Impact of training data composition on the generalizability of convolutional neural network aortic cross-section segmentation in four-dimensional magnetic resonance flow imaging by Chiara Manini, Markus Hüllebrand, Lars Walczak, Sarah Nordmeyer, Lina Jarmatz, Titus Kuehne, Heiko Stern, Christian Meierhofer, Andreas Harloff, Jennifer Erley, Sebastian Kelle, Peter Bannas, Ralf Felix Trauzeddel, Jeanette Schulz-Menger, Anja Hennemuth

“…Conclusion: This study highlights the importance of considering a heterogeneous dataset for the training of widely applicable automatic CNN segmentations in 4D flow CMR, with a particular focus on the inclusion of different pathologies and technical aspects of data acquisition.…”
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CanCellCap: robust cancer cell capture across tissue types on single-cell RNA-seq data by multi-domain learning by Jiaxing Bai, Yichun Gao, Feng Zhou, Yushuang He, Chen Lin, Xiaobing Huang, Ying Wang

“…Its strong generalization to unseen cancers, tissues, and even species, combined with its adaptability to spatial transcriptomics data, underscores its versatility for both research and clinical applications.…”
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A meta-analysis for the nighttime light remote sensing data applied in urban research: Key topics, hotspot study areas and new trends by Baiyu Dong, Ruyi Zhang, Sinan Li, Yang Ye, Chenhao Huang

“…Nighttime light (NTL) data have become an essential tool for urban remote-sensing research in the past 25 years because of its ability to intuitively detect human activities. …”
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Multi-century mean summer temperature variations in the Southern Rhaetian Alps reconstructed from <i>Larix decidua</i> blue intensity data by R. Cerrato, M. C. Salvatore, M. C. Salvatore, M. Brunetti, A. Somma, C. Baroni, C. Baroni

“…The results from this investigation will extend the current knowledge on the applicability of using BI data to study the European larch, and the reconstruction described herein is the first attempt to determine whether this proxy can be used for dendroclimatic aims. …”
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Development and Validation of a Machine Learning Model for Early Prediction of Delirium in Intensive Care Units Using Continuous Physiological Data: Retrospective Study by Chanmin Park, Changho Han, Su Kyeong Jang, Hyungjun Kim, Sora Kim, Byung Hee Kang, Kyoungwon Jung, Dukyong Yoon

“…ObjectiveWe aimed to create a novel machine learning model for delirium prediction in ICU patients using only continuous physiological data. MethodsWe developed models integrating routinely available clinical data, such as age, sex, and patient monitoring device outputs, to ensure practicality and adaptability in diverse clinical settings. …”
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Evaluation of diabetes care services, data quality, and availability of resources in Ethiopia: Difference-in-differences analysis of the NORAD-WHO NCDs’ midterm project evaluation... by Yimer Seid Yimer, Meaza Gezu Shentema, Zenawi Hagos Gufue, Awgichew Kifle Zemelak, Zeytu Gashaw Asfaw, Sefonias Getachew, Mulugeta Tamire, Kalkidan Solomon, Asmamaw Bezabeh Workneh, Girma Taye Aweke

“…Clinical trial number Not applicable.…”
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Peat oxic and anoxic controls of <i>Sphagnum</i> decomposition rates in the Holocene Peatland Model decomposition module estimated from litterbag data by H. Teickner, H. Teickner, E. Pebesma, K.-H. Knorr

“…<p>The Holocene Peatland Model (HPM) is a widely applied model for understanding and predicting long-term peat accumulation, but it is difficult to test due to its complexity, measurement errors, and lack of data. Instead of testing the complete model, tests of individual modules may avoid some of these problems. …”
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Global ground-based tropospheric ozone measurements: reference data and individual site trends (2000–2022) from the TOAR-II/HEGIFTOM project by R. Van Malderen, A. M. Thompson, A. M. Thompson, D. E. Kollonige, D. E. Kollonige, R. M. Stauffer, H. G. J. Smit, E. Maillard Barras, C. Vigouroux, I. Petropavlovskikh, I. Petropavlovskikh, T. Leblanc, V. Thouret, P. Wolff, P. Effertz, P. Effertz, D. W. Tarasick, D. Poyraz, G. Ancellet, M.-R. De Backer, S. Evan, V. Flood, M. M. Frey, J. W. Hannigan, J. L. Hernandez, M. Iarlori, B. J. Johnson, N. Jones, R. Kivi, E. Mahieu, G. McConville, K. Müller, T. Nagahama, J. Notholt, A. Piters, N. Prats, R. Querel, D. Smale, W. Steinbrecht, K. Strong, R. Sussmann

