Manufacturing test flows in high-volume electronics production are typically fixed during product development and executed unchanged on every unit, even as failure patterns and process conditions evolve. This protects quality, but it also imposes unnecessary test cost, while existing data-driven methods mostly optimize static test subsets and neither adapt online to changing defect distributions nor explicitly control escape risk. In this study, we present an adaptive test-selection framework that combines offline minimum-cost diagnostic subset construction using greedy set cover with an online Thompson-sampling multi-armed bandit that switches between full and reduced test plans using a rolling process-stability signal. We evaluate the framework on two printed circuit board assembly stages-Functional Circuit Test and End-of-Line test-covering 28,000 board runs. Offline analysis identified zero-escape reduced plans that cut test time by 18.78% in Functional Circuit Test and 91.57\% in End-of-Line testing. Under temporal validation with real concept drift, static reduction produced 110 escaped defects in Functional Circuit Test and 8 in End-of-Line, whereas the adaptive policy reduced escapes to zero by reverting to fuller coverage when instability emerged in practice. These results show that online learning can preserve manufacturing quality while reducing test burden, offering a practical route to adaptive test planning across production domains, and offering both economic and logistics improvement for companies.
Preserving clusters and correlations: a dimensionality reduction method for exceptionally high global structure preservation
University of Oslo (UiO)（奥斯陆大学）；Oslo University Hospital (OUS)（奥斯陆大学医院）；University of Lübeck (UzL)（吕贝克大学）；University Medical Center Schleswig-Holstein (UKSH)（石勒苏益格-荷尔斯泰因大学医学中心）；University of Latvia (LU)（拉脱维亚大学）；Tel Aviv University (TAU)（特拉维夫大学）
A Global Medical Data Security and Privacy Preserving Standards Identification Framework for Electronic Healthcare Consumers
Thumbay Institute for AI in Healthcare（坦贝医疗人工智能研究院）；Gulf Medical University（海湾医科大学）；National Sun Yat-sen University（国立中山大学）；University of Aizu（会津大学）；Indian Institute of Information Technology Bhopal（博帕尔印度信息技术学院）；University of Economics and Human Sciences（经济与人文科学大学）
Anti-drift in electronic nose via dimensionality reduction: a discriminative subspace projection approach
School of Computing, National University of Singapore（新加坡国立大学计算机学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）
An unsupervised clustering analysis of breast cancer data derived from electronic health records enhanced through UMAP dimensionality reduction
Dipartimento di Informatica Sistemistica e Comunicazione, Università di Milano-Bicocca（信息系统与通信系，米兰比可卡大学）；Institute of Health Policy Management and Evaluation, University of Toronto（卫生政策管理与评估研究所，多伦多大学）
Instance-based Learning with Prototype Reduction for Real-Time Proportional Myocontrol: A Randomized User Study Demonstrating Accuracy-preserving Data Reduction for Prosthetic Embedded Systems
German Aerospace Center (DLR)（德国航空航天中心）；CERN（欧洲核子研究组织）；University of Trento（特伦托大学）
DIRESA, a distance-preserving nonlinear dimension reduction technique based on regularized autoencoders
Department of Electronics and Informatics, Vrije Universiteit Brussel（比利时布鲁塞尔自由大学电子与信息学院）；Observations Scientific Service, Royal Meteorological Institute（皇家气象研究所观测科学部门）
Differentially Private Normalizing Flows for Density Estimation, Data Synthesis, and Variational Inference with Application to Electronic Health Records
