10 research outputs found
Temporal Variation of Reference Evapotranspiration in Lower River Kaduna Catchment Area, Nigeria
No full textLower River Kaduna catchment area is located in north part of Nigeria where climate variability is pronounce such as high rainfall which result to drought, ground water scarcity and high surface water to evaporation. As such it is importance to investigate the temporal variation evapotranspiration of this area. Because it determined the atmospheric water demand in the catchment. Meteorological data were collected from Nigeria Meteorological Agency which include mean temperature and solar radiation for the period of 21 years (1994-2014). Makkink method of estimating evapotranspiration was used. Trend analysis, normality test and coefficient of variance were carried out respectively. The result revealed that the evapotranspiration has mean value of 4.22 mm day-1, solar radiation has mean value of 21.48 MJ m−2 day−1 and temperature has mean value of 32.07°C. Both variables show a moderate variability in the catchment. Evapotranspiration and solar radiation show a negative annual trend while the temperature reveals positive annual trend. April to October has decrease of solar radiation, temperature and evapotranspiration while the remaining month has an increase trend. Seasonal trend shows that MAM and JJA have a decrease while SON and DJF have an increase in solar radiation and evapotranspiration. Seasonal trend of temperature show that MAM, JJA and SON has a decrease while DJF has an increase
Estimation of land surface temperature of Kaduna metropolis, Nigeria using landsat images
No full textUnderstanding the spatial variation of Land Surface Temperature (LST), will be helpful in urban micro climate studies. This study estimates the land surface temperature of Kaduna metropolis, Nigeria. For the present study Landsat ETM+ images of 2001, 2006, 2009 and OLI 2015 was obtained from USGS of the study area. Normalized Difference Vegetation Index (NDVI) image was developed. The digital number of thermal infrared band is converted in to spectral radiance using the equation supplied by the Landsat user’s hand book. The effective at-sensor brightness temperature is obtained from the spectral radiance using Plank’s inverse function. The surface emissivity based on NDVI is used to retrieve the final LST. It was noted that 2006 has the highest maximum value with the highest mean value of 0.177 and standard deviation of 0.0903 while 2001 has the minimum value of NDVI. So also 2001 has the maximum value with highest mean value of 0.999 and standard deviation of 0.00161 while 2015 has the minimum value of surface emissivity. The coefficient determinant R2 (0.837) show a strong positive correlation between mean of surface emissivity with date and season which shows downward trend in average over the study period. 2015 has the highest mean value of 39.42 with standard deviation of 1.92 of LST and coefficient determinant R2 (0.46) show a positive correlation between mean of LST with date and season, with an upward trend in average LST over the study period. Lastly, NDVI is found to have negative correlation with LST. The Coefficient of determination (R2) (0.66) of surface temperature with NDVI and surface emissivity show a better prediction power of land surface temperature.Keywords: LST, NDVI, Surface Emissivity, Landsat images, Temperatur
Spatio-Temporal Variation of Actual Evapotranspiration of Lower River Kaduna Catchment, Nigeria
No full textEstimation of Biophysical Properties in Lower River Kaduna Catchment Area Kaduna, Nigeria
No full textIMPACT OF FINANCIAL ACCOUNTING SYSTEM ON CORPORATE PERFORMANCE OF MANUFACTURING FIRMS IN NIGERIA
No full textThis study examined the impact of financial accounting system on corporate performance of manufacturing firms in Nigeria. The reason for this study was because financial report is expected to show the economic and financial situation of the company, in order to inform managers and shareholders and is of crucial importance in decision making, when the interests of both shareholders and creditors must be taken into account. The specifics objectives of the study were to ascertain the effect of financial reporting quality on return on assets, effect of financial reporting quality on return on equity and effect of financial reporting quality on net profit margin all in manufacturing companies in Nigeria. Secondary data was used in this study and Ordinary Least Square (OLS) multiple regression was used to analysed the data with the aid of SPSS version 23 output statistical software. The study employed an ex post factor research design because the data for the study was extracted from annual report and account of the manufacturing companies in Nigerian. A purposive sampling technique was employed to select 10 financial firms listed on the Nigerian Exchange Group as at 31st December, 2023. The study covered a time frame of five (5) years, starting from 2019 – 2023. The study found out that positive relationship between financial reporting quality and returns on assets, a negative relationship between financial reporting quality and returns on equity and a positive relationship between financial reporting quality and net profit margin of selected manufacturing companies. We therefore, recommended timeliness of audited corporate annual financial reports is considered to be a crucial and an essential factor affecting the usefulness of information made available to various users, the use of joint auditors is also encouraged, as the volume of transactions in most Nigerian firms are constantly increasing, the possibility of efficiency and effectiveness in audit might be likely eroded and appointing outside directors to the board is an effective board leadership style to reduce the agency problem and increase reporting quality.
