The International Trauma Interview (ITI): development of a semi-structured diagnostic interview and evaluation in a UK sample by Neil P. Roberts, Philip Hyland, Robert Fox, Alice Roberts, Catrin Lewis, Marylene Cloitre, Chris R. Brewin, Thanos Karatzias, Mark Shevlin, Odeta Gelezelyte, Kristina Bondjers, Andrés Fresno, Alistair Souch, Jonathan I. Bisson

“…This study aimed to investigate a psychometric evaluation of the ITI and to finalise the English language version.Method: The latent structure, internal consistency, interrater agreement, and convergent and discriminant validity were evaluated with data from a convenience sample, drawn from an existing research cohort, of 131 trauma exposed participants from the United Kingdom reporting past diagnosis for PTSD or who had screened positively for traumatic stress symptoms. …”
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Impact of a structured food sequence and mobile health monitoring on gestational diabetes outcomes: a clinical trial by Ria Murugesan, Shubhashree Thiruselvam, Kakithakara Vajravelu Leela, Abhishek Satheesan, K. Geetha, Mohan Ram, Janardanan Kumar

“…The intervention group followed a structured food order—fiber first, then protein, and carbohydrates last—and tracked their intake using a mobile health application (JotForm). …”
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Features of Structured, One-to-One Videoconference Interventions That Actively Engage People in the Management of Their Chronic Conditions: Scoping Review by Yu-Ting Chen, Michelle Lehman, Toni Van Denend, Jacqueline Kish, Yue Wu, Katharine Preissner, Matthew Plow, Tanya L Packer

“…The included studies reported on structured, one-on-one, synchronous videoconferencing interventions that actively engaged adults in the management of their chronic conditions at home. …”
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Autoencoder-Augmented Graph Neural Networks for Accurate and Scalable Structure Recognition in Analog/Mixed-Signal Schematics by Mohamed Salem, Witesyavwirwa Vianney Kambale, Ali Deeb, Sergii Tkachov, Anjeza Karaj, Joachim Pichler, Manuel Ludwig Lexer, Kyandoghere Kyamakya

“…The increasing complexity of Analog/Mixed-Signal (AMS) schematics has been posing significant challenges in structure recognition, particularly in the intellectual property (IP) industry, where data scarcity and confidentiality constraints limit model training. …”
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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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Constraints on the frontal crustal structure of a continental collision from an integrated geophysical research: The central‐western Betic Cordillera (SW Spain) by A. Ruiz‐Constán, A. Pedrera, J. Galindo‐Zaldívar, J. Pous, J. Arzate, F. J. Roldán‐García, C. Marin‐Lechado, F. Anahnah

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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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SARS‐CoV‐2 structural coverage map reveals viral protein assembly, mimicry, and hijacking mechanisms by Seán I O’Donoghue, Andrea Schafferhans, Neblina Sikta, Christian Stolte, Sandeep Kaur, Bosco K Ho, Stuart Anderson, James B Procter, Christian Dallago, Nicola Bordin, Matt Adcock, Burkhard Rost

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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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