Abstract Background: Healthcare organizations increasingly require information systems that support not only clinical data storage but also efficient retrieval, integration, and advanced analytics. Poorly designed database schemas introduce redundancy, update anomalies, and integrity risks that compromise both operational efficiency... Background: Healthcare organizations increasingly require information systems that support not only clinical data storage but also efficient retrieval, integration, and advanced analytics. Poorly designed database schemas introduce redundancy, update anomalies, and integrity risks that compromise both operational efficiency and data quality. Objective: This study presents a normalized relational database schema for hospital management and demonstrates its capacity to support diverse operational and patient level analyses. Methods: A fourteen table relational schema was designed applying first, second, and third normal forms (1NF-3NF) with primary and foreign key constraints to ensure referential integrity across patient demographics, staff management, departmental organization, physical resources, appointments, clinical records, surgical events, shift scheduling, and billing. The schema was then evaluated through mediation analysis, moderation analysis, bipartite patient doctor network analysis, four class Gaussian mixture latent profile analysis, group comparisons with effect sizes, and robustness/sensitivity checks. Results: Mediation of payment through appointment completion was not statistically supported (bootstrap 95% CI:- 53.45 to +6.08). Moderation by age and department was non-significant. The patient doctor network exhibited strong centralization, with one physician accounting for 505 weighted links and 426 unique patients among 726 patients and 1,000 appointment edges. Four distinct utilization phenotypes emerged. Appointment modality showed negligible practical effects on payment or completion ( 2 = 0.0012; Cramér's V = 0.056). Findings were robust across alternative specifications. Conclusion: The normalized schema functions as an integrated analytical foundation bridging data management and data services, supporting network analytics, patient phenotyping, and reusable clinical phenotype development for future hospital information infrastructures. Read More Read Less

Abstract Effective workload characterization is essential for resource management in cloud data centres, yet the relative importance of different workload descriptors remains unclear. This study analyses a corpus of 15,000 workload observations to identify the dominant empirical dimensions... Effective workload characterization is essential for resource management in cloud data centres, yet the relative importance of different workload descriptors remains unclear. This study analyses a corpus of 15,000 workload observations to identify the dominant empirical dimensions of workload variation and to evaluate whether secondary behavioural metrics provide independent discriminatory information beyond read/write orientation. Observations were classified into read heavy (74.77%), mixed (25.09%), and writeheavy (0.14%) categories. We further cross classified a six type workload taxonomy with four data modalities to examine finer descriptive structure. Principal Component Analysis followed by silhouette optimised Kmeans clustering (K = 2, silhouette = 0.1957) partitioned the feature space into two groups distinguished almost exclusively by read/write composition, while request rate, hotspot intensity, access skew, data locality, and workload change rate remained virtually identical across clusters. Kruskal Wallis testing confirmed that read and write ratios differed highly significantly among workload classes (H = 8493.179, p < .001), whereas all secondary metrics were statistically homogeneous (p > .20). The findings demonstrate that read/write orientation constitutes the principal organising axis of workload behaviour in this corpus, while secondary characteristics do not form independent classification dimensions. The results support a parsimonious, hierarchical workload representation in which read/write ratio serves as the primary classification feature and secondary metrics function as continuous operational descriptors. This paper discusses implications for workload aware database selection, cloud native architecture, and resource management frameworks. Read More Read Less

Abstract This study examines the convergence, persistence, catch up dynamics, and heterogeneous trajectories of R&D researcher capacity across OECD and selected partner economies during 2015-2023. Using an integrated analytical framework combining  convergence, absolute  convergence, the Phillips... This study examines the convergence, persistence, catch up dynamics, and heterogeneous trajectories of R&D researcher capacity across OECD and selected partner economies during 2015-2023. Using an integrated analytical framework combining  convergence, absolute  convergence, the Phillips Sul log-t test, Markov transition analysis, trajectory clustering, compound annual growth rate (CAGR) analysis, and a growth level typology, the study investigates whether national researcher stocks move toward a common long run equilibrium or instead follow differentiated development paths. The results decisively reject universal convergence: cross country dispersion did not decline systematically, the  -convergence coefficient was statistically insignificant ( = +0.000599, p = 0.867), and the Phillips Sul test rejected overall convergence (t = -22.002). Markov transition analysis revealed strong structural persistence, with probabilities of remaining in the same capacity state ranging from 74.4% to 94.1%. Nevertheless, the study identified selective catch up: Poland, Türkiye, Hungary, Slovenia, and Croatia showed rapid researcher capacity growth and formed a statistically distinct trajectory cluster. The growth level typology classified countries into four regimes: strategic leaders, mature leaders, catch up economies, and persistent laggards. The study concludes that R&D researcher capacity constitutes a multi regime system characterized by simultaneous persistence, selective catch up, and multiple trajectories rather than a single convergence process. These findings carry significant implications for differentiated science, technology, and innovation policy design. Read More Read Less