The Profit-Splitting Model In The Sharing Economy

In the sharing economics era, platform modes have become much more prevalent as information and communication technologies (ICT) have made it easier to build marketplaces for providing individuals, corporations, non-profits and governments with information that enables the optimization of resources through the redistribution, sharing and reuse of excess capacity in goods and services. There are many new organizations around us that practice the platform model, for instance, Airbnb and Uber. These organizations seem much more like associations of independent professionals and companies that connect to customers through a common platform. Furthermore, each association acts like a conglomeration in the ability of empowering its associated professionals and companies to deliver services independently while enforcing consistency in order to build its brand. Over the platform, there are a collaborative consumption phenomenon in which participants share access to products or services, rather than having individual ownership, and a common premise that when information about goods is shared (typically via an online marketplace), the value of those goods may increase for the business, for individuals, for the community and for society in general. In sum, the platform should embed with a profit splitting model that benefits all associated professionals and companies. This study use the new platform set up by the Tripbaa to explore the profit splitting model. In brief, around the world, there are more than 0.1 billion Destinations Tourism that want (1) the pleasure of exploring the tourism destination, (2) the free, convenient, and instant travel, and (3) the share of travel inspiration. The Tripbaa tries to fill in the demand gap to become the top brand for Chinese Destinations Tourism around the world. To accomplish this, the Tripbaa sets up a platform that connects FIT to professional tour guides. The Tripbaa platform also provides a one-stop FIT service with customized itinerary, charter, accommodation, and so on. With a well-established platform, the Destinations Tourism tour arrangement is integrated into the smart tourism and becomes much easier. The Tripbaa platform also provides a profit splitting model that attracts all strategic partners, including professional tour guides, charters, accommodation providers, and travel agents

Artificial Intelligence in Smart Tourism: A Conceptual Framework

Smart tourism destination as: an innovative tourist destination, built on an infrastructure of state-of-the-art technology guaranteeing the sustainable development of tourist areas, accessible to everyone, which facilitates the visitor’s interaction with and integration into his or her surroundings, increases the quality of the experience at the destination, and improves residents’ quality of life. Lopez de Avila (2015). Smart tourism involves multiple components and layers of “smart” include (1) Smart Destinations which was special cases of smart cities integration of ICT’s into physical infrastructure, (2) Smart experience which specifically focus on technology-mediated tourism experience and their engagement through personalization, context-awareness and real-time monitoring, (3) Smart business refer to the complex business ecosystem that creates and supports the exchange of touristic resource and the co-creation of tourism experience. Gretzel et al, (2015). Smart tourism also clearly relies on the ability to not only collect enormous of data but to intelligently store, process, combine, analyze and use big data to inform business innovation, operations and services by artificial intelligence and big data technique. The rapid development of information communication technology (ICT) such as artificial intelligent, cloud computing, mobile device, big data mining and social media cause computing, storage and communication relevant software and hardware popular. Facebook, Amazon, Apple, Microsoft and Google have risen rapidly since 2000. In recent years, Emerging technologies such as Artificial Intelligence, Internet of Thing, Robotic, Cyber Security, 3D printer and Block chain also accelerate the development of industry toward digital transformation trend such as Fintech, e-commerce, smart cities, smart tourism, smart healthcare, smart manufacturing... This study proposes a conceptual framework that integrates (1) artificial intelligence/machine learning, (2) institution/organizational and (3) business processes to assist smart tourism stake holder to leverage artificial intelligence to integrate cross-departmental business and streamline key performance metrics to build a business-level IT Strategy. Artificial intelligence as long as the function includes (1) Cognitive engagement to (voice/pattern recognition function) (2) Cognitive process automation (Robotic Process Automation) (3) Cognitive insight (forecast, recommendation)

A New Method for Calculating Viscosity and Solubility of Lubricant- Refrigerant Mixtures

A new model was developed to determine viscosity and solubility of lubricant-refrigerant mixture, using concepts from lattice model in liquid state, reaction rate theory for viscosity, and local composition theory. The computing results from our new model showed high degree of accuracy, and are comparable to the results from NRTL model and Flory-Huggins model. Various type of POE lubricants (viscosity ranges from 68~220cst) in R134a refrigerant have been fitted for the new model to describe the viscosity and pressure of binary systems. The tests were conducted in temperature ranging from 0? to 100?. Typical average absolute deviation (AAD%) of these calculation results in the model is between 1.0~3.5%

