Sales compensation in the real estate sector is the most important factor in determining an agent’s performance and retention. Fixed salaries, straight commissions, and split commissions, along with other conventional compensation models, struggle to keep up with market changes, agent performance, and consumer preferences. Based on this, this paper studies how modern analytics techniques, such as predictive modeling and agent segmentation, can improve and optimize real estate sales compensation programs. These techniques also provide brokerages with ways to customize compensation plans, reward top performers better, and make incentives in line with organizational goals. Predictive modeling uses real-time data integration to calculate what agent performance will be and, therefore, forecast revenue and various tiers of commission structure and even have it adjust compensation accordingly to market shifts. The practicality of using data analytics to optimize commission structures is demonstrated by presenting a case study using regression analysis on turnstile systems in the transportation industry, which are decreasing times of service in order to reduce prices and the uncapped shift. It also details the best practice of implementing what the author refers to as a data-driven compensation System, as he highlights the need to align the incentive with business objectives and transparency to prevent fraud and nonmonetary rewards. With volatility in the real estate market and stiff competition both emerging, embracing data-driven compensation lands more motivated agents, higher retention rates, and more profitable estate agents. The current state of real estate sales compensation depends on adapting to new market conditions using the tool of data insights and applying the new technology coming to the market, like AI, machine learning, and block chain, to build fair, flexible, and dynamic compensation models for the future.

The development of reconfigurable run-flat tires offers a significant leap in tire technology, blending the benefits of traditional run-flat tires with dynamic adaptability. This paper explores the innovative design, prototyping, and testing of a reconfigurable run-flat tire (RRT) that maintains optimal performance under various conditions, including after punctures. By integrating advanced materials, intelligent sensors, and an adaptable structural design, the RRT aims to enhance vehicle safety, reduce maintenance costs, and improve overall driving experience. The paper covers the design principles, the prototyping process, and the results from preliminary testing. The findings suggest that reconfigurability in run-flat tires can substantially improve vehicle reliability while offering better user experience and performance.

In this study, we present a deep learning-based approach for real-time credit card fraud detection in banking systems, with a primary focus on Long Short-Term Memory (LSTM) networks. Using a highly imbalanced credit card transaction dataset, we implemented comprehensive preprocessing, feature engineering, and model evaluation strategies to enhance the detection accuracy. Our experimental results reveal that the LSTM model significantly outperformed traditional machine learning algorithms such as Logistic Regression, Decision Tree, and Random Forest. The LSTM achieved an accuracy of 99.38%, precision of 99.40%, recall of 99.22%, and F1-score of 99.31%, demonstrating its superior capability to detect fraud while minimizing false positives. Through comparative analysis, we establish that deep learning not only improves predictive performance but also adapts better to temporal patterns inherent in financial transactions. This research underscores the transformative potential of AI-driven fraud detection in modern banking infrastructures, ensuring enhanced security, operational efficiency, and customer trust.

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.

This article examines the logistics of perishable and deep-frozen products from a multidisciplinary standpoint, with a focus on strategic, technological, and sustainability-oriented frameworks. Drawing on prior research and international regulatory standards, the study delves into three key areas. First, it addresses logistical processes and the inherent vulnerabilities of perishable items such as dairy, meat, fruits, and vegetables, highlighting how temperature, humidity, and delivery speed collectively shape product quality. Second, it explores the transportation of deep-frozen products by emphasizing ATP guidelines, hazard prevention measures under HACCP and ISO 22000, and the relevance of integrated monitoring tools. Third, it advocates a cross-functional approach that reconciles commercial objectives with environmental responsibilities, illustrating how “green logistics” methods can reduce emissions, energy consumption, and food waste. The article proposes that robust cold chain management—supported by inter-organizational collaboration and real-time data analytics—can not only uphold consumer safety but also drive cost reductions and enhance corporate reputations. Ultimately, the synthesis offers practical recommendations for industry practitioners, policymakers, and academic researchers seeking to advance cold chain efficiency and sustainability for perishable and deep-frozen commodities.

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.

This article explores conceptual approaches to optimizing ETL processes in distributed systems using a hybrid algorithmic solution based on the integration of Grey Wolf Optimizer (GWO) and Tabu Search (TS) methods. The study analyzes the characteristics of ETL under cloud-based architectures and identifies key challenges, such as high computational complexity, data redundancy, and the difficulty of clustering when handling large volumes of information. The results confirm the hypothesis that the synergy between GWO and TS algorithms leads to more efficient ETL processes, which is especially relevant for modern distributed systems and cloud computing environments. The article will be of interest to other researchers and graduate students specializing in distributed computing systems, big data processing, and ETL process optimization, as it presents an analysis of methodological approaches aimed at improving data integration efficiency within scalable architectures. The findings are also valuable for IT practitioners, enterprise system architects, and developers seeking to integrate advanced ETL optimization methods into modern information systems to enhance their performance and resilience.

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.

