As the aging population grows, particularly for the baby boomer generation, the United States is witnessing a significant increase in the elderly population experiencing multifunctional disabilities. These disabilities, stemming from a variety of chronic diseases, injuries, and impairments, present a complex challenge due to their multidimensional nature, encompassing both physical and cognitive aspects. Traditional methods often use univariate regression-based methods to model and predict single degradation conditions and assume population homogeneity, which is inadequate to address the complexity and diversity of aging-related degradation. This study introduces a novel framework for multi-functional degradation modeling that captures the multidimensional (e.g., physical and cognitive) and heterogeneous nature of elderly disabilities. Utilizing deep learning, our approach predicts health degradation scores and uncovers latent heterogeneity from elderly health histories, offering both efficient estimation and explainable insights into the diverse effects and causes of aging-related degradation. A real-case study demonstrates the effectiveness and marks a pivotal contribution to accurately modeling the intricate dynamics of elderly degradation, and addresses the healthcare challenges in the aging population.
Benchmarking Early Agitation Prediction in Community-Dwelling People with Dementia Using Multimodal Sensors and Machine Learning
KITE Research Institute（KITE研究机构）；Toronto Rehabilitation Institute（多伦多康复研究所）；University Health Network（大学健康网络）；Lawrence Bloomberg Faculty of Nursing（劳伦斯布罗德利护理学院）；University of Toronto（多伦多大学）；College of Engineering and Technology（工程与技术学院）；American University of the Middle East（中东美国大学）
DiffBatt: A Diffusion Model for Battery Degradation Prediction and Synthesis
Clausthal University of Technology（克劳斯塔尔工业大学）；Battery LabFactory Braunschweig (BLB)（布伦瑞克电池实验室工厂）；Technische Universität Braunschweig（布伦瑞克工业大学）；Institute of Chemical and Electrochemical Process Engineering（化学与电化学过程工程研究所）；Institute of Automotive Management and Industrial Production（汽车管理与工业生产研究所）；Institute of Machine Tools and Production Technology（机床与生产技术研究所）
Long-Term and Short-Term Transistor Aging in Deep Neural Networks: Impact and Mitigation
Dept. of Electrical and Computer Engineering, New York University (NYU) Tandon School of Engineering（纽约大学电气与计算机工程系（Tandon工程学院））；School of Computation, Information and Technology, Technical University of Munich (TUM)（慕尼黑技术大学计算、信息与技术学院）
Data-efficient crack quantification in lithium-ion cathodes using foundation model transfer
RWTH Aachen University（亚琛工业大学）；Honda Research Institute Europe GmbH（本田欧洲研究院）；Zenules GmbH（Zenules有限公司）；Honda Research Institute USA, Inc.（本田美国研究院）；Juelich Aachen Research Alliance, JARA-Energy（于利希-亚琛研究联盟JARA能源分部）
