Research on Missing Data Estimation Method for UPFC Submodules Based on Bayesian Multiple Imputation and Support Vector Machines by Xiaoming Yu, Jun Wang, Ke Zhang, Zhijun Chen, Ming Tong, Sibo Sun, Jiapeng Shen, Li Zhang, Chuyang Wang

“…Experimental validation focusing on capacitor voltage, current, and temperature parameters of UPFC submodules under a 50% missing data scenario demonstrates that the proposed method achieves an 18.7% average error reduction and approximately 30% computational efficiency improvement compared to single imputation and traditional multiple imputation approaches, significantly outperforming neural network models. …”
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An Improved Wavelet Soft-Threshold Function Integrated with SVMD Dual-Parameter Joint Denoising for Ancient Building Deformation Monitoring by Jiaxing Zhao, Houzeng Han, Yang Deng, Youqiang Dong, Jian Wang, Wenjin Chen

“…In simulation experiments, compared to wavelet soft-threshold function (WST) with the sqtwolog threshold, the root-mean-square error (RMSE) of SVMD-Dual-CC-WST (sqtwolog threshold), SVMD-DP-IWT (sqtwolog threshold), and SVMD-DP-IWT (minimaxi threshold) improved by 51.44%, 52.13%, and 52.49%, respectively. …”
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ANN and Multilayer-ELM based prediction of combustion, performance and emission characteristics of a diesel engine fuelled with Diesel-DTBP blends by Hiren Dave, Vinay Vakharia, Hitesh Panchal, Md Irfanul Haque Siddiqui, Dan Dobrotă

“…Moreover, ML models achieved least error metrics for all three key engine performance metrics. …”
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Spatiotemporal Analysis and Anomalous Trends of Asia AOD (2001–2024): Insights from a Deep Learning Fusion Model and EOF Decomposition by Yu Ding, Wenjia Ni, Jiaxin Dong, Jie Yang, Shiyao Meng, Siwei Li

“…The fused dataset demonstrates significant improvements over the original MERRA-2 AOD, with an increase in the coefficient of determination (R²) by 0.1065 and a reduction in root mean square error (RMSE) by 0.0369. …”
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Optimized stress-strain modeling of eco-friendly fiber-reinforced concrete members using meta-heuristic algorithms by Sawsan Akram Hassan, Mahir M. Hason, Ammar N. Hanoon, Ali A. Abdulhameed

“…In terms of absolute relative error (ARE), the dataset proves that the suggested model has both the greatest (ARE 5 %) and worst (ARE > 15 %) frequencies. …”
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Clinical Outcome Scores Post Medial Unicompartmental Knee Arthroplasty: A Comparison of the MAKO Robotic Arm versus the Oxford Conventional Approach by Tan CMP, Shih SSW, Ravichandran V, Quah ESH, Kunnasegaran R

“…Robotic-assisted UKA offers more accurate implant positioning, soft tissue balancing, improved lower limb alignment, and a reduction in surgical error. There are few studies studying functional outcomes post robotic-assisted UKA. …”
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Blood profile of proteins and steroid hormones predicts weight change after weight loss with interactions of dietary protein level and glycemic index. by Ping Wang, Claus Holst, Malene R Andersen, Arne Astrup, Freek G Bouwman, Sanne van Otterdijk, Will K W H Wodzig, Marleen A van Baak, Thomas M Larsen, Susan A Jebb, Anthony Kafatos, Andreas F H Pfeiffer, J Alfredo Martinez, Teodora Handjieva-Darlenska, Marie Kunesova, Wim H M Saris, Edwin C M Mariman

“…A predictor panel of 15 variables enabled an optimal classification by Random Forests with an error rate of 24±1%. A logistic regression model with independent variables from 9 blood analytes had a prediction accuracy of 92%.…”
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Validity of pulse oximetry measures for heart rate and oxygen saturation during profound hypoxia in normobaric simulated extreme altitudes. by Harald Vikne, Jon Arild Kjeserud, Willy Westgaard, Ruben Baalsrud Westlie, Jan Ivar Kåsin, Jon Ingulf Medbø, Terje Gjøvaag, Nils Henrik Holmedahl

“…The validity was assessed in intervals of 55-70%, 70-85% and 85-100% SaO2 using the Bland-Altman method (bias and 95% limits of agreement (LoA)) and the root mean square error for variability. The demarcation criteria for agreement between methods were ±6 percentage points O2Hb saturation and ±6 bpm HR.…”
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Optimizing mRNA Vaccine Degradation Prediction via Penalized Dropout Approaches by Hwai Ing Soon, Azian Azamimi Abdullah, Hiromitsu Nishizaki, Latifah Munirah Kamarudin

“…The experimental outcomes highlight significant advancements in convergence rates and error reduction, particularly in complex models like 3-layer-wrapped Bidirectional Long Short-Term Memory (3wBiLSTM). …”
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Joint learning equation of state surfaces with uncertainty-aware physically regularized neural networks by Dongyang Kuang, Shiwei Li, Buxuan Wang, Chao Xiong, Shichang Zhang, Yanyao Zhang

