Context-based Pricing for Revenue Optimization with Applications to the Airline Industry

Most airlines use dynamic pricing to optimize the price of their base economy product by maximizing the expected revenue. However, when it comes to pricing of premium products, airlines often uses a static price increments that are applied to the best available economy fare based on simple business rules for adjusting the price based on supply. In this paper, we present a suite of machine learning algorithms that take advantage of the rich booking session context available at the time of the booking to make its predictions. The challenge is to accurately predict bookings for new combinations of attributes by market and segment (departure time, length of stay, advance purchase, length of haul, …) while accounting for cross-product price effects in a scalable manner. To generate practical pricing policies, the approach accommodates a variety of real-world business requirements into the decision optimization problem. We present a scalable approach based on a novel path-based mixed-integer program (MIP) reformulation that can efficiently recover near-optimal pricing policies. We demonstrate the efficacy of our model with extensive experiments on synthetic and real-life data. Finally, we present an airline case study on deriving profitable prescriptive policies for premium cabin tickets based on easily interpretable pricing rules

A Multiple-Objects Recognition Method Based on Region Similarity Measures: Application to Roof Extraction from Orthophotoplans

In this paper, an efficient method for automatic and accurate detection of multiple objects from images using a region similarity measure is presented. This method involves the construction of two knowledge databases: The first one contains several distinctive textures of objects to be extracted. The second one is composed with textures representing background. Both databases are provided by some examples (training set) of images from which one wants to recognize objects. The proposed procedure starts by an initialization step during which the studied image is segmented into homogeneous regions. In order to separate the objects of interest from the image background, an evaluation of the similarity between the regions of the segmented image and those of the constructed knowledge databases is then performed. The proposed approach presents several advantages in terms of applicability, suitability and simplicity. Experimental results obtained from the method applied to extract building roofs from orthophotoplans prove its robustness and performance over popular methods like K Nearest Neighbours (KNN) and Support Vector Machine (SVM)

Anesthesia for endoscopic retrograde cholangiopancreatography: target-controlled infusion versus standard volatile anesthesia

Abstract Background Endoscopic retrograde cholangiopancreatography (ERCP) is a technique used both for diagnosis and for the treatment of biliary and pancreatic diseases. ERCP has some anesthetic implications and specifi c complications. Th e primary outcome aim was to compare two protocols in terms of time of extubation. We also compared anesthetic protocols in terms of hemodynamic and respiratory instability, antispasmodics needs, endoscopist satisfaction, and recovery room stay

Persistance du 5ème arc aortique associé à une interruption de l’arche aortique

Les auteurs rapportent un cas de persistance du 5ème arc aortique associé à une interruption totale de l'arche aortique. Ce cas clinique montre le piège diagnostic posé par la persistance du 5ème arc aortique et son effet bénéfique hémodynamique. Le tableau clinique était trompeur en préopératoire en raison de la persistance des pouls fémoraux et des signes cliniques d'un shunt gauche-droite via un large canal artériel. Le diagnostic a été redressé en peropératoire grâce au monitorage de la pression artérielle par un cathéter placé dans l'artère fémorale

Early results for active infective endocarditis

Introduction: Cardiac surgery is frequently needed during active phase of infective endocarditis (IE). The purpose of this study was to analyze the immediate and late results and determine the risk factors for death.Methods: We retrospectively reviewed 101 patients with IE operated in the active phase. The mean age was 40.5 ± 12.5 years. 16 patients (15.8%) were diagnosed with prosthetic valve endocarditis (PVE). 81 (80.9%) were in NYHA functional class III-IV. Blood cultures were positive in only 24 cases (23.9%).Results: in-hospital mortality rate was 17.9% (18 cases). Multivariate analysis indentified five determinant predictor factors: congestive heart failure (CHF), renal insufficiency, high Euroscore, prolonged cardiopulmonary bypass time (> 120 min) and long ICU stay. The median follow-up period was 4.2 (2-6.5) years. Overall survival rate for all patients who survived surgery was 97% at 5 years and 91% at 10 years.Conclusion: Despite high in-hospital mortality rate, when patients receive operation early in the active phase of their illness, late outcome may be good.Keywords: Infective endocarditis, valvular surgery, active phas

