Open Source Software (OSS) projects follow diverse lifecycle trajectories shaped by evolving patterns of contribution, coordination, and community engagement. Understanding these trajectories is essential for stakeholders seeking to assess project organization and health at scale. However, prior work has largely relied on static or aggregated metrics, such as project age or cumulative activity, providing limited insight into how OSS sustainability unfolds over time. In this paper, we propose a hierarchical predictive framework that models OSS projects as belonging to distinct lifecycle stages grounded in established socio-technical categorizations of OSS development. Rather than treating sustainability solely as project longevity, these lifecycle stages operationalize sustainability as a multidimensional construct integrating contribution activity, community participation, and maintenance dynamics. The framework combines engineered tabular indicators with 24-month temporal activity sequences and employs a multi-stage classification pipeline to distinguish lifecycle stages associated with different coordination and participation regimes. To support transparency, we incorporate explainable AI techniques to examine the relative contribution of feature categories to model predictions. Evaluated on a large corpus of OSS repositories, the proposed approach achieves over 94\% overall accuracy in lifecycle stage classification. Attribution analyses consistently identify contribution activity and community-related features as dominant signals, highlighting the central role of collective participation dynamics.
Analysis and Evaluation of Explainable Artificial Intelligence on Suicide Risk Assessment
The University of Western Australia（西澳大利亚大学）；Armadale Mental Health Service（阿玛代尔心理健康服务中心）；Bethesda Clinic（贝塞斯达诊所）；Co-HIVE Mental Health（Co-HIVE心理健康中心）；Harry Perkins Institute of Medical Research（哈里·珀金斯医学研究所）；Fiona Stanley Hospital（菲奥娜·斯坦利医院）；University of Western Australia（西澳大利亚大学）
XAI for In-hospital Mortality Prediction via Multimodal ICU Data
Wuhan University（武汉大学）；University of Oxford（牛津大学）；The University of Sydney（悉尼大学）；The Hong Kong Polytechnic University（香港理工大学）；University of Edinburgh（爱丁堡大学）
From Black Box to Insight: Explainable AI for Extreme Event Preparedness
Department of Cybersecurity University at Albany, SUNY Albany, NY, USA；Dept. of Atmospheric \& Environmental Sciences University at Albany, SUNY Albany, NY, USA；Lally School of Management Rensselaer Polytechnic Institute Albany, NY, USA；Goddard Space Flight Center NASA Greenbelt, MD, USA
SCOPE-PD: Explainable AI on Subjective and Clinical Objective Measurements of Parkinson's Disease for Precision Decision-Making
Computing & Information Sciences（计算与信息科学）；Florida International University（佛罗里达国际大学）；Computer Science & Engineering（计算机科学与工程）；University of South Florida（佛罗里达州立大学）
Explainable AI for Engineering Design: A Unified Approach of Systems Engineering and Component- Based Deep Learning Demonstrated by Energy- Efficient Building Design
Leibniz University Hannover（汉诺威莱布尼茨大学）；Technical University of Munich（慕尼黑工业大学）
