Complex modulation of fingertip forces during precision grasp and lift after theta burst stimulation over the dorsal premotor cortex by Drljačić Dragana, Pajić Sanja, Nedeljković Aleksandar, Milanović Slađan D., Ilić Tihomir V.

“…On the contrary, the cTBS reduced variable error (VE) for non-dominant hand (NH), p = 0.005. …”
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RETRACTED ARTICLE: Machine learning intelligent based hydromagnetic thermal transport under Soret and Dufour effects in convergent/divergent channels: a hybrid evolutionary numeric... by Muhammad Naeem Aslam, Nadeem Shaukat, Arshad Riaz, Ilyas Khan, Shafiullah Niazai

“…The minimum fitness function value is 1.32 × 10−8 computed across these multiple runs. The absolute error, between the HAM and machine learning approach addressed ranging from 3.55 × 10−7 to 1.90 × 10−8. …”
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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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Sound-Based Unsupervised Fault Diagnosis of Industrial Equipment Considering Environmental Noise by Jeong-Geun Lee, Kwang Sik Kim, Jang Hyun Lee

“…The VAE primarily learns from normal state acoustic data and determines the occurrence of faults based on reconstruction error. To achieve this, statistical features of Mel frequency cepstral coefficients were extracted, generating features applicable regardless of signal length. …”
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Revealing the Next Word and Character in Arabic: An Effective Blend of Long Short-Term Memory Networks and ARABERT by Fawaz S. Al-Anzi, S. T. Bibin Shalini

“…We extensively assessed the model’s performance using new Arabic text, focusing on evaluation metrics like accuracy, word error rate, character error rate, BLEU score, and perplexity. …”
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A hybrid steganography framework using DCT and GAN for secure data communication in the big data era by Kaleem Razzaq Malik, Muhammad Sajid, Ahmad Almogren, Tauqeer Safdar Malik, Ali Haider Khan, Ayman Altameem, Ateeq Ur Rehman, Seada Hussen

“…The experimental results validate the effectiveness of the proposed steganography approach, showcasing superior visual image quality with a mean square error (MSE) of 93.30%, peak signal-to-noise ratio (PSNR) of 58.27%, root mean squared error (RMSE) of 96.10%, and structural similarity index measure (SSIM) of 94.20%, in comparison to existing leading methodologies. …”
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Printing outsourced orthognathic surgical splints in-house: a dimensional verification process for point-of-care printing by Talal Beidas, Luther Light, Caroline Carrico, Shayne Kondor, Prasanth Ravi, Yotom A. Rabinowitz

“…Accounting for this error, the 3D-printed splints exhibited an average RMS of 0.20 mm for both the Formlabs 3B + and the Phrozen 4 K. …”
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An efficient and robust MRI-guided radiotherapy planning approach for targeting abdominal organs and tumours in the mouse. by Veerle Kersemans, John S Beech, Stuart Gilchrist, Paul Kinchesh, Philip D Allen, James Thompson, Ana L Gomes, Zenobia D'Costa, Luke Bird, Iain D C Tullis, Robert G Newman, Aurelien Corroyer-Dulmont, Nadia Falzone, Abul Azad, Katherine A Vallis, Owen J Sansom, Ruth J Muschel, Borivoj Vojnovic, Mark A Hill, Emmanouil Fokas, Sean C Smart

“…<h4>Results</h4>The BANG gel phantoms demonstrated a targeting efficiency error of 0.56 ± 0.18 mm. The in vivo stability tests of body motion during MR-IGRT and the associated cradle transfer showed that the residual body movements are within this MR-IGRT targeting error. …”
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Physics-Informed Neural Networks for Advanced Thermal Management in Electronics and Battery Systems: A Review of Recent Developments and Future Prospects by Zichen Du, Renhao Lu

“…However, challenges such as training convergence in fine-grained or large-scale applications remain. Notably, research combining PINNs with LSTM networks for battery thermal management at a 2.0 C charging rate has achieved impressive results—an R² of 0.9863, a mean absolute error (MAE) of 0.2875 °C, and a root mean square error (RMSE) of 0.3306 °C—demonstrating high predictive accuracy. …”
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Design and control of a permanent magnet-based robotic system for navigating tetherless magnetic devices in viscous environments by Zhengya Zhang, Anke Klingner, Sarthak Misra, Islam S. M. Khalil

