Integrated geotechnical and geophysical investigation of a proposed construction site at Mowe, Southwestern Nigeria

The subsurface of a proposed site for building development in Mowe, Nigeria, using Standard Penetration Test (SPT), Cone Penetrometer Test (CPT) and Horizontal Electrical Profiling (HEP), was investigated with the aim of evaluating the suitability of the strata for foundation materials. Four SPT and CPT were conducted using 2.5 tonnes hammer. HEP utilizing Wenner array were performed with inter-electrode spacing of 10 – 60 m along four traverses coincident with each of the SPT and CPT. The HEP data were processed using DIPRO software and textural filtering of the resulting resistivity sections was implemented to enable delineation of hidden layers. Sandy lateritic clay, silty lateritic clay, clay, clayey sand and sand horizons were delineated. The SPT “N” value defined very soft to soft sandy lateritic (<4), stiff silty lateritic clay (7 – 12), very stiff silty clay (12 - 15), clayey sand (15- 20) and sand (27 – 37). Sandy lateritic clay (5-40 kg/cm2) and silty lateritic clay (25 - 65 kg/cm2) were defined from the CPT response. Sandy lateritic clay (220-750 Ωm), clay (< 50 Ωm) and sand (415-5359 Ωm) were delineated from the resistivity sections with two thin layers of silty lateritic clay and clayey sand defined in the texturally filtered resistivity sections. Incompetent clayey materials that are unsuitable for the foundation of the proposed structure underlain the study area to a depth of about 18m. Deep foundation involving piling through the incompetent shallow layers to the competent sand at 20 m depth was recommended

Geophysical Investigation of Some Flood Prone Areas in Ota, Southwestern Nigeria

Thirteen shallow vertical electrical resistivity soundings using Schlumberger array were conducted within the study area. The aim of the study was to investigate the nature of the subsurface in some flood prone areas within the study area by determining the lithology and the corresponding inverse model resistivities at the depths investigated and hence the cause of flooding in the area during the wet season. The resistivity sounding data were collected along seven traverses using a Campus Tigre terrameter. The observed data were interpreted quantitatively using curve matching and computer assisted iteration method. The results of the inversion show a lithology that comprises of the top soil and a paralic sequence of sand and lateritic clay at the depth investigated with varied resistivity and thickness. The flooding is thought to be due to the shallow lateritic clay layer at an average depth of 5.2 m with thickness ranging from 14.5m to 31.8m at the various points of investigation and the shallow depth of the water tabl

Separation of Digital Audio Signals using Least-Mean-Square (LMS) Adaptive Algorithm

Adaptive filtering is one of the fundamental technologies in digital signal processing (DSP) in today’s communication systems and it has been employed in a wide range of applications such as adaptive noise cancellation, adaptive equalization, and echo cancellation.Signal separation remains a task that has called for attention in digital signal processing and different techniques have been employed in order to achieve efficient and accurateresult. Implementation of adaptive filtering can separate wanted and interference signals so as to improve performance of communication systems. In the light of this, this paper usesa least-mean-square (LMS) adaptive algorithm for separation of audio signals.The simulated results show that the designed LMS based adaptive filtering techniqueconverge faster than conventional LMS adaptive filter.DOI:http://dx.doi.org/10.11591/ijece.v4i4.621

Effect of Multicolinearity and Autocorrelation on Predictive Ability of Some Estimators of Linear Regression Model

