Assessing Gonipterus defoliation levels using multispectral unmanned aerial vehicle (UAV) data in Eucalyptus plantations by Phumlani Nzuza, Michelle L. Schröder, Rene J. Heim, Louis Daniels, Bernard Slippers, Brett P. Hurley, IIaria Germishuizen, Benice Sivparsad, Jolanda Roux, Wouter. H Maes

“…However, the method was less reliable when trained and validated on separate fields. This study highlights the potential of multi-site datasets in increasing the model's generalization, using UAV based multispectral imagery to assess Gonipterus sp. n. 2 damage and demonstrating reliable upscaling from individual tree assessments to stand scale. …”
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Characteristics of the gut virome in patients with premalignant colorectal adenoma by Pan Zhang, Xiaofeng Tuo, Jiong Jiang, Yue Zhang, Juhui Zhao, Chengzhao Deng, Gang Zhao, Yan Cheng, Lingqin Song, Yan Yang, Ruochun Guo, Huan Zhang, Hongli Zhao, Shiyang Ma, Lu Li, Haitao Shi

“…., bacteriome, mycobiome, and virome) characteristics of colorectal cancer have been extensively studied, yet there is still an insufficient description of the microbiota features in its early-stage, colorectal adenoma, particularly in the gut virome aspect. …”
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The Bioethics-CSR Divide by Caio Caesar Dib

“…Mirowski, “The Philosophical Bases of Institutionalist Economics.” [39]. Glenn McGee, ed., Pragmatic Bioethics, 2nd ed, Basic Bioethics (Cambridge, Mass: MIT Press, 2003)…”
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Digital augmentation of aftercare for patients with anorexia nervosa: the TRIANGLE RCT and economic evaluation by Janet Treasure, Katie Rowlands, Valentina Cardi, Suman Ambwani, David McDaid, Jodie Lord, Danielle Clark Bryan, Pamela Macdonald, Eva Bonin, Ulrike Schmidt, Jon Arcelus, Amy Harrison, Sabine Landau

“…Eur Eat Disord Rev 2023) and ECHO-based (Philipp J, Truttmann S, Zeiler M, Franta C, Wittek T, Schöfbeck G, et al. …”
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Protecting the Autonomy of Patients with Severe Mental Illness Through Psychiatric Advance Directive Peer-Facilitation by Nicholas Karasik

“…A Systematic Review of Argument-based Ethics Literature.," BMC Medical Ethics 20, no. 1 (2019): 76, https://doi.org/10.1186/s12910-019-0417-3…”
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Machine Learning Prediction of Airfoil Aerodynamic Performance Using Neural Network Ensembles by Diana-Andreea Sterpu, Daniel Măriuța, Grigore Cican, Ciprian-Marius Larco, Lucian-Teodor Grigorie

“…In this study, a hybrid deep learning model is proposed, combining convolutional neural networks (CNNs) and operating directly on raw airfoil geometry, with parallel branches of fully connected deep neural networks (DNNs) that process operational parameters and engineered features. The model is trained on an extensive database of NACA four-digit airfoils, covering angles of attack ranging from −5° to 14° and ten Reynolds numbers increasing in steps of 500,000 from 500,000 up to 5,000,000. …”
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Supervised machine learning algorithms for the classification of obesity levels using anthropometric indices derived from bioelectrical impedance analysis by Rodrigo Yáñez-Sepúlveda, Aldo Vásquez-Bonilla, Rodrigo Olivares, Pablo Olivares, Juan Pablo Zavala-Crichton, Claudio Hinojosa-Torres, Catalina Muñoz-Strale, Frano Giakoni-Ramírez, Josivaldo de Souza-Lima, Jacqueline Páez-Herrera, Jorge Olivares-Arancibia, Tomás Reyes-Amigo, Guillermo Cortés-Roco, Juan Hurtado-Almonacid, Eduardo Guzmán-Muñoz, Nicole Aguilera-Martínez, José Francisco López-Gil, Boryi A. Becerra-Patiño, Juan David Paucar-Uribe, Exal Garcia-Carrillo, Vicente Javier Clemente-Suárez

“…SHapley Additive exPlanations analysis revealed that FMI, FFMI, and BMI were the most influential features, while sex had minimal predictive impact. …”
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Suppression and control of unbalanced and misalignment coupler vibration of a double-rotor permanent magnet drive by Yongcun GUO, Xin MA, Shuang WANG, Deyong LI, Tun YANG

“…The neural network iterative learning control algorithm makes the network able to fit the nonlinear features of the system by introducing nonlinear activation function and depth structure. …”
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Classification of Individuals With COVID-19 and Post–COVID-19 Condition and Healthy Controls Using Heart Rate Variability: Machine Learning Study With a Near–Real-Time Monitoring C... by Carlos Alberto Sanches, Andre Felipe Henriques Librantz, Luciana Maria Malosá Sampaio, Peterson Adriano Belan

