With the accelerating growth of the aging population and changes in family structures, the demand for supportive living environments and social interaction among older adults has grown significantly. However, traditional qualitative methods often fail to fully capture the nuanced needs of older adults due to communication barriers, memory limitations, and psychological stress. This study proposes an innovative research approach using an AI-driven age-inclusive digital agent in Mixed Reality (MR) to explore long-term interactions with older adults ageing in place. By creating intelligent digital agents designed after individuals, pets, or objects familiar to older adults, these agents aim to unobtrusively investigate how the residential environment influences daily routines among older people. Insights gained through natural interactions in MR can inform the design of residential spaces, better satisfying the needs of aging populations. This proposed approach aims to improve both home redevelopments and future residential designs for older adults.

The radio map, which describes spatial signal strength and network coverage information, is crucial in modern wireless systems for network planning and resource management. Fine-grained radio maps rely on measurements collected by sparsely deployed spectrum sensors in the area of interest. However, due to physical limitations and security considerations, these measurements may exhibit non-uniform distribution and be entirely absent in certain inaccessible areas, making it challenging for accurate radio map reconstruction. Thus, in this work, considering the issues of non-uniform sampling and inaccessible areas, we propose a deep completion partial convolution network for radio map reconstruction. This approach captures the spatial characteristics by separating the missing measurements from sampled ones and does not require prior knowledge of emitters. We evaluate our method using a simulated dataset for campus environments and demonstrate its effectiveness over several baselines for reconstructing radio maps.

Identification of drug-drug interaction(DDI) is critical for safer and more effective drug co-prescription. As wetlab screening assays are time-consuming, labor-intensive and expensive, it is highly desired to develop an effective computational method to predict drug-drug interactions. In this work, we aim to predict of drug-drug interactions and synergistic drug combinations by proposing an end-to-end multi-task learning framework based on graph convolutional network (GCN). Precisely, we first convert the drug into a molecular graph, in which vertices represent atoms and edges represent chemical bonds. Next, the r-radius subgraph method is applied to molecular graph so that a series of subgraphs are produced for each drug. Next, the subgraphs are used as the input of the graph convolutional network to learn the embedding vector. Finally, the pairwise drug embeddings learned by GCN are concatenated as input into a fully-connected layer for predicting drug-drug interaction and synergistic effects. We conducted extensive performance evaluations on different data sets, including benchmark DDI data sets and manually collected drug combination data sets, and the results show that our proposed method is significantly better than the newly proposed methods (DeepCCI) and four typical machine learning methods (FFNN, SVM, RF, AdaBoost). In addition, our case study showed that 11 our of top 20 predicted DDIs have been reported by PubMed literature.

Predicting adverse drug reactions (ADRs) plays a critical role in developing new drugs and preventing adverse reactions during the treatment of existing drugs. However, with the rapid progress of machine learning technology, a new situation has been opened up in ADRs prediction. Using appropriate machine learning methods with existing data can achieve high prediction performance, attracting more researchers. This review describes commonly used features (biological, chemical, and phenotypic features) and machine learning algorithms.