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Research Infrastructures in Citizen Science: State of Knowledge and Taxonomic Framework As a Pathway to Sustainability by Karen Soacha-Godoy, Pranesh Prakash, Ana Álvarez, Adriana Giraldo Tejada, Alexandre López-Borrull, Xavier Salvador, Carlos Rodero, Diego Torres, Ivan Rodero, Mabel Ayure, Jaume Piera

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Análisis de habilidades y percepciones sobre mHealth en el manejo de pacientes crónicos por profesionales de atención primaria by Daniel Monasor Ortola, José Joaquín Mira Solves, Antonio Esteve Ríos

“…Measurements: Participants completed an anonymized ad hoc questionnaire divided into two sections: sociodemographic data and questions regarding the use of mobile health technologies and the internet. …”
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Seasonal effects in the application of the MOment MAtching (MOMA) remote calibration tool to outdoor PM_2.5 air sensors by L. F. Weissert, G. S. Henshaw, A. L. Clements, R. M. Duvall, C. Croghan, C. Croghan

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Application Of ArtifiCial Intelligence in E-Governance: A Comparative Study of Supervised Machine Learning and Ensemble Learning Algorithms on Crime Prediction. by Niyonzima, Ivan, Muhaise, Hussein, Akankwasa, Aureri

“…Therefore, this study proposes the application of supervised machine learning techniques in the prediction of crimes basing on the past crime data. …”
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Determination and Application of Critical Control Points on Ebi Fry Production (Value Added Product) at PT. Jala Sembilan Semarang, Central Java by Revanda Rizky Putraisya, Juni Triastuti

“…The working method used is a descriptive method with data collection including primary data and secondary data. …”
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Federated Learning Based on an Internet of Medical Things Framework for a Secure Brain Tumor Diagnostic System: A Capsule Networks Application by Roman Rodriguez-Aguilar, Jose-Antonio Marmolejo-Saucedo, Utku Köse

“…Federated learning has been successfully applied in medical settings, including diagnostic applications involving medical images such as MRI data. …”
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Deep learning identification of reward-related neural substrates of preadolescent irritability: A novel 3D CNN application for fMRI by Johanna C. Walker, Conner Swineford, Krupali R. Patel, Lea R. Dougherty, Jillian Lee Wiggins

“…Irritability, defined as a lowered threshold for angry responses to blocked rewards, is a promising neurodevelopmental marker for mental health risk due to its robust, transdiagnostic predictive power in youth. In this study, data from the Adolescent Brain and Cognitive Development (ABCD) baseline sample (N = 6065) were utilized for a novel application of a 3D CNN to whole-brain fMRI data acquired during the reward anticipation period of the monetary incentive delay task to predict parent-reported youth irritability severity, measured dimensionally. …”
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Establishment and application of a one-step multiplex real-time PCR assay for detection of A, B, and C subtypes of avian metapneumovirus by Mingjun Su, Jiongze Cheng, Xiangwen Xu, Yijia Liu, Yulin Zhao, Yutao Wang, Xiaoxu Du, Jiale Ying, Junfang Yan, Huihua Zheng, Changyong Cheng, Jing Sun

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Application of K-Means Cluster Analysis for Grouping State Islamic University in Indonesia based on the Readiness Indicators for World Class University (WCU) by Marhayati Marhayati, Arini Mayan Fa'ani, Sulistya Umie Ruhmanasari, Siti Faridah

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Drought models for the study of the vegetation moisture content; analysis of performance of different models in two mediterranean ecosystems for application in forest fire preventi... by Bonora Laura, Vincenzi Matteo De

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Edge AI-Based Smart Intersection and Its Application for Traffic Signal Coordination: A Case Study in Pyeongtaek City, South Korea by Seongjin Lee, Seungeon Baek, Wang-Hee Woo, Chiwon Ahn, Jinwon Yoon

“…First, there are inevitable delays in transmitting and processing the video data. Second, there is still a need to develop a real-time signal control method leveraging the acquired data from smart intersections. …”
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EDHD-FG-XGBoost: Entropy-driven high-dimensional feature gain XGBoost model for short-term wind power prediction and application by Xing Liu, Xingyu Mou, Ying Zhang, Hui Chen, Xin Wang, Xihao Sun, Chengcheng Wang, Ruixiang Shong, Yunfeng Li

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