Showing papers in "Journal of Nigerian Society of Physical Sciences in 2022"

The corrosion characteristics of SS316L stainless steel in a typical acid cleaning solution and its inhibition by 1-benzylimidazole: Weight loss, electrochemical and SEM characterizations

Abstract: Acid cleaning, an inevitable industrial practice used to descale chemical reactors, usually causes serious corrosion attack on underlying alloy substrates. Ameliorating this phenomenon requires the addition of effective corrosion inhibitors into the acid solution. Current global regulations encourage environmentally–benign molecules as corrosion inhibitors. Consequently, 1-benzylimidazole has been investigated for its inhibitive characteristics against the corrosion of SS316L stainless steel in a typical acid cleaning solution containing 2 % HCl + 3.5 % NaCl. Weight loss measurements confirm that the corrosion inhibition property of 1-benzylimidazole increases with concentration but depreciates with increased temperature. Electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization (PDP) measurements confirm that 1-benzylimidazole adsorb on the stainless steel surface to isolate its surface from the acid solution. 1-benzylimidazole is a mixed-type inhibitor with greater anodic influence, and its adsorption enhances the formation and protectiveness of a passive film. Weight loss and the electrochemical measurements agree to an average inhibition efficiency > 70 % at 1000 ppm. The inhibitor adsorbs via physisorption and obeys the Temkin isotherm model. SEM surface characterization confirm the ability of 1-benzylimidazole to protect the surface microstructure of the stainless steel during the corrosion.

Application of Machine Learning and Resampling Techniques to Credit Card Fraud Detection

Abstract: The application of machine learning algorithms to the detection of fraudulent credit card transactions is a challenging problem domain due to the high imbalance in the datasets and confidentiality of financial data. This implies that legitimate transactions make up a high majority of the datasets such that a weak model with 99% accuracy and faulty predictions may still be assessed as high-performing. To build optimal models, four techniques were used in this research to sample the datasets including the baseline train test split method, the class weighted hyperparameter approach, and the undersampling and oversampling techniques. Three machine learning algorithms were implemented for the development of the models including the Random Forest, XGBoost and TensorFlow Deep Neural Network (DNN). Our observation is that the DNN is more effcient than the other 2 algorithms in modelling the under-sampled dataset while overall, the three algorithms had a better performance in the oversampling technique than in the undersampling technique. However, the Random Forest performed better than the other algorithms in the baseline approach. After comparing our results with some existing state-of-the-art works, we achieved an improved performance using real-world datasets.

Numerical Solution of Stiff and Oscillatory Problems using Third Derivative Trigonometrically Fitted Block Method

Abstract: This paper considered the formulation of continuous third derivative trigonometrically fitted method for the solution of oscillatory first order initial value problems using the technique of interpolation and collocation of the approximate solution by combining polynomial and trigonometric functions. Solving for the unknown parameters and substituting the results into the approximate solution yielded a continuous linear multistep method, which was evaluated at some selected grid points where two cases were considered at equal intervals to give the discrete schemes which are implemented in block form. The blocks are convergent and stable. Numerical experiments show that the methods compete favorably with existing method.This paper considered the formulation of continuous third derivative trigonometrically fitted block method for the solution of stiff and oscillatory problems. The development of the technique involved the interpolation and collocation of the approximate solution which is the combination of polynomial and trigonometric functions. Solving for the unknown parameters and substituting the results into the approximate solution yielded a continuous linear multistep method, which is evaluated at some selected grid points where two cases were considered at equal intervals to give the discrete schemes which are implemented in block form. The blocks are convergent and stable. Numerical experiments show that the methods compete favorably with existing method and efficient for the solution of stiff and oscillatory problems.

