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During the last three years, numerous research papers have been reported which use social media data to explore several issues related to the COVID-19 pandemic. Bibliometric methods in this work are used to analyze 1427 peer-reviewed documents from the last three years extracted from the Web of Science database. The results of this study show that there was high growth in publications in open access journals with an annual rate reaching 19.3% and they also identify the top cited journals and research papers. The thematic analysis of papers shows that research topics related to social media for surveillance and monitoring of public attitudes and perceptions, mental health, misinformation, and fake news are important and well-developed, whereas topics related to distance-learning education with social media are emerging. The results also show that the USA, China, and the UK have published many papers and received a high number of citations because of their strong international collaboration. [ABSTRACT FROM AUTHOR]

The land use, land-use change and forestry (LULUCF) sector, as a source and a sink of greenhouse gas (GHG) emissions, is critical for achieving carbon neutrality. Many academic journals have published papers on land use carbon emission or sink (LUCES), but LUCES reviews are relatively rare, which poses great challenges in accurately understanding the research progress and future prospects. This work analyzes the research characteristics, hotspots and future perspectives of LUCES research by using a bibliometric analysis (such as DDA, VOSviewer, CiteSpace software) and a review based on the data (6115 scientific papers) during 1991–2023 from the Web of Science (WoS) platform. We found that (1) over the past 33 years, it first presented a steady growth, then fluctuating growth, and finally a rapid growth trend in the yearly number of publications in LUCES research. The USA (17.31%), China (14.96%), and the UK (7.37%) occupy a dominant position in this research field. (2) The related LUCES research is interdisciplinary, which mainly cover science and technology, meteorology and atmospheric sciences, geology, and environmental sciences and ecology disciplines. (3) The research hotspot analysis on LUCES shows that these articles mostly covered the follow three aspects: ecosystem services, climate change, and carbon neutrality. (4) A review of the past LUCES literature suggests that it is mainly focused on exploring the forefront issues in terms of the definition and boundaries, evaluation method and influencing factors, etc. This work suggests that further research could explore the main scientific problems on quantification of land-based carbon neutrality, quantitative analysis of the impact mechanisms, as well as interdisciplinary research and collaborative governance needed for carbon neutrality. [ABSTRACT FROM AUTHOR]

This paper explores the development of a smart Structural Health Monitoring (SHM) platform tailored for long-span bridge monitoring, using the Forth Road Bridge (FRB) as a case study. It discusses the selection of smart sensors available for real-time monitoring, the formulation of an effective data strategy encompassing the collection, processing, management, analysis, and visualization of monitoring data sets to support decision-making, and the establishment of a cost-effective and intelligent sensor network aligned with the objectives set through comprehensive communication with asset owners. Due to the high data rates and dense sensor installations, conventional processing techniques are inadequate for fulfilling monitoring functionalities and ensuring security. Cloud-computing emerges as a widely adopted solution for processing and storing vast monitoring data sets. Drawing from the authors' experience in implementing long-span bridge monitoring systems in the UK and China, this paper compares the advantages and limitations of employing cloud- computing for long-span bridge monitoring. Furthermore, it explores strategies for developing a robust data strategy and leveraging artificial intelligence (AI) and digital twin (DT) technologies to extract relevant information or patterns regarding asset health conditions. This information is then visualized through the interaction between physical and virtual worlds, facilitating timely and informed decision-making in managing critical road transport infrastructure. [ABSTRACT FROM AUTHOR]

The world is facing the challenge of increasing grain production and improving the environment, in which the treatment of diffuse water pollution from agriculture is the important content. Traditional administrative means are still unable to solve the problem of market failure and government failure in controlling water pollution. Economic policy instruments have more advantages in improving market economics and reducing the cost of environmental governance and supervision. They have become an important way to solve pollution and promote the transformation of water pollution prevention and control management. This paper puts forward suggestions and countermeasures for improving China's economic policy instruments by systematically sorting out and analyzing the EPIs in China and the UK. Starting from the whole process of agricultural production, China's water quality governance needs to follow three principles to innovate and comprehensively utilize economic policy instruments. A transparent multi-party information sharing and an efficient supervision system are invisible to water quality governance. China also needs to continue to deepen reforms and pilot projects, especially in terms of governance objectives, public welfare funds, water prices, tradable water rights, and emission rights. This paper could also provide a reference for water pollution control in other developing countries. [ABSTRACT FROM AUTHOR]

