Regulation of the Adrenal Cortex Function During Stress

A proposal to study the function of the adrenal gland in the rat during stress is presented. In the proposed project, three different phases of experimentation will be undertaken. The first phase includes establishment of the circadian rhythm of both brain amines and glucocoticoids, under normal conditions and under chronic and acute stressful conditions. The second phase includes the study of the pharmacokinetics of glucocorticoid binding under normal and stress conditions. The third phase includes brain uptake and binding under different experimental conditions. In the outlined experiments brain biogenic amines will be evaluated, adrenal functions will be measured and stress effect on those parameters will be studied. It is hoped that this investigation can explain some of the complex relationships between the brain neurotransmitter and adrenal function

Framework for assessing the quality of quality management programs

A model for assessing the quality of quality management programs is presented in this paper. The role of Strategic Gaps and Knowledge Gaps in evaluating the quality of quality management programs is discussed in this paper. In addition, the paper presents a method for identification of any possible Strategic Gaps and Knowledge Gaps which may exist in organisational quality management processes. The existence of such gaps may adversely affect the expected outcome from the implemented quality management programs. Furthermore, the paper explores the relationship between the perception of the developers or implementers of quality management programs and other related organizational attributes. Finally, the study seeks to identify other management characteristics associated with success or otherwise of quality management programs in HR departments. In so doing, the importance of addressing issues arising from Strategic Gaps and Knowledge Gaps is addressed

Calcitonin control of calcium metabolism during weightlessness

The main objective of this proposal is to elucidate calcitonin role in calcium homeostasis during weightlessness. In this investigation our objectives are to study: the effect of weightlessness on thyroid and serum calcitonin, the effect of weightlessness on the circadian variation of calcitonin in serum and the thyroid gland, the role of light as zeitgeber for calcitonin circadian rhythm, the circadian pattern of thyroid sensitivity to release calcitonin in response to calcium load, and the role of serotonin and norepinephrine in the control of calcitonin release. The main objective of this research/proposal is to establish the role of calcitonin in calcium metabolism during weightlessness condition. Understanding the mechanism of these abnormalities will help in developing therapeutic means to counter calcium imbalance in spaceflights

Genetic relationships of cotton (Gossypium barbadense L.) genotypes as studied by morphological and molecular markers

Fifteen (15) morphological traits and three different types of molecular markers [inter simple sequence repeats (ISSR), simple sequence repeat (SSR) and expressed sequence tag (EST) markers] were used to study the genetic relationships among 24 cotton (Gossypium barbadense L.) genotypes (commercial varieties and new germplasm). High significant differences were observed among the genotypes for all the studied traits and the interaction between genotypes and years ranged from highly significant to significant for the most studied traits. The value of phenotypic coefficient of variation (PCV) was higher than genotypic coefficient of variation (GCV) for all studied traits which means that the apparent variation is not only due to genotypes but also due to the influence of environmental factors. The cluster analysis of the 24 cotton genotypes depending upon the morphological traits divided them into two main groups (A and B) while molecular data divided them into six groups. The cotton genotypes were distributed according to principal coordinate analysis (PCOORDA) analysis of both morphological traits and molecular markers regardless of their fiber characteristics. According to this analysis, the cotton genotypes were distributed into three distinct parts. Most molecular markers showed polymorphism in their patterns. The highest number of total and polymorphic bands was generated from ISSR markers while the least number of total and polymorphic bands was obtained from the ESTSSR markers. According to both morphological and molecular analyses, the following genotypes could be used to hybridize and produce high growth and yield potential: Giz87, Giza45, Giza88 and Giza70 as a first parent and Karshansky, Giza80, Giza83, Australian10229 and Russian6022 as a second parent in the cross.Keywords: Cotton, simple sequence repeat (SSR), expressed sequence tag (EST), inter simple sequence repeats (ISSR), morphological traits, cluster analysis, principal coordinate analysis (PCOORDA).African Journal of Biotechnology Vol. 12(30), pp. 4736-474

On Using Active Learning and Self-Training when Mining Performance Discussions on Stack Overflow

Abundant data is the key to successful machine learning. However, supervised learning requires annotated data that are often hard to obtain. In a classification task with limited resources, Active Learning (AL) promises to guide annotators to examples that bring the most value for a classifier. AL can be successfully combined with self-training, i.e., extending a training set with the unlabelled examples for which a classifier is the most certain. We report our experiences on using AL in a systematic manner to train an SVM classifier for Stack Overflow posts discussing performance of software components. We show that the training examples deemed as the most valuable to the classifier are also the most difficult for humans to annotate. Despite carefully evolved annotation criteria, we report low inter-rater agreement, but we also propose mitigation strategies. Finally, based on one annotator's work, we show that self-training can improve the classification accuracy. We conclude the paper by discussing implication for future text miners aspiring to use AL and self-training.Comment: Preprint of paper accepted for the Proc. of the 21st International Conference on Evaluation and Assessment in Software Engineering, 201

