Analysis of Performance Evaluation Model for Higher Education Informatization by Means of PLS-BP Model by Kunjing Dong, Dan Zhang, Xu Liu, Dan Guo

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Graphical Notation for Document Database Modeling by M. V. Smirnov, R. S. Tolmasov

“…The selected materials were analyzed and the main graphical notations used to describe the relational data model were identified. Three notations were selected from them, a set of graphic stereotypes, which were most different from each other, the analysis of which allowed us to identify the main image patterns of the components of the relational model.The resulting patterns were applied to the main elements of the document database, which were obtained by analyzing the documentation of the popular MongoDB DBMS.Results. …”
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Analysis on the Pressure Loss Law of Water Nozzles inLayered Water Injection Wells by LIU Xingbin, LI Shanwen, DENG Yuheng, HE Zhuang, JIANG Panqin, CHEN Lidong, SHEN Chen

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Improving National Forest Mapping in Romania Using Machine Learning and Sentinel-2 Multispectral Imagery by Mohamed Islam Keskes, Aya Hamed Mohamed, Stelian Alexandru Borz, Mihai Daniel Niţă

“…This study addresses these challenges by integrating machine learning algorithms with high-resolution remotely sensed data and rigorously collected ground truth measurements to produce accurate, national-scale maps of forest attributes in Romania. …”
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Prediction of Landslide Displacement Based on GreyRelational Analysis and VMD-SES-BP Model by AN Bei, JIANG Yanan, ZENG Qifei

“…Aiming at the “stepped” landslide displacement in the Three Gorges area,this paper proposes a new landslide displacement time series prediction model,namely VMD-SES-BP prediction model by combining variational mode decomposition (VMD),second exponential smoothing (SES),and BP neural network (BPNN);conducts the VMD of the GPS monitoring displacement data of landslide at Baishuihe River of the Three Gorges through this model to obtain the trend component and other sub-sequence components;makes rolling predictions of trend components by the SES,determines the influencing factors of other displacement components of the landslide through gray relational analysis (GRA),and learns and predicts by considering it as the training sample of BPNN.Comparing the prediction results of each component with the true value,the average relative error of prediction is 0.78%,the mean square error is 3.14 cm,and the correlation coefficient is 0.986.The experimental results show that the model is well applicable to the prediction of “stepped” landslide displacement,with high prediction accuracy,which provides a certain reference value for landslide displacement prediction.…”
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Poisson mixture distribution analysis for North Carolina SIDS counts using information criteria by Tyler Massaro

“…Mixture distribution analysis provides us with a tool for identifying unlabeled clusters that naturally arise in a data set.  …”
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An Improved Decline Curve Analysis Method via Ensemble Learning for Shale Gas Reservoirs by Yu Zhou, Zaixun Gu, Changyu He, Junwen Yang, Jian Xiong

“…We evaluated this method using data from 22 shale gas wells in region L, China, comparing it to six traditional DCA models, including Arps and the Logistic Growth Model (LGM). …”
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SPBO-ANFIS Model of Combined Monthly Runoff Forecasting Based on Singular Spectrum Analysis by ZHANG Yajie, CUI Dongwen

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Life Prediction Modeling Based on FOA and Interface Shapes Simulation Applicability Analysis of TBCs by Xiao Hu, Jing Tian, Yanting Ai, Yudong Yao, Tiannan Bao, Peng Guan

“…Subjective selection of simulation interface shapes may introduce errors in the strength and fatigue analysis of thermal barrier coatings (TBCs). …”
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Compressive strength prediction for sandcrete blocks with metakaolin: experiment and multiple linear regression analysis by Akintoye Olumide Oyelade, Ayandele Joshua Ayandeji, Afeez Adeniyi Yekeen, Yetunde O. Abiodun, Oluwasola Oribayo

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Impact of INSAT‐3D land surface temperature assimilation via simplified extended Kalman filter‐based land data assimilation system on forecasting of surface fields over India by Abhishek Lodh, Ashish Routray, Devajyoti Dutta, Vivek Singh, John. P. George

“…An observing system experiment (OSE) was carried out during both the summer (May) and winter (February) months by assimilating the INSAT‐3D LT data in a coupled land‐atmosphere analysis‐forecast system. …”
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Water scarcity in Hebei Province, China: A predictive model and analysis of contributing factors by Wanliang Qian, Shun Wu, Min Fang, Xiaoyan Qian

