Multi-Source Data Fusion-Based Grid-Level Load Forecasting by Hai Ye, Xiaobi Teng, Bingbing Song, Kaiming Zou, Moyan Zhu, Guangyu He

“…Performance gains are particularly significant during critical peak periods, with up to 62.6% error reduction under high-temperature conditions. …”
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Inferring chromosome segregation error stage and crossover in trisomic disorders with application to Down syndrome by Zhenhua Li, Wenjian Yang, Gang Wu, Ti-Cheng Chang, Zhongshan Cheng, Meenakshi Devidas, Mary Shago, Andrew J. Carroll, Nyla A. Heerema, Julie M. Gastier-Foster, Brent L. Wood, Lauren Sanclemente, Elizabeth A. Raetz, Stephen P. Hunger, Mignon L. Loh, Eleanor Feingold, Tracie C. Rosser, Emily G. Allen, Stephanie L. Sherman, Karen R. Rabin, Philip J. Lupo, Jun J. Yang

“…Herein, we develop Mis-segregation Error Identification through Hidden Markov Models (MeiHMM), a method for inferring NDJ error stage and crossover events based on only genomic data of trisomic probands. …”
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The Investigation into Determinants of Regional Exports Base: Panel Analysis for Spanish and Polish Regions by Tomasz Brodzicki, Laura Márquez-Ramos, Stanisław Umiński

“…Research Design & Methods:We apply the Prais-Winsten method for Panel Corrected Standard Errors (PCSE) on a data panel allowing for heteroskedasticity and autocorrelation estimating a model of regional exports for a joint sample of Polish and Spanish NUTS-2 regions. …”
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COMPARING FORECASTS OF AGRICULTURAL SECTOR EXPORT VALUES USING SARIMA AND LONG SHORT-TERM MEMORY MODELS by Aleytha Ilahnugrah Kurnadipare, Sri Amaliya, Khairil Anwar Notodiputro, Yenni Angraini, Laily Nissa Atul Mualifah

“…As part of the government's vision to make Indonesia the world's food barn by 2045, increasing the volume and value of agricultural product exports is a primary focus, making export value forecasting essential for supporting strategic decision-making. Sequential data analysis is an important approach in analyzing data collected over a specific period. …”
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The MuLeCo project: A learner corpus of L1 German learners of romance languages by Stephan Lücke, Patricia de Crignis, Johanna Wolf, Florian Zacherl

“…At the core of the corpus lies the categorisation of errors. The relational database used for storing and handling the highly structured corpus data allows for multifold analysis. …”
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Simple model based on artificial neural network for early prediction and simulation winter rapeseed yield by Gniewko Niedbała

“…An important feature of the created predictive model is the ability to forecast the current agrotechnical year based on current weather and fertilizing data. The lowest value of the MAPE error was obtained for a neural network model based on the MLP network of 21:21-13-6-1:1 structure, which was 9.43%. …”
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ANALYSIS OF THE INFLUENCE OF ROMANIA’S MONETARY POLICY INTEREST RATE ON THE ANNUAL AVERAGE EURO-RON EXCHANGE RATE by NADIA ELENA STOICUȚA

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AI-Based Deep Learning of the Water Cycle System and Its Effects on Climate Change by Hasib Khan, Wafa F. Alfwzan, Rabia Latif, Jehad Alzabut, Rajermani Thinakaran

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Optimising Solar Power Plant Reliability Using Neural Networks for Fault Detection and Diagnosis by Mohammed Bouzidi, Abdelfatah Nasri, Omar Ouledali, Messaoud Hamouda

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Employing Data Mining Techniques and Machine Learning Models in Classification of Students’ Academic Performance. by Hussein, Alkattan, Alhumaima, Ali Subhi, Oluwaseun, Adelaja A., Abotaleb, Mostafa, Mijwil, Maad M., Pradeep, Mishra, Sekiwu, Denis, Bamwerinde, Wilson, Turyasingura, Benson

“…The research indicates that the use of machine learning models and data mining methods can reveal hidden patterns and relationships in big data, making them indispensable tools in the field of education analysis. …”
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Square-root lasso under correlated regressors: Tight statistical analysis with a wireless communications application by Ayed M. Alrashdi, Masad A. Alrasheedi

“…We analyzed its performance using several metrics, such as root-mean-squared error (r.m.s.e.), mean absolute error (m.a.e.), cosine similarity, and true/false recovery rates. …”
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A novel approach towards the robustness of centrality measures in networks by Jens Dörpinghaus, Vera Weil, Robert Rockenfeller, Meetkumar Pravinbhai Mangroliya

