Search

To investigate the differences in aroma components release between the heated cigarette and traditional cigarette, 10 unblended leaf tobacco were prepared into two types of cigarettes. The aroma components in the aerosol of heated cigarettes and in the mainstream smoke of traditional cigarette were analyzed by GC/MS. And orthogonal partial least squares discriminant analysis (OPLS-DA) model were used to evaluate the differences of aroma components in the two types of cigarettes. The results showed that a total of 53 and 77 aroma components were identified in the aerosol of heated cigarettes and in the mainstream smoke of traditional cigarettes, respectively. Among the 47 common components, the release of 13 aroma components in the heated cigarettes were more than traditional cigarettes, such as, acetic acid, γ-Butyrolactone and the other 11 aldehyde ketone compounds. 53 different aroma components between heated cigarettes and traditional cigarettes were screened by OPLS-DA, including 7 pyrazine-pyridines,12 ketones, 4 furans, 9 acids, 3 esters, 8 phenols, 9 hydrocarbons, and 1 alcohol. According to the proportion of differential aroma components in the total identified components of the same category, the most significant differences in the release of phenolic components were observed between the two types of cigarettes, followed by hydrocarbons, pyrazine-pyridines, acids, esters, ketones, furans, alcohols. [ABSTRACT FROM AUTHOR]

In response to the construction of green data centers for sustainable development, reducing data center power consumption and carbon dioxide emissions has become an urgent issue. A data center model considering cooling systems was established. The model transforms cost functions of data center power costs, bandwidth costs, carbon emission costs, and cooling costs into a cost minimization problem. The data center operational costs are reduced by effective workload scheduling and rational servers on-off strategies. The electricity prices and carbon emission rates are difficult to predict accurately due to the fluctuation. A feature of the Lyapunov optimization framework was utilized to propose an online control strategy. The strategy only requires knowledge of the current system information. An alternating direction method of multipliers (ADMM) is introduced to enable the control center to coordinate workload distribution among various data centers. Data centers only need to exchange workload decisions, effectively reducing computational complexity and safeguarding user privacy. Simulation results based on real-world data demonstrate that the proposed algorithms can effectively reduce data center operational costs. [ABSTRACT FROM AUTHOR]

Service robots as an example of service innovation has been of great interest to researchers as it could produce greater value‐in‐use for consumers during service encounters. However, the question of how and why service robots may affect consumers remains inadequately understood. Leveraging service‐dominant logic and the heuristic‐systematic model, Study 1 examines the impacts of service innovation types on brand equity and the moderating role of consumer expertise. Study 2 explores whether cognitive and emotional trust can bridge the underlying mechanism. We find that consumers with higher levels of service expertise rate firms with supportive innovation (vs. interactive innovation) higher in brand equity. On the other hand, service novices rate firms with both types of service innovation similarly. Emotional trust significantly mediates the effect mentioned above. In addition, consumers with high technology expertise will better recognize firms' service innovation efforts regardless of innovation type. Our findings extend the service innovation literature by demonstrating how individual‐level factors such as consumer expertise help explain the relationships between various types of service robots and consumer response. Moreover, we reveal the importance for service brands to invest in different service robots based on target groups and build emotional trust with consumers. [ABSTRACT FROM AUTHOR]

In sharing accommodation business such as Airbnb, while the provision of personalized amenities and services may seem like good business, hosts should be aware of the potential unintended consequences when they are not able to deliver what they promise. The present research examines how expectation gaps created by guest reviews interact with different types of preferential services to subsequently affect consumer behavior in the peer-to-peer accommodation economy. Grounded in attribution theory, this study offers new insights on customer responses to unfulfilled preferential treatment. The results suggest that in the condition of utilitarian services (e.g., airport transportation), participants in the low dispersion condition exhibited more negative attitudes, a lower level of repurchase intention, and a decreased willingness to write an online review. Conversely, in the condition of hedonic services (e.g., perform a talent show), expectation discrepancy did not result in different consumer evaluations across the dispersion conditions. [ABSTRACT FROM AUTHOR]