Accuracy Evaluation of Multi-Source Precipitation Data in Mountain Flood Simulation in Guoning Village， Xiahe County by Wubin HUANG, Jing FU, Runxia GUO, Junxia ZHANG, Yu LEI

“…From 22:00 on September 6， 2023 to 04:00 （Beijing Time） on September 7， Xiahe County in Gansu Province experienced severe convective weather， with short-term heavy rainfall in some areas， causing flash floods in Guoning Village， Xiahe County， resulting in casualties.In this study， the characteristics of Radar Quantitative Precipitation Estimation （Radar-QPE）， FengYun 4B Quantitative Precipitation Estimation （FY4B-QPE）， and CMA Multi-source Precipitation Analysis （CMPA） precipitation products were contrastive analyzed based on meteorological station observations.These precipitation data were used to drive the hydrodynamic hydrological model and evaluate the effect of different precipitation data in the flash flood simulation.The results showed that： （1） Among the 12-hour cumulative precipitation amounts， CMPA demonstrated higher accuracy in terms of the position of large value areas and differences in local precipitation levels； Radar-QPE was closer to AWS （Automatic Weather Station） in terms of cumulative precipitation level but showed significant differences in spatial distribution； FY4B-QPE overestimated the cumulative precipitation level by 33.8%.（2） In terms of hourly distribution， CMPA was most similar to AWS in terms of temporal evolution， spatial distribution， and precipitation level； Radar-QPE's peak values were smaller， and the peak times were lagged， with negative deviations in precipitation being dominant； FY4B-QPE's peak values and peak times were consistent with reality， but there were deviations in the start and end times of precipitation， with positive deviations in precipitation being dominant.（3） In the hydrological simulation study， CMPA， Radar-QPE， and FY4B-QPE all overestimated water levels， but the timing of water level peaks was more consistent with AWS.CMPA performed best in terms of RMSE （Root Mean Square Error）， NSE （Nash Efficiency Coefficient）， and Bias （Relative Deviation）， followed by Radar-QPE， and FY4B-QPE performed relatively poorly.Although existing site-observed precipitation cannot fully meet the needs of research and early warning for small and medium scale mountain floods， the high precision of CMPA data could effectively supplement the deficiencies of traditional meteorological observation stations to some extent.Meanwhile， the algorithms and accuracy of Radar-QPE and FY4B-QPE needed to be further improved and enhanced.…”
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A Data-Driven Approach to Engineering Instruction: Exploring Learning Styles, Study Habits, and Machine Learning by Lauren Genith Isaza Dominguez, Antonio Robles-Gomez, Rafael Pastor-Vargas

“…Several machine learning models, including Random Forest and Voting Ensemble, were tested to predict academic performance using study behavior data. Voting Ensemble was found to be the strongest model, explaining 83% of the variance in final exam scores, with a mean absolute error of 3.18. …”
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POSSIBILITIES OF CARIES PROGNOSIS IN CHILDREN OF SCHOOL-AGE ACCORDING TO DATA GAINED FROM THEM AND THEIR PARENTS QUESTIONNAIRE by L.F. Kaskova, T.B. Mandziuk, L.P. Ulasevych, L.D. Korovina, M.A. Sadovski

“…The correlation coefficient was considered significant in the case of of error probability “p <0,05”. Discriminant analysis was conducted in order to determine the factors of classification. …”
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Advice on better utilization of validation data to adjust odds ratios for differential exposure misclassification (recall bias) by Igor Burstyn, George Luta

“…Applying Quantitative Bias Analysis to Epidemiologic Data: Springer; 2021. 4. …”
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The Radius of the High-mass Pulsar PSR J0740+6620 with 3.6 yr of NICER Data by Tuomo Salmi, Devarshi Choudhury, Yves Kini, Thomas E. Riley, Serena Vinciguerra, Anna L. Watts, Michael T. Wolff, Zaven Arzoumanian, Slavko Bogdanov, Deepto Chakrabarty, Keith Gendreau, Sebastien Guillot, Wynn C. G. Ho, Daniela Huppenkothen, Renee M. Ludlam, Sharon M. Morsink, Paul S. Ray

“…We report an updated analysis of the radius, mass, and heated surface regions of the massive pulsar PSR J0740+6620 using Neutron Star Interior Composition Explorer (NICER) data from 2018 September 21 to 2022 April 21, a substantial increase in data set size compared to previous analyses. …”
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Statistical Analysis of Industrial Processes by T. I. Chepeleva, A. N. Chepelev

“…Statistical multidimensional analysis of complex production data allows to analyze the work of individual units, production blocks. …”
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Bias in regression coefficient estimates when assumptions for handling missing data are violated: a simulation study by Sander MJ van Kuijk, Wolfgang Viechtbauer, Louis L Peeters, Luc Smits

“…The study focused on the point estimation of regression coefficients and standard errors.</p><p><strong>Results</strong></p><p>When data were MAR conditional on Y, CC analysis resulted in biased regression coefficients; they were all underestimated in our scenarios. …”
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Assimilation of Doppler Radar Data and Its Impact on Prediction of a Heavy Meiyu Frontal Rainfall Event by Hongli Li, Xiangde Xu, Yang Hu, Yanjiao Xiao, Zhibin Wang

