Extrapolation-Based Scale Effect Model for Granular Heap Modulus by Zhigang Ma, Shuaifeng Wu, Ran Wei

“…The numerical analysis was used to predict the rockfill dam displacement, and the model parameters were calibrated using the triaxial experiments on scale-down rockfill samples. …”
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Near-Infrared Spectroscopy and Chemometrics for the Routine Detection of Bilberry Extract Adulteration and Quantitative Determination of the Anthocyanins by Claudio Gardana, Antonio Scialpi, Christian Fachechi, Paolo Simonetti

“…Spectra were recorded in the range of 4000–12500 cm−1, and a good prediction model was obtained in the range of 9400–6096 and 5456–4248 cm−1 with r2 of 99.5% and a root-mean-square error of 0.3%. …”
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A Centralized Algorithm with Collision Avoidance for Trajectory Planning in Preflight Stage by Zheng Xiang, Wenqi Zhang, Deyang He, Yu Tang

“…Firstly, through establishing a flight performance prediction model, in which the flight plan data is extracted and the time when an aircraft passed a specified waypoint is calculated, a 4D flight prediction can be derived. …”
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Thermal Infrared Anomalies of Several Strong Earthquakes by Congxin Wei, Yuansheng Zhang, Xiao Guo, Shaoxing Hui, Manzhong Qin, Ying Zhang

“…In summary, we should be sure that earthquake thermal infrared anomalies as useful earthquake precursor can be used in earthquake prediction and forecasting.…”
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Traffic Anomaly Detection Algorithm for Wireless Sensor Networks Based on Improved Exploitation of the GM(1,1) Model by Qin Yu, Jibin Lyu, Lirui Jiang, Longjiang Li

“…In the paper, we improve exploitation of GM(1,1) model to make traffic prediction and judge the traffic anomaly in WSNs. Based on our systematical researches on the characteristics of WSN traffic, the causes of WSN abnormal traffic, and latest related research and development, we better exploit the GM(1,1) model following four guidelines: using a sliding window to determine historical data for modeling, optimizing initial value of one-order grey differential equation, making traffic prediction by short step exponential weighted average method, and judging whether the traffic of the next moment is abnormal by Euclidean distance. …”
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Selecting the Best Routing Traffic for Packets in LAN via Machine Learning to Achieve the Best Strategy by Bo Zhang, Rongji Liao

“…Exploiting the gaps in previous studies, which are represented in the lack of training of the system and the inaccurate prediction as a result of not taking into consideration the hidden layers' feedback, leads to great performance. …”
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Evaluation Models for Soil Nutrient Based on Support Vector Machine and Artificial Neural Networks by Hao Li, Weijia Leng, Yibing Zhou, Fudi Chen, Zhilong Xiu, Dazuo Yang

“…Results show that the average prediction accuracies of SVM models are 77.87% and 83.00%, respectively, while the general regression neural network (GRNN) model’s average prediction accuracy is 92.86%, indicating that SVM and GRNN models can be used effectively to assess the levels of soil nutrient with suitable dependent variables. …”
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The Utility of the Bering Sea and East Asia Rules in Long-Range Forecasting by Joseph S. Renken, Joshua J. Herman, Travis R. Bradshaw, Patrick S. Market, Anthony R. Lupo

“…Using the National Center for Atmospheric Research/National Centers for Environmental Prediction (NCAR/NCEP) reanalyses and the daily Pacific North American (PNA) index values from the Climate Prediction Center from 1 January 1950 to 31 December 2016, the utility of the Bering Sea Rule (BSR) and the East Asia Rule (EAR) for making forecasts in the two-to-four-week time frame for the central USA region is examined. …”
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A Simple Solution for Estimating the Smear Effect Permeability Ratio Using Finite Element Method by Rufaizal Che Mamat, Azuin Ramli, Sri Atmaja P. Rosyidi

“…Incorporating the smear effect into the numerical analysis of vertical drains improved prediction accuracy. The article proposes a new approach for estimating the smear effect permeability ratio for soft soil stabilized with PVDs.…”
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Forecasting Carbon Emissions with Dynamic Model Averaging Approach: Time-Varying Evidence from China by Siqi Xu, Yifeng Zhang, Xiaodan Chen

