Knowledge-infused Deep Learning Enables Interpretable Landslide Forecasting

Forecasting how landslides will evolve over time or whether they will fail is a challenging task due to a variety of factors, both internal and external. Despite their considerable potential to address these challenges, deep learning techniques lack interpretability, undermining the credibility of the forecasts they produce. The recent development of transformer-based deep learning offers untapped possibilities for forecasting landslides with unprecedented interpretability and nonlinear feature learning capabilities. Here, we present a deep learning pipeline that is capable of predicting landslide behavior holistically, which employs a transformer-based network called LFIT to learn complex nonlinear relationships from prior knowledge and multiple source data, identifying the most relevant variables, and demonstrating a comprehensive understanding of landslide evolution and temporal patterns. By integrating prior knowledge, we provide improvement in holistic landslide forecasting, enabling us to capture diverse responses to various influencing factors in different local landslide areas. Using deformation observations as proxies for measuring the kinetics of landslides, we validate our approach by training models to forecast reservoir landslides in the Three Gorges Reservoir and creeping landslides on the Tibetan Plateau. When prior knowledge is incorporated, we show that interpretable landslide forecasting effectively identifies influential factors across various landslides. It further elucidates how local areas respond to these factors, making landslide behavior and trends more interpretable and predictable. The findings from this study will contribute to understanding landslide behavior in a new way and make the proposed approach applicable to other complex disasters influenced by internal and external factors in the future

The Policy Transfer Situation among Chinese National Independent Innovation Demonstration Zones

Chinese national independent innovation demonstration zones are a major organizational mode of Chinese scientific and technological innovation, and a paragon of first movers of technological policies. Since foundation of these demonstration zones, transfer and mutual learning of policies among them have accompanied their growth. Currently, in demonstration zones, policy transfer has become a common phenomenon or even a major source of policy formation. However, due to lack of a systematic knowledge, policy transfer is still largely blind and random, thus seriously restricting the policy innovation ability of demonstration zones and making research into the policy transfer of demonstration zones imperative. This paper adopts the technological policies issued by some national independent innovation demonstration zones from 2006 to 2015 as samples. Through a comprehensive review, these policies are classified into five types, namely technological talents, technological industries, technological enterprises, technological finance and others. Based on the transfer-in and transfer-out of different policies, the development trend of policy transfer in demonstration zones is studied. Meanwhile, combining the importance degree of policy transfer-out, the competitiveness of different types of policies in demonstration zones is analyzed, and characteristics of policy transfer among Chinese demonstration zones are examined. It is hoped that this research can fill the gap of empirical research into transfer of technological policies in China. Keywords: Independent innovation, Demonstration zone, Policy transfer, Policy competitivenes

Evolution of Zhangjiang National Independent Innovation Demonstration Zone’s Administration Function based on Ground Theory

Upon setting up, Zhangjiang National Independent Innovation Demonstration Zone has been playing its pioneer and model roles, who not only had made great achievements in the field of science & technology innovation and industrial park construction, but also had attempted successful reform in the aspect of administration function. The development and innovation of committee’s administration function can influence zhangjiang's capacity for independent innovation profoundly. This paper through the grounded theory analysis of Zhangjiang Demonstration Zone’s work plans from 2011 to 2014, studied the evolution of its administration committee’s administration functions and explored the development tendency of its administrative system reform, so as to provide effective guidance for the future development of Zhangjiang National Independent Innovation Demonstration Zone. Keywords: Grounded theory, Zhangjiang National independent innovation demonstration zone, Administration functio

SWCGAN: Generative Adversarial Network Combining Swin Transformer and CNN for Remote Sensing Image Super-Resolution

