., Vlad, M., Voitsekhovitch, I, Voltolina, D., von Toussaint, U., Vondracek, P., Vuksic, M., Wakeling, B., Waldon, C., Walkden, N., Walker, R., Walker, M., Walsh, M., Wang, N., Wang, E., Warder, S., Warren, R., Waterhouse, J., Watts, C., Wauters, T., Webb, M., Weckmann, A., Weiland, J., Weiland, M., Weisen, H., Weiszflog, M., Welch, P., West, A., Wheatley, M., Wheeler, S., Whitehead, A. M., Whittaker, D., Widdowson, A. M., Wiesen, S., Wilkie, G., Williams, J., Willoughby, D., Wilson, J., Wilson, I, Wilson, H. R., Wischmeier, M., Withycombe, A., Witts, D., Wolfrum, E., Wood, R., Woodley, R., Woodley, C., Wray, S., Wright, J. C., Wright, P., Wukitch, S., Wynn, A., Xiang, L., Xu, T., Xue, Y., Yadikin, D., Yakovenko, Y., Yanling, W., Yavorskij, V, Young, I, Young, R., Young, D., Zacks, J., Zagorski, R., Zaitsev, F. S., Zakharov, L., Zanino, R., Zarins, A., Zarins, R., Fernandez, D. Zarzoso, Zastrow, K. D., Zerbini, M., Zhang, W., Zhou, Y., Zilli, E., Zocco, A., Zoita, V, Zoletnik, S., Zwingmann, W., Zychor, I, Saarelma, S., Frassinetti, L., Bilkova, P., Challis, C. D., Chankin, A., Fridström, R., Garzotti, L., Horvath, L., Maggi, C. F., Chiariello, A. G., and JET Contributors

Subjects
Technology and Engineering, Hydrogen, Physics::Instrumentation and Detectors, PLASMAS, chemistry.chemical_element, edge localized modes, CONFINEMENT, ELMY H-MODES, Thermal diffusivity, 01 natural sciences, 010305 fluids & plasmas, Pedestal, Physics::Plasma Physics, 0103 physical sciences, Kinetic isotope effect, 010306 general physics, Edge-localized mode, EDGE LOCALIZED MODES, ELMy h-modes, plasmas, confinement, Physics, Mechanics, Penetration (firestop), Plasma, elmy h-modes, Condensed Matter Physics, chemistry, Deuterium
Abstract

The self-consistent core-pedestal prediction model of a combination of EPED1 type pedestal prediction and a simple stiff core transport model is able to predict Type I ELMy (edge localized mode) pedestals of a large JET-ILW (ITER-like wall) database at the similar accuracy as is obtained when the experimental global plasma β is used as input. The neutral penetration model [R. J. Groebner et al., Phys. Plasmas 9, 2134 (2002)] with corrections that take into account variations due to gas fueling and plasma triangularity is able to predict the pedestal density with an average error of 15%. The prediction of the pedestal pressure in hydrogen plasma that has higher core heat diffusivity compared to a deuterium plasma with similar heating and fueling agrees with the experiment when the isotope effect on the stability, the increased diffusivity, and outward radial shift of the pedestal are included in the prediction. However, the neutral penetration model that successfully predicts the deuterium pedestal densities fails to predict the isotope effect on the pedestal density in hydrogen plasmas.The self-consistent core-pedestal prediction model of a combination of EPED1 type pedestal prediction and a simple stiff core transport model is able to predict Type I ELMy (edge localized mode) pedestals of a large JET-ILW (ITER-like wall) database at the similar accuracy as is obtained when the experimental global plasma β is used as input. The neutral penetration model [R. J. Groebner et al., Phys. Plasmas 9, 2134 (2002)] with corrections that take into account variations due to gas fueling and plasma triangularity is able to predict the pedestal density with an average error of 15%. The prediction of the pedestal pressure in hydrogen plasma that has higher core heat diffusivity compared to a deuterium plasma with similar heating and fueling agrees with the experiment when the isotope effect on the stability, the increased diffusivity, and outward radial shift of the pedestal are included in the prediction. However, the neutral penetration model that successfully predicts the deuterium pedestal densiti...

Published
2019

36. A Hospital Based Study On Knowledge In Women Regarding Food And Nutrients Intake During Pregnancy

Author

Hafiza Nimra Shabbir, Aqsa Javed, Sidra Shabbir

Subjects
Pregnancy, diet intake, nutritional status
Abstract

Objectives: To know the knowledge of the diet plan in pregnant women according to food and nutrient levels and to know the level of education, the economy, and popular women's choice of food. Place and Duration: The study was performed in the Obstetrics and gynecology department of Services Hospital, Lahore for the period of one year from January 2015 to December 2015. Study Design: An Observational Study Results: 380 pregnant women were interviewed 3 times using a 24-hour diet reminder with recipes. Negotiations were made on health, nutrition and socioeconomic status and on women's weight and height measurements. The result shows nutritional status should be monitored in pregnant women and should be acknowledged to the pregnant women. Conclusions: According to the Pakistan diet, women's (unreinforced) diets were not sufficient for all nutrients except energy and fat. A low level of micronutrient intake of food outside the rice was detected. Food habits and the economy have shown that women have a negative impact on food choice, family type, education level, number of pregnancies and popular dietetics. negative. Recommended foods in proper amount, green vegetables especially, should be given to women. Nutritional recommendations provided by healthcare providers at all levels should be the same and should be local cheap foods that are widely consumed. Keywords: Pregnancy, diet intake, nutritional status.

