NitroNet – a machine learning model for the prediction of tropospheric NO₂ profiles from TROPOMI observations by L. Kuhn, L. Kuhn, S. Beirle, S. Osipov, S. Osipov, A. Pozzer, T. Wagner, T. Wagner

“…In comparison to TROPOMI satellite data, NitroNet even shows significantly lower errors and stronger correlation than a direct comparison with WRF-Chem numerical results. …”
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Obliczanie Sił Krytycznych dla Sprężystych Prętów Niepryzmatycznych Metoda Interpolacji Częściowej by M. Życzkowski

“…This enables a considerable reduction of the final interpolation error ba(x). The paper gives an example of the evaluation of the error for the ordinary and the accelerated partial interpolations Eqs. (8.17) and (8.21), respectively and presents formulae for the coefficients of basic approximations assumed in the form of a polynomial, (9.3), (9.7), and (9.9). …”
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Development of an upper limb muscle strength rehabilitation assessment system using particle swarm optimisation by Chuangan Zhou, Siqi Wang, Meiyi Wu, Wei Lai, Junyu Yao, Xingyue Gou, Hui Ye, Jun Yi, Dong Cao

“…Model performance was evaluated using R-squared (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Mean Bias Error (MBE).ResultsThe system successfully collected electromyographic and kinematic data. …”
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Machine Learning Models Informed by Connected Mixture Components for Short- and Medium-Term Time Series Forecasting by Andrey K. Gorshenin, Anton L. Vilyaev

“…For geophysical spatiotemporal data, the decrease in Root Mean Square Error (RMSE) was up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>27.7</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and the reduction in Mean Absolute Percentage Error (MAPE) was up to <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>45.7</mn><mo>%</mo></mrow></semantics></math></inline-formula> compared with ML models without probability informing. …”
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Multi-scale attention-enhanced deep learning approach for detecting seven trunk pests and diseases in Shanghai’s urban plane trees by Tianyang Song, Guohua Hu, Tianci Yu, Xing Meng, Yanting Zhang, Ruiqing Yang, Benyao Wang, Xia Li

“…Traditional manual inspections are labor-intensive and error-prone. This study introduces an enhanced YOLOv8-based detection framework to address multi-scale variability in pest and disease datasets. …”
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Explosion characteristics and overpressure prediction of hydrogen-doped natural gas under ambient turbulence conditions by Ranran Li, Zhongmo Xu, Mingzhi Li, Shuhong Li, Zhenyi Liu, Zihao Xiu, Qiqi Liu

“…However, when the wind speed exceeds 2 m/s, turbulence suppresses flame propagation, leading to a reduction in maximum overpressure by up to 50.5 %. …”
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Electric Vehicle charging station load forecasting with an integrated DeepBoost approach by Joveria Siddiqui, Ubaid Ahmed, Adil Amin, Talal Alharbi, Abdulelah Alharbi, Imran Aziz, Ahsan Raza Khan, Anzar Mahmood

“…For the dataset of Adaptive Charging Networks (ACN), the Mean Absolute Error (MAE) of DeepBoost improves by 9.4%, 32.7% and 88% as compared to CatBoost, XgBoost and LSTM networks, respectively.…”
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Unraveling overestimated exposure risks through hourly ozone retrievals from next-generation geostationary satellites by Siwei Li, Ge Song, Jia Xing, Jiaxin Dong, Maolin Zhang, Chunying Fan, Shiyao Meng, Jie Yang, Lechao Dong, Wei Gong

“…Here, we utilize a next-generation geostationary satellite with ultraviolet capabilities to retrieve hourly O3 concentrations, achieving high accuracy (R2 = 0.94) and improving daily maximum 8-hour estimates, particularly in semi-urban areas (R2 + 0.10, error reduction >7 μg/m³). Our analysis reveals a 30% drop in O3-related health risks compared to traditional polar-orbit estimates, with the greatest impact in semi-urban and rural areas where satellite data plays an important role due to the lack of ground measurements. …”
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Wavefront Detection and Event Segmentation Method for Partial Discharge Signal Analysis by Francisco de A. Oliveira Nascimento, Rodrigo De A. Coelho, George V. R. Xavier, Pedro D. Alvim, Almir C. Dos Santos Junior, Hugerles S. Silva

“…Experimental results showed substantial improvements in signal-to-noise ratio (SNR), high cross-correlation between the original and denoised signals, and a significant reduction in normalized mean squared error, confirming the robustness of the method under low-SNR conditions.…”
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FASQuiC: Flexible Architecture for Scalable Spin Qubit Control by Mathieu Toubeix, Eric Guthmuller, Adrian Evans, Antoine Faurie, Tristan Meunier