Eyring-Powell MHD nanoliquid and entropy generation in a porous device with thermal radiation and convective cooling

Abstract: This study investigates the flow of magnetohydromagnetic (MHD) Eyring-Powell chemical reaction nanoliquid in a permeable boundless device with wall cooling and thermal radiation. The fully developed Cauchy non-Newtonian fluid model is stimulated by species reaction and the stretching sheet under gravity influence. Using the Rosseland radiation approximation model with an appropriate similarity variable, the dimensionless coupled derivatives are obtained. A shooting numerical technique is utilized to determine the thermophysical effects on the flow characteristics. The solution results are computed and given in graphs and tables for clear demonstration and clarification. The results show that entropy is minimized by augmenting the magnetic field, porosity, and thermodynamic equilibrium. Also, parameters that enhance internal heat must be monitored to prevent chemical reaction nanoliquid blowup.

Statistical Analysis and Distribution of Global Solar Radiation and Temperature Over Southern Nigeria

Abstract: The intensity of solar energy that is received by a particular location is affected by most meteorological conditions including, the solar irradiance received by the location, precipitation, extreme heat as a result of the surface or ambient temperature, etc. We obtain the monthly global solar irradiation and ambient temperature for the three (3) eco-climatic zones in the south of Nigeria (17 locations) for 12 years (2005 - 2016) from the Photovoltaic Geographical Information System (PVGIS) Satellite. The goal of this study is to understand how regional meteorological conditions affect radiation and temperature reception. Monthly and annual trends were plotted and compared for both variables in each region to show the similarity or dichotomy in their trends. The Mann-Kendall (M-K) trend test has been adopted to reveal that the changes in the variations on an annual basis, and results showed that the trend were not significant for both variables. Box plots have been used to give a better description of the data, and compared to show similarities and differences. Finally, we adopted the Gaussian (normal) distribution to show, understand and compare the data distribution. Linear regression plots for each zone shows that the relationship between the solar irradiation and temperature is high. Results show that the climate and vegetation of a region contributes majorly to the variation of radiation and temperature. Inhomogeneityof data or results for locations in the same zones may be attributed to local meteorological conditions. The results obtained here will prove vital in decision making relating to the adoption of solar energy technologies in the region. Results show that the climate and vegetation of a region contributes majorly to the variation of radiation and temperature. Inhomogeneity of data or results for locations in the same zones may be attributed to local meteorological conditions.

Modeling and Forecasting the Third wave of Covid-19 Incidence Rate in Nigeria Using Vector Autoregressive Model Approach

Abstract: Modeling the onset of a pandemic is important for forming inferences and putting measures in place. In this study, we used the Vector autoregressive model to model and forecast the number of confirmed covid-19 cases and deaths in Nigeria, taking into account the relationship that exists between both multivariate variables. Before using the Vector Autoregressive model, a co-integration test was performed. An autocorrelation test and a heteroscedasticity test were also performed, and it was discovered that there is no autocorrelation at lags 3 and 4, as well as no heteroscedasticity. According to the findings of the study, the number of covid-19 cases and deaths is on the rise. To forecast the number of cases and deaths, a Vector Autoregressive model with lag 4 was used. The projection likewise shows a steady increase in the number of deaths over time, but a minor drop in the number of confirmed Covid-19 cases.

Implicit Four-Point Hybrid Block Integrator for the Simulations of Stiff Models

Abstract: Over the years, the systematic search for stiff model solvers that are near-optimal has attracted the attention of many researchers. An attempt has been made in this research to formulate an implicit Four-Point Hybrid Block Integrator (FPHBI) for the simulations of some renowned rigid stiff models. The integrator is formulated by using the Lagrange polynomial as basis function. The properties of the integrator which include order, consistency, and convergence were analyzed. Further analysis showed that the proposed integrator has an A-stability region. The A-stability nature of the integrator makes it more robust and fitted for the simulation of stiff models. To test the computational reliability of the new integrator, few well-known technical stiff models such as the pharmacokinetics, Robertson and Van der Pol models were solved. The results generated were then compared with those of some existing methods including the MATLAB solid solvent, ode 15s. From the results generated, the new implicit FPHBI performed better than the ones with which we compared our results with.

