Showing 81 - 100 results of 2,825 for search '((reduction OR education) OR ((predictive OR prediction) OR predicting)) stress', query time: 0.17s Refine Results
  1. 81

    Evaluating the predictive power of combined gene expression dynamics from single cells on antibiotic survival by Razan N. Alnahhas, Virgile Andreani, Mary J. Dunlop

    Published 2025-06-01
    “…We developed a Bayesian inference model to predict how the combination of dual reporter expression levels and growth rate impacts ciprofloxacin survival in Escherichia coli. …”
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    Article
  2. 82

    Relationship Between 8-iso-prostaglandin-F<sub>2α</sub> and Predicted 10-Year Cardiovascular Risk in Hypertensive Patients by Giulio Geraci, Alessandra Sorce, Luca Zanoli, Giuseppe Cuttone, Vincenzo Calabrese, Francesco Pallotti, Valentina Paternò, Pietro Ferrara, Ligia J. Dominguez, Riccardo Polosa, Jacob George, Giuseppe Mulè, Caterina Carollo

    Published 2025-03-01
    “…The aim of this study was to assess the relationship between 8-iso-PGF<sub>2α</sub> and 10-year CV risk, as predicted by validated equations in hypertension patients without CV diseases. …”
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    Article
  3. 83

    Classification Prediction of Rockburst in Railway Tunnel Based on Hybrid PSO-BP Neural Network by Min Zhang

    Published 2022-01-01
    “…In order to accurately predict the rockburst intensity level of the railway tunnel, the rock stress coefficient σθ/σc, rock brittleness coefficient σc/σt, and elastic energy index Wet are used as evaluation indexes of rockburst intensity, and a BP neural network rockburst prediction model based on hybrid particle swarm optimization algorithm is proposed. …”
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  4. 84
  5. 85

    Biomarker panels for improved risk prediction and enhanced biological insights in patients with atrial fibrillation by Pascal B. Meyre, Stefanie Aeschbacher, Steffen Blum, Tobias Reichlin, Moa Haller, Nicolas Rodondi, Andreas S. Müller, Alain Bernheim, Jürg Hans Beer, Giorgio Moschovitis, André Ziegler, Bianca Wahrenberger, Elia Rigamonti, Giulio Conte, Philipp Krisai, Leo H. Bonati, Stefan Osswald, Michael Kühne, David Conen

    Published 2025-07-01
    “…We identify 5 biomarkers including D-dimer, growth differentiation factor 15 (GDF-15), interleukin-6 (IL-6), N-terminal pro-B-type natriuretic peptide (NT-proBNP), and high-sensitivity troponin T (hsTropT) that independently predict cardiovascular death, stroke, myocardial infarction, and systemic embolism, significantly enhancing predictive accuracy. …”
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  6. 86

    Prediction method of surface deformation around soft base dump based on viscoelastic theory by Dong WANG, Li YIN, Lanzhu CAO, Yiming FU, Liguo ZHANG, Chunhui ZHANG, Chunjian DING

    Published 2025-06-01
    “…A series of quantitative data is obtained through calculations and predictions of surface deformation on layered viscoelastic substrates under pile load actions in the discharging yard. …”
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  7. 87

    Optimizing corn yield prediction: Integrating multi-temporal UAS data and machine learning by Huihui Zhang, Yuting Zhou, Shengfang Ma, Kevin Yemoto

    Published 2025-12-01
    “…The integration of VNIR and LWIR imagery improves yield prediction accuracy, particularly in water-stressed conditions, highlighting the importance of advanced sensing technologies and complex models such as RF and GB. …”
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  8. 88

    Multi‐trait multi‐environment genomic prediction of preliminary yield trial in pulse crop by Rica Amor Saludares, Sikiru Adeniyi Atanda, Lisa Piche, Hannah Worral, Francoise Dariva, Kevin McPhee, Nonoy Bandillo

    Published 2024-09-01
    “…Multi‐trait multi‐environment enabled genomic prediction (MTME‐GP) offers a valuable alternative to predict missing phenotypes of selection candidates for multiple traits and diverse environments. …”
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  9. 89
  10. 90

    Advancements in predicting soil liquefaction susceptibility: a comprehensive analysis of ensemble and deep learning approaches by Divesh Ranjan Kumar, Warit Wipulanusat

    Published 2025-07-01
    “…The BI-LSTM model has the highest accuracy, with 0.9791 in training and 0.8889 in testing, indicating strong predictive ability and good generalizability. LSTM follows closely with training and testing accuracies of 0.9433 and 0.8750, respectively, offering consistent performance. …”
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  11. 91

