Global gbest guided-artificial bee colony algorithm for numerical function optimization

Numerous computational algorithms are used to obtain a high performance in solving mathematics, engineering and statistical complexities. Recently, an attractive bio-inspired method—namely the Artificial Bee Colony (ABC)—has shown outstanding performance with some typical computational algorithms in different complex problems. The modification, hybridization and improvement strategies made ABC more attractive to science and engineering researchers. The two well-known honeybees-based upgraded algorithms, Gbest Guided Artificial Bee Colony (GGABC) and Global Artificial Bee Colony Search (GABCS), use the foraging behavior of the global best and guided best honeybees for solving complex optimization tasks. Here, the hybrid of the above GGABC and GABC methods is called the 3G-ABC algorithm for strong discovery and exploitation processes. The proposed and typical methods were implemented on the basis of maximum fitness values instead of maximum cycle numbers, which has provided an extra strength to the proposed and existing methods. The experimental results were tested with sets of fifteen numerical benchmark functions. The obtained results from the proposed approach are compared with the several existing approaches such as ABC, GABC and GGABC, result and found to be very profitable. Finally, obtained results are verified with some statistical testing

Detection of mesocarp oleoyl-thioesterase gene of the South American oil palm Elaeis oleifera by reverse transcriptase polymerase chain reaction

The thioesterase enzyme functions in lipid synthesis by cleaving the acyl-ACP bond and liberating the fatty acid. Thioesterases have been isolated from a number of plant sources. The gene for this enzyme was detected in Elaeis oleifera by reverse transcriptase polymerase chain reaction (RT-PCR), cloned and sequenced and found to have considerable sequence similarity with other previously cloned thioesterases. Its highest homology is to the Brassica napus oleoyl-ACP thioesterase, 72% at the nucleotide level over the coding region examined, and 83% identity (90% positives) at the amino acid level. Key Words: Elaeis oleifera, mesocarp, oleoyl-ACP thioesterase, RT-PCR. African Journal of Biotechnology Vol.3(11) 2004: 595-59

Effects of SMS Texting on Academic Writing Skills of Undergraduate Students at a Public Sector University in Pakistan

The growing concern about the use of texting endangering the standard forms in language prompted the present research to determine the presence or absence of SMS features in the academic writing of the participants. Triangulation was used for data collection i.e. questionnaires for learners and educators and samples of the learners’ English written work were examined for SMS features. Simple average and ratio were used for descriptive analysis of the data. Contrary to the expectation, there were no significant evidences of these features in the sample. It seems being proficient in standard forms, these learners are context conscious and can switch to the appropriate register or style when writing formally .Thus the present study has demystified the popular belief about texting adversely affecting writing and thus destroying Standard English. Moreover, the evidences of one punctuation mark used in place of another indicate there can be other factors like carelessness or lack of knowledge of students and the lack of training, feedback or emphasis by educators or the system. So the matter of concern should be the general neglect of punctuation even out of the context of texting. It is found that the higher the exposure to the SMS, more the negative effect on the writing skills of the university students. The excessive use of this medium is leading students towards writing wrong spellings and using SMS language’s short abbreviations that are not standard in examinations and daily academic work that is very harmful in academia. Keywords: Orthography, Phonetic Transcriptions, SMS (texting), Writing skills DOI: 10.7176/JLLL/75-05 Publication date: January 31st 202

Assessing feedback practices in classroom assessment at federal government educational institutions of Lahore, Pakistan

This study aimed at investigating current feedback practices in classroom assessment. A sample of 300 participants including 150 teachers and 150 students each was selected using a stratified random sampling technique. Under the positivist paradigm, a survey method was deployed to conduct the research. In this study, a self-developed questionnaire comprising 20 items was used for data collection from the participants. The collected data was analyzed using SPSS (24.0). Frequencies and percentages were calculated in descriptive stats, whereas an independent sample t-test was used to verify research hypotheses. The study explored that class tests, class exercises, homework, and trial work during lessons were the most commonly used assessment tools whereas essay-type questions, and multiple-type questions were the most commonly used assessment formats. Moreover, it was found that delayed marking and returning of assessment tasks, less or no motivation for better performance, and lack of contact with parents were the major issues in feedback on classroom assessment at Federal Government Educational Institutions (FGEIs). The study recommended that the workload of teachers should be reduced so that they may have sufficient time to design and evaluate assessment tasks. Professional training on assessment on regular basis may be arranged for the faculty. A comprehensive plan of classroom assessment may be proposed by school principals along with a defined syllabus and be timely communicated the same to all stakeholders. An effective mechanism of monitoring to assess classroom assessment feedback practices may also be established