Masses and thermal properties of a Charmonium and Bottomonium Mesons

Abstract: In this research, we model Hulthén plus generalized inverse quadratic Yukawa potential to interact in a quark-antiquark system. The solutions of the Schrödinger equation are obtained using the Nikiforov-Uvarov method. The energy spectrum and normalized wave function were obtained. The masses of the heavy mesons for different quantum states such as 1S, 2S , 1P, 2P 3S, 4S, 1D, and 2D were predicted as 3.096 GeV, 3.686 GeV, 3.327 GeV, 3.774GeV, 4.040 GeV, 4.364GeV, 3.761 GeV, and 4.058 GeV respectively for charmonium (cc). Also, for bottomonium (bb) we obtained 9.460 GeV, 10.023 GeV, 9.841 GeV, 10.160 GeV, 10.345 GeV, 10.522 GeV, and 10.142GeV for different states of 1S , 2S , 1P , 2P , 3S , 4S , 1D respectively. The partition function was calculated from the energy spectrum, thereafter other thermal properties were obtained. The results obtained showed an improvement when compared with the work of other researchers and excellently agreed with experimental data with a percentage error of 1.60 % and 0.46 % for (cc) and (bb), respectively.

Time-Fractional Differential Equations with an Approximate Solution

Abstract: This paper shows how to use the fractional Sumudu homotopy perturbation technique (SHP) with the Caputo fractional operator (CF) to solve time fractional linear and nonlinear partial differential equations. The Sumudu transform (ST) and the homotopy perturbation technique (HP) are combined in this approach. In the Caputo definition, the fractional derivative is defined. In general, the method is straightforward to execute and yields good results. There are some examples offered to demonstrate the technique's validity and use.

An Internet-of-Things based Real-time Monitoring System for Smart Classroom

Abstract: The Development in technology has brought about a common norm of communicating and interacting with appliances remotely using portable devices like laptops and smartphones that have Internet connections. This is possible with the use of the Internet of Things, popularly referred to as IoT. This paper presents a system by which classroom held interactions specifically seat allotment is made a simpler process by secured automation coupled with the Internet of Things to develop a system that enables a person or group of people to remotely monitor seat allotment, de-allotment with precision over a wide distance. Traditional means of allotment are quite slow and tiresome especially when dealing with a large number of students. Hence there is a need to develop a system to automatically allot, track, and monitor real-time seats in a classroom. In this research, an IoT seat allotment system is proposed; the system is developed and tested within a smart classroom environment using the Federal University of Agriculture Abeokuta (FUNAAB), Nigeria as a case study. The system is implemented on Arduino IDE using C++ programming language and a prototype of the mobile IoT seat allotment application is developed using Java programming language. Through experimental analysis, it was discovered that the IoT Seat allotment achieved the ranges of 30 % to 50 % reduced time, and higher accuracy when compared with the traditional seat allotment method.

Jackknife Kibria-Lukman M-Estimator: Simulation and Application

Abstract: The ordinary least square (OLS) method is very efficient in estimating the regression parameters in a linear regression model under classical assumptions. If the model contains outliers, the performance of the OLS estimator becomes imprecise. Multicollinearity is another issue that can reduce the performance of the OLS estimator. This study proposed the Robust Jackknife Kibria-Lukman (RJKL) estimator based on the M-estimator to deal with multicollinearity and outliers. We examine the superiority of the estimator over existing estimators using theoretical proofs and Monte Carlo simulations. We put the estimator to the test once more using real-world data. We observed that the estimator performs better than the existing estimators.

Optimal Control of the Coronavirus Pandemic with Impacts of Implemented Control Measures

Abstract: This paper considers the current global issue of containing the coronavirus pandemic as an optimal control problem. The goal is to determine the most advantageous levels of effectiveness of the various control and preventive measures that should be attained in order to cost effectively drive the epidemic towards eradication within a relatively short time. Thus, the problem objective functional is constructed such that it minimizes the prevalence as well as the cost of implementing the various control measures subject to a model for the disease transmission dynamics which incorporates the existing controls. The optimality system of the model is derived based on Pontryagin's maximum principle while the resulting system is solved numerically using the Runge-Kutta fourth order scheme with forward-backward sweep approach. Findings from our results show that the new cases and the prevalence of the disease can be remarkably reduced in a cost effective way, if the specified optimal levels of effectiveness of the various preventive and control measures are upheld continuously for at least a month. Moreover, the results also show that the disease can be eventually eradicated if these effectiveness levels are sustained over a reasonable length of time.