Acknowledging the extreme risk COVID-19 poses to humans, this paper attempted to analyze and compare case fatality rates, identify the existence of learning curves for COVID-19 medical treatments, and examine the impact of vaccination on fatality rate reduction. Confirmed cases and deaths were extracted from the "Daily Situation Report" provided by the World Health Organization. The results showed that low registration and low viral test rates resulted in low fatality rates, and the learning curve was significant for all countries except China. Treatment for COVID-19 can be improved through repeated experience. Vaccinations in the U.K. and U.S.A. are highly effective in reducing fatality rates, but not in other countries. The positive impact of vaccines may be attributed to higher vaccination rates. In addition to China, this study identified the existence of learning curves for the medical treatment of COVID-19 that can explain the effect of vaccination rates on fatalities. [ABSTRACT FROM AUTHOR]

75.6% of land comprising Hong Kong remains undeveloped according to the special administrative region's planning department. In turn, Hong Kong's constricted real estate, now estimated to be the world's costliest, has created eye-popping living arrangements historically and contemporarily. Denizens' colorful reputation and imagination for flouting city ordinances, zoning laws, and spatial management stand emblematic of tenacious self-sufficiency and a free-spirited brand of runaway capitalist initiative. Why is this conspicuous trademark of Hong Kong's societal fabric very much alive in the 21st Century? Why does this matter in a rapidly urbanizing world witnessing the ascension of mega-urban centers alongside ever-widening socioeconomic chasms? This paper intends to illuminate the peculiar origins and longevity of the KowloonWalled City, an urban monolith of notoriety and autonomy that blossomed in a semi-legal grey zone unencumbered under British protectorate rule for nearly a century. Parallels will connect the linear trajectory between Kowloon's hardnosed living to today's comparable Chungking Mansions and the hundreds of thousands of cage homes appearing in all corners of the city. This paper aims to answer why these residential paradoxes continue to function with efficiency and relevancy, posing solutions for indigent housing while exacerbating the stigma of social and economic ostracism. [ABSTRACT FROM AUTHOR]

Over the past three decades, there have been many comprehensive studies related to public–private partnerships (PPP), but the analysis at the macro level still lacks comprehensiveness and interpretability. Through the application of bibliometric analysis, 2-mode network, and strategic coordinate analysis, we systematically analyzed the derivative characteristics of the literature data and the coupling characteristics of countries and keywords. Moreover, through the frequency and betweenness centrality, etc., this paper determines the evolution path of keywords and the evolution direction of theme words and realizes the prediction of theme words and keywords in the future. The results show that: (1) Through the three-stage biclustering analysis, we determined the hot theme words and hot keywords for each stage and focused the theme direction and main research content of the evolution, which led to great interpretability of the data analysis in the literature characteristics; (2) Through the distribution characteristics of time and space, the USA, China, the UK and other mainstream publishing countries and their main research hotspots were determined. Among them, developing countries have strong willingness in academic cooperation and great potential for academic development; (3) According to the derivative characteristics of the literature data, it is predicted that the future research hotspots are: the integration of business economy and sustainability, the integration of policy support and innovative technology application, and the urbanization promotion of developing countries. Based on the findings, this study makes concrete and targeted research methods and provides reference value and application value for the future research and analysis of PPP. [ABSTRACT FROM AUTHOR]

Various facial colour cues were identified as valid predictors of facial attractiveness, yet the conventional univariate approach has simplified the complex nature of attractiveness judgement for real human faces. Predicting attractiveness from colour cues is difficult due to the high number of candidate variables and their inherent correlations. Using datasets from Chinese subjects, this study proposed a novel analytic framework for modelling attractiveness from various colour characteristics. One hundred images of real human faces were used in experiments and an extensive set of 65 colour features were extracted. Two separate attractiveness evaluation sets of data were collected through psychophysical experiments in the UK and China as training and testing datasets, respectively. Eight multivariate regression strategies were compared for their predictive accuracy and simplicity. The proposed methodology achieved a comprehensive assessment of diverse facial colour features and their role in attractiveness judgements of real faces; improved the predictive accuracy (the best-fit model achieved an out-of-sample accuracy of 0.66 on a 7-point scale) and significantly mitigated the issue of model overfitting; and effectively simplified the model and identified the most important colour features. It can serve as a useful and repeatable analytic tool for future research on facial impression modelling using high-dimensional datasets. [ABSTRACT FROM AUTHOR]

