With the expansion of urban road network, the importance of road maintenance is increasing, which guarantee the operation efficiency of transportation infrastructure. Compare to road construction, ...road maintenance is more sophisticated. Even though invested a lot of manpower and financial resources, management departments are troubled by insufficient and lagging road maintenance. The development and widely application of technologies including IoT, Big Data and Artificial Intelligence(AI) in various industries offer possible solutions to this issue. However, how to utilize these technologies is a challenge for related administration department because of their weak technical force. To address the problem, a pavement management system(PMC) is developed in this research. Combined with IoT and big data, the PMS provide an overall management structure of road maintenance. Composed of three subsections: Pavement detection and 3D modeling, Data analysis and Decision support, the PMS offer an automated and intelligent solution to related administrative departments and firms. Besides, two road maintenance related firms, one is a road maintenance company and the other is a technical firm that offer smart solutions to road maintenance, are selected as cases to illustrate how PMS are applied to support the daily operations and road maintenance.
With the rise of satellite-as-a-service subscriptions as technology improves, our paper examines how satellite operators (SOs) can choose between traditional channels for selling satellites, ...satellite-as-a-service (SataaS), and dual-channel strategies. In the space supply chain, commercial satellites display several distinct characteristics that differentiate them from other physical commodities in traditional supply chains, most notably their exposure to significant launch risks. In addition, SataaS suffers from the risk of secure data transmission. However, the emergence of novel technologies such as blockchain technology (BCT) could help to mitigate such risks. Therefore, our study analyzes the optimal strategy among the sell, SataaS, and dual-channel approaches for SOs by considering the above distinctive characteristics in the case without blockchain and the case with blockchain. Our paper reveals the existence of a dual-channel strategy that generates the highest profit for the SO and yields the highest consumer utility when launch success is high and when SataaS fixed costs are low. When the launch success rate is very high, the dual-channel strategy transforms into a pure sell strategy. After the adoption of BCT, when the cost of BCT is low, the SO’s profit is enhanced in all three sales channels compared to the case without blockchain. Notably, in SataaS as well as in the dual-channel approach, the adoption of BCT always improves consumer utility compared to the case without blockchain. However, in the sell channel, the consumer benefits depend on the relatively low cost of blockchain customization.
The ranking of network node importance is one of the most essential problems in the field of network science. Node ranking algorithms serve as an essential part in many application scenarios such as ...search engine, social networks, and recommendation systems. This paper presents a systematic review on three representative methods: node ranking based on centralities, PageRank algorithm, and HITS algorithm. Furthermore, we investigate the latest extensions and improvements of these representative methods, provided with several main application fields. Inspired by the survey of current literatures, we attempt to propose promising directions for future research. The conclusions of this paper are enlightening and beneficial to both the academic and industrial communities.
Fiscal sustainability is an issue of great concern for governments globally and air pollution control has become an important factor affecting fiscal sustainability. This study aims to examine the ...impact of air pollution on fiscal sustainability in the short and long run. We conducted an empirical analysis based on air pollution and local government debt data on China’s prefecture-level cities in 2014–2019, using regression discontinuity design (RDD) and a panel data model. The results show that air pollution reduces the debt burden of governments in the short run. However, in the long run, addressing the negative impacts of air pollution adds to the debt burden of local governments, hindering fiscal sustainability. Fiscal freedom and the level of public services significantly moderate the negative impact of air pollution on fiscal sustainability. A higher level of fiscal freedom generally indicates a greater incentive for local governments to raise pollutant emission standards, strengthen the construction of green infrastructure, and subsidize green enterprises. Furthermore, a higher level of public services reflects better infrastructure and higher levels of investment in environmental protection, which help to reduce the negative impact of air pollution. The governments are suggested to take measures to effectively control air pollution, so as to enhance fiscal stability in the long run.
In the era of big data, stock markets are closely connected with Internet big data from diverse sources. This paper makes the first attempt to compare the linkage between stock markets and various ...Internet big data collected from search engines, public media and social media. To achieve this purpose, a big data-based causality testing framework is proposed with three steps, i.e., data crawling, data mining and causality testing. Taking the Shanghai Stock Exchange and Shenzhen Stock Exchange as targets for stock markets, web search data, news, and microblogs as samples of Internet big data, some interesting findings can be obtained. 1) There is a strong bi-directional, linear and nonlinear Granger causality between stock markets and investors’ web search behaviors due to some similar trends and uncertain factors. 2) News sentiments from public media have Granger causality with stock markets in a bi-directional linear way, while microblog sentiments from social media have Granger causality with stock markets in a unidirectional linear way, running from stock markets to microblog sentiments. 3) News sentiments can explain the changes in stock markets better than microblog sentiments due to their authority. The results of this paper might provide some valuable information for both stock market investors and modelers.
