Multimodal fusion for athlete state prediction leveraging XLNet and deep generative models by Yafeng Feng, Yong Sun, Chengfang Hang

“…To address these challenges, we propose a model that combines XLNet with a context window adjustment mechanism and Deep Generative VAE for psychological and physiological data integration. The model leverages advanced feature extraction techniques to capture both emotional tendencies from self-reports and sentiment analysis texts, as well as sequential physiological signals such as ECG and EDA. …”
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Patients’, clinicians’ and developers’ perspectives and experiences of artificial intelligence in cardiac healthcare: A qualitative study by Lesley Baillie, Adele Stewart-Lord, Nicola Thomas, Dan Frings

“…Semi-structured interviews were conducted with: patients ( n =  9), clinicians ( n =  16) and AI software developers ( n =  5). Data were analysed using thematic analysis. Results Potential benefits identified were increasing consistency and reliability through reducing human error, and greater efficiency. …”
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A New Mathematical Model for Predicting the Surface Vibration Velocity on the Step Topography by Xu Wu, Qifeng Guo, Yunpeng Zhang

“…The regression analysis results show that the fitting coefficient of determination of the new prediction model is 0.8152 in horizontal and 0.8902 in vertical, respectively, and the prediction error is less than 20%, which is much better than other formulas. …”
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Highly concentrated iron ore slurry flow through pipeline with and without chemical additive; part I: Experimental investigations and proposed model for the prediction of pressure... by Mishra Stuti, Kaushal Deo Raj

“…Modified Slatter’s method is proposed by replacing d85 with a more accurate optimum particle diameter using the rheological and pilot plant pipe loop testing data collected in the present study. Based on comparison with experimental data, it is observed that the proposed modified Slatter’s method can predict the pressure drop with an error of ±15%.…”
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Verification of Energy Usage Based on Standard Building Model Development of Low-Rise Residential Buildings in South Korea by KyungSoo Kim, DongChul Yoo, ChangHo Choi, HyangIn Jang

“…The standard model was developed based on reliable related standards, national statistical data, and national reports, and the energy variables applied were validated through a sensitivity analysis. …”
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Prediction of ‘Gigante’ cactus pear yield by morphological characters and artificial neural networks by Bruno V. C. Guimarães, Sérgio L. R. Donato, Alcinei M. Azevedo, Ignacio Aspiazú, Ancilon A. e Silva Junior

“…Six vegetative agronomic characters were evaluated in 500 plants in the third production cycle. The data were subjected to ANN analysis using the R software. …”
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Hyperparameter Optimization of Neural Networks Using Grid Search for Predicting HVAC Heating Coil Performance by Yosef Jaber, Pasidu Dharmasena, Adam Nassif, Nabil Nassif

“…Future research should extend the analysis to incorporate cooling operation and real-world building operation data for broader applicability.…”
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Evaluation of aerodynamic coefficients of supercritical airfoil NASA sc(2) 0712: A comparative study of 2D and 3D flow models with experimental validations by Šukunda Teodora, Svorcan Jelena, Ivanov Toni

“…This study presents a comparative numerical analysis of 2D and 3D compressible, turbulent flows around the supercritical airfoil NASA sc(2) 0712 at Mach number M = 0.5 using the finite volume method implemented in ANSYS Fluent, focusing on the estimation of drag coefficients in relation to existing experimental data. …”
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Digital biomarkers for psoriatic arthritis: a qualitative focus group study on patient-perceived opportunities and barriers by Ilja Tchetverikov, Marijn Vis, Marc R. Kok, Jolanda J. Luime, Yvonne P.M. Goekoop-Ruiterman, Wendy Wagenaar, Laura C. Coates, Patty de Groot, Jasper Foolen

“…Reducing the time of trial and error in learning to manage the disease is regarded beneficial. …”
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Fiber Bragg grating flexible sensor for deformation monitoring in steel structure engineering by Chenguang Huang, Hui Zong, Xuepeng Chen, Peng Luo, Zongli Luo, Haixing Luo, Xiang Wu, Jie Huang

“…The produced sensors were applied to deformation monitoring in steel structure engineering, and the wavelet analysis method was used to preprocess the sensor data. …”
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Climate-Based Prediction of Rice Blast Disease Using Count Time Series and Machine Learning Approaches by Meena Arumugam Gopalakrishnan, Gopalakrishnan Chellappan, Santhosh Ganapati Patil, Santosha Rathod, Kamalakannan Ayyanar, Jagadeeswaran Ramasamy, Sathyamoorthy Nagaranai Karuppasamy, Manonmani Swaminathan