 
Modelling the signature of human influence on vegetation dynamic in Kamuku National Park, Nigeria
No full textThe sustainable development goal (SDG 15) recognizes the necessity to investigate important biodiversity because of human disturbance. This research models the signature of human influence on vegetation dynamics in the protected area. The Vegetation Index 16-Day from MODIS was used. A novel method for computing the vegetation deficit index (standardized vegetation deficit index: SVDI) on a 3 to 24 month scale was proposed. In addition, NDVI and VCI were used to assess the vegetation condition. Time series decomposition, Mann-Kendall, coefficient of variance, landscape fragmentation matrix, correlation, and wavelet analysis were used. Key informant interviews were conducted to assess the socioeconomic and driving factors of vegetation changes. The findings revealed that across all five period scales of SVDI, and the wavelet result in the last decade, human activities have had a tremendous footprint on the vegetation dynamics. It was also revealed that because of the insecurity that happened in the area, there was vegetation regeneration (from 2015 to 2021), which was revealed in the result of vegetation indices and their spectrum. The changes in vegetation revealed by this studies were justified by the changes perceived by the communities around the national park. The community's livelihood activities were hampered because of the occurrence of insecurity in and around the national park
Application of machine learning for coastal flooding
No full textAbstract The rise in coastal flooding has been an issue of global concern, particularly to populations Living in low-lying coastal areas. It has resulted in several casualties, destruction of infrastructure worth billions of dollars, displacement of communities, and ecological damage. This necessitates a review to provide an appraisal of how machine learning can be used for coastal flooding management. The study considered articles published between 2000 and 2025 from academic databases. A systematic literature assessment was carried out based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines to ensure an all-inclusive and organized reviewing process. Findings revealed that traditional physical models have a number of weaknesses that include high data needs, calibration, validation, and scaling up. Machine Learning (ML) outperform conventional techniques by effectively handling intricate nonlinear relationships and large dataset burdens. Contemporary challenges to the use of ML in predicting coastal flooding include the requirement for extensive, high-resolution datasets and the risk of overfitting; besides, there are interpretability issues and difficulties associated with incorporating machine learning models into traditional hydrological and hydraulic frameworks. The prospects of ML in coastal flooding are good, as some recent improvements like deep learning architectures, transfer learning, reinforcement learning, and the integration of IoT, hybrid modeling approaches and remote sensing technologies are expected to change entirely how floods are predicted or managed. The study highlights hybrid models as a key future direction, combining the strengths of both ML and traditional methods to improve prediction accuracy and operational efficiency. The study concludes that integrating advanced approaches, such as hybrid models, can help address some of the limitations of conventional models, leading to improved accuracy and efficiency in coastal flood prediction and management. However, while machine learning models show great promise, they should be seen as complementary rather than as complete replacements for traditional methods
High pollution loads engineer oxygen dynamics, ecological niches, and pathogenicity shifts in freshwater environments
No full textThe current study comprehensively reviews the ecological niche and pathogenicity shift in the freshwater microbial community in response to the stress induced by a high pollution load. The study provides a unique understanding of how a change in oxygen level tends to affect the survival of aquatic biota by delving into how an increase in pollutant load affects freshwater stability. The review indicated that high pollution loads alter the balance of freshwater resources such as organic matter, dissolved gases, light penetration, and essential nutrients. This causes oxygen dynamics and a species-dependent change in the community and niche of microorganisms in freshwater environments. This oxygen dynamics also causes the alteration of the genome of freshwater microorganisms, leading to the development of antibiotic resistance genes and thereby increasing the pathogenicity of freshwater microorganisms. The oxygen dynamic created lowers the natural defence strategies of the freshwater environment, thereby increasing the efficacy of the pathogens to infest the respective host. A detailed study of the mechanisms involved in freshwater exotoxins production and interaction with microorganisms will give an important insight into the niche shift in response to the effect of the exotoxin. The effect of the change in the pathogenicity of freshwater microorganisms is of importance to both environmental and medical interests. This is because the change in pathogenicity is not only detrimental to aquatic organisms but also resists improperly treated drinking water. Such water could retrogress wellness and quality of life when used continuously. An extensive study on how specific pollutants cause a shift in the niche and pathogenicity of freshwater microbiota will provide a detailed understanding of the impact of pollution on the stability of freshwater environment
Executive Compensation and Value of Listed Deposit Money Banks in Nigeria
No full textThe increasing failure of banks has made it important to seek for ways to enhance its value in order to attract investors and potential investors. To make this reality, scholars have argued from various quarters that the people who manage the banks must be adequately compensated if the desired value needs to be achieved. Therefore, the study examines the relationship between executive compensation and value of listed deposit money banks (DMB) in Nigeria. The study adopted correlational research design with balanced panel data of 14 listed banks which served as population of the study for the period of 2010-2021 using Generalized Least Square (GLS) regression as a tool of analysis. The study found that CEO Pay and Chairman’s compensation have positive effect on the value of listed banks, while the highest paid director exact negative influence on the banks’ value. This implies that the CEO Pay and Chairman’s compensation improves the value of banks. Therefore, it is recommended among others that the management of banks should increase the CEO pay and place more emphasis on performance as a basis of increased pay to guarantee continuous improvement in the value of the banks
Global, regional, and national burden of chronic respiratory diseases and impact of the COVID-19 pandemic, 1990–2023: a Global Burden of Disease study
No full textChronic respiratory diseases, including chronic obstructive pulmonary disease (COPD), asthma, pneumoconiosis, interstitial lung disease (ILD) and pulmonary sarcoidosis, are major global causes of mortality and morbidity. Although the COVID-19 pandemic has influenced acute respiratory health, its impact on chronic respiratory conditions remains unclear. We estimated the global, regional and national burden of chronic respiratory diseases from 1990 to 2023, including risk factors, and evaluated how these burdens have shifted during the COVID-19 pandemic using the Global Burden of Disease Study 2023. In 2023, chronic respiratory diseases accounted for 569.2 million (95% uncertainty interval (UI), 508.8–639.8) cases and 4.2 million (3.6–5.1) deaths. The age-standardized death rate declined by 25.7% globally from 1990 to 2023 despite an increase in ILD and pulmonary sarcoidosis. Mortality declined in younger males, especially for asthma, whereas older adults experienced a rise in ILD and pulmonary sarcoidosis. Smoking was the primary risk factor for COPD, whereas high body mass index and silica exposure were key risk factors for asthma and pneumoconiosis. During the pandemic, the incidence of chronic respiratory diseases increased modestly, but the decline in mortality rates became more pronounced, highlighting the need for sustained global attention and action to address their long-term burden
