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Strain YJY-8, a new γ-polyglutamic acid producer, was separated from fermented soybean paste samples. The strain was identified as a genus of Bacillus by morphological and 16S rDNA sequence analysis and was named Bacillus sp. YJY-8. The optimal medium composition and cultural conditions were studied using a single-factor experiment and a response surface experiment. The optimized medium consisted of monosodium glutamate 70 g/L, glucose 54.3 g/L, glycerol 31.8 g/L, ammonium sulfate 11.1 g/L, yeast extract 3.2 g/L, tryptone 1.5 g/L, L-glutamic acid 6.8 g/L, MgSO4 7H2O 0.5 g/L, FeCl3 6H2O 0.02 g/L, KH2PO4 0.9 g/L, CaCl2 0.03 g/L, MnSO4 H2O 0.3 g/L, ammonium molybdate 0.02 g/L, pH 7.0. The optimal cultivation conditions were 35 °C and pH 7.0. Under the optimized conditions, after 48 hr of cultivation, the highest shaking flask fermentation level of γ-PGA reached 65.2 ± 0.36 g/L. In addition, through fed-batch fermentation in 30 L fermenters, the fermentation level of γ-PGA reached its highest level at 88.42 g/L and productivity was 1.23 g/(L hr) after 72 hr. Then, the effect of γ-PGA on tomato yield was investigated. At the seedling stage, the plant height and stem diameter of γ-PGA treated plants increased by 5.69 and 15.735% after spraying γ-PGA for 19 days. During the flowering and fruiting period, the stem diameter of the γ-PGA treatment group increased by 6.74%, with a maximum increase of 11.65%. The number of fruit branches increased by 0.56–16.29% and the number of fruit sets increased by 1.01–28.47%. At the fruit maturation stage, the yield of tomatoes increased by 10.51, 14.27, and 5.83%. [ABSTRACT FROM AUTHOR]

This study investigates the heterogeneous effects of online reviews on hotel revenue when online reviews and American Customer Satisfaction Index (ACSI) coexist. Using a sample of reviews for 303 Houston hotels, the research finds that online reviews are influential for the revenue of non-ACSI hotels. Our results also demonstrate a substituting role of the positive hotel reviews for hotel brand reputation. The study further highlights implications for online reviews and reputation management. [ABSTRACT FROM AUTHOR]

The hospital system is a complex service system in which patients and hospitals interact and make their decisions based on bounded rationality and information. In this paper, we develop a generative agent-based model to simulate the behavior of a hospital service system. Our model combines agent-based simulation and queueing models to mimic the hospital service processes. Our goal is to simulate and understand the growth and size distribution of hospitals. This simulation model includes agents for supply elements (i.e., hospitals with different resources and expansion strategies) and demand elements (i.e., patients with different preferences for their hospital selections) in a hospital service system. Three important questions are investigated: 1) what is the emergent size distribution of hospitals? 2) what key factors influence the size distribution? and 3) how sensitive is the size distribution to these key factors? Simulation results show that the size distribution is neither power law nor lognormal. Rather, the distribution is leptokurtic, and more skewed than normal but less skewed than lognormal. This result contradicts those in extant literature on the size distributions of human and natural systems, including cities, firms, power-grids, and citation network. We conduct a set of experiments to identify the generative mechanisms for the hospital size distribution and to test the robustness of the results. The model was validated empirically by using a U.S. hospital size dataset. [ABSTRACT FROM AUTHOR]

The successful management of crowdsourcing risk has gained considerable attention because of the challenges involved in obtaining the satisfactory performance from crowdsourcing activities. However, the mechanism of how various critical risk factors affect crowdsourcing performance has been poorly investigated. Empirical evidence based on a dataset of 169 samples from China shows that three technical (i.e., complexity, requirement, and task) and three social (i.e., seeker, solver, and relationship) subsystem risks not only directly but also interactively influence the crowdsourcing task performance. Technical subsystem risk factors, while worsen performance, diminish the negative effects of social subsystem risk factors on the crowdsourcing task performance. The diminishing effects of three technical subsystem risk factors are also differentiated. High requirement and task risks lessen the negative effects of seeker, solver, and relationship risks on the task performance, whereas the high complexity risk only weakens the negative effect of the solver risk. Therefore, various crowdsourcing risk factors affect the performance in different ways and directions (either negatively or positively). Risk can perform dual roles in influencing the performance. Hybrid strategies that integrate social and technical subsystem elements may be effective in achieving a superior task performance for the crowdsourcing risk management. Managers of crowdsourcing platforms can also optimize social and technical environments to improve the fit between social and technical systems. [ABSTRACT FROM AUTHOR]