Violation of the assumptions of independent regressors and error terms in linear regression model has respectively resulted into the problems of multicollinearity and autocorrelation. Each of these problems separately has significant effect on parameters estimation of the model parameters and hence prediction.  This paper therefore attempts to investigate the joint effect of the existence of multicollinerity and autocorrlation on Ordinary Least Square (OLS) estimator, Cochrane-Orcutt (COR) estimator, Maximum Likelihood (ML) estimator and the estimators based on Principal Component (PC) analysis on prediction of linear regression model through Monte Carlo studies using the adjusted coefficient of determination goodness of fit statistic of each estimator. With correlated normal variables as regressors, it further identifies the best estimator for prediction at various levels of sample sizes (n), multicollinearity  and autocorrlation . Results reveal the pattern of performances of COR and ML at each level of multicollinearity over the levels of autocorrelation to be generally and evidently convex especially when  and while that of OLS and PC is generally concave. Moreover, the COR and ML estimators perform equivalently and better; and their performances become much better as multicollinearity increases. The COR estimator is generally the best estimator for prediction except at high level of multicollinearity and low levels of autocorrelation. At these instances, the PC estimator is either best or competes with the COR estimator. Moreover, when the sample size is small (n=10) and multicollinearity level is not high, the OLS estimator is best at low level of autocorrelation whereas the ML is best at moderate levels of autocorrelation. .Keywords: Prediction, Estimators, Linear Regression Model, Multicollinearity, Autocorrelation

Determinants of Attitudes of Oil and Gas Companies to Host Communities: A Social Responsibility Perspective

The study was designed to explore the social responsibility attitudes of oil and gas companies to host communities. It presumed that these determinants fall broadly into endogenous and exogenous factors. Three oil and gas companies were sampled from the twelve listed in the Nigeria Stock Exchange for the study. The study found that for Oando Plc., Return on Assets, Earnings size and Debt-Equity ratio were determinants of attitudes of oil and gas companies but not so with MRS Plc. and Seplat Plc. Exogenous factors were found to be largely responsible for the social responsibility attitudes of oil and gas companies towards their communities. Keywords: Oil and Gas Companies, Host Communities, Social Responsibility, Attitude

Piezoelectric effects on bone modeling for enhanced sustainability

© 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)Bone tissue possesses piezoelectric properties, allowing mechanical forces to be converted into electrical potentials. Piezoelectricity has been demonstrated to play a crucial role in bone remodelling and adaptability. Bone remodelling models that consider strain adaptation, both with and without piezoelectric effects, were simulated and validated in this study. This simulation help to better comprehend the interplay between mechanical and electrical stimulations during these processes. This study aimed to optimise the modelling of piezoelectric effects in bone modelling analysis. The connection between mechanical loads applied to bones and the resulting electrical charges generated by the piezoelectric effect was examined. Furthermore, mathematical modelling and simulation techniques were employed to enhance the piezoelectric effect and promote bone tissue growth and repair. The findings from this research have substantial implications for developing novel therapies for bone-related diseases and injuries. It was observed that electrically stimulated bone surfaces increased bone deposition. In some instances of physical disability or osteoporosis, therapeutic electrical stimulation can supplement the mechanical stresses of regular exercise to prevent bone loss. Consequently, the bone remodelling method on the software platform enables easy application and repetition of finite element analysis. This study significantly benefits bone tissue/biomedical engineering, particularly in bone remodelling, healing, and repair.Peer reviewe

SURVIVAL PRIORITY FOR NIGERIAN BANKS: INVESTIGATING THE NEED FOR DIVERSIFICATION STRATEGIES IN A DOWNTURN ECONOMY

This study examined diversification strategies from a different perspective by evaluating the survival indicators from sampled of Nigerian banks through the exploitation of new product tactics, related and unrelated diversification options. Using survey design to sift data from 372 sampled respondents of five randomly selected money deposit banks in Oyo and Ogun states Nigeria; and by adopting the triangulation analytical technique involving combination of questionnaires and interviews, it was found that there was a significant positive effect of new product/service strategies on the profit growth of selected banks in Nigeria; further it was discovered that unrelated diversification strategies influenced positively on the banking firms’ ability to outperform their competing rivals; and also, banking firms in Nigeria that considered related or unrelated diversification grow faster and perform better than those who remain undiversified. The regression analysis was used to test the three hypothesized questions and results showed significant figures on the variables. The study concludes that the corporate survival of Nigerian banking organizations would be significantly affected by the mode of diversification utilized by such firms. It was advised that the Nigerian banking organizations should pay greater attention to the new-products, related and unrelated diversifications in order to enjoy continuing successful operations. Further, the study admonished that the banking firms need to enhance and improve on their quality design, innovations and unique features. Due to the forces faced from domestic and international competition, a strategy of diversification would be a more viable option for Nigerian banks than strategies based on efficiency and price. JEL: E58, G21, E02  Article visualizations

