Comparative Assessment of the Dynamics of Visual Performance of a Patient with Visually Intense Work with Binocular Cataract after Various Technologies of Cataract Phacoemulsificat... by D. F. Pokrovsky, I. B. Medvedev, N. I. Ovechkin, I. G. Ovechkin, A. I. Pavlov

“…Patients were divided into three equals in number (n = 100), age (from 38 to 66 years, mean age 55.8 ± 1.3 years), gender (78 % — men; 22 % — women), refractive values of the group who underwent PEС using the following technologies: delayed sequential bilateral cataract surgery (DSBCS), immediate sequential bilateral cataract surgery according to the traditional (trISBCS) and developed algorithms (maISBCS). …”
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Multiomics-Based Deep Learning Prediction of Prognosis and Therapeutic Response in Patients With Extensive-Stage Small Cell Lung Cancer Receiving Chemoimmunotherapy: A Retrospectiv... by Nie F, Pei X, Du J, Shi W, Wang J, Feng L, Liu Y

“…Patients were divided into reactive and non-reactive groups based on their response to chemoimmunotherapy.Machine learning algorithms (including random forests, decision trees, artificial neural networks, and generalized linear regression) were used to predict the combined treatment response. …”
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Estimating vegetation indices and biophysical parameters for Central European temperate forests with Sentinel-1 SAR data and machine learning by Daniel Paluba, Bertrand Le Saux, Francesco Sarti, Přemysl Štych

“…In the comparison of ML models, the traditional ML algorithms, Random Forest Regressor and Extreme Gradient Boosting (XGB) slightly outperformed the Automatic Machine Learning (AutoML) approach, auto-sklearn, for all forest parameters, achieving high accuracies (R2 between 70% and 86%) and low errors (0.055–0.29 of mean absolute error). …”
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A machine learning model for predicting acute respiratory distress syndrome risk in patients with sepsis using circulating immune cell parameters: a retrospective study by Kaihuan Zhou, Lian Qin, Yin Chen, Hanming Gao, Yicong Ling, Qianqian Qin, Chenglin Mou, Tao Qin, Junyu Lu

“…Feature importance analysis revealed that mean arterial pressure, monocyte count, neutrophil count, pH, and platelet count were key predictors of ARDS risk in patients with sepsis. …”
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Proportional myoelectric control of a virtual bionic arm in participants with hemiparesis, muscle spasticity, and impaired range of motion by Caleb J. Thomson, Fredi R. Mino, Danielle R. Lopez, Patrick P. Maitre, Steven R. Edgley, Jacob A. George

“…We compared the signal-to-noise ratio (SNR) of the recorded electromyography (EMG) signals used to train the control algorithms and the task performance using root mean square error, percent time in target, and maximum hold time within the target window. …”
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Towards a Digital Twin for Gas Turbines: Thermodynamic Modeling, Critical Parameter Estimation, and Performance Optimization Using PINN and PSO by Jian Tiong Lim, Achnaf Habibullah, Eddie Yin Kwee Ng

“…Ultimately, the proposed methods aim to enhance GT operations via a data-driven digital twin concept combination of deep learning and optimization algorithms.…”
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Development of intelligent tools to predict neuroblastoma risk stratification and overall prognosis based on multiphase enhanced CT and clinical features by Wei Zhao, Yahui Han, Xiaokun Yu, Jianing Liu, Jiao Zhang, Juan Li

“…Prognostic models were constructed using a combination of multiple machine learning algorithms in conjunction with CT image features and clinical characteristics.ResultsSwin-ART based on arterial phase images was the best risk stratification classifier with an AUC of 0.770 (95% CI: 0.613–0.909) and an accuracy of 0.780 in the testing cohort. …”
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A novel approach to wind energy modeling in the context of climate change at Zaafrana region in Egypt by Bassem Khaled Kamel, Almoataz Y. Abdelaziz, Mahmoud A. Attia, Amr Khaled Khamees

“…Moreover, Exponential Distribution Optimizer (EDO), Aquila Optimizer (AO), and Equilibrium Optimizer (EO) algorithms are used to find various probability distribution functions (PDFs) parameters to model wind speed data from Zaafrana region in Egypt using Root Mean Square Error (RMSE) and Coefficient of Correlation (R^2) as judging criteria. …”
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Spatial and Temporal Expansion of Photovoltaic Sites and Thermal Environmental Effects in Ningxia Based on Remote Sensing and Deep Learning by Heao Xie, Peixian Li, Fang Shi, Chengting Han, Ximin Cui, Yuling Zhao