Published
2018
Full Text
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38. Correlation analysis for energy losses, waiting times and durations of type I edge-localized modes in the Joint European Torus

Author

Shabbir, Aqsa, Verdoolaege, Geert, Hornung, Grégoire, Kardaun, O. J. W. F., Zohm, H., Abduallev, S., Abhangi, M., Abreu, P., Afzal, M., Aggarwal, K. M., Ahlgren, T., Ahn, J. H., Aho-Mantila, L., Aiba, N., Airila, M., Albanese, R., Aldred, V., Alegre, D., Alessi, E., Aleynikov, P., Alfier, A., Alkseev, A., Allinson, M., Alper, B., Alves, E., Ambrosino, G., Ambrosino, R., Amicucci, L., Amosov, V., Sunden, E. Andersson, Angelone, M., Anghel, M., Angioni, C., Appel, L., Appelbee, C., Arena, P., Ariola, M., Arnichand, H., Arshad, S., Ash, A., Ashikawa, N., Aslanyan, V., Asunta, O., Auriemma, F., Austin, Y., Avotina, L., Axton, M. D., Ayres, C., Bacharis, M., Baciero, A., Baiao, D., Bailey, S., Baker, A., Balboa, I., Balden, M., Balshaw, N., Bament, R., Banks, J. W., Baranov, Y. F., Barnard, M. A., Barnes, D., Barnes, M., Barnsley, R., Wiechec, A. Baron, Orte, L. Barrera, Baruzzo, M., Basiuk, V., Bassan, M., Bastow, R., Batista, A., Batistoni, P., Baughan, R., Bauvir, B., Baylor, L., Bazylev, B., Beal, J., Beaumont, P. S., Beckers, M., Beckett, B., Becoulet, A., Bekris, N., Beldishevski, M., Bell, K., Belli, F., Bellinger, M., Belonohy, E., Ben Ayed, N., Benterman, N. A., Bergsaker, H., Bernardo, J., Bernert, M., Berry, M., Bertalot, L., Besliu, C., Beurskens, M., Bieg, B., Bielecki, J., Biewer, T., Bigi, M., Bilkova, P., Binda, F., Bisoffi, A., Bizarro, J. P. S., Bjorkas, C., Blackburn, J., Blackman, K., Blackman, T. R., Blanchard, P., Blatchford, P., Bobkov, V., Boboc, A., Bodnar, G., Bogar, O., Bolshakova, I., Bolzonella, T., Bonanomi, N., Bonelli, F., Boom, J., Booth, J., Borba, D., Borodin, D., Borodkina, I., Botrugno, A., Bottereau, C., Boulting, P., Bourdelle, C., Bowden, M., Bower, C., Bowman, C., Boyce, T., Boyd, C., Boyer, H. J., Bradshaw, J. M. A., Braic, V., Bravanec, R., Breizman, B., Bremond, S., Brennan, P. D., Breton, S., Brett, A., Brezinsek, S., Bright, M. D. J., Brix, M., Broeckx, W., Brombin, M., Broslawski, A., Brown, D. P. D., Brown, M., Bruno, E., Bucalossi, J., Buch, J., Buchanan, J., Buckley, M. A., Budny, R., Bufferand, H., Bulman, M., Bulmer, N., Bunting, P., Buratti, P., Burckhart, A., Buscarino, A., Busse, A., Butler, N. K., Bykov, I., Byrne, J., Cahyna, P., Calabro, G., Calvo, I., Camenen, Y., Camp, P., Campling, D. C., Cane, J., Cannas, B., Capel, A. J., Card, P. J., Cardinali, A., Carman, P., Carr, M., Carralero, D., Carraro, L., Carvalho, B. B., Carvalho, I., Carvalho, P., Casson, F. J., Castaldo, C., Catarino, N., Caumont, J., Causa, F., Cavazzana, R., Cave-Ayland, K., Cavinato, M., Cecconello, M., Ceccuzzi, S., Cecil, E., Cenedese, A., Cesario, R., Challis, C. D., Chandler, M., Chandra, D., Chang, C. S., Chankin, A., Chapman, I. T., Chapman, S. C., Chernyshova, M., Chitarin, G., Ciraolo, G., Ciric, D., Citrin, J., Clairet, F., Clark, E., Clark, M., Clarkson, R., Clatworthy, D., Clements, C., Cleverly, M., Coad, J. P., Coates, P. A., Cobalt, A., Coccorese, V., Cocilovo, V., Coda, S., Coelho, R., Coenen, J. W., Coffey, I., Colas, L., Collins, S., Conka, D., Conroy, S., Conway, N., Coombs, D., Cooper, D., Cooper, S. R., Corradino, C., Corre, Y., Corrigan, G., Cortes, S., Coster, D., Couchman, A. S., Cox, M. P., Craciunescu, T., Cramp, S., Craven, R., Crisanti, F., Croci, G., Croft, D., Crombé, Kristel, Crowe, R., Cruz, N., Cseh, G., Cufar, A., Cullen, A., Curuia, M., Czarnecka, A., Dabirikhah, H., Dalgliesh, P., Dalley, S., Dankowski, J., Darrow, D., Davies, O., Davis, W., Day, C., Day, I. E., De Bock, M., de Castro, A., de la Cal, E., de la Luna, E., De Masi, G., de Pablos, J. L., De Temmerman, G., De Tommasi, G., de Vries, P., Deakin, K., Deane, J., Agostini, F. Degli, Dejarnac, R., Delabie, E., den Harder, N., Dendy, R. O., Denis, J., Denner, P., Devaux, S., Devynck, P., Di Maio, F., Di Siena, A., Di