The paper gives a profound evolution of the business of Manicure Development. It started from the transformation of the manual traditional techniques to the use of technological solutions in the present day. The paper also depicts the original historical background from the civilization of ancient Egyptian and Chinese, manicure popularization in the twentieth century up to the current time of copious use of automation, artificial intelligence, and robotics within the industry. It centers the discussion on the change that technology is bringing into the beauty industry with promises to make services more efficient, accessible, and personalized to redefine the customer experience. The paper basically summarizes the main stages in the development of classical methods through electric tools, software for salon management, and the most recent state-of-the-art innovation.

The study aims to explore the influences of up-to-date technologies on the business of manicure, tracking changes that occur due to automation and artificial intelligence, compare them with traditional methods, and put forward, if it is possible, the improved quality and works due to better productivity of services. The relevance comes from the breathtaking speed that changes the beauty business with technology use to optimize processes and satisfy increased demands from customers for more individual experiences. What is new in this article is an extended comparative presentation of historical references with a contemporary view of technological trends, which enables one to spotlight the main driving forces of movement in the sphere of beauty and make reasoned forecasts.

Beauty industry specialists and manicurists would find the article quite practical since it gives practical ideas on how to apply technologies that would leverage competitiveness and the level of service. Entrepreneurs would find the work capable of shedding light on market trends and innovation incentives in the manicure business. The examples of the use of AI and robotics will inspire technologists and developers to create new solutions.

This study examines the efficiency of automated testing in container orchestration using Kubernetes as an example. Modern IT environments require rapid, reliable, and scalable application deployment, made possible by advancements in containerization technologies and CI/CD automation. The research is based on an analysis of existing studies. The paper explores the theoretical foundations of container orchestration, including Kubernetes architecture, and the principles of automated testing, encompassing unit, integration, performance, and security testing. Practical aspects of integrating testing into CI/CD processes are presented, with a focus on rolling updates, blue-green, and canary deployments, which help minimize the risk of deploying defective code and reduce downtime. The study also discusses future developments in the field, emphasizing AI/ML integration for failure prediction, improved multi-cluster management, and enhanced security measures. The findings demonstrate that implementing automated testing improves the reliability and efficiency of container orchestration, playing a crucial role in optimizing modern IT infrastructures. The information provided in this study will be of interest to researchers in DevOps, automated testing, and container orchestration, as it contributes to a deeper theoretical understanding and practical optimization of quality assurance processes in distributed systems amid ongoing digital transformation.

Cold start latency in serverless computing, particularly in Java-based AWS Lambda functions, presents a significant challenge for latency-sensitive applications. This study investigates the performance characteristics of three modern Java frameworks - Spring Boot, Micronaut, and Quarkus - deployed on AWS Lambda using the ARM64 (Graviton2) architecture. It evaluates cold start latency across three deployment configurations: managed runtime (with and without SnapStart) and GraalVM native images. Metrics were collected at varying memory allocations using Java 21. Results show that Quarkus consistently outperforms others in cold start latency on standard JVM, while SnapStart and GraalVM significantly reduce the number of cold starts and achieve sub-second latency, respectively. We discuss the implications of these findings for choosing a Java framework and runtime strategy on AWS Lambda, considering the trade-offs in deployment time, complexity, and performance. The paper concludes with recommendations for leveraging SnapStart and native images to mitigate cold start issues in Java serverless applications on ARM64.

The article examines the features of integrating artificial intelligence algorithms (Explainable AI, XAI) into CRM systems aimed at enhancing customer experience (Customer Experience, CX). Based on an analysis of recent publications, the study explores the principles of personalization as well as approaches to the explainability of machine learning algorithms, including chatbots and recommendation systems. It demonstrates that transparency and interpretability of model outputs positively influence customer trust and loyalty while simultaneously improving the efficiency of internal business processes. The article analyzes the implementation experience of XAI in the banking sector, insurance call centers, and online retail, which has led to improvements in retention, conversion, and satisfaction metrics. The information presented in the article is intended for researchers and professionals in the field of artificial intelligence focused on developing interpretable machine learning algorithms, as well as for analysts seeking to optimize CRM systems to enhance customer experience management. In addition, the material is useful for professionals in corporate governance and marketing who aim to integrate advanced Explainable AI methods into personalization strategies and decision-making processes, ensuring the transparency and adaptability of services under dynamic market conditions.

