Machine Learning Approach to Aerodynamic Analysis of NACA0005 Airfoil: ANN and CFD Integration by Taiba Kouser, Dilek Funda Kurtulus, Srikanth Goli, Abdulrahman Aliyu, Imil Hamda Imran, Luai M. Alhems, Azhar M. Memon

“…This study presents a machine learning approach to predict the unsteady aerodynamic performance of a NACA0005 airfoil. …”
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JAK2 Inhibitors and Emerging Therapies in Graft-Versus-Host Disease: Current Perspectives and Future Directions by Behzad Amoozgar, Ayrton Bangolo, Abdifitah Mohamed, Charlene Mansour, Daniel Elias, Christina Cho, Siddhartha Reddy

“…Recent advances in biomarker development, such as the MAGIC Algorithm Probability (MAP), are enabling early risk stratification and response prediction. …”
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Ultrasound Imaging and Machine Learning to Detect Missing Hand Motions for Individuals Receiving Targeted Muscle Reinnervation for Nerve-Pain Prevention by Anna Rita E. Moukarzel, Justin Fitzgerald, Marcus Battraw, Clifford Pereira, Andrew Li, Paul Marasco, Wilsaan M. Joiner, Jonathon Schofield

“…We found that attempted missing hand movements resulted in unique patterns of deformation in the reinnervated muscles and applying a K-nearest neighbors machine learning algorithm, we could predict 4-10 hand movements for each participant with 83.3-99.4% accuracy. …”
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Machine learning and response surface methodology forecasting comparison for improved spray dry scrubber performance with brine sludge-derived sorbent by B.J. Chepkonga, L. Koech, R.S. Makomere, H.L. Rutto

“…The response surface methodology (RSM) model also proved to be a reliable forecasting tool, indicating its potential as a practical alternative to complex algorithmic computations in scenarios with limited raw data.…”
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Detecting cognitive motor dissociation by functional near-infrared spectroscopy by Yan Wang, Yan Wang, Yan Wang, Wentao Zeng, Wentao Zeng, Wentao Zeng, Leyao Zou, Leyao Zou, Leyao Zou, Qijun Wang, Bingkai Ren, Bingkai Ren, Bingkai Ren, Qi Xiong, Yang Bai, Yang Bai, Yang Bai, Zhen Feng, Zhen Feng, Zhen Feng

“…The support vector machine combined with genetic algorithm was employed to classify and predict the brain's response to spoken commands and to identify CMD patients among prolonged DOC individuals.ResultsWe identified seven CMD patients using fNIRS, of whom four were in VS/UWS and three were in MCS–. …”
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Diagnostic performance of actigraphy in Alzheimer’s disease using a machine learning classifier – a cross-sectional memory clinic study by Mathias Holsey Gramkow, Andreas Brink-Kjær, Frederikke Kragh Clemmensen, Nikolai Sulkjær Sjælland, Gunhild Waldemar, Poul Jennum, Steen Gregers Hasselbalch, Kristian Steen Frederiksen

“…We evaluated the performance of our classifier by assessing the accuracy and precision of predictions. Results We found that movement patterns as well as the robustness and fragmentation of the circadian rhythm differed significantly between groups. …”
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Artificial intelligence enhanced electrochemical immunoassay for staphylococcal enterotoxin B by Yuliang Zhao, Tingting Sun, Huawei Zhang, Chao Lian, Zhongpeng Zhao, Yongqiang Jiang, Huiqi Duan, Yuhao Ren, Xuyang Sun, Zhikun Zhan, Mingyue Qu, Shaolong Chen

“…Lastly, a multivariate linear regression algorithm is employed to effectively train and fit the extracted feature data. …”
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Exploration of metastasis-related signatures in osteosarcoma based on tumor microenvironment by integrated bioinformatic analysis by Shiyao Liao, Xing Gao, Kai Zhou, Yao Kang, Lichen Ji, Xugang Zhong, Jun Lv

“…Conclusion: We identified three principal genes as promising signatures for predicting the survival the prognosis of OS patients. …”
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Pork-YOLO: Automated collection of pork quality traits by Jiacheng Wei, Xi Tang, Jinxiu Liu, Ting Luo, Yan Wu, Junhui Duan, Shijun Xiao, Zhiyan Zhang

“…For marbling scoring, an image classification task yielded an average accuracy of 98.9 %, with a strong correlation (R2 = 0.999) between predicted and actual values. This study presents an innovative method for rapid automated assessment of pork quality traits, offering valuable insights for future phenotypic measurement automation.…”
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Multi-Omics Profiling Reveals Glycerolipid Metabolism-Associated Molecular Subtypes and Identifies ALDH2 as a Prognostic Biomarker in Pancreatic Cancer by Jifeng Liu, Shurong Ma, Dawei Deng, Yao Yang, Junchen Li, Yunshu Zhang, Peiyuan Yin, Dong Shang

