Evaluation of the influence of hydrodynamic cavitation treatment of dark petroleum products on the yield of fractions with boiling points up to 400°C by B. V. Peshnev, E. V. Burlyaeva, V. B. Terenteva, D. V. Nikishin, A. I. Nikolaev, K. S. Andronov

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Reliability of a VO2-derived running-speed threshold in trained men by Marialaura Espaillat, David M. Falkins, Brandi Antonio, David H. Fukuda, Jeffrey R. Stout

“…Reliability statistics included ICC(2,1), standard error of measurement (SEM), coefficient of variation (CV), and minimal detectable change at 95% confidence (MDC95).Results RSVO₂ showed excellent reliability: ICC(2,1) = 0.96 (95% CI 0.89–0.99); SEM = 0.35 km·h−1; CV = 3.0%; MDC95 = 0.98 km·h−1. …”
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Deep learning for sunflower (Helianthus annuus L.) mapping and stand counting: Trade-offs between closed vs. open-access methods by Maria Villamil-Mahecha, Harsh Pathak, Nitin Rai, Paul Overby, Xin Sun

“…The root mean squared error (RMSE) for stand count was 26.7 sunflowers per tile further confirmed the model’s accuracy. …”
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Stability of Blood DNA Methylation Across Two Timepoints in Three Cohorts by Mikołaj Danielewski, Jarosław Walkowiak, Karolina Wielgus, Jan Krzysztof Nowak

“…A resource describing probe stability is made publicly available, with the intention to support biomarker studies and the investigation of early epigenetic programming. The absolute error and correlation are two complementary facets of probe stability that may be considered in further research, especially to determine the stability of probes in health and disease across different tissues and populations.…”
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Modeling and simulation of optical wireless communication channels in IoUT considering water types turbulence and transmitter selection by M. Mokhtar Zayed, Mona Shokair

“…Performance metrics, including received optical power, signal-to-noise ratio (SNR), and bit error rate (BER), are evaluated to provide in-depth insights into system behavior. …”
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Strategies for Soil Salinity Mapping Using Remote Sensing and Machine Learning in the Yellow River Delta by Junyong Zhang, Xianghe Ge, Xuehui Hou, Lijing Han, Zhuoran Zhang, Wenjie Feng, Zihan Zhou, Xiubin Luo

“…Notably, under Strategy IX, the SVR model achieved the best predictive performance, with a coefficient of determination (R²) of 0.62 and a root mean square error (RMSE) of 0.38 g/kg. Analysis based on SHapley Additive exPlanations (SHAP) values and feature importance indicated that Vegetation Type Factors contributed significantly and consistently to the model’s performance, maintaining higher importance than traditional salinity indices and playing a dominant role. …”
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Benchmarking farm-level cotton water productivity using on-farm irrigation measurements and remotely sensed yields by Zitian Gao, Danlu Guo, Dongryeol Ryu, Andrew W. Western

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Translation and validation of the Chinese version of quality of physician-patient interaction scale by Jing Li, Thitipong Tankumpuan, Aurawamon Sriyuktasuth, Patricia M. Davidson

“…Its use can support clinical communication improvement and research on patient-centered care.…”
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Assessing the Test-Retest Reliability of MyotonPRO for Measuring Achilles Tendon Stiffness by Krystof Volesky, Jan Novak, Michael Janek, Jakub Katolicky, James J. Tufano, Michal Steffl, Javier Courel-Ibáñez, Tomas Vetrovsky

“…Reliability was calculated using the intraclass correlation coefficient (ICC), its 95% confidence interval, coefficient of variation, standard error of measurement, and minimal detectable change. …”
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Sistema de servocontrol visual empleando redes neuronales y filtros en el dominio de CIELAB//Visual servo-control system using neural networks and filters based on CIELAB by Germán Buitrago Salazar, Olga Ramos Sandoval

“…El resultado de esta investigación demostró que la distancia estimada por la red tiene un margen de error menor, que el algoritmo propuesto en otros trabajos. …”
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Wind farm capacity factor forecasting: An Australian case study by Aiman Albatayneh, Ragheb AbuAlRous, Merlinde Kay, Ramez Abdallah, Adel Juaidi, Amos García-Cruz, Francisco Manzano-Agugliaro

