Private banking institutions serve the financial sector’s wealthiest clientele via a dedicated value proposition. Based on the relevant tendencies and statistics, a remarkable expansion can be outlined since the mid-1990s. The aim of this study is to elaborate the Hungarian private banking market’s development as a case study. The paper also intends to add to the literature on this unique segment of the financial market. Based on the available statistics, the analysis primarily focuses on the Hungarian private banking market’s rapid development process. This can be underpinned by the clientele’s savings, number of accounts and respective segmentation limits of the institutions. Referring to the amount of savings, a correlation analysis indicates significant co-movements with specific social and economic variables. The growth rate of the Hungarian clientele’s savings outperformed the respective indicator in Western Europe during the review time period (2007–2020). The current paper also includes a section that summarises general challenges that private banking managers need to address during the development process. Generally, the literature on private banking can still be considered scarce, whereas there is a lack of studies on the Central-Eastern European region. The analysis of the Hungarian sector’s development path can serve with relevant information to any financial expert in the field.

In today's highly competitive environment, enterprises strive for competitive advantages by actively responding to changes in the network environment through digital technology. This approach fosters continuous innovation and establishes new paradigms by creating new network structures and relationships. However, research on the relationship and transmission mechanisms between digital technology and innovation performance in dynamic environments is still in its early stages, which does not fully address the demands of current social practice. Therefore, exploring the impact mechanisms of digital technology applications on enterprise innovation performance is an important research area. Based on the dynamic capability theory, this paper utilized SPSS 26.0 and AMOS 24.0 software to conduct an empirical analysis of 490 valid samples from the network perspective, exploring the pathways through which digital technology capability influences enterprise innovation performance. The results indicate that (1) digital technology capability is positively correlated with enterprise innovation performance; (2) digital technology capability is positively correlated with network responsiveness; (3) network responsiveness is positively correlated with enterprise innovation performance; (4) network responsiveness plays a mediating role in the impact of digital technology capability on enterprise innovation performance; (5) environmental dynamism positively moderates the relationship between digital technology capability and enterprise innovation performance. This paper enhances the understanding of how digital technology capability influences enterprise innovation performance in dynamic environments, offering new insights for future research. The results suggest that enterprises should focus on enhancing their digital technology capabilities, optimizing network structures, and strengthening network relationships to drive digital innovation.

This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.

Service composition enables the integration of multiple services to create new functionalities, optimizing resource utilization and supporting diverse applications in critical domains such as safety-critical systems, telecommunications, and business operations. This paper addresses the challenges in comparing load-balancing algorithms within service composition environments and proposes a novel dynamic load-balancing algorithm designed specifically for these systems. The proposed algorithm aims to improve response times, enhance system efficiency, and optimize overall performance. Through a simulated service composition environment, the algorithm was validated, demonstrating its effectiveness in managing the computational load of a BMI calculator web service. This dynamic algorithm provides real-time monitoring of critical system parameters and supports system optimization. In future work, the algorithm will be refined and tested across a broader range of scenarios to further evaluate its scalability and adaptability. By bridging theoretical insights with practical applications, this research contributes to the advancement of dynamic load balancing in service composition, offering practical implications for high-tech system performance.

The study of the performance of high-efficiency heat pump systems has been a hot issue of general interest in the field of heat pump air conditioning. For the designed and developed two-stage casing tandem heat exchanger of heat pump system, the 3D finite volume method and the realizable k-ε model are used to numerically analyze the influence law of inlet fluid temperature and flow velocity on the overall heat transfer coefficient as well as the Nussle number of inner and outer tubes. The results show that decreasing the inlet water temperature or increasing the inlet refrigerant temperature can improve the overall heat transfer performance; Nu_in increases with the increase of water and refrigerant flow rates, while Nu_out increases with the increase of water flow rate but decreases with the increase of refrigerant flow rate; Nu_in and Nu_out both increase with the decrease of water temperature or refrigerant temperature increases.