Enhanced Particle Swarm Optimizer for Power System Applications

Power system networks are complex systems that are highly nonlinear and non-stationary, and therefore, their performance is difficult to optimize using traditional optimization techniques. This paper presents an enhanced particle swarm optimizer for solving constrained optimization problems for power system applications, in particular, the optimal allocation of multiple STATCOM units. The study focuses on the capability of the algorithm to find feasible solutions in a highly restricted hyperspace. The performance of the enhanced particle swarm optimizer is compared with the classical particle swarm optimization (PSO) algorithm, genetic algorithm (GA) and bacterial foraging algorithm (BFA). Results show that the enhanced PSO is able to find feasible solutions faster and converge to feasible regions more often as compared with other algorithms. Additionally, the enhanced PSO is capable of finding the global optimum without getting trapped in local minima

Grasses and Legumes for Cellulosic Bioenergy

Human life has depended on renewable sources of bioenergy for many thousands of years, since the time humans fi rst learned to control fi re and utilize wood as the earliest source of bioenergy. The exploitation of forage crops constituted the next major technological breakthrough in renewable bioenergy, when our ancestors began to domesticate livestock about 6000 years ago. Horses, cattle, oxen, water buffalo, and camels have long been used as sources of mechanical and chemical energy. They perform tillage for crop production, provide leverage to collect and transport construction materials, supply transportation for trade and migratory routes, and create manure that is used to cook meals and heat homes. Forage crops—many of which form the basis of Grass: The 1948 Yearbook of Agriculture (Stefferud, 1948), as well as the other chapters of this volume—have composed the principal or only diet of these draft animals since the dawn of agriculture

The inner centromere is a biomolecular condensate scaffolded by the chromosomal passenger complex.

The inner centromere is a region on every mitotic chromosome that enables specific biochemical reactions that underlie properties, such as the maintenance of cohesion, the regulation of kinetochores and the assembly of specialized chromatin, that can resist microtubule pulling forces. The chromosomal passenger complex (CPC) is abundantly localized to the inner centromeres and it is unclear whether it is involved in non-kinase activities that contribute to the generation of these unique chromatin properties. We find that the borealin subunit of the CPC drives phase separation of the CPC in vitro at concentrations that are below those found on the inner centromere. We also provide strong evidence that the CPC exists in a phase-separated state at the inner centromere. CPC phase separation is required for its inner-centromere localization and function during mitosis. We suggest that the CPC combines phase separation, kinase and histone code-reading activities to enable the formation of a chromatin body with unique biochemical activities at the inner centromere

Development and Validation of the Behavioral Tendencies Questionnaire

At a fundamental level, taxonomy of behavior and behavioral tendencies can be described in terms of approach, avoid, or equivocate (i.e., neither approach nor avoid). While there are numerous theories of personality, temperament, and character, few seem to take advantage of parsimonious taxonomy. The present study sought to implement this taxonomy by creating a questionnaire based on a categorization of behavioral temperaments/tendencies first identified in Buddhist accounts over fifteen hundred years ago. Items were developed using historical and contemporary texts of the behavioral temperaments, described as “Greedy/Faithful”, “Aversive/Discerning”, and “Deluded/Speculative”. To both maintain this categorical typology and benefit from the advantageous properties of forced-choice response format (e.g., reduction of response biases), binary pairwise preferences for items were modeled using Latent Class Analysis (LCA). One sample (n1 = 394) was used to estimate the item parameters, and the second sample (n2 = 504) was used to classify the participants using the established parameters and cross-validate the classification against multiple other measures. The cross-validated measure exhibited good nomothetic span (construct-consistent relationships with related measures) that seemed to corroborate the ideas present in the original Buddhist source documents. The final 13-block questionnaire created from the best performing items (the Behavioral Tendencies Questionnaire or BTQ) is a psychometrically valid questionnaire that is historically consistent, based in behavioral tendencies, and promises practical and clinical utility particularly in settings that teach and study meditation practices such as Mindfulness Based Stress Reduction (MBSR)

Relationship of a big five personality questionnaire to the symptoms of affective disorders

