Ladoke Akintola University of Technology

About: Ladoke Akintola University of Technology is a education organization based out in Ogbomoso, Nigeria. It is known for research contribution in the topics: Population & Adsorption. The organization has 2786 authors who have published 3066 publications receiving 36850 citations. The organization is also known as: Oyo State University of Technology & LAUTECH.

Cytogenotoxicity potentials of cocoa pod and bean-mediated green synthesized silver nanoparticles on Allium cepa cells

Abstract: Nanotechnology is a ground-breaking scientific innovation, but there are possible hazards to environment and human health. Therefore, there is a need to understand the toxic potential of nanopartic...

Self-reported physical activity among health care professionals in South-West Nigeria

Abstract: Introduction:Physical activity (PA) is a key requirement for maintaining good health. There is growing evidence of declining PA worldwide. Physical inactivity is linked with the global obesity pandemic and increasing burden of noncommunicable diseases (NCDs) in developing countries. A barrier to PA counseling by health care providers (HCPs) is personal PA habits. Information regarding PA among HCPs in Nigeria is limited. We aimed to determine the adequacy and predictors of PA among HCPs of a tertiary health care facility in Lagos, Nigeria. Methods: A cross-sectional study was carried out with the International Physical Activity Questionnaire-Short Form (IPAQ-SF). Using the World Health Organization (WHO) guideline, PA was categorized as adequate or inadequate. Predictors of PA were explored with multivariate logistic regression. Results: A total of 300 HCPs were recruited, comprising 47.7% doctors and dentists, 43.3% nurses and 9.0% other HCPs. Mean age was 39.9 (9.0 years), 79.2%, 9.7% and 11.1% of the HCPs had low, moderate or high PA levels respectively. Thus, only 20.8% had adequate PA. 71.3% had body mass index (BMI) above the recommended value. BMI of ≥25 kg/m2 was associated with inadequate PA (Adjusted Odds Ratio-2.1,P= 0.018). Conclusion:Majority of the HCPs had inadequate PA levels according to WHO guidelines. BMI ≥25 kg/m2 was associated with inadequate physical inactivity. The low level of PA implies that these HCPs are at risk for NCDs. This will have a negative impact on availability of human resource for health. There is an urgent need to establish programs to increase PA among HCPs.

Optimization of biodiesel production from sandbox (Hura crepitans L.) seed oil using two different catalysts

Abstract: Biodiesel is a renewable and environmentally friendly fuel used in internal combustion engines. The major challenges in the biodiesel industry are those of food-fuel crisis associated with the use of edible oils for biodiesel production and the type of catalysts used for production. This work focused on the determination of optimal conditions for biodiesel production from inedible sandbox (Hura crepitans L.) seed using calcined snail shell (CSS) and potassium hydroxide (KOH) as catalysts. Oil was extracted from sandbox seeds using solvent extraction. The raw sandbox oil was used for biodiesel production using the transesterification method with KOH and CSS as catalysts. D-optimal design of response surface methodology (RSM) with 24 experimental runs was used to study the optimum yield response of biodiesel using temperature, time, and catalyst concentration as reaction the parameters. Selected fuel properties (specific gravity, viscosity, cloud point, pour point, density, and refractive index) of ...

Modeling of an activated sludge process for effluent prediction—a comparative study using ANFIS and GLM regression

Abstract: In this paper, nonlinear system identification of the activated sludge process in an industrial wastewater treatment plant was completed using adaptive neuro-fuzzy inference system (ANFIS) and generalized linear model (GLM) regression. Predictive models of the effluent chemical and 5-day biochemical oxygen demands were developed from measured past inputs and outputs. From a set of candidates, least absolute shrinkage and selection operator (LASSO), and a fuzzy brute-force search were utilized in selecting the best combination of regressors for the GLMs and ANFIS models respectively. Root mean square error (RMSE) and Pearson’s correlation coefficient (R-value) served as metrics in assessing the predicting performance of the models. Contrasted with the GLM predictions, the obtained modeling results show that the ANFIS models provide better predictions of the studied effluent variables. The results of the empirical search for the dominant regressors indicate the models have an enormous potential in the estimation of the time lag before a desired effluent quality can be realized, and preempting process disturbances. Hence, the models can be used in developing a software tool that will facilitate the effective management of the treatment operation.