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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' 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' 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' choice to optimize the loyalty. The findings of this study provide airline companies useful insights to better understand the passengers' choices and develop effective strategies for growing customer relationship.

We define two data paradigms for Causal Discovery: definite data: a singleskeleton structure with observed nodes single-value, and indefinite data: a set of multi-skeleton structures with observed nodes multi-value (e.g., video or dialogue data). Multi skeletons induce low sample utilization, and multi values induce incapability of the distribution assumption, both leading to the fact that recovering causal relations from indefinite data is, as of yet, largely unexplored. We design the causal strength variational model to settle down these two problems. Specifically, we leverage the causal strength instead of independent noise as the latent variable to construct evidence lower bound. By this design ethos, The causal strength of different skeletons is regarded as a distribution and can be expressed as a singlevalued causal graph matrix. Moreover, considering the latent confounders, we disentangle the causal graph G into two relation subgraphs O and C. O contains pure relations between observed nodes, while C represents the relations from latent variables to observed nodes. We summarize the above designs as Confounding Disentanglement Causal Discovery (biCD), tailored to learn causal representation from indefinite data under latent confounding. Finally, we conduct comprehensive experiments on synthetic and real-world data to demonstrate the effectiveness of our method.However, suppose you want to analyze multi-value data, for example, which locates the relevant subimage determined for labels, or multi-skeleton data, which measures the activity of different brain regions -how can you infer the causal influence from indefinite data? We likewise define the Preprint. Under review.

An 18-year-old woman developed Stevens-Johnson syndrome (SJS) with ocular involvement after taking ibuprofen. She was admitted to another hospital, received saline flushes and bacitracin ophthalmic ointment to the eyes, and became unable to open them. Upon transfer to this burn center 3 weeks after symptom onset, there was complete fusion of both eyelids with no visible cornea or sclera. She underwent bilateral operative scar release. After opening the lids, meticulous debridement of cicatricial membranes and release of symblephara were performed with subsequent placement of amniotic membrane grafts. Her vision slowly improved, though her long-term visual prognosis remains guarded. Early recognition and treatment of SJS or toxic epidermal necrolysis (TEN) with ocular involvement is imperative. Even mild cases may require intensive topical lubrication, steroids, and antibiotics, with early placement of amniotic membrane grafts in severe cases. Prompt intervention and daily evaluation are paramount in preventing lifelong visual disability.