Calibration Strategies for Optical Underwater 3D-Scanners by C. Bräuer-Burchardt, R. Ramm, M. Heinze, P. Kühmstedt, G. Notni, G. Notni

“…The precise three-dimensional (3D) reconstruction of underwater objects is required in many different applications. Optical systems are gaining popularity due to their high accuracy potential. …”
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MoRPI: Mobile Robot Pure Inertial Navigation by Aviad Etzion, Itzik Klein

“…Mobile robots are used in a variety of applications indoors and outdoors. In real-world scenarios, frequently, the navigation solution relies only on the inertial sensors. …”
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An overview of AI in Biofunctional Materials by Dazhou Li

“…The integration of artificial intelligence (AI) into biofunctional materials is transforming material design, synthesis, and optimization for medical applications. Machine learning and deep learning models now predict material properties (e.g., mechanical strength, degradation rate) with > 90% accuracy, dramatically reducing trial-and-error in scaffold and nanoparticle fabrication. …”
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Structural flexural strengthening through material bonded to the concrete substrate by Victor Hugo Dalosto de Oliveira, Rodolfo Alves Carvalho, Rodolfo Rabelo Neves, Luana Ferreira Borges, Paulo Chaves de Rezende Martins, Marcos Honorato de Oliveira, João Paulo de Almeida Siqueira, Rafael de Almeida Sobral, Sidney Luan Teixeira de Nazaré, Carlos Henrique de Moura Cunhac

“…As result, it was verified that the proposed procedure provides values similar to the trial-and-error method used in the FRP strengthening design. …”
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Failure pressure prediction of an API 5L X120 pipe elbow with longitudinally aligned interacting corrosion defects subjected to internal pressure by Sangetha Devi Vijaya Kumar, Suria Devi Vijaya Kumar, Saravanan Karuppanan, Mark Ovinis, Veeradasan Perumal

“…High accuracy with R2 values of 0.99 and mean squared error of 0.000319 was obtained by developing ANN models for defects found at the intrados, crown, and extrados. …”
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A Novel Framework for Solar Irradiance Prediction Integrating Signal Decomposition With Hybrid Time-Series Models by Keng-Hsi Lin, Po-Yen Hsu, Po-Han Chen, Mu-Yen Chen

“…Among all configurations, the EEMD–Transformer–GRU model achieved the best performance, with a MAPE of 7.87%, reducing prediction error by approximately 57% compared to non-decomposed models. …”
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Virtual sensing-enabled digital twin framework for real-time monitoring of nuclear systems leveraging deep neural operators by Raisa Hossain, Farid Ahmed, Kazuma Kobayashi, Seid Koric, Diab Abueidda, Syed Bahauddin Alam

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Artificial neural network (ANN) approach in predicting the thermo-solutal transport rate from multiple heated chips within an enclosure filled with hybrid nanocoolant by Tawsif Mahmud, Jiaul Haque Saboj, Preetom Nag, Goutam Saha, Bijan K. Saha

“…A hybrid nano-coolant containing ϕMWCNT = 1.5 %, ϕCuO = 0.5 %, and ϕAl2O3 = 2 % showed a 3.11% improvement in heat transfer rate compared to the base fluid, highlighting its potential for thermal management applications. This study also investigates various machine learning models for predicting the heat and mass transfer rate, and an error analysis is conducted on the K-Nearest Neighbour Regressor, Random Forest Regressor, Decision Tree Regressor, and ANN model. …”
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The Design and Evaluation of a Direction Sensor System Using Color Marker Patterns Onboard Small Fixed-Wing UAVs in a Wireless Relay System by Kanya Hirai, Masazumi Ueba

“…Among the several usages of unmanned aerial vehicles (UAVs), a wireless relay system is one of the most promising applications. Specifically, a small fixed-wing UAV is suitable to establish the system promptly. …”
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Intelligent predictive networks for MHD nanofluid with carbon nanotubes and thermal conductivity along a porous medium by Hafiz Muhammad Shahbaz, Iftikhar Ahmad

“…For the assessment of the performance of the applied LMA-RNNs, the fitness of mean squared error, regression plots and error distribution in histograms is presented. …”
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AE-BPNN: autoencoder and backpropagation neural network-based model for lithium-ion battery state of health estimation by Abdullah Ahmed Al-Dulaimi, Muhammet Tahir Guneser, Raghad Al-Shabandar, Yeonghyeon Gu, Muhammad Syafrudin, Norma Latif Fitriyani