“…The hardware for a single channel is very compact, 2&#x0025; of ZCU111 logic resources for one DAC lane in the default configuration, leaving significant circuit resources for integrated feedback, calibration, and quantum error correction.…”
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Digital biomarkers for interstitial glucose prediction in healthy individuals using wearables and machine learning by Xinyu Huang, Franziska Schmelter, Christian Seitzer, Lars Martensen, Hans Otzen, Artur Piet, Oliver Witt, Torsten Schröder, Ulrich L. Günther, Lisa Marshall, Marcin Grzegorzek, Christian Sina

“…Abstract A personalized low-glycemic diet, maintaining stable blood glucose levels, aids in weight reduction and managing (pre-)diabetes and migraines in individuals. …”
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Time-of-flight-based advanced surface reconstruction methods for real-time volume estimation of bulk harvested wild blueberries by Connor C. Mullins, Travis J. Esau, Qamar U. Zaman, Ahmad A. Al-Mallahi, Aitazaz A. Farooque, Craig B. MacEachern

“…Convex hull exhibited the highest initial MAE (0.0495 ± 0.0235 m³) but showed significant error reduction post-bias correction (to 0.0122 ± 0.0128 m³), leading to a reduced variability. …”
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Accurate sub-seasonal root-zone soil moisture prediction using attention-based autoregressive transfer learning and SMAP data by Lei Xu, Xihao Zhang, Xi Zhang, Tingtao Wu, Hongchu Yu, Wenying Du, Zeqiang Chen, Nengcheng Chen

“…The results showed that compared with LSTM, the skills of the MAATL model were significantly improved, with an average correlation coefficient increase of 18.26% and a root mean square error (RMSE) reduction of 42.55%. Furthermore, 118 in-situ soil moisture stations are used for predictive validation and the proposed MAATL model demonstrates higher accuracy compared to the Global Forecast System (GFS) and the LSTM model, with an average correlation skill improvement of 16.02% and 15.08% for MAATL over GFS and LSTM, respectively. …”
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Streptococcus agalactiae colonization and screening approach in high-risk pregnant women in southern Brazil by Jeane Zanini da Rocha, Jéssica Feltraco, Vanessa Radin, Carla Vitola Gonçalves, Pedro Eduardo Almeida da Silva, Andrea von Groll

“…Among the pregnant women treated, a fivefold reduction in the rate of prematurity and rate of neonatal infection was observed. …”
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Wave forecast investigations on downscaling, source terms, and tides for Aotearoa New Zealand by R. Santana, R. Santana, R. Gorman, E. Lane, S. Moore, C. Bosserelle, G. Reeve, C. Rautenbach, C. Rautenbach

“…This variability was also evident in the Tm01 predictions, with notable improvements in bias reduction through model downscaling, particularly at Baring Head. …”
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Nonlinear Compensation of Multi-CAP VLC System Employing Clustering Algorithm Based Perception Decision by Xingyu Lu, Kaihui Wang, Liang Qiao, Wen Zhou, Yiguang Wang, Nan Chi

“…The CAPD method can outperform the Volterra series based nonlinear equalizer with a lower BER value (at least 10&#x0025; reduction) and relatively lower complexity. To the best of our knowledge, this is the first time that the clustering algorithm in machine learning is successfully applied to VLC systems.…”
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LightHand99K: A Synthetic Dataset for Hand Pose Estimation With Wrist-Worn Cameras by Jeongho Lee, Changho Kim, Jaeyun Kim, Seon Ho Kim, Younggeun Choi, Sang-Il Choi

“…Incorporating three data augmentation techniques, LightHand99K demonstrated a 36% increase in area under the curve (AUC) and a 6.2-mm reduction in average endpoint error (EPE) compared to existing datasets. …”
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Elbow Joint Angle Estimation Using a Low-Cost and Low-Power Single Inertial Device for Daily Home-Based Self-Rehabilitation by Manon Fourniol, Rémy Vauché, Guillaume Rao, Eric Watelain, Edith Kussener

“…Moreover, its power consumption can be reduced by more than the increase in the error when reducing the rate of the data output by the sensor. …”
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Short-term and long-term inertia forecasting with low-inertia event prediction in IBR-integrated power systems using a deep learning approach by Santosh Diggikar, Arunkumar Patil, Katkar Siddhant Satyapal, Kunal Samad

“…The proposed hybrid model achieves superior predictive performance, with a mean absolute percentage error (MAPE) of 2.74%, mean absolute error (MAE) of 4.55 GVAs, root mean square error (RMSE) of 6.65 GVAs, mean squared error (MSE) of 44.22 GVAs2, and combined accuracy (CA) of 3.70 GVAs. …”
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Neuro-fuzzy inference system and white shark optimization of coagulation-flocculation of aquaculture wastewater treatment by A.F. Mohamed, H. Rezk

“…The R-squared values for training and testing are 1.0 and 0.82, respectively, and adaptive neuro-fuzzy inference system reduced the root mean square error from 6.8 with analysis of variance to 1.135 with adaptive neuro-fuzzy inference system achieving an 83.5 percent reduction. …”
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