Troia, C., Dinca, P., D'Inca, R., Ding, B., Dittmar, T., Doerk, H., Doerner, R. P., Donne, T., Dorling, S. E., Dormido-Canto, S., Doswon, S., Douai, D., Doyle, P. T., Drenik, A., Drewelow, P., Drews, P., Duckworth, Ph., Dumont, R., Dumortier, P., Dunai, D., Dunne, M., Duran, I., Durodie, F., Dutta, P., Duval, B. P., Dux, R., Dylst, K., Dzysiuk, N., Edappala, P. V., Edmond, J., Edwards, A. M., Edwards, J., Eich, Th., Ekedahl, A., El-Jorf, R., Elsmore, C. G., Enachescu, M., Ericsson, G., Eriksson, F., Eriksson, J., Eriksson, L. G., Esposito, B., Esquembri, S., Esser, H. G., Esteve, D., Evans, B., Evans, G. E., Evison, G., Ewart, G. D., Fagan, D., Faitsch, M., Falie, D., Fanni, A., Fasoli, A., Faustin, J. M., Fawlk, N., Fazendeiro, L., Fedorczak, N., Felton, R. C., Fenton, K., Fernades, A., Fernandes, H., Ferreira, J., Fessey, J. A., Fevrier, O., Ficker, O., Field, A., Fietz, S., Figueiredo, A., Figueiredo, J., Fil, A., Finburg, P., Firdaouss, M., Fischer, U., Fittill, L., Fitzgerald, M., Flammini, D., Flanagan, J., Fleming, C., Flinders, K., Fonnesu, N., Fontdecaba, J. M., Formisano, A., Forsythe, L., Fortuna, L., Fortuna-Zalesna, E., Fortune, M., Foster, S., Franke, T., Franklin, T., Frasca, M., Frassinetti, L., Freisinger, M., Fresa, R., Frigione, D., Fuchs, V., Fuller, D., Futatani, S., Fyvie, J., Gal, K., Galassi, D., Galazka, K., Galdon-Quiroga, J., Gallagher, J., Gallart, D., Galvao, R., Gao, X., Gao, Y., Garcia, J., Garcia-Carrasco, A., Garcia-Munoz, M., Gardarein, J. -L., Garzotti, L., Gaudio, P., Gauthier, E., Gear, D. F., Gee, S. J., Geiger, B., Gelfusa, M., Gerasimov, S., Gervasini, G., Gethins, M., Ghani, Z., Ghate, M., Gherendi, M., Giacalone, J. C., Giacomelli, L., Gibson, C. S., Giegerich, T., Gil, C., Gil, L., Gilligan, S., Gin, D., Giovannozzi, E., Girardo, J. B., Giroud, C., Giruzzi, G., Gloeggler, S., Godwin, J., Goff, J., Gohil, P., Goloborod'ko, V., Gomes, R., Goncalves, B., Goniche, M., Goodliffe, M., Goodyear, A., Gorini, G., Gosk, M., Goulding, R., Goussarov, A., Gowland, R., Graham, B., Graham, M. E., Graves, J. P., Grazier, N., Grazier, P., Green, N. R., Greuner, H., Grierson, B., Griph, F. S., Grisolia, C., Grist, D., Groth, M., Grove, R., Grundy, C. N., Grzonka, J., Guard, D., Guerard, C., Guillemaut, C., Guirlet, R., Gurl, C., Utoh, H. H., Hackett, L. J., Hacquin, S., Hagar, A., Hager, R., Hakola, A., Halitovs, M., Hall, S. J., Cook, S. P. Hallworth, Hamlyn-Harris, C., Hammond, K., Harrington, C., Harrison, J., Harting, D., Hasenbeck, F., Hatano, Y., Hatch, D. R., Haupt, T. D. V., Hawes, J., Hawkes, N. C., Hawkins, J., Hawkins, P., Haydon, P. W., Hayter, N., Hazel, S., Heesterman, P. J. L., Heinola, K., Hellesen, C., Hellsten, T., Helou, W., Hemming, O. N., Hender, T. C., Henderson, M., Henderson, S. S., Henriques, R., Hepple, D., Hermon, G., Hertout, P., Hidalgo, C., Highcock, E. G., Hill, M., Hillairet, J., Hillesheim, J., Hillis, D., Hizanidis, K., Hjalmarsson, A., Hobirk, J., Hodille, E., Hogben, C. H. A., Hogeweij, G. M. D., Hollingsworth, A., Hollis, S., Homfray, D. A., Horacek, J., Hornung, G., Horton, A. R., Horton, L. D., Horvath, L., Hotchin, S. P., Hough, M. R., Howarth, P. J., Hubbard, A., Huber, A., Huber, V., Huddleston, T. M., Hughes, M., Huijsmans, G. T. A., Hunter, C. L., Huynh, P., Hynes, A. M., Iglesias, D., Imazawa, N., Imbeaux, F., Imrisek, M., Incelli, M., Innocente, P., Irishkin, M., Ivanova-Stanik, I., Jachmich, S., Jacobsen, A. S., Jacquet, P., Jansons, J., Jardin, A., Jarvinen, A., Jaulmes, F., Jednorog, S., Jenkins, I., Jeong, C., Jepu, I., Joffrin, E., Johnson, R., Johnson, T., Johnston, Jane, Joita, L., Jones, G., Jones, T. T. C., Hoshino, K. K., Kallenbach, A., Kamiya, K., Kaniewski, J., Kantor, A., Kappatou, A., Karhunen, J., Karkinsky, D., Karnowska, I., Kaufman, M., Kaveney, G., Kazakov, Y., Kazantzidis, V., Keeling, D. L., Keenan, T., Keep, J., Kempenaars, M., Kennedy, C., Kenny, D., Kent, J., Kent, O. N., Khilkevich, E., Kim, H. T., Kim, H. S., Kinch, A., King, C., King, D., King, R. F., Kinna, D. J., Kiptily, V., Kirk, A., Kirov, K., Kirschner, A., Kizane, G., Klepper, C., Klix, A., Knight, P., Knipe, S. J., Knott, S., Kobuchi, T., Koechl, F., Kocsis, G., Kodeli, I., Kogan, L., Kogut, D., Koivuranta, S., Kominis, Y., Koeppen, M., Kos, B., Koskela, T., Koslowski, H. R., Koubiti, M., Kovari, M., Kowalska-Strzeciwilk, E., Krasilnikov, A., Krasilnikov, V., Krawczyk, N., Kresina, M., Krieger, K., Krivska, A., Kruezi, U., Ksiazek, I., Kukushkin, A., Kundu, A., Kurki-Suonio, T., Kwak, S., Kwiatkowski, R., Kwon, O. J., Laguardia, L., Lahtinen, A., Laing, A., Lam, N., Lambertz, H. T., Lane, C., Lang, P. 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Subjects
Waiting time, Physics, Nuclear and High Energy Physics, ELMS, waiting times, correlation analysis, Joint European Torus, Inverse, Plasma, Type (model theory), Edge (geometry), Condensed Matter Physics, 01 natural sciences, 010305 fluids & plasmas, Computational physics, PEDESTAL, Physics and Astronomy, JET, 0103 physical sciences, Correlation analysis, 010306 general physics, edge-localized mode (ELM), Energy (signal processing), PARTICLE LOSSES, slow transport event
Abstract

