The Michigan Model of Infant Mental Health Home Visiting increases preventative services while decreasing emergency services for children by Jerrica Pitzen, Danielle Rice, Barbara Durán, Jennifer Jester, Jessica Riggs, Megan Julian, Brendan Appold, Maria Muzik, Katherine Rosenblum, Michigan Collaborative for Infant Mental Health, Emily Alfafara, Carla Barron, Holly E. Brophy-Herb, Nora L. Erickson, Hiram E. Fitzgerald, Alissa C. Huth-Bocks, Meriam Issa, Jennifer M. Jester, Megan M. Julian, Jamie M. Lawler, Rena Menke, Alyssa S. Meuwissen, Alison L. Miller, Maria Muzik, Larissa N. Niec, Jerrica Pitzen, Julie Ribaudo, Jessica Riggs, Katherine L. Rosenblum, Sarah E. Shea, Paul Spicer, Ann M. Stacks, Chioma Torres, Laurie Van Egeren, Rachel Waddell, Christopher L. Watson, Deborah J. Weatherston, Kristyn VanDahm

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Centering peers in design and training for a peer-delivered contingency management program for self-identified harm reduction and treatment goals by Linda Peng, Erin Stack, Alexis Cooke, Bryan Hartzler, Ryan Cook, Gillian Leichtling, Christi Hildebran, Judith Leahy, Kelsey Smith Payne, Lynn Kunkel, Kim Hoffman, P. Todd Korthuis

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Ergonomic Risk Assessment of Warehouse Workers in the Courier Service Industry: A Case Study from Kuantan, Malaysia by Alya Nadhirah binti Ismail, Mirta Widia, Ezrin Hani binti Sukadarin, Wan Norlinda Roshana Binti Mohd Nawi, Mohammad Faizal bin Zainal Abidin

“…The REBA analysis indicated a very high-level risk for tiered stacking storage, necessitating immediate ergonomic interventions. …”
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Costs of transitioning the livestock sector to net-zero emissions under future climates by Franco Bilotto, Karen Michelle Christie-Whitehead, Bill Malcolm, Nicoli Barnes, Brendan Cullen, Margaret Ayre, Matthew Tom Harrison

“…In contrast, stacking several interventions to mitigate enteric methane, improve FCE and sequester carbon entirely negated enterprise emissions in a profitable way. …”
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Predicting Diabetic Retinopathy and Nephropathy Complications Using Machine Learning Techniques by D. R. Manjunath, J. J. Lohith, S. Selva Kumar, Abhijit Das

“…Ensemble methods like Voting and Stacking Classifiers performed better and achieved near perfect metrics (AUC = 1.0) in oversampled datasets. …”
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Optimizing hypertension prediction using ensemble learning approaches. by Isteaq Kabir Sifat, Md Kaderi Kibria

“…This research contributes to ongoing efforts to optimize HTN prediction models, ultimately supporting early intervention and personalized healthcare management.…”
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Machine learning for grading prediction and survival analysis in high grade glioma by Xiangzhi Li, Xueqi Huang, Yi Shen, Sihui Yu, Lin Zheng, Yunxiang Cai, Yang Yang, Renyuan Zhang, Lingying Zhu, Enyu Wang

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Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study by Jingchao Lei, Jia Zhai, Yao Zhang, Jing Qi, Chuanzheng Sun

“…ObjectiveThis study aims to develop an explainable machine learning model, incorporating stacking techniques, to predict the occurrence of liver injury in patients with sepsis and provide decision support for early intervention and personalized treatment strategies. …”
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Explainable artificial intelligence for predicting medical students’ performance in comprehensive assessments by Haniye Mastour, Toktam Dehghani, Ehsan Moradi, Saeid Eslami

“…These insights empower educators to prioritize curriculum reforms and implement early interventions for at-risk students while delivering personalized feedback for learners to enhance learning outcomes.…”
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Exploring the use of solid fuels for cooking and household air pollution in informal settlements through photovoice: The Fuel to Pot study in Ndirande (Malawi) and Mukuru (Kenya). by Isabelle Uny, Tracy Chasima, Line Caes, Lusizi Kambalame, Moses V M Chamba, Limbani Kalumbi, Fred Orina, Heather Price, Sian Lucas, Maria Nyikuri, Sean Semple, Hellen Meme

