Emergency Online Teaching and Learning in a Nigerian Private University: An Activity Theory Perspective

The purpose of this study is to investigate the challenges of emergency online teaching and learning adopted in a Nigerian private university due to the suspension of face-to- face learning, as a result of the COVID-19 pandemic. Activity theory was used to examine the elements that make up the activity, that is, the virtual learning environment, as well as their associated relationships in order to reveal existing and potential tensions within the activity. Data used for the analysis of this case study was obtained by observation of the online class sessions, student responses to an online survey, emails (requesting support) received by the instructional technology support team, and interviews with participants of the activity system. The findings show that majority of the challenges and contradictions observed were a result of a hurried decision to migrate to online learning in order to complete the academic semester. This brought about several issues with regards to the tools, rules, and roles within the activity system. The most significant contradiction observed was as a result of the influence of an external activity on the studied activity system. The study provides insights to policymakers in the education sector on the current barriers to online learning, especially in the Nigerian context

A dai-liao hybrid hestenes-stiefel and fletcher-revees methods for unconstrained optimization

Some problems have no analytical solution or too difficult to solve by scientists, engineers, and mathematicians, so the development of numerical methods to obtain approximate solutions became necessary. Gradient methods are more efficient when the function to be minimized continuously in its first derivative. Therefore, this article presents a new hybrid Conjugate Gradient (CG) method to solve unconstrained optimization problems. The method requires the first-order derivatives but overcomes the steepest descent method’s shortcoming of slow convergence and needs not to save or compute the second-order derivatives needed by the Newton method. The CG update parameter is suggested from the Dai-Liao conjugacy condition as a convex combination of Hestenes-Stiefel and Fletcher-Revees algorithms by employing an optimal modulating choice parameterto avoid matrix storage. Numerical computation adopts an inexact line search to obtain the step-size that generates a decent property, showing that the algorithm is robust and efficient. The scheme converges globally under Wolfe line search, and it’s like is suitable in compressive sensing problems and M-tensor systems

A dai-liao hybrid conjugate gradient method for unconstrained optimization

One of todays’ best-performing CG methods is Dai-Liao (DL) method which depends on non-negative parameter  and conjugacy conditions for its computation. Although numerous optimal selections for the parameter were suggested, the best choice of  remains a subject of consideration. The pure conjugacy condition adopts an exact line search for numerical experiments and convergence analysis. Though, a practical mathematical experiment implies using an inexact line search to find the step size. To avoid such drawbacks, Dai and Liao substituted the earlier conjugacy condition with an extended conjugacy condition. Therefore, this paper suggests a new hybrid CG that combines the strength of Liu and Storey and Conjugate Descent CG methods by retaining a choice of Dai-Liao parameterthat is optimal. The theoretical analysis indicated that the search direction of the new CG scheme is descent and satisfies sufficient descent condition when the iterates jam under strong Wolfe line search. The algorithm is shown to converge globally using standard assumptions. The numerical experimentation of the scheme demonstrated that the proposed method is robust and promising than some known methods applying the performance profile Dolan and Mor´e on 250 unrestricted problems.  Numerical assessment of the tested CG algorithms with sparse signal reconstruction and image restoration in compressive sensing problems, file restoration, image video coding and other applications. The result shows that these CG schemes are comparable and can be applied in different fields such as temperature, fire, seismic sensors, and humidity detectors in forests, using wireless sensor network techniques

Structure and conduct of risk returns-characteristics of residential property investment in Kaduna metropolis, Nigeria

Residential property investment is one of the most subscribed investments in the world. However, its risk-return characteristics is least understood especially in the Nigeria context. Though past studies have critically established the performance of mostly isolated  residential and commercial properties in southern regions of Nigeria. Disentangling and identifying empirically risk-return characteristic of residential property in Kaduna metropolis Northwest Nigeria is an unresolved challenge. This paper presents an empirical analysis of the performance of residential properties to gain a better understanding of the property market dynamics in Nigeria, survey research approach was employed to collect quantitative data required for the study. To determine residential property returns and asset risk, descriptive (weighted means, standard deviation and percentages) and inferential statistics were utilised. The outcome demonstrated that residential properties have diverse total returns and riskreturn characteristic. Furthermore, this study  established that total returns from residential properties ranged between 7.93% to 12.68 % and the risk features ranged from 2.37% to6.81% among the classes of properties. The result demonstrates a direct positive relationship between total returns and risk profile. Hence, recommends that Malali market is the most desirable location for risk-averse investors. Keywords: Risk- return analysis, residential investment, total return, portfolio, propert