Modified Szmidt and Kacprzyk’s Intuitionistic Fuzzy Distances and their Applications in Decision-making

Abstract: Intuitionistic fuzzy models are significant in resolving decision-making. Distance measures under intuitionistic fuzzy environment are reliable techniques deployed to express the application of IFSs. Some approaches of estimating distances between IFSs have been explored by Szmidt and Kacprzyk, where the complete parameters of IFSs are considered. Albeit, the distance operators lack reliability because of certain setbacks. In this paper, we modified Szmidt and Kacprzyk's distance operators between IFSs to enhance reliability in terms of applications. Some theorems are given to substantiate the validity of the modified intuitionistic fuzzy distance operators. Futhermore, decision-making cases of pattern recognition and disease identification are discussed using the Szmidt and Kacprzyk's distances and their improved versions where information are represented in intuitionistic fuzzy pairs. From the study, it is observed that the modified Szmidt and Kacprzyk's distance operators between IFSs yield better results compare to the Szmidt and Kacprzyk's distance operators between IFSs.

Analysis of Hydromagnetic Double Exothermic Chemical Reactive Flow with Convective Cooling through a Porous Medium under Bimolecular Kinetics

Abstract: In this study, the analytical solution of steady hydromagnetic double exothermic combustible reaction fluid flow in a porous medium with convective cooling wall is presented. The viscous heating reactive liquid is totaling exothermic without consumption of material. The combustion reaction of the fluid takes place in a Poiseuille device, and it is been propelled by pressure gradient and pre-exponential bimolecular kinetics. The device is exposed to convective cooling to keep the reactive hydromagnetic fluid from distortion. The weighted residual method (WRM) is analytically used to get the numerical values for the dimensionless nonlinear governing equations. The solution to temperature and velocity distribution is carried out and the result is graphically depicted. The Nusselt number and skin friction coefficient is also showed for some significant parameters engrained in the flow and the solution obtained is compared with numerical method. As obtained in the study, the second exothermic reaction term increases the combustion process; hence the term will assist in reducing toxic discharge from the engines that pollute the environment. The Frank-Kamenetskii parameter contributes highly to system thermo-fluid destruction; as such it must be monitored.

Performance Evaluation and Statistical Analysis of Solar Energy Modeling: A Review and Case Study

Abstract: The main target of this research is a quantitative review of literature on global solar radiation (GSR) models available for different stations around the world. The statistical analysis of 400 existing sunshine-based GSR models on a horizontal surface is compared using 40-year meteorological data in the selected locations in Egypt. The measured data is divided into two sets. The first sub-data set from 1980 to 2019 was used to develop empirical correlation models between the monthly average daily global solar radiation fraction (H/H0) and the monthly average of desired meteorological parameters. The second sub-data set from 2015–2019 was used to validate and evaluate the derived models and correlations. The developed models were compared with each other and with the experimental data of the second subset based on the statistical error indicators such as RMSE, MBE, MABE, MPE, and correlation coefficient (R). The statistical test of the correlation, coefficient (R), for all models gives very good results (above 0.92). The smallest values of t-Test occur around the models (M 272, M 261, M 251, and M 238). The accuracy of each model is tested using ten different statistical indicator tests. The Global Performance Indicator (GPI) is used to rank the selected GSR models. According to the results, the Rietveld model (Model 272) has shown the best capability to predict the GSR on horizontal surfaces, followed by the Katiyar et al. model (Model 251) and the Aras et al. model (Model 261).

Introducing article numbering to Journal of the Nigerian Society of Physical Sciences

Abstract: Journal of the Nigerian Society of Physical Sciences is a peer-reviewed journal published quarterly (February, May, August & November) by the Nigerian Society of Physical Sciences. The Editor in Chief is Babatunde James Falaye.