“…<h4>Results</h4>With their pictures, participants described fuel stacking and switching behaviours in their communities. …”
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Interpretable Machine Learning Models for PISA Results in Mathematics by Ismael Gomez-Talal, Luis Bote-Curiel, Jose Luis Rojo-Alvarez

“…In addition, a stacking meta-model integrating the strengths of eight distinct predictive models was developed to enhance prediction accuracy. …”
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Exploring the Integration of Atomic Habits in Pedagogical Frameworks: A Qualitative Analysis of Teachers’ Implementation and Students’ Outcomes by Fakhar Ul-Zaman, Muhammad Safdar Bhatti, Samina Semab, Muhammad Junaid Aslam, Muhammad Awais Anwer, Maria Noor

“…This study adds to our knowledge of how small-scale, incremental changes in students' behavior will, through habit-building interventions, have the potential to greatly improve educational performance. …”
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A Multi-Algorithm Machine Learning Model for Predicting the Risk of Preterm Birth in Patients with Early-Onset Preeclampsia by Xu Y, Zu Y, Zhang Y, Liang Z, Xu X, Yan J

“…The Stacking model (XGBoost+GBDT+SVM) achieved superior performance (AUROC=0.865). …”
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A Comparative Analysis of Machine Learning Models for Predicting EFL Student Language Performance in Smart Learning Environments by Banchakarn Sameephet, Wirapong Chansanam, Mahboubeh Rakhshandehroo, Chawin Srisawat, Kittichai Nilubol, Arnon Jannok, Bhirawit Satthamnuwong, Kornwipa Poonpon

“…The analysis results indicated that ensemble models, particularly XGBoost and Stacking Ensemble, performed the best in predicting students’ English language performance. …”
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Implementation and Applications of Intelligent Batch Processing Technology for Isotope Injection Profile Logging Interpretation by SUN Liang

“…By utilizing technologies such as big data monitoring and machine learning, key technical challenges such as basic data input, raw data decomposition, deep matching, tool recognition, curve stacking, and comprehensive evaluation have been solved, forming intelligent batch processing technology. …”
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Assessing Physiological Stress Responses in Student Nurses Using Mixed Reality Training by Kamelia Sepanloo, Daniel Shevelev, Young-Jun Son, Shravan Aras, Janine E. Hinton

“…Among the models tested, the Stacking Classifier demonstrated the highest classification accuracy of 96.4%, outperforming both Random Forest (96.18%) and Gradient Boosting (95.35%). …”
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CELM: An Ensemble Deep Learning Model for Early Cardiomegaly Diagnosis in Chest Radiography by Erdem Yanar, Fırat Hardalaç, Kubilay Ayturan

“…In addition, we introduced a novel stacking-based ensemble model—Combined Ensemble Learning Model (CELM)—that integrates complementary CNN features via a meta-classifier. …”
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Materialized disagreements: interpreting urban dual language signage and murals in Northern Ireland by John-Guy Perrem

“…Two practical interventions are proposed. First, shifting from the conventional horizontally stacked format of dual-language street signs to a vertically aligned side-by-side design reduces implicit hierarchies between English and Irish, ensuring equal linguistic representation. …”
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Prediction of Myopia Among Undergraduate Students Using Ensemble Machine Learning Techniques by Isteaq Kabir Sifat, Tajin Ahmed Jisa, Jyoti Shree Roy, Nourin Sultana, Farhana Hasan, Md Parvez Mosharaf, Md. Kaderi Kibria

“…Model performance was evaluated using accuracy, precision, recall, F1‐score, and area under the curve (AUC). Results The stacking ensemble model achieved the highest performance, with an accuracy of 95.42%, recall of 93.42%, precision of 98.85%, F1‐score of 96.08%, and AUC of 0.979. …”
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Tumor vessel-adaptable adhesive and absorbable microspheres for sustainable transarterial chemoembolization therapy by Jiakun Guo, Jintao Huang, Zuliang Huang, Di Hu, Hujing Tan, Yan Wang, Chao Deng, Xiaoli Zhu, Zhiyuan Zhong

“…Abstract Transarterial chemoembolization (TACE) is a common clinical intervention used for unresectable liver tumors, but conventional embolic microspheres generally exhibit slack vascular stacking and suboptimal drug release. …”
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