Depression is a prevalent and recurrent mental disorder affecting over one billion individuals annually, imposing significant burdens on healthcare systems and patient families. Although smart technologies facilitate real-time monitoring and data collection in depression care, research seldom examines mechanisms for preserving shared well-being between patients and family caregivers. This study introduces CloudEcho, a collaborative home-care management tool powered by fine-tuned Large Conversational Language Models (LLMs). CloudEcho enables free-form patient–LLM dialogue that adheres to mental health guidelines and supports timely sharing of personal experiences and vital clinical information when caregivers are unavailable. An exploratory evaluation involving 50 patients and five mental health professionals demonstrated enhancements in patient–caregiver communication, caregiver empathy, and the richness of patient daily records for diagnostic use. These findings offer insights into LLM integration in mental health and inform design recommendations for future AI-supported home-care systems.

This study aims to investigate the key factors affecting the use and interaction of wearable ECG devices from the user's perception. A conceptual model is proposed that combines an expectation-confirmation model with facilitating and inhibiting factors. Besides, health consciousness is set as a moderating variable. A quantitative study is conducted with users who have real-world experience with wearable ECG devices, the findings suggest that the perceived availability and compatibility of wearable ECG devices have a positive effect on confirmation and satisfaction. Technical anxiety and transition costs negatively affect satisfaction but have no impact on confirmation. Health consciousness can mitigate the negative effects of technological anxiety and transition costs while positively moderating the impact of perceived compatibility on satisfaction. The study recommendations focus on optimizing product reliability and real-time visual feedback, while ensuring multi-scenario compatibility and adopting differentiated design strategies. Additionally, incorporating health education and reward mechanisms is suggested.

Micro Advice is a mobile platform for democratic governance in China, allowing access to voice social issues and advice to the government with the aim of improving people's livelihood. However, due to the lack of first-hand experience, the current understanding of how end-users utilize Micro Advice to participate in democratic governance is incomplete. We interviewed 12 users to understand their practices and challenges in using the platform. Specifically, we illustrate the user's experience, introduce what difficulties they encountered, and how they strategically use the platform to improve people's livelihood. We also investigate the socio-technical aspects of Micro Advice within the Chinese political context, discussing how to accept and utilize Micro Advice in China's social environment, and develop technological solutions adapted to these backgrounds. Finally, we propose some design implications for civic technology participation platforms. Micro Advice provides a novel, open, and real-time channel for civic engagement, showcasing the practical effects and impact of digitized civic engagement in China. It offers researchers a new perspective for expressing and addressing societal issues. We believe that the innovation and insights of Micro Advice can extend to other types of digitized civic engagement initiatives. We will continue to explore the interactive processes between the government and the public, along with innovative technological approaches.

Collaboration can amalgamate diverse ideas, styles, and visual elements, fostering creativity and innovation among different designers. In collaborative design, sketches play a pivotal role as a means of expressing design creativity. However, designers often tend to not openly share these meticulously crafted sketches. This phenomenon of data island in the design area hinders its digital transformation under the third wave of AI. In this paper, we introduce a Federated Generative Artificial Intelligence Clothing system, namely StyleWe, employing federated learning to aid in sketch design. StyleWe is committed to establishing an ecosystem wherein designers can exchange sketch styles among themselves. Through StyleWe, designers can generate sketches that incorporate various designers' styles from their peers, drawing inspiration from collaboration without the need for data disclosure or upload. Extensive performance evaluations and user studies indicate that our StyleWe system can produce multi-styled sketches of comparable quality to human-designed ones while significantly enhancing efficiency compared to hand-drawn sketches.

Hand-drawn sketches and sketch colourization are the most laborious but necessary steps for fashion designers to design exquisite clothes, especially when the fashion design requires distinctive and personal characteristics from designer style. This paper presents an artificial intelligent aided fashion design system, namely StyleMe, to support the automatic generation of clothing sketches with designer style. Given the clothing pictures specified by the designer, StyleMe can use deep learning based generative model to generate clothing sketches that are consistent with the designer style. The system also supports intelligent colourization on clothing sketch by style transfer, according to specified styles from the real fashion images. Through a series of performance evaluations and user studies, we found that our system can generate effective clothing sketches as good as fashion designers’ human work, and significantly improve the efficiency of fashion design with its sketch colourization method.

Recently, deep learning has been introduced to classify hyperspectral images (HSIs) and achieved effective performance. In general, the previous networks are not enough deep, which might not extract very discriminant features for classification. In addition, they do not consider strong correlations among different hierarchical layers. Due to the two problems, a hybrid deep residual network is presented for HSIs classification in this paper. The proposed method firstly employs deep residual network (DRN) to extract very deep and discriminant features of HSIs. The DRN can help to overcome the decrease of classification accuracy that is caused by the increasing network depth and limited available training samples. Moreover, by incorporating different hierarchical features of network with a hybrid mechanism, the classification results can be further improved. Experimental results on a real hyperspectral image demonstrate that the proposed method outperforms other competitive methods.