“…This study analyzed the spatiotemporal expansion trends and microclimatic impacts of PV installations (2015–2024) using Gaofen-1 (GF-1) and Landsat8 satellite imagery with deep learning algorithms and multidimensional environmental metrics. …”
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The Systemic Immune‐Inflammation Index and Its Association With Biologic Therapy Switching and Response in Psoriasis by Gregg Murray, Niamh Kearney, Conor Smith, Kieran Carty, Yasmine Safta, Olwyn Conlon, Brian Kirby, Ali Alsharqi

“…Integrating SII into psoriasis treatment algorithms may enable clinicians to identify high‐risk patients early, optimise biologic selection, and improve long‐term outcomes. …”
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Land Surface Condition-Driven Emissivity Variation and Its Impact on Diurnal Land Surface Temperature Retrieval Uncertainty by Lijuan Wang, Ping Yue, Yang Yang, Sha Sha, Die Hu, Xueyuan Ren, Xiaoping Wang, Hui Han, Xiaoyu Jiang

“…This model reduced the root mean square error (RMSE) of LST retrieved from geostationary satellites from 6.02 °C to 2.97 °C, significantly enhancing retrieval accuracy. …”
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Artificial Intelligence Control Methodologies for Shape Memory Alloy Actuators: A Systematic Review and Performance Analysis by Stefano Rodinò, Giuseppe Rota, Matteo Chiodo, Antonio Corigliano, Carmine Maletta

“…Future research should prioritize adaptive algorithms for fatigue compensation, lightweight AI models for embedded deployment, and standardized benchmarking to bridge material-specific performance gaps. …”
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Construction and validation of risk prediction models for renal replacement therapy in patients with acute pancreatitis by Fei Zuo, Lei Zhong, Jie Min, Jinyu Zhang, Longping Yao

“…Results The mean age of the cohort was 59.17 years, with an average Acute Physiology and Chronic Health Evaluation II (APACHE II) score of 17.55. …”
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Precision analysis of NGC 2158 with Gaia DR3 by Nasser M. Ahmed, A. L. Tadross

“…Using the PARSEC stellar isochrones fit, the mean cluster age and its relaxation time are 1.95 $$\pm$$ 0.28 Gyr and 89.0 $$\pm$$ 12.54 Myr, respectively. …”
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Characterization and stratification of risk factors of stroke in people living with HIV: A theory-informed systematic review by Martins Nweke, Nombeko Mshunqane

“…Results Thirty studies (22 cohorts and eight case–control) with a sample size of 353,995 participants were included in this review. The mean age of the participants was 45.1 ± 10.7 years. …”
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A Distributed Collaborative Navigation Strategy Based on Adaptive Extended Kalman Filter Integrated Positioning and Model Predictive Control for Global Navigation Satellite System/... by Wanqiang Chen, Yunpeng Jing, Shuo Zhao, Lei Yan, Quancheng Liu, Zichang He

“…In the field of multi-robot cooperative localization and task planning, traditional filtering algorithms encounter synchronization and consistency issues during multi-source data fusion. …”
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Automated process assessment of primary healthcare for hyperlipidemia: preliminary findings and implications form Anhui, China by Ningjing Yang, Yuning Wang, Ying Li, Dongying Xiao, Ruirui Cui, Nana Li, Rong Liu, Jing Chai, Xingrong Shen, Debin Wang

“…., lipid lowering medication prescription) using self-designed algorithms. While the encounter-based measures included number or rate of visits for HL, currently-noticed hyperlipidemia (CNHL, or HL noticed during the current consultation), and ever-diagnosed hyperlipidemia (EDHL). …”
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Improved Asphalt Pavement Crack Detection Model Based on Shuffle Attention and Feature Fusion by Tursun Mamat, Abdukeram Dolkun, Runchang He, Yonghui Zhang, Zulipapar Nigat, Hanchen Du

“…The experimental results demonstrate a stepwise improvement in the model’s mean average precision (mAP) with each enhancement to the original network. …”
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Analysis of spatio-temporal fungal growth dynamics under different environmental conditions by Liselotte De Ligne, Guillermo Vidal-Diez de Ulzurrun, Jan M. Baetens, Jan Van den Bulcke, Joris Van Acker, Bernard De Baets

“…Other growth characteristics such as the mean hyphal segment length did not vary significantly over time. …”
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Growth dynamics of splenic artery aneurysms: morphology, comorbidities, and vascular anatomical factors by Ahmet Tanyeri, Aygün Katmerlikaya, Rıdvan Akbulut, Mehmet Burak Çildağ

“…A p < 0.05 was considered statistically significant. Results The mean age of the study cohort was 71 ± 16 years, with 43% of the patients being male. …”
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