“…Secondly, all these predictors present a distinct predictive ability for carbon emission in China. The proportion of industry production in GDP (IP) shows the greatest predictive power, while the proportion of FDI in GDP has the smallest forecasting ability. …”
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Comparing ALADIN-CZ and ALADIN-LAEF Precipitation Forecasts for Hydrological Modelling in the Czech Republic by Martin Vokoun, Martin Hanel

“…Furthermore, the relationship between synoptic weather types, hydrological regions, and predictability was considered. It was found that the worst prediction results are related to weather situation C (cyclone over central Europe), which dominantly affects Berounka and Lower Elbe catchments.…”
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A Data Decomposition and End-to-End Optimization-Based Monthly Carbon Emission Intensity of Electricity Forecasting Method by Yue Yan, Haoran Feng, Jinwei Song, Shixu Zhang, Shize Zhang, Qi He

“…Case studies conducted using actual data from Guangdong Province, China, demonstrate that the proposed method can effectively enhance monthly data, thereby improving prediction accuracy.…”
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Dynamic Characteristics of an Offshore Wind Turbine with Tripod Suction Buckets via Full-Scale Testing by Yun-Ho Seo, Moo Sung Ryu, Ki-Yong Oh

“…The comparison shows that the stiffness of the suction bucket cap and strain dependency of the soil play a significant role in predicting natural frequency, suggesting that these two factors should be considered in finite element analysis for the accurate prediction of dynamic responses of an offshore wind conversion system. …”
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Applying System Dynamics of Discrete Supported Track to Analyze the Rail Corrugation Causation on Curved Urban Railway Tracks by Xianxian Yin, Xiukun Wei, Haichao Zheng

“…However, little attention has been paid to the key causes of the track resonance and the practical prediction of the occurrence probability of rail corrugation on the certain track. …”
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Learning model combined with data clustering and dimensionality reduction for short-term electricity load forecasting by Hyun-Jung Bae, Jong-Seong Park, Ji-hyeok Choi, Hyuk-Yoon Kwon

“…In this study, we investigate short-term load forecasting (STLF) for large-scale electricity usage datasets. We propose a new prediction model for STLF that combines data clustering and dimensionality reduction schemes to handle large-scale electricity usage data effectively. …”
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Research on cutting mechanism and process optimization method of gear skiving by Peng Wang, Yuanchao Ni, Xiaoqiang Wu, Jiaxue Ji, Geng Li, Jiahao Wu

“…This prediction model allows for the construction of a multi-objective optimization model for the process parameters. …”
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Viscoelastic Fluid Factor Inversion and Application in Luojia Oilfield Based on Broadband Impedance by Ruyi Zhang, Huazhong Wang

“…Based on the physical quantity of log data, the accurate identification of oil- and gas-bearing properties may be caused by the prestack inversion of fluid prediction, which will affect the success rate of exploration and development. …”
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UniLF: A novel short-term load forecasting model uniformly considering various features from multivariate load data by Shiyang Zhou, Qingyong Zhang, Peng Xiao, Bingrong Xu, Geshuai Luo

“…Experiments conducted on three load datasets from Australia, Panama and Austria show that UniLF achieves superior forecasting accuracy with competitive practical efficiency under different prediction lengths, providing a new solution for STLF.…”
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Using crafted features and polar bear optimization algorithm for short-term electric load forecast system by Mansi Bhatnagar, Gregor Rozinaj, Radoslav Vargic

“…Short-term load forecasting (STLF) can be utilized to predict usage fluctuation in a short time period and accurate forecasting can save a big chunk of a country's economic loss. …”
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A Novel Model Using Virtual State Variables and Bayesian Discriminant Analysis to Classify Surrounding Rock Stability by Jinglai Sun, Darui Ren, Yu Song, Mingyuan Yu, Zhaofei Chu, Baoguo Liu, Shaogang Li, Xinyang Guo

“…ANN and BDA models are also constructed based on the same training samples. The predictions by the three models for the testing samples are compared; the results show that the proposed VSV-BDA model has high prediction accuracy and can be applied in practical engineering.…”
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