Several important ELM control techniques are in large part motivated by the empirically observed inverse relationship between average ELM energy loss and ELM frequency in a plasma. However, to ensure a reliable effect on the energy released by the ELMs, it is important that this relation is verified for individual ELM events. Therefore, in this work the relation between ELM energy loss (W-ELM) and waiting time (Delta t(ELM)) is investigated for individual ELMs in a set of ITER-like wall plasmas in JET. A comparison is made with the results from a set of carbon-wall and nitrogen-seeded ITER-like wall JET plasmas. It is found that the correlation between W-ELM and Delta t(ELM) for individual ELMs varies from strongly positive to zero. Furthermore, the effect of the extended collapse phase often accompanying ELMs from unseeded JET ILW plasmas and referred to as the slow transport event (STE) is studied on the distribution of ELM durations, and on the correlation between W-ELM and Delta t(ELM). A high correlation between W-ELM and Delta t(ELM), comparable to CW plasmas is only found in nitrogen-seeded ILW plasmas. Finally, a regression analysis is performed using plasma engineering parameters as predictors for determining the region of the plasma operational space with a high correlation between W-ELM and Delta t(ELM).

Published
2017

41. Pattern recognition in spaces of probability distributions for the analysis of edge-localized modes in tokamak plasmas

Author

Shabbir, Aqsa, Verdoolaege, Geert, and Zohm, Hartmut

Subjects
information geometry, pattern recognition, FOS: Physical sciences, Physics and Astronomy, classification, Physics::Plasma Physics, probability theory, Nuclear fusion, ELM, Nuclear fusion, Edge-localized modes, pattern recognition, information geometry, classification, edge-localized mode, tokamak, geodesic distance
Abstract