In recent years, the proportion of China’s elderly population is gradually increasing, and the real estate market is changing dramatically. Consequently the impact of population aging on housing ...demand has become increasingly prominent. On the theoretical basis of the relationship between population aging, family structure and housing demand, this paper firstly constructs a system dynamics model of the impact of population aging and family structure on housing demand in China. Then, the model is simulated, and several scenarios of population, family structure, and real estate policies are set up. The main conclusions of this paper are as follows: China’s population will rise at first and then decrease, reaching a peak of 1.401 billion by 2023. As the proportion of elderly population increases, the degree of population aging is becoming increasingly serious. The size of the family will gradually shrink to 2.39 people per household in 2050. The housing demand will increase first and then decrease. Through the results of scenario simulation, this paper puts forward the following suggestion to effectively balance the housing demand in China: Completely relaxing family planning policies, imposing a real estate tax, canceling the pre-sale policy, and raising the loan rate.
Since 2010, Chinese government has introduced a series of administrative policies to limit speculation in the housing market to stabilize price fluctuations and keep the housing market in a healthy ...state of development. In order to investigate whether administrative policy can play its due role, this paper constructs a comprehensive bottom-up housing market heterogeneous households multiagent model (HHMAM) to undertake research on the differentiated effect of administrative policy in different cities. The empirical studies find that: 1) Administrative policy that increases interest rates will cause housing prices to continue to decline in the long term, but they will resume a rising trend after reaching the lowest point; 2) If the government cancels a property-purchasing limitation, housing prices will continue to rise; and 3) investors tend to invest in 1
st
-tier cities due to the high demand and greater likelihood of appreciation in these cities.
The impact of human resource management (HRM) on corporate growth is a crucial research topic, especially for growth-oriented firms. This paper aims to study how different payment policies (such as ...recruitment and dismissal strategies and payment plans) affect the human resource market system. Based on the HRM characteristics of growth-oriented firms, we develop an agent-based model to simulate the decision-making and interaction behaviors of firms and workers. The system performance is measured by six indicators: the average profit, the profit Gini coefficient, the average output of firms, the average payment, the payment Gini coefficient, and the employment rate of workers. According to the simulation results and statistical analysis, the recruitment plan is the only key factor that significantly impacts all performance indicators other than the employment rate, and companies should pay extra attention to such plans. This study also finds that the changing worker’s payment gap is influenced by industry growth and their abilities, and that the payment cap policy has a positive impact on the development of growth-oriented firms in the startup stage.
Differences in progress across sustainable development goals (SDGs) are widespread globally; meanwhile, the rising call for prioritizing specific SDGs may exacerbate such gaps. Nevertheless, how ...these progress differences would influence global sustainable development has been long neglected. Here, we present the first quantitative assessment of SDGs’ progress differences globally by adopting the SDGs progress evenness index. Our results highlight that the uneven progress across SDGs has been a hindrance to sustainable development because (1) it is strongly associated with many public health risks (e.g., air pollution), social inequalities (e.g., gender inequality, modern slavery, wealth gap), and a reduction in life expectancy; (2) it is also associated with deforestation and habitat loss in terrestrial and marine ecosystems, increasing the challenges related to biodiversity conservation; (3) most countries with low average SDGs performance show lower progress evenness, which further hinders their fulfillment of SDGs; and (4) many countries with high average SDGs performance also showcase stagnation or even retrogression in progress evenness, which is partly ascribed to the antagonism between climate actions and other goals. These findings highlight that while setting SDGs priorities may be more realistic under the constraints of multiple global stressors, caution must be exercised to avoid new problems from intensifying uneven progress across goals. Moreover, our study reveals that the urgent needs regarding SDGs of different regions seem complementary, emphasizing that regional collaborations (e.g., demand-oriented carbon trading between SDGs poorly performed and well-performed countries) may promote sustainable development achievements at the global scale.
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•Integrating progress evenness reveals hidden challenges and opportunities toward sustainable development goals (SDGs).•Uneven SDGs progress is related to health risks, ecosystem damage, and social inequality.•Uneven SDGs progress is common in the low-income and extremely arid countries.•Many high-income countries face antagonism between climate actions and other goals.•Complementary collaborations are crucial to achieve SDGs.
A challenge for investors in P2P lending marketplaces is to find an effective allocation of their funds across different loans by assessing the widespread collapse of lending platforms and each ...loan's default risk. This paper investigates risk-averse/risk-tolerant/risk-neutral investors' and borrowers' transaction behaviours, as well as P2P lending platforms' operating mechanism. A Heterogeneous Investors Multi-Agent Model (HIMAM) is built to simulate agents' reactions to variance in platform's bankruptcy risk. This enables an assessment of changes in investors' average return rates and investment decisions. This study finds that platform bankruptcy risk influences total investment amounts and average return rates. The correlation between the transaction fee and total investment amount presents as an inverted U-shape. Furthermore, there is a negative correlation between two-part tariffs and both investors' average return rates and the number of investors. This is because two-part tariffs are dependent on investors' transaction volumes. High two-part tariffs increase investors' transaction costs. Additionally, considering bankruptcy and default risk, credit E are the most popular type of borrower because such borrower tradeoff higher risk for higher return; credits B and C are the least popular type of borrower.
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