Estimating Customer Lifetime Value Using Machine Learning Techniques

With the rapid development of civil aviation industry, high-quality customer resources have become a significant way to measure the competitiveness of the civil aviation industry. It is well known that the competition for high-value customers has become the core of airline profits. The research of airline customer lifetime value can help airlines identify high-value, medium-value and low-value travellers. What is more, the airline company can make resource allocation more rational, with the least resource investment for maximum profit return. However, the models that are used to calculate the value of customer life value remain controversial, and how to design a model that applies to airline company still needs to be explored. In the paper, the author proposed the optimised China Eastern Airlines passenger network value assessment model and examined its fitting degree with the TravelSky value score. Besides, the author combines China Eastern Airlines passenger network value assessment model score with loss model score to help airlines find their significant customers

Change Support in Cross-Organizational Dynamic Process-Aware Software Architecture – A Pattern-Based Analysis

Process-aware information systems (PAIS) offer promising perspectives in this respect and are increasingly employed for operationally supporting business processes. In this paper, we describe the emergence of different process support paradigms and the lack of methods for comparing existing change approaches have made it difficult for process-aware software architecture (PASA) engineers to choose the adequate technology. A pattern-based analysis combines self-adapting and self-evolution theory in PAIS, we adopt a set of changes patterns and change support features to put forwards four kinds of model of PASA according to the situation of the needs business processer facing and changeable environment. Based on these change patterns and features, we provide a detailed mechanism analysis and case study evaluation in the healthcare industry of the relationship between cross-organizational dynamic process-aware software architecture (CD-PASA) and change patterns of business processes. In summary, we identified change patterns and change support features facilitate the comparison of change support frameworks, and consequently will support PASA engineers in selecting the right technology for realizing flexible PASA. In addition, this work can be used as a reference for implementing more flexible PASA

A Federated Learning-Based Civil Aviation Passenger Value Analysis Method and MaaS Construction Considerations in the Epidemic Background

Airline customer demand has plummeted since the COVID-19 pandemic, with about two-thirds of the world’s fleet grounded. Under such circumstances, the airline needs to adjust its market strategy. Mining the value of passengers and providing differentiated services for passengers with different values are key to the differentiated competition of airlines. In the case of ensuring data privacy, this study introduces a privacy-preserving federated learning method, which combines airline internal data with external operator data, comprehensively considers multiple dimensional characteristics of passengers. This study compares a unilateral model using airline data with a joint model combining airline internal data and operators through federated learning. The result shows that the joint model based on federated learning is more accurate than the unilateral model. Based on this result, this study puts forward the thinking about passenger mining and insight in the construction of MaaS under the epidemic situation, constructs a customer journey map according to the characteristics of the segmented population, and proposes the idea of providing different transportation services for the segmented population. This research provides important theoretical and practical implications for the airline digital transformation and MaaS construction under the epidemic

Airlines Content Recommendations Based on Passengers\u27 Choice Using Bayesian Belief Networks

Faced with the increasingly fierce competition in the aviation market, the strategy of consumer choice has gained increasing significance in both academia and practice. As ever-increasing travel choices and growing consumer heterogeneity, how do airline companies satisfy passengers\u27 needs? With a vast amount of data, how do airline managers combine information to excavate the relationship between independent variables to gain insight about passengers\u27 choices and value system as well as determining best personalized contents to them? Using the real case of China Southern Airlines, this paper illustrates how Bayesian belief network (BBN) can enable airlines dynamically recommend relevant contents based on predicting passengers\u27 choice to optimize the loyalty. The findings of this study provide airline companies useful insights to better understand the passengers\u27 choices and develop effective strategies for growing customer relationship

Enhancement of brain-type creatine kinase activity ameliorates neuronal deficits in Huntington's disease

AbstractHuntington's disease (HD) is a hereditary neurodegenerative disorder caused by a CAG repeat expansion in the huntingtin (HTT) gene. Brain-type creatine kinase (CKB) is an enzyme involved in energy homeostasis via the phosphocreatine–creatine kinase system. Although downregulation of CKB was previously reported in brains of HD mouse models and patients, such regulation and its functional consequence in HD are not fully understood. In the present study, we demonstrated that levels of CKB found in both the soma and processes were markedly reduced in primary neurons and brains of HD mice. We show for the first time that mutant HTT (mHTT) suppressed the activity of the promoter of the CKB gene, which contributes to the lowered CKB expression in HD. Exogenous expression of wild-type CKB, but not a dominant negative CKB mutant, rescued the ATP depletion, aggregate formation, impaired proteasome activity, and shortened neurites induced by mHTT. These findings suggest that negative regulation of CKB by mHTT is a key event in the pathogenesis of HD and contributes to the neuronal dysfunction associated with HD. In addition, besides dietary supplementation with the CKB substrate, strategies aimed at increasing CKB expression might lead to the development of therapeutic treatments for HD