Magnetically confined fusion plasmas provide several data analysis challenges due to the occurrence of massive data sets, substantial measurement uncertainty, stochasticity and data dimensionality, and often nonlinear interactions between measured quantities. Recently, methods from the fields of machine learning and probability theory - some standard, some more advanced - have come to play an increasingly important role in analyzing data from fusion experiments. The capabilities offered by such methods to efficiently extract, possibly in real time, additional information from the data that is not immediately apparent to human experts, has attracted attention from an increasing number of researchers. In addition, innovative methods for real-time data processing can play an important role in plasma control, in order to ensure safe and reliable operation of the machine. Pattern recognition is a discipline within the information sciences that concerns the exploration of structure in (multidimensional) data sets using computer-based methods and algorithms. In this doctoral work, pattern recognition techniques are developed and applied to data from tokamak plasmas, in order to contribute to a systematic analysis of edge-localized modes (ELMs). ELMs are magnetohydrodynamic (MHD) instabilities occurring in the edge region of high-confinement (H-mode) fusion plasmas. The type I ELMy H-mode is the reference scenario for operation of the next-step fusion device ITER. On the one hand, ELMs have a beneficial effect on plasma operation through their role in impurity control. On the other hand, ELMs eject energy and particles from the plasma and, in ITER, large unmitigated ELMs are expected to cause intolerable heat loads on the plasma-facing components (PFCs). In interpreting experiments focused on ELM understanding and control, a significant challenge lies in handling the measurement uncertainties and the inherent stochasticity of ELM properties. In this work, we employ probabilistic models (distributions) for a quantitative data description geared towards an enhanced systematization of ELM phenomenology. Hence, we start from the point of view that the fundamental object resulting from the observation of a system is a probability distribution, with every single measurement providing a sample from this distribution. We argue that, particularly for richly stochastic phenomena like ELMs, the probability distribution of physical quantities contain significantly more information compared to mere averages. Consequently, in exploring the patterns emerging from the various ELM regimes and relations, we need methods that can handle the intrinsic probabilistic nature of the data. The original contributions of this work are twofold. First, several novel pattern recognition methods in non-Euclidean spaces of probability distribution functions (PDFs) are developed and validated. The second main contribution lies in the application of these and other techniques to a systematic analysis of ELMs in tokamak plasmas. In regard to the methodological aims of the work, we employ the framework of information geometry to develop pattern visualization and classification methods in spaces of probability distributions. In information geometry, a family of probability distributions is considered as a Riemannian manifold. Every point on the manifold represents a single PDF and the distribution parameters provide local coordinates on the manifold. The Fisher information plays the role of a Riemannian metric tensor, enabling calculation of geodesic curves on the surface. The length of such curves yields the geodesic distance (GD) on probabilistic manifolds, which is a natural similarity (distance) measure between PDFs. Equipped with a suitable distance measure, we extrapolate several distance-based pattern recognition methods to the manifold setting. This includes k-nearest neighbor (kNN) and conformal predictor (CP) methods for classification, as well as multidimensional scaling (MDS) and landmark multidimensional scaling (LMDS) for data visualization (dimensionality reduction). Furthermore, two new classification schemes are developed: a distance-to-centroid classifier (D2C) and a principal geodesic classifier (PGC). D2C classifies on the basis of the minimum GD to the class centroids and PGC considers the shape of the class on the manifold by determining the minimum distance to the principal geodesic of each class. The methods are validated by their application to the classification and retrieval of colored texture images represented in the wavelet domain. Both methods prove to be computationally efficient, yield high accuracy and also clearly exhibit the adequacy of the GD and its superiority over the Euclidean distance, for comparing PDFs. This also aids in demonstrating the utility and adaptability of the developed methods to a wide range of applications other than ELMs, which are the prime focus of analysis in this work. The second main goal of the work targets ELM analysis at three fronts, using pattern recognition and probabilistic modeling : i). We first concentrate on visualization of ELM characteristics by creating maps containing projections of multidimensional ELM data, as well as the corresponding probabilistic models. Such maps can provide physicists and machine operators with a convenient means and a useful tool for plasma monitoring and for studying data patterns reflecting key regimes and their underlying physics. In particular, GD-based MDS is used for representing the complete distributions of the multidimensional data characterizing the operational space of ELMs onto two-dimensional maps. Clusters corresponding to type I and type III ELMs are identified and the maps enable tracking of trends in plasma parameters across the operational space. It is shown that the maps can also be used with reasonable accuracy for predicting the values of the plasma parameters at a certain point in the operational space. ii). Our second application concerns fast, standardized and