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Application of Multivariate Analysis of Broadband Transmission Spectra for Calibration of Physico-Chemical Parameters of Wines by M. A. Khodasevich, E. A. Scorbanov, M. V. Rogovaya

“…Interval methods of multivariate data analysis allow signifi reducing the root mean square calibration error in comparison with the broadband multivariate methods. …”
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Tissue gene expression analysis approach in a context of high technical and biological heterogeneity by Evan Bagley, Souvik Seal, Lauren R. Fanning, Jean-Sebastien Anoma, Tami Crawford, Benjamin G. Vincent, Elizabeth L. Barry, Martha J. Shrubsole, Erin Kirk, John A. Baron, Dale C. Snover, David N. Lewin, Todd A. Mackenzie, Xiaohua Gao, Melissa A. Troester, Alexander V. Alekseyenko, Kristin Wallace

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Estimating petrophysical properties using Geostatistical inversion and data-driven extreme gradient boosting: A case study of late Eocene McKee formation, Taranaki Basin, New Zeala... by John Oluwadamilola Olutoki, Mohamed Elsaadany, Numair Ahmed Siddiqui, AKM Eahsanul Haque, Syed Haroon Ali, Alidu Rashid, Oluwaseun Daniel Akinyemi

“…Subsequently, we estimated porosity using various seismic attributes, employing a Data-driven Extreme Gradient Boosting (Xgboost) approach within the reservoir analysis. …”
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Deep neural networks for endemic measles dynamics: Comparative analysis and integration with mechanistic models. by Wyatt G Madden, Wei Jin, Benjamin Lopman, Andreas Zufle, Benjamin Dalziel, C Jessica E Metcalf, Bryan T Grenfell, Max S Y Lau

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Comparative Performance Analysis of Optimization Algorithms in Artificial Neural Networks for Stock Price Prediction by Ekaprana Wijaya, Moch. Arief Soeleman, Pulung Nurtantio Andono

“…Performance metrics such as Mean Squared Error (MSE), Mean Absolute Error (MAE), and R Square are utilized to evaluate the effectiveness of each method. …”
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SentiTSMixer: A Specific Model for Sales Forecasting Using Sentiment Analysis of Customer by Partha Ghosh, Subhashis Das, Subhankar Roy, Ankur Bhattacharjee, Agostino Cortesi, Soumya Sen

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Genome-wide meta-analysis of myopia and hyperopia provides evidence for replication of 11 loci. by Claire L Simpson, Robert Wojciechowski, Konrad Oexle, Federico Murgia, Laura Portas, Xiaohui Li, Virginie J M Verhoeven, Veronique Vitart, Maria Schache, S Mohsen Hosseini, Pirro G Hysi, Leslie J Raffel, Mary Frances Cotch, Emily Chew, Barbara E K Klein, Ronald Klein, Tien Yin Wong, Cornelia M van Duijn, Paul Mitchell, Seang Mei Saw, Maurizio Fossarello, Jie Jin Wang, DCCT/EDIC Research Group, Ozren Polašek, Harry Campbell, Igor Rudan, Ben A Oostra, André G Uitterlinden, Albert Hofman, Fernando Rivadeneira, Najaf Amin, Lennart C Karssen, Johannes R Vingerling, Angela Döring, Thomas Bettecken, Goran Bencic, Christian Gieger, H-Erich Wichmann, James F Wilson, Cristina Venturini, Brian Fleck, Phillippa M Cumberland, Jugnoo S Rahi, Chris J Hammond, Caroline Hayward, Alan F Wright, Andrew D Paterson, Paul N Baird, Caroline C W Klaver, Jerome I Rotter, Mario Pirastu, Thomas Meitinger, Joan E Bailey-Wilson, Dwight Stambolian

“…Refractive error (RE) is a complex, multifactorial disorder characterized by a mismatch between the optical power of the eye and its axial length that causes object images to be focused off the retina. …”
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Influence of underwater light fields on pigment characteristics in the Baltic Sea - results of statistical analysis by Joanna Stoń-Egiert, Roman Majchrowski2, Mirosław Darecki, Alicja Kosakowska, Mirosława Ostrowska

“…In the case of the C_{chl <i>c</i> tot} approximations, the logarithmic statistical error is lower for Baltic waters than for case 1waters: &sigma;_ = 34.6% for Baltic data and &sigma;_ = 39.4% for ocean data. …”
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