The Evolution of Internal Representation

To develop an appropriate internal representation, a deterministic learning algorithm that has an ability to adjust not only weights but also the number of adopted hidden nodes is proposed. The key mechanisms are (1) the recruiting mechanism that recruits proper extra hidden nodes, and (2) the reasoning mechanism that prunes potentially irrelevant hidden nodes. This learning algorithm can make use of external environmental clues to develop an internal representation appropriate for the required mapping. The encoding problem and the parity problem is used to demonstrate the performance of the proposed algorithm. The experimental results are clearly positive

Profiling Interactive Television Research: A Bibliometric Review

Though the recent revolution in digital processing ushers the broadcasting industry into a new era, the interactive television (iTV) has been regarded as the third generation of broadcasting services and relevant issues of iTV have gained tremendous interests from both academics and practitioners. This article endeavours to profile the scholarly development of the interactive television literatures by utilizing bibliometric technique to review the literature material in SCIE, SSCI, and A&HCI databases appeared in 1970 to 2009. There are 228 documents in total. The analysis is conducted on such as most productive authors, authors’ background, geographic diversity analysis (including countries and institutions), subject areas, publication year, and the citation analysis. The conclusions about the promising future, research direction, and the attribute of interactive television research are derived from this study

POE Lubricant Candidates For Low GWP Refrigerants

Several series of polyol ester (POE) refrigeration lubricants have been investigated for low GWP refrigerant R32 (R-410A replacement) and HFO-1234ze (R-134a replacement). The main problem of R32/HFO refrigeration lubricant development can be summarized as balancing between miscibility, solubility and lubricity. Generally speaking, refrigerant-lubricant mixture with highly miscible property in low temperature evaporator will lead to more soluble phenomenon in high temperature compressor. Therefore, when refrigerant is well miscible with refrigeration lubricant, dissolved refrigerant will reduce working viscosity of refrigerant-oil mixture in compressor, and thus results in lower lubricity, wear of sliding parts, and compressor durability shortage. In our studies, the key factor which result in aforementioned phenomenon was found, and can be controlled independently by using optimized chemical structure. For R32 compressor system, we have successfully developed a series of POE refrigeration lubricant, with viscosities ranging from 32cSt to 90cSt at 40°C, and with a wide range of miscibility (20% oil) from -40℃ to 2℃. From results of PVT experiments and lubricity tests (Falex P/V and four ball), it demonstrated to be possible to develop a POE oil with high miscibility, low solubility and high working viscosity. All results in R32 system were better than traditional refrigeration lubricant in R410A system. Meanwhile, we also were able to identify the relationship between surface tension of chemical structure and lubricity. For HFO-1234ze compressor system, incumbent refrigeration lubricants suitable for R134a are almost fully miscible in HFO-1234ze, which could lead to severe refrigerant dilution of lubricant viscosity and poor lubricity due to high solubility. Through studies of chemical structure of refrigeration lubricants, reliable experimental tests and rigorous thermodynamic calculation, we created a range of POE lubricants (ISO68 to ISO220) with miscibility (20% oil) from -33℃ to -13℃, all the while, maintaining solubility and working viscosity on par with the common POE refrigeration lubricants currently used in R-134a system

The Influence of Lubricant in HFC & HFO Blend Refrigeration system

Although HFO refrigerants are considered the low GWP solutions to the global warming problem, some properties of HFO refrigerants prohibited the direct drop in replacement application in the refrigeration system. As a result, to replace the high GWP HFC refrigerants, HFC & HFO refrigerants are blended to combine their properties to become a workable solution. Both HFC and HFO refrigerants have similar basic properties; however, one of the HFO refrigerants properties is good miscibility to incumbent refrigeration lubricants, especially HFO-1234ze. Generally speaking, refrigerant-oil mixture with highly miscible property in low temperature evaporator will lead to more soluble phenomenon in high temperature compressor. Therefore, when refrigerant is well miscible with refrigeration oil, the dissolved refrigerant will reduce working viscosity of refrigerant-oil mixture in compressor, which could results in lower lubricity, increase wear of sliding parts, and shorter compressor durability. In our studies, we discuss the influence of lubricant in HFC & HFO blend refrigeration system, such as R513A or R450A. Compared to the lubricant performance in HFO refrigerants, the same lubricant in the HFC & HFO blend refrigerants do not lead to severe refrigerants dilution of lubricant viscosity, which causes poor lubricity due to high solubility. According to our previous study, we know the better miscibility brings the better solubility in R1234ze system. We also developed lubricants with different miscibility properties in HFC & HFO blend refrigerants in order to investigate the miscibility, solubility and lubricity performance in the HFC & HFO refrigeration systems