Trend Models for Analysis of Socio-Economic Security by D. N. Shvaiba

“…Various approaches can be used to calculate a magnitude of the forecast error. Thus, a question pertaining to selection of trend models for an analysis of socio-economic security is natural due to difference in reliability of data when using different models, and correctness of the selection will improve an efficiency of the analysis. …”
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Leveraging moisture elimination and hybrid deep learning models for soil organic carbon mapping with multi-modal remote sensing data by Yilin Bao, Xiangtian Meng, Weimin Ruan, Huanjun Liu, Mingchang Wang, Abdul Mounem Mouazen

“…Results indicate that (1) the proposed paradigm achieves optimal SOC content prediction accuracy in humid regions, with a root mean square error (RMSE) of 3.58 g kg−1, a coefficient of determination (R2) of 0.76, a ratio of performance to interquartile distance (RPIQ) of 2.26, and a mean absolute error (MAE) of 4.73 g kg−1. …”
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Generating Seamless Three-Dimensional Maps by Integrating Low-Cost Unmanned Aerial Vehicle Imagery and Mobile Mapping System Data by Mohammad Gholami Farkoushi, Seunghwan Hong, Hong-Gyoo Sohn

“…The spatial accuracy of the 3D model was improved by integrating the matched features as ground control points into a structure from the motion pipeline. Validation using data from the campus of Yonsei University and the nearby urban area of Yeonhui-dong yielded notable accuracy gains and a root mean square error of 0.131 m. …”
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Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods by Huifang Wang, Min Wang, Pan Jiang, Fanshu Ma, Yanhu Gao, Xinchen Gu, Qingzu Luan

“…The satellite remote sensing of Aerosol Optical Depth (AOD) products is crucial in environmental monitoring and atmospheric pollution research. However, data gaps in AOD products from satellites like Fengyun significantly hinder continuous, seamless environmental monitoring capabilities, posing challenges for the long-term analysis of atmospheric pollution trends, responses to sudden ecological events, and disaster management. …”
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Prediction of instability of formwork concrete pier based on big data machine learning for secondary mining without coal pillar mining by Yanhui Zhu, Ye Tian, Peilin Gong, Kang Yi, Tong Zhao

“…Through field research, numerical simulation, theoretical analysis, big data machine learning, and field testing, the stress migration patterns and destabilization mechanisms of flexible formwork concrete pier columns under secondary mining conditions were investigated. …”
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Forecasting Delivery Time of Goods in Supply Chains Using Machine Learning Methods by V. K. Rezvanov, O. M. Romakina, E. V. Zaytseva

“…The open data set DataCo Smart supply chain for big data analysis on deliveries in online trade was used. …”
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Bite mark analysis: A brief overview by Reshma Priyanka Danam, Mary Sujatha Mekala

“…Despite these challenges, the field presents opportunities for improvement through advancements in technology, such as three-dimensional imaging and computer-aided analysis, which can enhance the precision of bite mark comparisons and reduce error rates. …”
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Applying machine learning to gauge the number of women in science, technology, and innovation policy (STIP): a model to accommodate missing data by Caitlin Meyer, Du Baogui, Mohamed Amin Gouda

“…This study addresses this gap by developing a comprehensive machine learning framework to accurately measure and predict women’s representation in STIP while accounting for missing domestic data. Using data from 60 countries, we implemented hybrid machine learning models—including Linear Regression, ElasticNet, Lasso Regression, and Ridge Regression, and Support Vector Regression—to forecast women’s representation in STIP. …”
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Quantitative research on aesthetic value of the world heritage karst based on UGC data: A case study of Huangguoshu Scenic Area. by Xi Zhao, Kangning Xiong, Meng Zhang

