Expedia Group (United States)

The increasing complexity and volume of digital payment transactions demand architectural solutions that ensure continuous service availability while maintaining strict security and compliance standards. This article presents a comprehensive framework for implementing event-driven architecture in payment systems, focusing specifically on failover mechanisms and redundancy patterns that enable seamless operation during service disruptions. The article examines how asynchronous event processing and message-driven communication patterns can decouple payment services, creating natural resilience boundaries that prevent cascading failures common in traditional synchronous architectures. Through detailed analysis of retry mechanisms, circuit breaker implementations, and dynamic gateway routing strategies, the article demonstrates how event-driven systems can automatically recover from transient failures while maintaining transaction integrity and audit compliance. The article explores critical implementation considerations, including idempotency handling, eventual consistency management, and distributed transaction coordination through saga patterns, providing practical guidance for organizations transitioning from legacy payment infrastructures. Case studies illustrate real-world applications of the proposed architecture in scenarios ranging from payment authorization retries to multi-region disaster recovery, highlighting both the technical benefits and operational challenges associated with event-driven payment processing. The article addresses essential security and compliance requirements through tokenization strategies, comprehensive audit logging, and real-time fraud detection integration that leverages event streams for immediate risk assessment. Performance evaluation reveals significant improvements in system throughput, fault tolerance, and recovery capabilities compared to traditional architectures, though implementation requires substantial investment in team expertise and monitoring infrastructure. This article contributes to the growing body of knowledge on distributed payment system design by providing a practical blueprint for achieving high availability and fault tolerance through event-driven architectural principles.

The field of medical imaging has been revolutionized by intelligent systems, especially those powered by machine learning and artificial intelligence (AI). Among various applications, brain tumor detection and classification stand out due to their critical impact on patient health outcomes. Brain tumors—whether benign or malignant—are among the most serious neurological conditions and often require prompt and accurate diagnosis. Traditionally, identifying brain tumors through imaging has been a manual and expertise-driven process, but with the integration of AI, especially deep learning, the accuracy and speed of diagnosis have improved significantly. This overview explores how machine learning contributes to brain tumor analysis, from the collection and pre-processing of MRI data to model training and classification. Key challenges such as data quality, model explainability, and tumor variability are also addressed, alongside modern advancements in deep learning models that continue to evolve in precision and clinical applicability.

This comprehensive article explores the evolution and implementation of scalable cloud architectures with advanced API integration strategies in modern enterprise environments. The article explores fundamental aspects of distributed computing, container orchestration, and high-availability patterns while examining their impact on system performance and business outcomes. Through an analysis of current industry practices, the article demonstrates how organizations leverage various API protocols, performance optimization techniques, and security measures to build resilient cloud infrastructures. The article further delves into real-world applications across e-commerce, financial services, logistics, and media streaming platforms, highlighting transformative improvements in operational efficiency and user experience. By examining implementation best practices, including architecture design patterns, security considerations, monitoring strategies, and DevOps integration, the research provides valuable insights for organizations pursuing cloud transformation initiatives. The article also explores emerging trends in serverless computing, edge computing, and artificial intelligence, offering a forward-looking perspective on the evolution of cloud architectures and their role in driving business innovation.

The influence of aqueous solutions of 2-(tetrafluoro(trifluoromethyl)-λ6-sulfanyl-ethan-1-ol (CF3SF4-ethanol) and 2,2,2-trifluoroethanol (TFE) on the secondary structure of melittin was studied using circular dichroism (CD) and molecular dynamics (MD) simulations. In water, melittin transitions into a random coil. However, upon addition of even as little as 1% by volume of CF3SF4-ethanol, the secondary structure of melittin stabilizes as a helix. Contrarily, the addition of 40% by volume of TFE is required for the greatest helicity. Fluoroalcohols stabilize melittin's hydrophobic side chain residues, thereby enhancing the helical structure. Locally alcohol concentrations approach nearly 70-90% in the near vicinity of the hydrophobic side chains increasing hydrophobic interactions and reducing water-peptide hydrogen bonding. Using the molecular mechanics-Poisson Boltzmann surface area method (MMPBSA), the free energy of binding between the peptide and fluoroalcohols highlighted the role of nonpolar residues in stabilizing the secondary structure. Secondary structure content analysis (SESCA) validated the simulation results, confirming CF3SF4-ethanol as an effective, eco-friendly enhancer of helicity at low concentrations. The far UV circular dichroism (CD) spectrum of melittin in solutions containing TFE corroborates previous findings and likewise affirms that the addition of CF3SF4-ethanol to an aqueous solution can enhance helicity. The agreement between the experimental and calculated helicities highlights the potential of CF3SF4-ethanol. This study offers insights into peptide stabilization by fluoroalcohols, with implications for peptide-based therapeutic design.

In this comprehensive study, we delve into the application of Reinforcement Learning from Human Feedback (RLHF) in fine-tuning large language models (LLMs) to align them with human preferences. We address the multifaceted challenges of potential bias in human feedback and propose robust mechanisms to mitigate these biases. Through extensive experimentation and analysis, we demonstrate the effectiveness of RLHF in refining model behaviors and improving alignment while ensuring adherence to ethical standards and diverse user expectations. Our research contributes to the ongoing effort to create more responsible and fair AI systems that can be safely deployed in various societal contexts.