automated classification of ELM types. ELM types have so far been identified and characterized on an empirical and phenomenological basis. The presented classification schemes are aimed at complementing the phenomenological characterization using standardized methods that are less susceptible to subjective interpretation, while considerably reducing the effort of ELM experts in identifying ELM types. To this end, different classification paradigms (parametric and non-parametric) are explored and put to use. Discriminant analysis (DA) is used for determining a linear separation boundary between type I and III ELMs in terms of global plasma parameters, which can then be used for the prediction of ELM types as well as the study of ELM occurrence boundaries and ELM physics. However, DA makes an assumption about the underlying class distribution and presently cannot be applied in spaces of probability distributions, leading to a sub-optimal treatment of stochasticity. This is circumvented by the use of GD-based CP and kNN classifiers. CP provides estimates of its own accuracy and reliability and kNN is a simple, yet powerful classifier of ELM types. It is shown that a classification based on the distribution of ELM properties, namely inter-ELM time intervals and the distribution of global plasma parameters, is more informative and accurate than the classification based on average parameter values. iii). Finally, the correlation} between ELM energy loss (ELM size) and ELM waiting times (inverse ELM frequency) is studied for individual ELMs in a set of plasmas from the JET tokamak upgraded with the ITER-like wall (ILW). Typically, ELM control methods rely on the empirically observed inverse dependence of average ELM energy loss on average ELM frequency, even though ELM control is targeted at reducing the size of individual ELMs and not the average ELM loss. The analysis finds that for individual ELMs the correlation between ELM energy loss and waiting times varies from zero to a moderately positive value. A comparison is made with the results from a set of carbon-wall (CW) JET plasmas and nitrogen-seeded ILW JET plasmas. It is found that a high correlation between ELM energy loss and waiting time comparable to CW plasmas is only found in nitrogen-seeded ILW plasmas. Furthermore, most of the unseeded JET ILW plasmas have ELMs that are followed by a second phase referred to as the slow transport event (STE). The effect of the STEs on the distribution of ELM durations is studied, as well as their influence on the correlation between ELM energy loss and waiting times. This analysis has a clear outcome for the optimization of ELM control methods, while presenting insights for an improved physics understanding of ELMs., Die Analyse von experimentellen Daten magnetisch eingeschlossener Fusionsplasmen stellt wegen der großen Datenmengen, der hohen Dimensionalität, der Messunsicherheiten und auch der oft nichtlinearen Beziehungen untereinander eine große Herausforderung dar. Methoden der Datenanalyse aus den Feldern des maschinellen Lernens sowie der Wahrscheinlichkeitstheorie spielen daher in letzter Zeit eine immer größere Rolle bei der Analyse von Daten aus Fusionsexperimenten. Dabei interessiert vor allem die Möglichkeit, zusätzliche Information welche dem menschlichen Beobachter verborgen bleiben, systematisch zu extrahieren. Zusätzlich können innovative Methoden der Echtzeit-Datenverarbeitung eine wichtige Rolle für Kontrollanwendungen in Fusionsexperimenten spielen. Mustererkennung ist eine Disziplin der Informationstheorie welche sich mit der Erforschung von Strukturen in multidimensionalen Datensätzen durch computergestützte Methoden und Algorithmen beschäftigt. In dieser Doktorarbeit werden Methoden der Mustererkennung auf Daten von Tokamakexperimenten für eine systematische Analyse von edge-localized modes (ELMs) angewendet. ELMs sind magnetohydrodynamische (MHD) Instabilitäten die am Plasmarand in ‘high-confinement‘ (H-mode) Fusionsplasmen auftreten. Die ‘Typ I ELMy H-mode' ist das Referenz-Betriebsszenario für das zukünftige ITER Experiment. ELMs spielen einerseits eine positive Rolle für den Plasmabetrieb da sie zur Verunreinigungskontrolle beitragen. Andererseits werfen ELMs Teilchen und Energie aus dem Plasma und könnten daher in ITER die Integrität der ersten Wand gefährden. Eine signifikante Herausforderung bei der Interpretation von Experimenten welche sich mit dem Verständnis und der Kontrolle von ELMs beschäftigen liegt in der Behandlung der Messunsicherheiten sowie der inhärenten Stochastizität der ELM Parameter. In der vorliegenden Arbeit werden probabilistische Modelle (Verteilungen) zur quantitativen Beschreibung der Daten mit dem Ziel einer verbesserten systematischen Einteilung der ELM-Phänomenologie verwendet. Dabei wird davon ausgegangen, dass die fundamentale Größe eines Systems eine Wahrscheinlichkeitsverteilung ist, wobei jede Einzelmessung eine Stichprobe dieser Verteilung darstellt. Dabei wird angenommen dass, im Besonderen für stark stochastische Ereignisse wie ELMs, die Wahrscheinlichkeitsverteilung der physikalischen Parameter deutlich mehr Information enthält als deren Mittelwerte. Folglich erfordert die Erforschung der Struktur der unterschiedlichen ELM Regimes Methoden, welche die intrinsisch stochastische Natur der Daten berücksichtigen kann. Diese Arbeit liefert zwei grundsätzlich neue Beiträge: zunächst werden neuartige Strukturerkennungs-Methoden in nicht-euklidischen Räumen von Wahrscheinlichkeitsverteilungen entwickelt und validiert. Der zweite grundsätzliche Beitrag liegt in der Anwendung dieser und anderer Methoden auf eine systematische Analyse von ELMs in Tokamakplasmen. Aus methodologischer Sicht wird in dieser Arbeit die Informationsgeometrie angewendet um Methoden zur Mustererkennung und –klassifizierung in Räumen von Wahrscheinlichkeitsverteilungen zu entwickeln. In der Informationsgeometrie wird eine Familie von Wahrscheinlichkeitsverteilungen als eine Riemannsche Mannigfaltigkeit aufgefasst. Jeder Punkt