During the outbreak of a disease caused by a pathogen with unknown characteristics, the uncertainty of its progression parameters can be reduced by devising methods that, based on rational assumptions, exploit available information to provide actionable insights. In this study, performed a few (~6) weeks into the outbreak of COVID-19 (caused by SARS-CoV-2), one of the most important disease parameters, the average time-to-recovery, was calculated using data publicly available on the internet (daily reported cases of confirmed infections, deaths, and recoveries), and fed into an algorithm that matches confirmed cases with deaths and recoveries. Unmatched cases were adjusted based on the matched cases calculation. The mean time-to-recovery, calculated from all globally reported cases, was found to be 18.01 days (SD 3.31 days) for the matched cases and 18.29 days (SD 2.73 days) taking into consideration the adjusted unmatched cases as well. The proposed method used limited data and provided experimental results in the same region as clinical studies published several months later. This indicates that the proposed method, combined with expert knowledge and informed calculated assumptions, could provide a meaningful calculated average time-to-recovery figure, which can be used as an evidence-based estimation to support containment and mitigation policy decisions, even at the very early stages of an outbreak. [ABSTRACT FROM AUTHOR]

Pig manure currently results in sizeable greenhouse gas emissions, during storage and spreading to land. Anaerobic digestion and hydrothermal carbonisation could provide significant greenhouse gas mitigation, as well as generate renewable heat and power (with anaerobic digestion), or a peat-like soil amendment product (with hydrothermal carbonisation). The greenhouse gas mitigation potential associated with avoidance of pig manure storage and spreading in the UK, EU, and China, as well as the potential to provide heat and power by anaerobic digestion and soil amendment products by hydrothermal carbonisation was herein determined. In each case, the mono-conversion of pig manure is compared to co-conversion with a 50:50 mixture of pig manure with grass. Anaerobic digestion displayed a greater greenhouse gas mitigation potential than hydrothermal carbonisation in all cases, and co-processing with grass greatly enhances greenhouse gas mitigation potential. China has the largest greenhouse gas mitigation potential (129 MT CO2 eq), and greatest mitigation per kg of pig manure (1.8 kgCO2/kg pig manure volatile solids). The energy grid carbon intensity has a significant impact on the greenhouse gas mitigation potential of the different approaches in the different regions. Pig manure is generated in large amounts in China, and the energy generated from biogas offsets a higher carbon intensity grid. Greenhouse gas savings from the anaerobic digestion of pig manure and grass have been calculated to provide a significant potential for reducing total greenhouse gas emissions representation in China (1.05%), the EU (0.92%), and the UK (0.19%). Overall, the utilisation of pig manure could bring about substantial greenhouse savings, especially through co-digestion of pig manure with grass in countries with large pig farming industries and carbon intense energy mixes. [ABSTRACT FROM AUTHOR]

The increase of urbanization is affecting the urban food system (UFS) in many areas, primarily production, processing, and consumption. The upgrading of the urban food consumption structure not only puts forward higher food production requirements, but also poses a challenge to resource consumption and technological innovation. Considerable case or review studies have been conducted on UFS, but there is no bibliometric review attempting to provide an objective and comprehensive analysis of the existing articles. In this study, we selected 5360 research publications from the core Web of Science collection from 1991 to 2020, analyzing contributions of countries, institutions, and journals. In addition, based on keyword co-occurrence and clustering analyses, we evaluated the research hotspots of UFS. The results show that global research interest in UFS has increased significantly during these three decades. The USA, China, and the UK are the countries with the highest output and closest collaborations. UFS research involves multiple subject categories, with environmental disciplines becoming mainstream. Food security, food consumption, and food waste are the three main research areas. We suggest that food sustainability and resilience, food innovation, and comparative studies between cities should be given more attention in the future. [ABSTRACT FROM AUTHOR]

When traditional financial institutions faced difficulties in the task of assisting micro, small and medium-sized enterprises (MSMEs) with capital allocations, crowdfunding can upsurge as an innovative and vibrant vehicle that can support and assist the activity of such MSMEffs, by financing their activity and instrumenting the process of risk-sharing. Simultaneously with its enormous growth and popularity, crowdfunding is faced by several key challenges, one of biggest such challenges referring to the problem of information asymmetry that can exist between fundraisers and potential backers. Based on the signaling theory, a research taxonomy has been developed for a comparative analysis between China and the UK. This has been accomplished by retrieving secondary data from the following crowdfunding platforms: Dreamore (Chinese platform) and Crowdfunder (UK platform). The objective of the study is to investigate both the effect and the impact that signals (goal setting, project comments and updates) have upon mitigating the problem of information asymmetry, in order to make the project successful. We have thus deployed an Ordinary Least Square (OLS) regression and validated the models through a robustness check. The findings reveal that signals actively mitigate the problem of information asymmetry in both countries, but this varies in the sense that higher goal setting has a more positive/impactful relationship with project success in the UK than it does in China. Project comments are more positively associated with project success in China as compared to the UK, whereas project updates are more negatively related to project success in China as compared to the UK. These findings demonstrate the importance that signals have upon successful crowdfunding activities/campaigns, highlighting the theoretical and practical influence and relevance for potential fundraisers in the two aforementioned economies. [ABSTRACT FROM AUTHOR]

