Search Results - "Perrakis, E."

Spigel hernia: a single center experience in a rare hernia entity by Perrakis, A., Velimezis, G., Kapogiannatos, G., Koronakis, D., Perrakis, E.

“…Introduction Spigelian Hernia (SH) is a rare ventral hernia with a high incarceration and obstruction risk. The purpose of this study is to present our…”
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No fixation of onlay patches in thin patients by Perrakis, E

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Randomized comparison between different insufflation pressures for laparoscopic cholecystectomy by Perrakis, E, Vezakis, A, Velimezis, G, Savanis, G, Deverakis, S, Antoniades, J, Sagkana, E

“…Laparoscopy using carbon dioxide insufflation induces adverse effects in both the cardiovascular and the respiratory function. The use of low pressure…”
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A new tension-free technique for the repair of umbilical hernia, using the Prolene Hernia System: early results from 48 cases by PERRAKIS, E, VELIMEZIS, G, VEZAKIS, A, ANTONIADES, J, SAVANIS, G, PATRIKAKOS, V

“…Tension-free repair using the Prolene Hernia System (PHS) has been widely adopted for inguinal hernias with excellent results. In our department, a new…”
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Real-time control of manufacturing cells using dynamic neural networks by Rovithakis, George A., Gaganis, Vassilis I., Perrakis, Stelios E., Christodoulou, Manolis A.

“…In this paper, a control aspect of the non-acyclic FMS scheduling problem is considered. Based on a dynamic neural network model derived herein, an adaptive,…”
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Neuro schedulers for flexible manufacturing systems by Rovithakis, George A., Gaganis, Vassilis I., Perrakis, Stelios E., Christodoulou, Manolis A.

“…In this paper a dynamic neural network (DNN)-based controller is constructed to provide the basis upon which a scheduler is developed to guarantee that system…”
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Incidence, diagnostics and management of iatrogenic bile duct injuries: 20 years experience in a high volume centre by Perrakis, E., Rapti, Nikoletta, Ioannidis, P., Rallis, G., Papadopoulos, K., Alifierakis, E., Perrakis, A.

“…Background-Aim The vast majority of bile duct injuries occur in cases of open or laparoscopic cholecystectomy. Numerous reports have demonstrated that the…”
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Fine-mapping identifies two additional breast cancer susceptibility loci at 9q31.2 by Orr, N., Dudbridge, F., Dryden, N., Maguire, S., Novo, D., Perrakis, E., Johnson, N., Ghoussaini, M., Hopper, J. L., Southey, M. C., Apicella, C., Stone, J., Schmidt, M. K., Broeks, A., Van't Veer, L. J., Hogervorst, F. B., Fasching, P. A., Haeberle, L., Ekici, A. B., Beckmann, M. W., Gibson, L., Aitken, Z., Warren, H., Sawyer, E., Tomlinson, I., Kerin, M. J., Miller, N., Burwinkel, B., Marme, F., Schneeweiss, A., Sohn, C., Guenel, P., Truong, T., Cordina-Duverger, E., Sanchez, M., Bojesen, S. E., Nordestgaard, B. G., Nielsen, S. F., Flyger, H., Benitez, J., Zamora, M. P., Arias Perez, J. I., Menendez, P., Anton-Culver, H., Neuhausen, S. L., Brenner, H., Dieffenbach, A. K., Arndt, V., Stegmaier, C., Hamann, U., Brauch, H., Justenhoven, C., Bruning, T., Ko, Y.-D., Nevanlinna, H., Aittomaki, K., Blomqvist, C., Khan, S., Bogdanova, N., Dork, T., Lindblom, A., Margolin, S., Mannermaa, A., Kataja, V., Kosma, V.-M., Hartikainen, J. M., Chenevix-Trench, G., Beesley, J., Lambrechts, D., Moisse, M., Floris, G., Beuselinck, B., Chang-Claude, J., Rudolph, A., Seibold, P., Flesch-Janys, D., Radice, P., Peterlongo, P., Peissel, B., Pensotti, V., Couch, F. J., Olson, J. E., Slettedahl, S., Vachon, C., Giles, G. G., Milne, R. L., McLean, C., Haiman, C. A., Henderson, B. E., Schumacher, F., Le Marchand, L., Simard, J., Goldberg, M. S., Labreche, F., Dumont, M., Kristensen, V., Alnaes, G. G., Nord, S., Borresen-Dale, A.-L., Zheng, W.

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Incisional Hernia: Difficult Cases 2 by Novitsky, Y., Fayezizadeh, M., Majumder, A., Yee, S., Petro, C., Orenstein, S., Woeste, G., Reinisch, A., Bechstein, W. O., Rosen, M., Carbonell, A., Cobb, W., Bauer, J., Selzer, D., Chao, J., Harmaty, M., Poulose, B., Matthews, B., Goldblatt, M., Jacobsen, G., Rosman, C., Hansson, B., Prabhu, A., Fathi, A., Skipworth, J., Younis, I., Floyd, D., Shankar, A., Olmi, S., Cesana, G., Ciccarese, F., Uccelli, M., Carrieri, D., Castello, G., Legnani, G., Lyo, V., Irwin, C., Xu, X., Harris, H., Zuvela, M., Galun, D., Petrovic, J., Palibrk, I., Koncar, I., Basaric, D., Tian, W., Fei, Y., Pittman, M., Jones, E., Schwartz, J., Mikami, D., Perrakis, A., Knüttel, D., Klein, P., Croner, R. S., Hohenberger, W., Perrakis, E., Müller, V., Grande, M., Villa, M., Lisi, G., Esser, A., De Sanctis, F., Petrella, G., Birolini, C., Miranda, J. S., Tanaka, E. Y., Utiyama, E. M., Rasslan, S., Shi, Y., Guo, X. B., Zhuo, H. Q., Li, L. P., Liu, H. J., Bauder, A., Gerety, P., Epps, G., Pannucci, C., Fischer, J., Kovach, S.

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Application of a neural-network scheduler on a real manufacturing system by Rovithakis, G.A., Perrakis, S.E., Christodoulou, M.A.

“…In this paper a neural adaptive scheduling methodology approached machine scheduling as a control regulation problem is evaluated by comparing its performance…”
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High order neural networks to control manufacturing systems-a comparison study by Rovithakis, G.A., Gaganis, V.I., Perrakis, S.E., Christodoulou, M.A.

“…In this paper the neuro adaptive scheduling methodology is evaluated by comparing its performance with conventional schedulers, through simulation studies. The…”
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