Retracted: Internet of Things Health Detection System in Steel Structure Construction Management by Qinglin Liu, Yongjiang Zhu, Xiongzhou Yuan, Jian Zhang, Rui Wu, Qianwen Dou, Song Liu

“…It is concluded that the time series model established in this study can objectively reflect the change trend and law of strain data, has certain prediction accuracy, and has certain value significance for monitoring data analysis and construction management of steel building structures.…”
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Super-resolution mapping of anisotropic tissue structure with diffusion MRI and deep learning by Alfredo Ordinola, David Abramian, Magnus Herberthson, Anders Eklund, Evren Özarslan

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NIMS fatigue data sheet on gigacycle fatigue properties of A2017 (Al-4.0Cu-0.6Mg) aluminium alloy by Yoshiyuki Furuya, Hideaki Nishikawa, Hisashi Hirukawa

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Predictive Technology Assessment by Means of a Structure-Based Method of Machine Learning by Manja Mai-Ly PFAFF, Uwe FRIEß, Andreas OTTO, Matthias PUTZ

“…Based on this algorithm, correlation considerations can be performed on the data structure, the measured variables, and the diagnostic parameters. …”
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The Historical Evolution and Significance of Multiple Sequence Alignment in Molecular Structure and Function Prediction by Chenyue Zhang, Qinxin Wang, Yiyang Li, Anqi Teng, Gang Hu, Qiqige Wuyun, Wei Zheng

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Calculation of Water Saturation of Dolomite Reservoir Based on Multi-Pore Structure Model by Xinzhong Qi, Xingneng Wu, Yong Ai, Ruokun Huang, Wenxing Duan, Yaqi Liu

“…The total conductivity of the rock is established by the parallel combination of the effective components to establish the porous structure response equation, the equation is fitted by the rock electrical experimental data, and the parameters such as the fracture porosity index (mj) and the interconnected void porosity (ml) are calculated. …”
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qPRF: A system to accelerate population receptive field modeling by Sebastian Waz, Yalin Wang, Zhong-Lin Lu

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From sequence to protein structure and conformational dynamics with artificial intelligence/machine learning by Alexander M. Ille, Emily Anas, Michael B. Mathews, Stephen K. Burley

“…The 2024 Nobel Prize in Chemistry was awarded in part for de novo protein structure prediction using AlphaFold2, an artificial intelligence/machine learning (AI/ML) model trained on vast amounts of sequence and three-dimensional structure data. …”
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Deep nested U-structure network with frequency attention for building semantic segmentation by Khaled Moghalles, Zaid Al-Huda, Dalal AL-Alimi, Yeong Hyeon Gu, Mugahed A. Al-antari

“…We have introduced a novel approach to address these issues: an end-to-end residual U-structure embedded within a U-Net, enhanced by a frequency attention module and a hybrid loss function. …”
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Local Scouring Visualization of Bed Channel Surface in Hydraulic Structure Using Surfer by Tifarah Dayansabila Paramadita, Nadia Azarine, Aburizal Azhar, Didit Puji Riyanto

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Research on Structure and Characteristics of Gear System of Crossflow in Flow Domain of Reactor Core by CHEN Guangliang, ZHANG Hanqi, TAO Wenquan

“…However, in traditional research, the states of flow resistance, thermal resistance, flow mixing and other states are measured by the macroscopic integration of lines, surfaces and volumes, and the fine data of tens of millions or hundreds of millions of computing grids are mostly converted into hundreds or thousands of sets of average data, the physical state is reduced from three-dimensional to two-dimensional or one-dimensional state, and the spatial resolution is reduced from submillimeter to decimeter or lower, and the fine data cannot be effectively and fully utilized. …”

Analysis and selection of the structure of a multiprocessor computing system according to the performance criterion by G. V. Petushkov, A. S. Sigov

