A Hybrid ARIMA-LSTM-XGBoost Model with Linear Regression Stacking for Transformer Oil Temperature Prediction by Xuemin Huang, Xiaoliang Zhuang, Fangyuan Tian, Zheng Niu, Yujie Chen, Qian Zhou, Chao Yuan

“…Experimental results demonstrate the hybrid model’s superiority: In 5000-data-point prediction, it achieves an MSE = 0.9908 and MAPE = 1.9824%, outperforming standalone XGBoost (MSE = 3.2001) by 69.03% in error reduction and ARIMA-LSTM (MSE = 1.1268) by 12.08%, while surpassing naïve methods 1–2 (MSE = 1.7370–1.6716) by 42.94–40.74%. …”
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A Novel Approach to Generate Large-Scale InSAR-Derived Velocity Fields: Enhanced Mosaicking of Overlapping InSAR Data by Xupeng Liu, Guangyu Xu, Yaning Yi, Tengxu Zhang, Yuanping Xia

“…This method integrates GNSS data with InSAR data and also considers the additional constraint of data overlap region. …”
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Multi-Dimensional Feature Fusion and Enhanced Attention Streaming Movie Prediction Algorithm by Hanqing Hu, Tianmu Tian, Chengjing Liu, Xueyuan Bai

“…The experimental results show that the proposed algorithm FFLSTMEA achieves better prediction results with an average absolute error (MAE) of 3.50, a root mean square error (RMSE) of 5.28, and a coefficient of determination (R-squared) of 0.87 in the evaluation index. …”
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Blended Ensemble Learning for Robust Normal Behavior Modeling of Wind Turbines by Jianghao Zhu, Tingting Pei, Le Su, Bin Lan, Wei Chen

“…The framework reduced mean absolute error by 25.1% and mean absolute percentage error by 33.4% compared to conventional methods. …”
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Stereo Visual Odometry and Real-Time Appearance-Based SLAM for Mapping and Localization in Indoor and Outdoor Orchard Environments by Imran Hussain, Xiongzhe Han, Jong-Woo Ha

“…Substantial improvements in both mapping and localization performance over the traditional approach were demonstrated, with an average error of 0.018 m against the ground truth for outdoor mapping and a consistent average error of 0.03 m for indoor trails with a 20.7% reduction in visual odometry trajectory deviation compared to traditional methods. …”
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Online Core Temperature Estimation for Lithium-Ion Batteries via an Aging-Integrated ECM-1D Coupled Model-Based Algorithm by Yiqi Jia, Lorenzo Brancato, Marco Giglio, Francesco Cadini

“…Validation under extreme conditions (high-rate cycling, aging, and ISCs) demonstrates 60% lower core temperature RMSE during high-rate cycling, a maximum estimation error below 0.9 K, and 58.9% reduction in SOC estimation error under aging conditions versus existing methods. …”
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Forecasting Day-Ahead Electricity Demand in Australia Using a CNN-LSTM Model with an Attention Mechanism by Laial Alsmadi, Gang Lei, Li Li

“…The results show a significant reduction in both Mean Absolute Error and Mean Absolute Percentage Error, confirming the model’s effectiveness. …”
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Quadrature-Phase-Locked-Loop-Based Back-Electromotive Force Observer for Sensorless Brushless DC Motor Drive Control in Solar-Powered Electric Vehicles by Biswajit Saha, Aryadip Sen, Bhim Singh, Kumar Mahtani, José A. Sánchez-Fernández

“…The experimental results demonstrate a significant reduction in commutation error, with a nearly flat value at 0 degrees during steady-state and less than 8 degrees under dynamic conditions. …”
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Study on Interfacial Interaction of Cement-Based Nanocomposite by Molecular Dynamic Analysis and an RVE Approach by A. K. Roopa, A. M. Hunashyal, Arun Y. Patil, Abhishek Kamadollishettar, Bharatkumar Patil, Manzoore Elahi M. Soudagar, Kiran Shahapurkar, T. M. Yunus Khan, M. A. Kalam

“…The experimental test results substantiate the analytical studies, and the error obtained from both approaches is less than 20%. …”
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Thermal Performance Study of Solar Air Dryers for Cashew Kernel: A Comparative Analysis and Modelling Using Response Surface Methodology (RSM) and Artificial Neural Network (ANN) by Vivekanand B. Huddar, Abdul Razak, Erdem Cuce, Sudarshana Gadwal, Mamdooh Alwetaishi, Asif Afzal, C. Ahamed Saleel, Saboor Shaik

