High‐Precision Prediction of Ionospheric TEC in the China Region Based on CMONOC High‐Resolution Data and an Auxiliary Attention Temporal Convolutional Network by Jianghe Chen, Pan Xiong, Haochen Wu, Xiaoran Zhang, Xuemin Zhang, Rongzi Chai, Ting Zhang, Kaixin Wang, Chaoyu Wang

“…Comparative analysis with multiple experiments under varying geomagnetic and solar conditions shows that the AuxATTCN model significantly outperforms traditional time‐series methods (such as ARIMA, Prophet), mainstream deep learning models (including ConvLSTM, CONGRU, and TCN), and international ionospheric models (IRI2020, NeQuick2) in terms of overall error, seasonal and diurnal variations, and prediction accuracy during geomagnetic storms and solar activity peaks. …”
Get full text

DEVELOPMENT AND STUDY OF THE OPERATION OF THE MODULE FOR DETERMINING THE ORIENTATION OF THE MANIPULATOR JOINT by Igor Nevlyudov, Sergiy Novoselov, Oksana Sychova

“…The following methods used are: experimental research was conducted on a real object - a model of the manipulator joint, created using methods and tools of 3D prototyping; to determine the position of the joint of the manipulator used methods of processing signals received from sensors; processing of experimental results and calculation of values of errors of positioning of a joint of the manipulator is based on methods of the statistical analysis of random sizes. …”
Get full text

Integration of Machine Learning and Wavelet Algorithms for Processing Probing Signals: An Example of Oil Wells by Zukhra Abdiakhmetova, Zhanerke Temirbekova

“…The evaluation performed using R-square and root mean square error to validate the proposed approach revealed values of 0.887 and 0.0091. …”
Get full text

Bus Network Adjustment Pre-Evaluation Based on Biometric Recognition and Travel Spatio-Temporal Deduction by Qingbo Wei, Nanfeng Zhang, Yuan Gao, Cheng Chen, Li Wang, Jingfeng Yang

“…The method includes stage of travel demand analysis, accessible path set calculation, passenger assignment, and evaluation of key indicators. …”
Get full text

Evaluating the Effectiveness of Artificial Intelligence in Prostate Cancer Detection using Biparametric Magnetic Resonance Imaging: A Comparative Study by Rossy Vlăduţ TEICĂ, Ioana Andreea GHEONEA

“…Error distribution included 36% false-negative findings, 21% false-positive findings, 20% PI-RADS (Prostate Imaging-Reporting and Data System) overestimations, and 23% PI-RADS underestimations. …”
Get full text

Integrated pixel-level crack detection and quantification using an ensemble of advanced U-Net architectures by Rakshitha R, Srinath S, N Vinay Kumar, Rashmi S, Poornima B V

“…By using Euclidean distance along skeletal paths, the algorithm minimized error rates in length and width estimation. This framework provides a scalable and efficient solution for automated pavement crack analysis. …”
Get full text

ASM-SS: the first quasi-global high-spatial-resolution coastal storm surge dataset reconstructed from tide gauge records by L. Yang, T. Jin, T. Jin, W. Jiang, W. Jiang

“…For annual maximum SSs, it is more stable than the numerical model, with the overall root mean square error and coefficient of determination optimizing by 22.3 % and 14.8 %, respectively. …”
Get full text

A mathematical phase field model predicts superparamagnetic nanoparticle accelerated fusion of HeLa spheroids for field guided biofabrication by Cristian F. Rodríguez, Valentina Quezada, Paula Guzmán-Sastoque, Juan Camilo Orozco, Luis H. Reyes, Johann F. Osma, Carolina Muñoz-Camargo, Juan C. Cruz

“…Increased system complexity introduced slightly higher error variability, but the model maintained robust predictive capabilities. …”
Get full text

Strategies for Soil Salinity Mapping Using Remote Sensing and Machine Learning in the Yellow River Delta by Junyong Zhang, Xianghe Ge, Xuehui Hou, Lijing Han, Zhuoran Zhang, Wenjie Feng, Zihan Zhou, Xiubin Luo

“…Notably, under Strategy IX, the SVR model achieved the best predictive performance, with a coefficient of determination (R²) of 0.62 and a root mean square error (RMSE) of 0.38 g/kg. Analysis based on SHapley Additive exPlanations (SHAP) values and feature importance indicated that Vegetation Type Factors contributed significantly and consistently to the model’s performance, maintaining higher importance than traditional salinity indices and playing a dominant role. …”
Get full text

Machine learning predicts improvement of functional outcomes in spinal cord injury patients after inpatient rehabilitation by Mohammad Rasoolinejad, Irene Say, Peter B. Wu, Xinran Liu, Yan Zhou, Yan Zhou, Nathan Zhang, Emily R. Rosario, Daniel C. Lu, Daniel C. Lu, Daniel C. Lu

“…The ability to accurately predict functional outcomes for SCI patients is essential for optimizing rehabilitation strategies, guiding patient and family decision making, and improving patient care.MethodsWe conducted a retrospective analysis of 589 SCI patients admitted to a single acute rehabilitation facility and used the dataset to train advanced machine learning algorithms to predict patients' rehabilitation outcomes. …”
Get full text

