Structural Optimization and Experimental Validation of a Composite Engine Mount Designed for VTOL UAV by Milica Milić, Jelena Svorcan, Toni Ivanov, Ivana Atanasovska, Dejan Momčilović, Željko Flajs, Boško Rašuo

“…Through joint efforts, engineers and researchers try to make these novel aircraft more maneuverable and reliable, but also lighter, more efficient and quieter. …”
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Empirical Models for Estimating Draught and Vertical Reaction Forces of a Duckfoot Tool in Compacted Soil: Effects of Moisture Content, Depth, Width, and Speed by Aleksander Lisowski, Daniel Lauryn, Tomasz Nowakowski, Jacek Klonowski, Adam Świętochowski, Michał Sypuła, Jarosław Chlebowski, Jan Kamiński, Krzysztof Kostyra, Magdalena Dąbrowska, Adam Strużyk, Leszek Mieszkalski, Mateusz Stasiak

“…The optimisation results showed good fitness with experimental results, and the negative relative errors (from −1.72% do −4.81%), indicating overestimating, confirmed the accuracy of the model’s predictions. …”
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Artificial Intelligence-based Approaches for Characterizing Plaque Components From Intravascular Optical Coherence Tomography Imaging: Integration Into Clinical Decision Support Sy... by Michela Sperti, Camilla Cardaci, Francesco Bruno, Syed Taimoor Hussain Shah, Konstantinos Panagiotopoulos, Karim Kassem, Giuseppe De Nisco, Umberto Morbiducci, Raffaele Piccolo, Francesco Burzotta, Fabrizio D’Ascenzo, Marco Agostino Deriu, Claudio Chiastra

“…Manual plaque assessment by experts is time-consuming, prone to errors, and affected by high inter-observer variability. …”
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Observations of Uranus at High Phase Angle as Seen by New Horizons by Samantha N. Hasler, L. C. Mayorga, William M. Grundy, Amy A. Simon, Susan D. Benecchi, Carly J. A. Howett, Silvia Protopapa, Heidi B. Hammel, Daniel D. Wenkert, S. Alan Stern, Kelsi N. Singer, Simon B. Porter, Pontus C. Brandt, Joel W. Parker, Anne J. Verbiscer, John R. Spencer, The New Horizons Planetary Science Theme Team

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Cómo Iniciar el Proceso de Mercadeo en la Empresa by Gerardo Lee Rojas

“…Sin un plan de mercadeo, la posibilidad decometer errores es altísima. En muchos casos, la desesperaciónque causa el avance de la competencia y lareducción del nivel de ventas, provocan que la empresase desenfoque, cometiendo el error de operar en camposque no estaban contemplados en su estrategia comercialbásica, y, por tanto, en los que no tiene suficiente habilidadcompetitiva. …”
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Controladores multivariables para un vehículo autónomo terrestre: Comparación basada en la fiabilidad del software by Norberto Cañas, Wilmar Hernández, Gabriel González, Oleg Sergiyenko

“…The reliability tests were conducted on perturbed controllers with injected faults, simulating typical programmer errors. These controllers were designed to operate in an autonomous ground vehicle, and they were tuned by using a genetic algorithm. …”
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Diseño, fiabilidad y validez de una herramienta para el análisis de las imágenes de los libros de texto de Educación Física (Design, reliability, and validity of a tool for the an... by Irene Moya-Mata, Julio Martín Ruiz, Laura Ruiz Sanchis, Concepción Ros Ros

“…Para ello se desarrolló SAIMEF (Sistema de Análisis de Imágenes en Educación Física), una aplicación php diseñada ad hoc, que facilita el acceso al sistema y minimiza los errores de análisis. La herramienta consta de dos sistemas de categorías para el análisis: SC-I para las imágenes en las que aparece la figura humana realizando actividad física (con 72 indicadores) y SC-II para el resto de las imágenes (con 47 indicadores). …”
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Review and assess of frequently used scales in OHRQoL by Abdulraof Alghadeer, Tim Newton, Stepphen Dunne

“…Guillemin et al. (1993) and Acquadro et al. (2008) recommend the following: care should be taken during cross-cultural adaptation to incorporate a team of varied experts, there should be regular contact with the original authors to minimizemajor errors and a pilot study should be conducted for new items before they are submitted to a larger group. …”
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Classification of Structural and Functional Development Stage of Cardiomyocytes Using Machine Learning Techniques by V. R. Bondarev, K. O. Ivanko, N. G. Ivanushkina

“…Images from the classes with a large number of mutual errors are also considered. Based on the analysis, several classes of structural and functional development of cardiomyocytes are combined. …”
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Dataset of noise signals generated by smart attackers for disrupting state of health and state of charge estimations of battery energy storage systemsMendeley by Alaa Selim, Huadong Mo, Hemanshu Pota