“…This result shows a significant improvement over the case without regularization, with an increase of 0.2 in $$R^2$$ and a reduction of 0.26 eV/atom in energy prediction error, and is even slightly better than the fully supervised case for traditional regression method based on the Mie-Gr $$\ddot{\text {u}}$$ neisen equation. …”
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Effect of concentric exercise-induced fatigue on proprioception, motor control and performance of the upper limb in handball players. by Stelios Hadjisavvas, Michalis A Efstathiou, Irene-Chrysovalanto Themistocleous, Katerina Daskalaki, Paraskevi Malliou, Christoforos D Giannaki, Jeremy Lewis, Manos Stefanakis

“…<h4>Results</h4>A significant increase in absolute angular error (AAE) was observed in all target angles in both external rotation (ER) and internal rotation (IR) directions (p < 0.01). …”
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Fundamental of constant electrode melting rate control for electroslag fusion welding by Haoran Xu, Baokuan Li, Xuechi Huang, Yu Wang, Yanchun Lou, Zhongqiu Liu

“…Slag regulation mechanism stabilizes slag pool depth (18.93 % reduction during stabilization) but reduces horizontal melting depth (10.42 % decrease at 42.4 kg/min). …”
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Dynamic Aggregation and Augmentation for Low-Resource Machine Translation Using Federated Fine-Tuning of Pretrained Transformer Models by Emmanuel Agyei, Xiaoling Zhang, Ama Bonuah Quaye, Victor Adeyi Odeh, Joseph Roger Arhin

“…A thorough evaluation of translation quality is provided by the use of SPBLEU, ROUGE (ROUGE-1, ROUGE-2, and ROUGE-L) measures, and Word Error Rate (WER) metrics. A pretrained mT5 model is used to set baseline performance, which acts as a standard for the optimized model. …”
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High-Level Synthesis: Productivity, Performance, and Software Constraints by Yun Liang, Kyle Rupnow, Yinan Li, Dongbo Min, Minh N. Do, Deming Chen

“…Such tools reduce design effort: high-level descriptions are more compact and less error prone. HLS tools promise hardware development abstracted from software designer knowledge of the implementation platform. …”
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Machine learning prediction and explainability analysis of high strength glass powder concrete using SHAP PDP and ICE by Muhammad Sarmad Mahmood, Tariq Ali, Inamullah Inam, Muhammad Zeeshan Qureshi, Syed Salman Ahmad Zaidi, Muwaffaq Alqurashi, Hawreen Ahmed, Muhammad Adnan, Abdul Hakim Hotak

“…Among these, XGB-GWO demonstrated the highest accuracy, with R² improving to 0.991 and MSE decreasing significantly from 83.95 to 14.42, resulting in an 82.82% error reduction. SHAP, PDP, and ICE analyses identified superplasticizer dosage, curing days, and coarse aggregate as the most influential parameters affecting compressive strength (CS). …”
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IonoBench: Evaluating Spatiotemporal Models for Ionospheric Forecasting Under Solar-Balanced and Storm-Aware Conditions by Mert Can Turkmen, Yee Hui Lee, Eng Leong Tan

“…SimVPv2 consistently provides the best performance, with superior evaluation metrics and stable error distributions. Compared to the C1PG baseline (the CODE 1-day forecast product), SimVPv2 achieves a notable RMSE reduction up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>32.1</mn><mo>%</mo></mrow></semantics></math></inline-formula> across various subsets under diverse solar conditions. …”
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Opioid prescriptions following behavioral health training among primary care providers by Shutong Huo, Tim A. Bruckner, Abhery Das, Glen L. Xiong, David Marcovitz, Ariel B. Neikrug, Robert McCarron

“…This result remains robust after controlling for several covariates (coef: − 0.209 ; standard error = 0.052, p < 0.001) as well as after clustering patient observations by provider. …”
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Optimizing PID control for multi-model adaptive high-speed rail platform door systems with an improved metaheuristic approach by Dong Zhan, Ai-Qing Tian, Shao-Quan Ni

“…The practical application of the IBM-Dy-SGA at Zigong Station successfully resolved platform door alignment issues under varying train models and parking error conditions, demonstrating its effectiveness and reliability in complex and dynamic high-speed rail operations. …”
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Chromolaena odorata and male fertility: evidence from the testes and epididymis of Wistar rats by Eguono Raphael Uwejigho, Felix O Enemali, Victor Oghenekparobo Emojevwe, Jesuyomike Marvellous Oluremi, Israel Olorunsola Adeyomoye

“…Results were described as the mean ± standard error of means (S.E.M.). One-way analysis of variance  (at a 95% confidence level) was used to compare the means of the control group and experimental groups using GraphPad Prism (version 10.2.0). …”
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Innovative approach for gauge-based QPE in arid climates: comparing neural networks and traditional methods by Bayan Banimfreg, Ernesto Damiani, Vesta Afzali Gorooh, Duncan Axisa, Luca Delle Monache, Youssef Wehbe

“…Results The neural network model outperformed traditional interpolation techniques, achieving a 47% reduction in Root Mean Square Error (RMSE), and a 0.56 increase in R2 compared to the best-performing IDW variant (RS-IDW). …”
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