Predicting adoption of location-based social media service in travel decisions

Advances in location-acquisition and mobile communication technologies have empowered people to use location-based social media. However, the technologies are relatively new, and there is little literature on the relevant factors determining location-based social media adoption. We examine if the online information reviews information can predict users' location-based social media usage for travel planning. The results of this study will be useful for location-based social media providers in formulating appropriate marketing strategies and in developing applications that will attract more users

Quantitative decision making in land banking: a Monte Carlo simulation for China's real estate developers

The real estate industry is one of the fast growing industries in many developing countries such as China and India. The Chinese real estate industry has gone through many reforms from offering housing as part of its social welfare system, to the current capitalist model based on demand and supply. Due to these reforms and the shortage of lands for development in China's urban cities, many Chinese property firms have resorted to land banking in order to secure land property for future developments. However, in China, land speculation is considered illegal, while failure to purchase the suitable land for future developments will hinder the real estate developers’ future business and growth. The purpose of this paper is to develop a decision making model for property developments in their land banking decisions and strategies. The paper employed mathematical modeling and Monte Carlo simulation to examine our decision model, and further validated our results by conducting the simulation by using China Vanke Co. Ltd as a case study. This study is one of the first few studies that develop a decision model for land banking in China. It also helps real estate enterprises to make rational and dynamic decision in the current dynamic property market. First Publish Online: 19 Dec 201

Modelling methane emissions and grain yields for a double-rice system in Southern China with DAYCENT and DNDC models

Acknowledgements This work contributed to the following projects: EU Horizon 2020 programme (SuperG) and The Scientific and Technological Innovation Special Fund Project of Carbon Peak and Carbon Neutrality in Jiangsu Province (No. BE2022311). The first author (Yang Guo) gratefully acknowledges financial support from China Scholarship Council (CSC).Peer reviewedPublisher PD

Predicting online e-marketplace sales performances: a big data approach

To manage supply chain efficiently, e-business organizations need to understand their sales effectively. Previous research has shown that product review plays an important role in influencing sales performance, especially review volume and rating. However, limited attention has been paid to understand how other factors moderate the effect of product review on online sales. This study aims to confirm the importance of review volume and rating on improving sales performance, and further examine the moderating roles of product category, answered questions, discount and review usefulness in such relationships. By analyzing 2,939 records of data extracted from Amazon.com using a big data architecture, it is found that review volume and rating have stronger influence on sales rank for search product than for experience product. Also, review usefulness significantly moderates the effects of review volume and rating on product sales rank. In addition, the relationship between review volume and sales rank is significantly moderated by both answered questions and discount. However, answered questions and discount do not have significant moderation effect on the relationship between review rating and sales rank. The findings expand previous literature by confirming important interactions between customer review features and other factors, and the findings provide practical guidelines to manage e-businesses. This study also explains a big data architecture and illustrates the use of big data technologies in testing theoretical framework

Correntropy-Based Evolving Fuzzy Neural System

In this paper, a correntropy-based evolving fuzzy neural system (correntropy-EFNS) is proposed for approximation of nonlinear systems. Different from the commonly used meansquare error criterion, correntropy has a strong outliers rejection ability through capturing the higher moments of the error distribution. Considering the merits of correntropy, this paper brings contributions to build EFNS based on the correntropy concept to achieve a more stable evolution of the rule base and update of the rule parameters instead of the commonly used meansquare error criterion. The correntropy-EFNS (CEFNS) begins with an empty rule base and all rules are evolved online based on the correntropy criterion. The consequent part parameters are tuned based on the maximum correntropy criterion where the correntropy is used as the cost function so as to improve the non-Gaussian noise rejection ability. The steady-state convergence performance of the CEFNS is studied through the calculation of the steady-state excess mean square error (EMSE) in two cases: i) Gaussian noise; and ii) non-Gaussian noise. Finally, the CEFNS is validated through a benchmark system identification problem, a Mackey-Glass time series prediction problem as well as five other real-world benchmark regression problems under both noise-free and noisy conditions. Compared with other evolving fuzzy neural systems, the simulation results show that the proposed CEFNS produces better approximation accuracy using the least number of rules and training time and also owns superior non-Gaussian noise handling capability

Macrophyte species strongly affects changes in C, N, and P stocks in shallow lakes after a regime shift from macrophyte to phytoplankton dominance

