Comparative study of microwave assisted and conventional synthesis of novel 2-quinoxalinone-3- hydrazone derivatives and its spectroscopic properties

A series of novel quinoxalin-2(1H)-one-3-hydrazone derivatives, 2a - 8d were synthesized via condensation of 3-hydrazinoquinoxalin-2(1H)-one, 1, with the corresponding ketones under microwave irradiation. The microwave assisted reaction was remarkably successful and gave hydrazones in higher yield at less reaction time compared to conventional heating method. The chemical structures of the compounds prepared were confirmed by analytical and spectral dat

EMPIRICAL EVIDENCE ON THE RELATIONSHIP BETWEEN BUSINESS COMPETENCIES AND ENTREPRENEURIAL PERFORMANCE IN NIGERIA

Abstract' This study examines the relationship between business competencies and entrepreneurial performance among the micro, small and medium enterprises (MSMEs) owners in Lagos State, Nigeria. Thus, to achieve the research objectives this study employed cross-sectional research. design with the adoption of survey method. The collected data were analysed using Structural Equation Modelling (SEM) to show the degree of correlation between the multiple variables under study. The structural path reveals statistical insignificant of human resource competency on entrepreneurial performance at (p=.049, .CR = . 741, p = .459). The financial competency on entrepreneurial performance is insignificant (p = -.023, CR · = -.356. p = . 722) while operational competency did not contributed significantly to entrepreneurial performance (p = .008, CR = .122, p = .903). However, the structural model further indicated that marketing competencies has contributed significantly to entrepreneurial performance (p = .148, CR = 2.181, p = .029). The researcher concludes that there is a partial significant relationship between business competencies and entrepreneurial performance. The study recommended that the individual-organisation characteristics such as knowledge, skills, and abilities are required to perform a specific job perfectly at the organisational level (e.g. human resource competency, marketing competency, financial management competency, and operational management competency). Therefore, the entrepreneurial training agencies ·can take a clue from this study finding when designing entrepreneurial training curriculum with effective state-of-the-art facilities by taking into consideration functional business competencies

Phytochemical Screening of the Bark of Vernonia Amygdalina

The bark of vernoniaamygdalina has been selected for this work in order to ascertain the natural and  medicinal endowment based on the ethnobotanical evidences of the plant. Phytochemical screening was carried out on the bark of vernoniaamygdalina (bitter leave), using both methanolic and chloroform extracts, this screening analysis confirmed the presence of saponins, alkaloids, cardiac glycosides, anthraquinones and phobatannins. Keywords: Phytochemical Screening, Vernoniaamygdalina

The Effect of Electrolyte on Dye Sensitized Solar Cells Using Natural Dye from Mango (M. indica L.) Leaf as Sensitizer

Dye-sensitized solar cells (DSSC) were fabricated with mango leaf dye extracts as natural dye sensitizers at pH value of 5.20 and temperature of 18.1˚C. Methanol was used as dye-extracting solvent. DSSCs from dye extract of M. indica L. with KMnO4 electrolyte had the highest photocurrent density of 1.3 mA/cm2 and fill factor FF of 0.46 for the sun at its peak. Potassium permanganate (KMnO4) had a photocurrent density of 1.3 mA/cm2 and FF of 0.8 at sundown. Potassium Iodide (KI), Potassium Bromide (KBr) and Mercury Chloride (HgCl2) electrolytes had 0.2 mA/cm2, 0.08 mA/cm2 and 0.02 mA/cm2 photocurrent densities respectively. The fill factors of 0.09, 0.03 and 0.003 respectively for sun overhead while 0.08 mA/cm2, 0.01 mA/cm2 and 0.01 mA/cm2 were the values of photocurrent densities respectively at sundown. The fill factors were 0.02, 0.0006 and 0.003 respectively at sundown. The maximum power Pmax of the DSSCs were 0.5 mW/cm2, 0.10 mW/cm2, 0.01 mW/cm2 and 0.012 mW/cm2 respectively at 1300 h at 1630 h 0.9 mW/cm2, 0.14 mW/cm2, 0.005 mW/cm2 and 0.0015 mW/cm2 respectively

Microwave-Assisted Synthesis and Antibacterial Activity of some Pyrazol-I-Ylquinoxalin-2(IH)-One Derivatives

3-Hydrazinoquinoxalin-2(1H)-one was prepared from quinoxaline-2,3-dione and subsequently used for the synthesis of some potentially biologically active 3-(pyrazol-1-ylquinoxalin-2(1H)-one derivatives. While 3-(3,5-dimethylpyrazol-1-yl)quinoxalin-2(1H)-one showed a comparative effect with streptomycin, 3-(5-oxo-3-phenyl-4,5-di- hydropyrazol-1-yl)quinoxalin-2(1H)-one was found to be the most active at MIC value of 7.8 μg/m