    Comparison of hemodynamic effects and negative predictive value of normal adenosine gated myocardial perfusion scan with or without caffeine abstinence by Maseeh uz Zaman, Nosheen Fatima, Areeba Zaman, Unaiza Zaman, Rabia Tahseen

    Published 2016-07-01
    “…The aim of this study was to compare the hemodynamic changes and negative predictive value (NPV) of normal MPIs with adenosine stress performed with or without caffeine abstinence. …”
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  12. 92

    Unveiling postpartum PTSD: predicting risk factors using decision trees and logistic regression in Chinese women by Xiao Fei Nie, Lan Lan Xu, Wen Ping Guo, Jin Hui Li, Li Cheng, Tao Tao Zhang, Jun-Yan Li

    Published 2025-08-01
    “…However, no studies have yet integrated both approaches to investigate postpartum posttraumatic stress disorder (PP-PTSD). This study aims to explore the factors associated with postpartum posttraumatic stress disorder (PP-PTSD) in Chinese women using decision tree and logistic regression models, while also comparing the predictive performance of both approaches. …”
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  13. 93

    Compression-compression fatigue life prediction model of T300/69 laminates under edge impact by ZHANG Peng, LIU Jianhui, WEI Yaobing

    Published 2025-07-01
    “…Therefore, it is of practical engineering significance to establish a fatigue life prediction model for low-velocity impact at the edge. …”
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    Article
  14. 94

    Evaluating the feasibility of using crystalline patterns induced by PBF-LB to predict strength enhancing orientations by José David Pérez-Ruiz, Luis Norberto López de Lacalle, Wilmer Velilla-Díaz, Jaime A. Mesa, Gaizka Gómez, Heriberto Maury, Gorka Urbikain, Haizea Gonzalez

    Published 2025-06-01
    “…A distinctive feature of the PBF-LB process is its capacity to develop crystalline patterns, which can be utilized to predict strength-enhancing orientations of the produced components. …”
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  15. 95

    Biomarkers for intensive care unit-acquired weakness: a systematic review for prediction, diagnosis and prognosis by Jiamei Song, Ting Deng, Qingmei Yu, Xun Luo, Yanmei Miao, Leiyu Xie, Yongming Mei, Peng Xie, Shaolin Chen

    Published 2025-07-01
    “…Biomarkers offer potential for diagnosing, predicting, and prognosticating ICU-AW, but a comprehensive synthesis of the available evidence is still lacking. …”
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  16. 96
  17. 97

    Incorporating soil moisture data into a machine learning framework improved the predictive accuracy of corn yields in the U.S. by Bishwoyog Bhattarai, Zachary Leasor, André Fróes de Borja Reis

    Published 2025-10-01
    “…We hypothesize that incorporating soil moisture and temperature data from land surface models into a ML framework will enhance accuracy of corn yield predictions. To test this, we employed XGBoost algorithm using long-term (2010–2022) yield data collected from multi-environment corn trials conducted by the Variety Testing Program at the University of Missouri, along with weather data from Daymet database, soil data from North American Land Data Assimilation System (NLDAS-2), and POLARIS soil database. …”
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  18. 98

    Histogram-based gradient boosting machine with SHAP-driven interpretability for predicting intensity of urban heat Island effect by Nhat-Duc Hoang

    Published 2025-08-01
    “…Abstract This study presents a novel framework for the analysis and prediction of urban heat stress. To conduct the analysis, urban Land Surface Temperature (LST) data were retrieved from Landsat 8 during the dry seasons in 2006, 2014, and 2022. …”
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  19. 99

    Improved Deflection Prediction Model for PSC Box Girder with Stay Cable System during Tensioning Phase by Gangnian Xu, Weimin Xu, Zhanhong Wang, Ruishuo Zhang

    Published 2024-01-01
    “…To improve the accuracy of deflection prediction for prestressed concrete (PSC) box-girder bridges with a stay cable system (SCS) during tensioning, this study employs the Latin hypercube sampling (LHS) technique to sample random variables affecting long-term deflection of the main girder. …”
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  20. 100

    Using machine learning to predict anesthetic dose in fish: a case study using nutmeg oil by Mert Minaz, Cem Alparslan, Akif Er

    Published 2025-08-01
    “…Application of anesthetic chemicals in aquaculture is important to minimize stress under normal operations such as handling, transport, and artificial breeding. …”
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