From Simulation to Reality: Transfer Learning for Automating Pseudo‐Labeling of Real and Infrared Imagery by Jeffrey Choate, Derek Worth, Scott L. Nykl, Clark Taylor, Brett Borghetti, Christine Schubert Kabban, Ryan Raettig

“…Training a convolutional neural network (CNN) for real‐world applications is challenging due to the requirement of high‐quality labeled imagery. …”
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Ground Vibrations Prediction Using Artificial Neural Networks by Ismail Derbal, Nouredine Bourahla, Ahmed Mebarki, Ramdane Bahar

“…Furthermore, the predictions of ground acceleration peaks have good accuracy, i.e. less than 2% error. Considering the unlimited possibilities to sample the soil properties combined to the homogenization and the synchronization techniques, the ANN model can be generalized to many practical applications.…”
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Optimal Dual Control Strategy for Multi-Interconnected Microgrids Under Unintentional Islanding and Fault Scenarios by H. K. Shaker, Ines Mahmoud, Magdi A. Mosa, K. M. Abdel-Latif, A. A. Ali, H. E. Keshta

“…The simulation results demonstrate that the ST-PI outperforms the PI controller under different disturbances in terms of overshoot, undershoot, settling time, and integral time absolute error (ITAE). Additionally, the paper utilizes an advanced bioinspired metaheuristic algorithm, the Newton-Raphson-based optimizer (NRBO), to optimize the design of the proposed controllers via the ITAE as a performance measure.…”
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Improving Cardiovascular Disease Prediction through Stratified Machine Learning Models and Combined Datasets by Tara Yousif Mawlood, Alla Ahmad Hassan, Rebwar Khalid Muhammed, Aso M. Aladdin, Tarik A. Rashid, Bryar A. Hassan

“…The datasets, sourced from the UCI repository, were pre-processed and evaluated using metrics such as accuracy, precision, F1-score, log loss, and error rate, with performance further assessed using confusion matrices. …”
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Enhanced nonlinear equalization for OFDM systems in IoT-based intelligent transportation using CGLS algorithms by Jafar Ababneh, Hani Attar, Zakaria Che Muda, Ilhami Colak, Mohanad A. Deif, Samir Bendoukha, Ahmed Solyman

“…Simulation results show that the proposed methods significantly reduce bit error rates and achieve up to a 5 dB performance improvement over traditional approaches, while keeping computational costs low enough for real-time IoT applications. …”
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A UAV Image Stitching Method for Complex Urban Environments by W. Niu, D. Qiu, R. Wu, R. Wu, Z. Wang, Y. Shi

“…Finally, a global energy function, including reprojection error and smoothness constraints, is constructed to iteratively optimize the homography matrix and suppress stitching distortions. …”
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Fast grouping fusion method of dual carbon monitoring data based on DBSCAN clustering algorithm by Rui Ma, Jiangbo Sha, Shuang Zhang, Dongge Zhu, Wenni Kang, Jia Liu

“…In the complex environment with frequent invalid abnormal data, the mean square error of the fusion results is small.…”
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Bayesian deep-learning structured illumination microscopy enables reliable super-resolution imaging with uncertainty quantification by Tao Liu, Jiahao Liu, Dong Li, Shan Tan

“…Moreover, the BayesDL uncertainty shows versatile utilities for daily super-resolution imaging, such as error estimation, data acquisition evaluation, etc. …”
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Validation of Taylor’s Frozen Hypothesis for DAS-Based Flow by Shu Dai, Lei Liang, Ke Jiang, Hui Wang, Chengyi Zhong

“…The results show that the vortex structure of turbulent flow in circular pipes remains stable in the convection direction, conforming to theoretical premises; the relative error of average flow velocity by this method is ≤3%, with significant improvements in accuracy and stability in high-flow zones. …”
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Removal of anthracene from vehicle-wash wastewater through adsorption using eucalyptus wood waste-derived biochar by Muhammad Ilyas, Yuxuan Liao, Jiajun Xu, Siyu Wu, Wanying Liao, Xuelin Zhao

“…The experiment's findings showed that the Langmuir isotherm model provided the most accurate fit to the data. Also, error function analysis was performed. The exothermic and spontaneous nature of the adsorption process is evident based on the values of thermodynamic parameters, including enthalpy (ΔH°), Gibbs free energy (ΔG°), and entropy (ΔS°). …”
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Automated Adaptation and Stabilization of a Tunable WDM Polarization-Independent Receiver on Active Silicon Photonic Platform by Minglei Ma, Hossam Shoman, Sudip Shekhar, Nicolas A. F. Jaeger, Lukas Chrostowski

