Kaduna State University is in Kaduna, Kaduna State, Nigeria. It was established in 2004. It has seven faculties with over 39 departments and a library that contains over 17,000 volumes of books. It has two campuses: Kafanchan and Kaduna.
PurposeIn light of the fast-paced digital transformation, this paper aims to examine the effectiveness and adoption of cybersecurity frameworks in higher education institutions (HEIs).Design/methodology/approachThe methodological approach involved a systematic literature review that synthesized evidence from recent studies to classify and evaluate cybersecurity frameworks that are relevant to HEIs.FindingsThe study identifies three main types of frameworks, which are maturity and capability models, governance-based and collaborative structures, and technical and operational mechanisms. COBIT 5, Zero Trust and risk assessment models are frameworks that enhance institutional safety only when they are aligned with governance, resources, cultural norms and user experience. Persistent issues include fragmented adoption, minimal cooperation among educational institutions, and a preference for normative rather than alternative methods. Integrating ethical considerations and training with cognitive theory, as well as postincident learning mechanisms, are among the best practices.Research limitations/implicationsThe limited number of empirical studies focusing on HEIs and the geographic preference for developed areas restrict the impact of the findings presented in this review.Practical implicationsThe results offer practical advice for HEIs to implement tailored, holistic cybersecurity frameworks that address resilience, collaboration and culture adaptation beyond just compliance.Social implicationsHEIs should prioritize cybersecurity to protect their teaching, research and innovation, as well as intellectual capital and public trust.Originality/valueThis paper presents a methodical examination of cybersecurity management frameworks in HEIs, emphasizing both technical and socio-technical aspects, providing premise development for culturally sensitive and sustainable strategies.
Proper orthogonal decomposition (POD) has been widely employed in dimensional reduction as an essential preprocessing step in modern modal analysis techniques for turbulent flows. However, previous studies have focused primarily on preserving energy content rather than investigating turbulence statistics fidelity during the dimensional reduction step. This study examines the impact of POD-based dimensionality reduction on high-order turbulence statistics, including skewness sk, kurtosis kt, and extreme event (99.9th percentile value) for flow over a cubical array. POD was applied to both wind speed s and individual velocity components (streamwise u, spanwise v, vertical w) at two pedestrian levels (0.1H and 0.5H, where H is cube length). Wind speed structures were analyzed in relation to the structures revealed by POD of the three velocity components. Results show that thirty POD modes (2.5% of total modes) of s contained about 70% of total energy at 0.1H, while preserving the main characteristics of turbulence statistics compared to the original data. The root mean square errors of s (RMSE) for sk, kt, and 99.9% percentile value were 0.15, 0.45, 0.06, respectively. When reconstructing flow using 100 POD modes (85% of total energy), these RMSE reduced further to 0.07, 0.21, 0.03. At 0.1H level, the first mode of s shared structures with the first mode of v in the wake region behind the cube, while the first mode of u contributed to the s structure in front of the cube. This study provides insights into preserving high-order statistics during dimensionality reduction of pedestrian-level turbulence.
Land surface temperature is a key indicator of environmental conditions, and it is affected by both natural and anthropogenic activities. This study assessed the environmental determinants of land surface temperature (LST) within the Kaduna River Basin (KRB) using an integrated framework of machine learning and GeoDetector analysis. Using satellite-derived datasets, including MODIS (LST and NDVI), Sentinel-2 (LULC), and SRTM (elevation), this study quantifies regional and seasonal LST drivers. The results demonstrate significant seasonal shifts: the mean NDVI increased from 0.34 in the dry season to 0.51 in the wet season, whereas the mean LST decreased from 33.57 degrees C to 31.99 degrees C. Stacked ensemble machine learning (R-2 = 0.72 dry; 0.66 wet) and GeoDetector analysis identified vegetation (NDVI) and elevation as primary controls on the LST. Notably, the interaction between the NDVI and elevation was found to be more influential than individual factors, highlighting the synergistic role of topography and green cover in regulating basin-scale thermal dynamics. This study provides critical policy insights by stressing the importance of sustaining plant cover, particularly in specific topographical zones, in managing basin-scale thermal dynamics in the face of growing regional warming.
Meteorological droughts pose a persistent threat to rainfed agriculture in Kaduna State, Nigeria. This study aimed to examine meteorological drought in Kaduna State, Nigeria, from 1981 to 2024 using the Standardized Precipitation Index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) at time scales of 3, 6, 9, 12, and 24 months, with trends assessed using the Mann–Kendall test, and the indices compared using the Pearson correlation, mean difference, Root Mean Square Error, Mean Absolute Error (MAE), and Kling-Gupta Efficiency (KGE). The results revealed that SPI and SPEI detected drought events, and their duration and severity increased over longer timescales. SPI-3 detected the most intense drought (−4.317), while the most severe SPEI-3 drought had a magnitude of −3.355. The SPI-24 detected a 26-month drought with a highest severity of −3.351, while SPEI-24 detected a 35-month drought event between 1983 and 1986. SPI showed significant wetting trends at 12- and 24-month scales (p < 0.05), SPEI exhibited no significant trend at any timescale. Results of the comparison between SPI and SPEI revealed a strong positive correlation, particularly for the 6-month timescale (r > 0.94), but diverged at short and long timescales, most likely due to temperature influence. The study concluded that the difference between SPI and SPEI may be attributed to the influence of warming temperatures on atmospheric demand. The masking of precipitation gains by temperature-induced evaporation represents a common but under-recognized climate-change impact across Savanna and semi-arid regions globally, emphasizing the need to prioritize SPEI over precipitation-only indices for drought monitoring and adaptation planning.
The increasing use of metal and metal oxide nanoparticles in industrial and biomedical applications has raised important environmental and health concerns. Conventional synthesis routes often depend on toxic chemicals and energy-intensive processes. This review aims to examine how isolated plant-derived polyphenols function as natural reducing and stabilizing agents in the green synthesis of nanoparticles, addressing the research question: How do specific polyphenols influence nanoparticle formation, morphology, and applicability? A comprehensive analysis of published experimental studies was conducted, focusing on isolated polyphenol classes such as flavonoids, tannins, lignans, and stilbenes. Mechanistic pathways of reduction and stabilization were examined, and the effects of synthesis parameters including polyphenol concentration, pH, and temperature on nanoparticle morphology were reviewed. Findings show that isolated polyphenols effectively mediate nanoparticle synthesis by reducing metal ions and stabilizing formed particles, enabling controlled size and shape formation. Variations in reaction conditions markedly influence nanoparticle morphology. Polyphenol-capped nanoparticles demonstrated enhanced biocompatibility and exhibited promising performance in catalysis, sensing, antimicrobial activity, and drug delivery applications. Polyphenol-mediated nanoparticle synthesis offers a sustainable, efficient, and tunable alternative to conventional chemical methods. Despite its potential, challenges remain—including the standardization of plant extracts and the consistent isolation of specific polyphenols. Addressing these limitations will be essential to fully harnessing this green synthesis approach for advanced industrial and biomedical applications.
