DIGITAL TOOLS FOR MATCHING QUALIFICATIONS TO THE LEVELS OF THE NATIONAL QUALIFICATIONS FRAMEWORK by Volodymyr Kovtunets, Sergiy Londar, Serhii Melnyk, Oles Kovtunets

“…Therefore, some simple approximation methods for eigenvector computing may be applied using only minimal means of Microsoft Excel or analogous applications. …”
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A Novel Method for Describing Texture of Scar Collagen Using Second Harmonic Generation Images by Guannan Chen, Gaoqiang Liu, Xiaoqin Zhu, Mingyu Liu, Encai Zhang, Jichun Li, Kun Zhang, Lihang Lin

“…Our proposed LOTP method requires less computation time than the extension of LTP and describes SHG images with higher accuracy compared to existing algorithms.…”
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Detection of litchi fruit maturity states based on unmanned aerial vehicle remote sensing and improved YOLOv8 model by Changjiang Liang, Changjiang Liang, Dandan Liu, Dandan Liu, Weiyi Ge, Weiyi Ge, Wenzhong Huang, Wenzhong Huang, Yubin Lan, Yubin Lan, Yubin Lan, Yongbing Long, Yongbing Long, Yongbing Long, Yongbing Long

“…The YOLOv8-FPDW model integrated FasterNet, ParNetAttention, DADet, and Wiou modules, achieving a mean average precision (mAP) of 87.7%. The weight, parameter count, and computational load of the model were reduced by 17.5%, 19.0%, and 9.9%, respectively. …”
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Quantitative Analysis of Structural Parameters Importance of Helical Temperature Microfiber Sensor by Artificial Neural Network by Juan Liu, Minghui Chen, Hang Yu, Jinjin Han, Hongyi Jia, Zhili Lin, Zhijun Wu, Jixiong Pu, Xining Zhang, Hao Dai

“…With the assistance of the evaluation algorithms based on the well-performed backpropagation neural network (BPNN), we quantitatively analyze the importance of the structural parameters of the supported helical microfiber (HMF) temperature sensor. …”
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Association between hemoglobin glycation index and the risk of cardiovascular disease in early-stage cardiovascular-kidney-metabolic syndrome: evidence from the China health and re... by Huiyi Liu, Shuai Mao, Shuai Mao, Yunzhang Zhao, Yunzhang Zhao, Lisha Dong, Lisha Dong, Yifan Wang, Yifan Wang, Chao Lv, Tong Yin, Tong Yin

“…Extreme gradient boosting (XGBoost) algorithm was applied, with the Shapley additive explanation (SHAP) method used to determine feature importance. …”
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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 RF model exhibited the highest predictive accuracy, with an R-squared value of 0.90 and a Mean Squared Error (MSE) of 0.29 on the training dataset, while achieving 0.52 R-squared and 1.37 MSE on the test dataset. …”
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Genome-wide identification and expression analysis of phytochrome gene family in Aikang58 wheat (Triticum aestivum L.) by Zhu Yang, Zhu Yang, Wenjie Kan, Wenjie Kan, Ziqi Wang, Caiguo Tang, Yuan Cheng, Yuan Cheng, Dacheng Wang, Dacheng Wang, Yameng Gao, Lifang Wu, Lifang Wu

“…The cis-acting element analysis indicates that the promoter regions of TaAkPHY genes contain a large number of CAT-box, CGTCA-motif, GC-motif, etc., which are mainly involved in plant development, hormone response, and stress response. …”
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Detecting Temporal Trends in Straw Incorporation Using Sentinel-2 Imagery: A Mann-Kendall Test Approach in Household Mode by Jian Li, Weijian Zhang, Jia Du, Kaishan Song, Weilin Yu, Jie Qin, Zhengwei Liang, Kewen Shao, Kaizeng Zhuo, Yu Han, Cangming Zhang

“…Regions A, B, C, and D exhibited SI rates of 34.76%, 33.05%, 57.88%, and 22.08%, respectively, with SI mainly concentrated in the eastern area of Gongzhuling City. …”
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Influence of micro-topography on the spatial heterogeneity of above-ground biomass: Lessons from the Qinglan Port Mangrove Nature Reserve, China by Meihuijuan Jiang, Penghua Qiu, Dezhi Wang, Minghui Wu, Ruiquan Lai, Xinqing Zou, Tingting Si, Hui Li, Qidong Shi, Yi Lin, Genzong Xie, Yanli Yang, Siang Wan

“…We employed the random forest algorithm to map the spatial distribution of mangrove above-ground biomass (AGB), and used geographic detectors and structural equation model (SEM) to study micro-topography’s impact on AGB spatial differentiation. …”
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Promising methods of prenatal diagnostics based on passive sensors and machine learning by A. A. Ivshin, V. M. Vorobyova, N. A. Malyshev

“…A key component of such technologies is the use of artificial intelligence for signal processing and interpreting, which increases the accuracy and monitoring information content. The main problem is generation of effective data processing algorithms for their accurate and unambiguous interpretation. …”
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Development of an ensemble prediction model for acute graft-versus-host disease in allogeneic transplantation based on machine learning by Lin Song, Xingwei Wu, Mengjia Xu, Ling Xue, Xun Yu, Zongqi Cheng, Chenrong Huang, Liyan Miao

“…Meanwhile, correlation analysis and recursive feature elimination were used for feature screening before machine learning model development. Then fifteen algorithms were used to establish models, and an ensemble model was established through soft voting based on the top five performance algorithms. …”
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Standardized conversion model for retinal thickness measurements between spectral-domain and swept-source optical coherence tomography based on machine learning by Zhongping Tian, Yinning Guo, Xi Chen, Qifeng Zhou, Yuan Liu, Zhizhu Yi, Li Zhang, Li Zhang

