Modeling and Performance for Seabed Airlift Device Based on Pressure Drop by Zhineng WANG, Junwei ZOU, Weiming LIN, Xiaochuan WANG

“…Finally, an experiment is conducted to validate this new model; the results showed that: 1) The new model aligns well with the experimental data with a maximum error of 17.6%. 2) A high gas flow rate increases the pressure drop but reduces the solid fraction. …”
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Effect of Public Health Care Expenditure on Malaria Incidence in Uganda. by Mugabe, Andrew

“…On the contrary, malaria incidence is increased in the long term by female-headed households and GDP per capita, though both factors lead to a short-term decrease, albeit at a slower initial rate. The error correction term (ECT) coefficient, -0.130, was statistically significant at the 1% level. …”
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The Effect of Eight Weeks of Motor Control Exercises and Myofascial Release on the Range of Motion of the Lumbar Spine and Disability in People with Non-Specific Chronic Low Back P... by S Jamali Brayjani, M.H Alizadeh, N Rahnama

“…Data analysis was done using repeated measures analysis of variance and Bonferroni's post hoc test at a five percent error level and using SPSS software version 26.ResultsMovement control exercises with and without myofascial release have caused a significant increase in the amount of range of motion of the lumbar spine and significant reduction in the severity of disability of the subjects, after training compared to before training (p<0.001). range of motion of the lumbar spine and severity of disability was not significantly different between the control and experimental groups (p>0.05). …”
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Adaptive Hierarchical Multi-Headed Convolutional Neural Network With Modified Convolutional Block Attention for Aerial Forest Fire Detection by Md. Najmul Mowla, Davood Asadi, Shamsul Masum, Khaled Rabie

“…Additionally, AHMHCNN-mCBAM outperformed HMHCNN-CBAM in recall (25% improvement), precision (21.95%), F1-score (14.94%), and fire detection rate (FDR) reduction (25.01%), while achieving a 100% reduction in error warning rate (EWR). …”
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DETERMINATION OF CHEWING EFFICIENCY IN PATIENTS WITH RESTORED TOOTH CROWN PART by V.V. Nikolov, D M. Korol, D.D. Kindiy, M.D. Korol

“…In a week after the fixation of fixed porcelain fused metal dentures, the reduction of the mean value of mastication test index by 0.2 was fixed. …”
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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. …”
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Assimilation of synthetic and real SWOT observations for the North Atlantic Ocean and Canadian east coast using the regional ice ocean prediction system by Guoqiang Liu, Guoqiang Liu, Gregory C. Smith, Audry-Anne Gauthier, Charlie Hébert-Pinard, Will Perrie, Will Perrie, Maryam Rashed Al Shehhi

“…The SSH error spectrum shows that the most notable improvements are for scales associated with the largest errors in the range of 200-400 km, with a 33% reduction compared to traditional data assimilation. …”
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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. …”
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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

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AI-driven demand forecasting for enhanced energy management in renewable microgrids: A hybrid LSTM-CNN approach by Bashiru olalekan ariyo, mutalub adesina lambe, olalekan ogunbiyi, musa abdulwaheed, bilkisu jimada ojuolape, monsurat omolara balogun

“…Results: The hybrid LSTM-CNN model demonstrated superior performance, achieving an R2 value of 0.87, a Mean Absolute Error (MAE) of 1.45 MWh, and a Root Mean Squared Error (RMSE) of 2.12 MWh. …”
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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. …”
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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. …”
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Cosmology with One Galaxy: Autoencoding the Galaxy Properties Manifold by Amanda Lue, Shy Genel, Marc Huertas-Company, Francisco Villaescusa-Navarro, Matthew Ho

“…In contrast, variations in other parameters such as σ _8 cause negligible error changes, suggesting galaxies shift along the manifold. …”
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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). …”
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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.…”
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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>. …”
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Graph2Mat: universal graph to matrix conversion for electron density prediction by Pol Febrer, Peter Bjørn Jørgensen, Miguel Pruneda, Alberto García, Pablo Ordejón, Arghya Bhowmik

“…The novel prediction model also allows for two new and promising measures of uncertainty (total charge error and self-consistency error) that will facilitate its practical usage, e.g. within active learning workflows. …”
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A clinical prediction model for blood pressure changes after renal denervation in patients with resistant hypertension by Yishuan Zhang, Ruiqing He, Chen Chen, Hong Zhang, Lingyan Li, Rongxue Xiao, Shuangyu Chen, Shuyi Wu, Zongjun Liu, Junqing Gao

“…In the prediction model on SBP change, compared to the OLS model, the Ridge regression exhibited lower prediction errors [mean absolute error [MAE]: 6.40 vs. 6.95; mean squared error [MSE]: 65.58 vs. 76.15] and a higher R² (0.79 vs. 0.72). …”
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Pharmacokinetic Model‐Informed Precision Dosing of Natalizumab in Multiple Sclerosis by Stefan P. H. van denBerg, Alyssa A. Toorop, Femke Hooijberg, Gertjan Wolbink, Nivea M. F. Voelkner, Liza M. Y. Gelissen, Joep Killestein, Zoé L. E. vanKempen, Thomas P. C. Dorlo, Theo Rispens

“…Corresponding precision (root mean square error) was 2.3 versus 4.0, and 1.5 versus 5 μg/mL. …”
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Sowing Depth Monitoring System for High-Speed Precision Planters Based on Multi-Sensor Data Fusion by Song Wang, Shujuan Yi, Bin Zhao, Yifei Li, Shuaifei Li, Guixiang Tao, Xin Mao, Wensheng Sun

“…High-speed precision planters are subject to high-speed (12~16 km/h) operation due to terrain undulation caused by mechanical vibration and sensor measurement errors caused by the sowing depth monitoring system’s accuracy reduction problems. …”
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