Checklist of pioneer benthic taxa found on Autonomous Reef Monitoring Structures (ARMS) in Terra Nova Bay (Ross Sea, Antarctica) by Valentina Cometti, Matteo Cecchetto, Alice Guzzi, Marco Grillo, Nicholas Noli, Simonetta Corsolini, Stefano Schiaparelli

“…This dataset provides essential baseline data on the occurrence and abundance of pioneer benthic communities in this study area, representing an important contribution for understanding the dynamics of benthic pioneer communities in an area where these structures have never been deployed and, in general, for an exposure time that largely exceed the standard one, which is usually of one year only.The 277 occurrences reported here have been classified at the lowest possible taxonomic level and comprise 39 recognised species, 49 genera and 43 families. …”
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The Performance of an ML-Based Weigh-in-Motion System in the Context of a Network Arch Bridge Structural Specificity by Dawid Piotrowski, Marcin Jasiński, Artur Nowoświat, Piotr Łaziński, Stefan Pradelok

“…In civil and bridge engineering, they can facilitate the identification of specific patterns through the analysis of data acquired from structural health monitoring (SHM) systems. …”
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Community-structures that facilitate engagement in health research: Ifakara Health Research Institute-Bagamoyo case study [version 2; peer review: 1 approved, 2 approved with reser... by Leah Bategereza, Dorcas Kamuya, Ally Olotu

“…The present research was aimed to describe the nature of community structures, that could be engaged in health research at the Ifakara Health Institute. …”
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Artificial Intelligence-Based Approach for Damage Localization in Ultrasonic Guided Wave-Based Structural Health Monitoring by Anastasiia Volovikova, Steffen Freitag, Oliver Schackmann, Vittorio Memmolo, Ahmed Bayoumi, Inka Mueller, Jochen Moll

“…Existing localization approaches are limited through them being applied on defined simple structures and requiring a large amount of data, which is usually unavailable in practical applications. …”
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Damage detection in structural health monitoring using hybrid convolution neural network and recurrent neural network by Dung Bui-Ngoc, Hieu Nguyen-Tran, Lan Nguyen-Ngoc, Hoa Tran-Ngoc, Thanh Bui-Tien, Hung Tran-Viet

“…Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. …”
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Damage Detection in Structural Health Monitoring using Hybrid Convolution Neural Network and Recurrent Neural Network by Thanh Bui-Tien, Dung Bui-Ngoc, Hieu Nguyen-Tran, Lan Nguyen-Ngoc, Hoa Tran-Ngoc, Hung Tran-Viet

“…Different machine learning techniques have been applied to attempt to extract features or knowledge from vibration data, however, they need to learn prior knowledge about the factors affecting the structure. …”
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Factors Influencing the Human Development Index in Indonesia Using Structural Equation Modeling-Partial Least Square by Rifki Saefullah, Besse Arnawisuda Ningsi

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Structural modeling of the effect of school culture on psychological well-being with the mediating role of narcissism and achievement emotions by maryam fallahi, samaneh salimi

“…The aim of the present study was to investigate the structural modeling of the effect of school culture on psychological well-being with the mediating role of narcissism and students' progress emotions. …”
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Gender-specific perception of job stressors and resources: a structural equation model-based secondary analysis by Angelina Heub, Andrea Schaller, Martin Lange

“…The national representative cross-sectional sample included 4,118 employees. The data were analyzed using structural equation modeling.ResultsCorrelations between quantitative demands, working time, co-worker support, quality of leadership, job control, and subjective perceived stress were first confirmed for a total sample of employees. …”
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Digital transformation of care for keratoconus patients: ML modeling structural outcomes of corneal collagen cross-linking by Yauhen Statsenko, Yauhen Statsenko, Yauhen Statsenko, Darya Smetanina, Roman Voitetskii, Gillian Lylian Simiyu, Mikalai Pazniak, Elena Likhorad, Aleh Pazniak, Pavel Beliakouski, Dmitri Abelski, Fatima Ismail, Klaus Neidl-Van Gorkom, Milos Ljubisavljevic, Milos Ljubisavljevic

