On-chip deterministic arbitrary-phase-controlling by Ma Rui, Li Chu, Yan Qiuchen, Wang Xinyi, Wang Ruiqi, Wang Yufei, Chen Yumeng, Li Yan, Lu Cuicui, Wang Jianwei, Hu Xiaoyong, Chan Che Ting, Gong Qihuang

“…Conventional phase-controlling methods face with serious limitation of unavoidable crosstalk, length distortion, and fabrication error. To date, it is still a great challenge to achieve deterministic and wide-range on-chip arbitrary-phase-controlling. …”
Get full text

The Time Difference of Arrival Estimation Method Utilizing an Inexact Reconstruction Within the Framework of Compressed Sensing by Shanhe Wang, Yu Xiang, Yuanyuan Gao, Yu Hua, Changjiang Huang, Xian Zhao

“…Simulation results demonstrate that when the signal-to-noise ratio (SNR) of the received signal is at least 0 dB and the compressed sampling length exceeds one-tenth of the original signal length, the TDOA estimation error of the proposed method is nearly identical to that of the cross-correlation method.…”
Get full text

Machine Learning Model for Predicting Global Ionospheric TEC Maps Based on Constraint Conditions by Qingfeng Li, Hanxian Fang, Chao Xiao, Die Duan, Hongtao Huang, Ganming Ren

“…The prediction and modeling of ionospheric total electron content (TEC) have consistently been a focal point for researchers, as it holds significant implications for satellite positioning, navigation, telemetry, control, and radio wave propagation. …”
Get full text

WetCH₄: a machine-learning-based upscaling of methane fluxes of northern wetlands during 2016–2022 by Q. Ying, Q. Ying, B. Poulter, J. D. Watts, K. A. Arndt, A.-M. Virkkala, L. Bruhwiler, Y. Oh, Y. Oh, B. M. Rogers, S. M. Natali, H. Sullivan, A. Armstrong, A. Armstrong, E. J. Ward, E. J. Ward, L. D. Schiferl, C. D. Elder, C. D. Elder, O. Peltola, A. Bartsch, A. R. Desai, E. Euskirchen, M. Göckede, B. Lehner, M. B. Nilsson, M. Peichl, O. Sonnentag, E.-S. Tuittila, T. Sachs, T. Sachs, A. Kalhori, M. Ueyama, Z. Zhang, Z. Zhang

Get full text

PEMETAAN PENGGUNAAN LAHAN SAWAH BERDASARKAN PENDEKATAN EKOLOGI BENTANG LAHANMENGGUNAKAN CITRA PEREKAMAN TUNGGAL by Algi Variski Hasibuan, Projo Danoedoro, Sigit Heru Murti

“…While multitemporal data has been widely used by researchers and proven effective, using single-date imagery can be more efficient. …”
Get full text

Radar Sensor Data Fitting for Accurate Linear Sprint Modelling by Daniel Geneau, Patrick Cormier, Ming-Chang Tsai, Dana Agar-Newman, Seth Lenetsky, Marc Klimstra

“…This study evaluated five different modelling approaches, including three established methods, a third-degree polynomial, and a sigmoid function, assessing their goodness-of-fit through the root mean square error (RMSE) and coefficient of determination (r²). …”
Get full text

Metric-based defect prediction from class diagram by Batnyam Battulga, Lkhamrolom Tsoodol, Enkhzol Dovdon, Naranchimeg Bold, Oyun-Erdene Namsrai

“…A software defect refers to a fault, failure, or error in software. With the rapid development and increasing reliance on software products, it is essential to identify these defects as early and easily as possible, given the efforts and budget invested in their creation and maintenance. …”
Get full text

Kolmogorov–Arnold Network in the Fault Diagnosis of Oil-Immersed Power Transformers by Thales W. Cabral, Felippe V. Gomes, Eduardo R. de Lima, José C. S. S. Filho, Luís G. P. Meloni

“…Our findings reveal that this pioneering employment of KAN_Diag achieved the minimal value of Hamming loss—0.0323—which minimized the classification error, guaranteeing enhanced reliability for the whole system. …”
Get full text

Emerging strontium isoscapes of Anatolia (Türkiye): new datasets and perspectives in bioavailable 87Sr/86Sr baseline studies by G. Bike Yazıcıoğlu, David C. Meiggs, Maxwell Davis, Suzanne E. Pilaar Birch, Suzanne E. Pilaar Birch

“…In closing, we comment on beneficial avenues for mobility studies in under-researched periods and regions in Anatolia.…”
Get full text

Assessment of Bone Aging—A Comparison of Different Methods for Evaluating Bone Tissue by Paweł Kamiński, Aleksander Gali, Rafał Obuchowicz, Michał Strzelecki, Adam Piórkowski, Marcin Kociołek, Elżbieta Pociask, Joanna Kwiecień, Karolina Nurzyńska

