Exploration and application of deep learning based wellbore deformation forecasting model by Hui LIU, Guoqiang LI, Xiaojun ZHU, Pengfei ZHANG, Hua CHENG, Jinzheng WANG, Peishuai LI

“…In response to the tilting and damage disasters of deep vertical shafts in thick water-bearing loose layers, the tilting and deformation monitoring of shafts was carried out by taking the deep vertical shaft (800 m) of a mine in Lunan as the research object, studying the spatial and temporal change characteristics of shaft tilting, and analyzing the main influencing factors of shaft tilting; based on this, based on the deep learning theory, four types of deep learning method, namely, recurrent neural network (RNN), long and short-term memory network (LSTM), gated recurrent unit (GRU), and one-dimensional convolutional neural network (1DCNN), were used. unit (GRU), and one-dimensional convolutional neural network (1DCNN) to construct a wellbore tilt deformation prediction model, and compare the prediction results with the measured values to analyze the accuracy of the wellbore deformation prediction model, validate the reliability of the model, studied overall wellbore and critical area prediction effects, and carry out engineering applications. The study shows that: ① The wellbore tilt mainly occurs in the loose layer, the tilt value decreases linearly from shallow to deep, and is biased towards the side of the extraction zone, with a maximum of 352 mm, and the deformation of the bedrock layer is smaller, with a maximum of 88 mm; the increase in the range of deformation propagation in the thick loose layer caused by the mining, and the change of seepage hydrophobicity of the aquifer at the bottom along the wall of the well and the seepage field of the groundwater are the main causes of the tilted deformation of the wellbore. ② The Spearman correlation coefficient between the model and the measured value is 0.978 at the maximum and 0.867 at the minimum;the maximum difference between the four models and the field measured offsets is 0.043 m, the mean absolute error EMA is within 0.003–0.009 m, and the root mean square error ERMS is within 0.004–0.011 m. …”
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Numerical Background-Oriented Schlieren for Phase Reconstruction and Its Potential Applications by Shiwei Liu, Yichong Ren, Haiping Mei, Zhiwei Tao, Shuran Ye, Xiaoxuan Ma, Ruizhong Rao

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Measurements of Periodic Signals Phase Shifts with Application of Direct Digital Synthesis by I. V. Gula, O. I. Polikarovskykh, K. I. Horiashchenko, I. V. Karpova, V. M. Melnychuk

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Exploring the Use of Mobile Health Applications in Palestinian Community Pharmacy Practice by Ahmed Nouri, PharmD, MSc

“…Adopting mobile tools can improve efficiency, reduce errors, and align pharmacy practice with modern standards, highlighting the need for future research.…”
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The Application of Kernel Ridge Regression for the Improvement of a Sensing Interferometric System by Ana Dinora Guzman-Chavez, Everardo Vargas-Rodriguez

“…Finally, the results show that by implementing KRR with a Gaussian kernel, the temperature could be estimated with a root-mean-square error of 0.094 °C for the measurement range from 4.5 to 50 °C, which indicates that it was widened by a factor of eight compared with traditional methods.…”
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Application of improved clustering algorithm in mixed teaching of modern educational technology by Lei Shu, Guirong Li

“…With advances in algorithm optimization and computational capabilities, cluster analysis holds broad potential for educational applications. Specifically, the improved clustering algorithm increased the silhouette score from 0.51 (K-means) and 0.58 (DBSCAN) to 0.68 and reduced the clustering error from 0.27 to 0.18. …”
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Application of SWAT Model for Runoff Calculation in Small Watershed of Zhejiang Province by LAN Xuechun, WANYAN Sheng, ZHANG Zhenqi