Influence of Precursor Temperature on Bi Doped ZnSe Material via Electrochemical Deposition Technique for Photovoltaic Application

Abstract: In this study, Bismuth (Bi) doped ZnSe thin films were deposited on conducting glass substrates by electrochemical deposition technique and the influence of precursor temperature (room, 50, 55, 60 oC) on their optical and structural properties were systematically studied using the combined effect of X-Ray Diffraction (XRD), Scanning Electron Microscope (SEM) and UV-VIS spectrophotometer. The XRD patterns show a face-centred cubic structure indexed with peaks at (220), (221) and (300). The grain size was in the range of 3.24056 to 4.60481 nm with a lattice constant of 7.189Å. The material deposited at room, 500C, 550C, and 600C reveals agglomeration of particle on the surface of the substrate indicating uniform deposition. The optical spectra show that at different temperature (say room, 50oC, 55oC and 60oC), the absorbance and reflectance of BiZnSe thin films decreases with increase in wavelength of the incident radiation while the transmittance shows direct proportionality with the increase in wavelength. The bandgap demonstrated an increase in the range 1.75-2.25 eV with increase in temperature.

A review on Transforming plastic wastes into fuel

Abstract: The application of plastics in various sectors led to its increased production globally and this demand, in turn, caused an overflow of plastic waste in landfills, illegal dumping in the sea, and environmental pollution. To overcome this issue, several alternatives for managing plastic wastes have been developed and among them, reuse, recycling, and energy recovery methods are highly acknowledged methods. Nonetheless, recycling methods come with certain disadvantages like mixing and segregation of wastes, high labour costs associated with segregation and processing, by-product disposal, and its usage. Researchers have shifted their focus to energy recovery systems because of these drawbacks. Extensive research in this area led to the development of converting waste plastics into liquid fuel through the process called pyrolysis. The pyrolysis process can thermally degrade plastics in the absence of oxygenproducing oil and monomers. The temperature has the most impact on the pyrolysis process and depending on the types of plastic wastes, the pyrolysis temperature varies between 300 – 800 oC. The oil yield due to the variation in temperature varies between 45 – 95 wt.% and the calorific value of the oil has been observed to be in the range of 9679 – 11428.5 kCal/kg, which is similar to the other commercial fuels. Also, the review indicates that it is possible to extract up to 84% of fuel from 1-kg plastic at 360 oC. As a result, following refining/blending with conventional fuels, pyrolysis oil can be utilised as an alternate source of energy and transportation fuel. Apart from the temperature, the other influencing factors include, the reactor design and its size, pressure, heating rate, residence time and feedstock composition. The pyrolysis process was examined in terms of plastic types and primary process factors that impacted the end result, such as oil, gaseous, and char. Temperatures, reactor types, residence duration, pressure, catalysts, and other critical factors were examined in this work. Furthermore, the study examines technological problems and current advances.

Effect of temperature on optical, structural, morphological and antibacterial properties of biosynthesized ZnO nanoparticles

Abstract: Nanomaterials can be produced by using nontoxic biological compounds that are both eco-friendly and economically viable. Temperature dependent ecological synthesis of ZnO nanoparticles was carried out with leaf extract of Ocimum sanctum. An electron microscope study confirmed that a temperature of 400 oC was optimal for the formation of ZnO nanoparticles generated by biosynthesizing ZnO nanoparticles. The normal crystalline size of biosynthesized ZnO nanoparticles calculated via XRD analysis are found to be 18, 12 and 17 nm for 300 - 500 oC, respectively. The direct optical band gap energy deducted from Tauc approximation range to be 3.32-3.20 eV. In SEM analysis, depending on the temperature of the synthesis conditions, different ZnO morphologies are also found. Functional groups analysis confirmed the incidence of carboxyl and amide groups in the O. sanctum leaf extract. The ZnO nanoparticles analysed at room temperature using photoluminescence, a broad visible band is observed around 382 nm for all samples. Furthermore, this study determines that the synthesized ZnO nanoparticles provide antimicrobial efficacy against clinical strains of Bacillus subtilis and Staphylococcus aureus, as well as against standard strains of Escherichia coli. Several fields, including cosmetics and pharmaceuticals, can benefit from biosynthesized nanoparticles.

