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  1. 1701

    GRB 221009A: Observations with LST-1 of CTAO and Implications for Structured Jets in Long Gamma-Ray Bursts by K. Abe, S. Abe, A. Abhishek, F. Acero, A. Aguasca-Cabot, I. Agudo, C. Alispach, D. Ambrosino, F. Ambrosino, L. A. Antonelli, C. Aramo, A. Arbet-Engels, C. Arcaro, T. T. H. Arnesen, K. Asano, P. Aubert, A. Baktash, M. Balbo, A. Bamba, A. Baquero Larriva, U. Barres de Almeida, J. A. Barrio, L. Barrios Jiménez, I. Batkovic, J. Baxter, J. Becerra González, E. Bernardini, J. Bernete, A. Berti, I. Bezshyiko, C. Bigongiari, E. Bissaldi, O. Blanch, G. Bonnoli, P. Bordas, G. Borkowski, G. Brunelli, A. Bulgarelli, M. Bunse, I. Burelli, L. Burmistrov, M. Cardillo, S. Caroff, A. Carosi, R. Carraro, M. S. Carrasco, F. Cassol, D. Cerasole, G. Ceribella, A. Cerviño Cortínez, Y. Chai, K. Cheng, A. Chiavassa, M. Chikawa, G. Chon, L. Chytka, G. M. Cicciari, A. Cifuentes, J. L. Contreras, J. Cortina, H. Costantini, M. Dalchenko, P. Da Vela, F. Dazzi, A. De Angelis, M. de Bony de Lavergne, R. Del Burgo, C. Delgado, J. Delgado Mengual, M. Dellaiera, D. della Volpe, B. De Lotto, L. Del Peral, R. de Menezes, G. De Palma, C. Díaz, A. Di Piano, F. Di Pierro, R. Di Tria, L. Di Venere, R. M. Dominik, D. Dominis Prester, A. Donini, D. Dorner, M. Doro, L. Eisenberger, D. Elsässer, G. Emery, J. Escudero, V. Fallah Ramazani, F. Ferrarotto, A. Fiasson, L. Foffano, F. Frías García-Lago, S. Fröse, Y. Fukazawa, S. Gallozzi, R. Garcia López, S. Garcia Soto, C. Gasbarra, D. Gasparrini, D. Geyer, J. Giesbrecht Paiva, N. Giglietto, F. Giordano, N. Godinovic, T. Gradetzke, R. Grau, D. Green, J. Green, S. Gunji, P. Günther, J. Hackfeld, D. Hadasch, A. Hahn, M. Hashizume, T. Hassan, K. Hayashi, L. Heckmann, M. Heller, J. Herrera Llorente, K. Hirotani, D. Hoffmann, D. Horns, J. Houles, M. Hrabovsky, D. Hrupec, D. Hui, M. Iarlori, R. Imazawa, T. Inada, Y. Inome, S. Inoue, K. Ioka, M. Iori, T. Itokawa, A. Iuliano, J. Jahanvi, I. Jimenez Martinez, J. Jimenez Quiles, I. Jorge Rodrigo, J. Jurysek, M. Kagaya, O. Kalashev, V. Karas, H. Katagiri, D. Kerszberg, T. Kiyomot, Y. Kobayashi, K. Kohri, A. Kong, P. Kornecki, H. Kubo, J. Kushida, B. Lacave, M. Lainez, G. Lamanna, A. Lamastra, L. Lemoigne, M. Linhoff, S. Lombardi, F. Longo, R. López-Coto, M. López-Moya, A. López-Oramas, S. Loporchio, A. Lorini, J. Lozano Bahilo, F. Lucarelli, H. Luciani, P. L. Luque-Escamilla, P. Majumdar, M. Makariev, M. Mallamaci, D. Mandat, M. Manganaro, D. K. Maniadakis, G. Manicò, K. Mannheim, S. Marchesi, F. Marini, M. Mariotti, P. Marquez, G. Marsella, J. Martí, O. Martinez, G. Martínez, M. Martínez, A. Mas-Aguilar, M. Massa, G. Maurin, D. Mazin, J. Méndez-Gallego, S. Menon, E. Mestre Guillen, D. Miceli, T. Miener, J. M. Miranda, R. Mirzoyan, M. Mizote, T. Mizuno, M. Molero Gonzalez, E. Molina, T. Montaruli, A. Moralejo, D. Morcuende, A. Moreno Ramos, A. Morselli, V. Moya, H. Muraishi, K. Murase, S. Nagataki, T. Nakamori, A. Neronov, D. Nieto Castaño, M. Nievas Rosillo, L. Nikolic, K. Nishijima, K. Noda, D. Nosek, V. Novotny, S. Nozaki, M. Ohishi, Y. Ohtani, T. Oka, A. Okumura, R. Orito, L. Orsini, J. Otero-Santos, P. Ottanelli, M. Palatiello, G. Panebianco, D. Paneque, F. R. Pantaleo, R. Paoletti, J. M. Paredes, M. Pech, M. Pecimotika, M. Peresano, F. Pfeifle, E. Pietropaolo, M. Pihet, G. Pirola, C. Plard, F. Podobnik, M. Polo, E. Prandini, M. Prouza, S. Rainò, R. Rando, W. Rhode, M. Ribó, V. Rizi, G. Rodriguez Fernandez, M. D. Rodríguez Frías, P. Romano, A. Roy, A. Ruina, E. Ruiz-Velasco, T. Saito, S. Sakurai, D. A. Sanchez, H. Sano, T. Šarić, Y. Sato, F. G. Saturni, V. Savchenko, F. Schiavone, B. Schleicher, F. Schmuckermaier, J. L. Schubert, F. Schussler, T. Schweizer, M. Seglar Arroyo, T. Siegert, G. Silvestri, A. Simongini, J. Sitarek, V. Sliusar, A. Stamerra, J. Strišković, M. Strzys, Y. Suda, A. Sunny, H. Tajima, M. Takahashi, J. Takata, R. Takeishi, P. H. T. Tam, S. J. Tanaka, D. Tateishi, T. Tavernier, P. Temnikov, Y. Terada, K. Terauchi, T. Terzic, M. Teshima, M. Tluczykont, F. Tokanai, T. Tomura, D. F. Torres, F. Tramonti, P. Travnicek, G. Tripodo, A. Tutone, M. Vacula, J. van Scherpenberg, M. Vázquez Acosta, S. Ventura, S. Vercellone, G. Verna, I. Viale, A. Vigliano, C. F. Vigorito, E. Visentin, V. Vitale, V. Voitsekhovskyi, G. Voutsinas, I. Vovk, T. Vuillaume, R. Walter, L. Wan, M. Will, J. Wójtowicz, T. Yamamoto, R. Yamazaki, Y. Yao, P. K. H. Yeung, T. Yoshida, T. Yoshikoshi, W. Zhang, (The CTAO-LST Collaboration)