“…In general, we may assume networks to be rather robust against small errors and few missing data if they follow a scale-free distribution. …”
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Parameter Optimization of Transformer Model Based onSimulation Credibility Evaluation by YING Ting, LI Jianghong

“…The concept of simulation credibility is introduced, and the quantitative error of simulation and test data is taken as the optimization objective. …”
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Debiasing Structure Function Estimates from Sparse Time Series of the Solar Wind: A Data-driven Approach by Daniel Wrench, Tulasi N. Parashar

“…Compared to conventional gap-handling methods, our approach reduces the mean error for missing data fractions above 20%, and the overall error is reduced by nearly 50% when averaged across all missing fractions tested. …”
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Review and Evaluation of Slip-ratio-based Void Fraction Prediction Models by HE Wen, HAN Jinyu, ZHAO Chenru, LI Yanlin, BO Hanliang

“…Then, an experimental database for void fraction was established, containing 380 sets of experimental data of air-water two-phase flow. The overall performance of these slip-ratio-based void fraction models was comprehensively evaluated both based on the error analysis and the grey correlation analysis. …”

Data-driven framework for prediction of mechanical properties of waste glass aggregates concrete by Kennedy C. Onyelowe, Shadi Hanandeh, Viroon Kamchoom, Ahmed M. Ebid, Hamza Imran, Miguel Angel Duque Vaca, Greys Carolina Herrera Morales, Nestor Ulloa, Krishna Prakash Arunachalam

“…Abstract This research presents a novel data-driven framework for predicting the mechanical properties of waste glass aggregate concrete using six advanced metaheuristic optimization algorithms: Bat Algorithm (Bat), Cuckoo Search Algorithm (Cuckoo), Elephant Herding Optimization (Elephant), Firefly Algorithm (Firefly), Rhinoceros Optimization Algorithm (Rhino), and Gray Wolf Optimizer (Wolf). …”
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Enhancing hydrometric monitoring in Ethiopia's Abbay basin: A collaborative framework for Data-Scarce Africa by Ermias Teferi, Greg O'Donnell, Woldeamlak Bewket, Gete Zeleke, Claire Walsh

“…Third, we performed rigorous quality checks through statistical analysis, including outlier analysis and homogeneity tests on daily streamflow data from more than 100 stations, ensuring data reliability and integrity. …”
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QUANTITATIVE ASSESSMENT OF RELATIVE HUMIDITY, K INDEX, AND TT INDEX USING PROGRAMMATIC ANALYSIS by Indrajit Ghosh, Ananya Roy, Vanshika Gupta, Shruti Bhattacharya

“…The primary objective was to establish predictive equations that could be validated against real-time data. The models demonstrated a low Mean Squared Error (MSE) of approximately 20.69, indicating their potential as reliable tools for significant statistical assessments.…”
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Large language models generating synthetic clinical datasets: a feasibility and comparative analysis with real-world perioperative data by Austin A. Barr, Joshua Quan, Eddie Guo, Emre Sezgin, Emre Sezgin

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SpecDis: Value Added Distance Catalog for 4 Million Stars from DESI Year-1 Data by Songting Li, Wenting Wang, Sergey E. Koposov, Ting S. Li, Youjia Wu, Monica Valluri, Joan Najita, Carlos Allende Prieto, Amanda Byström, Christopher J. Manser, Jiaxin Han, Carles G. Palau, Hao Yang, Andrew P. Cooper, Namitha Kizhuprakkat, Alexander H. Riley, Leandro Beraldo e Silva, Jessica Nicole Aguilar, Steven Ahlen, David Bianchi, David Brooks, Todd Claybaugh, Axel de la Macorra, John Della Costa, Arjun Dey, Peter Doel, Jaime E. Forero-Romero, Enrique Gaztañaga, Satya Gontcho A Gontcho, Gaston Gutierrez, Klaus Honscheid, Mustapha Ishak, Stephanie Juneau, Robert Kehoe, Theodore Kisner, Anthony Kremin, Martin Landriau, Laurent Le Guillou, Michael Levi, Marc Manera, Aaron Meisner, Ramon Miquel, John Moustakas, Nathalie Palanque-Delabrouille, Will Percival, Claire Poppett, Francisco Prada, Ignasi Pérez-Ràfols, Graziano Rossi, Eusebio Sanchez, David Schlegel, Michael Schubnell, Hee-Jong Seo, Joseph Harry Silber, David Sprayberry, Gregory Tarlé, Benjamin Alan Weaver, Rongpu Zhou, Hu Zou

“…To build up an unbiased training sample, we do not apply selections on parallax error or signal-to-noise (S/N) of the stellar spectra, and instead, we incorporate parallax error into the loss function. …”
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