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A new modified incomprehensible but intelligible-in-time logics algorithm for modeling solid oxide fuel cell by Shahr Alshahr, Fatma A. Hashim, Ahmed Alshahir, Ahmed Fathy, Hegazy Rezk

“…Then three famous constrained engineering problems (cantilever beam design, welded beam design, and speed reducer design) are applied to show the effectiveness of REILA in solving real world applications. The goal is to mitigate the sum mean squared error (SMSE) between the experimental and estimated curves. …”
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Exoskeleton for Upper Limb Rehabilitation (EULR) with 3D printing technology based on force sensor by Triwiyanto Triwiyanto, Levana Forra Wakidi, I. Putu Alit Pawana

“…The mean linearity error of load cell across all data points was 0.2292 %. …”
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Artificial neural network-enhanced unconditionally stable finite-difference time-domain technique for multiscale problems by Oluwole John Famoriji, Thokozani Shongwe

“…The gradient of the ANN's output with respect to the input vector indicates the error of the system. Labeled samples are not required for training as the backpropagation (BP) algorithm uses this error value to update the ANN parameters. …”
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Borehole TV Imaging Technology for Horizontal Directional Drilling Based on an Optimization Algorithm for Structural Planes by Dong Yang, Wangsuo Cai, Junchao Wang, Yong Fang

“…As indicated by the experiment results, the optimization algorithm proposed in this study is applicable to many types of structural planes, with a dip direction error of less than 10° and a dip angle error of less than 5°, thus meeting the production requirements.…”
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A feasibility study on using soft insoles for estimating 3D ground reaction forces with incorporated 3D-printed foam-like sensors by Nick Willemstein, Saivimal Sridar, Herman van der Kooij, Ali Sadeghi

“…In addition, the identified HW models showed the best estimation performance (on average root mean squared (RMS) error 9.3%, $ {R}^2 $ =0.85 and mean absolute error (MAE) 7%) of the vertical, mediolateral, and anteroposterior GRFs, thereby showing that these sensors can estimate the resulting 3D force reasonably well. …”
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Corrosion Predictive Model in Hot-Dip Galvanized Steel Buried in Soil by Lorena-De Arriba-Rodríguez, Francisco Ortega-Fernández, Joaquín M. Villanueva-Balsera, Vicente Rodríguez-Montequín

“…Specifically, the mean square error was 290.6 g/m2 (range of the objective variable is from 51.787 g/m2 to 5950.5 g/m2), R2 was 0.96, and from a relative error of 0.14, the success of the estimate was 100%. …”
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Strength prediction of ECC-CES columns under eccentric compression using adaptive sampling and ML techniques by Khaled Megahed

“…Based on evaluation metrics, the Gaussian Process Regression (GPR), CatBoost (CATB), and LightGBM (LGBM) models emerged as the most accurate and reliable, with over 97% of the finite element (FE) samples falling within a 10% error range. While the ML models demonstrate impressive performance, their black-box nature restricts their practical use in design applications. …”
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RSSI-Based Passive Localization in the Wild, At Streetscape Scales by Fanchen Bao, Stepan Mazokha, Jason O. Hallstrom

“…While we formulate the problem and validate our solution within an outdoor context, the work is equally applicable to large indoor environments. It achieves a mean localization error of 3.16 and 4.21 m, with 73&#x0025; and 66&#x0025; chance of reaching an error <inline-formula><tex-math notation="LaTeX">$\le$</tex-math></inline-formula>4 m, and 17&#x0025; and 21&#x0025; of the data discarded due to poor quality in the 500 and 400 block, respectively. …”
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Integrated irrigation of water and fertilizer with superior self-correcting fuzzy PID control system. by Wanjun Zhang, Jingsheng Tong, Feng Zhang, Wanliang Zhang, Jingxuan Zhang, Jingyi Zhang, Jingyan Zhang, Honghong Sun, Derek O Northwood, Kristian E Waters, Hao Ma

“…Traditional PID control, due to its static parameters, suffers from reduced stability and error accumulation under dynamic variations (e.g., irrigation flow fluctuations, environmental disturbances) or nonlinear interactions (e.g., coupling effects of fertilizer concentration and pH). …”
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