Community Facilities and the Health of Older Adults in China

Previous research has indicated that there is an association between community characteristics and health status among older adults, but the mechanisms underlying this relationship, and what factors may moderate this relationship are unclear. This study attempts to fill this gap by assessing whether physical/social activities mediate the relationship between community facilities and the health of older adults using CHARLS 2011 survey including 6,651 older adults in China. In addition, this study tests gender differences in this relationship. Communities are primarily operationalized using government-defined boundaries. Health status is characterized by overall self-rated health and functional limitation. As predicted, this study found out older adults who live in the community with more of a variety of community facilities are healthier. The current study shows that physical activity and social activity are significantly positively associated with self-rated health and negatively associated with functional limitation, but they do not mediate the relationship between variety of community facilities and health outcomes. This study also found out positive effects of variety is more evident on women than men

CD-CNN: A Partially Supervised Cross-Domain Deep Learning Model for Urban Resident Recognition

Driven by the wave of urbanization in recent decades, the research topic about migrant behavior analysis draws great attention from both academia and the government. Nevertheless, subject to the cost of data collection and the lack of modeling methods, most of existing studies use only questionnaire surveys with sparse samples and non-individual level statistical data to achieve coarse-grained studies of migrant behaviors. In this paper, a partially supervised cross-domain deep learning model named CD-CNN is proposed for migrant/native recognition using mobile phone signaling data as behavioral features and questionnaire survey data as incomplete labels. Specifically, CD-CNN features in decomposing the mobile data into location domain and communication domain, and adopts a joint learning framework that combines two convolutional neural networks with a feature balancing scheme. Moreover, CD-CNN employs a three-step algorithm for training, in which the co-training step is of great value to partially supervised cross-domain learning. Comparative experiments on the city Wuxi demonstrate the high predictive power of CD-CNN. Two interesting applications further highlight the ability of CD-CNN for in-depth migrant behavioral analysis.Comment: 8 pages, 5 figures, conferenc

The Art of SOCRATIC QUESTIONING: Zero-shot Multimodal Reasoning with Recursive Thinking and Self-Questioning

Chain-of-Thought prompting (CoT) enables large-scale language models to solve complex reasoning problems by decomposing the problem and tackling it step-by-step. However, Chain-of-Thought is a greedy thinking process that requires the language model to come up with a starting point and generate the next step solely based on previous steps. This thinking process is different from how humans approach a complex problem e.g., we proactively raise sub-problems related to the original problem and recursively answer them. In this work, we propose Socratic Questioning, a divide-and-conquer fashion algorithm that simulates the self-questioning and recursive thinking process. Socratic Questioning is driven by a Self-Questioning module that employs a large-scale language model to propose sub-problems related to the original problem as intermediate steps and Socratic Questioning recursively backtracks and answers the sub-problems until reaches the original problem. We apply our proposed algorithm to the visual question-answering task as a case study and by evaluating it on three public benchmark datasets, we observe a significant performance improvement over all baselines on (almost) all datasets. In addition, the qualitative analysis clearly demonstrates the intermediate thinking steps elicited by Socratic Questioning are similar to the human's recursively thinking process of a complex reasoning problem.Comment: 15 pages, 12 figure, 2 algorithm

Thermoacoustic heat pump utilizing medium/low-grade heat sources for domestic building heating

Thermoacoustic heat pumps are a promising heating technology that utilizes medium/low-grade heat to reduce reliance on electricity. This study proposes a single direct-coupled configuration for a thermoacoustic heat pump, aimed at minimizing system complexity and making it suitable for domestic applications. Numerical investigations were conducted under typical household heating conditions, including performance analysis, exergy loss evaluation, and axial distribution of key parameters. Results show that the proposed thermoacoustic heat pump achieves a heating capacity of 5.7 kW and a coefficient of performance of 1.4, with a heating temperature of 300 °C and a heat-sink temperature of 55 °C. A comparison with existing absorption heat pumps reveals favorable adaptability for large temperature lift applications. A case study conducted in Finland over an annual cycle analyzes the economic and environmental performance of the system, identifying two distinct modes based on the driving heat source: medium temperature (≥250 °C) and low temperature (<250 °C), both of which exhibit favorable heating performance. When the thermoacoustic heat pump is driven by waste heat, energy savings of 20.1 MWh/year, emission reductions of 4143 kgCO$_2$/year, and total environmental cost savings of 1629 €/year are obtained. These results demonstrate the potential of the proposed thermoacoustic heat pump as a cost-effective and environmentally friendly option for domestic building heating using medium/low-grade heat sources

Standardized Volume Power Density Boost in Frequency-Up Converted Contact-Separation Mode Triboelectric Nanogenerators

The influence of a mechanical structure’s volume increment on the volume power density (VPD) of triboelectric nanogenerators (TENGs) is often neglected when considering surface charge density and surface power density. This paper aims to address this gap by introducing a standardized VPD metric for a more comprehensive evaluation of TENG performance. The study specifically focuses on 2 frequency-up mechanisms, namely, the integration of planetary gears (PG-TENG) and the implementation of a double-cantilever structure (DC-TENG), to investigate their impact on VPD. The study reveals that the PG-TENG achieves the highest volume average power density, measuring at 0.92 W/m3. This value surpasses the DC-TENG by 1.26 times and the counterpart TENG by a magnitude of 69.9 times. Additionally, the PG-TENG demonstrates superior average power output. These findings introduce a new approach for enhancing TENGs by incorporating frequency-up mechanisms, and highlight the importance of VPD as a key performance metric for evaluating TENGs

Sentence Specified Dynamic Video Thumbnail Generation

With the tremendous growth of videos over the Internet, video thumbnails, providing video content previews, are becoming increasingly crucial to influencing users' online searching experiences. Conventional video thumbnails are generated once purely based on the visual characteristics of videos, and then displayed as requested. Hence, such video thumbnails, without considering the users' searching intentions, cannot provide a meaningful snapshot of the video contents that users concern. In this paper, we define a distinctively new task, namely sentence specified dynamic video thumbnail generation, where the generated thumbnails not only provide a concise preview of the original video contents but also dynamically relate to the users' searching intentions with semantic correspondences to the users' query sentences. To tackle such a challenging task, we propose a novel graph convolved video thumbnail pointer (GTP). Specifically, GTP leverages a sentence specified video graph convolutional network to model both the sentence-video semantic interaction and the internal video relationships incorporated with the sentence information, based on which a temporal conditioned pointer network is then introduced to sequentially generate the sentence specified video thumbnails. Moreover, we annotate a new dataset based on ActivityNet Captions for the proposed new task, which consists of 10,000+ video-sentence pairs with each accompanied by an annotated sentence specified video thumbnail. We demonstrate that our proposed GTP outperforms several baseline methods on the created dataset, and thus believe that our initial results along with the release of the new dataset will inspire further research on sentence specified dynamic video thumbnail generation. Dataset and code are available at https://github.com/yytzsy/GTP