Persona-based, empathetic approaches can foster sustainable long-term user-agent engagement in aging-in-place contexts. We present PersonaBot, a persona-driven persuasive agent built on a Dual-Persona framework that constructs user personas and generates culturally diverse, gender- and personality-varied agent personas, pairing users with preferred agent personas and adapting them over time. In an eight-week field deployment (8 participants; 1005 participant messages; 2432 agent messages), PersonaBot significantly increased perceived empathy, slowed engagement decline relative to a non-persona baseline, and elicited more elaborative interactions. Effectiveness varied with users’ technological self-efficacy, autonomy preferences, cultural identity, and social patterns, underscoring heterogeneous persona needs. Contrary to our initial assumptions, participants sometimes chose cross-cultural agents for perceived professionalism (over demographic similarity) and favored teacher-like personas balancing authority and warmth. Many framed the agent as a co-pilot rather than a caregiver replacement and engaged selectively, indicating agent personas should respect autonomy and invite—rather than demand—interaction.

Dementia is one of the major causes of disability and dependency among older people. Early detection is the key for preserving the quality of life of the patients and reducing caring costs. The Clock Drawing Test (CDT) is commonly used by clinicians to screen for early signs of dementia. We build an automated CDT that runs on mobile platforms, enabling convenient and frequent self-monitoring and testing at minimal costs. Our system combines both a spatial-temporal approach and a purely image-based deep learning approach to analyze and evaluate the hand-drawn clocks based on established clinical criteria. Our system produces scores that are highly correlated with expert human raters.

Population aging is becoming an increasingly important issue around the world. As people live longer, they also tend to suffer from more challenging medical conditions. Currently, there is a lack of a holistic technology-powered solution for providing quality care at affordable cost to patients suffering from co-morbidity. In this paper, we demonstrate a novel AI-powered solution to provide early detection of the onset of Dementia + Parkinson's disease (DPD) co-morbidity, a condition which severely limits a senior's ability to live actively and independently. We investigate useful in-game behaviour markers which can support machine learning-based predictive analytics on seniors' risk of developing DPD co-morbidity.

L\X-band Full Polarization Aperture Synthesis Microwave Radiometer (FPASMR) is a 2-D aperture synthesis radiometer which can measure the sea salinity and soil moisture. As a key component of FPASMR, the digital correlator has been introduced in this paper from three abstract: the system design, the hardware design and the test results of the demonstrator. The results demonstrate that the digital correlator referred in this paper can ensure the realtime signal sampling and processing of 140 ADC channels which meet the practical requirements.

High-resolution digital elevation models (DEMs) are often limited in spatial coverage; they also may possess systematic artifacts when compared to comprehensive low-resolution maps. Here we correct artifacts and interpolate regions of missing data in airborne Topographic Synthetic Aperture Radar (TOPSAR) DEMs using a low-resolution Shuttle Radar Topography Mission (SRTM) DEM. We use a prediction error (PE) filter to interpolate and fill missing data so that the interpolated regions have the same spectral content as the valid regions of the TOPSAR DEM. The SRTM DEM is used as an additional constraint in the interpolation. We use cross-validation methods to obtain the optimal weighting for the PE filter and SRTM DEM constraints.