Measuring Carbon Monoxide With TROPOMI: First Results and a Comparison With ECMWF‐IFS Analysis Data by T. Borsdorff, J. Aan de Brugh, H. Hu, I. Aben, O. Hasekamp, J. Landgraf

“…Using the operational processing algorithm, we analyze six subsequent days of measurements during the commissioning phase. …”
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Trading in the Quantum Era: optimizing Bitcoin gains and energy costs by Simona-Vasilica Oprea, Adela Bâra, Cristian Bucur, Bogdan-George Tudorică, Niculae Oprea

“…This paper presents an in-depth analysis of a Quantum-inspired Multi-objective Optimization Algorithm (QMOA) applied to a unique problem: maximizing trading profits while minimizing energy costs. …”
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Computational Prediction and Structural Analysis of α-Hairpinins, a Ubiquitous Family of Antimicrobial Peptides, Using the Cysmotif Searcher Pipeline by Anna A. Slavokhotova, Andrey A. Shelenkov, Eugene A. Rogozhin

“…We integrated this algorithm into the Cysmotif searcher pipeline and then analyzed all transcriptomes available from the One Thousand Plant Transcriptomes project. …”
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Predicting Ship Waiting Times Using Machine Learning for Enhanced Port Operations by Min-Hwa Choi, Woongchang Yoon

“…The XGBoost Regressor (XGBR) is optimized using genetic-algorithm-based hyperparameter tuning, reducing mean squared error (RMSE) from 20.9531 to 19.6387, mean absolute error (MAE) from 13.6821 to 12.6753, and improving coefficient of determination (R2) from 0.2791 to 0.2949. …”
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Thermal-Hydraulic Network Model for steady-state thermal analysis and design of power transformer magnetic cores by L.H. Medeiros, M.M. Oliveira, C.E.G. Falcão, V.C. Bender, T.B. Marchesan

“…The constructive characteristics, as the stacking sheets forming different steps and the anisotropy for heat transfer in different directions, are considered. …”
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An ensemble learning method with GAN-based sampling and consistency check for anomaly detection of imbalanced data streams with concept drift. by Yansong Liu, Shuang Wang, He Sui, Li Zhu

“…Next, we introduce double encoders into GAN to better capture the distribution characteristics of imbalanced data for oversampling. Then, we apply the stacking ensemble learning to deal with concept drift. …”
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A numerical study on tread wear and fatigue damage of railway wheels subjected to anti-slip control by Yunfan Yang, Liang Ling, Jiacheng Wang, Wanming Zhai

“…The on-board anti-slip controllers are of essence aiming to hold back the striking slipping of the powered wheelsets under low-adhesion wheel/rail conditions. …”
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Average miniature post-synaptic potential size is inversely proportional to membrane capacitance across neocortical pyramidal neurons of different sizes by Martynas Dervinis, Guy Major

“…Here, using a new software package called ‘minis,’ we describe a novel quantal analysis method that estimates the effective ‘electrical sizes’ of synapses by comparing events detected in somatic recordings from the same neuron of (a) real minis and (b) background noise (with minis blocked pharmacologically) with simulated minis added by a genetic algorithm. The estimated minis’ distributions reveal a striking inverse dependence of mean excitatory mPSP amplitude on total cell membrane capacitance (proportional to cell size, or more exactly, extracellular membrane surface area) suggesting that, in rat somatosensory cortex at least, the average charge injected by single excitatory synapses (ca. 30 fC) is conserved across neocortical pyramidal neurons of very different sizes (across a more than three-fold range).…”
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Multi-Task Reinforcement Learning Based on Parallel Recombination Networks by Manlu Liu, Qingbo Zhang, Weimin Qian

“…By combining the proposed ’Soft Parallel Recombination Network’ method with the SAC algorithm (PRSAC) and validating it on the Meta-world multi-task training platform, the experimental results demonstrate that the proposed method significantly outperforms existing baseline algorithms in terms of sample efficiency and performance.…”
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A Hybrid Machine Learning Approach for Estimating Aboveground Biomass and Carbon Stock in Tanzania’s Miombo Woodlands by Emmanuel F. Chifunda, Ramkumar T. Balan, Peter J. Kirigiti

“…Then, a stacking technique was used to integrate both the ANN and RF models. …”
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Triplet Spatial Reconstruction Attention-Based Lightweight Ship Component Detection for Intelligent Manufacturing by Bocheng Feng, Zhenqiu Yao, Chuanpu Feng