auf der Mannigfaltigkeit stellt eine Wahrscheinlichkeitsverteilung dar und die Verteilungsparameter sind lokale Koordinaten auf der Mannigfaltigkeit. Die Fisher Information spielt dabei die Rolle des Riemannschen metrischen Tensors und erlaubt es, geodätische Kurven auf der Fläche zu berechnen. Die Länge einer solchen Kurve ergibt den geodätischen Abstand auf der Mannigfaltigkeit, welcher ein natürliches Maß für den Abstand zwischen Verteilungsfunktionen ist. Mit diesem geeigneten Abstandsmaß werden mehrere Mustererkennungsmethoden welche auf dem Abstand basieren auf die Mannigfaltigkeit angewandt. Diese schließen die ‘k-nearest neighbor’ (kNN) und ‘conformal predictor’ (CP) Klassifikationsmethoden ein sowie ‘multidimensional scaling’ (MDS) und ‘landmark multidimensional scaling‘ (LMDS) zur Datenvisualisierung mit dem Ziel der Dimensionsreduktion. Desweitern werden zwei neue Klassifikationsmethoden entwickelt: ein ‘distance-to-centroid classifier’ (D2C) und ein ‘principal geodesic classifier’ (PGC). D2C klassifiziert auf Basis des minimalen geodätischen Abstands vom Schwerpunkt der Daten und PGC berücksichtigt die Form der Klasse auf der Mannigfaltigkeit indem der Abstand zur Hauptgeodätischen jeder Klasse bestimmt wird. Diese Methoden werden durch Anwendung auf die Klassifizierung und Rekonstruktion von farbigen Texturbildern in der Waveletdarstellung validiert. Beide Methoden stellen sich als effizient im Rechenaufwand heraus und liefern hohe Genauigkeit, wobei der geodätische Abstand dem euklidischen Abstand deutlich überlegen ist und somit als angemessen für den Vergleich von Verteilungsfunktionen bestätigt wird. Dies dient auch dem Nachweis der Eignung der entwickelten Methoden für eine Vielzahl von Anwendungen über das in dieser Arbeit vorrangig behandelte Feld der ELMs hinaus. Das zweite Hauptziel der Arbeit ist die Analyse von ELMs mit den Methoden der Mustererkennung und der wahrscheinlichkeitstheoretischen Modellierung auf drei Gebieten: i). Zunächst wird die Visualisierung von ELM Eigenschaften durch Erstellung von Abbildungen behandelt welche multidimensionale ELM Daten projizieren. Solche Abbildungen können für Physiker und Experimentatoren ein nützliches Werkzeug zur Überwachung der Plasmaentladung darstellen und dienen darüber hinaus zu Studien von Datenmustern, welche prinzipielle Regimes und deren zugrundeliegende Physik charakterisieren. Im speziellen wird die GD-basierte MDS zur Darstellung der gesamten Verteilung der multidimensionalen Daten, welche das Auftreten von ELMs beschreiben in zweidimensionalen Abbildungen verwendet. Cluster in welchen ‘Typ I’ und ‘Typ III’ ELMs auftreten werden identifiziert und die Abbildung ermöglicht es, Trends in der Veränderung von Plasmaparametern im Parameterraum zu erkennen. Es wird gezeigt, dass diese Abbildungen auch dazu verwendet werden können, die Plasmaparameter für einen bestimmten Punkt im Betriebsbereich vorherzusagen. ii). Eine zweite Anwendung beschäftigt sich mit einer schnellen, standardisierten Klassifizierung des ELM Typs. ELM Typen wurden bisher auf einer empirisch-phänomenologischen Basis identifiziert. Die hier vorgestellten Klassifizierungs-Schemata dienen der Ergänzung der phänomenologischen Beschreibung durch standardisierte Methoden welche weniger anfällig für subjektive Wahrnehmung und Interpretation sind und sollen auch den Aufwand bei der Bestimmung des ELM Typs verringern. Verschiedene Klassifizierungsmethoden, parametrisch und nicht-parametrisch, werden untersucht und eingesetzt. Discriminant Analysis (DA) wird für die Bestimmung einer linearen Grenze zwischen Typ I und Typ III ELMs in globalen Plasmaparametern eingesetzt, die dann sowohl zur Vorhersage des ELM Typs als auch zur Untersuchung der Bereiche, in denen die unterschiedlichen ELM Typen auftreten, verwendet wird. Dabei basiert die DA allerdings auf einer Annahme über die zugrunde liegende Verteilung der Klassen und kann nach derzeitigem Stand nicht auf Räume von Verteilungsfunktionen angewendet werden, was zu einer unzureichenden Behandlung der Stochastizität führt. Dies wird durch die Verwendung von GD-basierter CP und von kNN Klassifikatoren behoben. CP liefert eine Abschätzung ihrer Genauigkeit und Zuverlässigkeit und kNN ist ein einfacher, aber leistungsstarker Klassifikator für ELM-Typen. Es wird gezeigt dass eine Klassifizierung basierend auf der Verteilung der ELM Eigenschaften, namentlich der inter-ELM Zeitintervalle und der Verteilung der globalen Plasmaparameter, mehr Information enthält als eine Klassifizierung welche auf gemittelten Werten basiert. iii).Schließlich wird die Korrelation zwischen ELM Energieverlust (ELM Größe) und ELM Wartezeiten (inverse ELM Frequenz) für individuelle ELMs aus einer Datenbasis von Plasmaentladungen des JET Tokamaks in der ‚ITER-like wall‘ (ILW) Konfiguration untersucht. ELM Kontrollmethoden basieren typischerweise auf dem empirisch beobachteten inversen Zusammenhang zwischen mittlerem ELM-Verlust und mittlerer ELM-Frequenz, obwohl ELM Kontrolle die Reduktion der Größe individueller ELMs zum Ziel hat. Die Analyse zeigt, dass für individuelle ELMs die Korrelation zwischen ELM-Energieverlust und Wartezeit generell niedrig ist. Dieses Ergebnis wird mit einem Datensatz von JET in der ‚carbon-wall‘ (CW) Konfiguration sowie einem Datensatz von Stickstoff-gekühlten ILW JET Plasmen verglichen. Es zeigt sich, dass eine hohe Korrelation zwischen ELM-Energieverlust und Wartezeit, vergleichbar zu CW Plasmen, nur in Stickstoff-gekühlten ILW Plasmen auftritt. Darüber hinaus treten in den meisten JET ILW Plasmen ohne Stickstoffkühlung ELMs auf, welche von einer zweiten Phase, slow transport event (STE) genannt, begleitet werden. Der Effekt der STEs auf die Verteilung der ELM Dauer sowie deren Einfluss auf die Korrelation zwischen ELM-Energieverlust und Wartezeit wird untersucht. Diese Untersuchung hat einerseits eine starke Relevanz für die Optimierung von Methoden zur ELM Kontrolle, andererseits trägt sie zum tieferen Einblick in die den ELMs zugrunde liegende Physik bei.