Investigations of the Elastic Moduli of Er2O3 NPs Doped TeO2 – B2O3 – SiO2 Glasses using Theoretical Models

Abstract: Elastic moduli of {[(TeO2)0.7 (B2O3)0.3]0.8 (SiO2)0.2}1-y (Er2O3 NPs)y glasses with y = 0.01, 0.02, 0.03, 0.04, 0.05 were studied in this work using the theoretical elastic models. The Makishima & Mackenzie, Rocherulle and bond compression models were employed for the study. In the Makishima and Mackenzie model, the packing density was calculated from the bulk glass molar weight and the bulk glass density whereas in Rocherulle model it is determined as the individual oxides. Young, shear and bulk moduli as well as the Poisson ratio were calculated for the glasses in the Makishima and Rocherulle models, while longitudinal, was calculated in addition to young, bulk and shear moduli using the bond compression model. Bond per unit volume number (nb), bulk modulus, bulk modulus ratio (Kbc/Ke), atomic ring size (?) and stretching force constant were also calculated and presented. The values of the Young, bulk and shear moduli obtained from Makishima model increased from 52.854 to 55.335 GPa, 35.754 to 39.862 GPa and 21.080 to 21.809 GPa respectively with Er2O3 NPs composition increase from 1% to 5%.. The Rocherulle model presented increasing values for Young, bulk and shear moduli as 56.910 to 58.432 GPa, 41.452 to 44.450 GPa and 22.385 to 22.809 GPa respectively with Er2O3 NPs composition increase from 1% to 5%. The bond compression model presented much higher values of the elastic moduli compared to the experimentally obtained values and showed an increasing trend as the Er2O3 NPs concentration increases. In the glass network, the atomic ring size value decreased from 0.5698 to 0.5091 nm indicating an increase in the close packing of atoms. Based on the elastic moduli values presented by all the models, Makishima and Mackenzie model presented a more reliable data and hence represents the best model for the studied glass system.

Age Prediction from Sclera Images using Deep Learning

Abstract: Automatic age classification has drawn the interest of many scholars in the fields of machine learning and deep learning. In this study, we looked at the problem of estimating age groups using different biometric modalities of human beings. We looked at the problem of determining age groups in humans using various biometric modalities. Specifically, we focused on the use of transfer learning for sclera age group classification. 2000 Sclera images were collected from 250 individuals of various ages, and Otsu thresholding was used to segment the images using morphological processes. Experiment was conducted to determine how accurately the age group of a person can be classified from sclera images using pretrained CNN architectures. The segmented images were trained and tested on four different pre-trained models (VGG16, ResNet50, MobileNetV2, EffcientNet-B1), which were compared based on different performance metrics in which ResNet-50 was shown to outperform the others, resulting in an accuracy, precision, recall and F1-score of 95% while VGG-16, EffcientNetB1, and MobileNetV2 had 94%, 93%, and 91%, respectively. The findings from the study showed that there is an aging template in the sclera that can be utilized to classify age.

Combating the Multicollinearity in Bell Regression Model: Simulation and Application

Abstract: Poisson regression model has been popularly used to model count data. However, over-dispersion is a threat to the performance of the Poisson regression model. The Bell Regression Model (BRM) is an alternative means of modelling count data with over-dispersion. Conventionally, the parameters in BRM is popularly estimated using the Method of Maximum Likelihood (MML). Multicollinearity posed challenge on the efficiency of MML. In this study, we developed a new estimator to overcome the problem of multicollinearity. The theoretical, simulation and application results were in favor of this new method.

