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Abstract. Due to the convenient transportation and construction, cities are prone to be situated in areas with flat terrain and unstable sediments, resulting in the concurrence of ground subsidence and urbanization. Here the interaction between geology, anthropogenic processes and ground subsidence geo-hazards were investigated in the Greater Pearl River Delta region of China. Geological evidences and 2006–2010 persistent scatterer data indicate that anthropogenic activities are dominant, although the distribution of river system and Quaternary sediments are also highly related to significant displacements (primarily at a rate of −15 to 15 mm a−1). The surface displacements derived by synthetic aperture radar interferometry suggest that the urbanization rhythm has to be routinely monitored. Considering analogous urbanization modes, particularly in developing countries, ground subsidence monitoring together with the analysis of its driving force are critical for geo-hazards early-warning, city planning as well as sustainable urbanization.

© 2020 IEEE. AI has powerful capabilities in prediction, automation, planning, targeting, and personalisation. Generally, it is assumed that AI can enable machines to exhibit human-like intelligence, and is claimed to benefit to different areas of our lives. Since AI is fueled by data and is a distinct form of autonomous and self-learning agency, we are seeing increasing ethical concerns related to AI uses. In order to mitigate various ethical concerns, national and international organisations including governmental organisations, private sectors as well as research institutes have made extensive efforts by drafting ethical principles of AI, and having active discussions on ethics of AI within and beyond the AI community. This paper investigates these efforts with a focus on the identification of fundamental ethical principles of AI and their implementations. The review found that there is a convergence around limited principles and the most prevalent principles are transparency, justice and fairness, responsibility, non-maleficence, and privacy. The investigation suggests that ethical principles need to be combined with every stages of the AI lifecycle in the implementation to ensure that the AI system is designed, implemented and deployed in an ethical manner. Similar to ethical framework used in biomedical and clinical research, this paper suggests checklist-style questionnaires as benchmarks for the implementation of ethical principles of AI.

The current COVID-19 pandemic and its uncertainty have given rise to various myths and rumours. These myths spread incredibly fast through social media, which has caused massive panic in society. In this paper, we comprehensively examined the prevailing myths related to COVID-19 in regard to the diffusion of myths, people's engagement with myths and people's subjective emotions to myths. First, we classified the myths into five categories: spread of infection, preventive measures, detection measures, treatment and miscellaneous. We collected the tweets about each category of myths from 1 January to 7 July in the year 2020. We found that the vast majority of the myth tweets were about the spread of the infection. Next, we fitted myths spreading with the SIR epidemic model and calculated the basic reproduction number R0 for each category of myths. We observed that the myths about the spread of infection and preventive measures propagated faster than other categories of myths, and more miscellaneous myths raised and quickly spread from later June 2020. We further analyzed people's emotions evoked by each category of myths and found that fear was the strongest emotion in all categories of myths and around 64% of the collected tweets expressed the emotion of fear. The study in this paper provides insights for authorities and governments to understand the myths during the eruption of the pandemic, and hence enable targeted and feasible measures to demystify the most concerned myths in due time.

The recent COVID-19 pandemic has caused unprecedented impact across the globe. We have also witnessed millions of people with increased mental health issues, such as depression, stress, worry, fear, disgust, sadness, and anxiety, which have become one of the major public health concerns during this severe health crisis. For instance, depression is one of the most common mental health issues according to the findings made by the World Health Organisation (WHO). Depression can cause serious emotional, behavioural and physical health problems with significant consequences, both personal and social costs included. This paper studies community depression dynamics due to COVID-19 pandemic through user-generated content on Twitter. A new approach based on multi-modal features from tweets and Term Frequency-Inverse Document Frequency (TF-IDF) is proposed to build depression classification models. Multi-modal features capture depression cues from emotion, topic and domain-specific perspectives. We study the problem using recently scraped tweets from Twitter users emanating from the state of New South Wales in Australia. Our novel classification model is capable of extracting depression polarities which may be affected by COVID-19 and related events during the COVID-19 period. The results found that people became more depressed after the outbreak of COVID-19. The measures implemented by the government such as the state lockdown also increased depression levels. Further analysis in the Local Government Area (LGA) level found that the community depression level was different across different LGAs. Such granular level analysis of depression dynamics not only can help authorities such as governmental departments to take corresponding actions more objectively in specific regions if necessary but also allows users to perceive the dynamics of depression over the time.