Determinants of learning management systems adoption in Nigeria: A hybrid SEM and artificial neural network approach

Research has shown that technology, when used prudently, has the potential to improve instruction and learning both in and out of the classroom. Only a handful of African tertiary institutions have fully deployed learning management systems (LMS) and the literature is devoid of research examining the factors that foster the adoption of LMS. To fill this void, the present research investigates the factors contributing to students’ acceptance of LMS. Survey data were obtained from registered students in four Nigerian universities (n = 1116); the responses were analyzed using artificial neural network (ANN) and structural equation modeling (SEM) techniques. The results show that social influence, facilitating conditions, system quality, perceived ease of use, and perceived usefulness are important predictors for students’ behavioral intention to use LMS. Students’ behavioral intention to use LMS also functions as a predictor for actual usage of LMS. Implications for practice and theory are discussed.No sponso

Comparative biocidal activities of some crude plant species powders against the cowpea weevil (Callosobrochus maculatus (F.)(Coleoptera: bruchidae))

Callosobruchus maculatus is one of the most important pests of cowpea in storage causing severe economic damage to the grain. This study investigated the efficacies of three plant materials (Azadirachta indica, Calotropis procera and Chromolaena odorata) leaves against the cowpea weevil. Concentrations of 0.1, 0.25 and 0.5g of the plant powders were used on 10g of grains with 10 adult weevils in each and a Control (untreated) in triplicates. The results showed significant (P< 0.05) negatively effects of the plant materials on the survival of C. maculatus at the highest concentration. In all trails, mean daily mortality in adult C. maculatus were significantly(p<0.05) increased. All plant powder type were effective but concentration-dependent, with C. procera recording significantly (P < 0.05) higher mortality at the various concentrations while C. odorata, elicited the least mean daily mortality. The lowest LD50 (0.63g) was obtained with C. procera. These plants materials were found to also affect the egg-laying capacity of C. maculatus. Treatment with C. odorata recorded significantly(P<0.05) higher number of eggs laid at all concentrations, though the egg-laying capacity was also concentration-dependent; whereas C. procera recorded the least number of eggs laid. All the three plants powders tested demonstrated significant insecticidal potency on stored cowpea weevils, with C. procera and C. odorata showing significantly higher and lower insecticidal potentials respectively. These findings will help in solving problem associated with food security especially with respect to stored produce

Laundry soap production from the respective tallows of goat, sheep and cow: evaluation of physicochemical properties for the best

Tallow mainly consists of triglycerides, whose major constituents are derived from stearic, palmitic and oleic acids, and its usage reduces production cost of soap, adds lather stability and hardness to soap. Laundry soaps were produced with variation on amount of tallow (sourced from cow, sheep and goat) and labelled as A, B, C, D and E formulations. The respective tallows were characterized in terms of saponification value and acid value and determined to be 192.14 and 2.24mg KOH/g (cow tallow); 200.56 and 2.38mgKOH/g (sheep tallow) and 197.75 and 1.96 mgKOH/g (goat tallow). The physicochemical properties of soap which determine its area of usage and cleansing properties were determined. The properties considered in this work were hardness, moisture content, foam capacity, pH, free acidity content, and total fatty matter. The hardness, moisture content, foam capacity, pH, free acidity content and total fatty matter of the produced soaps were determined and ranged between mild-deep penetration level; 11-21%; 1-9cm; 8-10.5; 0.16-0.82% and 40-86% respectively. From the comparative analysis, soap made from sheep tallow has the lowest penetration level (with formulations B and E), lowest free acidity content of 0.16% (with formulation A), highest total fatty matter of 86% (using formulation E), highest foam height of 9cm (with formulation A), lowest moisture content of 11% (with formulation A) and mild alkalinity of 8 (with formulations A, B and E). These results showed that the soaps produced from sheep tallow are the best in terms of hardness, lather and skin friendliness, due to its high degree of longer carbon chain lengths of fatty acids. These values satisfy the standard limit set for good quality laundry soap by National Agency for Food and Drug Administration and Control and Encyclopaedia of Industrial Chemical Analysis, respectively

Factors affecting the adoption of e-learning technologies among higher education students in Nigeria : A structural equation modelling approach

