Global sorghum production dataset for temperate to subtropical regions at subnational scale over 2000–2020Zendo by Mohsen Davoudkhani, Nicolas Guilpart, David Makowski, Nicolas Viovy, Philippe Ciais, Ronny Lauerwald

“…The dataset can be used to develop crop models, machine learning algorithms, and statistical models for predicting sorghum yields under different climate scenarios. …”
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Artificial Neural Network Modelling of Photodegradation in Suspension of Manganese Doped Zinc Oxide Nanoparticles under Visible-Light Irradiation by Yadollah Abdollahi, Azmi Zakaria, Nor Asrina Sairi, Khamirul Amin Matori, Hamid Reza Fard Masoumi, Amir Reza Sadrolhosseini, Hossein Jahangirian

“…Moreover, the model predicted the relative importance of the variables which showed none of them was neglectable in this work.…”
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A hybrid learning network with progressive resizing and PCA for diagnosis of cervical cancer on WSI slides by Nitin Kumar Chauhan, Krishna Singh, Amit Kumar, Ashutosh Mishra, Sachin Kumar Gupta, Shubham Mahajan, Seifedine Kadry, Jungeun Kim

“…Furthermore, two ML methods, Support Vector Machine (SVM) and Random Forest (RF) models, are trained on this reduced feature set, and their predictions are integrated using a majority voting approach for evaluating the final classification results, thereby enhancing overall accuracy and reliability. …”
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Machine learning identifies PYGM as a macrophage polarization–linked metabolic biomarker in rectal cancer prognosis by Chengyuan Xu, Chengyuan Xu, Siqi Zhang, Bin Sun, Zicheng Yu, Hailong Liu

“…Functional analyses, single-cell RNA sequencing, immunohistochemistry of clinical specimens, and in vitro cellular assays were employed to investigate the role of the MPGS hub gene, PYGM, in tumor biology and immune modulation.ResultsThe MPGS exhibited robust prognostic capability and effectively predicted responses to immunotherapy and various chemotherapeutic agents. …”
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Association between serum hypertriglyceridemia and hematological indices: data mining approaches by Somayeh Ghiasi Hafezi, Amin Mansoori, Alireza Kooshki, Marzieh Hosseini, Sahar Ghoflchi, Mark Ghamsary, Gordon Ferns, Habibollah Esmaily, Majid Ghayour-Mobarhan

“…RF model showed to have higher accuracy in predicting the TG level in both males and females. Conclusion Our model assessed the association between serum TG with several hematological factors like RLR, RPR, and PHR. …”
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Ferroptosis-disulfidptosis-related CHMP6 is a clinico-immune target in colorectal cancer by Yifei Zhu, Huixia Huang, Jiayu Chen, Keji Chen, Yanxi Yao, Yaxian Wang, Yuxue Li, Zhibing Qiu, Dawei Li, Ping Wei

“…Conclusions The FDRG score is a robust tool for predicting CRC prognosis, tumor microenvironment dynamics, and response to immunotherapy. …”
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Simulation of energy consumption processes at the metallurgical enterprises in the energy-saving projects implementation by Kiyko S. G., Druzhinin E. A., Prokhorov O. V., Haidabrus B. V.

“…With this software package’s help, a comparison of the permissible values and the adjusting of the predicted consumption of active power by a metallurgical enterprise for each fixed point in time are carried out. …”
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Matching of Observation Footprints in the FY-3G MWRI-RM Using BGI by Ke Chen, Bowen Cai, Wei Han, Zihao Suo

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Machine Learning G-Code Optimization by Héctor Lasluisa-Naranjo, David Rivas-Lalaleo, Joaquín Vaquero-López, Christian Cruz-Moposita

“…This research applies the K-means machine learning clustering algorithm to optimize G-code parameters such as extruder and heated bed temperature, deposition speed, and flow control. …”
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Global Methane Retrieval, Monitoring, and Quantification in Hotspot Regions Based on AHSI/ZY-1 Satellite by Tong Lu, Zhengqiang Li, Cheng Fan, Zhuo He, Xinran Jiang, Ying Zhang, Yuanyuan Gao, Yundong Xuan, Gerrit de Leeuw

“…The results show that the improved MF algorithm effectively suppresses noise in retrieval results over both land and ocean surfaces, enhancing algorithm robustness. …”
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Control of Overfitting with Physics by Sergei V. Kozyrev, Ilya A. Lopatin, Alexander N. Pechen