“…However, there is still a gap in the evaluation of natural landscape aesthetic value based on UGC(User Generated Content) data and deep learning models. This article is based on a public perspective, using social media UGC data, crawling images and texts as data sources, and combining SegFormer deep learning models, ArcGIS spatial analysis, natural Language Processing Technology (NLP) and other methods to conduct quantitative research on aesthetic value. …”
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A Data Reconstruction Method for Inspection Mode in GBSAR Monitoring Using Sage–Husa Adaptive Kalman Filtering and RTS Smoothing by Yaolong Qi, Jialiang Guo, Jiaxin Hui, Ting Hou, Pingping Huang, Weixian Tan, Wei Xu

“…The problem of missing data in the inspection mode not only destroys the spatial and temporal continuity of the data but also affects the accuracy of the subsequent deformation analysis. …”
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Canopy height mapping in French Guiana using multi-source satellite data and environmental information in a U-Net architecture by Kamel Lahssini, Nicolas Baghdadi, Guerric le Maire, Guerric le Maire, Ibrahim Fayad, Ibrahim Fayad, Ludovic Villard

“…However, challenges remain, particularly in characterizing tall canopies, as our CHNET model exhibits a tendency to underestimate canopy heights greater than 35 m. A large part of this error arises from the use of GEDI measurements as reference, given the fact these data exhibit certain saturation in tropical biomes. …”
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Estimation of Potential Evapotranspiration across Sri Lanka Using a Distributed Dual-Source Evapotranspiration Model under Data Scarcity by Udara Senatilleke, Himasha Abeysiriwardana, Randika K. Makubura, Faisal Anwar, Upaka Rathnayake

“…The country was divided into a grid with 6km×6km cells. The meteorological data, including rainfall, temperature, relative humidity, wind speed, net solar radiation, and pan evaporation, for 14 meteorological stations were used in this analysis. …”
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A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors by Nahshon Obiri, Kristof van Laerhoven

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THE ANALYSIS OF METHODS OF RECEPTION OF LONGITUDINAL PROFILE OF ROADS by V. G. Mikhailov

“…So in case of use of the popular data of radar scanning of the earth from companions the error can reach 30–50%. …”
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Analysis of space labeling through binary fingerprinting by Marouan Mizmizi, Luca Reggiani

“…Here, it developed the performance estimation, exploiting the association of this deployment to an error correcting code. The analysis and numerical and experimental results allow a deeper understanding of the impact of the proposed solution and show that it is robust and computationally efficient with respect to the traditional fingerprinting technique.…”
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Retrieval of NO2 Columns by Exploiting MAX-DOAS Observations and Comparison with OMI and TROPOMI Data during the Time Period of 2015–2019 by Ahmad Iqbal, Naveed Ahmad, Hassan Mohy ud Din, Michel Van Roozendael, Muhammad Shehzaib Anjum, Muhammad Zeeshan Ali Khan, Muhammad Fahim Khokhar

“…The error analysis indicates that for TROPOMI measurements calculated biases are significantly improved in case of TROPOMI as compared to OMI measurements. …”
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Monitoring multiyear cropping patterns in the arid regions of northwestern China from 2005 to 2022 using Landsat time-series data by Yongquan Lu, Guilin Liu, Xiuyuan Zhang, Jingwen Wang, Weijia Chen, Yuecheng Li, Desheng Jiang

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Model building and analysis of S-type flexible hinges by YU Mingwei, LI Zongxuan, ZHANG Defu, LI Qingya, XU Jiakun

“…The translational compliance of the samples was then measured using finite element analysis and an experimental testing platform.ResultsThe results demonstrate good agreement between the analytical values, simulation values, and experimental measurement data, with a maximum relative error of 7.16%. …”
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On The Pros and Cons of Using Excel for Regression Analysis by Sencer Buzrul

“…It is also possible to visually examine the model fit and experimental data together on the same graph. For linear models (linear in parameters) Excel Add-In Data Analysis-Regression tool creates a summary output, and parameter estimates, parameter uncertainties, adjusted R2 (R2adj) and root mean square error (RMSE) values can be found even for the models that do not exist in Excel. …”
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Paid Services to Population of the Russian Regions by Boris Ivanovich Alekhin

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