We explore whether individuals are averse to telling a Pareto white lie—a lie that benefits both themselves and another. We first review and summarize the existing evidence on Pareto white lies. We find that the evidence is relatively limited and varied in its conclusions. We then present new experimental results obtained using a coin-tossing experiment. Results are provided for both the UK and China. We find evidence of willingness to tell a partial lie (i.e., inflating reports slightly) and high levels of aversion to telling a Pareto white lie that would maximize payoffs. We also find no significant difference between willingness to tell a Pareto white lie and a selfish black lie—a lie that harms another. We find marginal evidence of more lying in China than the UK, but the overall results in the UK and China are very similar. [ABSTRACT FROM AUTHOR]

Clinical criteria/Family history-based BRCA testing misses a large proportion of BRCA carriers who can benefit from screening/prevention. We estimate the cost-effectiveness of population-based BRCA testing in general population women across different countries/health systems. A Markov model comparing the lifetime costs and effects of BRCA1/BRCA2 testing all general population women ≥30 years compared with clinical criteria/FH-based testing. Separate analyses are undertaken for the UK/USA/Netherlands (high-income countries/HIC), China/Brazil (upper–middle income countries/UMIC) and India (low–middle income countries/LMIC) using both health system/payer and societal perspectives. BRCA carriers undergo appropriate screening/prevention interventions to reduce breast cancer (BC) and ovarian cancer (OC) risk. Outcomes include OC, BC, and additional heart disease deaths and incremental cost-effectiveness ratio (ICER)/quality-adjusted life year (QALY). Probabilistic/one-way sensitivity analyses evaluate model uncertainty. For the base case, from a societal perspective, we found that population-based BRCA testing is cost-saving in HIC (UK-ICER = $−5639/QALY; USA-ICER = $−4018/QALY; Netherlands-ICER = $−11,433/QALY), and it appears cost-effective in UMIC (China-ICER = $18,066/QALY; Brazil-ICER = $13,579/QALY), but it is not cost-effective in LMIC (India-ICER = $23,031/QALY). From a payer perspective, population-based BRCA testing is highly cost-effective in HIC (UK-ICER = $21,191/QALY, USA-ICER = $16,552/QALY, Netherlands-ICER = $25,215/QALY), and it is cost-effective in UMIC (China-ICER = $23,485/QALY, Brazil−ICER = $20,995/QALY), but it is not cost-effective in LMIC (India-ICER = $32,217/QALY). BRCA testing costs below $172/test (ICER = $19,685/QALY), which makes it cost-effective (from a societal perspective) for LMIC/India. Population-based BRCA testing can prevent an additional 2319 to 2666 BC and 327 to 449 OC cases per million women than the current clinical strategy. Findings suggest that population-based BRCA testing for countries evaluated is extremely cost-effective across HIC/UMIC health systems, is cost-saving for HIC health systems from a societal perspective, and can prevent tens of thousands more BC/OC cases. [ABSTRACT FROM AUTHOR]

Fatality rates such as fatalities per full-time equivalent workers are officially used to compare the risk level of the construction industry among various countries. However, each country evaluates the fatality rate using different conditions. This paper presents the comparison of fatality rates of various countries using conventional (national data) and pair (equivalent condition) methods through a time-series approach. The research was conducted in three stages. The risk level was evaluated in order in South Korea (1.54), Japan (0.84), Mexico (0.83), China (0.70), United Kingdom (0.15), and Singapore (0.13) in terms of national data. However, the risk level was re-evaluated in order in China (2.27), South Korea (2.05), Mexico (1.23), Singapore (0.98), Japan (0.80), and United Kingdom (0.47) in terms of equivalent conditions. The risk level of each can be changed when the fatality rate is compared under given equivalent conditions.

