Spatiotemporal Dynamics and Future Projections of Carbon Use Efficiency on the Mongolian Plateau: A Remote Sensing and Machine Learning Approach by Xinyu Yang, Qiang Yu, Buyanbaatar Avirmed, Yu Wang, Jikai Zhao, Weijie Sun, Huanjia Cui, Bowen Chi, Ji Long

“…This research offers valuable insights for improving carbon sequestration and climate resilience in arid ecosystems, with significant implications for carbon management under high-emission scenarios.…”
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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 results show that the PGVAE-label model has superior TEC prediction capability, producing TEC prediction maps with the lowest average root-mean-square error values of 1.79, 1.80, and 1.83 TECU, and that the PGVAE-label model is also superior to the PGVAE and CODE models in the region of the peak ionospheric structure. …”
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An AI-Based Deep Learning with K-Mean Approach for Enhancing Altitude Estimation Accuracy in Unmanned Aerial Vehicles by Prot Piyakawanich, Pattarapong Phasukkit

“…The outcomes of our study are particularly noteworthy, with the model demonstrating remarkably low Mean Squared Error (MSE) values across all data clusters, ranging from 0.011 to 0.072. …”
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A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures by Sajjad Hussain, Carman Ka Man Lee, Yung Po Tsang, Saad Waqar

“…Extensive testing was performed to evaluate the performance of the algorithms, employing R-squared values and root mean square error (RMSE). The results showed that the machine learning models, specifically the RF and DT algorithms, performed the best, achieving R-squared scores of 0.993 and 1.0 and RMSE scores of 0.1180 and 0.0795, respectively. …”
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Research and application of a novel grey multivariable model in port scale prediction under the impact of Free Trade Zone by Yuyu Sun, Yuchen Zhang, Zhiguo Zhao

“…Findings – In the Tianjin and Fujian cases, the new model outperforms the other comparison models, with the least mean absolute percentage error (MAPE) values of 6.07% and 4.16% in the simulation phase, and 6.70% and 1.63% in the forecasting phase, respectively. …”
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Optimizing demulsifier selection for crude oil dehydration: a fuzzy TOPSIS-based multi-criteria decision-making approach by Jianyong Yu, Merwa Alhadrawi, Farag M. A. Altalbawy, Ahmed Rasol Hasson, M. Mehdi Shafieezadeh

“…Compared to conventional selection approaches that often rely on trial-and-error or single-criterion assessments, this study demonstrates that Fuzzy TOPSIS provides a structured, quantitative, and transparent approach to demulsifier selection. …”
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Long-term trends and chemometric analysis of atmospheric air quality matrices in Nigeria (2003–2023) using NASA GIOVANNI satellite data by Daniel Omeodisemi Omokpariola, John Kanayochukwu Nduka, Martin Osita Anagboso, Patrick Leonard Omokpariola

“…Statistical analysis including mean, range, standard error of the mean (SEM), variance, coefficient of variance, standard deviation, median, skewness, kurtosis, regression plot, correlation, Pearson Correlation, Dendrogram Clustering, and Scatter Plot were conducted to determine the trends and relationships between atmospheric parameters. …”
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The combined impact of AI and VR on interdisciplinary learning and patient safety in healthcare education: a narrative review by Emmanuel Aoudi Chance

“…Strategic implementation can contribute to error reduction, improved patient outcomes, and a culture of safety. …”
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Inertial measurement unit technology for gait detection: a comprehensive evaluation of gait traits in two Italian horse breeds by Vittoria Asti, Michela Ablondi, Arnaud Molle, Andrea Zanotti, Matteo Vasini, Alberto Sabbioni

“…Overall, the GBM model exhibits the highest accuracy and the lowest error. In conclusion, integrating IMU technology into horse performance evaluation offers valuable insights, with implications for breeding and training.…”
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Efficient and Accurate Calibration of Discrete Element Method Parameters for Black Beans by Xuezhen Wang, Qinghang Zhai, Shihao Zhang, Qianwen Li, Hanmi Zhou

“…A static friction coefficient of 0.202 and rolling friction coefficient of 0.0104 between BLBs were obtained based on the optimization results. A low relative error (0.74%) and insignificant difference (<i>p</i> > 0.05) between the simulated and measured repose angles were found. …”
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COVID-19 pandemic, oil prices and Saudi stock market: empirical evidence from ARDL modeling and Bayer–Hanck cointegration approach by Jamel Boukhatem, Ali M. Alhazmi