Microfinance and Growth of MSMEs: The Moderating Role of Entrepreneurial Thrust

The main objective of the study is to investigate the role of entrepreneurial thrust in the relationship between growth in microfinance and growth of micro, small and medium enterprises (MSMEs). A panel of 15 countries from 2004 to 2013 is investigated in the analysis. Microfinance index is developed by using PCA and impact of microfinance index is examined on growth of MSMEs along with other dimensions of microfinance. The study documented that in isolation microfinance may not increase growth of MSMEs but if a borrower possess entrepreneurial thrust then growth in MSMEs is evident. Entrepreneurial thrust plays a role of catalyst in the relationship. Furthermore role of entrepreneurial thrust in growth of MSMEs is found to be more important than role of growth in microfinance itself. It is also established that in the presence of entrepreneurial thrust if even small loans are given to the borrowers then it leads to increase in business activities. It is concluded that microfinance may be a better tool to alleviate poverty but for creating new enterprises entrepreneurial thrust is found to be a prerequisite. In absence of entrepreneurial thrust microfinance may not be workable. Keywords: Microfinance, Entrepreneurial Thrust, MSMEs Growth

An improved artificial bee colony algorithm for training multilayer perceptron in time series prediction

Learning an Artificial Neural Network (ANN) is an optimization task since it is desirable to find optimal weight sets of an ANN in the training process. Different equations are used to guide the network for providing an accurate result with less training and testing error. Most of the training algorithms focus on weight values, activation functions, and network structures for providing optimal outputs. Backpropagation (BP) learning algorithm is the well-known learning technique that trained ANN. However, some difficulties arise where the BP cannot get achievements without trapping in local minima and converge very slow in the solution space. Therefore, to overcome the trapping difficulties, slow convergence and difficulties in finding optimal weight values, three improved Artificial Bee Colony (ABC) algorithms built on the social insect behavior are proposed in this research for training ANN, namely the widely used Multilayer Perceptron (MLP). Here, three improved learning approaches inspired by artificial honey bee's behavior are used to train MLP. They are: Global Guided Artificial Bee Colony (GGABC), Improved Gbest Guided Artificial Bee Colony (IGGABC) and Artificial Smart Bee Colony (ASBC) algorithm. These improved algorithms were used to increase the exploration, exploitation and keep them balance for getting optimal results for a given task. Furthermore, here these algorithms used to train the MLP on two tasks; the seismic event's prediction and Boolean function classification. The simulation results of the MLP trained with improved algorithms were compared with that when trained with the standard BP, ABC, Global ABC and Particle Swarm Optimization algorithm. From the experimental analysis, the proposed improved algorithms get better the classification efficacy for time series prediction and Boolean function classification. Moreover, these improved algorithm's success to get high accuracy and optimize the best network's weight values for training the MLP

Using new artificial bee colony as probabilistic neural network for breast cancer data classification

Purpose – Breast cancer is an important medical disorder, which is not a single disease but a cluster more than 200 different serious medical complications. Design/methodology/approach – The new artificial bee colony (ABC) implementation has been applied to probabilistic neural network (PNN) for training and testing purpose to classify the breast cancer data set. Findings – The new ABC algorithm along with PNN has been successfully applied to breast cancers data set for prediction purpose with minimum iteration consuming. Originality/value – The new implementation of ABC along PNN can be easily applied to times series problems for accurate prediction or classification

Endostatin concentration in plasma of healthy human volunteers

Background: Angiogenesis is involved in many cardiovascular and cancerous diseases, including atherosclerosis and is controlled by a fine balance between angiogenic and angiostatic mediators. Endostatin is one of the main angiostatic mediators, and inhibits angiogenesis and prevents progression of atherosclerosis. The available literature shows a broad range of concentrations in relatively small samples of healthy controls and is calculated by using different techniques. This study was aimed to determine the basal endostatin concentration in plasma of healthy volunteers, to fully understand its physiological role. Methods: Fifty healthy adult volunteers were recruited to the study. Participants were advised not to participate in any physical activity on the day before the blood sampling. The volunteers’ physical activity, height, weight, heart rate and blood pressure were recorded. The samples were analysed for plasma endostatin concentration, using ELISA. The participants were divided by gender and ethnic groups to calculate any difference. Results: Endostatin and other variables were normally distributed. Most of the participants had a moderate level of physical activity with no gender related difference (p=0.370). The mean value for plasma endostatin in all samples was 105±12 ng/ml with range of 81–132 ng/ml. For males, it was 107±13 ng/ml, while for females; 102±12 ng/ml. There were no significant gender or ethnicity related differences in endostatin concentration. Moreover, endostatin was not significantly related with any anthropometric and physical variable. Conclusion: This study gives endostatin levels in normal healthy people and show no gender and ethnicity related differences in endostatin levels. Endostatin was not related with any anthropometric and physical variable