Model description of combined numerical and stochastic groundwater flow in Bandung-Soreang Groundwater Basin, West Java, Indonesia by Achmad Darul, Dasapta Erwin Irawan, Prihadi Sumintadireja, Gumilar Ramadhan

“…This diverse dataset provided a robust foundation for the analysis. Hydraulic conductivity (K) was estimated using the ordinary kriging method, a geostatistical technique that allows for optimal interpolation based on regression against observed values of surrounding data points. • Analyses: This study examines K distribution using a block model, employing finite difference modeling with an structured grid 0.1 km². …”
Get full text

Fuzzy-FMECA: Right Solution for Jet Dyeing Machine Damage Prevention by Tiaradia Ihsan, Didit Damur Rochman, Rendiyatna Ferdian

“…The dominant machine failures identified in jet dyeing components are leakage, short circuits, and installation errors. The Pareto analysis shows that leaks, tears, and short circuits are responsible for over 70% of total failures. …”
Get full text

Impact of multiple radar wind profiler data assimilation on convective-scale short-term rainfall forecasts: OSSE studies over the Beijing–Tianjin–Hebei region by J. Zhao, J. Guo, X. Zheng

“…A detailed examination of the 21 July 2023 case reveals that the FH_RD experiment generally exhibits more skillful storm forecasts in terms of areal coverage, storm mode, and orientation, benefiting from refined mesoscale wind analysis. Particularly, in the RD experiment, RWP data assimilation notably reduces wind errors and improves the representation of mesoscale atmospheric features near the Taihang Mountains upstream of Beijing, crucial for convective initiation (CI). …”
Get full text

A Near-Infrared Imaging System for Robotic Venous Blood Collection by Zhikang Yang, Mao Shi, Yassine Gharbi, Qian Qi, Huan Shen, Gaojian Tao, Wu Xu, Wenqi Lyu, Aihong Ji

“…Results show that, compared to U-Net, the BYOL+U-Net+ResNet18 method achieves an 8.31% reduction in Binary Cross-Entropy (BCE), a 5.50% reduction in Hausdorff Distance (HD), a 15.95% increase in Intersection over Union (IoU), and a 9.20% increase in the Dice coefficient (Dice), indicating improved image segmentation quality. The average error of the optimized AD-Census stereo matching algorithm is reduced by 25.69%, and the improvement of the image stereo matching performance is more obvious. …”
Get full text

A multi-level, multi-scale comparison of LiDAR- and LANDSAT-based habitat selection models of Mexican spotted owls in a post-fire landscape by Ho Yi Wan, Michael A. Lommler, Samuel A. Cushman, Jamie S. Sanderlin, Joseph L. Ganey, Andrew J. Sánchez Meador, Paul Beier

“…Both models had low out-of-bag (OOB) error rates (0.037 for LANDSAT and 0.050 for LiDAR), indicating high classification reliability. …”
Get full text

FL-QNNs: Memory Efficient and Privacy Preserving Framework for Peripheral Blood Cell Classification by Meenakshi Aggarwal, Vikas Khullar, Nitin Goyal, Bhavani Sankar Panda, Hardik Doshi, Nafeesh Ahmad, Vivek Bhardwaj, Gaurav Sharma

“…Consequently, blood analysis enables clinicians to evaluate a person’s physiological state. …”
Get full text

Enhancing Arabic Language Education through Virtual Education: Validation of a Teaching Model in Iraqi Secondary Schools by Abbas Taher Allawi Gharabat, Nasrolah Ghashghaeizadeh, Jalal Shanta Jaber, Faranak Mosavi

“…Qualitative data were analyzed using inductive content analysis, and a significance level (p-value) of 0.05 was applied to determine statistical significance. …”
Get full text

Breast lesion classification via colorized mammograms and transfer learning in a novel CAD framework by Abbas Ali Hussein, Morteza Valizadeh, Mehdi Chehel Amirani, Sedighe Mirbolouk

“…As a result, Computer-Aided Design (CAD) systems have become increasingly popular due to their ability to operate independently of human analysis. Current CAD systems use grayscale analysis, which lacks the contrast needed to differentiate benign from malignant lesions. …”
Get full text

Predicting PTSD and complex PTSD from interpersonal violence in Japanese school-based extracurricular sports activities: using the International Trauma Questionnaire (ITQ) by Hayato Toyoda, Hayato Toyoda, Katsuhiko Ishikawa, Yasuhiro Omi

“…The ITQ was examined using confirmatory factor analysis with maximum likelihood with robust standard errors, fit indices comparisons, a graded response model, differential item functioning, and rank correlation designs. …”
Get full text

CNN Based Fault Classification and Predition of 33kw Solar PV System with IoT Based Smart Data Collection Setup by K. Punitha, G. Sivapriya, T. Jayachitra

“…These faults can arise from a variety of factors, including environmental conditions, manufacturing defects, installation errors, and wear and tear of the components. Fault diagnosis in solar PV systems involves the detection, identification, and rectification of faults or abnormalities that can occur due to various reasons. …”
Get full text