The evolving SARS-CoV-2 epidemic in Africa: Insights from rapidly expanding genomic surveillance

INTRODUCTION Investment in Africa over the past year with regard to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) sequencing has led to a massive increase in the number of sequences, which, to date, exceeds 100,000 sequences generated to track the pandemic on the continent. These sequences have profoundly affected how public health officials in Africa have navigated the COVID-19 pandemic. RATIONALE We demonstrate how the first 100,000 SARS-CoV-2 sequences from Africa have helped monitor the epidemic on the continent, how genomic surveillance expanded over the course of the pandemic, and how we adapted our sequencing methods to deal with an evolving virus. Finally, we also examine how viral lineages have spread across the continent in a phylogeographic framework to gain insights into the underlying temporal and spatial transmission dynamics for several variants of concern (VOCs). RESULTS Our results indicate that the number of countries in Africa that can sequence the virus within their own borders is growing and that this is coupled with a shorter turnaround time from the time of sampling to sequence submission. Ongoing evolution necessitated the continual updating of primer sets, and, as a result, eight primer sets were designed in tandem with viral evolution and used to ensure effective sequencing of the virus. The pandemic unfolded through multiple waves of infection that were each driven by distinct genetic lineages, with B.1-like ancestral strains associated with the first pandemic wave of infections in 2020. Successive waves on the continent were fueled by different VOCs, with Alpha and Beta cocirculating in distinct spatial patterns during the second wave and Delta and Omicron affecting the whole continent during the third and fourth waves, respectively. Phylogeographic reconstruction points toward distinct differences in viral importation and exportation patterns associated with the Alpha, Beta, Delta, and Omicron variants and subvariants, when considering both Africa versus the rest of the world and viral dissemination within the continent. Our epidemiological and phylogenetic inferences therefore underscore the heterogeneous nature of the pandemic on the continent and highlight key insights and challenges, for instance, recognizing the limitations of low testing proportions. We also highlight the early warning capacity that genomic surveillance in Africa has had for the rest of the world with the detection of new lineages and variants, the most recent being the characterization of various Omicron subvariants. CONCLUSION Sustained investment for diagnostics and genomic surveillance in Africa is needed as the virus continues to evolve. This is important not only to help combat SARS-CoV-2 on the continent but also because it can be used as a platform to help address the many emerging and reemerging infectious disease threats in Africa. In particular, capacity building for local sequencing within countries or within the continent should be prioritized because this is generally associated with shorter turnaround times, providing the most benefit to local public health authorities tasked with pandemic response and mitigation and allowing for the fastest reaction to localized outbreaks. These investments are crucial for pandemic preparedness and response and will serve the health of the continent well into the 21st century

Analyzing the Impact of Complaints on Customer Satisfaction in the Travel Industry

Customer satisfaction is crucial for the long term success of any travel service provider. Therefore, identifying situations that can lead to customer dissatisfaction is critical. The strongest evidence of customers dissatisfaction are their complaints. While complaints do not occur very often, they often lead to loss of customer goodwill which can cost travel providers millions of dollars in compensation and future revenue. In this paper, we describe an approach to proactively identify high value and high risk customers that have the highest propensity to complain, thereby empowering customer service teams with information to deliver a more timely, relevant and impactful service experience. We use three key aspects in this approach: (i) specialized feature engineering for the travel industry; (ii) handling extremely imbalanced data and (iii) adaptation of binary classification, anomaly detection and learning to rank models to our specific task. This research is an important step towards more individualized understanding of customer behavior, and potential service enhancements to further increase customer satisfaction