The article examines issues related to the effectiveness of CRM in preventing aviation accidents. Modern civil aviation demands extremely high standards of flight safety, which necessitates the improvement of the methodological foundation for crew resource management. Despite the widespread implementation of relevant mechanisms, unresolved challenges remain that are directly linked to the effectiveness of various training methods, the role of technological innovations, and the impact of the human factor. Some studies emphasize the particular importance of digitalization and the integration of artificial intelligence into decision-making processes, while others focus on enhancing traditional interpersonal skills within the crew. These contradictions require detailed analysis. The study aims to investigate the efficacy of CRM in the context of preventing aviation accidents, taking into account the different approaches applied by airlines. Key aspects addressed include the role of meteorological data, the implementation of virtual reality and artificial intelligence in training programs, and the human and organizational determinants that shape the successful application of CRM. It is concluded that the effectiveness of CRM depends not only on the quality of crew training but also on the systematic integration of technological solutions, training methodologies, and organizational culture. The author’s contribution is evident in the formulation of recommendations regarding the development of training programs. The results will be useful for airlines, aviation training centers, developers of simulation technologies, and safety professionals.

The increasing complexity of credit risk management in banking systems has led to the adoption of machine learning techniques to improve the prediction of loan defaults. This study evaluates and compares the performance of several machine learning models—Logistic Regression, Random Forest, Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Neural Networks—in predicting credit risk. The models were tested on a comprehensive dataset containing demographic, financial, and historical loan data. Performance was assessed based on accuracy, precision, recall, F1-score, AUC, and confusion matrix analysis. The results indicate that Gradient Boosting (XGBoost) outperformed the other models with the highest accuracy (88.7%), precision (89.5%), recall (80.3%), and AUC (91.3%), demonstrating its superior ability to predict loan defaults and manage credit risk effectively. Random Forest followed closely in performance, while Logistic Regression showed solid results with a focus on interpretability. Neural Networks and SVM performed well in accuracy but were more resource-intensive and less interpretable. The study concludes that Gradient Boosting (XGBoost) is the most suitable model for large-scale credit risk management due to its balance of high predictive power and ability to handle complex, imbalanced datasets. However, the choice of model should consider computational resources, interpretability requirements, and specific operational constraints of the banking institution.

Graph Neural Networks (GNNs) present significant potential to revolutionize automated Test Case Prioritization (TCP) in Quality Assurance (QA) by effectively modeling intricate software-test relationships. This study evaluates the performance of Graph Convolutional Networks (GCN) and Graph Attention Networks (GAT) against traditional prioritization methods, including random, coverage-based, and historical-data-based prioritization. Employing five publicly available software project datasets, results indicate that GNN-based methods, particularly GCN, demonstrate superior performance with an average APFD (Average Percentage Faults Detected) score of 84.2%, outperforming conventional approaches. Despite their effectiveness, GNN methods face substantial challenges, notably computational complexity, scalability issues, data availability and quality concerns, and limited interpretability. Practical adoption also demands sophisticated graph construction, rigorous hyperparameter tuning, and integration into existing QA workflows. The findings emphasize the necessity for strategic implementation and further research in hybrid modeling, incremental learning, and explainable AI to maximize the benefits of GNNs in TCP.

Function-as-a-Service (FaaS) in cloud computing is a critical optimization problem that needs to be tackled, including cold start latency, resource inefficiency, state management, and more. While FaaS provides obvious scalability and lower cost benefits, the lack of availability of resources and the problem of cold starts to prevent it from being used for high-performance applications. Pre-warming, snapshotting, and on-demand instantiation with lightweight runtimes, such as WebAssembly, are other ways to minimize cold start delays. It also benchmarks major FaaS platforms (AWS Lambda, Google Cloud Functions, Azure Functions, and OpenFaaS) and measures latency, throughput, and scalability metrics. The study also considers how to manage the resource, for instance, using auto-scaling, memory allocation, and request batching to enhance cost efficiency and performance. The study covers security challenges in multi-tenant environments and solutions for stateful applications in usually stateless serverless architectures with Faast.js, Knative, and OpenWhisk. Another area of research is edge computing and architectures for multiple clouds to improve the deployment of FaaS. Incorporating the lessons from this study gives it enough flexibility to adjust functions as applications in a real-world enterprise environment, especially in high-performance and data-sensitive applications. The study also provides security practices like function isolation and encryption to secure data in multi-tenancy environments for reliable, secure, and efficient serverless computing. The contribution to FaaS optimization and security for various use cases is achieved.