Modeling the human as a controller in a multitask environment

Modeling the human as a controller of slowly responding systems with preview is considered. Along with control tasks, discrete noncontrol tasks occur at irregular intervals. In multitask situations such as these, it has been observed that humans tend to apply piecewise constant controls. It is believed that the magnitude of controls and the durations for which they remain constant are dependent directly on the system bandwidth, preview distance, complexity of the trajectory to be followed, and nature of the noncontrol tasks. A simple heuristic model of human control behavior in this situation is presented. The results of a simulation study, whose purpose was determination of the sensitivity of the model to its parameters, are discussed

Carbon Nanotubes by a CVD Method. Part II: Formation of Nanotubes from (Mg, Fe)O Catalysts

The aim of this paper is to study the formation of carbon nanotubes (CNTs) from different Fe/MgO oxide powders that were prepared by combustion synthesis and characterized in detail in a companion paper. Depending on the synthesis conditions, several iron species are present in the starting oxides including Fe2+ ions, octahedral Fe3+ ions, Fe3+ clusters, and MgFe2O4-like nanoparticles. Upon reduction during heating at 5 °C/min up to 1000 °C in H2/CH4 of the oxide powders, the octahedral Fe3+ ions tend to form Fe2+ ions, which are not likely to be reduced to metallic iron whereas the MgFe2O4-like particles are directly reduced to metallic iron. The reduced phases are R-Fe, Fe3C, and ç-Fe-C. Fe3C appears as the postreaction phase involved in the formation of carbon filaments (CNTs and thick carbon nanofibers). Thick carbon nanofibers are formed from catalyst particles originating from poorly dispersed species (Fe3+ clusters and MgFe2O4-like particles). The nanofiber outer diameter is determined by the particle size. The reduction of the iron ions and clusters that are well dispersed in the MgO lattice leads to small catalytic particles (<5 nm), which tend to form SWNTS and DWNTs with an inner diameter close to 2 nm. Well-dispersed MgFe2O4-like particles can also be reduced to small metal particles with a narrow size distribution, producing SWNTs and DWNTs. The present results will help in tailoring oxide precursors for the controlled formation of CNTs

Carbon Nanotubes by a CVD Method. Part I: Synthesis and Characterization of the (Mg, Fe)O Catalysts

The controlled synthesis of carbon nanotubes by chemical vapor deposition requires tailored and wellcharacterized catalyst materials. We attempted to synthesize Mg1-xFexO oxide solid solutions by the combustion route, with the aim of performing a detailed investigation of the influence of the synthesis conditions (nitrate/urea ratio and the iron content) on the valency and distribution of the iron ions and phases. Notably, characterization of the catalyst materials is performed using 57Fe Mo¨ssbauer spectroscopy, X-ray diffraction, and electron microscopy. Several iron species are detected including Fe2+ ions substituting for Mg2+ in the MgO lattice, Fe3+ ions dispersed in the octahedral sites of MgO, different clusters of Fe3+ ions, and MgFe2O4-like nanoparticles. The dispersion of these species and the microstructure of the oxides are discussed. Powders markedly different from one another that may serve as model systems for further study are identified. The formation of carbon nanotubes upon reduction in a H2/CH4 gas atmosphere of the selected powders is reported in a companion paper

Genetic diversity analysis of pearl millet (Pennisetum glauccum [L.] R. Br.) accessions using molecular markers

Random amplified polymorphic DNA (RAPD) analysis was applied to pearl millet genotypes in order to assess the degree of polymorphisms within and among genotypes and to investigate if this approachwas suitable for genetic studies of pearl millet. 20 genotypes were evaluated using 30 different 10-mer primers of arbitrary sequence. Most of the primers did not reveal any polymorphism; however 12primers revealed scorable polymorphism between genotypes of pearl millet and these can be further evaluated for use in genetic mapping. Pair-wise comparisons of unique and shared polymorphic amplification products were generated by Jaccard’s similarity co-efficient. These similarity co-efficientswere employed to construct a dendrogram showing phylogenetic relationships using unweighted paired group method with arithmetic averages (UPGMA). The UPGMA analysis indicated a higher similaritybetween genotype PT 2835/1 and PT 5552 and lowest similarity index was observed between PT 5554 and PT 2835/1. Analysis of RAPD data appears to be helpful in determining the genetic relationship among 20 pearl millet genotypes. The associations among the 20 genotypes were also examined with Principle components analysis (PCA) from Jaccard’s similarity co-efficient and it is more informative to analyze the extreme genotypes

Zirconia nanotubes

Hollow nanotubes of zirconia as well as of yttria-stabilized zirconia are successfully prepared by first coating the carbon nanotubes appropriately with the oxidic material and then burning off the carbon of the template

