A secure and efficient user selection scheme in vehicular crowdsensing by Min Zhang, Qing Ye, Zhimin Yuan, Kaihuan Deng

“…The SEUS-VCS scheme has advantages in reducing loss function (Loss), Mean Square Error (MSE), and Mean Absolute Error (MAE), and the predicted results match the true data very well. …”
Get full text

A Predictive Method for Greenhouse Soil Pore Water Electrical Conductivity Based on Multi-Model Fusion and Variable Weight Combination by Jiawei Zhao, Peng Tian, Jihong Sun, Xinrui Wang, Changjun Deng, Yunlei Yang, Haokai Zhang, Ye Qian

“…The experimental results demonstrate that the PCLBX model achieves a mean square error (MSE) of 0.0016, a mean absolute error (MAE) of 0.0288, and a coefficient of determination (R²) of 0.9778. …”
Get full text

Study on the localization of tropical cyclone rainfall climatology and persistence model R-CLIPER by Ying LI, Weihua FANG, Yanting YE, Yuejun LI, Yefeng CHEN

Get full text

Comparison of Two System Identification Approaches for a Four-Wheel Differential Robot Based on Velocity Command Execution by Diego Guffanti, Moisés Filiberto Mora Murillo, Marco Alejandro Hinojosa, Santiago Bustamante Sanchez, Javier Oswaldo Obregón Gutiérrez, Nelson Gutiérrez, Miguel Sánchez

“…Similarly, the maximum position error averaged 0.522 m for MBM and 0.710 m for SM, confirming that MBM is more accurate and consistent in position tracking. …”
Get full text

Frequency Regulation Provided by Doubly Fed Induction Generator Based Variable-Speed Wind Turbines Using Inertial Emulation and Droop Control in Hybrid Wind–Diesel Power Systems by Muhammad Asad, José Ángel Sánchez-Fernández

“…As a result, the FD in the WDPS on San Cristobal Island was reduced by 1.05 Hz, and other quality indices, such as the integral absolute error (IAE), integral square error (ISE), and controller quality index (Z), were improved by 159.65, 16.75, and 83.80%, respectively. …”
Get full text

A Near-Real-Time Model for Predicting Electricity Disruptions in Texas During Winter Storms by Jangjae Lee, Sangkeun Lee, Supriya Chinthavali, Stephanie Paal

“…For model optimization, Bayesian optimization was employed using Root Mean Squared Error (RMSE) as the objective function. In results, when comparing Group 2 (the optimized group) with Group 1 (the non-optimized group), it was found that optimization did not always lead to a reduction in RMSE and Mean Absolute Error (MAE). …”
Get full text

AirTrace-SA: Air Pollution Tracing for Source Attribution by Wenchuan Zhao, Qi Zhang, Ting Shu, Xia Du

“…By reducing the reliance on labor-intensive data collection and providing scalable, high-precision source tracing, AirTrace-SA offers a powerful tool for environmental management that supports targeted emission reduction strategies and sustainable development.…”
Get full text

Symmetrized Neural Network Operators in Fractional Calculus: Caputo Derivatives, Asymptotic Analysis, and the Voronovskaya–Santos–Sales Theorem by Rômulo Damasclin Chaves dos Santos, Jorge Henrique de Oliveira Sales, Gislan Silveira Santos

“…Numerical experiments demonstrate a relative error reduction of up to <b>92.5%</b> when compared to classical quasi-interpolation operators, with observed convergence rates reaching <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi mathvariant="script">O</mi><mfenced separators="" open="(" close=")"><msup><mi>n</mi><mrow><mo>−</mo><mn>1.5</mn></mrow></msup></mfenced></mrow></semantics></math></inline-formula> under Caputo derivatives, using parameters <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>λ</mi><mo>=</mo><mn>3.5</mn></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>q</mi><mo>=</mo><mn>1.8</mn></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mi>n</mi><mo>=</mo><mn>100</mn></mrow></semantics></math></inline-formula>. …”
Get full text

Study on carbon emissions of a small hydropower plant in Southwest China by Caihong Tang, Yiling Leng, Pengyu Wang, Jian Feng, Shanghong Zhang, Yujun Yi, Hui Li, Shaoliang Tian

“…The uncertainty was evaluated using the error propagation method. Following analysis, suggestions for carbon footprint reduction measures were proposed. …”
Get full text

Global surface eddy mixing ellipses: spatio-temporal variability and machine learning prediction by Tian Jing, Ru Chen, Chuanyu Liu, Chunhua Qiu, Chunhua Qiu, Cuicui Zhang, Mei Hong

“…This resulted in a spatially averaged correlation increase of over 0.5 for predicting the minor axis and anisotropy, along with a reduction of more than 0.15 in the Normalized Root Mean Square Error. …”
Get full text