Maize (Zea mays L.) is the most prominent crop in the Northeast Farming Region of China (NFR), and drought has been the largest limitation for maize production in this area during recent decades. The question of how to adapt maize production to drought has received great attention from policy makers, researchers and farmers. In order to evaluate the effects of adaptation strategies against drought and examine the influences of policy supports and farmer households' characteristics on adopting decisions, a large scale household survey was conducted in five representative maize production counties across NFR. Our survey results indicated that using variety diversification, drought resistant varieties and dibbling irrigation are the three major adaptation strategies against drought in spring, and farmers also adopted changes in sowing time, conservation tillage and mulching to cope with drought in spring. About 20% and 18% of households enhanced irrigation against drought in summer and autumn, respectively. Deep loosening tillage and organic fertilizer are also options for farmers to resist drought in summer. Maize yield was highly dependent on soil qualities, with yields on land of high soil quality approximately 1050 kg/ha and 2400 kg/ha higher than for normal and poor soil conditions, respectively. Using variety diversification and drought resistant varieties can respectively increase maize yield by approximately 150 and 220 kg/ha under drought. Conservation tillage increased maize yield by 438–459 kg/ha in drought years. Irrigation improved maize yield by 419–435 kg/ha and 444–463 kg/ha against drought in summer and autumn, respectively. Offering information service, financial and technical support can greatly increase the use of adaptation strategies for farmers to cope with drought. However, only 46% of households received information service, 43% of households received financial support, and 26% of households received technical support against drought from the local government. The maize acreage and the irrigation access are the major factors that influenced farmers' decisions to apply adaptation strategies to cope with drought in each season, but only 25% of households have access to irrigation. This indicates the need for enhanced public support for farmers to better cope with drought in maize production, particularly through improving access to irrigation.

This working paper was produced under the European Union Horizon 2020 funded AGRUMIG project and traces the impact of Covid-19 on migration trends in seven project countries – China, Ethiopia, Kyrgyzstan, Moldova, Morocco, Nepal and Thailand. The context of global migration has changed dramatically due to the coronavirus pandemic. Both within and between countries there has been a substantial curtailment of movement. As a result of multiple lockdowns, economic activity has severely declined and labor markets have ground to a halt, with mass unemployment in industrialized economies looming on the horizon. For both migrant hosting and origin countries – some are substantially both – this poses a set of complex development challenges. Partners of the AGRUMIG project undertook a rapid review of impacts across project countries, exploring the impacts on rural households but also identifying the persistent desire to migrate in spite of restrictions.

The nodal-line semimetals have attracted immense interest due to the unique electronic structures such as the linear dispersion and the vanishing density of states as the Fermi energy approaching the nodes. Here, we report temperature-dependent transport and scanning tunneling microscopy (spectroscopy) [STM(S)] measurements on nodal-line semimetal ZrSiSe. Our experimental results and theoretical analyses consistently demonstrate that the temperature induces Lifshitz transitions at 80 and 106 K in ZrSiSe, which results in the transport anomalies at the same temperatures. More strikingly, we observe a V-shaped dip structure around Fermi energy from the STS spectrum at low temperature, which can be attributed to co-effect of the spin-orbit coupling and excitonic instability. Our observations indicate the correlation interaction may play an important role in ZrSiSe, which owns the quasi-two-dimensional electronic structures. © 2020 American Physical Society. ; This work was supported by the National Key R&D Program (Grants No. 2016YFA0300404, No. 2016YFA0401803, No. 2017YFA0303201, No. 2015CB921103, and No. 2019YFA0308602), the National Nature Science Foundation of China (Grants No. 11674326, No. 11674331, No. 11774351, No. 11874357, No. 11625415, No. 11374260, No. U1432139, No. U1832141, and No. U1932217), Key Research Program of Frontier Sciences, CAS (Grant No. QYZDB-SSW-SLH015), the "Strategic Priority Research Program (B)" of the Chinese Academy of Sciences, Grant No. XDB33030100, the "100 Talents Project" of the Chinese Academy of Sciences, CASHIPS Director's Fund (Grant No. BJPY2019B03) and Science Challenge Project (Grant No. TZ2016001). A portion of this work was supported by the High Magnetic Field Laboratory of Anhui Province, the Fundamental Research Funds for the Central Universities in China, the European Research Council under the European Union's Seventh Framework Program (FP/2007-2013) through ERC Grant No. 338957 and by NWO via Spinoza Prize, and the Cluster of Excellence "The Hamburg Centre for Ultrafast Imaging (CUI)" of the German Science Foundation (DFG).