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)

“…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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Improving Survey Data Interpretation: A Novel Approach to Analyze Single-Item Ordinal Responses with Non-Response Categories by Ewa Roszkowska

Subjects: “…belief structure…”
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Analysis of industrial transformation and upgrading in resource-exhausted cities: evidence from city-level and enterprise-level panel data. by Yiwen Peng

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Revealing the City Influence and Its Pattern Using Web Search Data: A New Perspective Through Attention Flow by Xiaoxiao Wang, Haiping Zhang, Zitong Li, Xin Yang, Ziyi Qian, Duo Bian, Yongxin Zhao

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Civil structural health monitoring and machine learning: a comprehensive review by Asraar Anjum, Meftah Hrairi, Abdul Aabid, Norfazrina Yatim, Maisarah Ali

“…More robust prediction models may be produced by combining test data collected in the laboratory or field with ML. …”
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Civil Structural Health Monitoring and Machine Learning: A Comprehensive Review by Asraar Anjum, Meftah Hrairi, Abdul Aabid, Norfazrina Yatim, Maisarah Ali

“…More robust prediction models may be produced by combining test data collected in the laboratory or field with ML. …”
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Graph compression algorithm based on a two-level index structure by Gaochao LI, Ben LI, Yuhai LU, Mengya LIU, Yanbing LIU

“…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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A structural biology compatible file format for atomic force microscopy by Yining Jiang, Zhaokun Wang, Simon Scheuring

“…Abstract Cryogenic electron microscopy (cryo-EM), X-ray crystallography, and nuclear magnetic resonance (NMR) contribute structural data that are interchangeable, cross-verifiable, and visualizable on common platforms, making them powerful tools for our understanding of protein structures. …”
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A Few Remarks on the Stochastic Structure of the Unemployment Rate in Poland by Gender by Stanisław Jaworski

“…It appeared that for Polish unemployment data that structure was not as it could have been expected. …”
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Mapping topographic structure in white matter pathways with level set trees. by Brian P Kent, Alessandro Rinaldo, Fang-Cheng Yeh, Timothy Verstynen

“…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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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

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

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

“…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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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

“…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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Advancing Structural Health Monitoring with Deep Belief Network-Based Classification by Álvaro Presno Vélez, Zulima Fernández Muñiz, Juan Luis Fernández Martínez

“…In recent years, deep learning techniques have emerged as powerful tools for analyzing the complex data generated by SHM systems. This study investigates the use of deep belief networks (DBNs) for classifying structural conditions before and after retrofitting, using both ambient and train-induced acceleration data. …”
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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

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

“…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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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

“…Abstract This study examines the impact of capital structure on the financial performance of Nigerian oil and gas companies. …”
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A Model-Based Substructuring Method for Local Damage Detection of Structure by Eun-Taik Lee, Hee-Chang Eun

“…It is impractical to collect the full set of data in finite element model for investigating the structural performance. …”
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Structural Assessment of Flexible Pavements Based on the Level of Detail of Management Functions by Edwin Antonio Guzmán Suárez, Diego Fernando Gualdrón Alfonso, Jorge Andrés Sarmiento-Rojas

“…Context: Pavement condition data are a fundamental component of pavement management systems (PMS) and play a critical role in structural evaluation. …”
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