On Modeling and Constrained Model Predictive Control of Open Irrigation Canals by Lihui Cen, Ziqiang Wu, Xiaofang Chen, Yanggui Zou, Shaohui Zhang

“…This paper proposes a model predictive control of open irrigation canals with constraints. …”
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Predicting the thickness of shallow landslides in Switzerland using machine learning by C. Schaller, C. Schaller, L. Dorren, M. Schwarz, C. Moos, A. C. Seijmonsbergen, E. E. van Loon

“…In this study, we developed a model providing improved predictions of potential shallow-landslide thickness for Switzerland. …”
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MORF: Multi-view oblique random forest for hepatotoxicity prediction by Binsheng Sui, Qingzhuo He, Bowei Yan, Kunhong Liu, Yong Xu, Song He, Xiaochen Bo

“…Summary: Hepatotoxicity prediction is significant in drug development, so the experts expect to get effective and reliable references. …”
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Asynchronous Distributed Model Predictive Control for Multi-Agent Systems by Xiaoxiao Mi, Yuanjiang Liao, Hongzheng Zeng

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Prediction of the Strength of Rubberized Concrete by an Evolved Random Forest Model by Yuantian Sun, Guichen Li, Junfei Zhang, Deyu Qian

“…The proposed BRF model can accurately predict the UCS of RC with a high correlation coefficient (0.96). …”
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Prediction of the Sex-Associated Genomic Region in Tunas (Thunnus Fishes) by Yoji Nakamura, Kentaro Higuchi, Kazunori Kumon, Motoshige Yasuike, Toshinori Takashi, Koichiro Gen, Atushi Fujiwara

“…As a result, we found a unique region carrying the male-specific haplotype, where a homolog of estrogen sulfotransferase gene was predicted to be encoded. The genome-wide mapping of previously resequenced data indicated that, among the functionally annotated genes, only this gene, named sult1st6y, was paternally inherited in the males of Pacific bluefin tuna. …”
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Using BIB-SEM Imaging for Permeability Prediction in Heterogeneous Shales by C. J. A. Sinn, J. Klaver, R. Fink, M. Jiang, J. Schmatz, R. Littke, J. L. Urai

“…SEM-visible porosities range from 0.1% to 1.8% increasing with TOC, in agreement with our measurements. The CTM predicts permeability correctly within one order of magnitude. …”
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The Perfect Way to Predict the Severity of Acute Pancreatitis: The Search Continues by Daniel C Sadowski

“…This study was designed to determine the clinical utility of three rating scales (Ranson's, Acute Physiology And Chronic Health Evaluation [APACHE] II and Glasgow) in predicting the severity of acute pancreatitis experienced by patients known to have human immunodeficiency virus (HIV) or acquired immunodeficiency syndrome (AIDS). …”
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Predicting Axillary Metastasis of Breast Cancer Patients with MRI Relaxometry by Roxana Pintican, Radu Fechete, Delia Ioana Radutiu, Manuela Lenghel, Ioana Bene, Carolina Solomon, Cristiana Ciortea, Anca Ciurea

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Prediction of Mine Dust Concentration Based on Grey Markov Model by Zhou Xu, Guo Liwen, Zhang Jiuling, Qin Sijia, Zhu Yi

“…The model was applied to the prediction of mine dust concentration and compared with the prediction results of the BP neural network model, grey prediction model, and ARIMA (1, 2, 1) model. …”
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Prediction the Compaction Properties of Lateritic Soils by Hybrid ANFIS Methods by Arivalagan Pugazhendhi, Ha Manh Bui

“…The main purpose of this study is to scrutinize the applicability of using the hybrid adaptive neuro-fuzzy inference system (ANFIS) models for predicting the γdmax and ωopt related to the standard proctor compaction test of lateritic soils. …”
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Assessment of Artificial Neural Networks for Hourly Solar Radiation Prediction by Tamer Khatib, Azah Mohamed, K. Sopian, M. Mahmoud

“…FFNN and CFNN efficacies were acceptable in general, but their predictive value was degraded in poor solar radiation conditions. …”
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Development of an Injury Burden Prediction Model in Professional Baseball Pitchers by Garrett Bullock, Charles Thigpen, Gary Collins, Nigel Arden, Thomas Noonan, Michael Kissenberth, Ellen Shanley

“…Separate elbow and shoulder prediction models demonstrated decreased performance. …”
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Prediction Model of Mining Subsidence Parameters Based on Fuzzy Clustering by Fei Cheng, Jun Yang, Ziwen Zhang, Jingliang Yu, Xuelian Wang, Yongdong Wu, Zhengyi Guo, Hui Li, Meng Xu

“…In view of the inaccuracy of rock movement observation data and the inaccuracy of mining subsidence prediction parameters, a prediction model of mining subsidence parameters based on fuzzy clustering is proposed. …”
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Single Machine Predictive Scheduling Using Inserted Idle Times by Hongli Zhu, Hong Zhou

“…The predictability of the schedule is measured by the completion time deviations between the predictive schedule and realized schedule. …”
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Statistical Prediction of the South China Sea Surface Height Anomaly by Caixia Shao, Weimin Zhang, Chunjian Sun, Xinmin Chai, Zhimin Wang

“…In addition, the prediction skill of SCS SSHA has remarkable decadal variability, with better phase forecast in 1997–2007.…”
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Predicting metabolite response to dietary intervention using deep learning by Tong Wang, Hannah D. Holscher, Sergei Maslov, Frank B. Hu, Scott T. Weiss, Yang-Yu Liu

“…Existing prediction methods are typically limited to traditional machine learning models. …”
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Dynamic Search Mechanism with Threat Prediction in a GNSS Receiver by Fang Liu, Meng Liu

“…Therefore, the searches with threat prediction in GNSS signals have been regarded as the important problem for GNSS receivers. …”
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Prediction of Charging Requirements for Electric Vehicles Based on Multiagent Intelligence by Shuai Ling, Xiang Zhu, Qing Wang, Paul Schonfeld

“…The performance of the charging system is then evaluated based on three indicators, the charging request rejection rate, charging pile utilization rate, and charging load deviation, thereby verifying the effectiveness of our charging demand prediction model. This is done using multiagent-based simulations applied to the National Household Travel Survey (NHTS) 2017 dataset. …”
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Efficient Artificial Neural Network for Smart Grid Stability Prediction by Saeed Mohsen, Mohit Bajaj, Hossam Kotb, Mukesh Pushkarna, Sadam Alphonse, Sherif S. M. Ghoneim

“…From this perspective, this process is too much time consuming, thus it should predict a smart grid stability via artificial intelligence (e.g., neural networks). …”
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