“…These signals serve as a complementary example of adversarial attacks, demonstrating how such noise can destabilize estimation algorithms and lead to critical control errors. This dataset is valuable for researchers and engineers aiming to understand and mitigate the effects of smart cyber-physical attacks on BESS. …”
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Kasimov Tsardom in the Tatar Memory of the 19th century by V.V. Trepavlov

“…In general, this picture was chronologically correct, but it was not full of details and abounded in factual errors.…”
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Graph neural networks embedded with domain knowledge for cyber threat intelligence entity and relationship mining by Gan Liu, Kai Lu, Saiqi Pi

“…However, current models for identifying network threat entities and extracting relationships suffer from limitations such as the inadequate representation of textual semantic information, insufficient granularity in threat entity recognition, and errors in relationship extraction propagation. To address these issues, this article proposes a novel model for Network Threat Entity Recognition and Relationship Extraction (CtiErRe). …”
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Benchmarking of bioinformatics tools for the hybrid de novo assembly of human and non-human whole-genome sequencing data by Adrián Muñoz-Barrera, Luis A. Rubio-Rodríguez, David Jáspez, Almudena Corrales, Itahisa Marcelino-Rodriguez, Lourdes Ortiz, Pablo Mendoza, José M. Lorenzo-Salazar, Rafaela González-Montelongo, Carlos Flores

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Predicting Marshall stability and flow parameters in asphalt pavements using explainable machine-learning models by Ibrahim Asi, Yusra I. Alhadidi, Taqwa I. Alhadidi

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Changes in Choroidal Thickness of Healthy Children during Myopia Progression over 4 Years: Boramae Myopia Cohort Study Report 5 by Jeong Hyun Lee, MD, MSc, Joo Young Shin, MD, PhD, Martha Kim, MD, PhD, Kyoung Min Lee, MD, PhD, Sohee Oh, PhD, Seok Hwan Kim, MD, PhD, Ho-Kyung Choung, MD, PhD, Jeeyun Ahn, MD, PhD

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A Fusion Strategy for High-Accuracy Multilayer Soil Moisture Downscaling and Mapping by Xu Zhang, Xin Liu, Xiang Zhang, Aminjon Gulakhmadov, Jiefeng Wu, Xihui Gu, Won-Ho Nam, Panda Rabindra Kumar, Veber Afonso Figueiredo Costa, Mahlatse Kganyago, Berhanu Keno Terfa, Wenying Du, Chao Wang, Peng Wang, Jing Yuan, Nengcheng Chen

“…Experimental results show that the Pearson correlation coefficient (PCC) of SSM and RZSM data downscaled by LightGBM are 0.699 and 0.754, with root mean square error (RMSE) values of 0.053 and 0.055 m³/m³, respectively. …”
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A Systematic Study of Popular Software Packages and AI/ML Models for Calibrating In Situ Air Quality Data: An Example with Purple Air Sensors by Seren Smith, Theodore Trefonides, Anusha Srirenganathan Malarvizhi, Shyra LaGarde, Jiakang Liu, Xiaoguo Jia, Zifu Wang, Jacob Cain, Thomas Huang, Mohammad Pourhomayoun, Grace Llewellyn, Wai Phyo, Sina Hasheminassab, Joe Roberts, Kevin Marlis, Daniel Q. Duffy, Chaowei Yang

“…Long Short-Term Memory (LSTM) models trained in RStudio and TensorFlow excelled, with high R² scores of 0.856 and 0.857 and low Root Mean Squared Errors (RMSEs) of 4.25 µg/m³ and 4.26 µg/m³, respectively. …”
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Effects of Physical Exercise Breaks on Executive Function in a Simulated Classroom Setting: Uncovering a Window into the Brain by Qian Yu, Zhihao Zhang, Sebastian Ludyga, Kirk I. Erickson, Boris Cheval, Meijun Hou, Dominika M. Pindus, Charles H. Hillman, Arthur F. Kramer, Ryan S. Falck, Teresa Liu‐Ambrose, Jin Kuang, Sean P. Mullen, Keita Kamijo, Toru Ishihara, David A. Raichlen, Matthew Heath, David Moreau, André O. Werneck, Fabian Herold, Liye Zou

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Development of a stand for measuring thrust of micro-jet thrusters based on machine vision by V. V. Fedyanin, V. V. Shalay, P. S. Yachmenev, I. S. Vavilov

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Genome data based deep learning identified new genes predicting pharmacological treatment response of attention deficit hyperactivity disorder by Yilu Zhao, Zhao Fu, Eric J. Barnett, Ning Wang, Kangfuxi Zhang, Xuping Gao, Xiangyu Zheng, Junbin Tian, Hui Zhang, XueTong Ding, Shaoxian Li, Shuyu Li, Qingjiu Cao, Suhua Chang, Yufeng Wang, Stephen V. Faraone, Li Yang

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