Optimization of Fuzzy Logic Controller for Supervisory Power System Stabilizers

This paper presents a powerful supervisory power system stabilizer (PSS) using an adaptive fuzzy logic controller driven by an adaptive fuzzy set (AFS). The system under study consists of two synchronous generators, each fitted with a PSS, which are connected via double transmission lines. Different types of PSS-controller techniques are considered. The proposed genetic adaptive fuzzy logic controller (GAFLC)-PSS, using 25 rules, is compared with a static fuzzy logic controller (SFLC) driven by a fixed fuzzy set (FFS) which has 49 rules. Both fuzzy logic controller (FLC) algorithms utilize the speed error and its rate of change as an input vector. The adaptive FLC algorithm uses a genetic algorithmto tune the parameters of the fuzzy set of each PSS. The FLC’s are simulated and tested when the system is subjected to different disturbances under a wide range of operating points. The proposed GAFLC using AFS reduced the computational time of the FLC, where the number of rules is reduced from 49 to 25 rules. In addition, the proposed adaptive FLC driven by a genetic algorithm also reduced the complexity of the fuzzy model, while achieving a good dynamic response of the system under study

Optimization of Fuzzy Logic Controller for Supervisory Power System Stabilizers

This paper presents a powerful supervisory power system stabilizer (PSS) using an adaptive fuzzy logic controller driven by an adaptive fuzzy set (AFS). The system under study consists of two synchronous generators, each fitted with a PSS, which are connected via double transmission lines. Different types of PSS-controller techniques are considered. The proposed genetic adaptive fuzzy logic controller (GAFLC)-PSS, using 25 rules, is compared with a static fuzzy logic controller (SFLC) driven by a fixed fuzzy set (FFS) which has 49 rules. Both fuzzy logic controller (FLC) algorithms utilize the speed error and its rate of change as an input vector. The adaptive FLC algorithm uses a genetic algorithmto tune the parameters of the fuzzy set of each PSS. The FLC’s are simulated and tested when the system is subjected to different disturbances under a wide range of operating points. The proposed GAFLC using AFS reduced the computational time of the FLC, where the number of rules is reduced from 49 to 25 rules. In addition, the proposed adaptive FLC driven by a genetic algorithm also reduced the complexity of the fuzzy model, while achieving a good dynamic response of the system under study

Genetic Algorithm Based Control System Design of a Self-Excited Induction Generator

This paper presents an application of the genetic algorithm (GA) for optimizing controller gains of the Self-Excited Induction Generator (SEIG) driven by the Wind Energy Conversion Scheme (WECS). The proposed genetic algorithm is introduced to adapt the integral gains of the conventional controllers of the active and reactive control loop of the system under study, where GA calculates the optimum value for the gains of the variables based on the best dynamic performance and a domain search of the integral gains. The proposed genetic algorithm is used to regulate the terminal voltage or reactive power control, by adjusting the self excitation, and to control the mechanical input power or active power control by adapting the blade angle of WECS, in order to adjust the stator frequency. The GA is used for optimizing these gains, for an active and reactive power loop, by solving the related optimization problem. The simulation results show a better dynamic performance using the GA than using the conventional PI controller for active and reactive control

Power System Stabilizer Driven by an Adaptive Fuzzy Set for Better Dynamic Performance

This paper presents a novel application of a fuzzy logic controller (FLC) driven by an adaptive fuzzy set (AFS) for a power system stabilizer (PSS).The proposed FLC, driven by AFS, is compared with a classical FLC, driven by a fixed fuzzy set (FFS). Both FLC algorithms use the speed error and its rate of change as input vectors. A single generator equipped with FLC-PSS and connected to an infinite bus bar through double transmission lines is considered. Both FLCs, using AFS and FFS, are simulated and tested when the system is subjected to different step changes in the reference value. The simulation results of the proposed FLC, using the adaptive fuzzy set, give a better dynamic response of the overall system by improving the damping coefficient and decreasing the rise time and settling time compared with classical FLC using FFS. The proposed FLC using AFS also reduces the computational time of the FLC as the number of rules is reduced.