Sequential Hybrid Beamforming for Radio Stripes by Joumana JOUMANA, Daniel Castanheira, Adao Silva, Rui Dinis, Atilio Gameiro

“…The APs hybrid equalizer is optimized, using as a metric, the mean squared error (MSE) between the transmitted and the AP received signals. …”
Get full text

Characterizing wind, wave, and Stokes drift interactions in the upper ocean during Typhoon Doksuri using the COAWST model by Yaoyao Han, Changsheng Zuo, Zhizu Wang, Yucui Wang, Chengchen Tao, Xu Zhang, Juncheng Zuo

“…The COAWST model provides a more accurate simulation of typhoon wind speeds compared to ERA5 reanalysis data and the WRF model, and it offers a more precise representation of significant wave heights (Hs) than ERA5 reanalysis data and the SWAN model. The root mean square error (RMSE) of wind speed shows a reduction of 90.97% and 61.09% compared to ERA5 and WRF, respectively, resulting in an RMSE of 1.71 m/s. …”
Get full text

The Application of Vibroacoustic Mean and Peak-to-Peak Estimates to Assess the Rapidly Changing Thermodynamic Process of Converting Energy Obtained from Various Fuel Compositions U... by Marek Waligórski, Maciej Bajerlein, Wojciech Karpiuk, Rafał Smolec, Jakub Pełczyński

“…Mathematical models of combustion and its variability were created using the mean, peak-to-peak amplitude, root mean square error, and peak amplitudes of vibration accelerations, which were also represented using vibration graphics. …”
Get full text

Quantitative Estimation of Net Primary Productivity by an Improved tCASA Model Using Landsat Time Series Data: A Case Study of Central Plains, China by Lida Xu, Zongze Zhao, Cheng Wang, Hongtao Wang, Chao Ma

“…The results indicate the following: the improved tCASA model exhibits a stronger linear correlation with the test dataset, achieving a reduction of 11.11 gC/m² in the root mean square error. …”
Get full text

Ionospheric Prediction With High Temporal Resolution Using a Local Data Ingestion Technique Over the Chinese Region by Junchen Xue, Peng Guo, Weihua Wang

“…Compared to the standard NeQuick-2 model, the updated model demonstrates a significant reduction in both the bias and root-mean-square error (RMSE) from approximately 2 TECu to approximately 0.2 TECu and from approximately 8 TECu to nearly 4 TECu, respectively. …”
Get full text

Effects of Trapezius Muscle Self-Stretching on Muscle Stiffness and Choroidal Circulatory Dynamics: An Evaluation Using Ultrasound Strain Elastography and Laser Speckle Flowgraphy by Miki Yoshimura, Takanori Taniguchi, Takeshi Yoshitomi, Yuki Hashimoto

“…Methods: Eighteen healthy adults in their 20s (median age ± standard error: 21.0 ± 4.9 years) and eight healthy adults in their 40s (age: 43.0 ± 15.2 years) were included. …”
Get full text

Large Language Model Enhanced Particle Swarm Optimization for Hyperparameter Tuning for Deep Learning Models by Saad Hameed, Basheer Qolomany, Samir Brahim Belhaouari, Mohamed Abdallah, Junaid Qadir, Ala Al-Fuqaha

“…Llama3 achieved a 20% to 40% reduction in model calls for regression tasks, whereas ChatGPT-3.5 reduced model calls by 60% for both regression and classification tasks, all while preserving accuracy and error rates. …”
Get full text

A novel multi-task learning model based on Transformer-LSTM for wind power forecasting by Rongquan Zhang, Siqi Bu, Yuxia Zheng, Gangqiang Li, Xiupeng Wan, Qiangqiang Zeng, Min Zhou

“…Compared to 23 state-of-the-art deterministic models, the proposed model reduces the mean absolute error by a minimum of 0.3174 and a maximum of 9.190, with an average reduction of 2.278. …”
Get full text

Postoperative Apnea‐Hypopnea Index Prediction of Velopharyngeal Surgery Based on Machine Learning by Jingyuan You, Juan Li, Yingqian Zhou, Xin Cao, Chunmei Zhao, Yuhuan Zhang, Jingying Ye

“…Surgical success was defined as a ≥50% reduction in AHI to a final AHI of <20 events/h. Results A total of 152 OSA adult patients (median [interquartile range] age = 40 [35, 48] years, male/female = 136/16) were included in this study. …”
Get full text

Design of an improved graph-based model integrating LSTM, LoRaWAN, and blockchain for smart agriculture by Ravi Kumar Munaganuri, Narasimha Rao Yamarthi, Sai Chandana Bolem

“…LSTM networks are chosen here for their high performance in timestamp series prediction tasks with an mean average error (MAE) of 0.02 m3/m3 over a 7-day forecast horizon. …”
Get full text