“…An application of this analogy allows us to explain the selection of wide likelihood maxima and ab overfitting reduction for GANs.…”
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Screening biomarkers related to cholesterol metabolism in osteoarthritis based on transcriptomics by ChenDeng Lao, Wei Wei, JianWen Cheng, ShiJie Liao, XiaoLin Luo, Qian Huang, HengZhen Huang, JinMin Zhao

“…Three machine learning algorithms identified ATF3, CHKA, CLU, CTNNB1, and FASN as potential biomarkers. …”
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Optimization of Flight Scheduling in Urban Air Mobility Considering Spatiotemporal Uncertainties by Lingzhong Meng, Minggong Wu, Xiangxi Wen, Zhichong Zhou, Qingguo Tian

“…Traditional flight plan adjustment and management methods often rely on deterministic trajectory predictions, ignoring the inherent temporal uncertainties in actual operations, which may lead to the underestimation of potential risks. …”
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A Novel Approach for Maize Straw Type Recognition Based on UAV Imagery Integrating Height, Shape, and Spectral Information by Xin Liu, Huili Gong, Lin Guo, Xiaohe Gu, Jingping Zhou

“…Accurately determining the distribution and quantity of maize straw types is of great significance for evaluating the effectiveness of conservation tillage, precisely estimating straw resources, and predicting the risk of straw burning. The widespread adoption of conservation tillage technology has greatly increased the diversity and complexity of maize straw coverage in fields after harvest. …”
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RETRACTED ARTICLE: Single-cell omics and machine learning integration to develop a polyamine metabolism-based risk score model in breast cancer patients by Xiliang Zhang, Hanjie Guo, Xiaolong Li, Wei Tao, Xiaoqing Ma, Yuxing Zhang, Weidong Xiao

“…These genes were analyzed using univariate COX and machine learning algorithms to develop a prognostic scoring algorithm. …”
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Thrombotic Lesion of the Pulmonary Vessels in Patients with Pulmonary Embolism by A. A. Klimenko, N. A. Demidova, N. A. Shostak, M. O. Anischenko

“…Pathogenetic aspects of this condition are not fully investigated. It is important to predict the development of postembolic syndrome and to develop algorithms for the diagnosis, treatment and rehabilitation of patients with symptoms and residual pulmonary thrombosis. …”
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Feasibility and case studies on converting small hydropower stations to pumped storage by Yangqing Dan, Qingyue Chen, Daren Li, Wenhuan Bai, Weiming Zhou, Anyu Yang, Jia Yang

“…The proposed conversion scheme has been assessed, and predictions regarding annual operating hours, power generation, and energy consumption have been formulated. …”
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Investigation of granuloma-targeted host-pathogen interactions identify vaccine correlates of immune protection associated with control of M. tuberculosis replication by David Forrest Ackart, Dr. Faye Lanni, Victoria Mitcham, Dr. G. Brooke Anderson, Dr. Michael Lyons, Dr. Marcela Henao-Tamayo, Dr Brendan Podell

“…While a number of blood transcriptomic signatures have been shown to predict clinical states of TB in human subjects, no correlates exist to indicate immune signatures that control bacterial growth, which would inform improved vaccine design. …”
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Trends in Russia’s GDP Growth under Environmental Constraints by Z. V. Bragina, A. R. Denisov, N. N. Masyuk, N. V. Steblyanskii

“…Methods used: empirical and statistical analysis, building regression models, algorithmic and predictions, building time trends, etc. …”
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Novel estrogen-related gene variants identified by whole-exome sequencing in pregnancy-associated intrahepatic cholestasis by Hua Lai, Siming Xin, Jinliang Zhang, Yang Hu, Wenjuan Fan, Hong Wan, Bowen Chen, Yang Zou, Xiaoming Zeng, Xianxian Liu

“…These variants exhibited the following characteristics: (1) complete absence in 1,237 controls and all public genomic databases (1000 Genomes, ExAC, and dbSNP); (2) evolutionary conservation of the affected residues, with unanimous pathogenic predictions from all algorithms (PolyPhen-2: damaging; SIFT: deleterious; MutationTaster: disease-causing); (3) molecular modeling demonstrating structural perturbations in critical functional domains, including steroid-binding and redox partner interaction sites. …”
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