“…To ascertain the existence of a long-run equilibrium relationship between the variables, we applied autoregressive distributed lag (ARDL) modeling and the error correction model, with this ultimately revealing the existence of strong cointegration in the long run. …”
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Physical Model Research on the Impact of Bridge Piers on River Flow in Parallel Bridge Construction Projects by Yu Zhang, Bo Chen, Shuo Wang, Xin Zhang

“…The experimental results demonstrate that the hydraulic physical model of this river section that was constructed is reliable, with a relative error of <20% in sediment deposition, in the simulation of sediment erosion and deposition, flow velocity patterns, water levels, and riverbed morphological changes during parallel bridge construction in bridge-clustered river sections. …”
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Neurocomputational Mechanisms of Sense of Agency: Literature Review for Integrating Predictive Coding and Adaptive Control in Human–Machine Interfaces by Anirban Dutta

“…Predictive coding frameworks, especially when implemented via Kalman filters and LQG control, provide a mechanistic basis for modeling motor learning, error correction, and adaptive control. Disruptions in these inference processes underlie symptoms in disorders such as functional movement disorder. …”
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Binding Affinity Prediction for Pancreatic Ductal Adenocarcinoma Using Drug-Target Descriptors and Artificial Intelligence by Pragya, A. Amalin Prince, Jac Fredo Agastinose Ronickom

“…We achieved a mean square error (MSE) value of 1.5 using Morgan-RDKit-PubChem-Conjoint descriptors and 1D-CNN on the PDAC dataset. …”
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DFN-YOLO: Detecting Narrowband Signals in Broadband Spectrum by Kun Jiang, Kexiao Peng, Yuan Feng, Xia Guo, Zuping Tang

“…Additionally, the model maintains an average time estimation error within <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.55</mn><mo>×</mo><msup><mn>10</mn><mrow><mo>−</mo><mn>5</mn></mrow></msup></mrow></semantics></math></inline-formula> s and provides preliminary center frequency estimation in the broadband spectrum. …”
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Unlocking students’ potential beyond traditional exams: the influence of collaborative testing on nursing students’ retention and soft skills by Amal Diab Ghanem Atalla, Mohamed Hussein Ramadan Atta, Amina Hemida Salem Ghattas

“…If not developed, they may negatively impact patient outcomes and safety, contributing to medical errors, higher mortality rates, and job dissatisfaction. …”
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Mungongi: Metric Ambiguity and Analytical Uncertainty in Zambian Luchazi Percussion by Jason Winikoff

“…I ruminate on the positives of uncertainty, embrace ambiguity, contemplate the possible colonial undertones of this venture, champion embodied knowledge, advocate for attention to performers’ errors, and question our tools of analysis.…”
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Assessing the spatial-temporal performance of machine learning in predicting grapevine water status from Landsat 8 imagery via block-out and date-out cross-validation by Eve Laroche-Pinel, Vincenzo Cianciola, Khushwinder Singh, Gaetano A. Vivaldi, Luca Brillante

“…The results of the study demonstrate that machine learning is accurate in predicting vine water status spatially within the training measurement dates with low errors (NRMSEΨstem = 2.7 %, NRMSEgs = 16.2 %, NRMSEAN = 11.2 %) and a high degree of accuracy (R2 greater than 0.8 in the prediction of all three measurements) as assessed by block-out cross-validation. …”
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Factors influencing burnout, stress levels, and coping strategies among nursing staff in intensive care units by Beata Wudarczyk, Sabina Krupa-Nurcek, Michał Czapla, Michał Czapla, Izabella Uchmanowicz, Izabella Uchmanowicz

“…It contributes to increased medical errors and diminished care standards. The objective of this study was to evaluate factors influencing professional burnout in nursing staff working in intensive care units.MethodologyThis quantitative study was conducted between March and November 2019 among nurses undergoing qualification and specialization training at the European Centre for Postgraduate Education in Wrocław. …”
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Exploring the association between socio-economic and environmental factors and food consumption in Iran: insights from time series data by Pegah Rahbarinejad, Seyyed Reza Sobhani, Negar Sangsefidi, Kiyavash Irankhah, Maryam Mohamadinarab

“…Time series regression with ARIMA errors (R 4.1.2) identified drivers of consumption trends. …”
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