“…The experimental results of this level of control demonstrate that the mean absolute error and the maximum tracking error of three-dimensional motion control are 1.18 mm and 2.64 mm, respectively. …”
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Enhancing eLoran Timing Accuracy via Machine Learning With Meteorological and Terrain Data by Taewon Kang, Seunghyeon Park, Pyo-Woong Son, Jiwon Seo

“…However, the primary sources of error for GNSS and eLoran differ. For eLoran, the main source of error is signal propagation delay over land, known as the additional secondary factor (ASF). …”
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Triaxial Response and Elastoplastic Constitutive Model for Artificially Cemented Granular Materials by Xiaochun Yu, Yuchen Ye, Anyu Yang, Jie Yang

“…Quantitative evaluation of the peak values indicates that the proposed constitutive model predicts peak deviatoric stress with an error of 1.36% and peak volumetric strain with an error of 3.78%. …”
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Prediction and evaluation of environmental quality for nursing sow buildings via multisource sensor information fusion by Chong Chen, Xingqiao Liu, Chaoji Liu, Chengyang Yu

“…The validation test results indicate that this model outperformed four other models, achieving a coefficient of determination (R2) of 0.9086, a Mean Absolute Error (MAE) of 0.0639, a Root Mean Squared Error (RMSE) of 0.1787, and a computational time of 7.5862 s. …”
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Compact Bistatic Iterative Passive Radar Based on Terrestrial Digital Video Broadcasting Signals by Víctor P. Gil Jiménez, Atilio Gameiro

“…This is especially effective when the signal-to-noise-ratio (SNR) at the receiver is moderate and/or there are several reflections in the environment that may introduce some error into schemes that perform the angle of arrival or time of arrival for the estimation. …”
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A dynamic localization network for regional navigation under global navigation satellite system denial environments by Hongwei Zhao, Yue Yan, Xiaozhu Shi

“…First, the collaborative positioning algorithm is given, and the architecture of localization system is proposed. Then the error sources of localization system are analyzed for performance evaluation. …”
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A High-Precision UWB-Based Indoor Positioning System Using Time-of-Arrival and Intersection Midpoint Algorithm by Wen-Piao Lin, Yi-Shun Lu

“…In contrast, IMA maintains stable and accurate positioning across all test points, achieving an average error of 12.87 cm. The system offers low power consumption, fast computation, and high positioning accuracy, making it suitable for real-time indoor applications such as hospital patient tracking and shopping malls where GPS is unavailable or unreliable.…”
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Enhancing Building-Integrated Photovoltaic Power Forecasting with a Hybrid Conditional Generative Adversarial Network Framework by Dong Ha Choi, Wei Li, Albert Y. Zomaya

“…These results highlight the potential of our framework to enhance prediction accuracy and optimize data utilization in renewable energy applications.…”
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Examination of Machining Parameters and Prediction of Cutting Velocity and Surface Roughness Using RSM and ANN Using WEDM of Altemp HX by I. V. Manoj, Hargovind Soni, S. Narendranath, P. M. Mashinini, Fuat Kara

“…The Altemp HX is a nickel-based superalloy having many applications in chemical, nuclear, aerospace, and marine industries. …”
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A New Biased Estimation Class to Combat the Multicollinearity in Regression Models: Modified Two--Parameter Liu Estimator by Mohamed Reda Abonazel

“…The results of the simulation study and two real-life applications show the superiority of the proposed estimator because the MSE of the proposed estimator is smaller than the other estimators.…”
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Real-Time Object Detection and Distance Measurement Enhanced with Semantic 3D Depth Sensing Using Camera–LiDAR Fusion by Ahmet Serhat Yildiz, Hongying Meng, Mohammad Rafiq Swash

“…Overall, segmentation mask methods are preferred for applications where accuracy is critical, despite their slower processing speed, while bounding box methods are suitable for real-time applications requiring faster depth extraction. …”
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