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

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

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.

Super-resolution imaging reveals the sub-diffraction phenotype of Zellweger Syndrome ghosts and wild-type peroxisomes.

Peroxisomes are ubiquitous cell organelles involved in many metabolic and signaling functions. Their assembly requires peroxins, encoded by PEX genes. Mutations in PEX genes are the cause of Zellweger Syndrome spectrum (ZSS), a heterogeneous group of peroxisomal biogenesis disorders (PBD). The size and morphological features of peroxisomes are below the diffraction limit of light, which makes them attractive for super-resolution imaging. We applied Stimulated Emission Depletion (STED) microscopy to study the morphology of human peroxisomes and peroxisomal protein localization in human controls and ZSS patients. We defined the peroxisome morphology in healthy skin fibroblasts and the sub-diffraction phenotype of residual peroxisomal structures ('ghosts') in ZSS patients that revealed a relation between mutation severity and clinical phenotype. Further, we investigated the 70 kDa peroxisomal membrane protein (PMP70) abundance in relationship to the ZSS sub-diffraction phenotype. This work improves the morphological definition of peroxisomes. It expands current knowledge about peroxisome biogenesis and ZSS pathoethiology to the sub-diffraction phenotype including key peroxins and the characteristics of ghost peroxisomes

Percevejo bronzeado em eucalipto.

Resumo

SYNTHESIS, ANTITUMOR ACTIVITY, PHARMACOPHORE MODELING AND QSAR STUDIES OF NOVEL PYRAZOLES AND PYRAZOLO [1, 5-A] PYRIMIDINES AGAINST BREAST ADENOCARCINOMA MCF-7 CELL LINE

Objective: The present work aimed to synthesize New series of pyrazoles 3 and pyrazolo[1,5-a]pyrimidines 5, 7, 9 in order to evaluate their antiproliferative activity against human breast adenocarcinoma MCF-7cell line and study the cell cycle progression of the most active compounds. In addition, Pharmacophore modeling and QSAR Studies of these new compounds were done.Methods: The diazonium salt of 4-aminoacetophenone 1 was coupled with malononitrile in ethanol using sodium acetate affords 2-[(4-acetylphenyl)diazenyl] malononitrile Cycloaddition of hydrazine hydrate, in molar ratios 1:1 or 1:2, on compound 2, furnished 3,5-diaminopyrazolederivatives 3a and 3b respectively. Moreover, new pyrazolo[1,5-a]pyrimidine derivatives 5a-f were obtained upon cyclocondensation of 3a, b with different chalcones 4a-c in EtOH/piperidine,while compounds 7a-f were prepared via cycloaddition of 3a, b with various arylidene malononitriles 6a-c in the same reaction condition. Finally, treatment of 3a, b with ethyl 2-cyano-3-ethoxyacrylate 8a or 2-(ethoxymethylene)malononitrile 8b in EtOH/TEA yielded the novel pyrazolo[1,5-a]pyrimidine derivatives 9a, b respectively. These target compounds were screened for their cytotoxic activity against MCF-7 (human breast Cell Line) followed by study cell cycle of 7a. Finally, Pharmacophore modeling and QSAR Studies was carried out.Results: The pyrazolopyrimidine 7a was the most active compound (IC50 = 3.25 µM), whereas, some of the tested compounds exploited moderate growth inhibitory activity. Its effect was further studied on cell cycle progression; results showed that compound 7a induced cell cycle arrest at S-phase verifying this compound as a promising selective anticancer agent.Conclusion: Compound 7a was found to be the most active member against MCF-7 breast cancer (IC50= 3.25 μM), Further biological assessment of 7a using flow-cytometric analysis, revealed that it induced cell cycle arrest at S phase.Keywords: Pyrazole, Pyrazolo[1,5-a]pyrimidine, MCF-7 breast cancer cell line, Cell cycle profile, 3D pharmacophore,1 QSAR stud

Closing oil palm yield gaps among Indonesian smallholders through industry schemes, pruning, weeding and improved seeds

Oil palm production has led to large losses of valuable habitats for tropical biodiversity. Sparing of land for nature could in theory be attained if oil palm yields increased. The efficiency of oil palm smallholders is below its potential capacity, but the factors determining efficiency are poorly understood. We employed a two-stage data envelopment analysis approach to assess the influence of agronomic, supply chain and management factors on oil palm production efficiency in 190 smallholders in six villages in Indonesia. The results show that, on average, yield increases of 65% were possible and that fertilizer and herbicide use was excessive and inefficient. Adopting industry-supported scheme management practices, use of high-quality seeds and higher pruning and weeding rates were found to improve efficiency. Smallholder oil palm production intensification in Indonesia has the capacity to increase production by 26%, an equivalent of 1.75 million hectares of land

A Predictive Model for Student Performance in Classrooms using Student Interactions with an eTextbook

With the rise of online eTextbooks and Massive Open Online Courses (MOOCs), a huge amount of data has been collected related to students’ learning. With the careful analysis of this data, educators can gain useful insights into their students’ performance and their behavior in learning a particular topic. This paper proposes a new model for predicting student performance based on an analysis of how students interact with an interactive online eTextbook. By being able to predict students’ performance early in the course, educators can easily identify students at risk and provide a suitable intervention. We considered two main issues: the prediction of good/bad performance and the prediction of the final exam grade. To build the proposed model, we evaluated the most popular classification and regression algorithms. Random Forest Regression and Multiple Linear Regression have been applied in Regression. While Logistic Regression, decision tree, Random Forest Classifier, K Nearest Neighbors, and Support Vector Machine have been applied in classification. Based on the findings of the experiments, the algorithm with the best result overall in classification was Random Forest Classifier with an accuracy equal to 91.7%, while in the regression it was Random Forest Regression with an R2 equal to 0.977