“…Radial velocity data are analyzed through the LAPS wind analysis-based successive correction method. …”
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Improving of the Generation Accuracy Forecasting of Photovoltaic Plants Based on <i>k</i>-Means and <i>k</i>-Nearest Neighbors Algorithms by P. V. Matrenin, A. I. Khalyasmaa, V. V. Gamaley, S. A. Eroshenko, N. A. Papkova, D. A. Sekatski, Y. V. Potachits

“…For the best case, the error in forecasting of a photovoltaic plant generation an hour ahead was 9 %.…”
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Visualization of large-scale user association feature data based on a nonlinear dimensionality reduction method by Nong Linlin

“…The prosperity of data science and the booming growth of the internet industry have made the analysis and processing of large-scale user-related feature data a particularly important issue in modern society. …”
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Nowcasting reported covid-19 hospitalizations using de-identified, aggregated medical insurance claims data. by Xueda Shen, Aaron Rumack, Bryan Wilder, Ryan J Tibshirani

“…We propose, implement, and evaluate a method for nowcasting the daily number of new COVID-19 hospitalizations, at the level of individual US states, based on de-identified, aggregated medical insurance claims data. Our analysis proceeds under a hypothetical scenario in which, during the Delta wave, states only report data on the first day of each month, and on this day, report COVID-19 hospitalization counts for each day in the previous month. …”
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FuXi-DA: a generalized deep learning data assimilation framework for assimilating satellite observations by Xiaoze Xu, Xiuyu Sun, Wei Han, Xiaohui Zhong, Lei Chen, Zhiqiu Gao, Hao Li

“…In this study, we introduce FuXi-DA, a generalized DL-based DA framework for assimilating satellite observations. By assimilating data from Advanced Geosynchronous Radiation Imager aboard Fengyun-4B, FuXi-DA consistently mitigates analysis errors and significantly improves forecast performance. …”
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Enhanced wheat yield prediction through integrated climate and satellite data using advanced AI techniques by Muhamad Ashfaq, Imran Khan, Rana Fezan Afzal, Dilawar Shah, Shujaat Ali, Muhammad Tahir

“…Among the tested models, those capable of capturing spatial and temporal patterns reduced prediction errors most effectively. These findings demonstrate the value of integrating environmental data and AI methods for enhancing crop yield forecasting across complex agricultural regions. …”
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Fast and accurate imputation of genotypes from noisy low-coverage sequencing data in bi-parental populations. by Cécile Triay, Alice Boizet, Christopher Fragoso, Anestis Gkanogiannis, Jean-François Rami, Mathias Lorieux

“…., the crossovers), and minimized mapping intervals for quantitative-trait locus analysis. The main issues with these low-coverage genotyping methods are (1) poor performance at heterozygous loci, (2) high percentage of missing data, (3) local errors due to erroneous mapping of sequencing reads and reference genome mistakes, and (4) global, technical errors inherent to NGS itself. …”
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AI for Data Quality Auditing: Detecting Mislabeled Work Zone Crashes Using Large Language Models by Shadi Jaradat, Nirmal Acharya, Smitha Shivshankar, Taqwa I. Alhadidi, Mohammad Elhenawy

“…Ensuring high data quality in traffic crash datasets is critical for effective safety analysis and policymaking. …”
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Data Governance Design for Optimization of Hospital Management Information System (SIM-RS) at ABC Regional Hospital by Muhammad Furqan Nazuli, Irfan Walhidayah, Neng Ayu Herawati, Lenny Putri Yulianti, Kridanto Surendro

“…A qualitative approach with a case study method was employed, involving interviews and document analysis to identify key challenges in data management. …”
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A Comparative Study of K-means Clustering Algorithms Using Euclidean and Manhattan Distance for Climate Data. by Bakhshan Hamad

“…The K-means clustering algorithms (Random, K-means++, Canopy, and Farthest First) are unsupervised machine learning techniques designed to group data points based on their similarities. The study examined the effects of clustering algorithms and distance metrics on climate data analysis from meteorological stations in the Kurdistan Region of Iraq (2020&ndash;2022). 8-attribute dataset with 1,095 cases was clustered using Random, K-means++, Canopy, and Farthest First methods, evaluated with Euclidean and Manhattan distance metrics via the WEKA tools, which is a versatile and accessible open-source tool for machine learning and data mining. …”
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Sky Temperature Forecasting in Djibouti: An Integrated Approach Using Measured Climate Data and Artificial Neural Networks by Hamda Abdi, Abdou Idris, Anh Dung Tran Le

“…A systematic comparison of known correlations for sky temperature estimation under various climatic conditions revealed their limited accuracy in the region, as indicated by low R² values and root mean square errors (RMSEs). To address these limitations, an ANN model was trained, validated, and tested on the collected data to capture complex patterns and relationships in the data. …”
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Sea Surface Wind Speed Retrieval Using Gaofen-3-02 SAR Full Polarization Data by Kuo Zhang, Yuxin Hu, Junxin Yang, Xiaochen Wang

“…The results showed a bias and root mean square errors (RMSEs) of 0.02 m/s and 1.36 m/s, respectively, when compared to the European Centre for Medium-Range Weather Forecasts (ECMWF) Reanalysis V5 (ERA5) data. …”
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Reversible Adversarial Examples with Minimalist Evolution for Recognition Control in Computer Vision by Shilong Yang, Lu Leng, Ching-Chun Chang, Chin-Chen Chang

“…As artificial intelligence increasingly automates the recognition and analysis of visual content, it poses significant risks to privacy, security, and autonomy. …”
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