Results 41 to 50 of about 1,462 (252)
Transferable Deep Reinforcement Learning With Edge‐Contour‐Depth Fusion for Autonomous Wireless Capsule Endoscopy Navigation
Advanced Science, EarlyView.This study presents an anatomical landmark‐guided DRL framework for autonomous wireless capsule endoscopy navigation. Using a lightweight edge‐contour‐depth fusion module, it achieves over 97% coverage across diverse gastric anatomies. To ensure reliability, a two‐stage sim‐to‐real pipeline with an adaptive dynamic programming controller mitigates ...Haoxuan Wu, Haitao Gao, Qingyang Liu, Sishen Yuan, Haiyang Fang, Mingwu Su, Baijia Liang, Yongzun Yang, Long Bai, Wenzhen Dong, Dihong Xie, Shijian Su, Jiewen Lai, Shing Shin Cheng, Zhen Li, Xiuli Zuo, Hongliang Ren +16 morewiley +1 more sourceGravitational Lensing [PDF]
AIP Conference Proceedings, 2009 Gravitational lensing of the cosmic microwave background by large‐scale structure in the late universe is both a source of cosmological information and a potential contaminant of primordial gravity waves. Because lensing imprints growth of structure in the late universe on the CMB, measurements of CMB lensing will constrain parameters to which the CMB ...Kendrick M. Smith, Asantha Cooray, Sudeep Das, Olivier Doré, Duncan Hanson, Chris Hirata, Manoj Kaplinghat, Brian Keating, Marilena LoVerde, Nathan Miller, Graça Rocha, Meir Shimon, Oliver Zahn, Scott Dodelson, Daniel Baumann, Asantha Cooray, Joanna Dunkley, Aurelien Fraisse, Mark G. Jackson, Alan Kogut, Lawrence Krauss, Matias Zaldarriaga, Kendrick Smith +22 moreopenaire +2 more sourcesA deconstruction of methods to derive one-point lensing statistics
The Open Journal of AstrophysicsGravitational lensing is a crucial tool for exploring cosmic phenomena, providing insights into galaxy clustering, dark matter, and dark energy. Given the substantial computational demands of $N$-body simulations, approximate methods like $\texttt ...Viviane Alfradique, Tiago Castro, Valerio Marra, Miguel Quartin, Carlo Giocoli, Pierluigi Monaco +5 moredoaj +1 more sourceEuclid Quick Data Release (Q1)
Astronomy & AstrophysicsWe present the first catalogue of strong-lensing galaxy clusters identified in the Euclid Quick Release 1 observations (covering 63.1 deg2). This catalogue is the result of the visual inspection of 1260 Euclid image cutouts of previously identified ...Bergamini P., Meneghetti M., Acebron A., Clément B., Bolzonella M., Grillo C., Rosati P., Abriola D., Acevedo Barroso J. A., Angora G., Bazzanini L., Cabanac R., Nagam B. C., Cooray A. R., Despali G., Di Rosa G., Diego J. M., Fogliardi M., Galan A., Gavazzi R., Granata G., Hogg N. B., Jahnke K., Leuzzi L., Li T., Lombardi M., Mahler G., Manjón-García A., Metcalf R. B., Oguri M., Olave C., Palencia J. M., Richard J., Rojas K., Ecker L. R., Scarlata C., Schirmer M., Schuldt S., Sluse D., Smith G. P., Tortora C., Vernardos G., Walth G. L., Wilde J., Xie Y., Zumalacarregui M., Aghanim N., Altieri B., Amara A., Amendola L., Andreon S., Auricchio N., Aussel H., Baccigalupi C., Baldi M., Balestra A., Bardelli S., Basset A., Battaglia P., Bender R., Biviano A., Bonchi A., Bonino D., Branchini E., Brescia M., Brinchmann J., Caillat A., Camera S., Cañas-Herrera G., Capobianco V., Carbone C., Carretero J., Casas S., Castander F. J., Castellano M., Castignani G., Cavuoti S., Chambers K. C., Cimatti A., Colodro-Conde C., Congedo G., Conselice C. J., Conversi L., Copin Y., Courbin F., Courtois H. M., Cropper M., Da Silva A., Degaudenzi H., De Lucia G., Di Giorgio A. M., Dolding C., Dole H., Dubath F., Dupac X., Dusini S., Ealet A., Escoffier S., Fabricius M., Farina M., Farinelli R., Faustini F., Ferriol S., Finelli F., Fosalba P., Fotopoulou S., Frailis M., Franceschi E., Fumana M., Galeotta S., George K., Gillis B., Giocoli C., Gómez-Alvarez P., Gracia-Carpio J., Granett B. R., Grazian A., Grupp F., Guzzo L., Haugan S. V. H., Hoekstra H., Holmes W., Hormuth F., Hornstrup A., Hudelot P., Jhabvala M., Joachimi B., Keihänen E., Kermiche S., Kiessling A., Kilbinger M., Kohley R., Kubik B., Kuijken K., Kümmel M., Kunz M., Kurki-Suonio H., Lahav O., Laureijs R., Le Boulc’h Q., Le Brun A. M. C., Le Mignant D., Liebing P., Ligori S., Lilje P. B., Lindholm V., Lloro I., Mainetti G., Maino D., Maiorano E., Mansutti O., Marcin S., Marggraf O., Martinelli M., Martinet N., Marulli F., Massey R., Maurogordato S., Medinaceli E., Mei S., Melchior M., Mellier Y., Merlin E., Meylan G., Mora A., Moresco M., Moscardini L., Mourre S., Nakajima R., Neissner C., Nichol R. C., Niemi S.