Daily allocation of energy consumption forecasting of a power distribution company using optimized least squares support vector machine by Marzia Ahmed, Mohd Herwan Sulaiman, Md. Maruf Hassan, Md. Atikur Rahaman, Mohammad Bin Amin

“…The optimized LSSVM hyper-parameters, specifically the regularization parameter (γ) and the kernel parameter (σ2), were applied to test data to enhance accuracy guided by the Mean Absolute Prediction Error (MAPE), ensuring precise alignment of forecasted values with actual energy consumption data. …”
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Incorporating Machine Learning in Capture-Recapture Estimation of Survey Measurement Error by Maaike Walraad, Jonas Klingwort, Joep Burger

“…In particular, we study differences in prediction quality, point estimates, variance estimates, and estimates of measurement error in five years. …”
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Early identification of dropouts during the special forces selection program by Ruud J. R. den Hartigh, Rik Huijzer, Frank J. Blaauw, Age de Wit, Peter de Jonge

“…Additionally, we found that dropout could often be predicted multiple weeks in advance. These findings offer novel insights in the use of prediction models on psychological and physical processes, specifically in the context of special forces selection. …”
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Study on Tooth Surface Roughness Modeling of Form Grinding Considering the Change of the Local Grinding Condition by Chen Xiaoqi, Tang Cheng, Liao Xianggui, Zhou Weihua, Tang Jinyuan

“…The results show that the prediction model of the surface roughness of formed grinding teeth presented in this study agrees with the experimental results. …”
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A Software Tool for Optimal Sizing of PV Systems in Malaysia by Tamer Khatib, Azah Mohamed, K. Sopian

“…The ANN based model for metrological prediction uses four meteorological variables, namely, sun shine ratio, day number and location coordinates. …”
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Effect of mesostructure on quasi-static and dynamic tensile strength of rock: insights from a breakable grain-based model by Cheng Pan, Xing Li, Guangming Zhao, Bing Cheng

“…Abstract Accurately predicting rock tensile strength under varying strain rates is crucial for tunnel construction, as rock often fails in tension. …”
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Nonlinear Model of Vibrating Screen to Determine Permissible Spring Deterioration for Proper Separation by Cristian G. Rodriguez, Manuel A. Moncada, Emilio E. Dufeu, Mario I. Razeto

“…The proposed model allows the prediction of vibrating screen behavior when there is a reduction in spring stiffness. …”
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Static Mechanical Properties and Microscopic Analysis of Hybrid Fiber Reinforced Ultra-High Performance Concrete with Coarse Aggregate by Shengbing Liu, Yonglei Zhang

“…Finally, three kinds of strength parameters are predicted based on the back propagation (BP) neural network system. …”
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Tumor Size as a Critical Prognostic Factor in T1-2 Stage Esophageal Cancer by Zilong Wu, Bentong Yu

“…Tumor size has been measured in esophageal cancer for decades, but the role of tumor size in relation to T stage in the prediction of survival is still underappreciated. Thus, the present study is aimed at investigating the influence of T stage on the predictive value of tumor size in clinical stage I–IV esophageal cancer patients. …”
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Online energy consumption forecast for battery electric buses using a learning-free algebraic method by Zejiang Wang, Guanhao Xu, Ruixiao Sun, Anye Zhou, Adian Cook, Yuche Chen

“…Abstract Accurately predicting the energy consumption plays a vital role in battery electric buses (BEBs) route planning and deployment. …”
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Feature engineering descriptors, transforms, and machine learning for grain boundaries and variable-sized atom clusters by C. Braxton Owens, Nithin Mathew, Tyce W. Olaveson, Jacob P. Tavenner, Edward M. Kober, Garritt J. Tucker, Gus L. W. Hart, Eric R. Homer

