Potential assessment of coordinated regulation of power load of emerging industrial users based on extreme scenarios of electric vehicle aggregators by Yuanshi Zhang, Bokang Zou, Yiheng Liang, Yifu Luo, Mengtong Chen, Jingwen Ye, Siqi Li, Yan Li, Xiaoyan Hu, Weiqi Pan

“…Finally, the effectiveness of the proposed model and algorithm is verified by simulation analysis. Compared with the traditional industrial user production optimization, the proposed model can tap the potential of multi‐agent scheduling operation in extreme scenarios.…”
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Exploring fecal microbiota signatures associated with immune response and antibiotic impact in NSCLC: insights from metagenomic and machine learning approaches by Wenjie Han, Wenjie Han, Yuhang Zhou, Yuhang Zhou, Yiwen Wang, Yiwen Wang, Xiaolin Liu, Tao Sun, Tao Sun, Junnan Xu, Junnan Xu, Junnan Xu

“…Among eight machine learning algorithms evaluated, the optimal model was selected to construct a predictive framework for immunotherapy response.ResultsMicrobial α-diversity was significantly elevated in responders compared to non-responders, with antibiotic administration further amplifying this difference—most notably at the species level. …”
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From homogeneity to heterogeneity: Refining stochastic simulations of gene regulation by Seok Joo Chae, Seolah Shin, Kangmin Lee, Seunggyu Lee, Jae Kyoung Kim

“…However, this algorithm typically assumes that DNA is homogeneously distributed throughout the nucleus, which is not realistic. …”
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Transformation of Nonmultiple Cluster Music Cyclic Shift Topology to Music Performance Style by Jing Li

“…Finally, according to the feature extraction method and the principle of the classifier algorithm studied in this paper, a music style classification system is implemented in the MATLAB environment. …”
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Automated ejection fraction and risk stratification in cardiomyopathy patients with diverse LV geometry using 2D echocardiography by Ziwei Zhu, Ke Fan, Shuyuan Zhang, Tingting Hu, Jingyi Li, Ze Zhao, Ye Jin, Shuyang Zhang

“…LASSO regression identified predicted LVEF, E/e′ ratio, and age as significant predictors, with AUC values of 0.833 (CR), 0.695 (CH), and 0.938 (EH) for adverse outcomes prediction. …”
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A Method for Improving Traffic Management Based on Computer Vision and Traffic Simulation by R. Huang, Z. Wang, Y. Lin, T. Du, K. Lu, K. Lu, C. Ding, Y. Zhang

“…Results demonstrate significant improvements: 16.7% reduction in eastbound queue lengths (240.23m), 80.4% decrease in vehicle delays (56.46s), 44.8% shorter travel times (51.61s), and 83% fewer pedestrian-vehicle conflicts. …”
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Effects of the lavage through fistula in treatment of spontaneous esophageal rupture by combined thoracoscopic and gastroscopic management by Zhao Huang, Penglong Zhao, Bingmei Qiu, Chuan Gao, Zhao Chen, Nan Yang, Yang Xu, Zhiqiang Zou, Yi Shen, Liwen Hu

“…The Lavage-Drainage group had lower 30-day mortality, fewer complications and adverse events, and a faster reduction in inflammatory factors, but a higher cost. …”
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Mapping Mountain Permafrost via GPR-Augmented Machine Learning in the Northeastern Qinghai–Tibet Plateau by Yao Xiao, Guangyue Liu, Guojie Hu, Defu Zou, Ren Li, Erji Du, Tonghua Wu, Xiaodong Wu, Guohui Zhao, Yonghua Zhao, Lin Zhao

“…Elevation-based spatial comparisons revealed that LightGBM and CatBoost produced more terrain-adaptive predictions at high altitudes and slope transitions. …”
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Artificial intelligence in psychiatry: A systematic review and meta-analysis of diagnostic and therapeutic efficacy by Moustaq Karim Khan Rony, Dipak Chandra Das, Most. Tahmina Khatun, Silvia Ferdousi, Mosammat Ruma Akter, Mst. Amena Khatun, Most. Hasina Begum, Md Ibrahim Khalil, Mst. Rina Parvin, Daifallah M Alrazeeni, Fazila Akter

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Modeling quinoa growth under saline and water-limiting conditions using SWAP-WOFOST by Diana C. Estrella Delgado, Tom De Swaef, Jan Vanderborght, Eric Laloy, Gerda Cnops, Maarten De Boever, Abdelaziz Hirich, Ayoub El Mouttaqi, Sarah Garré