Easy and efficient acquisition of high-resolution remote sensing images is of importance in geographic information systems. Previously, deep neural networks composed of convolutional layers have achieved impressive progress in super-resolution reconstruction. However, the inherent problems of the convolutional layer, including the difficulty of modeling the long-range dependency, limit the performance of these networks on super-resolution reconstruction. To address the abovementioned problems, we propose a generative adversarial network (GAN) by combining the advantages of the swin transformer and convolutional layers, called SWCGAN. It is different from the previous super-resolution models, which are composed of pure convolutional blocks. The essential idea behind the proposed method is to generate high-resolution images by a generator network with a hybrid of convolutional and swin transformer layers and then to use a pure swin transformer discriminator network for adversarial training. In the proposed method, first, we employ a convolutional layer for shallow feature extraction that can be adapted to flexible input sizes; second, we further propose the residual dense swin transformer block to extract deep features for upsampling to generate high-resolution images; and third, we use a simplified swin transformer as the discriminator for adversarial training. To evaluate the performance of the proposed method, we compare the proposed method with other state-of-the-art methods by utilizing the UCMerced benchmark dataset, and we apply the proposed method to real-world remote sensing images. The results demonstrate that the reconstruction performance of the proposed method outperforms other state-of-the-art methods in most metrics

Heterogeneous Data Fusion Considering Spatial Correlations using Graph Convolutional Networks and its Application in Air Quality Prediction

Heterogeneous data are commonly adopted as the inputs for some models that predict the future trends of some observations. Existing predictive models typically ignore the inconsistencies and imperfections in heterogeneous data while also failing to consider the (1) spatial correlations among monitoring points or (2) predictions for the entire study area. To address the above problems, this paper proposes a deep learning method for fusing heterogeneous data collected from multiple monitoring points using graph convolutional networks (GCNs) to predict the future trends of some observations and evaluates its effectiveness by applying it in an air quality predictions scenario. The essential idea behind the proposed method is to (1) fuse the collected heterogeneous data based on the locations of the monitoring points with regard to their spatial correlations and (2) perform prediction based on global information rather than local information. In the proposed method, first, we assemble a fusion matrix using the proposed RBF-based fusion approach; second, based on the fused data, we construct spatially and temporally correlated data as inputs for the predictive model; finally, we employ the spatiotemporal graph convolutional network (STGCN) to predict the future trends of some observations. In the application scenario of air quality prediction, it is observed that (1) the fused data derived from the RBF-based fusion approach achieve satisfactory consistency; (2) the performances of the prediction models based on fused data are better than those based on raw data; and (3) the STGCN model achieves the best performance when compared to those of all baseline models. The proposed method is applicable for similar scenarios where continuous heterogeneous data are collected from multiple monitoring points scattered across a study area.Comment: 32 page

Trends in insufficient physical activity among adults in China 2010–18: a population-based study

Abstract Background The global prevalence of insufficient physical activity (PA) was reported to be 27.5% in 2016, and there were stable levels of insufficient PA worldwide between 2001 and 2016. The global target of a 10% reduction in insufficient PA by 2025 will not be met if the trends remain. The relevant data for trends in China were still scarce. This study aimed to determine nationwide temporal trends in insufficient PA among adults in China from 2010 to 2018. Methods 645 903 adults aged 18 years or older were randomly selected from four nationally representative cross-sectional surveys of the China Chronic Disease and Risk Factor Surveillance conducted in 2010, 2013, 2015, and 2018. PA was measured using the Global Physical Activity Questionnaire. Temporal changes in insufficient PA prevalence and participation of domain-specific moderate- to vigorous-intensity PA (MVPA) were analyzed using logistic regression. Results From 2010 to 2018, the age-adjusted prevalence of insufficient PA in China increased from 17.9% (95% confidence interval 16.3% to 19.5%) in 2010 to 22.3% (20.9% to 23.8%) in 2018 (P for trend < 0.001). By age group, with a significant increase in insufficient PA in adults aged 18–34 years (P for trend < 0.001), which rose more rapidly than in adults aged ≥ 35 years (P for interaction < 0.001). Insufficient PA has increased significantly among adults engaged in agriculture-related work, non-manual work, and other manual work (all P for trend < 0.05). And among the occupational groups, those engaged in agriculture-related work had the fastest increase (P for interaction = 0.01). The percentage of adults participating in work-related MVPA decreased from 79.6% (77.8% to 81.5%) to 66.8% (64.9% to 68.7%) along with a decrease in time spent on work-related MVPA, while percentages of adults participating in recreation-related MVPA increased from 14.2% (12.5% to 15.9%) to 17.2% (16.0% to 18.4%) (all P for trend < 0.05). Conclusions Among Chinese adults, an increasing trend was found in insufficient PA from 2010 to 2018, with more than one-fifth of adults failing to achieve the recommendation of adequate PA. More targeted PA promotion strategies should be developed to improve population health