“…These findings establish a novel avenue for studying prognostic prediction and precision medicine in PC patients.…”
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Using Machine Learning and Feature Importance to Identify Risk Factors for Mortality in Pediatric Heart Surgery by Lorenz A. Kapsner, Manuel Feißt, Ariawan Purbojo, Hans-Ulrich Prokosch, Thomas Ganslandt, Sven Dittrich, Jonathan M. Mang, Wolfgang Wällisch

“…All models were then applied to predict the outcome in an independent holdout test dataset (40% of the cohort). …”
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Identification of copper metabolism-related subtypes, the development of a prognosis model, and characterization of the immune landscape in colorectal cancer by Geng Peng, Lin Zhong, Nan Lai, Lina Luo, Fu Cheng, Manzhao Ouyang

“…CMGs are effective biomarkers for predicting the prognosis of CRC patient and guiding immunotherapy.…”
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CD79A and GADD45A as novel immune-related biomarkers for respiratory syncytial virus severity in children: an integrated machine learning analysis and clinical validation by Juan Juan Chen, Zhang Ze Lu, Yu Xin Jing, Xing Mei Nong, Yi Qin, Jin Yang Huang, Na Lin, Jie Wei

“…Potential drug interactions for hug genes were predicted using the Drug-Gene Interaction Database (DGIdb). …”
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The alternative prey hypothesis revisited: Still valid for willow ptarmigan population dynamics. by Jo Inge Breisjøberget, Morten Odden, Per Wegge, Barbara Zimmermann, Harry Andreassen

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A large-scale ENIGMA multisite replication study of brain age in depression by Laura K.M. Han, Richard Dinga, Ramona Leenings, Tim Hahn, James H. Cole, Lyubomir I. Aftanas, Alyssa R. Amod, Bianca Besteher, Romain Colle, Emmanuelle Corruble, Baptiste Couvy-Duchesne, Konstantin V. Danilenko, Paola Fuentes-Claramonte, Ali Saffet Gonul, Ian H. Gotlib, Roberto Goya-Maldonado, Nynke A. Groenewold, Paul Hamilton, Naho Ichikawa, Jonathan C. Ipser, Eri Itai, Sheri-Michelle Koopowitz, Meng Li, Go Okada, Yasumasa Okamoto, Olga S. Churikova, Evgeny A. Osipov, Brenda W.J.H. Penninx, Edith Pomarol-Clotet, Elena Rodríguez-Cano, Matthew D. Sacchet, Hotaka Shinzato, Kang Sim, Dan J. Stein, Aslihan Uyar-Demir, Dick J. Veltman, Lianne Schmaal

“…Conclusions: This study further validates our previously developed ENIGMA brain age algorithm. Importantly, we replicated the brain age gap in depression with a comparable effect size. …”
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iCARE: An R package to build, validate and apply absolute risk models. by Parichoy Pal Choudhury, Paige Maas, Amber Wilcox, William Wheeler, Mark Brook, David Check, Montserrat Garcia-Closas, Nilanjan Chatterjee

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Theoretical open-loop model of respiratory mechanics in the extremely preterm infant. by Laura Ellwein Fix, Joseph Khoury, Russell R Moores, Lauren Linkous, Matthew Brandes, Henry J Rozycki

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CAFiKS: Communication-Aware Federated IDS With Knowledge Sharing for Secure IoT Connectivity by Ogobuchi Daniel Okey, Demostenes Zegarra Rodriguez, Frederico Gadelha Guimaraes, Joao Henrique Kleinschmidt

“…In many cases, deep neural networks (DNNs) serve as the backbone algorithm in federated processes. However, their computational demands make them impractical for deployment in Internet of Things (IoT) environments, which are characterised by resource-constrained devices; hence, the need for lightweight and adaptable solutions. …”
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Prognostic role of hemostasis-regulating genetic factors and their interaction with conventional risk factors at the early stages of coronary heart disease development by E. Yu. Andreenko, L. M. Samokhodskaya, A. V. Balatskyi, P. I. Makarevich, S. A. Boytsov

“…In participants with HCH, the PLA2/PLA2 genotype of GPIIIa was linked to increased MI risk for CHD patients (p=0,01; OR=6,0). Conclusion. The algorithm for predicting the genetic CHD risk may incorporate the assessment of the genetic polymorphism of GPIa (C807T), GPIIIa (PLA1/PLA2), factor XIII (V34L), and factor VII (R353Q). …”
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Multiple-omics analysis of aggrephagy-related cellular patterns and development of an aggrephagy-related signature for hepatocellular carcinoma by Jiafen Xie, Xiaoming Wang

“…At the single-cell level, the AGG scores were calculated using AUCell algorithm, and cell interactions and pseudotime trajectory analyses were conducted. …”
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