Spatial clustering based gene selection for gene expression analysis in microarray data classification

A typical application of categorization in data mining is to uncover interesting distributions and significant patterns in the information that underlies it using density-based spatial clustering for workloads with noise. In these conditions, it is anticipated that the classification of the microarray gene expression database will have the necessary clustering property that may be utilized to emphasize the effects of the alterations. The proposed method typically guarantees that the subsequent identification of gene clusters’ best global arrangement of genes. It provides an iterative method for figuring out the precise number of clusters needed for each data collection. The technique is based on practices frequently used in statistical tests. The key idea is to coordinate gene redistribution optimization across clusters with the search for the optimal number of groups. An experiment that finds the most effective number of genes over time was used to evaluate the effectiveness of the suggested strategy. It used this stringent statistical test to show that our technique accurately clusters more than 95% of the genes. Finally, since the basic principles of gene development and gene cluster assignment have been well characterized by earlier studies and the technique was verified using real gene expression information

Path coefficient analysis in local pearl millet germplasm for grain minerals and agronomic characters

Path coefficient analysis for grain minerals and quality traits affecting grain yield was accomplished in 61 indigenous germplasm lines of pearl millet. Maximum direct effect on grain yield was contributed by crude fat content which is positive and highly significant (P < 0.01) followed by number of productive tillers (P < 0.01), hundred grain weight and panicle length (p<0.05). The rest of the yield attributes showed moderate to low positive direct effect on grain yield plant−1. Productive tillers per plant, panicle length, 100-grain weight and crude fat content had positive and significant indirect effect on grain yield. These results interestingly showed that either higher grain yield or seed weight often have higher crude fat content which increased the feed value in poultry farms. It was also noted that, breeding for high grain minerals as well as other quality characters did reduce the yield potential of the cultivars

PSO based optimized PI controller design for hybrid active power filter

This research study presents the design and simulation of a hybrid active power filter (HAPF) for reducing harmonics. The reference currents have been determined using the synchronous reference frame technique. To achieve its goals, the proposed HAPF employs AI algorithm known as particle swarm optimization (PSO) to fine-tune the proportional-integral PI controller's parameters. With the help of PI-PSO controller the DC link voltage is regulated in the HAPF-inverter. A non-linear current control strategy based on hysteresis employed here to construct the pulse gate by comparing the retrieved reference and real currents necessitated by the HAPF. Simulations were carried out in MATLAB and shown that the proposed method is extremely adaptable and efficient in reducing harmonic currents caused by non-linear loads

Correlation Studies for Grain Yield Components and Nutritional Quality Traits in Pearl Millet (Pennisetum glaucum (L.) R. Br.) Germplasm

Before selecting genotypes for nutritional quality characters (protein, oil and micronutrients content) and anti-nutritional factor (phytic acid), it is important to know how much selection is likely to affect yield. Using a diverse range of genotypes, relationships between quality traits with yield and yield attributes in pearl millet were investigated. The number of productive tillers, panicle length, panicle girth, days to maturity, 100-grain weight were most important traits for maximizing grain yield owing to their high significant positive association with grain yield. Phytate phosphorus and total phosphorus were significantly positive correlated and it is therefore inferred that simple selection against phytate phosphorus is unlikely to lower phosphorus concentration in pearl millet. No correlation between grain yield and protein content. These suggested that, there is possibility of selection for increased protein content without detrimental effect on grain yield

Extremely-randomized-tree-based Prediction of N(6)-Methyladenosine Sites in Saccharomyces cerevisiae

INTRODUCTION: N(6)-methyladenosine (m6A) is one of the most common post-transcriptional modifications in RNA, which has been related to several biological processes. The accurate prediction of m6A sites from RNA sequences is one of the challenging tasks in computational biology. Several computational methods utilizing machine-learning algorithms have been proposed that accelerate in silico screening of m6A sites, thereby drastically reducing the experimental time and labor costs involved. METHODOLOGY: In this study, we proposed a novel computational predictor termed ERT-m6Apred, for the accurate prediction of m6A sites. To identify the feature encodings with more discriminative capability, we applied a two-step feature selection technique on seven different feature encodings and identified the corresponding optimal feature set. RESULTS: Subsequently, performance comparison of the corresponding optimal feature set-based extremely randomized tree model revealed that Pseudo k-tuple composition encoding, which includes 14 physicochemical properties significantly outperformed other encodings. Moreover, ERT-m6Apred achieved an accuracy of 78.84% during cross-validation analysis, which is comparatively better than recently reported predictors. CONCLUSION: In summary, ERT-m6Apred predicts Saccharomyces cerevisiae m6A sites with higher accuracy, thus facilitating biological hypothesis generation and experimental validations