“…This new method has resulted in batch drying of cashew kernels of up to 30 kg capacity in a time span of 360 minutes of solar irradiation with an average consumption of 255 kJ. …”
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Comprehensive Analysis of the Influence of Technical and Biological Variations on De Novo Assembly of RNA-Seq Datasets by Gonzalez Sergio Alberto, Rivarola Maximo, Ribone Andres, Lew Sergio, Paniego Norma

“…This selection will affect the completeness of represented genes and assembled isoforms, as well as contribute to error reduction.…”
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Improving Model Robustness With Frequency Component Modification and Mixing by Hyunha Hwang, Se-Hun Kim, Kyujoong Lee, Hyuk-Jae Lee

“…Experimental results demonstrate that FCMM achieves a 1.4%p reduction in mean corruption error (mCE) on both CIFAR-10-C and CIFAR-100-C, and 1.3%p on ImageNet-C compared to PixMix, which uses additional data. …”
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Optimization of four-diode equivalent circuit models for solar cells: Analytical formulation and performance enhancement by Martin Calasan, Snezana Vujosevic, Mohammed Alruwaili, Moustafa Ahmed Ibrahim

“…For the PHOTOWATT PWP 201 solar panel, the proposed models achieved up to a 29 % reduction in root-mean-square error (RMSE) compared to the most accurate method reported in the literature. …”
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Finite Element Analysis of a Novel Temporary External Fixator (TEF) in Mandibular Reconstruction by Rungsan Chaiyachet, Surasith Piyasin, Weerayut Jina, Teerawat Paipongna, Apichart Boonma

“…A mandibular model was created with cortical and cancellous bones segmented by threshold method. The screw plate, designed in SolidWorks, featured a 12 mm inter-hole distance, 2.0 mm thickness, and 2.4 mm screws. …”
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Deep learning for predicting porosity in ultra-deep fractured vuggy reservoirs from the Shunbei oilfield in Tarim Basin, China by Ziyan Deng, Dongsheng Zhou, Hezheng Dong, Xiaowei Huang, Shiping Wei, Zhijiang Kang

“…Validation using blind wells from the Shunbei oilfield shows that this approach achieves a 76% reduction in Mean Square Error (MSE) compared to traditional impedance inversion techniques, highlighting its high predictive accuracy. …”
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Phase Difference of Arrival Aided Downlink Time Difference of Arrival Ultra-Wide Band Positioning System by Josef Krska, Christian Gentner, Vaclav Navratil

“…The GNSS-like Downlink Time Difference of Arrival (TDoA) method is well suited for these problems, enabling unlimited number of simultaneous users. …”
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Improving Georeferencing Accuracy in Drone Imagery: Combining Drone Camera Angles with High and Variable Fields of View by Vishal Nagpal, Manoj Devare

“…The proposed approach further shows a quantitative improvement of 12.50% to 75.0% in the geolocation error reduction claimed. It was achieved by the decrease of MAE from 0.108 km to 0.055 km while RMSE was lowered from 0.111 km to 0.057 km indicating the reliability of the method. …”
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Flood Classification and Improved Loss Function by Combining Deep Learning Models to Improve Water Level Prediction in a Small Mountain Watershed by Rukai Wang, Ximin Yuan, Fuchang Tian, Minghui Liu, Xiujie Wang, Xiaobin Li, Minrui Wu

“…The optimized loss function further improves the prediction performance, resulting in a significant improvement in the accuracy of flood peak prediction, with a reduction of 0.26% in the relative error of the peak prediction by the GWN model. …”
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Integrated top-down process and voxel-based microstructure modeling for Ti-6Al-4V in laser wire direct energy deposition process by Yousub Lee, Peeyush Nandwana, Brian Gibson, Paritosh Mhatre, Julio Ortega Rojas, Bhagyashree Prabhune, Aaron Thornton, Joshua Vaughan, Srdjan Simunovic

“…A voxel-by-voxel simulation method is used to predict individual phase fractions of Ti-6Al-4 V. …”
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Investigating the Capabilities of Ensemble Machine Learning Model in Identifying Near-Fault Pulse-Like Ground Motions by Jafar Al Thawabteh, Jamal Al Adwan, Yazan Alzubi, Ahmad Al-Elwan

“…The study evaluates the effectiveness of these ensemble models in comparison to traditional methods, focusing on their ability to manage the unique attributes of pulse-like ground motions. …”
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