Forecasting of the epidemiological situation: Case of COVID-19 in Morocco

Abstract: Since the coronavirus pandemic started, many people have died due to the disease. The epidemic has been challenging to predict, as it progresses and spreads throughout the world. We used Auto-Regressive Integrated Moving Average (ARIMA) models to predict the outbreak of COVID-19 in the upcoming months in Morocco. In this work, we measured the effective reproduction number using the real data and the forecasted data produced by the two commonly used approaches, to reveal how effective the measures taken by the Moroccan government have been in controlling the COVID-19 outbreak. The prediction results for the next few months show a strong evolution in the number of confirmed and death cases in Morocco. We study the spread of COVID-19 in Morocco to see how many cases are discovered, recovered, and dead, and the forecasting of further cases is used as a basic novel method. It is based on time series models. We used coronavirus outbreak data from March 02, 2020, to August 04, 2021. ARIMA (Autoregressive integrated moving average) and Prophet time-series models are used to forecast the development of COVID-19, which is not a novel method. The mean absolute error, root mean square error, and coefficient of determination R2 were computed to assess the model's performance. Our study aims to provide a better understanding of the infectious disease outbreak that affected Morocco. It also provides information on the disease outbreak's epidemiology. Our study shows that the FBProphet model is more accurate in predicting the prevalence of COVID-19. It can help guide the government's efforts to prevent the virus' spread.

Graph Theory: A Lost Component For Development in Nigeria

Abstract: Graph theory is one of the neglected branches of mathematics in Nigeria but with the most applications in other fields of research. This article shows the paucity, importance, and necessity of graph theory in the development of Nigeria. The adjacency matrix and dual graph of the Nigeria map were presented. The graph spectrum and energies (graph energy and Laplacian energy) of the dual graph were computed. Then the chromatic number, maximum degree, minimum spanning tree, graph radius, and diameter, the Eulerian circuit and Hamiltonian paths from the dual graph were obtained and discussed.

Collocation Approach for the Computational Solution Of Fredholm-Volterra Fractional Order of Integro-Differential Equations

Abstract: In this work, a collocation technique is used to determine the computational solution to fractional order Fredholm-Volterra integro-differential equations with boundary conditions using Caputo sense. We obtained the linear integral form of the problem and transformed it into a system of linear algebraic equations using standard collocation points. The matrix inversion approach is adopted to solve the algebraic equation and substituted it into the approximate solution. We established the uniqueness and convergence of the method and some modelled numerical examples are provided to demonstrate the method’s correctness and efficiency. It is observed that the results obtained by our new method are accurate and performed better than the results obtained in the literature. The study will be useful to engineers and scientists. It is advantageous because it addresses the difficulty in tackling fractional order Fredholm-Volterra integro-differential problems using a simple collocation strategy. The approach has the advantage of being more accurate and reducing computer running time.

Green Information and Communication Technologies Implementation in Textile Industry Using Multicriteria Method

Abstract: It has been recognized that green Information Communication Technology (ICT) is fast becoming popular in every sector. It’s fundamental relevance to textile industry requires urgent attention than ever. Over the years selections of technologies to drive the textile industry has been a debate. There is a need for an efficient technology selection method to realize this goal. Selection of green ICT alternatives is a multicriteria decision making problem which has been sparsely explored in open literature. This study presents green ICT adoption in the textile industry using a fuzzy-TOPSIS multi-criteria approach for the most preferred ICT alternative in a textile industry. Criteria for Green ICT selection were identified by administering interview with selected textile and ICT industry experts at the managerial cadre of organizations and academics. Criteria considered were Implementation Cost (IC), Operating and Maintenance Cost (OMC), Environmental Impact (EI), Improved System Performance and Use (ISPU), Supply Chain Management (SCM) and Employment Opportunities (EO). Results shows that the most preferred ICT alternative is power management with overall coefficient of 0.60 while the least preferred is software optimization with coefficient of 0.23. This work will allow clean industrial process in the textile industry and also promote sustainable cities and communities through responsible consumption and production as highlighted by sustainable development goals (SDG) 11 and 12.

Countermeasure to Structured Query Language Injection Attack for Web Applications using Hybrid Logistic Regression Technique

Abstract: The new generation of security threats has been promoted by real-time applications, where several users develop new ways to communicate on the internet via web applications. Structured Query Language injection Attacks (SQLiAs) is one of the major threats to web application security. Here, unauthorised users usually gain access to the database via web applications. Despite the giant strides made in the detection and prevention of SQLiAs by several researchers, an ideal approach is still far from over as most existing techniques still require improvement, especially in the area of addressing the weak characterisation of input vectors which often leads to low prediction accuracy. To deal with this concern, this paper put forward a hybrid optimised Logistic Regression (LR) model with Improved Term Frequency Inverse Document-Frequency (ITFIDF-LR). To show the effectiveness of the proposed approach, attack datasets is used and evaluated using selected performance metrics, i.e., accuracy, recall, specificity and False Positive Rate. The experimental results via simulation when compared with the benchmarked techniques, achieved performance record of 0.99781 for accuracy, recall and F1-score as well as 0.99782, 0.99409 and 0.00591 for precision, specificity and False Positive Rate (FPR) respectively. This is an indication that the proposed approach is efficient and when deployed is capable of detecting SQLiA on web applications.