“…In addition, we implement a long-duration experiment, in which we measure the bit-error-ratio for continuously varying polarization states, and changing chip temperatures. …”
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Technical Aspects of Deploying UAV and Ground Robots for Intelligent Logistics Using YOLO on Embedded Systems by Wissem Dilmi, Sami El Ferik, Fethi Ouerdane, Mustapha K. Khaldi, Abdul-Wahid A. Saif

“…Automation of logistics enhances efficiency, reduces costs, and minimizes human error. Image processing—particularly vision-based AI—enables real-time tracking, object recognition, and intelligent decision-making, thereby improving supply chain resilience. …”
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An Experimental Performance Evaluation and Compatibility Study of the Bluetooth Low Energy Based Platform for ECG Monitoring in WBANs by Farid Touati, Ochirkhand Erdene-Ochir, Waiser Mehmood, Ammad Hassan, Adel Ben Mnaouer, Brahim Gaabab, Mohd Fadlee A. Rasid, Lazhar Khriji

“…The performance of the system is evaluated experimentally and the results showed good potential of this proposed BLE platform in meeting the main QoS requirements of medical applications in terms of throughput, end-to-end delay, and packet error rate, while staying energy efficient.…”
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The Synergistic Effects of GCPs and Camera Calibration Models on UAV-SfM Photogrammetry by Zixin Wang, Leyan Shi, Jinzhou Li, Wen Dai, Wangda Lu, Mengqi Li

“…The correlation matrix and root mean squared error (RMSE) were used to analyze the synergistic effects of GCPs and camera models. …”
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Research on real time measurement model and measurement system for gas concentration in extraction drilling by Dayang Yu, Junhao Zhu, Xing Li, An He, Huaiqian Liu, Changjiang Chen, Yong Liu

“…This work provides a compact, accurate, and environment-adaptive solution for methane monitoring at borehole outlets and supports the development of intelligent gas sensing systems in coal mining applications.…”
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Optimization and predictive performance of fly ash-based sustainable concrete using integrated multitask deep learning framework with interpretable machine learning techniques by Bhupesh P. Nandurkar, Jayant M. Raut, Pawan K. Hinge, Boskey V. Bahoria, Tejas R. Patil, Sachin Upadhye, Vikrant S. Vairagade, Sagar D. Shelare

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Explainable, Flexible, Frequency Response Function-Based Parametric Surrogate for Guided Wave-Based Evaluation in Multiple Defect Scenarios by Paul Sieber, Rohan Soman, Wieslaw Ostachowicz, Eleni Chatzi, Konstantinos Agathos

“…For most sensor locations, the surrogate achieves an error between 1% and 4%, while providing a computational speedup of three to four orders of magnitude.…”
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A Methodology for Turbine-Level Possible Power Prediction and Uncertainty Estimations Using Farm-Wide Autoregressive Information on High-Frequency Data by Francisco Javier Jara Ávila, Timothy Verstraeten, Pieter Jan Daems, Ann Nowé, Jan Helsen

“…The results demonstrate that the proposed method improves predictive accuracy over the manufacturer’s power curve, achieving a reduction in error measurements of around 1%. Improvements of around 5% were seen in dominant wind directions (200°–300°) using 2 and 3 Latent GPs, with similar improvements observed on the test set. …”
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AFHNet: Attention-Free Hybrid Network for Salient Object Detection in Underwater Images by Qian Tang, Zhen Wang, Xuqi Wang, Shan-Wen Zhang

“…Comprehensive experiments conducted on an underwater optical image dataset, comparing AFHNet with 36 state-of-the-art methods, demonstrate its superior performance, achieving a mean absolute error (MAE) of 1.94%. These results highlight the potential of AFHNet to advance underwater vision applications and establish a new benchmark for salient object detection in challenging environments. …”
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Reliable, efficient, and scalable photonic inverse design empowered by physics-inspired deep learning by Shao Guocheng, Zhou Tiankuang, Yan Tao, Guo Yanchen, Zhao Yun, Huang Ruqi, Fang Lu

“…EMNN increases the design speed by 17,000 times than that of the analytical model and reduces the modeling error by two orders of magnitude compared to the numerical model. …”
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