Objective: This study aimed to analyze the progression and trends of multimorbidity in the elderly in China and internationally from a bibliometric perspective, and compare their differences on hotspots and research fronts., Methods: Publications between January 2001 and August 2021 were retrieved from WOS and CNKI databases. Endnote 20 and VOSviewer 1.6.8 were used to summarize bibliometric features, including publication years, journals, and keywords, and the co-occurrence map of countries, institutions, and keywords was drawn., Results: 3857 research papers in English and 664 research papers in Chinese were included in this study. The development trends of multimorbidity in the elderly are fully synchronized in China and other countries. They were divided into germination period, development period, and prosperity period. Research literature in English was found to be mainly focused on public health, and the IF of the literature is high; In China, however, most research papers are in general medicine and geriatrics with fewer core journals. Co-occurrence analysis based on countries and institutions showed that the most productive areas were the United States, Canada, the United Kingdom, and Australia, while the Chinese researchers have made little contribution. The clustering analysis of high-frequency keywords in China and around the globe shows that the hotspots have shifted from individual multimorbidity to group multimorbidity management. Sorting out the top 10 highly cited articles and highly cited authors, Barnett, K's article published in Lancet in 2012 is regarded as a milestone in the field., Conclusion: Multimorbidity in the elderly leads to more attention in the world. Although China lags behind global research the research fronts from disease-centered to patient-centered, and individual management to population management is consistent.

At the end of December 2019, an outbreak of COVID-19 occurred in Wuhan city, China. Modelling plays a crucial role in developing a strategy to prevent a disease outbreak from spreading around the globe. Models have contributed to the perspicacity of epidemiological variations between and within nations and the planning of desired control strategies. In this paper, a literature review was conducted to summarise knowledge about COVID-19 disease modelling in three countries-China, the UK and Australia-to develop a robust research framework for the regional areas that are urban and rural health districts of New South Wales, Australia. In different aspects of modelling, summarising disease and intervention strategies can help policymakers control the outbreak of COVID-19 and may motivate modelling disease-related research at a finer level of regional geospatial scales in the future.

This paper studies the interplay between social distancing and the spread of the COVID-19 disease-a global pandemic that has affected most of the world's population. Our goals are to (1) to observe the correlation between the strictness of social distancing policies and the spread of disease and (2) to determine the optimal adoption level of social distancing policies. The earliest instances of the virus were found in China, and the virus has reached the United States with devastating consequences. Other countries severely affected by the pandemic are Brazil, Russia, the United Kingdom, Spain, India, Italy, and France. Although it is impossible to stop it, it is possible to slow down its spread to reduce its impact on the society and economy. Governments around the world have deployed various policies to reduce the virus spread in response to the pandemic. To assess the effectiveness of these policies, the system's dynamics of the society needs to be analyzed, which is generally not possible with mathematical linear equations or Monte Carlo methods because human society is a complex adaptive system with continuous feedback loops. Because of the challenges with the other methods, we chose agent-based methods to conduct our study. Moreover, recent agent-based modeling studies for the COVID-19 pandemic show significant promise in assisting decision-makers in managing the crisis by applying policies such as social distancing, disease testing, contact tracing, home isolation, emergency hospitalization, and travel prevention to reduce infection rates. Based on modeling studies conducted in Imperial College, increasing levels of interventions could slow the spread of disease and infection. We ran the model with six different percentages of social distancing while keeping the other parameters constant. The results show that social distancing affects the spread of COVID-19 significantly, in turn decreasing the spread of disease and infection rates when implemented at higher levels. We also validated these results by using the behavior space tool with ten experiments with varying social distancing levels. We conclude that applying and increasing social distancing policy levels leads to a significant reduction in infection spread and the number of deaths. Both experiments show that infection rates are reduced drastically when social distancing intervention is implemented between 80% to 100%.

This paper examines the linkages in financial markets during coronavirus disease 2019 (COVID-19) pandemic outbreak. For this purpose, daily stock market returns were used over the period of December 31, 2019-April 20, 2020 for the following economies: USA, Spain, Italy, France, Germany, UK, China, and Romania. The study applied the autoregressive distributed lag (ARDL) model to explore whether the Romanian stock market is impacted by the crisis generated by novel coronavirus. Granger causality was employed to investigate the causalities among COVID-19 and stock market returns, as well as between pandemic measures and several commodities. The outcomes of the ARDL approach failed to find evidence towards the impact of Chinese COVID-19 records on the Romanian financial market, neither in the short-term, nor in the long-term. On the other hand, our quantitative approach reveals a negative effect of the new deaths' cases from Italy on the 10-year Romanian bond yield both in the short-run and long-run. The econometric research provide evidence that Romanian 10-year government bond is more sensitive to the news related to COVID-19 than the index of the Bucharest Stock Exchange. Granger causality analysis reveals causal associations between selected stock market returns and Philadelphia Gold/Silver Index.