Online assessments allow cost-effective, large-scale screening for psychiatric vulnerability (e.g., university undergraduates or military recruits). However, conventional psychiatric questionnaires may worsen mental health outcomes due to overmedicalizing normal emotional reactions. Personality questionnaires designed for occupational applications could circumvent this problem as they utilise non-clinical wording and it is well-established that personality traits influence susceptibility to psychiatric illness. Here we present a brief, free-to-use occupational personality questionnaire, and test its sensitivity to symptoms of Bipolar Disorder (BD) and Major Depressive Disorder (MDD) in an online sample. Our study used a cross-sectional, self-report design to assess the relationship between self-reported symptoms of affective disorders and scores on the personality dimensions of openness, conscientiousness, extraversion, agreeableness and neuroticism. We used SEM to compare affective symptoms in 8,470 individuals (mean age 25.6 ± 7.0 years; 4,717 male) with scores on an online adaption of the TSDI, a public-domain ‘Big Five’ personality questionnaire. ROC curve analyses assessed cut off scores for the best predictors of overall vulnerability to affective disorders (represented by a composite screening score). Neuroticism was the most robust predictor of QIDS-16 depression symptoms and MDQ Hypomania symptoms (β = 0.68 and 0.39 respectively, p < .0001). Extraversion was the most robust predictor of HCL-16 Hypomania symptoms (β = 0.34, p < .0001). ROC curve analyses suggest if the TSDI was used for screening in this sample, neuroticism cut offs of approximately 58 for men and 70 for women would provide the most useful classification of overall vulnerability to affective disorders

Taxonomy and structure of the Romanian personality lexicon

We identified 1746 personality-relevant trait-adjectives in a Romanian dictionary, of which 412 were classified as descriptors of dispositions by 10 judges. Self-ratings were collected from 515 participants on those 412 adjectives, and the ratings were factored using principal components analysis. Solutions with different numbers of factors were analysed. The two- and three-factor solutions, respectively, confirmed the Big Two and Big Three of personality traits. A five-factor solution reflected the Big Five model with a fifth factor emphasising Rebelliousness versus Conventionality. The five-factor solution was related to the International Personality Item Pool-Big Five scales, and the highest correlations were indeed between the corresponding factors and scales. A six-factor solution was indicative of the six-factor model as expressed in the HEXACO model, yet with a weak Honesty-Humility factor. Additional analysis with self-ratings from 218 participants on marker-scales for the six-factor solution and on the six scales of the HEXACO did not produce a clear one-to-one correspondence between the two sets of scales, confirming indeed that the six-factor model was only partially found

A Bayesian method for inferring quantitative information from FRET data

<p>Abstract</p> <p>Background</p> <p>Understanding biological networks requires identifying their elementary protein interactions and establishing the timing and strength of those interactions. Fluorescence microscopy and Förster resonance energy transfer (FRET) have the potential to reveal such information because they allow molecular interactions to be monitored in living cells, but it is unclear how best to analyze FRET data. Existing techniques differ in assumptions, manipulations of data and the quantities they derive. To address this variation, we have developed a versatile Bayesian analysis based on clear assumptions and systematic statistics.</p> <p>Results</p> <p>Our algorithm infers values of the FRET efficiency and dissociation constant, <it>K_d</it>, between a pair of fluorescently tagged proteins. It gives a posterior probability distribution for these parameters, conveying more extensive information than single-value estimates can. The width and shape of the distribution reflects the reliability of the estimate and we used simulated data to determine how measurement noise, data quantity and fluorophore concentrations affect the inference. We are able to show why varying concentrations of donors and acceptors is necessary for estimating <it>K_d</it>. We further demonstrate that the inference improves if additional knowledge is available, for example of the FRET efficiency, which could be obtained from separate fluorescence lifetime measurements.</p> <p>Conclusions</p> <p>We present a general, systematic approach for extracting quantitative information on molecular interactions from FRET data. Our method yields both an estimate of the dissociation constant and the uncertainty associated with that estimate. The information produced by our algorithm can help design optimal experiments and is fundamental for developing mathematical models of biochemical networks.</p

Five-factor model personality traits in opioid dependence

<p>Abstract</p> <p>Background</p> <p>Personality traits may form a part of the aetiology of opioid dependence. For instance, opioid dependence may result from self-medication in emotionally unstable individuals, or from experimenting with drugs in sensation seekers. The five factor model (FFM) has obtained a central position in contemporary personality trait theory. The five factors are: Neuroticism, Extraversion, Openness to Experience, Agreeableness and Conscientiousness. Few studies have examined whether there is a distinct personality pattern associated with opioid dependence.</p> <p>Methods</p> <p>We compared FFM personality traits in 65 opioid dependent persons (mean age 27 years, 34% females) in outpatient counselling after a minimum of 5 weeks in buprenorphine replacement therapy, with those in a non-clinical, age- and sex-matched sample selected from a national database. Personality traits were assessed by a Norwegian version of the Revised NEO Personality Inventory (NEO PI-R), a 240-item self-report questionnaire. Cohen's d effect sizes were calculated for the differences in personality trait scores.</p> <p>Results</p> <p>The opioid-dependent sample scored higher on Neuroticism, lower on Extraversion and lower on Conscientiousness (d = -1.7, 1.2 and 1.7, respectively) than the controls. Effects sizes were small for the difference between the groups in Openness to experience scores and Agreeableness scores.</p> <p>Conclusion</p> <p>We found differences of medium and large effect sizes between the opioid dependent group and the matched comparison group, suggesting that the personality traits of people with opioid dependence are in fact different from those of non-clinical peers.</p