“…Machine learning-derived conversion algorithms significantly improve cross-device comparability, offering a robust standardization framework for multicenter research and longitudinal data integration. …”
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A Novel Forest Dynamic Growth Visualization Method by Incorporating Spatial Structural Parameters Based on Convolutional Neural Network by Linlong Wang, Huaiqing Zhang, Kexin Lei, Tingdong Yang, Jing Zhang, Zeyu Cui, Rurao Fu, Hongyan Yu, Baowei Zhao, Xianyin Wang

“…The results show that: first, spatial structural parameters C and U have a certain contribution to the forest growth, and C and U can explain 21.5&#x0025;, 15.2&#x0025;, and 9.3&#x0025; of the variance in DBH, H, and CW growth models, respectively; second, CNN model outperformed machine learning algorithms SVR, MARS, Cubist, RF, and XGBoost in terms of prediction performance; third, based on FDGVM-CNN-SSP, we simulated Chinese fir plantations at individual tree level and stand level from 2018 to 2022 and found that DBH and H&#x0027;s fitting performance in measured and predicted data was highly consistent with <italic>R</italic>² and root-mean-square error (RMSE) of 86.8&#x0025;, 2.06 cm in DBH and 79.2&#x0025;, 1.11 m in H, but CW&#x0027;s <italic>R</italic>² and RMSE of 72.2&#x0025;, 0.65 m caused crowding (C) inconsistency.…”
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Comparison of pulse pressure and stroke volume variations measured by three monitors in high-risk surgical patients by Barbora Cenková, Miloš Chobola, Vladimír Šrámek, Michal Šitina, Pavel Suk

“…Introduction: Dynamic indices of fluid responsiveness (FR) such as pulse pressure variation (PPV) and stroke volume variation (SVV) differ among hemodynamic monitors, which use proprietary algorithms, and vary even over a short period of time. …”
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γ‐Glutamyl Transferase and Long‐Term Survival in the SYNTAXES Trial: Is It Just the Liver? by Kai Ninomiya, Patrick W. Serruys, Scot Garg, Shigetaka Kageyama, Nozomi Kotoku, Shinichiro Masuda, Pruthvi C. Revaiah, Neil O'leary, Arie Pieter Kappetein, Michael J. Mack, David R. Holmes, Piroze M. Davierwala, Friedrich W. Mohr, Daniel J. F. M. Thuijs, Yoshinobu Onuma

“…Background Recently, machine learning algorithms have identified preprocedural γ‐glutamyl transferase (GGT) as a significant predictor of long‐term mortality after coronary revascularization in the SYNTAX (Synergy Between PCI [Percutaneous Coronary Intervention] With Taxus and Cardiac Surgery) trial. …”
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DMP-PUNet: A novel network for two-dimensional InSAR phase unwrapping under severe noise and complex fringes conditions by Yu Chen, Shuai Wang, Yandong Gao, Yanjian Sun, Jinqi Zhao, Kun Tan, Peijun Du

“…This simulation, combined with quasi-real interferometric phase data obtained from DEM inversion algorithms, forms the comprehensive training dataset. …”
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Predicting climate-driven shift of the East Mediterranean endemic Cynara cornigera Lindl by Heba Bedair, Heba Bedair, Yehia Hazzazi, Asmaa Abo Hatab, Marwa Waseem A. Halmy, Mohammed A. Dakhil, Mohammed A. Dakhil, Mubaraka S. Alghariani, Mubaraka S. Alghariani, Mari Sumayli, A. El-Shabasy, Mohamed M. El-Khalafy

“…Our analysis involved inclusion of bioclimatic variables, in the SDM modeling process that incorporated five algorithms: generalized linear model (GLM), Random Forest (RF), Boosted Regression Trees (BRT), Support Vector Machines (SVM), and Generalized Additive Model (GAM).Results and discussionThe ensemble model obtained high accuracy and performance model outcomes with a mean AUC of 0.95 and TSS of 0.85 for the overall model. …”
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Coupling HEC-RAS and AI for River Morphodynamics Assessment Under Changing Flow Regimes: Enhancing Disaster Preparedness for the Ottawa River by Mohammad Uzair Anwar Qureshi, Afshin Amiri, Isa Ebtehaj, Silvio José Guimere, Juraj Cunderlik, Hossein Bonakdari

“…Despite significant advancements in flood forecasting using machine learning (ML) algorithms, recent events have revealed hydrological behaviors deviating from historical model development trends. …”
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In Silico Analysis of Coding/Noncoding SNPs of Human RETN Gene and Characterization of Their Impact on Resistin Stability and Structure by Lamiae Elkhattabi, Imane Morjane, Hicham Charoute, Soumaya Amghar, Hind Bouafi, Zouhair Elkarhat, Rachid Saile, Hassan Rouba, Abdelhamid Barakat

“…The 3D structure of human resistin was generated by homology modeling using Swiss model. Root-mean-square deviation (RMSD), hydrogen bonds (h-bonds), and interactions were estimated. …”
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Bridge Deformation Prediction Using KCC-LSTM With InSAR Time Series Data by Zechao Bai, Chang Shen, Yanping Wang, Yun Lin, Yang Li, Wenjie Shen

“…Our results demonstrate that compared to standard LSTM, the proposed approach reduces root mean square error of Bridge 1 from 3.6 to 0.5 mm and Bridge 2 from 3.6 to 1.3&#x00A0;mm, improving prediction accuracy. …”
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