“…Nevertheless, closer inspection of these methods reveals their error rate is much higher than real wind farm capacity factor estimates. …”
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A Combined Deep Learning Method with Attention-Based LSTM Model for Short-Term Traffic Speed Forecasting by Pan Wu, Zilin Huang, Yuzhuang Pian, Lunhui Xu, Jinlong Li, Kaixun Chen

“…Short-term traffic speed prediction is a promising research topic in intelligent transportation systems (ITSs), which also plays an important role in the real-time decision-making of traffic control and guidance systems. …”
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Design of an improved model using federated learning and LSTM autoencoders for secure and transparent blockchain network transactions by R. Vijay Anand, G. Magesh, I. Alagiri, Madala Guru Brahmam, Balamurugan Balusamy, Chithirai Pon Selvan, Haya Mesfer Alshahrani, Masresha Getahun, Ben Othman Soufiene

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White Matter Imaging Phenotypes Mediate the Negative Causality of Mitochondrial DNA Copy Number on Sleep Apnea: A Bidirectional Mendelian Randomization Study and Mediation Analysis by Ying Q, Wang M, Zhao Z, Wu Y, Sun C, Huang X, Zhang X, Guo J

“…A two-step approach was employed to evaluate causal relationships between BIPs, mtDNA-CN and SA, utilising the “product of coefficients” method to assess the mediating effects of BIPs. Multiple testing errors were corrected using the Benjamini–Hochberg method.Results: Genetically predicted mtDNA-CN had a negative causal effect on SA (OR = 0.859, 95% CI = 0.785– 0.939, P = 3.20× 10− 4), whereas SA did not have a causal effect on mtDNA-CN (OR = 1.0056, 95% CI = 0.9954– 1.0159, P = 0.2825). …”
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Performance of wind assessment datasets in United States coastal areas by L. M. Sheridan, J. Wang, C. Draxl, N. Bodini, C. Phillips, D. Duplyakin, H. Tinnesand, R. K. Rai, J. E. Flaherty, L. K. Berg, C. Jung, E. Young, R. Kotamarthi

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Real-Time, Risk-Based Clinical Trial Quality Management in China: Development of a Digital Monitoring Platform by Min Jiang, Shuhua Zhao, Yun Mei, Zhiying Fu, Yannan Yuan, Jie Ai, Yuan Sheng, Ying Gong, Jingjing Chen

“…However, traditional clinical trial quality management models largely rely on human monitoring and counting, which can be time-consuming and are likely to generate errors and biases. There is an urgent need to upgrade and improve the efficiency and accuracy of clinical trial quality monitoring systems in hospital-based research institutions within China. …”
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Pengambilan Data secara Bergerak pada Automatic Meter Reading Bertopologi Mesh by Fandisya Rahman, Achmad Basuki, Ismiarta Aknuranda

“…The more number of houses to be visited, the heavier the workload for data recording and the recording errors may occur. Inaccurate data correlate with financial losses borne by utilities. …”
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Biochemical indicators, quality and safety of domestic and wild pig slaughter products by S. V. Furman, I. M. Sokulskyi, D. V. Lisohurska, O. V. Lisohurska, B. V. Gutyj

“…The correct methodical approach to the inspection of organs and tissues of slaughtered animals allows to avoid errors in the diagnosis of various diseases, in the sanitary assessment of slaughter products, to prevent meat losses and the release of food products that are dangerous for humans. …”
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Komparasi Data Mining Naive Bayes dan Neural Network memprediksi Masa Studi Mahasiswa S1 by Azahari Azahari, Yulindawati Yulindawati, Dewi Rosita, Syamsuddin Mallala

“…The evaluation and analysis process are conducted to see where the errors and truths are in the results of the study period predictions. …”
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Improving automated deep phenotyping through large language models using retrieval-augmented generation by Brandon T. Garcia, Lauren Westerfield, Priya Yelemali, Nikhita Gogate, E. Andres Rivera-Munoz, Haowei Du, Moez Dawood, Angad Jolly, James R. Lupski, Jennifer E. Posey

“…While large language models (LLMs) hold promise for more context-driven phenotype extraction, they are prone to errors and “hallucinations,” making them less reliable without further refinement. …”
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