    Published 2025-01-01
    “…The results are compared with various models of afterglows from structured jets that are consistent with the published multiwavelength data but entail significant quantitative and qualitative differences in the VHE emission after 1 day. …”
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    Article
  2. 1702
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  6. 1706
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  8. 1708

    Graph compression algorithm based on a two-level index structure by Gaochao LI, Ben LI, Yuhai LU, Mengya LIU, Yanbing LIU

    Published 2018-06-01
    “…The demand for the analysis and application of graph data in various fields is increasing day by day.The management of large-scale graph data with complicated structure and high degree of coupling faces two challenges:one is querying speed too slow,the other is space consumption too large.Facing the problems of long query time and large space occupation in graph data management,a two-level index compression algorithm named GComIdx for graph data was proposed.GComIdx algorithm used the ordered Key-Value structure to store the associated nodes and edges as closely as possible,and constructed two-level index and hash node index for efficient attribute query and neighbor query.Furthermore,GComIdx algorithm used a graph data compressed technology to compress the graph data before it directly stored in hard disk,which could effectively reduce the storing space consumption.The experimental results show that GComIdx algorithm can effectively reduce the initialization time of the graph data calculation and the disk space occupancy of the graph data storing,meanwhile,the query time is less than common graph databases and other Key-Value storage solutions.…”
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    Article
  9. 1709

    A Review Investigation of Damage Identification in Structures via Internet of Things by Abolfazl Yosefi, Hamidreza Nasseri

    Published 2024-01-01
    “…The Internet of Things (IoT) offers a method for collecting and analyzing data on structures. This innovative approach holds the promise of identifying and preventing structural impairment by the IoT, thereby enhancing the safety and excellence of concrete and steel constructions. …”
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    Article
  10. 1710

    A Few Remarks on the Stochastic Structure of the Unemployment Rate in Poland by Gender by Stanisław Jaworski