“…Decision tree models trained solely on HRV features achieved 76.4% accuracy with high discriminative performance for active COVID-19 (F1-score=88%; area under the curve=0.85) but limited detection of post–COVID-19 condition (F1-score=56%). …”
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Mean pulmonary artery pressure prediction with explainable multi-view cardiovascular magnetic resonance cine series deep learning model by Li-Hsin Cheng, Xiaowu Sun, Charlie Elliot, Robin Condliffe, David G. Kiely, Samer Alabed, Andrew J. Swift, Rob J. van der Geest, David G Kiely, Lisa Watson, Iain Armstrong, Catherine Billings, Athanasios Charalampopoulos, Robin Condliffe, Charlie Elliot, Abdul Hameed, Neil Hamilton, Judith Hurdman, Allan Lawrie, Robert A Lewis, Smitha Rajaram, Alex Rothman, Andy J. Swift, Steven Wood, AA Roger Thompson, Jim Wild

“…Conclusion: Hemodynamic parameters can be estimated non-invasively with a CNN, using MR cine series from four views, revealing key contributing features at the same time.…”
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Software with artificial intelligence-derived algorithms for detecting and analysing lung nodules in CT scans: systematic review and economic evaluation by Julia Geppert, Peter Auguste, Asra Asgharzadeh, Hesam Ghiasvand, Mubarak Patel, Anna Brown, Surangi Jayakody, Emma Helm, Dan Todkill, Jason Madan, Chris Stinton, Daniel Gallacher, Sian Taylor-Phillips, Yen-Fu Chen

“…Artificial intelligence-based software has been developed to reduce the number of missed or misdiagnosed lung nodules on computed tomography images. …”
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Machine-learning detection of stress severity expressed on a continuous scale using acoustic, verbal, visual, and physiological data: lessons learned by Marketa Ciharova, Khadicha Amarti, Ward van Breda, Ward van Breda, Martin J. Gevonden, Sina Ghassemi, Annet Kleiboer, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Christiaan H. Vinkers, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Milou S. C. Sep, Sophia Trofimova, Alexander C. Cooper, Xianhua Peng, Xianhua Peng, Mieke Schulte, Mieke Schulte, Eirini Karyotaki, Eirini Karyotaki, Eirini Karyotaki, Pim Cuijpers, Pim Cuijpers, Pim Cuijpers, Heleen Riper, Heleen Riper

“…When applying all available features (i.e., visual, acoustic, verbal, and physiological), or a combination of visual, acoustic and verbal features, performance ranged from acceptable to good, but only for the presentation task (accuracy up to.71, F1-score up to.73).ConclusionsThe complexity of input features needed for machine-learning detection of stress severity based on multimodal data requires large sample sizes with wide variability of stress reactions and inputs among participants. …”
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Employing the concept of stacking ensemble learning to generate deep dream images using multiple CNN variants by Lafta Alkhazraji, Ayad R. Abbas, Abeer S. Jamil, Zahraa Saddi Kadhim, Wissam Alkhazraji, Sabah Abdulazeez Jebur, Bassam Noori Shaker, Mohammed Abdallazez Mohammed, Mohanad A. Mohammed, Basim Mohammed Al-Araji, Abdulkareem Z. Mohmmed, Wasiq Khan, Bilal Khan, Abir Jaafar Hussain

“…Performance of the proposed model was evaluated across three octaves to amplify the maximum possible patterns and features of the base image. The resulting dream-like images contain shapes that reflect elements from the ImageNet dataset on which the above pre-trained models were trained. …”
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Stacking Ensemble Learning Process to Predict Rural Road Traffic Flow by Arash Rasaizadi, Seyedehsan Seyedabrishami

“…In this study, an ensemble learning process is proposed to predict the hourly traffic flow. First, three base models, including K-nearest neighbors, random forest, and recurrent neural network, are trained. …”
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Museums and Geoconservation by Michael J. Benton, Sarah King

“…Importantly, geoconservation is based on the fundamental scientific evaluation of those locations, such as open landscapes, coastlines, and quarries in terms of their regional and international significance. …”
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Autocorrelation Matrix Knowledge Distillation: A Task-Specific Distillation Method for BERT Models by Kai Zhang, Jinqiu Li, Bingqian Wang, Haoran Meng

“…Pre-trained language models perform well in various natural language processing tasks. …”
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Construction of a Model for the Cross-Domain Opinion Word Extraction by N. V. Loukachevitch, I. I. Chetviorkin

“…The extraction model was trained in the movie domain and then applied to four other domains. …”
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Normalizing flows for high-dimensional detector simulations by Florian Ernst, Luigi Favaro, Claudius Krause, Tilman Plehn, David Shih

“…We use fast and expressive coupling spline transformations applied to the CaloChallenge datasets. In addition to the base flow architecture we also employ a VAE to compress the dimensionality and train a generative network in the latent space. …”
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Development and validation of machine learning models for osteoporosis prediction in chronic kidney disease patients: Data from National Health and Nutrition Examination survey by Hui Li, Ya Zhang, Chong Zhang

“…Separate models for male and female CKD patients were developed using 59 potential predictors, with key variables selected through the Least Absolute Shrinkage and Selection Operator and Boruta algorithms. Seven single-base models, including logistic regression, support vector machine, extreme gradient boosting, K-nearest neighbors, gradient boosting decision tree, random forest (RF), and neural network, were trained. …”
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Determining minimum population size and demographics of black rhinos in the Salient of Aberdare National Park, Kenya by Felix Patton, Martin Jones

“…The database and a standard method of describing the identification features of each of the rhinos enabled details of individuals to be disseminated, patrol rangers trained to identify individuals accurately, minimum population demography to be described and changes in minimum population size, from potentially 23 in 2003 down to possibly only 7 individuals in 2005, to be observed. …”
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