Property-based biomass feedstock grading using k-Nearest Neighbour technique

Abstract: Energy generation from biomass requires a nexus of different sources irrespective of origin. A detailed and scientific understanding of the class to which a biomass resource belongs is therefore highly essential for energy generation. An intelligent classification of biomass resources based on properties offers a high prospect in analytical, operational and strategic decision-making. This study proposes the -Nearest Neighbour (-NN) classification model to classify biomass based on their properties. The study scientifically classified 214 biomass dataset obtained from several articles published in reputable journals. Four different values of (=1,2,3,4) were experimented for various self normalizing distance functions and their results compared for effectiveness and efficiency in order to determine the optimal model. The -NN model based on Mahalanobis distance function revealed a great accuracy at =3 with Root Mean Squared Error (RMSE), Accuracy, Error, Sensitivity, Specificity, False positive rate, Kappa statistics and Computation time (in seconds) of 1.42, 0.703, 0.297, 0.580, 0.953, 0.047, 0.622, and 4.7 respectively. The authors concluded that -NN based classification model is feasible and reliable for biomass classification. The implementation of this classification models shows that -NN can serve as a handy tool for biomass resources classification irrespective of the sources and origins

Towards low-carbon energy state in South Africa: a survey of energy availability and sustainability

Abstract : The drive towards low-carbon economy in South Africa has necessitated alternative energy sources for electricity generation. More alternative sources have evolved in recent times with a view to making energy available to all and sundry. However, asides proliferation of these sources and extensions in form of micro-grids, the questions of increased availability and sustainability has become a growing concern. This survey investigates the state of the renewable energy system in South Africa with focus on the elements, which enhance energy availability and sustainability in the emerging transition to a low- carbon economy. Case studies of other countries were reviewed and considered in the South African context. It was observed that energy availability on the journey to the low-carbon economy is influenced by physical, climatic, human, prosumer concept and political factors. In sustaining the transition and progressing to a green economy, intelligent use of data from power generation, transmission, and distribution sectors for intelligent data-driven decision-making processes was also found as essential. As part of the sustainability roadmap, efficiency at the end-user side of the value chain and a system thinking paradigm in the harvesting of renewable energy sources (RES) and formulation of supporting policies were also identified. In the overall, the study reveals that South Africa is replete with abundance of RES, however, their continuous availability and sustainability depends on joint interventions of both stakeholders and the government with viable environment for the growth of the sector

Wind turbine power output short-term forecast : a comparative study of data clustering techniques in a PSO-ANFIS model

Abstract:The emergence of new sites for wind energy exploration in South Africa requires an accurate prediction of the potential power output of a typical utility-scale wind turbine in such areas. However, careful selection of data clustering technique is very essential as it has a significant impact on the accuracy of the prediction. Adaptive neurofuzzy inference system (ANFIS), both in its standalone and hybrid form has been applied in offline and online forecast in wind energy studies, however, the effect of clustering techniques has not been reported despite its significance. Therefore, this study investigates the effect of the choice of clustering algorithm on the performance of a standalone ANFIS and ANFIS optimized with particle swarm optimization (PSO) technique using a synthetic wind turbine power output data of a potential site in the Eastern Cape, South Africa. In this study a wind resource map for the Eastern Cape province was developed. Also, autoregressive ANFIS models and their hybrids with PSO were developed. Each model was evaluated based on three clustering techniques (grid partitioning (GP), subtractive clustering (SC), and fuzzy-c-means (FCM)). The gross wind power of the model wind turbine was estimated from the wind speed data collected from the potential site at 10 min data resolution using Windographer software. The standalone and hybrid models were trained and tested with 70% and 30% of the dataset respectively. The performance of each clustering technique was compared for both standalone and PSO-ANFIS models using known statistical metrics. From our findings, ANFIS standalone model clustered with SC performed best among the standalone models with a root mean square error (RMSE) of 0.132, mean absolute percentage error (MAPE) of 30.94, a mean absolute deviation (MAD) of 0.077, relative mean bias error (rMBE) of 0.190 and variance accounted for (VAF) of 94.307. Also, PSO-ANFIS model clustered with SC technique performed the best among the three hybrid models with RMSE of 0.127, MAPE of 28.11, MAD of 0.078, rMBE of 0.190 and VAF of 94.311. The ANFIS-SC model recorded the lowest computational time of 30.23secs among the standalone models. However, the PSO-ANFIS-SC model recorded a computational time of 47.21secs. Based on our findings, a hybrid ANFIS model gives better forecast accuracy compared to the standalone model, though with a trade-off in the computational time. Since, the choice of clustering technique was observed to play a vital role in the forecast accuracy of standalone and hybrid models, this study recommends SC technique for ANFIS modeling at both standalone and hybrid models

Natural Radionuclides in Natural Spring Water Samples in Ikere – Ekiti Local Government Area, Ekiti State, Nigeria

The presence of  the radionuclides in drinking water poses a number of health hazards.  This study estimated the committed effective doses due the natural radiounuclides via consumption of spring water in the study area. 80 spring water samples were measured using gamma spectrometry.  The annual committed effective doses in Ikere local government , Ekiti State, Nigeria varied from 0.16 mSv/y to 0.22 mSv/y with a mean value of 0.20 ± 0.03 mSv/y.  The calculated annual committed effective dose is  lower than WHO recommended limit of 1.0 mSv/y for public exposure. Due to the consumption of natural spring water there is no radiological health hazards to the public within the study area. Keywords:Radionuclides, Activity concentration, Committed effective dose, Spring water, Drinking wate