A comparative study of some robust ridge and liu estimators

In multiple linear regression analysis, multicollinearity and outliers are two main problems. When multicollinearity exists, biased estimation techniques such as Ridge and Liu Estimators are preferable to Ordinary Least Square. On the other hand, when outliers exist in the data, robust estimators like M, MM, LTS and S Estimators, are preferred. To handle these two problems jointly, the study combines the Ridge and Liu Estimators with Robust Estimators to provide Robust Ridge and Robust Liu estimators respectively. The Mean Square Error (MSE) criterion was used to compare the performance of the estimators. Application to the proposed estimators to three (3) real life data set with multicollinearity and outliers problems reveals that the M-Liu and LTS-Liu Estimator are generally most efficient..Keywords: Ordinary Least Squares, Ridge Regression Estimator, Liu Estimator, Robust Estimator, Robust Ridge Regression Estimator, Robust Liu Estimato

Stabilization of Lateritic Soil with Rubber Wood Ash and Lime for Road Construction

This study involved the investigation of the stabilizing lateritic soil with rubber wood ash (RWA) and lime for road construction. The index and engineering properties of the soil were carried out. Stabilization of the soil was carried out by mixing the soil by weight with 0%, 2%, 4% and 6% RWA mixed with 0.5%, 1%, 1.5% and 2% lime. The soil was classified as A-6 soil group according to AASHTO soil classification system possessing the following characteristics in its natural state; average moisture content of 18.7%, average specific gravity of 2.83 average liquid limit of 34.72%, average plastic limit of 22.02%, average plasticity index of 12.70% and average California Bearing Ratio of 2.47%. Data obtained revealed that the optimum mix ratio for economic and effectiveness was 4% RWA mixed with 1.5% lime which gave a result of 11.34% for soaked sample and 14.30% for the unsoaked sample respectively. Test results also showed that increase in RWA content increased the optimum moisture content but decreased the maximum dry density. The addition of 2% constant RWA with varying lime from 0.5%-2% lime shows that the MDD decreases consistently from 1.89g/cm3 to 1.63g/cm3 with an increase in OMC from 14.00% to 21.50%. The addition of 4% constant RWA with varying lime from 0.5%-1.5% lime reduce the MDD from the initial value of 1.63g/cm3 at 2% RWA to a constant value of 1.62g/cm3 and then increases to 1.68g/cm3 at 2% lime variation when the additives became too much and vice versa for the OMC. The CBR value of the natural soil is 2.47% which shows that the sample is very poor as subgrade material. The addition of RWA only shows a little improvement but the addition of RWA and lime gave a better result

Shape memory polymer review for flexible artificial intelligence materials of biomedical

The self-healing and biocompatibility of polymer composites for biomedicine have made them a preferred approach for small-scale tissue engineering elements. By moving from static to dynamic pressure, 4D printing simulates the natural physical-mechanical changes of living tissue over time. A promising new platform with excellent controllability actuation is required to enhance the significance of 4D printing for biological applications. This study systematically analyses current 4D printing technologies for the flexible fabrication of artificial intelligence (AIM) materials. In addition, many potential applications of flexible 4D printing in composite biological engineering are thoroughly investigated. We found that knowledge about this new category of flexible AIM composites is relatively limited, and the potential for practical applications has not yet been demonstrated. Finally, we discuss the problems and limitations of flexible 4D printing technology, AIM, and future approaches and applications.</p