Shallow lakes are important stocks of carbon (C), nitrogen (N), and phosphorus (P), yet little is known about the influence of alternative primary producer dominance on C stocks or the impact of different macrophyte species on the role of shallow lakes as elemental stocks. We used Yangtze shallow lakes dominated by a monsoon climate as a research site to test the hypothesis that changes in elemental stocks in the water column and sediment after a shift to a phytoplankton-dominated state depend on the macrophyte species originally present. We used a dual approach, combining multi-year monitoring and multi-lake comparisons of lakes that were, at least once, dominated either by fast-decomposing Potamogeton crispus or slow-decomposing P. maackianus. Elemental concentrations generally decreased in the water column and increased in sediment after a shift from P. maackianus presence to absence. Only a minor reallocation of elemental stocks was found in lakes where P. crispus disappeared. This difference is likely caused by a combination of the different biomass and decomposition rates between species, further illustrated by the amount of dead plant material in the sediment after loss of plants. After P. maackianus loss, plant material was found in the sediment in high amounts for up to 6 years, whereas after P. crispus loss the coarse material was absent in <1 year. Suspended and dissolved concentrations (i.e., the mobile pool) of C increased 1.5–1.9-fold and P increased 2.0–4.3-fold after the shift, whereas N tended to decrease or stay unchanged. Higher mobile pools of C and P after macrophytes loss implies a more vulnerable watershed, supporting higher phytoplankton biomass in the lakes and causing serious downstream eutrophication problems

Brucella Dysregulates Monocytes and Inhibits Macrophage Polarization through LC3-Dependent Autophagy

Brucellosis is caused by infection with Brucella species and exhibits diverse clinical manifestations in infected humans. Monocytes and macrophages are not only the first line of defense against Brucella infection but also a main reservoir for Brucella. In the present study, we examined the effects of Brucella infection on human peripheral monocytes and monocyte-derived polarized macrophages. We showed that Brucella infection led to an increase in the proportion of CD14++CD16− monocytes and the expression of the autophagy-related protein LC3B, and the effects of Brucella-induced monocytes are inhibited after 6 weeks of antibiotic treatment. Additionally, the production of IL-1β, IL-6, IL-10, and TNF-α from monocytes in patients with brucellosis was suppressed through the LC3-dependent autophagy pathway during Brucella infection. Moreover, Brucella infection inhibited macrophage polarization. Consistently, the addition of 3-MA, an inhibitor of LC3-related autophagy, partially restored macrophage polarization. Intriguingly, we also found that the upregulation of LC3B expression by rapamycin and heat-killed Brucella in vitro inhibits M2 macrophage polarization, which can be reversed partially by 3-MA. Taken together, these findings reveal that Brucella dysregulates monocyte and macrophage polarization through LC3-dependent autophagy. Thus, targeting this pathway may lead to the development of new therapeutics against Brucellosis

Macrophyte species strongly affects changes in C, N, and P stocks in shallow lakes after a regime shift from macrophyte to phytoplankton dominance

Shallow lakes are important stocks of carbon (C), nitrogen (N), and phosphorus (P), yet little is known about the influence of alternative primary producer dominance on C stocks or the impact of different macrophyte species on the role of shallow lakes as elemental stocks. We used Yangtze shallow lakes dominated by a monsoon climate as a research site to test the hypothesis that changes in elemental stocks in the water column and sediment after a shift to a phytoplankton-dominated state depend on the macrophyte species originally present. We used a dual approach, combining multi-year monitoring and multi-lake comparisons of lakes that were, at least once, dominated either by fast-decomposing Potamogeton crispus or slow-decomposing P. maackianus. Elemental concentrations generally decreased in the water column and increased in sediment after a shift from P. maackianus presence to absence. Only a minor reallocation of elemental stocks was found in lakes where P. crispus disappeared. This difference is likely caused by a combination of the different biomass and decomposition rates between species, further illustrated by the amount of dead plant material in the sediment after loss of plants. After P. maackianus loss, plant material was found in the sediment in high amounts for up to 6 years, whereas after P. crispus loss the coarse material was absent in <1 year. Suspended and dissolved concentrations (i.e., the mobile pool) of C increased 1.5–1.9-fold and P increased 2.0–4.3-fold after the shift, whereas N tended to decrease or stay unchanged. Higher mobile pools of C and P after macrophytes loss implies a more vulnerable watershed, supporting higher phytoplankton biomass in the lakes and causing serious downstream eutrophication problems