Taxation plays a pivotal role in shaping the financial framework of public sector organizations, influencing their decision-making processes. This study explores the effects of taxation on management decision-making in the public sector, focusing specifically on Ekiti State, Nigeria. The research examines key elements such as tax revenue, tax compliance rates, tax burdens, and effective tax rates, providing insight into their implications for governance and resource allocation. The study highlights how tax revenue serves as a critical funding source for public sector operations. An adequate and consistent tax collection system enables government agencies to plan and execute development projects effectively. Conversely, irregularities in tax revenue streams may constrain decision-making, causing delays or cancellations of essential projects. Tax compliance rates, tax burdens, and their equitable distribution further impact management strategies, requiring a balance between sustainable revenue generation and taxpayer satisfaction. The research employed both primary and secondary data to analyze the relationship between taxation and public sector management. Primary data was collected through structured interviews and surveys targeting public sector managers and tax administrators, while secondary data was sourced from government reports, policy documents, and relevant academic literature. This mixed-method approach provides a comprehensive perspective on the issue. This study concludes that taxation significantly shapes management decision-making in the public sector in Nigeria. Recommendations include adopting transparent tax policies, fostering taxpayer engagement, and ensuring the judicious use of tax revenue to enhance public trust and governance outcomes. These measures can improve the capacity of public sector organizations to make informed decisions, ultimately fostering sustainable development.

A total of 43 urine samples were examined for the presence of uropathogenic bacteria. Escherichia coli was isolated from 33 of these samples, while Klebsiella species were found in seven (7). Bacterial identification was confirmed using standard biochemical tests following CLSI guidelines. E. coli tested positive for Indole and Catalase, whereas Klebsiella spp. were negative for Indole and Oxidase; both organisms showed lactose fermentation on MacConkey agar. All confirmed isolates were screened for Metallo-?-lactamase (MBL) and Extended-Spectrum ?-lactamase (ESBL) production. MBL activity was observed in two E. coli isolates using the Meropenem-EDTA Disk Synergy Test, indicated by a ?7 mm increase in the inhibition zone. ESBL production was confirmed in one E. coli and three Klebsiella isolates using the Double Disc Diffusion Test with Ceftriaxone and Clavulanic Acid, where a zone enhancement of ?8 mm signified ESBL activity. Three selected isolates underwent Sanger sequencing and were analyzed through BLAST. These results underscore the presence of multidrug-resistant pathogens in urinary tract infections and highlight the importance of ongoing antimicrobial resistance surveillance.

Pile foundations are widely used to transfer structural loads to deeper and more competent soil strata, particularly in areas with weak or heterogeneous ground conditions. Accurate prediction of pile performance is essential for safe and economical foundation design. This review paper presents a comprehensive comparative study of pile foundation performance based on in-situ testing methods and numerical analysis techniques. Commonly adopted in-situ tests, such as static pile load tests, cone penetration tests (CPT), standard penetration tests (SPT), and dynamic pile testing, are reviewed with emphasis on their ability to evaluate load–settlement behavior, bearing capacity, and soil–pile interaction mechanisms. The interpretation approaches, advantages, and limitations of these field-based methods are critically discussed. In parallel, numerical modeling techniques using finite element and finite difference methods are examined, focusing on constitutive soil models, boundary conditions, pile–soil interface modeling, and calibration procedures. A comparative assessment highlights the consistency and discrepancies between field test results and numerical predictions, identifying key factors influencing performance such as soil variability, modeling assumptions, and construction effects. The review underscores the importance of integrating in-situ test data with numerical analysis to improve the reliability of pile design and optimize foundation performance. Finally, research gaps and future directions are identified to enhance the accuracy of predictive models and promote performance-based pile foundation design.