“…Mean Squared Error (MSE) and Root Mean Squared Error (RMSE) were used to assess the model’s prediction ability. …”
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Thermal resistance capacity model for transient simulation of capillary-box heat exchangers by Huili Yu, Yuhao Wang, Guangrui Xv, Di Wu, Songtao Hu

“…The model is verified against experimental data, achieving very good agreement with the mean bias error (MBE) of 7.2 %. …”
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Positional Accuracy of Dental Implants Placed by Means of Fully Guided Technique in Partially Edentulous Patients: A Retrospective Study by Mariano Tia, Alessia Teresa Guerriero, Antonio Carnevale, Ilaria Fioretti, Gianrico Spagnuolo, Gilberto Sammartino, Roberta Gasparro

“…For all patients, 3D cone‐beam computed tomography (CBCT) and intraoral scans were obtained and superimposed by matching the resulting DICOM and STL data files in a software to create the tooth‐supported surgical guide. …”
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Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML by Mahdi Zeynali, Khalil Alipour, Bahram Tarvirdizadeh, Mohammad Ghamari

“…The results showed an average root mean squared error (RMSE) of 19.7 mg/dL, with 76.6% accuracy within the A zone and 23.4% accuracy within the B zone of the Clarke Error Grid Analysis (CEGA), indicating a 100% clinical acceptance. …”
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Study of the corrective ability of sync codes for the matched processing decoder by A. V. Sadchenko, O. A. Kushnirenko, A. G. Yurkevych, V. S. Sevastianov

“…The authors carry out an analysis of the correcting ability of the decoder model with matched processing for Barker codes of different lengths under the conditions of a one, two, and threefold error. …”
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Estimating Gait Speed in the Real World With a Head-Worn Inertial Sensor by Paolo Tasca, Francesca Salis, Samanta Rosati, Gabriella Balestra, Claudia Mazza, Andrea Cereatti

“…The stride detector achieved high detection rate (F1-score > 92%) and accuracy (mean absolute error < 40 ms). Very strong correlation between target and predicted speed (Spearman coefficient > 0.86) and low mean absolute error (< 0.085 m/s) were observed. …”
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Predicting pull-out strength and failure modes of metal anchors embedded in masonry structures using explainable machine learning models and empirical equations by Aryan Baibordy, Mohammad Yekrangnia

“…Among all the models, the tuned Voting model, identified as the best for predicting pull-out force, showed an average error of about 3.0 kN and an R2 score of 93%. On the other hand, CatBoost demonstrated a strong performance with an accuracy of 90% and F1-score of about 89% in both the training and test data for predicting the failure modes. …”
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The accuracy of forecasted hospital admission for respiratory tract infections in children aged 0–5 years for 2017/2023 by Fredrik Methi, Karin Magnusson, Karin Magnusson

“…Now, in 2024, we aim to examine the accuracy and usefulness of our forecast models.MethodsWe conducted a retrospective analysis using data from 753,070 children aged 0–5 years, plotting the observed monthly number of RTI admissions, including influenza coded RTI, respiratory syncytial virus (RSV) coded RTI, COVID-19 coded RTI, and other upper and lower RTI, from January 1st, 2017, until May 31st, 2023. …”
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RELATIVE AND ABSOLUTE RELIABILITY OF ISOMETRIC AND ISOKINETIC SHOULDER MAXIMAL MOMENT AND FLEXION/EXTENSION RATIOS IN GYMNASTS by Dimitrios Milosis, Theophanis A. Siatras, Kosmas I. Christoulas, Dimitrios A. Patikas

“…Bland-Altman analysis showed that the bias was lower than 10% and limits of agreement (LOAs) were lower than 35%.  …”
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Evaluation of the fit of preformed nickel titanium arch wires on normal occlusion dental arches by Rakhn G. Al-Barakati, Nasser D. Alqahtani, Abdulaziz AlMadi, Sahar F. Albarakati, Eman A. ALKofide

“…Preformed 0.016″ × 0.022″ NiTi archwires from Rocky Mountain Orthodontics (RMO), 3 M Unitek, Ormco, and Dentaurum were evaluated in terms of their fits on dental arches from male, female, and combined cases. Data were analyzed by using fourth- and sixth-order polynomial equations, analysis of variance (ANOVA), and the Duncan post hoc test. …”
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