Get full text

Optimization of Serum and Salivary Cortisol Interpolation for Time-Dependent Modeling Frameworks in Healthy Adult Males by Nathaniel T. Berry, Travis Anderson, Christopher K. Rhea, Laurie Wideman

“…Model fit and performance were assessed using the coefficient of determination (R²) and the root mean square error (RMSE), as well as an examination of the equivalence, via two one-sided t-tests (TOST), of 24 h cortisol output between the observed and interpolated data. …”
Get full text

Back analysis of geomechanical parameters based on a data augmentation algorithm and machine learning technique by Hui Li, Weizhong Chen, Xianjun Tan

“…It is also indicated that the relative error of the predicted displacement values reduces from almost 20% to 5% for the OPSO-SVM model trained with 25 samples and that trained with 625 samples. …”
Get full text

Identifying Factors Affecting Self-Development of Iranian TV School Teachers: Report of A Qualitative Study by abbas mohammadi, maede arshadrad

“…Then the text of the interviews was read several times by the researcher to get to know him completely, and the least error would occur in the coding. …”
Get full text

Psychometric properties of the Breast Cancer Awareness Measurement among Chinese women: a cross-sectional study by Ping Li, Na Liu, Dan-dan Chen, Wei-jia Sun, Xue-hui Zhang

“…This reliable instrument can help researchers and health professionals evaluate women’s knowledge about the symptoms and risk factors of breast cancer and identify their barriers to seeking medical help. …”
Get full text

Performance Evaluation of Metaheuristics for LQR Controller Optimization: A Two-Wheel Balancing Robot Case Study by Jose Ricardo Rivera-Ruiz, Ricardo Rojas-Galvan, Jose R. Garcia-Martinez, Edson Eduardo Cruz Miguel, Omar A. Barra-Vazquez, Maria Ines Cruz Orduna, Jesus Enrique Escalante-Martinez, Juvenal Rodriguez-Resendiz

“…Experimental results demonstrate that the controller optimized by differential evolution achieves the lowest root mean square error (0.4910 degrees), outperforming ABC (0.5187 degrees) and GHS (0.5195 degrees). …”
Get full text

Deep Learning‐Coupled Metabolic Heat Integrated Sensing System for Noninvasive Continuous Monitoring of Blood Glucose by Haolin Wang, Chuanjie Yao, Zhibo Liu, Xinze Wang, Zhengjie Liu, Tao Zhang, Xinshuo Huang, LinGe Wang, Yuedan Wang, Gemin Xiao, Shady Farah, Hui‐jiuan Chen, Xi Xie

Get full text

Computerised analysis of non-conjugate spiral bevel gear mesh using an advanced and fast-converging tooth contact model by Maksat Temirkhan, Andas Amrin

“…This application aims to examine the impact of misalignment or profile modifications on the transmission error, path of contact, and stress.…”
Get full text

The relationship between proteinuria and coronary risk: a systematic review and meta-analysis. by Vlado Perkovic, Christine Verdon, Toshiharu Ninomiya, Federica Barzi, Alan Cass, Anushka Patel, Meg Jardine, Martin Gallagher, Fiona Turnbull, John Chalmers, Jonathan Craig, Rachel Huxley

“…MEDLINE and EMBASE were searched for studies reporting an age- or multivariate-adjusted estimate and standard error of the association between proteinuria and coronary heart disease. …”
Get full text

Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods by Abhinav Kumar, Paul Rodrigues, A. K. Kareem, Tingneyuc Sekac, Sherzod Abdullaev, Jasgurpreet Singh Chohan, R. Manjunatha, Kumar Rethik, Shivakrishna Dasi, Mahmood Kiani

Get full text

Is AI currently capable of identifying wild oysters? A comparison of human annotators against the AI model, ODYSSEE by Brendan Campbell, Alan Williams, Kleio Baxevani, Kleio Baxevani, Alyssa Campbell, Alyssa Campbell, Rushabh Dhoke, Rileigh E. Hudock, Xiaomin Lin, Xiaomin Lin, Vivek Mange, Vivek Mange, Bernhard Neuberger, Arjun Suresh, Alhim Vera, Arthur Trembanis, Herbert G. Tanner, Edward Hale, Edward Hale

“…In addition, we identified potential sources of prediction error. Although the model can make inferences significantly faster than expert and non-expert annotators (39.6 s, 2.34±0.61 h, 4.50±1.46 h, respectively), the model overpredicted the number of live oysters, achieving lower accuracy (63%) in identifying live oysters compared to experts (74%) and non-experts (75%) alike. …”
Get full text