The aim of the study is to investigate factors that influence the adoption and use of educational technology by students of a higher education institution in developing countries. The study employed the unified theory of acceptance and use of technology (UTAUT). The online survey method was used to collect data from 286 students of a higher education institution in Nigeria. The maximum likelihood method based on structural equation modelling (SEM) using IBM Amos 22.0 application was used to analyse the data. The study determined that performance expectancy and effort expectancy (p 0.05). Facilitating conditions and behavioural intentions were determined to be salient factors that positively influence the actual usage of Canvas by the students. The results from the data obtained partially support the UTAUT’s ability to explain the factors responsible for the acceptance of educational technology in developing countries, in Nigeria to be specific. Furthermore the study contributes to the formulation of approaches and guidelines to enhance the adoption of educational technologies in developing countries

Empirical analysis of tree-based classification models for customer churn prediction

Customer churn is a vital and reoccurring problem facing most business industries, particularly the telecommunications industry. Considering the fierce competition among telecommunications firms and the high expenses of attracting and gaining new subscribers, keeping existing loyal subscribers becomes crucial. Early prediction of disgruntled subscribers can assist telecommunications firms in identifying the reasons for churn and in deploying applicable innovative policies to boost productivity, maintain market competitiveness, and reduce monetary damages. Controlling customer churn through the development of efficient and dependable customer churn prediction (CCP) solutions is imperative to attaining this goal. According to the outcomes of current CCP research, several strategies, including rule-based and machine-learning (ML) processes, have been proposed to handle the CCP phenomenon. However, the lack of flexibility and robustness of rule based CCP solutions is a fundamental shortcoming, and the lopsided distribution of churn datasets is deleterious to the efficacy of most traditional ML techniques in CCP. Regardless, ML-based CCP solutions have been reported to be more effective than other forms of CCP solutions. Unlike linear-based, instance-based, and function-based ML classifiers, tree-based ML classifiers are known to generate predictive models with high accuracy, high stability, and ease of interpretation. However, the deployment of tree-based classifiers for CCP is limited in most cases to the decision tree (DT) and random forest (RF). Hence, this research investigated the effectiveness of tree-based classifiers with diverse computational properties in CCP. Specifically, the CCP performances of diverse tree-based classifiers such as the single, ensemble, enhanced, and hybrid tree-based classifiers are investigated. Also, the effects of data quality problems such as the class imbalance problem (CIP) on the predictive performances of tree-based classifiers and their homogeneous ensemble variants on CCP were assessed. From the experimental results, it was observed that the investigated tree-based classifiers outperformed other forms of classifiers such as linear-based (Support Vector Machine (SVM)), instance-based (K-Nearest Neighbour (KNN)), Bayesian-based (Naïve Bayes (NB)) and function-based (MultiLayer Perceptron (MLP)) classifiers in most cases with or without the CIP. Also, it was observed that the CIP has a significant effect on the CCP performances of investigated tree-based classifiers, but the combination of a data sampling technique and a homogeneous ensemble method can be an effective solution to CIP and also generate efficient CCP models

Intelligent Decision Forest Models for Customer Churn Prediction

Customer churn is a critical issue impacting enterprises and organizations, particularly in the emerging and highly competitive telecommunications industry. It is important to researchers and industry analysts interested in projecting customer behavior to separate churn from non-churn consumers. The fundamental incentive is a firm’s intent desire to keep current consumers, along with the exorbitant expense of gaining new ones. Many solutions have been developed to address customer churn prediction (CCP), such as rule-based and machine learning (ML) solutions. However, the issue of scalability and robustness of rule-based customer churn solutions is a critical drawback, while the imbalanced nature of churn datasets has a detrimental impact on the prediction efficacy of conventional ML techniques in CCP. As a result, in this study, we developed intelligent decision forest (DF) models for CCP in telecommunication. Specifically, we investigated the prediction performances of the logistic model tree (LMT), random forest (RF), and Functional Trees (FT) as DF models and enhanced DF (LMT, RF, and FT) models based on weighted soft voting and weighted stacking methods. Extensive experimentation was performed to ascertain the efficacy of the suggested DF models utilizing publicly accessible benchmark telecom CCP datasets. The suggested DF models efficiently distinguish churn from non-churn consumers in the presence of the class imbalance problem. In addition, when compared to baseline and existing ML-based CCP methods, comparative findings showed that the proposed DF models provided superior prediction performances and optimal solutions for CCP in the telecom industry. Hence, the development and deployment of DF-based models for CCP and applicable ML tasks are recommended