-M., Nightingale J. W., Padilla C., Paltani S., Pasian F., Pedersen K., Percival W. J., Pettorino V., Pires S., Polenta G., Poncet M., Popa L. A., Pozzetti L., Raison F., Rebolo R., Renzi A., Rhodes J., Riccio G., Romelli E., Roncarelli M., Rusholme B., Saglia R., Sakr Z., Sapone D., Sartoris B., Schewtschenko J. A., Schneider P., Secroun A., Seidel G., Seiffert M., Serrano S., Simon P., Sirignano C., Sirri G., Spurio Mancini A., Stanco L., Steinwagner J., Tallada-Crespí P., Taylor A. N., Teplitz H. I., Tereno I., Tessore N., Toft S., Toledo-Moreo R., Torradeflot F., Tsyganov A., Tutusaus I., Valentijn E. A., Valenziano L., Valiviita J., Vassallo T., Verdoes Kleijn G., Veropalumbo A., Wang Y., Weller J., Zacchei A., Zamorani G., Zerbi F. M., Zucca E., Allevato V., Ballardini M., Bozzo E., Burigana C., Cappi A., Casenove P., Di Ferdinando D., Escartin Vigo J. A., Gabarra L., Martín-Fleitas J., Matthew S., Maturi M., Mauri N., Nucita A. A., Pezzotta A., Pöntinen M., Porciani C., Risso I., Scottez V., Sereno M., Tenti M., Viel M., Wiesmann M., Akrami Y., Andika I. T., Anselmi S., Archidiacono M., Atrio-Barandela F., Benoist C., Benson K., Bertacca D., Bethermin M., Blanchard A., Blot L., Böhringer H., Borgani S., Brown M. L., Bruton S., Calabro A., Camacho Quevedo B., Caro F., Carvalho C. S., Castro T., Cogato F., Cucciati O., Davini S., De Paolis F., Desprez G., Díaz-Sánchez A., Diaz J. J., Di Domizio S., Duc P.-A., Enia A., Fang Y., Ferrari A. G., Ferreira P. G., Finoguenov A., Fontana A., Franco A., Ganga K., García-Bellido J., Gasparetto T., Gautard V., Gaztanaga E., Giacomini F., Gianotti F., Gonzalez A. H., Gozaliasl G., Guidi M., Gutierrez C. M., Hall A., Hartley W. G., Hernández-Monteagudo C., Hildebrandt H., Hjorth J., Ilbert O., Jauzac M., Kajava J. J. E., Kang Y., Kansal V., Karagiannis D., Kiiveri K., Kirkpatrick C. C., Kruk S., Le Graet J., Legrand L., Lembo M., Lepori F., Leroy G., Lesci G. F., Lesgourgues J., Liaudat T. I., Liu S. J., Loureiro A., Macias-Perez J., Maggio G., Magliocchetti M., Mannucci F., Maoli R., Martins C. J. A. P., Maurin L., Migliaccio M., Miluzio M., Monaco P., Moretti C., Morgante G., Murray C., Nadathur S., Naidoo K., Navarro-Alsina A., Nesseris S., Passalacqua F., Paterson K., Patrizii L., Pisani A., Potter D., Quai S., Radovich M., Reimberg P., Rocci P.-F., Rodighiero G., Sacquegna S., Sahlén M., Sanders D. B., Sarpa E., Schneider A., Schultheis M., Sciotti D., Sellentin E., Shankar F., Smith L. C., Stanford S. A., Tanidis K., Tao C., Testera G., Teyssier R., Tosi S., Troja A., Tucci M., Valieri C., Venhola A., Vergani D., Verza G., Vielzeuf P., Walton N. A., Soubrie E., Scott D. +376 moredoaj +1 more sourceModeling the separation of water‐in‐oil emulsions in continuously fed gravity settlers using millifluidic experiments
AIChE Journal, EarlyView.Abstract
Emulsion separation remains a persistent challenge in chemical and process industries due to the metastable nature of dispersed droplets. In gravity separators, the overall separation rate is governed by the formation of a densely packed zone (DPZ) of deforming and coalescing droplets that mediates between the dispersed and continuous phases ...Andrei Zlobin, José Esteban Andino‐Enriquez, Thomas Gouëzec, Maurice Bourrel, Enric Santanach‐Carreras, Nicolas Passade‐Boupat, Laurence Talini, François Lequeux, Pascal Panizza +8 morewiley +1 more sourceA Novel Test for MOND: Gravitational Lensing by Disk Galaxies
The Astrophysical JournalDisk galaxies represent a promising laboratory for the study of gravitational physics, including alternatives to dark matter (DM), owing to the possibility of coupling rotation curves’ dynamical data with strong gravitational lensing (SGL) observations ...Christopher Harvey-Hawes, Marco Galoppodoaj +1 more sourceParamNet: A Physics‐Guided Deep Learning Framework for Intelligent Self‐Inversion of Vacuum Optical Levitation Systems
Advanced Intelligent Systems, EarlyView.A physics‐guided deep learning framework, ParamNet, is introduced for the intelligent self‐inversion of vacuum optical tweezers. By fuzing dual‐branch time–frequency features with physical dynamical constraints, it achieves high‐accuracy calibration of trap parameters from short‐window, low‐frequency trajectories, outperforming traditional methods ...Qi Zheng, Wenfeng Fan, Feng Li, Jian Wu, Wei Quan +4 morewiley +1 more source