Pollution Status of Groundwater Resources Through Hydrochemical Characteristics - A Case Study From Southern India

Abstract: Mayiladuthurai is situated on the banks of river Cauveri. This research work aims to determine the quality of water which were collected from twenty groundwater sources. From the identified twenty locations, water samples were collected. The sampling stations include three taluks in the Mayiladuthurai district. The samples were analysed for various physicochemical qualities of water and compared with BIS & WHO standards. Frequency histogram and statistical analysis were applied to analyse the obtained data. Results of hydrochemistry revealed the dominance of hardness, magnesium and fluoride ions. The present investigation of hydrochemistry concludes that the drinking nature of the analyzed samples was very weak quality-wise and could not be used for drinking as such. Besides, fluoride ion related health problems were raised in some packets of the study area. The analytical report reveals that the quality of water has deteriorated and this may have a severe impact on human beings and other organisms in the study area.

Mechanical Evaluation and Minerals Phases Identification of Fine and Coarse Okelele Block Clay Composites for Furnace Lining Application

Abstract: The suitability of fine and coarse Okelele clays as refractory raw materials for furnace lining application was investigated. The clay samples were crushed and pounded with a mortar and pestle to a particle size of 20 microns. 230 g each of fine clay was mixed with 50 mls of water inside a bowl and stirred thoroughly to form homogenous plastic paste. 10 g, 15 g, 25 g, 35 g and 45 g of coarse clay were added respectively to the 230 g of homogenous fine clay paste in different container. The fine and coarse clays composites weighing 240 g, 245 g, 255 g, 265 g and 275 g were respectively put in a mold of dimension 3 x 5 x 6 cm and air dried for 7 days. The samples were fired at temperature of 1200 oC for five hours using Carbolite Furnace. After cooling, the fine and coarse clay composites of 240 g and 245g were broken by the heat and composites blocks 255 g, 265g and 275g were hardened and remove for compressive test analysis. The fine and coarse clays were characterized using X-ray Diffractometer PW1830forminerals phases’ identification. The result of XRD shows that the clay was majorly composed of Quartz and Kaolinite with the traces of other minerals such as Smectile, Illite/Mica, Albite, Jarosite, Gypsum and Pyrite. The Kaolinite contains aluminum silicate (Al2O3·2SiO2) and Quartz has the silicon and oxygen atoms. The compressive strength test result judged the 275 g fire block of clays composite the best with the maximum force breaks of 7652 N with deflection of 3.734 mm and Young Modulus of 212 N/mm2 for the time to failure of 22 seconds. The results proved that Okelele clays are suitable as refractory material for furnace lining application.

Half-metallic Characteristics of the Novel Half Heusler Alloys XCrSb (X=Ti, Zr, Hf )

Abstract: Ab-initio calculations are performed to examine the structural, mechanical, electronic, magnetic and thermodynamic properties of the half-Heusler ternary alloys XCrSb (X = H f , Ti, Zr). In this study, the spin-polarized density functional theory (DFT) method that is spin-polarized with generalised gradient approximation (GGA) are used to perform ab-initio calculations to investigate the physical properties of a novel half-Heusler ternary alloys XCrSb (X = H f , Ti, Zr). It was confirmed that the alloys are stable mechanically and exhibit ferromagnetic states (FM). The study reveals that the alloys portray half-metallic character with narrow energy gaps. And it also shows that they have a total magnetic moment of approximately 3ub. From the formation energy calculation, it shows that the alloys can be synthesized experimentally. Also, it was observed that they are mechanically stable. The heat capacities and Debye temperatures were also computed and they show high thermodynamic stability.