This article addresses the issues related to ensuring information security in mobile gaming applications. The rapid growth of this sector is accompanied by a surge in cyber threats, primarily targeting user data compromise, scenario manipulation, and unauthorized access to application functionality. The technical complexity of such systems—particularly those interacting with cloud and edge computing infrastructures—has given rise to a range of new vulnerabilities that fall outside the scope of traditional security measures. The aim of this study is to identify and systematize the problem areas in securing mobile games, particularly at the intersection of network architecture, user behavior, and cryptographic solutions. A review of the academic literature reveals a disconnect: while individual aspects such as authentication and encryption are explored in depth, architectural and behavioral risks are often treated superficially. This work categorizes the key threats and emphasizes how poor coordination between application design, technical implementation, and user practices creates a vulnerable environment—especially in the context of widespread use of third-party SDKs and monetization systems. The article presents indicative performance estimates for encryption and authentication mechanisms to provide an initial assessment of their applicability. The author’s contribution lies in integrating interdisciplinary approaches to analyzing the security of mobile gaming solutions and highlighting areas that remain underexplored in the current literature. The material will be of interest to cybersecurity specialists, developers, digital communication researchers, and interface designers.

This article explores methods for automating continuous integration processes in the context of distributed teams. With the rise of remote work and globalization, development process optimization has become a crucial factor in the success of modern projects. The objective of this study is to analyze approaches to automation that enhance collaboration among remote team members, reduce testing and deployment time, improve system stability, and enhance the overall quality of the final product.

The methodology involves a comparative analysis of scientific publications available in open sources. The article examines the capabilities of various tools in the context of remote work and identifies challenges teams face during implementation. It discusses key principles of CI/CD pipeline creation, including test automation, deployment, and monitoring strategies.

The results indicate that configuring and integrating CI/CD tools significantly reduce development time, improve code quality, and minimize human errors in testing and deployment. A critical aspect is the establishment of infrastructure that ensures workflow continuity in distributed teams while addressing synchronization and communication challenges.

The material will be useful for IT project managers, DevOps engineers, automation specialists, and technical managers working in distributed teams. The conclusion highlights the necessity of a comprehensive approach to CI/CD automation.

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.

This article explores existing strategies for embedding security measures into the Software Development Lifecycle (SDLC), with a particular focus on hybrid models such as Agile and DevSecOps. The study is grounded in a theoretical analysis, which identifies the foundational principles of secure software development, evaluates the significance of automated security testing within CI/CD pipelines, and examines the role of interdisciplinary approaches in fostering a security-oriented culture within organizations. The research highlights current challenges and limitations associated with balancing development flexibility and stringent security requirements, while also outlining promising directions for advancement, including increased automation, the implementation of unified standards, and the development of professional upskilling programs. The proposed strategies aim to reduce system vulnerabilities, improve software quality, and optimize security-related costs. This article will be of interest to researchers and practitioners in the fields of information security and software engineering who seek to integrate contemporary security practices into the development lifecycle to enhance cyber risk management. It may also attract attention from professionals involved in interdisciplinary research, as it analyzes the synergy between development methodologies and modern organizational security mechanisms.

Sodium-ion batteries (SIBs) are gaining attention as safer and cost-effective alternatives to lithium-ion batteries, but challenges remain in improving their safety, performance, and sustainability. This study explores advancements in electrolyte additives, polymer electrolytes, separators, and poly-ionic membranes to enhance SIB efficiency and safety. Sodium bis(oxalato)borate (NaBOB) was identified as a non-flammable and fluoride-free alternative to toxic NaPF6 in trimethyl phosphate (TMP), achieving thermal stability up to 300°C, high ionic conductivity (5 × 10⁻³ S cm⁻¹), and 97% coulombic efficiency. Incorporating vinylene carbonate (VC) mitigates discharge capacity degradation over cycling.

Flexible polymer electrolytes, such as PPEGMA-gel systems, demonstrate resilience to mechanical shocks with a capacity retention of 91% after 400 cycles and a wide voltage range of 4.8 V, though high-temperature performance requires further investigation. Organic electrolyte blends with 10 vol% fluoroethylene

carbonates (FEC) improve electrode stability, enabling energy densities of up to 1246 Wh kg⁻¹ after 300 cycles. Advanced separators, such as ZrO₂/PVDF-HFP-coated polyolefins, exhibit enhanced Na⁺ conductivity (7 × 10⁻⁴ S cm⁻¹) but require ceramic modifications for higher thermal resilience, achieving stability up to 500°C with barium titanate integration.

Hierarchical poly-ionic liquid-based solid electrolytes (HPILSE) outperform conventional membranes in flexibility, thermal stability (up to 300°C), and resistance to mechanical stress. Future studies are essential to optimize ionic conductivity through additive research. This comprehensive exploration of materials and configurations offers promising directions for the development of safe, efficient, and durable sodium-ion batteries.