“…BackgroundStructural outcomes of corneal collagen cross-linking (CXL) have not been thoroughly investigated. …”
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Structural knowledge-driven meta-learning for task offloading in vehicular networks with integrated communications, sensing and computing by Ruijin Sun, Yao Wen, Nan Cheng, Wei Wang, Rong Chai, Yilong Hui

“…To tackle this issue, for tasks taking environmental data as input, the data perceived by roadside units (RSU) equipped with several sensors can be directly exploited for computation, resulting in a novel task offloading paradigm with integrated communications, sensing and computing (I-CSC). …”
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Floral diversity, structural integrity, and regeneration patterns of Endba-Zend dry Afromontane forest in Northwestern Ethiopia by Yitayih Dessie, Nigussie Amsalu, Belayneh Awoke, Getaneh Gebeyehu

“…Abstract This study was carried out at Endba-Zend Forest in Sekela District, Northwestern Ethiopia, to investigate the plant species composition, structure, and regeneration status of a remnant forest. …”
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Association of intermediate health determinants with gestational diabetes self-care: a structural equation modeling approach by Fatemeh Vakili, Zohreh Mahmoodi, Malihe Nasiri, Saiedeh Hanieh Alamolhoda, Zeinab Hamzehgardeshi, Vajiheh Arabshahi Hoseinabadi

“…After data collection, data analysis was performed using SPSS-21 and Lisrel-8 software. …”
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Analyzing Impact of Intellectual Capital on Business Performance Using Structural Models Based on Customer Knowledge Management by Seyedeh Maryam Ameli kalkhoran, Kamran Rabiei, Seyed Mehdi Seyed Alizadeh, Hakimeh Morabbi Heravi, Yaser Rouzpeykar

“…It was confirmed by a consensus of experts that the research instrument was valid, and the reliability of the research was 0.894%. Structural equation modeling was used to analyze the data. …”
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Evaluating Theoretical Solvent Models for Thermodynamic and Structural Descriptions of Dacarbazine–Cyclodextrin Complexes. The Theoretical and Conductometric Study by Zdzisław Kinart, Marta Hoelm, Martyna Imińska

“…In this study, the influence of various implicit solvent models on the structural and thermodynamic properties of dacarbazine complexes with three cyclodextrins—α-CD, HP-β-CD, and HE-β-CD—was evaluated. …”
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WDNET-YOLO: Enhanced Deep Learning for Structural Timber Defect Detection to Improve Building Safety and Reliability by Xiaoxia Lin, Weihao Gong, Lin Sun, Xiaodong Yang, Chunwei Leng, Yan Li, Zhenyu Niu, Yingzhou Meng, Xinyue Xiao, Junyan Zhang

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Integrating Environmental Covariates into Adaptability and Stability Analyses: A Structural Equation Modeling Approach for Cotton Breeding by Matheus Massariol Suela, Moysés Nascimento, Ana Carolina Campana Nascimento, Camila Ferreira Azevedo, Paulo Eduardo Teodoro, Francisco José Correia Farias, Luiz Paulo de Carvalho, Diego Jarquin

“…In the context of phenotypic adaptability and stability analysis, structural equation modeling (SEM) offers an interesting approach to integrating environmental covariates. …”
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Understanding and reducing delayed dental care for early childhood caries: a structural equation model approach by Mengmeng Wang, Yu Zhang, Xiaofei Li, Xin Liu

“…The data in this study were analyzed using a variety of statistical tests, including the Kruskal-Wallis and Mann-Whitney tests, correlation analysis, multiple stepwise regression analysis, and structural equation modeling (SEM). …”
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Research on sinusoidal load identification method under structural natural frequency excitation based on LSTM-CNN by HE Wenbo, SUN Hanyu, XIE Jiang, ZHANG Xiaoqiang

“…For model training and load identification，the response data and excitation data from the structure are corrected. …”
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Multi-Task Learning for Real-Time BSIM-CMG Parameter Extraction of NSFETs With Multiple Structural Variations by Seunghwan Lee, Seungjoon Eom, Jinsu Jeong, Junjong Lee, Sanguk Lee, Hyeok Yun, Yonghwan Ahn, Rock-Hyun Baek

“…To mitigate overfitting due to disparate data sources, such as Monte Carlo simulations for training data and technology computer-aided design (TCAD) simulations or hardware measurements for test data, additive noise was incorporated into the training data. …”
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