“…To verify the applicability of the SWAT model in runoff calculation in small watershed of Zhejiang province,this paper collects daily records for the past 20 years from Wenling meteorological station,topographic map,land use and soil data,and establishes the SWAT model of the Human reservoir by using the SWAT2012 module loaded with Arcgis 10.2 software.In calibration period (1999—2008),R² (correlation coefficient) is 0.89 and E_NS (Nash-Sutcliffe efficiency) is 0.88;in validation period (2009—2018),R² is 0.93 and E_NS is 0.91.With an error of 1.3%,the simulated annual average flow is 0.8 m³/s and the observed flow is 0.79 m³/s,which indicating a high accuracy of the model.The sensitivity test of input parameters shows that the SOL_AWC.sol (available water capacity of soil) and CN2.mgt (runoff coefficient of different land use) are significant to simulated results.The model can be further used for forecasting the flood process and peak flow of Human reservoir,as well as optimizing the reservoir operation scheme and formulating the protection program of water source.…”
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MZAP—Mobile Application for Basketball Match Tracking and Digitalization of Endgame Reports by Predrag Pecev, Branko Markoski

“…This paper presents MZAP, a mobile application designed to digitalize basketball match tracking and generate secure, searchable endgame reports. …”
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Comparing Outlier Detection Methods: An Application on Indonesian Air Quality Data by Anwar Fitrianto, Amalia Kholifatunnisa, Anang Kurnia

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An Application of Quasi Newton Algorithm and Improvement of Sliding Surface for Robot Control by Chi-Ngon Nguyen, Thanh Tung Pham

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Generating Automatically Print/Scan Textures for Morphing Attack Detection Applications by Juan E. Tapia, Maximilian Russo, Christoph Busch

“…Our proposed methods achieve an Equal Error Rate (EER) of 3.84% and 1.92% on the FRGC/FERET database when incorporating our synthetic and texture-transferred print/scan images at 600 dpi alongside handcrafted images, respectively.…”
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Numerical study of the singular nonlinear initial value problem with applications in astrophysics by Mahmoud Abd El-Hady, Homan Emadifar, Galal I. El-Baghdady, Atallah El-shenawy

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Data-Driven Computational Methods in Fuel Combustion: A Review of Applications by Jacek Lukasz Wilk-Jakubowski, Lukasz Pawlik, Damian Frej, Grzegorz Wilk-Jakubowski

“…This review article provides a comprehensive analysis of the recent advancements in combustion science and engineering, focusing on the application of machine learning and genetic algorithms from 2015 to 2024. …”
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Application of deep learning for rice leaf disease detection in the Mekong Delta by Duc Luu Ngo, Thi Thuy Diem Le, Thi Phuong Anh Ha

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Application of deep learning for rice leaf disease detection in the Mekong Delta by Duc Luu Ngo, Thi Thuy Diem Le, Thi Phuong Anh Ha

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Application of Homogenization Method in Free Vibration of Multi-Material Auxetic Metamaterials by Kadir Gunaydin, Orhan Gülcan, Aykut Tamer

“…It was shown that the implicit models predict the natural frequencies with an error range of less than 6.5% when compared with the explicit models in all of the mode shapes for both honeycomb and re-entrant auxetic lattices. …”
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Application of Mathematical Models for Blood Flow in Aorta and Right Coronary Artery by Monica Minniti, Vera Gramigna, Arrigo Palumbo, Gionata Fragomeni

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Advanced Applications of Artificial Intelligence in Pharmacovigilance: Current Trends and Future Perspectives by Yogesh S Ahire, Jayesh H Patil, Harshita N Chordiya, Rashmi A Deore, Vinod A Bairagi

“…Conventional approaches suffer from biases in human error, inefficiency, and scalability problems. A new era in pharmacovigilance is being ushered in by the introduction of artificial intelligence (AI), which holds the promise of vast data analysis, automated procedures, and enhanced safety signal detection. …”
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Design and Application of Full-Time Dimension Dispatching Schedule Safety Check by Di WU, Zhengfeng WANG, Hang LIU

“…For the monthly safety check, a power generation feasible domain is constructed based on the power flow section, and the generation feasible range is optimized to compensate for the error of monthly load forecast. For the weekly safety check, the generator start-up mode is determined through optimization. …”
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Application of the numerically obtained fundamental solutions in the field point-source method by Sergey Yu. Knyazev, Elena E. Shcherbakova

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