“…Recent efforts use machine learning to derive these relationships, but the way the atomic grain boundary structure is represented can have a significant impact on the predictions. Key steps for property prediction common to grain boundaries and other variable-sized atom clustered structures include: (1) describing the atomic structure as a feature matrix, (2) transforming the variable-sized feature matrix to a fixed length common to all structures, and (3) applying a machine learning algorithm to predict properties from the transformed matrices. …”
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The Enlightenment of Domestic and International Legal and Policies Related to the Sea Level Rise Adaptation by ZHANG Zefei, REN Shutong, XIA Dongdong, SHEN Xiaoying

“…By conducting an in-depth analysis from the perspectives of socialist rule of law and local legislation, we suggest that coastal local governments should strive to balance the conflicts between coastal development and environment protection under the backdrop of climate change by: a) strengthening research on local relative sea level rise prediction and its impact, b) applying research outcomes to coastal development management, c) raising public awareness about sea level rise, and d) progressively initiating the local legislative process.…”
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AVP-GPT2: A Transformer-Powered Platform for De Novo Generation, Screening, and Explanation of Antiviral Peptides by Huajian Zhao, Gengshen Song

“…A multi-modal screening approach, incorporating Star-Transformer and Vision Transformer, enables accurate prediction of antiviral activity and toxicity, leading to the identification of potent and safe candidates. …”
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Estimation of Critical Safety Thickness of the Base against Confined Water Inrush for a Rectangular Foundation Pit by Qin-xing Li, Hong-tao Wang, Yan-qing Men, Xiao Yu, Chi Liu, Hua-jun Zhang

“…Then, based on the limit equilibrium theory, the prediction method for the safety thickness of the base against confined water inrush was deduced, and a comparison with existing research works was conducted. …”
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Predictores de vasoespasmo cerebral sintomático en la hemorragia subaracnoidea espontánea by Ezequiel López Espinosa, Miguel Ángel Serra Valdés, Maira Sarmiento Matamoros

“…<br /><strong>Objective:</strong> to identify the factors contributing to the prediction of symptomatic cerebral vasospasm in patients diagnosed with subarachnoid hemorrhage. …”
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CFD Turbulence Study of PWR Spacer-Grids in a Rod Bundle by C. Peña-Monferrer, J. L. Muñoz-Cobo, S. Chiva

“…Calculations were performed with the commercial code ANSYS CFX using large eddy simulation (LES) turbulence model and the CFD modeling procedure validated by comparison with measurements to determine their suitability in the prediction of the turbulence phenomena.…”
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A New Variational Assimilation Method Based on Gradient Information from Satellite Data by Bo Zhong, Yun-Feng Wang, Gang Ma, Xin-Yuan Ma, Lu Yang

“…With the development of meteorological observation technology, satellite data have found increasingly wide use in the numerical weather prediction field. However, there are various observational biases in satellite data, including a random bias brought about by complex weather systems and a systematic bias caused by the instrument itself, which greatly influence the quality of satellite data. …”
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A System of Remote Patients’ Monitoring and Alerting Using the Machine Learning Technique by M. Dhinakaran, Khongdet Phasinam, Joel Alanya-Beltran, Kingshuk Srivastava, D. Vijendra Babu, Sitesh Kumar Singh

“…Machine learning is utilized in various areas, but mainly in the healthcare industry, where it provides significant advantages via appropriate decision and prediction methods. The proposed work introduces a remote system that can continuously monitor the patient and can produce an alert whenever necessary. …”
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Exploring the Relationships between Subjective Evaluations and Objective Metrics of Vehicle Dynamic Performance by Jianyou Zhao, Jing Liu, Liping Yang, Ping He

“…The established overall subjective evaluation model was relatively reliable, with a prediction accuracy of 90%. This study furthered the existing knowledge of the methods for evaluating vehicle dynamic performance. …”
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Automatic Quantitative Analysis of Internal Quantum Efficiency Measurements of GaAs Solar Cells Using Deep Learning by Zubair Abdullah‐Vetter, Brendan Wright, Tien‐Chun Wu, Ali Shakiba, Ziv Hameiri

“…Therefore, this study proposes using a deep learning method to automatically predict multiple key parameters from IQE measurements of gallium arsenide cells. …”
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