This paper examines the integration of causal analysis (causality) and machine learning methods to accurately predict the effects of interventions. The first part introduces the rationale for the importance of the causal approach when classical statistical models and purely associative ML methods face problems of hidden factors and incorrect extrapolation of results. The second part discusses the basic theoretical concepts of causal graphs, do-operator, intervening and counterfactual distributions, and the role of identifiability assumptions in the presence of unobserved confounders. Next, methods for integrating causality and machine learning - causal supervised learning (to deal with spurious correlations and increase robustness to distributional shifts), causal generative modeling (with a focus on generating counterfactual data), and other state-of-the-art approaches (causal model explanation, causal fairness, causal reinforcement learning) - are discussed in detail. It is shown how such methods can better account for the real-world structure of the data and produce more reliable predictions, especially in heterogeneous environments. The results can be applied to medicine, economics, social sciences, and other fields where it is important to accurately predict the effects of potential interventions.

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.

The rapid expansion of e-commerce and the growing demand for faster delivery have significantly reshaped the role of warehouse logistics in modern business. Traditional warehouse management methods are no longer sufficient to handle the rising volume of goods, underscoring the urgent need for innovative technological solutions. This study focuses on the evolution of smart warehousing—from basic barcoding systems to sophisticated technologies involving robotics, drones, and artificial intelligence. A noticeable gap remains in the academic literature between theoretical research on warehouse optimization and its practical applications. While many publications emphasize technical advancements, they often overlook the economic and social implications of automation. Moreover, there is a lack of interdisciplinary research that bridges technological innovation with the transformation of business models and the evolution of labor relations. This article analyzes key technological trajectories and demonstrates how the integration of digital twins, predictive analytics, and autonomous robotics not only enhances operational performance but also fundamentally redefines warehouse management practices. The insights presented are relevant for logistics company executives, technology developers, infrastructure investors, and supply chain management researchers.

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.

This article explores the theoretical and methodological foundations of developing cloud computing solutions, which have become one of the primary tools for optimizing business processes and accelerating innovation in today’s digital landscape. The relevance of the topic stems from the growing volume of data and the need for flexible resource management, making the cloud an indispensable mechanism for ensuring sustainable organizational growth. The novelty of this study lies in its synthesis of economic, architectural, and organizational approaches, with particular attention to the risk of vendor lock-in and the integration potential of edge computing. The article examines various service models (IaaS, PaaS, SaaS), their specific features and areas of application, and identifies factors influencing successful migration to cloud environments and the development of flexible risk mitigation strategies. Special focus is given to implementation experiences within small and medium-sized enterprises and educational institutions. The primary goal of the article is to establish a systematic understanding of cloud computing solutions and to provide recommendations for their effective deployment. To achieve this, the study employs comparative analysis, source systematization, and a critical methodological approach. The conclusion presents key insights into how cloud computing influences organizational resilience and competitiveness. This article will be of interest to researchers, IT professionals, and managers aiming to integrate cloud technologies into their operations.

The study examines the principles of ensuring cybersecurity during software testing. The focus is placed on the fact that testing should not be limited to validation checks but must also incorporate risk assessment, compliance with standards, and early-stage vulnerability analysis throughout the software development lifecycle. The study reviews key regulatory requirements (GDPR, HIPAA, PCI DSS, ISO/IEC 27001, NIST Cybersecurity Framework) and analyzes their impact on testing strategies and quality control processes. Special attention is given to the CIA triad (confidentiality, integrity, and availability) and proactive incident planning. The necessity of integrating automated tools (SAST/DAST, SIEM, RPA, etc.) and artificial intelligence algorithms is substantiated to optimize protection procedures and enhance vulnerability detection efficiency. The conclusions emphasize that achieving a high level of product resilience is only possible through the close alignment of security requirements with test scenarios and the continuous refinement of testing methodologies. The findings presented in this study will be of interest to researchers and professionals in information security, software testing specialists, and developers seeking to integrate advanced methods into the protection of information assets.

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.

This study examines a decentralized approach to implementing a service mesh for microservice-based systems designed for scalable data processing. Unlike traditional solutions dominated by the pipes-and-filters pattern and a centralized control plane, this approach utilizes the concept of Eblocks—unified modules that incorporate service discovery, authentication, monitoring, and load management components. This allows for the formation of various patterns (manager-worker, divide-and-conquer, hybrid models) directly at the microservice level without strict dependence on centralized logic. It is demonstrated that such an architecture accelerates data processing through automatic scaling and parallel execution, simplifies configuration, and provides flexible security and observability mechanisms. The proposed results, supported by findings from other researchers, indicate a significant increase in system throughput when handling documents requiring pipeline, parallel, and distributed processing. The presented information is of interest to researchers and professionals in distributed systems, cloud computing, and microservice architecture, aiming for a deeper understanding and implementation of innovative service mesh architectures to enhance the scalability, reliability, and efficiency of modern IT applications.