“…Analysis of the various architectures of computing systems (CSs) used in recent decades has allowed us to identify the most common structures. One of the key features is the use of mass-produced equipment to create data processing subsystems (for example, multicore processors and high-capacity semiconductor memory), as well as network equipment to build communication subsystems. …”
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Limitations of risk-based artificial intelligence regulation: a structuration theory approach by Lily Ballot Jones, Julia Thornton, Daswin De Silva

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On the Variability of Neural Network Classification Measures in the Protein Secondary Structure Prediction Problem by Eric Sakk, Ayanna Alexander

“…In this context, neural network mappings are constructed between protein training set sequences and their assigned structure classes in order to analyze the class membership of test data and associated measures of significance. …”
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Exploring the evolution of novel enzyme functions within structurally defined protein superfamilies. by Nicholas Furnham, Ian Sillitoe, Gemma L Holliday, Alison L Cuff, Roman A Laskowski, Christine A Orengo, Janet M Thornton

“…In order to understand the evolution of enzyme reactions and to gain an overview of biological catalysis we have combined sequence and structural data to generate phylogenetic trees in an analysis of 276 structurally defined enzyme superfamilies, and used these to study how enzyme functions have evolved. …”
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HERGAI: an artificial intelligence tool for structure-based prediction of hERG inhibitors by Viet-Khoa Tran-Nguyen, Ulrick Fineddie Randriharimanamizara, Olivier Taboureau

“…Multiple structure-based artificial intelligence (AI) binary classifiers for predicting hERG inhibitors were developed, employing, as descriptors, protein–ligand extended connectivity (PLEC) fingerprints fed into random forest, extreme gradient boosting, and deep neural network (DNN) algorithms. …”
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The Music of Rivers: The Mathematics of Waves Reveals Global Structure and Drivers of Streamflow Regime by Brian C. Brown, Aimee H. Fullerton, Darin Kopp, Flavia Tromboni, Arial J. Shogren, J. Angus Webb, Claire Ruffing, Matthew Heaton, Lenka Kuglerová, Daniel C. Allen, Lillian McGill, Jay P. Zarnetske, Matt R. Whiles, Jeremy B. Jones Jr., Benjamin W. Abbott

“…Across all these methods, the flow data showed low‐dimensional structure (i.e., simple and consistent patterns), suggesting that fundamental mechanisms are constraining flow regime. …”
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STRUCTURE OF PERIODONTAL TISSUE DISEASES IN WORKERS OF AGRICULTURAL PRODUCTION IN THE OPEN AND COVERED SOIL by V.T. Dyryk

“…Thus, as a result of the analysis of structure of periodontal disease depending on experience, in working in the conditions of contact with pesticides, on the closed soil investigated: • it is lower frequency of occurrence of intact periodontium all analtube intervals than in working of open soil and not contacting with pesticides; • it is lower frequency of occurrence of the initial forms of the inflammation of periodontal tissues (2,2 times) relatively to the obtained data in persons of the main group and the comparison group (1.3 times); • it is lower the incidence of primary generalized forms of periodontitis in relation to data of the comparison group (1.4 times) for the same number examined, compared with the values of the core group; • large frequency of occurrence of advanced forms of generalized periodontitis: in 1.6 times more than obtained data in the main group and in 3.3 times more than obtained data in the comparison group. …”
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Bayesian Structure Learning and Sampling of Bayesian Networks with the R Package BiDAG by Polina Suter, Jack Kuipers, Giusi Moffa, Niko Beerenwinkel

“…The package includes tools to search for a maximum a posteriori (MAP) graph and to sample graphs from the posterior distribution given the data. A new hybrid approach to structure learning enables inference in large graphs. …”
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ANALYSIS OF SUPPLY STRUCTURE AND TRENDS OF FORMAL FUNDING OF AGRICULTURE IN NIGERIA (1992-2012) by Sylvanus Ibeabuchi OGBONNA, Charles Kelechi OSONDU

“…This study analyzed supply structure and trends of formal funding of agriculture in Nigeria. …”
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