Retrieval-augmented generation (RAG) enhances large language models (LLMs) by integrating external knowledge sources, enabling more useful information and generating accurate responses. This paper explores RAG's architecture and applications, combining generator and retriever models to access and utilize vast external data repositories. While RAG holds significant promise for various Natural Language Processing (NLP) processes like dialogue generation, summarization, and question answering, it also presents unique security challenges that must be addressed to ensure system integrity and reliability. RAG systems face several security threats, including data poisoning, model manipulation, privacy leakage, biased information retrieval, and harmful outputs generation. Generally, in the traditional RAG application, security threat is one of the major concerns. To tighten the security system and enhance the efficiency of the model on processing more complex data this paper outlines key strategies for securing RAG-based applications to mitigate these risks paper outlines key strategies for securing RAG-based applications to mitigate these risks. Ensuring data security through filtering, sanitization, and provenance tracking can prevent data poisoning and enhance the quality of external knowledge sources. Strengthening model security via adversarial training, input validation, and anomaly detection improves resilience against manipulative attacks. Implementing output monitoring and filtering techniques, such as factual verification, language moderation, and bias detection, ensures the accuracy and safety of generated responses. Additionally, robust infrastructure and access control measures, including secure data storage, secure APIs, and regulated model access, protect against unauthorized access and manipulation. Moreover, this study analyzes various use cases for LLMs enhanced by RAG, including personalized recommendations, customer support automation, content creation, and advanced search functionalities. The role of vector databases in optimizing RAG-driven generative AI is also discussed, highlighting their ability to efficiently manage and retrieve large-scale data for improved response generation. By adhering to these security measures and leveraging best practices from leading industry sources such as Databricks, AWS, and Milvus, developers can ensure the robustness and trustworthiness of RAG-based systems across diverse applications.

Objetivo. Analisar uma ação de promoção à saúde auditiva por meio da Wikipédia. Método. Trata-se de uma pesquisa descritivo-analítica, transversal, do tipo intervenção com abordagem quantitativa que utilizou a estratégia de estudo de caso único. Foram utilizadas ferramentas estatísticas próprias da Wikipédia para analisar o impacto das edições promovidas, com relação à tradução, edição e elaboração de um novo verbete. Resultados. Esse processo resultou na edição de 10 verbetes, representando cerca de 20 mil palavras acrescentadas e mais de 17 mil visualizações dos conteúdos, resultando no aumento significativo nos acessos. Conclusão. Os resultados obtidos com a melhoria, tradução e criação de conteúdos de saúde auditiva na Wikipédia demonstraram o potencial desta plataforma na contribuição para mudanças significativas na promoção de saúde.

Introduction Falls are a major cause for injuries in older adults and are associated with motor and cognitive impairments (Hsu, 2012). Thus, sensitive assessment tools capturing subtle changes in motor and cognitive functioning are crucial for the prediction of falls. Aim of this study was to test the ability of a novel, instrumented, cognitive-motor assessment battery, to distinguish between older persons who experienced a fall in the past 12 months (fallers) and persons who did not (non-fallers) as well as its added value to predict fall status compared to gold-standard clinical fall risk assessments. Methods This is a secondary analysis using baseline data of an international randomized controlled trial. Participants (age > 60, categorized in two groups: fallers and non-fallers) performed five instrumented tests (Reaction time test (psychomotor speed), Flexibility test (mental flexibility), Go/No-Go task (inhibition), Sway test (postural control), and Coordinated Stability test (dynamic balance control), as well as the Timed-up and Go (TUG) test as a gold standard assessment. Binominal logistic regression analyses were conducted for each cognitive-motor assessment separately to identify test variables significantly associated with group assignment which could then be included in a further logistic regression model. Furthermore, a Receiver Operating Characteristic (ROC) analysis was performed. Results Preliminary results of n = 38 showed among the instrumented assessments only inhibition (p = .016) and dynamic balance control (p = .014) were significantly associated with fall status and therefore included in the second regression model. In this second model, both functions contributed significantly in predicting fall status (inhibition: p = .040, dynamic balance control: p = .028). This regression model was statistically significant and resulted in a good amount of explained variance (χ2(2) = 14.283 (p = .001), R2 = .418) with a sensitivity of 84.2% and a specificity of 73.7%. Similarly, the ROC analysis showed significant Area under the Curve (AUC) values only for inhibition (AUC = .751, p = .008) and dynamic balance control (AUC = .765, p = .005), whereas the AUC value of the TUG test remained non-significant (AUC = .681, p = .056). Discussion/Conclusion Based on this preliminary analysis, two tests of the novel instrumented cognitive-motor assessment battery assessing inhibition and dynamic balance control were able to distinguish between fallers and non-fallers and these tests showed better discriminative properties compared with the TUG. This supports the results of previous research which showed only limited discriminant validity of the TUG in terms of fall risk (Schoene et al., 2013). Therefore, the cognitive-motor assessments might provide an added value for clinical fall risk assessment. The study is still ongoing, and thus, the complete results (of N = 145) will be presented at the conference. References Hsu, C. L., Nagamatsu, L. S., Davis, J. C., & Liu-Ambrose, T. (2012). Examining the relationship between specific cognitive processes and falls risk in older adults: A systematic review. Osteoporosis International, 23, 2409-2424. https://doi.org/10.1007/s00198-012-1992-z Schoene, D., Wu, S. M. S., Mikolaizak, A. S., Menant, J., Smith, S. T., & Delbaere, K. (2013). Discriminative ability and predictive validity of the timed up and go test in identifying older people who fall: Systematic review and meta-analysis. Journal of the American Geriatrics Society, 61, 202-208. https://doi.org/10.1111/jgs.12106