With the increasingly rapid aging of the global population, the population of middle-aged and older people with disabilities has been increasing, and thus disability has gradually been highlighted as an important issue for the middle-aged and elderly population. In this article, a 1-D convolutional neural network (1DCNN) model incorporating the convolutional block attention module (CBAM), i.e., the 1DCBAMCNN model, is designed and applied to the risk assessment of disability in the elderly. The 1DCNN effectively captures both local and global features within the data through convolutional operations. By directly analyzing tabular data related to disabilities in the elderly, it circumvents the information loss and increased computational complexity associated with dimensionality transformation. Additionally, the CBAM, which comprises channel attention and spatial attention modules, facilitates fine-grained feature selection and weighting of the input data. The integration of these two components not only leverages the robust feature extraction capabilities of the 1DCNN but also utilizes the attention mechanism of CBAM to delve deeper into the abstract characteristics of the data, significantly enhancing the model's sensitivity to data features and effectively improving predictive performance. The model proposed in this article (1DCBAMCNN) experiments using the China Health and Retirement Longitudinal Study (CHARLS) dataset, and the model is compared with deep learning methods such as convolutional neural network (CNN), artificial neural network, and long short-term memory, as well as with the CNN model that incorporates the squeeze-and-excitation module, the efficient channel attention module, and the self-attention, respectively. The experimental results show that 1DCBAMCNN has better prediction results on the test set compared to other models, with accuracy and F1 score reaching 92.46% and 80.25%, respectively. The 1DCBAMCNN model presented in this article can more effectively predict disabilities within the elderly population, providing a basis for diagnosis and treatment.

This article presents a multiattribute group decision-making (MAGDM) method using cloud models to handle decision problems with gray uncertain linguistic variables that contain both subjective and objective uncertainty. In this new method, the new concepts of gray uncertain linguistic variable and gray comprehensive cloud are defined, and a method for converting gray uncertain linguistic variables into gray comprehensive clouds is provided. A gray comprehensive clouds-weighted averaging (GCC-WA) operator is proposed to aggregate the information with multiple gray comprehensive clouds. A distance measure between two gray comprehensive clouds is defined to form the gray comprehensive cloud-hybrid closeness degree (GCC-HCD), which is then applied to rank the alternatives. An example of an offshore mining investment is applied to validate the method. Sensitivity analysis and comparison analysis are carried out with other congeneric methods.

Comparison of fuzzy variables is considered one of the most important topics in fuzzy theory. A new approach for ranking fuzzy variable via credibility measure — fuzzy dominance is presented in this paper. Some basic properties of fuzzy dominance are investigated. As an illustration, the cases of fuzzy dominance rule for triangular fuzzy variables are examined.