Individual-level and community-level effect modifiers of the temperature-mortality relationship in 66 Chinese communities

Objectives - To examine the modification of temperature-mortality association by factors at the individual and community levels. Design and methods - This study investigated this issue using a national database comprising daily data of 66 Chinese communities for 2006–2011. A ‘threshold-natural cubic spline’ distributed lag non-linear model was utilised to estimate the mortality effects of daily mean temperature, and then examined the modification of the relationship by individual factors (age, sex, education level, place of death and cause of death) using a meta-analysis approach and community-level factors (annual temperature, population density, sex ratio, percentage of older population, health access, household income and latitude) using a meta-regression method. Results - We found significant effects of high and low temperatures on mortality in China. The pooled excess mortality risk was 1.04% (95% CI 0.90% to 1.18%) for a 1°C temperature decrease below the minimum mortality temperature (MMT), and 3.44% (95% CI 3.00% to 3.88%) for a 1°C temperature increase above MMT. At the individual level, age and place of death were found to be significant modifiers of cold effect, while age, sex, place of death, cause of death and education level were effect modifiers of heat effect. At the community level, communities with lower socioeconomic status and higher annual temperature were generally more vulnerable to the mortality effects of high and low temperatures. Conclusions - This study identifies susceptibility based on both individual-level and community-level effect modifiers; more attention should be given to these vulnerable individuals and communities to reduce adverse health effects of extreme temperatures

Additional file 1 of Trends in insufficient physical activity among adults in China 2010–18: a population-based study

Additional file 1:  Appendix2. Data collection of CCDRFS 2010-18. Appendix 3. Analysis plan. Appendix 4.Global Physical Activity Questionnaire. Appendix 5. List of the typical physical activities. Appendix figure 1. Map of China Chronic Disease and Risk Factor Surveillance (CCDRFS) Sites. Appendix figure 2. Percentages of participants interviewed by month and survey. Appendix table 1. Trends in insufficient physical activity in urban and rural adults in China, 2010-18.Appendix table 2. Trends in adults undertaking 150-299 min/week of MVPA in China, 2010 -18. Appendix table 3. Trends in percentages of adults participating in domain-specific MVPA in China, 2010-18.Appendix table 4. Trends in mean min/week of domain-specific MVPA among adults in China, 2010-18. Appendix table 5. Mean domain-specific relative contribution to total MVPA among adults in China, 2010-18. Appendix table 6. Trends in percentages of adults without intensity-specific MVPA in China, 2010-18

The way forward.

Good public-health decisionmaking is dependent on reliable and timely statistics on births and deaths (including the medical causes of death). All high-income countries, without exception, have national civil registration systems that record these events and generate regular, frequent, and timely vital statistics. By contrast, these statistics are not available in many low-income and lower-middle-income countries, even though it is in such settings that premature mortality is most severe and the need for robust evidence to back decisionmaking most critical. Civil registration also has a range of benefits for individuals in terms of legal status, and the protection of economic, social, and human rights. However, over the past 30 years, the global health and development community has failed to provide the needed technical and financial support to countries to develop civil registration systems. There is no single blueprint for establishing and maintaining such systems and ensuring the availability of sound vital statistics. Each country faces a different set of challenges, and strategies must be tailored accordingly. There are steps that can be taken, however, and we propose an approach that couples the application of methods to generate better vital statistics in the short term with capacity-building for comprehensive civil registration systems in the long run