    Published 2020-01-01
    “…It appeared that for Polish unemployment data that structure was not as it could have been expected. …”
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    Article
  11. 1711

    Mapping topographic structure in white matter pathways with level set trees. by Brian P Kent, Alessandro Rinaldo, Fang-Cheng Yeh, Timothy Verstynen

    Published 2014-01-01
    “…We show that level set trees can also be generalized to model pseudo-density functions in order to analyze a broader array of data types, including entire fiber streamlines. Finally, resampling methods show the reliability of the level set tree as a descriptive measure of topographic structure, illustrating its potential as a statistical descriptor in brain imaging analysis. …”
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    Article
  12. 1712

    An Ultrafast Optical Imaging System with Anamorphic Transformation Based on STEAM Structure by Guoqing Wang, Yuan Zhou, Rui Min, Fang Zhao, E Du, Xingquan Li, Cong Qiu, Dongrui Xiao, Chao Wang

    Published 2024-12-01
    “…In this paper, we propose an ultrafast optical imaging system with anamorphic transformation (AT) based on the STEAM structure, which has the benefit of data compression and changing group delay-related resolution. …”
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    Article
  13. 1713

    Dynamic graph structure and spatio-temporal representations in wind power forecasting by Zang Peng, Dong Wenqi, Wang Jing, Fu Jianglong

    Published 2025-01-01
    “…However, due to the stochastic and unstable nature of wind, it poses a real challenge to effectively analyze the correlations among multiple time series data for accurate prediction. In our study, an end-to-end framework called Dynamic Graph structure and Spatio-Temporal representation learning (DSTG) framework is proposed to achieve stable power forecasting by constructing graph data to capture the critical features in the data. …”
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    Article
  14. 1714

    KARAŞAR, AN ALEVI-BEKTASHI SETTLEMENT IN BEYPAZARI (ADMINISTRATIVE, ECONOMIC AND SOCIAL STRUCTURE) by İsmail Yaşayanlar

    Published 2024-12-01
    “…This study evaluates the economic and social structure of Karaşar, an Alevi-Bektashi settlement, on the basis of data from tahrir, population and temettuat books in the Ottoman Archives. …”
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    Article
  15. 1715

    Numerical Simulation of Earthquake Impacts on Marine Structures: A Comprehensive Review by Adel Kabi, Jersson X. Leon-Medina, Francesc Pozo

    Published 2024-12-01
    “…This review examines advanced numerical modeling approaches, including CFD, FEM, DEM, FVM, and BEM, to assess the impacts of earthquakes on these structures. These methods provide cost-effective and reliable simulations, demonstrating strong alignment with experimental and theoretical data. …”
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    Article
  16. 1716

    Bayesian variable selection with graphical structure learning: Applications in integrative genomics. by Suprateek Kundu, Yichen Cheng, Minsuk Shin, Ganiraju Manyam, Bani K Mallick, Veerabhadran Baladandayuthapani

    Published 2018-01-01
    “…This has motivated systematic data-driven approaches to integrate multi-dimensional structured datasets, since cancer development and progression is driven by numerous co-ordinated molecular alterations and the interactions between them. …”
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    Article
  17. 1717

    Using the Size Structure of Populations to Infer Range Dynamics and the Frequency of Recruitment by Jenny Ann Sweatman, J. David Aguirre, Adam N. H. Smith, Libby Liggins

    Published 2025-06-01
    “…Our study demonstrates that size‐structure data can be a valuable resource in understanding range dynamics and recruitment in the absence of time‐series data.…”
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  18. 1718

    Efficient structure learning of gene regulatory networks with Bayesian active learning by Dániel Sándor, Péter Antal

    Published 2025-06-01
    “…Abstract Background Gene regulatory network modeling is a complex structure learning problem that involves both observational data analysis and experimental interventions. …”
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    Article
  19. 1719

    Impact of capital structure on financial performance of oil and gas firms in Nigeria by Obumneme Renato Anozie, Taiwo Adewale Muritala, Victor Edet Ininm, Nurudeen Salako Yisau

    Published 2023-03-01
    “…Abstract This study examines the impact of capital structure on the financial performance of Nigerian oil and gas companies. …”
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  20. 1720

    A Model-Based Substructuring Method for Local Damage Detection of Structure by Eun-Taik Lee, Hee-Chang Eun

    Published 2014-01-01
    “…It is impractical to collect the full set of data in finite element model for investigating the structural performance. …”
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    Article