Enhancing environmental models with a new downscaling method for global radiation in complex terrain by A. Druel, J. Ruffault, H. Davi, A. Chanzy, O. Marloie, M. De Cáceres, A. Olioso, F. Mouillot, C. François, K. Soudani, N. K. Martin-StPaul

“…Finally, by using two different PBMs (CASTANEA, a PBM simulating tree growth, and SurEau, a plant hydraulic model simulating hydraulic failure risk), we showed that accounting for fine-resolution radiation can have a great impact on predictions of forest functions.</p>…”
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Hybrid Long Short-Term Memory Wavelet Transform Models for Short-Term Electricity Load Forecasting by Agbassou Guenoukpati, Akuété Pierre Agbessi, Adekunlé Akim Salami, Yawo Amen Bakpo

“…This approach offers significant advantages in reducing algorithmic complexity and effectively processing patterns within the same class of data. …”
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An Enhanced Deep Neural Network Approach for WiFi Fingerprinting-Based Multi-Floor Indoor Localization by Shehu Lukman Ayinla, Azrina Abd Aziz, Micheal Drieberg, Misfa Susanto, Afidalina Tumian, Mazlaini Yahya

“…To address these concerns, Artificial Intelligence (AI) techniques such as Machine Learning (ML) and Deep Learning (DL) have been used to classify and predict indoor locations. While these techniques have shown promising results, they face two significant challenges. …”
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Management and edaphoclimatic factors that determine soybean top-performing farmers in Brazil by Victor Delino Barasuol Scarton, Ivan Ricardo Carvalho, Christiane de Fátima Colet, Leonardo Cesar Pradebon, Willyan Júnior Adorian Bandeira, Jaqueline Piesanti Sangiovo, Murilo Vieira Loro, José Antonio Gonzalez da Silva

“…Machine learning models proved superior to traditional methods for predicting productivity, offering a strategic tool for agribusiness. …”
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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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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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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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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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Spatial and temporal heterogeneity in high-grade serous ovarian cancer: a phylogenetic analysis. by Roland F Schwarz, Charlotte K Y Ng, Susanna L Cooke, Scott Newman, Jillian Temple, Anna M Piskorz, Davina Gale, Karen Sayal, Muhammed Murtaza, Peter J Baldwin, Nitzan Rosenfeld, Helena M Earl, Evis Sala, Mercedes Jimenez-Linan, Christine A Parkinson, Florian Markowetz, James D Brenton

“…<h4>Conclusions</h4>This study demonstrates that quantitative measures of intra-tumour heterogeneity may have predictive value for survival after chemotherapy treatment in HGSOC. …”
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