Anti-inflammatory effect of chamomile from randomized clinical trials: a systematic review and meta-analyses by Jason Valmy, Stephanie Greenfield, Satoru Shindo, Toshihisa Kawai, Jorge Cervantes, Bo-Young Hong

“…The mean difference, confidence intervals, and standard error from the extracted means and standard deviations for relevant outcomes were calculated. …”
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Enhanced nonlinear sliding mode control technique for wind power generation systems application: Theoretical design and comparative study by Yattou El Fadili, Ismail Boumhidi

“…The controller also achieves a high efficiency of 47.45%, robustly driving the system to its desired state in finite time with tracking error standard deviations of 0.3171 in the first test and 0.1652 in the second test.…”
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A Novel Hybrid Deep Learning Framework for Evaluating Field Evapotranspiration Considering the Impact of Soil Salinity by Yao Rong, Weishu Wang, Peijin Wu, Pu Wang, Chenglong Zhang, Chaozi Wang, Zailin Huo

“…During testing, DL‐SS consistently showed optimal performance, yielding root mean square error (RMSE) values of 37.4 W m−2 for sunflower and 39.2 W m−2 for maize. …”
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Enhanced quantitation of pathological α-synuclein in patient biospecimens by RT-QuIC seed amplification assays. by Ankit Srivastava, Qinlu Wang, Christina D Orrù, Manel Fernandez, Yaroslau Compta, Bernardino Ghetti, Gianluigi Zanusso, Wen-Quan Zou, Byron Caughey, Catherine A A Beauchemin

“…The use of 2-fold versus 10-fold dilution factors and 12 versus 4 replicate reactions per dilution reduced ED-RT-QuIC assay error by as much as 70%. This enhanced assay format discriminated as little as 2-fold differences in αSynD seed concentration besides detecting ~2-16-fold seed reductions caused by inactivation treatments. …”
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Quantized Convolutional Neural Networks Robustness under Perturbation [version 1; peer review: 2 approved] by Guy Kember, Issam Hammad, Jack Langille

“…This process introduces significant reductions in inference time and simplifies the hardware requirements. …”
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Optimizing Energy Performance of Phase-Change Material-Enhanced Building Envelopes Through Novel Performance Indicators by Abrar Ahmad, Shazim Ali Memon

“…Simulation outputs were validated against experimental cubicle data, yielding a mean absolute indoor temperature error ≤ 4.5%, well within the ±5% tolerance commonly accepted for building thermal simulations. …”
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MTL-PlotCounter: Multitask Driven Soybean Seedling Counting at the Plot Scale Based on UAV Imagery by Xiaoqin Xue, Chenfei Li, Zonglin Liu, Yile Sun, Xuru Li, Haiyan Song

“…PlotCounter achieves a root mean square error (RMSE) of 6.98 and a relative RMSE (rRMSE) of 6.93%. …”
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Virtual-Vector-Based Predictive Torque Control for Six-Phase IM With Reduced Computational Burden and Copper Losses by Osvaldo Gonzalez, Jesus Doval-Gandoy, Magno Ayala, Jorge Rodas, Paola Maidana, Christian Medina, Carlos Romero, Larizza Delorme, Raul Gregor, Ricardo Maciel

“…Experimental results confirm reductions of approximately 79.7% in computational burden and 98.4% in copper losses when using PTC-VV compared to classic PTC. …”
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Coordinated Multi-Input and Single-Output Photonic Millimeter-Wave Communication in W-Band Using Neural Network-Based Waveform-To-Symbol Converter by Kexin Liu, Boyu Dong, Zhongya Li, Yinjun Liu, Yaxuan Li, Fangbing Wu, Yongzhu Hu, Junwen Zhang

“…The results show that the NNWSC-based receiver achieves significant bit error rate (BER) reductions compared to conventional receivers across all configurations. …”
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Estimation of Footprint-Scale Across-Track Slopes Based on Elevation Frequency Histogram from Single-Track ICESat-2 Photon Data of Strong Beam by Qianyin Zhang, Hui Zhou, Yue Ma, Song Li, Heng Wang

“…The results show that the mean absolute error (MAE) obtained by our method is 11.45°, which is comparable to the ICESat-2 method (11.61°) and the plane fitting method (12.51°). …”
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Evaluation of the LI-710 evapotranspiration sensor in comparison to full eddy covariance for monitoring energy fluxes in perennial and annual crops by Srinivasa Rao Peddinti, Isaya Kisekka

“…H improved similarly (processing tomato: 0.86–0.97; citrus: 0.13–0.67; almond: −0.35 to 0.75; pistachio: 0.20–0.85), while LE accuracy rose from 0.44 to 0.95 in processing tomatoes, −0.10 to 0.77 in citrus, −1.51 to 0.76 in almonds, and 0.71–0.93 in pistachios. Corresponding reductions in root mean square error (RMSE) confirmed that EBR-corrected LI-710 measurements closely aligned with EC observations, effectively capturing seasonal peaks and phenological transitions. …”
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Processor-in-the-Loop validation of direct power control based on fractional-order modified super-twisting algorithm for doubly-fed induction generators by Mourad Yessef, Habib Benbouhenni, Ahmed Lagrioui, Nicu Bizon, Badre Bossoufi, Saad F. Al-Gahtani, Z. M. S. Elbarbary