“…The resulting crop parameters provide insights into the stress tolerance mechanisms of quinoa, including reduction of transpiration and uptake of solutes. The salinity stress function of SWAP effectively represents these tolerance mechanisms and accurately predicts the impact on yield, under arid conditions. …”
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PCA-based denoising and automatic recognition of marine biological sounds to estimate Bio-voice Count Index for marine monitoring by Bingjia Huang, Yi Wu, Yihua Lyu, Xi Yan, Mengmeng Tong, Xiaoping Wang

“…To address this, we present two novel computational workflows: (1) a Principal Component Analysis (PCA)-driven noise reduction algorithm that selectively suppresses anthropogenic noise (e.g., vessel sounds) overlapping with biological frequency bands; and (2) an automatic Bio-voice Count Index (BCI) that quantifies target biological sounds through energy thresholding and adjustable frequency-weighting curve. …”
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Development of a method for differential diagnosis of iron deficiency anemia and anemia of chronic disease based on demographic data and routine laboratory tests using machine lear... by N. V. Varekha, N. I. Stuklov, K. V. Gordienko, R. R. Gimadiev, O. B. Shchegolev, S. N. Kislaya, E. V. Gubina, A. A. Gurkina

“…To select an appropriate artificial intelligence algorithm for predicting serum ferritin (SF) levels and to evaluate its applicability for differential diagnosis of iron deficiency anemia and anemia of chronic diseases.   …”
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INFLAMMATION's cognitive impact revealed by a novel "Line of Identity" approach. by Donald R Royall, Raymond F Palmer

“…Our approach also distinguishes individuals resilient to individual biomarker effects allowing for more accurate prediction and precision intervention.…”
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The Prognostic Significance of TMSB4X in Glioma Patients by Li S, Fan T, Hao Z, Zhang Z, Han X, Li J, Wang Y, Zhou H, Xue X

“…The top 50 core genes were then screened using the cyto Hubba algorithm, these genes were analyzed using The Gene Ontology Database (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG). …”
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Inflammatory markers mediate the association between alternative adiposity indices and mortality in patients with rheumatoid arthritis: data from NHANES 1999–2018 by Feng Luo, Jia-jie Guo, Xue-mei Yuan, Heng Zhou, Qiu-yi Wang, Chang-ming Chen, Xue-ming Yao, Wu-kai Ma

“…Traditional obesity indicators inadequately predict the mortality risk in this population. Thus, the research aimed to evaluate new obesity indicators to explore their close association with RA mortality. …”
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CcpA-mediated regulation of cellular energy metabolism in the ruminal bacterium Streptococcus bovis by Yunan Weng, Yaqian Jin, Wenze Zhang, Lihong Wang, Hongrong Wang

“…Based on the random forest algorithm, glutamine and UDP-GlcNac were identified as biomarkers under varying glucose conditions and in the presence or absence of ccpA. …”
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Comparing and Combining Artificial Intelligence and Spectral/Statistical Approaches for Elevating Prostate Cancer Assessment in a Biparametric MRI: A Pilot Study by Rulon Mayer, Yuan Yuan, Jayaram Udupa, Baris Turkbey, Peter Choyke, Dong Han, Haibo Lin, Charles B. Simone

“…Combining Z-SSMNet with spectral/statistical approaches significantly enhanced tumor grade prediction, possibly providing an alternative to current prostate tumor assessment.…”
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Biomarkers of microvascularture by ultra Micro-angiography (UMA) assist to identify papillary thyroid carcinoma (PTC) with atypia of undetermined significance by Qingsong Wang, Zhewei Li, Jie Zhang, Sijie Zhang, Lijun Wang, Hongjian Yao, Hong Zhang, Jing Li, Shuo Wang, Jinglai Sun, Wenhui Zhang, Hui Yu

“…Conclusions This study demonstrated the potential value of microvasculature in the prediction of PTC in AUS Cases and improved the performance of ultrasound examination. …”
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Sustainable analysis of COVID-19 Co-packaged paxlovid: exploring advanced sampling techniques and multivariate processing tools by Shymaa S. Soliman, Nisreen F Abo- Talib, Mohamed R. Elghobashy, Mona A. Abdel Rahman

“…Abstract The drawbacks of random sampling not only hinder the development of more reliable and efficient methods but also weaken their accuracy, predictive abilities, and validity across several domains. …”
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Machine Learning Models Decoding the Association Between Urinary Stone Diseases and Metabolic Urinary Profiles by Lin Ma, Yi Qiao, Runqiu Wang, Hualin Chen, Guanghua Liu, He Xiao, Ran Dai

“…Our analyses revealed that the Random Forest algorithm exhibited the highest predictive accuracy, with AUC values of 0.809 for kidney stones, 0.99 for ureter stones, and 0.775 for multiple location stones. …”
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