“…Experimental validation on a small-scale actual ship component dataset demonstrates that the improved network achieves 88.7% mean Average Precision (mAP), 84.2% precision, and 87.1% recall, representing improvements of 3.5%, 2.2%, and 3.8%, respectively, compared to the original YOLOv8n algorithm, requiring only 2.6 M parameters and 7.5 Giga Floating-point Operations per Second (GFLOPs) computational cost, achieving a good balance between detection accuracy and lightweight model design. …”
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Shearing Performance of Lime-Reinforced Iron Tailing Powder Based on Energy Dissipation by Ping Jiang, Lingqi Qiu, Na Li, Wei Wang, Aizhao Zhou, Jingping Xiao

“…The back propagation (BP) neural network algorithm was used to fit the F-s curve, and the fitting equation that met the accuracy requirement was obtained. …”
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Decoding Colon Cancer Heterogeneity Through Integrated miRNA–Gene Network Analysis by Qingcai He, Zhilong Mi, Tianyue Liu, Taihang Huang, Mao Li, Binghui Guo, Zhiming Zheng

“…This study establishes a multi-dimensional stratification framework through integrative analysis of miRNA–gene co-expression networks, employing the MRNETB algorithm coupled with Markov flow entropy (MFE) centrality quantification. …”
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A novel voting ensemble model empowered by metaheuristic feature selection for accurate flash flood susceptibility mapping by Radhwan A. Saleh, Ahmed M. Al-Areeq, Amran A. Al Aghbari, Mustafa Ghaleb, Mohammed Benaafi, Nabil M. Al‑Areeq, Baqer M. Al-Ramadan

“…Statistical validations using the Friedman and Wilcoxon signed-rank tests confirmed the significant superiority of MSA over competing algorithms. Ensemble classifiers (bagging, boosting, stacking) were then applied to the reduced feature space. …”
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Sex-Specific Ensemble Models for Type 2 Diabetes Classification in the Mexican Population by Mendoza-Mendoza MM, Acosta-Jiménez S, Galván-Tejada CE, Maeda-Gutiérrez V, Celaya-Padilla JM, Galván-Tejada JI, Cruz M

“…Data are split by sex, and feature selection is performed using GALGO, a genetic algorithm-based tool. Classification models including Random Forest, K-Nearest Neighbor, Support Vector Machine, and Logistic Regression are trained and evaluated. …”
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Machine Learning-Based Classification and Statistical Analysis of Liver Cancer: A Comprehensive Study of Model Performance and Clinical Significance by Pratyush Kumar MAHARANA, Tapan Kumar BEHERA, Pradeep Kumar NAIK

“…Conclusion: After performing the complete process, we conclude that the extra tree classifier out of 17 models is the most suitable machine learning algorithm for liver cancer prediction. …”
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Opinion mining in e-commerce: Evaluating machine learning approaches for sentiment analysis by L. Lakshmi, Ali B.M. Ali, K Dhana Sree Devi, Muhammad Rafiq, Iskandar Shernazarov, Nashwan Adnan Othman, M. Ijaz Khan

“…We employ three machine learning algorithms—stacking, random forest, and LogitBoost—to evaluate the performance of these approaches. …”
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A Composite Network for CS ISAR Integrating Deep Adaptive Sampling and Imaging by Lianzi Wang, Ling Wang, Miguel Heredia Conde, DaiYin Zhu

“…However, the existing CS ISAR imaging methods based on deep learning (DL) mainly focus on improving the performance of the reconstruction algorithm while ignoring the potential room for improvement given by the design of the measurement matrix. …”
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Decoding Depression from Different Brain Regions Using Hybrid Machine Learning Methods by Qi Sang, Chen Chen, Zeguo Shao

“…To clarify the impact of brain region segmentation on the detection accuracy of moderate-to-severe major depressive disorder (MDD) and identify the optimal brain region for detecting MDD using electroencephalography (EEG), this study compared eight traditional single-machine learning algorithms with a hybrid machine learning model based on a stacking ensemble technique. …”
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Balancing Performance and Portability: A Study on CsI(Tl) Crystal Sizes for Real-Time Gamma-Ray Spectrum and Dose Monitoring by Nikolaos Voulgaris, Hikari Nishimura, Shingo Tamaki, Sachie Kusaka, Isao Murata

“…To achieve this, we used an improved sequential Bayesian estimation algorithm. The dose rate was then derived from the energy spectrum by applying a flux-to-dose conversion coefficient. …”
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