Published
2016

42. Relating Probability Distributions Using Geodesic Least Squares Regression: Application to Edge-Localized Modes in Fusion Plasmas.

Author

Verdoolaege, Geert and Shabbir, Aqsa

Subjects
*PROBABILITY theory, *ISOTONIC regression, *SUM of squares, *VARIANCE inflation factors (Statistics), *MULTILEVEL models
Abstract

Geodesic least squares regression (GLS) is a new robust, but simple regression technique based on minimization of the Rao geodesic distance on a probabilistic manifold. It is particularly useful in the presence of large or unknown sources of uncertainty, as it relates probability distributions rather than individual measurements. The GLS method is employed here to estimate the dependence between the probability distributions of two important characteristics of a repetitive instability occurring in the boundary region of fusion plasmas, namely the edge-localized mode (ELM). Specifically, we study the relation between the plasma energy loss following an ELM and the time since the previous ELM. GLS is shown to produce consistent results, whether using measurements on individual ELMs or averaged quantities, even in the presence of questionable modeling assumptions. The method is illustrated using the pseudosphere as an intuitive model of the Gaussian manifold. [ABSTRACT FROM AUTHOR]

Published
2017
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46. Corporate Governance through Ownership Structure: Evidence from KSE-100 Index.

Author

Shabbir, Aqsa, Tahir, Safdar H., and Aziz, Bilal

Subjects
CORPORATE governance, ORGANIZATIONAL performance, DIVIDEND policy, MULTIPLE regression analysis, FOREIGN investments, STOCKHOLDERS
Abstract

This paper examined the association between ownership structure, firm performance and dividend policy with respect to Governance perspective of companies present in Karachi Stock Exchange (KSE). A sample of 45 Nonfinancial KSE-100 Index listed firms for a period of 2010 to 2013 has been taken for analysis of this study. Multiple Regression Models are applied on the panel data to measure the relationship between ownership structure, firm performance and dividend policy. The empirical results provide evidence that ownership structure significantly affects the dividend policy and firm performance in connection with family ownership concentration. The empirical results also exhibit a significant negative relation between Dividend payments and ownership concentration and thus support the Entrenchment Theory; it means that the major shareholders protect their own benefit at the cost of minor stakeholders. Furthermore, the results also showed a positive relation of firm performance with foreign holdings, which means better performance is an attractive sign for the foreign investment and financial health of the economy. [ABSTRACT FROM AUTHOR]

Published
2013

47. Optimization of Thermophotovoltaic Power System With a Spectrally Controlled Multi-Concentric Emitter

Author

Kazim, Ali Hussain, Salman, Ali, Khan, Abu Bakar, Shaukat, Umer, and Shabbir, Aqsa

Abstract

Thermophotovoltaic energy conversion is a solid-state thermal-to-electric energy conversion technique which uses only infrared light i.e., heat. Its maximum achieved efficiency by proper bandgap energy selection and utilizing spectral control strategies is merely 30% which is currently still low to be used as a standalone device for energy harvesting. Recently, researchers used molten metal storage and concentrated solar power (CSP) as an input to derive these thermophotovoltaic blocks. Such blocks usually consist of an emitter and a photovoltaic power converter which boosted the system efficiency by adding a silver-based reflector to bounce unused photons back to the emitter. In this work, we have demonstrated another means of photon recycling on the conversion efficiency by passing the radiations through spectral filters before incident on these cells. The spectral filters were selected by aiming at gaining maximum conversion efficiency and a cylindrical power block was used in order to reduce the conversion losses due to edge effects. The modified model in terms of geometrical shape and spectrally controlled filters was able to achieve a conversion efficiency up to 50% even without using the back surface reflectors (BSRs). This work provides the framework for a cheap and efficient alternative to the combined-cycle electric turbines currently used to convert heat into energy inside utility-scale concentrated solar power facilities. This work enables widespread use wherever solar energy is available in abundance.

Published
2022
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