“…In a comparable analysis, reductions of 70.90%, 52.63%, and 63.46% were observed in SSE, overshoot, and reactive power (Qs) ripple, respectively, when contrasted with the conventional DPC method.…”
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Mapping near-real-time soil moisture dynamics over Tasmania with transfer learning by M. T. Widyastuti, J. Padarian, B. Minasny, M. Webb, M. Taufik, D. Kidd

“…Results showed that (1) models calibrated from the Australian dataset performed worse than Tasmanian models regardless of the type of DL approaches; (2) Tasmanian models, calibrated solely using local data, resulted in shortcomings in predicting soil moisture; and (3) transfer learning exhibited remarkable performance improvements (error reductions of up to 45 % and a 50 % increase in correlation) and resolved the drawbacks of the two previous models. …”
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Quantitative determination of blended proportions in tobacco formulations using near-infrared spectroscopy and transfer learning by Qinlin Xiao, Qinlin Xiao, Ruifang Gu, Li Li, Jing Wen, Xixiang Zhang, Yi Shen, Yang Liu, Lan Xiao, Qinqin Tang, Jun Yang, Yong He, Juan Yang

“…The results show that TCA-PLSR achieved substantial reductions in prediction error in most transfer tasks involving large discrepancies in feature distributions. …”
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A Bayesian-Optimized Surrogate Model Integrating Deep Learning Algorithms for Correcting PurpleAir Sensor Measurements by Masrur Ahmed, Jing Kong, Ningbo Jiang, Hiep Nguyen Duc, Praveen Puppala, Merched Azzi, Matthew Riley, Xavier Barthelemy

“…BaySurcls reduced root mean square error (RMSE) by an average of 20% in collocated scenarios, with reductions of up to 25% in highvariation sites. …”
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Projected future changes in extreme climate indices affecting rice production in China using a multi-model ensemble of CMIP6 projections by Xinmin Chen, Dengpan Xiao, Dengpan Xiao, Yongqing Qi, Yongqing Qi, Zexu Shi, Huizi Bai, Yang Lu, Yang Lu, Man Zhang, Man Zhang, Peipei Pan, Peipei Pan, Dandan Ren, Xiaomeng Yin, Xiaomeng Yin, Renjie Li, Renjie Li

“…The results indicate that the multi-model ensemble constructed via the Independence Weighted Mean method (IWM) significantly outperformed both the arithmetic mean method (AM) and individual GCMs in replicating observed trends of 11 ECIs during the historical period (1981–2014), with notable reductions in root mean square error (RMSE) for certain indices. …”
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HIDRA3: a deep-learning model for multipoint ensemble sea level forecasting in the presence of tide gauge sensor failures by M. Rus, M. Rus, H. Mihanović, M. Ličer, M. Ličer, M. Kristan

“…Results show that HIDRA3 outperforms HIDRA2 and the Mediterranean basin Nucleus for European Modelling of the Ocean (NEMO) setup of the Copernicus Marine Environment Monitoring Service (CMEMS) by <span class="inline-formula">∼</span> 15 % and <span class="inline-formula">∼</span> 13 % mean absolute error (MAE) reductions at high SSH values, creating a solid new state of the art. …”
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Optimized step size control within the Rosenbrock solvers for stiff chemical ordinary differential equation systems in KPP version 2.2.3_rs4 by R. Dreger, T. Kirfel, T. Kirfel, A. Pozzer, S. Rosanka, R. Sander, D. Taraborrelli, D. Taraborrelli

“…Our analysis indicates that the local error, which is the key factor for the step size selection, is often overestimated, leading to very small substeps. …”
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A Framework for Autonomous UAV Navigation Based on Monocular Depth Estimation by Jonas Gaigalas, Linas Perkauskas, Henrikas Gricius, Tomas Kanapickas, Andrius Kriščiūnas

“…In this study, fine-tuned models using synthetic RGB and depth image data were used for each environment, demonstrating a noticeable improvement in depth estimation accuracy, with reductions in Mean Absolute Percentage Error (MAPE) from 120.45% to 33.41% in AirSimNH, from 70.09% to 8.04% in Blocks, and from 121.94% to 32.86% in MSBuild2018. …”
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Gait stability prediction through synthetic time-series and vision-based data by Mauricio C. Cordeiro, Ciaran O. Cathain, Ciaran O. Cathain, Ciaran O. Cathain, Vitor B. Nascimento, Thiago B. Rodrigues

“…The model trained exclusively on synthetic data (TSTR) outperformed the model trained on real data (TRTR), with error reductions (RMSE decreased by 56.3%, MAE by 58.2%, and MSE by 80.9%) and improved variance explanation (R2 increase of 31.2%). …”
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